AI-Driven Entrepreneurship Research (2023–2026): Bibliometric Trends, Thematic Evolution, and a Future Research Agenda

Acep Supriadi; Asep  Mahpudz; Ade Budhi Salira; Harfandi Harfandi

doi:10.62794/je3s.v7i4.423

Authors

Acep Supriadi Universitas Pendidikan Indonesia, Bandung, Indonesia
Asep Mahpudz Universitas Pendidikan Indonesia, Bandung, Indonesia
Ade Budhi Salira Universitas Pendidikan Indonesia, Bandung, Indonesia
Harfandi Harfandi Universitas Islam Negeri Sjech M.Djamil Djambek Bukittinggi, Bukittinggi, Indonesia

DOI:

https://doi.org/10.62794/je3s.v7i4.423

Keywords:

Artificial intelligence, AI-driven entrepreneurship, Bibliometric analysis, Thematic evolution, Science mapping

Abstract

This study maps publication trends, intellectual structure, thematic evolution, and future research directions in Scopus-indexed conference papers, book chapters, and books on AI-driven entrepreneurship. Using a bibliometric and science-mapping approach, the analysis draws on metadata for 2,119 documents from 1,248 sources indexed in Scopus during 2023–2026, comprising 6,139 authors, 5,072 author keywords, and 9,890 Keywords Plus terms. Performance analysis, co-authorship, co-citation, bibliographic coupling, keyword co-occurrence, and thematic-evolution mapping were conducted using Bibliometrix/Biblioshiny and VOSviewer, with Python-based verification of key figures. Publications grew 22.02% annually, with 3.17 authors per document on average and a 22.27% international co-authorship rate; India and China emerged as leading centers of productivity and collaboration. Publication sources remain dominated by conference proceedings, book chapters, and Lecture Notes series, a pattern reflecting the document-type restriction applied during retrieval. The intellectual structure links entrepreneurship theory, innovation, technology management, decision-making, and computing systems, with artificial intelligence as the most central theme; keyword trends show a gradual, still-emerging rise in generative-AI and hybrid-AI terms alongside continued dominance of core AI and machine-learning concepts. A stratified two-rater relevance screening of a 327-record sample (Cohen’s κ = 0.707) indicates that about 51% of the retrieved corpus is substantively relevant to AI-driven entrepreneurship, so the mapped patterns should be read as a provisional, database-bounded picture. The study proposes a future research agenda testing how AI capabilities shape opportunity recognition, creativity, business-model innovation, financing access, performance, and sustainability, while calling for stronger theory, longitudinal evidence, and attention to ethics and governance.

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