Abstract

How does regulation affect entrepreneurship outcomes? We examine the effect of two regulatory policy mechanisms—costs and procedures—on entrepreneurship quality and quantity. Based on the national systems of entrepreneurship perspective, we apply public interest and public choice theories to hypothesize how regulatory costs and regulatory procedures can affect entrepreneurship quality and quantity differently. Using a multi-level approach, we test the direction, size, and shape of these effects with data on 51,330 innovation-oriented entrepreneurs (reflecting quality) and 871,241 entrepreneurs who started new ventures (reflecting quantity) across 76 countries during 2008–2017. We find that regulatory procedures in a country often have an inverted U-shaped relationship with entrepreneurship quality, suggesting that both too few and too many procedures might be detrimental when policymakers target innovation. We find that regulatory costs tend to have negative or inverted U-shaped effects on entrepreneurship quality and quantity. Our findings show that the way regulations are administered—by imposing financial costs or administrative requirements—is a boundary condition for entrepreneurship that affects the overall quantity of entrepreneurship and the innovation-centered quality of entrepreneurship.

Introduction

There is growing interest in how policies may better target specific goals (World Bank, 2023) and which levers can be adjusted to induce a particular entrepreneurship effect. Scholars and policymakers increasingly see regulation as a tool to influence entrepreneurship, and in some cases to target activities such as innovation, firm growth, and exporting. How the regulatory environment affects entrepreneurs is a complicated question. For example, should regulators simplify regulations or reduce filing fees for mandatory forms if their goal is to encourage more entrepreneurship in general? What if the goal is to achieve more innovation? Some policies favor prioritizing the “quality” of entrepreneurship, such as job creation, new products development, opportunity exploration, and innovation, versus generally a higher volume of entrepreneurship. Other perspectives consider whether the role of public policy is to focus on knowledge dissemination and creation via entrepreneurship. For example, will innovation be undersupplied without public support (see Mazzucato and Perez, 2015)?

The economic effects of differences in regulation on entrepreneurship are often not well understood (Audretsch et al., 2019; Fritsch et al., 2021). Entrepreneurship and institutions research examines which regulatory domains matter, such as tax policy, property registration, entry regulation, bankruptcy laws, and export permitting (e.g., Autio et al., 2014; Busenitz et al., 2000; Estrin et al., 2016, Estrin et al., 2019; Klapper et al., 2006), but how the effects are induced needs more attention (Audretsch et al., 2022). Findings on the direction and size of key relationships can be inconclusive (Stenholm et al., 2013; Estrin et al., 2013a), with some recent studies point to non-linear relationships (Braunerhjelm et al., 2021; Braunerhjelm, 2022; Audretsch et al., 2019). This could stem from the heterogeneity of both regulation and entrepreneurship; regulation varies widely in type and nature of change, and entrepreneurship can take many forms (see Klapper and Love, 2016; Braunerhjelm et al., 2015, Braunerhjelm et al., 2021; Chowdhury et al., 2019; Colombelli et al., 2016). Prior work often examined the rate of entrepreneurship activity, such as events, entry, and flows (e.g., Parker, 2009; Reynolds et al., 2002), and the research lacks consensus about determinants, including regulation, of entrepreneurship quality and quantity (see Cole et al., 2016; Colombelli et al., 2016; Sobel, 2008).

Results

We use a six-step approach to test our hypotheses. First, we test the significance of the country-year group variance for the dependent variable by excluding individual and country-year level independent variables and controls (i.e., the null random model) (specification 1, Table 4, Table 5) to justify the use of multilevel models (Rabe-Hesketh et al., 2005). Second, we add individual predictors and year and industry controls to test the effects of entrepreneurs’ internal resources and capabilities on change in the likelihood of engaging in innovation and new entry. Third, in addition to our individual variables, we include country-year explanatory variables in levels (specification 3, Table 4, Table 5). Fourth, we add country-year explanatory variables for regulatory costs and procedures in levels, together with the quadratic term, to establish our baseline specification and to test H1 and H2 (specification 4, Table 4, Table 5). Fifth, we add country-level control variables that predict how other socioeconomic conditions may affect entrepreneurship quality and quantity in a country (specification 5, Table 4, Table 5). Finally, we use individual-level controls, along with one-year lagged linear and quadratic terms of regulatory cost and procedures and other country-level variables (one year lagged), to test the role of regulation in shaping entrepreneurship.

Abstract

This study aims to determine whether big business or institutions comprise a more significant entrepreneurship determinant. In addition, we have three subobjectives: We examine (1) how the relationship between big businesses and entrepreneurship changes according to the economic development stage; (2) if big businesses affect the two different entrepreneurship types, namely, opportunity- and necessity-driven entrepreneurship, differently; and (3) which institutional factors are effective for these two different entrepreneurship types. To this end, we compare the effects of big businesses and institutions on entrepreneurship among 33 countries covering lower-middle-, upper-middle-, and high-income countries during the 2001–2015 period. Among the various determinants of entrepreneurship, we focus on two key factors that affect entrepreneurship, namely, big businesses and institutions while controlling for the demand- and supply-side factors that influence entrepreneurship. This study conducts fixed-effect estimations. We find that the relationship among entrepreneurship, big businesses, and economic development is “N-shaped.” In particular, there is a positive relationship between big businesses and opportunity-driven entrepreneurship in high-income countries. Moreover, among the various institutional factors, only supportive public policies toward new firms and positive social perceptions toward entrepreneurs are effective for opportunity-driven entrepreneurs, whereas none of the institutional factors are effective for necessity-driven entrepreneurs.

Introduction

Because entrepreneurship has come to be regarded as a vital element of a nation’s economic development (Huang et al., 2022; Méndez-Picazo et al., 2021; Urbano et al., 2019; Audretsch, 2018, 2007; Schumpeter, 1934), understanding the main factors driving entrepreneurship has become essential. Previous studies have uncovered various explanatory variables influencing entrepreneurship, including (1) economic factors (e.g., gross domestic product [GDP] and globalization), (2) institutions (e.g., tax policy and corruption), (3) culture (e.g., social and religious beliefs), (4) legal systems, and (5) geography (e.g., coastal and temperate zones). Currently, studies on the effects of digital technologies (Dana et al., 2022; Yang et al., 2021) and artificial intelligence (Obschonka & Audretsch, 2020) on entrepreneurship have been conducted. Among these determinants, in studying entrepreneurship within or across countries, the broad connection between entrepreneurship and various institutional factors constitutes a critical research area (Audretsch et al., 2021; Goduscheit et al., 2021; Carbonara et al., 2016; Lu & Tao, 2010; Acs et al., 2008). In particular, institutions influence the choice of opportunity- and necessity-driven entrepreneurship (Audretsch et al., 2021). Given this importance, this study regards institutions as an essential factor that influences entrepreneurship.

