Entrepreneurial ecosystems: analysing the status quo

ABSTRACT Entrepreneurial ecosystem is an emerging field that prioritises in-depth discussions of the sustainable development of entrepreneurship. Scientometric analysis of the results of entrepreneurial ecosystem research helps understand the dynamics and development trends, providing new ideas for research on the sustainable development of entrepreneurial activities. This study conducts a quantitative examination of the development status of entrepreneurial ecosystem research using scientometric analysis and 286 articles focusing on entrepreneurial ecosystems. We identified the most influential institutions, authors, journals, references, betweenness centrality, as well as disciplines and topics in the field. Our paper summarises the literature on the entrepreneurial ecosystem from the perspective of scientometrics, analyses the research dynamics, and provides a foundation for future research on entrepreneurial ecosystems.


Introduction
The entrepreneurial ecosystem which introduces ecological thought into the human socioeconomic order is a new direction in entrepreneurial research. It refers to an interconnected group of actors in a local geographic community committed to sustainable development through the support and facilitation of new sustainable ventures (Cohen, 2006). Accompanied by the evolution of entrepreneurial behaviour, entrepreneurial ecosystem research involves the dual areas of ecologically sustainable development and entrepreneurial management. As an emerging area in entrepreneurial research, the topic of entrepreneurial ecosystem must traverse a long journey before it officially enters the research field. Academic history has witnessed, two waves in the introduction of ecological thought into the study of human social order. The first wave occurred in the first half of the twentieth century. The concept of ecology was applied to management, economics, and sociology. Tansley (1935) was the first ecologist to propose the concept of ecosystem: he introduced "system" into ecological research, which defines ecosystem elements as a collection of complex organisms and habitat-related environments. The second wave, which started from Michael and John (1977), systematically summed up the relationship between organisational type and environment from the perspective of organisational ecology (Michael & John, 1977). Scholars have simulated and empirically explored the competition and cooperation between population evolution and inter-industry. Ecological population theory is used to discuss the development path of environment through industrial development (Hannan, Dundon, Carroll, & Torres, 1995). Research on such organisational evolution has similar conclusions to that on the evolution of natural ecology, and scholars have applied it to in-depth research. With the development of society and an increasingly complicated economic structure, traditional entrepreneurial management theory cannot explain real-world dynamic entrepreneurial activities any longer. Ecological theory can use biological genetics, variations, and selection mechanisms to compare the entrepreneurial ecosystem with the natural ecosystem, and present a better dynamic research framework for sustainable development of the entrepreneurial ecosystem. Although a new field, research on the entrepreneurial ecosystem has been rapidly progressing, particularly in terms of defining the significance of the entrepreneurial ecosystem and evaluating the elements and functional characteristics of the entrepreneurial ecosystem.
As a new research field, there is significant interest in the development of the entrepreneurial ecosystem. Owing to the novelty of the topic, there were only 286 records of entrepreneurial ecosystem in the Web of Science Core Collection before March 2019, excluding Current Chemical Reactions (CCR-EXPANDED) and Index Chemicus (IC). However, it is obvious that the number of studies is rising, as Figure 1 shows.
Besides, more scholars from different countries are participating in related research. Authors from the USA, England, Italy, Germany, Netherlands, Canada, Scotland, France, Australia, and China have contributed the most to achievements in the field. Therefore, the entrepreneurial ecosystem can exist in societies with different polities, economies, and cultures, and has high research value. Figure 2 shows the proportion of different countries/regions in the overall literature on entrepreneurial ecosystem.
However, most of this literature is relatively new, without systematic bibliometric analysis (e.g., Ferreira et al., 2019;Vallaster et al., 2019). For a better understanding of the entrepreneurial ecosystem and the state of development of related research, this study conducts a scientometric analysis. Section 2 presents an introduction to the entrepreneurial ecosystem. Besides, the scientometric method is introduced, and the data source is confirmed. Section 3 then analyses the most influential institutions, authors, journals, references, betweenness centrality, as well as disciplines and topics.
2. Introduction to the research on entrepreneurial ecosystem 2.1. Definitions Spigel (2017) believes that entrepreneurial ecosystems represent the types of cultural, social, economic, and political environments within a region that supports high-growth entrepreneurship. Moreover, they are seen as part of a regional economic development strategy based on creating supportive environments that foster innovative start-ups (Spigel, 2017). Scholars of the entrepreneurial ecosystem examine its connotation, especially the ecological characteristics of its sustainable development and network characteristics as a system of entrepreneurial management. First, the ecological characteristics of entrepreneurial ecosystem are reflected in sustainable development with a spontaneous cycle. Spilling is the first author who proposed the concept of entrepreneurial ecosystem and incorporated entrepreneurial activities in the market into a systematic system for research (Spilling, 1996). Moreover, the entrepreneurial ecosystem was considered a three-part ecological environment in which relevant entities within a certain region play a decisive role in entrepreneurial activities. From the perspective of the university, Dunn puts forward the basic outline of the entrepreneurial ecosystem and interprets the example of the Massachusetts Institute of Technology to understand the concept of the entrepreneurial ecosystem (Katharine, 2005). He believes that many organisations foster entrepreneurship within the entrepreneurial ecosystem. The entrepreneurial ecosystem has a spontaneous cycle, which is similar to the natural ecosystem, and can embody  sustainability. Grobbelaar defines the entrepreneurial ecosystem as an ecosystem of synergistic symbiosis between science and technology, as well as innovation units and other innovative species, communities, and innovation environments (Sara, 2018). Promoting the development of the entrepreneurial ecosystem may benefit the local community (Sara, 2018). Second, defining the entrepreneurial ecosystem from the network perspective embodies the network system as a network resource allocation system. The entities are combined into an interdependent complementary system. The government and other powerful actors do not establish the entrepreneurial ecosystem; it is created by a network system formed by the daily interaction of common goals, resources and the main body of the infrastructure (Acs, Estrin, Mickiewicz, & Szerb, 2018;Thompson, Purdy, & Ventresca, 2017). The focus in this definition is on the system stakeholders. The entrepreneurial ecosystem is a collaborative network of stakeholders in a specific entrepreneurial environment, in which entrepreneurial ecosystem participants, stakeholders, and socioeconomic and political agents are playing the same important roles in creating economic value and solving social problems (Ben & Richard, 2018;Sarma & Sunny, 2017). The mutual competition and interests of multiple stakeholders embedded in the entrepreneurial ecosystem can aid in the emergence, prosperity and disappearance of the entrepreneurial ecosystem, and contribute to the sustainable development of the regional entrepreneurial ecosystem, thus achieving the competitive aspects of sustainable economic growth (Carayannis, Grigoroudis, Campbell, Meissner, & Stamati, 2018;Jung, Eun, & Lee, 2017).

