Building a bridge: Knowledge sharing flows into entrepreneurial ecosystems

Andrade, Roberta; Pinheiro, Paulo; Carvalho, Luísa Cagica; Rocha, Raysa Geaquinto Article Building a bridge: Knowledge sharing flows into entrepreneurial ecosystems Journal of Open Innovation: Technology, Market, and Complexity Provided in Cooperation with: Society of Open Innovation: Technology, Market, and Complexity (SOItmC) Suggested Citation: Andrade, Roberta; Pinheiro, Paulo; Carvalho, Luísa Cagica; Rocha, Raysa Geaquinto (2022) : Building a bridge: Knowledge sharing flows into entrepreneurial ecosystems, Journal of Open Innovation: Technology, Market, and Complexity, ISSN 2199-8531, MDPI, Basel, Vol. 8, Iss. 3, pp. 1-18, https://doi.org/10.3390/joitmc8030144 This Version is available at: https://hdl.handle.net/10419/274445 Standard-Nutzungsbedingungen: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden. 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If the documents have been made available under an Open Content Licence (especially Creative Commons Licences), you may exercise further usage rights as specified in the indicated licence. https://creativecommons.org/licenses/by/4.0/ Citation: Andrade, R.; Pinheiro, P.; Carvalho, L.; Rocha, R. Building a Bridge: Knowledge Sharing Flows into Entrepreneurial Ecosystems. J. Open Innov. Technol. Mark. Complex. 2022,8, 144. https://doi.org/ 10.3390/joitmc8030144 Received: 12 July 2022 Accepted: 9 August 2022 Published: 13 August 2022 Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. Copyright: © 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/). Journal of Open Innovation: Technology, Market, and Complexity Article Building a Bridge: Knowledge Sharing Flows into Entrepreneurial Ecosystems Roberta Andrade 1, Paulo Pinheiro 1, Luísa Carvalho 2and Raysa Rocha 1,* 1Research Center in Business Sciences (NECE-UBI), University of Beira Interior, 6200-209 Covilhã, Portugal 2Institute Polytechnic of Setúbal, Research Center for Advanced Studies in Management and Economics (CEFAGE), 8005-139 Faro, Portugal *Correspondence: geaquinto.r[email protected] Abstract: Entrepreneurial ecosystems remain under-theorised and conceptually fragmented, making it challenging to comprehend their disposition and performance in the business process. Accordingly, in this research, we explored how knowledge sharing flows through entrepreneurial ecosystems to make analyses and trials to assess new ventures’ creation, continuity, and development opportunities. We carried out a systematic literature review on the Web of Science database. The analysis was carried out in two stages: (i) content analysis using NVivo software and (ii) statistical processing and clustering with the support of VOSviewer and Bibliometrix software. Moreover, we reviewed entrepreneurial literature and proposed conceptual model mapping relations through all main actors and knowledge flow in ecosystems. Our findings suggest the knowledge path in the near field sharing mechanisms resulting in a new conception of traditional structures and relations used to judge and decide how to assess opportunities for new ventures’ opening, maintenance, and growth. This study contributes to entrepreneurial literature, demonstrating knowledge sharing flow through entrepreneurial ecosystems, considering an embracing, dynamic, and multilevel approach. Furthermore, it highlights political and social contributions to include new emergent perspectives: resource scarcity and structural and institutional gaps. This representation is the first knowledge management model applied to different economies and areas, respecting their singularities. Keywords: entrepreneurial ecosystems; knowledge sharing; start-ups; bibliometric analysis; systematic literature review 1. Introduction The concept of entrepreneurial ecosystems (EEs) emerged to clarify the continuum of regions with high-growth entrepreneurship [ 1 , 2 ]. A view widely used in academia to designate an entrepreneurial ecosystem refers to a community with numerous stakeholders that provides a favourable environment for creating and developing new ventures inside a region [ 3 , 4 ]. Along with the broad academic and political interest [ 5 , 6 ], this concept has gained considerable attention among practitioners, researchers, and policy makers [ 6 , 7 ]. The emerging domain of entrepreneurial ecosystems remains unsystematic, unclear, and conceptually fragmented with blurred boundaries [ 2 , 8 ], making it hard to perceive its structure and leverage on the entrepreneurship procedure [ 9 ]. Although these ecosystems emerge as a global phenomenon, research has been largely static and concentrated in central regions of advanced economies, not covering dynamic factors or emerging economies [10–13]. An integrated perspective is a current stage in entrepreneurship policy [ 14 , 15 ], highlighting the purpose of the EE and the mechanisms of how it is sustained, moulded, and developed. This integrated perspective advocates entrepreneurial activity as an individual behaviour of entrepreneurs ingrained in the local matter [ 2 , 8 ] instead of focusing on disconnected entrepreneurial movements [ 7 , 16 , 17 ]. The most theoretical model proposed in the literature is limited to analysing advanced economies and highlighting static attributes focusing on a one-level approach [18]. J. Open Innov. Technol. Mark. Complex. 