In contrast to the existing literature, this study’s primary emphasis is on a new variable, that is, the role of big businesses. The role of big businesses has been importantly emphasized not only in the rise of current advanced economies in prominent studies such as Chandler (1990) but also in the context of newly emerging economies as a crucial vehicle for achieving rapid economic growth (Chandler & Hikino, 1997). In particular, Lee et al. (2013) pointed out a positive causal relationship between the number of big businesses and economic growth after controlling for the endogeneity that a larger economy is expected to generate more large firms than a small economy. Moreover, big businesses contain unique information that is commonly related to economic growth (Lee et al., 2013; Chandler & Hikino, 1997; Chandler, 1990, 1959; Schumpeter, 1934). Historically, large industrial firms have played a leading role in booming global economies and the upgrading of industrial structures (Chandler & Hikino, 1997). Because growth has a path-dependent tendency, it can be inferred that the leading players of the Fourth Industrial Revolution are big businesses. Despite the importance of big businesses in a country’s economy, the role of big businesses in country-level entrepreneurial activity has remained unexplored. Although some studies have investigated the spillover effect of big businesses, they are limited to the study of corporate entrepreneurship1 (Urbano et al., 2022). Consequently, we consider the presence of big businesses as a predictive factor for entrepreneurship and compare this factor with the spillover effect of institutions.

Summary and conclusions

In summary, this study finds that the roles of big businesses and institutions in entrepreneurship have three interconnected aspects as follows: (1) A basic and negative relationship exists between big businesses and entrepreneurship. Specifically, an increasing share of large firms negatively affects both overall and opportunity-driven entrepreneurship, while it does not affect necessity-driven entrepreneurship. These findings hold when not considering the stage of economic development, thus supporting Hypothesis 1-a; (2) Among the various institutional factors, supportive public policies for new firms prove to be effective for fostering opportunity-driven entrepreneurs. Moreover, positive social perceptions toward entrepreneurs positively affect opportunity-driven entrepreneurship. However, none of the institutional factors appears effective for necessity-driven entrepreneurs. These empirical results support Hypothesis 2; (3) In lower-middle-income economies, an increase in the relative presence of big businesses is accompanied by a rise in necessity-driven entrepreneurship. In upper-middle-income countries, both necessity- and opportunity-driven entrepreneurship decrease as the proportion of big businesses in the economy increases. In high-income economies, an increase in the relative presence of big businesses leads to an increase in opportunity-driven entrepreneurship. These results precisely support Hypotheses 3–5. The threefold effect outlined above can be viewed as a partial answer to which actor, that is, big business or institutions, is a more critical determinant of entrepreneurship, which is our core research question. The answer is that as the economy develops, big businesses play a more important role in entrepreneurship, especially in the fostering of opportunity-driven entrepreneurship.

Abstract

his paper introduces a conceptual model with perceived opportunity and perceived capability as mediators in the process by which entrepreneurship education helps improve the established business activities. Specifically, we hypothesize that the prevalence of entrepreneurship education relates positively with the rate of established business activity, and that perceived opportunity and capability mediate this relationship. We find support for all our hypotheses using Poisson regression with the Global Entrepreneurship Monitor data collected from 826 participants across 107 countries during the 2001–2018 period. Our findings confirm that entrepreneurship education has a significant positive impact on the performance of established businesses by helping the entrepreneurs improve their perceived capabilities and opportunities. These findings extend the current research on the impact of entrepreneurship education on entrepreneurial activities at different stages of development for entrepreneurial businesses.

Introduction

Entrepreneurship education (EE) can contribute to economic growth by improving entrepreneurial activities (EA), as reflected by growing research on the link between EE and EA (e.g., Abodohoui & Su, 2020; Abreu, Demirel, Grinevich, & Karataş-Özkan, 2016; Audretsch, 2014; Cohen, Hsu, & Shinnar, 2021). Based on two alternative EE models—experiential model and contingency model (Honig, 2004), EE may influence EA by making entrepreneurial learning process experiential (Politis, 2005) and engaging individuals in key learning behaviors, such as exploratory and exploitative learning (Nogueira, 2019; Wang & Chugh, 2014) to acquire entrepreneurial knowledge. This knowledge is needed to effectively cope with the liabilities of newness and recognize and act on entrepreneurial opportunities (Hahn, Minola, Bosio, & Cassia, 2020; Karlsson & Moberg, 2013; Politis, 2005). In addition, EE can impact EA (e.g., new business creation) by strengthening entrepreneurial self-efficacy (McGee, Peterson, Mueller, & Sequeira, 2009; Zhao, Seibert, & Hills, 2005) and competencies (Klarin, Inkizhinov, Nazarov, & Gorenskaia, 2021; Santos, Neumeyer, & Morris, 2019; Vidal, Pittz, Hertz, & White, 2021).

Some empirical studies also confirm the relationships between EE and EA (Souitaris, Zerbinati, & Al-Laham, 2007). Walter and Block (2016) show the strong link between entrepreneurship education and entrepreneurial activity (i.e., start a new business), with the sample of 11,230 individuals in 32 countries. Using data of 75 countries, Berrill, O’Hagan-Luff, and van Stel (2020) find that entrepreneurship education can reinforce the total early-stage entrepreneurial activities (either nascent or new entrepreneurial activities). Bergmann, Hundt, and Sternberg (2016) empirically find that entrepreneurship education has a positive effect on new business creations at the regional level with the 2011 Global University Entrepreneurial Spirit Students’ Survey data and multi-level analysis techniques. Shirokova, Osiyevskyy, Morris, and Bogatyreva (2017) also adopt the same dataset to test the effect of EE on student entrepreneurs from 26 countries, and the results indicate that entrepreneurial curricula have positive effect on causation and effectuation approaches in nascent entrepreneurship activities. By utilizing the data about Stanford alumni, Eesley and Lee (2021) find that entrepreneurship education programs may not improve new business creation but can help the individuals understand themselves well becoming entrepreneurs. Rahman, Hasibuan, Syah, Sagala, and Prayogo (2022) also examine how entrepreneurship education in universities can teach and introduce business students to entrepreneurial thinking, which includes intrapreneurial spirits (Delić & Alibegović, 2016). This improves individuals’ readiness to become entrepreneurs or persist in a professional environment.

