Volume 25 Issue 4 Article 3 December 2023 SMEs “Growing Smart”: The Complementarity of Intangible and SMEs “Growing Smart”: The Complementarity of Intangible and Digital Investment in Small Firms and Their Contribution to Firm Digital Investment in Small Firms and Their Contribution to Firm Performance Performance Eva Erjavec University of Ljubljana, School of Economics and Business, PhD Student, Ljubljana, Slovenia, eva.erjavec@ef.uni-lj.si Tjaš a Redek University of Ljubljana, School of Economics and Business, Ljubljana, Slovenia, tjasa.redek@ef.uni-lj.si Č rt Kostevc University of Ljubljana, School of Economics and Business, Ljubljana, Slovenia, crt.kostevc@ef.uni-lj.si Follow this and additional works at: https://www.ebrjournal.net/home Part of the Entrepreneurial and Small Business Operations Commons, Human Resources Management Commons, and the Technology and Innovation Commons Recommended Citation Recommended Citation Erjavec, E., Redek, T., & Kostevc, Č . (2023). SMEs “Growing Smart”: The Complementarity of Intangible and Digital Investment in Small Firms and Their Contribution to Firm Performance. Economic and Business Review, 25(4), 216-232. https://doi.org/10.15458/2335-4216.1328 This Original Article is brought to you for free and open access by Economic and Business Review. It has been accepted for inclusion in Economic and Business Review by an authorized editor of Economic and Business Review. ORIGINAL ARTICLE SMEs “Growing Smart”: The Complementarity of Intangible and Digital Investment in Small Firms and Their Contribution to Firm Performance Eva Erjavec a, * , Tjaša Redek b , ˇ Crt Kostevc b a University of Ljubljana, School of Economics and Business, PhD Student, Ljubljana, Slovenia b University of Ljubljana, School of Economics and Business, Ljubljana, Slovenia Abstract Like large companies, small and medium-sized companies (SMEs) are turning to new digital technologies and knowledge-based capital to bolster their productivity and growth. However, data show that smaller companies lag signicantly in implementing new Industry 4.0 technologies and in the intensity of their use. Lack of skills and human capital is often cited as one of the biggest barriers. This paper examines the benets of digital technologies, intangible capital, and in particular the role of complementary investments in new technologies and intangible capital to maximize the impact on productivity growth. The analysis draws on extensive rm-level datasets combining business and employee registry data and the harmonized EU ICT usage survey for the period 2007–2020 in Slovenia. While SMEs lag behind large companies in the use of ICT on average, the use of ICT and other new technologies signicantly increases the productivity of companies in the SME sector, especially when combined with the intangible investments that enhance the contribution of new technologies. Several conclusions emerge from the results, in particular the need to grow and invest intelligently, that is, to invest in intangible assets and new technologies simultaneously, even in SMEs. Keywords: Digitalization, SMEs, Complementary intangible investments, Productivity JEL classication: E22, O34 Introduction S mall and medium-sized companies (SMEs), like large companies, benet signicantly from new digital technologies that offer new opportunities for growth and increase business productivity and com- petitiveness (Remes et al., 2018; Wagner, 2007). SMEs have improved their performance through digital- ization. In addition, companies are building their internal capabilities to cope with the external dif- culties posed by the digitalization process (Teruel et al., 2022). However, SMEs lag behind in digital transformation compared to large companies. For example, in 2020, 27.23% of SMEs used a single digital technology and 21.57% used multiple digi- tal technologies, compared to 28.16% and 46.28% of large companies, respectively (European Investment Bank, 2019). Apart from the delay in implementation, the potential of digital technologies for innovation and growth is often underutilized by SMEs due to the lack of other required complementary re- sources, mostly human or intangible resources as well as nancial resources (Vitezi ´ c & Peric, 2015). As a result, the majority of SMEs do not fully benet from the productivity and competitiveness that result from the adoption of digital technolo- gies because they cannot clearly identify their needs or effectively use digital technologies (Organisation for Economic Cooperation and Development, 2022). Those companies that have the necessary (digital) Received 10 May 2023; accepted 4 September 2023. Available online 5 December 2023 * Corresponding author. E-mail addresses: eva.erjavec@ef.uni-lj.si (E. Erjavec), tjasa.redek@ef.uni-lj.si (T. Redek), crt.kostevc@ef.uni-lj.si ( ˇ C. Kostevc). https://doi.org/10.15458/2335-4216.1328 2335-4216/© 2023 School of Economics and Business University of Ljubljana. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/ licenses/by-nc-nd/4.0/). ECONOMIC AND BUSINESS REVIEW 2023;25:216–232 217 capabilities are able to integrate the IT and busi- ness planning process more effectively, conceive of and develop reliable and cost-effective applications that support the business needs of the rm faster than competition, communicate and work with busi- ness units more efciently and anticipate future business needs of the rm and innovate valuable new product features before competitors (Bharadwaj, 2000). This complementarity between digital and intan- gible business resources has led rms to focus on investments in intangibles such as intellectual prop- erty, innovative capital, competencies and skills, busi- ness model development, brand strengthening, and others, as these investments provide competitive ad- vantages to rms. However, SMEs also lag behind in investing in intangible capital, as a large proportion of SMEs do not invest in intangibles at all (Kostevc & Redek, 2021). The paper examines the following research ques- tions: (1) What are the characteristics of investment in new technologies and intangible investment as a function of rm size? (2) How important are simultaneous investments in intangible and new tech- nologies for productivity growth? Methodologically, the analysis relies on a combination of ofcial reg- ister microdata sets: (1) annual nancial statement data available for the entire population of Slove- nian rms provided by the Agency of the Republic of Slovenia for Public Legal Records and Related Services (AJPES), (2) microdata from the ofcial har- monized survey on the “Use of ICT in Companies,” and (3) the register of employees in Slovenia used to assess intangible investments in line with the H2020 Globalinto approach. The paper examines compa- nies in Slovenia in the period between 2007 and 2020. The results show that both investments in new tech- nologies and intangible investments have a positive impact on SME productivity. However, the strongest impact is found for companies that invest in both at the same time or that “grow smartly.” To our knowledge, this is the rst study of its kind to ex- amine the impact of complementary investments in new technologies and intangible capital in SMEs from a productivity growth perspective. It applies an innovative approach to estimating rm intangible capital based on the Globalinto method, overcom- ing the lack of data on intangible investment in rms. The paper rst provides a theoretical background to develop the hypotheses, followed by a discussion of the methodology and results. It then discusses the results and implications and concludes by summariz- ing the main ndings. 1 Theoretical background 1.1 The impact of technology and digitalization on rm performance The literature stresses the positive impact of digi- talization, which has led to a number of innovative advances improving businesses’ productivity and efciency (Björkdahl, 2020; Bui & Le, 2023). New tech- nologies help increase rms’ short-run efciency and long-run growth and competitiveness (Coad & Srhoj, 2020; Gao et al., 2012; Müller et al., 2018). Digital transformation generates additional revenue streams (Chawla & Goyal, 2022) through sales growth (Ba- hadir et al., 2009), allows better customer care and more efcient operations (Al Awadhi et al., 2021; Rekettye & Rekettye, 2019). Digital resources can reduce transaction costs and production expenses, improving operational efciency (Mithas & Rust, 2016) and increasing internal efciency, for instance, through better working and organizational practices (Schildt, 2017; Trittin-Ulbrich et al., 2021). Research also acknowledges the relationships between digital- ization and businesses’ innovative capacities, which can have a favourable impact on performance in terms of growth and innovation (Ferreira et al., 2019; Tsou & Chen, 2021). Studies have also shown linkages between soft-skill development and digital resources, that is, IT and big data, as a way to connect human and technological components for better performance (Caputo et al., 2019; Kristoffersen et al., 2021). Addi- tional value added is generated also through inno- vative combinations of technologies involved in the process of digital transformation (Bharadwaj et al., 2013). Digital technologies also reduce costs, rapidly and cost-efciently improve old or customize new products and processes (Chawla & Goyal, 2022). New technologies such as 3D printing, blockchain, and oth- ers, also have a positive effect on product innovation and a rm’s performance (Menguc & Ozanne, 2005). Digitalization, which is faster in large companies (Organisation for Economic Cooperation and Devel- opment, n.d.), is important for all rms, including SMEs. It allows rms to compete more efciently by integrating operational and management information systems (Abdullah & Chatwin, 1994), become more exible (Bharadwaj, 2000), and use ICT more ef- ciently (Ivanova & Castellano, 2012; Santoro et al., 2019). A 2019 economic survey of Singapore’s rst- quarter results showed that SMEs adopting digital tools increased their value by 25% and their pro- ductivity by 16% on