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Cited by 371Job Satisfaction and Organizational BehaviorAI and HR TechnologiesHuman-Automation Interaction and Safety

The DOI matches its Crossref record; 218 of the 218 references it cites were found in a registry or an index.

When Conscientious Employees Meet Intelligent Machines: An Integrative Approach Inspired by Complementarity Theory and Role Theory

Man Tang, Pok, Koopman, Joel, McClean, Shawn T., Zhang, Jack H., Li, Chi Hon, De Cremer, David, Lu, Yizhen, Ng, Chin Tung Stewart·2022·Academy of Management Journal
DOI 10.5465/amj.2020.1516
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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.

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Citations

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Metadata

Authors
Man Tang, P., Koopman, J., McClean, S. T., Zhang, J. H., Li, C. H., De Cremer, D., Lu, Y., & Ng, C. T. S.
Affiliations
  • University of Georgia
  • Texas A&M University (US)
  • University of Oklahoma (US)
  • Washington University in St. Louis (US)
  • National University of Singapore
  • National University of Singapore (SG)
  • National Sun Yat-sen University (TW)
ORCID
Venue
Academy of Management Journal
Publisher
Academy of Management
Published
2022-06
Volume
65
Issue
3
Pages
1019-1054
ISSN
  • 0001-4273 (ISSN-L)
  • 1948-0989
DOI
10.5465/amj.2020.1516
OpenAlex
W3211155880
Semantic Scholar
d804dd90e7632bee1eaad8d0c8d696d620ec5731
Cited by
370
References
215
Citations by year
2026: 162 · 2025: 117 · 2024: 54 · 2023: 27 · 2022: 9
FWCI
22.07
Type
journal article
Language
en
Open access
closed
Locations
  • Academy of Management Journal (publishedVersion)
  • ePrints Soton (University of Southampton) (submittedVersion)
Topics
  • Social Sciences › Business, Management and Accounting › Organizational Behavior and Human Resource Management › Job Satisfaction and Organizational Behavior
  • Social Sciences › Business, Management and Accounting › Organizational Behavior and Human Resource Management › AI and HR Technologies
  • Social Sciences › Human Factors and Ergonomics › Human-Automation Interaction and Safety
Keywords
conscientiousness, intelligent machines, artificial intelligence, robots, algorithms, complementarity principle, role theory, orderliness, employee performance, autonomous decision-making, human-machine interaction

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