Evaluation research on elements of entrepreneurial ecosystem
This aspect of study is an extension of the initial hierarchy of elemental structure classifications (Cohen, 2006) to measure and test the causal effects of ecological elements on entrepreneurial ecological output (Martha & Nataliya, 2018;Nicotra, Romano, Giudice, & Schillaci, 2018). Research on the entrepreneurial ecosystem has largely taken a macro-perspective to map the determinants of the entrepreneurial ecosystem better while ignoring the research on its systemic elements (Cunningham, Menter, & Wirsching, 2019). The research may only involve a brief description of the successful region, but it cannot be summarised. Therefore, the ecological elements of the entrepreneurial system can be used to measure the indicators of system health assessment more comprehensively. The key elements of the entrepreneurial ecosystem can identify emerging economies through system elements recognition, control key technologies and future market users, such as investment capital, skilled workers and entrepreneurial knowledge (Ferràs-Hernández, Tarrats-Pons, & Arimany-Serrat, 2017;Spigel & Harrison, 2017). The interactive research in technical and network elements has gradually become a new research field of entrepreneurial ecosystem. Considering the nonequilibrium among the elements of the entrepreneurial ecosystem, the evaluation of its elements must focus on synergistic symbiosis and the spiralling of stakeholders within the system (Carayannis et al., 2018;Miller, Puthusserry, McAdam, Moffett, & Alexander, 2016). In the process of factor evaluation, Auerswald combines the ecological characteristics of ecosystem and uses ecosystem vitality indicators to assess the vitality and development trajectory of a regional entrepreneurial ecosystem (Auerswald & Dani, 2017).