average (Abanmai, 2020). Pro- ductivity gains are bigger for high-productivity rms (Berlingieri, 2018), although gains from technology depend also on the type of technology (e.g., cloud 218 ECONOMIC AND BUSINESS REVIEW 2023;25:216–232 computing is more benecial for small rms as a means to avoid investing in a large IT infrastructure; Bloom & Pierri, 2018). Consequently, we expect (H1) that investments in digital technologies are positively as- sociated with value added in SMEs. 1.2 Intangible capital, rm performance, and technology implementation Intangible capital consists of (1) computerized in- formation, (2) innovative capital, and (3) economic competencies (Corrado et al., 2009). Higher lev- els of competition and a more digitalized economy results in businesses focusing on investments in in- tangibles such as intellectual property since these investments bring companies certain competitive ad- vantages (Khan et al., 2019). Human capital, which represents a major part of intangible capital (eco- nomic competencies), is dened as the collective capability of a rm’s employees comprising the skills, knowledge, experiences, and prociency (Edvinsson, 1997). It represents an essential form of the competi- tive advantage of a rm (Liu & Jiang, 2020) and can leverage strategic capabilities such as digital capa- bility through its sub-dimensions including human capital and their distinct roles (Altman et al., 2022; Chu et al., 2006). Similarly, the innovative property, R&D, design, and so forth enhance rm performance (Corrado et al., 2017; Maggi, 2019; Piekkola & Rahko, 2020; Roth, 2020); in particular, the companies that are more digitalized also benet from more innova- tiveness, from product innovation to business model innovation (Menguc & Ozanne, 2005). Digital tech- nology that is used for digital transformation does not necessarily have to be new, because its value added comes from the innovative combinations of information generation, extraction, computation, and communication technologies involved in the process of digital transformation (Bharadwaj et al., 2013), which again requires the use of intangible capital and a suitable company strategy with forms of digital transformation (Kane et al., 2015) to maximize the impact of technology. We additionally argue that intangible investment and investments in new technologies are complemen- tary and that rms that simultaneously invest in both intangible capital and new technologies will perform better. First, the lack of human capital, knowledge, and skills is a highly cited obstacle in new technology implementation ( ˇ Cater et al., 2021) and investment at large (Andrews et al., 2018; European Investment Bank, 2019). Skills are important in the development of innovative business models needed for new tech- nologies implementation (Baima et al., 2020), while new technologies can further increase the efciency in completing organizational tasks, the speed and responsiveness of rms, and decision making (Pin- zone et al., 2017). The number of qualied employees impacts the adoption of new technologies (Petroni et al., 2012). Educated people are also good innovators (De Albuquerque et al., 2012) and are better in- formed about the latest technologies available (more advanced technologies require a higher level of skills; Gruber, 2017; Walk et al., 2015). Therefore, we expect that (H2) simultaneous presence of investment in both new technologies and intangible as- sets is positively associated with value added. 1.3 The characteristics and role of intangible investment in SMEs SMEs’ characteristics differ from those of large rms, due to their limitations regarding nancial and human resources (Müller et al., 2018). SMEs largely lag behind in the use of both more complex digital technologies as well as in terms of the employment of ICT specialists ( ˇ Cater et al., 2019; Maravi´ c et al., 2021) and the use of intangible capital (Kostevc & Redek, 2021). Evidence also shows that SMEs have larger skills deciencies than large companies and invest less (on a per-employee basis) in trainings com- pared to large companies. SMEs have difculties in attracting a highly qualied workforce (Organisation for Economic Cooperation and Development, 2017). In addition, SMEs’ challenges include not only the lack of resources (human and nancial), but also a low degree of processes standardization and less au- tomated production processes (Müller et al., 2018). It has been shown that the level of technology usage differs among rms’ sizes and industries (Berlingieri, 2018; Denicolai et al., 2021; Lu & Ramamurthy, 2011). The rst reason for the differences lies in industry specics, as not all technologies are appropriate for all industries (Banerjee et al., 2003; Utterback, 1974). The rm size also affects the level of technology us- age as well as the optimal technology intensity level. First, the implementation of new technologies is often very costly, so only rms with substantial resources can afford it (and usually larger companies have rela- tively more resources available to invest; Berlingieri, 2018). The second reason lies in economies of scale, which will lead to a different optimal number of technologies used; third, the implementation of new technologies requires complementary investments in other intangible assets such as human capital in order for a rm to take full advantage of new technologies (Corrado et al., 2017). Nonetheless, the literature shows that intangible investments have a positive impact on rm perfor- mance, which includes SMEs (Mansion & Bausch, ECONOMIC AND BUSINESS REVIEW 2023;25:216–232 219 2020; Seo & Kim, 2020; Yadiati et al., 2019), although there were signicant differences in the intensity and contribution of intangibles to rm performance (Kostevc & Redek, 2021). For example, the distribu- tion of intangible investment is very skewed; many rms invest very little or nothing at all (Kaus et al., 2020), among them mainly small rms. For example, in Slovenia in 2020 there were around 75% of mi- crorms with no intangible capital compared to only around 5% of large companies. On average in Slove- nia, 70% of SMEs had no intangible capital, though the estimations showed that it was in fact the smallest rms (up to 9 employees) that recorded the strongest impact of intangible capital on rm performance (Kostevc & Redek, 2021). Based on this, we hypothe- size that (H3) smaller companies on average report lower intangible investment intensity and digital intensity than larger rms and, as already stated, that (H4) intangible capital has a positive impact on rm performance in SMEs . 1.4 Exporting status and productivity Since the mid-1990s the availability of rm-level data has supported extensive research on the asso- ciation between rm engagement in exporting and its performance. The correlation between productiv- ity and export status has been proven to be robust over numerous datasets (Greenaway et al., 2005, and Wagner, 2007, provide extensive literature re- views). Theoretical models such as Bernard et al. (2003) and Melitz and Ottaviano (2008) emphasize the self-selection of rms into export markets based on an underlying productivity distribution, creating a strong correlation between productivity and export status. Given the small domestic market, a large pro- portion of Slovenian rms export (around 50% of companies in 2021, excluding sole proprietors and two thirds of companies in the estimation sample), leading to a relative scarcity of non-exporting control observations, in particular in the cohort of large rms in industries reliant on scale economies. Based on this, we control for exporting status in the productivity specication by including an indicator for rms that exported at least 50% of their total sales in addition to the standard exporting status indicator. 2 Methodology and data The paper studies the link between productivity (value added) and its determinants, with the focus on evaluating the contribution of new technologies, intangible capital, and complementary investments in both. Methodologically, the estimation strategy is based on an evaluation of the production func- tion, which relies on a modied approach utilized in H2020 Globalinto procedure for estimating the contribution of knowledge-based capital (i.e., intan- gible capital). We extend the specication by adding the perspective of ICT usage as well as the im- pact of openness (exports). The analysis relies on a combination of individual (employee) and rm-level datasets—accounting registry data for the population of companies and ofcial national/Eurostat harmo- nized survey data. 2.1 Empirical approach We employed a production-function-based investi- gation into the contributions of standard production factors: capital, non-intangible employees, intangible employees of three types (organizational, ICT, and R&D), and the intensity of use of new technologies. We followed the approach suggested by Piekkola et al. (2021a), where the elasticity of value added is derived based on an extended production function: Y it D AK b K it L b L it L b Lorg ORGit L b Lict ICTit L b Lrd RDit . K it is capital per rm in a given year; L ORGit, L ICTit , L RDit are the organizational, ICT, and R&D workers, respectively. b denotes the relevant elasticities in each case. The estimation was also extended with dummy variables, capturing the combined intensity of investment in technology and intangibles (D) for each of the groups j (as explained below). The relevant estimation equation is (1): lnY it D b 0 C b L lnL it C b K lnK it C b ORG lnL ORGit C b R&D lnL R&Dit C b ICT lnL ICTit C6 j b j D jt C e it (1) Fixed-effects estimation was used controlling for industry (NACE, level 2) and year, as well as rm size to differentiate between different sub-classes of SMEs (Clark & Linzer, 2015). Additionally, in some specications, the technological type of the industry as dened by the Organisation for Economic Cooperation and Development (OECD) (see Piekkola et al., 2021b) was used to control for differences across sectors by technological intensity rather than NACE sector. 