A Complex Leadership Perspective on Generative AI Adoption in SMEs: The Interplay of TAM, TMT, and RBV

Abstract

Although Generative Artificial Intelligence (GenAI) is one of the strategic choices for digital transformation in small and medium-sized enterprises (SMEs), its adoption remains constrained by leadership decision-making that must balance strategic aspirations with resource limitations and organizational inertia. Organizational leadership must face the dynamic and complex characteristics of digital transformation in the knowledge era. Drawing on Complexity Theory and integrating the Technology Acceptance Model (TAM), Temporal Motivation Theory (TMT), and the Resource-Based View (RBV), this study proposes a conceptual framework reflecting distinct strategic leadership orientations. Following a qualitative approach based on semi-structured interviews with SME leaders and an Interpretative Phenomenological Analysis (IPA) this conceptual framework contributes by reframing GenAI adoption as a complex, nonlinear process rather than a straightforward diffusion model, that includes four strategic profiles (Strategic Adopters, Aspiring Adopters, Opportunistic Adopters, and Operational Stabilizers) that affect a dynamic relationship between three key adoption dimensions: intention, motivation, and resource allocation. SME leaders can benefit from a delimitation of their strategic and operational goals while overcoming adoption barriers.