Data analysis and results

Table 1 reports mean, standard deviation, and correlations of the variables. Before we conduct the regression analysis, we standardize the variables to reduce the dimensional issue. To test multicollinearity, we compute the variance inflation factor (VIF) for the variables included in each regression equation. The results indicate that the maximum VIF value is 3.98 among all the equations. Following the suggestions from De Clercq, Lim, and Oh (2014), Dheer (2017), and Hechavarría and Ingram (2019), we also calculate the mean VIF value for each equation, and the maximum value is 2.18. All the VIF values are below the threshold of 10.0 (Hair, Tatham, Anderson, & Black, 2006; Wooldridge, 2013), which indicates that there is no multicollinearity in our sample. In Table 1, the correlation results show that there are significant positive correlations between perceived opportunity and established business activity (r = 0.260, p < 0.001), between perceived capability and established business activity (r = 0.393, p < 0.001), and between entrepreneurship education and perceived opportunity (r = 0.136, p < 0.001). The results provide initial evidence for the analysis on the effect of entrepreneurship education on established business activity.

Abstract

Newly established enterprises in China face significant challenges and opportunities, with persistently high mortality rates. Navigating market challenges and establishing sustainable competitive advantages are pressing issues for contemporary businesses. This study delves into the bridging role of business model innovation between entrepreneurial bricolage and entrepreneurial performance, with market orientation influencing the relationship boundaries. We examined 288 Chinese small and medium-sized enterprises, investigating the relationships among entrepreneurial bricolage, business model innovation, market orientation, and entrepreneurial performance. Empirical results indicate: (1) Entrepreneurial bricolage positively influences business model innovation, and business model innovation positively impacts entrepreneurial performance. (2) Business model innovation plays a fully mediating positive role between entrepreneurial bricolage and entrepreneurial performance. (3) Market orientation positively moderates the impact of entrepreneurial bricolage on business model innovation and entrepreneurial performance, and it also positively moderates the impact of business model innovation on entrepreneurial performance. (4) Market orientation positively moderates the impact of entrepreneurial bricolage, mediated by business model innovation, on entrepreneurial performance. The study results contribute to a more effective understanding of the mechanisms through which entrepreneurial bricolage and business model innovation influence entrepreneurial performance, as well as how market orientation moderates their relationships and how enterprises sustain competitive advantages.

Introduction

In recent years, entrepreneurship has become a new focal point for economic and social development worldwide, seen as a new driving force for promoting economic and social progress. In today’s highly competitive business environment, new startups face significant challenges and opportunities. While there have been many successful new startups that have achieved brilliance through business model innovation, such as Didi, Xiaomi, and Xiami Music, the mortality rate of new startups remains high. For instance, in the bike-sharing industry, apart from the leading companies Mobike and Ofo, almost all second and third-tier companies’ financing processes have been concentrated around Series A or A+ rounds, with many experiencing a wave of closures since the second half of 2017. In the complex and rapidly changing environment, due to inherent “new” and “small” disadvantages, new startups generally have a higher failure rate [1]. Therefore, how new startups can address market challenges and build sustainable competitive advantages is a pressing issue for enterprises today and will continue to be a research hotspot in the field of strategic management for a long time (see Table 12).

Entrepreneurship can undoubtedly contribute significant value to the economy and society [2]. For new startups, enhancing entrepreneurial performance becomes a practical challenge. Read points out that the success of most startups often stems from initially overlooked idle resources [3]. Entrepreneurial bricolage is a way of rationalizing the allocation of these idle (or redundant) resources, allowing startups to obtain enterprise value at the lowest cost, thereby contributing to performance improvement [4]. Entrepreneurial bricolage also facilitates entrepreneurs in innovating and adjusting business models [5]. Therefore, entrepreneurial bricolage, as a precursor to business model innovation, can provide new business opportunities and innovative ideas for enterprises [6]. However, startups are constantly in a predicament of resource constraints, and to build novel business models, they have no choice but to creatively combine existing resources [7]. Business model innovation also has a positive impact on entrepreneurial performance [8]. It is an essential means for enterprises to achieve performance, but this innovation process continually evolves in overcoming market uncertainty [9]. Thus, enterprises must be market-oriented, continuously adjusting and optimizing their products, services, and market strategies [10]. Market orientation is a culture that emphasizes creating excellent value for customers, creating more value for customers through shared value concepts, behavioral norms, and effective process combinations, thereby achieving outstanding organizational performance [11]. In the early stages of entrepreneurship, startups face the problem of resource constraints. A market-oriented strategy that focuses on customer needs and actively captures market opportunities is an effective way to gain competitive advantages for enterprises [12]. Research indicates that market orientation can enhance entrepreneurs’ sensitivity to market demand, thus promoting entrepreneurial bricolage and business model innovation [5,8], ultimately boosting entrepreneurial performance.

Conclusion

This study links entrepreneurial bricolage, business model innovation, and entrepreneurial performance through the regulation of market orientation, and uniquely constructs and explores a moderated mediation model. The study reveals several important findings: (1) Entrepreneurial bricolage significantly and positively impacts business model innovation. (2) Business model innovation, in turn, has a significant and positive effect on entrepreneurial performance. It also serves as a complete mediator between entrepreneurial bricolage and entrepreneurial performance. (3) Market orientation plays a crucial role in regulating the impact of entrepreneurial bricolage on business model innovation. It also positively regulates the impact of business model innovation on entrepreneurial performance. (4) Market orientation further exhibits a significant regulatory effect on the impact of entrepreneurial bricolage on entrepreneurial performance, acting as a mediator in this relationship. In summary, entrepreneurial bricolage, business model innovation, and market orientation are interconnected factors that influence entrepreneurial performance in a positive and significant manner.

