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

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. [ABSTRACT FROM AUTHOR]