In Pursuit of Eco-innovation Jana Hojnik University of Primorska Press Faculty of Management Monograph Series Editorial Board Editor in Chief Katarina Babnik Matjaž Novak Štefan Bojnec Editorial Board Aleksandra Brezovec Ana Arzenšek Boris Horvat Štefan Bojnec Dejan Hozjan Dubravka Celinšek Alenka Janko Spreizer Armand Faganel Alen Ježovnik Viktorija Florjančič Lenka Kavčič Borut Kodrič Alan Orbanič Suzana Laporšek Gregor Pobežin Mirko Markič Andraž Teršek Franko Milost Jonatan Vinkler Matjaž Nahtigal Mitja Ruzzier In Pursuit of Eco-innovation Drivers and Consequences of Eco-innovation at Firm Level Jana Hojnik In Pursuit of Eco-innovation: Drivers and Consequences of Eco-innovation at Firm Level Jana Hojnik Reviewers Boštjan Antončič Andrea Tracogna Typesetting: Jonatan Vinkler Published by Založba Univerze na Primorskem / University of Primorska Press (for the Publisher: Prof. Dragan Marušič, PhD., rector) Titov trg 4, SI-6000 Koper Editor-in-Chief Jonatan Vinkler Managing Editor Alen Ježovnik Koper 2017 isbn 978-961-7023-53-4 (pdf) http://www.hippocampus.si/isbn/978-961-7023-53-4.pdf isbn 978-961-7023-54-1 (html) http://www.hippocampus.si/isbn/978-961-7023-54-1/index.html DOI: https://doi.org/10.26493/978-961-7023-53-4 © 2017 University of Primorska Press Kataložni zapis o publikaciji (CIP) pripravili v Narodni in univerzitetni knjižnici v Ljubljani COBISS.SI-ID=292854784 ISBN 978-961-7023-53-4 (pdf) ISBN 978-961-7023-54-1 (html) Contents Introduction • 17 Eco-innovation • 21 Why to distinguish eco-innovation from regular innovation • 21 Defining eco-innovation • 26 Review of current eco-innovation definitions • 27 Features of eco-innovation • 31 Main dimensions of eco-innovation • 36 Target • 37 Mechanisms • 37 Eco-innovation’s impact on the environment • 38 Types of eco-innovation • 39 Product eco-innovation • 40 Process eco-innovation • 41 Technological eco-innovation • 42 Organizational eco-innovation • 43 Marketing eco-innovation • 45 Social eco-innovation • 46 System eco-innovation • 46 Measuring eco-innovation • 46 Toward a new definition of eco-innovation • 61 Drivers of Eco-innovation • 63 Environmental policy instruments • 63 Regulation • 69 Taxation (taxes and tax incentives) and subsidies • 74 Demand side • 75 Competition • 78 In Pursuit of Eco-innovation Society • 80 Expected benefits from eco-innovation • 81 Sources of information • 84 Organizational capabilities • 85 Managerial environmental concern • 86 Company’s general characteristics (firm size and firm age) • 90 Consequences of Eco-innovation Adoption • 95 Firm performance • 98 Internationalization • 106 Competitive advantage • 108 Hypotheses Development • 111 Hypotheses concerning antecedents of eco-innovations • 111 Environmental policy instruments and eco-innovation • 111 6 Customer demand and eco-innovation • 114 Managerial environmental concern and eco-innovation • 115 Expected benefits and eco-innovation • 116 Firm reputation • 117 Cost savings • 118 Competition and eco-innovation • 118 Hypotheses concerning consequences of eco-innovation • 119 Eco-innovation and firm performance • 119 Eco-innovation and economic performance • 121 Eco-innovation and competitive benefits • 121 Eco-innovation and internationalization • 122 Methodology • 125 Preliminary testing of questionnaire • 125 Research instrument and operationalization of variables and measures • 126 Measures for eco-innovation antecedents • 126 Measures for eco-innovation dimensions • 129 Measures for consequences/outcomes of eco-innovation • 131 Sampling and data collection • 133 Common method variance assessment • 136 Data analyses • 136 Evaluation of the results • 140 Results • 147 Sample characteristics • 147 Eco-innovation determinants • 154 Managerial environmental concern • 155 Expected benefits • 158 Environmental policy instruments • 163 Contents Customer demand • 170 Competition (Competitive intensity and Competitive pressure) • 173 Eco-innovation types • 179 Product eco-innovation • 179 Process eco-innovation • 183 Organizational eco-innovation • 187 Eco-innovation construct • 191 Convergent and discriminant validity of the eco-innovation construct • 199 Eco-innovation outcomes • 204 Competitive benefits • 204 Economic benefits • 212 Company performance • 218 Internationalization • 224 Eco-innovation models • 233 7 Product eco-innovation model • 233 Construct validity of product eco-innovation model • 234 Statistical analysis and results (path analysis) • 239 Process eco-innovation model • 242 Construct validity of process eco-innovation model • 243 Statistical analysis and results (path analysis) • 247 Organizational eco-innovation • 251 Construct validity of organizational eco-innovation model • 251 Statistical analysis and results (path analysis) • 257 The expanded construct-level model of eco-innovation • 260 Construct validity for the expanded construct-level model of eco- innovation • 261 The expanded construct-level model of eco-innovation (path analysis) • 269 Summary of findings and discussion • 273 Conclusion • 287 Contributions • 287 Implications • 292 Implications for theory and research • 292 Implications for policy makers • 294 Implications for entrepreneurs • 298 Limitations • 299 Future research directions and opportunities • 302 References and sources • 307 References • 307 Sources • 326 Recenziji • 331 List of Figures 19 • Figure 1: Structure of the study 33 • Figure 2: Product lifecycle stages 38 • Figure 3: Conceptual relationships between sustainable manufacturing and eco-innovation 96 • Figure 4: Business case for eco-innovation 124 • Figure 5: The eco-innovation conceptual model (for the construct-level model) 158 • Figure 6: Diagram of construct Managerial environmental concern with the standardized solution 162 • Figure 7: Diagram of construct Expected benefits with the standardized solution 167 • Figure 8: Diagram of construct Command-and-control instrument with the standardized solution 170 • Figure 9: Diagram of construct Economic incentive instrument with the standardized solution 177 • Figure 11: Diagram of construct Competitive intensity with the standardized solution 178 • Figure 12: Diagram of construct Competitive pressure with the standardized solution 183 • Figure 13: Diagram of eco-innovation dimension of Product eco-innovation with the standardized solution 187 • Figure 14: Diagram of eco-innovation dimension of Process eco-innovation with the standardized solution 190 • Figure 15: Diagram of eco-innovation dimension of Organizational eco-innovation with the standardized solution 200 • Figure 16: Eco-innovation construct (with the standardized solution) 209 • Figure 17: Diagram of construct Competitive benefits with the standardized solution 212 • Figure 18: Diagram of construct Competitive benefits with the standardized solution In Pursuit of Eco-innovation 215 • Figure 19: Diagram of construct Economic benefits with the standardized solution 217 • Figure 20: Diagram of construct Economic benefits with the standardized solution 224 • Figure 21: Diagram of company performance dimension – construct Company profitability with the standardized solution 225 • Figure 22: Frequency and percentage of use of operation modes (types) by the analyzed companies 227 • Figure 23: Frequency and percentage of use of operation modes (number) by the analyzed companies 228 • Figure 24: Frequency and percentage of the total number of countries where analyzed companies sell their products/services 231 • Figure 25: Diagram of construct Internationalization with the standardized solution 240 • Figure 26: Product eco-innovation model (standardized solution) 248 • Figure 27: Process eco-innovation model (standardized solution) 258 • Figure 28: Organizational eco-innovation model (standardized solution) 10 270 • Figure 29: The expanded construct-level model of eco-innovation (standardized solution) List of Tables 23 • Table 1: Main peculiarities of environmental innovations as compared to other types of innovations (identified by neoclassical contributions in the environmental innovation economics literature) 29 • Table 2: Selected definitions of eco-innovation 36 • Table 3: Example of GOM (The Green Option Matrix) 48 • Table 4: Types of eco-innovation used in previous studies examining more than one eco-innovation type 62 • Table 5: Main characteristics of eco-innovation 65 • Table 6: Five models of policy to encourage environmental adaptation within organizations 92 • Table 7: Summary of drivers of eco-innovation found in previous research works (focusing on factors explored in our study) 103 • Table 8: Summary of the past findings and measures used to test the relationship between eco-innovation and firm performance 123 • Table 9: Summary of research hypotheses 127 • Table 10: Items for three latent variables (Managerial environmental concern, Expected benefits, Customer demand) 128 • Table 11: Items for two latent variables (Command-and-control instrument, Economic incentive instrument) 129 • Table 12: Items for two latent variables (Competitive intensity and Competitive pressure) 130 • Table 13: Items for the latent variable of Product eco-innovation 130 • Table 14: Items for the latent variable of Process eco-innovation 131 • Table 15: Items for the latent variable of Organizational eco-innovation 132 • Table 16: Items for latent variable of Firm performance (growth and profitability) 132 • Table 17: Items for latent variable of Economic performance 133 • Table 18: Items for latent variable of Competitive benefits In Persuit of Eco-innovation 149 • Table 19: Sample characteristics 151 • Table 20: Main industry types in which analyzed companies operate 152 • Table 21: Environmental certificates/prizes that have obtained the included companies 153 • Table 22: The level of innovativeness of included companies in the past three years (2011-2013) 154 • Table 23: The sample in comparison with the population 155 • Table 24: Descriptive statistics for determinant Managerial environmental concern 156 • Table 25: KMO and Bartlett’s test of sphericity (Managerial environmental concern) 157 • Table 26: Standardized coefficients and their squares (Managerial environmental concern) 159 • Table 27: Descriptive statistics for determinant Expected benefits 160 • Table 28: KMO and Bartlett’s test of sphericity (Expected benefits) 160 • Table 29: Standardized coefficients and their squares (Expected benefits) 12 162 • Table 30: Standardized coefficients and their squares (Expected benefits) 164 • Table 31: Descriptive statistics for determinant Environmental policy instruments 166 • Table 32: KMO and Bartlett’s test of sphericity (Command-and-control instrument) 167 • Table 33: Standardized coefficients and their squares (Command-and-control instrument) 169 • Table 34: KMO and Bartlett’s test of sphericity (Economic incentive instrument) 169 • Table 35: Standardized coefficients and their squares (Economic incentive instrument) 171 • Table 36: Descriptive statistics for determinant Customer demand 172 • Table 37: KMO and Bartlett’s test of sphericity (Customer demand) 172 • Table 38: Standardized coefficients and their squares (Customer demand) 173 • Figure 10: Diagram of construct Customer demand with the standardized solution 174 • Table 39: Descriptive statistics for determinant Competition (Competitive intensity and Competitive pressure) 175 • Table 40: KMO and Bartlett’s test of sphericity (Competitive intensity) 176 • Table 41: KMO and Bartlett’s test of sphericity (Competitive pressure) 176 • Table 42: Standardized coefficients and their squares (Competitive intensity) 178 • Table 43: Standardized coefficients and their squares (Competitive pressure) 179 • Table 44: Descriptive statistics for Product eco-innovation 182 • Table 45: KMO and Bartlett’s test of sphericity (Product eco-innovation) 182 • Table 46: Standardized coefficients and their squares (Product eco-innovation) 184 • Table 47: Descriptive statistics for Process eco-innovation 186 • Table 48: KMO and Bartlett’s test of sphericity (Process eco-innovation) 186 • Table 49: Standardized coefficients and their squares (Process eco-innovation) 188 • Table 50: Descriptive statistics for Organizational eco-innovation 189 • Table 51: KMO and Bartlett’s test of sphericity (Organizational eco-innovation) 190 • Table 52: Standardized coefficients and their squares (Organizational eco- innovation) List of Tables 192 • Table 53: The eco-innovation dimensions’ (product and process eco-innovation factor and organizational eco-innovation factor) items factor loadings 194 • Table 54: The eco-innovation dimension’s item factor loadings (three eco-innovation factors) 196 • Table 55: The eco-innovation dimension’s item factor loadings 199 • Table 56: Eco-innovation dimension’s scale convergence – summary for all three eco- innovation dimensions and eco-innovation construct 201 • Table 57: Standardized coefficients and their squares (eco-innovation construct) 202 • Table 58: Eco-innovation construct convergent and discriminant validity 203 • Table 59: The dimensions-only vs. the one common factor model 204 • Table 60: Descriptive statistics for Competitive benefits 205 • Table 61: KMO and Bartlett’s test of sphericity (Competitive benefits) 206 • Table 62: Competitive benefits dimension’s item factor loadings 207 • Table 63: Model good-fit and reliability indexes for 1-factor and 2-factor solution of construct Competitive benefits 13 208 • Table 64: Standardized coefficients and their squares (Competitive benefits) 211 • Table 65: KMO and Bartlett’s test of sphericity (Competitive benefits) 211 • Table 66: Standardized coefficients and their squares (Competitive benefits) 213 • Table 67: Descriptive statistics for Economic benefits 214 • Table 68: KMO and Bartlett’s test of sphericity (Economic benefits) 214 • Table 69: Standardized coefficients and their squares (Economic benefits) 216 • Table 70: KMO and Bartlett’s test of sphericity (Economic benefits) 217 • Table 71: Standardized coefficients and their squares (Economic benefits) 218 • Table 72: Descriptive statistics for Company performance 220 • Table 73: Company performance – frequency and percentage of different financial and non-financial indicators 221 • Table 74: KMO and Bartlett’s test of sphericity (Company performance) 222 • Table 75: Company performance dimension’s item factor loadings 223 • Table 76: Standardized coefficients and their squares (Company profitability) 226 • Table 77: Descriptive statistics for internationalization variable – operation modes 229 • Table 78: Share of sales in foreign market in 2013 229 • Table 79: Descriptive statistics for internationalization 230 • Table 80: KMO and Bartlett’s test of sphericity (Internationalization) 231 • Table 81: Standardized coefficients and their squares (Internationalization) 235 • Table 82: Measurement model of latent variables and Cronbach’s alpha for latent variables 239 • Table 83: Results of correlations between latent variables 243 • Table 84: Measurement model of latent variables and Cronbach’s alpha for latent variables 247 • Table 85: Results of Correlations between latent variables 252 • Table 86: Measurement model of latent variables and Cronbach’s alpha for latent variables 257 • Table 87: Results of correlations between latent variables In Pursuit of Eco-innovation 261 • Table 88: Measurement items and Cronbach’s alpha for latent variables 264 • Table 89: Measurement model of latent variables 269 • Table 90: Results of Correlations between latent variables 277 • Table 91: Summary of hypotheses-related findings (structural equation modeling) 14 Abbreviations CfSD Centre for Sustainable Design CIS Community Innovation Survey CMV Common Method Variance EIO Eco-Innovation Observatory EMAS ECO - Management and Audit Scheme EMS Environmental Management Systems ENGO Environmental Non-Governmental Organization EQS Structural Equation Modeling Software EU European Union IMPRESS Impact of Clean Production on Employment in Europe ISO International Organization for Standardization MEI Measuring Eco-Innovation research project NGO Non-Governmental Organization OECD Organization for Economic Co-operation and Development QMS Quality Management Systems R&D Research and Development ROA Return on assets ROE Return on equity ROS Return on sales SEM Structural Equation Modeling In Pursuit of Eco-innovation SME Small and Medium-sized Enterprises SPSS Statistical Package for the Social Sciences TQEM Total Quality Environmental Management VDI German Association of Engineers (Verein Deutscher Ingenieure) ZEW The Centre for European Economic Research in Mannheim (Zentrum für Europäische Wirtschaftsforschung) 16 Introduction There is no business to be done on a dead planet. David Brower, Executive Director, Sierra Club In recent years, eco-innovations have gained importance and generated vast interest in both the academic and business worlds. Due to the salient issues, among which are primarily scarce resources and increasing population, the conservation of environmental quality has become essential (Govindan et al. 2014). Moreover, resource management, pollution control and climate change phenomena are all issues that, by their nature, reach beyond geographic borders (i.e., economic trends that occur in one country and/or internationalization of production and international trade all affect also other national economies) and thus make the challenges of sustainability a priority shared by countries and communities worldwide (Strange and Bayley 2014). The equilibrium in the environment has been distorted; therefore, the key challenge that must be undertaken is to reestablish that equilibrium. The interest in eco-innovation in research and practice has increased, particularly because of companies’ adverse impacts on the environment, which have resulted in serious global environmental problems and rising global concern for the environment on the other hand. Related to those, the data (OECD 2009) demonstrate that manufacturing companies account for a significant part of the world’s consumption of resources and generation of waste and were estimated to account for nearly a third of global energy usage. Therefore, the manufacturing industries carry the potential to become a driving force for the creation of sustainable soci- In Pursuit of Eco-innovation ety, through the development and implementation of products, services and other integrated sustainable practices in order to improve the environmental performance (OECD 2009). On the other hand, as aforementioned, the practice of green activities and conservation of the environment has become mandatory due to the scarce resources and increasing population (Govindan et al. 2014). The subject of our study is eco-innovation, which is a subset of all innovations in an economy (Wagner 2008). According to the Measuring eco-innovation project (MEI project),1 eco-innovation is defined as: “production, application or exploitation of a good, service, production process, organizational structure, or management or business method that is novel to the firm or user and which results, throughout its lifecycle, in a reduction of environmental risk, pollution and the negative impacts of 18 resources use (including energy use) compared to relevant alternatives” (Kemp and Pearson 2007, 7). Likewise, Eco-Innovation Observatory (2013) defined eco-innovation as any innovation that reduces the use of natural resources and decreases the release of harmful substances across the whole lifecycle, which reflects its environmental component. Eco-innovation therefore is identified by the feature of providing solutions that are more environmentally benign than relevant alternatives, even if the environmental component is not planned. It is increasingly apparent and widely accepted that eco-innovations are environmentally benign; addi-tionally, some types of eco-innovations may be beneficial for the environment and the end-user (e.g., providing energy and material savings). Moreover, eco-innovations are considered a path to new business opportunities, encompassing growth and competitive advantage (Aschhoff and Sofka 2009; Laperche and Uzunidis 2012). In eco-innovation hence lies the potential to create and provide a win-win situation, pertaining to both the environment and the company (Horbach 2008). Therefore, companies should know more about the possible benefits to be obtained from eco-innovation’s implementation and should be encouraged to implement eco-innovation to a larger extent, which we believe is a critical point to gain a competitive advantage, expand on foreign markets and improve firm performance in the long run. The way to reach 1 MEI is a project for DG Research of the European Commission (Call FP6-2005-SSP-5A, Area B, 1.6, Task 1). The project has been carried out in collaboration with Eurostat, the European Environment Agency (EEA) and the Joint Research Center (JRC) of the European Commission. MEI offers a conceptual clarification of eco-innovation (developing a typology) and discusses possible indicators, leading to proposals for eco-innovation measurement (Kemp and Pearson 2007). Introduction for sustainability is through implementation of eco-innovation, which by bringing benefits to the environment and companies presents a win-win situation. Therefore, we will strive to fill the gap by empirically testing an integrative model of eco-innovation. Finally, our aim is also to propose a definition of eco-innovation, with more focus on entrepreneurial orientation and its influence on company competitiveness. In this study we thus aim to analyze the relationships between the drivers of eco-innovation, implementation of different types of eco-innovation (product, process and organizational eco-innovation and, lastly, eco-innovation construct) and its outcomes at firm-level, based on a sample of Slovenian companies. We have first conducted the qualitative analysis to determine whether the identified drivers are appropriate for the Slovenian environment/companies. Drivers for implementation of eco-innovation were tested in this way by employing a qualitative study 19 in the first stage (interviews with companies’ environmental managers about the drivers and outcomes of eco-innovation). While the qualitative research was followed by a quantitative study in which we empirically tested the integrative model based on Slovenian companies. The structure of the study is presented below in Figure 1, and it is as follows: 1) Introduction, 2) Eco-innovation (definition and its main dimensions), 3) Drivers of eco-innovation, 4) Consequences of eco-innovation, 5) Research design, 6) Methodology, 7) and 8) Results, 9) Summary of findings and discussion, and 10) Conclusion. Figure 1: Structure of the study Eco-innovation In this section, we will focus on several issues pertaining to eco-innovation. The first subsection will focus on the main peculiarities of eco-innovation, which differentiate it from regular innovation (2.1). Next, we will define eco-innovation (2.2) and present its distinct features (2.3), main dimensions (2.4), types (2.5) and measurement (2.6). Finally, we will conclude this section with our own proposed eco-innovation definition (2.7). Why to distinguish eco-innovation from regular innovation Environmental innovations can be defined as a subset of all innovations in an economy (Wagner 2008). As such, they present an answer to the problems which already have or in the future will have a global dimension (Jänicke 2008). Based on global concerns and discourses regarding global warming, eco-innovations have a global market potential, while political support is required to trigger them, especially when pertaining to renewable energy technologies (Karakaya et al. 2014). Therefore, researchers (van den Bergh et al. 2011) argue that the main difference between “regular” innovation and eco-innovation pertains to the combination of an urgent environmental problem, which requires a solution associated with external costs (these costs do not enter the private costs of the polluter). This results in the need for adoption and investments in new technologies, which create less pollution and thus are less harmful for the environment (resulting in beneficial environmental characteristics), while there are no incentives for the polluter or other companies to induce adoption and implementation of such technologies (van In Pursuit of Eco-innovation den Bergh et al. 2011). This would lead to reduced social costs, while the private costs would increase (van den Bergh et al. 2011). Van den Bergh et al. (2011) argue that the cost structure becomes more incentive compatible and tends to improve the likelihood of eco-innovations when external (social) costs are translated into private ones through a public policy (regulation of the environmental externality). Hence, eco-innovations are increasingly at the center of the policy action, and therefore a crucial question pertaining to eco-innovations regards whether or not they actually require a specific theory and policy (Rennings 2000; De Marchi 2012). Other important characteristics that differentiate eco-innovation from regular innovation are that eco-innovation is not an open-ended concept and that eco-innovation explicitly pinpoints reduction of environmental impacts, whether these are intentional or not (Kemp and Fox-22 on 2007; Arundel and Kemp 2009; OECD 2009; Machiba 2010; Fawzi and Rundquist 2011; Rave et al. 2011; Fleiter et al. 2012; Horbach, Rammer and Rennings 2012; Antonioli, Mancinelli, and Mazzanti 2013; Cainelli and Mazzanti 2013). The existing literature (especially neoclassical contributions) focuses on and emphasizes two main aspects that differentiate eco-innovations from other innovations (De Marchi 2012). These two aspects concern their externalities and drivers (see Table 1), which has already pointed out Rennings (2000), who named them the “double externality problem” and “the regulatory push/pull effect”. The double externality problem is one of the most important and well-known peculiarities of environmental innovations and regards production of the common spillovers of innovations in general and at the same time creation of less environmental external costs (Rennings 2000; Ziegler and Rennings 2004; Rennings et al. 2006). This means that the whole society exploits the benefits from an environmental innovation, while a single company carries all the costs by itself (Ziegler and Rennings 2004; Beise and Rennings 2005). Moreover, even if a company successfully markets an environmental innovation, the company’s appropriation of the profits for this innovation is difficult, especially if the access to the corresponding knowledge about this environmental innovation is easily accessible to possible imitators and when environmental benefits result to have a good public character (Ziegler and Rennings 2004; Beise and Rennings 2005). Researchers (Rennings 2000; Ziegler and Rennings 2004) emphasize that the double externality problem leads to an increase of the importance of regulatory framework (because both externalities result in a suboptimal investment in environmental innovations). Eco-innovation Table 1: Main peculiarities of environmental innovations as compared to other types of innovations (identified by neoclassical contributions in the environmental innovation economics literature) Environmental innovations Other innovations Knowledge externalities Externalities and environmental externalities Knowledge externalities Demand-pull, Demand-pull and Drivers technology push and regulatory push/pull factors technology push factors Source: De Marchi 2012. We follow Rennings (2000), who argues that three peculiarities of eco-innovation actually exist, which he further identifies as follows: 1) 23 the double externality problem, 2) the regulatory push/pull effect, and 3) the increasing importance of institutional and social innovation. In more detail, we describe the aforementioned peculiarities of eco-innovation identified by Rennings (2000). Focusing first on institutional and social innovation, we mention an important peculiarity regarding the nature and development of eco-innovation. Eco-innovations can be developed by companies or non-profit organizations and traded or not on markets, while their nature can be technological, organizational (pertaining to management instruments at the firm level, like eco-audits), social (regarding changes of lifestyles and consumer behavior; Scherhorn et al. 1997, 16, in Rennnings 2000, 323) or institutional (e.g., Rennings 2000, 324, posits promotion of sustainable transport or improvement of material flow management in a certain region by a network of scientists, public authorities and NGOs). The second peculiarity of eco-innovation peculiarity pointed out by Rennings (2000) regards the issue of eco-innovation placed between two different economic sub-disciplines, which are innovation economics and environmental economics. In order to provide an adequate analysis of eco-innovation, an interdisciplinary approach is required. Meanwhile, a valuable contribution derived from innovation economics pertains to identification of innovation determinants and the complexity of drivers that spur innovation, while from the side of environmental economics, the main contribution regards how to assess environmental policy instruments (Rennings 2000). Combining both approaches would lead to identification and assessment of the state regulation role to induce innovation (Rennings 2000). On the one hand, environmental economics was ori- In Pursuit of Eco-innovation ented towards environmental policy instruments (encompassing tradable permits, taxes) and regulatory framework concerning innovation methods and strategies in order to valuate and internalize the negative external costs (Rennings 2000). On the other hand, innovation economics focused on positive spillovers of basic R&D efforts in companies (Rennings 2000). Eco-innovations produce positive spillovers in the innovation and the diffusion phase (e.g. “a smaller amount of external costs compared to competing goods and services on the market”; Rennings 2000, 326). This leads to the double externality problem, which results in the reduction of incentives for companies to invest in eco-innovation (Rennings 2000). With a better coordination of environmental and innovation policy, the main aim of innovation policy would be to cut the costs of technological, institutional and social innovation (especially required would 24 be in phases of invention (financial support for pilot projects) and market introduction (improvement of performance characteristics of eco-innovations)) (Rennings 2000). The key role of environmental policy regarding the diffusion phase would comprise internalization of external costs, which are imposed by competing, non-ecological products or services (Rennings 2000). The markets’ non-punishment for products and services that harm the environment leads to the distortion of competition between environmental and non-environmental innovation (Ren- nings 2000). Therefore, the competition between environmental and non-environmental innovation continues to be distorted, unless markets reward environmental improvements and punish environmentally harmful impacts (Beise and Rennings 2005). In conclusion, all innovations produce common knowledge spillovers, while eco-innovations also bring positive externalities (environmental spillovers), which result in benefits to society, while the costs are borne by the enterprises that practice and introduce eco-innovations (Rennings et al. 2006). Because of those two positive externalities created by eco-innovation (usual knowledge externalities through research and innovation phases as well as environmental externalities in the adoption and diffusion phases), eco-innovations are socially desirable (Belin et al. 2009). Moreover, the double externality problem (i.e., both externalities result in sub-optimal investment in eco-innovations) leads to the last peculiarity of eco-innovation, which pertains to the determinants of eco-innovation adoption (Rennings 2000). Innovation economics should also consider regulatory framework as an important driver of eco-innovation adoption (Rennings 2000). Although new eco-efficient technologies can be spurred under technology push factors, it is also well known that mar- Eco-innovation ket pull factors induce environmentally friendly products or image (Rennings 2000). Hence, due to the externality problem regarding eco-innovation, the determinants of eco-innovation should also include the regulatory framework (the regulatory push/pull effect), because the regulatory framework and environmental policy both strongly affect eco-innovation (Rennings 2000). Therefore, neoclassical environmental economics considers environmental regulation to remedy a market failure through the internalization of costs that occur from the negative externalities (Testa et al. 2011). While environmental regulation corrects the negative externalities, it also burdens companies with additional costs deriving from increased expenditures in environmental protection in order to comply with regulations (Testa et al. 2011). Higher production costs lead to a lower competitiveness of companies’ products on the domestic and foreign markets (Testa et al. 2011). In contrast, the second stream ar-25 gued that environmental regulation could be beneficial. The Porter hypothesis suggests that environmental regulation stimulates innovation (Testa et al. 2011) by providing incentives that affect companies’ production routines in a way that ensures compliance and leads to cost reductions (through decrease of resource inputs or increased efficiency) or even to new marketable products that entirely offset the costs of compliance (Testa et al. 2011). Thereby, environmental innovation represents a source of comparative advantages (Costantini and Crespi 2008). Ford et al. (2014) found some support for the original version of the Porter hypothesis, which claims that regulation spurs innovation. Additionally, Jaffe and Palmer (1997) differentiated the Porter hypothesis into “weak”, “narrow” and “strong” versions, with the results of their study confirm-ing the “weak” version. The “narrow” version claims that a certain type of regulation motivates innovation (Jaffe and Palmer 1997), the “weak” version posits that only regulation will induce certain types of innovation, and the “strong” version postulates that properly designed regulation induces innovation and more than offsets the costs of compliance (i.e., leads to compliance with regulation and increased profits) (Jaffe and Palmer 1997). Furthermore, other researchers (Mazzanti and Costantini 2010) found support for the weak and the strong Porter hypothesis on export performance, while Lanoie et al. (2011), based on seven OECD countries, found strong support for the weak version, found qualified support for the narrow version and rejected the strong version (no support found). Regarding the strong version of the Porter hypothesis, Mazzanti and Costantini (2010) found that the overall impact of environmental policies is not in conflict with export competitiveness. For the weak version In Pursuit of Eco-innovation of the Porter hypothesis, empirical support has been found – all (e.g., use of export flows of environmental goods, environmental policies, public R&D expenditures and all patenting activities) induce competitive advantages of green exports (Mazzanti and Costantini 2010). In addition, the overall impact of environmental policies does not negatively affect export competitiveness in the manufacturing sector, and the strong version of the Porter hypothesis is confirmed – specific innovation efforts and energy tax policies positively affect export flows dynamics (Costantini and Mazzanti 2012). Researchers also found support for the narrowly strong version, arguing that environmental policies foster green exports (Costantini and Mazzanti 2012). In contrast, Rexhäuser and Rammer (2013) have come to somewhat opposite findings, arguing that the strong version of the Porter hypothesis does not hold in general, but rather de-26 pends on the type of environmental innovation. Defining eco-innovation Eco-innovation is a type of innovation that steers companies towards reduction of environmental impact, whether this effect is intentional or not (Machiba 2010; Fawzi and Rundquist 2011). Fleiter et al. (2012) discussed the fact that the introduction of eco-innovation is not necessarily dependent on environmental harm reduction. Therefore, if technology is less environmentally harmful than its conventional alternative, it can be defined as eco-innovation (Kemp and Foxon 2007). Laperche and Picard (2013) suggest that firms, through eco-innovation, try to transform constraints into opportunities, which can results in cost reduction, enjoy-ment of better reputation and gain of new markets. Eco-innovation observatory (2010 in EIO 2013a) proposed a defi- nition of eco-innovation as: “introduction of any new or significantly improved product (good or service), process, organizational change or marketing solution that reduces the use of natural resources (including materials, energy, water and land) and decreases the release of harmful substances across the lifecycle”. Given this broad definition, we can recognize that the emphasis is put on different types of eco-innovation, such as product, process, marketing and organizational innovation, which induce a reduction of the use of natural resources and the release of harmful substances, highlighting the entire lifecycle of it. Hence, the environmental benefits should pertain to the production of goods or services within companies as well as the after-sale use of the end-user (Arundel and Kemp 2009; Doran and Ryan 2012; Horbach et al. 2012). More information about eco-innovation definitions will follow in section 2.2.1. Eco-innovation With regard to eco-innovation activities, the survey of Eurobarometer (2011) has shown that approximately three out of 10 companies in the EU (29%) had introduced a new or significantly improved eco-innovation production process or method in the past two years, whereas 24% had introduced a new or significantly improved eco-innovative product or service on the market. On the other hand, summarizing the Eco-Innovation scoreboard, Slovenia advanced from the 10th place to the 7th between 2011 and 2012 and has remained among the best-performing new member states, even though that some indicators have regressed (EIO 2011a; EIO 2013b). However, in 2010 was noted an increase in the R&D in all sectors compared to the previous year, and also number of policy measures have supported public spending on R&D and intended to reinforce the knowledge triangle: research, education and innovation. (EIO 2011a). While, the situation regarding eco-innovation in Slovenia has changed 27 over the years. Compared to 2011 and 2012, Slovenia has decreased in the ranking, it ranked only 15th in 2013. Review of current eco-innovation definitions Today, most people have a general knowledge or opinion about the meaning of the words “eco”, “green”, and “environmental”. Nonetheless, the definition of eco-innovation in research is still evolving. For instance, Rennings (2000, 322) summarizes that eco-innovations can be developed by firms or non-profit organizations, they can be traded on markets or not, their nature can be technological, organizational, social or institutional, while the Eco-innovation Observatory (hereinafter EIO) defined eco-innovation as “introduction of any new or significantly improved product (good or service), process, organizational change or marketing solution that reduces the use of natural resources (including materials, energy, water and land) and decreases the release of harmful substances across the lifecycle” (EIO 2010 in EIO 2013a, 2). Within the literature, all definitions definitely acknowledge that eco-innovation contributes to the environmental benefit or at least decreases the environmental burden. The definitions proposed by various organizations and researchers will be presented in more detail further ahead (see Table 2). When reviewing eco-innovation in the literature, we can also notice the use of different terms when referring to eco-innovation. Some confusion still exists regarding eco-innovation’s definition as well as the terms used for eco-innovation activities. In the review of the existing literature, we find three synonyms implying the same meaning or addressing the same type of innovation: “eco”/“ecological”, “green” and “environmental” In Pursuit of Eco-innovation innovation. Through our study, we will use interchangeably all three expressions, and we will also clarify the difference between eco/green/environmental and sustainable innovation. The use of these synonyms (eco, green and environmental innovation) depends largely on how each individual researcher addresses the same type of innovation. Here we briefly present the research of Angelo et al. (2012), who have done a literature review focusing on eco, green and environmental innovation and on the frequency of used terms. Reviewing scientific articles published up to the year 2012 using the terms “environmental innovation”, “green innovation” and “eco-innovation” revealed that the term “environmental innovation” is used in 65% of the analyzed articles, followed by the term “eco-innovation” (22%) and finally “green innovation” (13%). Likewise, Schiederig et al. (2012) have also noted confusion about different notions 28 and terminology in describing innovations that have a reduced negative impact on the environment. Thus, the terms green, eco/ecological and environmental innovation are used as synonyms, and they suggest that we should be aware of the broader concept of sustainable innovation, which also includes a social dimension (Schiederig et al. 2012). Further ahead, we explain the main difference between eco-innovation and sustainable innovation. We cite a few brief but meaningful definitions and conclude with a summary of the difference between eco-innovation and sustainable innovation. James (1997 in Charter and Clark 2007, 9) has defined eco-innovation as the “process of developing new products, processes or services which provide customer and business value but significantly decrease environmental impact”. Moreover, eco-innovation can be considered as “any form of innovation aiming at significant and demonstrable progress towards the goal of sustainable development, through reducing impacts on the environment or achieving a more efficient and responsible use of natural resources, including energy” (Competitiveness and Innovation Framework (2007 to 2013) in Charter and Clark 2007, 9) . The main differences between eco-innovation and sustainable innovation therefore lie in the different dimensions they encompass. Eco-innovation addresses economic and environmental dimensions, while sustainable innovation includes these as well as two broader dimensions: social and ethical (Charter and Clark 2007). Table 2 illustrates all the selected definitions of eco-innovation encompassed in our literature review. Eco-innovation Table 2: Selected definitions of eco-innovation Author Definition of eco-innovation Fussler and James (1996 in Car- Eco-innovation is the process of developing new products, processes or services, il o-Hermosil a et al. 2010, which provide customer and business value but significantly decrease environmen-1074) tal impact. Eco-innovations are new products and processes that provide customer and busi-James (1997) ness value but significantly decrease environmental impact. Eco-innovations include all measures of relevant actors (firms, politicians, unions, Rennings associations, churches, private households), which develop new ideas, behavior, (2000, 322) products and processes, apply or introduce them and which contribute to a reduction of environmental burdens or to ecological y specified sustainability targets. Environmental innovations consist of new or modified processes, techniques, practices, systems and products to avoid or reduce environmental harms. Environmental innovations may be developed with or without the explicit aim of reducing Rennings et al. (2004, 8) environmental harm. They may be motivated by the usual business goals such as re-29 ducing costs or enhancing product quality. Many environmental innovations combine an environmental benefit with a benefit for the company or user. Green innovation is a hardware or software innovation that is related to green products or processes, including the innovation in technologies that are involved in en-Chen et al. (2006, 332) ergy-saving, pollution prevention, waste recycling, green product designs, or corporate environmental management. Although no consumer product has a zero impact on the environment, in business, the terms ‘green products’ or ‘environmental product’ are used commonly to de-Ottman et al. (2006, 24) scribe those that strive to protect or enhance the natural environment by conserv-ing energy and/or resources and reducing or eliminating the use of toxic agents, pollution, and waste. Eco-innovation is any form of innovation aiming at significant and demonstrable Competitiveness and Innova-progress towards the goal of sustainable development, through reducing impacts tion Framework 2007 to 2013 on the environment or achieving a more efficient and responsible use of natural re- (in Charter and Clark 2007, 9) sources, including energy. Eco-innovation is the production, application or exploitation of a good, service, MEI – Measuring Eco-Innova- production process, organizational structure, or management or business method tion – research project (Kemp that is novel to the firm or user and that results, throughout its lifecycle, in a reduc-and Foxon 2007, 4; Kemp and tion of environmental risk, pollution and the negative impacts of resources use (in-Pearson 2007, 7) cluding energy use) compared to relevant alternatives. Eco-innovation is “the creation of novel and competitively priced goods, process-Reid and Miedzinski (2008, es, systems, services, and procedures designed to satisfy human needs and provide 2) – The EUROPE INNO-a better quality of life for everyone with a lifecycle minimal use of natural resourc-VA panel es (materials including energy and surface area) per unit output, and a minimal release of toxic substances”. A new or significantly improved product (good or service), process, organizational method or marketing method that creates environmental benefits compared to al-Community Innovation ternatives. The environmental benefits can be the primary objective of the innova-Surveys (CIS) in Belin et al. tion or the result of other innovation objectives. The environmental benefits of an (2009) innovation can occur during the production of a good or service or during the after-sale use of a good or service by the end user. In Pursuit of Eco-innovation Author Definition of eco-innovation Huppes and Ishikawa (2009, Eco-innovation is a change in economic activities that improves both the econom-1698) ic performance and the environmental performance of society. Environmental innovations are all innovations that have a beneficial effect on the Kammerer (2009, 2286) natural environment regardless of whether this was the main objective of the innovation. Environmental innovations can be defined as innovations that consist of new or Oltra and Saint Jean (2009, modified processes, practices, systems and products, which benefit the environ-567) ment and so contribute to environmental sustainability. Eco-innovations are innovations that consist of new or modified products, process-Ahmed and Kamruzzaman es, techniques, practices, organizations, markets and systems to avoid or reduce en- (2010, 10) vironmental harms. Eco-innovation is defined as an innovation that improves environmental perfor-30 mance (Carril o-Hermosil a et al., 2009), in line with the idea that the reduction in Carril o-Hermosil a et al. environmental impacts (whether intentional or not) is the main distinguishing fea- (2010, 1075) ture of eco-innovation. From the social point of view, it does not matter very much if the initial motivation for the uptake of eco-innovation is purely an environmental one. Eco-innovation is innovation that reduces the use of natural resources and decreases the release of harmful substances across the whole lifecycle. The understanding Eco-innovation Scoreboard of eco-innovation has broadened from a traditional understanding of innovating (2011b, VII) to reduce environmental impacts towards innovating to minimize the use of natural resources in the design, production, use, re-use and recycling of products and materials. Environmental innovation is defined as a sub-group of general innovations that contribute to an improvement of environmental quality or the use of fewer natural resources. This includes the advancement of existing or the development and mar-Rave, Goetzke and Larch ket introduction of new environmental y friendly products or environmental im- (2011, 12) provements through the modification or replacement of existing processes (add-on or integrated technologies). Environmental improvements may not be directly intended (i.e., they may only be a side effect of the innovation). Environmental innovations are organizational implementations and changes focusing on the environment, with implications for companies’ products, manufacturing processes and marketing, with different degrees of novelty. They can be merely incremental improvements that intensify the performance of something that already Angelo et al. (2012, 117) exists, or radical ones that promote something completely unprecedented, where the main objective is to reduce the company’s environmental impacts. In addition, environmental innovation has a bilateral relationship with the level of pro-active environmental management adopted by companies. Eco-innovation is the introduction of any new or significantly improved product Eco-innovation Scoreboard (good or service), process, organizational change or marketing solution that reduc- (2012b, 8) es the use of natural resources (including materials, energy, water and land) and decreases the release of harmful substances across the lifecycle. Eco-innovation Author Definition of eco-innovation Eco-innovation can be found in all forms of new, or significantly improved, prod-European Commission ucts, goods, services, processes, marketing methods, organizational structures, insti- (2012, 29) tutional arrangements and lifestyle and social behaviors, which lead to environmental improvements compared to relevant alternatives. Eco-innovation is defined as product, process, marketing, and organizational innovations, leading to a noticeable reduction in environmental burdens. Positive en-Horbach, Rammer and Ren- vironmental effects can be explicit goals or side effects of innovations. They can nings (2012, 119) occur within the respective companies or through customer use of products or services. The singularity of eco-innovation with regard to conventional innovation resides in its favorable effect on the environment, which improves social wellbeing. The con-Pereira and Vence (2012, 91) cept tries to highlight the compatibility between the two traditional y opposed goals of improving business competitiveness and the environmental care. From a theoretical perspective, eco-innovation has become an interdisciplinary concept; as a research field, it is established on the principles of innovation theories 31 and environmental science. Eco-innovation is studied as an aspect of innovation and thus is compared to the general innovation measures, even though it specifi-Dong et al. (2013, 2) cal y aims to improve firms’ long-term ecological performance, rather than to promote business operational efficiencies and/or profitability per se. Eco-innovation focuses on reducing the negative effects of excessive natural resource exploitation, environmental pollutant emissions, and ecological risks that emerge along the lifecycle of specific products and/or services. Eco-innovation can be a new good or service, process, organizational change, or marketing method in a company, but also a wider change with systemic implica-Wilts et al. (2013, 824) tions for economy and society (e.g., new production–consumption models based on services). Source: Fussler and James (1996 in Caril o-Hermosil a et al. 2010); James (1997); Rennings (2000); Rennings et al. (2004); Chen et al. (2006); Ottman et al. (2006); Competitiveness and Innovation Framework 2007 to 2013 (in Charter and Clark 2007); Kemp and Foxon (2007); Kemp and Pearson (2007); Reid and Miedzinski (2008); Belin et al. (2009); Huppes and Ishikawa (2009); Kammerer (2009); Oltra and Saint Jean (2009); Ahmed and Kamruzzaman (2010); Carril o-Hermosil a et al. (2010); Eco-innovation scoreboard (2011b); Rave, Goetzke and Larch (2011); Angelo et al. (2012); Eco-innovation scoreboard (2012b); European Commission (2012); Horbach, Rammer and Rennings (2012); Pereira and Vence (2012); Dong et al. (2013); Wilts et al. (2013). Features of eco-innovation In the following pages, we extract and delineate the main characteristics of eco-innovation, beginning with the lifecycle perspective (Kemp and Pearson 2007; Speirs, Pearson and Foxon 2008; EIO 2010 in EIO 2013a; EIO 2011b; EIO and CfSD 2013). The definition proposed by EIO emphasizes the full lifecycle perspective and not just environmental aspects of individual lifecycle stages (EIO and CfSD 2013). Inventing new prod- In Pursuit of Eco-innovation ucts and delivering new services is not the only issue of eco-innovation, which also includes reduction of environmental impacts in the way products are designed, produced, used, reused and recycled (EIO and CfSD 2013). The lifecycle perspective of eco-innovation includes the following stages (EIO and CfSD 2013): - resource extraction (reduction of environmental pressure and impacts by limiting extraction of virgin resources and also limi- ting “unused” extraction), - manufacture (with regard to using fewer resources – including energy), - use or substitution of materials with less environmental impacts, less pollution and waste production, 32 - distribution (reduction of impacts through better packing de- sign, reuse and recycling, reduction of fuel and energy in tran- sportation and storage), - use (use of less resources (e.g., materials, energy, land and water), less pollution and waste), - “end-of-life” (reduction of impacts of waste disposal by improving the quality of waste or decreasing the volume of waste). Reid and Miedzinski (2008, 4) summarize as follows: “All types of innovations leading to a lower resource and energy intensity at the stages of material extraction, manufacturing (both in relation to the components and final product), distribution, use, reuse and recycling as well as disposal are considered eco-innovations if they lead to a decreased resource-intensity from the perspective of the whole lifecycle of the product or a service. Indeed, the concept of cradle-to-cradle takes the minimization of waste to a logical extreme”. Furthermore, Figure 2 summarizes product lifecycle stages, which have been presented by Maxwell and van der Vorst (2003, 885). They have presented concept SPSD (sustainable product and/or service development) defined as “the process of making products and/or services in a more sustainable way throughout their entire lifecycle, from conception to end of life” (Maxwell and van der Vorst 2003, 884). These products and/or services are developed in order to balance economic, environmental and social aspects – they imply development towards sustainability regarding the Triple Bottom Line (Maxwell and van der Vorst 2003, 884). As we can see from Figure 2, the product and/or service lifecycle starts at conception (the stage of concept and design of a potential product, service or product service systems), followed Eco-innovation by remaining stages encompassing raw materials and all till the end of life of product/service/system as well as potential “recovery” and “reuse” options after the end of life (Maxwell and van der Vorst 2003). Therefore, the focus of eco-innovation should be oriented towards eco-innovation’s lifecycle, which implies that we should consider the use of resources from the beginning (the conception phase of product) till the end of the production process as well as when the product ‘expires’, referring to the end life of the product (i.e., waste), to prevent release of harmful substances into the environment. 33 Figure 2: Product lifecycle stages Source: Maxwell and van der Vorst 2003, 885. The second characteristic of eco-innovation is that of being more resource efficient (Competitiveness and Innovation Framework (2007 to 2013) in Charter and Clark 2007; Kemp and Foxon 2007; EIO 2010 in EIO 2013a). Kemp and Foxon (2007) argue that eco-innovation is not limited to new or better environmental technologies but includes every environmentally improved product or service and organizational change for the environment; that is, all new processes that are more resource efficient are eco-innovations (Kemp and Foxon 2007). As a third eco-innovation characteristic, we emphasize the environmental impact (James 1997 in Charter and Clark 2007; Competitiveness and Innovation Framework 2007 to 2013 in Charter and Clark 2007; Rennings 2000; Rennings et al. 2004; Kemp and Foxon 2007; Speirs, Pearson and Foxon 2008; Kammerer 2009; Ahmed and Kamruzzaman 2010; EIO 2011b; Angelo et al. 2012; Horbach, Rammer and Ren- nings 2012). The literature acknowledges eco-innovations to be environmentally benign and/or to benefit the environment, either intentionally or unintentionally, by introducing new or significantly improved products, processes, organizational changes or marketing methods (Kammer- In Pursuit of Eco-innovation er 2009: Machiba 2010; EIO 2010 in EIO 2013a; Belin et al. 2011; Fawzi and Rundquist 2011; Horbach, Rammer and Rennings 2012). In addition, we have to highlight that an innovation’s effects determine whether an innovation is environmental; therefore, the determinant is not an innovation’s intention (Fawzi and Rundquist 2011). In accordance to the previous, Belin et al. (2011) have emphasized that the environmental objective is generally not the direct and only intention of eco-innovation. They argue that the environmental objective comes in addition to other objectives (i.e., companies follow their main purposes such as competitiveness and productivity, while also seeking to stay in compliance with environmental regulatory requirements). Machiba (2010) summarizes that eco-innovation is innovation with an explicit emphasis on reducing environmental impact, whether this effect is intended or not. Therefore, 34 eco-innovation is not limited to environmentally motivated innovations but also includes “unintended reduction of environmental impact” (Kemp and Foxon 2007; Arundel and Kemp 2009; Machiba 2010; Fawzi and Rundquist 2011; Rave et al. 2011; Fleiter et al. 2012; Horbach, Rammer and Rennings 2012; Antonioli, Mancinelli, and Mazzanti 2013; Cainelli and Mazzanti 2013). Therefore, environmental improvements can happen by chance; they are not required to be the primary goal of a new eco-product or eco-process (Horbach et al. 2012). Fourth, eco-innovations can be introduced in various industries or sectors of the economy, such as in manufacturing, services, organizations, management styles, urban and rural planning and design, agriculture, and many other sectors (European Commission 2012). An important characteristic of eco-innovation, thus, is that eco-innovation can take place in any economic activity and is neither technology- nor sector-specific (Antonioli, Mancinelli, and Mazzanti 2013; Cainelli and Mazzanti 2013). Summarizing, we can see that eco-innovation in not just innovation or introduction of novelties regarding “eco/environmental area” but also involves improvement of already existing products, processes, services, technologies, organizations, marketing, and so on, with the aim of using more efficient and less harmful natural resources and materials, leading to less adverse effects on the environment and consequently bringing benefits to the environment or at least reducing the negative impacts released in the environment. Schiederig et al. (2012), in their review of different terminology encompassing green, eco, environmental and sustainable innovation, have identified six important aspects that create a linkage between them: 1) innovation object (product, process, service and method); Eco-innovation 2) market orientation, where the goal is to satisfy needs and be competitive on the market; 3) the environmental aspect, all four innovation notions aim to reduce negative impact (optimum or zero impact); 4) phase in the lifecycle; 5) impulse, where the intention for reduction is ecological or economical; and 6) level – setting up a new innovation or green standard for the firm (Schiederig et al. 2012). Finally, we should differentiate sustainable innovation from eco/green/environmental innovation, because sustainable innovation implies a broader concept and adds to the aforementioned dimensions a social dimension (Schiederig et al. 2012). In order to provide an instrument to identify and analyze the different characteristics and features of green products and practices, Dangelico and Pontrandolfo (2010) have developed The Green Option Matrix (GOM), which integrates different dimensions of green products. The three-dimensional GOM encompasses the following dimensions (see Ta-35 ble 3): - Phase of the product lifecycle: with regard to this dimension, the authors have considered three main phases: before usage (included materials extraction, production processes and transportati- on processes), usage and after usage (end-of-life); - The main environmental focus of the product: this dimension distinguishes the focus of green products on materials, energy and pollution; - The type of impact on the environment: this can be less negative (when green products have a lower environmental impact then conventional ones), null or positive (positive contribution to the environment). In Pursuit of Eco-innovation Table 3: Example of GOM (The Green Option Matrix) Focus Green product with focus on Green product with focus Green product with focus on materials on energy pollution Environmental impact The green product is more During production, uses less energy efficient than a con- Pollute less than convention- Less negative materials than convention- ventional one or part of the al products. al products. used energy derives from re- newable energy sources. During production, uses only recycled materials or natural/ Energy use only from renew- Green products that do not 36 Nul biodegradable materials at a able sources. pollute. sustainable rate. Is designed in such a man- ner as to be reused, disassem- bled and manufactured or is made of such materials that can be recycled, leading to re- Energy production from re- duction of the environmen- newable sources, leading to Reduction of pollution Positive tal impact of other products reduction of environmen- caused by other products. that will not require the vir- tal impact caused by other gin materials consumption. products. Those products, by al ow- ing a new life for materials, recall the concept of “cradle to cradle”. Source: adapted from Dangelico and Pontrandolfo 2010. Main dimensions of eco-innovation Prior research works, the objective of which was to delineate the main dimensions of eco-innovation and develop a psychometrically reliable and valid scale, have in common the same conclusion. Eco-innovation’s nature is a multi-aspect concept, which comprises production of an eco-product, carrying out an eco-process and at last managing an eco-organization (Arundel and Kemp 2009; Cheng and Shiu 2012; Tseng et al. 2013); therefore, we have to deal with it from a multidimensional perspective (Cheng and Shiu 2012). Arundel and Kemp (2009) noted that past research works and measurement activities focused merely on pollution control and abatement activities or on the environmental goods and services sector. Moreover, they have argued (Arundel and Kemp 2009) that Eco-innovation research and data collection encompassing eco-innovation should not be oriented to only environmentally motivated innovation; rather, researchers should overcome this limitation in the sense of comprising products, processes and/or organizational innovations with environmental benefits. In addition, Arundel and Kemp (2009) pointed out the fact that the attention should be broadened in order to include innovation oriented towards the following characteristics: resource use, energy efficiency, greenhouse gas reduction, waste minimization, reuse and recycling, new materials (e.g., nanotechnology) and eco-design. In the following pages, we first describe the concept of eco-innovation provided by OECD (2007 in OECD 2009). This concept comprises three dimensions, which are targets, mechanisms and impacts (see Figure 3). Moreover, we briefly summarize the dimensions as they did in OECD (2009, referring to the Oslo manual, OECD 2007), followed by dimen-37 sions of eco-innovation features proposed by Dong et al. (2013) and by a description of the main types of eco-innovation in more detail. Target Target refers to the basic focus of eco-innovation. Following the Oslo manual in OECD (2009), the target of eco-innovation can be: products (goods and services), processes (production method or procedure), marketing methods (promotion and pricing of products and other market-oriented strategies), organizations (in the sense of structure of management and responsibility distribution) and finally institutions (including broader societal area beyond a single organization’s control – such as institutional arrangements, social norms and cultural values). The target’s nature can be technological or non-technological. As we can also see from the scheme below (see Figure 3), eco-innovation products and processes tend to rely mainly on technological development, while eco-innovations in marketing, organizations and institutions rely more on non-technological changes (OECD 2007 in OECD 2009). In addition, researchers (Rennings 2000; Reid and Miedzinski 2008) suggest that eco-innovation includes innovation in social and institutional structures and therefore should not be limited to innovation in products, processes, marketing methods and organizational methods. Mechanisms The second dimension of eco-innovation is that of mechanisms. Adapted by Stevels (1997; Charter and Clark 2007), four main levels of eco-in- In Pursuit of Eco-innovation novation can be defined in the context of environmental improvement. The first level (i.e., modification) is incremental and regards small or progressive improvements to existing products. The second level (i.e., re-design) is the complete re-design of existing product concepts or “green limits”, where there is a major re-design of existing products (while the level of improvement that is technically feasible is limited). The third level (i.e., alternatives) regards functional or “product alternatives”; this refers to new product or service concepts that satisfy the same functional need (e.g., teleconferencing instead of travelling). Finally, the last level (i.e., creation) is that of systems as designs suitable for sustainable society (e.g., design and introduction of entirely new products, processes, procedures, organizations and institutions). Thus, mechanisms are related to where eco-innovation target takes place or is introduced (OECD 2009). 38 Eco-innovation’s impact on the environment Figure 3: Conceptual relationships between sustainable manufacturing and eco-innovation Source: OECD 2009, 15, Figure 5. The last dimension is eco-innovation’s impact. The impact that eco-innovation brings across its lifecycle or some other focus area refers to its effect on the environment (OECD 2009). Eco-innovation’s target and mechanism interplay with socio-technical surroundings and bring potential environmental impacts (OECD 2009). Certain mechanisms (e.g., alternatives and creation) generally bring higher potential environ- Eco-innovation mental benefits, but they are more difficult to co-ordinate, while mechanisms such as modification and re-design bring lower potential environmental benefits (OECD 2009). The environmental benefits do not have to be necessarily the primary objective of the innovation; they can be the result of other innovation objectives and can occur during the production of a good or service, or during the after sales use of a good or service by the end user (Arundel and Kemp 2009). In more detail, environmental impact concerns reduction of material and energy use; reduction of air, water, soil and noise emissions/pollution; replacement of hazardous substances and improved recycling of water, waste or materials during the production and after use of products (Horbach et al. 2012). Moreover, Dong et al. (2013) argue that the typology of eco-innovation dimensions is based on the categorization dimensions of general innovation. Dong et al. (2013) have summarized the eco-innovation fea-39 tures derived from the current literature and presented the dimensions of eco-innovation. The three dimensions identified by Dong et al. (2013) are innovation content, ecological/environmental target and innovation intensity. We scrutinize briefly the literature from which these dimensions are drawn. Rennings (2000) categorized eco-innovations into four types (focusing on their subjects): technology, society, organization and institution. Oltra and Saint (2009) distinguished the following eco-innovation types: product innovation, process innovation and organizational innovation. Examples of other categories include: disruptive innovation, sustainable innovation and system innovation proposed by MEI (Kemp and Foxon 2007). Further, Reid and Miedzinski (2008) developed an eco-innovation classification system in which they take into account environmental performance and therefore differentiate four types of eco-innovation: lifecycle innovation, product and process innovation, organizational innovation and marketing innovation. By eco-innovation’s technical characteristics and environmental impact, OECD (2009) has divided eco-innovation in the following categories: pollution management, clean technologies and products, natural resource management and eco-friendly products. Types of eco-innovation This chapter presents eco-innovation types. Types of eco-innovation are not congruent among research works; therefore, in this section, we present in more detail the classification of eco-innovation provided by EIO (2013). According to EIO (2013) types of eco-innovation are as follows: In Pursuit of Eco-innovation product, process, organizational, marketing, social and system eco-innovation. Product eco-innovation EIO (2013a) argue that product innovation encompass both goods (those that tend to minimize the overall impact on the environment though their production, while also emphasizing eco-design) and services. Eco-design as a part of product innovation regards resource constraints in the sense of designing a product in such a manner as to provide a reduction of environmental impact and less use of resources during operation and recovery options, which comprise repairing, remanufacturing or recycling (EIO 2013a). Product innovation, according to OECD (2005), is defined as the introduction of a good or service that is new or significantly improved 40 with respect to its characteristics or intended uses. This includes significant improvements in technical specifications, components and material, incorporated software, user friendliness or other functional characteristics (OECD 2005). Meanwhile, Dong et al. (2013) describe product eco-innovation as innovation that responds to the environmental needs of the market and the government and thereby aims to achieve long-term environmental performance by improving the resource effectiveness and optimization of environmental benefits in a product’s lifecycle. Implemented eco-product innovation brings environmental improvements to existing eco-products or the development of new eco-products (Cheng and Shiu 2012). In the previous description, we can see that Cheng and Shiu (2012) identify eco-innovation as the improvement of something “old” or already existing and eco-innovation as a total novelty. Product eco-innovations include novelties and existing products or services that are significantly improved in a way that minimizes their overall impact on environment (Reid and Miedzinski 2008). Furthermore, eco-product implementation focuses mainly on a product’s lifecycle in order to reduce environmental impact, because the principal environmental impact of many products stems from their use (e.g., fuel consumption and CO 2 emissions of cars) and disposal (e.g., heavy metals in batteries) (Cheng and Shiu 2012). According to Kemp and Foxon, product or service eco-innovation refers to a new or improved product/good/service that offers environmental benefits and is less pollution- and resource-intensive, including eco-houses, eco-buildings and eco-services such as car sharing. According to Reid and Miedzinski (2008), products can include various goods with different numbers of components (e.g., just a household appliance or an entire house) and various types of services (e.g., new public Eco-innovation mobility schemes, car sharing and environmental services, waste management, environmental consulting). In summary, product eco-innovation tends to use less or non-polluting/toxic materials (using environmentally friendly material); improving and designing environmentally friendly packaging (e.g., less paper and plastic material used) for existing and new products; recovery of a company’s end-of-life products and recycling; and using eco-labeling (Chiou et al. 2011; Ar 2012). Product eco-innovation also consists of development of new eco-products through new technologies to simplify their packaging, construction and components, with the goal of easily recycling their components and easily decomposing their materials, followed by development of new eco-products through new technologies to avoid the use of processed materials and instead use natural materials and reduce of waste and damage by waste as much as possible with as litte use of energy as possible (Cheng and Shiu 2012). Chassa-41 gnon and Haned (2014, p: 3) argue that product eco-innovation requires the development of new eco-friendly goods or services, such as products free of harmful chemicals (e.g., phosphates or solvents). Process eco-innovation The main characteristics that define process innovations are reduction of material use, lower risk and cost savings as a result (EIO 2013a). Furthermore, OECD (2005) defined process innovation in general as implementation of a new or significantly improved production or delivery method. This includes significant changes in techniques, equipment and/or software (OECD 2005). Rennings et al. (2006) argue that environmental process innovations comprise or are commonly subdivided into end-of-pipe technologies and cleaner production technologies (i.e., innovation in integrated technologies). In more detail, end-of-pipe technologies reduce the impact of pollution at the end of the production process without modifying it, while cleaner technologies imply a change in the production process, such as the use of an alternative process that is less harmful to the environment than the conventional one or a reduction of input (Chassagnon and Haned 2014). Process eco-innovation, according to Dong et al. (2013), is not limited to explicit environmental performance (reduction of clean production cost and decrease of the pollutant emission in order to achieve compliance with environmental regulations) but also encompasses the tacit environmental performance (i.e., increase of resource utilization and pollution protection). According to Cheng and Shiu (2012) eco-process innovations refer to the introduction or manufacturing of processes that lead to a reduction of environmental impact, In Pursuit of Eco-innovation such as closed loops for solvents, material recycling or filters. Furthermore, process eco-innovation also involves the improvement of existing production processes or the implementation of new processes to reduce environmental impact (Cheng and Shiu 2012). Process eco-innovation reflects support for novel technological and non-technological solutions, which result in the reduction of material and energy costs of companies (European Commission 2012). In summary, process eco-innovation includes low consumption of energy sources such as water, electricity, gas and petrol during production/use/disposal; recycle, reuse and remanufacture of material; and use of cleaner technology to produce savings and prevent pollution (such as energy, water and waste) (Chen et al. 2006; Chen 2008; Chiou et al. 2011; Wong 2012; Tseng et al. 2013). 