2022,8, 144. https://doi.org/10.3390/joitmc8030144 https://www.mdpi.com/journal/joitmc J. Open Innov. Technol. Mark. Complex. 2022,8, 144 2 of 18 Further in-depth research is required to provide integrated, active, and multilevel dynamics to capture how knowledge sharing is influenced by resource availability and institutional structures and positions [ 19 ]. Thus, we aim to address the research question: how do knowledge sharing flows extend approaches to understanding entrepreneurial ecosystems? Accordingly, we address the horizontal and vertical interactions by embracing a variety of resources, structures, and institutional gaps. Moreover, understanding EE studies focused on advanced economies emphasising big central areas, proximity to universities, and complex industries, the concept of latecomer regions was proposed for emerging economies and contingency areas [ 20 , 21 ]. This broader perspective leads to a rich stream of discussion on EEs from a knowledge sharing perspective considering some peculiarities linked to resource access, structures, and institutional gaps. In brief, the literature evolution is unsystematic and probably reflects the authors’ hermeneutic biases. Despite the literature continuing to grow to explore distinct research trends and with complexity increasing around 2018 [ 22 – 24 ], there is no systematic analysis exploring the role of knowledge sharing evolution in EEs. With this paper, we aim to fill this gap using bibliometric analysis tools to verify how intellectual bases have evolved. For both experienced researchers and newcomers, our analysis affords a systematic review of literature development plus demanding turning points and emerging interests detailing potential meaningful directions for future research. 2. Background: Knowledge Sharing through Entrepreneurial Ecosystems The interest in entrepreneurial ecosystems has been growing in recent years. Despite the similarities in proximity and territorial delimitation focusing on the entrepreneurial process’ social and economic context, the entrepreneurial ecosystem concept positions the individual entrepreneur as the leading actor [ 25 ]. The definition of entrepreneurial ecosystems appears with Spilling [ 26 ]. The current description found so far is Riaz [ 27 ], who understands entrepreneurial and business ecosystems as linked subsets of a regional economic ecosystem with the commercial exploitation of ideas as complements to organisational assets. Notwithstanding the growing political and academic interest in EEs, the theoretical domain remains emerging, under-theorised, and fragmented [ 11 ]. The proposed conceptual models are primarily based on advanced economies and present challenges in replicating different economies and contexts. Such challenges could be explained by the differences between the logic of resources (provision, access, and mobilisation), interaction logic (regular business process, horizontal network standards and particular structural elements), and governance logic (coordination, alignment of benefits, and commitment) [10]. Given the salient features common to emerging and non-central regions—the scarcity of resources, structural gaps, and institutional gaps [ 28 – 32 ]—an in-depth study on entrepreneurial ecosystems is fundamental to see them in their entirety. Perceived as an intrinsic part of the entrepreneurial ecosystem, start-ups play a fundamental role in the progress of nations. Without these entities, several economies would be subject to lesser exploitation of opportunities in new market niches and minor attraction of investors interested in developing these organisations [ 33 – 35 ]. Thus, the flow of knowledge in start-ups promotes innovation by creating new organisational learning by adopting new collaborative and innovative technologies [36–38]. Start-up regions, commonly referred to as EEs, are global phenomena [ 39 ], and creating new businesses became a crucial part of fostering economic growth and regional development [ 40 ]. The systemic approach to this phenomenon could help reduce the gaps between advanced and emerging economies. While such institutional gaps can hamper economic development, they can also provide opportunities for institutional development [ 41 , 42 ]. Although the growth of EEs in non-central regions and emerging economies has been increasing in the past two decades, little is known about how they operate in these environments [ 43 – 47 ]. These new emerging regions assume an increasingly relevant J. Open Innov. Technol. Mark. Complex. 