A structure for promoting entrepreneurship education: Understanding the interactions within the entrepreneurial ecosystem

A structure for promoting entrepreneurship education: Understanding the interactions within the entrepreneurial ecosystem | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article A structure for promoting entrepreneurship education: Understanding the interactions within the entrepreneurial ecosystem Peng Wang, Yuying Zhang, Guobiao Li, Guojing Zhao This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8395537/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 11 You are reading this latest preprint version Abstract Despite extensive investigations into the impact of entrepreneurship education (EE), only a few studies have focused on the impact of the antecedents of entrepreneurship education (EE) and their combined effects. Based on an entrepreneurial ecosystem perspective, we concentrate on country-level factors influencing entrepreneurship education (EE, social and cultural norms, infrastructure, ease of entry, R&D transfers, government policy, entrepreneurial finance, government entrepreneurial programs). We applied fsQCA to investigate the impact of the interactions between the above factors on the development of entrepreneurship education (EE). We used a dataset from the latest Global Entrepreneurship Monitor (GEM), including data from 50 economies in the Entrepreneurship Framework 2021. Our findings suggest that the development of global entrepreneurship education (EE) under the COVID-19 was characterised by a focus on R&D transfer, including the provision of infrastructure and funding for R&D transfer. Based on this, three configurational pathways can achieve high performing entrepreneurship education (EE), including a policy-project combination configuration, a policy-deficit configuration and a government-project-deficit configuration. Business and commerce/Economics Social science/Economics Business and commerce/Information systems and information technology fsQCA entrepreneurship education Entrepreneurial Ecosystem GEM Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 1. Introduction The UN's 2005 prediction that youth unemployment would cause socio-economic problems seems to have come true (UN, 2005 ; Thomassen et al., 2020 ). Along with concerns about population growth, governments have continued to warn of an aging society, for example in China, where a 'three-child policy' has been proposed to address the social problems of an unbalanced workforce in the future (CPC, Central Committee and State Council of PRC, 2022). Although many jobs will be vacated as a result, this will not be enough compared to the growing number of young people seeking employment (Thomassen et al., 2020 ). At the same time, the sudden arrival of COVID-19 has changed EE and created massive unemployment, and we know very little about the future of entrepreneurship education (Ratten and Jones, 2020 ; Alzaidi and Shehawy, 2022 ; Mahajan et al., 2022 ). Therefore, without more jobs being created, the negative impact of unemployment may be unimaginable. How entrepreneurship education will develop in the aftermath of the COVID-19 is a matter of concern and common ground for policymakers and educators alike. Governments in both developed and developing countries see EE as a panacea for stagnant or declining economic performance and unemployment (Matlay and Carey, 2006; O'Connor, 2013) because, on the one hand, entrepreneurship education and training appears to be the driving force behind the rise of entrepreneurship and small business development (EC, 2002 ), on the other hand, EE is recognised as a place to promote self-employment (BalanVnuk et al., 2014; Mwasalwiba et al., 2014 ). The literature points out that contemporary youth are the most educated generation. However, the role of the formal education system in entrepreneurship development has been grossly underestimated, especially at the tertiary level (Nowak, 2016 ). Kuratko ( 2005 ) cautions that despite the achievements in legitimising EE, there are still serious challenges ahead. At this stage, scholarly research has primarily focused on the impact of EE on entrepreneurial intentions, entrepreneurial competencies, and other entrepreneurial behaviours but has neglected a more important premise: what factors influence EE? A recent review (Fellnhofer, 2019 ) shows that literature about EE appears to be heavily biased towards higher education (especially business schools), self-employment, and new ventures, neglecting education that promotes entrepreneurial behaviour in other contexts. Meanwhile, the literature falls into several broad categories (Fellnhofer, 2019 ), including social and policy-driven EE research, human capital research related to self-employment, organisational EE, triple helix research, (re)design and (re)evaluation of EE initiatives, entrepreneurial learning, impact studies of EE and opportunities-related issues. A concept that has not been explicitly included in the research and is closely linked to the above themes is rarely mentioned, namely the entrepreneurial ecosystem (Motoyama and Watkins, 2014 ; Abootorabi et al., 2021 ). Only a few of these papers explicitly refer to higher education institutions as a component of the entrepreneurial ecosystem (e.g., Streeter et al., 2002 ; Isenberg, 2011 ), a proposition that remains to be tested. The entrepreneurial ecosystem is a dynamic, institutional interaction between individuals' entrepreneurial attitudes, abilities, and ambitions that drives resource allocation through the creation and operation of new ventures (Acs et al., 2014 ). Different scholars have provided differentiated lists of relevant ecological factors (Isenberg, 2011 ; Spigel, 2017 ; Nicotra et al., 2018 ). Unfortunately, however, there is little clear empirical evidence (Stam, 2014 ; Nicotra et al., 2018 ). Entrepreneurial ecosystems reveal complex, interconnected configurations (Bertalanffy, 1968 ; Müller, 1997 ; Cohen, 2006 ; Theodoraki et al., 2022 ), while studies related to entrepreneurship and EE are limited to independent net effects (Lim et al., 2016 ; Jiang et al., 2021 ), ignoring the fact that their relationship with the entrepreneurial ecosystem is multidimensional and heterogeneous, failing to reveal synergies between the elements (Isenberg, 2010 ; Guerrero and Urbano, 2012 ; Clark et al., 2021 ). Furthermore, Ratten and Jones ( 2020 ) also emphasise the urgency of an enhanced analysis of coping mechanisms, including recovery and change, by considering EE as a holistic process from the perspective of entrepreneurial stakeholders. General systems theory (Boulding, 1956 ) assumes that the main task in studying social phenomena is to consider the interdependence of the elements of the system as a whole (Ragin, 2008 ). Although scholars recognise that entrepreneurship is a process that is influenced by multiple synergistic factors, most research still focuses on traditional linear regression analysis (Walter and Block, 2016 ; Dou et al., 2019 ; Debarliev et al., 2022 ). Therefore, exploring the mechanisms of coupled roles in EE is a research gap. In recent years, scholars in management and management information systems have often used qualitative comparative analysis (QCA) when faced with complex causal relationships that are difficult to test in standard statistical methods (Fainshmidt et al., 2021 ; Leppänen et al., 2021 ). Before we go on, we need to remind you in particular that, unlike linear regression, QCA is neither a purely quantitative nor a strictly qualitative analysis tool but rather integrates the advantages of both quantitative and qualitative aspects, using quantitative thinking to analyze case data. QCA (Fiss, 2007 , 2011 ; Vilmos et al., 2017 ) has become a popular tool for analysing complex causal and configurational situations, using boolean algebra (the algebra of sets and logic) to examine which combinations of attributes lead to the outcome and to conceptualise the situation as a combination of attributes explicitly. Based on these considerations, this study aims to apply a systems approach to analyse the impact of different combinations of elements in the entrepreneurial ecosystem on EE, rather than the traditional isolated net effect analysis, and in turn to draw different conclusions from previous single-factor impacts, and in this way fill the gaps in existing research. Our research questions are, therefore, as follows: RQ How the interaction of elements within the entrepreneurial ecosystem affects EE? The paper is organised as follows: In the next section, we review the research on EE and the entrepreneurial ecosystem, and in doing so, we identify the research debates. We will then report on our research methodology and analysis. Finally, the findings and limitations of the study are summarised and discussed, and in doing so, recommendations for future research are made. Exploring the antecedent variables of EE has both theoretical and practical implications. Although there has been a great deal of research conducted by scholars worldwide on EE, the findings have been mixed (Nabi et al., 2018 ). At a time when the vast majority are keen on the impact of EE, this study shifts the focus to antecedent variables, which helps to reveal the relationship between entrepreneurial knowledge and entrepreneurship practice, while clarifying the nature and complexity of the relationship between ecosystems and EE (Lin and Xu, 2017 ), which Mukesh and RajasekharanPillai ( 2020 ) argue is an essential prerequisite for a proper understanding of entrepreneurship formation among university students. The theoretical model for this study is shown in the following diagram. 2. Theoretical Background and Literature Review 2.1 Entrepreneurial ecosystem theory Entrepreneurial ecosystem theory (EET) was developed based on the concept of regional innovation system, but differs in that EET emphasises the analysis of social phenomena from a systems perspective, focusing on the concurrent influence of multiple factors (Cohen, 2006 ; Isenberg, 2010 ). Since its introduction, the theory has received widespread attention, but academic research is still in the exploratory stage (Simatupang et al., 2015 ; Bischoff et al., 2018 ) and can currently be divided into two schools of thought: the environmentalist school and the subject-environment school. Early scholars considered the entrepreneurial ecosystem the external entrepreneurial environment in which the entrepreneurial firm operates (Cohen, 2006 ; Isenberg, 2010 ). The subject-environment school emphasises the relationship between the individual and the whole, advocating that the relationship between the entrepreneurial subject and its environment is explored with a focus on the entrepreneurial subject and that, in addition to the external entrepreneurial environment, the entrepreneurial firm itself is part of the entrepreneurial ecosystem (Vogel, 2013 ). Previous research has failed to account for the complex impact of the components of the ecosystem as a whole structure, so recent research has begun to focus on the interrelationships between the components of the entrepreneurial ecosystem (Abdurakhmonov et al., 2021 ). The entrepreneurial ecosystem (Bischoff et al., 2018 ) or external stakeholders (Matlay, 2009 ) is highly relevant to EE, and Cheng ( 2012 ) argues that the significance of the entrepreneurial ecosystem to EE lies in the synergistic relationship between elements that each play their part and function as part of a whole. A better understanding of the nature of EE requires the integration of several different factors within a holistic framework (Lin and Xu, 2017 ). It is therefore appropriate for this study to use the theory to explore the combined impact of antecedent and causal variables in EE. 2.2 Entrepreneurship education and its antecedents The rise of EE is due to its role in promoting economic growth and national transformational development, poverty and unemployment reduction, wealth creation, and innovation by providing business ideas (Fayolle et al., 2016 ). Gavron et al. ( 1998 ) and Reynolds et al. ( 1999 ) argue that entrepreneurship education and training can stimulate entrepreneurial activity by promoting skills and attitudes conducive to developing entrepreneurship. Thus, EE refers to how individuals take advantage of entrepreneurial opportunities and take action to develop entrepreneurial skills (McIntyre and Roche, 1999 ). Currently, mainstream topics in EE include: social and policy-driven EE research (O'Connor, 2013; Hoppe, 2016 ; Senathiraja et al. 2021 ; Yi and Duval-Couetil, 2021 ), entrepreneurial learning (Fayolle and Gailly, 2008 ; Fayolle, 2013 ; Hoppe, 2016 ; Li et al., 2022 ), and impact studies of EE (Duong, 2021 ; Pinto Borges et al. 2021 ) Previous studies have more often drawn from the theory of planned behaviour (Hatak and Zhou, 2019 ; Yu-Yu et al.,2021; Li et al., 2022 ), stakeholder theory (Bischoff et al., 2018 ; Crammond, 2020 ) and other perspectives to explore EE. Although the relationship between EE and entrepreneurship activity is strongly correlated, research in this field of study has yielded mixed and even conflicting results (Fayolle and Gailly, 2008 ; Nabi et al., 2016 ), with methodology may be an important reason (Matlay, 2006 ). Scholars point out that the concept of how EE influences entrepreneurial orientation and behaviour is becoming increasingly complex and still requires subsequent empirical research (Fleck et al., 2021 ). In developed countries, the integration of higher education, including EE, and the entrepreneurial ecosystem has received increasing attention (Ismail et al., 2018 ), and the introduction of system thinking into the research and practice of EE has become a new perspective in EE research in recent years (Wang et al., 2021 ). Therefore, it is necessary to find effective ways to improve the effectiveness of EE based on an ecosystem perspective. Only a few studies have noted the importance of antecedent influences on EE (Lin and Xu, 2017 ; Thomassen et al., 2020 ). The literature highlights four main categories of drivers: firstly, EE is driven by government policy or institutional factors (Matlay, 2005a , b ;Connor, 2013), which is the most common type of factor; secondly, the requirement of national economic development to develop EE (O'Connor, 2013); and thirdly, the labour market influence that EE is to address the rising unemployment rate is urgent (Lin and Xu, 2017 ; Liguori et al., 2021 ); finally, in more limited studies, socio-cultural norms and values have also been found to have an impact on the development of EE (García-Rodríguez et al., 2017 ). Among these factors, economic development and the labour market are demand-driven, while policies and institutions are supply-driven (Lin and Xu, 2017 ). Despite the attention paid to the antecedent influences on EE, on the one hand, exploring the impact of these factors on EE is not sufficient and thorough; on the other hand, these studies tend to emphasise the role of single factors only (but policy and economic factors are often mentioned simultaneously), neglecting the combined impact of the ecological factors in which EE is embedded. Most are case studies that lack empirical testing. 2.3 Entrepreneurial ecosystem and its composition Publications in the field of entrepreneurial ecosystem have increased significantly in the last five years but have not been fully theorised (Simatupang et al., 2015 ; Theodoraki et al., 2022 ; Spigel, 2017 ; Autio et al., 2018 ; Wurth et al., 2021 ), and they attempt to ecosystem conceptualised and made a list of its constituent elements. The ecosystem is holistic (Theodoraki et al., 2020 ; Abootorabi et al., 2021 ). The literature in this field uses a systems theory perspective to emphasise the interaction between the individual and the whole (Jack and Anderson, 2002 ) and the importance of the complex interconnectedness of the elements in an entrepreneurial ecosystem (Bertalanffy, 1968 ; Müller, 1997 ; Binkley, 2015 ). Originating from a biological concept, the entrepreneurial ecosystem was first considered the environment in which firms use business networks to succeed in modern competition (Moore, 1993 ). The term was later replaced by business ecosystem (Iansiti and Levien, 2004 ), but with the difference that it began to emphasise the collective attributes of ecosystem participants. Scholars have generally agreed that entrepreneurial ecosystem is the environment in which various ecological elements interact and influence each other to achieve specific goals. Cohen ( 2006 ) defines entrepreneurial ecosystems as a diverse set of interdependent, interacting actors within a geographical area that influences the formation and eventual trajectory of an entire group of actors, which can also potentially impact the economy as a whole. Jackson ( 2011 ) argues that innovation ecosystems consist of essential actors and entities whose functional goal is to promote technology development and innovation. Zaidi et al. ( 2021 ) see the entrepreneurial ecosystem as dynamic social, institutional, and cultural processes and actors at the local level that facilitates and encourage the creation and growth of new businesses. Acs et al. ( 2014 ) argue that the entrepreneurial ecosystem is a dynamic, institutional interaction between individuals' entrepreneurial attitudes, capabilities, and ambitions that drives resource allocation by creating and operating new businesses. The discussion among researchers on the components of an entrepreneurial ecosystem is not yet unified, and widely used indicators include Isenberg ( 2011 ) and GEM. Isenberg ( 2011 ) proposes a six-factor model of the entrepreneurial ecosystem, comprising six interrelated domains: policy, finance, support, culture, human capital, and markets. Specifically, it refers to an enabling culture, facilitating policies and leadership, providing dedicated finance, relevant human capital, a market for entrepreneurial-friendly products, and extensive institutional and infrastructural support. GEM provides a comprehensive set of entrepreneurship indicators (Fig. 1 ), which is considered to be the most authoritative and informative indicator system available on entrepreneurship (Herrington and Coduras, 2019 ), and it includes Entrepreneurial Finance, Ease of Access to Entrepreneurial Finance, Government Policy: Support and Relevance, Government Policy: Taxes and Bureaucracy, Government Entrepreneurial Programs. Entrepreneurial Education at School, Entrepreneurial Education Post-School, Research and Development Transfers, Commercial and Professional Infrastructure, Ease of Entry: Market Dynamics, Ease of Entry: Burdens and Regulation, Physical Infrastructure and Social and Cultural Norms (GEM, 2022 ). Despite the systematic approach taken in ecosystem research, scholars have only recently begun to examine the impacts and challenges associated with the ecosystem (Theodoraki et al., 2022 ; DiVito and Ingen-Housz, 2021 ; Pankov et al., 2021 ) Thus, research on the entrepreneurial ecosystem is still in the developmental stage (Theodoraki et al., 2022 ). Existing research lacks an account of the causal relationship between the entrepreneurial ecosystem and its outputs (Nicotra et al., 2018 ; Corrente et al., 2018 ). Although previous research on EE, the entrepreneurial ecosystem has increased, little overlap can be observed between the two (Motoyama and Watkins 2014 ). Theodoraki et al. ( 2022 ) show that research on entrepreneurial ecosystems has focused on academic entrepreneurship, innovation and regional development, social entrepreneurship, sustainable development, networks, and clusters. The theme of EE has not been the focus of research. While a limited number of these papers explicitly include higher education institutions as a component of the entrepreneurial ecosystem (e.g., Streeter et al., 2002 ; Isenberg, 2011 ), with the increasing focus on the integration of entrepreneurship universities and entrepreneurial ecosystem in developed countries (Ismail et al., 2013), EE has been added to the list of components of the entrepreneurial ecosystem (GEM, 2022 ). Naturally, there is no clear empirical evidence of a link between entrepreneurial ecosystem factors and EE, and such lists are based mainly on the direct experience of researchers (Spigel, 2017 ; Corrente et al., 2018 ; Theodoraki et al., 2022 ). For EE, the significance of the entrepreneurial ecosystem lies in the synergistic relationships between elements (Cheng, 2012 ; Scheidgen, 2021 ). The introduction of ecological thinking into EE research and practice has become a new perspective in EE research in recent years, but there is still a breakthrough in methodological implications (Wang et al., 2021 ; Theodoraki et al., 2022 ). The relationship between ecosystem and EE is not simply linear. It presents a juxtaposed, complex causal relationship, and it is challenging to develop an appropriate methodology based on this (Neumeyer and Santos, 2018 ; Mukesh and RajasekharanPillai, 2020 ). 