160 Organizacija, V olume 55 Issue 2, May 2022 Research Papers 1 Received: 22nd February 2022; revised: 18th April 2022; accepted: 17th May 2022 The Interplay of Restaurant SMEs’ Entrepreneurial and Environmental Characteristics, Management of the Requisite Assets, and Operational Efficiency 1 University of Primorska, Faculty of Tourism Studies – Turistica, Obala 11a, 6320 Portorož, Slovenia, tanja.planinc@ fts.upr.si (corresponding author), marko.kukanja@fts.upr.si 2 University of Maribor, Faculty of Organizational Sciences, Kidričeva cesta 55a, 4000 Kranj, Slovenia, anja.znidar- sic@um.si Background/Purpose: SMEs are subject to different factors in the business environments that influence their business performance. Considering the importance of restaurants’ environmental characteristics, entrepreneurs can also, through their entrepreneurial characteristics, influence SMEsʼ management of the requisite assets (MRA). Accordingly, this study examines the influence of restaurant SMEs’ entrepreneurial (self-efficacy, orientation, and demographics) and environmental (location, size, and competition) characteristics on MRA and, consequently, on SMEs’ operational efficiency. Methods: Primary data relating to the environmental (location, size, and competition) and entrepreneurial (self-effi- cacy, orientation, and demographic) characteristics were obtained using a survey questionnaire, while the secondary data were obtained from SMEs’ official financial reports. The sample consists of 266 restaurant SMEs in the Repub- lic of Slovenia. Efficiency was analysed using data envelopment analysis (DEA), and structural equation modelling (SEM) was used to test the research model. Results: The results indicate that environmental characteristics have a much more significant impact on MRA than entrepreneurial characteristics. Entrepreneurial self-efficacy and most demographic characteristics (age, gender, ed- ucation, and experience) proved not to influence significantly MRA and, consequently, SMEs’ operational efficiency. Conclusion: SMEs’ external environment is generally not directly influenced by managerial decisions. Therefore, it is critical to strengthen the influence of the internal environment through an active development of entrepreneurial characteristics, which could result in a more effective MRA and higher efficiency. The conclusion provides sugges- tions for future research and valuable information for entrepreneurs, academia, and policymakers. Keywords: SMEs, Restaurant industry, Slovenia, Efficiency, Environmental and entrepreneurial characteristics, Requisite assets DOI: 10.2478/orga-2022-0011 1 Introduction Tourism, and therefore the restaurant industry, is an important economic activity. Until 2019 (before the out- break of the Covid-19 pandemic), the growth rates of gross domestic product (GDP) in tourism were higher than in the global economy (WTTC, 2020). The restaurant industry has also achieved strong growth in sales volumes and prof- Tanja PLANINC 1 , Marko KUKANJA 1 , Anja ŽNIDARŠIČ 2 161 Organizacija, V olume 55 Issue 2, May 2022 Research Papers itability in this context. Globally, tourism accounted for 10.4% of GDP in 2019, while in the Republic of Slovenia, a small European economy, tourism contributed 10.6% to GDP. At the same time, the restaurant industry has offered numerous opportunities for the development of micro, small, and medium-sized enterprises (SMEs), which are a hallmark of the tourism and restaurant industry (Morri- son et al., 2010). SMEs represent 99.8% of all enterprises in the European Union (EU). Similarly, SMEs represent 99.8% of all business entities in the Republic of Slovenia (a totally of 206.220 enterprises), among which approxi- mately 4% (8,266) operate in the restaurant sector (SURS, 2021). Statistical data (Ajpes, 2021) show that most res- taurant businesses are micro SMEs employing less than ten employees, which are mostly registered as sole propri- etorship businesses (5,840) (see also Section 4.1 Sample Characteristics). Apart from being mainly represented by micro SMEs, the restaurant industry has several other industry-specif- ic characteristics, such as labour intensives, volatility of demand, intense competition, combined ownership and managerial (entrepreneurial) function and active involve- ment of family members in the operational process, the importance of the location for business success, monop- olistic competition, and many others (Hallak et al., 2018). According to Peters and Kallmuenzer (2018), SMEs can be characterised as a complex system of personal and busi- ness dimensions, which have advantages (e.g. flexibility) and disadvantages (e.g. lack of strategic planning) regard- ing their business performance. The characteristics mentioned above belong to the in- ternal business environment (within managerial purview - controllable) and external business environment (beyond managerial (direct) purview - uncontrollable). Restau- rant industry characteristics significantly affect restaurant firms’ financial performance, generally characterised by low revenue profitability and low survival rates (Lee et al., 2016). Despite its unique context, the restaurant industry is the most significant sector and the largest employer within the tourism industry (Dube et al., 2020). All restaurant businesses strive to operate efficient- ly and effectively. Efficiency refers to the relationship between the observed and optimal values of inputs and outputs (Fried et al., 2008), while effectiveness primarily refers to financial performance. Research on efficiency in tourism has mainly focused on the hotel (lodging) indus- try, while the restaurant sector has been analysed to a less- er extent (Kukanja & Planinc, 2018). This finding was also supported by Assaf and Josiassen (2016), who conducted a literature review on efficiency measurement in tourism. Their findings also revealed a lack of a standardised set of efficiency measurement variables within the tourism in- dustry. Accordingly, studies on efficiency analysis provide a set of heterogeneous variables, making the comparability of research virtually impossible. In terms of restaurant efficiency measurement, many researchers have tried to measure restaurant efficiency us- ing many input and output variables (studies are presented in Table 1). Input variables mainly include the different internal (e.g. the number of employees, cost of salaries, cost of goods and materials) and external (e.g. size, loca- tion, competition) restaurant business characteristics. On the contrary, sales revenues were most often used as the output variable. Interestingly, few studies (e.g. Kukanja & Planinc, 2020, 2019, 2018; Planinc & Kukanja, 2020, 2019) implemented a generic approach to restaurant effi- ciency measurement. The authors mentioned above used the requisite assets (labour costs, cost of goods and mate- rials sold, depreciation, and cost of services) as inputs into the business process and sales revenues as outputs (see Ta- ble 1). Namely, both dependant variables (requisite assets and sales revenues) present the base of any business pro- cess regardless of the business activity, enabling the com- parison of research results and presenting a solid base for benchmarking and efficiency improvement. Interestingly, to the best of our knowledge, no efficiency studies using requisite assets and sales revenues as research variables were performed for other tourism sectors (e.g. hotels and tourist agencies). Consequently, the influence of the inter- nal (entrepreneurial) and external (environmental) busi- ness characteristics on the management of the requisite assets (MRA), and consequently their (potential) influence on efficiency performance, remains unanswered. However, previous research confirmed the importance of the different environmental characteristics such as loca- tion (Giménez-García et al., 2007) and competition (Reyn- olds, 2004) for restaurant efficiency performance. To the extent of our knowledge, there is no empirical evidence exploring the relationship between entrepreneurial charac- teristics and efficiency performance in restaurant SMEs so far. Nevertheless, very few studies from other (non-restau- rant) service sectors confirmed the importance of the dif- ferent entrepreneurial characteristics for SMEsʼ efficiency performance. For example, Tajeddini et al. (2013) proved the importance of entrepreneurial orientation (EO) for the efficient performance of small retailers and Tajeddini (2015) confirmed a positive correlation between EO and the efficiency of hotels in Switzerland. In this view, it is es- sential to highlight that these results have to be interpreted with caution, as the authors presented above used financial performance indicators (e.g. profitability achievement), which are considered measures of effectiveness (Turk, 2006), which further contribute to the inability of com- paring research results. However, in terms of EO, Haber and Reichel (2007) reported the importance of education for small tourism ventures’ business performance (profit growth) in Israel. Similarly, Bujan (2020) confirmed the importance of business-related education, risk preference, and proactivity for the financial and non-financial business performance of small family hotels in the nearby Republic 162 Organizacija, V olume 55 Issue 2, May 2022 Research Papers of Croatia. Although we can substantially learn about the importance of entrepreneurial characteristics from previ- ous research outside the restaurant sector, it is impossi- ble to compare research findings due to the differences in methodological approaches. As a result, the authors wanted to overcome this short- coming in the literature by developing a generic efficiency measurement model for the restaurant industry that puts the requisite assets at its centre (see Figure 1). We also wanted to examine the impact of the environmental (ex- ternal) and entrepreneurial (internal) characteristics on MRA and, consequently, analyse their impact on restaurant SMEs’ operational efficiency. The external environmental characteristics refer to the size and location of the facility and the number of competitors, while the internal entre- preneurial characteristics are represented by EO, entrepre- neurial self-efficacy (ESE), and demographic characteris- tics of entrepreneurs (DC) (Mhlanga, 2018; Roh & Choi, 2010; Tajeddini, 2015). Accordingly, the aim of this paper is to (1) analyse the impact of the external (environmental) and internal (entre- preneurial) characteristics on MRA, (2) investigate the im- pact of MRA on efficiency, with (3) the goal of designing and testing a generic measurement model that could apply to the rest of the tourism and service sectors since it is based on generic and comparable business characteristics (Figure 1). This study employs a mixed methodological approach. After the literature review, primary data were collected from managers and secondary data were obtained from restaurant SMEs’ official financial reports. For the empir - ical analysis, different statistical techniques were applied (see Section 3). This paper is divided into several sections. First, a the- oretical background of efficiency analysis, entrepreneuri- al, and demographic characteristics is provided. Next, the research methodology is presented, followed by a pres- entation and discussion of the results. In conclusion, sug- gestions for future research and valuable information for restaurant managers are provided. 2 Theoretical background 2.1 Efficiency Analysis in the Restaurant Industry Efficiency refers to the relationship between the ob- served and optimal values of inputs and outputs (Fried et al., 2008). The most commonly used econometric meth- od for restaurant efficiency measurement is the data en- velopment analysis (DEA) (Kukanja & Planinc, 2020). DEA analyses the efficiency of firms (units) in a sample based on a linear programming method. The most efficient units in the sample represent the frontier, while the ineffi- cient units are positioned below the frontier according to their efficiency score (Coelli et al., 2005). There are two DEA models for evaluating firms’ efficiency: the constant returns to scale (CCR) model and the variable returns to scale (BCC) model. The CCR model assumes that all units in the sample perform optimally. In contrast, the BCC model assumes that the production possibilities frontier is convex and fits the most efficient units in the sample (As - saf & Josiassen, 2016). DEA enables the simultaneous analysis of a larger number of variables. The selected variables can either be influenced by the management and are considered control- lable (e.g., food costs) or outside managerial influence and are considered uncontrollable (e.g., number of competi- tors). Despite its flexibility, the main disadvantage of DEA is its sensitivity to measurement errors, meaning that any deviation from the frontier is treated as a consequence of a firm’s inefficiency (Assaf & Josiassen, 2016). The origins of efficiency analysis using DEA in the res- taurant industry date back to the 1980s. In Table 1, DEA studies in the restaurant industry are presented in chrono- logical order. As shown in Table 1, in previous DEA studies, authors used different input and output variables to analyse res- taurant efficiency with the different research goals, which hinders the comparison of research results. For example, Banker and Morey (1986) focused on the impact of fixed uncontrollable inputs. Taylor et al. (2009) employed effi- ciency analysis to develop a multidimensional methodolo- gy for menu analysis in the USA. Giokas et al. (2015) used panel data to determine the efficiency of Greece’s pre-re- cession and recessionary periods. In their study, Mhlanga (2018) used panel data to identify factors impacting restau- rant efficiency in South Africa. The literature review also reveals that researchers are heterogeneous in selecting input variables. Input variables predominately include the following restaurant character- istics: number of employees, the cost of