2022,8, 144 3 of 18 position and demand more attention to their gaps [ 48 – 50 ], going through a transition to an economy based on knowledge, entrepreneurship, and innovation [49–51]. If economic incentives are adequate and institutions and structures are well-positioned early, the knowledge usually follows the desired sharing [ 51 ]. The goals of some actors can cause conflicts and misalignment in the EE. This strife could be addressed by plural coordination through the alignment of needs, regional priorities, mentoring experience, and the combination of entrepreneurs and intermediaries [ 52 , 53 ]. In emerging regions, shortterm economic incentives may even support collective benefits, but long-term stakeholder involvement and commitment are needed [53,54]. Commitment engagements in emerging EEs can be reflected through investor risk sharing, mentor volunteer time, and updating business models focusing on continued innovation [ 48 , 56 ]. Among the gaps, the authors pointed out several restrictions of different natures: institutional regulatory frameworks, market settings, access to financing, establishment and dissemination of knowledge and innovation, formation of entrepreneurial capacities and cultures, lack of effective interaction between educational and research institutions, and entrepreneurs and the fear of failure [ 57 , 58 ]. When neither worked nor properly filled, such gaps can stagnate the evolution of SEs. Stagnation generates adverse effects such as excessive bureaucracy, high taxes, lack of institutional support, informal trade, inconsistencies in articulations, depersonalised networks, limited funding, and difficulties in the internationalisation of new firms [58]. Accordingly, it is necessary to seek an EE management model whose knowledge sharing encourages and ensures new ventures’ creation, survival, and development [59]. 3. Method A systematic literature review protocol was followed to ensure primary research’s greatest strength, synthesis, and reliability in a specific field [ 60 , 61 ]. The review focused on papers about entrepreneurial ecosystems, considering their components, resources, results, and moderating and control variables to reveal their characteristics and specificities and shed light on future research paths. We carried out the data analysis in two stages to identify its intellectual bases and evolution: (i) bibliometric analysis using citation, co-citation, and co-occurrence techniques was performed to explore trends in documents, keywords, authors, and journals with the support of the VOSviewer software version 1.6.17 [ 62 ] and Bibliometrix R-tool [ 63 ]; (ii) qualitative content analysis was performed manually and using the NVivo software to identify turning points, intellectual structure, and how research has evolved. By assessing the comprehensive indicators’ results, biases can be attenuated, increasing the validity of the research and reducing the likelihood of omitting important information about the field [ 64 ]. The systematic literature review protocol and the article selection process are detailed in Table 1. Table 1. Systematic literature review protocol. Step Input Description Search method and scope Database Web of Science—WoS Types of articles Published in journals—full search Scope Topic (title, abstract, and keywords) Web of Science research. Available online: https://www.webofscience.com/wos/woscc/summary/edd0b5 d8-3560-4a91-8e24-7489bc4f6080-346e01e9/relevance/1 (accessed on 2 April 2022). Research equation (“entrepreneur*”) AND [(ecosystem*) OR (eco-system*)] AND [(“startup*”) OR (“start-up*”)] AND (“knowledg*”) Research date 2 April 2022 J. Open Innov. Technol. Mark. Complex. 2022,8, 144 4 of 18 Table 1. Cont. Step Input Description Research selection process Research selection process WoS—4698 articles Records screened Only full article (3476 articles) Only manager, business, and economics categories (2102 articles) Only in English (2029 pieces) Full-text articles assessed for eligibility Manual scrutiny after reading: screen title, abstract, keywords, and conclusions (56 articles) Manual scrutiny after reading method and results sections (52 articles) Studies included in the qualitative synthesis Thoroughly analysed articles (52 articles) Criteria for inclusion Study methods Qualitative, quantitative, and mixed Focus Entrepreneurial ecosystems Years 1900–2022: full search Types of studies Provide a theoretical contribution, both theoretical and empirical studies Exclusion criteria Related to Not related to management, business, or economics Languages English only Types of publications Non-papers Topic parameters Topic field to exclude studies without a primary focus on entrepreneurial ecosystems Unrelated discipline Duplicated studies Pure empirical and descriptive studies with the little theoretical contribution Results and conclusion parameters Studies focused on only one component of the entrepreneurial ecosystem rather than the system all at once Results unavailable electronically or by other reasonable means Studies centred on a large corporation or only innovation alternately entrepreneurship To obtain better results, the search strategy used the "*" with word roots to retrieve singular and plural variations or differences in spelling and endings of words. 