Digital Transformation, Artificial Intelligence, and Value Creation in Mergers & Acquisitions: Theoretical Synthesis, Empirical Insights, and an Integrated Research Agenda

Abstract

This article synthesizes interdisciplinary literature on the influence of digital transformation and artificial intelligence (AI) on merger and acquisition (M&A) outcomes, proposes a unified conceptual framework linking digital capabilities to acquisition performance, and outlines a rigorous research agenda for empirical testing. Drawing on strategic management, international business, information systems, and finance literatures, the paper first maps how digital transformation alters pre-deal target search, valuation, due diligence, contract design, integration planning, and post-merger value capture (Bauer, Matzler, & Schüssler, 2020; Christensen et al., 2011). It then examines the emergent role of AI-driven analytics and automation in augmenting human expertise across M&A phases (Brown et al., 2019; Antwi, Adelakun, & Eziefule, 2024), and situates these developments within classical explanations for merger synergies—taxes, market power, and efficiency improvements (Devos, Kadapakkam, & Krishnamurthy, 2009). The paper integrates microfoundations from organizational learning, dynamic capabilities, and resource-based views to explain heterogeneous performance effects (Devers et al., 2013; Cording, Christmann, & King, 2008). Methodologically, the article lays out a mixed-method, multi-phase empirical approach—combining archival event-study style performance analysis, large-scale survey measurement of digital competences, and in-depth qualitative case studies of AI-enabled diligence teams—to measure both financial and strategic outcomes while capturing boundary conditions. Results from a simulated synthesis of extant empirical findings suggest digital maturity positively moderates the relationship between deal-related complexity and integration success, yet exposes firms to new sources of risk and overvaluation when managerial attention is misallocated (Bauer et al., 2020; Devos et al., 2009). The discussion elaborates theoretical implications, practical prescriptions for dealmakers, and limitations of current knowledge, offering directions for future research including measurement refinement, causal identification strategies, and cross-border considerations (Buckley, Elia, & Kafouros, 2017). The article concludes by arguing that digitally capable acquirers with appropriate governance and learning mechanisms are better positioned to realize M&A synergies in the AI era, but achieving such advantages demands deliberate investment in human capital, integration processes, and transparency in AI-enabled decision tools (Betts & Jaep, 2017; Baskin, 2023).