goods and mate- rials sold, labour cost, rent, taxes and insurance, employee satisfaction, restaurant size, and the number of competi- tors. On the contrary, scholars are relatively homogeneous in selecting the output variables as sales revenue is the pre- dominant output variable in most studies (twenty-one out of twenty-eight). Analysis of previous studies has also revealed that some of the input variables, such as the number of employ- ees (Assaf et al., 2011), labour costs (Fang & Hsu, 2014), operating expenses (Giokas et al., 2015), and the value of assets (Parte & Alberca, 2019) were considered as control- lable. On the contrary, other input variables, such as size (Hadad et al., 2007), the number of competitors (Gimén- ez-García et al., 2007), and location (Donthu & Yoo, 1998) were treated as uncontrollable. To our best knowledge, the only studies that applied a systematic approach to efficiency measurement were those 163 Organizacija, V olume 55 Issue 2, May 2022 Research Papers Authors Inputs Outputs Hruschka (1986) no. of seats, labour costs, cost of goods and materials sold, other operating expenses sales revenue Banker & Morey (1986) cost of goods and materials sold, labour costs, age of the facility, advertising costs, location, presence of a drive-in counter sales revenue Andersson & Hartman (1995) no. of seats, no. of employees, fixed costs, labour costs no. of guests, contribution margin Donthu & Yoo (1998) size of the facility, years of experience as a manager, location, advertising costs sales revenue, guest satisfaction Reynolds (2003) labour hours sales revenue Reynolds (2004) labour hours, the average salary of employees, no. of seats, no. of competitors sales revenue, tips Lan et al. (2006) salary of employees, cost of social insurance, cost of water, electricity and gas income, no. of guests, cash flow Reynolds & Thompson (2007) salary of employees, no. of seats sales revenue, tips Reynolds & Biel (2007) cost of goods and materials sold, labour costs, em- ployee satisfaction, no. of seats, tax and insurance costs income, retention equity Hadad et al. (2007) no. of seats, average no. of total employees, average no. of employees per shift, size of the facility average no. of guests per day, average selling price Giménez-García et al. (2007) no. of employees, no. of seats and counters, location, no. of competitors, average consumption per guest sales revenue, service quality Taylor et al. (2009) meal preparation method, no. of suppliers, number of kitchen stations gross profit, meal popularity Roh & Choi (2010) size of the facility, size of the dining room, size of the kitchen, no. of seats and tables, no. of all employees, no. of kitchen and dining room staff, the salary of employees, rent, overheads sales revenue, net income Assaf et al. (2011) no. of employees, food and beverage costs, no. of seats sales revenue (separate for food and beverage) Joo et al. (2012) labour costs and hours sales revenue, no. of guests, no. of receipts Gharakhani et al. (2012) labour hours, size of the facility, years of experience as a manager no. of guests, sales revenue Fang & Hsu (2014) labour costs, cost of goods and materials sold, no. of suppliers gross profit, meal popularity Giokas et al. (2015) operating expenses (excluding the cost of goods and materials sold), the value of assets sales revenue Mhlanga (2018) no. of employees, no. of seats, labour costs, other operating expenses sales revenue, total covers Alberca & Parte (2018) labour costs, other operating expenses, the value of assets sales revenue Kukanja & Planinc (2018) requisite assets sales revenue Parte & Alberca (2019) no. of employees, labour costs, other operating expenses, the value of assets sales revenue Table 1: Efficiency analysis in the restaurant industry using DEA (1986-2020) – selection of variables 164 Organizacija, V olume 55 Issue 2, May 2022 Research Papers Table 1: Efficiency analysis in the restaurant industry using DEA (1986-2020) – selection of variables (continues) Authors Inputs Outputs Planinc & Kukanja (2019) requisite assets sales revenue Kukanja & Planinc (2019) requisite assets sales revenue Karakitsiou et al. (2020) no. of local units, number of employees and invest- ments turnover Kukanja & Planinc (2020) requisite assets sales revenue Planinc & Kukanja (2020) requisite assets sales revenue Hodžić et al., (2020) no. of entrepreneurs in the restaurant sector, aver- age no. of employees, expenses for employee wages and contributions total revenues, net profits, tourist overnights performed by Kukanja and Planinc (2020, 2019, 2018) and Planinc and Kukanja (2020, 2019). The authors stressed the necessity of a standardised approach to efficiency measurement and proposed a standardised set of inputs (the requisite assets) and output (sales revenue). Moreover, we found no previous studies investigating the importance of entrepreneurial characteristics (as indicators of the in- ternal environment) for restaurant efficiency performance (see Table 1). Another critical finding is that in previous DEA stud- ies, all input variables (despite controllable or uncontrol- lable) were equally treated as direct inputs into the DEA. Specifically, the prerequisite assets should be considered as a dependent variable since their management is influ- enced by the different factors arising from the internal and external business environment. Accordingly, we intend to close the gap in the literature by systematically analysing the impact of entrepreneurial (internal) and environmental (external) characteristics on MRA and restaurant efficien- cy. For this study, variables were selected based on the literature review (Table 1). The identified environmental characteristics are size, location of the facility, and the number of competitors, while the identified entrepreneur- ial characteristics are EO, ESE, and DC (see also Section 3.1 – Instrument design). 2.2 Entrepreneurial Characteristics 2.2.1 Entrepreneurial Orientation (EO) EO is often referred to as a precursor of competitive advantage, growth and business performance and refers to the policies and practices that form the basis for entrepre- neurial decision-making and action. Three main dimen- sions (constructs) best explain EO: innovativeness, proac- tiveness, and risk-taking (Kraus et al., 2012). Innovativeness refers to creativity and is most evident in developing and implementing new business ideas. The risk-taking dimension refers to how entrepreneurs are will- ing to take risky and bold decisions, while proactiveness refers to taking business initiatives or exploiting market opportunities (Rauch et al., 2009). In terms of analysing the influence of EO on efficien - cy, the review of the literature (Kallmuenzer et al., 2019; Tajeddini, 2015; Tajeddini et al., 2013) has confirmed the correlation between the two concepts. In this view, we have to point out that the authors mentioned above have equated efficiency with effectiveness (financial perfor - mance), which further contributes to the inability to carry out comparative analyses. 