4. Bibliometric Results 4.1. Dataset The first analysis provided by Bibliometrix is the description of the metadata about the collection, as shown in Table 2. Next, this report presents a summary of the results collected and analysed. Thus, 128 authors wrote 52 papers which I analysed—only six pieces are single-authored. The descriptive results also indicate the main research types (empirical (43), conceptual (3), and systematic reviews (4)) and research methods (case and multiple case studies (33), regressions (9), factor analysis (3), and mixed and other methods (6)). The main regions investigated were Europe (16), the USA (13), Asia (11), Latin America (9), and others (4). The Europe region includes the U.K., most Asian studies focused on China [ 65 ], and Latin American studies did not focus on more than one country [66,67]. 4.2. Journals Figure 1a,b shows the most relevant network and overlay visualisation journals. The most pertinent network (a) research sources for bibliographic coupling were Small Business Economics and The Journal of Technology Transfer, leading the purple and green clusters. Technological Forecasting and Social Change and Strategic Entrepreneurship Journal led the blue cluster by the same criteria. By the temporal overlap criterion (b), the journals with more recent highlights, from 2020 onwards, were Journal of Entrepreneurship in Emerging Economies and Knowledge Management Research. J. Open Innov. Technol. Mark. Complex. 2022,8, 144 5 of 18 Table 2. Dataset by Bibliometrix. Description Results Main Information About Data Timespan 2015–2021 Sources (journals) 34 Documents 52 Average years from publication 3.33 Average citations per document 36.96 Average citations per year per document 7146 References 3506 Document Type Article 51 Document Contents Keywords plus (ID) 239 Author’s keywords (DE) 190 Authors Authors 128 Authors of single-authored documents 6 Authors of multi-authored documents 122 Author Collaboration Single-authored documents 6 Documents per author 0.406 Authors per document 2.46 Co-authors per documents 2.65 Collaboration index 2.65 J. Open Innov. Technol. Mark. Complex. 2022, 8, x FOR PEER REVIEW 6 of 19 (a) (b) Figure 1. Journals’ bibliographic coupling (VOSviewer). (a) Network visualisation; (b) overlay visualisation. 4.3. Authors Considering the authors and their most relevant publications by network co-citation criteria, we verified the existence of four clusters with sizeable temporal overlap (Figure 2). However, the dark blue cluster, led by Shane (2000) and Eisenhardt (1989), presents the seminal and oldest studies in the area as Schumpeter (1934) and Johanson (1977). The light blue cluster is led by more recent studies: Spigel [65] and Stam (2015). Figure 1. Journals’ bibliographic coupling (VOSviewer). ( a ) Network visualisation; ( b ) overlay visualisation. J. Open Innov. Technol. Mark. Complex. 2022,8, 144 6 of 18 4.3. Authors Considering the authors and their most relevant publications by network co-citation criteria, we verified the existence of four clusters with sizeable temporal overlap (Figure 2). However, the dark blue cluster, led by Shane (2000) and Eisenhardt (1989), presents the seminal and oldest studies in the area as Schumpeter (1934) and Johanson (1977). The light blue cluster is led by more recent studies: Spigel (2017) and Stam (2015). J. Open Innov. Technol. Mark. Complex. 2022, 8, x FOR PEER REVIEW 7 of 19 Figure 2. Network co-citation analysis (VOSviewer). 4.4. Institutions We set the following criteria: minimum of four documents with fractional counting in the bibliographic coupling and co-authorship analysis. Therefore, scientific publications by institutions identified four clusters based on co-authorship (Figure 3). The two largest were led by U.S. and U.K. universities. The blue cluster is formed by North Carolina, Toronto, and Cambridge universities, and the purple one by Indiana, Utrecht, Northumbria, and Tennessee universities. The green cluster was primarily led by Italian universities, namely, the polytechnic universities of Milan, Bologna, Torino, and Bergamo. Figure 3. Network visualisation—institutions’ co-authorships (VOSviewer). 