CoChat
Sign Up
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
Open this paper in CoChat — free account

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.

Overview

Integrity signals

Registry

Crossref · 10.5465/amj.2020.1516

Cites

218 of 218 references verified0 citation errors · 0 not checkable

Statistics

Recomputing p-values needs the paper’s full text.

Verification

Claims the paper makes

This paper’s full text isn’t openly available, so its claims and self-check weren’t run. The registry and citation checks still apply.

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

Citations

Cites

Research works printed but not linked: 16

  1. Agrawal A., 2017, MIT Sloan Management Review, 58, 28
  2. Bauer J. C., 2000, Journal of Modern Business, 3, 41
  3. Brislin R. W., 1980, Handbook of cross-cultural psychology, vol. 2: Methodology:, 389
  4. Brynjolfsson E., 2018, American Economic Association Papers and Proceedings, 108, 43
  5. Cohen J., 1992, Psychological Bulletin, 112, 155, 10.1037/0033-2909.112.1.155 · DOI · Paper page
  6. Davenport T. H., 2019, Artificial intelligence: The insights you need from Harvard Business Review.
  7. Davenport T. H., 2016, Only humans need apply: Winners and losers in the age of smart machines.
  8. De Cremer D., 2020, Leadership by algorithm: Who leads and who follows in the AI era.
  9. Diekmann J., 2015, Employee recruitment, selection, and assessment: Contemporary issues for theory and practice, 117
  10. 10.5465/3069307 · DOI · Paper page
  11. Goldberg L. R., 1990, Journal of Personality and Social Psychology, 59, 1216, 10.1037/0022-3514.59.6.1216 · DOI · Paper page
  12. Langer E. J., 1976, Journal of Personality and Social Psychology, 34, 191, 10.1037/0022-3514.34.2.191 · DOI
  13. Lee M. K., 2015, CHI ’15: Proceedings of the 33rd Annual ACM Conference on Human Factors in Computing Systems, 1603
  14. Muthén L., 2015, Mplus user’s guide.
  15. Parsons T., 1951, The social system.
  16. Wilson H. J., 2018, Harvard Business Review, 96, 114

Cited by

Sorted by citations

See all 371

Related

See all

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

About these checks

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.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review