2.2.2 Entrepreneurial Self-efficacy (ESE) The ESE dimension encompasses an individual’s be- lief that he or she can successfully perform a variety of en- trepreneurial tasks. ESE mainly refers to developing new products or services and market opportunities, creating an innovative environment, finding potential investors, de- veloping a clear business concept, coping with unexpected challenges and developing human resources (Hallak et al., 2018). In reviewing the literature, we found that research- ers have studied ESE relating to financial performance (Bratkovič Kregar et al., 2019; Hallak et al., 2014; Lee & Hallak, 2018). Previous research mostly confirms a posi- tive correlation between ESE and financial performance. Moreover, the authors mentioned above highlighted the 165 Organizacija, V olume 55 Issue 2, May 2022 Research Papers importance of training and education, which significantly contribute to higher levels of ESE. Interestingly, no stud- ies were found on efficiency. Interestingly, no studies were found on efficiency. 2.2.3 Demographic Characteristics of Entrepreneurs (DC) In the efficiency analysis of restaurant SMEs, it is also essential to underscore the importance of entrepreneurs’ DC. Previous studies mainly analysed the importance of DC for business decision-making (Goll & Rasheed, 2005; Tavitiyaman et al., 2014). However, research findings do not provide a clear-cut answer on the importance of DC for SMEs’ financial performance. For example, Mazzarol et al. (1999) found that women are less likely to enter the world of business, Kristiansen et al. (2003) found that DCs were only marginally related to financial performance, and Bujan (2020) reported a positive relationship between business-related education and business performance. Again, no studies were found on efficiency. In previous research (Kallmuenzer et al., 2019; Lee & Hallak, 2018; Tajeddini, 2015), entrepreneurial character- istics significantly influenced business performance. Ac- cording to the characteristics of the restaurant industry pre- sented in Section 1, the manager is actively involved in the operational process. Therefore, we can assume that his EO, ESE, and DC can (hypothetically) directly influence MRA and, consequently, restaurant efficiency performance. 2.3 Environmental Characteristics Entrepreneurs mostly have no (direct) influence on environmental factors. Authors who studied the impact of the external environment on restaurant efficiency have come to different conclusions. For example, Gharakhani et al. (2012) found that larger restaurants are more effi- cient, while Gimenez (2004) reported higher efficiency in smaller restaurants. Sanjeev (2007) also confirmed a weak, positive correlation between size and efficiency. In contrast, research examining the correlation be- tween location and efficiency is relatively scarce. Reyn- olds (2000) and Sanjeev (2007) found that restaurants lo- cated in/or near cities achieve higher efficiency. Regarding the number of competitors, Giménez-García et al. (2007) confirmed a positive relationship between market compe- tition and efficiency. Based on the theoretical findings presented above, we can conclude that entrepreneurial and environmental characteristics are important determinants of restaurant performance. Nevertheless, the two characteristics do not present direct inputs into an operational process. However, they should be considered as internal and external factors that (hypothetically) influence restaurant MRA and, conse- quently, restaurant efficiency performance. Accordingly, we propose a research model (Figure 1) in which we investigate the influence of environmental and entrepreneurial characteristics (independent variables) on MRA (dependent variable), which are considered as an input into the efficiency analysis model. Since we are in- terested in operational efficiency, operational sales are a dependent output variable. Accordingly, we propose two main hypotheses: RH1: Environmental characteristics (size, location, and the number of competitors) impact MRA and, conse- quently, efficiency. RH2: Entrepreneurial characteristics (ESE, DC (gen- der, age, education, work experience, ownership, and EO) impact MRA and, consequently, efficiency. 3 Methods 3.1 Research process, instrument design, and data analyses Environmental and entrepreneurial characteristics were analysed based on instruments collected from pre- vious research. For measuring EO, we adopted a 12-item scale from a study by Kostanjevec and Gomezelj Omerzel (2013), while for measuring ESE, we applied a 23-item scale developed by De Noble et al. (1999). The importance of entrepreneur DC was measured based on the follow- ing variables: age, formal education, years of experience, ownership, and work experience (Goll & Rasheed, 2005; Reynolds, 2000; Tavitiyaman et al., 2014). Since the the- ory does not provide a clear answer about the importance of DC, each DC variable was measured only at the indi- vidual level (see Figure 1). Environmental characteristics were assessed based on the following variables: restaurant size (Gharakhani et al., 2012), location (Reynolds, 2000; Sanjeev, 2007), and the number of competitors (Gimén- ez-García et al., 2007). Additionally, we collected some basic physical information about the restaurant facilities, such as the number of employees and years of business activity. Financial (secondary) data for the efficiency anal- ysis were obtained from the Agency of the Republic of Slovenia for Public Legal Records and Related Services (Ajpes, 2021). For this study, various statistical approaches were ap- plied. Efficiency was analysed using DEA; factor structure was investigated with exploratory factor analysis (EFA), and the measurement model was validated using confirm- atory factor analysis (CFA). Finally, the relationships be- tween the observed variables were tested using structural equation modelling (SEM). 166 Organizacija, V olume 55 Issue 2, May 2022 Research Papers 3.2 Sample description and data collection The sample consists of restaurant SMEs in the Repub- lic of Slovenia classified according to the EU standard classification of activities (NACE) as I56.101 (Restaurants and inns) and I56.102 (Snack bars and similar). In 2019, there were 3,226 business entities in both classifications (Ajpes, 2021), representing almost 50% of the Food and Beverage (F&B) sector (6,496 business entities) in Slove- nia. The unavailability of information regarding the char- acteristics of restaurant SMEs included in our model led us to use a convenience sampling method. The survey took place between October and the end of December 2019 and was conducted by ten interviewers. Only those SMEs generating operating revenue from res- taurant sales were included in the survey. Accordingly, to verify that SMEs are appropriate for inclusion in the anal- ysis, respondents were asked to confirm that they have no other sources of revenue. At the end of the data collection, the sample consisted of 266 restaurant