Reconfiguring competitive advantage: a resource dynamic framework for generative AI adoption in digital content marketing

Giuseppe Lanfranchi, Alessandra Cioli, Andrea Amanti and Luca Marinelli

European Journal of Innovation Management, 2025, vol. 29, issue 3, 770-798

Abstract: PurposeThis multidisciplinary study investigates the transformative role of Generative Artificial Intelligence (Gen-AI) in the digital content marketing (DCM) domain through the lens of the Resource-Based View (RBV). The research examines Gen-AI's impact on DCM strategies and practices, including ethical, legal, and professional implications, thereby highlighting the interplay between rapidly accessible AI solutions and distinctive internal assets that are difficult for competitors to replicate.Design/methodology/approachA Delphi method was employed through a panel of 14 experts to formulate consensus-based recommendations for stakeholders in key areas of content marketing, covering Gen-AI's benefits, content quality and creativity, DCM strategies, ethical and legal considerations, and future technological developments. This expert-based process was combined with an RBV-oriented theoretical framework and a critical review of extant literature, providing a structured approach to capturing how Gen-AI adoption intersects with an organization's unique resources and capabilities.FindingsThe findings underscore Gen-AI's advantages in augmenting efficiency, innovation, and customization in content creation, emphasizing the growing need for AI-focused skill development, particularly “prompt engineering.” However, the real source of competitive advantage emerges not merely from adopting new technologies, but from integrating them with proprietary data, specialized know-how, and a culture of innovation. The RBV framework elucidates how these intangible resources, when effectively harnessed and preserved in a dynamic context, foster strategies capable of sustaining a durable competitive edge in DCM.Originality/valueThe study contributes to both theory and practice by merging the insights of the RBV with the evolving landscape of Gen-AI in DCM. It is among the first to provide a comprehensive framework that illustrates how AI-centric innovations, combined with hard-to-imitate organizational assets, can reinforce long-term strategic benefits. The recommendations derived from the Delphi process offer valuable guidance for stakeholders seeking to navigate the Gen-AI landscape responsibly, ensuring that ethical and legal considerations remain central to a robust and future-oriented DCM strategy.

Keywords: Dynamic capabilities; Delphi method; Resource-based view; VRIO; Digital content marketing (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:eme:ejimpp:ejim-03-2024-0317

DOI: 10.1108/EJIM-03-2024-0317

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