Back to Search Start Over

WHEN CONSCIENTIOUS EMPLOYEES MEET INTELLIGENT MACHINES: AN INTEGRATIVE APPROACH INSPIRED BY COMPLEMENTARITY THEORY AND ROLE THEORY.

Authors :
POK MAN TANG
KOOPMAN, JOEL
MCCLEAN, SHAWN T.
ZHANG, JACK H.
CHI HON LI
DE CREMER, DAVID
YIZHEN LU
CHIN TUNG STEWART NG
Source :
Academy of Management Journal; Jun2022, Vol. 65 Issue 3, p1019-1054, 36p, 1 Diagram, 6 Charts, 5 Graphs
Publication Year :
2022

Abstract

Over the past century, conscientiousness has become seen as the preeminent trait for predicting performance. This consensus is due in part to these employees' ability to work with traditional 20th-century technology. Such pairings balance the systematic nature of conscientious employees with the technology's need for user input and direction to perform tasks--resulting in a complementary match. However, the 21st century has seen the incorporation of intelligent machines (e.g., artificial intelligence, robots, and algorithms) into employees' jobs. Unlike traditional technology, these new machines are equipped with the capability to make decisions autonomously. Thus, their nature overlaps with the orderliness subdimension of conscientious employees--resulting in a non-complementary mismatch. This calls into question whether the consensus about conscientious employees' effectiveness with 20th-century technology applies to 21st-century jobs. Integrating complementarity and role theory, we refine this consensus. Across three studies using distinct samples (an experience sampling study, a field experiment, and an online experiment from working adults in Malaysia, Taiwan, and the United States), each focused on a different type of intelligent machine, we show not only that using intelligent machines has benefits and consequences, but, importantly, that conscientious (i.e., orderly) employees are less likely to benefit from working with them. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
00014273
Volume :
65
Issue :
3
Database :
Complementary Index
Journal :
Academy of Management Journal
Publication Type :
Academic Journal
Accession number :
157854322
Full Text :
https://doi.org/10.5465/amj.2020.1516