Functional characteristics of the entrepreneurial ecosystem
The inductive functional characteristics of entrepreneurial ecosystem focus on knowledge spillover function, incubator function, sustainable development function, and innovative function. The first function is knowledge spillover. Schillo (2016) believes that research-based derivatives (RBSO) as agents of the entrepreneurial ecosystem determine the function of the knowledge spillover effects of the derivative company's on the entrepreneurial ecosystem and analyses the method to sustain economic growth and knowledge usage. The spilled knowledge ecosystems lead to the emergence of technology platforms, and then the platforms react upon the entrepreneurial ecosystems and provide the resources they need (Attour & Lazaric, 2018;Schillo, 2016). The second function is to be an incubator. Colleges and universities engage in entrepreneurship and commercialisation, not only for students but also for both students and teachers. Heodoraki analyses an important element of entrepreneurial supportthe university business incubatorwhich plays an important role in the underlying entrepreneurial ecosystem in keeping the university sustainable (Heodoraki, Messeghem, & Rice, 2018;Huang-Saad, Fay, & Sheridan, 2017). The third function is innovation. Scholars define the entrepreneurial ecosystem while focusing on its innovative functions (Chao & Guan, 2017). Research on the entrepreneurial ecosystem leads to emerging technologies and their integration into complex technology systems and affects the new entrepreneurial ecosystem (Antonio, Ha-Joon, Sue, & Alan, 2018;Laguir & Den, 2016;Salter & McKelvey, 2016). Moreover, innovation is reflected in available assets, freedom and diversity as a frontier of the entrepreneurial ecosystem (Miller & Acs, 2017). The fourth function is that of sustainable development. Audretsch builds complex models based on entrepreneurial, regional and sustainability indices, and explores six areas of entrepreneurship (Audretsch & Belitski, 2017). Erina establishes an ecosystem model that contributes to regional sustainable development planning and design (Erina, Shatrevich, & Gailesarkane, 2017). The theory of sustainable development based on resource symbiosis is an important function of the entrepreneurial ecosystem. The emergence and sustainable diffusion of social enterprises in the entrepreneurial ecosystem help the local entrepreneurial market satisfy the needs of self-circulation (Debnath & Bardhan, 2018;Mcmullen, 2018). The positive effects of the ecosystem on sustainable business models involve the impact of organisational-level factors (including the type of risk and duration of venture investment) and individual-level factors (including types of network participants and their demographics) of social network connectivity. Thus, risk types and social network become a new element that affects the sustainability of the entire entrepreneurial ecosystem (Mustonen, Matinmikko, Holland, & Roberson, 2017;Neumeyer & Santos, 2018).
In general, the ecological characteristics of sustainable development and the system network characteristics of enterprise management have become a significant direction in the defining entrepreneurial ecosystem research. Stakeholders in the entrepreneurial ecosystem form a synergy in the entrepreneurial environment. Research has expanded from the initial classification of elemental structures to the measurement and testing of the causal effects of ecological factors on the ecological output of the entrepreneurial ecosystem. The dynamic function of the development and self-circulation system ultimately promotes the sustainable development of the entire entrepreneurial ecosystem.