2.2 Construction of key variables 2.2.1 Intangible capital measurement To measure intangible capital, the methodological approach proposed by Piekkola et al. (2021b) within the H2020 Globalinto “Capturing the value of intan- gible assets in microdata to promote EU’s growth and competitiveness” project was used. The methodol- ogy utilizes the microdata from the population-based statistical registry of employed workers, matched with rm-level data to overcome the lack of data 220 ECONOMIC AND BUSINESS REVIEW 2023;25:216–232 on intangible capital. It proxies intangible capital by deriving a measure based on intangible work—for example, innovative capital depends on R&D or in- novative work. To compose a measure of “intangible capital” work, the International Standard Classica- tion of Occupations (ISCO) is used. The amount of intangible capital in a company was assessed by the number of people (and shares) in certain “intangi- ble capital work” occupations according to the ISCO classication (for more details see Table A1 of the Appendix). Each employee was categorized into one of four groups: either non-intangible capital work or one of three intangible categories of workers (orga- nizational, ICT, and R&D). The data were collapsed and merged with rm accounting data. Respective numbers and shares of each category of work were constructed for each company. 2.2.2 Use of new technologies The variables for the use of new technologies in companies were created using the standardized EU survey “The use of ICT in companies” (see, e.g., Statistiˇ cni urad Republike Slovenije, 2019). As the ICT questionnaire has evolved considerably since 2007 and the number of different technologies used has in- creased, both the number per year and the proportion of all available/investigated technologies each year were used to examine the intensity of use. In addition, we had to consider sectoral and size differences. Consequently, several different variables were used. First, each company position was calcu- lated relative to the maximum number of technolo- gies used in each respective year, by dividing the number of technologies used by the rm in compar- ison to the maximum in that specic year (absolute maximum regardless of rm size and industry). Com- pany size also affects the rationale of investment into new technologies, as not all technologies are rele- vant for microcompanies, depending on what their specialization is. On the other hand, among medium companies, the size and economies of scale allow companies to implement and use a wider range of technologies. Size also impacts the structure of em- ployment and the share of intangible workers. The second indicator compared the number of technolo- gies used to the best performing company, where the maximum was identied for each rm size group in every year and the actual number of technologies in a company was compared to the best performer in the size group (not controlling for industry). The literature highlights the differences among sectors in the use of technology using two measures, which we also considered. The third indicator considered the relative intensity of the rm (i.e., the number of tech- nologies used in a rm) to the best performer in a specic NACE1 sector and size class. 2.2.3 Other variables The empirical approach relies on production- function estimation. To evaluate the impact of rel- evant variables on rm performance, value added was required rst. It was calculated using a standard approach, subtracting material costs from revenue. The number of non-intangible capital employees was constructed next, followed by the three types of intan- gible capital work (as described above). For each rm, average education of employees in years was calcu- lated by averaging across educational attainments of all individuals that worked in a specic rm. The share of exports was calculated by dividing the sales in foreign markets by total sales. Since roughly two thirds of rms in the sample were exporters, a dummy variable was created, indicating whether the rm had at least a 50% export share, signifying export intensity. 2.3 Data The analysis draws on four different registry databases. The proprietary data of AJPES provide the basic demographic characteristics about Slovenian businesses within the “Slovenian Business Register data” and the “AJPES” data about the entire pop- ulation of Slovenian businesses (130 thousand). The “Statistical registry of employees” in Slovenia (about 800 thousand employees annually), which provides information on the structure of employees, their ed- ucation, and occupation, was used to create the variables on intangible investments in companies. The datasets were merged with the microdata from the ofcial, EU-wide harmonized survey on the “Use of ICT in companies,” which is conducted by the Slovenian Statistical Ofce among about 1100–1500 companies per year. Atotal of 15,338 micro-, small and medium companies were analysed in the period be- tween 2007 and 2020, ranging from 774 observations (2020) to 1471 observations (2013) yearly. 1 The sample included 16.4% or 2512 microcompa- nies, 61.9% or 9503 small companies, and 21.7% or 3323 medium companies. The size was determined by employment (0–9, 10–49, 50–249 employees, re- spectively). More than one third of companies were from the manufacturing sector (33.4%, NACE C), 1 The panel is unbalanced, depending on the sampling by the Statistical ofce of the Republic of Slovenia, for each survey round. On average, a company was included in the survey four times; around 12% of companies were included at least 10 times. The sampling is done in accordance with the methodology of the Statistical ofce of the Republic of Slovenia and Eurostat. ECONOMIC AND BUSINESS REVIEW 2023;25:216–232 221 followed by the retail sector (21.8%, NACE G); around 9% were from the accommodation and food ser- vices sector (NACE I), and 9.6% from professional, scientic, and technical activities (NACE M), fol- lowed by transportation and storage (7.9%) and ICT (5.8%, NACE J). The companies were also divided according to the technology intensity of their respec- tive NACE 2-digit sector according to the OECD classication (see also Bloch et al., 2021). Around 31% of companies belonged to the medium-low- and low-technology-intensive manufacturing sectors, and 7.5% to the high-technology-intensive manufacturing sector, while 43.6% of companies surveyed belonged to the low-knowledge-intensity services sector; the rest were knowledge-intensive services (18%). 3 Results 3.1 The differences in the intensity of use of new technologies and intangible investment in SMEs Overall, 25.7% of the studied companies did not employ intangible workers, and 3.5% did not use technologies during the whole period of the study (2007–2020) (Table 1). The percentage was higher among microcompanies; no fewer than 50.0% of mi- crocompanies did not employ intangible workers. Among small companies, 26.1% did not employ any intangible workers, and 5.9% of medium companies employed no intangible workers. Among large com- panies, only 0.4% (11 in total) did not employ any intangible workers. The share of companies which used no technology declined between 2007 and 2020. If in 2007 around 1.3% of companies used no tech- nology (not even a computer), this share declined to only 0.3% by 2020. By size, the share of compa- nies not using any technology was the largest among microcompanies, where in the entire sample 7.2% of observed companies did not use technologies, while there were only a handful of such companies among the 3300 medium companies observed in the sam- ple. Also, the share of companies without intangible capital work declined in the observed period. Since 2007, the percentage of microcompanies that did not employ intangible labour varied but has on average decreased and was around 45% in 2020. In small com- panies, the volatility was lower, and the percentage of companies without intangible capital work also de- clined, from around 34% to 24%. The gap is the most pronounced between micro- and small companies on the one hand and medium companies on the other, where the share of companies without intangible cap- ital work is around 5 to 6%. A closer look at the technologies used in 2020 shows that while almost all companies, regardless of their size, used computers and the Internet (over 99%), there were signicant differences in the use of the mo- bile Internet, which was utilized only by 79% and 87% of micro- and small companies, respectively, and 96% of medium companies. While 96% of medium compa- nies had a website, only 68% of microcompanies did. The cloud was used in 23% of microcompanies, 43% of small companies, and 52% of medium companies. The use of more advanced technologies was lower in all size categories, but larger companies used them more frequently. Electronic invoices were used in 64% Table 1. Share of companies by size class with no intangible workers or no technologies used. Without intangible capital Without technologies Total Year Micro Small Medium Total Micro Small Medium Total Number of observations 2007 .49 .34 .07 .37 SP SP .000 .013 977 2008 .51 .27 .07 .35 .267 .134 .051 .181 1027 2009 .56 .30 .05 .38 .022 .011 .000 .014 1185 2010 .61 .29 .07 .26 SP SP SP .006 1058 2011 .53 .28 .06 .25 SP SP SP .004 1156 2012 .53 .27 .04 .23 SP SP .000 .008 1180 2013 .46 .25 .06 .28 .029 .007 .000 .012 1471 2014 .53 .25 .06 .22 .074 .009 .000 .011 1101 2015 .38 .24 .06 .20 SP SP .000 .006 1112 2016 .32 .27 .06 .22 SP SP .000 .004 1146 2017 .47 .23 .05 .20 SP SP SP SP 1150 2018 .46 .26 .06 .22 SP SP SP .005 1150 2019 .46 .22 .06 .20 SP .000 .000 SP 851 2020 .45 .24 .06 .21 .000 SP .000 SP 774 Total .50 .26 .06 .26 .072 .011 .004 .019 15,338 Total number of observations by company size Number 2512 9503 3323 15,338 2512 9503 3323 15,338 SP: “statistical protection,” very small number of observations, actual number protected by the statistical data protection framework and are condential. Data: Statistical Ofce of the Republic of Slovenia, own calculations. 