42 Technological eco-innovation Process innovations can be grouped in two broader categories: end-of-pipe technologies and clean technologies (del Río 2005; Triguero et al. 2013). End-of-pipe technologies are defined as “devices or plants added at the end of the production process with the aim to transform primary emissions into substances easier to handle. They do not involve changes in the production processes”; on the other hand, clean technologies are “changes in production processes that reduce the quantity of wastes and pollutants generated in the production process or during the whole lifecycle of the product (clean products)” (del Río 2005, 22). According to the VDI (2001 in Rennings et al. 2006, 47-48) typical examples of end-of-pipe technologies are: incineration plants (waste disposal), wastewater treatment plants (water protection), sound absorbers (noise abatement) and exhaust-gas cleaning equipment (air quality control). Examples ofclean-er production technologies are (according to the VDI 2001 in Rennings et. al 2006, 48): the recirculation of materials, the use of environmentally friendly materials (replacement of organic solvents by water) and the modification of the combustion chamber design (process integrated systems). In summary, end-of-pipe technologies (incremental innovations) require an increase in capital and also costs derived from maintenance but do not lead to an increase in production, while clean technologies (radical innovations), through a reduction of materials and energy consumption, lead to improved efficiency of the production process and furthermore have the potential to increase firm productivity and competitiveness (del Río 2005). Cleaner production technologies follow a preventive approach to environmental problems by reducing emissions at the source (i.e., they do not need to be dealt with afterwards), while end-of-pipe technologies Eco-innovation follow a reactive approach, treating emissions and discharges after they have been generated (del Río 2009). Cleaner technologies seem to be economically superior and may lead to economic benefits for adopting companies acquired through reduced energy costs, material cost savings and/ or greater revenues (del Río 2009). The economical superiority of cleaner technologies can be recognized even though that they require significant up-front investments (e.g., total reconfiguration of the company’s production process or other major changes such as hiring specialized staff or retraining the workforce) (del Río 2009). On the other hand, end-of-pipe technologies do not lead to efficiency in the production process; they involve only sunk costs (del Río 2009). According to Tseng et al. (2012), technological eco-innovations are the key player in giving information to comprehensive material-saving plans and management of documentation and information. With regard to Tseng et al. (2012) investment in 43 green equipment and installation of advanced green production technology plays a strategic role as a motive/stimulus and as a support for innovation effort. By reducing the consumption of energy and other resources and consequently contributing to the decrease of waste and emissions, environmental technologies lead to cost reduction and improved competitiveness (Klassen and Whybark 1999 in Murovec et al. 2012). Therefore, environmental technologies can be divided into two groups: those that aim to reduce the negative effects of pollution and/or improve the production process (such as cleaner technologies and end-of-pipe technologies) and those that are a part of the manufacture of environmentally-friendlier products (UN-DESA 1999 in Murovec et al. 2012). Organizational eco-innovation Organizational innovation implies implementation of a new organizational method in the firm’s business practices, workplace organization or external relations (OECD 2005). Therefore, organizational eco-innovation aims to enhance the total environmental performance on the basis of firm’s environmental vision – that is, to improve and sustain the ecological benefits and resource efficiency and expand the firm’s social responsibility as well (Dong et al. 2013). Rennings et al. (2006) explain that environmental organizational innovations aim to reduce environmental impacts and encompass reorganization of processes and responsibilities within the company (e.g., EMS). Their contribution can also lead to technological opportunities for the company, and they may act as supporting factors for technical environmental innovations (Rennings et al. 2006). Several researchers (Kemp and Foxon 2007; Kemp and Pear- In Pursuit of Eco-innovation son 2007) have written that organizational eco-innovation refers to the introduction of new organizational methods and management systems for coping with environmental issues in production and products. Furthermore, they have classified organizational eco-innovation as: pollution prevention schemes (prevention of pollution through input substitution, a more efficient operation of processes and small changes to production plants); environmental management and auditing systems (formal system that involves measurement, reporting and responsibilities for dealing with issues of material use, energy, water and waste; e.g., EMAS and ISO 14001); and chain management (cooperation between companies to close material loops and to avoid environmental damage across the value chain – “from cradle to grave”). Among the management instruments on a firm level are eco-audits (Rennings 2000). “The Eco-audit should provide a 44 list of recommended actions, in terms of increasing cost-effectiveness in addressing the critical environmental issues. This list should include in-terim and long-term targets and a timetable for achieving them, together with an indication of the investments and other resources (human, information, and so on) that would be required. The following points relate to the procedures for the execution of an Eco-Audit” (World Bank, 1995 in Sarkar 2013, 214). Cheng and Shiu (2012) distinguish the following types of organizational eco-innovation: use of novel systems to manage eco-innovation, use of eco-innovation as one of a unit’s management policies, collection of information on eco-innovation trends, active engagement in eco-innovation activities, communication of eco-innovation information to employees, applying the concept of eco-innovation to unit management, investment of a high ratio of R&D in eco-innovation and communication of experiences among various departments involved in eco-innovation. Organizational eco-innovations include any reorganization in the company intended to reduce the negative impact on the environment, such as environmental management systems (Chassagnon and Haned 2014, 3). Organizational eco-innovations, therefore, comprise: environmental management systems (e.g., ISO 14000 family standards or the voluntary EU instrument on the Eco-Management and Au- dit Scheme (EMAS)) or other specific environmental management tools such as process control tools, environmental audits or “chain” management (Reid and Miedzinski 2008). In addition, ISO 14001 is more a response to external pressure (customer requirements, public image, stakeholders’ and regulatory pressure), while EMAS tends to be motivated internally by corporate culture and influential individuals (Neugebauer 2012). Regarding environmental management systems (focusing on Eco-innovation ISO 14001 and EMAS), Frondel et al. (2008) find that the EMS adoption strongly correlates with an expected enhancement of corporate image, while it is negatively linked to the expected cost savings (EMS adoption can be assumed to be costly). Moreover, neither the occurrence of environmental incidents nor environmental regulatory compliance seem to be effective drivers for spurring EMS adoption, although those two drivers effectively induce eco-innovation and abatement activities (Frondel et al. 2008). Marketing eco-innovation Marketing innovation is the implementation of a new marketing meth-od involving significant changes in product design or packaging, product placement, product promotion or pricing (OECD 2005; EIO 2013a). 45 Eco-innovation in marketing comprises new ways of integrating environmental aspects in communication and sales strategies (OECD 2009). For example, a company improves a general product, then further develops it and/or sells eco-efficient products through better market research, contacting its consumers directly and using marketing practices that appeal environmentally aware consumers (OECD 2009). Therefore, mar- keting eco-innovation tends to discover which marketing techniques can be used to stimulate people to buy, use or implement eco-innovations; thus, it involves changes or development in product design or packaging, product placement, product promotion, pricing and also eco-labeling (EIO 2013a). In addition to the previous types of marketing eco-innovation, Kinoti (2011) suggest the following marketing green innovations: green products strategies, green consumption and green probe strategies (marketing information system). Herbig et al. (1993 in Kinoti 2011) has stressed that green marketing refers to products and packages that have one or more of the following characteristics: they are less toxic, are more durable, contain reusable materials and/or are made of recyclable materials. For companies and in marketing terms, brand is key to understanding the process of commercialization of products or services (EIO 2013a). A brand represents a collection of symbols, experiences and associations, which are linked with a product or service by potential customers (EIO 2013a). Moreover, green branding is important, but it is not the only or the best way to sell eco-innovations. Another important aspect of eco-innovation’s marketing, as aforementioned, is eco-labeling (EIO 2013a). In Pursuit of Eco-innovation Social eco-innovation One of the aspects of social eco-innovation is that any discussion of resource consumption considers the human element to be integral. Social eco-innovation includes market-based dimensions of behavioral and lifestyle change and consequently focuses on ensuring the demand for green goods and services. Some companies try to follow and practice the so-called user-led innovation, through which the functionality of new goods is developed with stakeholders and the risk of superfluous product features is minimized. Another important aspect that leads to an absolute decrease of material use without decreasing the provided quality of services to the user is product sharing (EIO 2013a). System eco-innovation 46 System eco-innovation refers to a series of connected innovations that bring improvement or create entirely new systems delivering specific functions with a reduced overall environmental impact (EIO 2013a). Its key feature is a collection of changes implemented by design. This means that, for example, a system eco-innovation related to a house is not just about window isolation or just use of a better heating system; it is about innovating the entire design to improve its functionality (EIO 2013a). EIO (2013a, 3) proposes another example of system innovations called “Green cities”: “when innovation and planning efforts lead to a combination of changes to make the functioning of the city and city life more ‘green’. This includes, for instance, new mobility concepts that tackle not only traditional public transportation services (e.g. buses) but also shared-bike systems (and related infrastructure like bike stations) as well as planning to reduce the need for travel (requiring that supermarkets, day care facilities, etc. are incorporated in new housing developments)” . Kemp and Foxon (2007) have defined green system innovations as alternative systems of production and consumption, which are more benign than already existing systems (e.g., biological agriculture, renewable-based energy systems). The European Commission (2012) includes in systemic eco-innovations comprehensive solutions based on innovative business models (e.g., smart cities), sustainable mobility and industrial ecology. Measuring eco-innovation Researchers (Speirs, Pearson, and Foxon 2008) argue that the lack of relevant data and indicators hinders policies and measures related to the pro- Eco-innovation motion of eco-innovation. Therefore, Arundel and Kemp (2009) summarized the following measures of eco-innovation: - Input measures: research and development (R&D) expenditures, innovation expenditures (inclusion of investment in intangibles, such as design expenditure, software and marketing costs) and R&D personnel (Acs and Audretsch 1993, 10 in Arundel and Kemp 2009, 15); - Intermediate output measures: the number of patents (regarding eco-innovation – patents covering eco-inventions), number and types of scientific publications, etc. (Acs and Audretsch 1993, 10 in Arundel and Kemp 2009, 15); - Direct output measures: number of innovations, individual description of innovation, data on sales of new products etc. 47 (Acs and Audretsch 1993, 10 in Arundel and Kemp 2009, 15); - Indirect impact measures derived from aggregate data: changes in resource efficiency and productivity using decomposition analysis (Arundel and Kemp 2009, 15). In the following pages, we depict types of eco-innovation and measures used in prior research (Table 4). We focused only on rese- arch works that have explored in their research at least two types of eco-innovation. In Pursuit of Eco-innovation Table 4: Types of eco-innovation used in previous studies examining more than one eco- -innovation type Author, year and Eco-innovation main dimensions publication name Eco-product Eco-process Eco-organization- innovation innovation al innovation Our unit often em- Our unit often up- Our unit manage- phasizes developing dates manufacturing ment often: new eco-products processes to: 1. Uses novel systems through new tech- 1. Protect against to manage eco-inno- nologies to: contamination vation 1. Simplify their pack- 2. Meet standards of 2. Collects informa-aging environmental law tion on eco-innova- 2. Simplify their con- 3. Our unit often in- tion trends struction troduces new tech- 3. Actively engages 48 3. Easily recycle their nologies into manu- in eco-innovation ac- Cheng and Shiu components facturing processes tivities (2012); Technovation 4. Easily decompose to save energy 4. Communicates their materials 4. Our unit often up- eco-innovation in- 5. Use natural ma- dates equipment in formation with em- terials manufacturing pro- ployees 6. Reduce damage cesses to save energy 5. Invests a high ratio by waste as much as of R&D in eco-inno- possible vation 7. Use as little energy 6. Communicates ex- as possible periences among var- ious departments involved in eco-inno- vation Eco-innovation Author, year and Eco-innovation main dimensions publication name Eco-product Eco-process Management Technological innovation innovation innovation innovation 1. Degree of new 1. Low energy con- 1. Redefine operation 1. Implementation of green product com- sumption such as wa- and production pro- comprehensive ma- petitiveness to un- ter, electricity, gas cesses to ensure in- terial saving plan derstand custom- and petrol during ternal efficiency that 2. Supervision sys- er needs production/use/dis- can help to imple- tem and technology 2. Evaluation of tech- posal ment green supply transfer nical, economic and 2. Recycle, reuse and chain management 3. Advanced green commercial feasibili- remanufacture of 2. Re-design and im- production tech- ty of green products material provement of prod- nology 3. Recovery of com- 3. Use of cleaner uct or service to 4. Management of 49 pany’s end-of-life technology to gener- obtain new environ- documentation and products and recy- ate savings and pre- mental criteria or di- information Tseng et al. (2013), cling vent pollution (such rectives Journal of Cleaner 4. Using eco-label- as energy, water and 3. Reduction of haz- Production ing, environment waste) ardous waste, emis- management system 4. Sending in-house sion, etc. and ISO 140001 auditor to appraise 4. Less consumption 5. Innovation of environmental per- of resources, e.g., wa- green products and formance of supplier ter, electricity, gas design measures 5. Process design and and petrol 6. Investment in innovation and en- 5. Install environmen- green equipment and hancement of R&D tal management sys- technology functions tem and ISO 14000 6. Low cost green series provider: unit cost 6. Providing environ- versus competitors’ mental awareness unit cost seminars and training for stakeholders In Pursuit of Eco-innovation Author, year and Eco-innovation main dimensions publication name Eco-product Eco-process innovation innovation Our firm often plac- Our firm often inno- es emphasis on devel- vatively updates man- oping new eco-prod- ufacturing process- ucts through new es to: technologies to: 1. Protect against 1. Simplify their pack- contamination aging 2. Meet standards of 2. Simplify their con- environmental law Cheng et al. (2013), struction 3. Our firm often uses Journal of Cleaner 3. Easily recycle their innovative technol- Production components ogies in manufactur- 4. Easily decompose ing processes to save their materials energy 50 5. Use natural ma- 4. Our firm of- terials ten innovatively up- 6. Reduce damage dates manufacturing from waste as much equipment in man- as possible ufacturing processes 7. Use as little energy to save energy as possible Eco-innovation Author, year and Eco-innovation main dimensions publication name Eco-product Eco-process innovation innovation The company choos- The manufacturing es the materials of process of the com- the product that: pany: 1. Produces the least 1. Effectively reduces amount of pollu- the emission of haz- tion for conducting ardous substances the product develop- or waste ment or design 2. Recycles waste and 2. Consumes the emissions that al ow least amount of ener- them to be treated gy and resources for and re-used conducting the prod- 3. Reduces the con- Chen et al. (2006), uct development or sumption of water, Journal of Business design electricity, coal or oil 51 Ethics; Chen (2008), 3. The company uses 4. Reduces the use of Journal of Business the smal est amount raw materials Ethics of materials to com- prise the product for conducting the prod- uct development or design 4. The company cir- cumspectly delib- erates whether the product is easy to re- cycle, reuse, and de- compose for con- ducting the product development or de- sign In Pursuit of Eco-innovation Author, year and Eco-innovation main dimensions publication name Eco-product Eco-process innovation innovation 1. Our new products 1. Our produc- use less or non-pol- tion processes con- luting/ toxic ma- sume less terials resource (e.g. water, 2. Our new products electricity, etc.) than use environmental y those of our com- friendly packing petitors 3. When designing 2. Our production new product, we take processes recycle, re- recycling and dispos- use and al at end-of-life into remanufacture mate- account rials or parts Wong (2012), Euro- 4. Our new prod- 3. Our production 52 pean Journal of Inno- ucts use recycled ma- processes use clean- vation Management terials er or renewable tech- 5. Our new products nology to generate use recyclable ma- savings (such as ener- terials gy, water and waste) 4. We redesign our production and op- eration processes to improve environ- mental efficiency 5. We redesign and improve our prod- ucts or services to meet new environ- mental criteria or di- rectives Eco-innovation Author, year and Eco-innovation main dimensions publication name Eco-product Eco-process Eco-organization- innovation innovation al innovation Our firm often plac- Our firm often inno- Our firm ’s manage- es emphasis on devel- vatively updates man- ment often: oping new eco-prod- ufacturing process- 1. Uses novel man- ucts through new es to: agement systems to technologies to: 1. Protect against manage eco-inno- 1. Simplify their pack- contamination vation aging 2. Meet standards of 2. Collects informa- 2. Simplify their con- environmental law tion on eco-innova- struction 3. Our firm often uses tion trends 3. Easily recycle their innovative technol- 3. Actively engages Cheng et al. (2013), components ogies in manufactur- in eco-innovation ac- Journal of Cleaner 4. Easily decompose ing processes to save tivities Production their materials energy 4. Communicates 5. Use natural ma- 4. Our firm of- eco-innovation in- 53 terials ten innovatively up- formation with em- 6. Reduce damage dates manufacturing ployees from waste as much equipment in man- 5. Invests a high ratio as possible ufacturing processes of R&D in eco-inno- 7. Use as little energy to save energy vation as possible 6. Communicates ex- perience among var- ious departments involved in eco-inno- vation In Pursuit of Eco-innovation Author, year and Eco-innovation main dimensions publication name Eco-product Eco-process innovation innovation The company choos- The manufacturing es the materials of process of the com- the product that: pany: 1. Produces the least 1. Effectively reduces amount of pollu- the emission of haz- tion for conducting ardous substances the product develop- or waste ment or design 2. Recycles waste and 2. Consumes the emissions that al ow least amount of ener- them to be treated gy and resources for and re-used conducting the prod- 3. Reduces the con- Chen et al. (2006), uct development or sumption of water, 54 Journal of Business design electricity, coal or oil Ethics; Chen (2008), 3. The company uses 4. Reduces the use of Journal of Business the smal est amount raw materials Ethics of materials to com- prise the product for conducting the prod- uct development or design 4. The company cir- cumspectly delib- erates whether the product is easy to re- cycle, reuse, and de- compose for con- ducting the product development or de- sign Eco-innovation Author, year and Eco-innovation main dimensions publication name Eco-product Eco-process innovation innovation 1. Our new products 1. Our produc- use less or non-pol- tion processes con- luting/ toxic ma- sume less terials resource (e.g. water, 2. Our new products electricity, etc.) than use environmental y those of our com- friendly packing petitors 3. When designing 2. Our production new product, we take processes recycle, re- recycling and dispos- use and al at end-of-life into remanufacture mate- account rials or parts Wong (2012), Euro- 4. Our new prod- 3. Our production pean Journal of Inno- ucts use recycled ma- processes use clean- 55 vation Management terials er or renewable tech- 5. Our new products nology to generate use recyclable ma- savings (such as ener- terials gy, water and waste) 4. We redesign our production and op- eration processes to improve environ- mental efficiency 5. We redesign and improve our prod- ucts or services to meet new environ- mental criteria or di- rectives In Pursuit of Eco-innovation Author, year and Eco-innovation main dimensions publication name Green product Green process Green managerial innovation innovation innovation 1. Using less or 1. Lower consump- 1. Redefine opera- non-polluting/toxic tion of resources, e.g., tion and production materials (using envi- water, electricity, gas processes to ensure ronmental y friendly and petrol during internal efficiency materials) production/use/dis- that can help to im- 2. Improving and de- posal plement GSCM signing environmen- 2. Recycle, reuse and (Green Supply Chain Chiou et al. (2011) tal y friendly pack- remanufacture of Management) Transportation Re- aging (e.g., less paper materials or parts 2. Re-designing and search Part E-Logis- and plastic material 3. Use of cleaner or improving product tics and Transporta- used) for existing and renewable technol- or service to obtain tion Review (based new products ogy to generate sav- new environmental on the items pro- 3. Recovery of com- ings (such as energy, criteria or directives 56 posed by Chen et pany’s end-of-life water, waste) al. (2006) and Chen products and recy- 4. Redesign of pro- (2008)) cling duction and opera- 4. Using eco-labeling tion processes to im- prove environmental efficiency 5. Redesigning and improving products or services to meet new environmental criteria or directives Eco-innovation Author, year and Eco-innovation main dimensions publication name Operational Tactical Strategic practices 1. Supply chain man- 1. Integration with 1. Recycling agement long-term business 2. Waste reduction 2. Early supplier in- strategy (proactive) volvement 2. Corporate policies 3. Waste reduction 3. Environmen- and procedures (reactive) tal standard for sup- 3. Environmental 4. Remanufacturing pliers mission statement 5. Substitution 4. Environmental au- 4. Employee pro- 6. Consume inter- dits suppliers grams nal y 5. Environmental 5. Environmental de- Montabon et al. 7. Packaging awards partments/teams (2007) Journal of 8. Spreading risk 6. Environmental 6. Surveil ance of the Operations Manage- 9. Market for waste participation market for environ- ment; 10. Energy: energy 7. Lifecycle analysis mental issues Hofer et al. (2012) conservation, effi- 8. Product develop- 7. Strategic al iance 57 Journal of Opera- ciency, recovery, fuel ment and innovation tions Management recovery 9. Design (eco-de- 11. Money spent on sign) environmental ini- 10. Design targets/ tiatives goals 12. Environmental in- 11. Environmental formation risk analysis 13. Rewards as incen- 12. Environmen- tive for environmen- tal management sys- tal project tems 13. Communication with stakeholders In Pursuit of Eco-innovation Author, year and Eco-innovation main dimensions publication name Environmental Organizational Product technologies: innovation: and service 1. Pollution control 1. Pollution preven- innovation: technologies includ- tion schemes: aimed 1. New or environ- ing waste water treat- at prevention of pol- mental y improved ment technologies lution through input products (goods) in- 2. Cleaning technol- substitution, a more cluding eco-houses ogies that treat pollu- efficient operation of and buildings tion released into the processes and small 2. Environmental ser- environment changes to produc- vices: solid and haz- 3. Cleaner process tion plants (avoiding ardous waste man- technologies: new or stopping leakages agement, water and manufacturing pro- and the like) waste water manage- cesses that are less 2. Environmental ment, environmen- Kemp and Foxon polluting and/or management and tal consulting, test- 58 (2007) Project Paper more resource ef- auditing systems: ing and engineering, Measuring Eco-inno- ficient formal systems of testing and analytical vation; 4. Waste manage- environmental man- services Kemp and Pearson ment equipment agement involving 3. Services that are (2007) Final Report 5. Environmental measurement, re- less pollution and re- MEI Project About monitoring and in- porting and respon- source intensive, such Measuring Eco-instrumentation sibilities for dealing as is car sharing novation 6. Green energy tech- with issues of materi- nologies al use, energy, water 7. Water supply and waste (EMAS 8. Noise and vibra- and ISO 14001 are tion control examples) 3. Chain manage- ment: cooperation between compa- nies to close materi- al loops and to avoid environmental dam- age across the value chain (from cradle to grave) Eco-innovation Author, year and Eco-innovation main dimensions publication name Operational Waste manage- Design for envi- practices ment practices ronmental prac- 1. Reduce fuel costs 1. Dispose of haz- tices 2. Optimize distribu- ardous waste appro- 1. Use non-hazardous tion network priately materials 3. Reduce polluting 2. Have a recycling 2. Design products emissions to air and program to be easy to repair water 3. Use re-useable and/or last longer 4. Set measurable tar- packaging 3. Design products to gets for reducing en- 4. Minimize product be easy to disassem- Lewis and Cassells ergy usage packaging ble and/or recycle (2010) Internation- 5. Treat or capture 5. Set measurable tar- 4. Replace virgin ma- al Journal of Business polluting emissions gets for waste re- terials with recycled Studies 6. Demonstrate a duction materials preference for green 6. Take back pack- products in pur- aging 59 chasing 7. Take back end-of- 7. Set measurable tar- life products gets for reducing wa- ter usage 8. Select cleaner methods of transpor- tation In Pursuit of Eco-innovation Author, year and Eco-innovation main dimensions publication name Pollutants/wastes Manufacturing Products or services (end-treatment) technique (cleaner 1. Environmental per- 1. Water treatment production) formance of prod- works or facilities are 1. Cleaner produc- ucts was evaluated available, such as bi- tion was assessed 2. Products were ological treatment, 2. Main equipment marketed as environ- and physical and was technical y mod- mental or green chemical treatment ernized with capacity 3. Products were au- equipment expansion thenticated to be 2. Air pollution con- 3. Process routes were environmental, en- trol projects or facili- improved or replaced ergy-saving or wa- ties are available, such 4. Raw materials ter-saving as precipitators, de- were replaced 4. Specific labels, Dong et al. (2013) sulfurization, denitri- 5. Energy system such as energy effi- Journal of Engineer- fication or incinera- was improved or re- ciency grade, recycla- 60 ing and Technology tors equipment placed, such as oil re- ble, energy- saving, Management 3. Solid waste or haz- placed by gas were attached on the ardous waste treat- 6. Toxic raw materi- products ment projects or fa- als were replaced or 5. Specific environ- cilities are available, abandoned mental performance such as incinerator, 7. Main waste was re- was addressed in the landfill cycled in plants R&D of novel prod- 4. Degraded, dam- 8. Main waste was re- ucts aged or destroyed cycled through other 6. Specific environ- ecosystems in plants company mental performance were recovered was indicated in the 5. Detection instru- product packaging ments were applied to environmental monitoring in plants Eco-innovation Author, year and Eco-innovation main dimensions publication name 1. Environ- 7. Use of waste of 13. Design consider- 18. Environmen- ment-friendly raw other companies ations tal improvement of materials 8. Recycling of ma- 14. Optimization of packaging 2. Choice of suppli- terials internal to the processes to reduce 19. Taking back pack- ers by environmental company water use aging criteria 9. Change to more 15. Optimization of 20. Use of alternative 3. Taking environ- environmen- processes to reduce sources of energy mental criteria into tal-friendly transpor- noise 21. Recovery of the consideration tation 16. Helping suppli- company’s end-of-life Rao and Holt (2005) 4. Optimization of 10. Providing con- ers to establish their products International Jour- processes to reduce sumers with infor- own EMS nal of Operations & solid wastes mation on environ- 17. Eco-labeling Production Man- 5. Optimization of mental y friendly agement processes to reduce products and/or pro- air emissions duction methods 6. Use of cleaner 11. Substitution of en- 61 technology processes vironmental y ques- to make savings (en- tionable materials ergy, water, wastes) 12. Urging/pressur- ing supplier(s) to take environmental actions Toward a new definition of eco-innovation Eco-innovation covers a variety of innovations, including products, processes, and organizational methods. They can be new (i.e., development of a new product, process or organizational method) or modified (in terms of significant improvements of an already existing product, process or organizational method). They can be either implemented or developed by the company (further divided into novelty in the company, novelty on the market where the company operates (domestic or global), or worldwide novelty (e.g. patented invention)). Eco-innovations can further stem from different reasons; the major driving force is competitive pressure, followed by market demand. Other effective drivers of eco-innovation in companies are managerial environmental concern and environmental policy instruments (the command-and-control instrument and the economic incentive instrument). Its outcome usually results in a decrease of the environmental burden (less adverse effects on the environment), as well as economic and competitive benefits to the company that adopts or develops them. In some cases, when significant improvements or developments occur, eco-innovations also benefit the company (higher company profitability, mostly stemming from cost savings). In sum, eco-innovations deliver several benefits to the company, including In Pursuit of Eco-innovation economic and competitive benefits and a higher degree of internationalization (i.e., entering more foreign countries, higher share of sales abroad and use of more operation types). Based on the results of the conducted study, we define eco-innovations as follows. Eco-innovations encompass environmental and economic dimensions and include a variety of new or significantly improved products, processes, organizational methods and systems that are more environmentally friendly than the existing ones. They stem mainly from competitive pressure and customer demand. The most important outcome of eco-innovations (which can be intentional or a side effect) pertains to decreased adverse effects to the environment. From the environmental point of view, eco-innovations decrease the company’s environmental burden, while from the economic point of view, being eco pays 62 off, as they result in a gain of competitive and economic benefits, as well as a higher degree of internationalization. Table 5: Main characteristics of eco-innovation Eco-innovation Eco-innovation Eco-innovation – main characteristics – the strongest drivers – outcomes Decreased adverse effects to the en- Encompass environmental and eco- Competitive pressure vironment (can be intentional or a nomic dimensions side effect) Include a variety of new or signifi- cantly improved products, process- es, organizational methods and sys- Customer demand Gain of competitive benefits tems that are more environmental y friendly than the existing ones Decrease the company’s environ- Gain of economic benefits mental burden Higher degree of international- Economical y pays off ization Source: own elaboration based on survey results Drivers of Eco-innovation Motives for companies’ adoption of eco-innovation may be legal, moral, financial, public relations image or human resources-related (Johnson 2009). Eco-innovations as such have its own peculiarities, which demand different treatment as regular innovations when exporing their drivers. Eco-innovation is distinct from general innovations mainly because of the production of two positive externalities, which require regulatory push/pull factors as a driver (De Marchi, 2012; Horbach, 2008; Rennings, 2000). Van den Bergh (2013) pointed out that eco-innovations cover a broader set of drivers than regular innovations. The reason for this lies in their inspiration; eco-innovations are driven not only by market opportunities but also by health, environmental and ethical concerns (van den Bergh 2013). In the following pages, we provide a more detailed description related to the findings of past research on drivers of eco-innovation. First, we describe drivers of eco-innovation pertaining to the environmental policy instruments, followed by demand side, competition, society, expected benefits, sources of information, organizational capabilities and managerial environmental concern. Environmental policy instruments “Public environmental policy affects the way organizations think and act, and therefore, their adaptation to the challenges relating to protection of the natural environment” (Camisón 2010, 346). The driving forces from the government consist of governmental incentives, regulations In Pursuit of Eco-innovation and assistance (Zeng et al. 2011). In our study, we use the term environmental policy instruments, as have several researchers (Horbach et al. 2012; Murovec et al. 2012), and in testing hypotheses we further break them down into two individual components: the command-and-control instrument and the economic incentive instrument (as practiced by Li 2014). In this section, we describe in more detail the effect of environmental policy measures on eco-innovation’s adoption. The traditional view divides policy instruments for inhibiting environmental degradation into two general categories: “command-and-control” standards and market-based approaches (Popp et al. 2009 in Ford et al. 2014). Likewise, Li (2014) distinguishes the command-and-control instrument and the economic incentive instrument. Testa et al. (2014) summarize del Brío et al. (2003), who base their classification of pol-64 icy instruments on how compulsory they are, resulting in the following three categories: direct regulation (command-and-control), marked-based instruments (economic instruments) and soft instruments. First, “polluter pays principle” (i.e., direct regulation) or command-and-control regulations include standards such as mandatory limitations and prohibitions (Camisón 2010). By direct regulations, which impose setting specific standards and limits on performance and/or requirements about the adoption of technologies and processes and later check their compliance with regulations through controls and inspections, companies are forced to adapt to new environmental changes (Camisón 2010). Second, we distinguish market-based or, more broadly, economic instruments. Zylicz (2010 in Testa et al. 2014) summarizes a number of potential advantages of market-based instruments over direct regulation: they provide a continuous incentive to reduce pollution (e.g., “pigouvian” taxes), are less costly to implement and can be applied through easily calculable parameters (e.g., energy or carbon taxes). Third, soft instruments comprise voluntary industry agreements, green procurement practices and environmental certification schemes (standards for EMS such as the worldwide ISO 14001 and the European EMAS) and can be extended to include incentives for other eco-innovations: products, processes and systems in organizations (Rennings et al. 2006; Camisón 2010). Regarding soft instruments, companies set their own objective and targets for environmental improvement and commit themselves publicly to pursuing these objectives and achieving these goals (Testa et al. 2014). Chappin et al. (2009) summarize that there are different mechanisms (e.g., coercion, consent and incentives) behind these instruments, which induce or inhibit certain behaviors. The underlying mechanisms are coercion for Drivers of Eco-innovation command-and-control regulation, consent for interactive regulation (voluntary agreements and covenants) and, lastly, incentives for positive and negative economic instruments (such as subsidies and taxes) (Chappin et al. 2009). Camisón (2010, 347-348) distinguishes between five models of poli-cy, which aim to encourage environmental adaptation within organizations. Table 6: Five models of policy to encourage environmental adaptation within organizations Type of policy Description Public administration develops coercive policies, followed by checks for compliance and imposed sanctions when the law is vi- olated. This kind of regulation imposes ways in which companies have to adapt to new environmental chal enges, and no flexibili- 65 ty in their application is tolerated. Moreover, command-and-con- Command-and-control regulation trol regulation is seen as the most appropriate approach in order to achieve objectives related to emissions in polluting industries, to establish norms related to products and processes, to establish direct regulation of the interaction between business-orient-ed activity and the natural environment and to restrict activity in some areas. These are coercive in nature as well, while they stimulate a more flexible adaptation in organizations. Flexible regulation gives the company the possibility to choose and apply the technology that fits better with their operations and strategy. There are also mechanisms that stimulate pollution control through total cost minimization. Thus, this approach is based on economic instruments and Market-based environmental approaches also establishes limits regarding pollution levels and applies controls and penalties. This kind of policy comprises marketable emission permits (related to quantity and used mainly in order to ob- tain cost savings) and emission charges (establishment of price or charge by emission and used in order to improve environmental quality through profit re-al ocation). These also impose mandatory obligations on companies, but the requirements are related to information. Therefore, companies have to communicate transparently regarding their environmental adaptation to their stakeholders by providing environmental re- ports of their environmental impact or environmental external au- Mandatory information-based environmental dits. Some countries (e.g., Canada, the United Kingdom and the approaches United States) apply the following approach and publish list of organizations that do not comply with regulations or have poor en- vironmental performance (for possible polluters, inspections and penalties are applied and public information is issued about annu-al polluting emissions). In Pursuit of Eco-innovation Type of policy Description The essence of this approach is that organizations, by voluntary and individual agreement, apply proactive environmental policies, without any coercive public pressure. In this case, public administration plays a key role in providing positive incentives (tax and Voluntary, individual environmental policies financial advantages and public contracts, which favor environ- mental adaptation). Green economic incentives also encompass insurers, which can reduce premiums in order to reward compa- nies for their extraordinary environmental efforts. This approach is used when organizations with voluntary, coop- erative environmental approaches act through a network (green clubs or associations, interorganizational networks, strategic al iances) in order to promote agreements of cooperation. Norms and standards are established within these networks to produce benefits by supporting changes in the behavior of associated com- panies, and the benefits of belonging to this network can be ex- Voluntary, cooperative environmental policies 66 ploited only if adhering to these norms. Therefore, companies included in this network have to demonstrate continuous fulfil - ment of auto-regulation if they wish to remain members. In addi- tion, effective mechanisms of control and sanctions are also present here to detect and differentiate opportunistic behaviors of companies that join this network in order to exploit the benefits from its reputation. Source: Camisón (2010) Cleff and Rennings (1999) stressed that market-based instruments (e.g., taxes and tradable permits) are the environmental policy instruments with the highest dynamic efficiency (innovation efficiency), as they give permanent incentives for further cost-efficient emissions reductions. Meanwhile, regulatory regimes driven by technical standards (command-and-control system or voluntary agreements in which standards are negotiated between government and industry) are not cost-efficient, and the incentives for progress in emission reduction disappears after the standards are met (Cleff and Rennings 1999). The researchers (Cleff and Rennings 1999; Rennings et al. 2006) stressed that the basic lesson from environmental economics was the assumption of the superiority of market-based instruments (such as taxes and tradable permits) over regulations in relation to spurring innovation. These instruments have been identified as the environmental policy instruments expressing the highest innovation efficiency (having an advantage in giving continuous incentives for further, cost-efficient reductions of environmental impacts) among environmental economists (Rennings et al. 2006). In addition, Oltra and Saint Jean (2009) argue that market-based instruments and standards cannot be complete substitutes and thus are not sufficient Drivers of Eco-innovation in order to spur innovation – other policy instruments are needed as well. Here we add the research of Chappin et al. (2009), who distinguished between command-and-control regulation (top-down regulation), interactive regulation (covenants and voluntary agreements) and positive and negative economic instruments (subsidies and taxes). The results of the study focused on paper and board factories revealed that governmental environmental policies are perceived to be relevant but constitute just one of the factors that influence adoption of environmental innovation (Chappin et al. 2009). Therefore, positive economic instruments turned out to be important (but not the most important) factors in almost all adoption processes, while the role of command-and-control regulation is limited. Finally, the role of interactive regulation appears to be important for several factories in the latest period of adoption (Chappin et al. 2009). The results indicate that, for adoption of cogeneration of heat 67 and power, the most important reason was a combination of high-energy prices and cost reduction or threat of additional regulation (Chappin et al. 2009). Camisón (2010) stressed that those companies that use voluntary approaches (cooperative and individual auto-regulation) have more advanced environmental adaptation, have higher environmental performance and, therefore, exhibit major adoption of preventive and proactive environmental practices. Companies that practice auto-regulation consequently deploy and implement preventive environmental productive tools, proactive environmental management systems, environmental reporting and measuring methods (Camisón 2010). On the other side, companies that are restricted to the command-and-control regulations and market-based approach are more likely to deploy end-of-pipe measures, while cleaner production and good green productive techniques are less likely to occur or occur to a lesser extent (Camisón 2010). Moreover, an information-based environmental approach seem to drive companies towards implementation of management practices (allowing them better communication and reporting towards stakeholders in companies) and prevention of negative environmental impacts (e.g., waste and emissions minimization plans, emergency plans, eco-efficiency indicator systems and environmental reports) (Camisón 2010, 359). Camisón (2010, 359) concludes that the diffusion of environmental good practices motivated by managerial voluntary initiatives, and especially cooperative auto-regulation, is better than promotion through legal impositions. Among various interpretations of public policy, Nemet (2009) de- composed public policy into technology-push policy and demand-pull policy. The main difference between these two is the way in which the In Pursuit of Eco-innovation government encourages innovation. The demand-pull policy pertains to government actions, which affect the size of the market for a new technology (government implements measures that increase the private payoff to successful innovation), while the technology-push policy affects the supply of new knowledge directly (government implements measures that reduce the private cost of producing innovation) (Nemet 2009, 702). Based on his case study focused on inventions related to wind power, the results revealed that inventions were not responding positively to the strong demand-pull policies (Nemet 2009). In addition, public procurement can play a significant role in inducing environmental innovation with the creation of niche markets for environmental technologies and by gathering feedback between experimental users and the emerging technology producers. A major poten-68 tial source of innovation is demand, which has yet to be recognized in government policy as a key driver of innovation (Edler and Georghiou 2007). However, public demand oriented towards innovative products and solutions has the potential to improve delivery of public policy and services (Edler and Georghiou 2007). Hence, public procurement as a demand-oriented measure has the potential to shape market demand conditions and provide the diffusion of environmental innovation (Edler and Georghiou 2007; Oltra and Saint Jean 2009). Georghiou et al. (2013) conclude that, while public procurement is increasingly seen as an important potential instrument of innovation policy, the evidence of public procurement effectiveness is largely anecdotal. The whole set of instruments discussed in this paragraph defines an environmental policy mix with the purpose of promoting more sustainable systems of production and consumption (Oltra and Saint Jean 2009). Researchers (Oltra and Saint Jean 2009) emphasize the key role of properly designed regulation, which can strengthen technology-push and market-pull effects as well but cannot be considered a simple and systematic response to regulatory pressure (many other factors may affect the technological response of companies). Related to the double externality problem of environmental innovation, various innovation policy instruments (e.g., R&D subsidies, information diffusion, public procurement and cooperative research programs) can be used to correct these market failures deriving from the positive externalities of environmental innovations and thus provide favorable conditions for knowledge creation and innovation (Oltra and Saint Jean 2009). Research works that focus on the effectiveness of policy measures on eco-innovation show different results. For instance, Murovec et al. (2012) have found a similar impact on environmental investments through Drivers of Eco-innovation group of measures that consist of financial incentives, tax measures, regulation and other non-market instruments. Similarly, Mickwitz et al. (2008), who explored effects of regulations, taxes and economic incentives on environmental innovations, rejected the popular claims pertaining to the ineffectiveness of regulations on eco-innovations. Their results indicate that, in some cases, regulations can result in the emergence or diffusion of new and more environmentally sound technologies, while the economic instruments were acknowledged as the most efficient policy instruments for triggering innovations (Mickwitz et al. 2008). Whether economic instruments will become efficient means to support the diffusion/emergence of environmental friendlier technologies largely depends on the political feasibility of setting economic instruments at sufficient-ly high levels (Mickwitz et al. 2008). Therefore, we should not generalize the role of policy instruments for innovation and diffusion without con-69 sidering the specific characteristics of the situation (regulations can hinder innovations in some cases, and taxpayers’ money can be wasted on inefficient R&D subsidies, but this conclusion is dangerous to generalize because is not self-evident in all cases) (Mickwitz et al. 2008). Regulation Due to the double externality problem (Rennings 2000), eco-innovations clearly differ from other innovations, and regulation becomes a key pre-requisite for them (Walz and Köhler 2014). Heyes and Kapur (2011, 337) argue that “environmental regulation aims to correct static market failures due to externalities but also to provide incentives for innovation and adoption of better abatement technologies”. Therefore, regional, national and cross-national regulations exert effects on the extent of environmental products and a company’s sustainable new product development (Gmelin and Seurin 2014). Past empirical works have revealed that regulatory design (considering its stringency, flexibility and limiting uncertainty) is a key factor affecting companies’ innovative response (Oltra and Saint Jean 2009). Stringency relates to the absolute reduction of environmental impacts as well as to the fact that compliance using existing technology is either costly or not possible; thus, stringent regulations should provide a spur for environmental innovation (Oltra and Saint Jean 2009). Stringent regulation is a key factor that paves the way for technological environmental innovations, while command-and-control regulations impose cumbersome application procedures (also prescribing the best available technology, which must be implemented) (Huber 2008). Usually performance standards are preferred In Pursuit of Eco-innovation and completed by the push and/or pull financial instruments – green taxes, emissions trading and subsidies (Huber 2008). Thus, environmental standards increasingly force developing countries to comply with global rules in order to enter global markets (Radonjič and Tominc 2006). Moreover, regulatory pressures derived from current and anticipated regulations play an important role in spurring voluntary environmental innovation (Khanna et al. 2009). Therefore, expected future regulations play an important role in encouraging eco-innovation’s adoption. Expected future regulations have been found to be highly important for the adoption of environmental product innovations (Horbach et al. 2012). Future regulations (such as anticipation of stringent environmental regulations for reducing currently unregulated pollutants), especially those targeted at toxic releases, can affect the adoption of pollution prevention 70 strategies and induce technological innovation by firms, which aim to reduce pollution at the source (Porter and van der Linde 1995b; Khanna et al. 2009). Rehfeld et al. (2007) found a positive relationship between environmental policy and environmental product innovations; therefore, 68.9% of all companies that have realized an environmental product innovation consider compliance with existing and future legal requirements to be an important innovation goal. Hence, regulation has been recognized as an important instrument with which companies are pushed towards improved environmental performance (Madsen and Ulhøi 2001). Moreover, regulations are significantly more important for eco-innovation than for any other kind of innovation (Horbach et al. 2012). Compliance with legal demands is the most basic environmental requirement for all business, while SMEs are even more affected by environmental regulations than larger business because of the lack of necessary resources (Lee 2009). The study undertaken by Dangelico and Pujari (2010), which focused on case studies comprising Italy and Canada, found that compliance with regulations is one of the motivations for companies to go green (i.e., develop green products). The authors (Dangelico and Pujari 2010) also expose the frequency of dec-larations and regulations, such as the restriction on chlorofluorocarbon (recommended by the Montreal Protocol, 1987); the restriction on CO 2 (recommended by the Kyoto Protocol, 1997); and the European Community directives on the restriction of the use of certain hazardous substances (RoHS) and on waste electronics and electrical equipment (WEEE), effective since 2006. Thereby, the following four agreements are related to remediation of universally recognized environmental problems: Agenda 218 (covers economic and social development that is consistent with Drivers of Eco-innovation future generations’ needs); the Montreal protocol (which covers ozone depletion substances); the Kyoto protocol (covers global warming gas emissions); and the Basel Convention on the Control of Transboundary Movements of Hazardous Waste and their Disposal (Müller and Sturm 2001). Furthermore, the executives from the conducted interviews add that regulations are not just compelling companies to introduce green practices and thus presenting constraints to them but also act as a “caution for avoiding risks of activity breakdown, money losses or damage to the company image” (Dangelico and Pujari 2010, 474). Related to the findings of Popp et al. (2011), results indicate that regulations play a role in both development and diffusion of environmental technologies (pertaining to the alternative bleaching technologies in the pulp industry). Environmental regulation may force companies to realize economi- cally benign environmental innovation, because companies are general-71 ly not able to recognize cost-saving potentials such as energy or material savings (Porter and van der Linde 1995b; Horbach 2008; Belin et al. 2011). With regard to the sustainable energy sector, environmental regulation is positively correlated with it and may affect international competitiveness in the export of energy technologies, while Costantini and Crespi (2010) suggest that environmental policies should be supported by technology policies. Studies of environmental innovation over the last 15 years have found that regulation is the most important stimulus for environmental innovation (Blum-Kusterer and Hussain 2001; Randjelovic et al. 2003; Green 2005 in Triebswetter and Wackerbauer 2008; Belin et al. 2009; Qi et al. 2010; Weng and Lin 2011; Chassagnon and Haned 2014). Prioritizing the existing regulations and complying with them has affected the most eco-product and eco-organizational innovations (Horbach 2008; Triguero et al. 2013). Meanwhile, environmental regulations are identified as the most important driver of eco-innovation, as they can change the level and nature of competition between firms (Porter and van der Linde 1995a; Kammerer 2009; Doran and Ryan 2012). When companies deal with more strict environmental regulations, they implement significantly more environmental product innovations, while there is weakly significant positive effect of stringent regulations on the novelty of environmental product innovations (Kammerer 2009). In other words, this means that more stringent regulations lead to environmental product innovations and their broad application, but they are not necessarily novel to the market (Kammerer 2009). In conclusion, environmental regulations intended to stimulate adoption actually lead to implementation of eco-innovation (Porter and van der Linde 1995b; Beise and In Pursuit of Eco-innovation Rennings 2005; Lai and Wong 2012) and can also enhance competitiveness (Porter and van der Linde 1995b) and may create lead markets (new markets, export opportunities for the pioneering country), but the regulations have to comply with international regulations, global demand or regulatory trends (Beise and Rennings 2005). Thereby, Porter and van der Linde (1995b) add that greater innovation and innovation offsets can be achieved by imposing stringent regulation. Therefore, incremental innovation and without innovation (i.e., end-of-pipe or secondary treatment solutions) are spurred by lax regulation, while more stringent regulations induce more fundamental solutions, such as reconfiguration of products and processes (Porter and van der Linde 1995b). More stringent environmental regulation provides a positive impulse for increasing investments in advanced technological equipment and innovative products (Testa et 72 al. 2011). The findings of Yang et al. (2012b) give support to the Porter hypothesis, which suggests that more stringent environmental regulations may enhance industrial competitiveness rather than lower it. Furthermore, if governmental regulations and institutional arrangements are correctly designed, they consequently positively affect eco-innovations (Porter and van der Linde 1995a; Beise and Rennings 2005; Testa et al. 2011; Murovec et al. 2012). Therefore, Jaffe and Palmer (1997) have distinguished and further divided the Porter hypothesis into two components: the weak version, which posits that environmental regulation spurs innovation, and the strong version, which states that innovation increases profits and competitiveness of the regulated firm more than it offsets the induced cost. Böhringer et al. (2012) examined the weak and the strong version of the Porter hypothesis and found support for the strong version, suggesting that improved environmental and economic performance can be accomplished through well-designed environmental regulations that spur environmental investment (induce innovation). Properly formed regulations can serve at least six purposes (Porter and van der Linde 1995b, 99-100): - Regulation can signal companies about their resource inefficiencies and potential technological improvements; - Regulation focused on information gathering can lead compani- es to major benefits by raising corporate awareness; - Regulation encourages investments to address the environment by reducing the uncertainty that these investments will be valu- able; - Regulation, through its creation of pressure, spurs innovati- on and progress (pressure for innovation can come from strong Drivers of Eco-innovation competitors, demanding customers or rising prices of raw ma- terials; while also properly crafted regulation can provide such pressure); - Regulation “levels the transitional playing field”, by ensuring that, during the transition period to innovation-based solutions, one company cannot opportunistically gain a position as well as avoid the environmental investments; - Regulation is “needed in the case of incomplete offsets”, mea- ning that innovation cannot always offset the cost of complian- ce in total; thus, in such cases, regulation is necessary in order to improve environmental quality. On the one hand, Holtbrügge and Dögl (2012) pointed out that com- panies cannot be trusted to self-regulate with regard to environmental 73 responsibility and that, therefore, external pressures (policy regulations, strict regulations and other political incentives) appear to be the most effective method to encourage companies to implement environmental practices that are not only good for firm performance but also best for the environment. In addition, when and if companies violate the law or fail to achieve the standard of prescribed government regulation, the government will force companies to follow the regulations through penalties or even by stopping their business (Zeng et al. 2011). On the other hand, environmental regulations can also reduce product costs by showing how to eliminate costly materials used in processes, reduce unnecessary packaging, simplify designs or use valuable materials that are more easily recyclable and recovered (Porter and van der Linde 1995b). Regulations play an important role in implementation of typical end-of-pipe technologies, such as other air emissions (SO or NOx) as well as dangerous substanc-2 es and noise reduction technologies, water and soil protection (Horbach et al. 2012). Moreover, regulations have the top priority and ensure that green manufacturing practices are mandatory, while certifications and internal and external audits are used for cultivating them (Govindan et al. 2014). Some environmental technology fields are more market oriented; for instance, end-of-pipe technologies in particular are more regulation driven (Horbach et al. 2012). Meanwhile, for the adoption of eco-innovations with regard to reduction of CO and energy consumption, the 2 results show that the most effective driving force is a combination of regulations and taxes with subsidies (Veugelers 2012). Proper regulations can also influence environmental technologies; leading to the conclusion that these regulations should be flexible and oriented towards specific targets to promote innovations based on the product lifecycle and not just in- In Pursuit of Eco-innovation duce innovations in order to achieve the specific recycling targets (Yabar et al. 2013). Leitner et al. (2010) pointed out the complexity of the relationship between regulation and innovation and suggested a focus on “smart” regulation, which has a positive effect on the environment as well as innovation, leading industries toward the common goal of sustainability. “Smart” regulation is seen as more effective and efficient than regulation by itself in order to achieve environmental goals and represent environmental issues to firms as a business challenge and opportunity (Leitner et al. 2010). Lastly, some research works lead also to the opposite findings from those mentioned above. Frondel et al. (2008) found that policy stringency demonstrates a positive effect on environmental innovation and abate-74 ment activities, while it is not associated with EMS adoption. Moreover, they found that no single policy instrument has demonstrated a tendency to push companies towards EMS adoption (Frondel et al. 2008). Likewise, Eiadat et al. (2008) found a negative and statistically significant effect of environmental regulation on the adoption of environmental strategy. Taxation (taxes and tax incentives) and subsidies Brouillat and Oltra (2012) in their simulation model found that tax subsidies and stringent norms seem to be the only instruments with the potential to bring radical innovations and significant changes in product designs. Furthermore, tax subsidies impact only recyclability, while the whole set of product characteristics is affected by stringent norms (Brouillat and Oltra 2012). Based on French service firms, Desmarchelier et al. (2013) found that service firms are sensitive to environmental policies, while the eco-tax policy seems to be more effective than the consumer information policy. Tax measures have a positive and significant effect on environmental investments (Murovec et al. 2012). In addition, taxa-tion has also been shown to be a driver of eco-innovation (Horbach et al. 2012). Subsidies especially trigger environmental innovations, mostly because of negative external effects of environmental problems (Horbach 2008). This means that public funding of private R&D activities is used as an innovation policy instrument by governments and can directly reduce the companies’ R&D costs (Aschhoff and Sofka 2009). Companies can apply to the call for public support and after the government selects specific R&D projects by choosing those, which could not been carried out without their support and present a high social return (hence not all com- Drivers of Eco-innovation panies benefit from R&D subsidies; Aschhoff and Sofka 2009). Based on a Slovenian sample, a positive and significant impact of financial incentives on investments in environmental technologies was found (Murovec et al. 2012). In the research of Horbach et al. (2012), subsidies turned out to be very important for energy and emission reduction products and in particular for CO emissions, which is a relatively young innovation area 2 and largely depends on basic research activities financed by public funds (or, e.g., by feed-in tariffs). Yalabik and Fairchild (2011) pointed out the effectiveness of subsidies especially regarding “dirty” industries (which, with received subsidies, free up the firm’s resources and invest in environmental innovation). As they provide financial support for companies, public subsidies can be classified as a direct instrument, mainly focused on the development of new technologies (Aschhoff and Sofka 2009). In contrast to the aforementioned findings, the results of Demirel and Kes-75 idou (2011) indicate that environmental taxes do not have any significant impact on eco-innovations in the UK (focusing on end-of-pipeline pollution control technologies, integrated cleaner production technologies and environmental R&D). In conclusion, some researchers (Zeng et al. 2011; Weng and Lin 2011) among the environmental policy instruments also counted in government assistance for eco-innovation adoption. Zeng et al. (2011) have defined government assistance as assistance such as technologies, information about environmental protection, project finance and other support with regard to corporate environmental/green products and technologies. Weng and Lin (2011) found that governmental support and regulatory pressure affect green innovation adoption, and, furthermore, government as a regulator should provide sufficient financial, technical and educational resources for the SMEs to adopt green innovations. Demand side Companies are challenged to satisfy consumers’ “green” demands by providing proper design, production, sales and recycling of products (Sarkar 2013). Environmentalism as a consumer attitude is increasing in importance worldwide, meaning that consumers are willing to choose environmental friendly products and prepared to pay higher prices for them (Chen 2013). Consumers are gaining environmental awareness regarding the environmental impacts of their purchasing choices, and they consequently put more pressure on companies to reduce these impacts (Kemp and Foxon 2007). Therefore, researchers (Doran and Ryan 2012) argue that consumer perception is also a strong driver of eco-innovation. Em- In Pursuit of Eco-innovation pirical evidence claims that the pressure to eco-innovate is the strongest when operating in product markets, which are close to final customers (Zeng et al. 2011; Doran and Ryan 2012). Market demand is critical in today’s business environment, because consumers require products to be produced in an environmentally friendly way (Chiou et al. 2011). Other researchers (Popp, Hafner, and Johnstone 2011) emphasize the consumer demand and expose the fact that most of the early demand regarding the reduction of chlorine during the production process derived from consumers. Companies that believe that customers expect environmentally friendly products also show a greater likelihood to eco-innovate (Doran and Ryan 2012). Customers’ demands and preferences have the potential to affect the direction and rate of eco-innovation (Horbach 2008). Thus, Van Hemel and Cramer (2002), in their research on eco-design, 76 found that customer demands are the most influential driver of eco-design innovations. Customer benefit plays a key role in environmental product innovation (Kammerer 2009), and market demand is positively correlated to both green product innovation performance and firm performance (Lin et al. 2013a). Green products will generate consumer demand and spur firms to implement green innovations, because of the public benefits and the consumer private environmental benefits (such as energy savings) (Kammerer 2009). Consumer benefits, besides cost and energy savings, can also pertain to more efficient appliances; improved product quality and durability; better repair, upgrade and disposal possibilities; and reduced health impacts (Kammerer 2009). Moreover, customer pressure has a significant influence/impact on green innovation adoption in SMEs (Weng and Lin 2011) and is also positively related to the implementation of green logistics management by Chinese manufacturing exporters (Lai and Wong 2012). In addition, a study encompassing Vietnamese hotels has shown that customer demand has a certain effect on the likelihood of adoption of environmentally friendly practices (Le et al. 2006). However, ISO 14001 accreditation is also often market driven in the sense that companies adopt it because customers require/ demand it or competitors have it (King et al. 2005 in Heras-Saizarbitoria et al. 2011; Potoski and Prakash 2005 in Heras-Saizarbitoria et al. 2011; Prajogo et al. 2012). Empirical evidence identifies customer requirements as an impor- tant source of eco-innovations, especially of products with improved environmental performance and process innovations that increase material efficiency and reduce energy consumption, waste and use of dangerous substances (Horbach et al. 2012). Kammerer (2009), in his research on Drivers of Eco-innovation German appliance manufactures, found that customer benefit plays a key role in environmental product innovations; thus, customer benefit not only fosters the implementation of environmental product innovations but also spreads its application and increases their level of novelty. German firms are orientated more towards eco-product innovations; therefore, market orientation plays a significant and important role with regard to eco-innovation adoption (Belin et al. 2011). In addition, environmental product innovation is significantly driven by the strategic market firms’ behavior (Rennings 2000). Brécard et al. (2009) stressed that the willingness to pay more for a green product that for a “brown” product reflects a higher marginal util-ity when buying the former (in addition to revealing the consumer’s environmental preferences). According to Manget, Roche and Münnich (2009 in Doran and Ryan 2012), customers from the following countries 77 are willing to pay from five to ten percent more for green goods: Canada, France, Germany, Italy, Japan, Spain, the United Kingdom and the United States. Guagnano (2001 in Doran and Ryan 2012) found that 86% of customers reported a willingness to pay more for household products if they are less harmful to the environment. Also, Kaenzig et al. (2013), in their survey encompassing 4968 experimental choices made by 414 retail consumers, found that German electricity customers expressed an im-plicit willingness to pay a premium of about 16% for electricity from renewable sources (to upgrade from the current default electricity mix in Germany to a more environmentally friendly default electricity mix). In contrast, Rehfeld et al. (2007) has shown that the higher prices of environmental product innovations seem to be one of the major reasons for the low performance of environmental products or commercial exploitation. Research conducted in Sweden by Jansson et al. (2010) found that once consumers adopt the use of eco-innovation, they demonstrate more willingness to purchase it again, and eco-innovation becomes an important and integrated part of their lives. The researchers found a strong positive influence of personal norms on willingness for the behaviors and a negative influence of habit strength, which is particularly the major barrier in strong car habits, where consumers do not express willingness for alternative fuel vehicle adoption nor willingness to reduce the negative impact of car use (Jansson et al. 2010). Even though, in the sector of cars and fuels, car habits have turned out to be important, while customer’s values, beliefs and norms are no less essential. Furthermore, researchers (Jansson et al. 2010; Jansson et al. 2011) suggested the use of attitudinal fac- In Pursuit of Eco-innovation tors (values, beliefs and norms) and habits to be more effective when using a market segmentation approach instead of just applying socio-demographic variables. Moreover, Belin et al. (2009) in their research on samples from France and Germany found that eco-innovation seems to be less correlated to demand pull effects than innovation in general (an explanation could be that eco-innovations are more oriented towards process and organizational innovations). Kesidou and Demirel (2012) found that consumer demand for environmentally friendly products and processes encourages a firm’s decision to invest in eco-innovation (they apply a minimum level of eco-innovation activities to respond to the market pressure, but they do not necessarily invest large amounts of resources into eco-innovation). Firms initiate the implementation of eco-innovations in order to satisfy the minimum of customer and social require-78 ments, while customer requirements do not affect the level of investment in eco-innovation, because increased investments in eco-innovations are driven by other factors: stricter regulations, cost savings and the firm’s organizational capabilities (Kesidou and Demirel 2012). In contrast, some researchers (Horbach 2008; Lee 2009) argue that customer demand represents one of the essential drivers of eco-innovations, because demand factors – especially calls for corporate responsibility and consumer demand for environmentally friendly products and processes – affect the firm’s decision to invest in eco-innovation (Kesidou and Demirel 2012). Customer pressure deriving from environmentally conscious core customers influences deployment of green practices in manufacturing and ranked as third in importance as a driver (Govindan et al. 2014). Dealing with environmentally conscious customers, who are also companies’ core customers, forces companies to implement green practices to avoid losing them (Govindan et al. 2014). Popp et al.’s (2011) study on the pulp sector demonstrated that the pressure imposed by consumers outperformed the effect of regulations (innovations occurred before regulations were in place); consumer demand for chlorine-free paper induced environmental technologies. To conclude, voluntary agreement and consumer perception variables together push firms to engage in at least a minimum level of eco-innovation in response to industry and social pressures and expectations (Doran and Ryan 2012). Competition Competitiveness has been identified as one of the major motivations for environmental responsiveness. Bansal and Roth (2000, 724) defined driver “competitiveness” as “the potential for ecological responsiveness to im- Drivers of Eco-innovation prove long-term profitability”. In a highly competitive market, implementation of green product innovation is required in order to achieve green competitive advantage through differentiation of a firm product (Lin et al. 2013a). Leonidou et al. (2013a) found that when competitive intensity is low, environmental marketing strategy positively affects competitive advantage, while this association gets stronger under high competitive intensity conditions. Furthermore, ecological responses that improved competitiveness encompass energy and waste management, source reductions, resulting in a higher output for the same inputs (process intensification), eco-labeling, green marketing and the development of eco-products (Bansal and Roth 2000). Firms motivated by competitiveness expect that their ecological responsiveness will lead to sustained advantage and improved long-term profitability (Bansal and Roth 2000). Competitive advantage as an antecedent of eco-innovations has a positive impact on 79 environmental marketing strategy and a greater effect on external environmental orientation (focused on the firm’s relationships with external stakeholders) and environmental corporate strategy in the industry of moderate environmental impact sectors (Banerjee et al. 2003), while it had an even greater effect on internal environmental orientation (focused on the development of corporate value and vision statements, typically from top management) in the industry of high environmental impact sectors (Banerjee et al. 2003). Therefore, the development of green products can represent a tool for achieving competitive advantage (Dangelico and Pontrandolfo 2010). Regarding the market concentration, Inoue et al. (2013) found that companies with fewer than five competitors do not have to fight short-term competition and, therefore, can afford to devote their resources to environmental R&D activities in a long-term perspective. The results indicate that if a company operated in an oligopolistic market, the environmental R&D expenditures as a percentage of total R&D expenditures may be higher (Inoue et al. 2013). Moreover, Yalabik and Fairchild (2011) examined the combination of consumer, regulatory and competitive pressure effects on the firm’s investment in environmental innovation, and they found that competition can be considered an effective driver of environmental innovation, when dealing with environmentally sensitive customers. When dealing with such customers, regulatory pressure also turns out to be an effective driver of environmental innovation; hence, the findings show that competition over environmentally sensitive customers has the potential to improve the effectiveness of environmental pressures (Yalabik and Fairchild 2011). Meanwhile, the empirical results of Li (2014) demonstrate a positive and significant im- In Pursuit of Eco-innovation pact of competitive pressure on environmental innovation practices, indicating the importance of strategy by providing green products through environmental innovation in order to establish a green image, increase market share and achieve sustainable development in an increasingly intense competitive environment. Society Among the variety of factors that influence companies’ decision to invest in or implement eco-innovations and “become eco-friendly” is pressure from society, which can be composed of many elements: communi-ty requirements, environmental associations and media exposure (Zeng et al. 2011). According to Qi et al. (2010), the positive or negative public opinion 