Abstract

How emotional competencies influence entrepreneurial behaviour especially among college students remains an under-researched subject in the entrepreneurship literature. In a unique perspective, this study examined the mediating roles of self-efficacy belief dimensions in the relationship between emotional intelligence and entrepreneurial intention of Electrical/Electronics Technology Education (EETE) students. The study was carried out among 192 EETE university students in 4 federal universities in South-East Nigeria. The hypothesized causal relationships based on literature were tested using covariance-based structural equation modelling (CB-SEM). Andrew Hayes’ PROCESS plugin in SPSS was used to simultaneously estimate the mediating effects of the self-efficacy components (perceived competence, perceived control, and perceived persistence). Data analysis results revealed that emotional intelligence had a significant positive effect on entrepreneurial intention in isolation, while the direct effect of emotional intelligence in the structural (mediation) model was not significant. Out of the three dimensions of self-efficacy, perceived competence and perceived persistence had significant partial mediating effects. The implications of the findings and recommendations were discussed.

Introduction

Entrepreneurship is widely recognized in literature as a catalyst of economic growth and sustainable development (Mirjana et al., 2018). In Nigeria and some other developing nations, the recognition of the vitality of entrepreneurship as an instrument of economic development is demonstrated in the provision of incentives in the form of grants and loans to encourage venture creation, particularly among the youths.

Technical Vocational Education and Training (TVET) programmes in Nigerian tertiary institutions are designed to equip the learners with the requisite skills and attitudes needed for economic self-reliance via entrepreneurship (Efuwape & Aremu, 2013; Ogbuanya & Chukwuedo, 2017; Ogbuanya et al., 2017). Thus, the Federal Government of Nigeria in her National Policy on Education described TVET programmes as a panacea for equity and economic development through the provision of skilled human resources required to boost the nation’s economy (Federal Republic of Nigeria, 2013). Electrical/Electronics Technology Education (EETE) programme in Nigerian universities is a subset of TVET aimed at producing skilled graduates to meet the technological needs of the country, thereby contributing to economic growth and sustainability. The programme modules are designed to equip the students with employable technical skills in electrical and electronics technology trades such as: electronic circuit designs, design and construction of innovative and usable electronic gadgets, electrical energy installation in private and medium scale commercial buildings, maintenance of electronic and electrical gadgets, etc. (Chukwuedo & Ohanu, 2022; Ohanu et al., 2022). The graduates of the programme are expected to be gainfully employed in related industries, or establish themselves as entrepreneurs based on the training received in school. However, previous studies have shown that many EETE graduates are not engaged as electrical/electronics technology entrepreneurs (Ohanu & Ogbuanya, 2018; Orji & Ogbuanya, 2020), indicating a negligible inclination to entrepreneurial venture creation on the part of the students. This anomaly has stimulated research on the possible factors associated with the entrepreneurial intention of the students (Nwibe & Bakare, 2022; Ohanu & Ogbuanya, 2018) as the programme’s mandate to produce entrepreneurial “spearheads” is being reduced to a mirage.

Conclusion

Electrical/Electronics Technology Education (EETE) as a strand of TVET programme is specifically aimed at preparing her graduates to contribute to economic growth of the society, especially via the ‘engine power’ of entrepreneurship. This study contributes immensely to the existing borders of knowledge on psychological factors and mechanisms associated with entrepreneurial behaviour of university students, particularly undergraduates. The study found that emotional intelligence had a significant impact on entrepreneurial intention of the students. Analysis of the mechanism of the impact of EI on entrepreneurial intention revealed that perceived competence and perceived control dimensions of self-efficacy belief were significant mediators. Therefore, developing the emotional quotient of the students will likely increase their propensity for entrepreneurship by enhancing self-apprehension of their competencies and control of the behaviour (entrepreneurship).

Abstract

     Recent literature suggests entrepreneurs struggle to pivot—or fundamentally change aspects of their venture—due to identity-based resistance to change. Yet, when entrepreneurs receive negative feedback, overcoming this resistance may be important to pivoting their business model. We adopt a convergent, mixed methods research design to explore when and why some entrepreneurs overcome resistance to change in response to negative feedback during early-stage business model experimentation. Building upon qualitative data that we gathered and analyzed, we theorize entrepreneurs may resist pivoting their value proposition relative to other business model components despite receiving negative feedback on this aspect of their business model. However, we find three factors – entrepreneurial experience, startup mentoring, and team size – may enable entrepreneurs to pivot in response to negative feedback. We theorize that these factors broaden a startup team’s perspective, enabling value proposition pivoting during early-stage business model experimentation. We test these relationships with quantitative data from 80 startups engaged in business model experimentation and find support across hypotheses. We contribute to understanding when and why entrepreneurs pivot aspects of their business models in response to negative feedback during early-stage business model experimentation.

Introduction

     The entrepreneurship literature suggests startups may benefit from experimentation and pivoting business model components in response to negative feedback from stakeholders (Andries et al., 2021; Blank and Eckhardt, 2023; Camuffo et al., 2020; Ries, 2011). In early stages of a new venture, a business model refers to a cognitive schema of a potential activity system designed to create and capture value (Massa et al., 2017; Shepherd and Gruber, 2021; Snihur & Eisenhardt, 2022). Business model experimentation is the process of testing assumptions underlying a potential business model schema and pivoting aspects of the business model in response to negative feedback (Andries et al., 2013; McDonald and Eisenhardt, 2020; Leatherbee and Katila, 2020). Building upon prior literature, we define business model pivoting as a fundamental change to a business model component (Berends et al., 2021; Shepherd et al., 2023; Shepherd et al., 2023). Yet, startup founders often struggle to change business model assumptions in response to negative feedback. Identity-based motives to preserve beliefs and assumptions related to founders’ self-concept may conflict with the capacity to change aspects of an emerging business model in response to negative feedback (Grimes, 2018; Kirtley and O’Mahony, 2023; Zuzul and Tripsas, 2020). Despite the general understanding that founders struggle to change their ideas, however, the entrepreneurship literature currently lacks precise insight into when and why some founders can overcome resistance to pivoting distinct business model components during early-stage business model experimentation.

     In this research, we use a convergent, mixed-methods research design to explore when and why startups pivot different aspects of their business model. We do so within the context of early-stage business model experimentation, wherein startup founders explicitly state business model assumptions, test those assumptions against stakeholder feedback, and are encouraged to pivot business model components in response to negative feedback. Through an exploratory qualitative analysis in Study One, we observe that founders are more willing to pivot when negative feedback relates to peripheral aspects of their business model—the activity system designed to deliver value. By contrast, founders resist pivoting the value proposition of their business model despite receiving negative feedback from stakeholders. This finding is important because the value proposition may be critical to pivot in response to negative feedback during early-stage business model experimentation to realize value creation (Camuffo et al., 2020; Danneels, 2007; Marvel et al., 2020; Zellweger and Zenger, 2021; Zott and Amit, 2007, 2008).