222 ECONOMIC AND BUSINESS REVIEW 2023;25:216–232 Table 2. Share of companies using a specic technology by company size and average number of selected technologies used in a company in 2020. Micro Small Medium Total IT intensity (average number of technologies used) 3.49 4.28 5.02 4.41 Computer SP SP SP .994783 Internet 1.00 SP 1.00 SP Mobile Internet .79 .87 .96 .89 Web page .68 .85 .96 .87 Cloud .23 .43 .52 .44 3D printing .00 .05 .12 .06 Robots SP .07 SP .10 Big data .10 .09 .19 .12 E-invoices .53 .59 .64 .60 E-sales .11 .23 .18 .21 Computer exchange of data (RIP) .00 .04 .15 .06 Internet of Things SP .16 SP .18 Use of computers and big data for 2018. For 2020, 10 technologies in total were captured. SP: “statistical protection,” very small number of observations, actual number protected by the individual data protection code of the Statistical Ofce of RS. Data: Statistical Ofce of the Republic of Slovenia, own calculations. of medium companies and 53% of microcompanies; e-sales were used in 11% of microcompanies and 18% of medium companies, and computerized data ex- change was used in 15% of medium companies and only 4% of small companies (Table 2). On average (over the entire period, not just in 2020 as in Table 2), microcompanies used 2.65 tech- nologies (8 was the maximum among them), small companies used 3.65 different technologies (9 was the maximum among them), and medium companies used 4.15 different technologies (9 was the maximum among them). The t-test for pairwise comparison of means shows that the differences between all combi- nations of groups are statistically signicant at pD .000, which conrms H3 that smaller companies on average report lower intangible investment intensity and digital intensity than larger rms. As for investment in intangible capital, as measured by the share of intangible employees, its share was low (Table 3). On average, 82% of employees were non-intangible workers. The share of R&D workers was Table 3. Share of intangible workers as % of all employees by company size over the observed period. Organizational ICT capital R&D capital capital work work work Total Mean .065 .038 .073 Median .033 .000 .000 Micro Mean .069 .035 .059 Median .000 .000 .000 Small Mean .070 .041 .073 Median .043 .000 .000 Medium Mean .048 .034 .083 Median .031 .000 .046 Data: Statistical Ofce of the Republic of Slovenia, own calculations. the highest in medium companies at 8.3%, while it was between 5.9% and 7.3% in micro- and medium companies. The share of organizational workers ranged from 4.8 to 6.9%, while the shares of ICT workers, which ranged from 3.4% to 4.1%, were the lowest (and comparatively the highest in small companies). Interestingly, the median microcompany had no intangible workers of any kind. Over the period studied, the mean and median proportions were relatively stable on average, in particular with regard to organizational capital workers, which remained around 6.5% on average, while the shares of ICT and R&D workers increased by around 1.5 and 1 percentage point, respectively, over the investigated period. Interestingly, the share of organizational, ICT, and R&D workers declined in microcompanies, which could be a result of different factors, such as company growth as well as less attractive jobs in microcompanies compared to larger companies. In small and medium companies, the shares of all intangible capital workers increased over the observed period, which is consistent with the rising importance of the knowledge economy, primarily in knowledge-intensive services (Piekkola et al., 2021a). To examine the impact of the use of new technolo- gies and intangible investments, as well as comple- mentary investments in intangibles and new tech- nologies, companies were divided into four groups (according to combined technology and intangible capital investment intensity): (1) rms with no in- tangibles and low technology intensity (below the median technology intensity measured as a share of total available technologies), (2) rms with intangible investment (measured as intangible employees) and low technology intensity, (3) rms with no intangible investment and high technology intensity, (4) rms with both intangible investment and high technology intensity (Table 4). ECONOMIC AND BUSINESS REVIEW 2023;25:216–232 223 Table 4. Division of companies by size and ICT–IT intensity : shares of companies by group. Both intangible No intangibles, With intangibles and No intangibles and investments and low tech. intensity low tech. intensity high tech. intensity high tech. intensity Total No. of rms Micro 28.8 20.3 21.3 29.7 100.0 2512 Small 13.5 26.3 12.6 47.6 100.0 9503 Medium 2.6 23.8 3.2 70.3 100.0 3323 Total 13.7 24.8 12.0 49.6 100.0 15,338 Below median technology intensity, measured as share of total available technologies. Data: Statistical Ofce of the Republic of Slovenia, own calculations. As the data in Table 4 show, 28.8% of microcom- panies belonged to the rst group, while almost 30% of all microcompanies were both technologically above average and had invested in intangible capital work. Among small companies, 47.6% had invested in both intangible capital and technologically ad- vanced products. Among medium companies, 70.3% had intangible capital and were also technologically advanced. 3.2 Relationship between ICT, intangible investment, and productivity On average (Table 5, details in Appendix, Table A3), companies that were more technologically advanced and used intangible capital also had higher value added per employee. Of all the companies, those that invested in both had an average value added of €42.8 thousand per employee in the observed period (2007– 2020), while those that did not use intangible assets and had low technological intensity only had €26.5 thousand value added per employee in the period studied, which speaks in favour of hypothesis H1 that investments in digital technologies are positively as- sociated with value added in SMEs. Data also show that, on average, productivity changes with rm size, with primarily microrms lagging behind. This pattern is also evident among micro-, small and medium rms if each group is divided fur- ther by intensity of investment in intangibles and new technologies. Firms that excel in productivity are those that invest in both intangibles and are also above average in technological intensity. Also, those rms that either invest only in technology and are technologically advanced, or those that employ intangible labour (while technologically underper- forming) are better than those that lag in both. In all three groups, value added is the highest in companies that both invest in intangibles and have above-average technological intensity. Companies which only use technology but have no intangible capital work have lower value added than companies that have only intangible capital work but invest little in new tech- nologies. The differences between the group with no Table 5. Descriptive statistics by company type (size and knowledge & tech intensity) , 2007–2020. No With No Both intangibles, intangibles, intangibles, intangibles low tech. low tech. high tech. and high tech. Micro Small Medium intensity intensity intensity intensity Total Value added per employee 33,222 39,335 38,339 26,462 38,983 29,844 42,840 38,046 Export share .13 .21 .31 .15 .27 .13 .24 .22 Number of employees 7.98 21.15 107.63 16.34 37.47 17.84 48.57 37.73 Share of max number of technologies .55 .61 .67 .39 .44 .71 .73 .61 Average years of education of employees 7.36 10.81 10.49 7.82 9.12 9.75 11.13 10.10 No. of employees 8.56 21.77 110.61 16.62 38.56 18.31 50.09 38.85 ORG workers share .07 .07 .05 .00 .09 .00 .09 .07 ICT workers share .03 .04 .03 .00 .04 .00 .06 .04 R&D workers share .06 .07 .08 .00 .09 .00 .10 .07 Average number of technologies used 2.63 3.64 4.11 2.48 3.27 3.61 4.02 3.57 Share of companies with at least 50% exports .11 .19 .32 .15 .26 .11 .22 .20 Real value added in euros, 2015 prices. Data: Statistical Ofce of the Republic of Slovenia, own calculations. 224 ECONOMIC AND BUSINESS REVIEW 2023;25:216–232 intangible capital work and low technology inten- sity and the group with both types is highest among medium companies, where the latter is 73% more pro- ductive (in the other two size groups, the group with both intangibles and high technological intensity is 62% and 68% more productive). 3.3 Relationship between simultaneous investment in intangibles and new technologies on the one hand and rm productivity on the other Regression results are in Table 6. Fixed-effects panel estimation was used, with specications including Table 6. Regression results on the impact on value added (coefcients and standard errors). 1 2 3 4 5 6 7 Value added b/se b/se b/se b/se b/se b/se b/se Average years of education .023 .022 .023 .023 .023 .018 .019 .006 .006 .006 .006 .006 .006 .005 Non-intangible capital work .627 .644 .647 .645 .643 .605 .605 .013 .014 .014 .014 .014 .018 .014 ICT work .044 .038 .043 .039 .040 .075 .019 .019 .02 .019 .019 .03 Organizational work .066 .042 .038 .042 .044 .037 .012 .013 .014 .014 .013 .02 R&D work .130 .136 .135 .136 .135 .147 .013 .013 .015 .013 .013 .017 Intangible capital work .090 .008 Capital .051 .051 .051 .051 .051 .049 .046 .021 .004 .004 .004 .004 .005 .021 Export (50% share) dummy .065 .065 .063 .063 .064 .021 .046 .021 .021 .021 .021 .021 .026 .021 Share of max ICT in relevant size group and NACE2 .036 .009 .027 .028 Share of max ICT##share of intangible organizational work .579 .147 Share of max ICT##share of intangible R&D work .025 .129 Share of max ICT##share of intangible ICT work .203 .143 Share of max ICT in relevant size group##share of intangible ICT work .251 .065 Share of max ICT in relevant size group and NACE2##share of intangible ICT work .158 .205 .220 .038 .055 .063 Share of max ICT ##share of intangible ICT work .078 .033 Industry dummy (NACE2) Yes Yes Yes Yes Yes Yes Yes Size dummy Yes Yes Yes Yes Yes Yes Yes Year dummy Yes Yes Yes Yes Yes Yes Yes Technological intensity category (OECD) Yes Yes Ownership dummy Yes Yes _cons 10.938 10.892 10.874 10.881 10.891 10.606 10.501 .400 .399 .4 .4 .4 .11 .148 N 15,876 15,838 15,876 15,876 15,876 12,015 12,015 r 2 _w .409 .412 .41 .41 .41 .356 .356 r 2 _b .681 .688 .687 .687 .687 .551 .545 r 2 _o .708 .714 .713 .714 .713 .544 .538 s_e .313 .312 .313 .313 .313 .311 .311 s_u .636 .629 .63 .63 .63 .614 .617 r .805 .803 .802 .802 .802 .796 .797 Note: Log-log form was used. Signicance levels: <.05, >.01, <.001. Data: Statistical Ofce of the Republic of Slovenia, own calculations. ECONOMIC AND BUSINESS REVIEW 2023;25:216–232 225 indicator variables for year, industry (NACE level 2), and size class (micro, small, medium). Double logarithmic form was used for the estimation equa- tion. The last two regressions additionally control for ownership type (private, public, mixed, other) and technological intensity according to the OECD. The results show that, on average, the elastic- ity of value added with respect to employment of non-intangible capital was the highest. However, the stock of human capital is important, as can be seen from the elasticity of value added with respect to the average years of education of employees in the company. With respect to the three types of in- tangible labour, which is included either separately (columns 1–6) or as whole (sum of all three cat- egories, column 7), the results show a systematic positive and signicant elasticity, as expected, which also conrms hypothesis H4 that intangible capi- tal has a positive impact on rm performance in SMEs. While technological intensity itself does not have a signicant impact 2 (column 1), the combined impact of technology and intangible capital work is positive and signicant regardless of specication. The rst specication studied the combined effect of the in- tangible work type (for each of the three categories) and relative share of the number of technologies the rm used, relative to the highest number used in a specic year (not controlling for rm size or sector, see Table A2 for variable description). The second controlled also for size (Share of max ICT in relevant size group), and the third for size and sector (Share of max ICT in relevant size group and NACE2). The last captured the combined effect of intangible capi- tal and relative share of the number of technologies the rm used, relative to the highest number used in a specic year. The relationship between simultane- ous investments in new technologies and intangible capital on the one hand and productivity on the other is, in all cases, positive and signicant, which conrms the hypothesis (H2) that simultaneous in- vestment in both new technologies and intangible assets is positively associated with value added, that is, productivity. After controlling for industry, year, as well as ownership and technological intensity of industries according to the OECD (see Bloch et al., 2021), the results remain positive and signicant. This conrms the combined importance of technology in- vestment/use when also accompanied by intangible capital investment. 4 Discussion 4.1 Main ndings and implications The Fourth Industrial Revolution with a wide range of new technologies, including robotization, smart factories, articial intelligence, and others, has a sig- nicant impact on productivity growth both in SMEs and in the economy at large (Aernoudt, 2019; Morrar et al., 2019; Szabo et al., 2020; Tsou & Chen, 2021). New technologies are expected to boost productivity also due to their impact on innovation and business model transformation (Bui & Le, 2023; Caputo et al., 2019), while intangible capital has been reported to con- tribute as much as 30% of total productivity growth (Nonnis et al., 2023; Piekkola et al., 2021b; Tsakanikas et al., 2020). The literature has also suggested that there is a link between knowledge (intangible cap- ital) and digitalization, which enhances the impact on innovativeness, creativity, and performance (Bui & Le, 2023; Caputo et al., 2019), especially when sup- ported by a solid strategy, which could be deemed intangible (organizational) capital as well. The liter- ature also points out that the lack of human capital and skills, which is a large part of intangible capital, that is, the part of economic competencies, is a ma- jor obstacle in identifying, implementing, and using appropriate technologies ( ˇ Cater et al., 2021). In the literature, knowledge and digitalization are increas- ingly important also for small rms (Aernoudt, 2019; Foroudi et al., 2017). This paper has focused on identifying whether there is a positive relationship between SME per- formance and simultaneous investment in both new technologies and intangible capital (proxied by intan- gible work). In particular, three elements have been investigated: rst, whether investments in digital tech- nologies are positively associated with value added in SMEs (H1). Second, we have been interested in whether simultaneous presence of investment in both new technolo- gies and intangible assets is positively associated with value added (H2). Third, we have explored the intensity of intangible investment in smaller rms relative to those in larger ones (H3) and the impact of intangible capital on rm performance in SMEs (H4). Results show that, on average, the rms that were more technologically advanced and used intangible capital also had higher value added per employee; specically, those that invested in both had an average value added of €42.8 thousand per employee during the 2007–2020 observation period, compared to only 2 Several different measures of technological intensity were used, from the number of technologies (absolute number) to relative measures. Here, only one such example is provided; however, the result is consistently insignicant, regardless of the measure. 226 ECONOMIC AND BUSINESS REVIEW 2023;25:216–232 half of the value added per employee produced by those that did not use intangible assets and had low technology intensity during the observation period. The results, obtained through a production- function estimation of the contributions of different factors, from intangible work to technology and combined effects of technology and intangible work, highlight that this positive relationship indeed exists and is highly signicant even after controlling for a number of other factors. The results also show that there is a signicant productivity gap between rms that lag in technology and intangible investments in comparison to rms that rely on both factors, with the difference reaching even over 70% higher produc- tivity (if measured by value added per employee). This nding has important implications for man- agers, particularly with respect to the existing lit- erature on productivity as well as barriers to im- plementation and use of new technologies. SMEs represent the majority of the business population in all countries; their share in Slovenia is over 99%, with microcompanies representing around 95% of all companies (Statistiˇ cni urad Republike Slovenije, n.d.). Improving their productivity by focusing on improv- ing technological intensity, knowledge intensity, or, ideally, both would have a signicant impact on over- all economic performance. The results highlight that to maximize the impact of new technologies, a rm’s decision to invest in new technologies to maximize the outcome should always be accompanied by strengthening (intangible) human capital and improving skills (education and training). In SMEs, the ability to invest in such resources may be limited, for nancial and non-nancial reasons (Aiello et al., 2020; Ferrando & Preuss, 2018; Oliveira Neto et al., 2017; Zubair et al., 2020). Consequently, their ability to grow is hampered twice—both due to the lack of investment as well as inability to unlock the potential of combined investment. Therefore, governments or stakeholders (such as chambers of commerce) could support the “smart growth” of SMEs by preparing training or providing support for technology adoption and use to maxi- mize the efciency of using new technologies. Given the possible nancial obstacles to investments, such “educational” campaigns should also be supported by investment programs (Organisation for Economic Cooperation and Development, 2019, 2022). 4.2 Contributions To the best of our knowledge, this paper is the rst to examine the complementarity of investments in new technologies and intangible capital in SMEs from the perspective of rm productivity in this manner. It is also, to our knowledge, the rst analysis of the complementarity of investments in technology and intangible capital, particularly from the perspective of emerging markets. The analysis applies an inno- vative approach to the estimation of rms’ intangible capital, based on the methodology of Innodrive and Globalinto, to help overcome the problem of lack of data on intangibles as dened by Corrado et al. (2009). The paper conrms that complementary investments are indeed the most benecial on average, which also supports the literature highlighting the role of human capital (which is part of intangible capital) in technol- ogy implementation. Indeed, it is reported that the lack of skills, the resistance of employees to change, and the lack of appropriate (technical) proles is one of the main barriers to technology adoption ( ˇ Cater et al., 2021). The analysis also draws on rich registry- based microdata collected through ofcial statistics, which is the most reliable source of data available. 