2.4 Entrepreneurial ecosystem and entrepreneurship education Although the impact of the entrepreneurial ecosystem is considered to provide a broader perspective on the development of EE (Hoppe, 2016 ; Bischoff et al., 2018 ; Mukesh and RajasekharanPillai, 2020 ), the factors of previous studies related to EE have mainly focused on factors internal to higher education institutions, while environmental factors were often neglected. In other words, the relationship between entrepreneurship education and the entrepreneurial ecosystem can be illustrated in two ways: on the one hand, the internal structures and conditions provided to improve and nurture entrepreneurship education; and on the other hand, the external conditions given by the policy conditions and business environment associated with higher education (Lehmann and Meoli, 2020). Admittedly, entrepreneurship education is an integral part of the entrepreneurial ecosystem. Still, as Lehmann and Meoli (2020) mention in their editorial, the relationship between entrepreneurship education and the entrepreneurial ecosystem is a two-way street, and more research has focused on the former, with the external conditions that entrepreneurship education is receiving minimal attention. Therefore, this study is concerned with the interaction of elements within the entrepreneurial ecosystem, specifically exploring the impact of external conditions on entrepreneurship education. Scattered case studies reveal the impact of single ecological factors, with social and policy-driven EE being more common (Hoppe, 2016 ). One of the most widely recognised facts is the increasing tendency of government policy to use EE to stimulate levels of economic activity, thus increasing entrepreneurial activity for economic efficiency (O'Connor, 2013). The most direct result of this generally supportive policy environment is the rapid growth of entrepreneurship programmes and training worldwide (Jiang et al., 2021 ). At the same time, it is widely accepted that the impact of entrepreneurship policies on higher education, it is important to note that disparities between different economies are objective. For example, the Swedish government's initiatives to promote entrepreneurship in higher education have been limited, although they are increasingly interested in EE (Hoppe, 2016 ). Similarly, EE under different socio-cultural norms has been identified as distinct by case studies (Stadler et al., 2021 ). For example, most French people consider entrepreneurial ventures insecure and a career destination for those who cannot find a job (Clark et al., 2021 ). In contrast, as a welfare state, Sweden emphasises collectivism (Bergström and Styhre, 2021 ), and therefore Swedish attitudes towards EE differ significantly from those of the French. As a part of an ecosystem, universities are characterised by various economic, institutional, legal, cultural, social and political factors. According to Spigel ( 2017 ), entrepreneurial systems can be described in terms of three interacting dimensions: physical, social, and cultural. Specifically, the physical dimension relies on the presence of local actors and the institutional environment; the social dimension is based on the linkages between actors in the ecosystem, and the cultural dimension determines the local demand for entrepreneurial activities. The few studies with a systemic perspective focus on stakeholders and within the triple helix theory perspective (Bischoff et al., 2018 ; Ribeiro et al., 2018 ; Mukesh and RajasekharanPillai, 2020 ). These studies consider entrepreneurial infrastructure, typified by incubation labs and science parks, as a medium for knowledge transfer from universities to companies (Ribeiro et al., 2018 ), and their active role in technology EE has important implications for understanding the university EE ecosystem (Bolzani et al., 2020 ). Although a few studies have started to focus on applying systems thinking in EE research, most of them only consider the influence of the triad of universities, government and business. It is crucial to acknowledge that the entrepreneurial environment is complex, and the factors that influence the development of EE are multiple, especially in different contexts. Based on the limitations of these studies, this study is significant in that, on the one hand, we explore the impact of the entrepreneurial ecosystem based on a holistic perspective, and, on the other hand, the methodology we use provides empirical evidence. *Note: Based on interactions within the entrepreneurial ecosystem, we have taken inspiration from the editorial by Lehmann and Meoli (2020), but they focus on the opposite relationship to ours. The framework is taken from GEM 2021/2022, and we have integrated the raw data according to its direct implications, from which the above analytical framework has been developed. 3. Methodology 3.1 Qualitative Comparative Analysis Based on a configurational and conformational approach, qualitative comparative analysis (QCA) is employed in this study to reveal the mechanisms by which combinations of elements in the entrepreneurial ecosystem contribute to EE. Unlike the symmetrical relationships between independent and dependent variables in traditional linear quantitative approaches, QCA emphasises complex non-linear relationships and argues that high performance is not achieved in isolation through individual factors or loosely coupled entities but rather through the interaction of interrelated structures in the system (Fiss, 2007 ). Further, within the antecedent variables that show large clusters, the different constituents are configured to analyse the impact of their combination on the outcome variable. The logical algorithm for QCA (Ragin, 2008 ; Fiss, 2007 , 2011 ) is Boolean algebra and draws on set-theoretic research. The set-theoretic approach provides a more rigorous way of combining verbal statements with logical relations and using this to construct a theory than the traditional view. Three assumptions inform the QCA: concurrent causality, equivalence, and asymmetry. Concurrent causality is the idea that outcomes are the effect of combining different elements. On the other hand, equivalence means that different path patterns can achieve the system reach the same final state. Finally, the asymmetry relationship can be understood as the realization that the opposite of a high-performance cause is not equal to a low-performance cause. In this way, QCA is divided into csQCA, mvQCA, and fsQCA. The first two can only deal with category phenomena (Cronqvist and Berg-Schlosser, 2009 ), while fsQCA can also deal with degree change and partisan affiliation problems. Due to the application of the Boolean algorithm, fsQCA has both quantitative and qualitative characteristics. In contrast to regression analyses that provide minimal conclusions, fsQCA takes complete account of the complexity of entrepreneurship and distinguishes in depth between multiple possibilities in the findings. fsQCA is made possible by creating different combinations of conditions and the set of countries associated with them (Coduras et al., 2016 ). Therefore, fsQCA was ultimately chosen as the research method for this paper to analyse a combination of antecedent and causal influence variables on EE. 3.2 Data Source and sample The data used in this study is based on the latest GEM 2021/2022 Report - a survey of national experts on the entrepreneurial ecosystem - sponsored by Babson College. It provides the most authoritative and informative body of indicators on entrepreneurship available (Herrington and Coduras, 2019 ). It is considered the most extensive and longest-running study of entrepreneurship globally. Increasingly, macroeconomic and quantitative approaches tend to use data from GEM for longitudinal or cross-case comparative studies (Bruns et al., 2017 ). QCA is based on empirical data modeled through a dialogue between theory and data and applies to small and medium-sized samples (Rihoux and Ragin, 2017 ). In recent years, empirical analyses based on GEM have yielded some results, e.g., Beynon et al. ( 2020 ) demonstrate entrepreneurial attitudes and entrepreneurial activity at the country level based on GEM and fsQCA; Xie et al. ( 2021 ) investigated the relationship between different national institutions and female entrepreneurship. While data from GEM were used for linear regression analysis, in contrast, fsQCA showed more substantial and more explicit explanatory power (Coduras et al., 2016 ). In fsQCA, different countries are classified into different sets, which distinguish them from linear regression, as one size does not fit all. Thus, the combination of GEM and fsQCA facilitates discovering entrepreneurial characteristics, types, and historical moments that are more appropriate for each country (Coduras et al., 2016 ; Bruns et al., 2017 ; Beynon et al., 2020 ; Xie et al., 2021 ). Based on the above works, we are assured that the adaptation of our sample to the research methodology is relevant and cutting edge. It is also important to note that in a challenging and turbulent period dominated by an unprecedented global pandemic, the latest GEM survey provides evidence from extensive interviews in 2021 with over 148,000 people in 50 different economies, which together account for approximately 68% of the global Gross Domestic Product (GDP) and 45% of the world's population. It is worth noting that according to World Bank GDP per capita data, 38% of these 50 economies have a GDP per capita of over US $ 40,000, a further 38% have a GDP per capita of between US $ 20,000 and US $ 40,000, and finally, 24% have a GDP per capita of less than US $ 20,000. This reflects the case at an economic level. 3.3 Measurement and Descriptive Statistical Analysis QCA does not have a clear rule on the number of conditions for the time being, and different scholars have given different suggestions. Berg-Schlosser and DeMeur (2009) suggest that research models with a caseload of 10–40 cases should identify up to seven antecedent variables. Marx and Dușa ( 2011 ) found specific probability distributions based on simulations with different sample sizes and conditions. They suggested that the number of conditions to the number of cases should be at least 1:3. In this study, the number of cases was 50 and based on the GEM classification. We further divided the 13 question items into seven variables based on the nature of the variables (see Table 1 ), which were named SCN (Social and Cultural Norms), INF (Infrastructure), EOE (Ease of Entry), RDT (Research and Development Transfers), GOP (Government Policy), EF (Entrepreneurial Finance), and GEP (Government Entrepreneurial Programs). Based on the connotations and definitions of the indicators, we have categorised them homogeneously. For example, commercial and professional infrastructure is also a category of entrepreneurial infrastructure, so we have grouped it with physical infrastructure. And the outcome variable EE includes both EE at school and post-school. Measures for each synthetic variable are calculated from the mean. Table 1 Construct and Variable Indicators Variables Indicators Items SCN Social and Cultural Norms Does culture encourage and celebrate entrepreneurship? INF Commercial and Professional Infrastructure Are these sufficient and affordable? Physical Infrastructure Is this sufficient and affordable? EOE Ease of Entry: Market Dynamics Are markets free, open and growing? Ease of Entry: Burdens and Regulation Do regulations encourage or restrict entry? RDT Research and Development Transfers Can research be translated into new businesses? GOP Government Policy: Support and Relevance Do they promote and support startups? Government Policy: Taxes and Bureaucracy Or are new businesses burdened? EF Entrepreneurial Finance Are there sufficient funds for new startups? Ease of Access to Entrepreneurial Finance And are those funds easy to access? GEP Government Entrepreneurial Programs Are quality support programs available? EE Entrepreneurial Education at School Do schools introduce entrepreneurship ideas? Entrepreneurial Education Post-School Do colleges offer courses in starting a business? Table 2 presents the results of the descriptive statistical analysis for each variable. The highest score in GEM scored for each variable was ten, and the lowest score was 1. As a whole, the performance of each element was not outstanding, with no variable scoring higher than seven on average. This reflects the poor performance of all aspects of entrepreneurship under the impact of the COVID-19 epidemic. Specifically, among the results of the descriptive statistics for the seven antecedent variables, GOP and INF performed the best, both scoring above six on average. In contrast, EE performed the worst, with a mean score of only 3.86, which should draw our attention. In terms of the difference between the highest and lowest scores, the highest difference is for GOP, followed by SCN and GEP, reflecting the vast disparity between the 50 economies surveyed in terms of government policy, social and cultural norms, and government entrepreneurial programs. Table 2 Descriptive statistical results of the research variables Statistical indicator Antecedent Variables Outcome Variables SCN INF EOE RDT GOP EF GEP EE Minimum 3.00 3.75 3.30 1.90 2.20 2.60 1.80 1.90 Maximum 7.90 7.75 6.75 6.20 7.25 6.85 6.50 6.05 Mean 4.99 6.04 4.76 4.01 6.85 4.49 4.64 3.86 Standard deviation 0.16 0.12 0.11 0.16 6.50 0.14 0.17 0.13 Difference 4.90 4.00 3.45 4.30 5.05 4.25 4.70 4.15 *Scale Note: 0 = very inadequate insufficient status, 10 = very adequate sufficient status. 4. Results This study uses fsQCA to analyse 50 economies from the GEM 2021/2022 report, each considered a case, to explore the impact of different combinations of factors on high-performance EE. Following Fiss's (2011) and Du and Jia's (2017) recommendations, we set the original consistency threshold at 0.8, the PRI consistency threshold at 0.7, and the case threshold at 1. From this, high-performance EE will be retained. 4.1 Calibration: affiliation scores of fuzzy sets Calibration is a process of assigning collective membership to individual cases (Du and Jia, 2017 ). Affiliation scores take values between 0 and 1. 1 represents complete affiliation with a set; conversely, 0 represents complete lack of affiliation with a set. In fuzzy set affiliation scores, between 0.5 and 1 (not included), a score close to 1 indicates strong affiliation (but not full affiliation); between 0 and 0.5 (not included), a score close to 0 indicates weak affiliation (not non-affiliation). Score 0.5 is a qualitative locus, which refers to the maximum fuzzy point when assessing whether a case belongs or does not belong to a set. Fiss ( 2011 ) suggested that based on theory and practice, a three-valued fuzzy set will be adopted for this study. Therefore, we set the three anchor points for the seven condition variables and one outcome variable to 0.25, 0.5, and 0.75. Table 3 lists the calibration anchor points for each variable. Table 3 Calibration anchor points of various variables Anchor point Fully affiliated Crossover point Completely unaffiliated Antecedent Variables SCN 7.3150 5.2000 3.3100 INF 7.5400 6.0000 4.5775 EOE 6.3225 4.7000 3.4825 RDT 6.0450 4.0500 2.2550 GOP 6.2175 4.5500 2.5775 EF 6.2950 4.3750 2.8100 GEP 6.4450 4.5500 2.3650 Outcome Variables EE 5.9950 3.8250 2.2775 4.2 Univariate necessity analysis The univariate analysis aims to test whether a variable would affect the outcome alone. Suppose there is a variable that would act on the dependent variable alone. In that case, that variable is considered a necessary presence in the results, regardless of the outcome of the combined analysis. In other words, if a single variable has a different effect, it must be necessary and central to the dependent variable. Conversely, it may form a grouping with other variables to influence the dependent variable. In arithmetic, the purpose of the univariate necessity analysis is to screen whether condition X is necessary for Y. It consists of two indicators: consistency and coverage. Suppose the consistency (Eq. 1) is more significant than 0.9. In that case, X is necessary for Y. Coverage (Eq. 2) reflects the explanatory power of a single or combination of variables, with a more significant result indicating that the variable or combination of variables is empirically more robust in explaining the results. Consistency (Xi ≤ Yi) = ∑[min (Xi, Yi)] /∑Xi (1) Coverage(Xi ≤ Yi) = ∑[min (Xi, Yi)] /∑Yi (2) Results of the analysis of the necessity of a single variable on the results shown in Table 4 were obtained through the fsQCA software analysis. As shown in Table 4 , none of the conditions have a consistency of more than 0.9, and all of the conditions only have some explanatory strength. Therefore, no single necessity condition emerges, and further analysis of the combination of condition variables is required to obtain more information. Table 4 High Entrepreneurial Education Analysis of Necessary Conditions Antecedent Variables Consistency Coverage SCN 0.793942 0.866387 ~ SCN 0.614335 0.504909 INF 0.879266 0.809187 ~ INF 0.562287 0.537301 EOE 0.763225 0.728717 ~ EOE 0.640785 0.590177 RDT 0.843003 0.827470 ~RDT 0.534556 0.479709 GOP 0.861348 0.807600 ~GOP 0.548208 0.514000 EF 0.856655 0.793049 ~EF 0.552474 0.524716 GEP 0.891638 0.781308 ~GEP 0.498294 0.502366 *Note: Due to the lack of evidence and theory on the exact direction in which these conditions affect outcomes, this study assumes that the presence or absence of each condition contributes to a high performance of entrepreneurship education when conducting a counterfactual analysis. 4.3 Analysis of combinations of conditions Combination analysis is one of the core parts of this study and aims to analyse possible combinatorial conditions patterns. After importing the truth tables into the fsQCA software, we can obtain solutions using the Fuzzy Truth Table Algorithm. We can obtain three solutions: complex, parsimonious, and intermediate. The impact on the results was determined by comparing the nested relationships between the intermediate and simplicial solutions. Then, the core condition of each solution is determined, that is, the condition that the intermediate solution also appears in the parsimonious solution. According to Du and Jia ( 2017 ), the presence of an Intermediate Solution plays an auxiliary role in determining the specific combination pattern and the number of combination patterns, that is, the edge condition. At the same time, a parsimonious solution determines the core conditions that generate the outcome. Therefore, this study uses the intermediate and parsimonious solutions to determine the combinatorial influence patterns of entrepreneurial ecological elements on EE (Table 5 ). Both "~" and "*" are concatenation symbols between variables. "~" means "not," that is, not present; "*" means "and" intersection relation, which indicates connected variables should be satisfied. By analysing the solution, it can be seen that, on the one hand, through the intermediate solution, we find that there are three indication paths, namely: INF*~ EOE* RDT*GOP*EF*GEP; SCN*INF*EOE*RDT*GOP*EF; SCN*INF*EOE*GOP*EF*GEP. On the other hand, by determining the core and edge condition through the parsimonious solution, we find five indicated paths, namely: SCN*INF*EOE; INF*EOE*RDT; INF*EOE*EF; INF*GOP*EF; INF*EF*GEP. Accordingly, we obtained the final three configurations (M1, M2, and M3) that could produce high-performance EE (Table 6 ). The consistency metrics for the three configurations were 0.9505, 0.9787, and 0.9779, respectively, indicating that the three configurations were sufficient for high-performing EE. Also, the solution consistency was 0.9588, indicating that the three configurations covering most cases are sufficient for high-performance EE. The model's Solution Coverage of 0.6651 indicates that these three configurations explain approximately 67% of the reasons for high performance in EE, with high explanatory strength of necessity. In addition, Raw Coverage reflects the adequacy of the combination of the variables, while Unique Coverage reflects the necessity of combining the variables. Studies generally use Unique Coverage as the primary basis for analysis. The following is a detailed analysis of each configuration that affects EE. Table 5 Configurations Leading to High Performance (EE) Antecedents Configurations Leading to High Performance (EE) M1 M2 M3 SCN ● ● INF ● ● ● EOE ⊗ ● ● RDT ● ● ● GOP ● ● EF ● ● ● GEP ● ● Consistency 0.9505 0.9787 0.9779 Raw Coverage 0.4177 0.5482 0.5474 Unique Coverage 0.1037 0.0141 0.0132 Solution Consistency 0.9588 Solution Coverage 0.6651 *Note: ⊗ indicates that the marginal condition is missing, ● indicates that the marginal condition is present, ● indicates that the core condition is present, and ⊗ indicates that the core condition is missing. A blank indicates that the presence of the condition variable is irrelevant to the result. 4.4 Robustness testing Robustness testing is a critical step in the QCA analysis methodology. Case or sample selection, conditional measurement and calibration, and analysis thresholds (case frequency, original consistency and PRI consistency) affect the number of sets, relationship states and relevant parameters. The robustness testing of the QCA can be conducted by examining whether these indicators vary significantly at different levels; if there is no significant change, in other words, if similar results to the original model are obtained, then the model is robust. In general, the options for robustness analysis include changing calibration, changing consistency, and dropping or adding cases (Schneider and Wagemann, 2012 ). Adjusting the analysis threshold is one of the most common choices because of its accuracy and ease of operation. Some scholars (White et al., 2021 ; Lewellyn and Muller-Kahle, 2021 ) have chosen to raise and lower the original consistency threshold to test the robustness of the model in their studies. Therefore, in testing the robustness of the antecedent variables affecting EE in high performance, we raise the original consistency threshold to 0.85 and lower it to 0.75, and the study results did not change. Therefore the results of this study are robust. 