Integration of Neuroscience and Entrepreneurship: A Systematic Review and Bibliometric Analysis.

In this study, we investigated the integration between neuroscience and entrepreneurship. First, we explored the concept of neuroentrepreneurship and the investigation of neuroentrepreneurship using scientific research methods. Second, we constructed a road map for entrepreneurial researchers interested in conducting neuroentrepreneurship-related research. This is an emerging research area; therefore, to more clearly analyze the dynamics of the research trends, we used a bibliometric method to capture patterns in current publications on subjects related to neuroentrepreneurship, examining papers published between 1999 and 2021 using the keywords "neuroscience" and "entrepreneurship" or "neuroentrepreneurship." To identify the keywords, we used two academic databases-the Social Science Citation Index and Science Citation Index-accessed through the Web of Science website. The three keywords were identified from studies integrating neuroscience with entrepreneurship. After carefully reviewing the research papers, we identified neuroentrepreneurship as a novel research area. The outcomes of this study provide a guide for describing the theoretical connection between neuroscience and entrepreneurship. In the future, this field of study should be empirically investigated.

Introduction

In recent years, global entrepreneurial research has increased in multiple disciplines (Shane, 2000; Davidsson, 2016). Entrepreneurship plays a crucial role in the economic and social development of nations (Davidsson, 2016); entrepreneurship activities not only promote economic growth and market innovation but also provide jobs, thus increasing employment levels (Shane, 2000). Entrepreneurship or specific aspects of entrepreneurship can be taught, and the spirit of entrepreneurship can be formed through learning (Kuratko, 2005). With the rise in entrepreneurial activities, entrepreneurship education is continuously developing. This trend has been generally valued by scholars focusing on entrepreneurship, which has become a rapidly developing research field (Fayolle et al., 2016). However, researchers approach this subject from different perspectives, particularly in relation to the factors that contribute to entrepreneurial behavior (Mustafa et al., 2016; Al-Jubari et al., 2019; Turulja et al., 2020; Su et al., 2021), and studies have been conducted in different regions and industries, using survey analysis, experiments, and interviews for data collection and the discussion of entrepreneurial behavior. Turulja et al. (2020) examined the influence of entrepreneurial intentions and informal support, and their results indicated that family and friends have a significant positive impact on entrepreneurial intentions. Similarly, Wegner et al. (2019) verified that the theory of entrepreneurial promotion strategy builds a strong connection between entrepreneurial education and entrepreneurial intentions. Lopes et al. (2020) researched entrepreneurial environmental factors, and their results indicate that students from metropolitan areas or major cities have higher levels of entrepreneurial intentions than do students from small cities (Rasool et al., 2021). Munir et al. (2019) conducted a comparative study in developing countries, such as China and Pakistan, to investigate the relationship between positive entrepreneurial intentions among students in those countries. The outcomes of their study suggested that students from large cities had more positive entrepreneurial intentions and a stronger urge to establish innovative businesses (Rasool et al., 2021).

Although knowledge relating to entrepreneurship has expanded, an understanding of entrepreneurs’ behavioral decisions is still lacking (Chin et al., 2021a,b). To promote the development of entrepreneurial research, entrepreneurs have also begun to consider the field of neuroscience (Nicolaou and Shane, 2009, 2014; Shane et al., 2010; Nofal et al., 2018; Jin et al., 2021; Lin et al., 2021; Mo et al., 2021), such as using electroencephalography (EEG) to analyze differences in behavioral decision-making between entrepreneurs and nonentrepreneurs (Ortiz-Terán et al., 2014) and functional magnetic resonance imaging (fMRI) to analyze decision-making tasks (Laureiro-Martínez et al., 2015), revealing that entrepreneurs were more effective than managers at decision-making; entrepreneurs make rapid decisions over a shorter period of time. In addition, Massaro et al. (2020) used functional neuroimaging to study the decision-making behavior of entrepreneurs.