4.5. Countries Figure 4 displays countries’ citations. Countries’ proximity indicates more collaborations with each other, while the size indicates their production. Likewise, their position in the figure reflects their relevance in the international citation structure [59,66]. The USA, England, Italy, Germany, and Spain are the most cited countries in this field. The evolution of colours, from dark blue to yellow, shows the emergence of quotes from some countries. America, Europe, and Australia had great prominence between 2016 and 2018. On the other hand, some emerging Latin, African, and Middle Eastern economies have shown nascent significance from 2019 onwards. Figure 2. Network co-citation analysis (VOSviewer). 4.4. Institutions We set the following criteria: minimum of four documents with fractional counting in the bibliographic coupling and co-authorship analysis. Therefore, scientific publications by institutions identified four clusters based on co-authorship (Figure 3). The two largest were led by U.S. and U.K. universities. The blue cluster is formed by North Carolina, Toronto, and Cambridge universities, and the purple one by Indiana, Utrecht, Northumbria, and Tennessee universities. The green cluster was primarily led by Italian universities, namely, the polytechnic universities of Milan, Bologna, Torino, and Bergamo. J. Open Innov. Technol. Mark. Complex. 2022, 8, x FOR PEER REVIEW 7 of 19 Figure 2. Network co-citation analysis (VOSviewer). 4.4. Institutions We set the following criteria: minimum of four documents with fractional counting in the bibliographic coupling and co-authorship analysis. Therefore, scientific publications by institutions identified four clusters based on co-authorship (Figure 3). The two largest were led by U.S. and U.K. universities. The blue cluster is formed by North Carolina, Toronto, and Cambridge universities, and the purple one by Indiana, Utrecht, Northumbria, and Tennessee universities. The green cluster was primarily led by Italian universities, namely, the polytechnic universities of Milan, Bologna, Torino, and Bergamo. Figure 3. Network visualisation—institutions’ co-authorships (VOSviewer). 4.5. Countries Figure 4 displays countries’ citations. Countries’ proximity indicates more collaborations with each other, while the size indicates their production. Likewise, their position in the figure reflects their relevance in the international citation structure [59,66]. The USA, England, Italy, Germany, and Spain are the most cited countries in this field. The evolution of colours, from dark blue to yellow, shows the emergence of quotes from some countries. America, Europe, and Australia had great prominence between 2016 and 2018. On the other hand, some emerging Latin, African, and Middle Eastern economies have shown nascent significance from 2019 onwards. Figure 3. Network visualisation—institutions’ co-authorships (VOSviewer). 4.5. Countries Figure 4displays countries’ citations. Countries’ proximity indicates more collaborations with each other, while the size indicates their production. Likewise, their position in the figure reflects their relevance in the international citation structure [ 62 – 64 , 68 ]. The J. Open Innov. Technol. Mark. Complex. 2022,8, 144 7 of 18 USA, England, Italy, Germany, and Spain are the most cited countries in this field. The evolution of colours, from dark blue to yellow, shows the emergence of quotes from some countries. America, Europe, and Australia had great prominence between 2016 and 2018. On the other hand, some emerging Latin, African, and Middle Eastern economies have shown nascent significance from 2019 onwards. J. Open Innov. Technol. Mark. Complex. 2022, 8, x FOR PEER REVIEW 8 of 19 Figure 4. Overlay visualisation of countries’ citations (VOSviewer). 4.6. Keywords Co-occurrence analysis of the authors’ keywords is valuable for identifying clusters. We used all 190 keywords and the complete count method. Thus, the study, by association, provided four clusters of temporal overlap, using non-normalised scores, shown in Figure 5a,b, with rotation differences. The software specified 2016 to 2020 as the default criteria because it displays a more colourful image, allowing a comprehensive temporal analysis. In 2016, the field of study focused on themes related to universities and generated opportunities, such as spin-offs, patents, resources, and technology transfer. (a) (b) Figure 5. Co-occurrence analysis of authors’ keywords (VOSviewer). (a) Horizontal rotation; (b) vertical rotation. The keyword entrepreneurship, which reached its peak in 2017, began to be associated with taxonomies and life cycles of start-ups and small businesses and gave rise to two new concepts. University entrepreneurship emerged along with entrepreneurship education and learning related to higher education. Social entrepreneurship is related to business models and sustainability, primarily in emerging economies. As of 2019, researchers began to focus their studies of entrepreneurial ecosystems on emerging themes related to digital technologies and platforms, sustainable development, and frugal innovation. Figure 4. Overlay visualisation of countries’ citations (VOSviewer). 