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Artificial Intelligence Adoption in Entrepreneurship and Digital Transformation: A Bibliometric–Thematic Review of Intellectual Structure and Emerging Frontiers: Comparison
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Please note this is a comparison between Version 2 by Wenqian Li and Version 1 by Leo Paul Dana.

This study examines the evolving intellectual structure of artificial intelligence (AI) adoption at the intersection of entrepreneurship and digital transformation. Using a hybrid bibliometric–thematic review design with VOSviewer software, the study analyzes 1085 Web of Science publications (2017–2026) selected through a screening protocol. Performance analysis and four types of science mapping—co-word analysis, term co-occurrence, co-authorship, and institutional/geographic networks—were employed to identify publication trajectories, influential contributors, and the conceptual architecture of the field. The findings reveal a pronounced J-curve in publication growth, with substantial acceleration after 2022. Five dominant thematic domains emerge: AI and ecosystem strategy, strategic management and dynamic capabilities, predictive analytics and SME applications, digital transformation and sustainability, and behavioral determinants of technology acceptance. The thematic synthesis suggests that AI adoption in entrepreneurial contexts is a multi-level organizational phenomenon shaped by capability development, institutional conditions, human–AI complementarity, and responsible governance. The review identifies a clear shift from predictive and technical applications toward strategic, human-centered, and sustainability-oriented perspectives. By integrating bibliometric evidence with interpretive thematic analysis, this study addresses literature fragmentation and develops a coherent future research agenda emphasizing longitudinal designs, cross-level theorization, contextual sensitivity, and ethical AI frameworks. The study provides a foundation for scholars, entrepreneurs, and policymakers seeking to understand AI’s role in sustainable competitive advantage.