4.5 Analysis of complementary and alternative relationships The frequency of the condition variables in the high-performance EE pathway was counted (see Table 6 ), with INF, RDT, and EF appearing most frequently (3 times) and SCN, EOE, GOP, and GEP appearing twice. This shows that entrepreneurial infrastructure, R&D transfer, and financial support for entrepreneurship play a vital role in the development of EE, followed by other conditions. This suggests that EE in the surveyed economies has been characterised by the development of R&D transfer activities in the post-COVID-19 era. Therefore, in the post-COVID-19 era, policymakers and university administrators should pay attention to the weight of investment in these three conditions in order to be able to target them to ensure the quality development of EE. Table 6 Frequency of Condition Variable in High-Performance Configuration Conditions SCN INF EOE RDT GOP EF GEP Frequency 2 3 2 3 2 3 2 By comparing the similarities and differences across the configurations, we further identify the mutual substitution or complementary relationships between the different conditions. Firstly, by comparing configurations 1 and 2, we find that government-initiated entrepreneurship projects for economies with well-developed entrepreneurship policies can be substituted by both socio-cultural norms and ease of market access when entrepreneurial infrastructure and finance are centered on R&D transfer activities (Fig. 2 ). In such economies where the government cannot initiate entrepreneurship programmes, a positive social entrepreneurial culture and easy access to markets can bring in fresh blood to replace government entrepreneurship programmes in achieving high-performance EE. Secondly, by comparing configurations 1 and 3, we find that entrepreneurship policy support can be replaced by cultural values that encourage praise for entrepreneurship and easy market access in economies with well-established government entrepreneurship programs that focus on R&D transfer activities to launch entrepreneurship infrastructure and finance (Fig. 3 ). A vibrant entrepreneurial market and a positive entrepreneurial culture can also drive EE in these economies where entrepreneurship policy does not explicitly encourage EE. Finally, the comparative analysis of configurations 2 and 3 concludes the previous two investigations-government-developed entrepreneurship policies and government-initiated entrepreneurship programmes can be substituted for each other in economies where entrepreneurial market access is easy, and the social culture encourages entrepreneurship, and where entrepreneurial infrastructure and finance is centered on R&D transfer activities (Fig. 4 ). In other words, either entrepreneurship policy or government entrepreneurship programmes can contribute to high-performance EE in conjunction with other conditions in these economies. The complementarity and substitution analysis results show that GEP and GOP play a more critical role than SCN and EOE. Under specific objective endowment conditions, both GEP and GOP can play a position that is only present when the combination of SCN and EOE is present (Figs. 2 and 3 ). In addition, the substitution relationship between GEP and GOP revealed in Fig. 4 further highlights the role of GEP in enhancing the development of EE. Given that the effects of GOP implementation often take some time to be seen, the launch of GEP can provide a platform for the implementation of EE in a short period, thus contributing to the high quality and speed of EE. 5. Discussion Before discussing the more specific findings, we relate this study to previous research. Some reflections on the entrepreneurial ecosystem emphasise a systems perspective to examine the interactions between different elements. Our study empirically demonstrates the impact of stakeholders in the entrepreneurial ecosystem on EE (Hoppe, 2016 ; Bischoff et al., 2018 ; Zelin et al., 2021), which echoes extensive previous research on the impact of EE on other elements. Thus, we complement the interactions between EE and other elements of the entrepreneurial ecosystem, i.e. EE as an element of the entrepreneurial ecosystem (Streeter et al., 2002 ; Isenberg, 2011 ). Furthermore, we find that EE after COVID-19 is characterised by R&D transfers, which is broadly consistent with the findings of Zaidi et al. ( 2021 ), who found that incubation centres were the reason behind the positive role of the entrepreneurial ecosystem and that entrepreneurship funding was the most important factor for entrepreneurship development. However, at this point, we cannot agree with the findings of Mukesh and RajasekharanPillai ( 2020 ). They argue that the ecosystem is closely related to EE but that finance, infrastructure and university structure play a minimal role in university education. One possible explanation is the timing of the research conducted. As we have said, our study data come from 2021, after COVID-19 had occurred. Therefore, this may be a difference in the factors affecting the development of EE before and after COVID-19. Finally, as Stadler et al. ( 2021 ) highlight when comparing Finland and Brazil in terms of differences in the relationship between EE and entrepreneurial ecosystems, the antecedents of EE differ across economies. The details of the findings are discussed next. Configuration 1: Policy-project combination configuration Based on the configurational results, we can further identify the differential fitness relationships of entrepreneurial ecological elements in driving high performance in EE. Specifically, configuration 1 suggests that for an economy with adequate entrepreneurial policies and finance, if the government provides entrepreneurial programmes and an excellent entrepreneurial infrastructure to facilitate R&D transfer, it can break down barriers to market entry and eliminate the adverse effects of an unmotivated entrepreneurial social culture, thereby contributing to high performance in EE. The core conditions are INF, GOP, EF, and GEP, and the RDT is the complementary condition. This pathway explains about 42% of the high-performance EE cases. In addition, about 10% of high-performance EE cases are defined by this pathway only. Economies that fall under this pathway include France, Switzerland, and Norway (Fig. 5 ). In France, for example, the French government's ever-growing interest in entrepreneurship has stimulated the creation of a vast number of business schools and university academic programmes over the last five years. At the same time, support for student entrepreneurs (for example, the growing number of incubators in business schools) and the regional administrative level are also on the rise. At the national level, the BPI (Public Bank for Investment) has developed many products, projects, and programmes to support the creation and development of companies. Access to finance is multifaceted in France. Examples include innovative business start-up funds, innovative loan funds, and investments in technological innovation in SMEs. In addition to the French Agency for Technological Innovation, local governments can invest shares in innovative SMEs. In terms of infrastructure, TEG provides offices and premises for student projects. These facilities can be opened at any time if required (Clark et al., 2021 ). Even though most French people consider entrepreneurial ventures unsafe and a career destination for those who cannot find a job, France still has a good ecosystem of innovation and entrepreneurship, which provides a sound basis for technological development and progress. For universities expanding their EE activities and outcomes, the entrepreneurial ecosystem provides a structure to identify and connect with key players and resources. The Entrepreneurial Garden (TEG) at the Business School of Burgundy (BSB) provides a solid case study for this research (Clark et al., 2021 ). TEG is a center for entrepreneurship and small business management established to promote sustainable entrepreneurial growth and applied EE and research. As an open learning environment, the TEG ecosystem provides students with a practice-oriented climate for EE. In this environment, all key players or partners are involved in the interaction of EE to develop entrepreneurial skills, creativity, and business ventures. The TEG rules explicitly support experiential teaching methods in entrepreneurship projects to ensure that the teaching and learning experience focuses on entrepreneurship and business growth, which is closely linked to the TEG vision and strategic themes. In addition, entrepreneurship funding from the BSB and business funding agencies is leveraged in the TEG entrepreneurial ecosystem to increase the visibility and reputation of entrepreneurial outcomes continuously. To ensure economies of scale, economies of scope, and increased economic efficiency and prevent inefficient resource allocation or excessive competition, government functionaries regulate market access for firms through approval and registration. On the one hand, Christina and Karim ( 2020 ) find that under this solid governmental guidance, the French market always maintains a dynamic balance between competition and cooperation; on the other hand, market access is mainly focused on firms. For example, WEF ( 2013 ) states that market access is necessary for identifying and evaluating potential target markets and is essential for start-up and growth-stage companies. Based on the current literature, we do not seem to find evidence of a link between market access and EE in France. Therefore, adequate entrepreneurship policies and finance, government entrepreneurship programmes, good entrepreneurial infrastructure, and R&D transfer are sufficient to break down market access barriers and counter the adverse effects of a disengaged entrepreneurial social culture for achieving high performance in EE in France. Configuration 2: Government-project-deficit configuration Configuration 2 reflects another possible pathway to achieving high-performance EE. In a society where entrepreneurship is encouraged, high-performance EE can be achieved by relaxing market entry conditions and encouraging R&D transfer. The policy, finance, and entrepreneurial infrastructures are in place if the government cannot provide entrepreneurial projects. However, it is essential to note that core conditions are SCN, INF, EOE, RDT, GOP, and EF. They are all necessary to make up for the government's lack of entrepreneurial projects. This pathway explains about 55% of the high-performing EE cases. In addition, about 1% of high-performing EE cases can only be explained by this pathway. Economies that fall into this pathway include the UK and the US (Fig. 6 ). In Configuration 1, we have explained the importance of historical cultural and social norms and tied them to market access. In environments with a historically negative bias towards entrepreneurship, the development of EE may be culturally challenging and challenging to change. At the same time, institutional deficiencies such as poor regulatory conditions may create insurmountable barriers. This is evident in any entrepreneurial system (Bedő et al., 2020 ). The US has always encouraged entrepreneurship, and its socio-cultural norms are supportive and lenient towards entrepreneurship. As a result, entry into the US market for entrepreneurial ventures is equally easy. Furthermore, the US is an innovation-driven economy and is considered to have a robust entrepreneurial ecosystem. This is due to the positive effect of the long-standing open capital, labour, and commodity markets on business in the US and solid property rights and a stable government. Nevertheless, another essential factor is that American culture places a high value on self-sufficiency and individual achievement. Entrepreneurs who fit this value (such as Mark Zuckerberg, Bill Gates, and Steve Jobs) are sought after by the public and the media. In this positive entrepreneurial environment and conditions, entrepreneurial projects from non-government sources are more common in the US than those initiated by the government. Many technology development projects in the Entrepreneurship Park at North Carolina State University encourage undergraduate students to participate, providing an EE platform. In addition, the entrepreneurship competition programmes run by universities offer a very effective platform for implementing EE (Jiang et al., 2021 ). As an entrepreneurial ecosystem with different business investments, Silicon Valley, entrepreneurship policy support, and abundant R&D transfer have been sufficient to promote EE. Configuration 3: Policy-deficit configuration Configuration 3 is the final possibility for achieving high-performance EE in this study. Unlike Configuration 2, Configuration 3 shows that in a society where entrepreneurship is encouraged, where the government initiates entrepreneurship projects to facilitate R&D transfer and provides a sound entrepreneurial infrastructure to do so, and where market access conditions are relaxed to expand collaboration and promote competition if social capital turns its focus to such R&D at this time, even if entrepreneurship policies are not in place EE can be developed to a high quality. It is also worth noting that SCN, INF, EOE, RDT, EF, and GEP are all core conditions. One of these is required to address the lack of government entrepreneurship programmes. Similar to Configuration 2, this pathway explains about 55% of the high-performing EE cases. In addition, about 1% of high-performing EE cases can only be explained by this pathway. Economies that fall under this pathway include economies such as Sweden (Fig. 7 ). Firstly, in complete contrast to France, the social culture in Sweden emphasises collectivism, as Sweden is a typical welfare state. The vigorous entrepreneurial activity provides a comprehensive source of tax revenue for policy, and, as a result, entrepreneurship and EE are popular in Sweden. Secondly, the shift towards liberalisation in the Swedish economy over the last half-century has been characterised primarily by an emphasis on free trade and deregulation of the formerly state-monopolised product markets. As a typically small, open economy, Sweden is highly dependent on international trade, and its reliance on exports from the global market is crucial. Furthermore, Swedish companies work more closely with universities than any other European country. Based on a well-established entrepreneurial infrastructure, Sweden invests a high proportion of GDP in R&D. Lerner and Tag (2013) find that Sweden invests far more in R&D than the US; furthermore, Sweden has a sizeable venture capital market (Lerner and Täg, 2013) and the Swedish government invests in supporting academic spin-offs "(Wennberg et al., 2011 ). Last but not least, despite its stability, Swedish state policy has undergone a process of change (Bergström and Styhre, 2021 ). The lesson learned by policymakers during the attempts to save the Swedish shipping industry in the 1970s was that it was better for the government not to intervene directly in business, but to act as a market benchmark, providing incentives to participants, for example, by providing insurance and sometimes subsidies for R&D projects, and building partnerships within and between industries. As a result, the development and implementation of the Swedish entrepreneurship policy is primarily a function of the social partners, with the state agencies playing only a supporting role. An advantage of this is that after a crisis such as the COVID-19 epidemic, measures are financed and organised by the social partners, and the government does not need to increase spending on crisis measures. This means that government spending in other areas of social policy is protected, reducing the need for social policy reforms and austerity measures and ensuring the continuation and stability of the welfare state. In short, it is sufficient for the government to provide support for entrepreneurial projects; the other elements, whether funding or policy purposes, are implemented by the social partners instead. 6. Conclusion The COVID-19 epidemic has caused an unprecedented impact on the global economy, putting the already difficult situation of university students' employment to an even more significant challenge. EE is an essential tool to enhance the employability of university students and an effective driver to stimulate entrepreneurial activities. However, entrepreneurship education and training have suffered a severe setback in the epidemic, with GEM 2021/2022 reporting that EE performs the worst in promoting entrepreneurial activity. How to re-energise the role of EE and promote its redevelopment has become an issue to be solved. The conditions that influence the development of EE and their complex interaction have not been explored in depth in existing studies. This paper uses data about the entrepreneurship ecosystem of the 50 economies surveyed in the GEM 2021/2022 report as cases. It uses fsQCA to conduct configuration analysis to examine the pathways by which conditions in the entrepreneurship ecosystem drive the development of EE. Our research found that, firstly , none of the states in the entrepreneurial ecosystem alone constitutes a necessary condition for high performance in EE. Instead, three types of configuration include the pathways that drive high-performing EE: the first configuration of INF, GOP, EF, GEP, and RDT; the project-deficit configuration of SCN, INF, EOE, RDT, GOP, and EF; and the policy-deficit configuration of SCN, INF, EOE, RDT, EF, and GEP. The project-deficient and policy-deficient configurations are more general, while the first grouping is more specific. Secondly , the complementary and substitution relationships between the conditions suggest that the development of global EE under the influence of the COVID-19 epidemic is characterised by a focus on R&D transfer, including the provision of infrastructure and finance for R&D transfer. Under certain endowment conditions, the GOP and GEP can contribute to the development of high-quality EE differently through equivalent substitution. Thirdly , on balance, like a time-sensitive condition, government entrepreneurship programmes are a viable option to activate EE development quickly and effectively. 5.1 Theoretical and Practical Contributions There are three main theoretical contributions of this study. Firstly , drawing on established findings in EE and using the entrepreneurial ecosystem as a framework based on data from 50 economies, this study enriches the research on the factors that influence the development of EE. While quantitative research is gaining attention in EE, established studies have focused more on the impact of EE on entrepreneurial competencies, entrepreneurial attitudes, and others, and the results of these studies have been mixed. Exploring the antecedent variables of EE would be beneficial in providing a clear framework for continuing this research with ambiguous results. In particular, the development of EE has been severely hindered and challenged in the two years of the COVID-19 epidemic. As such, this study provides a basis for a country-based comparison of the future direction of EE. Secondly , this paper empirically explores the entrepreneurial ecosystem framework's concurrent synergies and linkage patterns in promoting the development of EE through a configurational perspective. It further expands the application of the entrepreneurial ecosystem framework in explaining causal complexity, contributing to the development of the theory to a certain extent and providing an empirical basis. Entrepreneurial ecosystem theory in its developmental period emphasises the synergy between elements, but there is only limited evidence of the details that underlie this role. In addition, previous research in EE has been widely confined to the net marginal effect of a single condition in a statistical regression path. Few studies have examined in detail the potential for linkage matching between multiple conditions to influence the development of EE. At the same time, the relative importance of the role of multiple conditions in a comprehensive analytical framework is rarely addressed. Based on this, this study helps to reveal the black box of multiple conditions in the entrepreneurship ecosystem framework in influencing the level of EE development and further deepens researchers' understanding of the complex mechanisms behind the contradictory findings of EE research. Thirdly , by using the fsQCA approach, this paper has been able to clarify the equivocal driving mechanisms and condition substitution relationships that improve EE performance. It is found that multiple configurations of different conditions at the core of the entrepreneurial ecosystem can achieve high levels of EE in a different ways. Among them, entrepreneurship policy support and government entrepreneurship programmes deserve sufficient attention. They can not only play a combined role of socio-cultural norms and market access alone but can also influence EE in a mutually substitutive manner. This finding enriches the explanatory layers of the entrepreneurial ecosystem framework in the development of EE. The findings of this study can provide insights for policymakers and higher education institutions in two ways. On the one hand , the existence of concurrent synergies between different elements in the entrepreneurial ecosystem reveals the complexity of EE development. This implies that higher education institutions in different economies can seek and exploit adequate conditions for the development of EE by focusing on adapting multiple conditions within a holistic perspective based on the country's social culture. It is worth noting that entrepreneurship infrastructure and entrepreneurship finance, centered on R&D transfer, are core conditions for EE development. On the other hand , the analysis of complementary and alternative relationships suggests that entrepreneurship policy support and government entrepreneurship programmes are important tools for reactivating EE and implementation dilemmas facing the development of EE in the wake of the COVID-19 epidemic. Therefore, it is also possible for policymakers to choose the right context-specific driver for EE to provide an impetus for the revitalisation of entrepreneurial activities and thus the recovery of the post-epidemic economy. In addition, at least one entrepreneurship policy innovation and entrepreneurship project should be on the agenda. 