Method and data
This study uses scientometric methods to analyse the cooperation network and research hotspots of entrepreneurial ecosystem research, which is common in the field of science research management (Chowdhury, 1998;Ying, 2011). A knowledge map is a scientometric tool, a type of measurement graph that shows the relationship between the scientific knowledge development process and structure. Chen Chaomei developed the Citespace information visualisation software using Java (Li & Chen, 2017). Citespace can analyse the subject field evolution and scholar cooperation path through citation network and cooperative network visualisation, as well as find the turning point of high mediation centre, analyse evolutionary trends, and predict research frontiers (Chen, 2006). Citespace can transform the literature data into a knowledge map. It can display the size of the network node according to the frequency of the article. Each cluster formed by the nodes is composed of the cooperation or citation behaviour of a group of scientists with the same or similar research directions. This study primarily uses Citespace to explore and analyse the evolution, status, and frontier of entrepreneurial ecosystem research.
This study selects 286 articles from the Web of Science Core Collection from 2000 to 2019 (by March), excluding CCR-EXPANDED and IC, as CCR-EXPANDED and IC are for chemical indexes which are not related to entrepreneurial ecosystem research. A visual analysis confirms that the literature has a high level of academic value and strong representation. Since the entrepreneurial ecosystem is a relatively new research direction, this study also considers the CPCI-S database, because conference papers sometimes have high novelty.

Institution cooperation network analysis
The entrepreneurial ecosystem is a new field of research; therefore, the total number of research institutions is not large. Because there is no clear classification of their exploration, the links between institutions are not particularly obvious. Figure 3 shows the institution cooperation network for entrepreneurial ecosystem research. The larger the node, the more research results the institution has. The different colours in the nodes indicate the different years in which the new research occurred; a cluster is obvious among them. It includes Alpen Adria Univ Klagenfurt, George Washington University, Natl Res Univ, Tech Univ Crete, and Univ Appl Arts Vienna. These five institutions have relatively close cooperation in this field. According to the size of the node, the top institutions with the most research results are as follows: Indiana Univ, Univ Cambridge, Univ Utrecht, Univ Edinburgh, Arizona State Univ, Univ N Carolina, Univ Glasgow, George Mason Univ, Northumbria Univ, London Sch Econ, and Stanford Univ.

Author co-citation analysis
Co-citation analysis means that two documents appear in the same bibliography of the third citing document, and the two documents form a co-citation relationship (Li & Chen, 2017). Author co-citation analysis extracts author information from the entire bibliography. It can not only calculate the author's distribution in a certain field, identify scholars with influence in the field but also excavate research topics. By analysing the author's common reference network and clusters, it identifies the distribution of similar authors and their subject areas in a certain field (e.g., Kraus et al., 2012). Figure 4 shows the network of author co-citation. Nodes are coloured and labelled with different clusters, and the most cited authors are marked with black text. Six clusters are indicated in with red text. They are entrepreneurial university, entrepreneurial ecosystem, market and demand creation, entrepreneurial performance, collaboration, business models, and mitigation banking. It means that the co-cited authors' published work is mainly focused on the above six topics. Table 1 lists the authors who are cited the most. Audretsch DB is the author of the highest frequency of citations in Cluster  #3 and the most cited author in the field of entrepreneurial ecosystem. Therefore, Audretsch DB is the most recognised author in entrepreneurial performance research. Etzkowitz H has the highest citation frequency in Cluster #0, indicating that the scholar has made a great contribution to entrepreneurial ecosystem research. By analogy, it is possible to identify other authors who have made outstanding contributions in the field of entrepreneurial ecosystem research and whose achievements have been widely accepted and cited.

