Abstract

Introduction and Related Work The emergence of artificial intelligence (AI) has significantly transformed the entrepreneurial scene by allowing startups to contend with well-established industry titans on a worldwide level. AI's sophisticated powers in automation, machine learning, and data analysis enable startups to recognize and exploit opportunities, grow quickly, maximize productivity, and innovate (Giuggioli & Pellegrini, 2022). They may now penetrate and upend markets that were previously controlled by big businesses thanks to their technological advantage (Chalmers, MacKenzie, & Carter, 2021). AI has an impact on business and the global economy by promoting innovation, increasing competition, and opening up new avenues for economic expansion (Ghura & Harraf, 2021). As a result, AI-powered firms have a significant impact on the global economy and are changing conventional business practices. They also increase productivity, create jobs, and accelerate technological developments. While artificial Intelligence (AI) is broadly defined as ability of machines, particularly digital computers and algorithms, to accomplish jobs and find complicated solutions that normally need human intelligence, or as John McCarthy defined it six decades ago "the science and engineering of making intelligent machines" (Andersen, 2002), entrepreneurship can be described as the process of identifying and exploiting opportunities to create new offerings that provide value to the market, typically through innovation and risk taking (Shane & Venkataraman, 2000). Despite the critical importance of AI and entrepreneurship, the research on the intersection of these ever evolving two subfields is scattered and premature. Method and Potential Contributions This study provides a comprehensive bibliometric analysis of research published in the intersection of AI and entrepreneurship. We aim at investigating trends and patterns in the field of AI and entrepreneurship using performance analysis and science mapping. A total of 733 articles covering the period from 1973 to 2023 were retrieved from the Scopus database for this analysis, and VOSviewer and Gephi software were employed to conduct our analysis. In this paper, we present the productivity of the field through a deep dive into the publication abstracts, year of publication, authorship, publication sources, keywords, and countries and institutions of the published works. We also undertook a bibliometric approach, analyzing keyword co-occurrence and bibliographic coupling to assess the scientific evolution of this emerging scholarly field. The primary research questions of the current research are: RQ1: What are the trends in publications within the field of artificial intelligence (AI) in entrepreneurship? RQ2: What have been the focal points of previous research, and which methodological and analytical approaches have researchers adopted in the field of AI in entrepreneurship? RQ3: What are the key concepts investigated, and what constitutes the intellectual structure of the knowledge base of AI in entrepreneurship? RQ4: What potential future research directions could be explored by researchers in the field of AI in entrepreneurship? Findings and Conclusions The initial results of our analysis of 733 publications (703 articles and 30 review papers) from 366 journals show that research on the intersection of AI and entrepreneurship has spanned from 1973 to 2023 with an average annual growth rate of 10.82%. However, our early findings indicate a substantial increase in publications and citations over the past decade with an annual growth rate exceeding 32%. Each publication has an average age of 5.79 years and obtained 24.46 citations on average, which adds up to 39,024 references overall. Our content analysis incorporated 2,292 Keywords Plus (initiated by journals) and 2,459 author-specified keywords. Overall, 1,907 authors have contributed to the research on AI and entrepreneurship; 135 of them are the authors of single-authored documents. With an average of 2.84 co-authors per publication and 26.88% of the documents involving foreign co-authorship, the writers' collaboration is clearly evident. The top publishing journals for research on AI and entrepreneurship are as follows: Technological Forecasting and Social Change (26 articles), Journal of Cleaner Production (14 articles), Decision Support Systems (13 articles), International Journal of Entrepreneurial Behaviour and Research (13 articles), Journal of Business Venturing Insights (13 articles), Small Business Economics (12 articles), Academy of Entrepreneurship Journal (10 articles), International Journal of Production Research (10 articles), Journal of Business Research (9 articles), and the International Entrepreneurship and Management Journal (8 articles). Our analysis demonstrates that the most productive countries in publishing research on AI and entrepreneurship are: USA (286 publications; 1812 total citations), China (225 publications; 1713 total citations), India (155 publications; 512 total citations), Spain (99 publications; 629 total citations), Italy (97 publications; 1238 total citations), and the UK (93 publications; 641 total citations). Our results identified the most prolific authors in the field. While our results show Jilin University, University of Tehran, Indian Institute Of Technology Delhi, Politecnico Di Torino, and Central South University as the five most productive intuitions in this field, we identify Gupta BB, Panigrahi Pk, Gaurav A, Kim J, Saura Jr, and Wang W as the six most prolific authors in this field of research. Running network analysis of the keywords co-occurrences revealed that research on AI and entrepreneurship is clustered into six primary clusters. These clusters can be initially described as follows: AI applications in automation and business digitization, decision systems in entrepreneurship, AI role in opportunity and business model creation, big data and analytics, social entrepreneurship and entrepreneurship education, and sustainability and sectoral applications. In conclusion, research on the intersection of AI and entrepreneurship is underdeveloped and scattered across different disciplines. However, this represent a huge opportunity for researchers to study and delve into this interesting and critical area of investigation. As such, the relatively high average citation per publication of 24.46 demonstrates the importance of the topic and the rising interest of the scientific community. Also, the recent spike in publications showcase that there is an increasing interest and a rising appetite of research to investigate this promising field of research. Furthermore, there are ample research opportunities and many potential avenues for future research inquiries. For instance, our thematic analysis of publications in the area of AI and entrepreneurship show that the applications of AI on the investment related topics in the entrepreneurship space is an under researched area despite of its critical importance. Furthermore, the influence of AI on cost optimization and entrepreneurs’ ability in leveraging resources represent another promising area for future research.

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