80 on a firm’s environmental performance strongly affects the way firms do business, because of their close association with business strategies in any industry. Public pressure (pressure from the public and media) seems to be an essential driver of eco-innovations (Horbach 2008; Lee 2009) and may stimulate companies to become more eco-friendly (Bansal and Roth 2000). In recent years, developing countries such as China experienced increased growth of environmental non-government organizations (ENGOs), which are gradually becoming active players in the development of environmental policies (Yang 2005 in Qi et al. 2010). NGOs, along with local governments, have played a key role in the promotion of low carbon techniques (Shi and Lai 2013). NGOs have changed the means of communication with enterprises; they do not attack firms for unawareness of environmental issues but rather offer them consultation services on how to become green (Yarahmadi and Higgins 2011). In addition, civil society actors such as NGOs, scientific organizations and the media, refuse to interact only with government and thus more often establish a direct relationship with the business community where both confrontation and cooperation are present (Jänicke 2008). NGOs and local communities play an active role in the relationship with the business community related to the environmental proactive companies; this relationship can result in providing access to knowledge networks, political dialogs and potential sales (Yang et al. 2012a). Hence, NGOs and communities become actors and not just the foundation for developing innovations (Yang et al. 2012a). In conclusion, project stakeholders, including the community, EN- GOs (environmental non-governmental organizations) and employees, generate effective pressure on the firm and demand better environmen- Drivers of Eco-innovation tal performance (Qi et al. 2010). Therefore, firms are forced to implement green practices in response to both government regulations and project stakeholders (Qi et al. 2010). In contrast to the previous studies, Blum-Kusterer and Hussain (2001) found that in the pharmaceutical industry, the pressures of NGOs were relatively insignificant; they presume that this insignificant effect is due to respect to biodiversity appropriation and exploitation, as well as the ethics of drug sales in the developing world. Expected benefits from eco-innovation Companies also start to deploy eco-innovation in order to pursue benefits derived from its implementation, including cost savings (Horbach 2008; Belin et al. 2011; Demirel and Kesidou 2011; Horbach et al. 2012; 81 Klewitz et al. 2012; Triguero et al. 2013; Chassagnon and Haned 2014) and improvement of firm reputation (Agan et al. 2013; Chen 2013; Sarkar 2013). Shrivastava (1995) summarized a few other benefits deriving from successful eco-innovation implementation: improved relationships with local communities, access to new green markets and gain of competitive advantage. Moreover, Sarkar (2013) distinguished direct benefits (cost savings, greater resource productivity, better logistics and sales from commercialization) and indirect benefits (better image, better relations with customers, suppliers and authorities, health and safety benefits, greater worker satisfaction and enhanced innovation capability). “Companies make their offerings competitive through price/quali- ty or prestige/image strategies from their competitors but eco-friendliness and social responsibility can make companies more profitable on a sustainable basis” (Sarkar 2013, 185). Corporate image as an expected soft benefit was found to be the strongest driver of environmental activities (Agan et al. 2013), while Sarkar (2013) notes that improved company image is an indirect benefit of innovation. It is well known that firm reputation is fragile and takes time to build; therefore, it is easier to destroy a good reputation than to create a solid one. Companies are integrating corporate social responsibility and environmental awareness in their business strategies with the goal of gaining reputational advantages (Hillestad et al. 2010). A reputation that marks a company as environmentally aware, is difficult or even impossible for competitors to imitate and, therefore, is valuable in its contribution to competitive advantage (Hillestad et al. 2010). Improved overall image or prestige of companies, followed by increased customer loyalty or support sales efforts, consequently can derive from companies’ efforts to reduce pollution and oth- In Pursuit of Eco-innovation er environmental impacts (Ambec and Lanoie 2008). A firm’s reputation is enhanced by adoption of environmental innovation strategy (Eiadat et al. 2008). However, adoption of environmental values in companies’ culture, with the goal to develop and gain a good reputation in the market-place, is not enough; environmental commitment must be translated into specific strategies that enable customers, community and other relevant stakeholders to identify and value it (Fraj-Andrés et al. 2009). Firms aim to maintain a certain image, which is consistent with the current external regulatory pressures (Holtbrügge and Dögl 2012). They focus on maintaining this image and avoiding a severe backflash from their stakeholders for not complying with existing regulations and therefore, putting firm reputation in jeopardy (Holtbrügge and Dögl 2012). Also, industry norms and monitoring systems ensure that non-comply-82 ing behavior is punished, primarily through loss of reputation and social pressures, which eventually have an impact on commercial activity (Pacheco et al. 2010). Furthermore, according to Shrivastava (1995) environmental technologies help companies to establish a social presence on markets where they operate, gain social legitimacy and maintain good public relations and corporate image. Improved firm reputation through the implementation of sustainable practices is an important driver for corporate businesses (Pellegrini-Masini and Leishman 2011; Klewitz et al. 2012); in particular, green product development seems to be driven by improvement of reputation and corporate image (Dangelico and Pujari 2010). Based on a sample of Italian companies, image improvement was found in 80% of companies to be the strongest stimulus for the introduction of environmental management systems (ISO 14001) (Salomone 2008). Moreover, as key factors in customers’ purchasing decisions are brand recognition and acceptance, having a “green brand” will become increasingly important for companies (Kemp and Foxon 2007). Therefore, voluntary agreements have the largest impact on eco-innovation implementation in firms; furthermore, firms are willing to pay to brand themselves as eco-friendly (Doran and Ryan 2012). Hence, Sarkar (2013) points out that the “going green” movement continues to build momentum, and thus firms are realizing that not becoming eco-friendly can put their business in risk (by not “going green,” firms risk being branded as socially irresponsible, being a target of criticism, being vulnerable also by risk-ing their brands). As many new firms are starting operations with green brands, older ones want to re-brand their products in order to be more eco-friendly (Sarkar 2013). Firm reputation seems to be stronger driver of green practices in developed countries than in developing ones (Govin- Drivers of Eco-innovation dan et al. 2014). Several researchers have identified corporate image and reputation as one of the strongest drivers of environmental activities (Eiadat et al. 2008; Hillestad et al. 2010; Pellegrini-Masini and Leishman 2011; Holtbrügge and Dögl 2012; Klewitz et al. 2012; Agan et al. 2013). Among the other expected benefits that can be seized from eco-innovation are cost savings, which play an important role in inciting eco-innovation implementation and motivating the reduction of energy and material use (Horbach et al. 2012). However, cost-saving potentials (e.g., energy or material savings) of environmental innovation are often not recognized by firms (Porter and van der Linde 1995b; Horbach 2008; Belin et al. 2011). Cost savings constitute one of the main reasons for investments in eco-innovations, while lack of knowledge about the potential of technologies for material and energy savings and the lack of immediate results act as barriers to the implementation of eco-innovations (Pereira 83 and Vence 2012). Better environmental performance and environmental innovations can lead to several reductions of costs in the following areas: cost of material, energy and services; cost of capital; cost of labor; risk management and relations with external stakeholders (Ambec and Lanoie 2008). Meanwhile, Horbach (2008) argues that cost savings represent a significant determinant for environmental innovations compared to other innovations; moreover, the chemical industry, which is an environmentally intensive industry, realizes more innovations with environmental effects than other sectors. Cost savings (especially material and energy savings) strongly trigger eco-innovations in Germany and in France (Belin et al. 2011), where they play a very important role as a trigger of eco-innovation implementation (Horbach 2008; Horbach et al. 2012). SMEs can benefit from cost savings (increased energy efficiency) when dealing with sustainability-related issues (Klewitz et al. 2012). In addition, environmental management practices that are associated with cost savings are as follows: recycling (through more efficient use of materials, they reduce the cost structure), proactive waste reduction and remanufacture (both of which focus on lowering cost structure) (Montabon et al. 2007). Shrivastava (1995) suggested that companies can make large financial gains by waste reduction, energy saving, material reuse and addressing lifecycle costs. Similarly, the findings of Govindan et al. (2014) emphasized the importance of cost savings, especially because companies have acknowledged that recycling leads to lower costs (instead of purchasing original material, they reuse it and consequently lower the costs). In Pursuit of Eco-innovation Additionally, cost savings are most closely associated with the most advanced eco-innovations, because they derive from elimination or reuse of waste, and they appear to be significant in driving investment in environmental R&D, while less advanced eco-innovations have a lower potential for creating such savings for companies (Kesidou and Demirel 2011). The findings of Triguero et al. (2013) demonstrate the significance of cost savings only for eco-process innovations (Triguero et al. 2013). Therefore, environmental technologies offer the opportunity to decrease operating costs by exploiting ecological efficiencies (Shrivastava 1995). Sources of information For successful implementation, eco-innovations also require sources of knowledge and information. Prior research works (Bansal and Roth 84 2000; Yarahmadi and Higgins 2012) argue that, to acquire competen-cy (access to resources such as funds, knowledge and skills) and to obtain legitimacy and compliance with environmental laws and regulations, firms cooperate with the following institutions: governmental agencies, NGOs, suppliers, customers and industry associations. They also argue that cooperation with competitors and knowledge leaders is spurred only by competency-oriented motivation (Yarahmadi and Higgins 2012). As aforementioned, eco-innovations require more external sourc- es of knowledge and information than innovations in general do (Belin et al. 2011). According to Belin et al. (2011), external information and knowledge are considered an important source for eco-innovation-related activities, while internal sources such as R&D are less important. It has been found (Belin et al. 2011) that, to French eco-innovators universities, consultants and conferences are very important as information sources, while to German eco-innovators state-dependent research institutes represent an important source. Belin et al. (2011) found a similar picture between the two countries (France and Germany) for external sources, while the results are more distinct with regard to internal sources of information. In France, eco-innovation activities depend more on external sources of information, although internal sources also remain very important, while Germany relies on and uses more external information (public sources) and fewer internal sources of information (Belin et al. 2011). The results of De Marchi’s (2012) survey, which focused on technological environmental innovation of Spanish manufacturing firms, indicate the importance of firms’ cooperation with external partners when dealing with environmental innovation, while the most important external partners seem to be suppliers and scientific agents (including uni- Drivers of Eco-innovation versities, consultants and research centers). Environmentally innovative firms cooperated with external partners on innovation to a greater extent than other innovative firms and engaged resources on a continuous basis for internal R&D activities and cooperation with external partners (De Marchi 2012; De Marchi and Grandinetti 2013). Regarding development of environmental innovations, companies demonstrate a higher re-course to external knowledge, including use of external sources of information such as R&D from external firms and cooperation (De Marchi and Grandinetti 2013). Robin and Schubert (2013) evaluated the relationship between innovation activities in general and cooperation with public research institutions in France and Germany. The findings show that cooperation with public research increases product innovation, while there is no impact on process innovation (Robin and Schubert 2013). In summary, eco-innova-85 tions demonstrate a greater tendency towards knowledge and information intensity than do innovations in general, and R&D is not the most important source of information. Hence, several researchers (Belin et al. 2011; De Marchi 2012; Pereira and Vence 2012; Yarahmadi and Higgins 2012; De Marchi and Grandinetti 2013) have argued that, regarding environmental innovation, companies enter into cooperation with external partners (external sources of information) to a greater extent than in the case of regular or other innovation. Organizational capabilities Several researchers have identified organizational capabilities as driving forces of product and process eco-innovation (Demirel and Kesidou 2011; Kesidou and Demirel 2012; Cuerva et al. 2013). Some call these environmental organizational measures (Ziegler and Rennings 2004; Rennings et al. 2006; Wagner 2008). Cai and Zhou (2014, 2) noted that implementation of EMS serves to help companies “to build organizational capabilities and practices such as resource reduction, recycling, pollution prevention, and green product design, which are intended to promote mainly process innovations toward improved environmental quality in combination with decreased costs. They may also facilitate product and service innovation in the field of eco-efficiency”. However, researchers used certified environmental management sys- tems – usually ISO 14001 and EMAS (Ziegler and Rennings 2004; Re- hfeld et al. 2007) to examine the effect of environmental management systems on the adoption of environmental innovation. In some cases, the effect of EMS on environmental innovation was rather negligible In Pursuit of Eco-innovation (Ziegler and Rennings 2004). Wagner (2008) pointed out that, when by following the neo-institutional organizational theory of DiMaggio and Powell (1983 in Wagner 2008, 394), which stresses that “certification is a symbolic gesture with little influence on environmental innovations but rather motivated out of institutional isomorphism and mimicry behavior,” it is not appropriate to include standards as a drivers of eco-innovation. Therefore, single measures such as product design with lifecycle analysis and take back systems for products have turned out to be important drivers of environmental product and process innovations (Ziegler and Rennings 2004). Ziegler and Rennings (2004) argue that certified environmental management systems seem to be statistically less reliable (while the ISO 14001 standard has shown a significantly weak positive impact, the EMAS standard has shown no significant impact on envi-86 ronmental innovations at all). Meanwhile, the findings of the study undertaken by Cuerva et al. (2013) revealed a strong impact of organizational capabilities on green innovation. The results indicate that implemented Quality Management Systems (QMS) seem to be the strongest driver of environmental innovation strategy (pertaining to the ISO 9000 family of standards) (Cuerva et al. 2013). In conclusion, a positive impact of environmental management systems (EMS) on environmental innovation has been found by several researchers (Rehfeld et al. 2007; Wagner 2008; Kammerer 2009; Demirel and Kesidou 2011; Weng and Lin 2011; Kesidou and Demirel 2012). Managerial environmental concern To the entrepreneur is entrusted an important task, which also involves adoption of eco-innovations and concern about the environment, employees, final consumers and society. Banerjee et al. (2003) argued that top management plays a key role in influencing corporate environmentalism directly and helps to modify the influence of other stakeholders. Moreover, Martinsons et al. (1996 in Ndubisi and Nair 2009) suggested that the so-called entrepreneurial spirit is more important in making green business than regulations. Environmentally concerned and trained human resources (or managers or employees) increase environmental process innovations (Triguero et al. 2013). Ndubisi and Nair (2009) argue that green entrepreneurial orientation is vital for the development of green value added. Somewhat similar findings are derived from a case study conducted by Hillestad et al. (2010), who argue that a founder or a leader of a company plays the role of “cultural architect” and thus positively affects assessment of the company’s image by external constituents, Drivers of Eco-innovation pertaining to the company’s innovations and its awareness of environmental issues. The company’s image can be shaped in two ways: first, a company’s leader or founder has a role of coordination and motivation of employees’ attitudes and behaviors related to environmental issues; second, a green innovator enforces a positive external company reputation (Hillestad et al. 2010). In addition, Lewis and Cassells (2010) argue that the personal commitment of the firm’s owner or manager can be an advantage for SMEs in terms of the improvement of environmental responsibility. In Lewis and Cassells’ (2010) study, personal commitment was ranked the third most relevant factor (in 45.3%) in driving firms towards implementation of environmental innovations; thus, more emphasis should be given to it in future research works. Mzoughi (2011) explored the role of social and moral concerns and emphasized the extent to which we try to show others our environmen-87 tal commitment (social concerns) and how guilty we feel about our choices (moral concerns). Moral concerns relate to so-called intrinsic motivations (individuals’ ethics, such as personal satisfaction), where rewards are not expected and motivation stems from the individual (Mzoughi 2011). Meanwhile social concerns shape the individual’s behavior in relation to its reference group (which can result in social recognition or mon-etary rewards if adopting a given behavior or threats of punishment for non-compliance with the prescribed behavior). Findings emphasize the significant effect of moral and social concerns on adoption of ecological-ly friendly practices and should be considered in research works as well (Mzoughi 2011). Regarding this point, we “borrow” the broader concept of sustainable entrepreneurship in order to demonstrate the importance of the entrepreneur’s role. Sustainable entrepreneurs “…balance economic health, social equity and environmental resilience though their entrepreneurial behavior” (Kuckertz and Wagner 2010, 525). Furthermore, sustainability orientation has been found to have an influence on entrepreneurial intention, although business experience destroys the positive relationship between them (Kuckertz and Wagner 2010). Bansal and Roth (2000) assume that managers respond only to the salient issues; moreover, they chose to operate within cohesive fields and hire managers who exhibit ecological concern. On the other hand, managers possess pre-existing values and capabilities that can affect eco-innovation and encourage environmental actions in the company where they work (Ramus and Steger 2000). They encourage and support employees’ involvement in environmental inno- In Pursuit of Eco-innovation vation and are open to new environmental innovation ideas by providing resources and other support for environmental projects to be realized (Ramus 2002). Personal values of managers shape their environmental attitudes and, through managers’ environmental attitudes, exert influence on corporate environmental responsiveness (Papagiannakis and Lioukas 2012). Managers’ subjective norms and high levels of self-efficacy in handling environmental issues have also been shown to be strong predictors of corporate environmental responsiveness (Papagiannakis and Lioukas 2012). Papagiannakis and Lioukas (2012) have highlighted the importance of managers’ environmental attitudes on corporate environmental responsiveness. Moreovet, they suggested that managers with an awareness of the consequences of human nature interaction and a sense of commitment to react and take the correct actions see their organization as an 88 opportunity to materialize their environmental concerns and make appropriate strategic decisions, which are reflected through firms’ environmental responsiveness (Papagiannakis and Lioukas 2012). In addition, Pujari et al. (2003) found that environmental new product development performance is positively affected by a higher degree of top management support. Managers also play a crucial role as mediators with regard to the stakeholders’ pressure and influence (Madsen and Ulhøi 2001). Therefore, managers who perceive environmental protection as an important and integral part of a company’s identity act accordingly, without formal controls and incentives (Sharma 2000). Similarly, Yen and Yen (2012) found that top management commitment positively and significantly affects environmental collaboration with suppliers and firm adoption of green purchasing and is thus the primary driver of firms’ successful adoption of green purchasing standards. According to the research of Qi et al. (2010) in the construction area, managerial concerns are one of the two most important driving forces for the adoption of green practices. Moreover, managerial environmental concern as a moderator generally affects the relationship between green product innovation, firm performance and competitive capability (Ar 2012). Furthermore, Triguero et al. (2013) have found that entrepreneurs to whom collaboration with research institutes, agencies, universities, and the increase of market demand for green products are important are also more active in all types of eco-innovation. According to Ferguson and Langford (2006 in Tseng et al. 2013) and Eiadat et al. (2008), firms are more motivated to adopt an environmental innovation strategy if their managers place a high value on and express concern about the Drivers of Eco-innovation environment and its protection. Therefore, managers with environmental knowledge, skills, and beliefs that environmental issues should be a top priority are key factors that trigger companies’ adoption of an environmental innovation strategy (Eiadat et al. 2008). In addition, Dibrell et al. (2011) found a moderating effect of top managers’ attitudes toward the environment. Even though they did not find a direct effect of managerial attitudes towards the environment on firm innovativeness, a significant moderating effect of managerial attitudes toward the environment was found on the relationship between market orientation and firm innovativeness. Moreover, Dibrell et al. (2011) showed that entrepreneurial activities toward the environment in the form of firm innovativeness are improved when the managerial environmental attitudes are embedded within a market-oriented firm. Personal environmental values and beliefs are the most significant factors that affect environmental over-compli-89 ance (Wu 2009). Hence, the greater is the degree to which companies’ managers interpret environmental issues as opportunities, the higher is the likelihood that companies will engage in voluntary environmental strategies (Sharma 2000). Finally, Rivera-Camino (2012) found that the relationship between managers’ behavior and environmental policy is largely affected by society (i.e., its perceptions and judgments). The results of the study (Rivera-Camino 2012) support the basic premise of institutional theory regarding organizations’ tendency towards compliance and conformance to the social influences from the environment (support and legitimacy can be achieved/acquired though conformance to social pressures). “Under certain conditions entrepreneurs are likely to supplement or surpass the efforts of governments, NGOs and existing firms to achieve environmental sustainability” (i.e., the uncertainty of environmental issues presents significant entrepreneurial opportunities) (York and Venkataraman 2010, 449). Researchers (York and Venkataraman 2010) add that, for known, well-understood environmental problems, clear command-and-control regulation is needed and effective, while with regard to more intractable and uncertain problems regulation can repress innovations and solutions. Thus, we conclude that managerial concerns with regard to the environment are positively related to the scope and the speed of firms’ response to environmental issues (Tseng et al. 2013). Managers who express a high level of environmental concern are more keen to dedicate time and resources to environmental initiatives compared to those with a lower level of environmental concern (Naffziger et al. 2003). In Pursuit of Eco-innovation Company’s general characteristics (firm size and firm age) Propensity to eco-innovate is positively related to firm size (De Marchi 2012), meaning that larger firms are more likely to eco-innovate (Horbach 2008; Kammerer 2009; Qi et al. 2010; Belin et al. 2011; Doran and Ryan 2012; Hofer et al. 2012; Agan et al. 2013; Horbach and Rennings 2013; Robinson and Stubberud 2013; Triguero et al. 2013). Dong et al. (2013), after a literature review, concluded that the majority of research works suggest a positive effect of firm size on eco-innovation performance (from the perspectives of resources, economies of scale, reputation advantage, R&D costs, risks, etc.). In more detail, Triguero et al. (2013) found a positive relationship between firm size and the decision to eco-innovate at all levels (product, process and organizational eco-innovation). Firm size can present a potential barrier to eco-innovation, 90 because small companies face more difficulties in introducing eco-innovations (Triguero et al. 2013). Small businesses are often challenged in competition with larger businesses; furthermore, small businesses also desire to provide valuable products (goods or services) to their consumers and see an opportunity in environmental innovation, which can be an effective and sustainable way to do so (Robinson and Stubberud 2013). Firm size, which is related to available financial and human capital resources, affects a firm’s decision to invest in eco-innovation. Therefore, on the one hand, larger businesses are more likely to undertake green innovation because they have more capital to invest (Robinson and Stubberud 2013), while on the other hand, small firms have the advantage to be more flexible and can more easily adapt and are thus more responsive to the needs and changes in customer demand to eco-innovate than larger firms (Sak and Taymaz 2004 in Doran and Ryan 2012). In summary, small firms have several advantages over larger ones regarding the adoption of environmental practices. First, smaller firms are seen by consumers as more environmentally friendly, and second, smaller firms are in a position to react more actively to the increasing demands of green products and services in almost all market segments (Osukoya 2007 in Ndubisi and Nair 2009). Firms with higher profitability (often equated with firm size) can engage more resources and longer time periods to the development and implementation of environmental management activities, because of costs and investments associated with environmental management activities, where a long-term payoff can be speculative and uncertain (Tate et al. 2010 in Hofer et al. 2012). Hofer et al. (2012) found that more profitable Drivers of Eco-innovation firms, when threatened by competition, would allocate their financial resources and engage actively in implementation of environmental management activities and, moreover, will respond more aggressively. In addition, Murovec et al. (2012) found a positive impact of firm performance on the introduction of environmental technologies. Related to the aforementioned, larger firms are in a better position with regard to the ability to fund long-term and more speculative projects because of a higher degree of financial and human capital (Baylis et al. 1998 in Doran and Ryan 2012). Larger firms implement more product eco-innovations than small firms, implement them on a wider range, and offer to market more novelties because of the aforementioned availability of financial and human resources (Kammerer 2009). For instance, investments in environmental management systems require substantial 91 investments in information technology, which represents a tremendous burden to firms; therefore, large firms promote and implement environmental management activities that require specialized human, technical, financial and physical resources within the boundaries of the firm (del Río 2009; Hofer et al. 2012). As SMEs get larger and consequently possess more resources, their environmental performance also improves (Agan et al. 2013). Based on the aforementioned research work, we summarize that firm size is positively correlated to the environmental activities of innovating (Horbach 2008); moreover, larger firms are more likely to innovate (Horbach 2008; Kammerer 2009; Qi et al. 2010; Belin et al. 2011; Doran and Ryan 2012; De Marchi 2012; Hofer et al. 2012; Agan et al. 2013; Horbach and Rennings 2013; Robinson and Stubberud 2013; Triguero et al. 2013). Alvarez Gil (2001) found a positive association between hotel size and deployment of environmental management techniques. Many research works indicate that the larger the company is, the larger will be the extent of eco-innovations, meaning that larger companies introduce more product and process eco-innovations (Rehfeld et al. 2007; Chen 2008; Qi et al. 2010). In contrast, some researchers came to different conclusions. A negative relationship between firm size and eco-innovation performance has been found by Cole et al. (2005 in Dong et al. 2013), while other research findings suggest that there is no effect of firm size on eco-innovation performance (Ofezu 2006 in Dong et al. 2013; Wagner 2008). In Pursuit of Eco-innovation Table 7: Summary of drivers of eco-innovation found in previous research works (focusing on factors explored in our study) Eco-innovation drivers References Le et al. (2006); Kemp and Foxon (2007); Chen (2008); Eiadat et al. (2008); Frondel et al. (2008); Dangelico and Pujari (2010); Hil estad et al. (2010); Lewis and Cassells (2010); Pellegrini-Masini and Leishman (2011); van den Firm reputation Bergh et al. (2011); Doran and Ryan (2012); Holtbrügge and Dögl (2012); Klewitz et al. (2012); Agan et al. (2013); Chen (2013); Sarkar (2013); van den Bergh (2013); Govindan et al. (2014) Shrivastava (1995); Montabon et al. (2007); Ambec and Lanoie (2008); Horbach (2008); Lewis and Cassells (2010); Belin et al. (2011); Demirel and Kes-Expected benefits idou (2011); Rave et al. (2011); Santolaria et al. (2011); Horbach et al. (2012); Cost savings Kesidou and Demirel (2012); Klewitz et al (2012); Pereira and Vence (2012); Oxborrow and Brindley (2013); Triguero et al. (2013); Chassagnon and Haned (2014); Govindan et al. (2014); Murakami et al. (2014) 92 Porter and van der Linde (1995b); Shrivastava (1995); Van Hemel and Cram-New markets er (2002); Lewis and Cassells (2010); Rave et al. (2011); Horbach et al. (2012); Chen (2013); Oxborrow and Brindley (2013) Le et al. 2006; Lewis and Cassells (2010); Horbach et al. (2012); Triguero et Market share al. (2013) Bansal and Roth (2000); Banerjee et al. (2003); Pujari et al. (2004); Ferguson and Langford (2006 in Tseng et al. 2013); Eiadat et al. (2008); Lewis and Managerial environmental concern Cassells (2010); Qi et al. (2010); Dibrell et al. (2011); Ar (2012); Yen and Yen (2012); Agan et al. (2013); Tseng et al. (2013) Rennings (2000); Van Hemel and Cramer (2002); Le et al. (2006); Triebswetter and Wackerbauer (2008); Kammerer (2009); Lee (2009); Lewis and Cassells (2010); Popp et al. (2011); Santolaria et al. (2011); van den Bergh et al. (2011); Weng and Lin (2011); Zeng et al. (2011); Doran and Ryan (2012); Hor-Market – customer demand bach et al. (2012); Kesidou and Demirel (2012); Lai and Wong (2012); Murovec et al. (2012); Simpson (2012); Yen and Yen (2012); Agan et al. (2013); Lin et al. (2013a), Lin et al. (2013b); van den Bergh (2013); Cai and Zhou (2014); Govindan et al. (2014); Li (2014) Bansal and Roth (2000); Banerjee et al. (2003); Brunnermeier and Cohen (2003); Zhu and Sarkis (2006); Triebswetter and Wackerbauer (2008); Yala-Competition bik and Fairchild (2011); Zeng et al. (2011); Inoue et al. (2013); Cai and Zhou (2014); Govindan et al. (2014); Li (2014) Drivers of Eco-innovation Eco-innovation drivers References Porter and van der Linde (1995b); Bansal and Roth (2000); Rennings (2000); Blum-Kusterer and Hussain (2001); Madsen and Ulhøi (2001); Van Hemel and Cramer (2002); Banerjee et al. (2003); Beise and Rennings (2005); Green (2005 in Triebswetter and Wackerbauer 2008); Zhu and Sarkis (2006); Rehfeld et al. (2007); Horbach (2008); Triebswetter and Wackerbauer (2008); Belin et al. (2009); Gadenne, Kennedy and McKeiver (2009); Kammerer (2009); Khanna et al. (2009); Camisón (2010); Belin et al. (2011); Demi-Regulations rel and Kesidou (2011); Heyes and Kapur (2011); Popp et al. (2011); Qi et al. (2010); Santolaria et al. (2011); Testa et al. (2011); van den Bergh et al. (2011); Environmen-Weng and Lin (2011); Zeng et al. (2011); Brouil at and Oltra (2012); Doran and tal policy instru-Ryan (2012); Holtbrügge and Dögl (2012); Horbach et al. (2012); Kneller and ments Manderson (2012); Murovec et al. (2012); Simpson (2012); Veugelers (2012); Yen and Yen (2012); Agan et al. (2013); Lin et al. (2013b); Triguero et al. (2013); van den Bergh (2013); Yabar et al. (2013); Cai and Zhou (2014); Chassagnon and Haned (2014); Ford et al. (2014); Govindan et al. (2014); Li (2014) Horbach (2008); Chappin et al. (2009); Rave et al. (2011); Brouil at and Ol-93 Subsidies tra (2012); Desmarchelier et al. (2012); Doran and Ryan (2012); Murovec et al. (2012); Veugelers (2012) Kesidou and Demirel (2011); Brouil at and Oltra (2012); Desmarchelier et al. Taxation (2013); Murovec et al. (2012); Horbach et al. (2012); Veugelers (2012) With regard to the general characteristics of companies – firm size and firm age – we can conclude that company size is positively associated with eco-innovation propensity (De Marchi, 2012), meaning that larger companies are more likely than smaller companies to deploy eco-innovation (Alvarez Gil et al. 2001; Hofer et al. 2012; Kesidou and Demirel 2012). Pertaining to firm age, researchers (Ziegler and Rennings 2004; Rehfeld et al. 2007) found a U-shaped relationship between company age and the probability of the realization of product eco-innovation and/ or process eco-innovation. In summary, the younger the company is, the more likely it is to be (eco-)innovative, while this (eco-)innovativeness decreases with company age (nonetheless, mature companies might have developed a broader internal knowledge base that consequently leads to the realization of further product eco-innovations) (Rehfeld et al. 2007). Likewise, Alvarez Gil (2001) found that the age of hotels’ facilities negatively affects the deployment of environmental management practices. In continuation, Table 7 offers a short summary synthesizing previously mentioned research works and their main findings, with an emphasis on the factors that we encompass in our research. Table 7 thus encompasses only references of research works that found positive and significant effect of these factors on eco-innovation. Consequences of Eco-innovation Adoption Companies initially hesitated to become environmentally friendly and adopt eco-innovations. At first, eco-innovations were seen as a response to legislation, pursuing compliance with regulations. In addition, eco-innovations were perceived as a burden, aiming to help only the environment while jeopardizing the firm’s performance, especially in terms of profitability. The perspective of eco-innovation started to change with Porter’s hypothesis, which many researchers were eager to test. Moreover, pioneering in innovation has been assumed to bring companies the opportunity to enjoy “first mover advantages” (Porter and van der Linde 1995a). Porter and van der Linde (1995a) stressed that properly designed environmental standards can trigger innovations, which lower the total cost of a product or improve its value. Such innovations allow companies to use a range of inputs more productively – from raw materials and energy to labor – and offset the costs of improving environmental impact. Meanwhile, regulations should be strict rather than lax, because lax regulations can be handled incrementally by end-of-pipe or secondary treatment solution, while stringent regulations promote real innovation (Porter and van der Linde 1995a). Eco-innovations are “central to the promotion of sustainable and smart growth in regions because of their wide-ranging benefits for the economy and the environment” (European Commission 2012, 28). In other words, eco-innovations both protect the environment and affect growth and employment, although this impact is likely to vary and depends on the innovation type and the context in which it is used (Arun- In Pursuit of Eco-innovation del and Kemp 2009). As an example, eco-innovations create jobs and wealth in the producing sector (Arundel and Kemp 2009). Figure 4: Business case for eco-innovation Source: EIO and CfSD 2013, 9, Figure 3. 96 Furthermore, eco-innovations are beneficial for the environment, with the aim of releasing less harmful substances into the environment, using natural resources during the production process more effectively, and so forth. We can see that the benefits derived from eco-innovation performance pertain also to companies (see the scheme below in Figure 4). Eco-innovations can and do bring benefits to the adopting companies, thus resulting in a win-win situation (Horbach 2008). The benefits that firms can exploit from the successful introduction of eco-innovation implementation are cost savings, enhanced corporate image, improved relationship with local communities, access to new green markets and gain of superior competitive advantage (Shrivastava 1995). Sarkar (2013) presented the benefits derived from eco-innovation and divided them into direct and indirect benefits. The direct benefits consist of operational advantages, which are seen in cost savings and derive from greater resource productivity and better logistics, followed by sales from commercialization (Sarkar 2013), while the indirect benefits include better image, better relations with customers, suppliers and authorities, health and safety benefits, greater worker satisfaction and an enhanced innovation capability overall (Sarkar 2013). Sarkar (2013) emphasizes that companies increasingly recognize that the greening of businesses by improving resource productivity may increase their short and long-term competitiveness and create new markets. According to Robinson and Stubberud (2013, 48) “many SMEs are reluctant to engage in eco-efficiency, possibly because they equate green with expensive”. However, as Johnson (2009, 22) says, “when done prop- Consequences of Eco-innovation Adoption erly, going green is good business”; in other words, it is not necessary for companies to choose between being green and being profitable. Even though some companies “avoid” implementation of eco-inno- vation because of initial investments or an expectation that such innovation will be expensive (Robinson and Stubberud 2013), the literature provides empirical evidence to support the idea that eco-innovation can be a win-win situation for both the company and the environment (Horbach 2008). Summarizing the scheme in Figure 4, which depicts possible consequences related to the introduction of eco-innovations, we can see that product and technological eco-innovations are an opportunity for companies to consolidate their position on the domestic market and internationalize by entering or expanding to foreign markets, while they can also reduce their costs through material saving innovations along international material supply chains with the adoption of process eco-innova-97 tions (EIO 2011b). Among the most important benefits for firms that go “green” and aim to create a more sustainable business model are the following: - possibility to gain a green competitive advantage and competi- tiveness on the international markets (Tien et. al 2005; Chen et al. 2006; Triebswetter and Wackerbauer 2008; Johnson 2009; European Commission 2012; Ar 2012; Hofer et al. 2012; Wong 2012; Leonidou et al. 2013a), - entry into foreign markets/internationalization (Beise and Rennings 2005; Martin-Tapia et al. 2010), - improvement of firm performance (Clemens 2006; Johnson 2009; Zeng et al. 2011; European Commission 2012; Ar 2012; Doran and Ryan 2012; Lin et al. 2013a), - gain of sustainable growth on domestic and international mar- kets (European Commission 2012), - achievement of global corporate sustainability goals and objectives in organizations (Paraschiv et al. 2012). Other benefits that firms can seize from the adoption of eco-innovation are as follows: through cost efficiency, firms can gain in cost savings, corporate image can be enhanced and relationships with local communities can be improved, followed by access to new green markets and gain of superior competitive advantage (Shrivastava 1995). In addition, firms can achieve a cost advantage (operating at a lower cost than competitors but offering a comparable product) or a differentiation advantage (when customers consistently perceive the firm’s offer as superior to its competitors’ In Pursuit of Eco-innovation offer) (Porter 1985 in Zhou et al. 2009). Therefore, eco-innovations can, by cost efficiency or by introduction of eco-innovations that differ from others and bring additional value to the customers, gain and achieve a competitive advantage, whether on the domestic or international market (Tien et al. 2005; Chen et al. 2006; Triebswetter and Wackerbauer 2008; Chiou et al. 2011; Ar 2012; Hofer et al. 2012; Wong 2012; Leonidou et al. 2013a). Therefore, sustainable orientation in eco-innovation practices can lower costs because companies reduce the inputs they use, while they also generate additional revenues from better products and enable companies to create new businesses; hence, smart companies now treat sustainability as innovation’s new frontier (Nidumolu et al. 2009). Sustainability and the eco-innovations related to it therefore impose pressure on companies to change the way they think about products, technologies, processes and 98 business models (Nidumolu et al. 2009) and force them to act. Companies’ orientation towards sustainability (usually expressed in companies’ objectives and behavior with regard to sustainability) leads to a competitive advantage, which is hard for competitors to imitate (Nidumolu et al. 2009). Lastly, Marin (2014) argues that environmental innovations guar-antee a return, but this return compared to return of non-environmental innovations is substantially lower. Moreover, referring to the Porter hypothesis, Marin (2014) concludes that the possible effects of policy-induced environmental innovation on competitiveness are likely to show up in the medium to long term (depending on early mover advantages of environmental innovation and on the creation of new markets for environmental technologies). Firm performance Ramanathan et al. (2010) warn that the relationship between environmental innovation and financial performance can be ambiguous. Mixed findings regarding this relationship identify environmental efforts as a financial burden, which can hurt firm’s profitability, although findings also show that companies that pursue sustainability and implement environmental innovations benefit from enhanced efficiency and can exploit new growth opportunities, leading to higher profitability and competitive advantage (Schrettle et al. 2013). Companies endeavor to eco-innovate and hence sacrifice their short-term profitability in order to acquire higher mid-term and long-term business goals, although it is generally known that environmental innovations require higher costs for their development and introduction than other general innovations (Triguero et al. 2013). Introduction of new environmentally friendly products or sig- Consequences of Eco-innovation Adoption nificantly improved existing ones (in order to become more environmentally friendly) can, through the reduction of needed inputs through production, lead to improved productivity and ensure the compatibility of cost savings and reduction of environmental harm (Triguero et al. 2013). Therefore, on the one hand, product innovations have the potential to create new markets, lead to competitive advantages through greater differentiation from competitors’ products and gain greater profit margins (Ramanathan et al. 2010). Meanwhile, researchers (Porter and van der Linde 1995a; Porter and van der Linde 1995b; Ramanathan et al. 2010) add that process innovations can also result in cost reduction through the increase of energy efficiency and less waste production. On the other hand, the uptake of innovation and its implementation may not necessarily result in benefits for the company that has undertaken those innovations; because of high initial investments in R&D, such financial benefits 99 are not acquired in the short-term (Ramanathan et al. 2010). Researchers (Ghisetti and Rennings 2014) emphasize another pecu- liarity regarding environmental innovations and their relationship with profitability, pertaining to different typologies of eco-innovation. Based on their research, they conclude that, while it pays to be green, the benefit depends on the way in which a company is green (Ghisetti and Rennings 2014). Their findings indicate that for those environmental innovations that aim to reduce externalities (e.g., harmful materials, air, water, noise and soil pollution), it does not pay to be green, in the sense that these innovations may be profitable in the long run (due to improved environmental regulation) but do not pay off in the short run (when companies cope with environmental regulations as restrictions) (Ghisetti and Rennings 2014). On the other hand, energy- and resource-efficient innovations lead to a potential “win win” situation (reduced environmental impact of production and improved companies’ economic performance). Hence, it definitely pays to be green when engaging in environmental innovations, which lead to reduction in the use of resources and energy (Ghisetti and Rennings 2014). Energy and resource efficient innovations exert a positive and strongly significant effect on companies’ profitability, while the externality-reducing innovations negatively affect companies’ operating margins (Ghisetti and Rennings 2014). Similarly, Rexhäuser and Rammer (2013) pointed out that environmental innovations related to reduction of energy and material input demonstrate a positive impact on companies’ profitability (driven by cost reduction), while environmental innovations focused on reduction of environmental pressures (driven by regulations) negatively and weakly affect companies’ profita- In Pursuit of Eco-innovation bility. Furthermore, the following environmental management practices have the greatest impact on firm performance with regard to the research of Montabon et al. (2007): recycling, waste reduction, remanufacturing, environmental design and surveillance of the markets. The majority of eco-innovations (80.4%) lead to lower or constant costs, while 32% of these eco-innovations are associated with higher turnover; in other words, these eco-innovations are also economically successful (Horbach et al. 2012). Molina-Azorin et al. (2009 in Huang and Wu 2010) reviewed 32 studies, 21 of which found a positive effect of environmental management and/or environmental performance on financial performance. Similar results about eco-innovation’s impact on firms’ financial performance were found by Paraschiv et al. (2012), who found that 35% of participant organizations have achieved encouraging results, 100 whereas another 21% have reported significant results with a strong impact on the organization’s financial performance, and 10% specified that the results of eco-innovations were insignificant. In more detail, we can see that material savings and energy-saving products within the firm lead to an increase in turnover, while an improvement of product recyclability significantly reduces turnover due to its relation to the higher costs within the firm (Horbach et al. 2012). The results regarding the relationship between eco-innovation and firm performance indicate that eco-product innovation had a relatively greater impact on firm performance than eco-organizational and eco-process innovations had (Cheng and Shiu 2012). A year later, Cheng et al. (2013) revealed that eco-product, process and organizational innovations directly and indirectly affect firm performance (measured by return on investment, profits, market share and sales). Lastly, Alvarez Gil et al. (2001) found a positive relationship between environmental management practices and firms’ financial performance, indicating a positive effect on short-term financial performance. Doran and Ryan (2012) conducted a survey from 2006 to 2008 in- cluding 2181 Irish firms. Their research showed that eco-innovation exerts a positive and significant impact on firm performance (eco-innovation can drive performance growth); therefore, firms that engage in eco-innovations have higher levels of turnover per employee (i.e., revenue per employee) than firms that do not introduce eco-innovations. A positive relationship between green innovations and financial performance has also been found in small firms (it is even greater when green economic incentives exist; Clemens 2006) and in manufacturing SMEs in Northern China (Zeng et al. 2011). Moreover, technological innovation efficiency (Cruz-Cázares et al. 2013), eco-friendly marketing strategy (Leonidou Consequences of Eco-innovation Adoption et al. 2013a), environmental innovation strategy (Eiadat et al. 2008) and green product innovation performance (Huang and Wu 2010; Ar 2012; Lin et al. 2013a) were all positively associated with firms’ financial performance. Several researchers (Rexhäuser and Rammer 2013; Ghisetti and Rennings 2014) found that environmental innovations that improve firms’ resource efficiency (in terms of material or energy consumption per unit of output) demonstrate a positive and significant effect on firm profitability, while this effect on firms’ profits is not valid for environmental innovations, which do not improve firms’ resource efficiency. In more detail, Fraj-Andrés et al. (2009) found that environmental marketing positively affects firms’ operational and commercial performance, and such improvement affects their economic results. We should stress that the effect of environmental performance on financial performance is a long-term project that brings long-term benefits. Horváthová 101 (2012) found that the relationship between financial performance and environmental performance was negative after one year and turned positive after two years. Meanwhile, another study based on hotel tourism (Molina-Azorin et al. 2009) has also found a positive relationship between environmental management and firm performance, with the conclusion that environmentally proactive hotels have higher levels of financial performance. Meanwhile, the results of a study focused on green supply chains (Rao and Holt 2005) indicate that greening the supply chain can lead to competitiveness and economic performance. Therefore, the companies can exploit substantial cost savings and new market opportunities (which lead to greater profit margins), enhance sales, and increase market share, and most of the captured benefits result in improved firm performance (Rao and Holt 2005). Furthermore, De-Burgos-Jiménez et al. (2013) emphasized in their analysis and survey that researchers adopt different measures, which consequently lead to different conclusions. After a review of contradictory research works, they have broken down the environmental variables into three different concepts: environmental activities (environmental management), environmental strategic orientation (environmental proactivity) and the real impact on the natural environment (environmental performance). The results of their survey (De-Burgos-Jiménez et al. 2013) found that the correlation between environmental management and financial performance is not significant, while it turned out to be positive and significant for environmental proactivity and environmental performance. This implies that firms with good environmental performance (especially environmentally proactive firms) tend to have positive financial performance (De-Burgos-Jiménez et al. 2013). In Pursuit of Eco-innovation Findings derived from a meta-analysis comprising 37 empirical works (Horváthová 2010) show that the empirical evidence regarding the relationship between environmental performance and financial performance is inconclusive: half of studies find that the impact is positive, while the rest document either a negative or an insignificant impact. Horváthová (2010) emphasizes that, under certain conditions, studies investigating the relationship between environmental performance and financial performance are more likely to find a positive effect of environmental performance on financial performance. These conditions include the following: common law countries, appropriate time coverage and qualitative measures of environmental performance (Horváthová 2010). Researchers also came to negative conclusions. In contrast to researchers who found a positive association between eco-innovation and firm performance (Rao and 102 Holt 2005; Clemens 2006; Montabon et al. 2007; Eiadat 2008; Fraj-An-drés et al. 2009; Molina-Azorin et al. 2009; Huang and Wu 2010; Zeng et al. 2011; Ar 2012; Cheng and Shiu 2012; Cheng et al. 2013; Cruz-Cázares et al. 2013; Leonidou et al. 2013a; Lin et al. 2013a), opposite findings also exist. Some researchers found a negative relationship between eco-innovation and firm performance in the short term (Ramanathan et al. 2010), while other researchers (Pickman 1998 in Ramananthan et al. 2010; Horváthová 2012; Ghisetti and Rennings 2014) argue that innovation brings benefits to companies after a few years’ lag, whereas no immediate benefits are brought to companies deriving from innovation efforts. Thereby, Triguero et al. (2013) argue that environmental product innovations can be more costly than non-environmental ones and, therefore, companies have to sacrifice the short-term profits in order to achieve mid-term and long-term business goals. In addition, Horváthová (2012) has found that the relationship between financial performance and environmental performance was negative after one year and turned positive after two years. Finally, Li (2014) has not found any significant effect of environmental innovation practices on firms’ financial performance. In conclusion, the relationship between eco-innovation and firm performance can vary according to the eco-innovation type – those focused on efficiency are profitable, while the externalities reducing eco-innovations are not (Rexhäus-er and Rammer 2013; Ghisetti and Rennings 2014). In Table 8 below, we summarize findings of past research related to the relationship between eco-innovation and firm performance. Consequences of Eco-innovation Adoption Table 8: Summary of the past findings and measures used to test the relationship between eco-innovation and firm performance Authors Measures Findings In the last two years, because of imple- The results of the study suggest that green- menting better management practices, ing companies’ supply chains would lead there have been specific benefits achieved not only to the achievement of substantial in each of the following categories (on a cost savings but also to the enhancement four-point scale of strongly disagree, dis- of sales, market share, and exploitation agree, agree, strongly agree). of new market opportunities (leading to Rao and Holt (2005), - Increased efficiency (C) greater profit margins) – all together con- International Journal of - Quality improvement (C) tributing to the economic performance of Operations & Produc- - Productivity improvement (C) the company. tion Management - Cost saving (C) - New market opportunities (EP) - Product price increase (EP) - Profit margin (EP) 103 - Sales (EP) - Market share (EP) Respondents answered on a 5-point Likert The findings of this study indicate a posi-scale, anchored by “much worse” and tive relationship between green and finan- “much better”. cial performance. Therefore, small firms - As compared to your competitors, your that perform better environmental y are growth in earnings has been _____. also financial y the most successful. - As compared to your competitors, your Clemens (2006), growth in revenue has been _____. Journal of Business Re- - As compared to your competitors, your search change in market share has been _____. - As compared to your competitors, your return on assets has been _____. - As compared to your competitors, your long run level of profitability has been _____. - Return on investments Environmental management practic- - Sales growth es are positively associated with firm per- Montabon et al. (2007), - Product innovation formance. Journal of Operations - Process innovation Management Respondents were asked what effects their The results of the study have found a sig-environmental practices have had on: (1) nificant positive relationship between en- market share, (2) sales growth, and (3) re- vironmental innovation strategy and firms’ turn on investment. 5-point scale (Cron- business performance. Eiadat et al. (2008), bach’s alpha = 0.80), anchored by ‘substan- Journal of World Business tial negative effect’ and ‘substantial positive effect’. - Sales growth - Market share - Return on investment In Pursuit of Eco-innovation Authors Measures Findings - Firm’s profitability Environmental marketing is positively as- - Sales growth sociated with the firm’s operational and - Firm’s economic results commercial performance and this im- Fraj-Andres et al. (2009), - Profit before tax provement will influence their economic Journal of Business Ethics - Market share results. Therefore, environmental strategies reduce environmental impact and positive- ly affect the firm’s competitiveness. - Room occupancy rate The findings of the study revealed a pos- - Market share gain itive relationship between environmen- - Average sales growth in the last five years tal management practices and firm per-Molina-Azorín et al. - Income per room formance. (2009), - Total gross profit Journal of Cleaner Pro- - Gross profit per room duction - Wealth creation (accounting value of the firm with respect to its market value) 104 - Capacity to generate profit in times of crisis - Green new product development proj- Green product innovation performance ect’s return on investment has a positive influence on financial per- Huang and Wu (2010), - Growth in earning formance. Management Decision - Sales growth - Market share - Gross value added (at constant prices) Environmental innovation in the short run Ramanathan et al. (2010), negatively affects economic performance Management Decision in industrial sectors. - Sales Environmental performance and econom- - Profitability ic performance for SMEs with high or low Zeng et al. (2011), - ROE pollution levels positively correlate. En- Journal of Cleaner Pro- - Market share vironmental performance is moderate- duction - Number of customers ly correlated with financial indexes but not significantly correlated with the non-finan- cial indexes. What effect have your environmental Green product innovation positively and product innovation practices had on these significantly influences firm performance. Ar (2012), items – negative effect (1) and strongly Procedia - Social and Be- positive effect (7) havioral Sciences - Sales growth - Market share - Return on investment Relative to competing new eco-products All three dimensions of eco-innovation during the last three years, our unit’s new (product, process and organizational) are eco-product performance is better with positively linked to firm performance. Cheng and Shiu (2012), respect to: Technovation - Return on investment - Sales - Market share - Profitability Consequences of Eco-innovation Adoption Authors Measures Findings - Turnover per worker (i.e., revenue per Eco-innovation has a positive and signifi- employee) cant effect on firm performance. Firms en- Doran and Ryan (2012), gaged in eco-innovation have higher lev- European Journal of Inno- els of turnover per worker than firms that vation Management do not engage in eco-innovation or any in- novation at all. - Debt to total assets The results of the study have revealed that - ROA the effect of environmental performance Horváthová (2012), - ROE on financial performance is negative after a Ecological Economics - Sales one-year lag, while it becomes positive for - Profit in current accounting period a two-year lag. Relative to competing new eco-products The results of the study demonstrate that during the last three years, our unit’s new eco-organizational innovation has the eco-product performance is better with strongest effect on business performance. Cheng et al. (2013), respect to: Moreover, eco-organizational, eco-pro- 105 Journal of Cleaner Pro- - Return on investment cess, and eco-product innovations have di- duction - Sales rect and indirect effect on business per- - Market share formance. - Profits - ROA (return on assets) The results indicate that it pays to be green. De Burgos-Jiménez et - ROS (return on sales) When environmental performance is bet- al. (2013), - Sales variation ter as an industrial average, and when com- International Journal of panies are environmental y proactive, Operations & Produc- there is a positive effect on financial per- tion Management formance. - Operating profits Eco-friendly marketing strategy has a posi- - Profit to sales ratio tive influence on financial performance. - Profit return on investment Leonidou et al. (2013a), - Return on assets Tourism Management - Market share - Sales volume - Sales return on investment - Cash-flow - Market position improvement Green product innovation performance Lin et al. (2013a), Journal - Enhancing sale volume is positively associated with firm perfor- of Cleaner Production - Enhancing the profit rate mance. - Enhancing the reputation - ROS (return on sales) Innovations that do not improve firms’ re- source efficiency do not provide positive Rexhäuser and Rammer returns to profitability, while innovations (2013), that increase a firm’s resource efficiency (in Environmental and Re- terms of material or energy consumption source Economics per unit of output) have as well a positive effect on profitability. In Pursuit of Eco-innovation Authors Measures Findings - Estimated Operating Margin (profit be- The results indicate that innovations lead- Ghisetti and Rennings fore taxes on income as a percentage of ing to a reduction in the use of energy or (2014), turnover) materials per unit of output positively af- Journal of Cleaner Pro- fect firms’ competitiveness. In contrast, ex- duction ternality-reducing innovations hamper firms’ competitiveness. - Improved capacity utilization The results indicate that resource com- - Decrease of fee for waste treatment mitment works as a moderator between Li (2014), - Increased profit through the sale of scrap environmental innovation practices and Journal of Cleaner Pro-and used materials and equipment financial performance. As resource com- duction - Decrease of penalty costs for environ- mitment increases, financial performance mental accidents regarding environmental innovation prac- tices will improve. 106 Internationalization Luostarinen (1979 in Ruzzier 2005) has defined internationalization as geographical expansion of economic activities over a national country’s border. The reasons to “go international” are many and can stem from limited absorption power of the national market (Reuber and Fisher 1997; Kafouros et al. 2008; Ciszewska-Mlinarič and Mlinarič 2010; Ky-lläheiko et al. 2011), desire to gain a competitive advantage through innovation (López Rodríguez and García Rodríguez 2005; Pla-Barber and Alegre 2007; Ramadani and Gerguri 2011; Adalikwu 2011) and exploitation of an innovation’s benefits (Kafouros et al. 2008; Kylläheiko et al. 2011; Ruzzier and Mlakar 2011). Moreover, internationalization is considered an important asset in order to enhance SMEs’ long-term growth and survival (Cerrato and Piva 2010); therefore, Lu and Beamish (2006) suggest that it is only a question of when many companies will expand their geographic scope from domestic to foreign markets. Internationalization, in its simplest form as an export activity, is a phenomenon that is gaining importance within small companies, where the propensity to export depends highly on the ability to innovate (Nassibeni 2001). We can add that “innovative firms are better equipped to exploit international market opportunities and perform better in such markets” (O’Cass and Weerawardena 2009, 1325). Researchers (Lu and Beamish 2006) suggest that once a company is ready for internationalization, it should not wait long to start the internationalization process, because the sooner it does so, the easier will be the learning in the international environment and the faster will be the firm growth. Meanwhile, Dai et al. (2013) found a positive relationship between internationalization and innovativeness; furthermore, they Consequences of Eco-innovation Adoption found that the least innovative firms have achieved greater international scope than firms that are moderately innovative. In more detail, they suggest that firms whose goal is entry in foreign markets should either use a low innovation strategy to minimize development costs or invest more effort to become sector leaders by investing in leading edge innovations (Dai et al. 2013). Cassiman and Golovko (2011) found that successful product inno- vation spurs firm to get involved in international activities, usually in exports; moreover, investments in product innovation are associated with success on global export markets (D’Angelo et al. 2013). Thereby, firms that successfully implement eco-innovations expand their operations on foreign markets; they have an opportunity to internationalize because of successful implementation of eco-innovation. Martin-Tapia et al. (2008) found a positive relationship between advanced environmental strate-107 gies and internationalization (i.e., export intensity); moreover, proactive environmental strategy is positively related to a company’s export performance (Martin-Tapia et al. 2010). This means that proactive environmental strategy helps to improve export performance, while its effect increases with firm size; that is, this effect is stronger for smaller enterprises than for micro enterprises, and it is greater for medium enterprises than for smaller ones (Martin-Tapia et al. 2010). Moreover, the study conducted on a sample of export firms from the Spanish food industry has shown that, for export firms that work and spend time in markets with different environmental institutional profiles, gaining a background of complex knowledge is positively related to the adoption of a proactive environmental strategy (Aguilera-Caracuel et al. 2012). Leonidou et al. (2013b) found in their study that eco-friendly marketing strategy contributes to the achievement of superior export performance (such a strategy has turned out to be of even greater necessity when firms are selling industrial goods versus consumer goods and targeting developed rather developing countries). In addition, Beise and Rennings (2005) argued that national regu- lations, which stimulate environmental innovation, have to be properly set and need to comply with international markets, demand and international regulations in order for the “doors to international markets [to] be opened”; otherwise, these eco-products and services will only be niches just in regional and national markets. Therefore, countries that apply more stringent environmental standards and possess higher innovation capabilities have a greater export capacity for those environmental friendly technologies whose adoption is induced by regulations (Costantini and In Pursuit of Eco-innovation Crespi 2008). Meanwhile, Costantini and Mazzanti (2012) have tested the Porter hypothesis (the weak and the strong version), and the results of their study revealed that environmental policies are not harmful for export competitiveness in the manufacturing sector, as well as that there is a positive impact of specific energy tax policies and innovation effort on export flow dynamics. They conclude that environmental policies and more incisive efforts of environmental innovation spur green exports (Costantini and Mazzanti 2012). Lastly, it has been found that internationalization modes vary from country to country; Romanian firms usually use exports through foreign agents for selling ecological products (they are export-oriented based on strategic alliances with foreign partners that often hold the organization for distribution of their products under foreign brand names), while British firms aim to control the foreign distribution 108 channels and sell on foreign markets through specialized distributors with their own brand name (Gurǎu and Ranchhod 2005). Internationalization for Romanian firms presents the main center of profits and source of future competitive advantage gained in the domestic market, while for British firms the goal is expansion of sales and taking advantage of the positive eco-brand image (Gurǎu and Ranchhod 2005). Competitive advantage General innovation implies newness (Chetty and Stangl 2010) and is considered an important and vital source of competitive advantage and companies’ productivity growth (López Rodríguez and García Rodríguez 2005; Carneiro 2007; Pla-Barber and Alegre 2007; Acs, Desai and Hessels 2008; Adalikwu 2011). According to López Rodríguez and García Rodríguez (2005), process innovation can generate competitive advantages through gains in process efficiencies, while product innovation can create a competitive advantage in customer value through greater differentiation in product characteristics. Nevertheless, to exploit an innovation’s benefits, companies need a sufficient degree of internationalization (Kafourus et al. 2008; Kylläheiko et al. 2011; Ruzzier and Mlakar 2011). Therefore, in this section, we present the consequences of proactive and successful implementation of eco-innovations. Previous research works have found a positive relationship between eco-innovation and competitive advantage (Tien et. al 2005; Chen et al. 2006; Triebswetter and Wackerbauer 2008; Ar 2012; Hofer et al. 2012; Wong 2012; Leonidou et al. 2013a; Robinson and Stubberud 2013). Investment in proactive environmental management contributes to enhanced competitiveness of the firm; thus, cost and differentiation com- Consequences of Eco-innovation Adoption petitive advantages positively affect financial performance (López-Gamero et al. 2010). Moreover, pioneering in innovation gives companies the opportunity to enjoy first mover advantages (Porter and van der Linde 1995a); that is, they can ask for higher prices for green products, and, at the same time, they improve the corporate image and have a chance to develop new markets and to gain competitive advantages (Peattie 1992 in Chen et al. 2006; Hart 1995 in Chen et al. 2006). Nevertheless, we should be aware that, without strict regulations and international policy diffusion, renewable energies would not be competitive (Beise and Rennings 2005). Dealing with sustainability-related issues brings to SMEs the opportunity to realize competitive advantage in the sense of successful new products (Klewitz et al. 2012). Investment in green innovations (Chen et al. 2006) was helpful to the business (the more companies invested 109 in green innovation, the stronger was their competitive advantage). The correlations between green product and green process innovation have turned out to be positively associated to the firm’s competitive advantage (Chen et al. 2006). However, green product innovation has a stronger influence on competitive advantage and new product success than green process innovation has (Wong 2012). Therefore, when there are limited resources, green product innovation should be pursued first (Wong 2012). Chiou et al. (2011) found that firms, by focusing on green product, process and managerial innovation, will gain cost savings, increase their efficiency and productivity and have better product quality, all of which will lead to improved competitive advantage. Indeed, many companies worldwide have followed environmental compliance and consequently transformed their entire business operations to become more eco-efficient and achieve a competitive advantage over their competitors (Mourad and Ahmed 2012). Fraj-Andrés et al. (2009) revealed that environmental marketing is an excellent strategy to pursue in order to obtain competitive advantages in costs and in product differentiation. Moreover, Hofer et al. (2012) argue that companies should note that competitive advantages derived from environmental management activities (environmental innovations) are likely to be short-lived because of imitation by rival companies, but this can be avoided by protecting the innovation (when involving intellectual property; e.g., manufacturing processes, methods, and materials), which can prevent or at least slow down the erosion of competitive advantage caused by the imitation activity of other companies. Furthermore, a study on Greek hotels found that environmental marketing strategy leads to achievement of competitive In Pursuit of Eco-innovation advantage, while the positive effect of a green marketing strategy on competitive advantage is even more imperative when hotels face acute competition (Leonidou et al. 2013a). Leonidou et al. (2013a, 104) further argue that “the favorable effect of an eco-friendly marketing strategy on gaining a competitive advantage indicates that the adoption of an environmentalism approach can seriously reduce the firm’s costs (e.g., energy savings, process efficiency, recyclable material) and/or differentiate its products/ services (e.g., refillable packages, eco-friendly image, unique features)”. Additionally, companies can, through environmental technologies, gain a competitive advantage by establishing unique and inimitable strategies; therefore, they distinguish themselves from the competition and become environmental leaders (Shrivastava 1995). 