Journal co-citation analysis
The journal co-citation analysis is similar to the author co-citation analysis. It is used to establish a co-citation network from the source of the literature extracted from the references. The journal co-citation network shows the distribution of important knowledge sources in a field, which can help scholars identify the most cited journals in the field, and the links between these journals. Figure 5 is the dual-map overplay of journal cocitation. The nodes on the left are citing journals, which are the application area of entrepreneurial ecosystem, and those on the right are cited journals, which are the foundation of entrepreneurial ecosystem. The curves indicate the relationship between citations and the strength of the curves indicates related to the number of citations. The vertical axis of the ellipse represents the number of papers, and the horizontal axis represents the number of authors. It is obvious that the most cited documents and cited authors are the largest oval in the lower right corner, and the journal is Research Policy. Table 2 lists the clusters of journal co-citations and the top co-cited journals in each cluster. The closer the silhouette value is to 1, the higher it reflects the homogeneity of the network. When the value is greater than 0.5, the clustering result is reasonable; when the value is greater than 0.7, the clustering result has high reliability. Therefore, all the clusters in Table 2 are reasonable. Each cluster is labelled by keywords of citing articles in the cluster. The subsequent discussion uses the labels chosen by the log-likelihood ratio test method. The largest cluster is #0, which is labelled education. The silhouette value is 0.687, and the most cited articles are from Research Policy. Therefore, Research Policy contributes the most cited articles. The second is labelled universities, with a size of 24; Cluster #2 is labelled collaboration, with a size of 24; biopharmaceutical  , 2015  3  60  Anonymous, 2011  6  59  Acs ZJ, 2015  3  55  Autio E, 2016  5  49  Etzkowitz H, 2013  0  47  Shane S, 2014  5  47  Isenberg DJ, 2014  3  45  Adner R, 2008  4  44 Stam E, 2017 1 Figure 5. Dual-map overplay of journal co-citation. networks is the fourth, with a size of 21; universitybusiness cooperation is the label of Cluster #4 and mitigation banking is the label of Cluster #7.

Document co-citation analysis
Because entrepreneurial ecosystem is an emerging research field, this study sets one year as a time slice and selects all the articles from each time slice, from 2000 to 2019, to construct an individual network. All the individual networks are linked to obtain an overview of the whole development period. The study inserts all these references into the data set. If the same article cites two papers, they are considered relevant. The link between them reflects their frequency of citations by the same article. The nodes in Figure 6 correspond to different publications, displayed using the three-ring method. The size of the node can reflect the number of times the node is cited or appears. The annual ring of the node represents the number of papers published in different years. The wider the annual ring of a year, the greater the frequency of quotation or appearance in the corresponding year. Figure 6 presents an overview of the network of knowledge map of the entrepreneurial ecosystem. The co-cited references are connected tightly in the same cluster with the same colour. The top largest clusters are entrepreneurial ecosystem, collaboration, entrepreneurship, entrepreneurial university, peripheral region, innovation policy, disruptive innovation, market and demand creation,and incubators. Table 3  grow organically; reforming legal, bureaucratic, and regulatory frameworks. It emphasised the importance of considering the overall entrepreneurial ecosystem. Table 3 shows that articles from Clusters #0-#2 occupy most of the top-cited references, especially Cluster #0. Tables 4-6 list the key authors of Clusters #0-#2 to indicate their research focus. This study selects three cited references with most cites and three citing articles with the highest coverage. Table 4 shows that three major citing articles, in terms of their citation coverage, are all published in 2017, which means Cluster #0 experienced rapid development in 2017. The article 'Examining the connections within the startup ecosystem: a case study of St. Louis' has the highest coverage. This article critically examines the structure of entrepreneurial ecosystem using an exploratory and bottom-up approach, and applies a social network approach by analysing the connections of the ecosystem over multiple layers (Motoyama & Knowlton, 2017).
As Table 3 shows, it is not difficult to find that articles in Cluster #1 account for nearly half of the top 10. This indicates that most people have focused on and cited the research of Cluster #1, and it has great academic value, which is probably the basis study of entrepreneurial ecosystem. Table 5 shows the top three cited references and citing articles in Cluster #1. The article 'Business ecosystem and new venture business models: an exploratory study of participation in the lead to win job-creation engine' has the highest coverage value. Table 6 lists three articles that have the highest citation and coverage value in Cluster #2. The highest coverage value of a citing paper is 51%; over half of the cited references in the article 'Looking inside the spiky bits: a critical review and conceptualization of entrepreneurial ecosystem' are related to this cluster. Therefore, there is high consistency between them. Notably, the citing documents with the highest coverage values in Cluster #0 and Cluster #2 both have the following article: 'The relational organization of entrepreneurial ecosystem'. Thus, this article is the important intersection between these two clusters. The article argues that ecosystems are composed of 10 cultural, social, and material attributes that provide benefits and resources to entrepreneurs and the relationships between these attributes reproduce the ecosystem (Spigel, 2017). Looking back at Clusters #0-#2, the most widely cited articles essentially appeared in 2017, which shows that 2017 is the burst year of the research results.