SMEs, representing slightly over 8% of the I56.102 and I56.101 populations. Figure 1: Theoretical Model 167 Organizacija, V olume 55 Issue 2, May 2022 Research Papers Table 2: Restaurants’ and respondents’ characteristics 4 Results 4.1 Sample Characteristics Characteristics of restaurants included in the research are presented in Table 2. The physical and environmental characteristics pre- sented in Table 2 indicate that slightly over half of res- taurants (50,8%) are located in cities. Their average size is just over 270 square metres. On average, they have 119 seats. The largest proportion of restaurants employs up to ten employees (80.5%). On average, restaurants have 22.7 years of business activity, and respondents reported approximately four competing restaurants in their vicini- ty. The sample was mainly composed of male respondents (59.4%), whose average age was 43. The majority of re- spondents (67.3%) had completed vocational or secondary Variables Frequency Restaurants’ physical and environmental characteristics Location city 135 suburban areas 99 rural areas 32 Restaurant size (number of seats) up to 50 25 over 50 up to 100 90 more than 100 151 Number of employees up to 10 214 over 10 up to 20 41 more than 20 11 Years of business activity up to 10 96 over 10 up to 20 67 more than 20 103 Number of competitors (within a 1 km radius) up to 5 202 over 5 up to 10 46 more than 10 18 Respondents’ demographic characteristics (DCs) Gender male 158 female 108 Age up to 35 63 over 35 up to 45 87 over 45 up to 55 95 more than 55 21 Level of education primary school 3 vocational or secondary school 179 higher education 84 Years of experience up to 10 59 over 10 up to 20 87 over 20 up to 30 82 more than 30 38 Ownership structure owner and manager 205 manager 61 168 Organizacija, V olume 55 Issue 2, May 2022 Research Papers education. In terms of their work experience, respondents have, on average, 20 years of experience in the restaurant sector, and the majority of them (77.1%) reported owning their restaurants. Results of secondary data reveal that the average value of labour cost is €116,986.74, depreciation is €11,738.61, ESE Variables M SD Developing new product and market opportunities 1 I can see new market opportunities for new products and services. 4.02 0.789 2 I can discover new ways to improve existing products. 4.09 0.779 3 I can identify new areas for potential growth. 4.11 0.810 4 I can design products that solve current problems. 4.06 0.919 5 I can create products that fulfil customers’ unmet needs. 3.97 0.931 6 I can bring product concepts to market in a timely manner. 3.95 0.886 7 I can determine what the business will look like. 3.66 0.886 Building an innovative environment 8 I can create a working environment that lets people be their own boss more. 3.79 0.951 9 I can develop a working environment that encourages people to try out something new. 4.05 0.876 10 I can encourage people to take initiative and responsibility for their ideas and decisions, regardless of outcome. 3.92 0.970 11 I can form partner or alliance relationships with others. 3.92 0.991 Initiating investor relationships 12 I can develop and maintain favourable relationships with potential investors. 3.70 1.197 13 I can develop relationships with key people who are connected to capital sources. 3.67 1.173 14 I can identify potential sources of funding for investment. 3.65 1.036 Defining core purpose 15 I can articulate vision and values of the organisation. 4.25 0.771 16 I can inspire others to embrace the vision and values of the firm. 3.98 0.864 17 I can formulate a set of actions in pursuit of opportunities. 3.94 0.894 Coping with unexpected challenges 18 I can work productively under continuous stress, pressure, and conflict. 4.05 0.956 19 I can tolerate unexpected changes in business conditions. 4.02 0.829 20 I can persist in the face of adversity. 4.30 0.727 Developing critical human resources 21 I can recruit and train key employees. 4.12 0.885 22 I can develop contingency plans to backfill key technical staff. 3.81 0.987 23 I can identify and build management teams. 4.12 0.930 Table 3: Entrepreneurial Self-Efficacy (ESE) the cost of goods and materials sold is €175,131.54, the cost of services is €73,571.34, and sales revenue is €401,896.76. Next, ESE was analysed. In Table 3, results indicating ESE using mean values (M) and standard deviations (SD) are presented. 169 Organizacija, V olume 55 Issue 2, May 2022 Research Papers Table 4: Entrepreneurial Orientation (EO) EO Variables M SD Innovation 1 Since the firm was founded, we have not introduced many new products and services to the market. 2.76 1.327 2 Changes in our products and services are usually minor. 3.10 1.203 3 There is not a strong focus on the development of new products and services. 2.73 1.221 4 The firm does not have a strong focus on introducing new technologies that emerge on the market. 2.83 1.222 5 From the time the firm was founded until today, there have not been many im- provements in products and services. 2.41 1.254 6 There is no emphasis on developing in-house solutions, both technological and administrative. 2.47 1.188 Risk Orientation 7 Preference is given to products and services that are risk-neutral and have an average return. 3.03 1.210 8 In our competitive environment, it is wiser to make conservative and incremental decisions. 3.06 1.156 9 We prefer to thoroughly investigate the opportunity first and then make a decision. 3.70 1.009 Proactivity 10 Our firm usually only reacts to actions triggered by other competitors in the mar- ket. 2.43 1.171 11 Compared to competitors, we are very rarely the first to introduce new products and services, process technologies and other business practices. 2.57 1.212 12 We usually wait for the leading competitor to enter the market first with new prod- ucts and services before we follow. 2.15 1.175 As shown in Table 3, the highest-rated indicator was ESE-20, indicating their ability to cope with difficult situ- ations (M=4.30). The lowest-rated indicator was ESE-14, indicating entrepreneurs’ ability to find financial sources (M=3.65). Next, EFA (principal axis factoring method was used) was employed to examine the factor structure of ESE (oblimin rotation was used). Based on the correlation co- efficient values (showing no multicollinearity issues), all 23 indicators were included in the analysis. Results of the KMO test value (0.878) as well as the value of the Bart- lett’s test (p = 0.000 < 0.05; approximate χ2 = 2970.126; df = 253) indicate the suitability of the data for perform- ing EFA. We followed Kaiser’s rule when determining the number of factors, suggesting that eigenvalues should be above one and at least 50% of variance should be explained with the obtained factors. Based on the results of EFA, six factors (ESE dimensions) were obtained. The final model explains 56.67% of the total variance. In the next step, respondents’ opinion on EO was in- vestigated. The results in Table 4 show that the highest-rated in- dicator was EO-9, indicating that managers generally prefer to explore business opportunities and then decide (M=3.70). The lowest scores relate to EO-12 (M=2.15), meaning that they usually do not wait for the leading com- petitors to enter the market first and then follow. Next, EFA (principal axis factoring method was used) with oblimin rotation was used to examine the factor struc- ture of EO. All 12 indicators were included in the analysis based on the correlation coefficient values. Results of the KMO test value (0.886) as well as the value of the Bart- lett’s test (p = 0.000 < 0.05; approximate χ2 = 1564.739; df = 66) indicate the suitability of the data for performing EFA. Results show that three dimensions of EO explain 57.83% of the total variance. 