After carefully reviewing research papers, we observed that neuroentrepreneurship is a novel research area. Previously, most researchers were unaware of the connection between neuroscience and entrepreneurship. Similarly, describing the theoretical and practical contribution of neuroentrepreneurship is difficult. Studies have investigated the practical implications of neuroscience in the field of management and entrepreneurship through coupling and co-citation analyses (Cucino et al., 2021), but these researchers have not clearly described the respective differences. In this study, we observed that neuroscience and entrepreneurship have a clear relationship; thus, discussing the common methodologies and trends underlying neuroentrepreneurship research is crucial.

This study examined two research directions. First, we explored the concept of neuroentrepreneurship and the investigation of neuroentrepreneurship by using scientific research methods. Second, we constructed a road map for entrepreneurial researchers interested in conducting neuroentrepreneurship-related research. Through a summary of key studies, an analysis of co-citation networks, research trends at different points in time, and a summary of neuroentrepreneurship research methods, we provide a guide for entrepreneurs and researchers on neuroentrepreneurship research.

Literature Review

Bibliometrics is a measurement method used to describe and analyze the development and progress of a discipline or research field. With the support of information technology, it can be used to visualize the results of literature analysis (Cobo et al., 2011; Merigó et al., 2015). Visual co-citation analysis can facilitate data interpretation and make the results more comprehensive while simultaneously helping to identify internal connections among the data (Ma and Xi, 1992). Bibliometrics was first applied to research methods in the field of library and information science. It enables a quantitative analysis of academic output and is mainly used to understand a researcher’s paper publishing trends, citation status, author network analysis, and co-citation. Bibliometrics is an analysis tool for researching topics, country distribution, and further analyzing the value and influence of a discipline (Lin and Yan, 2016; Hsu and Chiang, 2017; Liang and Liu, 2018; Su et al., 2020; Jia et al., 2022). In this study, we mainly employed VOSviewer because this software is based primarily on the data standardization method of probability theory. It provides visuals in relation to keywords, coorganizations, and coauthors, and it included network visualization, overlay visualization, and density visualization software, with outstanding features for drawing and creating images. In addition, it provides a text mining function, which can be used to construct a co-occurrence network from key terms extracted from visual science literature (Van Eck and Waltman, 2010; Flis and van Eck, 2018; Zhao et al., 2021). Most relevant studies have used bibliometric methods to explore related topics through a document co-citation and coupling analysis (Kakouris and Georgiadis, 2016; Zhou et al., 2018; Dolhey, 2019; Hwang and Tu, 2021; Yi and Gao, 2021) with the aim of elucidating research trends in literature knowledge networks (Table 1).

Table 1

Application of bibliometric methodology.