4.6. Keywords Co-occurrence analysis of the authors’ keywords is valuable for identifying clusters. We used all 190 keywords and the complete count method. Thus, the study, by association, provided four clusters of temporal overlap, using non-normalised scores, shown in Figure 5a,b, with rotation differences. The software specified 2016 to 2020 as the default criteria because it displays a more colourful image, allowing a comprehensive temporal analysis. In 2016, the field of study focused on themes related to universities and generated opportunities, such as spin-offs, patents, resources, and technology transfer. J. Open Innov. Technol. Mark. Complex. 2022, 8, x FOR PEER REVIEW 8 of 19 Figure 4. Overlay visualisation of countries’ citations (VOSviewer). 4.6. Keywords Co-occurrence analysis of the authors’ keywords is valuable for identifying clusters. We used all 190 keywords and the complete count method. Thus, the study, by association, provided four clusters of temporal overlap, using non-normalised scores, shown in Figure 5a,b, with rotation differences. The software specified 2016 to 2020 as the default criteria because it displays a more colourful image, allowing a comprehensive temporal analysis. In 2016, the field of study focused on themes related to universities and generated opportunities, such as spin-offs, patents, resources, and technology transfer. (a) (b) Figure 5. Co-occurrence analysis of authors’ keywords (VOSviewer). (a) Horizontal rotation; (b) vertical rotation. The keyword entrepreneurship, which reached its peak in 2017, began to be associated with taxonomies and life cycles of start-ups and small businesses and gave rise to two new concepts. University entrepreneurship emerged along with entrepreneurship education and learning related to higher education. Social entrepreneurship is related to business models and sustainability, primarily in emerging economies. As of 2019, researchers began to focus their studies of entrepreneurial ecosystems on emerging themes related to digital technologies and platforms, sustainable development, and frugal innovation. Figure 5. Co-occurrence analysis of authors’ keywords (VOSviewer). ( a ) Horizontal rotation; ( b ) vertical rotation. The keyword entrepreneurship, which reached its peak in 2017, began to be associated with taxonomies and life cycles of start-ups and small businesses and gave rise to two new concepts. University entrepreneurship emerged along with entrepreneurship education and learning related to higher education. Social entrepreneurship is related to business J. Open Innov. Technol. Mark. Complex. 2022,8, 144 8 of 18 models and sustainability, primarily in emerging economies. As of 2019, researchers began to focus their studies of entrepreneurial ecosystems on emerging themes related to digital technologies and platforms, sustainable development, and frugal innovation. 5. Thematic Analysis The bibliometric analysis results support the thematic discussion in this section. The software NVivo 11 (QSR International, Massachusetts, US) [ 69 ] was used to analyse the co-occurrence of words between articles, as shown on Figure 6, to identify the main subjects in each cluster listed in the next section to help interpret the similarities of themes inside sets and their differences. Moreover, qualitative content analysis was performed to diagnose turning points, intellectual arrangement, and how the research has evolved. J. Open Innov. Technol. Mark. Complex. 2022, 8, x FOR PEER REVIEW 9 of 19 5. Thematic Analysis The bibliometric analysis results support the thematic discussion in this section. The software NVivo 11 (QSR International, Massachusetts, US) [67] was used to analyse the co-occurrence of words between articles, as shown on Figure 6, to identify the main subjects in each cluster listed in the next section to help interpret the similarities of themes inside sets and their differences. Moreover, qualitative content analysis was performed to diagnose turning points, intellectual arrangement, and how the research has evolved. Figure 6. Word cloud (NVivo). 