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How Does Intelligent System Knowledge Empowerment Yield Payoffs? Uncovering the Adaptation Mechanisms and Contingency Role of Work Experience

Liwei Chen¹; J. J. Po-An Hsieh²; Arun Rai^2,3

¹ Department of Operations, Business Analytics, and Information Systems, Carl H. Lindner College of Business, University of Cincinnati, Cincinnati, Ohio 45221; · ² Department of Computer Information Systems, Robinson College of Business, Georgia State University, Atlanta, Georgia 30303 · ³ Center for Digital Innovation, Robinson College of Business, Georgia State University, Atlanta, Georgia 30303

Intelligent systems—incorporating computational tools, learning algorithms, and statistical models—can generate knowledge to empower employees in how they conduct their work and increase their job performance. How can organizations realize this potential? Our in-depth study of transformation of work with intelligent systems in a technology maintenance service company provides managerial insights to this question. Although knowledge from intelligent systems can empower employees, employees will need to adapt how they work with intelligent systems to improve their job performance. Interestingly, they can leverage the empowerment to adapt in two ways: maximize benefits, where they use the system to its full potential in conducting work, and minimize disturbances, where they reduce role conflict with the system in conducting work. Although inexperienced employees leverage the empowerment to use the system to its full potential, experienced employees leverage the empowerment to minimize role conflict with the system. How empowered employees realize job performance gains requires understanding how employees channel their empowerment: maximize benefits through use of the system or minimize disturbances through role conflict with the system. Differentiating how inexperienced and experienced employees channel empowerment to increase job performance will enable managers to effectively manage the transformation of work for these two groups.

DOI

10.1287/isre.2021.1097

Volume

33 (3)

Pages

1042-1071

Language

en

Export

BibTeX

Sources

crossref