5.2 Limitations and further research Firstly , this is a macro-level study, reflecting overall levels across economies. Our understanding of the results may vary between regions within a country. As we are aware, there are differences in the level of development in different regions of each country. Therefore, our interpretation of the results represents only one possibility and has a limited impact on different regions within the same economy. Future research with different data is needed to reveal more possibilities between EE and the entrepreneurial ecosystem. Secondly , the impact of the COVID-19 epidemic is dynamic, complex, and uncertain, and this study is still a cross-sectional study that does not take into account the diffusion process of fusion histories over time. Future comparative analysis of continuous data could explain the evolution of EE. Finally , this study explores the combined effects of the antecedents of EE based on a configurational perspective. While this goes some way to compensate for the lack of focus on net effects in previous studies, in-depth longitudinal case studies and other multiple approaches are needed to enrich the explanatory power of our findings in the future. Declarations Competing interests The authors declare no competing interests. Ethical approval This study uses publicly available third-party data from the Global Entrepreneurship Monitor (GEM) 2021/2022. All data were used in compliance with the terms and conditions of the first author’s institution and do not require additional ethical approval. The study was conducted in accordance with the Declaration of Helsinki. Informed consent No animal studies are presented in this manuscript. No human studies are presented in this manuscript. No potentially identifiable human images is presented in this study. Since this dataset is used for scientific purposes and only contains public information from Global Entrepreneurship Monitor (GEM) 2021/2022, it meets all terms and conditions required from the first author’s institution. Author Contribution Conceptualization, Investigation, Writing-original draft preparation, Writing-review and editing and Validation: GL, GZ, PW, & YZ; Data curation, Formal Analysis, Software and Methodology: GL; Funding acquisition: PW; Visualization and Project administration: YZ; Resources and Supervision: GZ. All authors have read and agreed to the published version of the manuscript. Acknowledgement This work was supported by the 2022 National Social Science Foundation (22CSH069): Research on the implementation path and social support system of self-entrepreneurship for people with disabilities in the digital era. Data Availability The datasets generated during and/or analyzed during the current study are not publicly available for reasons of privacy, but are available from the corresponding author on reasonable request. 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1","display":"","copyAsset":false,"role":"figure","size":47757,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eTheoretical Model of Configuration Matching\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e*Note: Based on interactions within the entrepreneurial ecosystem, we have taken inspiration from the editorial by Lehmann and Meoli (2020), but they focus on the opposite relationship to ours. The framework is taken from GEM 2021/2022, and we have integrated the raw data according to its direct implications, from which the above analytical framework has been developed.\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8395537/v1/e45e8f39dd0e217d8c67c084.jpeg"},{"id":100252270,"identity":"a844227b-68f9-4d88-806e-ee88c698fb10","added_by":"auto","created_at":"2026-01-14 15:20:33","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":21790,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAlternative relationships between GEP and “SCN+EOE”\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-8395537/v1/96d94a123a7283fa00ea77e2.png"},{"id":100372244,"identity":"1bcbe8ba-84ab-45f2-b97d-5e445c076208","added_by":"auto","created_at":"2026-01-16 08:11:52","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":19283,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAlternative relationships between GOP and “SCN+EOE”\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-8395537/v1/7677cb69dc0e0f92bc3f176c.png"},{"id":100252273,"identity":"68b6ebf6-c33b-4ef6-9c84-6fad2c42e5d5","added_by":"auto","created_at":"2026-01-14 15:20:33","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":17830,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAlternative relationships between GEP and GOP\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-8395537/v1/a14b46dbaa9232bee8226e22.png"},{"id":100252274,"identity":"82be5b7b-34b8-4043-bdcc-eeff66c6d531","added_by":"auto","created_at":"2026-01-14 15:20:33","extension":"jpeg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":52737,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe case distribution of configuration 1\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage5.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8395537/v1/55a36ba037290f0a757f0938.jpeg"},{"id":100252275,"identity":"11da2cc9-2bc6-4977-b8d3-83be3c118fab","added_by":"auto","created_at":"2026-01-14 15:20:33","extension":"jpeg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":32724,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe case distribution of configuration 2\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage6.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8395537/v1/0ce2feb8e48265bb4cd1d727.jpeg"},{"id":100371346,"identity":"65f74be5-426e-47b0-b6bb-fd2a3169eed9","added_by":"auto","created_at":"2026-01-16 08:09:53","extension":"jpeg","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":74506,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe case distribution of configuration 3\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage7.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8395537/v1/bca02006ad3ca41264c83f17.jpeg"},{"id":100383612,"identity":"80fd7f0f-8b66-4456-8c9d-f907890f45e0","added_by":"auto","created_at":"2026-01-16 10:47:40","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1699981,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8395537/v1/8cb7c844-eded-419a-8402-f302a300a062.pdf"},{"id":100370625,"identity":"dc9bedc6-2f5c-4117-a07b-f03417dc7c33","added_by":"auto","created_at":"2026-01-16 08:06:56","extension":"xlsx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":13523,"visible":true,"origin":"","legend":"","description":"","filename":"Data.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-8395537/v1/b931e44c6e9845d775a8db33.xlsx"},{"id":100371297,"identity":"9a5fed3c-def5-402c-9ed5-84d47a16af5c","added_by":"auto","created_at":"2026-01-16 08:09:46","extension":"xlsx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":10529,"visible":true,"origin":"","legend":"","description":"","filename":"Truthtable.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-8395537/v1/54bb060b4090fc1650ecb935.xlsx"}],"financialInterests":"No competing interests reported.","formattedTitle":"A structure for promoting entrepreneurship education: Understanding the interactions within the entrepreneurial ecosystem","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eThe UN's 2005 prediction that youth unemployment would cause socio-economic problems seems to have come true (UN, \u003cspan citationid=\"CR102\" class=\"CitationRef\"\u003e2005\u003c/span\u003e; Thomassen et al., \u003cspan citationid=\"CR101\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Along with concerns about population growth, governments have continued to warn of an aging society, for example in China, where a 'three-child policy' has been proposed to address the social problems of an unbalanced workforce in the future (CPC, Central Committee and State Council of PRC, 2022). Although many jobs will be vacated as a result, this will not be enough compared to the growing number of young people seeking employment (Thomassen et al., \u003cspan citationid=\"CR101\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). At the same time, the sudden arrival of COVID-19 has changed EE and created massive unemployment, and we know very little about the future of entrepreneurship education (Ratten and Jones, \u003cspan citationid=\"CR87\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Alzaidi and Shehawy, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Mahajan et al., \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Therefore, without more jobs being created, the negative impact of unemployment may be unimaginable. How entrepreneurship education will develop in the aftermath of the COVID-19 is a matter of concern and common ground for policymakers and educators alike. Governments in both developed and developing countries see EE as a panacea for stagnant or declining economic performance and unemployment (Matlay and Carey, 2006; O'Connor, 2013) because, on the one hand, entrepreneurship education and training appears to be the driving force behind the rise of entrepreneurship and small business development (EC, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2002\u003c/span\u003e), on the other hand, EE is recognised as a place to promote self-employment (BalanVnuk et al., 2014; Mwasalwiba et al., \u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). The literature points out that contemporary youth are the most educated generation. However, the role of the formal education system in entrepreneurship development has been grossly underestimated, especially at the tertiary level (Nowak, \u003cspan citationid=\"CR82\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Kuratko (\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2005\u003c/span\u003e) cautions that despite the achievements in legitimising EE, there are still serious challenges ahead. At this stage, scholarly research has primarily focused on the impact of EE on entrepreneurial intentions, entrepreneurial competencies, and other entrepreneurial behaviours but has neglected a more important premise: what factors influence EE?\u003c/p\u003e \u003cp\u003eA recent review (Fellnhofer, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) shows that literature about EE appears to be heavily biased towards higher education (especially business schools), self-employment, and new ventures, neglecting education that promotes entrepreneurial behaviour in other contexts. Meanwhile, the literature falls into several broad categories (Fellnhofer, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), including social and policy-driven EE research, human capital research related to self-employment, organisational EE, triple helix research, (re)design and (re)evaluation of EE initiatives, entrepreneurial learning, impact studies of EE and opportunities-related issues. A concept that has not been explicitly included in the research and is closely linked to the above themes is rarely mentioned, namely the entrepreneurial ecosystem (Motoyama and Watkins, \u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Abootorabi et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Only a few of these papers explicitly refer to higher education institutions as a component of the entrepreneurial ecosystem (e.g., Streeter et al., \u003cspan citationid=\"CR98\" class=\"CitationRef\"\u003e2002\u003c/span\u003e; Isenberg, \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2011\u003c/span\u003e), a proposition that remains to be tested. The entrepreneurial ecosystem is a dynamic, institutional interaction between individuals' entrepreneurial attitudes, abilities, and ambitions that drives resource allocation through the creation and operation of new ventures (Acs et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Different scholars have provided differentiated lists of relevant ecological factors (Isenberg, \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Spigel, \u003cspan citationid=\"CR95\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Nicotra et al., \u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Unfortunately, however, there is little clear empirical evidence (Stam, \u003cspan citationid=\"CR96\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Nicotra et al., \u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Entrepreneurial ecosystems reveal complex, interconnected configurations (Bertalanffy, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e1968\u003c/span\u003e; M\u0026uuml;ller, \u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e1997\u003c/span\u003e; Cohen, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Theodoraki et al., \u003cspan citationid=\"CR100\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), while studies related to entrepreneurship and EE are limited to independent net effects (Lim et al., \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Jiang et al., \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), ignoring the fact that their relationship with the entrepreneurial ecosystem is multidimensional and heterogeneous, failing to reveal synergies between the elements (Isenberg, \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Guerrero and Urbano, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Clark et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Furthermore, Ratten and Jones (\u003cspan citationid=\"CR87\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) also emphasise the urgency of an enhanced analysis of coping mechanisms, including recovery and change, by considering EE as a holistic process from the perspective of entrepreneurial stakeholders.\u003c/p\u003e \u003cp\u003eGeneral systems theory (Boulding, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e1956\u003c/span\u003e) assumes that the main task in studying social phenomena is to consider the interdependence of the elements of the system as a whole (Ragin, \u003cspan citationid=\"CR86\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). Although scholars recognise that entrepreneurship is a process that is influenced by multiple synergistic factors, most research still focuses on traditional linear regression analysis (Walter and Block, \u003cspan citationid=\"CR105\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Dou et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Debarliev et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Therefore, exploring the mechanisms of coupled roles in EE is a research gap. In recent years, scholars in management and management information systems have often used qualitative comparative analysis (QCA) when faced with complex causal relationships that are difficult to test in standard statistical methods (Fainshmidt et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Lepp\u0026auml;nen et al., \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Before we go on, we need to remind you in particular that, unlike linear regression, QCA is neither a purely quantitative nor a strictly qualitative analysis tool but rather integrates the advantages of both quantitative and qualitative aspects, using quantitative thinking to analyze case data. QCA (Fiss, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2007\u003c/span\u003e, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Vilmos et al., \u003cspan citationid=\"CR103\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) has become a popular tool for analysing complex causal and configurational situations, using boolean algebra (the algebra of sets and logic) to examine which combinations of attributes lead to the outcome and to conceptualise the situation as a combination of attributes explicitly. Based on these considerations, this study aims to apply a systems approach to analyse the impact of different combinations of elements in the entrepreneurial ecosystem on EE, rather than the traditional isolated net effect analysis, and in turn to draw different conclusions from previous single-factor impacts, and in this way fill the gaps in existing research. Our research questions are, therefore, as follows:\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eRQ\u003c/strong\u003e \u003cp\u003eHow the interaction of elements within the entrepreneurial ecosystem affects EE?\u003c/p\u003e \u003c/p\u003e \u003cp\u003eThe paper is organised as follows: In the next section, we review the research on EE and the entrepreneurial ecosystem, and in doing so, we identify the research debates. We will then report on our research methodology and analysis. Finally, the findings and limitations of the study are summarised and discussed, and in doing so, recommendations for future research are made.\u003c/p\u003e \u003cp\u003eExploring the antecedent variables of EE has both theoretical and practical implications. Although there has been a great deal of research conducted by scholars worldwide on EE, the findings have been mixed (Nabi et al., \u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). At a time when the vast majority are keen on the impact of EE, this study shifts the focus to antecedent variables, which helps to reveal the relationship between entrepreneurial knowledge and entrepreneurship practice, while clarifying the nature and complexity of the relationship between ecosystems and EE (Lin and Xu, \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), which Mukesh and RajasekharanPillai (\u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) argue is an essential prerequisite for a proper understanding of entrepreneurship formation among university students. The theoretical model for this study is shown in the following diagram.\u003c/p\u003e"},{"header":"2. Theoretical Background and Literature Review","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Entrepreneurial ecosystem theory\u003c/h2\u003e \u003cp\u003eEntrepreneurial ecosystem theory (EET) was developed based on the concept of regional innovation system, but differs in that EET emphasises the analysis of social phenomena from a systems perspective, focusing on the concurrent influence of multiple factors (Cohen, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Isenberg, \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). Since its introduction, the theory has received widespread attention, but academic research is still in the exploratory stage (Simatupang et al., \u003cspan citationid=\"CR94\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Bischoff et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) and can currently be divided into two schools of thought: the environmentalist school and the subject-environment school. Early scholars considered the entrepreneurial ecosystem the external entrepreneurial environment in which the entrepreneurial firm operates (Cohen, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Isenberg, \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). The subject-environment school emphasises the relationship between the individual and the whole, advocating that the relationship between the entrepreneurial subject and its environment is explored with a focus on the entrepreneurial subject and that, in addition to the external entrepreneurial environment, the entrepreneurial firm itself is part of the entrepreneurial ecosystem (Vogel, \u003cspan citationid=\"CR104\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). Previous research has failed to account for the complex impact of the components of the ecosystem as a whole structure, so recent research has begun to focus on the interrelationships between the components of the entrepreneurial ecosystem (Abdurakhmonov et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). The entrepreneurial ecosystem (Bischoff et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) or external stakeholders (Matlay, \u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e2009\u003c/span\u003e) is highly relevant to EE, and Cheng (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2012\u003c/span\u003e) argues that the significance of the entrepreneurial ecosystem to EE lies in the synergistic relationship between elements that each play their part and function as part of a whole. A better understanding of the nature of EE requires the integration of several different factors within a holistic framework (Lin and Xu, \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). It is therefore appropriate for this study to use the theory to explore the combined impact of antecedent and causal variables in EE.