Author (year)	Research results	Research field
Mou et al., 2019	CiteSpace was used to quantitatively and visually analyze the papers of six e-commerce journals published between1999 and 2016 and predict future trends in the e-commerce field.	E-commerce
Du and Li, 2019	Literature on cross-border e-commerce coordination mechanisms obtained from the Science Citation Index (SCI) and Social Science Citation Index (SSCI) databases was analyzed to identify knowledge fields and emerging trends.	Cross-border e-commerce
Cui et al., 2018b	Social commerce is an emerging field in the field of e-commerce. This study quantitatively examined the knowledge structure, development, and evolution of social business to systematically review the status quo of social business literature.	Social business
Hsu et al., 2015	Reflecting the influence of the electronic market on various sectors of modern life, this study identified six groups of researchers pursuing different projects and topics in e-commerce research and further expounded the research structure within the smaller management information system journal group.	E-commerce
Cui et al., 2018a	This study analyzed papers from the SCI and SSCI databases published between 2005 and 2016. CiteSpace software was used to visually and quantitatively analyze organizational culture. In addition, the evolution of organizational culture was described and potential future research focuses predicted.	Group culture
Zhou et al., 2018	This study used bibliometric methods to analyze CiteSpace results and visually presented the main conclusions on energy security research in relation to the most effective countries, institutions, sources, authors, and research directions. A comprehensive analysis of energy security research was conducted, yielding guidance for future in-depth research.	Energy security
Yi and Gao, 2021	This study clarified and reviewed the field of online medical care, exploring the current research status, revealing research hotspots and frontiers, and providing references for other related research in this field.	Online medical care
Dolhey, 2019	This study used VOSviewer to explore research trends in the field of entrepreneurial intentions between 2000 and 2018 and proposed a research context to clarify the literature on entrepreneurial intentions. The results can guide researchers who are interested in conducting research on this subject.	Entrepreneurial intentions
Kakouris and Georgiadis, 2016	Through keyword analysis, co-occurrence networks, and citation analysis, this study revealed the lack of literature associating entrepreneurship with lifelong learning, vocational training, and career consultation. This study can be used as a reference for future researchers.	Entrepreneurship education
Hwang and Tu, 2021	In this study, the authors searched for relevant articles and conducted a bibliometric mapping analysis and systematic review to explore the role of artificial intelligence in mathematics education and related research trends. This study discussed application fields, research trends, research methods, adopted technologies, research questions, and artificial intelligence references and partnerships. Finally, progress in this field was described and research topics for future research recommended.	Artificial intelligence applied to mathematics education

Data Sets

Although this is an emerging yet rapidly expanding research field, studies focusing on relevant topics are limited. To more comprehensively analyze the dynamics of the research trends, we used bibliometric methods to capture the patterns of current publications on subjects related to neuroentrepreneurship. First, by searching academic databases, such as SCOPUS, through the Web of Science (WOS) database, we identified papers published between 1999 and 2021 using the following keywords: “entrepreneurship” OR “neuroentrepreneurship” OR “neuro entrepreneurship” AND “FMRI” OR “functional magnetic resonance imaging” OR “eye tracking event-related” OR “electroencephalography” OR “EEG” OR “eye fixation related potential.” These keywords were selected with reference to studies that focused on the integrative perspective of neuroscience and entrepreneurship. The WOS database was selected because of its wide coverage of quality journals in the social science field; it has been widely recognized as a benchmark database for academic journals of international standards. In accordance with the study by Garza-Reyes (2015), we constructed a meaningful data set by excluding irrelevant publications such as book reviews, magazine articles, replicated studies, unpublished conference proceedings, dissertations, and short research notes. Thus, the final number of articles included in our study was 56 (Figure 1).

Figure 1

Data distribution of articles related to neuroentrepreneurship, 1999–2021.

Results

Data Collection

The titles of the six journals that have made the greatest contribution to the development of the concept of neuroentrepreneurship are listed in Table 2. An analysis of the descriptive statistics relating to the number of relevant articles published in these journals provides insight into the knowledge structure development of this discipline and serves as a guide for researchers submitting relevant papers. The Business Strategy and the Environment journal has made the greatest contribution to neuroentrepreneurship, with 15 relevant papers published and a cumulative citation count of 1,847; the average number of citations per article is 123.13. Most of the articles in this journal focus on the relationship between entrepreneurship and sustainable development, with some centering on how small and medium-sized enterprises overcome the obstacles to sustainable development in their development process. This highlights the journal’s emphasis on how enterprises can benefit society and the environment, thereby promoting enterprise transformation and sustainable development. Corporate Social Responsibility and Environmental Management is the journal with the second highest number of relevant articles published (n = 5); the cumulative number of citations is 168, and the average number of citations per article is 33.6. Sustainable Development has the third highest number of relevant articles published, with 103 cumulative citations and an average number of citations per article of 34.33. Regarding the topic of neuroentrepreneurship in entrepreneurship studies, a highly cited paper by Hodgkinson et al. (2009; cited 132 times) reported on the analysis of entrepreneurial decision-making through fMRI experimentation. Journal citation analysis revealed that the three aforementioned journals pertain to the same cluster. In this study, we reviewed these journals and identified a clear thematic overlap between Business Strategy and the Environment and the other two journals regarding sustainable development. Therefore, the classification of these journals in the same cluster is validated.

