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

abstract

  • AbstractOrganizations 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 selfregulation theory, we theorize that dependence on intelligent machines is discrepancyreducingenhancing work goal progress, which in turn boosts employees task performance. On the other hand, such dependence may be discrepancyenlargingthreatening employee selfesteem, which in turn detracts from employees task performance. Drawing further from selfregulation theory, we submit that employees core selfevaluation (CSE) may influence these effects of dependence on intelligent machines. Across an experiencesampling field study conducted in India (Study 1) and a simulationbased 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.

published proceedings

  • HUMAN RESOURCE MANAGEMENT

author list (cited authors)

  • Tang, P. M., Koopman, J., Yam, K. C., De Cremer, D., Zhang, J. H., & Reynders, P.

publication date

  • September 2023

publisher