To break or not to break? The dual effects of employee-AI collaborative identification on employee pro-customer rule breaking
Shiyao Jiang, Zihao Li, Runnan Gao, Junqi Jia, Bao Cheng · Journal of Hospitality Marketing & Management · 2025
The hospitality industry is rapidly integrating AI, yet research has mainly focused on employees’ negative or neutral responses to AI collaboration. Positive psychological elements, such as employee-AI collaborative identification, and their effects on service outcomes remain underexplored. Grounded in conservation of resources theory, we adopted a resource-based perspective to suggest such identification exerts dual influences on pro-customer rule breaking (PCRB), mediated by job-growth and self-growth mindsets and moderated by organizational AI readiness. Using a mixed-methods approach, including three experiments and a time-lagged survey, our results demonstrate that employee-AI collaborative identification fosters resource gain by cultivating job-growth mindset, which in turn promotes PCRB, while simultaneously causing resource depletion by diminishing self-growth mindset and thereby inhibiting PCRB. Moreover, organizational AI readiness amplifies the positive impacts of such identification on job-growth mindset while buffering its negative impacts on self-growth mindset. We discussed current research implications and future research avenues.