110 Hypotheses Development This chapter pertains to the research hypotheses, which are developed and formulated based on prior research works on eco-innovation. Hypothesized relationships and development of hypotheses will be presented in two main groups, which include hypotheses about: a) the antecedents of eco-innovations (environmental policy instruments, customer demand, expected benefits, managerial environmental concern and competition) in Section 5.1, and b) the consequences of eco-innovation (impact of eco-innovations on firm performance, competitive benefits and internationalization) in section 5.2. Detailed hypotheses development and its theoretical underpinning are provided in the forthcoming pages. Hypotheses concerning antecedents of eco-innovations In this section, we provide theoretical arguments, which underpin hypotheses related to the drivers of eco-innovation. In our study we posited and tested the following determinants as driving forces of eco-innovation: environmental policy instruments (Section 5.1.1), customer demand (Section 5.1.2), managerial environmental concern (Section 5.1.3), expected benefits (Section 5.1.4) and competition (Section 5.1.5). Environmental policy instruments and eco-innovation Studies of environmental innovation over the last 15 years found regulation to be the most important stimulus of eco-innovation (Porter and van der Linde 1995b; Rennings 2000; Blum-Kusterer and Hussain 2001; Madsen and Ulhøi 2001; Van Hemel and Cramer 2002; Beise and Ren- In Pursuit of Eco-innovation nings 2005; Green 2005 in Triebswetter and Wackerbauer 2008; Rehfeld et al. 2007; Horbach 2008; Belin et al. 2009; Khanna et al. 2009; Popp et al. 2011; Qi et al. 2010; Testa et al. 2011; Weng and Lin 2011; Zeng et al. 2011; Brouillat and Oltra 2012; Holtbrügge and Dögl 2012; Horbach et al. 2012; Murovec et al. 2012; Triguero et al. 2013; Yabar et al. 2013; Chassagnon and Haned 2014). Prioritizing and complying with the existing regulations (Horbach 2008) has shaped the most eco-product and eco-organizational innovations (Triguero et al. 2013). Environmental regulation may “force” or “drive” firms to realize economically benign environmental innovation, while strict environmental regulations intended to stimulate implementation of eco-innovation (Porter and van der Linde 1995b; Beise and Rennings 2005) can also enhance competitiveness and may create lead markets (new markets, export opportunities for the pio-112 neering country) (Porter and van der Linde 1995b). Hence, the regulations need to comply with international regulations, global demand or regulatory trends (Beise and Rennings 2005). Furthermore, Desmarchelier et al. (2013), in a study of French service firms, found a sensitivity to environmental policies; especially effective seem to be eco-taxes, which along with financial incentives exert a positive and significant effect on environmental investments (Murovec et al. 2012). Moreover, subsidies trigger environmental innovations in particular, mostly because of negative external effects of environmental problems (Horbach 2008). Several research works pointed out important and positive effect of subsidies (Horbach 2008; Brouillat and Oltra 2012; Murovec et al. 2012; Veugelers 2012; Desmarchelier et al. 2013) and tax-ation (Kesidou and Demirel 2011; Brouillat and Oltra 2012; Murovec et al. 2012; Horbach et al. 2012; Veugelers 2012; Desmarchelier et al. 2013) on the implementation of eco-innovation. In summary, previous literature and research works pinpointed the key role of regulation in spurring eco-innovation, which stems from the well-known eco-innovation peculiarity of the double externality problem (Porter and van der Linde 1995b; Rennings 2000; Horbach 2008; Wagner 2008; De Marchi 2012). Regulations have the influence to push companies into eco-innovation and therefore force companies to respond. However, the companies may be tempted to comply only minimally, or as little as possible, with the regulation (Nidumolu et al. 2009). On the other hand, companies that seek to exceed the minimum level of compliance often enjoy first mover advantages by pioneering in innovation (Porter and van der Linde 1995; Nidumolu et al. 2009). Porter and van der Linde (1995a) stressed that properly designed environmental regulations can Hypotheses Development trigger innovations, which lower the total cost of a product or improve its value. Several researchers (Horbach 2008; Qi et al. 2010; Zeng et al. 2011; Holtbrügge and Dögl 2012; Yabar et al. 2013; Chassagnon and Haned 2014) found that environmental regulation provides sufficient incentives to induce eco-innovation. However, the regulations’ impact on eco-innovation is not always straightforward. For instance, Frondel et al. (2008) found that policy stringency has a positive effect on environmental innovation and abatement activities, while is not related to EMS adoption, while Eiadat et al. (2008) found a significant negative effect of environmental regulation on eco-innovation. Another important issue pertains to the stream of research that focuses on the influences of different environmental policies – the command-and-control instrument vs. the economic incentive instrument – on eco-innovation practices. The basic lesson from ecological economics 113 for a long time was that the economic incentive instrument is far more effective in triggering eco-innovation and is therefore superior to the command-and-control instrument (Cleff and Rennings 1999; Rennings et al. 2006). In contrast, Kemp and Pontoglio’s (2011) synthesized findings indicated that the economic incentive instrument influence is far weaker than assumed. Furthermore, empirical evidence of a study undertaken by Li (2014) indicates that the command-and-control instrument works as a driver of eco-innovation, while the economic incentive instrument does not work. Oltra and Saint Jean (2009) argued that market-based instruments cannot be complete substitutes for the other policy instruments and by themselves are not sufficient for inducing environmental innovation; the most effective seems to be combination of both environmental and innovative policy. Brouillat and Oltra (2012) argued that the use and impact of each instrument depends on the policy design – in particular, on the level of stringency and on the reward system – that is implemented. Therefore, similar to Li (2014), we investigate the individual effects of the command-and-control instrument and the economic incentive instrument on eco-innovation practices. Therefore, we propose that: Hypothesis 1a: There is a positive and significant relationship between the command-and-control instrument and companies’ implementation of eco-innovation. Hypothesis 1b: There is a positive and significant relationship between the economic incentive instrument and companies’ implementation of eco-innovation. In Pursuit of Eco-innovation Customer demand and eco-innovation Environmentalism as a consumer attitude is spreading and growing in importance worldwide. As a result, consumers are willing to choose environmentally friendly products and are prepared to pay higher prices for them (Chen 2013). By gaining environmental awareness and expressing concerns related to environmental impacts, which affect their purchasing choices, they exert more pressure on companies to reduce their adverse impacts on the environment (Kemp and Foxon 2007). Consumers’ “green” demands challenge companies to provide proper design, production, sales and recycling of products (Sarkar 2013). In addition, companies have realized that the market demand for environmentally friendly products is growing and can become profitable as a segment (Nidumolu et al. 2009). Consumer demand seems to be a strong driver of eco-innovation, 114 especially when operating in product markets, which are close to final customers, where the pressure to eco-innovate is the strongest (Zeng et al. 2011; Doran and Ryan 2012). This in in line with other research works that found customer demand to be the most effective driver of eco-product innovation (Kammerer 2009; Horbach et al. 2012; Lin et al. 2013a) and eco-process innovations that increase material efficiency, reduce energy consumption, waste and the use of dangerous substances (Horbach et al. 2012). Furthermore, Van Hemel and Cramer (2002) found that customer demand is to be the most influential driver of eco-design innovations and has a significant influence on green innovation adoption in SMEs (Weng and Lin 2011). Thus, consumer demand for environmentally friendly products and processes encourages firms’ decision to invest in and implement eco-innovation (they apply some or a minimum level of eco-innovation activities to respond to the market pressure, but they do not necessarily invest large amounts of resources into eco-innovation) (Kesidou and Demirel 2012). In conclusion, customer demand plays a critical role in today’s business environment, because consumers demand that products are produced in an environmentally friendly way (Chiou et al. 2011). Moreover, customer demands and preferences have the potential to affect the direction and rate of eco-innovation (Horbach 2008). Based on the findings of previous research works (Rennings 2000; Van Hemel and Cramer 2002; Le et al. 2006; Kammerer 2009; Lee 2009; Lewis and Cassells 2010; Popp et al. 2011; Weng and Lin 2011; Zeng et al. 2011; Doran and Ryan 2012; Horbach et al. 2012; Kesidou and Demirel 2012; Lai and Wong 2012; Murovec et al. 2012; Lin et al. 2013a), we can conclude that one of the essential drivers of eco-innovations is customer demand. Therefore, we expect that: Hypotheses Development Hypothesis 2: There is a positive and significant relationship between customer demand and companies’ implementation of eco-innovation. Managerial environmental concern and eco-innovation Managers are entrusted with the responsibility to behave socially and environmentally responsibly, demonstrating their corporate social responsibility and environmental awareness. They also have an important task concerning the adoption of eco-innovations and concern for all stakeholders – the environment, employees, final consumers and society. According to the research of Qi et al. (2010), managerial concerns are one of the two most important drivers of the adoption of green practices. In addition, Ar (2012) found managerial environmental concern to be a mod-115 erator of the relationship between green product innovation, firm performance and competitive capability. Companies are more motivated to adopt an environmental innovation strategy if their managers place a high value on and express concern about the environment and its protection (Ferguson and Langford 2006 in Tseng et al. 2013). Moreover, top management commitment positively and significantly affects environmental collaboration with suppliers as well as firms’ adoption of green purchasing (Yen and Yen 2012), while entrepreneurial activities towards the environment in the form of firm innovativeness are improved when the managerial environmental attitudes are embedded within a market-oriented firm (Dibrell et al. 2011). Managers who express a high level of environmental concern are also keener to dedicate more time and resources to environmental initiatives (Naffziger et al. 2003). Likewise, managerial concerns with regard to the environment are positively related to the scope and speed of the firm’s response to environmental issues (Tseng et al. 2013), and are thus the strongest driver of environmental innovation strategy (Eiadat et al. 2008). In summary, managerial environmental concern is one of the two most important drivers of eco-innovation adoption (Qi et al. 2010) and is the strongest driver of environmental innovation strategy (Eiadat et al. 2008). In addition, managerial environmental concern exerts a positive effect on the increase of environmental process innovations (Triguero et al. 2013) and works as a stimulus of corporate environmental responsiveness (Papagiannakis and Lioukas 2012), environmental new product development (Pujari et al. 2003) and environmental collaboration with suppliers, reflected in the company’s green purchasing (Yen and Yen 2012). In line with the aforementioned research works, we posit that: In Pursuit of Eco-innovation Hypothesis 3: There is a positive and significant relationship between managerial environmental concern and companies’ implementation of eco-innovation. Expected benefits and eco-innovation When companies are in pace with regulations, they start to act more proactively concerning environmental issues, and they try to make their value chains more sustainable by focusing on reduction of material, more efficient use of raw materials and manufacturing facilities and also reduction of waste (Nidumolu et al. 2009). The aim of companies’ implementation of eco-innovation usually concerns creation of a better image, but outcomes also include reduced costs and new market opportunities (Nidumolu et al. 2009). Implementation of eco-innovation leads to ben-116 efits that concern the environment as well as the company, providing a win-win situation for both of them (Horbach 2008). The benefits that the company can exploit from successful introduction and implementation of eco-innovation are cost savings, enhanced corporate image, improved relationship with local communities, access to new green markets and gain of superior competitive advantage (Shrivastava 1995). Sarkar (2013) stated that eco-innovation’s implementation can result in direct and indirect benefits. Among these, the direct benefits include operational advantages, which result in cost savings and derive from greater resource productivity and better logistics, followed by sales from commercialization (Sarkar 2013), while the indirect benefits include better image; better relations with customers, suppliers and authorities; health and safety benefits; greater worker satisfaction; and, because of knowledge holders, an enhanced innovation capability overall (Sarkar 2013). Past findings (Sarkar 2013) emphasize that among companies there is an increasing recognition that the greening of business by improving resource productivity may increase their short and long-term competitiveness and create new markets. In prior research works, the most frequently mentioned and acknowledged benefits of eco-innovation implementation are: - enhanced / improved firm reputation (Le et al. 2006; Kemp and Foxon 2007; Eiadat et al. 2008; Dangelico and Pujari 2010; Hillestad et al. 2010; Lewis and Cassells 2010; Pellegrini-Masi- ni and Leishman 2011; Doran and Ryan 2012; Holtbrügge and Dögl 2012; Klewitz et al. 2012; Agan et al. 2013; Chen 2013; Sarkar 2013), Hypotheses Development - cost savings (Shrivastava 1995; Montabon et al. 2007; Ambec and Lanoie 2008; Horbach 2008; Lewis and Cassells 2010; Belin et al. 2011; Demirel and Kesidou 2011; Horbach et al. 2012; Klewitz et al 2012; Pereira and Vence 2012; Triguero et al. 2013; Chassagnon and Haned 2014), - entry on new markets (Porter and van der Linde 1995b; Shrivastava 1995; Van Hemel and Cramer 2002; Lewis and Cassells 2010; Horbach et al. 2012; Chen 2013), - increase of market share (Le et al. 2006; Lewis and Cassells 2010; Horbach et al. 2012). Firm reputation Firms aim to maintain a certain image that is consistent with the current 117 external regulatory pressures (Holtbrügge and Dögl 2012). A growing body of empirical studies demonstrates that companies foster eco-innovations in order to avoid social pressures, to comply with external regulatory pressures and thus to improve their reputation (Holtbrügge and Dögl 2012). Non-complying behavior is punished, while loss of reputation and social pressures eventually affect commercial activity (Pacheco et al. 2010). Companies’ business strategies integrate corporate social responsibility and environmental awareness in order to gain and enhance reputational advantages (Eiadat et al. 2008; Hillestad et al. 2010). While the effect of environmental awareness on a company’s gain of competitive advantage is indirect rather than direct (Hillestad et al. 2010), this indirect impact is valuable, especially because environmental awareness is difficult for competitors to imitate and provides the company an improved reputation as environmentally aware (Hillestad et al. 2010). Moreover, key factors in customer purchasing decisions are brand recognition and acceptance; therefore, being the owner of a “green brand” will be increasingly important for companies (Kemp and Foxon 2007). Doran and Ryan (2012) have shown identified that voluntary agreement has the largest impact on eco-innovations’ implementation in firms, while firms are willing to pay to brand themselves as eco-friendly. To conclude, expected gain of firm reputation stemming from engagement in eco-innovations is recognized as an important driver for companies’ implementation of eco-innovations (Eiadat et al. 2008; Hillestad et al. 2010; Pellegrini-Masini and Leishman 2011; Holtbrügge and Dögl 2012). In Pursuit of Eco-innovation Cost savings Cost savings are a more significant determinant of environmental innovations than of other innovations (Horbach 2008). Furthermore, cost savings (especially material and energy savings) were found to strongly trigger eco-innovations in Germany and in France (Belin et al. 2011). They play a very important role as a trigger of eco-innovation (Horbach et al. 2012); although some researchers (Triguero et al. 2013) found their effect to be significant only for eco-process innovations. Cost savings constitute one of the main criteria for decisions to invest in eco-innovations, although there are no immediate visible results; therefore, the lack of knowledge about the potential of technologies, material and energy savings can be seen as a barrier to the implementation of eco-innovations (Pereira and Vence 2012). In addition, cost savings are most closely asso-118 ciated with the most advanced eco-innovations, because they are derived from elimination or re-use of waste; hence, they appear to have a lower potential for creating savings for companies with less advanced eco-innovations (Demirel and Kesidou 2011). Regarding the expected benefits captured from successful implementation of eco-innovation, we propose the following hypothesis: Hypothesis 4: There is a positive and significant relationship between expected benefits and companies’ implementation of eco-innovation. Competition and eco-innovation Another important driver that triggers eco-innovation is competition (Bansal and Roth 2000; Dangelico and Pontrandolfo 2010; Yalabik and Fairchild 2011; Inoue et al. 2013; Li 2014). Competitiveness has been defined as “the potential for ecological responsiveness to improve long-term profitability” (Bansal and Roth 2000, 724). In addition, improved competitiveness encompasses energy and waste management, source reductions resulting in a higher output for the same inputs (process intensifica-tion), eco-labeling and green marketing and, finally, the development of eco-products (Bansal and Roth 2000). Firms motivated by competitiveness expect that their implemented ecological responsiveness will lead to a sustained advantage and, consequently, to improved long-term profitability (Bansal and Roth 2000). Therefore, competition can be considered an effective driver of environmental innovation when dealing with environmentally sensitive customers (Yalabik and Fairchild 2011). In our study, our hypothesis related to competition as a driver of eco-innova- Hypotheses Development tion has been broken into two individual components – competitive intensity and competitive pressure. Building on institutional theory, we assume that companies’ implementation of eco-innovation can result from a mimetic pressure, as a result of which companies follow their competitors’ actions and pursue the same goals – that is, they mimic their actions, especially those that turn out to be lucrative (Spence et al. 2010; Li 2014). Eco-innovations have become an area in which companies have an opportunity to gain a competitive advantage over competitors through differentiation of a firm product, especially when operating in a highly competitive market (Lin et al. 2013a). Therefore, companies that operate in fiercely competitive markets are more likely to seek to be greener than their competitors (implementing new products or new management methods) to yield extra profits in future (Lin et al. 2013b). Past research on competitive pressure found it to be an effective driver of eco-innova-119 tion practices as well (Li 2014). The importance of providing green products through environmental innovation in order to establish a green image, increase market share and achieve sustainable development in an increasingly intense competitive environment is rising worldwide (Li 2014). The development of green products serves as means for companies to achieve a competitive advantage (Dangelico and Pontrandolfo 2010). The above discussion leads to the following hypotheses: Hypothesis 5a: There is a positive and significant relationship between competition intensity and companies’ implementation of eco-innovation. Hypothesis 5b: There is a positive and significant relationship between competition pressure and companies’ implementation of eco-innovation. Hypotheses concerning consequences of eco-innovation This section describes the development of hypotheses focused on eco-innovation outcomes. Hypotheses related to the eco-innovation outcomes pertain to firm performance (Section 5.2.1), economic performance (Section 5.2.2), competitive performance (Section 5.2.3) and internationalization (Section 5.2.4). Eco-innovation and firm performance Findings of previous research pertaining to the exploration of eco-innovation’s influence on firm performance provide mixed results and can lead to misleading conclusions. Researchers (Rexhäuser and Rammer 2013; In Pursuit of Eco-innovation Ghisetti and Rennings 2014) found that process eco-innovations that increase a company’s resource efficiency (in terms of material or energy consumption per unit of output) lead to higher profitability and also increase the company’s competitiveness. Meanwhile, externality-reducing innovations hamper both profitability and competitiveness (Ghisetti and Rennings 2014). Additionally, process eco-innovations exert a positive impact on the number of employees and the level of turnover (Rennings et al. 2006) and therefore contribute positively to the company’s growth. Moreover, companies that engage in eco-innovation demonstrate high-er levels of turnover per employee than companies which introduce non eco-innovations, and companies which do not engage in innovation activity (Doran and Ryan 2012, 435). In our study, we posit that eco-innovations exert a positive and significant impact on firm performance (in 120 terms of company growth and profitability). We rely on the results of prior research works, which found a positive association between eco-innovations and firm performance. The relationship between eco-innovation and financial performance was found to be positive in a study focused on SMEs (Clemens 2006) and for specific industries such as manufacturing (Zeng et al. 2011). Moreover, technological innovation efficiency and firm performance were positively related (Cruz-Cázares et al. 2013), eco-friendly marketing strategy showed a positive impact on financial performance (Leonidou et al. 2013a), environmental innovation strategy was positively related to a firm’s positive business performance (Eiadat et al. 2008) and green product innovation performance has been positively associated with firm financial performance (Huang and Wu 2010; Ar 2012; Lin et al. 2013a). In addition, prior research works that focused on eco-product, process and organizational innovation found a positive impact of these factors on firm performance (Cheng and Shiu 2012; Cheng et al. 2013), while other studies (Ar 2012; Lin et al. 2013a) also found that green product innovation positively affects firm performance. Concluding with an overview of eco-innovation performance, Horbach et al. (2012) found that the majority of eco-innovations (80.4%) lead to lower or constant cost, while 32% of these eco-innovations are associated with higher turnover; in other words, these eco-innovations are also economically successful. Likewise, the results of the study undertaken by Paraschiv et al. (2012) revealed that 35% of participant organizations achieved encouraging results, whereas another 21% reported significant results with a strong impact on the organization’s financial performance; finally, 10% specified that the results of eco-innovations were insignificant. Therefore, we expect that: Hypotheses Development Hypothesis 6a: The relationship between eco-innovation’s performance and company growth is direct and positive. Hypothesis 6b: The relationship between eco-innovation’s performance and company profitability is direct and positive. Eco-innovation and economic performance In addition to the previous hypothesis, we also explore the relationship between eco-innovation’s performance and economic performance by using self-reported measures. This hypothesis is added because in the previous one (Hypotheses 6a and 6b), we employ “harder” measures of firm performance, pertaining to company growth (in terms of growth over two business years pertaining to number of employees and net sales) and profitability (profitability indicator ratios, such as ROA, ROE and ROS), 121 and those data are collected through the database GVIN. In this case, we adopt “soft” self-reported measures, in order to test the relationship between a company’s adoption of eco-innovation and the effect on economic performance. This results from the discussion of how financial performance (especially regarding profitability indicator ratios) of a company’s eco-innovation implementation becomes positive over a two-year lag, while it is negative after a one-year lag (Horváthová 2012). Economic performance, therefore, will be tested in this hypothesis by obtaining company respondents’ perception of the effect of eco-innovation on companies’ economic performance. Many research works have adopted self-reported measures to estimate the effect of eco-innovation on firm performance (Rao and Holt 2005; Clemens 2006; Eiadat et al. 2008; Cheng and Shiu 2012; Cheng et al. 2013). In our case, this approach presents added value, because we will be able to see whether there are any differences between using profitability indicator ratios or self-reported measures when testing the relationship between eco-innovation and firm performance. The above discussion leads us to postulate the following hypothesis: Hypothesis 7: The relationship between eco-innovation’s performance and economic performance is direct and positive. Eco-innovation and competitive benefits Implementation of eco-innovations may result in other competitive benefits related to company performance (Sharma and Vredenburg 1998). The benefits that companies can seize from successful implementation of eco-innovation are as follows: cost savings, enhanced corporate image, improved relationship with local communities, access to new green mar- In Pursuit of Eco-innovation kets and gain of superior competitive advantage (Shrivastava 1995). Further, Sarkar (2013) differentiated direct and indirect benefits. Direct benefits consist of operational advantages, which are seen in cost savings and derive from greater resource productivity and better logistics, followed by sales from commercialization (Sarkar 2013). Indirect benefits include better image; better relations with customers, suppliers and authorities; health and safety benefits; greater worker satisfaction; and, because of knowledge holders, enhanced innovation capability overall. Chen et al. (2006) stressed that investing in eco-innovation helps companies to improve their competitive advantage. The association of product and process eco-innovation with the company’s competitive advantage has been found to be positive (Chen et al. 2006), whereas product eco-innovation exerts a stronger influence on competitive advantage than does process 122 eco-innovation (Wong 2012). Moreover, companies’ deployment of green product, process and managerial innovation will lead to cost savings, better product quality, increased efficiency and productivity and, consequently, improved competitive advantage (Chiou et al. 2011). Indeed, many companies worldwide have transformed their entire business operations to become more eco-efficient and thereby achieved a competitive advantage over their competitors (Mourad and Ahmed 2012). In summary, the past findings (Sarkar 2013) emphasize that companies increasingly recognize the fact that greening their business by improving resource productivity may increase their short and long-term competitiveness and create new markets. The above discussion leads us to propose the following hypothesis: Hypothesis 8: The relationship between eco-innovation’s performance and competitive benefits is direct and positive. Eco-innovation and internationalization This hypothesis aims to test the relationship between eco-innovation’s performance and firms’ internationalization. Prior research has found a positive relationship between advanced environmental strategies and internationalization (i.e., export intensity; Martin-Tapia et al. 2010). Furthermore, a proactive environmental strategy is positively related to a company’s export performance (Martin-Tapia et al. 2008). In addition, Beise and Rennings (2005) argued that regulations to stimulate eco-innovation have to be properly set; if national regulations comply with international markets, demand and international regulations, the “doors to international markets will be open”; otherwise, these eco-products and Hypotheses Development services will be only niches just in regional and national markets. Thus, we postulate the following hypothesis: Hypothesis 9: The relationship between eco-innovation’s performance and internationalization is direct and positive. The overall hypotheses presented in this section can be summarized (Table 9) to form the basis of the eco-innovation model (Figure 5). Table 9: Summary of research hypotheses Construct variable Hypothesis Hypothesized relationships Environmental policy instruments Command-and-control in- H1a Command-and-control instrument (+) Eco-innovation 123 strument Economic incentive in- H1b Economic incentive instrument (+) Eco-innovation strument Customer demand H2 Customer demand (+) Eco-innovation Managerial environmen- H3 Managerial environmental concern (+) Eco innovation tal concern Expected benefits from H4 Expected benefits (+) Eco-innovation eco-innovation Competition Competitive intensity H5a Eco-innovation (+) Competitive intensity Competitive pressure H5b Eco-innovation (+) Competitive pressure Company performance Company growth H6a Eco-innovation (+) Company growth Company profitability H6b Eco-innovation (+) Company profitability Economic benefits H7 Eco-innovation (+) Economic benefits Competitive benefits H8 Eco-innovation (+) Competitive benefits Internationalization H9 Eco-innovation (+) Internationalization In Pursuit of Eco-innovation 124 Figure 5: The eco-innovation conceptual model (for the construct-level model) Methodology The methodology will be discussed in terms of preliminary testing of the questionnaire, sampling and data collection and, lastly, data analysis and its evaluation. Preliminary testing of questionnaire Prior to data collection, the survey instrument was pre-tested for content validity through two stages. In the first stage, we asked eight experienced researchers to review and comment on the questionnaire to determine the clarity, ambiguity and appropriateness of the items used to operationalize each construct. We have prepared a list with all constructs’ descrip-tions, including the definition and main aim of measurement for each one. Then, based on the feedback received from these eight researchers, we modified the instrument in order to enhance the clarity and appropriateness of the measures. In the second stage, we asked five environmental managers from five different industry sectors and different company sizes to agree to a one-hour meeting in which they would review and comment on the questionnaire. We asked them in face-to-face interviews to review and comment on the questionnaire regarding its clarity, ambiguity, completeness, readability and structure. Finally, feedback on the questionnaire was also received from person who was dealing with ISO 14001 certification. We used their feedback to further improve the questionnaire. In this study, we have measured 15 latent variables: the com- mand-and-control instrument, the economic incentive instrument, customer demand, managerial environmental concern, expected benefits, In Pursuit of Eco-innovation competitive intensity, competitive pressure, eco-innovation practices (eco-product, eco-process and eco-organizational innovation), company performance (company growth and profitability), economic performance, competitive benefits and internationalization. The validity and reliability of the survey instrument, as aforementioned, were supported by a comprehensive literature review and pilot tests using in-depth managerial interviews in five Slovenian companies active in eco-innovating, and the final version of questionnaire was completed online by respondents from 10 Slovenian companies. Wordings for some items were modified based on feedback and insights from the managerial interviews to tailor them to Slovenian eco-innovation practices, and some items were also added upon their suggestion. A seven-point Likert scale was utilized in this study. 126 Research instrument and operationalization of variables and measures Our questionnaire is composed of five different content areas. In the first area, we asked respondents to indicate their level of agreement with items linked to the antecedents of eco-innovation. In the second area, we focused on eco-innovation implementation (encompassing three dimen- sions of eco-innovation: product eco-innovation, process eco-innovation and organizational eco-innovation). In the third area, we asked respondents about the consequences related to eco-innovation implementation (competitive benefits, company performance, economic performance and internationalization). The fourth area is dedicated to the company data (year of company’s establishment, type of industry, size of the company in terms of the number of employees and overall sales, year when eco-innovation activities were started, and commerce transactions – B2B/B2C). Finally, the fifth area is related to general information about the respondents who completed the questionnaire. A seven-point Likert scale was utilized. Measures for eco-innovation antecedents Respondents were asked to indicate their level of agreement for each statement on a seven-point Likert scale (1 = strongly disagree, 7 = strongly agree). The measures were adopted and adapted from previous research works, while some of items were added and adapted based on the comments and insight from pilot tests using in-depth managerial interviews. First, following Eiadat et al. (2008), we used four items to measure managerial environmental concern. Expected benefits were measured by Methodology nine items, adapted from Agan et al. (2013). We also added one item of our own, based on the literature review and recommendations from the in-depth interviews with Slovenian environmental managers during the pilot testing of the questionnaire. Finally, four items, following Agan et al. (2013), measured customer demand. We asked each respondent to indicate the extent (1 = strongly disagree, 7 = strongly agree) to which they agreed with the posited statements. Table 10: Items for three latent variables (Managerial environmental concern, Expected benefits, Customer demand) Measurement variable Source Managerial environmental concern Eco-innovation is an important component of the company’s environmental man-127 Eiadat et al. (2008) agement strategy. Most eco-innovations are worthwhile. Eiadat et al. (2008) Eco-innovation is necessary to achieve high levels of environmental performance. Eiadat et al. (2008) Environmental innovation is an effective environmental management strategy. Eiadat et al. (2008) Expected benefits Adapted from Agan et al. To reduce costs (energy, material, etc.). (2013) To improve profitability. Agan et al. (2013) To increase productivity. Agan et al. (2013) To increase market share. Agan et al. (2013) To enter new markets. Own Adapted from Agan et al. To improve firm reputation. (2013) To strengthen the brand. Agan et al. (2013) Competitive advantage. Agan et al. (2013) Adjustment to EU. Agan et al. (2013) Customer demand Environment is a critical issue for our important customers. Agan et al. (2013) Our important customers often bring up environmental issues. Agan et al. (2013) Customer demands motivate us in our environmental efforts. Agan et al. (2013) Our customers have clear demands regarding environmental issues. Agan et al. (2013) In Table 11, we show the items used to measure the command-and-control instrument and the economic incentive instrument, both of which are latent variables pertaining to the construct environmental policy in- In Pursuit of Eco-innovation struments. The first latent variable was measured using a 4-item scale, which was tailored to adapt to the Slovenian environment, with regard to the environmental policy instruments. The second latent variable, the economic incentive instrument, was measured using a 7-item scale, adapted from Li (2014) to align to the Slovenian environment. We asked each respondent to indicate the extent of their agreement with the statements given in Table 11 (1 = strongly disagree, 7 = strongly agree). Table 11: Items for two latent variables (Command-and-control instrument, Economic incentive instrument) Measurement variable Source Command-and-control instrument 128 Our products should meet the requirements of national environmental regulations. Li (2014) Our products should meet the requirements of international and/or EU environ-Adapted from Li (2014) mental regulations. Our production processes should meet the requirements of national environmen-Adapted from Li (2014) tal regulations. Our production processes should meet the requirements of international and/or Adapted from Li (2014) EU environmental regulations. Economic incentive instrument The government provides preferential subsidies for environmental innovation (availability of government grants, subsidies or other financial incentives for environmen- Adapted from Li (2014) tal innovation). The government provides preferential tax policies for environmental innovation. Li (2014) Environmental taxes – taxes on energy, transport, pollution/resources. Own The government promotes environmental protection. Li (2014) The government provides green public procurement. Own The government provides opportunity to undertake environmental tenders/cal s. Own The government provides opportunity to undertake environmental projects. Zeng et al. (2011) In Table 12, we present the items used for measuring two latent variables: competitive intensity and competitive pressure. Competitive intensity is oriented more towards the general intensity in the industry, while competitive pressure focuses on competition through the green concept. Three items, adopted from Jaworski (1993), measured competitive intensity. In addition, three items were used to measure the variable of competitive pressure, adopted from Li (2014) and focusing on the green concept. Methodology For the variables of competitive intensity and competitive pressure, we used a 7-point scale anchored by “strongly disagree” and “strongly agree”. Table 12: Items for two latent variables (Competitive intensity and Competitive pressure) Measurement variable Source Competitive intensity Jaworski and Kohli (1993), Competition in our industry is cutthroat. Slovenian translation Bod- laj (2009) Jaworski and Kohli (1993), Anything that one competitor can offer, others can match readily. Slovenian translation Bod- laj (2009) Jaworski and Kohli (1993), Price competition is a hal mark of our industry. Slovenian translation Bod- 129 laj (2009) Competitive pressure We establish the company’s environmental image compared to competitors Li (2014) through the green concept. We increase the company’s market share through green concept. Li (2014) We improve the company’s competitive advantage over competitors through the Li (2014) green concept. Measures for eco-innovation dimensions Eco-innovation activities were measured with three latent variables: product, process and organizational eco-innovation. Respondents were asked to indicate their level of agreement with each statement on a seven-point Likert scale (1 = strongly disagree, 7 = strongly agree). The measures were adapted from previous research works, while some of the items were added and adapted based on the comments and insight from pilot tests using in-depth managerial interviews. Following Chen et al. (2006), Chen et al. (2008) and Chiou et al. (2011), we used seven items to measure product eco-innovation (see Table 13) using a seven-point Likert scale (1 = strongly disagree, 7 = strongly agree). In Pursuit of Eco-innovation Table 13: Items for the latent variable of Product eco-innovation Measurement variable Source The company is using less or non-polluting/toxic materials (i.e., using environmen-Chiou et al. (2011), based on tal y friendly material). Chen et al. (2006, 2008) The company is improving and designing environmental y friendly packaging (e.g., Chiou et al. (2011), based on using less paper and plastic materials) for existing and new products. Chen et al. (2006, 2008) Chiou et al. (2011), based on The company is recovering end-of-life products and recycling. Chen et al. (2006, 2008) Chiou et al. (2011), based on The company is using eco-labeling. Chen et al. (2006, 2008) The company chooses product materials that consume the least amount of energy Chen et al. (2006, 2008) and resources for conducting the product development or design. The company uses the smal est amount of materials necessary for the product devel-130 Chen et al. (2006, 2008) opment or design. The company deliberately evaluates whether the product is easy to recycle, reuse and Chen et al. (2006, 2008) decompose when conducting the product development or design. Table 14: Items for the latent variable of Process eco-innovation Measurement variable Source Low energy consumption such as water, electricity, gas and petrol during produc-Chiou et al. (2011), based on tion/use/disposal. Chen et al. (2006, 2008) Chiou et al. (2011), based on Recycle, reuse and remanufacture material. Chen et al. (2006, 2008) Use of cleaner technology to generate savings and prevent pollution (e.g., energy, Chiou et al. (2011), based on water and waste). Chen et al. (2006, 2008) The manufacturing process of the company effectively reduces the emission of haz- Chen et al. (2006, 2008) ardous substances or waste. The manufacturing process of the company reduces the use of raw materials. Chen et al. (2006, 2008) Items for process eco-innovation are adopted from Chen et al. (2006), Chen et al. (2008) and Chiou et al. (2011) and adapted based on interviews conducted in June 2014 with environmental managers from five different Slovenian companies active in implementation of eco-innovation. Five items were used to measure process eco-innovation (see Table 14), and respondents were asked to indicate their level of agreement with each statement on a seven-point Likert scale (1 = strongly disagree, 7 = strongly agree). Methodology Finally, six items were selected to measure the variable of organizational eco-innovation (see Table 15), adapted from Cheng and Shiu (2012). A seven-point Likert scale (1 = strongly disagree, 7 = strongly agree) was utilized. Table 15: Items for the latent variable of Organizational eco-innovation Measurement variable Source Our firm management often uses novel systems to manage eco-innovation. Cheng and Shiu (2012) Our firm management often collects information on eco-innovation trends. Cheng and Shiu (2012) Our firm management often actively engages in eco-innovation activities. Cheng and Shiu (2012) Our firm management often communicates eco-innovation information with em-Cheng and Shiu (2012) ployees. Our firm management often invests substantial y in R&D on eco-innovation. Cheng and Shiu (2012) 131 Our firm management often communicates experiences among various depart-Cheng and Shiu (2012) ments involved in eco-innovation. Measures for consequences/outcomes of eco-innovation The consequences of eco-innovation implementation were measured by four latent variables: company performance (measured as company growth and profitability), economic performance, competitive benefits and internationalization. Where the measures were self-reported (in the case of the last three variables listed above), a seven-point Likert scale was utilized. For the variable of company performance (company growth and profitability), we gathered the data from an objective source, the commercial firm database GVIN. Companies’ business performance (Table 16) was operationalized in terms of sales growth (Montabon et al. 2007; Eiadat et al. 2008; Fraj-Andres et al. 2009; Huang and Wu 2010; Ar 2012), return on assets (ROA; Horváthová 2012; Leonidou et al. 2013a), return on equity (ROE; Zeng et al. 2011; Horváthová 2012), return on sales (ROS; De Burgos-Jimén-ez et al. 2013); Rexhäuser and Rammer 2013) and number of employees (growth over two business years). The financial data of the analyzed companies were obtained from the commercial firm database GVIN, part of the international business group Bisnode AB, which is Europe’s largest provider of business and credit information, operating in 17 European countries (GVIN 2015). The database provides firms’ full balance sheets and profit-loss statements for Slovenian companies. In Pursuit of Eco-innovation Table 16: Items for latent variable of Firm performance (growth and profitability) Measurement variable Source ROA (return on assets) GVIN database, secondary data ROE (return on equity) GVIN database, secondary data ROS (return on sales) GVIN database, secondary data Number of employees – growth through 2 business years GVIN database, secondary data Net sales – growth through 2 business years GVIN database, secondary data We also measured economic performance (Table 17) using self-re- ported measures. We used nine items, following Wagner (2011). Re- spondents were asked what effects their environmental practices have had on: (1) sales, (2) market share, (3) new market opportunities, (4) corporate 132 image, (5) management satisfaction, (6) employee satisfaction, (7) short-term profits, (8) cost savings and (9) productivity (we used a 7-point scale, anchored by ‘substantial negative effect’ and ‘substantial positive effect’). Table 17: Items for latent variable of Economic performance Measurement variable Source Sales Wagner (2011) Market share Wagner (2011) New market opportunities Wagner (2011) Corporate image Wagner (2011) Management satisfaction Wagner (2011) Employee satisfaction Wagner (2011) Short-term profits Wagner (2011) Cost savings Wagner (2011) Productivity Wagner (2011) Twelve items, following Sharma and Vredenburg (1998), measured competitive benefits (see Table 18). The respondents were asked to indicate the extent to which the company’s environmental practices have led to a variety of competitive benefits (1 = no contribution, 7 = very large contribution). Methodology Table 18: Items for latent variable of Competitive benefits Measurement variable Source Reduction in material costs Sharma and Vredenburg (1998) Reduction in process/production costs Sharma and Vredenburg (1998) Reduction in costs of regulatory compliance Sharma and Vredenburg (1998) Increased process/production efficiency Sharma and Vredenburg (1998) Increased productivity Sharma and Vredenburg (1998) Increased knowledge about effective ways of managing operations Sharma and Vredenburg (1998) Improved process innovations Sharma and Vredenburg (1998) Improved product quality Sharma and Vredenburg (1998) Improved product innovations Sharma and Vredenburg (1998) Better relationships with stakeholders, such as local communities, regulators, 133 Sharma and Vredenburg (1998) and environmental groups Improved employee morale Sharma and Vredenburg (1998) Overall improved company reputation or goodwil Sharma and Vredenburg (1998) Three items adopted from Ruzzier et al. (2014a; 2014b) measured internationalization as a latent factor. Thus, following Ruzzier et al. (2014a; 2014b), we used a combination of three different measures: number of foreign markets, number of operation modes and percentage of sales abroad in 2013. The items number of foreign markets and number of operation modes both measure the qualitative scope of internationalization, while the performance dimension of internationalization was measured by the extent of sales on foreign markets in 2013, ranging from 0 to 100%. The dependent variable of number of operation modes was construct-ed by summing up all operation modes (including direct export, export through intermediary, franchising, product or service licensing, contract, joint venture direct investment, sole venture direct investment). Sampling and data collection Data were collected using web research (email with attached link to the survey). The questionnaire and letter of intent were emailed to Slovenian companies in November 2014. The questionnaire was addressed to a top executive or environmental manager of the selected companies (in the larger companies, we addressed environmental managers or consultants that deal with environmental issues of the company or take care of ISO 14001 or EMAS in that company, while in the smaller companies gen- In Pursuit of Eco-innovation erally the top executive was addressed, called the “director” in Slovenia). They were chosen as respondents because they were considered to be the most knowledgeable person with respect to the issue of environmental care in their company. However, if these respondents felt that they were not the most appropriate informants to complete the survey, we asked them to pass the questionnaires on to the most appropriate informants in their companies (a cover letter highlighting the study’s background and objectives and a link to the survey were included in the email) or to introduce them to us, to finish the survey. Moreover, the respondents were as-sured of anonymity in reporting results. A variety of industries and company sizes were included, since the focus of the study is on eco-innovation in companies, which required us to include companies operating in all industry sectors, excluding public administration. 134 Data were collected from companies in Slovenia, in collaboration with an external research company specializing in data collection, which sent the questionnaire and invitation letter to a total of 6564 email addresses. The data were collected between November 2014 and January 2015. In the first round, the questionnaire was sent to 864 email addresses of ecological companies in Slovenia (i.e., companies with environmental certificates, such as ISO 14001, EMAS or environmental certificates, environmental prizes as identifies them also Gospodarska Zbornica Slovenije – Slovenian Chamber of Commerce) as well as to to 5,700 other email addresses (companies without environmental certificates but that might implement eco-innovations as well). In total, three reminders were sent to companies in order to urge them to collaborate in this study and complete the questionnaire. The usual response rate for postal surveys in Slovenia varies from 10% to 25% (Ruzzier 2005), while research distributed via email gives a much lower response – as high as 2% at best (Nagy 2013). Given the fact that our survey has been conducted through email, the response rate was much lower than the average for postal surveys. Due to the length of the questionnaire in this study (requiring at least 20 minutes to complete), a conservative response rate was expected. As aforementioned, the questionnaire, with a short description of the project and invitation to executives/environmental managers to collaborate in this study, was emailed to 6564 companies in total. At the end of the questionnaire, respondents were asked to choose whether they wanted to receive a summary of the research results, an invitation to the public presentation of the results or neither. The number of responses received was 223 (a 3.40% response rate), which was similar to what we expected due to the length of the Methodology questionnaire, the lack of an established relationship with the companies and the use of email to gather the results. Many of the emails sent were not delivered to the recipients, and not all of the companies to which the questionnaire was sent were dealing with eco-innovations. The 223 completed questionnaires were further analyzed for missing data. We followed Hair et al. (2006), who suggest that an observed unit (in our case, one questionnaire) missing less than 10% of values can be retained for further analysis and that a separate variable (measurement item) missing less than 15% of values can be retained as well. Therefore, the extent and the pattern of missing data were checked. We first checked for the extent of missing data concerning variables. The overall amount of missing data was small, totaling 3.947% of missing values. In more detail, only two variable measurement items in the questionnaire demonstrated missing data (measurement items had 0.4% 135 to 1.3% missing values) and therefore no variable measurement items were removed from the analysis. The percentage of missing data is a bit higher for firm performance, for which the data were required separately from the collected questionnaires for all the included companies (secondary data). With regard to firm performance (profitability indicator ratios and company growth), two measurement items (growth of net sales through two business years and ROS) had 0.4% of missing values, one measurement item (ROE) had 1.3% of missing values and one measurement item had 9.9% of missing values (growth of number of employees over two business years). We did not remove these items from the analysis. The higher proportion of missing data for these items may be due to the fact that some companies do not report certain data because they do not pertain to them (e.g., growth of employees through the last two business years does not apply to a sole proprietorship). The pattern of missing data was also examined. Missing data must always be addressed if the missing data are in a nonrandom pattern or more than 10 percent of the data are missing (Hair et al. 2006). Missing data can be considered random if the pattern of missing data for a variable does not depend on any other variable in the data set or on the values of the variable itself (Hair et al. 2006). We checked for a pattern among the cases (questionnaires/companies) and found that there are only four cases (companies) with missing values, and the overall amount of missing data was 1.794%. When we add the firm performance variables (profitability indicator ratios and company growth), there are 25 cases with a total of 11.21% of missing data. As we have explained previously, companies that are of the legal form of a sole proprietorship do not report some data, such In Pursuit of Eco-innovation as growth of employees, which can lead to the missing data. We investigated data for missing values and concluded that data were missing completely at random since no pattern of missing data was found. Regarding the missing data related to the company’s financial data (profitability indicator ratios and regarding the company’s growth), as we explained above, in such a case imputation is not an appropriate solution. Thus, the number of retained responses usable for analysis is 223. Common method variance assessment Since we used a single informant from each of the companies to complete the survey, concerns of common method variance (hereinafter CMV) should be addressed (Podsakoff et al. 2003). CMV is addressed because the majority of data are self-reported (using a single informant from each 136 of the companies) and the data were collected through the same questionnaire during the same period of time with a cross-sectional research design. Thus, the CMV is attributed to the measurement method rather than the constructs of interest and may cause systematic measurement error and further bias the estimates of the true relationship among the theoretical constructs. Therefore, we also analyzed data for common method variance problems by following the recommendations of Podsakoff et al. (2003). The potential for common method variance has been reduced by ensuring confidentiality to respondents participating in our study and, as aforementioned, by pre-testing the questionnaire items for their unambiguity, clearness and familiarity of wording. In this study, CMV is examined by Harman’s single factor test, which is the most widely used method to assess the possibility of CMV. Podsakoff and Organ (1986) stressed that if CMV is present, a single factor will emerge from the factor analysis of all survey items. Therefore, we used all survey items from the 223 questionnaires to conduct an exploratory factor analysis in SPSS. The un-rotated principal components factor analysis results demonstrate that no single factor accounts for the majority of the variance and that the first factor captures only 34.189% of the variance, which suggests that CMV is not present. Data analyses The data were analyzed using univariate and multivariate statistical methods conducted with the statistical program SPSS (version 21). For each construct used in our eco-innovation model, we tested the reliability of the construct (using Cronbach’s alpha), and we further conduct- Methodology ed exploratory and confirmatory factor analysis for all constructs used in the eco-innovation model using two statistical packages, SPSS and EQS 6.1. Furthermore, to test the hypotheses pertaining to the influence of eco-innovation antecedents (environmental policy instruments, managerial environmental concern, customer demand, expected benefits and competitive pressure) on eco-innovation (product, process and organizational eco-innovation and eco-innovation construct) and its consequences (company performance (in terms of growth and profitability), economic performance, competitive benefits and internationalization), we used the multivariate technique of structural equation modeling (hereinafter SEM) employing the statistical program EQS 6.1. Therefore, the model and hypotheses were tested by using SEM, which allows for simultaneous evaluation of multiple related dependent and independent relationships and takes into account measurement error (estimates) in the evaluation 137 process (Hair et al. 1998). Most scales used in this study were examined for convergent and discriminant validity using exploratory and confirmatory factor analyses. For each construct used in this study, exploratory factor analysis has been performed. We have therefore tested whether the number of factors proposed by the exploratory factors analysis is in line with the expected number of factors. We used the Maximum Likelihood method and Direct Oblimin rotation (oblique rotation, which expects correlations between factors). After conducting the exploratory factors analysis, we have also conducted confirmatory factor analysis for each construct to assess the reliability, validity and goodness-of-fit of each construct. Confirmatory factor analysis (CFA) enables us to test how well the measured variables represent the constructs (Hair et al. 2009). Regarding the eco-innovation construct, which was measured as a second-order construct, we first checked for construct reliability, which measures the reliability and internal consistency of the measured variables representing a latent construct (Hair et al. 2009). Before assessing the construct validity which deals with the accuracy of measurement (the extent to which a set of measured variables actually reflects the theoretical latent construct those items are designed to measure), we have to establish construct reliability (Hair et al. 2009). After this, we checked the eco-innovation construct, which includes three dimensions for convergent validity (the extent to which indicators of a specific construct converge or share a high proportion of variance in common) and discriminant validity (the extent to which a construct is truly distinct from other constructs). There are several ways to estimate the relative amount of convergent validity among In Pursuit of Eco-innovation item measures. The size of factor loadings is one important consideration. In the case of high convergent validity, high loadings on a factor would indicate that they converge on some common point. At a minimum, all factor loadings should be statistically significant, and the standardized loading estimates should be 0.50 or higher, and ideally 0.70 or higher. The rationale behind this rule can be understood in the context of an item’s communality; the square of standardized factor loading represents how much variation in an item is explained by the latent factor (meaning that a loading of 0.71 squared equals 0.50; the factor explains half the variation in the item, with the other half being error variance). The second indicator of convergence is variance extracted; with CFA, the average percentage of variance extracted among a set of construct items is a summary indicator of convergence. A value of variance extracted of 0.50 138 or higher is a good indicator of adequate convergence. Moreover, reliability is also an indicator of convergent validity; coefficient alpha remains a commonly applied estimate, although it may understate reliability. The rule of thumb for either reliability estimate is that 0.70 or higher suggests good reliability, while reliability between 0.60 and 0.70 may be acceptable. High construct reliability indicates that internal consistency exists, meaning that the measures all consistently represent the same latent construct (construct reliability should be 0.70 or higher to indicate adequate convergence or internal consistency) (Hair et al. 2009). The eco-innovation construct in our study is composed of three dimensions – product, process and organizational eco-innovation – and is thus a second-order latent factor. Items were grouped together in the expected grouping by dimension. Poorly fitting items – those that had low communalities, or had low correlations with other items pertaining to the same dimension or loaded onto two dimensions – have been excluded. The convergence and divergence of dimensions were checked by assessing the fit of confirmatory models and inter-dimension correlations. Furthermore, the contributions of the eco-innovation dimensions-only model versus contributions of the overall factor-only model were examined by comparing nested models (dimensions-only and one factor-on-ly) with an overall model that included both dimension factors and the overall eco-innovation factor, by employing confirmatory factor analysis. These contributions were analyzed using a test of significant improvements in the model fit (the NFI for the two model differences, computed with a formula from Bentler 1990). For testing the proposed hypotheses, we used structural equation modeling (SEM). The typical application of SEM is to a system of rela- Methodology tions, collectively referred to as a model. A model can include relations among measured variables and latent variables (i.e., factors, constructs) as well as nondirectional and directional (direct and indirect) relations. The model typically is presented at two levels: conceptual and statistical (Hoyle and Panter 1995). The conceptual model specifies the relations among concepts that are operationalized in the empirical study, while the precise statistical model that will be tested cannot be deduced from the presentation of the conceptual model. As such, each construct represented in the conceptual model must be operationalized, and the model must be translated into the statistical manifestation that has been or is to be tested. A path diagram can be an effective means of communicat-ing structural equation models at the statistical level (Hoyle and Panter 1995). Also, in our study, a structural equation model was used to test the theoretical model. SEM is a statistical methodology that takes a confirm-139 atory (i.e., hypothesis-testing) approach to the multivariate analysis of a structural theory bearing on some phenomenon (Byrne 2006 in Murovec et al. 2012). Typically, this theory represents “causal” processes that generate observations on multiple variables (Bentler 1995). SEM is a family of statistical models that seek to explain the relationships among multiple variables (Hair et al. 2009). It therefore examines the structure of interrelationships expressed in a series of equations, similar to a series of multiple regression equations. These equations depict all of the relationships among constructs (the dependent and independent variables) involved in the analysis. Constructs are unobservable or latent factors represented by multiple variables (much like variables representing a factor in factor analysis). So far, each multivariate technique has been classified either as an interdependence or a dependence technique. SEM can be thought of as a unique combination of both types of techniques because SEM’s foundation lies in two familiar multivariate techniques: factor analysis and multiple regression analysis (Hair et al. 2009). Hair et al. (2009) emphasized three main characteristics based on which SEM models can be distinguished: - Estimation of multiple and interrelated dependence relation- ships, - An ability to represent unobserved concepts in these relation- ships and correct for measurement error in the estimation pro- cess, - Defining a model to explain the entire set of relationships. In Pursuit of Eco-innovation SEM has also the ability to incorporate latent variables into the analysis. A latent variable, or latent construct, is a hypothesized and unobserved concept that can be represented by observable or measurable variables. It is measured indirectly by examining consistency among multiple measured variables, sometimes referred to as manifest variables, or indicators, which are gathered through various data collection methods (e.g., surveys, tests, observational methods) (Hair et al. 2009). The standard method of estimating free parameters in SEM is to employ maximum likelihood (ML). A growing body of research indicates that ML performs reasonably well under a variety of less-than-optimal analytic conditions (e.g., small sample size, excessive kurtosis) (Hoyle and Panter 1995). Moreover, Hair et al. (2006) pointed out that several readily available statistical programs are convenient for performing SEM. Traditionally, the 140 most widely used program is LISREL. EQS is another widely available program that also can perform regression and factor analysis and can test structural models. AMOS is a third program that has gained popularity because it is user-friendly and available as an addition to SPSS. Evaluation of the results For all the constructs measured in this survey, we first conducted an exploratory factor analysis, which is a class of procedures primarily used for data reduction and summarization (Malhotra 1993). Our main aim was to see how many factors are extracted based on the variables that were used to measure different constructs. When evaluating exploratory factor analysis, the key statistics associated with factor analysis are as follows (Malhotra 1993): - Bartlett’s test of sphericity is a test statistic used to examine the hypothesis that the variables are uncorrelated in the population. In other words, the population correlation matrix is an identity matrix in which each variable correlates perfectly with itself ( r = 1) but has no correlation with the other variables ( r = 0). - Correlation matrix is a lower triangle matrix showing the simple correlations between all possible pairs of variables included in the analysis. - Communality is the amount of variance a variable shares with all the other variables being considered. This is also the proportion of variance explained by the common factors. - Eigenvalue represents the total variance explained by each factor. Methodology - Factors loadings are simple correlations between the variables and the factors. - Factor matrix contains the factor loadings of all the variables on all the factors extracted. - Kaiser-Meyer-Olkin (KMO) measure of sampling adequacy is an index use to examine the appropriateness of factor analysis. High values (between 0.50 and 1) indicate that factor analysis is appropriate. Values below 0.50 imply that factor analysis may not be appropriate. - Percentage of variance is the percentage of the total variance attributed to each factor. Exploratory factor analysis was performed using the Maximum Like- lihood extraction method and Direct Oblimin rotation. Most research 141 using EFA has extracted factors that are orthogonal – that is, uncorrelated with or independent of one another (Maruyama 1998). In our study, we used the oblique rotation (e.g., Direct Oblimin), which predicts that factors are correlated with or dependent on one another and is in line with what the structural equation approaches hypothesize; factors in structural equation models usually will be hypothesized to correlate with one another (Maruyama 1998). Moreover, Bartlett’s test of sphericity and the Kaiser-Meyer-Olkin (KMO) test were used to determine whether data were appropriate for factor analysis. KMO values of 0.80 or above are excellent, 0.70 or above are middling, 0.60 or above are mediocre, 0.50 or above are poor, and below 0.50 are unacceptable (Hair et al. 1998). In our study, all the KMO values were above 0.50, and the sig. value of Bartlett’s test of sphericity was less than 0.05, which means that our data jus-tify the use of exploratory factor analysis. After the exploratory factor analysis, we also conducted confirmatory factor analysis. Evaluating the results of SEM involves theoretical criteria, statistical criteria, and an assessment of fit. Although the issue of fit is discussed in literature in greater detail than the other issues, it should be remembered that fit is of no interest unless the results meet theoretical and statistical criteria. A model submitted to an SEM program should be based as much as possible on “theory” in the sense of a systematic set of relationships providing a consistent and comprehensive explanation of a phenomenon. After the parameters of the models are estimated, they should be assessed from a theoretical perspective (e.g., the signs and mag-nitudes of the coefficients should be consistent with “theory”). Besides the theoretical criteria mentioned above, there are also two major statistical criteria. The first pertains to the identification status of the model In Pursuit of Eco-innovation (Klem 2000); a model can be either under-identified (this happens when a structural model has a negative number of degrees of freedom, meaning that we aim to estimate more parameters than is possible with the input matrix) or over-identified (this happens when a structural model has a positive number of degrees of freedom and thus indicates that some level generalizability may be possible). The researchers’ objective is always steered towards achievement of maximum model fit, with the largest number of degrees of freedom (Ruzzier 2005). The statistical reasona-bleness of the parameters concerns the second major statistical criterion. A model with negative variances and correlations greater than one is misspecified and can further result in improper results (Klem 2000). When it comes to determining the adequacy of a structural equation model, various measures of model fit are available. The two most popular 142 ways of evaluating model fit are those that involve the chi-square goodness-of-fit statistic (χ2 test) and the so-called fit indexes that have been offered in order to supplement the χ2 test (Hu and Bentler 1995). The χ2 test enjoyed substantial popularity at first, while the problems associated with the goodness-of-fit χ2 tests were recognized quite early. One of the concerns has centered on the sample size issue. The statistical theory for T is asymptotic; that is, it holds as sample size gets arbitrarily large. Therefore, T may not be χ2 distributed in a small sample; therefore, it may not be correct for model evaluation in practical situations. Furthermore, T may not be χ2 distributed when the typical underlying assumption of multivariate normality is violated. Therefore, the standard χ2 test may not be a sufficient guide to model adequacy, because a significant goodness-of-fit χ2 value may be a reflection of model misspecification, the power of the test, or a violation of some technical assumption underlying the estimation method (Hu and Betler 1995). When an SEM model that looks theoretically sensible is identified and there are no signs of statistically improper estimates, we check whether the data fit the model using various goodness-of-fit measures (Ruzzier 2005). With the measurement model specified, sufficient data collected, and key decisions such as the estimation technique already made, the researcher comes to the most fundamental question in SEM testing: “Is the measurement model valid?” Measurement model validity depends on goodness-of-fit for the measurement model and specific evidence of construct validity (Hair et al. 2009). Among the fit indexes, we can distinguish three types of fit measures: 1) absolute fit measures, 2) incremental fit measures and 3) parsimonious fit measures. An absolute fit index directly assesses how well an Methodology a priori model reproduces the sample data (Hu and Bentler 1995). Absolute fit indexes are a direct measure of how well the model specified by the researcher reproduces the observed data; they provide the most basic assessment of how well a researcher’s theory fits the sample data (Hair et al. 2009). They do not explicitly compare the goodness-of-fit of a specified model to any other model; rather, each model is evaluated independently of other possible models (Hair et al. 2009). Absolute-fit measures (e.g., χ2 statistic, GFI, RMSR, SRMR, RMSEA etc.) only assess the overall goodness-of-fit for both the structural and measurement models collectively and do not make any comparison to a specified null model (incremental fit measure) or adjust for the number of parameters in the estimated model (parsimonious fit measure) (Hair et al. 2006). An incremental fit index measures the proportionate improvement in fit by comparing a target model with a more restricted, nested baseline model (Hu and Bentler 143 1995). Incremental fit indexes differ from absolute fit indexes in that they assess how well a specified model fits relative to some alternative baseline model (Hair et al. 2009). The most common baseline model is referred to as a null model, one that assumes all observed variables are uncorrelated (Hair et al. 2009). It implies that no data reduction could possibly improve the model because it contains no multi-item factors, thus making impossible any multi-item constructs or relationships between them (Hair et al. 2009). Incremental fit indexes are: NFI, CFI, TLI, RNI (Hair et al. 2009). Finally, the third group of indexes is designed specifically to provide information about which model among a set of competing models is best, considering its fit relative to its complexity. A parsimony fit measure (e.g. PR, PGFI, PNFI) is improved either by a better fit or by a simpler model. In this case, a simpler model is one with fewer estimated parameter paths. Parsimony fit indexes are conceptually similar to the notion of an adjusted R2 in the sense that they relate model fit to model complexity. More complex models are expected to fit the data better. The indexes are not useful in assessing the fit of a single model but are quite useful in comparing the fit of two models when one is more complex that the other (Hair et al. 2009). There are three major problems involved in using fit indexes for evaluating goodness of fit: a) small sample bias, b) estimation effects and c) effects of violation of normality and independence. The previously mentioned problems are a natural consequence of the fact that these indexes typically are based on χ2 tests. As noted previously, these χ2 tests may not perform adequately at all sample sizes; moreover, because the adequacy of an χ2 statistic may depend on the particular assumptions it requires In Pursuit of Eco-innovation about the distributions of variables, these same factors can be expected to influence evaluation of model fit (Hu and Bentler 1995). In our study, we report the values of χ2 tests notwithstanding that these are high and consistently statistically significant, which is the result of the influence exerted by the sample size – performing more poorly in smaller samples that are considered to be not “asymptotic” enough. In addition, some other fit indexes, such as NFI, perform more poorly when they have a small sample size. Therefore, Bearden, Sharma and Teel (1982 cited in Hu and Bentler 1995) found that the mean of NFI is positively related to sample size and that NFI values tend to be far less than 1.00 when sample size is small (NFI is therefore not a good indicator for evaluating model fit when N is small). In our study, we will report the following fit indexes: 144 - Chi-square (χ2 test) – the fundamental measure used in SEM to quantify the differences between the observed and estimated covariance matrices. Chi-square is influenced by the difference in covariance matrices and by sample size. Moreover, increasing the size of the covariance matrix (i.e., using more indicator variables) increases the chance that the differences in matrices will be large (i.e., significant p-values can be expected). In SEM, we do not want the p-value for the χ2 test to be small (statistically significant). Rather, if our theory is to be supported by this test, we want a small χ2 value (and corresponding large p-value), thus in- dicating no statistically significant difference between the matrices (meaning that the observed sample and SEM estimated co- variance matrices are equal and the model fits perfectly) (Hair et al. 2009). - SRMR (Standardized Root Mean Square Residual) – an alternative statistic based on the residuals is the standardized root mean residual, which is a standardized value of RMSR and thus is more useful for comparing fit across models. Lower SRMR values represent better fit and higher values represent worse fit, which puts the SRMR into a category of indexes sometimes known as badness-of-fit measures, in which high values are in- dicative of poor fit (Hair et al. 2009). The average SRMR value is 0, meaning that both positive and negative residuals can oc- cur. Thus, a predicted covariance lower than the observed value results in a positive residual, while a predicted covariance larger than the observed value results in a negative residual. It is difficult to provide a hard-and-fast rule indicating when a residual is Methodology too large, but the researcher should carefully scrutinize any standardized residual exceeding |4.0| (below -4.0 or above 4.0) (Hair et al. 2006) With regard to the SRMR values, Hu and Bentler (1999 in Murovec et al. 2012) suggest that SRMR values of less than 0.08 indicate an acceptable fit. - RMSEA (Root Mean Square Error of Approximation) – another measure that attempts to correct for the tendency of the χ2 goodness-of-fit test statistic to reject models with large samples or a large number of observed variables. It differs from the RMSR in that it has a known distribution. Thus, it better represents how well a model fits a population, not just a sample used for estimation. Lower RMSEA values indicate better fit; typically, “good” RMSEA values are below 0.10 for most acceptable models (Hair et al. 2009). 