Adapt, absorb, or resist? A process model and nested typology of service employee responses to organizational AI adoption
Harish Yadav, Rajib Lochan Dhar · Journal of Service Theory and Practice · 2025
Purpose This study explores how service sector employees perceive, appraise, and cope with the disruptive impacts of organizational AI adoption—an aspect often overshadowed in literature by strategic and technical concerns. Design/methodology/approach The study proposes an eight-stage process model based on the transactional theory of stress and coping and the conservation of resources theory. It introduces a novel nested 2 × 2 coping typology that categorizes 16 distinct strategies (reactive, preventive, anticipatory, and proactive, further differentiated by active/passive and constructive/destructive dimensions) and defines an appraisal pivot point (APP) as a critical juncture for strategy reassessment. Findings The findings indicate that employees' responses to organizational AI adoption are strongly influenced by personal and managerial resources. Diverse evaluations of AI-induced stressors trigger evolving coping strategies, with the APP marking a decisive moment where adjustments lead to 16 unique coping responses. Research limitations/implications As a conceptual model, its propositions require empirical validation. Future research should test the model across varied industries and cultural settings using longitudinal designs and develop reliable measurement scales for constructs like the APP. Practical implications The study provides actionable insights for service organizations, guiding the design of targeted HR interventions and leadership strategies. Emphasizing resource replenishment through training, support, and feedback, the model assists in shifting employee behaviors from destructive to constructive coping during AI transitions. Originality/value The current study contributes to the service literature by proposing a holistic framework that integrates temporal, individual, and organizational dimensions into understanding employees' perspectives amid AI-driven workplace transformations, thereby bridging a critical gap in current research and offering fresh research directions and practical insights for managing change.