Table 2

Top 10 most productive journals in relation to neuroentrepreneurship, 1999–2021 (documents ≥2).

Rank	Journal	Papers	TC	P|TC
1	Business Strategy and the Environment	15	1847	123.13
2	Corporate Social Responsibility and Environmental Management	5	168	33.6
3	Long Range Planning	2	132	132
4	Sustainable Development	3	103	34.33
5	Journal of Business Venturing	2	29	14.5
6	Sustainability	2	28	28

TC: total citations; P|TC: average number of citations per article.

Countries and Institutions

Researchers in a total of 32 countries have engaged in research on neuroentrepreneurship, and the largest number of countries connected through one article was 14. Large-scale international collaboration has therefore become a trend. A review of the data on the number of neuroentrepreneurship-related articles published per country revealed that the United Kingdom was the country that had contributed the most articles, with 11 relevant papers published and 564 citations in total. Germany had produced the greatest number of cited articles, with a total of 848 citations relating to four papers. The United States had the highest degree of centrality score (9), followed by the United Kingdom (6). The three countries that shared the third highest degree of centrality score were Germany, Spain, and Switzerland. In addition, scholars from China had accumulated four published papers; the citation frequency was 0, and no collaborative relationship with other countries was observed (Figure 2; Table 3).

Figure 2

Network of international collaboration in neuroentrepreneurship research.

Table 3

Twenty most productive countries in relation to neuroentrepreneurship research, 1999–2021 (documents ≥ 2).

Rank	Country	Cluster	Papers	Citations
1	United Kingdom	1	11	564
2	United States	2	8	373
3	Germany	4	5	848
4	Netherlands	4	4	207
5	Australia	3	4	94
6	China	9	4	0
7	Spain	1	3	126
8	India	6	3	61
9	Switzerland	2	3	46
10	Canada	1	2	148
11	Chile	1	2	91
12	South Africa	5	2	71
13	Austria	1	2	67
14	Sweden	2	2	46
15	Poland	5	2	21
16	Czech Republic	5	2	20
17	France	4	2	19
18	Finland	2	2	12
19	Ukraine	13	2	7
20	Greece	10	2	5

A total of 131 institutions engaged in neuroentrepreneurship studies. An institutional collaboration network was formed, with these institutions as the nodes and their collaborative relationships as the connecting lines. Small-scale institutional collaboration has become a trend in this field. Analysis of the institutional article production statistics revealed a collaborative relationship between the two institutions producing the most cited articles, the Centre for Sustainability Management at Leuphana University of Lüneburg and the Chair of Entrepreneurship and Corporate Growth, University of Würzburg, both located in Germany, indicating that these two institutions conducted relatively advanced research in this field. The collaboration information listed in Table 4 reveals a strong connection between the two institutions that is likely related to the high number of citations of their articles (Figure 3; Table 4).

Table 4

Institutional collaboration in neuroentrepreneurship research.

