The double-edged sword effect of AI-employee collaboration on hotel employees’ digital performance: a job crafting perspective

Abstract

This study aims to examine the differential effects of artificial intelligence (AI)-employee collaboration on hotel employees’ digital innovation performance (DIP) and digital task performance (DTP) through job crafting, with leadership empathy (LEMP) as a moderator. This study was conducted using multitemporal survey data from 411 hotel employees and their leaders in China. The results indicate that AI-employee collaboration positively affects promotion-focused job crafting (POJC) and prevention-focused job crafting (PEJC). POJC positively affects DIP and DTP, whereas PEJC positively affects DTP but negatively affects DIP. LEMP positively moderates the link with POJC and negatively with PEJC. This study deepens understanding of AI-employee dynamics and offers insights for improving digital performance and innovation in the digital era. Based on regulatory focus theory, this study distinguishes employee digital performance, unveils the dual pathways of AI-employee collaboration’s influence from a job crafting perspective and expands boundary conditions by incorporating LEMP, offering theoretical insights for effective hotel AI-employee collaboration.