When Conscientious Employees Meet Intelligent Machines: An Integrative Approach Inspired by Complementarity Theory and Role Theory
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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Placing people in the role of an editor will likely pose a threat to the editor's experience of self-efficacy because AI occupies the proactive role of generating a creative product and the editor occupies the reactive role of aiding with improving its work.
“This will likely pose a threat to the editor’s experience of self-efficacy because AI occupies the proactive role of generating a creative product and the editor occupies the reactive role of aiding with improving its work24,25.”
From Establishing the importance of co-creation and self-efficacy in creative collaboration with artificial intelligence (2024) · cites this paper as [25]
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- 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)
- 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
- OpenAlex
- W3211155880
- Semantic Scholar
- d804dd90e7632bee1eaad8d0c8d696d620ec5731
- Cited by
- 370
- References
- 215
- Citations by year
- 2026: 162 · 2025: 117 · 2024: 54 · 2023: 27 · 2022: 9 · 2021: 1
- 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 › Psychology › Social Psychology › 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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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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