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Journal of Management Information Systems Vol. 40 No. 4 2023

Perceived Fairness of Human Managers Compared with Artificial Intelligence in Employee Performance Evaluation

Shaojun (Marco) Qin1; Nan Jia2; Xueming Luo1; Chengcheng Liao3; Ziyao Huang3

1 Fox School of Business, Temple University, Philadelphia, PA, USA · 2 Marshall School of Business, University of Southern California, Los Angeles, CA, USA · 3 Business School Sichuan University, Chengdu, P. R. China

Abstract

Human managers are increasingly challenged by artificial intelligence (AI) technologies in performing managerial functions. We undertook a field experiment that used AI vis-à-vis human managers to perform structured, data-intensive evaluations of employee performance. We generate two sets of insights. First, employees considered AI to be both fairer and more accurate in evaluating their performance than the average human manager. Second, to catch up with AI, human managers’ fairness perceived by employees played a first-order role by (a) helping human managers, to a greater extent than those managers’ evaluation accuracy, to close the performance gap of the employees evaluated by them compared with that of those evaluated by AI, and (b) constraining the effect of human managers’ perceived accuracy of evaluations on employees’ performance. Thus, facing the competition from AI, it is all the more important for human managers to treat employees fairly and build positive interpersonal relationships with employees.

DOI
10.1080/07421222.2023.2267316
Volume
40
Issue
4
Pages
1039-1070
Language
en
Sources
crossref openalex

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