4.3 Limitations and challenges for future research Several limitations provide opportunities for fu- ture research. In the future, analyses should focus on each segment of SMEs separately and examine the causes of differences in their behaviour, also separating fast-growing rms from those that grow slowly. The possible presence of “overinvestment” or diminishing returns and the suitability of different technologies for SMEs could be explored. Previous research has pointed to the importance of strat- egy in business development (Björkdahl, 2020), with ownership also playing a major role in determining behaviour and goals in small businesses. It has been reported that family businesses focus more on stabil- ity as they are often a source of social security for the owner and family, which means they are less risk- taking and less ambitious (Redek & Oblak, 2016). One of the challenges for future research is also to test for the link between rm size and rm growth. This is known as Gibrat’s law (Bojnec & Fert˝ o, 2020). Although we have not studied rm growth, but rather their productivity level, this could also be linked to rm size, as suggested by Gibrat’s law. To take this possibility into account, size dummies have been in- cluded, following the H2020 Globalinto methodology. However, we acknowledge that we have not been able to appropriately test for the issue of Gibrat’s law (Fiala & Hedija, 2019; Srhoj et al., 2018), since the dataset used combines administrative data and survey data (with varying structure of companies per survey wave). The estimation sample is a non- balanced panel, often comprising only one data point (ID, year) per rm. Therefore, we have not been able to test appropriately for the challenge of Gibrat’s law, ECONOMIC AND BUSINESS REVIEW 2023;25:216–232 227 as was for example suggested and done by Bojnec and Fert˝ o (2020) for Slovenian agricultural compa- nies, who relied on a number of different tests, from simple CD to multiple other unit root tests, such as Pesaran, HS, HMW and other. Depending on the availability of data, similar in- vestigations could be relevant also at sectoral level, potentially using combined dataset from Eurostat (on digital technology) and EU Klems (for intangible in- vestments). 5 Conclusion Digital transformation is expected to signicantly boost productivity growth, just as other industrial revolutions have done. Large companies will take the lead in technological transformation, while smaller ones will lag behind signicantly. In addition, smaller companies lag behind in complementary investments in intangible capital that enable rms to use digital technologies more efciently. SMEs also lag behind in intangible investments, especially when consid- ering the median rm. However, the results show that complementary investments in both technology and intangible capital boost productivity growth the most, which is a very important nding in a re- structured economy like Slovenia’s or other emerging economies. Results of the regression conrm that more technologically advanced rms using intangible capital have higher value added per employee com- pared to rms with low technology intensity and no intangible assets in the observed period. This is an outcome that is not only important for small busi- nesses. However, given the discrepancy, SMEs will lag behind comparatively more and will not be able to reap all the productivity benets unless they invest in both technology and intangible capital and focus especially on human capital development. Relating to our results this means that SMEs should improve their productivity by focusing on technological inten- sity and knowledge intensity; primarily investing in both would have a signicant impact on overall eco- nomic performance. Acknowledgements The analysis was partially co-nanced from the projects V5-2264, V5-2121, P5-0128, and J5-4575, (co)- funded by the Slovenian Research and Innovation Agency. It was prepared using linked employer– employee datasets, provided by the Statistical Ofce of the Republic of Slovenia. This analysis would not be possible without their expert support, in particular the User Relations Section of the Data Publication and Communication Division. References Abanmai, O. (2020, July 21). The Importance of going digital for SMEs. SME Finance Forum. https://www.smenanceforum .org/post/the-importance-of-going-digital-for-smes Abdullah, H., & Chatwin, C. (1994). Distributed C 3 envi- ronment for small to medium-sized companies. Integrated Manufacturing Systems, 5(3), 20–28. https://doi.org/10.1108/ 09576069410064724 Aernoudt, R. (2019). Unleashing rms’ growth potential. Ekono- miaz, 95(1), 135–155. Aiello, F., Bonanno, G., & Rossi, S. P . S. (2020). How rms nance innovation. Further empirics from European SMEs. Metroeco- nomica, 71(4), 689–714. https://doi.org/10.1111/meca.12298 Al Awadhi, J., Obeidat, B., & Alshurideh, M. (2021). The impact of customer service digitalization on customer satisfaction: Ev- idence from telecommunication industry. International Journal of Data and Network Science, 5(4), 815–830. https://doi.org/10 .5267/j.ijdns.2021.x.002 Altman, E., Balzano, M., Giannozzi, A., & Srhoj, S. (2022). Revisiting SME default predictors: The Omega Score. Journal of Small Busi- ness Management. Advance online publication. https://doi.org/ 10.1080/00472778.2022.2135718 Andrews, D., Nicoletti, G., & Timiliotis, C. (2018). Digital technology diffusion: A matter of capabilities, incentives or both? (OECD Eco- nomics Department Working Papers No. 1476). Organisation for Economic Cooperation and Development. https://doi.org/ 10.1787/7c542c16-en Bahadir, S. C., Bharadwaj, S. G., & Parzen, M. I. (2009). A meta-analysis of the determinants of organic sales growth. Inter- national Journal of Research in Marketing, 26(4), 263–275. https:// doi.org/10.1016/j.ijresmar.2009.06.003 Baima, G., Forliano, C., Santoro, G., & Vrontis, D. (2020). Intellectual capital and business model: A systematic literature review to explore their linkages. Journal of Intellectual Capital, 22(3), 653– 679. https://doi.org/10.1108/JIC-02-2020-0055 Banerjee, S. B., Iyer, E. S., & Kashyap, R. K. (2003). Corporate en- vironmentalism: Antecedents and inuence of industry type. Journal of Marketing, 67(2), 106–122. https://doi.org/10.1509/ jmkg.67.2.106.18604 Berlingieri, G. (2018). Last but not least: Laggard rms, technology dif- fusion and its structural and policy determinants. OECD Directorate for Science, Technology and Innovation. https://one.oecd.org/ document/DSTI/CIIE(2018)11/en/pdf Bernard, A. B., Eaton, J., Jensen, J. B., & Kortum, S. (2003). Plants and productivity in international trade. American Economic Review, 93(4), 1268–1290. https://doi.org/10.1257/ 000282803769206296 Bharadwaj, A., El Sawy, O. A., Pavlou, P . A., & Venkatraman, N. V . (2013). Digital business strategy: Toward a next generation of insights. MIS Quarterly, 37(2), 471–482. https://misq.umn.edu/ misq/downloads/download/editorial/581/ Bharadwaj, A. S. (2000). A resource-based perspective on informa- tion technology capability and rm performance: An empirical investigation. MIS Quarterly, 24(1), 169–196. https://doi.org/ 10.2307/3250983 Björkdahl, J. (2020). Strategies for digitalization in manufacturing rms. California Management Review, 62(4), 17–36. https://doi .org/10.1177/0008125620920349 Bloch, C., Piekkola, H., Rybalka, M., Eklund, C., & van Criekin- gen, K. (2021). Measuring intangible assets at the rm level— Development of an occupation based approach (Globalinto Deliver- able 4.3). Aarhus University. Bloom, N., & Pierri, N. (2018, August 31). Research: Cloud computing Is helping smaller, newer rms compete. Har- vard Business Review. https://hbr.org/2018/08/research-cloud -computing-is-helping-smaller-newer-rms-compete Bojnec, Š., & Fert˝ o, I. (2020). Testing the validity of Gibrat’s law for Slovenian farms: Cross-sectional dependence and unit root tests. Economic Research/Ekonomska Istraživanja, 33(1), 1280–1293. https://doi.org/10.1080/1331677X.2020.1722722 Bui, M.-T., & Le, H.-L. (2023). Digital capability and creative capa- bility to boost rm performance and formulate differentiated 228 ECONOMIC AND BUSINESS REVIEW 2023;25:216–232 CSR-based strategy. Heliyon, 9(3), Article e14241. https://doi .org/10.1016/j.heliyon.2023.e14241 Caputo, F., Cillo, V ., Candelo, E., & Liu, Y. (2019). Innovating through digital revolution: The role of soft skills and Big Data in increasing rm performance. Management Decision, 57(8), 2032– 2051. https://doi.org/10.1108/MD-07-2018-0833 ˇ Cater, B., ˇ Cater, T., ˇ Cerne, M., Koman, M., & Redek, T. (2019). Nove tehnologije industrije 4.0 v majhnih in srednjih podjetjih v Sloveniji [New Industry 4.0 technologies in small and medium- sized enterprises in Slovenia]. Economic and Business Review, 21(4), 175–184. https://doi.org/10.15458/2335-4216.1074 ˇ Cater, T., ˇ Cater, B., ˇ Cerne, M., Koman, M., & Redek, T. (2021). In- dustry 4.0 technologies usage: Motives and enablers. Journal of Manufacturing Technology Management, 32(9), 323–345. https:// doi.org/10.1108/JMTM-01-2021-0026 Chawla, R. N., & Goyal, P . (2022). Emerging trends in digital transformation: A bibliometric analysis. Benchmarking: An In- ternational Journal, 29(4), 1069–1112. https://doi.org/10.1108/ BIJ-01-2021-0009 Chu, P . Y., Lin, Y. L., Hsiung, H. H., & Liu, T. Y. (2006). Intellec- tual capital: An empirical study of ITRI. Technological Forecasting and Social Change, 73(7), 886–902. https://doi.org/10.1016/j .techfore.2005.11.001 Clark, T. S., & Linzer, D. A. (2015). Should I use xed or ran- dom effects? Political Science Research and Methods, 3(2), 399–408. https://doi.org/10.1017/psrm.2014.32 Coad, A., & Srhoj, S. (2020). Catching gazelles with a lasso: Big data techniques for the prediction of high-growth rms. Small Busi- ness Economics, 55(3), 541–565. https://doi.org/10.1007/s11187 -019-00203-3 Corrado, C., Haskel, J., & Jona-Lasinio, C. (2017). Knowledge spillovers, ICT and productivity growth. Oxford Bulletin of Eco- nomics and Statistics, 79(4), 592–618. https://doi.org/10.1111/ obes.12171 Corrado, C., Hulten, C., & Sichel, D. (2009). Intangible capital and U.S. economic growth. Review of Income and Wealth, 55(3), 661– 685. https://doi.org/10.1111/j.1475-4991.2009.00343.x De Albuquerque, N. R., Vellasco, M. M. M. R., Mun, J., & Housel, T. J. (2012). Human Capital valuation and return of investment on corporate education. Expert Systems with Applications, 39(15), 11934–11943. https://doi.org/10.1016/j.eswa.2012.03.002 Denicolai, S., Zucchella, A., & Magnani, G. (2021). Internation- alization, digitalization, and sustainability: Are SMEs ready? A survey on synergies and substituting effects among growth paths. Technological Forecasting and Social Change, 166, Article 120650. https://doi.org/10.1016/j.techfore.2021.120650 Edvinsson, L. (1997). Developing intellectual capital at Skandia. Long Range Planning, 30(3), 