Disciplines involved in entrepreneurial ecosystem
As an emerging direction in the field of entrepreneurial research, the disciplines involved in the entrepreneurial ecosystem are relatively concentrated. Figure 7 shows the top 10 disciplines involved in the entrepreneurial ecosystem. They are business & economy, management, business, engineering, economics, environmental sciences &   ecology, public administration, regional & urban planning, environmental studies,and engineering & industrial. Figure 8 shows the timeline of entrepreneurial ecosystem research. It shows that the five most important clusters can be summarised as entrepreneurial individual, business plan, mitigation banker, disaster mitigation and patient capital. Ecology and environment science is the earliest discipline involved in the entrepreneurial ecosystem. The nodes with the purple outer circle indicate that the literature on these areas contributes the most to entrepreneurial ecosystem research. Figure 8 shows nodes with purple circles as follows (arranged by time): management, business and economics, engineering, science and technology, computer science. With development, the research in entrepreneurial ecosystem has gradually appeared in several new fields in the past three years as follows: computer science, education & educational research, public administration, information science & library science, communication,operations research & management science, geography,and business and finance. Table 7 shows the top 10 specific citation counts; half of them are related to Cluster #1 and last for a long time. Therefore, Cluster #1 plays the most important role in the entrepreneurial ecosystem. The lasted most cited fields concentrate in Cluster #3. Generally, Cluster #3 gathers emerging important fields.

Topics involved in entrepreneurial ecosystem
The topics involved in entrepreneurial ecosystem can be delineated in terms of the keywords assigned to each article in the dataset. Figure 9 highlights the cluster result   Furthermore, the focus of the existing results cannot form a normative branch. There is no dark connection between the keywords, indicating that research has been concentrated in recent years, further verifying the innovation in this field. Table 9 lists the highest-covered articles in every cluster. Cluster #1 is the largest cluster, and the most related reference is Mission impossible? Entrepreneurial universities and peripheral regional innovation systems. Besides, the clustering effect of Cluster #5 (by silhouette) is the best. Table 10 indicates that industry, university, research and development, technology transfer, and entrepreneurial university are the keywords in Cluster #5. The article with the highest coverage value is 'Campus leadership and the entrepreneurial university: a dynamic capabilities perspective'-Academy of Management Perspectives. The article with the highest coverage cites the most references in the assigned cluster. In either Cluster #1 or #5, the research objects of the highest-covered articles are both universities, indicating that universities create entrepreneurial ecosystem easily.