Rank	Institution	Papers	Citations
1	University of Natural Resources and Life Sciences, Vienna, Austria	2	67
2	Aalto University School of Business, Finland	2	12
3	Centre for Sustainability Management, Leuphana University of Lüneburg, Germany	1	663
4	Chair of Entrepreneurship and Corporate Growth, University of Würzburg, Germany	1	663
5	University of Leeds, United Kingdom	1	225
6	Department of Business Administration, Darden Graduate School of Business Administration, University of Virginia, United States	1	179
7	University of Virginia, United States	1	179
8	Technische University München, Freising, Germany	1	158
9	Technische Universität München, Tum School of Management, Freising, Bavaria, Germany	1	158
10	Centre for Innovation and Corporate Sustainability Cimo, Faculty of Earth and Life Sciences, Free University, Amsterdam, the Netherlands	1	141
11	Vrije Universiteit, Amsterdam, the Netherlands	1	141
12	Faculty of Business, University of Victoria, Canada	1	132
13	Rawls College of Business, Texas Tech. University, Lubbock, Texas, United States	1	132
14	Eada Business School, Barcelona, Spain	1	91
15	Management School, University of Liverpool, Liverpool, United Kingdom	1	91
16	Universidad del Desarrollo, Santiago, Chile	1	91
17	Universitat de Vic, Barcelona, Spain	1	91
18	Department of Strategy and Business Systems, Portsmouth Business School, University of Portsmouth, United Kingdom	1	86
19	Centre for Geography and Environmental Science, Monash University, Clayton, Victoria, Australia	1	78
20	Copernicus Institute for Sustainable Development and Innovation, Utrecht University, the Netherlands	1	51

Figure 3

Network of institutional collaboration in neuroentrepreneurship research.

Authors’ Cooperation Network

A network graph of author collaboration in neuroentrepreneurship research was drawn, with the authors as the nodes and their collaborative relationships as the connecting lines. A network visualization map of this collaboration, presented in Figure 4, contains 138 nodes and 153 connecting lines, forming a total of 50 clusters. The collaboration network is generally fragmented, similar to most other scientific research collaboration networks. The entire collaboration network in neuroentrepreneurship research is composed of multiple nonconnected components, indicating a lack of collaborative relationships between different scientific research groups. We further analyzed the degree of author collaboration in neuroentrepreneurship research.

Figure 4

Authors’ cooperation network, 1999–2021.

According to the information in Table 5, the rate of collaboration among authors (researchers) in the field of neuroentrepreneurship reached 0.84, indicating that collaboration among authors is common in modern international academia; few scientific studies were completed by a single author. Further investigation demonstrated that the scale of collaboration in the field of neuroentrepreneurship was small and generally concentrated among two to four authors. This indicated that most research in this field is conducted through small-scale collaboration among authors.

Table 5

Rate of author collaboration in neuroentrepreneurship research.

Author collaboration
Number of authors	1	2	3	4	5	6
Number of collaborations	9	20	14	10	2	1
Collaboration rate	0.16	0.36	0.25	0.18	0.04	0.02

A core knowledge community has not yet been formed in the field of neuroentrepreneurship. We ranked the eight authors who had published more than two papers in the field of neuroentrepreneurship, details are listed in Table 6. Notably, these authors were not highly cited. Two papers published by Cohen et al. (2008) and Muñoz and Cohen (2018) had a total of 223 citations, with both papers exploring the effect of sustainable entrepreneurship. This result provides further evidence that highly cited papers are produced through collaboration.

Table 6

Author productivity in neuroentrepreneurship research.

Rank	Author	Cluster	Papers	Citations
1	Holt D.	34	3	75
2	Cohen B.	11	2	223
3	Provasnek A.K.	7	2	67
4	Schmid E.	7	2	67
5	Cerf M.	1	2	20
6	Drover W.	1	2	20
7	Halko M.-L.	2	2	12
8	Lahti T.	2	2	12

The collaboration network analysis of countries, institutions, and authors revealed that the use of collaborative models for scientific research collaboration represents a development trend in the field of neuroentrepreneurship, with scientific research papers produced through collaboration being frequently cited.

Research Methods Used in Neuroentrepreneurship Research

Figure 5 presents the research methods employed in the neuroentrepreneurship-related papers considered in this study. Most papers used quantitative methods (42 papers), followed by theoretical or descriptive analysis (five papers), mixed methods (seven papers), and finally, qualitative methods (two papers).

Figure 5

Research methods, 1999–2021.