145 - NFI (Normed Fit Index) – the NFI is a ratio of the difference in the χ2 value for the fitted model and a null model divided by the χ2 value for the null model. It ranges between 0 and 1; a model with perfect fit would produce an NFI of 1 (Hair et al. 2009). - CFI (Comparative Fit Index) – the CFI is an incremental fit index that is an improved version of the normed fit index (NFI). The CFI is normed so the values range between 0 and 1, with higher values indicating better fit (Hair et al. 2009). The ultimate goal of any of these fit indexes is to assist the researcher in discriminating between acceptably and unacceptably specified models (Hair et al. 2009). Academic journals are replete with SEM results citing a 0.90 value on key indexes, such as the TFI, CFI, NFI and GFI, as indicating an acceptable model (Hair et al. 2009). Hoyle and Panter (1995) suggest that 0.90 stands as the agreed-upon cutoff for overall fit indexes (in our case, pertaining to the NFI, NNFI and CFI). In general, 0.90 is the “magic number” for good-fitting models (Hair et al. 2009). In addition, Hair et al. (2009) stressed that more complex models with larger samples should not be held to the same strict standards; thus, when samples are large and the model contains a large number of measured variables and parameter estimates, cutoff values of 0.95 on key goodness-of-fit measures are unrealistic. Results Findings on the eco-innovations of analyzed companies will first be analyzed in terms of general findings. This section presents the study results in four sections. First, we present the sample characteristics (Section 7.1), followed by analyses of different constructs – general descriptive statistics, followed by exploratory and confirmatory analyses for each construct. The findings on eco-innovations of analyzed companies will first be analyzed in terms of general findings (descriptive statistics). Second, all the constructs will be analyzed and tested by employing an exploratory analysis in SPSS and further conducting confirmatory factor analysis in EQS. Therefore, we will first present the relevant descriptive statistics, conduct exploratory and confirmatory analyses for the determinants of eco-innovation (Sections 7.2), followed by the same analyses done for all three eco-innovation types (product, process and organizational eco-innovation), presented in Section 7.3. We will conclude with the constructs that measure eco-innovation outcomes (competitive benefits, economic benefits, company performance and internationalization), which will be presented in the same way as previously described (Section 7.4). Sample characteristics We received 223 usable responses from 223 companies. As previously mentioned, the questionnaire, with cover letter and invitation, was sent via email and addressed to the top executives, who were asked to forward the questionnaire to the most appropriate person in their company for responding on the subject of environmental issues. The respondents held In Pursuit of Eco-innovation the following positions: top executive ( N = 49), environmental manager or management representative for environment ( N = 36), quality manager or management representative for quality ( N = 29), HSE (health and safety) manager ( N = 2), ecologist ( N = 3), management representative for EMS (environmental management systems) ( N = 8), R&D sector ( N = 8), HR manager ( N = 3), consultant for the environment ( N = 6), tech-nologist ( N = 9), commercialist ( N = 11), assistant director ( N = 7), ad-ministrator ( N = 3), board member ( N = 2), owner ( N = 5), founder or cofounder ( N = 3), project manager ( N = 2), accountant ( N = 4), business secretary ( N = 5), and procurator ( N = 4). Additionally, the respondents’ demographic structure shows that the majority of respondents were men (124; 55.6%), and the majority (81 respondents; 36.3%) were between 41-50 years old, followed by 73 respond-148 ents (32.7%) who were between 31 and 40 years old and 53 respondents (23.8%) who were older than 51. Only 16 respondents (7.2%) were less than 30 years old. Related to their years of working experience, the majority (67 respondents; 30%) have between 21 and 30 years of working experience, followed by 46 respondents (20.6%) with between 16 and 20 years of working experience and 38 respondents (17%) with 31 or more years of working experience. Continuing, 28 respondents (12.6%) have between 11 and 15 years of working experience, while 26 respondents (11.7%) indicated that they have between 6 and 10 years of working experience, followed by 11 respondents (4.5%) that have between 1 and 3 years of working experience and seven respondents (3.1%) with between 4 and 5 years of working experience. Lastly, we asked them about their highest degree of education. The majority of respondents have acquired a bachelor’s degree (78 respondents; 35%), followed by 77 respondents (34.5%) who have finished high/higher professional college and 39 respondents (17.5%) who have finished vocational or high school. Also, 22 respondents (9.9%) reported finishing a specialization, MBA or master’s degree and 7 respondents (3.1%) have completed doctorate. Regarding the sample characteristics (see Table 19) and focusing on firm size (number of employees), the results of the descriptive statistics show that the sample of analyzed companies includes 52 (23.3%) micro companies (having less than 9 employees), followed by 68 (30.5%) small companies (between 10-49 employees), 56 (25.1%) medium-sized companies (between 50-249 employees) and 47 (21%) large companies (250 or more employees). When focusing on firm size with regard to the company’s profitability (annual sales in 2013), we can see that 27 (12.1%) companies had earned 400,000 EUR or less in year 2013, while another 27 Results (12.1%) of companies reported between 400,000-800,000 EUR of annual sales in 2013. Moreover, 29 (13%) companies reported between 800,0001,600,000 EUR of annual sales in 2013, followed by 41 (18.4%) companies that reported between 1,600,000-4,000,000 EUR; lastly, 46 (20.6%) of companies reported between 4,000,000 and 20,000,000 EUR of sales in 2013. Table 19 shows also the firm age of the analyzed companies. We can see that the majority of companies included in our sample are between 21 and 50 years old (78 companies; 35%), followed by 62 companies (27.6%) that are more than 50 years old and 52 companies (23.3%) that are between 11 and 20 years old. Additionally, 20 companies (9%) have between 6 and 10 years, followed by 9 companies (4%) that are between 2 and 5 years old and only 2 companies (0.9%) that are less than 2 years old. 149 Table 19: Sample characteristics Number of companies Percent Firm size (number of employees) 0-9 employees 52 23.3% 10-49 employees 68 30.5% 50-100 employees 27 12.1% 101-249 employees 29 13.0% 250-500 employees 17 7.6% 501-1000 employees 15 6.7% More than 1000 employees 15 6.7% Firm size (annual sales in 2013) 400,000 EUR or less 27 12.1% Between 400,000-800,000 EUR 27 12.1% Between 800,000-1,600,000 EUR 29 13.0% Between 1,600,000-4,000,000 EUR 41 18.4% Between 4,000,000 and 20,000,000 EUR 46 20.6% Above 20,000,000 EUR 53 23.8% Firm age Less than 2 years 2 0.9% Between 2-5 years 9 4% Between 6-10 years 20 9% Between 11-20 years 52 23.3% In Pursuit of Eco-innovation Number of companies Percent Between 21-50 years 78 35% More than 50 years 62 27.8% Operating on foreign markets Yes 151 67.7% No 72 32.3% Commerce transactions B2B (business-to-business) 113 50.7% B2C (business-to-customer) 109 48.9% Both 1 4% Concerning the internationalization aspect, 151 companies (67.7%) 150 are internationalized (operating on foreign markets), while 72 companies (32.3%) are not. Lastly, pertaining to the commerce transactions, 113 (50.7%) of companies operate on a business-to-business level and 109 (48.9%) operate on a business-to-customer level, while one company (4%) operates on both. Furthermore, Table 20 illustrates the main industry in which the analyzed companies operate. We can see that the majority of companies operates in the service industry (110 companies; 49.3%), followed by manufacturing (82 companies, 36.8%), while some companies have not identified themselves with either of the given categories (28 companies; 12.6%). These companies indicated that they operate in several types of industries, such as: ICT (6 companies; 2.7%), followed by cleaning services (5 companies; 2.2%), waste management and distribution (4 companies each; 1.8%). Moreover, companies also identified energy industry and municipal activities as their main industry type (3 companies each; 1.4%), followed by the electro industry and, lastly, the automotive industry (0.4%). Results Table 20: Main industry types in which analyzed companies operate Main industry Number Percent Total Production of industrial goods 58 26.0% Manufacturing Production of consumer goods 15 6.7% 82 (36.8%) Other manufacturing 9 4.0% Construction 25 11.2% Retail and wholesale 24 10.8% Transportation and public goods 14 6.3% Engineering, research and development 8 3.6% Consulting and business services 7 3.1% Services 110 (49.3%) Consumer services 6 2.7% Tourism 4 1.8% 151 Banking, investment banking, insurance 2 0.9% Mining, extraction, oil 1 0.4% Other services 19 8.5% ICT 6 2.7% Cleaning services 5 2.2% Waste management 4 1.8% Distribution 4 1.8% Other (please specify) 28 (12.6%) Energy industry 3 1.4% Municipal activities 3 1.4% Electro industry 2 0.9% Automotive industry 1 0.4% Not specified: 3 1.4% 3 (1.4%) Total 223 100% 223 (100%) Continuing, Table 21 depicts the environmental certificates or prizes acquired by the companies in our sample. We can see that more than a third of the included companies are ISO 14001 accredited (86 companies or 38.6%). In addition, 4 companies (1.8%) are EMAS accredited. Moreover, there are also other environmental certificates; the Chamber of Commerce Slovenia on their website gathers them together and publishes a list of environmental certificates/prizes with the names of companies that obtained them. Therefore, 25 companies (11.2%) possess Environment friendly company certificates, followed by 20 companies (9%) that have acquired certificates for Energy efficient projects, 18 companies (8.1%) have acquired Energy efficient company certificates and 17 com- In Pursuit of Eco-innovation panies (7.6%) with Clean production certificates. There are also 13 companies (5.8%) that obtained Environment friendly process certificates, followed by 12 companies (5.4%) that possess Responsible care certificates (common for the chemical industry). Companies have also acquired other certificates, such as: eco-product of the year (7; 3.1%), Eco label (6; 2.7%), International environmental partnership (4; 1.8%) and Eco profit (3; 1.3%). Lastly, 12 companies (5.4%) reported other certificates, such as ISO 50001, ISO 9001 and ISO 18001. Table 21: Environmental certificates/prizes that have obtained the included companies Environmental certificates/prizes Number of companies Percent ISO 14001 86 38.6% 152 EMAS 4 1.8% Eco label 6 2.7% Eco profit 3 1.3% Clean production 17 7.6% Environment friendly company 25 11.2% Responsible care 12 5.4% Eco-product of the year 7 3.1% International environmental partnership 4 1.8% Environment friendly process 13 5.8% Energy efficient company 18 8.1% Energy efficient project 20 9% Other (e.g., ISO 50001, ISO 9001, ISO 18001) 12 5.4% In addition, we asked companies when they started engaging in en- vironmental activities in their company. On average, they started in the year 2002. However, 101 companies (45.3%) began environmental activities less than 10 years ago, while 107 companies (48%) started more than 10 years ago with their first activities related to the environment. Lastly, we present the level of innovativeness in the analyzed companies (see Table 22). We can see that 100 companies (44.8%) indicated that they have not launched a new product or service in the global level, while 22 (9.9%) have. Furthermore, 44 companies (19.7%) have launched a new product or service in their company’s offering even though similar products or services exist on the market, while 58 companies (26%) have not introduced either type of innovation. Moreover, 43 companies (19.3%) enlarged their present offering with new types, while 32 companies (14.3%) Results have not done so. Quite encouraging is the fact that almost half of the analyzed companies, 107 companies (48%), rejected the statement that they have not implemented any innovation or new product/service, while only 23 companies (10.3%) stated that they have not implemented any innovation (new product or service) in the past three years. Lastly, 135 companies (60.5%) rejected the statement that they have reduced their offering of products and services, while only one company (0.4%) agreed. This leads us to the conclusion that innovations are no longer only a source of competitive advantage, as has been traditionally assumed; rather, they are becoming vital for companies’ survival. If they want to stay on the market and operate successfully, they are forced to innovate, to expand their offering, and to present new products and services within the company and the market they serve. 153 Table 22: The level of innovativeness of included companies in the past three years (2011-2013) N (%) N (%) N (%) N (%) N (%) N (%) N (%) Level of innovativeness Neither Com- Not true Partially Partially Not true true nor True pletely at all true true false true Company has launched 100 new product or service 27 (12.1%) 21 (9.4%) 23 (10.3%) 18 (8.1%) 12 (5.4%) 22 (9.9%) (44.8%) at the global level. Company has launched new product or service in your company’s of- 58 (26%) 19 (8.5%) 17 (7.6%) 30 (13.5%) 33 (14.8%) 22 (9.9%) 44 (19.7%) fer, even though that on marker already exist sim- ilar products or services. Company has enlarged present offering with 32 (14.3%) 15 (6.7%) 23 (10.3%) 35 (15.7%) 31 (13.9%) 44 (19.7%) 43 (19.3%) new types. Company has not im- plemented any innova- 107 (48%) 27 (12.1%) 13 (5.8%) 21 (9.4%) 17 (7.6%) 15 (6.7%) 23 (10.3%) tion or new products/ services. Company has reduced 7 1 its offering of products 135 (60.5%) 29 (13%) 13 (5.8%) 23 (10.3%) 15 (6.7%) (3.1%) (0.4%) and services. Concluding with the sample characteristics, we can summarize that the average company in the sample had 50-100 employees, between In Pursuit of Eco-innovation 1,600,000 and 4,000,000 EUR of annual sales in year 2013, was 43 years old and began its environmental activities in the year 2002. In order to compare the distribution of the sample to the population, a Chi-square was used. It was found that the distribution of the sample differs from the population (see Table 23). We compared the distribution of the sample to the population regarding company size. The results indicate a significant difference for company size in terms of full-time employees. This difference is mainly due to the lower number of responses received from micro companies (0-9 employees) and the higher rate of participation of small (10-49 employees), medium (50-249 employees) and large companies (250 or more employees). Table 23: The sample in comparison with the population 154 Sample Population* Difference Firm size (number of em- ployees) N of compa- N Percent Percent Chi-square nies of companies Micro company 52 23.32% 172983 94.99% (0-9 employees) χ2 = 6393.619 Small company 68 30.49% 6788 3.73% (10-49 employees) df = 3 Medium company 56 25.11% 1988 1.09% (50-249 employees) sig. = 0.000 Large company (more than 47 21.08% 330 0.18% 250 employees) Σ 223 100% 182089 100% Note: *Population in our case stands for the entire number of all Slovenian companies. Data were retrieved from Statistical Office RS, 2015. Eco-innovation determinants In this section, we will present the analyses of all factors that work as determinants/drivers of eco-innovation. The descriptive statistics will be presented, and the normality of distribution of various constructs will also be checked. This will be followed by exploratory factor analysis, conducted in SPSS and concluded by confirmatory factor analysis for each determinant of eco-innovation. The five determinants of eco-innovation that we will encompass in our eco-innovation model are as follows: managerial environmental concern (Section 7.2.1), expected benefits (Section 7.2.2), environmental policy instruments (further divided into two individual components, the command-and-control instrument and the eco- Results nomic incentive instrument; Section 7.2.3), customer demand (Section 7.2.4) and competition (Section 7.2.5). Managerial environmental concern Table 24: Descriptive statistics for determinant Managerial environmental concern St. Err. St. Err. N Mean St. Dev. Skew Kurt Skew Kurt Eco-innovation is an import- ant component of the com- 223 4.87 1.743 -0.372 0.163 -0.797 0.324 pany’s environmental man- agement strategy. Most eco-innovations are 223 5.43 1.412 -0.854 0.163 0.192 0.324 worthwhile. 155 Eco-innovation is necessary to achieve high levels of envi- 223 5.87 1.272 -1.357 0.163 2.040 0.324 ronmental performance. Eco-innovation is an effective environmental management 223 5.83 1.274 -1.198 0.163 1.375 0.324 strategy. Note: N = number of observations; Mean = mean value on the Likert scale, which ranges from 1 to 7 (1 = strongly disagree, 7 = strongly agree); St. Dev. = standard deviation; Skew = skewness; St. Err. of Skew = standard error of skewness; Kurt = kurtosis; St. Err. Kurt = standard error of kurtosis. Table 24 illustrates the level of respondents’ agreement with statements related to the managerial environmental concern. Respondents on average agreed to the largest extent with the statement that eco-innovation is necessary to achieve high levels of environmental performance (mean value 5.87 on a seven-point Likert scale), followed by the statement “Eco-innovation is an effective environmental management strategy” (M = 5.83). The statement that most eco-innovations are worthwhile also shows a high mean value (M = 5.43), which is very encouraging, because eco-innovations are typically believed to be expensive and more of a burden for the company that implements them than for others. It can be seen that the common thinking about eco-innovations is paving the way towards the belief that eco-innovations can be a win-win situation – meaning that they are beneficial for both the environment and the company that implements them. The lowest level of agreement among respondents (M = 4.87) was received by the statement “Eco-innovation is an impor- In Pursuit of Eco-innovation tant component of the company’s environmental management strategy”; however, this mean value is still above average relative to the 7-point scale and thus reflects more agreement than disagreement. Exploratory factor analysis was further conducted by using the whole sample (all 223 observations) and by employing statistical package SPSS version 21. Before the analysis, all measurement items were checked for normality of distribution (see Table 24). Results have shown that the ratio of standard errors of kurtosis and skewness range between values of -2 and 2, and thus the normality of distribution is confirmed. If the value of this ratio is lower than -2 or higher than 2, then the normality of distribution is rejected (Gomezelj Omerzel 2008). In our case, all the values of all items range between -2 and 2. The method of extraction in the exploratory analysis was the Maximum Likelihood Method, while the se-156 lected rotation was Direct Oblimin rotation, which assumes that different factors are related. The appropriateness of factor analysis was determined by examining the correlation matrix of managerial environmental concern items. The existence of sufficient correlations (the Bartlett’s test of sphericity) and the Kaiser-Meyer-Olkin measure of sampling adequacy higher than 0.50 are more critical issues. The Bartlett’s test of sphericity that statistically tests for the presence of correlations among the underlying variables showed that the correlation matrix has significant correlations (p < 0.05). In our case, the Bartlett’s test of sphericity showed that the correlation matrix has significant correlations (sig. = 0.000 for all items). Furthermore, the Kaiser-Meyer-Olkin measure of sampling adequacy was examined and indicated similar results; specifically, the KMO value was 0.732, which indicates a middling sample adequacy. Table 25: KMO and Bartlett’s test of sphericity (Managerial environmental concern) KMO and Bartlett’s test Kaiser-Meyer-Olkin Measure of Sampling Adequacy 0.732 Approx. chi-square 435.886 Bartlett’s test of sphericity df 6 Sig. 0,000 The number of expected factors was one, and the extracted factor was one, as expected and already tested in previous research works, when using this construct. In addition, the scree plot of the initial run indi- Results cated one factor as an appropriate number. Further, one factor explains 59.816% of variance. After consideration of each item’s communality index and its contribution, we retained all the items (the lowest communality after extraction was 0.331). In the process of analysis, usually researchers delete or exclude the items that have low communalities after extraction – below the threshold of 0.20. Further, a confirmatory factor analysis was conducted in order to validate the findings of the exploratory factor analysis, which resulted in one factor composed of four items. This has also been confirmed by the confirmatory factor analysis. The eco-innovation determinant of managerial environmental concern comprises four items. All the coefficients were found to be positive, high and significant, and are indicated in Table 26 and Figure 6. 157 Table 26: Standardized coefficients and their squares (Managerial environmental concern) Standard. coeff. R-square Eco-innovation is an important component of the company’s environ-0.58 0.34 mental management strategy. Most eco-innovations are worthwhile. 0.79 0.62 Eco-innovation is necessary to achieve high levels of environmental 0.85 0.72 performance. Eco-innovation is an effective environmental management strategy. 0.84 0.71 Note: Standard. coeff. = Standardized coefficients; R-square = Coefficient of Determination. Statistical information of the construct managerial environmen- tal concern, pertaining to reliability (reliability coefficients) and convergence (goodness-of-fit model indexes) based on the overall sample (N = 223), is indicated in Figure 6. The construct of managerial environmental concern showed good reliability (Cronbach’s alpha = 0.836). Also, the goodness-of-fit indexes are shown in Figure 6 (NFI = 0.909; NNFI = 0.724; CFI = 0.909; SRMR = 0.058; RMSEA = 0.29). NFI and CFI showed good fit (over the threshold of 0.90), while NNFI and RMSEA showed slightly worse fit. In Pursuit of Eco-innovation 158 Figure 6: Diagram of construct Managerial environmental concern with the standardized solution Note: Measurement items: Q1A = Eco-innovation is an important component of the company’s environmental management strategy; Q1B = Most eco-innovations are worthwhile; Q1C = Eco-innovation is necessary to achieve high levels of environmental performance; Q1D = Environmental innovation is an effective environmental management strategy; Chi- -square = 39.39; p = 0.00; Goodness-of-fit indexes: NFI = 0.909; NNFI = 0.724; CFI = 0.909; SRMR = 0.058; RMSEA = 0.29; Reliability coefficients: Cronbach’s alpha = 0.836; RHO = 0.832; Internal consistency reliability = 0.879. Expected benefits Respondents were also asked what benefits they expected to seize from eco-innovation implementation. The results (Table 27) show that the most commonly expected benefit from eco-innovation was improvement of firm reputation (mean value 5.90 on a seven-point Likert scale), followed by cost reduction (M= 5.68). Among the expected benefits from eco-innovation, the following also showed high mean values: adjustment to EU (M = 5.30), to strengthen the brand (M = 5.29), to gain a competitive advantage (M = 5.28) and to enter new markets (M = 4.94). On the other hand, improvement of profitability (M = 4.78), increase of market share (M = 4.78) and increase of productivity (M = 4.70) seem to be the least commonly expected benefits among those listed. However, the mean values of all three are still above the central anchor. These findings Results lead us to the conclusion that Slovenian companies are aware of the potential of eco-innovation for their companies. Table 27: Descriptive statistics for determinant Expected benefits St. Err. St. Err. N Mean St. Dev. Skew Kurt Skew Kurt To reduce costs 223 5.68 1.459 -0.987 0.163 0.166 0.324 (energy, material, etc.) To improve profitability 223 4.78 1.597 -0.251 0.163 -0.635 0.324 To increase productivity 223 4.70 1.600 -0.286 0.163 -0.673 0.324 To increase market share 223 4.78 1.631 -0.408 0.163 -0.658 0.324 To enter new markets 223 4.94 1.650 -0.580 0.163 -0.453 0.324 To improve firm reputation 223 5.90 1.264 -1.304 0.163 1.420 0.324 159 To strengthen the brand 223 5.29 1.565 -0.834 0.163 0.104 0.324 Competitive advantage 223 5.28 1.537 -0.761 0.163 -0.107 0.324 Adjustment to EU 223 5.30 1.431 -0.736 0.163 0.074 0.324 Note: N = number of observations; Mean = mean value on the Likert scale, which ranges from 1 to 7 (1 = strongly disagree, 7 = strongly agree); St. Dev. = standard deviation; Skew = skewness; St. Err. of Skew = standard error of skewness; Kurt = kurtosis; St. Err. Kurt = standard error of kurtosis. Exploratory factor analysis was also conducted for this construct by using the overall sample (all 223 observations) and by employing statistical package SPSS version 21. Before the analysis, all measurement items were checked for normality of distribution (see Table 27). Results have shown that the ratio of standard errors of kurtosis and skewness range between values of -2 and 2, which implies normality of distribution. The method of extraction in the exploratory analysis was Maximum Likelihood Method, while the selected rotation was Direct Oblimin rotation, which assumes that different factors are related. The appropriateness of factor analysis was determined by examining the correlation matrix of expected benefits items. The existence of sufficient correlations (the Bartlett’s test of sphericity) and the Kaiser-Meyer-Olkin measure of sampling adequacy higher than 0.50 are more critical issues. The Bartlett’s test of sphericity, which statistically tests for the presence of correlations among the underlying variables, showed that the correlation matrix has significant correlations (p < 0.05). Furthermore, the Kaiser-Meyer-Olkin measure of sampling adequacy was exam- In Pursuit of Eco-innovation ined and indicated similar results; specifically, the KMO value was 0.909, which indicates an excellent sample adequacy. The number of expected factors was one, and the extracted factor was one. In addition, the scree plot of the initial run indicated one factor as an appropriate number. Further, one factor explains 53.902% of variance. After consideration of each item’s communality index and its contribution, we retained all the items (the lowest communality after extraction was 0.335). In the process of analysis, researchers usually delete or exclude items that have low communalities after extraction – below the threshold of 0.20. Table 28: KMO and Bartlett’s test of sphericity (Expected benefits) 160 KMO and Bartlett’s test Kaiser-Meyer-Olkin Measure of Sampling Adequacy 0.909 Approx. chi-square 1190.240 Bartlett’s test of sphericity df 36 Sig. 0.000 Table 29: Standardized coefficients and their squares (Expected benefits) Standard. coeff. R-square To reduce costs (energy, material, etc.) 0.58 0.34 To improve profitability 0.76 0.58 To increase productivity 0.77 0.59 To increase market share 0.85 0.72 To enter new markets 0.80 0.64 To improve firm reputation 0.62 0.38 To strengthen the brand 0.77 0.59 Competitive advantage 0.85 0.72 Adjustment to EU 0.53 0.28 Note: Standard. coeff. = Standardized coefficients; R-square = Coefficient of Determination. A confirmatory factor analysis was conducted in order to validate the findings of the exploratory factor analysis, which resulted in one factor composed of nine items. This was also confirmed by the confirmatory factor analysis. The eco-innovation determinant of expected benefits Results comprises nine items. All the coefficients were found to be positive, high and significant; these are indicated in Table 29 and Figure 7. Statistical information of the construct expected benefits, pertaining to reliability (reliability coefficients) and convergence (goodness-of-fit model indexes) based on the overall sample (N= 223), is as follows. The construct expected benefits showed good reliability (Cronbach’s alpha= 0.911). The goodness-of-fit indexes are as follows: NFI = 0.889; NNFI = 0.879; CFI = 0.909; SRMR = 0.058; RMSEA = 0.13. CFI showed good fit (over the threshold of 0.90), while other goodness-of-fit indexes showed slightly worse fit. From Table 29, we can see that three items have lower standardized coefficients (approximately 0.60); these are: “To reduce costs (energy, material, etc.)”, “To improve firm reputation” and “Adjustment to EU”. In addition, the goodness-of-fit model indexes are also low. Therefore, we 161 decided to conduct another exploratory factor analysis, in which we eliminated these three items due to their low correlations with other items. For instance, the item “To reduce costs (energy, material, etc.)” had correlations with other items ranging between 0.310 and 0.571, followed by the item “To improve firm reputation”, which had correlations with other items ranging between 0.363 and 0.666 and “Adjustment to EU,” which had correlations with other items ranging between 0.310 and 0.488. Moreover, communalities of those items are as follows: “To reduce costs (energy, material, etc.)” = 0.335, “To improve firm reputation” = 0.383 and “Adjustment to EU” = 0,283. After eliminating those three items, we conducted exploratory factor analysis once more, and the value of the Kaiser-Meyer-Olkin measure for sampling adequacy was 0.896. Bartlett’s test of sphericity also showed a statistically significant value (chi-square = 865.338; df = 15; p = 0.000), meaning that the correlation matrix has significant correlations. The communality index shown good communal- ities for almost all items (the lowest communality after extraction was 0.546), while variance explained was estimated at 64.096%. We can see that with fewer items (six instead of nine items), we are able to explain more variance; therefore, we conducted the confirmatory factor analysis again to check whether the goodness-of-fit indexes are any better. A confirmatory factor analysis was conducted in order to validate the findings of the exploratory factor analysis, which resulted in one factor composed of six items. This has also been confirmed by the confirmatory factor analysis. The eco-innovation determinant expected benefits comprises six items. All the coefficients were found to be positive, high and significant. These are indicated in Table 30 and Figure 7. In Pursuit of Eco-innovation Table 30: Standardized coefficients and their squares (Expected benefits) Standard. coeff. R-square To improve profitability 0.74 0.55 To increase productivity 0.77 0.59 To increase market share 0.88 0.77 To enter new markets 0.82 0.67 To strengthen the brand 0.74 0.55 Competitive advantage 0.85 0.72 Note: Standard. coeff. = Standardized coefficients; R-square = Coefficient of Determination. 162 Figure 7: Diagram of construct Expected benefits with the standardized solution Note: Measurement items: Q2B = To improve profitability; Q2C = To increase productivity; Q2D = To increase market share; Q2E = To enter new markets; Q2G = To strengthen the brand; Q2H = To gain competitive advantage; Chi-square = 37.418; p = 0.00; Goodness-of-fit indexes: NFI = 0.957; NNFI = 0.945; CFI = 0.967; SRMR = 0.033; RMSEA = 0.119; Reliability coefficients: Cronbach’s alpha = 0.914; RHO = 0.914; Internal consistency reliability = 0.922. Results Statistical information of the construct expected benefits, pertaining to reliability (reliability coefficients) and convergence (goodness-of-fit model indexes) based on the overall sample (N= 223), is as follows. The construct expected benefits showed good reliability (Cronbach’s alpha= 0.914), while the goodness-of-fit indexes also improved and are as follows: NFI = 0.957; NNFI = 0.945; CFI = 0.967; SRMR = 0.033; RMSEA = 0,119. We can see that CFI, NFI and NNFI all showed good fit (over the threshold of 0.90), also SRMR showed good fit (less than 0.05), while RMSEA showed slightly worse fit; however, the fit is better than it was initially for all nine items. Environmental policy instruments The driver called environmental policy instruments is divided into two 163 separate dimensions (see Table 31): the command-and-control instrument and the economic incentive instrument. We followed Li (2014) in distinguishing these two separate dimensions in order to obtain more valuable and detailed insights. The command-and-control instrument covers regulations, while the economic incentive instrument covers preferential tax policy, subsidies and government’s promotion of environmental protection. In this way, we can test individual effects of both on eco-innovation in order to see if the alleged superiority of the economic incentive instrument over the command-and-control instrument really holds. We can see (Table 31) that, when focusing on the command-and-con- trol instrument, all of the listed statements had high average values, expressing high levels of respondents’ agreement with the statements. The command-and-control instrument focuses on regulations. Respondents agreed at the highest level with the statement that their production processes should meet the requirements of national environmental regulations (mean value of 6 on a seven-point Likert scale), followed by the statement that products should meet the requirements of national environmental regulations (M = 5,99). The highest level of agreement was therefore found for statements pertaining to the national environmental regulations, followed closely by the mean values of the statements that focus on international and/or EU environmental regulations. This can be expected, because more than two thirds of the analyzed companies (67.7%) are operating on foreign markets and therefore have to comply with the foreign regulations of those markets. Therefore, respondents also agreed with the statements that production processes should meet the requirements of international and/or EU environmental regulations In Pursuit of Eco-innovation (M = 5.95) and that products should meet the requirements of international and/or EU environmental regulations (M = 5.89). Table 31: Descriptive statistics for determinant Environmental policy instruments St. Err. St. Err. N Mean St. Dev. Skew Kurt Skew Kurt Command-and-control instrument Our products should meet the requirements 223 5.99 1.547 -1.828 0.163 2.867 0.324 of national environ- mental regulations. Our products should meet the requirements 164 of international and/or 223 5.89 1.577 -1.600 0.163 1.969 0.324 EU environmental reg- ulations. Our production pro- cesses should meet the requirements of nation- 223 6.00 1.519 -1.845 0.163 2.990 0.324 al environmental reg- ulations. Our production pro- cesses should meet the requirements of inter- 223 5.95 1.468 -1.578 0.163 2.080 0.324 national and/or EU environmental regu- lations. Economic incentive instrument The government pro- vides preferential subsi- dies for environmental innovation (availability of government grants, 223 4.00 1.649 0.188 0.163 -0.810 0.324 subsidies or other fi- nancial incentives for environmental inno- vation). The government pro- vides preferential tax 223 3.43 1.699 0.540 0.163 -0.526 0.324 policies for environ- mental innovation. The government pro- vides environmental taxes – taxes on energy, 223 4.80 1.713 -0.368 0.163 -0.818 0.324 transport, pollution/re- sources. Results St. Err. St. Err. N Mean St. Dev. Skew Kurt Skew Kurt The government pro- motes environmental 223 4.18 1.710 0.016 0.163 -0.960 0.324 protection. The government pro- vides green public pro- 223 3.78 1.727 0.275 0.163 -0.770 0.324 curement. The government pro- vides an opportunity to 223 4.02 1.594 0.092 0.163 -0.817 0.324 undertake environmen- tal tenders/cal s. The government pro- vides an opportunity to 223 3.93 1.638 0.171 0.163 -0.790 0.324 undertake environmen- tal projects. 165 Note: N = number of observations; Mean = mean value on the Likert scale, which ranges from 1 to 7 (1 = strongly disagree, 7 = strongly agree); St. Dev. = standard deviation; Skew = skewness; St. Err. of Skew = standard error of skewness; Kurt = kurtosis; St. Err. Kurt = standard error of kurtosis. With regard to the economic incentive instrument (Table 31), the results show that respondents agreed at the highest level with the statement “The government provides environmental taxes on energy, transport, pollution/resources” (M = 4.80). Concerning incentives, we can see that only two statements were above the central anchor: “The government promotes environmental protection” (M = 4.18) and “The government provides the opportunity to undertake environmental tenders/calls” (M = 4.02). Respondents agreed the least with the statement “The government provides preferential tax policy on environmental innovation” (M = 3.43). Concerning environmental policy measures, we can see from the descriptive statistics that there are more regulations imposed from the side of government than incentives offered to companies to eco-innovate or engage in environmental activities. As the other constructs presented in previous sections, for the environmental policy instruments an exploratory factor analysis was conducted by using the overall sample (all 223 observations), and by employing statistical package SPSS version 21. Before the analysis, all measurement items were checked for normality of distribution (see Table 31). Results have shown that the ratio of standard errors of kurtosis and skewness range between values of -2 and 2, which implies normality of distribution. The method of extraction in the exploratory analysis was Maximum In Pursuit of Eco-innovation Likelihood Method, while the selected rotation was Direct Oblimin rotation, which assumes that different factors are related. The appropriateness of factor analysis was determined by examining the correlation matrix of the command-and-control instrument items. The existence of sufficient correlations (the Bartlett’s test of sphericity) and the Kaiser-Meyer-Olkin measure of sampling adequacy higher than 0.50 are more critical issues. The Bartlett’s test of sphericity, which statistically tests for the presence of correlations among the underlying variables, showed that the correlation matrix has significant correlations (p < 0.05). In our case, the Bartlett’s test of sphericity showed that correlation matrix has significant correlations (sig. = 0.000 for all items). Furthermore, the Kaiser-Meyer-Olkin measure of sampling adequacy was examined and indicated similar results; specifically, the KMO value was 0.741, 166 which means a middling sample adequacy. The number of expected factors was one, and the extracted factor was one, as expected and already tested in previous research works, when using this construct. In addition, the scree plot of the initial run indicated one factor as an appropriate number. Further, one factor explains 81.487% of variance. After consideration of each item’s communality index and its contribution, we retained all the items (the lowest communality after extraction was 0.779). In the process of analysis, researchers usually delete or exclude the items that have low communalities after extraction – below the threshold of 0.20. Table 32: KMO and Bartlett’s test of sphericity (Command-and-control instrument) KMO and Bartlett’s test Kaiser-Meyer-Olkin Measure of Sampling Adequacy 0.741 Approx. chi-square 967.439 Bartlett’s test of sphericity df 6 Sig. 0.000 A confirmatory factor analysis was conducted in order to validate the findings of the exploratory factor analysis, which resulted in one factor composed of four items. This has also been confirmed by the confirmatory factor analysis. The eco-innovation determinant the command-and-control instrument comprises four items. All the coefficients were found to be positive, high and significant, and are indicated in Table 33 and Figure 8. Results Table 33: Standardized coefficients and their squares (Command-and-control instrument) Standard. coeff. R-square Our products should meet the requirements of national environmental regu-0.90 0.81 lations. Our products should meet the requirements of international and/or EU envi-0.93 0.86 ronmental regulations. Our production processes should meet the requirements of national environ-0.89 0.79 mental regulations. Our production processes should meet the requirements of international and/or 0.88 0.77 EU environmental regulations. Note: Standard. coeff. = Standardized coefficients; R-square = Coefficient of Determination. 167 Figure 8: Diagram of construct Command-and-control instrument with the standardized solution Note: Measurement items: Q3A = Our products should meet the requirements of national environmental regulations; Q3B = Our products should meet the requirements of international and/or EU environmental regulations; Q3C = Our production processes should meet the requirements of national environmental regulations; Q3D = Our production processes should meet the requirements of international and/or EU environmental regulations; Chi-square = 119.95; p = 0.00; Goodness-of-fit indexes: NFI = 0.877; NNFI = 0.636; CFI = 0.879; SRMR = 0.043; RMSEA = 0.52; Reliability coefficients: Cronbach’s alpha = 0.946; RHO = 0.947; Internal consistency reliability = 0.949. In Pursuit of Eco-innovation Statistical information of the construct command-and-control in- strument, pertaining to reliability (reliability coefficients) and convergence (goodness-of-fit model indexes) based on the overall sample (N = 223), is indicated in Figure 8. The construct command-and-control instrument showed good reliability (Cronbach’s alpha = 0,946). Also the goodness-of-fit indexes are shown in Figure 8 (NFI = 0.877; NNFI = 0.636; CFI = 0.879; SRMR = 0.043; RMSEA = 0.52), where we can see that all the goodness-of-fit indexes showed slightly worse fit, except for SRMR and RMSEA. Second, the appropriateness of factor analysis was determined by examining the correlation matrix of the economic incentive instrument items. The existence of sufficient correlations (the Bartlett’s test of sphericity) and the Kaiser-Meyer-Olkin measure of sampling adequacy higher 168 than 0.50 are more critical issues. The Bartlett’s test of sphericity showed that the correlation matrix has significant correlations (p < 0.05). Furthermore, the Kaiser-Meyer-Olkin measure of sampling adequacy was examined and indicated similar results; specifically, the KMO value was 0.860, which indicates an excellent sample adequacy. The number of expected factors was one, and the extracted factor was one. In addition, the scree plot of the initial run indicated one factor as an appropriate number, explaining 60.265% of variance. Furthermore, the communality index showed good communalities (above the threshold of 0.20), except for the item “The government provides environmental taxes on energy, transport, pollution/resources,” which had a communality index of 0.182. We deleted the item that had low communalities after extraction – below the threshold of 0.20 and conducted the exploratory factor analysis once again. This time, the KMO value was a bit lower (0.848), while the Bartlett’s test of sphericity showed that the correlation matrix has significant correlations (sig. = 0.000 for all items). Moreover, the communality index showed good communalities (all items after extraction had communalities above the threshold of 0.20; the lowest communality was 0.448) and one factor was extracted, explaining 67.169% of variance. However, we decided to remove the other three items that had high correlations with each other in the correlation matrix (“The government provides green public procurement”, “The government provides an opportunity to undertake environmental tenders/calls” and “The government provides an opportunity to undertake environmental projects”). The third time we conducted an exploratory factor analysis, the value of KMO was 0.660, and the Bartlett’s test of sphericity showed that Results thecorrelation matrix has significant correlations (sig. = 0.000 for all items). Furthermore, the communality index showed good communalities (above the threshold of 0.20), where the lowest communality was 0.372. One factor was extracted (comprising three items), explaining 66.538% of variance, which is similar to the variance explained when measuring this construct with six items. Table 34: KMO and Bartlett’s test of sphericity (Economic incentive instrument) KMO and Bartlett’s test Kaiser-Meyer-Olkin Measure of Sampling Adequacy 0.660 Approx. chi-square 323.764 Bartlett’s test of sphericity df 3 169 Sig. 0.000 Further, a confirmatory factor analysis was conducted in order to validate the findings of the exploratory factor analysis, which resulted in one factor composed of three items. This has also been confirmed by the confirmatory factor analysis, where all the coefficients were found to be positive, high and significant (Table 35 and Figure 9). Table 35: Standardized coefficients and their squares (Economic incentive instrument) Standard. coeff. R-square The government provides preferential subsidies for environmental innovation (availability of government grants, subsidies or other financial incentives for 0.71 0.50 environmental innovation). The government provides preferential tax policies for environmental inno-1.00 1 vation. The government provides propagations on environmental protection. 0.54 0.29 Note: Standard. coeff. = Standardized coefficients; R-square = Coefficient of Determination; since this construct has been measured by only three items, an additional constraint (factor has been fixed to one) has been imposed in order to estimate the goodness-of-fit indexes. Statistical information of the construct economic incentive instrument, pertaining to reliability (reliability coefficients) and convergence (goodness-of-fit model indexes) based on the overall sample (N = 223), is indicated in Figure 9. The construct economic incentive instrument showed good reliability (Cronbach’s alpha = 0.838), and the following goodness-of-fit indexes: NFI = 0.945; NNFI = 0.843; CFI = 0.948; In Pursuit of Eco-innovation SRMR = 0.196; RMSEA = 0.276. We can see that NFI, NNFI and CFI showed good fit, while RMSEA and SRMR showed worse fit. 170 Figure 9: Diagram of construct Economic incentive instrument with the standardized solution Note: Measurement items: Q3E = The government provides preferential subsidy on environmental innovation (availability of government grants, subsidies or other financial incentives for environmental innovation); Q3F = The government provides preferential tax policy on environmental innovation: Q3H = The government promotes environmental protection; Chi- -square = 17.879; p = 0.00; Goodness-of-fit indexes: NFI = 0.945; NNFI = 0.843; CFI = 0.948; SRMR = 0.196; RMSEA = 0.276; Reliability coefficients: Cronbach’s alpha = 0.838; RHO = 0.800; Internal consistency reliability = 1.000. Customer demand Moreover, Table 36 illustrates the level of respondents’ agreement with statements related to the driver customer demand. We can see that respondents, on average, agreed to the greatest extent with the statement “The environment is a critical issue for our important customers” (mean value 4.69 on a seven-point Likert scale), while they agreed the least with the statement “Our customers have clear demands regarding environmental issues” (M = 4.24). We can see that all four statements concerning customer demand are above the central anchor, reflecting the importance of customer demand pertaining to eco-innovations and environmental issues. Results Table 36: Descriptive statistics for determinant Customer demand St. Err. St. Err. N Mean St. Dev. Skew Kurt Skew Kurt Environment is a critical issue for 223 4.69 1.703 -0.353 0.163 -0.716 0.324 our important cus- tomers. Our important cus- tomers often bring 223 4.33 1.762 -0.246 0.163 -0.933 0.324 up environmental issues. Customer demands motivate us in our 223 4.54 1.710 -0.319 0.163 -0.751 0.324 environmental ef- forts. 171 Our customers have clear demands re- 223 4.24 1.764 -0.065 0.163 -0.870 0.324 garding environ- mental issues. Note: N = number of observations; Mean = mean value on the Likert scale, which ranges from 1 to 7 (1 = strongly disagree, 7 = strongly agree); St. Dev. = standard deviation; Skew = skewness; St. Err. of Skew = standard error of skewness; Kurt = kurtosis; St. Err. Kurt = standard error of kurtosis. Continuing, an exploratory factor analysis (the method of extraction was the Maximum Likelihood Method, and the selected rotation was Direct Oblimin rotation) was also conducted for this construct. Before the analysis, all measurement items were checked for normality of distribution (see Table 36). Results have shown that the ratio of standard errors of kurtosis and skewness range between values of -2 and 2, which implies normality of distribution. The appropriateness of factor analysis was determined by examining the correlation matrix of customer demand items. The Bartlett’s test of sphericity showed that the correlation matrix has significant correlations (p < 0.05) and, the Kaiser-Meyer-Olkin measure of sampling adequacy was 0.867, which indicates an excellent sample adequacy. The number of expected factors was one, and the extracted factor was one, explaining 79.711% of variance. After consideration of each item’s communality index and its contribution, we retained all the items (the lowest communality after extraction was 0.721). In Pursuit of Eco-innovation Table 37: KMO and Bartlett’s test of sphericity (Customer demand) KMO and Bartlett’s test Kaiser-Meyer-Olkin Measure of Sampling Adequacy 0.867 Approx. chi-square 793.942 Bartlett’s test of sphericity df 6 Sig. 0.000 A confirmatory factor analysis was conducted in order to validate the findings of the exploratory factor analysis, which resulted in one factor composed of four items. This has also been confirmed by the confirmatory factor analysis. The eco-innovation determinant customer demand comprises four items. All the coefficients were found to be positive, high 172 and significant, and are indicated in Table 38 and Figure 10. Table 38: Standardized coefficients and their squares (Customer demand) Standard. coeff. R-square Environment is a critical issue for our important customers. 0.91 0.83 Our important customers often bring up environmental issues. 0.92 0.85 Customer demands motivate us in our environmental efforts. 0.85 0.72 Our customers have clear demands regarding environmental issues. 0.89 0.79 Note: Standard. coeff. = Standardized coefficients; R-square = Coefficient of Determination Statistical information of the construct customer demand, pertaining to reliability (reliability coefficients) and convergence (goodness-of-fit model indexes) based on the overall sample (N= 223), is indicated in Figure 10. The construct customer demand showed good reliability (Cronbach’s alpha = 0.940). In addition, the goodness-of-fit indexes are shown in Figure 10 (NFI = 0.999; NNFI = 1.005; CFI = 1.000; SRMR = 0.004; RMSEA = 0.000). We can see that NFI, NNFI, CFI (over the threshold of 0.90) and RMSEA (below the threshold of 0.10) showed good fit. Results 173 Figure 10: Diagram of construct Customer demand with the standardized solution Note: Measurement items: Q4A = The environment is a critical issue for our important customers; Q4B = Our important customers often bring up environmental issues; Q4C = Customer demands motivate us in our environmental efforts; Q4D = Our customers have clear demands regarding environmental issues; Chi-square = 0.70; p = 0.71; Goodness-of-fit indexes: NFI = 0.999; NNFI = 1.005; CFI = 1.000; SRMR= 0.004; RMSEA = 0.000; Reliability coefficients: Cronbach’s alpha = 0.940; RHO = 0.940; Internal consistency reliability = 0.944. Competition (Competitive intensity and Competitive pressure) In addition, we also focused on competition as a driver of eco-innovation in companies. According to the institutional theory, companies can engage in environmental activities, acquire environmental certificates or start to eco-innovate as a result of mimicking their competitors’ successful actions. In this section, we focus on competition, which we divide into two different individual components that are tested separately: competitive intensity, which focuses on competition in the industry in which a company operates, and competitive pressure, which focuses on environmental activities – that is, the establishment of the green concept in companies. We can see in Table 39 that respondents most agreed with the statement that competition in their industry is cutthroat (mean value 5.88 on a seven-point Likert scale), followed by the statements “Price competition is a hallmark of our industry” (M= 5.73) and “Anything that one In Pursuit of Eco-innovation competitor can offer, others can match readily” (M= 5.39). All the statements are above the central anchor, reflecting their importance and high level of agreement. Moreover, concerning competitive pressure, the results show that respondents most agreed with the statement that they establish a company’s environmental image compared to competitors through the green concept (M= 4.09), and they agreed the least with the statement that they increase a company’s market share through the green concept (M = 3.67). Table 39: Descriptive statistics for determinant Competition (Competitive intensity and Competitive pressure) St. Err. St. Err. N Mean St. Dev. Skew Kurt Skew Kurt 174 Competitive intensity Competition in our in- 223 5.88 1.385 -1.260 0.163 0.869 0.324 dustry is cutthroat. Anything that one com- petitor can offer others 223 5.39 1.393 -0.745 0.163 0.206 0.324 can match readily. Price competition is a 223 5.73 1.539 -1.261 0.163 0.793 0.324 hal mark of our industry. Competitive pressure We establish a company’s environmental image compared to competi- 223 4.09 1.662 -0.119 0.163 -0.805 0.324 tors through the green concept. We increase a company’s market share through 223 3.67 1.656 0.029 0.163 -0.864 0.324 the green concept. We improve a com- pany’s competitive ad- vantage over competi- 223 4.03 1.719 -0.112 0.163 -0.976 0.324 tors through the green concept. Note: N = number of observations; Mean = mean value on the Likert scale, which ranges from 1 to 7 (1 = strongly disagree, 7 = strongly agree); St. Dev. = standard deviation; Skew = skewness; St. Err. of Skew = standard error of skewness; Kurt = kurtosis; St. Err. Kurt = standard error of kurtosis. An exploratory factor analysis was also conducted for these two constructs. As in the case of environmental policy instruments, we have in Results this case investigated competitive intensity and competitive pressure individually. Before the analysis, all measurement items were checked for normality of distribution (see Table 39). Results have shown that the ratio of standard errors of kurtosis and skewness range between values of -2 and 2, which implies normality of distribution. The method of extraction in the exploratory factor analysis was Maximum Likelihood Method, while the selected rotation was Direct Oblimin rotation. As in the previous analyses, the appropriateness of factor analysis was determined by examining the correlation matrix of competitive intensity items. The existence of sufficient correlations (the Bartlett’s test of sphericity) and the Kaiser-Meyer-Olkin measure of sampling adequacy higher than 0.50 are critical issues. The Bartlett’s test of sphericity showed that the correlation matrix has significant correlations (p < 0.05). Furthermore, the Kaiser-Meyer-Olkin measure of sampling adequacy was 0.633. 175 The number of expected and extracted factors for the construct competitive intensity was one, explaining 36.733% of variance. After consideration of each item’s communality index and its contribution, we retained all the items (the lowest communality after extraction was 0.274). Table 40: KMO and Bartlett’s test of sphericity (Competitive intensity) KMO and Bartlett’s test Kaiser-Meyer-Olkin Measure of Sampling Adequacy 0.633 Approx. chi-square 78.141 Bartlett’s test of sphericity df 3 Sig. 0.000 After conducting analysis for competitive intensity, an exploratory factor analysis was conducted for competitive pressure. As in the previous analyses, the appropriateness of factor analysis was determined by examining the correlation matrix of competitive pressure items. The Bartlett’s test of sphericity showed that the correlation matrix has significant correlations (p < 0.05), and the Kaiser-Meyer-Olkin measure of sampling adequacy was 0.750, which indicates a middling sample adequacy. The number of expected factors was one, and the extracted factor was one. In addition, the scree plot of the initial run indicated one factor as an appropriate number, explaining 82.367% of variance. After consideration of each item’s communality index and its contribution, we retained all the items (the lowest communality after extraction was 0.773). In Pursuit of Eco-innovation Table 41: KMO and Bartlett’s test of sphericity (Competitive pressure) KMO and Bartlett’s test Kaiser-Meyer-Olkin Measure of Sampling Adequacy 0.750 Approx. chi-square 560.824 Bartlett’s test of sphericity df 3 Sig. 0.000 When considering the results of the exploratory factor analyses, these demonstrate better fit for the construct of competitive pressure (more variance explained for the competitive pressure than for the competitive intensity). Therefore, we decided that, in the final testing of the model, we would retain only the construct of competitive pressure and leave out the 176 construct of competitive intensity, which explains too low share of variance (only 36.733%)sx and thus seems to not play as important a role as a driver of eco-innovation in the analyzed companies as does competitive pressure. In the literature, we can see that the construct of competitive pressure fits better when focusing on eco-innovations, and researchers have used it in models of eco-innovation, while the construct of competitive intensity is used more often for regular innovation. Even though we decided to eliminate competitive intensity from further analyses, we have still conducted a confirmatory factor analysis to validate the findings of the previous exploratory factor analysis for competitive intensity. The eco-innovation determinant competitive intensity comprises three items, and the standardized coefficients were found to be positive and significant but demonstrating lower values. They are shown in Table 42 and Figure 11. Table 42: Standardized coefficients and their squares (Competitive intensity) Standard. coeff. R-square Competition in our industry is cutthroat. 0.72 0.52 Anything that one competitor can offer others can match readily. 0.52 0.27 Price competition is a hal mark of our industry. 0.55 0.30 Note: Standard. coeff. = Standardized coefficients; R-square = Coefficient of Determination; since this construct has been measured by only three items, an additional constraint (factor has been fixed to one) has been imposed in order to estimate the goodness-of-fit indexes. Results Statistical information of the construct of competitive intensity, pertaining to reliability (reliability coefficients) and convergence (goodness-of-fit model indexes) based on the overall sample (N= 223), is indicated in the Figure 11. The construct of competitive intensity showed acceptable reliability (Cronbach’s alpha = 0.621), while the goodness-of-fit indexes showed excellent fit (NFI = 1.000; NNFI = 1.040; CFI = 1.000; SRMR = 0.001 and RMSEA = 0.000). 177 Figure 11: Diagram of construct Competitive intensity with the standardized solution Note: Measurement items: Q5A = Competition in our industry is cutthroat; Q5B = Anything that one competitor can offer, others can match readily; Q5C = Price competition is a hal mark of our industry; Chi-square = 0.001; p = 0.979; Goodness-of-fit indexes: NFI = 1.000; NNFI = 1.040; CFI = 1.000; SRMR = 0.001; RMSEA = 0.000; Reliability coefficients: Cronbach’s alpha = 0.621; RHO = 0.626; Internal consistency reliability = 0.656. Further, a confirmatory factor analysis was also conducted for the construct of competitive pressure in order to validate the findings of the exploratory factor analysis, which resulted in one factor composed of three items. This has also been confirmed by the confirmatory factor analysis. The eco-innovation determinant competitive pressure comprises three items. All the coefficients were found to be positive, high and significant, and they are indicated in Table 43 and Figure 12. In Pursuit of Eco-innovation Table 43: Standardized coefficients and their squares (Competitive pressure) Standard. coeff. R-square We establish a company’s environmental image compared to competitors 0.75 0.56 through the green concept. We increase a company’s market share through the green concept. 0.83 0.69 We improve a company’s competitive advantage over competitors through 0.97 0.94 the green concept. Note: Standard. coeff. = Standardized coefficients; R-square = Coefficient of Determination; since this construct has been measured by only three items, an additional constraint (factor has been fixed to one) has been imposed in order to estimate the goodness-of-fit indexes. Statistical information of the construct of competitive pressure, per-178 taining to reliability (reliability coefficients) and convergence (goodness-of-fit model indexes) based on the overall sample (N = 223), is indicated in the Figure 12. The construct of competitive pressure showed good reliability (Cronbach’s alpha = 0.933), while the majority of goodness-of-fit indexes also show good fit (NFI = 0.936; NNFI = 0.812; CFI = 0.937), except for SRMR = 0.295 and RMSEA = 0.398 showed worse fit. Figure 12: Diagram of construct Competitive pressure with the standardized solution Note: Measurement items: Q6A = We establish the company’s environmental image by comparing to competitors through the green concept; Q6B = We increase the company’s market share through the green concept; Q6C = We improve the company’s competitive advantage over competitors through the green concept; Chi-square = 36.174; p = 0.00; Goodness-of-fit indexes: NFI = 0.936; NNFI = 0.812; CFI = 0.937; SRMR = 0.295; RMSEA = 0.398; Reliability coefficients: Cronbach’s alpha = 0.933; RHO = 0.893; Internal consistency reliability = 0.950. Results Eco-innovation types This section deals with different eco-innovation types (product, process and organizational eco-innovation, as well as the eco-innovation construct, which contains all the aforementioned dimensions). Therefore, we present the analyses for each eco-innovation type separately. The descriptive statistics will be presented, and we will also check for the normality of distribution of various constructs, followed by exploratory factor analysis, conducted in SPSS, and finally confirmatory factor analysis for each eco-innovation type. This section is divided into three subsections; we reveal the findings that pertain to product eco-innovation (Section 7.3.1), followed by process eco-innovation (7.3.2) and organizational eco-innovation (Section 7.3.3), and we conclude with the eco-innovation construct, which covers all three dimensions (Section 7.3.4). 179 Product eco-innovation Regarding product eco-innovation, Table 44 depicts descriptive statistics for each item related to product eco-innovation. We can see that, among the listed types of product eco-innovations, the analyzed companies on average implement environmentally friendly materials the most (mean value 5.20 on a seven-point Likert scale), followed by environmentally friendly packaging (M = 5.13) and eco-labeling (M = 2.59) the least. Table 44: Descriptive statistics for Product eco-innovation St. Err. St. Err. N Mean St. Dev. Skew Kurt Skew Kurt The company is using less or non-polluting/ toxic materials (i.e., using 223 5.20 1.605 -0.828 0.163 -0.042 0.324 environmental y friendly material). The company is improv- ing and designing en- vironmental y friendly packaging (e.g., using less 223 5.13 1.685 -0.934 0.163 0.140 0.324 paper and plastic materi- als) for existing and new products. The company is recover- ing and recycling end-of- 223 3.91 2.321 0.023 0.163 -1.551 0.324 life products. In Pursuit of Eco-innovation St. Err. St. Err. N Mean St. Dev. Skew Kurt Skew Kurt The company is using 223 2.59 1.993 0.979 0.163 -0.410 0.324 eco-labeling. The company chooses materials of the product that consume the least amount of energy and 223 4.61 1.789 -0.443 0.163 -0.718 0.324 resources for conduct- ing the product develop- ment or design. The company uses the smal est amount of ma- terials to comprise the 223 4.89 1.786 -0.686 0.163 -0.467 0.324 product for conduct- ing the product develop- 180 ment or design. The company deliber- ately evaluates whether the product is easy to re- cycle, reuse and decom- 223 4.50 1.917 -0.391 0.163 -0.972 0.324 pose for conducting the product development or design. Note: N = number of observations; Mean = mean value on the Likert scale, which ranges from 1 to 7 (1 = strongly disagree, 7 = strongly agree); St. Dev. = standard deviation; Skew = skewness; St. Err. of Skew = standard error of skewness; Kurt = kurtosis; St. Err. Kurt = standard error of kurtosis. Exploratory factor analysis (the method of extraction was the Maximum Likelihood Method, while the selected rotation was Direct Oblimin rotation) was also conducted for this construct (see Table 44). Results have shown that the ratio of standard errors of kurtosis and skewness range between values of -2 and 2, which implies normality of distribution.In the first exploratory factor analysis, we comprised all seven items to measure product eco-innovation. The appropriateness of factor analysis was determined by examining the correlation matrix of product eco-innovation items. The Bartlett’s test of sphericity showed that the correlation matrix has significant correlations (sig. = 0.000 for all items). Furthermore, the Kaiser-Meyer-Olkin measure of sampling adequacy was examined and indicated similar results; specifically, the KMO value was 0.856, which indicates an excellent sample adequacy. Results The number of expected factors was one, and the extracted factor was one. In addition, the scree plot of the initial run indicated one factor as an appropriate number. Further, one factor explains 50.254% of variance. After consideration of each item’s communality index and its contribution, we removed one item called “The company is using eco-labeling”, which had communality index below the threshold of 0.20 (0.194). We then conducted exploratory factor analysis again to see how the factor characteristics behave with six items to measure the construct of product eco-innovation. In the second run, we noted in the correlation matrix that one item –“The company is recovering end-of-life products and recycling” – has low correlations with other items, ranging between 0.299 and 0.354. However, KMO was 0.846, which is still excellent for sampling adequacy, and the Bartlett’s test of sphericity showed that the correlation matrix has significant correlations (sig. = 0.000 for all items). 181 Moreover, the communalities after extraction were all above the threshold of 0.20. The aforementioned item, “The company is recovering end-of-life products and recycling”, had the lowest communality (0.283); however, this value did not imply that it should be removed. Moreover, the percentage of variance explained has risen. With six items, we could explain 55.245% of variance. For the aforementioned reasons, we decided to eliminate the item “The company is recovering end-of-life products and recycling” and conducted the exploratory factor analysis again. This time, KMO was 0.836, still demonstrating an excellent sampling adequacy. Moreover, the Bartlett’s test of sphericity showed that the correlation matrix has significant correlations (sig. = 0.000 for all items). After extraction, all the communalities were above the threshold of 0.20 (the lowest was 0.401), and we retained all five items. The percentage of variance explained has risen for approximately 5%. With five items, we are able to explain 60.687% of variance. The reason we retained only four items in the final model to measure product eco-innovation is explained further in Section 7.3.4. When conducting an exploratory factor analysis for all three dimensions (product, process and organizational eco-innovation), one item (“The company is using less or non-polluting/toxic materials (i.e., using environmentally friendly material)”) loaded on both the product and process eco-innovation factors. Moreover, while it loaded a bit higher on process eco-innovation, it loaded on both with a low loading value. Therefore, we excluded this item to improve the results. In addition, Table 45 indicates the KMO value and Bartlett’s test of sphericity for product eco-innovation, including only four items. The lowest extracted communality was 0.355, In Pursuit of Eco-innovation and no further items were excluded; all four items were retained to measure product eco-innovation. Additionally, with four items, we are able to explain 64.316% of variance (a higher percentage than for the five items tested above); lastly, one factor is extracted. Table 45: KMO and Bartlett’s test of sphericity (Product eco-innovation) KMO and Bartlett’s test Kaiser-Meyer-Olkin Measure of Sampling Adequacy 0.808 Approx. chi-square 484.648 Bartlett’s test of sphericity df 6 Sig. 0.000 182 A confirmatory factor analysis was conducted to validate the findings of the exploratory factor analysis, which resulted in one factor composed of four items. This has also been confirmed by the confirmatory factor analysis. The dimension of product eco-innovation comprises four items. All the coefficients were found to be positive, high and significant, and they are indicated in Table 46 and Figure 13. Table 46: Standardized coefficients and their squares (Product eco-innovation) Standard. coeff. R-square The company is improving and designing environmental y friendly packaging (e.g., using less paper and plastic materials) for existing and new 0.60 0.36 products. The company chooses materials of the product that consume the least amount of energy and resources for conducting the product develop-0.89 0.79 ment or design. The company uses the smal est amount of materials to comprise the 0.89 0.79 product for conducting the product development or design. The company deliberately evaluates whether the product is easy to recycle, reuse and decompose for conducting the product development or 0.80 0.64 design. Note: Standard. coeff. = Standardized coefficients; R-square = Coefficient of Determination. Statistical information of the dimension product eco-innovation, pertaining to reliability (reliability coefficients) and convergence (goodness-of-fit model indexes) based on the overall sample (N = 223), is in- Results dicated in Figure 13. The dimension of product eco-innovation showed good reliability (Cronbach’s alpha = 0.872). Also, the goodness-of-fit indexes are showed in Figure 13 (NFI = 0.993; NNFI = 0.992; CFI = 0.997; SRMR = 0.017; RMSEA = 0.053); NFI, NNFI and CFI all showed good fit (over the threshold of 0.90), and the other goodness-of-fit indexes (SRMR and RMSEA) also showed good fit. 183 Figure 13: Diagram of eco-innovation dimension of Product eco-innovation with the standardized solution Note: Measurement items: Q8B = The company is improving and designing environmentally friendly packaging (e.g., using less paper and plastic materials) for existing and new products; Q8E = The company chooses materials of the product that consume the least amount of energy and resources for conducting the product development or design; Q8F = The company uses the smal est amount of materials to comprise the product for conducting the product development or design; Q8G = The company deliberately evaluates whether the product is easy to recycle, reuse and decompose for conducting the product development or design; Chi-square = 3.257; p = 0.196; Goodness-of-fit indexes: NFI = 0.993; NNFI = 0.992; CFI = 0.997; SRMR = 0.017; RMSEA = 0.053; Reliability coefficients: Cronbach’s alpha = 0.872; RHO = 0.879; Internal consistency reliability = 0.907. Process eco-innovation When focusing on process eco-innovation, we can see (Table 47) that the analyzed companies primarily implement waste treatment (mean value 6.48 on a seven-point Likert scale) as a type of process eco-innovation, In Pursuit of Eco-innovation followed by low energy consumption during production/use/disposal (M = 5.78). Companies implement closed water loops (reuse of water) the least frequently (M = 4.40%). Table 47: Descriptive statistics for Process eco-innovation St. Err. St. Err. N Mean St. Dev. Skew Kurt Skew Kurt *Low energy con- sumption such as wa- ter, electricity, gas and 223 5.78 1.502 -1.286 0.163 0.870 0.324 petrol during produc- tion/use/disposal. *Recycle, reuse and re- 223 5.33 1.818 -1.084 0.163 0.149 0.324 manufacture material. 184 Closed water loops 223 4.40 2.211 -0.273 0.163 -1.371 0.324 (reuse of water). Recycle, reuse and re- 223 4.99 1.978 -0.752 0.163 -0.643 0.324 manufacture waste. Waste treatment. 223 6.48 0.900 -2.449 0.163 0.010 0.324 Decreasing use of sol- vents or replacing 223 5.32 1.688 -0.999 0.163 0.179 0.324 them with substitutes. *Use of cleaner tech- nology to generate savings and prevent 223 5.40 1.573 -0.982 0.163 0.326 0.324 pollution (such as en- ergy, water and waste). *The manufacturing process of the compa- ny effectively reduc- 223 5.49 1.530 -1.160 0.163 0.969 0.324 es the emission of haz- ardous substances or waste. *The manufacturing process of the compa- 223 5.31 1.571 -1.037 0.163 0.522 0.324 ny reduces the use of raw materials. Reduced CO2 emis- 223 5.42 1.639 -1.070 0.163 0.384 0.324 sions. Reduced other air emissions (e.g., SOx, 223 5.38 1.743 -1.042 0.163 0.170 0.324 NOx). Reduced water pol- 223 5.69 1.519 -1.262 0.163 1.107 0.324 lution. Results St. Err. St. Err. N Mean St. Dev. Skew Kurt Skew Kurt Reduced soil pol- 223 5.73 1.507 -1.400 0.163 1.579 0.324 lution. Reduced noise pol- 223 5.53 1.524 -1.190 0.163 1.008 0.324 lution. Replaced materials with less hazardous 223 5.60 1.433 -1.285 0.163 1.513 0.324 substitutes. Note: N = number of observations; Mean = mean value on the Likert scale, which ranges from 1 to 7 (1 = strongly disagree, 7 = strongly agree); St. Dev. = standard deviation; Skew = skewness; St. Err. of Skew = standard error of skewness; Kurt = kurtosis; St. Err. Kurt = standard error of kurtosis. *Measurement items for process eco-innovation used also in the final analyses pertaining to the model testing. 185 As above, we conducted an exploratory factor analysis. Before the analysis, all measurement items were checked for normality of distribution (see Table 47). Results have shown that the ratio of standard errors of kurtosis and skewness range between values of -2 and 2, which implies normality of distribution. The method of extraction in the exploratory analysis was Maximum Likelihood Method, while the selected rotation was Direct Oblimin rotation, which assumes that different factors are related. The appropriateness of factor analysis was determined by examining the correlation matrix of process eco-innovation items. The Bartlett’s test of sphericity showed that correlation matrix has significant correlations (sig. = 0.000 for all items). Furthermore, the Kaiser-Meyer-Olkin measure of sampling adequacy was examined and indicated similar results; specifically, the KMO value was 0.861, which indicates an excellent sample adequacy. The number of expected factors was one, and the extracted factor was one, explaining 68.441% of variance. After consideration of each item’s communality index and its contribution, we retained all the items (the lowest communality after extraction was 0.512). In Pursuit of Eco-innovation Table 48: KMO and Bartlett’s test of sphericity (Process eco-innovation) KMO and Bartlett’s test Kaiser-Meyer-Olkin Measure of Sampling Adequacy 0.861 Approx. chi-square 807.261 Bartlett’s test of sphericity df 10 Sig. 0.000 A confirmatory factor analysis was conducted to validate the findings of the exploratory factor analysis, which resulted in one factor composed of five items. This has also been confirmed by the confirmatory factor analysis. The dimension process of eco-innovation comprises five items. All the coefficients were found to be positive, high and significant and are 186 indicated in Table 49 and Figure 14. Table 49: Standardized coefficients and their squares (Process eco-innovation) Standard. coeff. R-square Low energy consumption such as water, electricity, gas and petrol during produc-0.76 0.58 tion/use/disposal. Recycle, reuse and remanufacture material. 0.72 0.52 Use of cleaner technology to generate savings and prevent pollution (such as en-0.81 0.66 ergy, water and waste). The manufacturing process of the company effectively reduces the emission of 0.92 0.85 hazardous substances or waste. The manufacturing process of the company reduces the use of raw materials. 