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Entrepreneurship education and its antecedents\u003c/h2\u003e \u003cp\u003eThe rise of EE is due to its role in promoting economic growth and national transformational development, poverty and unemployment reduction, wealth creation, and innovation by providing business ideas (Fayolle et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Gavron et al. (\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e1998\u003c/span\u003e) and Reynolds et al. (\u003cspan citationid=\"CR88\" class=\"CitationRef\"\u003e1999\u003c/span\u003e) argue that entrepreneurship education and training can stimulate entrepreneurial activity by promoting skills and attitudes conducive to developing entrepreneurship. Thus, EE refers to how individuals take advantage of entrepreneurial opportunities and take action to develop entrepreneurial skills (McIntyre and Roche, \u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e1999\u003c/span\u003e). Currently, mainstream topics in EE include: social and policy-driven EE research (O'Connor, 2013; Hoppe, \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Senathiraja et al. \u003cspan citationid=\"CR93\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Yi and Duval-Couetil, \u003cspan citationid=\"CR112\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), entrepreneurial learning (Fayolle and Gailly, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Fayolle, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Hoppe, \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Li et al., \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), and impact studies of EE (Duong, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Pinto Borges et al. \u003cspan citationid=\"CR85\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) Previous studies have more often drawn from the theory of planned behaviour (Hatak and Zhou, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Yu-Yu et al.,2021; Li et al., \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), stakeholder theory (Bischoff et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Crammond, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) and other perspectives to explore EE. Although the relationship between EE and entrepreneurship activity is strongly correlated, research in this field of study has yielded mixed and even conflicting results (Fayolle and Gailly, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Nabi et al., \u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e2016\u003c/span\u003e), with methodology may be an important reason (Matlay, \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e2006\u003c/span\u003e). Scholars point out that the concept of how EE influences entrepreneurial orientation and behaviour is becoming increasingly complex and still requires subsequent empirical research (Fleck et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). In developed countries, the integration of higher education, including EE, and the entrepreneurial ecosystem has received increasing attention (Ismail et al., \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), and the introduction of system thinking into the research and practice of EE has become a new perspective in EE research in recent years (Wang et al., \u003cspan citationid=\"CR106\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Therefore, it is necessary to find effective ways to improve the effectiveness of EE based on an ecosystem perspective.\u003c/p\u003e \u003cp\u003eOnly a few studies have noted the importance of antecedent influences on EE (Lin and Xu, \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Thomassen et al., \u003cspan citationid=\"CR101\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). The literature highlights four main categories of drivers: firstly, EE is driven by government policy or institutional factors (Matlay, \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e2005a\u003c/span\u003e, \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003eb\u003c/span\u003e;Connor, 2013), which is the most common type of factor; secondly, the requirement of national economic development to develop EE (O'Connor, 2013); and thirdly, the labour market influence that EE is to address the rising unemployment rate is urgent (Lin and Xu, \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Liguori et al., \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2021\u003c/span\u003e); finally, in more limited studies, socio-cultural norms and values have also been found to have an impact on the development of EE (Garc\u0026iacute;a-Rodr\u0026iacute;guez et al., \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Among these factors, economic development and the labour market are demand-driven, while policies and institutions are supply-driven (Lin and Xu, \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Despite the attention paid to the antecedent influences on EE, on the one hand, exploring the impact of these factors on EE is not sufficient and thorough; on the other hand, these studies tend to emphasise the role of single factors only (but policy and economic factors are often mentioned simultaneously), neglecting the combined impact of the ecological factors in which EE is embedded. Most are case studies that lack empirical testing.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Entrepreneurial ecosystem and its composition\u003c/h2\u003e \u003cp\u003ePublications in the field of entrepreneurial ecosystem have increased significantly in the last five years but have not been fully theorised (Simatupang et al., \u003cspan citationid=\"CR94\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Theodoraki et al., \u003cspan citationid=\"CR100\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Spigel, \u003cspan citationid=\"CR95\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Autio et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Wurth et al., \u003cspan citationid=\"CR110\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), and they attempt to ecosystem conceptualised and made a list of its constituent elements. The ecosystem is holistic (Theodoraki et al., \u003cspan citationid=\"CR99\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Abootorabi et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). The literature in this field uses a systems theory perspective to emphasise the interaction between the individual and the whole (Jack and Anderson, \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2002\u003c/span\u003e) and the importance of the complex interconnectedness of the elements in an entrepreneurial ecosystem (Bertalanffy, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e1968\u003c/span\u003e; M\u0026uuml;ller, \u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e1997\u003c/span\u003e; Binkley, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Originating from a biological concept, the entrepreneurial ecosystem was first considered the environment in which firms use business networks to succeed in modern competition (Moore, \u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e1993\u003c/span\u003e). The term was later replaced by business ecosystem (Iansiti and Levien, \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2004\u003c/span\u003e), but with the difference that it began to emphasise the collective attributes of ecosystem participants. Scholars have generally agreed that entrepreneurial ecosystem is the environment in which various ecological elements interact and influence each other to achieve specific goals. Cohen (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2006\u003c/span\u003e) defines entrepreneurial ecosystems as a diverse set of interdependent, interacting actors within a geographical area that influences the formation and eventual trajectory of an entire group of actors, which can also potentially impact the economy as a whole. Jackson (\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2011\u003c/span\u003e) argues that innovation ecosystems consist of essential actors and entities whose functional goal is to promote technology development and innovation. Zaidi et al. (\u003cspan citationid=\"CR113\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) see the entrepreneurial ecosystem as dynamic social, institutional, and cultural processes and actors at the local level that facilitates and encourage the creation and growth of new businesses. Acs et al. (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2014\u003c/span\u003e) argue that the entrepreneurial ecosystem is a dynamic, institutional interaction between individuals' entrepreneurial attitudes, capabilities, and ambitions that drives resource allocation by creating and operating new businesses. The discussion among researchers on the components of an entrepreneurial ecosystem is not yet unified, and widely used indicators include Isenberg (\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2011\u003c/span\u003e) and GEM. Isenberg (\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2011\u003c/span\u003e) proposes a six-factor model of the entrepreneurial ecosystem, comprising six interrelated domains: policy, finance, support, culture, human capital, and markets. Specifically, it refers to an enabling culture, facilitating policies and leadership, providing dedicated finance, relevant human capital, a market for entrepreneurial-friendly products, and extensive institutional and infrastructural support. GEM provides a comprehensive set of entrepreneurship indicators (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e1\u003c/span\u003e), which is considered to be the most authoritative and informative indicator system available on entrepreneurship (Herrington and Coduras, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), and it includes Entrepreneurial Finance, Ease of Access to Entrepreneurial Finance, Government Policy: Support and Relevance, Government Policy: Taxes and Bureaucracy, Government Entrepreneurial Programs. Entrepreneurial Education at School, Entrepreneurial Education Post-School, Research and Development Transfers, Commercial and Professional Infrastructure, Ease of Entry: Market Dynamics, Ease of Entry: Burdens and Regulation, Physical Infrastructure and Social and Cultural Norms (GEM, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eDespite the systematic approach taken in ecosystem research, scholars have only recently begun to examine the impacts and challenges associated with the ecosystem (Theodoraki et al., \u003cspan citationid=\"CR100\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; DiVito and Ingen-Housz, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Pankov et al., \u003cspan citationid=\"CR84\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) Thus, research on the entrepreneurial ecosystem is still in the developmental stage (Theodoraki et al., \u003cspan citationid=\"CR100\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Existing research lacks an account of the causal relationship between the entrepreneurial ecosystem and its outputs (Nicotra et al., \u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Corrente et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Although previous research on EE, the entrepreneurial ecosystem has increased, little overlap can be observed between the two (Motoyama and Watkins \u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Theodoraki et al. (\u003cspan citationid=\"CR100\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) show that research on entrepreneurial ecosystems has focused on academic entrepreneurship, innovation and regional development, social entrepreneurship, sustainable development, networks, and clusters. The theme of EE has not been the focus of research. While a limited number of these papers explicitly include higher education institutions as a component of the entrepreneurial ecosystem (e.g., Streeter et al., \u003cspan citationid=\"CR98\" class=\"CitationRef\"\u003e2002\u003c/span\u003e; Isenberg, \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2011\u003c/span\u003e), with the increasing focus on the integration of entrepreneurship universities and entrepreneurial ecosystem in developed countries (Ismail et al., 2013), EE has been added to the list of components of the entrepreneurial ecosystem (GEM, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Naturally, there is no clear empirical evidence of a link between entrepreneurial ecosystem factors and EE, and such lists are based mainly on the direct experience of researchers (Spigel, \u003cspan citationid=\"CR95\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Corrente et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Theodoraki et al., \u003cspan citationid=\"CR100\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). For EE, the significance of the entrepreneurial ecosystem lies in the synergistic relationships between elements (Cheng, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Scheidgen, \u003cspan citationid=\"CR92\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). The introduction of ecological thinking into EE research and practice has become a new perspective in EE research in recent years, but there is still a breakthrough in methodological implications (Wang et al., \u003cspan citationid=\"CR106\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Theodoraki et al., \u003cspan citationid=\"CR100\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). The relationship between ecosystem and EE is not simply linear. It presents a juxtaposed, complex causal relationship, and it is challenging to develop an appropriate methodology based on this (Neumeyer and Santos, \u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Mukesh and RajasekharanPillai, \u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Entrepreneurial ecosystem and entrepreneurship education\u003c/h2\u003e \u003cp\u003eAlthough the impact of the entrepreneurial ecosystem is considered to provide a broader perspective on the development of EE (Hoppe, \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Bischoff et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Mukesh and RajasekharanPillai, \u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), the factors of previous studies related to EE have mainly focused on factors internal to higher education institutions, while environmental factors were often neglected. In other words, the relationship between entrepreneurship education and the entrepreneurial ecosystem can be illustrated in two ways: on the one hand, the internal structures and conditions provided to improve and nurture entrepreneurship education; and on the other hand, the external conditions given by the policy conditions and business environment associated with higher education (Lehmann and Meoli, 2020). Admittedly, entrepreneurship education is an integral part of the entrepreneurial ecosystem. Still, as Lehmann and Meoli (2020) mention in their editorial, the relationship between entrepreneurship education and the entrepreneurial ecosystem is a two-way street, and more research has focused on the former, with the external conditions that entrepreneurship education is receiving minimal attention. Therefore, this study is concerned with the interaction of elements within the entrepreneurial ecosystem, specifically exploring the impact of external conditions on entrepreneurship education.\u003c/p\u003e \u003cp\u003eScattered case studies reveal the impact of single ecological factors, with social and policy-driven EE being more common (Hoppe, \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). One of the most widely recognised facts is the increasing tendency of government policy to use EE to stimulate levels of economic activity, thus increasing entrepreneurial activity for economic efficiency (O'Connor, 2013). The most direct result of this generally supportive policy environment is the rapid growth of entrepreneurship programmes and training worldwide (Jiang et al., \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). At the same time, it is widely accepted that the impact of entrepreneurship policies on higher education, it is important to note that disparities between different economies are objective. For example, the Swedish government's initiatives to promote entrepreneurship in higher education have been limited, although they are increasingly interested in EE (Hoppe, \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Similarly, EE under different socio-cultural norms has been identified as distinct by case studies (Stadler et al., \u003cspan citationid=\"CR97\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). For example, most French people consider entrepreneurial ventures insecure and a career destination for those who cannot find a job (Clark et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). In contrast, as a welfare state, Sweden emphasises collectivism (Bergstr\u0026ouml;m and Styhre, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), and therefore Swedish attitudes towards EE differ significantly from those of the French.\u003c/p\u003e \u003cp\u003eAs a part of an ecosystem, universities are characterised by various economic, institutional, legal, cultural, social and political factors. According to Spigel (\u003cspan citationid=\"CR95\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), entrepreneurial systems can be described in terms of three interacting dimensions: physical, social, and cultural. Specifically, the physical dimension relies on the presence of local actors and the institutional environment; the social dimension is based on the linkages between actors in the ecosystem, and the cultural dimension determines the local demand for entrepreneurial activities. The few studies with a systemic perspective focus on stakeholders and within the triple helix theory perspective (Bischoff et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Ribeiro et al., \u003cspan citationid=\"CR89\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Mukesh and RajasekharanPillai, \u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). These studies consider entrepreneurial infrastructure, typified by incubation labs and science parks, as a medium for knowledge transfer from universities to companies (Ribeiro et al., \u003cspan citationid=\"CR89\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), and their active role in technology EE has important implications for understanding the university EE ecosystem (Bolzani et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Although a few studies have started to focus on applying systems thinking in EE research, most of them only consider the influence of the triad of universities, government and business. It is crucial to acknowledge that the entrepreneurial environment is complex, and the factors that influence the development of EE are multiple, especially in different contexts. Based on the limitations of these studies, this study is significant in that, on the one hand, we explore the impact of the entrepreneurial ecosystem based on a holistic perspective, and, on the other hand, the methodology we use provides empirical evidence.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e*Note: Based on interactions within the entrepreneurial ecosystem, we have taken inspiration from the editorial by Lehmann and Meoli (2020), but they focus on the opposite relationship to ours. The framework is taken from GEM 2021/2022, and we have integrated the raw data according to its direct implications, from which the above analytical framework has been developed.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Methodology","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Qualitative Comparative Analysis\u003c/h2\u003e \u003cp\u003eBased on a configurational and conformational approach, qualitative comparative analysis (QCA) is employed in this study to reveal the mechanisms by which combinations of elements in the entrepreneurial ecosystem contribute to EE. Unlike the symmetrical relationships between independent and dependent variables in traditional linear quantitative approaches, QCA emphasises complex non-linear relationships and argues that high performance is not achieved in isolation through individual factors or loosely coupled entities but rather through the interaction of interrelated structures in the system (Fiss, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). Further, within the antecedent variables that show large clusters, the different constituents are configured to analyse the impact of their combination on the outcome variable.