Conclusion

     Our research explored how startups experiment with and pivot their business models in response to negative feedback. Using a mixed-methods research design, we highlight founders are more likely to pivot certain aspects of their business model as opposed to other aspects of their business model. To overcome this resistance to change, startups can gain entrepreneurial experience, engage frequently with startup mentors, and expand the size of their team. We suggest these factors enable pivoting because they increase breadth of perspective, which in turn allows founders to consider more alternative solutions when facing negative feedback. Overall, we contribute to understanding the social psychological mechanisms that enable startups to pivot different aspects of their emerging business model schemas during early-stage experimentation.

Abstract

     In hacker communities, tech-savvy pioneers collect and share information on nascent technologies. The pool of information shared among users reduces uncertainty about digital technology, but, first and foremost, it reveals entrepreneurial opportunities to users in the community, which is a central tenet of innovation commons theory. In this paper, we are first to explore the role of local hackerspaces for digital entrepreneurship in German counties using cross-sectional time series data. We find that longer-lasting hackerspaces are strongly correlated with the level of digital entrepreneurship in regions, particularly in agglomerations and urban contexts.

Motivation

     Hackerspaces are “community-operated physical places, where people share their interest in tinkering with technology, meet and work on their projects, and learn from each other” (Hackerspace.org, 2021). This definition encompasses a variety of third places of similar nature in terms of their characteristics and external features (van Holm, 2014), such as hacklabs (Maxigas, 2012), makerspaces (Smith et al., 2013, Hatch, 2014, van Holm, 2017), and fablabs (Gershenfeld, 2005, Walter-Herrmann and Büching, 2013). Hackerspaces bring together communities of tech-savvy users around the use of nascent technologies, “engaging them in collective action and developing rules to generate, share and govern innovation resources” (Cohendet et al., 2021 p. 2). A growing number of hackerspaces have been founded worldwide (Guimarães Pereira et al., 2017) and there is an ongoing debate about their economic significance (Anderson, 2012, Williams and Hall, 2015, van Holm, 2017, Svensson and Hartmann, 2018, Boutillier et al., 2020), in particular with regard to their impact on entrepreneurship (Mortara and Parisot, 2016). The relevance of hackerspaces for innovation and regional development is supported by anecdotes from Silicon Valley, where many successful entrepreneurs were born out of hackerspace environments.2

Conclusion

     In this paper, we are first to explore and provide quantitative evidence for a weak positive correlation between hackerspaces and digital entrepreneurship in regions. In a series of fixed-effect Poisson panel models, we find that the existence of a hackerspace can help explain some of the variation in start-up rates observed between 2001 and 2014. The longer a local hackerspace exists, the higher is the digital start-up rate as measured by the annual number of newly founded firms per civilian worker. Results continue to hold once we correct for some of the plausible treatment selection and omitted variable bias in our data using propensity score matching methods, as hackerspaces are not randomly assigned to regions but are more likely to emerge from larger population of tech-savvy users and aspiring entrepreneurs.

     Still, tentative findings point to the great importance of hackerspaces as a prime example of innovation commons structures and, potentially, a novel explanation for new firm formation in regions. Hackerspaces as supporting facilities help generate and reveal valuable, complementary information on nascent technology and entrepreneurial opportunities that is otherwise dispersed and privately held by users. With more empirical research on the horizon, a new generation of regional and entrepreneurial policies might also want to consider the support of hackerspaces. This could potentially bring in an alternative set of innovation agents in the ‘middleground’, previously not accounted for by most policies. The findings of this paper suggest that hackerspaces, at least in urban contexts, have been instrumental in fostering digital entrepreneurship in Germany and expanding the range of commercial opportunities.

Abstract

     Taking the digital marketing activities carried out by female-led microenterprises as its jumping-off point, this paper aims to accomplish two main goals: firstly, to analyze the effects of social media marketing (SMM) actions on social performance (SP) and, secondly, to determine whether the qualities of female microentrepreneurs have an impact on the SMM-SP relationship. Entrepreneurship affords many female a solid work-life balance. With scarce financial resources, they often turn to creative social media strategies to secure financial sustainability beyond the short term, as well as to enhance their medium- and long-term social performance. Our regression and moderation analysis of a sample of 127 microenterprises, the data for which was collected by online surveys administered to female microentrepreneurs, reveals SMM actions’ direct effect on social performance. Our findings also show that female microentrepreneurs’ human capital directly influences SP and moderates the effects of marketing strategies on SP. The paper ends by presenting relevant conclusions for both academics and marketing practitioners.

Introduction

     The 18.5 % drop in GDP during the second quarter of 2020 foregrounded entrepreneurship as an appealing alternative for people to improve their personal economy. Data provided by the Global Entrepreneurship Monitor (GEM) for 2020 underscore three key traits of this activity in Spain:

(1) The entrepreneurship rate in Spain was lower than in the rest of Europe. Whereas 10 % of citizens in the world’s most developed countries and 7.7 % of those in the EU-28 were involved in an entrepreneurial initiative, this figure stood at 6.4 % for Spain.

(2) Entrepreneurship has a high self-employment component. In Spain, the self-employment rate among entrepreneurs was 19 %, slightly higher than the EU-28 average of 17 %.

(3) Female entrepreneurship in Spain is growing steadily. While for the 2009–2010 period, the female TEA (Total early-stage Entrepreneurial Activity) was 3 %, during the 2019–2020 period it reached 6 %. Currently in Spain, eight women start businesses for every 10 men, thus exceeding the European average, where only six women are counted for every ten male entrepreneurs.

     Although these figures seem to suggest a narrowing gender gap, an entirely different picture comes into focus when we apply a socioeconomic perspective. Most notably, there is a severe gap in the proportion of consolidated businesses led by men (60 %) versus those led by women (40 %). That being said, men and women seem to be on equal footing when it comes to people hoping to create a business in the next three years (50.9 % of which are men and 49.1 %, women). The numbers even turn in women’s favor with respect to business abandonment, where men account for 52.6 % of such cases.

Conclusions

     The results obtained through our data analysis show that, among digital marketing strategies, those carried out on social media influence the performance of microenterprises, especially their social performance. In the current context, 2.0 technologies guarantee interactivity and quality between companies and consumers. Despite the multiple options available to companies to establish and consolidate relationships, social media appear to be the most efficient from a marketing standpoint, as people have integrated technology into their daily lives.