0.91 0.83 Note: Standard. coeff. = Standardized coefficients; R-square = Coefficient of Determination. Statistical information of the dimension of process eco-innovation, pertaining to reliability (reliability coefficients) and convergence (goodness-of-fit model indexes) based on the overall sample (N = 223), is indicated in the Figure 14. The dimension of process eco-innovation showed good reliability (Cronbach’s alpha = 0.912). Also, the goodness-of-fit indexes are shown in Figure 14 (NFI = 0.964; NNFI = 0.939; CFI = 0.970; SRMR = 0.036; RMSEA = 0.15); the majority of goodness-of-fit indexes showed good fit: NFI, NNFI and CFI (over the threshold of 0.90) and also the SRMR (below the threshold of 0.08), while RMSEA showed somewhat worse fit. Results 187 Figure 14: Diagram of eco-innovation dimension of Process eco-innovation with the standardized solution Note: Measurement items: Q9A = Low energy consumption such as water, electricity, gas, and petrol during production/use/disposal; Q9B = Recycle, reuse, and remanufacture material; Q9G = Use of cleaner technology to create savings and prevent pollution (such as energy, water, and waste); Q9H = The manufacturing process of the company effectively reduces the emission of hazardous substances or waste; Q9I = The manufacturing process of the company reduces the use of raw materials; Chi-square = 29.41; p = 0.00; Goodness-of-fit indexes: NFI = 0.964; NNFI = 0.939; CFI = 0.970; SRMR = 0.036; RMSEA= 0.15; Reliability coefficients: Cronbach’s alpha = 0.912; RHO = 0.911; Internal consistency reliability = 0.937. Organizational eco-innovation Lastly, Table 50 illustrates the types of organizational eco-innovation that the analyzed companies implement. We can see that companies, on average, use the environmental management system the most (mean value 5.30 on a seven-point Likert scale), while the least implemented organizational eco-innovation type among the analyzed companies is the use of life cycle analysis (M = 3.62). In Pursuit of Eco-innovation Table 50: Descriptive statistics for Organizational eco-innovation N Mean St. Dev. Skew St. Err. Skew Kurt St. Err. Kurt *Our firm management often uses novel systems to 223 4.39 1.670 -0.225 0.163 -0.792 0.324 manage eco-innovation. *Our firm management often collects information 223 4.61 1.715 -0.399 0.163 -0.728 0.324 on eco-innovation trends. *Our firm management often actively engages in 223 4.49 1.770 -0.296 0.163 -0.818 0.324 eco-innovation activities. *Our firm management often communicates 223 4.41 1.763 -0.307 0.163 -0.786 0.324 eco-innovation informa- 188 tion with employees. *Our firm management often invests a high ratio of 223 3.92 1.864 0.082 0.163 -1.064 0.324 R&D in eco-innovation. *Our firm management often communicates ex- periences among various 223 4.24 1.779 -0.199 0.163 -0.882 0.324 departments involved in eco-innovation. The firm uses an envi- ronmental management 223 5.30 1.935 -0.993 0.163 -0.152 0.324 system. The firm publishes an envi- 223 4.74 2.233 -0.463 0.163 -1.273 0.324 ronmental policy. The firm has specific tar- gets for environmental 223 5.06 1.946 -0.703 0.163 -0.680 0.324 performance. The firm publishes an an- 223 3.93 2.432 0.027 0.163 -1.640 0.324 nual environmental report. The firm applies environ- mental considerations to 223 4.95 1.791 -0.518 0.163 -0.794 0.324 purchasing decisions. The firm provides employ- 223 4.63 2.088 -0.350 0.163 -1.236 0.324 ee environmental training. The firm uses life cycle 223 3.62 2.135 0.217 0.163 -1.320 0.324 analysis. Note: N = number of observations; Mean = mean value on the Likert scale, which ranges from 1 to 7 (1 = strongly disagree, 7 = strongly agree); St. Dev. = standard deviation; Skew = skewness; St. Err. of Skew = standard error of skewness; Kurt = kurtosis; St. Err. Kurt = standard error of kurtosis. *Measurement items for organizational eco-innovation included in the final testing on the path model. Results Next, we conducted an exploratory factor analysis by using the overall sample (the method of extraction in the exploratory analysis was the Maximum Likelihood Method, while the selected rotation was Direct Oblimin rotation). Before the analysis, all measurement items were checked for normality of distribution (see Table 50). The appropriateness of factor analysis was determined by examin- ing the correlation matrix of organizational eco-innovation items. The Bartlett’s test of sphericity, which statistically tests for the presence of correlations among the underlying variables, showed that the correlation matrix has significant correlations (p < 0.05). Furthermore, the Kaiser-Meyer-Olkin measure of sampling adequacy was 0.901, which indicates means an excellent sample adequacy. The number of expected factors was one, and the extracted factor was one. Further, one factor explains 78.368% of variance. After considera-189 tion of each item’s communality index and its contribution, we retained all the items (the lowest communality after extraction was 0.642). Table 51: KMO and Bartlett’s test of sphericity (Organizational eco-innovation) KMO and Bartlett’s test Kaiser-Meyer-Olkin Measure of Sampling Adequacy 0.901 Approx. chi-square 1454.634 Bartlett’s test of sphericity df 15 Sig. 0.000 A confirmatory factor analysis was conducted in order to validate the findings of the exploratory factor analysis, which resulted in one factor composed of six items. This has also been confirmed by the confirmatory factor analysis. The dimension of organizational eco-innovation comprises six items. All the coefficients were found to be positive, high and significant, and they are indicated in Table 52 and Figure 15. In Pursuit of Eco-innovation Table 52: Standardized coefficients and their squares (Organizational eco-innovation) Standard. coeff. R-square Our firm management often uses novel systems to manage eco-innovation. 0.80 0.64 Our firm management often collects information on eco-innovation trends. 0.89 0.79 Our firm management often actively engages in eco-innovation activities. 0.93 0.87 Our firm management often communicates eco-innovation information with 0.93 0.87 employees. Our firm management often invests a high ratio of R&D in eco-innovation. 0.87 0.77 Our firm management often communicates experiences among various de-0.89 0.79 partments involved in eco-innovation. Note: Standard. coeff. = Standardized coefficients; R-square = Coefficient of Determinati-190 on. Figure 15: Diagram of eco-innovation dimension of Organizational eco-innovation with the standardized solution Results Note: Measurement items: Q10A = Our firm management often uses novel systems to manage eco-innovation; Q10B = Our firm management often collects information on eco-innovation trends; Q10C = Our firm management often actively engages in eco-innovation activities; Q10D = Our firm management often communicates eco-innovation information with employees; Q10E = Our firm management often invests a high ratio of R&D in eco-innovation; Q10F = Our firm management often communicates experiences among various departments involved in eco-innovation; Chi-square = 80.33; p = 0.00; Goodness-of-fit indexes: NFI = 0.945; NNFI = 0.918; CFI = 0.951; SRMR = 0.030; RMSEA = 0.19; Reliability coefficients: Cronbach’s alpha = 0.956; RHO = 0.956; Internal consistency reliability = 0.962. Statistical information of the dimension of organizational eco-innovation, pertaining to reliability (reliability coefficients) and convergence (goodness-of-fit model indexes) based on the overall sample (N = 223), are indicated in the Figure 15. The dimension of organizational eco-innovation showed good reliability (Cronbach’s alpha = 0.956). Also, the goodness-of-fit indexes are shown in Figure 15 (NFI = 0.945; NNFI = 0.918; 191 CFI = 0.951; SRMR = 0.030; RMSEA = 0.19). We can see that the majority of the goodness-of-fit indexes – NFI, NNFI and CFI – showed good fit (over the threshold of 0.90) and also the SRMR (below the threshold of 0.08), while RMSEA showed somewhat worse fit. Eco-innovation construct We repeated the same procedure as above for eco-innovation construct. First the exploratory factor analysis was conducted, followed by the confirmatory factor analysis. The exploratory factor analysis was conducted by using the overall sample (all 223 observations) and by employing statistical package SPSS version 21. The method of extraction in the exploratory analysis was the Maximum Likelihood Method, while the selected rotation was Direct Oblimin rotation, which assumes that different factors are related. The appropriateness of factor analysis was determined by examining the correlation matrix of eco-innovation items. The existence of sufficient correlations (the Bartlett’s test of sphericity) and the Kaiser-Meyer-Olkin measure of sampling adequacy higher than 0.50 are more critical issues. The Bartlett’s test of sphericity, which statistically tests for the presence of correlations among the underlying variables, showed that the correlation matrix has significant correlations (p < 0.05). Furthermore, the Kaiser-Meyer-Olkin measure of sampling adequacy was examined and in- dicated similar results; specifically, the KMO value was 0.939, which indicates an excellent sample adequacy. The number of expected factors was three, while the extracted factors were two (Table 53). The product eco-innovation dimension loaded to- In Pursuit of Eco-innovation gether with the process eco-innovation dimension into one factor, while organizational eco-innovation represents an independent factor. In addition, the scree plot of the initial run indicated two factors as an appropriate number, explaining 66.326% of variance. After consideration of each item’s communality index and its contribution, we retained all the items (the lowest communality after extraction was 0.422). Table 53: The eco-innovation dimensions’ (product and process eco-innovation factor and organizational eco-innovation factor) items factor loadings Factors Items Product & Process Organizational 192 eco-innovation eco-innovation Product and process eco-innovation (PD & PC) The manufacturing process of the company effectively reduces 0.917 the emission of hazardous substances or waste. The manufacturing process of the company reduces the use of 0.852 raw materials. Use of cleaner technology to generate savings and prevent pollu- 0.821 tion (such as energy, water and waste). Low energy consumption such as water, electricity, gas and petrol 0.792 during production/use/disposal. The company uses the smal est amount of materials to comprise 0.762 the product for conducting the product development or design. The company chooses materials of the product that consume the least amount of energy and resources for conducting the product 0.708 development or design. The company is using less or non-polluting/toxic materials (i.e., 0.703 using environmental y friendly material). Recycle, reuse and remanufacture material. 0.702 The company is improving and designing environmental y friend- ly packaging (e.g., using less paper and plastic materials) for exist-0.659 ing and new products. The company deliberately evaluates whether the product is easy to recycle, reuse and decompose for conducting the product de- 0.603 velopment or design. Results Factors Items Product & Process Organizational eco-innovation eco-innovation Organizational eco-innovation (OR) Our firm management often communicates eco-innovation in- -1.003 formation with employees. Our firm management often actively engages in eco-innovation -0.952 activities. Our firm management often invests a high ratio of R&D in -0.856 eco-innovation. Our firm management often collects information on eco-inno- -0.852 vation trends. Our firm management often communicates experiences among -0.831 various departments involved in eco-innovation. 193 Our firm management often uses novel systems to manage -0.736 eco-innovation. N = 2234 Extraction Method: Maximum Likelihood Rotation Method: Oblimin with Kaiser Normalization (absolute factor loadings equal or higher than 0.20 displayed) Bartlett’s test of sphericity: Chi-square = 3297.073; 120 df; sig. = 0.000 Kaiser-Meyer-Olkin measure of sample adequacy = 0.933 Variance explained = 66.326 According to the theory, a three-factor solution was expected. Therefore, we again conducted an exploratory factor analysis by prior determination of three expected factors (product, process and organizational eco-innovation). We fixed the number of extracted factors to three. The exploratory factor analysis was conducted by using the overall sample (all 223 observations) and by employing statistical package SPSS. The method of extraction in the exploratory analysis was the Maximum Likelihood Method, while the selected rotation was Direct Oblimin rotation, which assumes that different factors are related. The Bartlett’s test of sphericity, which statistically tests for the presence of correlations among the underlying variables, showed that the correlation matrix has significant correlations (p < 0.05). Furthermore, the Kaiser-Meyer-Olkin measure of sampling adequacy was 0.933, which indicates an excellent sample adequacy. The number of extracted factors was three, as previously determined. In addition, the scree plot of the initial run indicated three factors as an appropriate number, explaining 70.513% of variance. After consideration In Pursuit of Eco-innovation of each item’s communality index and its contribution, we retained all the items (the lowest communality after extraction was 0.414). Table 54: The eco-innovation dimension’s item factor loadings (three eco-innovation factors) Factors Items Process eco-inno- Product eco-inno- Organizational vation vation eco-innovation Process eco-innovation (PC) The manufacturing process of the company ef- fectively reduces the emission of hazardous sub- 1.015 stances or waste. 194 The manufacturing process of the company re- 0.891 duces the use of raw materials. Use of cleaner technology to generate sav- ings and prevent pollution (such as energy, wa- 0.737 ter and waste). Low energy consumption such as water, elec- tricity, gas and petrol during production/use/ 0.725 disposal. Recycle, reuse and remanufacture material. 0.592 Product eco-innovation (PD) The company uses the smal est amount of ma- terials to comprise the product for conducting 0.930 the product development or design. The company chooses materials of the product that consume the least amount of energy and 0.839 resources for conducting the product develop- ment or design. The company deliberately evaluates wheth- er the product is easy to recycle, reuse and de- 0.711 compose for conducting the product develop- ment or design. The company is improving and designing en- vironmental y friendly packaging (e.g., using 0.287 0.379 less paper and plastic materials) for existing and new products. The company is using less or non-polluting/ toxic materials (i.e., using environmental y 0.362 0.348 friendly material). Results Factors Items Process eco-inno- Product eco-inno- Organizational vation vation eco-innovation Organizational eco-innovation (OR) Our firm management often communicates -0.991 eco-innovation information with employees. Our firm management often actively engages in -0.954 eco-innovation activities. Our firm management often collects informa- -0.862 tion on eco-innovation trends. Our firm management often invests a high ratio -0.859 of R&D in eco-innovation. Our firm management often communicates ex- periences among various departments involved -0.822 195 in eco-innovation. Our firm management often uses novel systems -0.750 to manage eco-innovation. N = 223 Extraction Method: Maximum Likelihood Rotation Method: Oblimin with Kaiser Normalization (absolute factor loadings equal to or higher than 0.20 displayed) Bartlett’s test of sphericity: Chi-square = 3297.073; 120 df; sig. = 0.000 Kaiser-Meyer-Olkin measure of sample adequacy = 0.933 Variance explained = 70.513 From Table 54, we can see that two items loaded on two factors, while the item “The company is using less or non-polluting/toxic materials (i.e., using environmentally friendly material)” was more problemating; it not only loaded on two factors but also had higher loading on the wrong dimension (i.e., it loaded on the process eco-innovation factor, while it should load on the product eco-innovation factor). As mentioned, this item should pertain to the dimension of product eco-innovation, but it loaded a bit higher on the dimension of process eco-innovation. Therefore, we decided to eliminate this item. Moreover, the item “The company is improving and designing environmentally friendly packaging (e.g., using less paper and plastic materials) for existing and new products” also loaded on two factors – product and process eco-innovation. However, it loaded with a higher value on product eco-innovation, and thus, because of its importance, it was retained in the further analyses. We repeated the exploratory factor analysis again (Table 55), eliminating the item “The company is using less or non-polluting/toxic materi- In Pursuit of Eco-innovation als (i.e., using environmentally friendly material)”. The extraction method remained Maximum Likelihood and the rotation Direct Oblimin, and we also determined the number of factors to be extracted as three. This time, the KMO value was 0.936, and the Bartlett’s test of sphericity demonstrated significant correlations (p < 0.05). The number of extracted factors was three, as previously determined. In addition, the scree plot of the initial run indicated three factors as an appropriate number, explaining 71.981% of variance. After consideration of each item’s communality index and its contribution, we retained all the items (the lowest communality after extraction was 0.395). Table 55: The eco-innovation dimension’s item factor loadings 196 Factors Items Process eco-inno- Product eco-inno- Organizational vation vation eco-innovation Process eco-innovation (PC) The manufacturing process of the company ef- fectively reduces the emission of hazardous 1.016 substances or waste. The manufacturing process of the company re- 0.902 duces the use of raw materials. Use of cleaner technology to generate sav- ings and prevent pollution (such as energy, wa- 0.738 ter and waste). Low energy consumption such as water, elec- tricity, gas and petrol during production/use/ 0.731 disposal. Recycle, reuse and remanufacture material. 0.598 Product eco-innovation (PD) The company uses the smal est amount of ma- terials to comprise the product for conducting 0.940 the product development or design. The company chooses materials of the product that consume the least amount of energy and 0.847 resources for conducting the product develop- ment or design. The company deliberately evaluates wheth- er the product is easy to recycle, reuse and de- 0.701 compose for conducting the product develop- ment or design. Results Factors Items Process eco-inno- Product eco-inno- Organizational vation vation eco-innovation The company is improving and designing en- vironmental y friendly packaging (e.g., using 0.293 0.350 less paper and plastic materials) for existing and new products. Organizational eco-innovation (OR) Our firm management often communicates -0.988 eco-innovation information with employees. Our firm management often actively engages -0.953 in eco-innovation activities. Our firm management often collects informa- -0.862 tion on eco-innovation trends. 197 Our firm management often invests a high ra- -0.857 tio of R&D in eco-innovation. Our firm management often communicates experiences among various departments in- -0.816 volved in eco-innovation. Our firm management often uses novel systems -0.756 to manage eco-innovation. N = 223 Extraction Method: Maximum Likelihood Rotation Method: Oblimin with Kaiser Normalization (absolute factor loadings equal or higher than 0.20 displayed) Bartlett’s test of sphericity: Chi-square = 3109.220; 105 df; sig. = 0.000 Kaiser-Meyer-Olkin measure of sample adequacy = 0.936 Variance explained = 71.981 In order to validate the findings of both solutions given by the exploratory factor analyses, pertaining to the two-factor and three-factor solutions, we conducted a confirmatory factor analysis, through which we examine the convergence of the eco-innovation dimensions. The model with two factors (product & process eco-innovation as one factor, organizational eco-innovation) showed worse goodness-of-fit indexes (NFI = 0.882; NNFI = 0.890; CFI = 0.907; SRMR = 0.050; RMSEA = 0.121) and the Cronbach’s alpha was 0.952. The standardized coefficients were all positive, high (above 0.50) and statistically significant. Correlation between the two dimensions was estimated at 0.72. In addition, we have conducted a confirmatory factor analysis to examine the convergence of the eco-innovation dimensions, with three dimensions as would be supposed and expected according to the theory. In Pursuit of Eco-innovation The model with three factors gave goodness-of-fit indexes (NFI = 0.928; NNFI = 0.945; CFI = 0.954; SRMR = 0.044; RMSEA = 0.086), and the Cronbach’s alpha was 0.952. The standardized coefficients were all positive, high (over 0.50) and statistically significant. Correlation between product and process eco-innovation was estimated at 0.79, correlation between product and organizational eco-innovation was 0.65, and the process and organizational eco-innovation dimensions also showed high correlation (0.68). All correlations were statistically significant. We can see that confirmatory factor analysis demonstrated better goodness-of-fit indexes for the model with three dimensions than for the model with two dimensions. Therefore, we decided to use the three-factor model solution. Furthermore, statistical information of each eco-innovation dimension’s internal consistency (Cronbach’s alpha reliability) and convergence 198 (goodness-of-fit model indexes) based on the overall sample (N = 223) is indicated in Table 56. Table 56 summarizes the statistics for all eco-innovation dimensions (product, process and organizational eco-innovation) and further illustrates, for each eco-innovation dimension, model fit indexes, range of standardized coefficients, Cronbach’s alpha reliability and the number of items included. We can see that Cronbach’s alpha is high in all cases – for the product, process and organizational eco-innovation dimensions separately as well as for the eco-innovation construct (over 0.80). More specifically, the dimension of product eco-innovation showed good reliability (Cronbach’s alpha = 0.872) and convergence in terms of coefficients. The other two dimensions, process eco-innovation (Cronbach’s alpha = 0.912) and organizational eco-innovation (Cronbach’s alpha = 0.956), showed excellent reliability and convergence in terms of coefficients. Moreover, standardized coefficients are all positive, high (over 0.50) and statistically significant. The goodness-of-fit indexes are also high; only RMSEA values for process and organizational eco-innovation showed slightly worse fit. Moreover, the goodness-of-fit indexes are better when related to the entire eco-innovation construct. Lastly, the model showed goodness-of-fit indexes (NFI = 0.928; NNFI = 0.945; CFI = 0.954; SRMR = 0.044; RMSEA = 0.086). We can see that the goodness-of-fit indexes are better with a three-dimension model of eco-innovation, which is in line with our theory. However, the exploratory factor analysis gave two dimensions as solutions (joining product and process eco-innovation dimensions), probably also because of highly related dimensions; product and process eco-innovation demonstrated high correlation (r = 0.79; see Table 58). Thus, we decided on three dimensions of eco-innovation on the basis of the results of the confirmatory factor analysis. Results Table 56: Eco-innovation dimension’s scale convergence – summary for all three eco-innovation dimensions and eco-innovation construct Range of Model fit indexes Cronbach’s standard- Dimension N. of items alpha reli- ized coeffi- ability NFI NNFI CFI SRMR RMSEA cients* Product eco-in- 4 0.872 0.60 to 0.80 0.993 0.992 0.997 0.017 0.05 novation Process eco-in- 5 0.912 0.72 to 0.92 0.964 0.939 0.970 0.036 0.15 novation Organizational 6 0.956 0.80 to 0.93 0.945 0.918 0.951 0.030 0.19 eco-innovation Eco-innovation 15 0.952 0.62 to 0.93 0.928 0.945 0.954 0.044 0.08 construct 199 Note: N. of items = number of items of each eco-innovation dimension; * all standardized coefficients are positive, high and significant (sig. < 0.05). Convergent and discriminant validity of the eco-innovation construct The eco-innovation dimensions were tested for convergent and discriminant validity together in the eco-innovation construct structural model, where dimensions were modeled as first-order latent constructs and correlated with each other (see Figure 16). The model showed good fit (NFI = 0.928; NNFI = 0.945; CFI = 0.954 – over the threshold of 0.90; SRMR = 0.044; RMSEA = 0.086 – below the threshold of 0.10). Moreover, all coefficients were found to be positive, high and significant. Also, the reliability coefficients (Cronbach’s alpha = 0.952; RHO = 0.968, were high – above the threshold of 0.70). Figure 16 illustrates the standardized solution for the eco-innovation construct, composed of the three dimensions of product, process and organizational eco-innovation. The 15 items of these three dimensions measure the entire eco-innovation construct. Table 57 offers results pertaining to the standardized coefficients and their squares for all items of the eco-innovation construct. In Pursuit of Eco-innovation 200 Figure 16: Eco-innovation construct (with the standardized solution) Results Note pertaining to Figure 16: Measurement items: Q8B = The company is improving and designing environmental y friendly packaging (e.g., using less paper and plastic materials) for existing and new products; Q8E = The company chooses materials of the product that consume the least amount of energy and resources for conducting the product development or design; Q8F = The company uses the smal est amount of materials to comprise the product for conducting the product development or design; Q8G = The company deliberately evaluates whether the product is easy to recycle, reuse and decompose for conducting the product development or design; Q9A = Low energy consumption such as water, electricity, gas, and petrol during production/use/disposal; Q9B = Recycle, reuse, and remanufacture material; Q9G = Use of cleaner technology to generate savings and prevent pollution (such as energy, water, and waste); Q9H = The manufacturing process of the company effectively reduces the emission of hazardous substances or waste; Q9I = The manufacturing process of the company reduces the use of raw materials; Q10A = Our firm management often uses novel systems to manage eco-innovation; Q10B = Our firm management often collects information on eco- -innovation trends; Q10C = Our firm management often actively engages in eco-innovation activities; Q10D = Our firm management often communicates eco-innovation information with employees; Q10E = Our firm management often invests a high ratio of R&D in eco-inno-201 vation; Q10F = Our firm management often communicates experiences among various departments involved in eco-innovation; Chi-square = 228.463; p = 0.00; Goodness-of-fit indexes: NFI = 0.928; NNFI = 0.945; CFI = 0.954; SRMR = 0.044; RMSEA = 0.086; Reliability coefficients: Cronbach’s alpha = 0.952; RHO = 0.968. Table 57: Standardized coefficients and their squares (eco-innovation construct) Standard. coeff. R-square The company is improving and designing environmental y friendly packaging 0.62 0.38 (e.g., using less paper and plastic materials) for existing and new products. The company chooses materials of the product that consume the least amount of energy and resources for conducting the product development 0.89 0.79 or design. The company uses the smal est amount of materials to comprise the product 0.87 0.76 for conducting the product development or design. The company deliberately evaluates whether the product is easy to recycle, re-0.81 0.66 use and decompose for conducting the product development or design. Low energy consumption such as water, electricity, gas and petrol during pro-0.76 0.58 duction/use/disposal. Recycle, reuse and remanufacture material. 0.73 0.53 Use of cleaner technology to make savings and prevent pollution (such as en-0.82 0.67 ergy, water and waste). The manufacturing process of the company effectively reduces the emission 0.91 0.83 of hazardous substances or waste. The manufacturing process of the company reduces the use of raw materials. 0.91 0.83 Our firm management often uses novel systems to manage eco-innovation. 0.80 0.64 Our firm management often collects information on eco-innovation trends. 0.89 0.79 Our firm management often actively engages in eco-innovation activities. 0.93 0.87 In Pursuit of Eco-innovation Standard. coeff. R-square Our firm management often communicates eco-innovation information 0.92 0.85 with employees. Our firm management often invests a high ratio of R&D in eco-innovation. 0.87 0.76 Our firm management often communicates experiences among various de-0.89 0.79 partments involved in eco-innovation. Note: Standard. coeff. = Standardized coefficients; R-square = Coefficient of Determination. Lastly, the model reliability, variance statistics and inter-dimensional correlations are indicated in Table 58. All dimensions demonstrated good composite reliability (over the threshold of 0.70). The average var-202 iance extracted was also good, over the threshold of 0.50. Correlations among dimensions ranged from 0.65 to 0.79, implying convergence. We can see that the correlations are high among all three dimensions. The lowest correlation was estimated at 0.65 between product eco-innovation and organizational eco-innovation, while process eco-innovation and organizational eco-innovation correlated a bit higher (0.68). The highest correlation (0.79) is between product eco-innovation and process eco-innovation. Table 58: Eco-innovation construct convergent and discriminant validity Overall model* Correlations Product eco-inno- Process eco-inno- Organizational CR AVE vation vation eco-innovation Product eco-innovation 0.878 0.65 1 0.79* 0.65* Process eco-innovation 0.916 0.69 0.79* 1 0.68* Organizational eco-inno- 0.956 0.78 0.65* 0.68* 1 vation Note: CR = composite reliability; AVE = average variance extracted; * Goodness of fit indexes: NFI = 0.928; NNFI = 0.945; CFI = 0.954; SRMR = 0.044; RMSEA = 0.086. Multidimensionality of the eco-innovation construct was also tested by comparing the relative contributions of the two models. The first model includes only one common eco-innovation first-order factor (the one common factor model) and is based on the assumption of the unidimensionality of the eco-innovation construct. The second model (eco-innova- Results tion dimensions-only model) is based on the assumption of the non-un-idimensionality of the eco-innovation concept. These two models are nested in the model with both the dimensions and the common factor, a method that allows for model comparisons (Antončič 2002; Ruzzier 2005). These comparisons are shown in Table 59. Table 59: The dimensions-only vs. the one common factor model Chi-square df NFI NNFI CFI SRMR RMSEA M1: One common 920.656 89 0.712 0.682 0.731 0.205 factor model *** 0.109 M2: Dimen- 228.463 sions-only model 87 0.928 0.945 0.954 0.044 0.086 *** (three dimensions) 203 M3: Model with both the dimen- 142.048 68 0.956 0.963 0.976 0.033 0.070 sions and the com- *** mon factor 778.608 M1-M3 21 0.846 *** 86.415 M2-M3 19 0.378 *** Note: Chi-square: * significant at p < 0.05; ** significant at p < 0.01; *** significant at p < 0.0001. The one common factor model indicated an overall poor fit relative to the dimensions-only model in all goodness-of-fit indexes. Model fit indexes of the dimensions-only model and the model with both the dimensions and the common factor are very high. The model with both the dimensions and the common factor has somewhat lower residuals (SRMS) and errors (RMSEA) and higher NFI, NNFI and CFI indexes. The contributions of the two models are shown in the last two rows of Table 59. Both Chi-square differences are significant (p < 0.0001), indicating that both models may contribute to explanatory power. However, the NFI for the two model differences, computed with the formula from Bentler (1990) by including models 1 and 3 respectively, demonstrates that the contribution of the dimensions seems to be quite substantial (NFI = 0.846), while the contribution of the overall-factor model seems to be relatively minimal (NFI = 0.378). Overall, the one common factor model seems to be inferior to the dimensions-only model. This can In Pursuit of Eco-innovation be considered a strong indication of the eco-innovation constructs’ multidimensionality. Eco-innovation outcomes In this section, we present the analyses for constructs, which in the following sections will be tested in relation to eco-innovations as their consequences. As in the previous sections, we will present the descriptive statistics and check for the normality of distribution of various constructs. Exploratory factor analysis conducted in SPSS will follow, and the section will conclude with confirmatory factor analysis for each construct. We will focus on the following consequences of eco-innovation: competitive benefits (Section 7.4.1), economic benefits (Section 7.4.2), company performance (Section 7.4.3) and internationalization (Section 7.4.4). 204 Competitive benefits Results (see Table 60) show which competitive benefits most analyzed companies reported as consequences of eco-innovation implementation. We asked respondents to indicate the extent to which the company’s environmental practices have led to any of the listed competitive benefits (on a seven-point Likert scale: 1 = no contribution to 7 = very large contribution). We can see that, overall, improved company reputation or goodwill is reported most frequently (mean value 4.78 on a seven-point Likert scale) as a competitive benefit of eco-innovation implementation. Meanwhile, improved product innovations (M = 3.59) seem to be the least frequently reported benefit. Table 60: Descriptive statistics for Competitive benefits St. Err. St. Err. N Mean St. Dev. Skew Kurt Skew Kurt Reduction in material costs 223 4.03 1.801 -0.120 0.163 -0.938 0.324 Reduction in process/pro- 223 3.97 1.772 -0.104 0.163 -0.925 0.324 duction costs Reduction in costs of regula- 223 3.73 1.750 -0.065 0.163 -1.019 0.324 tory compliance Increased process/produc- 223 3.77 1.745 -0.053 0.163 -0.950 0.324 tion efficiency Increased productivity 223 3.79 1.728 -0.053 0.163 -0.954 0.324 Results St. Err. St. Err. N Mean St. Dev. Skew Kurt Skew Kurt Increased knowledge about effective ways of managing 223 4.00 1.707 -0.117 0.163 -0.830 0.324 operations Improved process innova- 223 3.82 1.686 -0.113 0.163 -0.896 0.324 tions Improved product quality 223 4.10 1.786 -0.198 0.163 -0.955 0.324 Improved product inno- 223 3.59 1.679 0.000 0.163 -0.877 0.324 vations Better relationships with stakeholders such as local 223 4.22 1.799 -0.287 0.163 -0.896 0.324 communities, regulators, and environmental groups Improved employee morale 223 4.19 1.586 -0.327 0.163 -0.657 0.324 205 Overall improved company 223 4.78 1.619 -0.628 0.163 -0.325 0.324 reputation or goodwil Note: N = number of observations; Mean = mean value on the Likert scale, which ranges from 1 to 7 (1 = strongly disagree, 7 = strongly agree); St. Dev. = standard deviation; Skew = skewness; St. Err. of Skew = standard error of skewness; Kurt = kurtosis; St. Err. Kurt = standard error of kurtosis. Further, we conducted an exploratory factor analysis (Maximum Likelihood Method of extraction and Direct Oblimin rotation). All measurement items were checked for normality of distribution (see Table 60). The appropriateness of factor analysis was determined by examining the correlation matrix of competitive benefits items. The Bartlett’s test of sphericity showed that the correlation matrix has significant correlations (p < 0.05), and the Kaiser-Meyer-Olkin measure of sampling adequacy was 0.917, which indicates an excellent sample adequacy. After consideration of each item’s communality index and its contribution, we retained all the items (the lowest communality after extraction was 0.511). Table 61: KMO and Bartlett’s test of sphericity (Competitive benefits) KMO and Bartlett’s test Kaiser-Meyer-Olkin Measure of Sampling Adequacy 0.917 Approx. chi-square 2697.222 Bartlett’s test of sphericity df 66 Sig. 0.000 In Pursuit of Eco-innovation The number of factors to be extracted was determined a priori based on previous research works that used this scale. The number of extracted factors should be one. The scree plot of the initial run indicated that two factors might be an appropriate number, and the latent root (eigenvalue) criterion also indicated two factors, which in total explain 71.406% of variance. The two factors that were extracted as a result of the exploratory factor analysis are presented in Table 62, together with the 12 related items and their factor loadings. The new competitive benefits dimension was split into two factors, one pertaining to the various improvements (Improvement factor) and the other to the various reductions (Reduction factor). Table 62: Competitive benefits dimension’s item factor loadings 206 Factors Items Factor 1 Factor 2 (Reduction factor) (Improvement factor) Better relationships with stakeholders such as lo- cal communities, regulators, and environmen- 0.872 tal groups Improved process innovations 0.820 Improved employee morale 0.820 Increased knowledge about effective ways of 0.801 managing operations Improved product innovations 0.753 Overall improved company reputation or good- 0.753 wil Improved product quality 0.724 Increased productivity 0.607 -0.344 Increased process/production efficiency 0.505 -0.445 Reduction in process/production costs -0.992 Reduction in material costs -0.898 Reduction in costs of regulatory compliance 0.251 -0.520 N = 223 Extraction Method: Maximum Likelihood Rotation Method: Oblimin with Kaiser Normalization (absolute factor loadings equal or higher than 0.20 displayed) Bartlett’s test of sphericity: Chi-square = 2697222; 66 df; sig. = 0.000 Kaiser-Meyer-Olkin measure of sample adequacy = 0.917 Variance explained = 71.406 Results A confirmatory factor analysis was conducted to validate the findings of the exploratory factor analysis, which resulted in two factors, an Improvement factor composed of nine items and a Reduction factor composed of three items. This scale originally was assumed to be composed of one factor, and previous researchers also used it as one factor (Sharma and Vredenburg 1998; Sharma 2001). Therefore, we first conducted confirmatory factor analysis in the sense that we put together all 12 items to measure competitive benefits; second, we conducted a confirmatory factor analysis in order to validate a two-factor solution. Table 63 illustrates the main results of the confirmatory factor analyses, related to the model goodness-of-fit indexes and reliability coefficient (Cronbach’s alpha). In Table 63, we can see that two-factor solution is not much better than the one-factor solution. Therefore, we decided to retain the one-factor solution composed of 12 items to measure com-207 petitive benefits. The Chi-Square and RMSEA had slightly better (lower) values in the two-factor solution, other model goodness-of-fit indexes, such as NFI, NNFI and CFI, were slightly higher in the two-factor solution, while the SRMR value was better (lower) in the one-factor solution. However, the differences were too low to decide on the two-factor solution, while the chi-square difference between the two models was statistically significant. Moreover, SRMR had better value in the one-factor solution than in the two-factor solutions, while Cronbach’s alpha for the scale was high (0.954). Finally, further in our analysis we tested competitive benefits as a one-dimensional construct, comprising 12 items. Table 63: Model good-fit and reliability indexes for 1-factor and 2-factor solution of construct Competitive benefits 1 factor 2 factors Chi-square (df) 613.583 (54) 542.818 (52) RMSEA 0.216 0.206 SRMR 0.080 0.227 NFI 0.777 0.803 NNFI 0.746 0.769 CFI 0.792 0.818 Cronbach’s alpha 0.954 0.954 Note: df = degrees of freedom; * the difference between models is statistical y significant (Chi-square = 70.765; df = 2; p< 0.0001). In Pursuit of Eco-innovation In addition, all the coefficients of the construct of competitive benefits were found to be positive, high and significant. These are presented in Table 64 and Figure 17. Table 64: Standardized coefficients and their squares (Competitive benefits) Standard. coeff. R-square Reduction in material costs 0.77 0.59 Reduction in process/production costs 0.76 0.58 Reduction in costs of regulatory compliance 0.68 0.46 Increased process/production efficiency 0.87 0.76 Increased in productivity 0.89 0.79 Increased knowledge about effective ways of managing operations 0.89 0.79 208 Improved process innovations 0.91 0.83 Improved product quality 0.82 0.67 Improved product innovations 0.83 0.69 Better relationships with stakeholders such as local communities, regula-0.71 0.50 tors, and environmental groups Improved employee morale 0.71 0.50 Overall improved company reputation or goodwil 0.68 0.46 Note: Standard. coeff. = Standardized coefficients; R-square = Coefficient of Determination. Statistical information of the construct competitive benefits, pertaining to reliability (reliability coefficients) and convergence (goodness-of-fit model indexes) based on the overall sample (N = 223), is presented in Figure 17. The construct competitive benefits showed good reliability (Cronbach’s alpha = 0.954). In addition, the goodness-of-fit indexes are showed in Figure 17 (NFI = 0.78; NNFI = 0.75; CFI = 0.79; SRMR = 0.080; RMSEA = 0.216). Results 209 Figure 17: Diagram of construct Competitive benefits with the standardized solution In Pursuit of Eco-innovation Note: Measurement items: Q17A = Reduction in material costs; Q17B = Reduction in process/production costs; Q17C = Reduction in costs of regulatory compliance; Q17D = Increased process/production efficiency; Q17E = Increased productivity; Q17F = Increased knowledge about effective ways of managing operations; Q17G = Improved process innovations; Q17H = Improved product quality; Q17I = Improved product innovations; Q17J = Better relationships with stakeholders such as local communities, regulators, and environmental groups; Q17K = Improved employee morale; Q17L = Overall improved company reputation or goodwill; Chi-square = 613.583; p = 0.000; Goodness-of-fit indexes: NFI = 0.78; NNFI = 0.75; CFI = 0.79; SRMR = 0.080; RMSEA = 0.216; Reliability coefficients: Cronbach’s alpha = 0.954; RHO = 0.954; Internal consistency reliability = 0.963. In the next step, we tried to improve the goodness-of-fit indexes by conducting another exploratory factor analysis, followed by a confirmatory factor analysis. Reduction of items was done step by step; in each step, we first eliminated the items that showed lower communalities and had lower correlations with other items (exploratory factor analy-210 sis). First, we eliminated items that had extracted communalities lower than 0.60 and correlations with other items below 0.60. After this step, we again conducted a confirmatory factor analysis to determine whether the goodness-of-fit indexes had improved, and then we eliminated the items that had lower standardized coefficients. Finally, after conducting several exploratory and confirmatory factor analyses, we came to the best and most parsimonious solution. We have reduced the number of items from 12 to four. We report further on all the values from the exploratory and confirmatory factors analyses. We conducted an exploratory factor analysis by using the overall sample. The method of extraction in the exploratory analysis was the Maximum Likelihood Method, while the selected rotation was Direct Oblimin rotation, which assumes that different factors are related. The appropriateness of factor analysis was determined by examining the correlation matrix of competitive benefits items. The Bartlett’s test of sphericity, which statistically tests for the presence of correlations among the underlying variables, showed that the correlation matrix has significant correlations (p < 0.05). Furthermore, the Kaiser-Meyer-Olkin measure of sampling adequacy was examined and indicated similar results; specifically, the KMO value was 0.810 (KMO value with 12 items was 0.917), which indicates an excellent sample adequacy. After consideration of each item’s communality index and its contribution, we retained all the items (the lowest communality after extraction was 0.749, while in the first version with 12 items the lowest communality was 0.511). As noted above, in order to improve the goodness-of-fit we removed all the items that showed lower communalities (approximately 0.60). Results Table 65: KMO and Bartlett’s test of sphericity (Competitive benefits) KMO and Bartlett’s test Kaiser-Meyer-Olkin Measure of Sampling Adequacy 0.810 Approx. chi-square 897.029 Bartlett’s test of sphericity df 6 Sig. 0.000 The number of factors to be extracted was determined a priori based on previous research works that used this scale. The number of extracted factors should be one, while in the previous version with 12 items, two factors were extracted, which in total explained 71.406% of variance. This time we had four items, and only one factor was extracted, which is in 211 line with expectations. Moreover, this one factor explains 81.419% of variance. Therefore, we conducted a confirmatory factor analysis in order to validate the findings of the exploratory factor analysis, which resulted in one factor composed of four items. Figure 18 illustrates the main results of the confirmatory factor analysis, related to the model goodness-of-fit indexes and reliability coefficient (Cronbach’s alpha). In addition, all the coefficients of the construct competitive benefits were found to be positive, high and significant. These are presented in Table 66 and Figure 18. Table 66: Standardized coefficients and their squares (Competitive benefits) Standard. coeff. R-square Increased process/production efficiency. 0.87 0.76 Increased productivity 0.91 0.83 Increased knowledge about effective ways of managing operations 0.92 0.85 Improved process innovations 0.92 0.85 Note: Standard. coeff. = Standardized coefficients; R-square = Coefficient of Determination. Statistical information of the construct competitive benefits, pertaining to reliability (reliability coefficients) and convergence (goodness-of-fit model indexes) based on the overall sample (N = 223), is indicated in the Figure 18. The construct competitive benefits showed good reliability (Cronbach’s alpha = 0.946). In addition, the goodness-of-fit in- In Pursuit of Eco-innovation dexes are shown in Figure 18 (NFI = 0.943; NNFI = 0.835; CFI = 0.945; SRMR = 0.029; RMSEA = 0.334). 212 Figure 18: Diagram of construct Competitive benefits with the standardized solution Note: Measurement items: Q17D = Increased process/production efficiency; Q17E = Increased productivity; Q17F = Increased knowledge about effective ways of managing operations; Q17G = Improved process innovations; Chi-square = 51.555; p = 0.000; Goodness-of- -fit indexes: NFI = 0.94; NNFI = 0.83; CFI = 0.94; SRMR = 0.029; RMSEA = 0.334; Reliability coefficients: Cronbach’s alpha = 0.946; RHO = 0.946; Internal consistency reliability = 0.948. Economic benefits Furthermore, we asked companies to specify the effects of their environmental activities on the listed economic benefits (on a seven-point Likert scale; 1 = very negative to 7 = very positive). Results (see Table 67) reveal that eco-innovations on average had the most positive effect on corporate image (M = 5.14), while the most negative effect was reported for short-term profits (M= 3.82). This is in line with the theory and also with practice, which indicate that eco-innovations, like innovations in general, demand high investments (this indeed depends on the type of eco-innovation that we aim to adopt, implement or develop) and pay off after a longer period of time. However, eco-innovations also exert positive effect on companies’ image and reputation, and many other beneficial effects of eco-innovation can be seen in Table 67. Results Table 67: Descriptive statistics for Economic benefits St. Err. St. Err. N Mean St. Dev. Skew Kurt Skew Kurt Sales 223 4.33 1.410 0.153 0.163 -0.346 0.324 Market share 223 4.02 1.349 0.067 0.163 -0.015 0.324 New market opportunities 223 4.32 1.431 -0.053 0.163 -0.414 0.324 Corporate image 223 5.14 1.367 -0.553 0.163 -0.028 0.324 Management satisfaction 223 4.78 1.531 -0.438 0.163 -0.317 0.324 Employee satisfaction 223 4.52 1.423 -0.329 0.163 -0.132 0.324 Short-term profits 223 3.82 1.419 0.111 0.163 -0.261 0.324 Cost savings 223 4.17 1.505 -0.094 0.163 -0.428 0.324 Productivity 223 4.00 1.430 -0.168 0.163 0.170 0.324 213 Note: N = number of observations; Mean = mean value on the Likert scale, which ranges from 1 to 7 (1 = strongly disagree, 7 = strongly agree); St. Dev. = standard deviation; Skew = skewness; St. Err. of Skew = standard error of skewness; Kurt = kurtosis; St. Err. Kurt = standard error of kurtosis. Further, we conducted an exploratory factor analysis by using the overall sample (the method of extraction was the Maximum Likelihood Method, while the selected rotation was Direct Oblimin rotation). Before the analysis, all measurement items were checked for normality of distribution (see Table 67). The appropriateness of factor analysis was determined by examining the correlation matrix of economic benefits items. The Bartlett’s test of sphericity, which statistically tests for the presence of correlations among the underlying variables, showed that the correlation matrix has significant correlations (p < 0.05). Furthermore, the Kaiser-Meyer-Olkin measure of sampling adequacy was 0.885, which indicates an excellent sample adequacy. After consideration of each item’s communality index and its con- tribution, we retained all the items (the lowest communality after extraction was 0.459). The number of expected factors was one, and the extracted factor was one. In addition, the scree plot of the initial run indicated one factor as an appropriate number. Further, one factor explains 65.087% of variance. In Pursuit of Eco-innovation Table 68: KMO and Bartlett’s test of sphericity (Economic benefits) KMO and Bartlett’s test Kaiser-Meyer-Olkin Measure of Sampling Adequacy 0.885 Approx. chi-square 1926.122 Bartlett’s test of sphericity df 36 Sig. 0.000 Further, a confirmatory factor analysis was conducted in order to validate the findings of the exploratory factor analysis, which resulted in one factor composed of nine items. This has also been confirmed by the confirmatory factor analysis. The construct of economic benefits comprises nine items. All the coefficients were found to be positive, high and signif-214 icant, and these are indicated in Table 69 and Figure 19. Table 69: Standardized coefficients and their squares (Economic benefits) Standard. coeff. R-square Sales 0.84 0.71 Market share 0.89 0.79 New market opportunities 0.85 0.72 Corporate image 0.78 0.61 Management satisfaction 0.81 0.66 Employee satisfaction 0.88 0.77 Short-term profits 0.74 0.55 Cost savings 0.68 0.46 Productivity 0.78 0.61 Note: Standard. coeff. = Standardized coefficients; R-square = Coefficient of Determination. Statistical information of the construct Economic benefits, pertaining to reliability (reliability coefficients) and convergence (goodness-of-fit model indexes) based on the overall sample (N = 223), is indicated in the Figure 19. The construct economic benefits showed good reliability (Cronbach’s alpha = 0.943). In addition, the goodness-of-fit indexes are shown in Figure 19 (NFI = 0.778; NNFI = 0.717; CFI = 0.788; SRMR = 0.083; RMSEA = 0.261). Results 215 Figure 19: Diagram of construct Economic benefits with the standardized solution Note: Measurement items: Q18A = Sales; Q18B = Market share; Q18C = New market opportunities; Q18D = Corporate image; Q18E = Management satisfaction; Q18F = Employee satisfaction; Q18G = Short-term profits; Q18H = Cost savings; Q18I = Productivity; Chi-square = 435.32; p = 0.00; Goodness-of-fit indexes: NFI = 0.778; NNFI = 0.717; CFI = 0.788; SRMR = 0.083; RMSEA = 0.261; Reliability coefficients: Cronbach’s alpha = 0.943; RHO = 0.943; Internal consistency reliability = 0.951. We have encountered a similar problem here as we did previously when dealing with the constuct of competitive benefits. Here, all nine items were extracted to one factor, and the goodness-of-fit indexes are again poor. Therefore, we tried to improve these results while maintain- In Pursuit of Eco-innovation ing the parsimony of the construct. We first eliminated the items that had lower extracted communalities and were correlated to a lower extent with other items, then again conducted exploratory factor analysis. If we appeared to be on the right track, we continued with confirmatory factor analysis and examined the goodness-of-fit indexes as well as the standardized coefficients. We repeated this procedure several times to find the best solution. We now present the results of the best solution. We conducted an exploratory factor analysis (the method of extraction was the Maximum Likelihood Method, while the selected rotation was Direct Oblimin rotation). The appropriateness of factor analysis was determined by examining the correlation matrix of economic benefits items. The Bartlett’s test of sphericity, which statistically tests for the presence of correlations 216 among the underlying variables, showed that the correlation matrix has significant correlations (p < 0.05). Furthermore, the Kaiser-Meyer-Olkin measure of sampling adequacy was 0.846 (with nine items, the KMO value was 0.885), which means an excellent sample adequacy. After consideration of each item’s communality index and its contribution, we retained all the items (the lowest communality after extraction was 0.631, while with all nine items the lowest one was 0.459). In the process of analysis, researchers usually delete or exclude the items that have low communalities after extraction – below the threshold of 0.20. Here, we have deleted all the items with communality less than 0.60. The number of expected factors was one, and the extracted factor was one. In addition, the scree plot of the initial run indicated one factor as an appropriate number. Further, one factor explains 77.242% of variance (with all nine items, the share of explained variance was 65.087%). Table 70: KMO and Bartlett’s test of sphericity (Economic benefits) KMO and Bartlett’s test Kaiser-Meyer-Olkin Measure of Sampling Adequacy 0.846 Approx. chi-square 753.649 Bartlett’s test of sphericity df 6 Sig. 0.000 Further, a confirmatory factor analysis was conducted in order to validate the findings of the exploratory factor analysis, which resulted in one factor composed of four items. This has also been confirmed by the confirmatory factor analysis. The construct Economic benefits comprises Results four items. All the coefficients were found to be positive, high and significant, and are indicated in Table 71 and Figure 20. Table 71: Standardized coefficients and their squares (Economic benefits) Standard. coeff. R-square Sales 0.86 0.74 Market share 0.95 0.90 New market opportunities 0.90 0.81 Employee satisfaction 0.79 0.62 Note: Standard. coeff. = Standardized coefficients; R-square = Coefficient of Determination. 217 Statistical information of the construct Economic benefits, pertaining to reliability (reliability coefficients) and convergence (goodness-of-fit model indexes) based on the overall sample (N = 223), is indicated in Figure 20. The construct Economic benefits showed good reliability (Cronbach’s alpha = 0.930). In addition, the goodness-of-fit indexes are shown in Figure 20 (NFI = 0.997; NNFI = 1.000; CFI = 1.000; SRMR = 0.008; RMSEA = 0.000). Figure 20: Diagram of construct Economic benefits with the standardized solution In Pursuit of Eco-innovation Note: Measurement items: Q18A = Sales; Q18B = Market share; Q18C = New market opportunities; Q18F = Employee satisfaction; Chi-square = 1.907; p = 0.385; Goodness-of-fit indexes: NFI = 0.997; NNFI = 1.000; CFI = 1.000; SRMR = 0.008; RMSEA = 0.000; Reliability coefficients: Cronbach’s alpha = 0.930; RHO = 0.930; Internal consistency reliability = 0.950. Company performance One of the last consequences that we measured in order to test them how do they relate to different eco-innovation types, is company performance. All the indexes presented in Table 72 were acquired and gathered for each company from the GVIN database, which contains business indicators for Slovenian companies. We have further divided companies into six classes/categories with regard to the financial and non-financial indicators (1 = less than 0%; 2 = 0-24%; 3 = 25-49%; 4 = 50-74%; 5 = 75-99%; 6 = more than 100%). Therefore, we can see that growth of employees and 218 growth of net sales through two business years in the analyzed companies were, on average, between 0 to 24%. Financial indicators, such as ROA, ROE and ROS, were also between 0 and 24%. Table 72: Descriptive statistics for Company performance St. Err. St. Err. N Mean St. Dev. Skew Kurt Skew Kurt Number of employees – growth through two busi- 201 1.5920 0.79545 2.253 0.172 8.671 0.341 ness years Net sales – growth through 222 1.6622 0.92639 2.032 0.163 5.799 0.325 two business years ROA (return on assets) 220 1.8682 0.41202 -0.514 0.164 4.278 0.327 ROE (return on equity) 212 2.0896 0.85791 2.645 0.167 9.706 0.333 ROS (return on sales) 220 1.8182 0.38657 -1.661 0.164 0.767 0.327 Note: N = number of observations; Mean = mean value on the Likert scale, which ranges from 1 to 7 (1 = strongly disagree, 7 = strongly agree); St. Dev. = standard deviation; Skew = skewness; St. Err. of Skew = standard error of skewness; Kurt = kurtosis; St. Err. Kurt = standard error of kurtosis. Furthermore, when we acquired the data for each company, for each type indicator, six different levels were proposed that were coded from 1–6: level 1 (less than 0%), level 2 (between 0-24%), level 3 (between 25-49%), level 4 (between 50-74%), level 5 (between 75-99%) and level 6 (more than 100%). The ‘mean value’ presented in the table actual y means the average ‘level’ of specific indicator; e.g., the mean of 2.08 for ‘ROE – return on equity’ actual y means that companies on average had between 0-24% of return on equity. In more detail, we present in Table 73 all the aforementioned indicators by frequency and percentage for the analyzed companies. The re- Results sults show that the majority of companies (106 or 47.5%) reported negative growth of number of employees through two business years, while 80 companies (35.9%) reported growth related to the number of employees through two business years between 0-24%. Moreover, 10 companies (4.5%) reported growth between 25-49%, followed by three companies (1.3%) that had growth between 50-74%, while only two companies had more than 100% of growth related to the number of employees through two business years. Regarding growth of net sales through two business years, 120 companies (53.8%) had less than 0%, while 72 companies (32.3%) had between 0-24% and 22 companies (9.9%) had between 25-49%. Continuing, four companies (1.8%) had between 50-74% of net sales growth through two business years, followed by one company (0.4%) with between 75-99% and 3 companies (1.3%) whose net sales growth through two busi-219 ness years was more than 100%. Table 73 also depicts other financial indicators, such as ROA (return on assets), ROE (return on equity) and ROS (return on sales). The results show that the majority of companies (182 or 81.6%) had ROA between 0-24%, followed by 34 companies (15.2%) with less than 0%, while three companies (1.3%) had between 25-49% and only one company (0.4%) had between 50-74%. Concerning the values of ROE, the results demonstrate that the majority of companies (158 or 70.9%) had ROE between 0-24%, followed by 29 companies (13%) that had less than 0% and 14 companies (6.4%) that had between 25-49%. Continuing, five companies (2.2%) had ROE more than 100%, followed by four companies (1.8%) that had between 50-74% and two companies (0.9%) that had between 75-99%. Finally, we also checked for ROS, and the results show that the majority of companies (180 companies or 80.7%) had between 0 and 24%, followed by 40 companies (17.9%) that had less than 0%. In Pursuit of Eco-innovation Table 73: Company performance – frequency and percentage of different financial and non- -financial indicators Frequency Percent Sample Number of employees – growth through two business years Less than 0% 106 47.5% Between 0-24% 80 35.9% Total = 201 Between 25-49% 10 4.5% (90.1%) Missing = 22 Between 50-74% 3 1.3% (9.9%) Between 75-99% 0 0 More than 100% 2 0.9% Net sales – growth through two business years 220 Less than 0% 120 53.8% Between 0-24% 72 32.3% Total = 222 Between 25-49% 22 9.9% (99.6%) Missing = 1 Between 50-74% 4 1.8% (0.4%) Between 75-99% 1 0.4% More than 100% 3 1.3% ROA (return on assets) Less than 0% 34 15.2% Between 0-24% 182 81.6% Total = 220 Between 25-49% 3 1.3% (98.7%) Missing = 3 Between 50-74% 1 0.4% (1.3%) Between 75-99% 0 0 More than 100% 0 0 ROE (return on equity) Less than 0% 29 13% Between 0-24% 158 70.9% Total = 212 Between 25-49% 14 6.3% (95.1%) Missing = 11 Between 50-74% 4 1.8% (4.9%) Between 75-99% 2 0.9% More than 100% 5 2.2% ROS (return on sales) Results Frequency Percent Sample Less than 0% 40 17.9% Between 0-24% 180 80.7% Total = 220 Between 25-49% 0 0 (98.7%) Missing = 3 Between 50-74% 0 0 (1.3%) Between 75-99% 0 0 More than 100% 0 0 Note: When we acquired the data for each company, for each type indicator, six different levels were proposed that were coded from 1–6: level 1 (less than 0%), level 2 (between 0-24%), level 3 (between 25-49%), level 4 (between 50-74%), level 5 (between 75-99%) and level 6 (more than 100%). Further, we conducted an exploratory factor analysis (the method 221 of extraction was the Maximum Likelihood Method, while the select-ed rotation was Direct Oblimin rotation). Before the analysis, all measurement items were checked for normality of distribution (see Table 72). Results have shown that the ratio of standard errors of kurtosis and skewness range between values of -2 and 2, which implies normality of distribution. The appropriateness of factor analysis was determined by examin- ing the correlation matrix of company performance items. The Bartlett’s test of sphericity, which statistically tests for the presence of correlations among the underlying variables, showed that the correlation matrix has significant correlations (p < 0.05). Furthermore, the Kaiser-Meyer-Olkin measure of sampling adequacy was 0.596, which indicates a sufficient sample adequacy. Table 74: KMO and Bartlett’s test of sphericity (Company performance) KMO and Bartlett’s test Kaiser-Meyer-Olkin Measure of Sampling Adequacy 0.596 Approx. chi-square 243.333 Bartlett’s test of sphericity df 10 Sig. 0.000 After consideration of each item’s communality index and its contribution, we retained all the items. In our case, one item – “Number of net sales – growth through two business years” –had a low communality af- In Pursuit of Eco-innovation ter extraction (0.11; which is below the threshold of 0.20). However, because of its importance, we retained it in the further analyses. The number of extracted factors should be two, one pertaining to the company growth and the other to the company profitability. As expected, the scree plot of the initial run indicated that two factors might be an appropriate number, and the latent root (eigenvalue) criterion also indicated two factors, which in total explain 53.722% of variance. The two factors that were extracted as a result of the exploratory factor analysis are presented in Table 75, together with the five related items and their factor loadings. The solution with two factors was retained. Therefore, the company performance construct was split into two individual factors, one pertaining to the company profitability (including three items) and the other to the company growth (including two items). 222 Table 75: Company performance dimension’s item factor loadings Factors Items Factor 1 Factor 2 (Company profitability) (Company growth) Return on assets (ROA) 1.016 Return on equity (ROE) 0.610 0.304 Return on sales (ROS) 0.564 Number of employees – growth through 0.861 two business years Net sales – growth through two busi- 0.269 ness years N = 223 Extraction Method: Maximum Likelihood Rotation Method: Oblimin with Kaiser Normalization (absolute factor loadings equal to or higher than 0.20 displayed) Bartlett’s test of sphericity: Chi-square = 243.333; 10 df; sig. = 0.000 Kaiser-Meyer-Olkin measure of sample adequacy = 0.596 Variance explained = 53.722 From Table 75, we can see that item “Return on equity (ROE)” load-ed on two factors – company growth (the wrong factor) and company profitability (the factor on which it should load). It had a higher loading on the right factor; nonetheless, we decided that company performance will be divided into two separate constructs – company profitability and company growth. Therefore, factors will not be covered under one second-order latent factor. Results Next, exploratory factor analysis was conducted only for items pertaining to the construct company profitability. The method of extraction was the Maximum Likelihood Method, while the selected rotation was Direct Oblimin rotation. The appropriateness of factor analysis was determined by examining the correlation matrix of company profitability items. The Bartlett’s test of sphericity showed that the correlation matrix has significant correlations (p < 0.05). Furthermore, the Kaiser-Meyer-Olkin measure of sampling adequacy was 0.596, which indicates a sufficient sample adequacy. After consideration of each item’s communality index and its contribution, we retained all the items. All items had values above the threshold of 0.20 (the lowest communality was 0.276). As expected, the scree plot of the initial run indicated that one factor might be an appropriate number, and the latent root (eigenvalue) criterion also indicated one factor, which in total explains 58.904% of variance. 223 A confirmatory factor analysis was conducted to validate the findings of the exploratory factor analysis, which showed that the construct company profitability is composed of three items: ROA (return on assets), ROE (return on equity) and ROS (return on sales). This has been confirmed by the confirmatory factor analysis. All the coefficients were found to be positive, high and significant, and they are indicated in Table 76 and Figure 21. Table 76: Standardized coefficients and their squares (Company profitability) Standard. coeff. R-square Return on assets (ROA) 0.95 0.90 Return on equity (ROE) 0.87 0.76 Return on sales (ROS) 0.63 0.39 Note: Standard. coeff. = Standardized coefficients; R-square = Coefficient of Determination; since this construct has been measured by only three items, an additional constraint (factor fixed to one and item ROE fixed to one) has been imposed in order to estimate the goodness-of-fit indexes. Statistical information of the dimension company profitability, pertaining to reliability (reliability coefficients) and convergence (goodness-of-fit model indexes) based on the overall sample (N = 223), is indicated in Figure 21. The dimension company profitability showed good reliability (Cronbach’s alpha = 0.681). In addition, the goodness-of-fit indexes are shown in Figure 21 (NFI = 0.820; NNFI = 0.468; CFI = 0.823; SRMR In Pursuit of Eco-innovation = 0.522; RMSEA = 0.418). We can see that all goodness-of-fit indexes showed a poor fit. 224 Figure 21: Diagram of company performance dimension – construct Company profitability with the standardized solution Note: Measurement items: ROA = Return on assets; ROE = Return on equity; ROS = Return on sales; Chi-square = 37.225; p = 0.00; Goodness-of-fit indexes: NFI = 0.820; NNFI = 0.468; CFI = 0.823; SRMR = 0.522; RMSEA = 0.418; Reliability coefficients: Cronbach’s alpha = 0.681; RHO = 0.870; Internal consistency reliability = 0.927. Moreover, the second construct related to company performance, company growth, is composed of only two items: number of employees (growth through two business years) and net sales (growth through two business years). Therefore, for the construct company growth, confirmatory factor analysis has not been conducted. Instead, we have calculated only correlation between those two items. Correlation was estimated at 0.249, which is quite low but showed to be positive and statistically significant at the 0.01 level. This construct has been retained for further analyses because of its importance. Internationalization In our survey, 151 companies out of 223 total are engaged in international activities (i.e., are active on foreign markets). This means that the majority of analyzed companies, which accounts for 67.7% of the total sample, are internationalized. In our study, we measured internationalization with three variables: operation modes, number of foreign markets (in which the company operates) and, lastly, company’s share of sales on foreign markets in the year 2013. Regarding company age when starting to operate on foreign markets, the results demonstrate that companies on average started to internationalize at between one year and three years. In more detail, among the internationalized companies, 34 compa- Results nies (22.5%) identified themselves as born global (immediately starting to operate on foreign markets), followed by 30 companies (19.9%) that started their internationalization process at between 1-3 years. The majority of companies – 39 companies (25.8%) – started operating on foreign markets at 21 years or more, followed by 18 companies (11.9%) that started with international operations at between 7-10 years old. Moreover, 16 companies (10.6%) started operating on foreign markets at between 4-6 years old and 13 companies (8.6%) started operating on foreign markets at between 11-20 years. Lastly, one company answered that they have not yet started the internationalization process. 225 Figure 22: Frequency and percentage of use of operation modes (types) by the analyzed companies First, we present briefly descriptive statistics of the variable called operation modes for the internationalized companies in our sample (Figure 22). Operation modes in our survey were classified into nine groups: 1) import; 2) direct export; 3) indirect export (export through intermediary); 4) sole venture direct investment; 5) joint venture direct investment; 6) contract; 7) product/service licensing; 8) franchising or 9) other. Figure 22 illustrates that the most frequently used operation mode for all analyzed companies was direct export, used by 111 companies (73.5%), followed by import (91 companies; 60.3%), indirect export (67 companies, 44.4%) and contract (64 companies; 42.4%). More rarely used by Slovenian companies were the following operation modes: sole venture direct investment (19 companies; 12.6%), followed by product/service li- In Pursuit of Eco-innovation censing (15 companies; 9.9%) and other (11 companies; 7.3%). A less used operation mode by the analyzed companies is joint venture direct investment (10 companies; 6.6%) and the least frequently used is franchising (5 companies; 3.3%). Furthermore, Table 77 illustrates the mean values of operation modes. We can see that direct export was the most frequently used mode of entry in international markets by analyzed companies, followed by indirect export and contract entry modes. Franchising and joint venture direct investment were found to be very rarely used by the analyzed companies. Table 77: Descriptive statistics for internationalization variable – operation modes St. Err. St. Err. N Mean St. Dev. Skew Kurt Skew Kurt 226 Import 151 0.60 0.491 -0.424 0.197 -1.845 0.392 Direct export 151 0.74 0.443 -1.076 0.197 -0.853 0.392 Indirect export 151 0.44 0.498 0.229 0.197 -1.974 0.392 Sole venture direct investment 151 0.13 0.333 2.279 0.197 3.237 0.392 Joint venture direct investment 151 0.07 0.250 3.524 0.197 10.557 0.392 Contract 151 0.42 0.496 0.311 0.197 -1.929 0.392 Product/service licensing 151 0.10 0.300 2.706 0.197 5.393 0.392 Franchising 151 0.03 0.180 5.271 0.197 26.131 0.392 Other 151 0.07 0.261 3.320 0.197 9.145 0.392 Note: N = number of observations; Mean = mean value on the Likert scale, which ranges from 1 to 7 (1 = strongly disagree, 7 = strongly agree); St. Dev. = standard deviation; Skew = skewness; St. Err. of Skew = standard error of skewness; Kurt = kurtosis; St. Err. Kurt = standard error of kurtosis; indirect export (export through intermediary). In addition, the total number of operation modes that a single company currently uses is also shown (see Figure 23). This indicator can further show the complexity of analyzed internationalized companies concerning international operations. Every single operation mode has its specifics, while we can conclude that companies that use more operation modes are more experienced in international operations than companies that use only one or two operation modes (Ruzzier 2005). We can see that among the internationalized companies, the majority of companies (47 companies; 31.1%) use only one operation mode, followed by 38 companies (25.2%), that use three operation modes and 33 companies (21.8%) use two operation modes. Moreover, 12 companies (7.9%) use four operation modes, 12 companies (7.9%) use five operation modes, six companies Results (3.9%) use six operation modes and two companies (1.3%) use seven operation modes. Lastly, only one company (0.7%) reported the use of eight operation modes. 227 Figure 23: Frequency and percentage of use of operation modes (number) by the analyzed companies The second internationalization variable is the number of foreign countries to which companies currently sell their products or services. Further, internationalization also concerns the market in which companies operate. A higher number of markets indicates greater complexity of companies’ operations and a wider range of knowledge that companies must possess in order to be successful (Ruzzier et al. 2014a). Figure 24 illustrates the total number of countries in which a single company operates. Based on companies included in the survey, we can see that the majority of companies (37 companies; 24.5%) sell their products/services to two or three countries, followed by 29 companies (19.2%) that sell their products/services in 6 to 10 countries, while 28 companies (18.5%) sell them in 21 and more countries and 26 companies (17.2%) sell their products/services to four or five countries. Moreover, we can see hat 13 companies (8.6%) sell their products/services in 11 to 15 countries, eight companies (5.3%) sell only in one country, and seven companies (4.6%) sell them in 16 to 20 countries. Lastly, of the surveyed companies, three companies (2%) do not sell their products/services in foreign countries. In Pursuit of Eco-innovation 228 Figure 24: Frequency and percentage of the total number of countries where analyzed companies sell their products/services The third internationalization variable pertains to the company’s share of sales abroad in year 2013 (see Table 78). The most frequent measure for internationalization performance is the percentage share of foreign sales. In Table 78, we can see that the analyzed companies, on average, make between 31-50% of their sales from international operations. In more detail, 28 companies (18.5%) reported between 51-70% of their sales on foreign markets, followed by 24 companies (15.9%) that estimated the share of sales on foreign markets in 2013 at 1-10% and 23 companies (15.2%) between 91-100%. This is followed by 22 companies (14.6%) whose share of sales on foreign markets is between 71-90%, followed by 17 companies (11.3%) that estimated the share of foreign sales between 11-20%, 16 companies (10.6%) between 21-30% and 15 companies (9.9%) between 31-50%. Finally, only six of the internationalized companies (4%) reported no share of international sales in 2013. Results Table 78: Share of sales in foreign market in 2013 Share of foreign sales in year 2013 N Mean St. Dev. St. Err. Min Max 151 4.97 2.189 0.178 1 8 Share of foreign sales in year 2013 N Frequency Percent 0% 151 6 4.0% 1-10% 151 24 15.9% 11-20% 151 17 11.3% 21-30% 151 16 10.6% 31-50% 151 15 9.9% 51-70% 151 28 18.5% 71-90% 151 22 14.6% 229 91-100% 151 23 15.2% Note: N = number of observations; Mean = mean value (variable Share of sales abroad in 2013 was measured by the extent of sales on foreign markets, ranging from 0 to 100% (level 1 = 0%; level 2 = 1-10%; level 3 = 11-20 %; level 4 = 21-30%; level 5 = 31-50%; level 6 = 51-70%; level 7 = 71-90%; level 8 = 91-100%); St. Dev. = standard deviation; St. Err. = standard error of Mean; Min = minimum; Max = maximum. Table 79 illustrates descriptive statistics for the construct internationalization, which was measured by three items: number of foreign markets, share of sales on foreign markets in 2013 and number of operation modes. We can see that analyzed companies, on average, operate on 6 to 10 foreign markets; they had between 31-50% of sales on foreign markets in 2013 and use two operation modes for international activities (Table 79). Table 79: Descriptive statistics for internationalization St. Err. St. Err. N Mean St. Dev. Skew Kurt Skew Kurt Number of foreign markets 151 4.83 1.971 0.340 0.197 -0.953 0.392 Share of sales abroad in 2013 151 4.97 2.189 -0.185 0.197 -1.258 0.392 Number of operation modes 151 2.60 1.567 0.988 0.197 0.571 0.392 Note: N = number of observations; Mean = mean value (variable Number of foreign markets has been coded as follows: level 1 = zero countries; level 2 = 1 country; level 3 = between 2-3 countries; level 4 = between 4-5 countries; level 5 = between 6-10 countries; level 6 = between 11-15 countries; level 7 = between 16-20 countries; level 8 = more than 21 countries; the performance dimension of internationalization (Share of sales abroad in 2013) was In Pursuit of Eco-innovation measured by the extent of sales on foreign markets, ranging from 0 to 100% (level 1 = 0%; level 2 = 1-10%; level 3 = 11-20%; level 4 = 21-30%; level 5 = 31-50%; level 6 = 51-70%; level 7 = 71-90%; level 8 = 91-100%); variable Number of operation modes was constructed by summing all operation modes); St. Dev. = standard deviation; Skew = skewness; St. Err. of Skew = standard error of skewness; Kurt = kurtosis; St. Err. Kurt = standard error of kurtosis. Further, we conducted an exploratory factor analysis (the method of extraction was the Maximum Likelihood Method, while the select-ed rotation was Direct Oblimin rotation). Before the analysis, all measurement items were checked for normality of distribution (see Table 79). Results have shown that the ratio of standard errors of kurtosis and skewness range between values of -2 and 2, which implies normality of distribution. The appropriateness of factor analysis was determined by examining 230 the correlation matrix of economic benefit items. The Bartlett’s test of sphericity showed that the correlation matrix has significant correlations (p < 0.05). Furthermore, the Kaiser-Meyer-Olkin measure of sampling adequacy was 0.514, which indicates a sufficient sample adequacy. After consideration of each item’s communality index and its contribution, we retained all the items (the lowest communality after extraction was 0.124). In the process of analysis, researchers usually delete or exclude the items that have low communalities after extraction (below the threshold of 0.20). However, we retained the item “Total number of operation modes” in further analyses despite the low communality after extraction, because of its relevance. The number of expected factors was one, and the extracted factor was one, explaining 50.377% of variance. Table 80: KMO and Bartlett’s test of sphericity (Internationalization) KMO and Bartlett’s test Kaiser-Meyer-Olkin Measure of Sampling Adequacy 0.514 Approx. chi-square 93.843 Bartlett’s test of sphericity df 3 Sig. 0.000 A confirmatory factor analysis was conducted in order to validate the findings of the exploratory factor analysis, which resulted in one factor composed of three items. This has also been confirmed by the confirmatory factor analysis. The construct internationalization comprises three items. All the coefficients were found to be positive, high and significant, and are indicated in Table 81 and Figure 25. Results Table 81: Standardized coefficients and their squares (Internationalization) Standard. coeff. R-square Number of foreign markets 1 1 Share of sales on foreign markets in 2013 0.85 0.72 Number of operation modes 0.62 0.38 Note: Standard. coeff. = Standardized coefficients; R-square = Coefficient of Determination; since this construct has been measured by only three items, an additional constraint (factor fixed to one) has been imposed in order to estimate the goodness-of-fit indexes. Statistical information of the construct internationalization, pertaining to reliability (reliability coefficients) and convergence (goodness-of-fit model indexes) based on the overall sample (N = 223), is indicated 231 in Figure 25. The construct internationalization showed good reliability (Cronbach’s alpha = 0.875). In addition, the goodness-of-fit indexes are shown in Figure 25 (NFI = 0.986; NNFI = 0.964; CFI = 0.988; SRMR = 0.132; RMSEA = 0.157). Figure 25: Diagram of construct Internationalization with the standardized solution Note: Measurement items: Number of foreign markets, Share of sales abroad and Number of operation modes; Chi-square = 6.373; p = 0.01; Goodness-of-fit indexes: NFI = 0.986; NNFI = 0.964; CFI = 0.988; SRMR = 0.132; RMSEA = 0.157; Reliability coefficients: Cronbach’s alpha = 0.875; RHO = 0.904; Internal consistency reliability = 1.000. Eco-innovation models The findings of the main effects among eco-innovations, its determinants (managerial environmental concern, expected benefits, the command-and-control instrument, the economic incentive instrument, customer demand and competitive pressure) and consequences (competitive benefits, economic benefits, company growth, company profitability and internationalization) will be discussed in this chapter. First, we present the findings that pertain to testing the hypotheses for the product eco-innovation (Section 8.1); second, we test the hypotheses for the process eco-innovation (Section 8.2); and third, for the organizational eco-innovation (Section 8.3). Lastly, we examine the expanded eco-innovation construct model (section 8.4), where all three dimensions of eco-innovation are covered under a second-order latent factor. Product eco-innovation model In the product eco-innovation model, the influence of various determinants on product eco-innovation was tested, and the influence of product eco-innovation on its outcomes was analyzed. Eco-innovation determinants were measured by six elements: the command-and-control instrument, the economic incentive instrument, managerial environmental concern, customer demand, expected benefits and competitive pressure. Eco-innovation outcomes were measured by five elements: company growth and company profitability, economic benefits, competitive benefits and internationalization. Two elements related to eco-innovation outcomes – company growth and company profitability – are objective In Pursuit of Eco-innovation measures, obtained from the GVIN database, which includes different companies’ parameters related to the profitability indicator ratios, company growth, and so on for most Slovenian companies. Construct validity of product eco-innovation model All measurement items and Cronbach’s alpha values are reported in Table 82. Content validity for the survey instrument is supported by the literature, in-depth interviews with environmental managers and a pilot test. A confirmatory factor analysis (CFA) using EQS 6.1 is estimated to assess the construct validity of the product eco-innovation model. In the model, each item is linked to its corresponding construct with freely estimated covariance. The model fit indexes are as follows: Chi-square = 1421.120; df = 836; NFI = 0.817; NNFI = 0.903; CFI = 0.914; RMSEA = 234 0.061; SRMR = 0.064. These results suggest that the measurement model is acceptable. In addition, the Cronbach’s alpha is 0.938, while the reliability coefficient RHO is 0.977. From Table 82, we can see that all factor loadings are greater than 0.50, and the p-values are significant at the 0.05 level, except for the item pertaining to the construct company growth (“Number of employees – growth through 2 business years”); therefore, convergent validity (pertaining to reliability) is ensured (Fornell and Larcker 1981). In addition, the composite reliability of all constructs (except for the construct company growth) is greater than 0.70, indicating acceptable reliability (Hair et al. 2009). Additionally, the square root of average variance extracted (AVE) for each construct is greater than 0.50, except for the construct company growth, for which has not been calculated (instead, the correlation between the two items pertaining to the construct company growth is given). Eco-innovation models Table 82: Measurement model of latent variables and Cronbach’s alpha for latent variables Completely Cronbach’s Composite Measurement items standardized p AVE alpha reliability loading Managerial environmental concern (MC) 1a Eco-innovation is an important component of the company’s envi- 0.61 ronmental management strategy. 