\u003c/p\u003e \u003cp\u003eThe logical algorithm for QCA (Ragin, \u003cspan citationid=\"CR86\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Fiss, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2007\u003c/span\u003e, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2011\u003c/span\u003e) is Boolean algebra and draws on set-theoretic research. The set-theoretic approach provides a more rigorous way of combining verbal statements with logical relations and using this to construct a theory than the traditional view. Three assumptions inform the QCA: concurrent causality, equivalence, and asymmetry. Concurrent causality is the idea that outcomes are the effect of combining different elements. On the other hand, equivalence means that different path patterns can achieve the system reach the same final state. Finally, the asymmetry relationship can be understood as the realization that the opposite of a high-performance cause is not equal to a low-performance cause. In this way, QCA is divided into csQCA, mvQCA, and fsQCA. The first two can only deal with category phenomena (Cronqvist and Berg-Schlosser, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2009\u003c/span\u003e), while fsQCA can also deal with degree change and partisan affiliation problems. Due to the application of the Boolean algorithm, fsQCA has both quantitative and qualitative characteristics.\u003c/p\u003e \u003cp\u003eIn contrast to regression analyses that provide minimal conclusions, fsQCA takes complete account of the complexity of entrepreneurship and distinguishes in depth between multiple possibilities in the findings. fsQCA is made possible by creating different combinations of conditions and the set of countries associated with them (Coduras et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Therefore, fsQCA was ultimately chosen as the research method for this paper to analyse a combination of antecedent and causal influence variables on EE.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Data Source and sample\u003c/h2\u003e \u003cp\u003eThe data used in this study is based on the latest GEM 2021/2022 Report - a survey of national experts on the entrepreneurial ecosystem - sponsored by Babson College. It provides the most authoritative and informative body of indicators on entrepreneurship available (Herrington and Coduras, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). It is considered the most extensive and longest-running study of entrepreneurship globally. Increasingly, macroeconomic and quantitative approaches tend to use data from GEM for longitudinal or cross-case comparative studies (Bruns et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). QCA is based on empirical data modeled through a dialogue between theory and data and applies to small and medium-sized samples (Rihoux and Ragin, \u003cspan citationid=\"CR90\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). In recent years, empirical analyses based on GEM have yielded some results, e.g., Beynon et al. (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) demonstrate entrepreneurial attitudes and entrepreneurial activity at the country level based on GEM and fsQCA; Xie et al. (\u003cspan citationid=\"CR111\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) investigated the relationship between different national institutions and female entrepreneurship. While data from GEM were used for linear regression analysis, in contrast, fsQCA showed more substantial and more explicit explanatory power (Coduras et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). In fsQCA, different countries are classified into different sets, which distinguish them from linear regression, as one size does not fit all. Thus, the combination of GEM and fsQCA facilitates discovering entrepreneurial characteristics, types, and historical moments that are more appropriate for each country (Coduras et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Bruns et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Beynon et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Xie et al., \u003cspan citationid=\"CR111\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Based on the above works, we are assured that the adaptation of our sample to the research methodology is relevant and cutting edge.\u003c/p\u003e \u003cp\u003eIt is also important to note that in a challenging and turbulent period dominated by an unprecedented global pandemic, the latest GEM survey provides evidence from extensive interviews in 2021 with over 148,000 people in 50 different economies, which together account for approximately 68% of the global Gross Domestic Product (GDP) and 45% of the world's population. It is worth noting that according to World Bank GDP per capita data, 38% of these 50 economies have a GDP per capita of over US\u003cspan\u003e$\u003c/span\u003e40,000, a further 38% have a GDP per capita of between US\u003cspan\u003e$\u003c/span\u003e20,000 and US\u003cspan\u003e$\u003c/span\u003e40,000, and finally, 24% have a GDP per capita of less than US\u003cspan\u003e$\u003c/span\u003e20,000. This reflects the case at an economic level.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Measurement and Descriptive Statistical Analysis\u003c/h2\u003e \u003cp\u003eQCA does not have a clear rule on the number of conditions for the time being, and different scholars have given different suggestions. Berg-Schlosser and DeMeur (2009) suggest that research models with a caseload of 10\u0026ndash;40 cases should identify up to seven antecedent variables. Marx and Dușa (\u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2011\u003c/span\u003e) found specific probability distributions based on simulations with different sample sizes and conditions. They suggested that the number of conditions to the number of cases should be at least 1:3. In this study, the number of cases was 50 and based on the GEM classification. We further divided the 13 question items into seven variables based on the nature of the variables (see Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e), which were named SCN (Social and Cultural Norms), INF (Infrastructure), EOE (Ease of Entry), RDT (Research and Development Transfers), GOP (Government Policy), EF (Entrepreneurial Finance), and GEP (Government Entrepreneurial Programs). Based on the connotations and definitions of the indicators, we have categorised them homogeneously. For example, commercial and professional infrastructure is also a category of entrepreneurial infrastructure, so we have grouped it with physical infrastructure. And the outcome variable EE includes both EE at school and post-school. Measures for each synthetic variable are calculated from the mean.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eConstruct and Variable Indicators\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIndicators\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eItems\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSCN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSocial and Cultural Norms\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDoes culture encourage and celebrate entrepreneurship?\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eINF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCommercial and Professional Infrastructure\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAre these sufficient and affordable?\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePhysical Infrastructure\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eIs this sufficient and affordable?\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eEOE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEase of Entry: Market Dynamics\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAre markets free, open and growing?\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEase of Entry: Burdens and Regulation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDo regulations encourage or restrict entry?\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRDT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eResearch and Development Transfers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCan research be translated into new businesses?\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eGOP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGovernment Policy: Support and Relevance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDo they promote and support startups?\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGovernment Policy: Taxes and Bureaucracy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eOr are new businesses burdened?\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eEF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEntrepreneurial Finance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAre there sufficient funds for new startups?\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEase of Access to Entrepreneurial Finance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAnd are those funds easy to access?\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGEP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGovernment Entrepreneurial Programs\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAre quality support programs available?\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eEE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEntrepreneurial Education at School\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDo schools introduce entrepreneurship ideas?\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEntrepreneurial Education Post-School\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDo colleges offer courses in starting a business?\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e presents the results of the descriptive statistical analysis for each variable. The highest score in GEM scored for each variable was ten, and the lowest score was 1. As a whole, the performance of each element was not outstanding, with no variable scoring higher than seven on average. This reflects the poor performance of all aspects of entrepreneurship under the impact of the COVID-19 epidemic. Specifically, among the results of the descriptive statistics for the seven antecedent variables, GOP and INF performed the best, both scoring above six on average. In contrast, EE performed the worst, with a mean score of only 3.86, which should draw our attention. In terms of the difference between the highest and lowest scores, the highest difference is for GOP, followed by SCN and GEP, reflecting the vast disparity between the 50 economies surveyed in terms of government policy, social and cultural norms, and government entrepreneurial programs.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDescriptive statistical results of the research variables\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"9\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eStatistical indicator\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"7\" nameend=\"c8\" namest=\"c2\"\u003e \u003cp\u003eAntecedent Variables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eOutcome Variables\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSCN\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eINF\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eEOE\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eRDT\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eGOP\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eEF\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eGEP\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eEE\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMinimum\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e2.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e1.90\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMaximum\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e7.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e6.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e7.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e6.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e6.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e6.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMean\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e6.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e4.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e4.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e3.86\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStandard deviation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e6.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.13\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDifference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e5.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e4.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e4.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e4.15\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e*Scale Note: 0\u0026thinsp;=\u0026thinsp;very inadequate insufficient status, 10\u0026thinsp;=\u0026thinsp;very adequate sufficient status.\u003c/p\u003e \u003c/div\u003e"},{"header":"4. Results","content":"\u003cp\u003eThis study uses fsQCA to analyse 50 economies from the GEM 2021/2022 report, each considered a case, to explore the impact of different combinations of factors on high-performance EE. Following Fiss's (2011) and Du and Jia's (2017) recommendations, we set the original consistency threshold at 0.8, the PRI consistency threshold at 0.7, and the case threshold at 1. From this, high-performance EE will be retained.\u003c/p\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e4.1 Calibration: affiliation scores of fuzzy sets\u003c/h2\u003e \u003cp\u003eCalibration is a process of assigning collective membership to individual cases (Du and Jia, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Affiliation scores take values between 0 and 1. 1 represents complete affiliation with a set; conversely, 0 represents complete lack of affiliation with a set. In fuzzy set affiliation scores, between 0.5 and 1 (not included), a score close to 1 indicates strong affiliation (but not full affiliation); between 0 and 0.5 (not included), a score close to 0 indicates weak affiliation (not non-affiliation). Score 0.5 is a qualitative locus, which refers to the maximum fuzzy point when assessing whether a case belongs or does not belong to a set. Fiss (\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2011\u003c/span\u003e) suggested that based on theory and practice, a three-valued fuzzy set will be adopted for this study. Therefore, we set the three anchor points for the seven condition variables and one outcome variable to 0.25, 0.5, and 0.75. Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e lists the calibration anchor points for each variable.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCalibration anchor points of various variables\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c5\" namest=\"c3\"\u003e \u003cp\u003eAnchor point\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFully affiliated\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCrossover point\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCompletely unaffiliated\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"6\" rowspan=\"7\"\u003e \u003cp\u003eAntecedent Variables\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSCN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7.3150\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5.2000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3.3100\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eINF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7.5400\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6.0000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4.5775\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEOE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6.3225\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.7000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3.4825\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRDT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6.0450\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.0500\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.2550\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGOP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6.2175\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.5500\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.5775\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6.2950\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.3750\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.8100\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGEP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6.4450\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.5500\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.3650\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOutcome Variables\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5.9950\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.8250\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.2775\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e4.2 Univariate necessity analysis\u003c/h2\u003e \u003cp\u003eThe univariate analysis aims to test whether a variable would affect the outcome alone. Suppose there is a variable that would act on the dependent variable alone. In that case, that variable is considered a necessary presence in the results, regardless of the outcome of the combined analysis. In other words, if a single variable has a different effect, it must be necessary and central to the dependent variable. Conversely, it may form a grouping with other variables to influence the dependent variable. In arithmetic, the purpose of the univariate necessity analysis is to screen whether condition X is necessary for Y. It consists of two indicators: consistency and coverage. Suppose the consistency (Eq.\u0026nbsp;1) is more significant than 0.9. In that case, X is necessary for Y. Coverage (Eq.\u0026nbsp;2) reflects the explanatory power of a single or combination of variables, with a more significant result indicating that the variable or combination of variables is empirically more robust in explaining the results.\u003c/p\u003e \u003cp\u003eConsistency (Xi\u0026thinsp;\u0026le;\u0026thinsp;Yi) = \u0026sum;[min (Xi, Yi)] /\u0026sum;Xi (1)\u003c/p\u003e \u003cp\u003eCoverage(Xi\u0026thinsp;\u0026le;\u0026thinsp;Yi) = \u0026sum;[min (Xi, Yi)] /\u0026sum;Yi (2)\u003c/p\u003e \u003cp\u003eResults of the analysis of the necessity of a single variable on the results shown in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e were obtained through the fsQCA software analysis. As shown in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e, none of the conditions have a consistency of more than 0.9, and all of the conditions only have some explanatory strength. Therefore, no single necessity condition emerges, and further analysis of the combination of condition variables is required to obtain more information.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eHigh Entrepreneurial Education Analysis of Necessary Conditions\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAntecedent Variables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eConsistency\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCoverage\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSCN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.793942\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.866387\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e~ SCN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.614335\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.504909\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eINF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.879266\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.809187\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e~ INF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.562287\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.537301\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEOE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.763225\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.728717\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e~ EOE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.640785\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.590177\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRDT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.843003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.827470\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e~RDT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.534556\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.479709\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGOP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.861348\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.807600\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e~GOP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.548208\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.514000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.856655\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.793049\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e~EF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.552474\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.524716\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGEP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.891638\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.781308\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e~GEP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.498294\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.502366\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e*Note: Due to the lack of evidence and theory on the exact direction in which these conditions affect outcomes, this study assumes that the presence or absence of each condition contributes to a high performance of entrepreneurship education when conducting a counterfactual analysis.