The self-regulatory consequences of dependence on intelligent machines at work: Evidence from field and experimental studies

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 is discrepancy-reducing—enhancing work goal progress, which in turn boosts employees’ task performance. On the other hand, such dependence may be discrepancy-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.