A conceptual model of employees’ behavioral intention to use generative artificial intelligence technology in mid-sized organizations

Rapid technological advancements have positioned Generative Artificial Intelligence (GenAI) as a strategic asset for businesses; however, its adoption in resource-constrained environments remains complex. Specifically, in the context of emerging economies where mid-sized firms drive significant employment yet face distinct digital hurdles, understanding these dynamics is crucial. This conceptual research develops a theoretical model explaining employees’ behavioral intention to use GenAI in mid-sized organizations, a sector often overlooked in favor of large corporations or small startups. The study integrates the Technology Acceptance Model (TAM), the Unified Theory of Acceptance and Use of Technology (UTAUT), Adaptive Governance Theory, and the Dynamic Capabilities Framework to capture the multidimensional interplay among organizational, governance, and psychological factors. Unlike traditional models that focus solely on utility, this research posits that organizational conditions and governance conditions act as primary antecedents influencing employees’ perceived risk and trust, which in turn determine behavioral intention toward GenAI use. Eleven research propositions (P1–P11) were formulated to describe both direct and indirect causal relationships, highlighting the mediating roles of psychological safety mechanisms. The study contributes theoretically by extending technology acceptance models beyond cognitive dimensions to include governance and ethical oversight as structural determinants of employee behavior. It also introduces perceived governance as an integrative construct linking organizational readiness to trust and risk perception. Practically, the framework provides actionable guidance for mid-sized organizations to design adaptive governance systems, strengthen transparency, and foster trust-based cultures that encourage responsible GenAI adoption. By highlighting the balance between innovation and accountability, this conceptual model establishes a robust foundation for future empirical validation and policy development aimed at promoting sustainable and ethical AI integration in organizational contexts.