The DOI matches its Crossref record; 61 of the 61 references it cites were found in a registry or an index.
The Janus face of artificial intelligence feedback: Deployment versus disclosure effects on employee performance
Open accessAbstract
Companies are increasingly using artificial intelligence (AI) to provide performance feedback to employees, by tracking employee behavior at work, automating performance evaluations, and recommending job improvements. However, this application of AI has provoked much debate. On the one hand, powerful AI data analytics increase the quality of feedback, which may enhance employee productivity (“deployment effect”). On the other hand, employees may develop a negative perception of AI feedback once it is disclosed to them, thus harming their productivity (“disclosure effect”). We examine these two effects theoretically and test them empirically using data from a field experiment. We find strong evidence that both effects coexist, and that the adverse disclosure effect is mitigated by employees' tenure in the firm. These findings offer pivotal implications for management theory, practice, and public policies. Managerial abstract Artificial intelligence (AI) technologies are bound to transform how companies manage employees. We examine the use of AI to generate performance feedback for employees. We demonstrate that AI significantly increases the accuracy and consistency of the analyses of information collected, and the relevance of feedback to each employee. These advantages of AI help employees achieve greater job performance at scale, and thus create value for companies. However, our study also alerts companies to the negative effect of disclosing using AI to employee that resul…
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“Some AI systems can also provide performance feedback and help employees recognise their successes so they can develop their self -management skills (Tong et al., 2021).”
From The Impact of Artificial Intelligence on Workers' Skills: Upskilling and Reskilling in Organisations (2023) · cites this paper as Tong et al., 2021
In the citing paper
“AI -controlled robots and machines, for example, can be programmed to perform tasks with a high degree of accuracy and repeatability, reducing the risk of error and improving overall quality (Tong et al., 2021).”
From The Impact of Artificial Intelligence on Workers' Skills: Upskilling and Reskilling in Organisations (2023) · cites this paper as Tong et al., 2021
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Cites
SPSS and SAS procedures for estimating indirect effects in simple mediation models
Preacher, K. J., & Hayes, A. F. (2004) · Behavior Research Methods, Instruments, & Computers · doi:10.3758/bf03206553 · Cited by 17,383
Machine learning: Trends, perspectives, and prospects
Jordan, M. I., & Mitchell, T. M. (2015) · Science · doi:10.1126/science.aaa8415 · Cited by 9,955
Formative assessment and the design of instructional systems
Sadler, D. R. (1989) · Instructional Science · doi:10.1007/bf00117714 · Cited by 4,285
A general approach to causal mediation analysis.
Imai, K., Keele, L., & Tingley, D. (2010) · Psychological Methods · doi:10.1037/a0020761 · Cited by 4,143
Robots and Jobs: Evidence from US Labor Markets
Acemoglu, D., & Restrepo, P. (2020) · Journal of Political Economy · doi:10.1086/705716 · Cited by 4,088
Artificial intelligence in service
Huang, M.-H., & Rust, R. T. (2018) · Journal of Service Research · doi:10.1177/1094670517752459 · Cited by 3,978
EMPLOYEE CREATIVITY: PERSONAL AND CONTEXTUAL FACTORS AT WORK.
Oldham, G. R., & Cummings, A. (1996) · Academy of Management Journal · doi:10.2307/256657 · Cited by 3,749
Algorithm aversion: People erroneously avoid algorithms after seeing them err.
