The Dark Side of AI : Examining Technology Overdependence, Accountability Laxity, and Negative Word‐of‐Mouth

Abstract

ABSTRACT This study examines the dark side of artificial intelligence (AI) by looking at the impact of technology overdependence and accountability laxity on negative consumer behavior. The research used a sequential exploratory mixed method design consisting of grounded theory interviews and a quantitative survey with 350 AI users in India. The proposed framework was tested using PLS‐SEM. The proposed model was tested first through PLS‐SEM, and the results confirmed significant effects of technology overdependence and laxity of accountability on negative affectivity, social isolation, cultural appropriation, and negative word‐of‐mouth. In turn, NCA has identified the necessary facilitating conditions, and fsQCA has identified multiple configurational paths to negative consumer behavior. The results of PLS‐SEM suggest that the overdependence on technology and the laxity in accountability significantly enhance negative affectivity, social isolation, and perceptions of cultural appropriation, which, in turn, fuel the negative word‐of‐mouth towards AI systems. Negative affectivity was the most powerful predictor of negative word‐of‐mouth, and trust in AI moderated the psychological and social relationships significantly. In addition, the findings show that AI mistrust significantly predicts the psychological and social dimension of negative behavior but does not significantly influence cultural appropriation, indicating that consumers' cultural beliefs directly influence their resistance to AI. The NCA findings point to negative affectivity, social isolation, and perceptions of cultural appropriation as important facilitating conditions for the development of NWOM. Furthermore, the fsQCA results reveal that different configurations of overdependence on technology, lax accountability, and socio‐psychological factors jointly induce adverse consumer behavior, supporting the asymmetrical and configurational properties of AI‐driven consumer responses. This study contributes to the growing literature on the dark side of AI by moving beyond cognitive and functional viewpoints and providing theoretical and managerial insights on the adoption of responsible and human‐centered AI.