The dark side of employee-generative AI collaboration in the workplace: an investigation on work alienation and employee expediency

Abstract

Generative AI (GenAI) has emerged as a powerful tool in the modern workplace, delivering significant benefits to both employees and organizations. As its adoption gains momentum, understanding the potential risks associated with employee-GenAI collaboration becomes increasingly important. While much of the existing research emphasizes the challenges GenAI presents to employees as individuals, this study shifts the focus to explore broader organizational risks, particularly unethical workplace behaviors. Drawing on human-AI collaboration research and the job demands-resources model, we develop and empirically test a novel model to explain how and when employee-GenAI collaboration may lead to employees’ unethical behavioral outcomes in daily organizational contexts. Using an experience sampling approach with longitudinal data from 229 service industry employees, encompassing 1050 matched daily observations, our findings reveal that employee-GenAI collaboration increases work alienation—a sense of disconnection from work—which, in turn, drives employee expediency that compromises work standards. Furthermore, we demonstrate that this effect is pronounced under high digital job demands. By highlighting this unintended consequence, our study contributes to theoretical advancements in understanding the darker side of employee-GenAI collaboration and provides practical insights to help organizations harness the benefits of GenAI while mitigating its potential ethical pitfalls.

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