Dietvorst, B. J., Simmons, J. P., & Massey, C. (2015) · Journal of Experimental Psychology: General · doi:10.1037/xge0000033 · Cited by 3,232
Acemoglu, D., & Restrepo, P. (2018) · American Economic Review · doi:10.1257/aer.20160696 · Cited by 3,121
Human trust in artificial intelligence: Review of empirical research
Glikson, E., & Woolley, A. W. (2020) · Academy of Management Annals · doi:10.5465/annals.2018.0057 · Cited by 2,921
Research works printed but not linked: 9
- Agrawal Ajay McHale J. &Oettl A. (2019).Artificial intelligence scientific discovery and commercial innovation.http://www.esri.go.jp/jp/workshop/190730/esri2019_second_presenter_paper.pdf
- Bughin J., 2019, Your AI efforts Won't succeed unless they benefit employees, Harvard Business Review
- Fountaine T., 2019, Building the AI‐powered organization, Harvard Business Review
- Ip G., 2019, For lower‐paid workers, the robot overlords have arrived ‐ WSJ
- Kahneman D., 2011, Thinking, Fast and Slow
- MacCarthy M., 2020, AI needs more regulation, not less
- Möhlmann M. &Zalmanson L.(2017).Navigating algorithmic management and drivers. Available fromhttps://www.researchgate.net/publication/319965259
- National Law Review. (2019).Keeping an eye on artificial intelligence regulation and legislation. Natlawreview.Com. Available fromhttps://www.natlawreview.com/article/keeping-eye-artificial-intelligence-regulation-and-legislation
- Premuzic T. C., 2018, As AI makes more decisions, the nature of leadership will change, Harvard Business Review
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Dell’Acqua, F., McFowland, E., Mollick, E., Lifshitz, H., Kellogg, K. C., Rajendran, S., Krayer, L., Candelon, F., & Lakhani, K. R. (2026) · Organization Science · doi:10.1287/orsc.2025.21838 · Cited by 1,189
Bankins, S., Ocampo, A. C., Marrone, M., Restubog, S. L. D., & Woo, S. E. (2024) · Journal of Organizational Behavior · doi:10.1002/job.2735 · Cited by 799
Dell'Acqua, F., McFowland III, E., Mollick, E. R., Lifshitz-Assaf, H., Kellogg, K., Rajendran, S., Krayer, L., Candelon, F., & Lakhani, K. R. (2023) · SSRN Electronic Journal · doi:10.2139/ssrn.4573321 · Cited by 794
The Impact of Artificial Intelligence on Workers' Skills: Upskilling and Reskilling in Organisations
Morandini, S., Fraboni, F., De Angelis, M., Puzzo, G., Giusino, D., & Pietrantoni, L. (2023) · Informing Science: The International Journal of an Emerging Transdiscipline · doi:10.28945/5078 · Cited by 439
“Some AI systems can also provide performance feedback and help employees recognise their successes so they can develop their self - management skills (Tong et al., 2021).”
BackgroundArtificial Intelligence in Tactical Human Resource Management: A Systematic Literature Review
Votto, A. M., Valecha, R., Najafirad, P., & Rao, H. R. (2021) · International Journal of Information Management Data Insights · doi:10.1016/j.jjimei.2021.100047 · Cited by 322
Metadata
- Authors
- Tong, S., Jia, N., Luo, X., & Fang, Z.
- Affiliations
- Nanyang Technological University (SG)
- University of Southern California (US)
- Temple University (US)
- Sichuan University (CN)
- Corresponding author
- Fang, Zheng
- Venue
- Strategic Management Journal
- Publisher
- Wiley
- Published
- 2021-07-12
- Volume
- 42
- Issue
- 9
- Pages
- 1600-1631
- ISSN
- 0143-2095 (ISSN-L)
- 1097-0266
- OpenAlex
- W3181487680
- Cited by
- 531
- References
- 59
- Citations by year
- 2026: 138 · 2025: 181 · 2024: 117 · 2023: 64 · 2022: 25 · 2021: 5
- FWCI
- 51.33
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Strategic Management Journal (publishedVersion, cc-by-nc-nd)
- SSRN Electronic Journal (publishedVersion)
- DR-NTU (Nanyang Technological University) (submittedVersion)
- Topics
- Social Sciences › Business, Management and Accounting › Organizational Behavior and Human Resource Management › AI and HR Technologies
- Physical Sciences › Computer Science › Artificial Intelligence › Ethics and Social Impacts of AI
- Social Sciences › Decision Sciences › Management Science and Operations Research › Impact of AI and Big Data on Business and Society
- Keywords
- artificial intelligence, performance feedback, employee performance, disclosure effects, employee perceptions of AI, employee tenure, labor productivity, internal communication
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