  • artificial intelligence
  • AI adoption
  • entrepreneurship
  • digital transformation
  • bibliometric analysis
  • thematic review
  • generative AI
  • dynamic capabilities
  • SMEs
  • responsible AI
Artificial Intelligence (AI) is increasingly recognized as a transformative force in strategic innovation management, reshaping organizational decision-making, long-term strategies, and sustainable competitive advantage [1]. The prominence of AI adoption in contemporary scholarly discourse reflects a critical shift: AI is no longer merely an isolated technological tool but a central catalyst that influences and accelerates organizational innovation. However, despite its rapid proliferation across sectors, the theoretical foundations regarding the underlying capabilities required for effective AI adoption remain increasingly vague and ambiguous [2]. Enterprises have realized that possessing robust AI capabilities has a significantly positive impact on organizational ambidextrous innovation, especially when supported by strong organizational learning and agility [3]. Consequently, AI adoption must be understood not just technically, but as a complex organizational phenomenon that demands rigorous interdisciplinary synthesis, ultimately driving firms toward holistic sustainable performance encompassing economic, environmental, and social dimensions [4].
Over the past decade, the literature on AI adoption at the intersection of entrepreneurship and digital transformation has grown exponentially, producing a vast and fragmented body of knowledge. Several systematic reviews and bibliometric analyses have sought to synthesize this expanding field. Islam et al. [5], in a systematic literature review published in the Journal of Engineering and Technology Management (Elsevier), examined AI-guided sustainable competitive advantage for SMEs through business model innovation, developing a comprehensive framework with external antecedents (market dynamics, technological infrastructure, government policies) and internal antecedents (digital leadership, dynamic capabilities, entrepreneurial mindset) contributing to sustainable performance. Blanco-González-Tejero et al. [6], in the International Journal on Semantic Web and Information Systems (Elsevier), utilized the SPAR-4-SLR protocol with natural language processing and VOSviewer to analyze 520 articles, revealing the semantic evolution of AI-entrepreneurship research and providing guidelines for researchers and entrepreneurs. Khoza [7], in a Taylor & Francis study published in the Journal of Small Business & Entrepreneurship, investigated AI-driven youth entrepreneurship in informal urban ecosystems through a systematic literature review of 146 articles, demonstrating that AI adoption is significantly mediated through mobile platforms but constrained by persistent digital divides.
Aquino et al. [8], in a Wiley study published in Applied AI Letters, employed the SPAR-4-SLR protocol with Biblioshiny and VOSviewer to analyze 245 articles, identifying exponential growth in AI-entrepreneurship publications post-2018 across marketing, product development, financing, and education domains. Redondo-Rodríguez et al. [9], in a Springer study published in The Journal of Technology Transfer, conducted a descriptive bibliometric analysis using SciMAT on 270 Web of Science articles (1987–2024), tracing the intellectual evolution from neural networks toward business models, crowdfunding, COVID-19, and e-health applications. Finally, Chotisarn and Phuthong [10], in a Taylor & Francis study published in Cogent Business & Management, provided a comprehensive bibliometric analysis of AI adoption in MSMEs (2014–2024), identifying key thematic clusters including e-commerce, manufacturing, digital transformation, and sustainability.
However, these existing reviews exhibit critical limitations that constrain their utility. First, they frequently treat entrepreneurship tangentially or conflate it broadly with “small businesses,” lacking specific theoretical framing of entrepreneurial innovation and opportunity recognition. Second, while some reviews acknowledge organizational learning and dynamic capabilities, the specific mechanisms through which capabilities mediate between AI infrastructure investments and actual entrepreneurial innovation outcomes remain inadequately theorized. Third, the bibliometric reviews in this domain have been limited by single-database constraints, narrow geographic coverage, or purely descriptive approaches lacking deep thematic synthesis. Fourth, current reviews suggest that digital technologies support sustainable entrepreneurship, but the precise implementation mechanisms linking AI investment to measurable entrepreneurial outcomes remain underspecified. Most importantly, no existing review systematically combines bibliometric landscape mapping with deep thematic interpretation specifically at the nexus of AI adoption, entrepreneurship, and organizational capabilities.
To address these gaps, this study employs a hybrid bibliometric–thematic review design to systematically map the intellectual structure, thematic evolution, and emerging frontiers of AI adoption in entrepreneurship and digital transformation. Table 1 provides a comprehensive comparison of the present review against the most relevant previous reviews, demonstrating how this study advances the field methodologically and theoretically.
Table 1. Alignment of Research Questions with Analytical Techniques.
Despite this explosive growth in publications, the literature on AI adoption in business contexts remains severely fragmented across disciplinary silos. Persistent challenges—including privacy concerns, algorithmic bias, data security risks, and the nuances of SME implementation—threaten equitable adoption and remain underexplored in longitudinal contexts [1]. Research currently exists in parallel streams examining innovation, entrepreneurship, technology adoption, digital transformation, and knowledge management, yet their interconnections remain undertheorized. Because the phenomenon of AI adoption is multi-dimensional, it cannot be adequately understood through a single disciplinary lens. There is a pressing need to bridge these fragmented findings to systematically understand how entrepreneurship drives technological innovation in the digital age, and how these capabilities translate into sustainable competitive advantage.
To address this fragmentation, a combined bibliometric and thematic analysis is highly appropriate. As demonstrated in recent robust mapping studies within entrepreneurial domains [11], utilizing a combined bibliometric approach—encompassing co-word and bibliographic coupling analyses—enables a comprehensive visualization of intellectual structures and emerging thematic clusters. This methodology provides a systematic examination of academic literature to identify publication trends, influential works, and the evolution of knowledge networks over time [12,13][12][13]. Therefore, this review aims to map the intellectual structure of AI adoption at the intersection of entrepreneurship and digital transformation. Specifically, this study addresses the following research questions (RQs): RQ1: What are the prominent publication trends, influential authors, and leading journals in the field of AI adoption within entrepreneurship and digital transformation? RQ2: What is the conceptual structure of the literature, and what core thematic clusters emerge from keyword co-occurrence, term co-occurrence, and collaboration network analyses? RQ3: What are the critical theoretical gaps, and what future research agenda can be proposed for scholars and practitioners?
The remainder of this article is structured as follows: Section 2 outlines the theoretical framing, explicitly identifying the literature gaps and positioning the novelty of this study. Section 3 details the research methodology and bibliometric protocols. Section 4 presents the performance analysis and scientific mapping results. Section 5 provides an in-depth thematic analysis of the identified clusters. Finally, Section 6 concludes the paper by discussing theoretical and practical implications, alongside future research directions.
To establish the novelty and contribution of this study, we systematically compare our review against the most relevant previous reviews in the field. A systematic comparison of 15 previous reviews against the present study is provided in Table S1 (Supplementary Materials). This comparison demonstrates the specific ways in which this study extends the existing literature and addresses the gaps identified in prior reviews.

References

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