4.2 Efficiency Analysis Next, we proceeded with DEA. Before performing DEA (the CCR model was used), we first checked whether 170 Organizacija, V olume 55 Issue 2, May 2022 Research Papers there is a correlation between inputs and outputs, which was established using the Pearson correlation coefficient. Results show that the average efficiency for all restaurants is 65.8%, 19 restaurants achieve 100% efficiency, 122 res- taurants achieve efficiency scores above the average, and 144 restaurants perform below the average. The results also suggest that restaurants that performed below the av- erage could reduce their inputs by 34% and still achieve the same level of sales revenue. The analysis of the requi- site assets shows that restaurants have the most place for efficiency improvement in terms of optimising their depre- ciation costs, which could be, on average, reduced by 37%. Restaurants performing below the average could therefore reduce their depreciation costs, on average, by 46%. 4.3 Validation of the model (CFA) After EFA, we conducted CFA on both constructs (ESE and EO) to justify the appropriateness of the obtained di- mensions or latent variables for inclusion in the measure- ment model. We excluded three indicators based on the low values of their standardised factor loadings (below 0.5). These indicators are ESE-7 (I can determine what the business will look like), ESE-11 (I can form partner or al- liance relationships with others), and EO-9 (We prefer to thoroughly investigate the opportunity first and then make a decision). We could not confirm discriminant validity in both cases, so we decided to introduce a second-order latent variable. ESE is measured with six latent variables referring to its six dimensions, while EO is measured by the three latent variables (Tables 3 and 4). The latent variables (obtained in EFA) can be defined as first-order latent variables. Consequently, both ESE and EO can be defined as second-order latent variables. In the case of the second-order latent variable, the first-order latent variables act as independent variables. Therefore, they are expected to be more strongly correlated, and consequently, discrimi- nant validity has no significant meaning in determining the model (Koufteros et al., 2009). In the case of ESE, the proposed measurement mod- el shows a good model fit. The χ2 ratio with respect to degrees of freedom is appropriate (χ2/df = 1.90 < 3) and goodness-of-fit indices (CFI = 0.91 > 0.9; SRMR = 0.059 < 0.08) show appropriate fit. The value of RMSEA is also satisfactory (0.068). Only the value of TLI (0.898) is slightly below 0.90. Nevertheless, the indicators point to a good fit of the model. The standardised factor loadings related to the second-order latent variables have values between 0.528 and 0.884, all statistically significant. This indicates a strong correlation between the second-order latent variable and the first-order latent variables. Conse- quently, the convergent validity of the second-order latent variable ESE is confirmed. In the case of EO, the fit indices also show a good mod- el fit. The χ2 ratio with respect to degrees of freedom is appropriate (χ2/df = 2.65 < 3), while fit indices are above the recommended threshold (CFI = 0.937 > 0.9; SRMR = 0.047 < 0.08). RMSEA (0.092) and TLI (0.916) also have satisfactory values. The standardised factor loadings relat- ing to the second-order latent variable have values between 0.725 and 0.904 and are all statistically significant. This indicates a strong association between the second-order latent variable EO and the six first-order latent variables. Consequently, the convergent validity of the second-order latent variable EO is confirmed. Next, we checked fit indices for the overall measure- ment model (ESE and EO). All standardised factor weights are above 0.5, all A VE values are above 0.5, and all CR values are above 0.7. Consequently, we can confirm the construct validity of the measurement model. In the analysis of the initial SEM, we were unable to confirm some hypotheses. In order to find the most parsi- monious model, the nested model approach was applied. By comparing the so-called nested models, we simplified the model to a form in which only variables connected with statistically significant paths were retained. The path with the weakest influence was removed in each step, and the resulting model was compared with the previous one (Kline, 2011). In subsequent steps, five paths were re- moved. The final model has satisfactory goodness-of-fit indicators (χ2/df = 1.59 < 3; SRMR = 0.061; RMSEA = 0.049). The CFI (0.880) and TLI (0.873) are slightly below the 0.9 thresholds; however, when all indicators are con- sidered together, the final model fits the data reasonably well. The location variable was measured as a nominal variable with three categories: rural area, suburban area, and urban (city) area. To include this variable in the re- search model, we formed two dichotomous variables (Lo- cation-suburban area and Location-rural area). The urban area was selected as the reference category. A summary of the final model results is presented in Table 5. Based on the results of SEM, we cannot confirm the influence of all environmental variables. Specifically, competition (RH1d) proved not to be significant for MRA. Since we employed the nested model procedure, only sig- nificant impacts will be discussed in detail. Size (RH1a; β = 0.3108) and location – suburban (RH1b; β = -0.155), and rural (RH1c; β = -0.117) have a significant impact on MRA. As emphasised before, the location was a nominal variable with three categories, and two dichotomous vari- ables were composed, selecting urban as a reference cate- gory. Negative coefficients for both dichotomous variables indicate that values of MRA are lower in suburban and ru- ral areas than the urban ones. In terms of entrepreneurial characteristics, we cannot confirm the correlations in the cases of the entire ESE di- mension (RH2a) and for most DCs (RH2b, RH2c, RH2d, and RH2e.) A statistically significant correlation was con- firmed only for ownership function (RH2f; β = -0.193). In contrast and interestingly, the entire EO dimension (RH2g) 171 Organizacija, V olume 55 Issue 2, May 2022 Research Papers Hypothesis Path Standardised