Keywords Network Analysis

We performed hierarchical network extraction on the basis of a frequency greater than or equal to two; subsequently, we generated a keyword network, with keywords as the nodes and the connections between keywords as the connecting lines. The keyword network analysis revealed 54 nodes and 316 connections; the network density was 0.223, which is much higher than the density value of 0.053 for random networks of the same scale, indicating a higher clustering coefficient. The findings reveal that the knowledge community in the field of neuroentrepreneurship has self-organized and converged toward certain topics, forming a highly clustered knowledge community. In this study, three clusters (knowledge communities) were formed in the field of neuroentrepreneurship. Relevant data are presented in Figure 6; Table 7.

Figure 6

Cluster analysis, 1999–2021.

Table 7

Knowledge community clusters in the field of neuroentrepreneurship.

Clusters	Terms
1 (8 terms)	Event-related potential; information management; small business; competition; Greece; implementation process; strategic planning; students
2 (17 terms)	Entrepreneurship; decision-making; cognition; economics; human; electroencephalography; emotion; functional magnetic resonance imaging; human experiment; industry; integration; magnetic resonance imaging; neuroscience; normal human; regional planning; social cognition; trust
3 (21 terms)	Entrepreneur; sustainability; sustainable development; innovation; environmental economics; environmental policy; stakeholder engagement; sustainable entrepreneurship; business development; conceptual framework; strategic approach; triple bottom line; environmental management; government; industrial performance; institutional framework; modeling; profitability; social; stakeholder

Cluster 1 contains eight keyword nodes, as listed in Table 7. These studies generally focused on research topics related to start-ups or entrepreneurial cooperation. Among them, Yang et al. (2013) adopted game theory and an event-related potential (ERP) experiment to explore the primary reasons for entrepreneurial failure, applied an experimental method to understand whether the success or failure of partnerships is critical for network relationships, and further investigated the role of partnerships in entrepreneurship through interviews. Hu and Tian (2018) clarified the neural mechanism behind trust cognition and behavior. These researchers proposed countermeasures to promote the long-term and stable development of trust among entrepreneurship partners with the aim of promoting the development of partnership enterprises. Liu and Xu (2018) employed an ERP experiment to examine the effect of risk preferences on cooperative behavior in entrepreneurship and analyze the behavioral choices of entrepreneurs.

Cluster 2 contains 17 keyword nodes, as listed in Table 7. For example, Hodgkinson et al. (2009) used fMRI experiments to explain the relationship between entrepreneurs’ strategic management and behavior in behavioral decision-making. Halko et al. (2017) explored the brain activities of some entrepreneurs and parents during emotional responses to a particular situation by using an empirical fMRI method. The results revealed similarities in brain responses between entrepreneurs’ emotional responses to business scenarios and parents’ emotional responses to their children. Lahti et al. (2019) conducted an experiment using fMRI on 42 groups of individuals to measure parental emotional connections to explore how entrepreneurs establish a connection with their business. The researchers attributed emotional resonance to entrepreneurs from a neuropsychology perspective and verified its influence on the establishment of entrepreneurial business relationships, focusing on entrepreneurship education and the similarities between entrepreneurship and the parent–child relationship. Massaro et al. (2020) used functional neuroimaging to study the decision-making behavior of entrepreneurs. Therefore, according to the results of cluster 2, the core research of this knowledge community is focused on analyzing entrepreneurial behavior as an aspect of entrepreneurial decision-making. In addition, differences in entrepreneurial behavior can be further expanded and explained through the analysis of these behavioral decisions in entrepreneurship, which can also have a decisive effect on corporate strategic planning.

Cluster 3 contains 21 keyword nodes, as listed in Table 7. Białek-Jaworska and Gabryelczyk (2015) adopted a simulation method to construct an experiment in which students experienced an entrepreneurship scenario; an empirical analysis was then conducted through questionnaires to understand the results of the students’ experiences. Shane et al. (2020) explored how changes in government policy affect informal investors’ interest in start-up pitches. Therefore, the knowledge nodes generated in cluster 3 revealed a focus on the correlation between sustainable entrepreneurship and government policy.