     The social use of such media has expanded and the number of people seeking emotional and hedonic benefits on them is increasing. People today turn to social media to stay in touch with friends and family, to partake in leisure activities or to give free rein to their creativity. For some, social media have even become a way to discover and engage in new experiences, a boundaryless place where they are free to be themselves. Social media offer users speed, convenience and, above all, security and anonymity, all qualities that encourage participation. Moreover, they greatly extend the reach of messages and content, thereby facilitating immediate and global dissemination. In short, it is easy to understand the reason for their success and why companies, aware of this, have sought to carry out SMM actions as a way to establish relationships with their customers.

Abstract

A robust literature has provided compelling evidence showing how open innovation impacts incumbent firms. However, only a paucity of research has linked open innovation strategies to different types of innovation in startups. This paper fills this gap in the literature by focusing on if, how and why open innovation enhances innovative activity in newly created firms. In particular, the paper examines how the role of both the specific external partner as well as the geographical location of partner matters in how product and process innovation is shaped in startups. The empirical evidence garnered in this paper suggest that not only do startups benefit from open innovation, but also the extent of product innovation and the propensity to innovate new processes in startups are significantly affected by specific external partner and its geographical location. The positive impact of open innovation reflects the heterogeneous effects of knowledge embedded in different partner types and the role that technological, institutional, and competitive arrangements play domestically and internationally in startup innovation. This study provides new light on how and why open innovation benefits not just incumbents but also startups as well.

Introduction

It is important to understand the effects of open innovation for innovation activity in startups. First, the rise of globalization and the growth of new digital technologies, and the evolution of knowledge in the field of engineering, technology and management (Cunningham and Kwakkel, 2011), have contributed to the emergence of open innovation models (Chesbrough, 2006, Chesbrough, 2007; Kafouros et al., 2020) and the rise of inter-firm collaboration and knowledge spillovers (Schilling and Phelps, 2007; Tucci et al., 2016). However, the returns to open innovation differ between startups and incumbent firms (Audretsch and Belitski, 2021). Second, ever-increasing innovation costs and a lack of resources push startups to search for external knowledge collaborators to co-create knowledge together and reduce innovation costs (van Beers and Zand, 2014).

Despite the theoretical underpinning and importance of knowledge collaboration for innovation, the entrepreneurship and management literatures (Tambe et al., 2012; Roper et al., 2017; Knoben and Bakker, 2019) have not identified the impact that knowledge partner type and location may have on a startup.

Conclusions

This study investigated the returns from knowledge collaboration across different partner types and geographical locations for innovation performance in startups. Knowledge collaboration with external partners directly affects the propensity for process innovation and the share of innovation sales. This study provides new insights on open innovation theory for entrepreneurs, by comparing how startups use the knowledge spillover of innovation (Audretsch and Belitski, 2022) across different partner types and locations.

Our findings pave the way for future studies that may address this study’s limitations. Firstly, our study was based on a sample of UK firms. Our findings are contextually limited and may not be generalizable to other countries, particularly developing countries and other developed countries. Future studies can expand the longitudinal dimension of data, aiming for longer lags as well as switching to internationalization vs. de-internationalization of collaboration for startups. Further research is needed to explore the breadth and depth of knowledge collaboration for startups and incumbent firms.

Abstract

     The purpose of the study is to analyse how green reverse logistics technology impacts agricultural entrepreneurial marketing firms’ operational efficiency and sustainable competitive advantage. Systematic literature review and meta-analytical methodology was adopted to execute the current study. Reverse Logistics Model has been used for the current study as the theoretical model grounding the study as it enables a quick reaction and finding resolution for the customer return whilst regaining the highest value that is possible on each returned item. Results indicate that green reverse logistics technology has a strong influence on agricultural entrepreneurial marketing firms’ operational efficiency and sustainable competitive advantage. The study further shows existing research gaps that can be closed by future research studies. The study has limitations which may affect the generalisability of the results since they can only be applied testing the validity and reliability of the developed conceptual model. The study adds to theoretical literature development by extending knowledge on the Reverse Logistics Model theoretical modelling framework since there is paucity of research that have directly applied the same model in agricultural inputs entrepreneurial marketing firms’ operational efficiency and sustainable competitive advantage. Practically, the study enhances the need for adoption of contemporary technologies to solve the current challenges facing agricultural inputs entrepreneurial marketing firms’ sustainable operational efficiency and competitive advantage in the marginalised areas, not only in developing and emerging economies, but also dotted around the world. The study contributes to the conceptual development, theoretical and practical policy directions applicable to any agricultural inputs entrepreneurial marketing firms. A model can be tested to prove its validity and reliability in future similar studies, using alternative methodologies.

Introduction

     Environmental concerns have affected how business is done world over (Alnoor, Eneizan, Makhamreh & Rahoma, 2018; Kiatcharoenpol & Sirisawat, 2020). This has caused agricultural entrepreneurial marketing firms to re-design and re-align their processes in such a way that they are environmentally friendly (Kariuki, Ngugi & Mburu, 2022; Kazancoglu, Ekinci, Mangla, Sezer & Kayikci, 2021). The need for agricultural entrepreneurial marketing firms and consumers to ensure the dismantling of used products in a bid to extract parts for reuse, recycling or alternatively to ensure safe disposal (Martín & Fernandez, 2022). This allows companies to seamlessly integrate value creating activities of different supply chain partners (Martínez, Carracedo, Comas & Siemens, 2021). Seamless integrating of value creating activities include the movement of end of useful life goods or products from consumers to agricultural entrepreneurial marketing firms for the purposes of recapturing value and or ensuring disposal in an environmentally friendly manner (Martínez, Puertas, Martín & Ribeiro-Soriano, 2022; Nyagadza, 2021). This process of moving goods or products from the consumers to the manufacturer for recapturing value or ensuring disposal in an environmentally friendly manner is known as reverse logistics (Hills & Hultman, 2013; Isernia, Passaro, Quinto & Thomas, 2019; Julianelli, Caiado, Scavarda & Cruz, 2020). Firms aim at attaining operational efficiency as they strategically manage the process of product returns (Guarnieri, Cerqueira-Streit & Batista, 2020; Alharthi, 2012; Hersleth et al., 2022). By gaining operational efficiency through strategic management of reverse logistics (RL) can result in the improvement of the agricultural entrepreneurial marketing firm’s competitive position. Operational efficiency (OE) lead to improved competitive advantage of a firm (Job, Njihia, Maalu & Iraki, 2020). In many agricultural inputs industries RL costs tend to exceed the costs of production. Lack of an integrated supply chain strategy may in the long-term influence the agricultural entrepreneurial marketing firm’s competitiveness (Nyagadza, Pashapa, Chare, Mazuruse & Hove, 2022).