Path Coefficient z-Value Hypothesis Supported? RH1a Size → RA 0.308 3.915 *** Yes RH1b Location – suburban area → RA - 0.155 - 2.709 ** Yes RH1c Location – rural → RA - 0.117 - 2.768 ** Yes RH1d Competition → RA No RH2a ESE→ RA No RH2b Gender → RA No RH2c Age → RA No RH2d Education → RA No RH2e Work experience → RA No RH2f Managerial-ownership function → RA -0.193 -2.510 * Yes RH2g OE → RA -0.130 -2.133 * Yes RH1/2 RA → Efficiency -0.195 3.951 *** Yes Table 5: Summary of hypothesis testing for SEM Note: *** p < 0,001; ** p < 0,01; * p < 0,05; RA = requisite assets Figure 2: SEM Note: Non-significant paths between variables are indicated with a grey dashed line 172 Organizacija, V olume 55 Issue 2, May 2022 Research Papers has a statistically negative impact on the values of the req- uisite assets (β = -0.130). The identified five characteristics explain 17.5% of the variability for the requisite assets’ variable (R2 = 0.175). As hypothesised in RH1/2, the requisite assets have a sta- tistically negative impact on efficiency (β = -0.195). Ac- cordingly, we can confirm RH1/2. The predictor variables explain 3.8% of variability for the efficiency variable (R2 = 0.038). 5 Discussion Our analysis has led us to some interesting conclu- sions. The analysis of the hypotheses revealed that RH1 (environmental characteristics) could be mostly con- firmed, while RH2 (entrepreneurial characteristics) can only be partially confirmed. In terms of size, larger res- taurants consume more assets in their business processes and, as expected, have higher values of requisite assets compared to smaller restaurants. In contrast, results show that restaurants located in rural areas have lower requisite assets values than restaurants located in cities. A possible explanation for this might be that restaurants in rural areas may have more possibilities to produce their ingredients or buy them directly from producers at lower prices. Addi- tionally, in urban areas, restaurants are more likely to offer high-end interiors (as an element of competitiveness), re- quiring higher values of requisite assets. Interestingly, in our case, the number of competitors has no impact on MRA and consequently on efficiency. This finding is not in line with the literature (Gimén- ez-García et al., 2007; Reynolds, 2004), which reported a correlation between the number of competitors and ef- ficiency. Nevertheless, in comparing the results of our study with previous research, we have to be cautious. For example, the study by Giménez-García et al. (2007) was performed in a specific environment (on restaurants oper- ating in shopping malls), while Reynolds (2004) did not report on testing the correlation between variables before performing DEA. Next, the importance of entrepreneurial characteristics for assuring restaurant efficiency was analysed. Research results confirmed only the importance of EO in manag- ing SMEs’ requisite assets and efficiency. Previous studies (e. g. Bujan, 2020; Haber & Reichel, 2007; Hallak et al., 2014; Lee & Hallak, 2018) confirmed the importance of EO for SMEs’ effectiveness. However, due to the differ - ences in methodological approaches (efficiency ≠ effec - tiveness), the results of our study cannot be compared to previous research. Our study found that those SMEs whose managers have a more pronounced EO have lower values of requi- site assets. Specifically, innovativeness, proactivity and risk-taking proved to be important determinants of effi- ciency performance. Another interesting finding relates to the highest and the lowest rated EO indicators (see Table 4). In terms of entrepreneurs’ risk orientation (EO-9), man- agers prefer to thoroughly investigate the opportunity first and then make a decision (M=3.70). On the contrary, in terms of proactivity (EO-12), they are less likely to wait for the leading competitor to enter the market first before they follow (M=2.15). Research results indicate that man- agers are relatively cautious when making decisions, al- though they do not like to follow the competition before launching new products and services. This result might be somehow related to the monopolistic characteristics of the restaurant industry (e.g. excess supply, many compet- itors offering slightly differentiated products and services, the decision regarding price or product of any firm does not significantly affect the competitive behaviour of other firms, low entry conditions to enter the market) (Hallak, 2018). Nevertheless, it is relatively difficult to explain these results due to the lack of comparative research. We might only assume that, based on their EO, managers have developed some entrepreneurial abilities that enable them to manage their business more efficiently. Interestingly, the analysis did not confirm the impact of ESE on MRA and efficiency. Again, it is relatively diffi - cult to explain these results since the literature (Lee et al., 2016; Lee & Hallak, 2018) only provides evidence related to the influence of ESE on effectiveness. The results also reveal that DCs (except for the owner- ship function) do not influence MRA. In cases in which the manager is also the restaurant owner, the values of the req- uisite assets are lower. This finding implies that the own- er-manager is more careful (and efficient) in managing the SME’s requisite assets than the manager. However, DC and ESE might be somehow intercorre- lated since ESE could also be gained through education, mentorship, and work experience (Lee et al., 2016). In our study, the average age of managers was 43 years, and most of them (67.3%) reported having vocational or secondary education. Accordingly, we might assume that they did not gain any (formal) entrepreneurial education since, more than twenty years ago, entrepreneurship was not taught in schools in Slovenia. Interestingly, concerning the impor- tance of education for business performance, Bujan (2020) reported that entrepreneurs of small family hotels in Cro- atia who had participated in business-related education performed better. An absence of correlation was also evident in work ex- perience. The result is surprising, but possible reasons may stem from the fact that there is no intergenerational trans- fer of mentoring skills (especially tacit knowledge) due to the deregulation of catering professions. Consequently, we might assume that managers are mostly self (incorrectly) taught. At the same time, respondents may have gained previous work experience at not necessarily managerial positions. Another possibility for these results could also be the relatively late start of strategic development of en- 173 Organizacija, V olume 55 Issue 2, May 2022 Research Papers trepreneurship in Slovenia, which only started in the late 1990s due to the transition process. Based on research results, we can conclude that envi- ronmental characteristics significantly impact MRA and restaurant efficiency compared to entrepreneurial char- acteristics. In this respect, the analysis also confirmed a weak negative impact of MRA on efficiency. This result was expected, as we had already reported that managers could have achieved the same level of efficiency with low- er values of requisite assets (on average: 34%). 