نوع مقاله : مقاله پژوهشی
نویسندگان
1 دانشجوی دکتری کارآفرینی، دانشکده کارآفرینی، دانشگاه تهران، تهران، ایران
2 استادیار گروه مدیریت گردشگری، دانشکدگان مدیریت دانشگاه تهران
چکیده
کلیدواژهها
عنوان مقاله [English]
نویسندگان [English]
Purpose: With the rapid growth of scientific output and the proliferation of research publications across disciplines, analyzing scientific trends and identifying the state of research have become essential. This study aims to conduct a bibliometric analysis of publications indexed in the Scopus database on the topic of artificial intelligence in entrepreneurship and to map the global scientific landscape from 1999 through the end of October 2025. The objective is to provide researchers and practitioners with a comprehensive overview of scholarly activities in this field.
Methodology: This applied study adopts an analytical–bibliometric approach, employing co-authorship and keyword co-occurrence techniques. The statistical population consists of 487 articles indexed in Scopus related to artificial intelligence in entrepreneurship. Data collection and analysis were conducted using the analytical tools available in Scopus and the network visualization software VOSviewer.
Findings: The results indicate that the field of artificial intelligence in entrepreneurship has experienced significant growth over the past two decades, evolving from an emerging topic into a prominent area of scientific inquiry. The publication trend shows a steady upward trajectory, with 2024 marking the peak of scientific output at 113 publications. Analysis of leading authors reveals that researchers such as Perida, Haftor, and Sjödin have demonstrated the highest levels of scholarly activity in this domain. The University of Vaasa and Luleå University of Technology were identified as leading academic institutions. Keyword network analysis highlights the formation of several major conceptual clusters, including data-driven business intelligence, intelligent business model innovation, sustainable technology entrepreneurship, platform ecosystem management, and financial technology adoption. The temporal evolution of themes shows a shift from analytical and data-driven tools toward broader issues such as digital transformation, sustainable innovation, and generative artificial intelligence.
Conclusion: Bibliometric analyses indicate that artificial intelligence has become a key driver in the formation and development of entrepreneurial activities, and research has evolved from technical applications toward strategic, ecosystem-oriented, and value-creating approaches. Based on the results, future research directions should address topics such as the integration of data-driven business intelligence with business model innovation, the advancement of sustainable technological entrepreneurship within digital ecosystems, the design of platform management and intelligent decision-support frameworks, and the adoption of financial technologies in trust-based and ethical contexts. These directions reveal a shift in research from a focus on technological analytics toward an understanding of the dynamics of creative and human-centered ecosystems. Such a perspective provides a foundation for sustainable value creation and innovative policy-making in AI-driven smart entrepreneurship. Overall, this study offers a structured vision of the scientific landscape and can serve as an effective guide for researchers, policymakers, and practitioners in the domain of AI-based entrepreneurship.
کلیدواژهها [English]
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