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e4.3 Analysis of combinations of conditions\u003c/h2\u003e \u003cp\u003eCombination analysis is one of the core parts of this study and aims to analyse possible combinatorial conditions patterns. After importing the truth tables into the fsQCA software, we can obtain solutions using the Fuzzy Truth Table Algorithm. We can obtain three solutions: complex, parsimonious, and intermediate. The impact on the results was determined by comparing the nested relationships between the intermediate and simplicial solutions. Then, the core condition of each solution is determined, that is, the condition that the intermediate solution also appears in the parsimonious solution. According to Du and Jia (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), the presence of an Intermediate Solution plays an auxiliary role in determining the specific combination pattern and the number of combination patterns, that is, the edge condition. At the same time, a parsimonious solution determines the core conditions that generate the outcome. Therefore, this study uses the intermediate and parsimonious solutions to determine the combinatorial influence patterns of entrepreneurial ecological elements on EE (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). Both \"~\" and \"*\" are concatenation symbols between variables. \"~\" means \"not,\" that is, not present; \"*\" means \"and\" intersection relation, which indicates connected variables should be satisfied. By analysing the solution, it can be seen that, on the one hand, through the intermediate solution, we find that there are three indication paths, namely: INF*~ EOE* RDT*GOP*EF*GEP; SCN*INF*EOE*RDT*GOP*EF; SCN*INF*EOE*GOP*EF*GEP.\u003c/p\u003e \u003cp\u003eOn the other hand, by determining the core and edge condition through the parsimonious solution, we find five indicated paths, namely: SCN*INF*EOE; INF*EOE*RDT; INF*EOE*EF; INF*GOP*EF; INF*EF*GEP. Accordingly, we obtained the final three configurations (M1, M2, and M3) that could produce high-performance EE (Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). The consistency metrics for the three configurations were 0.9505, 0.9787, and 0.9779, respectively, indicating that the three configurations were sufficient for high-performing EE. Also, the solution consistency was 0.9588, indicating that the three configurations covering most cases are sufficient for high-performance EE. The model's Solution Coverage of 0.6651 indicates that these three configurations explain approximately 67% of the reasons for high performance in EE, with high explanatory strength of necessity. In addition, Raw Coverage reflects the adequacy of the combination of the variables, while Unique Coverage reflects the necessity of combining the variables. Studies generally use Unique Coverage as the primary basis for analysis. The following is a detailed analysis of each configuration that affects EE.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eConfigurations Leading to High Performance (EE)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eAntecedents\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003eConfigurations Leading to High Performance (EE)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eM1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eM2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eM3\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSCN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e●\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e●\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eINF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e●\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e●\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e●\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEOE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026otimes;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e●\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e●\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRDT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e●\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e●\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e●\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGOP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e●\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e●\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e●\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e●\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e●\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGEP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e●\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e●\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eConsistency\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.9505\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.9787\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.9779\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRaw Coverage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.4177\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.5482\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.5474\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnique Coverage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.1037\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0141\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0132\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSolution Consistency\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003e0.9588\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSolution Coverage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003e0.6651\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e*Note: \u0026otimes; indicates that the marginal condition is missing, ● indicates that the marginal condition is present, ● indicates that the core condition is present, and \u0026otimes; indicates that the core condition is missing. A blank indicates that the presence of the condition variable is irrelevant to the result.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e4.4 Robustness testing\u003c/h2\u003e \u003cp\u003eRobustness testing is a critical step in the QCA analysis methodology. Case or sample selection, conditional measurement and calibration, and analysis thresholds (case frequency, original consistency and PRI consistency) affect the number of sets, relationship states and relevant parameters. The robustness testing of the QCA can be conducted by examining whether these indicators vary significantly at different levels; if there is no significant change, in other words, if similar results to the original model are obtained, then the model is robust. In general, the options for robustness analysis include changing calibration, changing consistency, and dropping or adding cases (Schneider and Wagemann, \u003cspan citationid=\"CR91\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). Adjusting the analysis threshold is one of the most common choices because of its accuracy and ease of operation. Some scholars (White et al., \u003cspan citationid=\"CR109\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Lewellyn and Muller-Kahle, \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) have chosen to raise and lower the original consistency threshold to test the robustness of the model in their studies. Therefore, in testing the robustness of the antecedent variables affecting EE in high performance, we raise the original consistency threshold to 0.85 and lower it to 0.75, and the study results did not change. Therefore the results of this study are robust.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e4.5 Analysis of complementary and alternative relationships\u003c/h2\u003e \u003cp\u003eThe frequency of the condition variables in the high-performance EE pathway was counted (see Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e), with INF, RDT, and EF appearing most frequently (3 times) and SCN, EOE, GOP, and GEP appearing twice. This shows that entrepreneurial infrastructure, R\u0026amp;D transfer, and financial support for entrepreneurship play a vital role in the development of EE, followed by other conditions. This suggests that EE in the surveyed economies has been characterised by the development of R\u0026amp;D transfer activities in the post-COVID-19 era. Therefore, in the post-COVID-19 era, policymakers and university administrators should pay attention to the weight of investment in these three conditions in order to be able to target them to ensure the quality development of EE.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eFrequency of Condition Variable in High-Performance Configuration\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eConditions\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSCN\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eINF\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eEOE\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eRDT\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eGOP\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eEF\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eGEP\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFrequency\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eBy comparing the similarities and differences across the configurations, we further identify the mutual substitution or complementary relationships between the different conditions. Firstly, by comparing configurations 1 and 2, we find that government-initiated entrepreneurship projects for economies with well-developed entrepreneurship policies can be substituted by both socio-cultural norms and ease of market access when entrepreneurial infrastructure and finance are centered on R\u0026amp;D transfer activities (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e2\u003c/span\u003e). In such economies where the government cannot initiate entrepreneurship programmes, a positive social entrepreneurial culture and easy access to markets can bring in fresh blood to replace government entrepreneurship programmes in achieving high-performance EE.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eSecondly, by comparing configurations 1 and 3, we find that entrepreneurship policy support can be replaced by cultural values that encourage praise for entrepreneurship and easy market access in economies with well-established government entrepreneurship programs that focus on R\u0026amp;D transfer activities to launch entrepreneurship infrastructure and finance (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e3\u003c/span\u003e). A vibrant entrepreneurial market and a positive entrepreneurial culture can also drive EE in these economies where entrepreneurship policy does not explicitly encourage EE.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFinally, the comparative analysis of configurations 2 and 3 concludes the previous two investigations-government-developed entrepreneurship policies and government-initiated entrepreneurship programmes can be substituted for each other in economies where entrepreneurial market access is easy, and the social culture encourages entrepreneurship, and where entrepreneurial infrastructure and finance is centered on R\u0026amp;D transfer activities (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e4\u003c/span\u003e). In other words, either entrepreneurship policy or government entrepreneurship programmes can contribute to high-performance EE in conjunction with other conditions in these economies.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe complementarity and substitution analysis results show that GEP and GOP play a more critical role than SCN and EOE. Under specific objective endowment conditions, both GEP and GOP can play a position that is only present when the combination of SCN and EOE is present (Figs.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e2\u003c/span\u003e and \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e3\u003c/span\u003e). In addition, the substitution relationship between GEP and GOP revealed in Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e4\u003c/span\u003e further highlights the role of GEP in enhancing the development of EE. Given that the effects of GOP implementation often take some time to be seen, the launch of GEP can provide a platform for the implementation of EE in a short period, thus contributing to the high quality and speed of EE.\u003c/p\u003e \u003c/div\u003e"},{"header":"5. Discussion","content":"\u003cp\u003eBefore discussing the more specific findings, we relate this study to previous research. Some reflections on the entrepreneurial ecosystem emphasise a systems perspective to examine the interactions between different elements. Our study empirically demonstrates the impact of stakeholders in the entrepreneurial ecosystem on EE (Hoppe, \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Bischoff et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Zelin et al., 2021), which echoes extensive previous research on the impact of EE on other elements. Thus, we complement the interactions between EE and other elements of the entrepreneurial ecosystem, i.e. EE as an element of the entrepreneurial ecosystem (Streeter et al., \u003cspan citationid=\"CR98\" class=\"CitationRef\"\u003e2002\u003c/span\u003e; Isenberg, \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). Furthermore, we find that EE after COVID-19 is characterised by R\u0026amp;D transfers, which is broadly consistent with the findings of Zaidi et al. (\u003cspan citationid=\"CR113\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), who found that incubation centres were the reason behind the positive role of the entrepreneurial ecosystem and that entrepreneurship funding was the most important factor for entrepreneurship development. However, at this point, we cannot agree with the findings of Mukesh and RajasekharanPillai (\u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). They argue that the ecosystem is closely related to EE but that finance, infrastructure and university structure play a minimal role in university education. One possible explanation is the timing of the research conducted. As we have said, our study data come from 2021, after COVID-19 had occurred. Therefore, this may be a difference in the factors affecting the development of EE before and after COVID-19. Finally, as Stadler et al. (\u003cspan citationid=\"CR97\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) highlight when comparing Finland and Brazil in terms of differences in the relationship between EE and entrepreneurial ecosystems, the antecedents of EE differ across economies. The details of the findings are discussed next.\u003c/p\u003e \u003cp\u003e \u003cb\u003eConfiguration 1: Policy-project combination configuration\u003c/b\u003e \u003c/p\u003e \u003cp\u003eBased on the configurational results, we can further identify the differential fitness relationships of entrepreneurial ecological elements in driving high performance in EE. Specifically, configuration 1 suggests that for an economy with adequate entrepreneurial policies and finance, if the government provides entrepreneurial programmes and an excellent entrepreneurial infrastructure to facilitate R\u0026amp;D transfer, it can break down barriers to market entry and eliminate the adverse effects of an unmotivated entrepreneurial social culture, thereby contributing to high performance in EE. The core conditions are INF, GOP, EF, and GEP, and the RDT is the complementary condition. This pathway explains about 42% of the high-performance EE cases. In addition, about 10% of high-performance EE cases are defined by this pathway only. Economies that fall under this pathway include France, Switzerland, and Norway (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn France, for example, the French government's ever-growing interest in entrepreneurship has stimulated the creation of a vast number of business schools and university academic programmes over the last five years. At the same time, support for student entrepreneurs (for example, the growing number of incubators in business schools) and the regional administrative level are also on the rise. At the national level, the BPI (Public Bank for Investment) has developed many products, projects, and programmes to support the creation and development of companies. Access to finance is multifaceted in France. Examples include innovative business start-up funds, innovative loan funds, and investments in technological innovation in SMEs. In addition to the French Agency for Technological Innovation, local governments can invest shares in innovative SMEs. In terms of infrastructure, TEG provides offices and premises for student projects. These facilities can be opened at any time if required (Clark et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eEven though most French people consider entrepreneurial ventures unsafe and a career destination for those who cannot find a job, France still has a good ecosystem of innovation and entrepreneurship, which provides a sound basis for technological development and progress. For universities expanding their EE activities and outcomes, the entrepreneurial ecosystem provides a structure to identify and connect with key players and resources. The Entrepreneurial Garden (TEG) at the Business School of Burgundy (BSB) provides a solid case study for this research (Clark et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). TEG is a center for entrepreneurship and small business management established to promote sustainable entrepreneurial growth and applied EE and research. As an open learning environment, the TEG ecosystem provides students with a practice-oriented climate for EE. In this environment, all key players or partners are involved in the interaction of EE to develop entrepreneurial skills, creativity, and business ventures. The TEG rules explicitly support experiential teaching methods in entrepreneurship projects to ensure that the teaching and learning experience focuses on entrepreneurship and business growth, which is closely linked to the TEG vision and strategic themes. In addition, entrepreneurship funding from the BSB and business funding agencies is leveraged in the TEG entrepreneurial ecosystem to increase the visibility and reputation of entrepreneurial outcomes continuously.\u003c/p\u003e \u003cp\u003eTo ensure economies of scale, economies of scope, and increased economic efficiency and prevent inefficient resource allocation or excessive competition, government functionaries regulate market access for firms through approval and registration. On the one hand, Christina and Karim (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) find that under this solid governmental guidance, the French market always maintains a dynamic balance between competition and cooperation; on the other hand, market access is mainly focused on firms. For example, WEF (\u003cspan citationid=\"CR107\" class=\"CitationRef\"\u003e2013\u003c/span\u003e) states that market access is necessary for identifying and evaluating potential target markets and is essential for start-up and growth-stage companies. Based on the current literature, we do not seem to find evidence of a link between market access and EE in France. Therefore, adequate entrepreneurship policies and finance, government entrepreneurship programmes, good entrepreneurial infrastructure, and R\u0026amp;D transfer are sufficient to break down market access barriers and counter the adverse effects of a disengaged entrepreneurial social culture for achieving high performance in EE in France.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eConfiguration 2: Government-project-deficit configuration\u003c/b\u003e \u003c/p\u003e \u003cp\u003eConfiguration 2 reflects another possible pathway to achieving high-performance EE. In a society where entrepreneurship is encouraged, high-performance EE can be achieved by relaxing market entry conditions and encouraging R\u0026amp;D transfer. The policy, finance, and entrepreneurial infrastructures are in place if the government cannot provide entrepreneurial projects. However, it is essential to note that core conditions are SCN, INF, EOE, RDT, GOP, and EF. They are all necessary to make up for the government's lack of entrepreneurial projects. This pathway explains about 55% of the high-performing EE cases. In addition, about 1% of high-performing EE cases can only be explained by this pathway. Economies that fall into this pathway include the UK and the US (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e6\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn Configuration 1, we have explained the importance of historical cultural and social norms and tied them to market access. In environments with a historically negative bias towards entrepreneurship, the development of EE may be culturally challenging and challenging to change. At the same time, institutional deficiencies such as poor regulatory conditions may create insurmountable barriers. This is evident in any entrepreneurial system (Bedő et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). The US has always encouraged entrepreneurship, and its socio-cultural norms are supportive and lenient towards entrepreneurship. As a result, entry into the US market for entrepreneurial ventures is equally easy.