366–373. https://doi.org/10.1016/ S0024-6301(97)90248-X European Investment Bank. (2019). Financial Report 2018. https:// doi.org/10.2867/704000 Ferrando, A., & Preuss, C. (2018). What nance for what in- vestment? Survey-based evidence for European companies. Economia Politica, 35(3), 1015–1053. https://doi.org/10.1007/ s40888-018-0108-4 Ferreira, J. J. M., Fernandes, C. I., & Ferreira, F. A. F. (2019). To be or not to be digital, that is the question: Firm innovation and performance. Journal of Business Research, 101, 583–590. https:// doi.org/10.1016/j.jbusres.2018.11.013 Fiala, R., & Hedija, V . (2019). Testing the validity of Gibrat’s law in the context of protability performance. Economic Research/Ekonomska Istraživanja, 32(1), 2850–2863. https://doi .org/10.1080/1331677X.2019.1655656 Foroudi, P ., Gupta, S., Nazarian, A., & Duda, M. (2017). Digital technology and marketing management capability: Achieving growth in SMEs. Qualitative Market Research, 20(2), 230–246. https://doi.org/10.1108/QMR-01-2017-0014 Gao, T., Leichter, G., & Wei, Y. (2012). Countervailing effects of value and risk perceptions in manufacturers’ adoption of expensive, discontinuous innovations. Industrial Marketing Management, 41(4), 659–668. https://doi.org/10.1016/j.indmarman.2011.09 .014 Greenaway, D., Gullstrand, J., & Kneller, R. (2005). Exporting may not always boost rm productivity. Review of World Eco- nomics/Weltwirtschaftliches Archiv, 141(4), 561–582. https://doi .org/10.1007/s10290-005-0045-5 Gruber, H. (2017). Innovation, skills and investment: A digital in- dustrial policy for Europe. Economia e Politica Industriale, 44(3), 327–343. https://doi.org/10.1007/s40812-017-0073-x Ivanova, O., & Castellano, S. (2012). Signalling legitimacy for SMEs transition environments—The case of the Bulgarian IT sector. Journal for East European Management Studies, 398–422. https:// doi.org/10.5771/0949-6181-2012-4-398 Kane, G. C., Palmer, D., Phillips, A. N., Kiron, D., & Buckley, N. (2015). Strategy, not technology, drives digital transformation: Becoming a digitally mature company. MIT Sloan Management Review and Deloitte University Press. https://www2.deloitte.com/content/dam/Deloitte/fr/ Documents/strategy/dup_strategy-not-technology-drives -digital-transformation.pdf Kaus, W., Slavtchev, V ., & Zimmermann, M. (2020). Intangible capital and productivity: Firm-level evidence from German manu- facturing (IWH Discussion Papers 1/2020). Halle Institute for Economic Research (IWH). https://ideas.repec.org/p/zbw/ iwhdps/12020.html Khan, S. Z., Yang, Q., & Waheed, A. (2019). Investment in intan- gible resources and capabilities spurs sustainable competitive advantage and rm performance. Corporate Social Responsibility and Environmental Management, 26(2), 285–295. https://doi.org/ 10.1002/csr.1678 Kostevc, ˇ C., & Redek, T. (2021). The impact of intangible capital on the productivity of small rms. Economic and Business Review, 24(3), 171–186. https://doi.org/10.15458/2335-4216.1305 Kristoffersen, E., Mikalef, P ., Blomsma, F., & Li, J. (2021). The effects of business analytics capability on circular economy implemen- tation, resource orchestration capability, and rm performance. International Journal of Production Economics, 239, Article 108205. https://doi.org/10.1016/j.ijpe.2021.108205 Liu, C.-H., & Jiang, J.-F. (2020). Assessing the moderating roles of brand equity, intellectual capital and social capital in Chinese luxury hotels. Journal of Hospitality and Tourism Management, 43, 139–148. https://doi.org/10.1016/j.jhtm.2020.03.003 Lu, Y., & Ramamurthy, K. (2011). Understanding the link be- tween information technology capability and organizational agility: An empirical examination. MIS Quarterly, 35(4), 931– 954. https://doi.org/10.2307/41409967 Maggi, B. (2019). ICT stochastic externalities, technology inno- vation and business services: Is there an evidence of missed opportunity growth? Economics of Innovation and New Tech- nology, 28(3), 265–278. https://doi.org/10.1080/10438599.2018 .1483485 Mansion, S. E., & Bausch, A. (2020). Intangible assets and SMEs’ export behavior: A meta-analytical perspective. Small Business Economics, 55(3), 727–760. https://doi.org/10.1007/s11187-019 -00182-5 Maravi´ c, D., Redek, T., & ˇ Cater, T. (2021). Industry 4.0 and robo- tisation in Croatia: Proactive motives and a long-term perspective [Unpublished manuscript]. School of Economics and Business, University of Ljubljana. Melitz, M. J., & Ottaviano, G. I. P . (2008). Market size, trade, and pro- ductivity. The Review of Economic Studies, 75(1), 295–316. https:// doi.org/10.1111/j.1467-937X.2007.00463.x Menguc, B., & Ozanne, L. K. (2005). Challenges of the “green imper- ative”: A natural resource-based approach to the environmental orientation–business performance relationship. Journal of Busi- ness Research, 58(4), 430–438. https://doi.org/10.1016/j.jbusres .2003.09.002 Mithas, S., & Rust, R. T. (2016). How information technology strat- egy and investments inuence rm performance: Conjecture and empirical evidence. MIS Quarterly, 40(1), 223–246. https:// doi.org/10.25300/MISQ/2016/40.1.10 Morrar, R., Abdeljawad, I., Jabr, S., Kisa, A., & Younis, M. Z. (2019). The role of information and communications technology (ICT) in enhancing service sector productivity in Palestine: An ECONOMIC AND BUSINESS REVIEW 2023;25:216–232 229 international perspective. Journal of Global Information Manage- ment, 27(1), 47–65. https://doi.org/10.4018/JGIM.2019010103 Müller, J. M., Kiel, D., & Voigt, K.-I. (2018). What drives the im- plementation of Industry 4.0? The role of opportunities and challenges in the context of sustainability. Sustainability, 10(1), Article 247. https://doi.org/10.3390/su10010247 Nonnis, A., Bounfour, A., & Kim, K. (2023). Knowledge spillovers and intangible complementarities: Empirical case of European countries. Research Policy, 52(1), Article 104611. https://doi.org/ 10.1016/j.respol.2022.104611 Oliveira Neto, G. C., Leite, R. R., Shibao, F. Y., & Lucato, W. C. (2017). Framework to overcome barriers in the implementation of cleaner production in small and medium-sized companies: Multiple case studies in Brazil. Journal of Cleaner Production, 142(1), 50–62. https://doi.org/10.1016/j.jclepro.2016.08.150 Organisation for Economic Cooperation and Development. (n.d.). Data warehouse. OECD.Stat. https://doi.org/10.1787/ data-00900-en Organisation for Economic Cooperation and Development. (2017). OECD digital economy outlook 2017. https://doi.org/10.1787/ 9789264276284-en Organisation for Economic Cooperation and Development. (2019). Enabling SMEs to scale up. In Strengthening SMEs and en- trepreneurship for productivity and inclusive growth: OECD 2018 ministerial conference on SMEs (pp. 35–52). https://doi.org/10 .1787/7fb3ae20-en Organisation for Economic Cooperation and Development. (2022). Slovenia. In Financing SMEs and entrepreneurs 2022: An OECD scoreboard. https://doi.org/10.1787/7f7c64c1-en Petroni, G., Venturini, K., & Verbano, C. (2012). Open innovation and new issues in R&D organization and personnel manage- ment. The International Journal of Human Resource Management, 23(1), 147–173. https://doi.org/10.1080/09585192.2011.561250 Piekkola, H., Bloch, C., Rybalka, M., & Redek, T. (2021a). Globalinto empirical analysis results [Unpublished results]. H2020 Global- into Deliverables. Piekkola, H., Bloch, C., Rybalka, M., & Redek, T. (2021b). Intangibles from innovative work—Their valuation and technological change (H2020 Globalinto Project Report, Deliverable 5.3]. University of Vaasa. https://globalinto.eu/wp-content/uploads/2023/ 02/D5.3-IntangiblesValuationTechnicalChange_2022_10_03 .pdf Piekkola, H., & Rahko, J. (2020). Innovative growth: The role of market power and negative selection. Economics of Innovation and New Technology, 29(6), 603–624. https://doi.org/10.1080/ 10438599.2019.1655878 Pinzone, M., Fantini, P ., Perini, S., Garavaglia, S., Taisch, M., & Miragliotta, G. (2017). Jobs and skills in Industry 4.0: An ex- ploratory research. In H. Lödding, R. Riedel, K.-D. Thoben, G. von Cieminski, & D. Kiritsis (Eds.), Advances in Production Management Systems. The Path to Intelligent, Collaborative and Sustainable Manufacturing (pp. 282–288). Springer International Publishing. https://doi.org/10.1007/978-3-319-66923-6_33 Redek, T., & Oblak, A. (2016). Characteristics of small and medium companies in Slovenia, export, innovation and growth. In J. Prašnikar, T. Redek, & M. Kozman (Eds.), Removing the barri- cades (pp. 157–180). ˇ Casnik Finance. Rekettye, G., & Rekettye, J. (2019). The effects of digitalization on cus- tomer experience. SSRN. https://doi.org/10.2139/ssrn.3491767 Remes, J., Manyika, J., Bughin, J., Woetzel, J., Mischke, J., & Krishnan, M. (2018). Solving the productivity puzzle: The role of demand and the promise of digitization. McKinsey Global Institute. https://www.mckinsey.com/~/media/mckinsey/ featured%20insights/Meeting%20societys%20expectations/ Solving%20the%20productivity%20puzzle/MG-Solving-the -Productivity-Puzzle--Report-February-2018.ashx Roth, F. (2020). Revisiting intangible capital and labour productiv- ity growth, 2000–2015: Accounting for the crisis and economic recovery in the EU. Journal of Intellectual Capital, 21(5), 671–690. https://doi.org/10.1108/JIC-05-2019-0119 Santoro, G., Ferraris, A., & Winteler, D. J. (2019). Open innovation practices and related internal dynamics: Case studies of Italian ICT SMEs. EuroMed Journal of Business, 14(1), 47–61. https://doi .org/10.1108/EMJB-05-2018-0031 Schildt, H. (2017). Big data and organizational design—The brave new world of algorithmic management and computer aug- mented transparency. Innovation, 19(1), 23–30. https://doi.org/ 10.1080/14479338.2016.1252043 Seo, H. S., & Kim, Y. (2020). Intangible assets investment and rms’ performance: Evidence from small and medium-sized compa- nies in Korea. Journal of Business Economics and Management, 21(2), 423–445. https://doi.org/10.3846/jbem.2020.12022 Srhoj, S., Zupic, I., & Jakliˇ c, M. (2018). Stylised facts about Slovenian high-growth rms. Economic Research/Ekonomska Is- traživanja, 31(1), 