Betweenness centrality analysis
The betweenness centrality (abbreviated in the following analysis as "centrality") of a node in the network is a character which measures the importance of an article. In the visualisation, the purple circle is used for identification. In general, the higher the betweenness centrality, the more the importance of the literature. Articles with high betweenness centrality are key hubs connecting two different domains. Betweenness centrality is defined as a fraction of the shortest path of a given node divided by the number of shortest paths between any pair of nodes, as Equation (1) shows.
where g st is the number of shortest paths from node s to node t, n i st is the shortest number of paths passing through node i in the shortest path from node s to node t.
Articles with high centrality are often the key hub connecting two different fields. Table 11 shows the top centrality references. The top-ranked item by centrality is Isenberg DJ (2010) in Cluster #0, with the centrality of 0.26. The second is Autio E (2014) in Cluster #1, with the centrality of 0.21. The third is Grimaldi R (2011) in Cluster #3, with the centrality of 0.21. The fourth is Eisenhardt KM (1989) in Cluster #1, with the centrality of 0.17. The fifth is Cooke P (1997) in Cluster #4, with the centrality of 0.15. The sixth is Adner R (2006) in Cluster #1, with the centrality of 0.12. The seventh is Audretsch DB (2014) in Cluster #5, with the centrality of 0.11. The eighth is Spigel B (2017) in Cluster #0, with the centrality of 0.09. The ninth is Neck HM (2004) in Cluster #0, with the centrality of 0.07. The tenth is Feld B (2012) in Cluster #0, with the centrality of 0.06. In general, if the centrality value is greater than 0.1, the literature is more important. The higher the centrality, the more important it is. Therefore, as Table 11 shows, seven articles are more important, and three of them are even over 0.2. Isenberg notes that entrepreneurship can transform the economy; however, creating a breeding ground for new ventures is not simple (Daniel, 2010). Autio focuses on the relationships between contexts and entrepreneurial innovation, discusses policy implications, including how public and private actors can meet these challenges (Autio & Rannikko, 2016). Grimaldi R considers the rationale for academic entrepreneurship and describes the evolving role of universities in the commercialisation of research (Rosa et al., 2011). Notably, in the development process of the entrepreneurial ecosystem, many studies focus on entrepreneurial universities. Research on entrepreneurial universities is an important component of the entrepreneurial ecosystem.

Conclusions
The analysis of the entrepreneurial ecosystem literature indicates its evolutionary trajectory from the conception proposed to the most recent knowledge. Scientometric analysis of the entrepreneurial ecosystem literature can help scholars understand the status quo. Cooperation network analysis enables the identification of primary cooperation networks in the research field. Whether it is the author or institution analysis in the cooperation network, it is important for resource sharing, exchange of ideas, and knowledge dissemination. Co-citation analysis of journals and documents can help scholars trace the research roots and identify trends in research and development. The analysis of disciplines and topics aids in finding the professional directions and hotspots involved in the   entrepreneurial ecosystem. This is why some documents use the word "hotspot" instead of "disciplines and topics" for analysis. Betweenness centrality analysis can help scholars find the most important studies. Literature streams with high betweenness centrality value are the key hubs that connect two different domains. The literature on high betweenness centrality value plays a crucial role in the research on the entrepreneurial ecosystem and provides fertile ground for further investigations. The detailed analysis above has led to the following conclusions: (1) The number of publications on entrepreneurial ecosystem increased every year, peaking in 2017.
(2) The United States leads the research on entrepreneurial ecosystem, and institutions in the States contribute more publications than in other countries. (3) The most cited and highest centrality article in entrepreneurial ecosystem is 'the big idea: how to start an entrepreneurial revolution' published by Harvard Business Review. It proposes nine prescriptions for creating an entrepreneurship ecosystem: stopping emulating silicon valley; shaping the ecosystem around local conditions; engaging the private sector from the start; favouring the high potentials; getting a big win on the board; tackling cultural change head-on; stressing the roots; not over-engineering clusters, but helping them grow organically; and reforming legal, bureaucratic, and regulatory frameworks. (4) Research Policy has the most cited articles and the most cited authors, according to the journal co-citation analysis. (5) Based on document co-citation analysis, Cluster #0 is the largest cluster, labelled as entrepreneurial ecosystem. The reference The relational organization of entrepreneurial ecosystem appears in Cluster #0 and Cluster #2 (labelled entrepreneurship) simultaneously. That is, this reference bridges Clusters #0 and #2. (6) Business & Economics is the most involved discipline, followed by management and business. (7) Innovation, entrepreneurship and entrepreneurial ecosystem are the hottest topics, as indicated by keywords with the highest frequency.
Entrepreneurial ecosystem is an emerging research discipline, and a low number of available documents caused difficulties in in-depth analysis. The research content is also relatively scattered, making it difficult to identify possible directions and trends for future research. However, the sharp increase in the number of documents since 2017 indicates increasing attention from scholars. Thus, research on entrepreneurial ecosystems will continue to grow exponentially for a long time.