1b Most eco-innovations are worth- 0.82 * while. 0.858 0.60 0.836 1c Eco-innovation is necessary to achieve high levels of environmen- 0.83 * tal performance. 1d Eco-innovation is an effective en- 0.83 * 235 vironmental management strategy. Expected benefits (EB) 2b To improve profitability. 0.77 2c To increase productivity. 0.79 * 2d To increase market share. 0.89 * 0.923 0.67 0.914 2e To enter new markets. 0.83 * 2g To strengthen the brand. 0.76 * 2h Competitive advantage. 0.85 * Command-and-control instrument (CCI) 3a Our products should meet the requirements of national environ- 0.88 mental regulations. 3b Our products should meet the requirements of international and/ 0.92 * or EU environmental regulations. 0.942 0.80 0.946 3c Our production processes should meet the requirements of 0.89 * national environmental regulations. 3d Our production processes should meet the requirements of 0.89 * international and/or EU environ- mental regulations. In Pursuit of Eco-innovation Completely Cronbach’s Composite Measurement items standardized p AVE alpha reliability loading Economic incentive instrument (EII) 3e The government provides pref- erential subsidy on environmental 0.85 innovation. 3f The government provides prefer- 0.847 0.66 0.838 ential tax policy on environmental 0.95 * innovation. 3h The government promotes envi- 0.59 * ronmental protection. Customer demand (CD) 4a Environment is a critical issue for 236 0.91 our important customers. 4b Our important customers often 0.93 * bring up environmental issues. 0.949 0.82 0.940 4c Customer demands motive us in 0.88 * our environmental efforts. 4d Our customers have clear de- mands regarding environmen- 0.91 * tal issues. Competitive pressure (CP) 6a We establish the company’s en- vironmental image compared to 0.90 competitors through green con- cepts. 6b We increase the company’s mar- 0.933 0.83 0.933 0.89 * ket share through green concepts. 6c We improve the company’s com- petitive advantage over competi- 0.95 * tors through green concepts. Eco-innovation models Completely Cronbach’s Composite Measurement items standardized p AVE alpha reliability loading Product eco-innovation (PD) 8b The company is improving and designing environmental y friendly packaging (e.g., using less paper and 0.61 plastic materials) for existing and new products. 8e The company chooses materials for the product that consume the least amount of energy and resourc- 0.91 * es for conducting the product de- velopment or design. 0.879 0.64 0.885 8f The company uses the smal - est possible amount of materials 237 to comprise the product for con- 0.88 * ducting the product development or design. 8g The company deliberately evalu- ates whether the product is easy to recycle, reuse and decompose for 0.79 * conducting the product develop- ment or design. Competitive benefits (CB) 17d Increased process/production 0.87 efficiency. 17e Increase in productivity. 0.92 * 0.949 0.82 0.946 17f Increased knowledge about ef- fective ways of managing oper- 0.92 * ations. 17g Improved process innovations. 0.92 * Economic benefits (ECB) 18a Sales. 0.89 18b Market share. 0.95 * 0.936 0.79 0.930 18c New market opportunities. 0.91 * 18f Employee satisfaction. 0.79 * In Pursuit of Eco-innovation Completely Cronbach’s Composite Measurement items standardized p AVE alpha reliability loading Company performance – growth (GR) Number of employees - growth 0.30 through 2 business years n.a. n.a. 0.249** Net sales - growth through 2 busi- 0.80 * ness years Company performance – profitability (PF) ROA 0.99 ROE 0.71 * 0.807 0.59 0.681 ROS 0.55 * 238 Internationalization (INT) Number of foreign countries where company currently sells its prod- 0.99 ucts/services 0.894 0.74 0.875 Share of sales on foreign markets 0.87 * in 2013 Total number of operation modes 0.70 * Note: * p-values are significant at 0.05 level; ** correlation between the two items pertaining to the construct company growth is significant at the 0.01 level; n.a. = not applicable, because the construct company growth is composed of only two items. Moreover, Table 83 depicts correlations between latent variables, where we can observe that all correlations are statistically significant. Eco-innovation models Table 83: Results of correlations between latent variables MC EB CCI EII CD CP PD CB ECB GR PF INT MC 1 EB 0.56* 1 CCI 0.26* 0.22* 1 EII 0.24* 0.30* 0.21* 1 CD 0.37* 0.40* 0.54* 0.19* 1 CP 0.45* 0.47* 0.37* 0.30* 0.49* 1 PD 0.36* 0.40* 0.41* 0.30* 0.51* 0.59* 1 CB 0.27* 0.29* 0.36* 0.18* 0.45* 0.49* 0.41* 1 ECB 0.38* 0.47* 0.30* 0.23* 0.49* 0.57* 0.47* 0.68* 1 239 GR 0.07* 0.10* -0.18* 0.03* -0.05* -0.08* -0.22* -0.04* 0.02* 1 PF 0.05* 0.07* -0.12* 0.05* -0.03* -0.04* 0.00* -0.04* 0.02* 0.27* 1 INT 0.08* 0.13* 0.29* -0.00* 0.37* 0.04* 0.25* 0.20* 0.27* 0.00* -0.02* 1 Note: MC = managerial environmental concern; EB = expected benefits; CCI = the command-and-control instrument; EII = the economic incentive instrument; CD = customer demand; CP = competitive pressure; PD = product eco-innovation; CB = competitive benefits; ECB = economic benefits; GR = growth (company performance); PF = profitability (company performance); INT = internationalization. Statistical analysis and results (path analysis) All construct dimensions were assessed using exploratory and confirmatory factor analyses in previous sections. We also present construct validity for the product eco-innovation model with its determinants and consequences (see Section 8.1.1). Reliability statistics for all construct dimensions were good (over 0.70), as were the goodness-of-fit measures, which indicated an acceptable model fit for all constructs (except for company growth, which showed worse goodness-of-fit measures). In this section, we use structural equation modeling to test all relationships between the latent variables and the observed variables as well as the relationships among multiple latent variables simultaneously. The resulting product eco-innovation model with estimated relationships (standardized solution) is depicted in Figure 26. The model shows a moderate fit to the data (NFI = 0.763; NNFI = 0.848; CFI = 0.859; SRMR = 0.212; RMSEA = 0.077). In Pursuit of Eco-innovation 240 Figure 26: Product eco-innovation model (standardized solution) Note: Chi-square = 1842.735 (879 df); p = 0.00; Goodness-of-fit indexes: NFI = 0.763; NNFI = 0.848; CFI = 0.859; SRMR = 0.212; RMSEA = 0.077; Reliability coefficients: Cronbach’s alpha = 0.939; RHO = 0.947. The results of testing the proposed hypotheses are depicted in Figure 26. We will focus first on the parts of the hypotheses pertaining to the determinants of product eco-innovation and then on the parts related to the consequences of product eco-innovation. Hypotheses 1a and 1b examined the relationship between the com- mand-and-control instrument, the economic incentive instrument and product eco-innovation, which for both types of environmental policy instruments was predicted to be positive and significant. The standardized coefficients for both relationships were in the expected direction (positive), quite substantial (the standardized coefficient was 0.15 for the command-and-control instrument and 0.12 for the economic incentive Eco-innovation models instrument) and significant. Therefore, the findings revealed that Hypotheses 1a and 1b are both supported. Strong support was found for Hypothesis 2, which postulated a positive and significant relationship between customer demand and product eco-innovation. The standardized coefficient was high (0.29) and significant. The relationship between managerial environmental concern and product eco-innovation was found to be positive and significant, while the standardized coefficient was small (0.01). Thus, Hypothesis 3 is supported. Hypothesis 4 postulated a positive and significant relationship between expected benefits and product eco-innovation. The association between expected benefits and product eco-innovation was found to be positive and significant (standardized coefficient 0.12). 241 Hypothesis 5a (the relationship between competitive intensity and product eco-innovation) was not tested, since the factor competitive intensity explains only 36.733% of variance and was therefore excluded from further analyses. However, Hypothesis 5b examined the relationship between competitive pressure and product eco-innovation, which was expected to be positive and significant. The standardized coefficient for this relationship is highly positive, significant and substantial (0.44), indicating strong support for Hypothesis 5b. When testing our hypotheses, as indicated in Figure 26, and focusing on consequences of product eco-innovation, we have to add that Hypothesis 6 was tested separately, as it was divided into two dimensions – company growth and company profitability – in order to obtain greater insight regarding how product eco-innovation affects company growth and company profitability. Hypothesis 6 predicted a positive and significant association between product eco-innovation and company performance. In more detail, Hypothesis 6a postulates a positive and significant relationship between product eco-innovation and company growth, while Hypothesis 6b posits a positive and significant association between product eco-innovation and company profitability. When testing the relationships between product eco-innovation and indicators of company performance (company growth and company profitability), statistically significant influences were detected, but the direction was the opposite of the predicted direction. We can see (Figure 26) that product eco-innovation was found to be quite substantially (standardized coefficient -0.14) related to company growth, but again in the opposite direction (i.e., negatively). Therefore, Hypothesis 6a is not supported. Similarly, the stand- In Pursuit of Eco-innovation ardized coefficient measuring the influence of eco-innovation on company profitability was significant, negative, and close to zero (standardized coefficient -0.00). The relationship was expected to be positive and significant, but, as aforementioned, we can see that the standardized coefficient estimating the relationship between product eco-innovation and company profitability is low (approximately zero) and statistically significant; thus, these findings indicate that Hypothesis 6b is not supported. In our model, we also used soft measures to measure economic per- formance of eco-innovation. Hypothesis 7 predicted a positive and significant relationship between product eco-innovation and economic benefits. In addition, the results further indicate that product eco-innovation was found to be highly, positively and significantly related to economic benefits (standardized coefficient 0.49). Therefore, Hypothesis 7 is sup-242 ported. Moreover, the relationship between product eco-innovation and competitive benefits was found to be highly positive and significant (standardized coefficient 0.43), offering support for the Hypothesis 8, which is confirmed. Finally, Hypothesis 9 examined the relationship between product eco-innovation and internationalization and postulated that product eco-innovation has a positive impact on internationalization. The standardized coefficient for this relationship is high, positive and significant (0.24), indicating support for Hypothesis 9. This means that more eco-innovative companies (in the sense of introducing more product eco-innovations) are also more internationalized (in terms of scale and scope). Process eco-innovation model In the process eco-innovation model, the influence of various determinants on process eco-innovation was tested, and the influence of process eco-innovation on its outcomes was also analyzed. Eco-innovation determinants were measured by six elements: the command-and-control instrument, the economic incentive instrument, managerial environmental concern, customer demand, expected benefits and competitive pressure. Eco-innovation outcomes were measured by five elements: company growth and company profitability (objective measures obtained from the GVIN database), economic benefits, competitive benefits and internationalization. Eco-innovation models Construct validity of process eco-innovation model All measurement items and values of Cronbach’s alpha are reported in Table 84. Content validity for the survey instrument is supported by the literature, in-depth interviews with environmental managers and a pilot test. A Confirmatory Factor Analysis (CFA) model using EQS 6.1 is estimated to assess the construct validity. In the model, each item is linked to its corresponding construct with freely estimated covariance. The model fit indexes are as follows: Chi-square = 1475.364; df = 879; NFI = 0.818; NNFI = 0.906; CFI = 0.916; RMSEA = 0.060; SRMR = 0.060, which suggests that the measurement model is acceptable. In addition, the Cronbach’s alpha is 0.939, while the reliability coefficient RHO is 0.978. Table 84: Measurement model of latent variables and Cronbach’s alpha for latent variables 243 Complete- Cronbach’s alpha ly standard- Composite Measurement items p AVE (for construct Growth is ized load- reliability given correlation) ing Managerial environmental concern (MC) 1a Eco-innovation is an im- portant component of the 0.63 company’s environmental management strategy. 1b Most eco-innovations are 0.83 * worthwhile. 0.856 0.60 0.836 1c Eco-innovation is nec- essary to achieve high lev- 0.81 * els of environmental per- formance. 1d Eco-innovation is an ef- fective environmental man- 0.81 * agement strategy. Expected benefits (EB) 2b To improve profitability. 0.77 2c To increase productivity. 0.79 * 2d To increase market share. 0.89 * 0.923 0.67 0.914 2e To enter new markets. 0.83 * 2g To strengthen the brand. 0.76 * 2h Competitive advantage. 0.85 * In Pursuit of Eco-innovation Complete- Cronbach’s alpha ly standard- Composite Measurement items p AVE (for construct Growth is ized load- reliability given correlation) ing Command-and-control instrument (CCI) 3a Our products should meet the requirements of 0.87 national environmental reg- ulations. 3b Our products should meet the requirements of 0.91 * international and/or EU en- vironmental regulations. 0.942 0.80 0.946 3c Our production process- es should meet the require- 244 0.90 * ments of national environ- mental regulations. 3d Our production process- es should meet the require- ments of international and/ 0.90 * or EU environmental reg- ulations. Economic incentive instrument (EII) 3e The government pro- vides preferential subsidy on 0.84 environmental innovation. 3f The government provides preferential tax policy on 0.96 * 0.848 0.66 0.838 environmental innovation. 3h The government pro- motes environmental pro- 0.59 * tection. Customer demand (CD) 4a Environment is a critical issue for our important cus- 0.91 tomers. 4b Our important custom- ers often bring up environ- 0.93 * mental issues. 0.949 0.82 0.940 4c Customer demands mo- tivate us in our environmen- 0.88 * tal efforts. 4d Our customers have clear demands regarding en- 0.91 * vironmental issues. Eco-innovation models Complete- Cronbach’s alpha ly standard- Composite Measurement items p AVE (for construct Growth is ized load- reliability given correlation) ing Competitive pressure (CP) 6a We establish a compa- ny’s environmental image 0.90 compared to competitors through green concepts. 6b We increase the compa- ny’s market share through 0.89 * 0.933 0.83 0.933 green concepts. 6c We improve the compa- ny’s competitive advantage 0.95 * over competitors through green concepts. 245 Process eco-innovation (PC) 9a Low energy consump- tion such as water, electric- 0.79 ity, gas and petrol during production/use/disposal. 9b Recycle, reuse and re- 0.73 * manufacture material. 9g Use of cleaner technol- ogy to generate savings and 0.83 * prevent pollution (such as 0.922 0.70 0.912 energy, water and waste). 9h The manufacturing pro- cess of the company effec- tively reduces the emission 0.92 * of hazardous substances or waste. 9i The manufacturing pro- cess of the company reduces 0.91 * the use of raw materials. Competitive benefits (CB) 17d Increased process/pro- 0.87 duction efficiency. 17e Increase in productivity. 0.92 * 17f Increased knowledge 0.949 0.82 0.946 about effective ways of man- 0.92 * aging operations. 17g Improved process in- 0.92 * novations. In Pursuit of Eco-innovation Complete- Cronbach’s alpha ly standard- Composite Measurement items p AVE (for construct Growth is ized load- reliability given correlation) ing Economic benefits (ECB) 18a Sales. 0.89 18b Market share. 0.95 * 0.935 0.78 0.930 18c New market oppor- 0.91 * tunities. 18f Employee satisfaction. 0.79 * Company performance – growth (GR) Number of employees - growth through 2 busi- 0.57 246 ness years n.a. n.a. 0.249** Net sales - growth through 0.43 * 2 business years Company performance – profitability (PF) ROA 0.95 ROE 0.74 * 0.804 0.59 0.681 ROS 0.56 * Internationalization (INT) Number of foreign coun- tries where company cur- 0.99 rently sells its products/ services 0.889 0.73 0.875 Share of sales on foreign 0.86 * markets in 2013 Total number of operation 0.69 * modes Note: * p-values are significant at 0.05 level; ** correlation between two items pertaining to the construct company growth is significant at the 0.01 level; n.a. = not applicable, because the construct company growth is composed of only two items. From Table 84, we can see that all factor loadings are greater than 0.50 and that the p-values are significant at 0.05 level, except for the item pertaining to the construct company growth (“Net sales – growth through 2 business years”); therefore, the convergent validity is ensured (Fornell and Larcker 1981). In addition, the composite reliability of all constructs (except for the construct company growth) is greater than 0.70, indicating acceptable reliability (Hair et al. 2009). Additionally, the square root Eco-innovation models of average variance extracted (AVE) for each construct is greater than 0.50, except for the construct company growth, for which has not been calculated (instead, the correlation between the two items related to the construct is given). Table 85 depicts correlations between latent variables, where we can observe that all correlations are statistically significant. Table 85: Results of Correlations between latent variables MC EB CCI EII CD CP PC CB ECB GR PF INT MC 1 EB 0.56* 1 CCI 0.26* 0.22* 1 247 EII 0.23* 0.29* 0.21* 1 CD 0.38* 0.40* 0.54* 0.19* 1 CP 0.46* 0.47* 0.37* 0.30* 0.49* 1 PC 0.44* 0.22* 0.47* 0.22* 0.51* 0.55* 1 CB 0.28* 0.29* 0.36* 0.18* 0.45* 0.49* 0.49* 1 ECB 0.39* 0.47* 0.30* 0.23* 0.49* 0.56* 0.48* 0.68* 1 GR 0.07* 0.03* -0.38* -0.10* -0.12* -0.07* -0.15* -0.07* 0.01* 1 PF 0.06* 0.07* -0.13* 0.05* -0.00* -0.00* 0.06* -0.04* 0.28* 0.38* 1 INT 0.08* 0.12* 0.29* -0.00* 0.36* 0.04* 0.16* 0.20* 0.26* -0.10* -0.01* 1 Note: MC = managerial environmental concern; EB = expected benefits; CCI = the command-and-control instrument; EII = the economic incentive instrument; CD = customer demand; CP = competitive pressure; PC = process eco-innovation; CB = competitive benefits; ECB = economic benefits; GR = growth (company performance); PF = profitability (company performance); INT = internationalization. Statistical analysis and results (path analysis) All construct dimensions were assessed using exploratory and confirmatory factor analyses in previous sections. We also present construct validity for the process eco-innovation model with its determinants and consequences (see Section 8.2.1). Reliability statistics for all construct dimensions were good (over 0.70), as were the goodness-of-fit measures, which indicated an acceptable model fit for all constructs (except for company growth, which showed worse goodness-of-fit measures). In this section, we use structural equation modeling to test all relationships be- In Pursuit of Eco-innovation tween latent variables and observed variables and the relationships among multiple latent variables simultaneously. The resulting process eco-innovation model with estimated relationships (standardized solution) is depicted in Figure 27. The model shows a moderate fit to the data (NFI = 0.766; NNFI = 0.852; CFI = 0.863; SRMR = 0.213; RMSEA = 0.075). 248 Figure 27: Process eco-innovation model (standardized solution) Note: Chi-square = 1902.195 (922 df); p = 0.00; Goodness-of-fit indexes: NFI = 0.766; NNFI = 0.852; CFI = 0.863; SRMR = 0.213; RMSEA = 0.075; Reliability coefficients: Cronbach’s alpha = 0.939; RHO = 0.948. We also tested hypotheses concerning determinants and consequenc- es for process eco-innovation. The results of testing our hypotheses are depicted in Figure 27. We will first focus on the hypotheses pertaining to the determinants of process eco-innovation and then on the hypotheses that pertain to the consequences of process eco-innovation. Eco-innovation models Hypotheses 1a and 1b examined the relationship between environ- mental policy instruments (the command-and-control instrument, the economic incentive instrument) and process eco-innovation, which for both environmental policy instruments was predicted to be positive and significant. The standardized coefficients for the relationship between the command-and-control instrument and process eco-innovation were in the expected direction (positive), quite high (the standardized coefficient of the command-and-control instrument was 0.22) and significant. Support was also found for Hypothesis 1b, which posited a positive and significant relationship between the economic incentive instrument and process eco-innovation (the standardized coefficient was 0.06). Therefore, the findings revealed that Hypotheses 1a and 1b are both supported, while the command-and-control instrument seems to play a more important role in spurring process eco-innovation than the economic incentive 249 instrument. Strong support was found for Hypothesis 2, which postulated a positive and significant relationship between customer demand and process eco-innovation. The standardized coefficient was high (0.28) and significant. The relationship between managerial environmental concern and process eco-innovation was found to be positive, high and significant (the standardized coefficient was 0.23). Hypothesis 3 is therefore supported. Hypothesis 4 postulated a positive relationship between expected benefits and process eco-innovation. The association between expected benefits and process eco-innovation was found to be negative and significant (standardized coefficient -0.18), which is the opposite of what we expected. We can thus see that expected benefits do not drive companies toward implementation of process eco-innovations. It is probable that companies expect higher investments in process eco-innovation and tradeoff, which can be seen after several years’ lag. Thus, Hypothesis 4 is not supported. Hypothesis 5a (the relationship between competitive intensity and process eco-innovation) was not tested, since the factor competitive intensity explains only 36.733% of variance and was therefore excluded from further analyses. Hypothesis 5b examined the relationship between competitive pressure and process eco-innovation, which was expected to be positive and significant. The standardized coefficient for this relationship is highly positive, significant and substantial (0.40), indicating strong support for Hypothesis 5b. In Pursuit of Eco-innovation When testing our hypotheses, as indicated in Figure 27 and focus- ing on consequences of process eco-innovation, we have to add that Hypothesis 6 was tested separately, as it was divided into two dimensions – company growth and company profitability – in order to obtain greater insights regarding how process eco-innovation affects company growth and company profitability. However, Hypothesis 6 predicted a positive and significant association between process eco-innovation and company performance. In more detail, Hypothesis 6a posits a positive and significant relationship between process eco-innovation and company growth, while Hypothesis 6b posits a positive and significant association between process eco-innovation and company profitability. When testing the relationships between process eco-innovation and indicators of company performance (company growth and company profitability), significant 250 influences were detected. We can see (Figure 27) that process eco-innovation was found to be quite substantially (standardized coefficient -0.15) related to company growth, but in a negative direction, which is the opposite of what we predicted. Therefore, Hypothesis 6a is not supported. Meanwhile, the standardized coefficient measuring the influence of process eco-innovation on company profitability was significant and positive, although that the association was weak (standardized coefficient 0.04). The relationship was expected to be positive and significant; thus, these findings indicate that Hypothesis 6b is supported. In our model, we also used soft measures to measure economic per- formance of eco-innovation. Hypothesis 7 predicted a positive and significant relationship between process eco-innovation and economic benefits. In addition, the results further indicate that process eco-innovation was found to be highly, positively and significantly related to economic benefits (standardized coefficient 0.48). Therefore, Hypothesis 7 is supported. Moreover, the relationship between process eco-innovation and competitive benefits was found to be highly positive and significant (standardized coefficient 0.49), offering support for Hypothesis 8. Finally, Hypothesis 9 examined the relationship between process eco-innovation and internationalization and postulated that process eco-innovation has a positive impact on internationalization. The standardized coefficient for this relationship is quite substantial, positive and significant (0.17), indicating support for Hypothesis 9. This means that more eco-innovative companies (in the sense of introducing more process eco-innovations) are also more internationalized (in terms of scale and scope). Eco-innovation models Organizational eco-innovation In the organizational eco-innovation model, the influence of various determinants on organizational eco-innovation and the influence of organizational eco-innovation on its outcomes were analyzed. Eco-innovation determinants were measured by six elements: the command-and-control instrument, the economic incentive instrument, managerial environmental concern, customer demand, expected benefits and competitive pressure. Eco-innovation outcomes were measured by five elements: company growth and company profitability (objective measures obtained from the GVIN database), economic benefits, competitive benefits and internationalization. Construct validity of organizational eco-innovation model 251 All measurement items and Cronbach’s alpha values are reported in Table 86. Content validity for the survey instrument is supported by the literature, in-depth interviews with environmental managers and a pilot test. A Confirmatory Factor Analysis (CFA) model is estimated to assess the construct validity. In the model, each item is linked to its corresponding construct with freely estimated covariance. The model fit indexes are as follows: Chi-square = 1600.916; df = 923; NFI = 0.819; NNFI = 0.902; CFI = 0.913; RMSEA = 0.063; SRMR = 0.060, suggesting that the measurement model is acceptable. In addition, the Cronbach’s alpha is 0.946, while the reliability coefficient RHO is 0.981. From Table 86, we can see that all factor loadings are greater than 0.50 and the p-values are significant at the 0.05 level, except for the item pertaining to the company growth (“Net sales - growth through 2 business years”), and convergent validity is ensured (Fornell and Larcker 1981). In addition, the composite reliability of all constructs (except for the construct company growth) is greater than 0.70, indicating acceptable reliability (Hair et al. 2009). Additionally, the square root of average variance extracted (AVE) for each construct is greater than 0.50, except for the construct company growth, for which has not been calculated (instead, the correlation between the two items pertaining to the construct is given). In Pursuit of Eco-innovation Table 86: Measurement model of latent variables and Cronbach’s alpha for latent variables Complete-Cronbach’s alpha ly standard- Composite reli- (for construct Measurement items p AVE ized load- ability Growth is given cor- ing relation) Managerial environmental concern (MC) 1a Eco-innovation is an important com- ponent of the com- 0.62 pany’s environmen- tal management strategy. 1b Most eco-innova- 0.83 * tions are worthwhile. 252 0.858 0.60 0.836 1c Eco-innovation is necessary to achieve high levels of envi- 0.82 * ronmental perfor- mance. 1d Eco-innovation is an effective environ- 0.82 * mental management strategy. Expected benefits (EB) 2b To improve prof- 0.77 itability. 2c To increase pro- 0.79 * ductivity. 2d To increase mar- 0.89 * ket share. 0.923 0.67 0.914 2e To enter new mar- 0.83 * kets. 2g To strengthen the 0.76 * brand. 2h Competitive ad- 0.85 * vantage. Eco-innovation models Complete- Cronbach’s alpha ly standard- Composite reli- (for construct Measurement items p AVE ized load- ability Growth is given cor- ing relation) Command-and-control instrument (CCI) 3a Our products should meet the re- quirements of na- 0.87 tional environmental regulations. 3b Our products should meet the re- quirements of inter- 0.91 * national and/or EU environmental regu- lations. 253 3c Our production 0.942 0.80 0.946 processes should meet the require- 0.90 * ments of national en- vironmental regu- lations. 3d Our production processes should meet the require- ments of interna- 0.90 * tional and/or EU environmental regu- lations. Economic incentive instrument (EII) 3e The government provides preferential 0.85 subsidy on environ- mental innovation. 3f The government provides preferen- 0.835 0.66 0.838 tial tax policy on en- 0.96 * vironmental inno- vation. 3h The government promotes environ- 0.59 * mental protection. In Pursuit of Eco-innovation Complete- Cronbach’s alpha ly standard- Composite reli- (for construct Measurement items p AVE ized load- ability Growth is given cor- ing relation) Customer demand (CD) 4a Environment is a critical issue for 0.91 our important cus- tomers. 4b Our import- ant customers of- 0.93 * ten bring up environ- mental issues. 0.949 0.82 0.940 4c Customer de- mands motivate us 254 0.88 * in our environmental efforts. 4d Our customers have clear demands 0.91 * regarding environ- mental issues. Competitive pressure (CP) 6a We establish the company’s environ- mental image com- 0.90 pared to competitors through green con- cepts. 6b We increase the company’s market 0.933 0.83 0.933 0.89 * share through green concepts. 6c We improve the company’s compet- itive advantage over 0.95 * competitors through green concepts. Eco-innovation models Complete- Cronbach’s alpha ly standard- Composite reli- (for construct Measurement items p AVE ized load- ability Growth is given cor- ing relation) Organizational eco-innovation (OR) 10a Our firm man- agement often uses novel systems to 0.81 manage eco-inno- vation. 10b Our firm man- agement often col- lects information 0.91 * on eco-innovation trends. 10c Our firm man- 255 agement often active- 0.93 * ly engages in eco-in- novation activities. 10d Our firm man- 0.958 0.79 0.956 agement often com- municates eco-inno- 0.92 * vation information with employees. 10e Our firm man- agement often in- vests a high ratio of 0.86 * R&D in eco-inno- vation. 10f Our firm manage- ment often commu- nicates experiences 0.90 * among various de- partments involved in eco-innovation. Competitive benefits (CB) 17d Increased pro- cess/production ef- 0.87 ficiency. 17e Increase in pro- 0.92 * ductivity. 0.949 0.82 0.946 17f Increased knowl- edge about effective 0.92 * ways of managing op- erations. 17g Improved pro- 0.92 * cess innovations. In Pursuit of Eco-innovation Complete- Cronbach’s alpha ly standard- Composite reli- (for construct Measurement items p AVE ized load- ability Growth is given cor- ing relation) Economic benefits (ECB) 18a Sales. 0.88 18b Market share. 0.95 * 18c New market op- 0.935 0.78 0.930 0.91 * portunities. 18f Employee satis- 0.79 * faction. Company performance – growth (GR) Number of employ- 256 ees - growth through 0.61 2 business years n.a. n.a. 0.249** Net sales - growth through 2 business 0.40 * years Company performance – profitability (PF) ROA 0.96 ROE 0.74 * 0.808 0.59 0.681 ROS 0.56 * Internationalization (INT) Number of for- eign countries where company current- 0.99 ly sells its products/ services 0.894 0.74 0.875 Share of sales on for- 0.87 * eign markets in 2013 Total number of op- 0.70 * eration modes Note: * p-values are significant at 0.05 level; ** correlation between two items pertaining to the construct company growth is significant at the 0.01 level; n.a. = not applicable, because the construct company growth is composed of only two items. Table 87 depicts correlations between latent variables, where we can observe that all correlations are statistically significant. Eco-innovation models Table 87: Results of correlations between latent variables MC EB CCI EII CD CP OR CB ECB GR PF INT MC 1 EB 0.56* 1 CCI 0.25* 0.22* 1 EII 0.23* 0.30* 0.21* 1 CD 0.37* 0.40* 0.54* 0.19* 1 CP 0.46* 0.47* 0.37* 0.30* 0.49* 1 OR 0.44* 0.42* 0.39* 0.31* 0.47* 0.73* 1 CB 0.27* 0.29* 0.36* 0.18* 0.45* 0.49* 0.56* 1 ECB 0.38* 0.47* 0.30* 0.23* 0.49* 0.56* 0.65* 0.68* 1 GR 0.06* 0.02* -0.37* -0.11* -0.12* -0.06* -0.06* -0.07* 0.06* 1 257 PF 0.06* 0.07* -0.12* 0.05* -0.00* -0.00* 0.06* -0.04* 0.03* 0.36* 1 INT 0.08* 0.12* 0.29* -0.00* 0.37* 0.04* 0.18* 0.20* 0.26* -0.11* -0.01* 1 Note: MC = managerial environmental concern; EB = expected benefits; CCI = the command-and-control instrument; EII = the economic incentive instrument; CD = customer demand; CP = competitive pressure; OR = organizational eco-innovation; CB = competitive benefits; ECB = economic benefits; GR = growth (company performance); PF = profitability (company performance); INT = internationalization. Statistical analysis and results (path analysis) All construct dimensions were assessed using exploratory and confirmatory factor analyses in previous sections. We also present construct validity for the organizational eco-innovation model with its determinants and consequences (see Section 8.3.1). Reliability statistics for all construct dimensions were good (over 0.70), as were the goodness-of-fit measures, which indicated an acceptable model fit for all constructs (except for company growth). In this section, we use structural equation modeling to test all relationships between latent variables and observed variables as well as the relationships among multiple latent variables simultaneously. The resulting organizational eco-innovation model with estimated relationships (standardized solution) is depicted in Figure 28. The model shows a moderate fit to the data (NFI = 0.775; NNFI = 0.860; CFI = 0.869; SRMR = 0.214; RMSEA = 0.075). In Pursuit of Eco-innovation 258 Figure 28: Organizational eco-innovation model (standardized solution) Note: Chi-square = 1982.385 (966 df); p = 0.00; Goodness-of-fit indexes: NFI = 0.755; NNFI = 0.860; CFI = 0.869; SRMR = 0.214; RMSEA = 0.075; Reliability coefficients: Cronbach’s alpha = 0.946; RHO = 0.958. The results of testing hypotheses related to the organizational eco-innovation model are depicted in Figure 28. As before, we will first focus on the hypotheses pertaining to the determinants of organizational eco-innovation and then on the hypotheses that pertain to the consequences of organizational eco-innovation. Hypotheses 1a and 1b examined the relationships between envi- ronmental policy instruments (the command-and-control instrument, the economic incentive instrument) and organizational eco-innovation, which for both types of environmental policy instruments was predicted to be positive and significant. The standardized coefficients for both relationships were positive (standardized coefficient was 0.11 for the command-and-control instrument and 0.09 for the economic incentive Eco-innovation models instrument) and significant. Therefore, the findings revealed that Hypotheses 1a and 1b are both supported. Support was also found for Hypothesis 2, which postulated a posi- tive and significant relationship between customer demand and organizational eco-innovation. The standardized coefficient was positive (0.12) and significant. The relationship between managerial environmental concern and or- ganizational eco-innovation was found to be positive and significant (the standardized coefficient was slightly lower, estimated at 0.08). Thus, Hypothesis 3 is supported. Hypothesis 4 postulated a positive relationship between expected benefits and organizational eco-innovation. The association between expected benefits and organizational eco-innovation was found to be positive and significant (standardized coefficient 0.06), indicating support for 259 Hypothesis 4. Hypothesis 5a (the relationship between competitive intensity and organizational eco-innovation) was not tested, since the factor competitive intensity explains only 36.733% of variance and was therefore excluded from further analyses. Hypothesis 5b examined the relationship between competitive pressure and organizational eco-innovation, which was expected to be positive and significant. The standardized coefficient for this relationship is highly positive, significant and substantial (0.64), offering strong support for Hypothesis 5b. When testing our hypotheses, as indicated in Figure 28 and focusing on consequences of organizational eco-innovation, we have to add that Hypothesis 6 was tested separately, as it was divided into two dimensions – company growth and company profitability – in order to obtain greater insights regarding how organizational eco-innovation affects company growth and company profitability. However, Hypothesis 6 predicted a positive and significant association between organizational eco-innovation and company performance. In more detail, Hypothesis 6a postulates a positive and significant relationship between organizational eco-innovation and company growth, while Hypothesis 6b posits a positive and significant association between organizational eco-innovation and company profitability. When testing the relationships between organizational eco-innovation and indicators of company performance (company growth and company profitability), significant influences were detected. We can see (Figure 28) that organizational eco-innovation was found to be negatively related to company growth (standardized coefficient -0.06), which is the opposite of what we expected. Therefore, Hypothesis 6a is In Pursuit of Eco-innovation not supported. Meanwhile, the standardized coefficient measuring the influence of organizational eco-innovation on company profitability was significant and positive, although the association was weak (standardized coefficient 0.05). The relationship was expected to be positive and significant; therefore, these findings indicate that Hypothesis 6b is supported. In our model, we also used soft measures to measure economic per- formance of eco-innovation. Hypothesis 7 predicted a positive and significant relationship between organizational eco-innovation and economic benefits. In addition, the results further indicate that organizational eco-innovation was found to be highly, positively and significantly related to economic benefits (standardized coefficient 0.62). Therefore, Hypothesis 7 is supported. Moreover, the relationship between organizational eco-innovation 260 and competitive benefits was found to be highly positive and significant (standardized coefficient 0.54), offering support for Hypothesis 8, which is confirmed. Finally, Hypothesis 9 examined the relationship between organiza- tional eco-innovation and internationalization and postulated that organizational eco-innovation has a positive impact on internationalization. The standardized coefficient for this relationship is quite substantial, positive and significant (0.17), therefore indicating support for Hypothesis 9. This means that more eco-innovative companies (in the sense of introducing more organizational eco-innovations) are also more internationalized (in terms of scale and scope). The expanded construct-level model of eco-innovation In the expanded construct-level model of eco-innovation, we have analyzed the influence of eco-innovation determinants on eco-innovation implementation and the influence of eco-innovation implementation on its outcomes. Eco-innovation determinants were measured by six elements: the command-and-control instrument, the economic incentive instrument, managerial environmental concern, customer demand, expect- ed benefits and competitive pressure. The eco-innovation construct was measured as a second-order latent factor composed of three dimensions: product, process and organizational eco-innovation. Eco-innovation outcomes were measured by five elements: company growth, company profitability, economic benefits, competitive benefits and internationalization. Two elements related to eco-innovation outcomes – company growth and company profitability – are objective measures, obtained from the GVIN database. Eco-innovation models Construct validity for the expanded construct-level model of eco-innovation All measurement items and the values of Cronbach’s alpha are reported in Tables 88 and 89. Content validity for the survey instrument is supported by the literature, in-depth interviews with environmental managers and a pilot test. A Confirmatory Factor Analysis (CFA) model is estimated to assess the construct validity. In the model, each item is linked to its corresponding construct with freely estimated covariance. The model fit indexes are as follows: Chi-square = 2230.569; df = 1339; NFI = 0.795; NNFI = 0.895; CFI = 0.905; RMSEA = 0.060; SRMR = 0.062, suggesting that the measurement model is acceptable. In addition, the Cronbach’s alpha is 0.958, while the reliability coefficient RHO is 0.984. Table 88 depicts Cronbach’s alpha values for all measurement items. It indicates that all constructs (with the exception of the construct company 261 growth) demonstrate good reliability (over the threshold of 0.70). Table 88: Measurement items and Cronbach’s alpha for latent variables Cronbach’s alpha Measurement items (for construct Growth is given correlation) Managerial environmental concern 1a Eco-innovation is an important component of the company’s environmental management strategy. 1b Most eco-innovations are worthwhile. 0.836 1c Eco-innovation is necessary to achieve high levels of environmental performance. 1d Eco-innovation is an effective environmental management strategy. Expected benefits 2b To improve profitability. 2c To increase productivity. 2d To increase market share. 0.914 2e To enter new markets. 2g To strengthen the brand. 2h Competitive advantage. In Pursuit of Eco-innovation Cronbach’s alpha Measurement items (for construct Growth is given correlation) Command-and-control instrument 3a Our products should meet the requirements of national environmental regulations. 3b Our products should meet the requirements of international and/or EU environmental regulations. 0.946 3c Our production processes should meet the requirements of national environmental regulations. 3d Our production processes should meet the requirements of international and/or EU environmental regulations. Economic incentive instrument 3e The government provides preferential subsidies for environmental innova-262 tion. 3f The government provides preferential tax policies on environmental innova-0.838 tion. 3h The government promotes environmental protection. Customer demand 4a Environment is a critical issue for our important customers. 4b Our important customers often bring up environmental issues. 0.940 4c Customer demands motivate us in our environmental efforts. 4d Our customers have clear demands regarding environmental issues. Competitive pressure 6a We establish the company’s environmental image compared to competitors through green concepts. 6b We increase the company’s market share through green concepts. 0.933 6c We improve the company’s competitive advantage over competitors through green concepts. Product eco-innovation 8b The company is improving and designing environmental y friendly packaging (e.g., using less paper and plastic materials) for existing and new products. 8e The company chooses materials of the product that consume the least amount of energy and resources for conducting the product development or design. 0.872 8f The company uses the smal est possible amount of materials to comprise the product for conducting the product development or design. 8g The company deliberately evaluates whether the product is easy to recycle, reuse and decompose for conducting the product development or design. Eco-innovation models Cronbach’s alpha Measurement items (for construct Growth is given correlation) Process eco-innovation 9a Low energy consumption such as water, electricity, gas and petrol during production/use/disposal. 9b Recycle, reuse and remanufacture material. 9g Use of cleaner technology to generate savings and prevent pollution (e.g., en-0.912 ergy, water and waste). 9h The manufacturing process of the company effectively reduces the emission of hazardous substances or waste. 9i The manufacturing process of the company reduces the use of raw materials. Organizational eco-innovation 10a Our firm management often uses novel systems to manage eco-innovation. 10b Our firm management often collects information on eco-innovation trends. 263 10c Our firm management often actively engages in eco-innovation activities. 10d Our firm management often communicates eco-innovation information 0.956 with employees. 10e Our firm management often invests a high ratio of R&D in eco-innovation. 10f Our firm management often communicates experiences among various departments involved in eco-innovation. Competitive benefits 17d Increased process/production efficiency. 17e Increase in productivity. 0.946 17f Increased knowledge about effective ways of managing operations. 17g Improved process innovations. Economic benefits 18a Sales. 18b Market share. 0.930 18c New market opportunities. 18f Employee satisfaction. Company performance – growth Number of employees - growth through 2 business years 0.249** Net sales - growth through 2 business years Company performance – profitability ROA ROE 0.681 ROS In Pursuit of Eco-innovation Cronbach’s alpha Measurement items (for construct Growth is given correlation) Internationalization Number of foreign countries where company currently sells its products/services 0.875 Share of sales on foreign markets in 2013 Total number of operation modes Note: ** correlation between two items pertaining to the construct company growth is significant at the 0.01 level. From Table 89, we can see that all factor loadings are greater than 0.50 and the p-values are significant at the 0.05 level, except for the item 264 pertaining to company growth (“Net sales - growth through 2 business years”); therefore, the convergent validity is ensured (Fornell and Larcker 1981). In addition, the composite reliability of all constructs (except for the construct company growth) is greater than 0.70, indicating acceptable reliability (Hair et al. 2009). Additionally, the square root of average variance extracted (AVE) for each construct is greater than 0.50, except for the construct growth, for which has not been calculated (instead, the correlation between the two items pertaining to the construct is given). Table 89: Measurement model of latent variables Completely stan- Composite reli- Measurement items p AVE dardized loading ability Managerial environmental concern (MC) 1a Eco-innovation is an important component of the company’s envi- 0.63 ronmental management strategy. 1b Most eco-innovations are 0.84 * worthwhile. 0.858 0.60 1c Eco-innovation is necessary to achieve high levels of environmen- 0.81 * tal performance. 1d Eco-innovation is an effec- tive environmental management 0.81 * strategy. Eco-innovation models Completely stan- Composite reli- Measurement items p AVE dardized loading ability Expected benefits (EB) 2b To improve profitability. 0.77 2c To increase productivity. 0.79 * 2d To increase market share. 0.89 * 0.923 0.67 2e To enter new markets. 0.83 * 2g To strengthen the brand. 0.76 * 2h Competitive advantage. 0.85 * Command-and-control instrument (CCI) 3a Our products should meet the requirements of national environ- 0.88 mental regulations. 265 3b Our products should meet the requirements of internation- 0.91 * al and/or EU environmental reg- ulations. 0.943 0.80 3c Our production processes should meet the requirements 0.90 * of national environmental reg- ulations. 3d Our production processes should meet the requirements of 0.90 * international and/or EU environ- mental regulations. Economic incentive instrument (EII) 3e The government provides pref- erential subsidies for environmen- 0.85 tal innovation. 3f The government provides pref- 0.847 0.66 erential tax policies on environ- 0.95 * mental innovation. 3h The government promotes en- 0.59 * vironmental protection. In Pursuit of Eco-innovation Completely stan- Composite reli- Measurement items p AVE dardized loading ability Customer demand (CD) 4a Environment is a critical issue 0.91 for our important customers. 4b Our important customers of- 0.93 * ten bring up environmental issues. 0.949 0.82 4c Customer demands motivate 0.88 * us in our environmental efforts. 4d Our customers have clear de- mands regarding environmen- 0.91 * tal issues. Competitive pressure (CP) 266 6a We establish the company’s en- vironmental image compared to 0.90 competitors through green con- cepts. 6b We increase the company’s 0.933 0.83 market share through green con- 0.89 * cepts. 6c We improve a company’s com- petitive advantage over competi- 0.95 * tors through green concepts. Product eco-innovation (PD) 8b The company is improving and designing environmental y friend- ly packaging (e.g., using less paper 0.63 and plastic materials) for existing and new products. 8e The company chooses materi- als of the product that consume the least amount of energy and re- 0.89 * sources for conducting the prod- uct development or design. 0.880 0.65 8f The company uses the smal - est possible amount of materials to comprise the product for con- 0.88 * ducting the product development or design. 8g The company deliberately eval- uates whether the product is easy to recycle, reuse and decompose 0.80 * for conducting the product devel- opment or design. Eco-innovation models Completely stan- Composite reli- Measurement items p AVE dardized loading ability Process eco-innovation (PC) 9a Low energy consumption such as water, electricity, gas and petrol 0.79 during production/use/disposal. 9b Recycle, reuse and remanufac- 0.73 * ture material. 9g Use of cleaner technology to generate savings and prevent pol- 0.84 * lution (e.g., energy, water and 0.922 0.70 waste). 9h The manufacturing process of the company effectively reduc- 0.92 * es the emission of hazardous sub- 267 stances or waste. 9i The manufacturing process of the company reduces the use of 0.90 * raw materials. Organizational eco-innovation (OR) 10a Our firm management of- ten uses novel systems to manage 0.81 eco-innovation. 10b Our firm management often collects information on eco-inno- 0.91 * vation trends. 10c Our firm management often actively engages in eco-innovation 0.93 * activities. 0.958 0.79 10d Our firm management often communicates eco-innovation in- 0.92 * formation with employees. 10e Our firm management of- ten invests a high ratio of R&D in 0.86 * eco-innovation. 10f Our firm management of- ten communicates experiences 0.91 * among various departments in- volved in eco-innovation. In Pursuit of Eco-innovation Completely stan- Composite reli- Measurement items p AVE dardized loading ability Competitive benefits (CB) 17d Increased process/production 0.87 efficiency. 17e Increased productivity. 0.92 * 0.949 0.82 17f Increased knowledge about effective ways of managing op- 0.92 * erations. 17g Improved process innovations. 0.92 * Economic benefits (ECB) 18a Sales. 0.88 268 18b Market share. 0.95 * 0.935 0.78 18c New market opportunities. 0.91 * 18f Employee satisfaction. 0.79 * Company performance – growth (GR) Number of employees - growth 0.48 through 2 business years n.a. n.a. Net sales - growth through 2 busi- 0.51 * ness years Company performance – profitability (PF) ROA 0.97 ROE 0.72 * 0.805 0.59 ROS 0.56 * Internationalization (INT) Number of foreign countries where company currently sells its 0.99 products/services 0.894 0.74 Share of sales on foreign mar- 0.87 * kets in 2013 Total number of operation modes 0.70 * Note: * p-values are significant at 0.05 level; n.a. = not applicable, because the construct company growth is composed of only two items. Moreover, Table 90 depicts correlations between latent variables, where we can observe that all correlations are statistically significant. Eco-innovation models Table 90: Results of Correlations between latent variables MC EB CCI EII CD CP PD PC OR CB ECB GR PF INT MC 1 EB 0.56* 1 CCI 0.26* 0.22* 1 EII 0.24* 0.30* 0.21* 1 CD 0.38* 0.40* 0.54* 0.19* 1 CP 0.46* 0.47* 0.38* 0.30* 0.49* 1 PD 0.37* 0.40* 0.41* 0.31* 0.51* 0.59* 1 PC 0.44* 0.22* 0.47* 0.22* 0.51* 0.55* 0.79* 1 OR 0.45* 0.42* 0.39* 0.31* 0.47* 0.73* 0.66* 0.69* 1 CB 0.28* 0.29* 0.36* 0.18* 0.45* 0.49* 0.41* 0.49* 0.56* 1 269 ECB 0.39* 0.47* 0.30* 0.23* 0.49* 0.57* 0.47* 0.48* 0.65* 0.68* 1 GR 0.08* 0.06* -0.36* -0.06* -0.11* -0.08* -0.21* -0.17* -0.08* -0.07* 0.13* 1 PF 0.06* 0.07* -0.12* 0.05* -0.02* -0.02* 0.01* 0.05* 0.06* -0.04* 0.26* 0.38* 1 INT 0.08* 0.13* 0.29* -0.04* 0.37* 0.04* 0.25* 0.16* 0.18* 0.20* 0.26* -0.08* -0.17* 1 Note: MC = managerial environmental concern; EB = expected benefits; CCI = the command-and-control instrument; EII = the economic incentive instrument; CD = customer demand; CP = competitive pressure; PD = product eco-innovation; PC = process eco- -innovation; OR = organizational eco-innovation; CB = competitive benefits; ECB = economic benefits; GR = growth (company performance); PF = profitability (company performance); INT = internationalization. The expanded construct-level model of eco-innovation (path analysis) In order to analyze the hypothesized relationships between determinants and outomes of eco-innovation construct, a new, expanded construct-level model of eco-innovation was designed. In this model, eco-innovation was presented as a second-order latent factor, defined by the underlying dimensions, which are product, process and organizational eco-innovation.All construct dimensions were assessed using exploratory and confirmatory factor analyses in previous sections. We also present a construct validity for the expanded eco-innovation model with its determinants and consequences (see Section 8.4.1). Reliability statistics for all construct dimensions were good (over 0.70), as were the goodness-of-fit measures, which indicated an acceptable model fit for all constructs (except for company growth). In this section, we use structural equation modeling In Pursuit of Eco-innovation to test all relationships between latent variables and observed variables as well as the relationships among multiple latent variables simultaneously. The resulting eco-innovation model with estimated relationships (standardized solution) is depicted in Figure 29. The model shows a moderate fit to the data (NFI = 0.755; NNFI = 0.857; CFI = 0.865; SRMR = 0.202; RMSEA = 0.070); specifically, NFI and SRMR show poor fit, while NNFI and CFI show acceptable fit and RMSEA shows good fit. 270 Figure 29: The expanded construct-level model of eco-innovation (standardized solution) Note: Chi-square = 2671.593 (1401 df); p = 0.00; Goodness-of-fit indexes: NFI = 0.755; NNFI = 0.857; CFI = 0.865; SRMR = 0.202; RMSEA = 0.070; Reliability coefficients: Cronbach’s alpha = 0.958; RHO = 0.967. In the expanded construct-level model of eco-innovation, we tested hypotheses related to determinants and consequences of eco-innovation (testing eco-innovation as a second-order latent construct, including the Eco-innovation models following three dimensions: product, process and organizational eco-innovation). The results related to the hypotheses testing are depicted in Figure 29. As before, we will focus first on the hypotheses pertaining to determinants of eco-innovation and then on hypotheses pertaining to the consequences of eco-innovation. Hypotheses 1a and 1b examined the relationships between environ- mental policy instruments (the command-and-control instrument, the economic incentive instrument) and eco-innovation, which were predicted to be positive and significant. The standardized coefficients for both relationships were positive, quite substantial and significant (standardized coefficient was 0.18 for the command-and-control instrument and 0.10 for the economic incentive instrument). The findings revealed that Hypotheses 1a and 1b can be both supported. Hypothesis 2, postulated a positive and significant relationship be-271 tween customer demand and eco-innovation. The standardized coefficient was positive, high and significant (standardized coefficient 0.30), and the results indicate strong support for Hypothesis 2. The relationship between managerial environmental concern and eco-innovation was found to be positive and significant (the standardized coefficient was 0.12). Thus, Hypothesis 3 is be supported. Hypothesis 4 predicted a positive relationship between expected benefits and eco-innovation. The association between expected benefits and eco-innovation was found to be positive and significant (standardized coefficient was 0.05), indicating support for Hypothesis 4. Hypothesis 5a (the relationship between competitive intensity and eco-innovation) was not tested, since the factor competitive intensity explains only 36.733% of variance and was therefore excluded from further analyses. Meanwhile, strong support was found for Hypothesis 5b, which examined the relationship between competitive pressure and eco-innovation, which was expected to be positive and significant. The standardized coefficient for this relationship is highly positive and significant (0.64), indicating strong support for Hypothesis 5b. When testing the relationships between eco-innovation and indica- tors of company performance (company growth and profitability), significant influences were detected. Hypothesis 6a postulates a positive and significant relationship between eco-innovation and company growth. The standardized coefficient was negative and significant (-0.11), indicating that Hypothesis 6a cannot be supported. Hypothesis 6b posits a positive and significant association between eco-innovation and company profitability. Eco-innovation was found to be weakly, positively and In Pursuit of Eco-innovation significantly related to company profitability (standardized coefficient 0.02), indicating support for Hypothesis 6b. In our model, we also used soft measures to measure economic per- formance of eco-innovation. Hypothesis 7 predicted a positive and significant relationship between eco-innovation and economic benefits. The results indicate that eco-innovation was highly, positively and significantly related to economic benefits (standardized coefficient 0.65). Therefore, Hypothesis 7 is supported. Moreover, the relationship between eco-innovation and competitive benefits was found to be highly positive and significant (standardized coefficient 0.59), offering support for Hypothesis 8, which is also confirmed. Finally, Hypothesis 9 examined the relationship between eco-inno- vation and internationalization and posited that eco-innovation has a 272 positive impact on internationalization. The standardized coefficient for this relationship is quite substantial, positive and significant (0.21), thus offering support for Hypothesis 9. In conclusion, it appears that companies that introduce more eco-innovations are also more internationalized (in terms of scale and scope). Summary of findings and discussion In this section, we briefly summarize the main findings of this study. First, we summarize the findings that pertain to the eco-innovation construct, which is composed of three dimensions (measured as a second-order latent factor) and which was developed and further tested in our study. We also present the findings of different eco-innovation models (product, process and organizational eco-innovation), for which we separately explored/tested drivers and outcomes. Therefore, the hypotheses developed and tested in this study can be divided into four groups. We have tested all hypotheses concerning eco-innovation determinants and outcomes separately for product, process and organizational eco-innovation. Lastly, the hypotheses were also tested for the construct-level model of eco-innovation. All the hypotheses were tested using structural equation modeling (SEM). The eco-innovation construct in our study was proposed to include three dimensions: product eco-innovation, process eco-innovation and organizational eco-innovation. As a result of the empirical analyses that were conducted in our study, we found that a three-dimensional structure was best to describe the phenomenon under investigation. The eco-innovation construct developed in this study (including the dimensions of product, process and organizational eco-innovation), demonstrated good convergent validity (NFI = 0.928; NNFI = 0.945; CFI = 0.954; SRMR = 0.044; RMSEA = 0.086; Cronbach’s alpha = 0.952) and moderate discriminant validity (correlations between product and organizational eco-innovation and between process and organizational eco-innovation were below 0.70, while the correlation between product and process eco-inno- In Pursuit of Eco-innovation vation was estimated at 0.79). When the eco-innovation construct was linked in the model with its determinants (drivers) and outcomes (consequences), the nomological validity of the eco-innovation construct was also shown. Second, we present findings pertaining to the product eco-innovation model. On the one hand, the findings of our study revealed that all the predicted determinants of eco-innovation (the command-and-control instrument, the economic incentive instrument, managerial environmental concern, customer demand, expected benefits and competitive pressure) exerted positive and significant effects (p < 0.05) on product eco-innovation. Among the tested determinants of eco-innovation, we found that competitive pressure works as the most effective driver of eco-innovation – its effect on product eco-innovation was the greatest among the tested 274 determinants of eco-innovation, followed by customer demand, which also exerted a large, positive and significant effect on product eco-innovation. A moderate (but still positive and significant) influence on product eco-innovation was also shown by other eco-innovation determinants, which are, in descending order with regard to the size of standardized coefficients, as follows: the command-and-control instrument, the economic incentive instrument and expected benefits (the last two had the same value of standardized coefficients, meaning that they both exert equal influence). The least effective determinant of product eco-innovation was found to be managerial environmental concern, which exerted the weakest influence on product eco-innovation; nevertheless, its influence was still positive and significant (p < 0.05). On the other hand, regarding eco-innovation outcomes, we found empirical evidence to support the hypotheses that predicted a positive and significant relationship between product eco-innovation and economic benefits, competitive benefits and internationalization (in descending order of the size of the standardized coefficients; p < 0.05). We have not found empirical evidence to support the hypotheses related to the objective measures of company performance – company growth and company profitability. The hypothe- ses can be partially supported in the sense that the relationship between product eco-innovation and both company growth and company profitability was direct and significant (p < 0.05); however, it was found to be negative, which is the opposite of what we expected. In the case of product eco-innovation’s effect on company profitability, the standardized coefficient is approximately zero and statistically significant, but it is also negative and thus not consistent with the hypothesis. In sum, companies reported the gain of competitive benefits and positive economic benefits Summary of findings and discussion to be related to product eco-innovation implementation, but the objective measures (company growth and company profitability) do not reflect this. This was expected to occur, because eco-innovation’s return on investment or payoff may take several years, and in our study we have not controlled the time since investment in product eco-innovation. Therefore, even when companies may already observe and reap some benefits from eco-innovation implementation, their positive effect on the “hard” measures pertaining to the company’s profitability indicator ratios cannot yet be seen. Moreover, those indicators related to company profitability and growth are derived from the period of the recent economic crisis; thus, we can infer that product eco-innovations contributed to companies’ survival and their existence during the crisis. Third, concerning the process eco-innovation model, we found that the following determinants (in descending order of importance by the 275 sizes of standardized coefficients) exerted positive and significant effects (p < 0.05) on process eco-innovation: competitive pressure, customer demand, managerial environmental concern, the command-and-control instrument and the economic incentive instrument. The only hypothesis pertaining to the determinants of eco-innovation for which we have not found empirical support was related to the expected benefits. We predicted a positive and significant relationship between expected benefits and process eco-innovation, while the association found between them was negative and significant (p < 0.05). It seems that when companies start to implement process eco-innovations, they do not consider them to be beneficial for the company; that is, they do not expect any benefits from their implementation in advance, or at least any such benefits are not the triggering factors that would steer them toward eco-innovation implementation. With regard to the eco-innovation outcomes, we found support for all eco-innovation outcomes except company growth, for which the association was significant and negative (p < 0.05), instead of the expected positive and significant association. Our findings indicate that process eco-innovation exerts a great positive and significant influence on competitive benefits, followed by economic benefits (p < 0.05).Meanwhile, the association between process eco-innovation and internationalization was moderately high, significant (p < 0.05) and positive. Lastly, we also found a weak but significant (p < 0.05) and positive association between process eco-innovation and company profitability. When examining the determinants and consequences of organiza- tional eco-innovation, the findings of our study indicate that all the tested determinants exerted positive and significant effects (p < 0.05) on or- In Pursuit of Eco-innovation ganizational eco-innovation, further, organizational eco-innovation also exerted significant influences (p < 0.05) on all examined consequences of eco-innovation. More specifically, the results reveal that organizational eco-innovation is driven to the greatest extent by competitive pressure. Other determinants exerted moderate, positive and significant effects on organizational eco-innovation as follows (in descending order by the size of standardized coefficients): customer demand, the command-and-control instrument, the economic incentive instrument, managerial environmental concern and expected benefits. Among the consequences of eco-innovation, the results indicate that organizational eco-innovation is associated to the greatest extent with economic benefits, followed by competitive benefits. Organizational eco-innovation also has a positive, moderately high and significant influence on internationalization and a 276 weaker but still positive and significant association with company profitability. In the organizational eco-innovation model, only one hypothesis has not been supported – the one that pertains to the relationship between organizational eco-innovation and company growth. The relationship between organizational eco-innovation and company growth was expected to be positive and significant, but it turned out to be significant and negative (p < 0.05). When we tested the construct-level model of eco-innovation, eco-innovation was measured as a second-order latent factor, including three dimensions (product, process and organizational eco-innovation). We found that all the tested determinants exerted positive and significant influences (p < 0.05) on the eco-innovation construct. Concerning the size of standardized coefficients, we can summarize that the empirical evidence gave the strongest support to the determinant competitive pressure, followed by customer demand. A moderate, positive and significant effect was also demonstrated by the following three determinants: the command-and-control instrument, managerial environmental concern and the economic incentive instrument. A weaker effect on the eco-innovation construct was exerted by the determinant expected benefits; however, it was still positive and significant. Concerning the eco-innovation outcomes, all the hypotheses, except the hypothesis pertaining to the company growth, were supported. The eco-innovation construct had the greatest influence on economic benefits, followed by competitive benefits. We also found a high, positive and significant association between the eco-innovation construct and internationalization, while the relationship between the eco-innovation construct and company profitability was significant and positive but weak. The only hypothesis that is par- Summary of findings and discussion tially rejected is the hypothesis about company growth. The relationship between the eco-innovation construct and company growth is significant and direct (as predicted in hypothesis 6a), but it is negative rather than positive and thus does not support the hypothesis. Table 91: Summary of hypotheses-related findings (structural equation modeling) Hypotheses Results – main findings with description Product EI Process EI Organizational EI EI construct There is a posi- tive and signifi- cant relationship between the com- H1a mand-and-con- Supported. Supported. Supported. Supported. trol instrument 277 and companies’ im- plementation of eco-innovation. There is a posi- tive and signifi- cant relationship between the eco- H1b nomic incen- Supported. Supported. Supported. Supported. tive instrument and companies’ im- plementation of eco-innovation. There is a positive and significant re- lationship between H2 customer demand Supported. Supported. Supported. Supported. and companies’ im- plementation of eco-innovation. There is a posi- tive and signifi- cant relationship between mana- H3 gerial environ- Supported. Supported. Supported. Supported. mental concern and companies’ im- plementation of eco-innovation. In Pursuit of Eco-innovation Hypotheses Results – main findings with description Product EI Process EI Organizational EI EI construct There is a positive and significant re- lationship between H4 expected bene- Supported. Not supported. Supported. Supported. fits and companies’ implementation of eco-innovation. There is a positive and significant re- lationship between H5a competitive inten- Not tested. Not tested. Not tested. Not tested. sity and companies’ implementation of 278 eco-innovation. There is a posi- tive and significant relationship be- tween competi- H5b tive pressure Supported. Supported. Supported. Supported. and companies’ im- plementation of eco-innovation. The relationship between eco-in- Partial y support- Partial y support- Partial y support- Partial y support- novation’s perfor- H6a ed (direct but neg- ed (direct but neg- ed (direct but neg- ed (direct but neg- mance and compa- ny growth ative). ative). ative). ative). is direct and positive. The relationship be- tween eco-innova- Partial y support- tion’s performance H6b ed (direct but close Supported. Supported. Supported. and company prof- itability to zero). is direct and positive. The relationship be- tween eco-innova- tion’s performance H7 Supported. Supported. Supported. Supported. and economic ben- efits is direct and positive. The relationship be- tween eco-innova- tion’s performance H8 Supported. Supported. Supported. Supported. and competitive benefits is direct and positive. Summary of findings and discussion Hypotheses Results – main findings with description Product EI Process EI Organizational EI EI construct The relationship be- tween eco-innova- tion’s performance H9 Supported. Supported. Supported. Supported. and internation- alization is direct and positive. Summarizing (see Table 91), we can conclude that several factors – the command-and-control instrument, the economic incentive instrument, customer demand, managerial environmental concern and competitive pressure – all drive implementation of the following eco-innovation types: product, process, organizational eco-innovation, and eco-inno-279 vation construct. The results revealed that expected benefits work as a driver of product eco-innovation, organizational eco-innovation and the eco-innovation construct, while expected benefits do not work as a driver of process eco-innovation. Finally, pertaining to the outcomes of eco-innovation, the results indicate that implementation of product, process and organizational eco-innovation and the eco-innovation construct leads to a higher level of internationalization (in terms of scope – number of operation modes and number of foreign markets – and scale) and also leads to greater competitive and economic benefits. Moreover, implementation of process eco-innovation, organizational eco-innovation and the eco-innovation construct is positively associated with company profitability (in terms of ROA, ROE and ROS), while this is not the case for product eco-innovation. We assume that this last finding pertaining to the effect of product eco-innovation on company profitability (where the standardized coefficient was close to zero, negative and statistically significant) relates to the longer process of product development, and thus the results pertaining to company profitability are also lagged in time. However, implementation of eco-innovation does not lead to company growth but rather shows a significant negative influence on it (in terms of employees and sales). This finding should be interpreted with care, however, because growth in number of employees and growth in sales can also be affected by other factors. Below, we summarize all the findings related to each hypothesis. We have tested four eco-innovation models (of which the main findings were briefly summarized above): we tested determinants and consequences of product, process and organizational eco-innovation and, lastly, the eco-innovation construct. In Pursuit of Eco-innovation Hypothesis 1a, which postulates a significant and positive effect of the command-and-control instrument on eco-innovations, has been supported for product, process and organizational eco-innovation and also for the eco-innovation construct, which measured eco-innovation as a second-order latent factor composed of product, process and organizational eco-innovation. Our findings are in line with prior research works (Noci and Verganti 1999; Triebswetter and Wackerbauer 2008; Kammerer 2009; Lin et al. 2013b; Triguero et al. 2013), which found regulations to be a driver of product eco-innovation. Furthermore, numerous studies also found support for the claim that regulations motivate companies to adopt process eco-innovation (Cleff and Rennings 1999; Wagner 2009; Agan et al. 2013; Lin et al. 2013b) and spur organizational eco-innovation adoption (Triguero et al. 2013). Moreover, our re-280 sults are in line with many research works that have found regulations to incite eco-innovation adoption/implementation (Cleff and Rennings 1999; Hall 2000; Blum-Kusterer and Salman Hussain 2001; Mazzanti and Zoboli 2006; Horbach 2008; Chappin et al. 2009; Lewis and Cassells 2010; Qi et al. 2010; Belin et al. 2011; Popp et al. 2011; Weng and Lin 2011; Yalabik and Fairchild 2011; Blind 2012; Doran and Ryan 2012; Dong et al. 2013; Bocken et al. 2014; Cai and Zhou 2014; Chassagnon and Haned 2014; Doran and Ryan 2014; Ford et al. 2014; Li 2014). An interesting finding from our results is that the standardized coefficient was greatest for the causal relationship between the command-and-control instrument and process eco-innovation, while it was lowest for the association between the command-and-control instrument and organizational eco-innovation. Based on our results, we can conclude that the command-and-control instrument drives process eco-innovation to the greatest extent (standardized coefficient 0.22), followed by the eco-innovation construct (standardized coefficient 0.18), product eco-innovation (standardized coefficient 0.15) and, lastly, organizational eco-innovation (standardized coefficient 0.11). It seems that the command-and-control instrument incites the most process eco-innovation, while it exerts a smaller effect on the other eco-innovation types (remaining high, positive and significant). In sum, the command-and-control instrument is an effective driver of all eco-innovation types, but only its relative strength varies depending on the eco-innovation type. Hypothesis 1b, which relates to the relationship between the eco- nomic incentive instrument and different types of eco-innovations, also turned out to be supported for all three eco-innovation types (product, process and organizational eco-innovation) and the eco-innovation con- Summary of findings and discussion struct. The economic incentive instrument exerted a positive, significant and moderate influence on product eco-innovation (standardized coefficient 0.12), on the eco-innovation construct (standardized coefficient 0.10), and on organizational eco-innovation (standardized coefficient 0.09), while it exerted the weakest influence (but still positive and significant) on process eco-innovation (standardized coefficient 0.06). We can conclude that our findings are consistent with those of prior research works, which found support for the role of the economic incentive instrument on eco-innovation implementation (Chappin et al. 2009; Oltra and Saint Jean 2009). Many researchers also found a positive and significant effect of government subsidies and grants (which are part of the economic incentive instrument) on different eco-innovation types (Yalabik and Fairchild 2011; Zeng et al. 2011; De Marchi 2012; Doran and Ryan 2012). 