Keyword Evolution Analysis

We also analyzed the evolution of knowledge communities in the fields of neuroscience and entrepreneurship over time. According to the results in Figure 7, the evolution hotspots in this field relate primarily to cluster 2. Cluster 2 is not only the largest knowledge community in the field of neuroentrepreneurship but also represents the research trends of the future. More than half the nodes in this knowledge community are new evolution hotspots from recent studies, with most of these studies published after 2017. This finding indicates that the topics of focus in this cluster represent the future direction of research in the neuroentrepreneurship field.

Figure 7

Keyword evolution analysis.

Conclusion

This study analyzed 56 articles in the field of neuroentrepreneurship published between 1999 and 2021, revealing that neuroentrepreneurship has become a research topic in entrepreneurship-related research in recent years. Most of the studies reviewed employed neuroscience-based tools, such as EEG, ERP, fMRI, and eye tracking. By considering the different experimental designs and techniques employed in these studies, we observed that current neuroentrepreneurship-related research focuses primarily on entrepreneurs or entrepreneurial teams, entrepreneurship opportunities, venture investment, and entrepreneurial decision-making, with considerable research findings achieved through conventional entrepreneurship research methods (Hodgkinson et al., 2009; Halko et al., 2017; Hu and Tian, 2018; Massaro et al., 2020).

The journal with the most relevant publications on neuroscience research is Business Strategy and the Environment, with a total of 15 articles. The study by Hodgkinson et al. (2009), which used fMRI to analyze entrepreneurial decision-making, was the most frequently cited paper. An analysis of cooperation networks between countries and institutions revealed that the United Kingdom conducts the most neuroentrepreneurship-related research, with a total of 564 citations, and Germany is the country with the most cited papers, with a total of 848 citations of four published papers. This situation reflects the cooperative networks of institutions. The Centre for Sustainability Management, Leuphana University of Lüneburg, and the Chair for Entrepreneurship and Corporate Growth, University of Würzburg, both German institutions, demonstrated the greatest cooperation. In terms of collaborative author networks, this study revealed the prevalence of coauthorship of research papers in the field of neuroentrepreneurship, with academic collaboration among researchers being a common phenomenon, which may also reflect the value of collaboration networks (Kocak et al., 2019; Xu et al., 2021). Regarding the research methodologies employed, most research in the field of neuroentrepreneurship is based on quantitative methods. However, an analysis of keywords revealed that studies mainly focus on three aspects of neuroentrepreneurship: start-ups or entrepreneurs’ cooperation, behavioral decision-making analyses of entrepreneurship, and sustainability and policy in relation to entrepreneurship. A keyword evolution analysis further revealed that behavioral decision-making in entrepreneurship (Lahti et al., 2019; Massaro et al., 2020; Dharmawan et al., 2021) is likely a primary direction of future research.

This study has some limitations. Neuroentrepreneurship is a new discipline and the number of related studies is relatively small (56 articles). Follow-up research is therefore suggested; in future studies, the search criteria for keywords can be expanded and the search can also include seminar papers in the field of neuroscience. This approach may yield more accurate discussions and contributions to reflect the development of neuroentrepreneurship.

Data Availability Statement

The original contributions presented in the study are included in the article/supplementary materials, further inquiries can be directed to the corresponding authors.

Author Contributions

WL and JY identified the research theme and provided meaningful guidance during the whole process. YX and YW conducted the literature analyzing and collecting and wrote the script. TX and ZY participated in translation and modification. All authors contributed to the article and approved the submitted version.

Funding

This research was funded by the project of soft science research of Zhejiang Provincial Science and Technology Plan Project, “the Influence of Difference Innovation Resource Endowment on Zhejiang Regional Economic Development” (2019C35083) and the soft science research of Zhejiang Provincial Science and Technology Plan Project, “Research on the Construction path of University Maker Space under Self-reliance in Science and Technology” (2022C35033).

Conflict of Interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Publisher’s Note

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