5.1. Entrepreneurial Ecosystems Components Development In this cluster, the studies are supported by field theories, socially situated entrepreneurial knowledge, knowledge spillover, resources, and constraints. The research focused on the relationships between the attributes that form EEs using clusters and regional innovation systems’ concepts. Moreover, the main objective is to measure these relationships’ causes, effects, and efficiency to identify the strong and weak links in the entrepreneurial ecosystem [68–70]. While some studies focus on identifying how resources flow between EE structures [30,71,72], others focus on understanding the differences between the relationships and distribution of resources in EEs over clusters and innovation systems [73,74]. The cluster presents research analysing at micro, meso, and macro levels with many empirical and qualitative studies, enabling a better reading and understanding of the EE formation process. Then, at the micro-level, the authors researched the founders of startups, mentors, and small enterprises in different studies, focusing on each one of the arrangements, desires, and abilities [75–77]. At the meso level, EE connection, development, coordination, selection expertise, and key and intermediary actors were analysed [78,79]. Furthermore, ecosystems were researched based on their territoriality at the macro level to understand how entrepreneurial education and policies to promote entrepreneurship and new firms are stimulated and developed [80–82]. By systematising the EE formation process, the authors seek to understand: (i) how resources flow and are produced internally, (ii) how successful and unsuccessful experiences are reused, and (iii) how the connections created by local entrepreneurs attract resources, people, and new ventures. Therefore, the EE is divided into inputs (financial, institutional, social, and knowledge capital), outputs (gross entrepreneurship, based on performance and based on productivity), and results (diffusion of new business models). Then, the relationships were framed considering processes (knowledge sharing and spillover, new scalable businesses, and testing of experimental business models), structures (digital and spatial resources and nature and lack of opportunities), and contingencies (policies and regulations, culture) [30,65,69,83]. The structural analysis of the EEs also presents the intermediaries as important disseminators of knowledge, since they connect industries and organisations in a specific context through sharing information, experiences, and understanding. Among the intermediaries listed are mediators (responsible for informal relationships), relational ones (in Figure 6. Word cloud (NVivo). 5.1. Entrepreneurial Ecosystems Components Development In this cluster, the studies are supported by field theories, socially situated entrepreneurial knowledge, knowledge spillover, resources, and constraints. The research focused on the relationships between the attributes that form EEs using clusters and regional innovation systems’ concepts. Moreover, the main objective is to measure these relationships’ causes, effects, and efficiency to identify the strong and weak links in the entrepreneurial ecosystem [70–72]. While some studies focus on identifying how resources flow between EE structures [ 73 , 74 ], others focus on understanding the differences between the relationships and distribution of resources in EEs over clusters and innovation systems [75–77]. The cluster presents research analysing at micro, meso, and macro levels with many empirical and qualitative studies, enabling a better reading and understanding of the EE formation process. Then, at the micro-level, the authors researched the founders of start-ups, mentors, and small enterprises in different studies, focusing on each one of the arrangements, desires, and abilities [ 78 – 80 ]. At the meso level, EE connection, development, coordination, selection expertise, and key and intermediary actors were analysed [ 81 , 82 ]. Furthermore, ecosystems were researched based on their territoriality at the macro level to understand how entrepreneurial education and policies to promote entrepreneurship and new firms are stimulated and developed [83–85]. By systematising the EE formation process, the authors seek to understand: (i) how resources flow and are produced internally, (ii) how successful and unsuccessful experiences are reused, and (iii) how the connections created by local entrepreneurs attract resources, people, and new ventures. Therefore, the EE is divided into inputs (financial, institutional, social, and knowledge capital), outputs (gross entrepreneurship, based on performance and based on productivity), and results (diffusion of new business models). Then, the relationships were framed considering processes (knowledge sharing and spillover, new scalable businesses, and testing of experimental business models), structures (digital and spatial resources and nature and lack of opportunities), and contingencies (policies and regulations, culture) [31,71,86,87]. The structural analysis of the EEs also presents the intermediaries as important disseminators of knowledge, since they connect industries and organisations in a specific context J. Open Innov. Technol. Mark. Complex. 2022,8, 144 15 of 18 14. Bosma, N.; Stam, E.; Schutjens, V. Creative Destruction and Regional Productivity Growth: Evidence from the Dutch Manufacturing and Services Industries. Small Bus. Econ. 2011,36, 401–418. [CrossRef] 15. 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