Discussion and conclusion

     With the perspective of the stated hypotheses, the modelling conceptual framework in Fig. 2 shows the antecedents of GRLT such as Reuse (X1), Repair (X2), Remanufacture (X3), and Recycle (X4), directly influence Operational Efficiency (OE) (M1) and Sustainable Competitive Advantage (SCA) (Y1 ) of the agricultural inputs entrepreneurial marketing firms. During the process of validating the structural modelling framework and its proposed hypotheses, future researchers need to make verifications of the absence of endogeneity through the simultaneity between Y1 and X1. If there is endogeneity on X1then an instrumental factor should be applied to have a solution for the problem under study.

     Further to this, suppliers and other supply chain participants receive returned goods, materials, and sub-assemblies for remanufacturing (Beamon, 1999). The basic design of a product must account for disassembly processes. Having suppliers on board is crucial for creating environmentally friendly products (Wu & Dunn, 1995). A company’s environmental impact can only be reduced by taking into account the entire system in which it operates (Andrushchak, 2018; Beamon, 1999; Richnák & Gubová, 2021). For widespread greening efforts, the green supply chain is the perfect emphasis since it reflects this comprehensive, systems-level perspective. The importance of constructing decision frameworks for reverse logistics processes for effective product take-back, reuse, and recycling of specific components and materials of end-of-life structures. The research also suggests effective choice approaches for reverse logistics vehicle routing (Lee & Lam, 2012; Kassem & Chen, 2012). Consequently, many experts in the field have proposed and executed several approaches to making decisions (Job et al., 2020). Recently, Chanintrakul, Coronado Mondragon, Lalwani and Wong (2009) provided a state-of-the-art review of the design of reverse logistics networks.

Highlights

Abstract

Keywords

1. Introduction

2. Literature and hypotheses

3. Data and method

4. Results

5. Discussion and conclusion

References

Vitae

Abstract

Drawing from perspectives on institutional hierarchy (Williamson) and social embeddedness (Granovetter), we examine the role of embeddedness, formal institutions and governance in shaping latent and emergent entrepreneurship. We examine the role of heterogeneous institutional conditions – corruption, social relationships, property rights and government size – matter across 66 countries between 2005 and 2015. Our findings demonstrate that heterogeneity of institutional conditions and heterogeneity of entrepreneurship outcome are important and not monolithic. Notably, we find that while corruption impedes both latent and emergent entrepreneurship, this effect lasts almost three times as long for latent entrepreneurship. We also find that entrepreneurs in countries with more corrupt contexts have lower aspirations to start and own a business.

1. Introduction

A consensus has emerged in the research on institutions and entrepreneurship that institutions have an important role to play (Audretsch, Belitski, Chowdhury, and Desai, 2021; McMullen et al., 2008; Stenholm et al., 2013; Colombo et al., 2016). This raises “next generation” questions related to institutional heterogeneity, such as how, which and when institutions matter for entrepreneurs (Audretsch and Moog, 2020; Dutta and Sobel, 2016, 2018, 2020).

These questions sit at the intersection of three streams of research that are relevant to entrepreneurship. Firstly, the emergence of entrepreneurship is not strictly dichotomous (Mickiewicz et al., 2017), and there can be stages and activities that precede new business activities (Bennett & Chatterji, 2017). Related to this, not everybody who is interested in starting a business might ultimately do so.

Secondly, institutions are heterogeneous, as are their impacts on entrepreneurship (Granovetter, 1985; Aidis et al., 2012; Bennett, 2020). Some institutions may matter while others do not, and there may be nonlinearities or permutations of institutions that shape how entrepreneurs perceive and act on opportunity (Bergmann and Stephan, 2013; Braunerhjelm and Eklund, 2014; Elert, Henrekson, and Sanders, 2019; Audretsch et al., 2019, 2021; Amoros et al., 2019 ´ ).

Thirdly, the nature and type of entrepreneurship outcomes and activities themselves can differ across institutional contexts (Autio et al., 2014; Acs et al., 2014; Audretsch and Belitski, 2017). The body of research on the allocation of entrepreneurship is growing, including research on formal and informal (North, 1990; Thai and Turkina, 2014), productive, unproductive and destructive, (Desai, Weitzel, and Acs. 2014, Sanders and Weitzel, 2013; Baumol, 1990), high-impact (Stenholm et al., 2013), and necessity and opportunity (Amoros et al., 2017; Angulo-Guerrero et al., 2017; Audretsch et al., 2021) entrepreneurship.

There has been relatively less research at the intersection of these three streams: how do different institutions affect different forms of entrepreneurship? In particular, there is a need for more insight on engagement in potential or aspirational entrepreneurship (latent) and newly formed ventures (emergent) (Parker, 2004, 2018; Grilo and Irigoyen, 2006 (Caiazza et al., 2019,2020)).

Abstract

     Entrepreneurs require human resources to establish and scale their ventures; however, constraints often prevent entrepreneurs from investing in formal human resource systems. How entrepreneurs overcome human resource challenges by leveraging their entrepreneurial ecosystems as informal inter-organizational talent management systems has been overlooked by scholars. We propose a model of entrepreneurial ecosystem human resource management, theorizing that ecosystem participants collectively perform the human resource management function for entrepreneurship communities. Drawing from economic rents theory, we explain how entrepreneurial ecosystems encourage a form of meta-organizational human resource management that allows ecosystem participants to coordinate talent acquisition, learning and development, performance management and rewards, and retention. Coordinated entrepreneurial ecosystems improve entrepreneurial performance by sourcing talent, onboarding selected members, enculturating ecosystem values, developing entrepreneurial skills, and retaining human resources, which in turn generates rents. We discuss how our theory catalyzes research at the HR and entrepreneurial ecosystems interface and reveals insights for practitioners.