6 Conclusion In this paper, we aimed to (1) analyse the impact of the environmental and entrepreneurial characteristics on MRA, (2) investigate the impact of MRA on efficiency, with (3) the goal of designing and testing a measurement model for efficiency analysis, which is based on generic SMEs and entrepreneurial variables. The present study is thus the first to comprehensively address and examine the relationship among environmental and entrepreneurial characteristics, MRA, and restaurant efficiency. The selec- tion of generic variables significantly contributes to our research and efficiency literature since it enables compara- tive analyses of efficiency measurement. Based on the literature review, we formulated two main research hypotheses. The results indicate that envi- ronmental (RH1) and entrepreneurial (RH2) characteris- tics influence MRA and restaurant efficiency. Specifically, the following characteristics influence MRA and efficien - cy: size and location (environmental characteristics), and ownership and EO (entrepreneurial characteristics). Based on the analysis, we conclude that SMEs’ exter- nal environment on MRA and efficiency is much stronger than the influence of the internal environment. The external environment is generally not directly influenced by mana- gerial decisions. This fact leads to the practical conclusion that it makes sense to strengthen the influence of the inter- nal environment through the active development of entre- preneurial characteristics, which could consequently result in a more effective MRA and higher efficiency. In terms of managerial application, managers could im- prove the potential of ESE and EO through active lifelong learning. Another recommendation refers to the improve- ment of restaurant efficiency. Results of DEA show that there is most potential for efficiency improvement in terms of depreciation costs. Nevertheless, according to Kukanja and Planinc (2018), this practice must be interpreted with caution. Namely, managers could use restaurants’ physical (tangible) elements (e.g. interior design, layout, restaurant equipment) as critical elements of differentiation strategy. As a result, this practice might lead to higher depreciation costs since the use of the physical elements may not (yet) be reflected in higher sales revenues. Additionally, manag- ers have also several (other) opportunities for improving SMEs’ efficiency by networking and collaborating with other restaurant providers, which can also lead to econ- omies of scale and cost optimisation (e.g. joint procure- ment). Regarding other recommendations for policymakers, we also suggest the introduction of entrepreneurial skills in curricula at all education levels. Organising educational events might also present a challenge for restaurant asso- ciations. Moreover, academia should be more actively in- cluded in disseminating research results among restaurant managers. Finally, we faced some limitations in conducting our research. The main limitations were the unwillingness of restaurant managers to participate in the study and the unavailability of industry-specific information related to the characteristics of restaurant SMEs. Specifically, res- taurants are classified following their primary business ac- tivity, which means that they might also generate revenue from other businesses and report their financial data at an aggregated level. Accordingly, based on official reports, it is impossible to identify the entire population of restaurant SMEs that generate revenue solely from restaurant sales. Consequently, the sample design of our study was based on a convenience sampling method, the main limitation of which is the inability to draw statistical inferences from a sample to the population. A potential limitation could also present the research location, as Slovenia is one of the smallest economies within the EU. Therefore, it remains unclear whether research findings could be similar for oth- er service industries and cultures. Another limitation refers to the time frame of the survey since it was carried out just before the outbreak of the Covid-19 pandemic. Finally, the lack of research on restaurant efficiency can also be con- sidered a limitation. We have also identified several opportunities for future research. The present study may represent an initial step in a longitudinal and cross-national (EU) comparative analy- sis of restaurant efficiency, providing valuable insights into the dynamics of assuring restaurant efficiency. Another possible aspect for future research is the effi- ciency analysis related to the business life cycle. In the ini- tial stages of the life cycle, the owner is also the restaurant manager; however, over time, there is often a need to re- organise or separate the managerial function by employing a manager (Lester et al., 2003). Further studies could also analyse the importance of managers’ overconfidence and emotional unfitness for restaurant efficiency performance, as their significance for restaurant business failure was al- ready reported by Camillo et al. (2008). Additional (state- of-the-art) variables such as green EO (Habib et al., 2020), characteristics of migrant restaurant entrepreneurs (Farrer, 2021), restaurant crowdfunding (Yang & Koh, 2022), or introduction of service robots (Seo & Lee, 2021) could also be introduced into the analysis. 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Travel & Tourism Global Economic Im- pact and Trends 2020 (p. 20). Tanja Planinc has a doctorate degree in Management. She is a senior lecturer of business management at the Faculty of Tourism Studies – TURISTICA, University of Primorska. Her main areas of work are basics of accounting in tourism, business finance, and tourism economics. Marko Kukanja has a doctorate degree in Quality Management. He is an associate professor of tourism and hospitality management at the Faculty of Tourism Studies – TURISTICA, University of Primorska. His academic career is based on fifteen years of international business and managerial experience in tourism and hospitality industry. His main areas of research interest are Food & Beverage management, Quality Management, and tourism entrepreneurship. His research has been published in scholarly journals such as Total Quality Management & Business Excellence, E+M: Economies and Management, Tourism and Hospitality Management, and Academica Turistica: Tourism & Innovation Journal, among others. 177 Organizacija, V olume 55 Issue 2, May 2022 Research Papers Anja Žnidaršič is an associate professor of Quantitative Methods at the Faculty of Organizational Sciences, Uni- versity of Maribor, Slovenia. Her main research interests are social network analysis, micro-enterprises, informa- tion-communication technology students’ performance in methodological courses, and technology adoption.