281 Related to the environmental policy instruments, which we have tested as two individual components – the command-and-control instrument and the economic incentive instrument – we can conclude that the results of our study do not offer empirical support for the superiority of the economic incentive instrument over the command-and-control instrument (Rennings et al. 2006). Rather, both instruments have shown a positive and significant association with all eco-innovation types (product, process and organizational eco-innovation as well as the eco-innovation construct). The empirical evidence indicates the slight superiority of the command-and-control instrument over the economic incentive instrument in all four tested models of eco-innovation. Concerning Hypothesis 2, which posits a positive and significant relationship between customer demand and eco-innovations, we found strong support for the construct-level eco-innovation model (standardized coefficient 0.30), which is consistent with numerous research works (Ziegler and Rennings 2004; Le et al. 2006; Kivimaa 2007; Horbach 2008; Lewis and Cassells 2010; Popp et al. 2011; Weng and Lin 2011; Zeng et al. 2011; Doran and Ryan 2012; Oxborrow and Brindley 2013; Bocken et al. 2014; Cai and Zhou 2014; Chassagnon and Haned 2014; Doran and Ryan 2014; Li 2014; Triguero et al. 2014). Furthermore, our results offer strong empirical evidence for the relationship between customer demand and product eco-innovation (standardized coefficient 0.29), in line with prior research (Ziegler and Rennings 2004; Rehfeld et al. 2007; Triebswetter and Wackerbauer 2008; Horbach et al. 2012; Lin et al. 2013a; Lin et al. 2013b; Triguero et al. 2013). Based on the results of our study, we can conclude that customer demand also drives pro- In Pursuit of Eco-innovation cess eco-innovation (standardized coefficient 0.28), in line with other research works (Ziegler and Rennings 2004; Agan et al. 2013). Moreover, we found support for the association between customer demand and organizational eco-innovation (standardized coefficient 0.12). The influence of customer demand on different types of eco-innovation was found to be positive and significant for all four models of eco-innovation; therefore, customer demand has been demonstrated to drive the implementation of different eco-innovation types in the analyzed companies. We can also observe that the causal relationships between customer demand and several eco-innovations – the eco-innovation construct, product eco-innovation, and process eco-innovation – have received stronger support than the causal relationship between customer demand and organizational eco-innovation. This can be also expected because, generally, custom-282 ers impose pressure on companies to operate in a more environmentally friendly way, and thus emphasize the role of process eco-innovation adoption and in such way steer companies toward environmentally friendly way of manufacturing or demand from companies’ ecological products as an outcome. Therefore, it is more likely that customers incite companies to meet their needs by introducing product eco-innovations, which may lead to certain benefits for the customer (e.g., energy savings) or will satisfy customers’ desire for ecological responsibility, awareness and environmental consciousness. Next, consistent with previous research, we found that managerial environmental concern (Hypothesis 3) exerts a positive and significant influence on process eco-innovation (standardized coefficient 0.23), on the eco-innovation construct (standardized coefficient 0.12), on organizational eco-innovation (standardized coefficient 0.08) and on product eco-innovation (standardized coefficient 0.01). This is in line with past research, which found support for the effect of managerial environmental concern on process eco-innovation (Agan et al. 2013), on eco-innovation in general (Qi et al. 2010; Bocken et al. 2014) and on product eco-innovation (Chang 2014). The influence of the determinant managerial environmental concern was positive and significant for all four models, while its effect was greatest on process eco-innovation and weakest on product eco-innovation, which is in line with past research. Thus, managerial environmental concern seems to affect mostly implementation of process eco-innovations in the analyzed companies. Hypothesis 4, with regard to expected benefits as a driver of eco-innovation, received mixed support when analyzing its effect on different eco-innovation types. We predicted a positive and significant influence Summary of findings and discussion on all eco-innovation models. In our study, we found the greatest empirical support for the causal relationship between expected benefits and product eco-innovation (standardized coefficient 0.12), followed by organizational eco-innovation (standardized coefficient 0.06); finally, we found a weaker, but still positive and significant, relationship between expected benefits and the eco-innovation construct (standardized coefficient 0.05). The relationships of the previously mentioned effects were all positive and significant. However, we have not found support for a positive effect of expected benefits on process eco-innovation; instead the association between them was negative, moderately high and significant (-0.18). It seems that companies do not engage in process eco-innovation because they expect benefits or positive outcomes from it. It is likely that, in the initial phase, companies expect only high investment costs and expenses related to process eco-innovation implementation, although in the 283 long term they provide several benefits for companies, especially cost savings. It is generally known that eco-innovations pay off after several years’ lag due to high initial investments (especially in the case of integrated cleaner technology). Moreover, process eco-innovation can be roughly divided into end-of-pipeline technology and cleaner technology; the first leads only to costs and does not deliver benefits to the company, because it works only on externality reduction, while the latter demands higher investments but can also be beneficial for the company (in terms of cost savings, e.g., energy, material and resource). However, the results of our study revealed that expected benefits do not drive implementation of process eco-innovations in the analyzed companies. On the other hand, our findings concerning product eco-innovation, organizational eco-innovation and the eco-innovation construct are in line with past findings that companies are motivated by expected benefits to adopt eco-innovation. Researchers found that companies expect primarily cost savings from eco-innovation implementation (Lewis and Cassells 2010; York and Venkataraman 2010; Belin et al. 2011; Pereira and Vence 2012; Oxborrow and Brindley 2013; Chassagnon and Haned 2014; Mondéjar-Jiménez et al. 2014), followed by other benefits like new market creation/increase of market share (Lewis and Cassells 2010; Triguero et al 2013; Mondé- jar-Jiménez et al. 2014), improvement of firm reputation/image (Lewis and Cassells 2010; van den Bergh et al. 2011; van den Bergh 2013; Bocken et al. 2014), expected increase of product quality (Lewis and Cassells 2010; Mondéjar-Jiménez et al. 2014), improved firm efficiency/productivity (Lewiss and Cassells 2010), potential revenue (York and Venkataraman 2010; Bocken et al. 2014) and gain of competitive advantage/differ- In Pursuit of Eco-innovation entiation (Triebswetter and Wackerbauer 2008; Lewis and Cassells 2010; York and Venkataraman 2010; Cuerva et al. 2013). We have not tested Hypothesis 5a related to competitive intensity, because the variance that this construct explained was too low. Instead, we examined the Hypothesis 5b, which predicts a significant and positive influence of competitive pressure on eco-innovation. This hypothesis received strong support in all four models (product eco-innovation, process eco-innovation, organizational eco-innovation and the eco-innovation construct). In more detail, competitive pressure exerted the greatest impact on the eco-innovation construct and organizational eco-innovation (both had standardized coefficients estimated at 0.64), followed by product eco-innovation (standardized coefficient 0.44) and process eco-innovation (standardized coefficient 0.40). Again, all relationships were high, 284 positive and significant, which is consistent with past research works (Le et al. 2006; Kemp and Pearson 2007; Yalabik and Fairchild 2011; Zeng et al. 2011; Bocken et al. 2014; Cai and Zhou 2014; Li 2014). Hypothesis 6a predicted a positive and significant relationship between company growth and eco-innovations. However, our study pro- duced no empirical evidence to support this relationship. Rather, negative and significant relationships were found for all four models of eco-innovation. These results are not surprising, because past researchers received mixed results when testing the relationship between eco-innovations and company growth. Likewise, we predicted positive and significant relationships between eco-innovations and company profitability and again found mixed support. Empirical evidence of our study gives support to causal relationships between process eco-innovation, organizational eco-innovation and the eco-innovation construct and company profitability, which is consistent with past research works (Rao and Holt 2005; Clemens 2006; Montabon et al. 2007; Eiadat et al. 2008; Molina-Azorín et al. 2009; Huang and Wu 2010; Zeng et al. 2011; Ar 2012; Cheng and Shiu 2012; Cheng et al. 2013; De Burgos-Jiménez et al. 2013; Leonidou et al. 2013a). Meanwhile, a significant and negative relationship (standardized coefficient close to zero) was found between product eco-innovation and company profitability; therefore, more research on this topic is required before making premature or ambiguous conclusions related to this relationship. The support found for causal associations between organizational eco-innovation and company profitability (standardized coefficient 0.05); process eco-innovation and company profitability (standardized coefficient 0.04), and eco-innovation construct and company profitability (standardized coefficient 0.02) was positive and Summary of findings and discussion significant, although the standardized coefficients were low. We can also see that only for product eco-innovation showed no support; product eco-innovation exerts a negative influence on company profitability, but this this relationship is close to zero, is negative and significant (standardized coefficient -0.00). The likely explanation for this finding is that innovations that do not improve a company’s resource efficiency do not provide positive returns on profitability, while innovations that increase a company’s resource efficiency (in terms of material or energy consumption per unit of output) are more likely to have a positive effect on profitability (Rexhäuser and Rammer 2013). Likewise, Ghisetti and Rennings (2014) stressed that innovations that lead to reduction in the use of energy or materials per unit of output positively affect a company’s competitiveness, while externality-reducing innovations hamper a company’s competitiveness. 285 Because we expected that eco-innovations would not exert a positive effect on company performance, when focusing on objective measures (i.e., profitability indicator ratios) related to company growth and company profitability (secondary data obtained from the GVIN database), we also measured economic benefits pertaining to the respondents’ assessment of company performance. We found strong support for Hypothesis 7, which predicted positive and significant relationships between eco-innovations and economic benefits. The eco-innovation construct exerts a significant positive effect on economic benefits (standardized coefficient 0.65), as do organizational eco-innovation (standardized coefficient 0.62), product eco-innovation (standardized coefficient 0.49) and, finally, process eco-innovation (standardized coefficient 0.48). Moreover, all eco-innovation types (product eco-innovation, process eco-innovation, organizational eco-innovation and the eco-innovation construct) exerted a positive and significant influence on competitive benefits. We found the strongest association when testing the causal relationship between the eco-innovation construct and competitive benefits (standardized coefficient 0.59), followed by organizational eco-innovation (standardized coefficient 0.54), process eco-innovation (standardized coefficient 0.49), and product eco-innovation (standardized coefficient 0.43). All the relationships were strong, positive and significant, thus offering strong support to Hypothesis 8. Lastly, we tested the relationships between eco-innovations and internationalization. Our study yields empirical evidence to support positive and significant causal relationships of these categories for all four models. We found the strongest support for the relationship between In Pursuit of Eco-innovation product eco-innovation and internationalization (standardized coefficient 0.24), followed by the relationship between the eco-innovation construct and internationalization (standardized coefficient 0.21). Moderately high standardized coefficients were found for the relationships between process eco-innovation and internationalization and between organizational eco-innovation and internationalization (in both relationships, the standardized coefficients were estimated at 0.17). This indicates that eco-innovations lead to higher degree of internationalization. 286 Conclusion This chapter is divided into four sections: contributions (Section 10.1), implications (for theory and research, for policy makers and for entrepreneurs; Section 10.2), limitations (Section 10.3), and, finally, future research directions and opportunities (Section 10.4). Contributions This study makes theoretical and methodological contributions to the field of eco-innovation research. The first theoretical contribution pertains to the literature review, which offers a synthesis regarding eco-innovation definitions, the main dimensions of eco-innovation, eco-innovation features, eco-innovation drivers and eco-innovation outcomes. This is followed by a proposal of our own definition of eco-innovation, developed based on the results and findings of this study. The second contribution of this study pertains to conceptual proposal and empirical verification of the eco-innovation construct, which is composed of three main dimensions – product, process and organizational eco-innovation – based on a sample of Slovenian companies (where such a study and validation of the eco-innovation construct has, to the best of our knowledge, not yet been conducted). The newly adapted and tested multidimensional measure of eco-innovation is comprehensive and parsimonious, reflecting good psychometric characteristics. It integrates three dimensions (product, process and organizational eco-innovation) and can be used as a reliable and validated measure of eco-innovation in future research works. Items for the measurement scales’ development were In Pursuit of Eco-innovation adapted to the Slovenian environment based on prior research works. Some items were retained while others were eliminated due to the content validation performed by a qualitative study involving interviewing environmental managers from companies that implement eco-innovations. The quality of scales has been verified by exploratory and confirmatory analyses for each construct. The eco-innovation construct has been demonstrated to have good validity (convergent, discriminant and nomological). The third key contribution of this study is the development and empirical testing of an integrative model of eco-innovation, which includes eco-innovation with its main dimensions (eco-innovation as a second-order latent factor, including product, process and organizational eco-innovation), its drivers and its consequences. The empirical testing of the 288 model clarified the nature of the relationships between eco-innovation, its drivers (the command-and-control instrument, the economic incentive instrument, managerial environmental concern, expected benefits, customer demand and competitive pressure) and its consequences (economic and competitive benefits, internationalization, company growth and profitability) based on a sample of Slovenian companies. The main contribution of testing this model is that it reveals the key role of competitive pressure as a driver of the eco-innovation construct, while the other drivers’ significantly positive influences are minor in comparison. This leads us to the conclusion that operating in highly competitive environments steers companies towards the adoption and development of environmentally friendly products (to satisfy customers’ demands), implementation of environmentally friendly production processes and organizational eco-innovation, in order to gain a competitive advantage over their competitors. Related to the consequences of the eco-innovation construct, based on the findings of the undertaken study, we can conclude that eco-innovation exerts significantly positive influences on companies’ economic and competitive benefits and contributes to higher degrees of internationalization and higher company profitability, whereas it is significantly negatively associated with company growth. This last finding should be interpreted with caution, however, because the values of standardized coefficients are very low; thus, this should be revised and measured/tested again after a few years’ lag (the research could be repeated next year to explore whether differences in company growth and profitability occur). One of the greatest contributions of this study is that it tests four different models, together with the previously mentioned construct-lev- Conclusion el model of eco-innovation. In our study, we examined drivers and outcomes of different eco-innovation types separately (product, process and organizational eco-innovation), which have led us to more detailed and profound insights regarding drivers and outcomes of different eco-innovation types. As aforementioned, the construct-level model of eco-innovation (measured and tested as a second-order latent factor comprising three dimensions: product, process and organizational eco-innovation) has also been tested. In more detail, we have explored the relative strengths of each driver on different eco-innovation types; likewise, the same has been done for the eco-innovation outcomes. This leads us to conclusions regarding which eco-innovation type leads to greater competitive and economic benefits, higher company profitability or increased internationalization, as well as which drivers are more relevant and effective in triggering certain eco-innovation types. The most important insight is 289 that all the tested drivers significantly positively affect all three eco-innovation types (with the exception of expected benefits, which demonstrated a significantly negative association with process eco-innovation). The strongest influence on product eco-innovation is exerted by competitive pressure, followed by customer demand. Meanwhile, process and organizational eco-innovation are largely driven by competitive pressure. Regarding the eco-innovation outcomes, relationships between all three eco-innovation types (product, process and organizational eco-innovation) and three of the outcomes (internationalization, competitive and economic benefits) are significantly positive, whereas all three eco-innovation types are significantly negatively associated with company growth (however, while the values of standardized coefficients were significant, they were low; thus, this finding should be interpreted with care, and the analysis should be repeated after a one-year lag). Moreover, process and organizational eco-innovation exert a significantly positive influence on company profitability, while the association between product eco-innovation and company profitability is significant but negative (again this finding should be interpreted with caution, because the standardized coefficient was close to zero despite being statistically significant). The fifth contribution pertains to the eco-innovation drivers, as we have tested many factors (both internal and external to the company) that may influence the implementation of eco-innovation in companies. Before the quantitative research, these factors were also verified through qualitative research, to explore whether the Slovenian companies identify them as driving forces of eco-innovation implementation. Their relevance was thus identified/verified by a prior qualitative study (i.e., in- In Pursuit of Eco-innovation terviews with environmental managers of five Slovenian companies that implement eco-innovations). Moreover, in this study, we identified the drivers of different eco-innovation types (product, process and organizational eco-innovation, and the eco-innovation construct). We found that all drivers spur eco-innovations (with the exception of excepted benefits as a driver of process eco-innovation, where a negative relationship was found), while competitive pressure can be considered the strongest driver of all three eco-innovation types. Another great contribution lies in testing driver environmental policy instruments as two individual components (the command-and-control instrument and the economic incen- tive instrument). This approach has also been adopted in prior research (Li 2014) and has proven to be rewarding in our study, where we were able to identify the individual influences of both instruments on differ-290 ent eco-innovation types. Furthermore, the contribution pertaining to the outcomes of eco-innovation is that we tested outcomes of eco-innovation at the firm level, meaning that we were interested in the consequences pertaining to the company that deploys eco-innovations. Our main aim was to explore whether eco-innovations are worthwhile for the company that adopts them, or whether they deliver benefits only to the environment. We tested the following outcomes of eco-innovation: competitive benefits, economic benefits, company growth and profitability, and internationalization as consequences of three eco-innovation types (product, process and organizational eco-innovation) and eco-innovation construct. Another important contribution in testing the consequences of eco-innovation is that we not only identified the consequences for each eco-innovation type but also used both self-reported (economic and competitive benefits, internationalization) and objective measures (company growth and profitability were obtained from the GVIN database). A combination of both types of measures enabled us to derive several insights. The literature offers rather ambiguous and mixed results pertaining to the eco-innovation outcomes. Eco-innovation by definition is more environmentally benign than relevant alternatives. The definition by itself does not emphasize any benefits for the company that either adopts or develops eco-innovation. However, it is known that some types of eco-innovation may be beneficial for the companies (e.g., cleaner production resulting in cost savings and consequently leading to higher profitability), while others (eco-innovations that tend only to reduce the negative externalities, such as end-of-pipeline technologies) are instead harmful to the company performance (competitiveness and profitability). Moreover, payoff relat- Conclusion ed to eco-innovation investments requires a few years’ lag (depending on the amount of resources invested). Therefore, for the majority of companies, at least for the first few years after implementation, eco-innovations were seen as a burden for the company. As previously mentioned, we tested the outcomes of eco-innovation in two ways: by asking respondents to evaluate the economic and competitive benefits and by using secondary data (such as ROA, ROE, ROS, company growth in terms of number of employees and growth in sales over two business years). Our findings indicate that companies perceive eco-innovations as beneficial, in terms of economic and competitive benefits (self-reported measures); however, in terms of the objective indicators of company performance, we found a negative association between all eco-innovation types and company growth, as well as between product eco-innovation and company profitability. While these values were statistically significant, they were low 291 (in the case of product eco-innovation’s effect on company profitability, the standardized coefficient was close to zero and thus requires further research). The finding that relations between eco-innovations and company performance are low, may be explained by the fact that eco-innovations generally pay off after several years’ lag; that is, the profitability indicator ratios are initially negative if the investments made were substantial and have not yet shown returns. Therefore, more research on this topic is needed in order for the association between eco-innovations and company performance (when using the profitability indicator ratios) to be fully understood. However, by including self-reported measures, the results indicate that eco-innovations deliver competitive and economic benefits to the company that implements them. Therefore, our findings reveal that the relationships between eco-innovations (all four models) and company growth are significant and negative, whereas the relationship between eco-innovations and company profitability was found to be positive and significant, with the exception of product eco-innovation (which had a significant and negative association with company profitability). However, eco-innovations lead to the gain of economic and competitive benefits. This approach (using both types of measures to test the effects of eco-innovations on company performance) proved to be rewarding, as the distinct effects of company performance would otherwise not be recognized or could lead to flawed conclusions. In this way, the results show that eco-innovations do deliver benefits to the company that implements them. We can thus conclude that eco-innovations are worthwhile (in terms of economic and competitive benefits) for companies that imple- In Pursuit of Eco-innovation ment them, even if the objective measures do not yet show the positive results. A major contribution of this study is that identifies which drivers work as driving forces of specific eco-innovation types and further explores how different eco-innovation types affect company performance in terms of competitive and economic benefits, company growth and profitability and internationalization. These results lead to several insights and implications, which especially concern entrepreneurs and policy makers. Through the acquired knowledge and insights regarding drivers of certain eco-innovation types as well as eco-innovations’ outcomes, this study can contribute to the development and further implementation of eco-innovations in the Slovenian entrepreneurial environment. The last contribution lies in the rigor test of the data analyses. All the 292 models were tested with structural equation modeling, and prior to testing all the constructs were validated (through exploratory and confirmatory factor analysis) and demonstrated good psychometric characteristics. Implications The results of this study deliver several implications. In the following subsections, we provide implications for theory and research (Section 10.2.1), policy makers (Section 10.2.2) and practice (entrepreneurs; Section 10.2.3). Implications for theory and research As already mentioned, many research works have explored determinants and outcomes of eco-innovation, usually focusing only on either determinants or outcomes and either focusing only on one eco-innovation type or combining all eco-innovation types under one factor (as we have done with the eco-innovation construct). Compared to the partial models that have been previously explored, we have employed a more integrative approach. We have first conducted a qualitative study by interviewing environmental managers in five Slovenian companies that implement eco-innovation in order to identify the drivers of eco-innovation that motivated them to implement eco-innovation and the outcomes of their eco-innovation implementation. Through these interviews, we were able to verify which drivers are relevant and important for the Slovenian environment with regard to implementation of eco-innovation. We then adapted the chosen drivers to the Slovenian environment (because many research Conclusion works are based on Chinese companies, whose eco-innovation drivers differ from those in Slovenia), and conducted the quantitative research. In our quantitative research, we encompassed several drivers and also focused on the outcomes (in terms of company growth and profitability, internationalization, competitive and economic benefits). The integrative approach that we have adopted highlights the relative importance and relevance of several eco-innovation determinants and outcomes pertaining to eco-innovation implementation. Concerning the drivers of eco-innovation with a focus on environ- mental policy instruments, researchers generally measure them as one construct. Following Li (2014), we have divided environmental policy instruments into the command-and-control instrument and the econom- ic incentive instrument. Thus, we examined the individual effects of each instrument on eco-innovation. This approach has turned out to be re-293 warding in our research, giving more profound insights into their individual effects on different eco-innovation types. Moreover, when testing outcomes of eco-innovation, we employed both objective (secondary financial data of analyzed companies gathered from the GVIN database) and self-reported measures, instead of only one or the other as in most previous research. This approach has proven to be rewarding. Eco-innovation generally pays off after several years’ lag due to the investments made, and thus the objective measures can reflect a more negative situation than occurs in reality, leading to the conclusion that eco-innovation is only a cost that the company must bear. The self-reported measures, on the other hand, reflect the outcomes that cannot yet be observed using the profitability indicator ratios (as the return on investments may take several years). Therefore, the financial indicators tend to show a negative image even after companies begin to see the benefits of implementing eco-innovation. In our case, we have seen that product, process and organizational eco-innovation all exerted a significantly negative influence on company growth, while only process and organizational eco-innovation demonstrated a low but significantly positive influence on company profitability. On the other hand, our self-reported measures showed that companies recognized economic and competitive benefits derived from product, process and organizational eco-innovation implementation. Another contribution to theory and research is our exploration of drivers and outcomes of different eco-innovation types, leading to greater insights and a deeper understanding of drivers and outcomes of different eco-innovation types (product, process and organizational eco-inno- In Pursuit of Eco-innovation vation). The integrative approach has taken into consideration a larger number of relevant variables that can work as drivers of eco-innovation, as well as a larger number of variables to gauge company performance (outcomes related to eco-innovation implementation). In contrast to partial approaches, which tend to explore few variables, the integrative approach takes into consideration a greater number of relevant variables while omitting the less important elements (checked with prior qualitative research). However, by identifying the relative importance of model elements (drivers and outcomes) for different eco-innovation types (product, process and organizational eco-innovation), we are able to draw more precise and accurate implications for entrepreneurs and policy makers, making our research interesting and beneficial for a wide range of au-diences. 294 Lastly, we have adapted and tested the eco-innovation construct, covering product, process and organizational eco-innovation, by verifying it on the sample of Slovenian companies. The eco-innovation construct developed in this study offers a relatively complete picture and thus can be used in future exploration of eco-innovation as a research framework. The eco-innovation construct was validated in this study and can be used as an eco-innovation measure both at the overall level and at the dimensional levels. Finally, based on the results of our study, we proposed the following definition of eco-innovations: Eco-innovations encompass environmental and economic dimensions and include a variety of new or significantly improved products, processes, organizational methods and systems that are more environmentally friendly than the existing ones. They stem mainly from competitive pressure and customer demand. The most important outcome of eco-innovations (which can be intentional or a side effect) pertains to decreased adverse effects to the environment. From the environmental point of view, eco-innovations decrease the company’s environmental burden, while from the economic point of view, eco-innovations pay off because they result in competitive and economic benefits, as well as a higher degree of internationalization. Implications for policy makers The implications for governmental policy makers are as follows. The results of our study indicate the greatest influence of competitive pressure and customer demand on product eco-innovation; moreover, competitive pressure seems to be the strongest driving force of process and organizational eco-innovation. By comparison, both the command-and-con- Conclusion trol instrument and the economic incentive instrument exert a smaller (though still significantly positive) effect on all eco-innovation types. The command-and-control instrument and the economic incentive instrument seem to be effective in motivating eco-innovation implementation, while the economic incentive instrument plays an even smaller role than the command-and-control instrument in spurring eco-innovation implementation in Slovenian companies. Policy makers could benefit from our results, as our findings revealed that the command-and-control instrument plays the most effective role in spurring process eco-innovation, followed by product eco-innovation, while it has the least effect on organizational eco-innovation. In addition, the economic incentive instrument is most effective in spurring implementation of product eco-innovation, followed by organizational eco-innovation, and, lastly, process eco-innovation. We believe that the economic incentive instrument in the Slo-295 venian environment is not developed enough, and therefore should be more emphasized, especially for eco-innovations that deliver higher value for the environment and economy and are related to large investments, which require more time to pay off and consequently hamper company performance in the meantime. It is likely that developing greater flexibility in the command-and-control instrument and combining it with the economic incentive instrument would deliver better results and gain more success in spurring eco-innovation. As stressed by other researchers (Rennings et al. 2006), for a long time it was assumed that the economic incentive instrument is more effective than and thus superior to the command-and-control instrument for triggering eco-innovation, whereas the findings of our study demonstrate the opposite result for all eco-innovation types. As argued by Oltra and Saint Jean (2009), we stress, based on the obtained findings, that the economic incentive instrument cannot entirely substitute for the command-and-control instrument, and by itself is not sufficient for spurring eco-innovation. Therefore, the use of different instruments may vary depending on the context and eco-innovation type, while the combination of both seems to be most effective. However, a significant insight of this study is that the most important driver of implementation of eco-innovation is not the environmental policy instruments but rather competitive pressure, which forces companies to become more environmentally friendly, be more eco-efficient in their use of resources (e.g., material, energy, water, etc.), and provide/offer to consumers more environmentally friendly solutions. According to the results of this study, competition is the strongest driving force of all eco-innovation types tested in this study (product, process, organization- In Pursuit of Eco-innovation al eco-innovation and eco-innovation construct). Thus, the policy makers should develop or propose instruments that would both diffuse eco-innovation adoption in companies and help companies to develop eco-innovations that lead to a gain of competitive advantage and consequently benefit the economy as well. In addition, product eco-innovation contributes to companies’ economic and competitive benefits but is negatively related to company profitability and growth. More research should be done on this topic, considering that the positive outcomes of eco-innovations can be lagged in time and thus control the time of investment in product eco-innovation. However, companies that implement product eco-innovation to a greater extent seem to enjoy a higher degree of internationalization. Policy makers should tackle this issue and dedicate more attention to the economic 296 incentive instrument in order to overcome the investment costs related to the product eco-innovation; a possible solution would be green public procurement or tax exemptions. Incentivizing product eco-innovation can also deliver benefits to the economy, in the sense that a higher degree of internationalization leads to higher profits and also reflects a country’s sustainable awareness worldwide. Moreover, companies with more process and/or organizational eco-innovations are also more internationalized, and this relationship is stronger in the case of product eco-innovation. Product eco-innovation exerts a low but negative influence on company growth and profitability, while process and organizational eco-innovation showed a positive relationship with company profitability and a negative relationship with company growth. The results of this study reveal that all eco-innovations lead to competitive and economic benefits. Therefore, it would be meaningful to incentivize companies in terms of subsidizing hiring additional employees, which would not only contribute to company growth in terms of number of employees but also deliver new insights, knowledge and competences to companies, as well as new human resources that can be exploited for the adoption or development of eco-innovation. Moreover, bearing in mind that process and organizational eco-innovations are positively associated with company profitability, such incentives (sunk costs, subsidies, grants) may be provided only for the initial investment, in the case of certain process eco-innovations that highly contribute to the environmental welfare and demand high investments that may pay off after several years through cost savings. Other incentives that could be helpful are tax exemptions, which, again, could be applied only for a few years, covering the investment period (e.g., a company that deploys a new Conclusion eco-innovative process and amortizes its investment over 10 years would acquire tax exemptions instead of other grants for the period before the investment becomes lucrative). Based on the findings of our study, we summarize the main sugges- tions for the policy makers. Companies have long seen eco-innovations as sunk costs and never-ending investments due to the environmental regulations and standards with which they needed to comply. Eco-innovations were therefore seen as a burden for companies, implemented only to comply with regulations. Today, however, we can observe a change in this mindset. Companies have begun to recognize that in eco-innovation lies the potential to acquire several benefits, such as the gain of competitive advantage and business opportunities. The findings of our study indicate that companies that implement product, process and/or organizational eco-innovation can exploit several benefits from it, such 297 as higher company profitability (with the exception of product eco-innovation, for which we found a low but negative association), economic and competitive benefits and a higher degree of internationalization. This indicates that eco-innovations are worthwhile for companies and do pay off in terms of the previously mentioned outcomes. Regarding the drivers of eco-innovation, the results of our study clearly indicate the prevailing effect of competitive pressure over other determinants for all three eco-innovation types (product, process and organizational eco-innovaton). Competitive pressure seems to be the strongest driving force of all three eco-innovation types, while noteworthy influences are also demonstrated by customer demand on product eco-innovation and customer demand and managerial environmental concern on process eco-innovation. The rest of the examined determinants, such as the command-and-control instrument, the economic incentive instrument, and expected benefits were also found to be effective in triggering eco-innovations, albeit to a lesser degree. These findings suggest an important implication for policy makers. Keeping in mind that companies are mostly motivated to eco-innovation implementation by competitive pressure, customer demand and managerial environmental concern, the environmental policy instruments (both the command-and-control instrument and the economic incentive instrument) should be adapted. For a successful enforcement of policies, the command-and-control instrument and the economic incentive instrument should be combined and made more market-oriented. Pursuing market demand and companies’ needs and including them in policy development would greatly contribute to the enhanced capacity and performance level of eco-innovations in companies. In Pursuit of Eco-innovation Second, environmental policy instruments should offer more assistance and support to companies through the entire lifecycle of eco-innovations. Market-oriented instruments (e.g., tradable permits and green taxes), regulations, and environmental standards are meant to hinder the operation of non-compliant companies; however, too often they also hamper companies that do comply. Moreover, tax exemptions could be applied more often, either to the company that implements eco-innovations or to the purchase of eco-products or services. For investments in process eco-innovation, which are more eco-efficient and pertain to the area of renewable energy (e.g., wind power, photovoltaic system, etc.), feed-in-tariffs have proved worldwide to be a good practice in order to spur their implementation. One of the last suggestions regarding environmental policy instruments concerns green public procurement, which is not yet ap-298 plied and practiced to a desirable extent in the Slovenian environment, but which has the potential to ease companies’ commercialization and sales and to support their environmental efforts. Finally, the results of our study reveal that managers play an important role in inducing eco-innovation in companies. Therefore, subsidies for education/training related to environmental topics, to either raise the level of environmental awareness among companies’ managers and present them with opportunities to seize from eco-innovation or to improve their knowledge and expertise, could deliver important outcomes. Another suggestion would be providing subsidies for employment of managers with backgrounds, expertise or knowledge related to environmental issues and solutions, making them more likely to pursue sustainability and steer companies towards eco-innovation implementation. Lastly, critically needed are a higher level of knowledge transfer among researchers and practitioners, a stronger connection between research and practice and better collaboration between public authorities (universities and research institutes) and companies, all of which could greatly contribute to companies’ efforts to implement or develop eco-innovations. A huge amount of hidden potential, opportunities and knowledge lies in this gap, which should be better exploited. Implications for entrepreneurs Lastly, our results hold several implications for entrepreneurs. The results of this study have revealed that implementation of process and/or organizational eco-innovations leads to more successful company performance in terms of company profitability, gain of competitive and economic benefits and a higher degree of internationalization. This indicates that com- Conclusion panies should invest more in process and organizational eco-innovations, which increase their profitability and lead to several benefits, or at least successfully pay off, according to our findings. Implementation of product, process and organizational eco-innovation also leads to several competitive and economic benefits for companies and therefore delivers value not only to the environment but also to the companies that implement them. Another important aspect to tackle from the perspective of entre- preneurs is that, based on our study’s findings, eco-innovations seem to increase the degree of internationalization. Companies that implement either product, process or organizational eco-innovation are also more internationalized (operating on more foreign markets, using many operation modes when entering foreign markets, and having a higher share of sales abroad). Implementation of eco-innovation therefore contributes to 299 success on the foreign markets (in terms of scale and scope). In sum, implementation of eco-innovations can provide a new business opportunity, offering entrance to or better performance on foreign markets. Limitations Our study has some limitations, which will be described in this section. Due to the complexity of the phenomenon and our effort to adopt an integrative approach (i.e., to test the drivers and outcomes of different eco-innovation types as well as the construct-level model of eco-innovation), high observations per parameter were required to test such a complex model. With regard to the data collection, our study is based on a sample of Slovenian companies; therefore, the study and its findings are somewhat limited to the Slovenian environment. However, since the conceptual basis was developed in research on other contexts and then adapted to the Slovenian environment, we infer that the findings may be generalizable to some degree to other European countries that are similar to Slovenia. Moreover, we collected data only from companies employing at least five employees in order to avoid dormant micro companies. Further, this study has encompassed companies from different industries that have implemented eco-innovation, whether or not they have acquired environmental certificates. Nevertheless, having been conducted on a middling large sample, our study already shows the prevailing effects of certain eco-innovation determinants on different eco-innovation types, as well as the effects of different eco-innovation types on the outcomes at firm level. A great contribution of this study is that it has In Pursuit of Eco-innovation not measured eco-innovation in general but rather distinguished several eco-innovation types – product, process and organizational – and further explored drivers and outcomes of different eco-innovation types. Some limitations in the study’s design can be also identified. This study used cross-sectional data. A longitudinal study would enable us to draw conclusions and causative implications regarding the effects of different eco-innovation determinants on eco-innovation practices, as well as the impact of different eco-innovation types on the outcomes at firm level. With this approach, we obtained insight into drivers and outcomes of eco-innovation pertaining to the moment at which companies completed the questionnaire. This limitation has been partially mitigated with the use of objective and self-reported measures concerning company performance. Objective measures gathered from a secondary database 300 present a current state, while the self-reported measures add reflective and subjective evaluation of eco-innovation performance and its ongoing effect on the company’s performance. Another limitation, which is partially related to the previous one, pertains to informant bias (i.e., the data were collected from only one person from each company). Since we used a single informant from each of the companies to complete the survey, concerns of common method variance (CMV) were addressed (Podsakoff et al. 2003) using Harman’s single factor test, which is the most widely used method to assess the possibility of CMV. Results indicated a low threat of common method variance. Our study used mainly self-reported measures, which reflect its subjective nature. It may be that different informants from the same company would respond differently to some degree, because they perceive the same situation and environment differently. However, we believe that this was not a major limitation in our study, because we were interested in factors that spur companies into implementation of eco-innovation and its outcomes. Another limitation could occur if the person who completed the questionnaire was not yet employed in the company when the company began its eco-innovation implementation, as that person might not have the necessary knowledge and insights into factors that incited the company to begin eco-innovation implementation, nor of its outcomes and their effects exerted on company performance. We have partially avoided this limitation by asking respondents to respond only if they are the most knowledgeable person in the company about its eco-innovations. Moreover, we measured the outcomes of eco-innovation using objective measures; for company growth and profitability we obtained data from Conclusion the available database, and therefore subjective factors provided only additional, complementary insight. By collecting the objective and subjective measures of company performance (in terms of economic benefits), we achieved equilibrium, in the sense that some types of eco-innovation become profitable after several years’ lag, while respondents may already recognize and be able to report their positive results. Moreover, in this study we endeavored to explore the determinants of different eco-innovation types based on the Slovenian sample. However, we have encompassed the most relevant determinants of eco-innovation according to the prior qualitative research we conducted. This implies that some other determinants were omitted. Due to the complexity of the phenomenon under study, only the most important drivers and outcomes of eco-innovation were selected and included in the model. We have decided to dedicate more attention to the company-related outcomes of 301 eco-innovation than to the environmental benefits, because we feel that it is of great importance to show companies the outcomes of eco-innovation related to company performance in order to answer the question of whether such innovations are worth implementing. This is quite a salient issue, especially because the literature offers mixed findings, and companies themselves usually consider eco-innovation to be expensive and beneficial only to the environment while it is harmful to company performance. Our aim was thus to explore which eco-innovations deliver potential benefits (company growth, profitability, higher degree of internationalization, and competitive and economic benefits) to the companies that implement them. Among the determinants of eco-innovation, we investigated only drivers of eco-innovation, while barriers to eco-innovation remains a topic for further research. It would be useful to know what barriers hinder companies from adopting eco-innovation, in order to get insights regarding why companies do not implement eco-innovation. Research on this question should focus on companies that are not engaged in any eco-innovation activity. These results would lead to important insights and suggest how to steer less motivated companies to implement eco-innovation. Another limitation of this study concerns the fact that we have not differentiated between companies that adopt and companies that develop certain types of eco-innovation. Adoption and development of eco-innovation can differ in their driving forces and in outcomes pertaining to company performance. Furthermore, related to the research methodology, we can analyze drivers of eco-innovation with either a qualitative or a quantitative ap- In Pursuit of Eco-innovation proach. Our study is a case of quantitative research (i.e., survey analysis), which means that the relative strength of the so-called “driver” is being studied, while its decisiveness remains a topic for further analysis (Hojnik and Ruzzier 2015). Despite the aforementioned limitations, this study’s methods and design were suitable for realizing the study’s goal and also delivering the important contributions discussed in the previous sections. Future research directions and opportunities The main goals of this study were as follows. First, we wanted to develop an eco-innovation construct with three dimensions – product, process and organizational eco-innovation – and empirically test it based on a sample of 223 Slovenian companies. Second, we wanted to develop and 302 empirically test a construct-level model of eco-innovation, by adopting an integrative approach and exploring eco-innovation’s drivers and consequences. Further, drivers and outcomes were also explored for the different eco-innovation types (product, process and organizational), thus delivering insights that are more detailed and provide a deeper understanding. We believe that our study delivers important insights and contributions and, to the best of our knowledge, presents the first integrative study in this research field in the Slovenian environment. However, many research gaps still remain open, as discussed below. The measures of the eco-innovation construct in our study encompass three dimensions and differentiate between product, process and organizational eco-innovation. Data analyses demonstrate good psychometric characteristics, also including sufficient discriminant validity, although the product and process eco-innovation dimensions do correlate with each other to a higher level than with organizational eco-innovation dimension. Thus, these dimensions could be further refined and improved. Furthermore, the eco-innovation construct measure should also be validated on samples of foreign companies in different countries. Moreover, the distinction or division of eco-innovation dimensions could be also more specific and go into more detail, in the sense that process eco-innovation could be divided into externality-reducing innovations and resource-reducing innovations (e.g., Ghisetti and Rennings 2014), or end-of-pipeline technologies and cleaner production technologies (as is more commonly done). This distinction could bring other important insights regarding the drivers and outcomes of different process eco-innovation types. Conclusion As abovementioned, in our study we have not differentiated between the adoption/implementation and innovation/development stages of eco-innovation (as strongly emphasized in various literature reviews undertaken by several researchers, e.g., del Río 2009; Hojnik and Ruzzier 2015). Great differences can emerge when exploring drivers of eco-innovation in these two different stages. These differences might also be present in the outcomes of eco-innovation (i.e., whether a company develops or adopts eco-innovation). Thus, future research could explore which drivers work better, which are most effective in the different stages of eco-innovation (development and adoption), for different types of eco-innovation (product, process and organizational), and also how the outcomes of different eco-innovation types differ in different eco-innovation stages. Future research should address the following questions: Is it better for companies to adopt or develop eco-innovation? Which is more benefi-303 cial – contributing to better company performance, providing competitive benefits or providing possible entry or expansion on foreign markets? Do first-mover advantages really occur, and are the companies that develop eco-innovations able to seize the benefits from them? Another future research direction pertains to the exploration of drivers and outcomes of radical and incremental innovations. Researchers (Kemp and Pearson 2008; Kemp and Pontoglio 2011) have argued that within the innovation literature, a distinction is made between incremental innovations and radical innovations. Incremental innovations are only minor modifications of already existing processes or products, while radical innovations present a technological discontinuity based on a break with existing competencies and technologies (Kemp and Pontoglio 2011). Based on a detailed literature review encompassing mixed-method studies and meta-analyses, Kemp and Pontoglio (2011) stressed that regulation is generally believed to motivate merely the diffusion of environmental technology; further, the common wisdom sees market-based instruments as superior to regulations when aiming to solicit innovative responses. However, based on their literature review, Kemp and Pontoglio (2011) conclude that there is more evidence of regulations inducing radical innovation than of market-based instruments doing so. Therefore, in future research it would be beneficial to explore which drivers trigger incremental eco-innovation and which trigger radical eco-innovation (i.e., what are the relative strengths of different drivers and which ones work best for which type), as well as the outcomes of the different types at the firm level. It would also be interesting to explore and control for the In Pursuit of Eco-innovation expected period of return on investment and the size of the return on investment for each eco-innovation type. Moreover, we have adopted a cross-sectional study design, while a longitudinal design would be more appropriate for exploring cause-and-effect relationships (i.e., drivers and outcomes of eco-innovations). In future research, it would thus be meaningful to perform a longitudinal study, since eco-innovation effects on company performance are known to have a few years’ lag (especially concerning company growth and profitability). A longitudinal study would explain the process of eco-innovation, especially in terms of which drivers of eco-innovation are important in the development phase and which in the adoption and diffusion phase. Knowing the drivers for all stages of eco-innovation would indeed be an important insight. Moreover, we could also obtain deeper in-304 sights, such as when a certain type of eco-innovation becomes profitable (bearing in mind that investments in cleaner technology can pay off after several years, while investment in end-of-pipe technology mainly benefits the environment rather than the company) and when different types of eco-innovation provide a return on investment. A longitudinal study would help to answer the question of whether eco-innovations over time only cover or offset the investment costs or really turn out to be lucrative for companies and offer them a first-mover advantage on the market. Pertaining to the effect of eco-innovations on company performance, we have found some statistically significant results in our study, but we were not able to support the hypotheses about company growth and profitability for all eco-innovation types. Therefore, further work and research on this topic related to companies’ profitability indicators ratios (i.e., growth in number of sales and employees, ROA, ROE and ROS) is recommended in order to clarify and understand the association between eco-innovations and company performance. In future research, more information (e.g., time of investment and resources invested in eco-innovation) would help us to establish a greater degree of accuracy on this matter. In the future, we will repeat this study in order to again estimate and analyze the relationship between eco-innovations and company performance after a few years’ lag, to see and control whether and how the results change over time.In future research, it would also be interesting to control the rate of R&D investment in eco-innovations. Several researchers (Ziegler and Rennings 2004; Rennings et al. 2006) found a positive and significant effect of R&D activities on implementation of product and process eco-innovation, while others (Rehfeld et al. 2007) found a weak effect of R&D Conclusion activities on product eco-innovation. Moreover, as aforementioned, not only the rate of R&D but also the stage of eco-innovation should be controlled, in order to see and gauge what kind and amount of investment the adoption of eco-innovation or the development of a certain eco-innovation type require and when the return on investments occurs. With these insights, we could suggest to companies which eco-innovation type is best for them to adopt or implement using their limited resources, as well as what period of time is predicted for a return on their investment. In addition, the outcomes of different eco-innovation types should also be examined in more detail – that is, when, whether, and which eco-innovation types deliver benefits to the company. In sum, return on investment and estimated time of payoff related to different eco-innovation types present further research directions, which would be of great help not only for companies but also for potential investors and policy mak-305 ers, in order to make it easier for them to plan the development and application of different environmental policy instruments. Researchers (Hojnik and Ruzzier 2015, 1) have stressed that a stimulus can act as a motivation-based factor (e.g., regulatory pressure, various expected benefits to be derived from eco-innovation implementation, profiling of company as environmentally friendly, competitive pressure, customer demand) or a facilitating factor (e.g., EMS, financial resources, technological capabilities). In our study, we defined and examined the drivers of eco-innovation as motivation-based factors, and thus the role of drivers that work as facilitating factors of eco-innovation remains a topic of future research. Thus, another interesting aspect to explore would be the effect of EMS (ISO 14001 and EMAS), because several researchers have found a positive effect of EMS on different eco-innovation types. Specifically, researchers have found a positive association between EMS (ISO 14001 and EMAS accreditation) and environmental product innovation (Rehfeld et al. 2007), environmental process innovations (Wagner 2008), and increased investments in eco-innovation (Kesidou and Demirel 2012). When tested separately, ISO 14001 exerted a positive and significant influence on environmental product and process innovation (Ziegler and Rennings 2004), as well as on end-of-pipeline technologies (Demirel and Kesidou 2011). In future research, it would be interesting to test the association between various types of EMS (such as ISO 14001 and EMAS) and different eco-innovation types (e.g., product, process and organizational eco-innovation). Moreover, we could also control the time of accreditation and the effect of accreditation on eco-innovation in the stages of adoption and development. In Pursuit of Eco-innovation In addition, as emphasized by Hojnik and Ruzzier (2015), past re- search mostly examined the proximate factors (i.e., the causes that immediately lead to the adoption or development of eco-innovation), while more attention should be devoted to the distal factors of eco-innovation adoption and development (i.e., the real reason that leads to eco-innovation adoption or development). Our study in not an exception with regard to this issue; therefore, the investigation of the distal factors of eco-innovation adoption and development remains a future research direction. Lastly, in future research we would suggest developing this study and applying it to a wider context (i.e., other countries) to test whether and how drivers and outcomes of eco-innovation vary across countries and eco-innovation types. It would also be fruitful to use this integrative ap-306 proach on different types of industries. 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Avtorica se osredotoči oziroma v svo-ji raziskavi zajame širok spekter določljivk/gonilnih sil eko inovacij (to so naslednje: predpise, ekonomske instrumente, povpraševanje kupcev, ma-nagerjeva skrb za okolje, pričakovane koristi in pritisk konkurence) in pa posledic (v raziskavi zajame naslednje: rast in dobičkonosnost podjetja, ekonomske koristi, konkurenčne koristi ter internacionalizacija). Sama monografija nudi strnjen pregled definicij eko inovacij, njihovih glavnih dimenzij, lastnosti in pa tudi merjenja, medtem ko s sintezo pomembnej- ših lastnosti eko inovacij pripomore k razjasnitvi koncepta eko inovacij. Avtorica na podlagi rezultatov raziskave ter preučevanja literature razvije svojo definicijo eko inovacij. Kot omenjeno, avtorica na začetku natančno opredeli eko inovacije, njihove dimenzije, načine merjenja, pomembnej- še lastnosti eko inovacij, nato pa sledi pregled določljivk eko inovacij in pa posledic eko inovacij na ravni podjetij. Na podlagi izčrpnega pregleda literature je predlagan konceptualni model eko inovacij, katerega avtorica na vzorcu 223 slovenskih podjetij tudi empirično preveri. Vsi konstrukti uporabljeni v raziskavi so prej ustrezno testirani/preverjeni – preverjene so njihove psihometrične značilnosti s pomočjo konfirmativne in eksplo-rativne faktorske analize. Nadalje avtorica predlagani konceptualni mo- In Pursuit of Eco-innovation del s pomočjo modeliranja strukturnih enačb tudi empirično preveri na vzorcu 223 slovenskih podjetij vseh velikosti (v vzorec so vključena podjetja, ki imajo vsaj 5 zaposlenih). Raziskava oziroma monografija nudi ce-losten pregled dosedanje/aktualne literature in empiričnih del/raziskav in pa tudi zanimive ter koristne ugotovitve nanašajoč se na uvajanje in spodbujanje eko inovacij v podjetjih. Velika dodana vrednost raziskave je posamično testiranje gonilnih sil in pa posledic različnih vrst eko inovacij: izdelčnih, procesnih in pa organizacijskih eko inovacij. S tem avtorica bolj natančno testira in tudi določi katere določljivke vplivajo na uvajanje izdelčnih, procesnih in pa organizacijskih eko inovacij. Nadalje, ta pri-stop omogoča tudi vpogled v to katere eko inovacije se podjetjem izpla- čajo in katere ne ali manj. Avtorica glavne ugotovitve raziskave strne in prikaže bolj jedrnato na koncu monografije. Za zaključek pa opozori na 332 omejitve raziskave, predstavi tudi možnosti za nadaljnje raziskovanje in poda predloge za podjetja, raziskovalce ter oblikovalce politik. Boštjan Antončič II The subject of monograph is very effectively identified and described and the main research questions are clearly expressed and positioned within the current academic conversation. A thorough literature review is carried out in the first part of the monograph, showing the multitude of perspectives that overlap. Furthermore, an entire chapter is dedicated to the clarification of the possible meanings of “eco-innovation” and their positioning within the broader concept of innovation. The literature review highlights the many facets of eco-innovation, its several determinants and its multiple consequences. The research conducted and presented in this monograph is based on a very sophisticated and complex model that includes most of the variables and dimensions covered by the rich and growing literature in the field. The hypotheses are tested through an econometric model, based on the well-known methodology of Structural Equations Modelling. The research is based on a non-randomized sample of Slovenian companies who are pursuing eco-innovation projects. While quantitative in nature, the research is mostly based on percep-tual measures of eco-innovation determinants and outcomes. Hypotheses are tested for relevance and significance. Furthermore, the monograph drives interesting and important con- clusions from the research, both at the theoretical and the managerial level. A specific section of the conclusions also addresses the potential ave- Recenziji nues for future research. It is also highlighted the need to further refine and adjust the research methodology to drive some conclusions on the time dimensions and profiles of eco-innovation. Indeed, the current research is cross-sectional and a longitudinal study would be more appropriate to test some of these research hypotheses. In sum, this monograph is a very well crafted research, based on solid academic ground and driving to interesting conclusions, based on significant statistical methods. Andrea Tracogna 333 Založba Univerze na Primorskem Document Outline Hojnik, Jana. 2017. In Persuit of Eco-innovation. Drivers and Consequences of Eco-innovation at Firm Level. Koper: University of Primorska Press (Cover) Hojnik, Jana. 2017. In Persuit of Eco-innovation. Drivers and Consequences of Eco-innovation at Firm Level. Koper: University of Primorska Press (Title Page) Colophone Contents List of Figures List of Tables Abbreviations Introduction Eco-innovation Why to distinguish eco-innovation from regular innovation Defining eco-innovation Review of current eco-innovation definitions Features of eco-innovation Main dimensions of eco-innovation Target Mechanisms Eco-innovation’s impact on the environment Types of eco-innovation Product eco-innovation Process eco-innovation Technological eco-innovation Organizational eco-innovation Marketing eco-innovation Social eco-innovation System eco-innovation Measuring eco-innovation Toward a new definition of eco-innovation Drivers of Eco-innovation Environmental policy instruments Regulation Taxation (taxes and tax incentives) and subsidies Demand side Competition Society Expected benefits from eco-innovation Sources of information Organizational capabilities Managerial environmental concern Company’s general characteristics (firm size and firm age) Consequences of Eco-innovation Adoption Firm performance Internationalization Competitive advantage Hypotheses Development Hypotheses concerning antecedents of eco-innovations Environmental policy instruments and eco-innovation Customer demand and eco-innovation Managerial environmental concern and eco-innovation Expected benefits and eco-innovation Firm reputation Cost savings Competition and eco-innovation Hypotheses concerning consequences of eco-innovation Eco-innovation and firm performance Eco-innovation and economic performance Eco-innovation and competitive benefits Eco-innovation and internationalization Methodology Preliminary testing of questionnaire Research instrument and operationalization of variables and measures Measures for eco-innovation antecedents Measures for eco-innovation dimensions Measures for consequences/outcomes of eco-innovation Sampling and data collection Common method variance assessment Data analyses Evaluation of the results Results Sample characteristics Eco-innovation determinants Managerial environmental concern Expected benefits Environmental policy instruments Customer demand Competition (Competitive intensity and Competitive pressure) Eco-innovation types Product eco-innovation Process eco-innovation Organizational eco-innovation Eco-innovation construct Convergent and discriminant validity of the eco-innovation construct Eco-innovation outcomes Competitive benefits Economic benefits Company performance Internationalization Eco-innovation models Product eco-innovation model Construct validity of product eco-innovation model Statistical analysis and results (path analysis) Process eco-innovation model Construct validity of process eco-innovation model Statistical analysis and results (path analysis) Organizational eco-innovation Construct validity of organizational eco-innovation model Statistical analysis and results (path analysis) The expanded construct-level model of eco-innovation Construct validity for the expanded construct-level model of eco-innovation The expanded construct-level model of eco-innovation (path analysis) Summary of findings and discussion Conclusion Contributions Implications Implications for theory and research Implications for policy makers Implications for entrepreneurs Limitations Future research directions and opportunities References and sources References Sources Recenziji I II