\u003c/p\u003e \u003cp\u003eFurthermore, the US is an innovation-driven economy and is considered to have a robust entrepreneurial ecosystem. This is due to the positive effect of the long-standing open capital, labour, and commodity markets on business in the US and solid property rights and a stable government. Nevertheless, another essential factor is that American culture places a high value on self-sufficiency and individual achievement. Entrepreneurs who fit this value (such as Mark Zuckerberg, Bill Gates, and Steve Jobs) are sought after by the public and the media. In this positive entrepreneurial environment and conditions, entrepreneurial projects from non-government sources are more common in the US than those initiated by the government. Many technology development projects in the Entrepreneurship Park at North Carolina State University encourage undergraduate students to participate, providing an EE platform. In addition, the entrepreneurship competition programmes run by universities offer a very effective platform for implementing EE (Jiang et al., \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). As an entrepreneurial ecosystem with different business investments, Silicon Valley, entrepreneurship policy support, and abundant R\u0026amp;D transfer have been sufficient to promote EE.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eConfiguration 3: Policy-deficit configuration\u003c/b\u003e \u003c/p\u003e \u003cp\u003eConfiguration 3 is the final possibility for achieving high-performance EE in this study. Unlike Configuration 2, Configuration 3 shows that in a society where entrepreneurship is encouraged, where the government initiates entrepreneurship projects to facilitate R\u0026amp;D transfer and provides a sound entrepreneurial infrastructure to do so, and where market access conditions are relaxed to expand collaboration and promote competition if social capital turns its focus to such R\u0026amp;D at this time, even if entrepreneurship policies are not in place EE can be developed to a high quality. It is also worth noting that SCN, INF, EOE, RDT, EF, and GEP are all core conditions. One of these is required to address the lack of government entrepreneurship programmes. Similar to Configuration 2, this pathway explains about 55% of the high-performing EE cases. In addition, about 1% of high-performing EE cases can only be explained by this pathway. Economies that fall under this pathway include economies such as Sweden (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e7\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eFirstly, in complete contrast to France, the social culture in Sweden emphasises collectivism, as Sweden is a typical welfare state. The vigorous entrepreneurial activity provides a comprehensive source of tax revenue for policy, and, as a result, entrepreneurship and EE are popular in Sweden. Secondly, the shift towards liberalisation in the Swedish economy over the last half-century has been characterised primarily by an emphasis on free trade and deregulation of the formerly state-monopolised product markets. As a typically small, open economy, Sweden is highly dependent on international trade, and its reliance on exports from the global market is crucial. Furthermore, Swedish companies work more closely with universities than any other European country. Based on a well-established entrepreneurial infrastructure, Sweden invests a high proportion of GDP in R\u0026amp;D. Lerner and Tag (2013) find that Sweden invests far more in R\u0026amp;D than the US; furthermore, Sweden has a sizeable venture capital market (Lerner and T\u0026auml;g, 2013) and the Swedish government invests in supporting academic spin-offs \"(Wennberg et al., \u003cspan citationid=\"CR108\" class=\"CitationRef\"\u003e2011\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eLast but not least, despite its stability, Swedish state policy has undergone a process of change (Bergstr\u0026ouml;m and Styhre, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). The lesson learned by policymakers during the attempts to save the Swedish shipping industry in the 1970s was that it was better for the government not to intervene directly in business, but to act as a market benchmark, providing incentives to participants, for example, by providing insurance and sometimes subsidies for R\u0026amp;D projects, and building partnerships within and between industries. As a result, the development and implementation of the Swedish entrepreneurship policy is primarily a function of the social partners, with the state agencies playing only a supporting role. An advantage of this is that after a crisis such as the COVID-19 epidemic, measures are financed and organised by the social partners, and the government does not need to increase spending on crisis measures. This means that government spending in other areas of social policy is protected, reducing the need for social policy reforms and austerity measures and ensuring the continuation and stability of the welfare state. In short, it is sufficient for the government to provide support for entrepreneurial projects; the other elements, whether funding or policy purposes, are implemented by the social partners instead.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"6. Conclusion","content":"\u003cp\u003eThe COVID-19 epidemic has caused an unprecedented impact on the global economy, putting the already difficult situation of university students' employment to an even more significant challenge. EE is an essential tool to enhance the employability of university students and an effective driver to stimulate entrepreneurial activities. However, entrepreneurship education and training have suffered a severe setback in the epidemic, with GEM 2021/2022 reporting that EE performs the worst in promoting entrepreneurial activity. How to re-energise the role of EE and promote its redevelopment has become an issue to be solved. The conditions that influence the development of EE and their complex interaction have not been explored in depth in existing studies. This paper uses data about the entrepreneurship ecosystem of the 50 economies surveyed in the GEM 2021/2022 report as cases. It uses fsQCA to conduct configuration analysis to examine the pathways by which conditions in the entrepreneurship ecosystem drive the development of EE.\u003c/p\u003e \u003cp\u003eOur research found that, \u003cb\u003efirstly\u003c/b\u003e, none of the states in the entrepreneurial ecosystem alone constitutes a necessary condition for high performance in EE. Instead, three types of configuration include the pathways that drive high-performing EE: the first configuration of INF, GOP, EF, GEP, and RDT; the project-deficit configuration of SCN, INF, EOE, RDT, GOP, and EF; and the policy-deficit configuration of SCN, INF, EOE, RDT, EF, and GEP. The project-deficient and policy-deficient configurations are more general, while the first grouping is more specific. \u003cb\u003eSecondly\u003c/b\u003e, the complementary and substitution relationships between the conditions suggest that the development of global EE under the influence of the COVID-19 epidemic is characterised by a focus on R\u0026amp;D transfer, including the provision of infrastructure and finance for R\u0026amp;D transfer. Under certain endowment conditions, the GOP and GEP can contribute to the development of high-quality EE differently through equivalent substitution. \u003cb\u003eThirdly\u003c/b\u003e, on balance, like a time-sensitive condition, government entrepreneurship programmes are a viable option to activate EE development quickly and effectively.\u003c/p\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003e5.1 Theoretical and Practical Contributions\u003c/h2\u003e \u003cp\u003eThere are three main theoretical contributions of this study. \u003cb\u003eFirstly\u003c/b\u003e, drawing on established findings in EE and using the entrepreneurial ecosystem as a framework based on data from 50 economies, this study enriches the research on the factors that influence the development of EE. While quantitative research is gaining attention in EE, established studies have focused more on the impact of EE on entrepreneurial competencies, entrepreneurial attitudes, and others, and the results of these studies have been mixed. Exploring the antecedent variables of EE would be beneficial in providing a clear framework for continuing this research with ambiguous results. In particular, the development of EE has been severely hindered and challenged in the two years of the COVID-19 epidemic. As such, this study provides a basis for a country-based comparison of the future direction of EE.\u003c/p\u003e \u003cp\u003e \u003cb\u003eSecondly\u003c/b\u003e, this paper empirically explores the entrepreneurial ecosystem framework's concurrent synergies and linkage patterns in promoting the development of EE through a configurational perspective. It further expands the application of the entrepreneurial ecosystem framework in explaining causal complexity, contributing to the development of the theory to a certain extent and providing an empirical basis. Entrepreneurial ecosystem theory in its developmental period emphasises the synergy between elements, but there is only limited evidence of the details that underlie this role. In addition, previous research in EE has been widely confined to the net marginal effect of a single condition in a statistical regression path. Few studies have examined in detail the potential for linkage matching between multiple conditions to influence the development of EE. At the same time, the relative importance of the role of multiple conditions in a comprehensive analytical framework is rarely addressed. Based on this, this study helps to reveal the black box of multiple conditions in the entrepreneurship ecosystem framework in influencing the level of EE development and further deepens researchers' understanding of the complex mechanisms behind the contradictory findings of EE research.\u003c/p\u003e \u003cp\u003e \u003cb\u003eThirdly\u003c/b\u003e, by using the fsQCA approach, this paper has been able to clarify the equivocal driving mechanisms and condition substitution relationships that improve EE performance. It is found that multiple configurations of different conditions at the core of the entrepreneurial ecosystem can achieve high levels of EE in a different ways. Among them, entrepreneurship policy support and government entrepreneurship programmes deserve sufficient attention. They can not only play a combined role of socio-cultural norms and market access alone but can also influence EE in a mutually substitutive manner. This finding enriches the explanatory layers of the entrepreneurial ecosystem framework in the development of EE.\u003c/p\u003e \u003cp\u003eThe findings of this study can provide insights for policymakers and higher education institutions in two ways. \u003cb\u003eOn the one hand\u003c/b\u003e, the existence of concurrent synergies between different elements in the entrepreneurial ecosystem reveals the complexity of EE development. This implies that higher education institutions in different economies can seek and exploit adequate conditions for the development of EE by focusing on adapting multiple conditions within a holistic perspective based on the country's social culture. It is worth noting that entrepreneurship infrastructure and entrepreneurship finance, centered on R\u0026amp;D transfer, are core conditions for EE development. \u003cb\u003eOn the other hand\u003c/b\u003e, the analysis of complementary and alternative relationships suggests that entrepreneurship policy support and government entrepreneurship programmes are important tools for reactivating EE and implementation dilemmas facing the development of EE in the wake of the COVID-19 epidemic. Therefore, it is also possible for policymakers to choose the right context-specific driver for EE to provide an impetus for the revitalisation of entrepreneurial activities and thus the recovery of the post-epidemic economy. In addition, at least one entrepreneurship policy innovation and entrepreneurship project should be on the agenda.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003e5.2 Limitations and further research\u003c/h2\u003e \u003cp\u003e \u003cb\u003eFirstly\u003c/b\u003e, this is a macro-level study, reflecting overall levels across economies. Our understanding of the results may vary between regions within a country. As we are aware, there are differences in the level of development in different regions of each country. Therefore, our interpretation of the results represents only one possibility and has a limited impact on different regions within the same economy. Future research with different data is needed to reveal more possibilities between EE and the entrepreneurial ecosystem. \u003cb\u003eSecondly\u003c/b\u003e, the impact of the COVID-19 epidemic is dynamic, complex, and uncertain, and this study is still a cross-sectional study that does not take into account the diffusion process of fusion histories over time. Future comparative analysis of continuous data could explain the evolution of EE. \u003cb\u003eFinally\u003c/b\u003e, this study explores the combined effects of the antecedents of EE based on a configurational perspective. While this goes some way to compensate for the lack of focus on net effects in previous studies, in-depth longitudinal case studies and other multiple approaches are needed to enrich the explanatory power of our findings in the future.\u003c/p\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003ch2\u003eCompeting interests\u003c/h2\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e\n\u003ch2\u003eEthical approval\u003c/h2\u003e\n\u003cp\u003eThis study uses publicly available third-party data from the Global Entrepreneurship Monitor (GEM) 2021/2022. All data were used in compliance with the terms and conditions of the first author\u0026rsquo;s institution and do not require additional ethical approval. The study was conducted in accordance with the Declaration of Helsinki.\u003c/p\u003e\n\u003ch2\u003eInformed consent\u003c/h2\u003e\n\u003cp\u003eNo animal studies are presented in this manuscript. No human studies are presented in this manuscript. No potentially identifiable human images is presented in this study. Since this dataset is used for scientific purposes and only contains public information from Global Entrepreneurship Monitor (GEM) 2021/2022, it meets all terms and conditions required from the first author\u0026rsquo;s institution.\u003c/p\u003e\n\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\n\u003cp\u003eConceptualization, Investigation, Writing-original draft preparation, Writing-review and editing and Validation: GL, GZ, PW, \u0026amp; YZ; Data curation, Formal Analysis, Software and Methodology: GL; Funding acquisition: PW; Visualization and Project administration: YZ; Resources and Supervision: GZ. All authors have read and agreed to the published version of the manuscript.\u003c/p\u003e\n\u003ch2\u003eAcknowledgement\u003c/h2\u003e\n\u003cp\u003eThis work was supported by the 2022 National Social Science Foundation (22CSH069): Research on the implementation path and social support system of self-entrepreneurship for people with disabilities in the digital era.\u003c/p\u003e\n\u003ch2\u003eData Availability\u003c/h2\u003e\n\u003cp\u003eThe datasets generated during and/or analyzed during the current study are not publicly available for reasons of privacy, but are available from the corresponding author on reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAbdurakhmonov M, Ridge JW, Hill AD, February (2021) Unpacking firm external dependence: how government contract dependence affects firm investments and market performance. 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Entrepreneurship Theory and Practice, p 104225872110181\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZaidi RA, Khan MM, Khan RA, Mujtaba BG (2021) Do entrepreneurship ecosystem and managerial skills contribute to startup development? South Asian Journal of Business Studies, Vol. ahead-of-print No. ahead-of-print. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1108/SAJBS-07-2020-0233\u003c/span\u003e\u003cspan address=\"10.1108/SAJBS-07-2020-0233\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"humanities-and-social-sciences-communications","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"palcomms","sideBox":"Learn more about [Humanities \u0026 Social Sciences Communications](http://www.nature.com/palcomms/)","snPcode":"41599","submissionUrl":"https://submission.springernature.com/new-submission/41599/3","title":"Humanities and Social Sciences Communications","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Nature AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"fsQCA, entrepreneurship education, Entrepreneurial Ecosystem, GEM","lastPublishedDoi":"10.21203/rs.3.rs-8395537/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8395537/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eDespite extensive investigations into the impact of entrepreneurship education (EE), only a few studies have focused on the impact of the antecedents of entrepreneurship education (EE) and their combined effects. Based on an entrepreneurial ecosystem perspective, we concentrate on country-level factors influencing entrepreneurship education (EE, social and cultural norms, infrastructure, ease of entry, R\u0026amp;D transfers, government policy, entrepreneurial finance, government entrepreneurial programs). We applied fsQCA to investigate the impact of the interactions between the above factors on the development of entrepreneurship education (EE). We used a dataset from the latest Global Entrepreneurship Monitor (GEM), including data from 50 economies in the Entrepreneurship Framework 2021. Our findings suggest that the development of global entrepreneurship education (EE) under the COVID-19 was characterised by a focus on R\u0026amp;D transfer, including the provision of infrastructure and funding for R\u0026amp;D transfer. Based on this, three configurational pathways can achieve high performing entrepreneurship education (EE), including a policy-project combination configuration, a policy-deficit configuration and a government-project-deficit configuration.\u003c/p\u003e","manuscriptTitle":"A structure for promoting entrepreneurship education: Understanding the interactions within the entrepreneurial ecosystem","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-01-14 15:20:28","doi":"10.21203/rs.3.rs-8395537/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-03-22T06:10:00+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-03-05T13:13:43+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"29927626768272785543125772837547983691","date":"2026-02-25T15:45:56+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-01-26T06:51:54+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"241452995702442553166098817767214664107","date":"2026-01-15T09:46:33+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"29927626768272785543125772837547983691","date":"2026-01-15T07:11:02+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-01-13T07:47:58+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-01-13T07:44:52+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-01-13T07:33:24+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-01-09T13:15:26+00:00","index":"","fulltext":""},{"type":"submitted","content":"Humanities and Social Sciences Communications","date":"2026-01-09T12:59:40+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"humanities-and-social-sciences-communications","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"palcomms","sideBox":"Learn more about [Humanities \u0026 Social Sciences Communications](http://www.nature.com/palcomms/)","snPcode":"41599","submissionUrl":"https://submission.springernature.com/new-submission/41599/3","title":"Humanities and Social Sciences Communications","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Nature AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"528295a8-c270-4662-9953-e770ab44597c","owner":[],"postedDate":"January 14th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":61044072,"name":"Business and commerce/Economics"},{"id":61044073,"name":"Social science/Economics"},{"id":61044074,"name":"Business and commerce/Information systems and information technology"}],"tags":[],"updatedAt":"2026-04-15T13:53:10+00:00","versionOfRecord":[],"versionCreatedAt":"2026-01-14 15:20:28","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8395537","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8395537","identity":"rs-8395537","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}