When conscientious employees meet intelligent machines: an integrative approach inspired by complementarity theory and role theory
When conscientious employees meet intelligent machines: an integrative approach inspired by complementarity theory and role theory
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.
Tang, Pok Man
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Koopman, Joel
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McClean, Shawn
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Zhang, Jack H.
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Li, Chi Hon
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Cremer, David De
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Lu, Yizhen
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Ng, Chin Tung Stewart
269c5be6-72a5-4ceb-a64a-e3c0e936ef59
Tang, Pok Man
abd945fb-dcd4-4021-909e-0b7044a39452
Koopman, Joel
b889aad5-df02-4aac-946f-f6656cce014e
McClean, Shawn
31dedad5-0529-4022-afa9-6b01243ee12b
Zhang, Jack H.
e66f5494-0487-43f8-aa2d-48829ecc8679
Li, Chi Hon
9f5a5275-316e-40a4-b142-12d21bb42524
Cremer, David De
f2425fce-e87e-4294-87e8-ffea9fff3898
Lu, Yizhen
5c34dda3-c1b4-40d9-a3e2-ab1a4e2e880d
Ng, Chin Tung Stewart
269c5be6-72a5-4ceb-a64a-e3c0e936ef59
Tang, Pok Man, Koopman, Joel, McClean, Shawn, Zhang, Jack H., Li, Chi Hon, Cremer, David De, Lu, Yizhen and Ng, Chin Tung Stewart
(2022)
When conscientious employees meet intelligent machines: an integrative approach inspired by complementarity theory and role theory.
Academy of Management Journal, 65 (3).
(doi:10.5465/amj.2020.1516).
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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e-pub ahead of print date: 30 June 2022
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Local EPrints ID: 482995
URI: http://eprints.soton.ac.uk/id/eprint/482995
ISSN: 0001-4273
PURE UUID: 38f56ff4-0c8e-442c-aac5-0227099bb262
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Date deposited: 18 Oct 2023 16:43
Last modified: 18 Mar 2024 04:12
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Contributors
Author:
Pok Man Tang
Author:
Joel Koopman
Author:
Shawn McClean
Author:
Jack H. Zhang
Author:
Chi Hon Li
Author:
David De Cremer
Author:
Yizhen Lu
Author:
Chin Tung Stewart Ng
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