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, Junji Jia, Bao Cheng
Abstract
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.