The self-regulatory consequences of dependence on intelligent machines at work: Evidence from field and experimental studies
Pok Man Tang, Joel Koopman, Kai Chi Yam, David De Cremer, Jack H. Zhang, Philipp Reynders
Human Resource Management · 2023 · Volume 62 · Issue 5
Abstract
Organizations are increasingly augmenting employee jobs with intelligent machines. Although this augmentation has a bright side, in terms of its ability to enhance employee performance, we think there is likely a dark side as well. Draw from self-regulation theory, we theorize that dependence on intelligent machines isdiscrepancy-reducing—enhancing work goal progress, which in turn boosts employees’ task performance. On the other hand, such dependence may bediscrepancy-enlarging—threatening employee self-esteem, which in turn detracts from employees’ task performance. Drawing further from self-regulation theory, we submit that employees’ core self-evaluation (CSE) may influence these effects of dependence on intelligent machines. Across an experience-sampling field study conducted in India (Study 1) and a simulation-based experiment conducted in the United States (Study 2), our results generally support a “mixed blessing” perspective of intelligent machines at work. We conclude by discussing the theoretical and practical implications of our work.
Keywords
Intelligent machines, self-regulation theory, work goal progress, self-esteem threat
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