Artificial Intelligence and Firm Value: A Bibliometric and Systematic Literature Review

Alexandros Koulis (), Constantinos Kyriakopoulos and Ioannis Lakkas
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Alexandros Koulis: School of Social Sciences, Hellenic Open University, 26331 Patras, Greece
Constantinos Kyriakopoulos: Department of Mathematics, National and Kapodistrian University of Athens, 15772 Athens, Greece
Ioannis Lakkas: School of Social Sciences, Hellenic Open University, 26331 Patras, Greece

FinTech, 2025, vol. 4, issue 4, 1-20

Abstract: Objective: This study investigates how artificial intelligence (AI) research relates to firm value, focusing on dominant thematic trends, theoretical foundations, and global collaboration patterns. Methods: A PRISMA-guided systematic review was conducted on 219 peer-reviewed articles published between 2013 and May 2025 in the Web of Science Social Sciences Citation Index. Bibliometric techniques, including co-word, co-citation, and collaboration network analyses, were performed using the bibliometrix (version 4.2.3) in R (version 4.4.2) package to map key concepts, intellectual structures, and international research partnerships. Results: The literature is primarily grounded in strategic management theories such as the resource-based view (RBV) and dynamic capabilities. Emerging research streams emphasize digital transformation, big data analytics, and decision support systems. Frequently co-occurring terms include “firm performance,” “artificial intelligence,” “dynamic capabilities,” “information technology,” and “decision-making.” Collaboration mapping highlights the United States, United Kingdom, and China as leading hubs, with increasing contributions from emerging economies such as India, Malaysia, and Saudi Arabia. The alignment between co-word and co-citation structures reflects a shift from foundational theory to applied AI capabilities in firm-value creation. Implications: By integrating a systematic review with advanced bibliometric and science-mapping methods, this work establishes a structured foundation for theory development, empirical testing, and policy formulation in AI-driven business landscapes.

Keywords: artificial intelligence; firm value; bibliometric analysis; systematic literature review; resource-based view (search for similar items in EconPapers)
JEL-codes: C6 F3 G O3 (search for similar items in EconPapers)
Date: 2025
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