1851–1879. https://doi.org/10.1080/1331677X .2018.1516153 Statistiˇ cni urad Republike Slovenije. (n.d.). SI-STAT podatkovni portal [SI-STAT data portal]. http://pxweb.stat.si/pxweb/ Database/Ekonomsko/Ekonomsko.asp Statistiˇ cni urad Republike Slovenije. (2019). Vprašalnik za statistiˇ cno raziskovanje: Uporaba informacijsko-komunikacijske tehnologije (IKT) v podjetjih, 2019 [Statistical survey questionnaire: Use of information and communication technology (ICT) in companies, 2019]. https://www.stat.si/StatWeb/File/ DocSysFile/10309 Szabo, R. Z., Vuksanovi´ c Herceg, I., Hanák, R., Hortovanyi, L., Romanová, A., Mocan, M., & Djuriˇ cin, D. (2020). Industry 4.0 implementation in B2B companies: Cross-country empir- ical evidence on digital transformation in the CEE region. Sustainability, 12(22), Article 9538. https://doi.org/10.3390/ su12229538 Teruel, M., Amaral-Garcia, S., Bauer, P ., Coad, A., Domnick, C., Harasztosi, P ., & Pál, R. (2022). COVID-19 and the resilience of European rms: The inuence of pre-crisis productivity, digitalisation and growth performance (EIB Working Paper 2022/13). European Investment Bank. https://www.eib.org/attachments/lucalli/ 20220232_economics_working_paper_2022_13_en.pdf Trittin-Ulbrich, H., Scherer, A. G., Munro, I., & Whelan, G. (2021). Exploring the dark and unexpected sides of digitalization: To- ward a critical agenda. Organization, 28(1), 8–25. https://doi .org/10.1177/1350508420968184 Tsakanikas, A., Roth, F., Calio, S., Caloghirou, Y., & Dimas, P . (2020). The contribution of intangible inputs and participation in global value chains to productivity performance: Evidence from the EU-28, 2000- 2014 (Hamburg Discussion Papers in International Economics 5). Hamburg University. Tsou, H.-T., & Chen, J.-S. (2021). How does digital technology us- age benet rm performance? Digital transformation strategy and organisational innovation as mediators. Technology Analy- sis & Strategic Management, 35(9), 1114–1127. https://doi.org/ 10.1080/09537325.2021.1991575 Utterback, J. M. (1974). Innovation in industry and the diffusion of technology. Science, 183(4125), 620–626. https://doi.org/10 .1126/science.183.4125.620 Vitezi´ c, V ., & Peric, M. (2015). Impact of global economic crisis on rm growth. Small Business Economics, 46(1), 1–12. https://doi .org/10.1007/s11187-015-9671-z Wagner, J. (2007). Exports and productivity: A survey of the ev- idence from rm-level data. The World Economy, 30(1), 60–82. https://doi.org/10.1111/j.1467-9701.2007.00872.x Walk, M., Greenspan, I., Crossley, H., & Handy, F. (2015). So- cial return on investment analysis: A case study of a job and skills training program offered by a social company. Nonprot Management and Leadership, 26(2), 129–144. https://doi.org/10 .1002/nml.21190 Yadiati, W., Nissa, N., Paulus, S., Suharman, H., & Meiryani, M. (2019). The role of green intellectual capital and organizational reputation in inuencing environmental performance. Inter- national Journal of Energy Economics and Policy, 9(3), 261–268. https://doi.org/10.32479/ijeep.7752 Zubair, S., Kabir, R., & Huang, X. (2020). Does the nancial crisis change the effect of nancing on investment? Evidence from private SMEs. Journal of Business Research, 110, 456–463. https:// doi.org/10.1016/j.jbusres.2020.01.063 230 ECONOMIC AND BUSINESS REVIEW 2023;25:216–232 Appendix Table A1. Classication of occupations into three intangible capital work categories. Minor ISCO code Minor ISCO label Organizational capital occupations 121 Business Services and Administration Managers 122 Sales, Marketing and Development Managers 131 Production Managers in Agriculture, Forestry and Fisheries 132 Manufacturing, Mining, Construction and Distribution Managers 134 Professional Services Managers 241 Finance Professionals 242 Administration Professionals R&D capital occupations 211 Physical and Earth Science Professionals 212 Mathematicians, Actuaries and Statisticians 213 Life Science Professionals 214 Engineering Professionals (excluding Electrotechnology) 215 Electrotechnology Engineers (excluding Telecommunications Engineers) 216 Architects, Planners, Surveyors and Designers 221 Medical Doctors 222 Nursing and Midwifery Professionals 223 Traditional and Complementary Medicine Professionals 311 Physical and Engineering Science Technicians 314 Life Science Technicians and Related Associate Professionals 321 Medical and Pharmaceutical Technicians ICT capital occupations 133 Information and Communications Technology Services Managers 251 Software and Applications Developers and Analysts 252 Database and Network Professionals 351 Software and Applications Developers and Analysts 352 Database and Network Professionals Source: Adapted from Bloch et al. (2021). ECONOMIC AND BUSINESS REVIEW 2023;25:216–232 231 Table A2. List of variables and source of variables in order of appearance in the regression table. Variable Description Data source Value added Revenue minus intermediate costs AJPES Average years of education Calculated as average years of education for all employees in the rm. Data on completed education provided in the registry. Number of years of education for each individual was calculated using a formula: primary education is 8 years, secondary 4 years, university 4 years, masters 2 years. Statistical registry of employees Non-intangible capital work Number of workers that do not fall into one of the intangible work categories (see Table A1 for intangible capital work categories) AJPES ICT work Number of workers in the ICT intangible work category (Table A1) Statistical registry of employees Organizational work Number of workers in the organizational intangible work category (Table A1) R&D work Number of workers in the R&D intangible work category (Table A1) Intangible capital work Total number of intangible workers (ICT, organizational, and R&D) Capital Reported in the nancial statements AJPES Export (50% share) dummy Calculated as share of sales abroad in total sales. Dummy was given value of 1 if the share was at least 50%. Share of max ICT Number of technologies used by a rm in a relevant year, divided by the maximum number of technologies used in any given rm in that year Use of ICT in companies Share of max ICT in relevant size group and NACE2 Number of technologies used by a rm in a relevant year, divided by the maximum number of technologies used in any given rm in that year Share of max ICT in relevant size group Number of technologies used by a rm in a relevant year and size group (micro-, small, medium), divided by the maximum number of technologies used in any given rm in that year and size group (micro-, small, medium) Share of max ICT in relevant size group and NACE2 Number of technologies used by a rm in a relevant year, NACE Level 2 group and size group (micro-, small, medium), divided by the maximum number of technologies used in any given rm in that year, NACE Level 2 group and size group (micro, small, medium) Industry (NACE2) NACE Level 2 industry dummy Slovenian Business Register data Size Micro, small, medium-sized dummy Year Year AJPES Technological intensity category (OECD) 8 groups (high-tech, medium-high-tech, medium-low-tech, and low-tech manufacturing, R&D, ICT, management services, and other services OECD classication adjusted by Bloch et al. (2021) Ownership Private, public/state, mixed, other Slovenian Business Register data Prepared using linked employer–employee datasets provided by the Statistical Ofce of the Republic of Slovenia. This analysis would not be possible without their expert support, in particular the User Relations Section of the Data Publication and Communication Division. 232 ECONOMIC AND BUSINESS REVIEW 2023;25:216–232 Table A3. Descriptive statistics by company size and type. Average Average Share of Share of max education of Employment OC ICT R&D number of companies Value added Export Number of number of employees number by workers workers workers technologies with at least per employee share employees technologies in years SRDAP share share share used 50% exports Micro mean 33,222 .13 7.98 .55 7.36 8.56 .07 .03 .06 2.63 .11 p50 26,096 .00 7.39 .60 10.67 8.00 .00 .00 .00 3.00 .00 sd 84,818 .26 3.45 .23 5.93 4.53 .12 .14 .15 1.20 .32 N 2413 2512 2512 2512 2329 2512 2512 2512 2512 2512 2512 Small mean 39,335 .21 21.15 .61 10.81 21.77 .07 .04 .07 3.64 .19 p50 30,062 .03 17.18 .60 11.61 18.00 .04 .00 .00 4.00 .00 sd 49,181 .31 11.84 .17 4.08 13.21 .10 .14 .15 1.17 .39 N 8365 9503 9503 9503 7583 9503 9503 9503 9503 9503 9503 Medium mean 38,339 .31 107.63 .67 10.49 110.61 .05 .03 .08 4.11 .32 p50 30,071 .10 90.00 .67 11.43 93.00 .03 .00 .05 4.00 .00 sd 39,878 .37 56.27 .16 3.96 61.81 .07 .12 .12 1.23 .46 N 2935 3323 3323 3323 2617 3323 3323 3323 3323 3323 3323 No intangibles, low mean 26,462 .15 16.34 .39 7.82 16.62 .00 .00 .00 2.48 .15 tech. intensity p50 22,519 .00 11.58 .43 10.67 12.00 .00 .00 .00 2.00 .00 sd 16,897 .30 17.17 .16 5.07 17.56 .00 .00 .00 1.34 .36 N 1888 2098 2098 2098 1657 2098 2098 2098 2098 2098 2098 With intangibles and low mean 38,983 .27 37.47 .44 9.12 38.56 .09 .04 .09 3.27 .26 tech. intensity p50 30,799 .05 19.66 .50 11.27 20.00 .06 .00 .04 3.00 .00 sd 39,320 .35 43.46 .13 5.26 45.41 .11 .14 .16 1.43 .44 N 3231 3797 3797 3797 2495 3797 3797 3797 3797 3797 3797 No intangibles, high mean 29,844 .13 17.84 .71 9.75 18.31 .00 .00 .00 3.61 .11 tech. intensity p50 25,153 .00 12.00 .75 11.20 12.00 .00 .00 .00 3.00 .00 sd 21,938 .26 18.71 .09 4.17 19.82 .00 .00 .00 0.81 .31 N 1720 1838 1838 1838 1703 1838 1838 1838 1838 1838 1838 Intangible investments, mean 42,840 .24 48.57 .73 11.13 50.09 .09 .06 .10 4.02 .22 high tech. intensity p50 32,350 .05 25.92 .75 11.88 27.00 .06 .00 .05 4.00 .00 sd 71,797 .32 53.71 .09 4.09 56.91 .11 .16 .16 1.00 .41 N 6874 7605 7605 7605 6674 7605 7605 7605 7605 7605 7605 Total mean 38,046 .22 37.73 .61 10.10 38.85 .07 .04 .07 3.57 .20 p50 29,333 .02 18.31 .60 11.46 19.00 .03 .00 .00 4.00 .00 sd 55,554 .32 46.35 .19 4.65 48.85 .10 .14 .14 1.27 .40 N 13,713 15,338 15,338 15,338 12,529 15,338 15,338 15,338 15,338 15,338 15,338 Data: Statistical Ofce of the Republic of Slovenia, own calculations.