Introduction

     The ecosystem perspective in business research draws attention to how organizational activity is shaped by complex systems of interdependent actors (e.g., consumers, investors, government officials) and socio-economic forces (e.g., values, institutions, narratives) that extend beyond firm, industry, and sectoral boundaries (Datt´ee, Alexy, & Autio, 2018; Fuller, Jacobides, & Reeves, 2019; Ganco, Kapoor, & Lee, 2020; Jacobides, Cennamo, & Gawer, 2018; Shipilov & Gawer, 2020). The ecosystem lens has become prevalent in studies of entrepreneurship, with scholars acknowledging that entrepreneurial activity does not occur in a vacuum but is placebased, context-specific, and embedded in entrepreneurial ecosystems (EEs)—the interconnected actors and forces that support entrepreneurial activity within geographic areas (Acs, Stam, Audretsch, & O’Connor, 2017; Berger & Kuckertz, 2016; Bouncken & Kraus, 2021; Roundy, Bradshaw, & Brockman, 2018; Spigel, 2017a; Stam, 2015; Thompson, Purdy, & Ventresca, 2017). Research focusing on vibrant EEs in cities and regions with high levels of entrepreneurial activity, such as Silicon Valley, Bangalore, and Stockholm, has made strides in understanding ecosystem development (Cao & Shi, 2020; Goswami, Mitchell, & Bhagavatula, 2018; Roundy, 2019). However, despite human resources (HR) appearing in most lists of the key components of ecosystems (e.g., Feld, 2012; World Economic Forum, 2013; Vedula & Kim, 2019), how human resources are acquired, developed, and coordinated in EEs—and by EEs—is unclear and has received scant attention in the literature.

Conclusion

     Entrepreneurial ecosystems are increasingly recognized as engines for economic and community development (e.g., Mason & Brown, 2014; Spigel & Harrison, 2018). However, navigating an ecosystem is especially challenging for resource-constrained entrepreneurs who may struggle to identify where talented individuals are located. To date, EE research has not been able to shed light on these challenges because the processes by which ecosystems influence HRM have not been explicitly explored. Our theoretical model addresses this issue by identifying a specific set of mechanisms through which coordinated EEs benefit entrepreneurs’ performance via talent management and rent generation. Our theory of EE-HRM is designed to spur further research on the human resource component of EEs and reveals new connections between HR and entrepreneurship research.

Abstract

     Since the COVID-19 pandemic arrived, various options have been created to export products, with the creation and development of startups becoming a viable and significant option. Previous research has described the influence of competitive business advantage on international performance. However, the novelty of the current research was to evaluate the influence of the company’s capabilities, through the competitive advantage of the company as a mediator in international performance. The study was based on the responses of 200 active startups in Peru. The PLS-SEM technique was utilized with the company’s capabilities linked to internationalization processes as independent variables, as well as market intelligence, product innovation, and pricing. The mediating variable was competitive advantage, and the dependent variable was international performance. It was found that all three capabilities influenced competitive advantage and, ultimately, international performance. It was concluded that competitive advantage does not act as a mediator between pricing capabilities and the international performance of startups. Moreover, it was determined that competitive advantage acts as a mediator between two of the three assessed capabilities: market intelligence capabilities and product innovation capabilities. The results were supported by the reflective and formative evaluation of the results obtained through the applied technique. These results could help create better programs to promote startups interested in internationalizing and exporting products.

Introduction

     In the last 30 years, the resource-based theory (RBT) [1] has been shown as one of the most relevant for the description, explanation and prediction organization. It should be noted that RBT only took shape in the 1990s, because in the 1980s, theories were focused on the outside of organizations, such as Porter’s forces. In the first decade of the new century, the application of RBT could be supported in different disciplines. Today, RBT can be recognized as an influential approach in strategic management. The framework of RBT is composed of four elements: (a) value, (b) being rare, (c) immobility, and (d) sustainability. These are the characteristics that a company needs to have for the strategic planning reference and hold a sustained competitive advantage. The four conditions of RBT suggest that poor organizational policies, processes and procedures can weaken a resource’s potential competitive advantage.

     There is also the concept of dynamic capabilities (DC), which can be considered an extension of the resource-based view (RBV) because of its ability to respond to rapid technological changes. Although this concept was born at the end of the 1950s, it was through the publication of Teece, Pisano and Shuen [2] in 1997 that it gained prominence in strategic management. DC is defined as “the firm’s ability to integrate, build, and reconfigure internal and external competences to address rapidly changing environments” [2]. First of all, dynamic capabilities can be conceptualized as those meta-capabilities that give companies the ability to adapt to new changes and that at the same time can be specific to the company, i.e., very difficult to imitate. There are six functions that can be performed by DCs to enter a new market or position themselves more strongly in a market already won: renew, recombine, redeploy, replicate, retrench, and retire the resources/capabilities. With this, it can be identified that it is not only a matter of creating resources, but also of eliminating those that do not contribute to the creation of competitive advantage. There is no consensus on when to use DC, since on the one hand, it can be argued that it works well in a scenario of rapid changes, especially technological ones, but it can also be argued that in non-repetitive changes, its use is not advisable because it could be reconfiguring the company’s resources for a very short period, and its benefit could be minimal. The organization who possesses DCs can meet the change that is necessary to build a competitive advantage. Both RBT and CD contain elements that are crucial to a company’s competitive advantage. On the one hand, the value of the processes, products and services that an organization must have must be identified, understood and measured, so that it can be known why the company’s capabilities are valuable. Rarity is also identified, i.e., these capabilities are unique in the marketplace; it is also expected that they cannot be copied by competitors. Dynamic capabilities need the company’s continuous work to make them evolve to ensure that they remain rare and inimitable, achieving such a positioning that they are not substitutable.

Results

     As previously mentioned, we performed PLS-SEM to examine our hypotheses, for this, a bootstrapping was used of 5000 subsamples, this technique was performed used to estimate summary statistics, such as the mean or standard deviations, considering that it is the most approved setting for PLS-SEM [137]. As seen in Table 5, the capabilities of product innovation, marketing intelligence, and price are firmly related to the variable of competitive advantage; in other words, hypotheses H1, H2, and H3 are supported. Additionally, Table 5 shows that competitive advantage is also firmly connected to the international performance of startups; as a result, hypothesis H4 is also supported. The variance inflation factor (VIF), which was also examined to identify if our result has multicollinearity problems, demonstrates that it is not a problem between the exogenous latent constructs. Every VIF value that is not a problem is between 1 and 2, below 5. In the case of the R square values in Table 5, our model explained 29.2% of the competitive advantage variance and 16.2% of the international performance of the startups, which means that this model fits our data very well because the predictive relevance confirms the rule of thumb that says it must not be negative [137].