AI FoMO in SME Decision-Makers’ Reflections on AI Adoption: A Mixed-Method Approach: Conceptualizing and exploring fear of missing out under conditions of AI-related uncertainty

Abstract

Small and medium-sized enterprises (SMEs) face increasing pressure to engage with artificial intelligence (AI). While prior research has largely focused on rational determinants of decision-making on AI adoption, less attention has been paid to affective and socially driven mechanisms. This paper explores AI Fear of Missing Out (FoMO) as a context-specific form of FoMO in SME decision-makers’ reflections on AI adoption. Using an exploratory sequential mixed-method design, we first analyze 11 interviews with SME decision-makers and then develop and assess an initial AI FoMO measure in a survey with 63 participants. The findings suggest that AI FoMO emerges through competitive pressure, social comparison under informational opacity, limited evaluative capability, and concerns about falling behind. Based on these insights, we propose an initial contextualized operationalization of AI FoMO and provide preliminary evidence of its internal coherence and its association with participation in practice-based AI qualification workshops.