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
Citations
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
Flagged first, then by citations
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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Kong, L.-Y., & Duraipandi, O. (2025) · Lex Localis-journal of Local Self-government · doi:10.52152/6jdejb72
“Data collection employs multi-source triangulation methods, integrating secondary and primary data to ensure research reliability (Tong, Jia, Luo, & Fang, 2021).”
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Grover, M., Kaur, G., Nazeer, I., Mishra, B. B., Parashar, K., & Hussain, N. (2025) · Multidisciplinary Science Journal · doi:10.31893/multiscience.2025ss0205
Rathore, A., Bhargava, M., Raj, N., Minhas, D., Bawa, R., & Sreevas, V. T. K. (2025) · Multidisciplinary Science Journal · doi:10.31893/multiscience.2025ss0206
van Zijl, A. L., & Koster, F. (2025) · Innovation & Management Review · doi:10.1108/inmr-06-2023-0094
AI Management and Emotional Disengagement: A Conceptual Overview
Raime, S., Rahman, N. A., Hashim, R. A., & Shamsudin, M. F. (2025) · International Journal of Academic Research in Business and Social Sciences · doi:10.6007/ijarbss/v15-i8/26186
“The absence of human interaction and emotional validation can result in a breakdown of trust between employees and the organisation (Tong et al., 2021).”
InfluentialMubarrak, H., Sari, V. W., Nurhayati, N., Wardayani, W., Suginam, S., & Mesran, M. (2025) · TIN: Terapan Informatika Nusantara · doi:10.47065/tin.v6i2.8071
“(Tong, 2021), for instance, looked at how AI affected the work attitudes and performance of teachers, but they didn't talk about administrative or support positions.”
NEP 2020 – Effectiveness of deployment and redeployment of faculty members for academic innovations
Gupta, P., Dubey, P., Dave, T., & Gupta, B. (2025) · Asian Journal of Management · doi:10.52711/2321-5763.2025.00037
Regis, C. F. T., & Vera, L. A. R. (2025) · Future Studies Research Journal: Trends and Strategies · doi:10.24023/futurejournal/2175-5825/2025.v17i1.887
Tešić, B., & Pavlović, M. M. (2025) · STED JOURNAL · doi:10.63395/stedjournal0701073e58
Managerial Support and Employee Performance of Small Enterprises at Tacurong City
Avila, S. M., Escalera, G., Galoy, E., Pande, C. J., Cruz, J., Talamor, C. J., Gamboa, H., & Decena, J. (2025) · Psychology and Education A Multidisciplinary Journal · doi:10.70838/pemj.370103
Unlocking Strategic Directions for AI-Powered HR: a Business Value Assessment Framework
Pour, M. J., & Emami, S. A. (2025) · International Conference on Web Research (ICWR) · doi:10.1109/icwr65219.2025.11006212
“By leveraging AI, organizations can not only make data-driven decisions but also significantly improve workforce efficiency and reduce supervision costs [11].”
Performance Management Model Innovation and Organisational Change Driven by Artificial Intelligence
Yan, T.-L. (2025) · Journal of Combinatorial Mathematics and Combinatorial Computing · doi:10.61091/jcmcc127a-036
BİLİŞİM SEKTÖRÜNDE ÇALIŞANLARIN SORUMLULUK VE DIĞERKAMLIK DUYGUSU ÜZERİNE BIR ARAŞTIRMA
Erdoğan, D., Özcan, A., & Erkasap, A. (2025) · İstanbul Ticaret Üniversitesi Girişimcilik Dergisi · doi:10.55830/tje.1592703
Dinh, T. H., Nguyen, T. B. L., Trang, T. V., & Nguyen, H. N. (2025) · Journal of Management World · doi:10.53935/jomw.v2024i4.716
“AI improves the significance of feedback given to specific employees by effectively catering to each individual's distinct work circumstances and difficulties (Tong et al., 2021).”
Meng, B., & Liu, W. (2024) · ICAIE · doi:10.1145/3722237.3722393
“Tong et al. [7] argue that companies are increasingly using AI to provide performance feedback to employees.”
Reinvigorating Unicorn Startup Employee Productivity Following Mass Layoffs
Dania, R. F. R., & Gulabdin, S. (2024) · International Student Conference on Business, Education, Economics, Accounting, and Management (ISC-BEAM) · doi:10.21009/isc-beam.011.32
“As a multidimensional concept influenced by many factors, observations regarding employee performance cannot stand alone (Singgih et al., 2021) Companies should invest more in managing employee performance, especially in critical times (Tong, Siliang Jia, N., Luo, X., & Fang, 2021).”
BackgroundMarketing faced with the organizational challenge of AI explainability
Vuarin, L., & Steyer, V. (2024) · Décisions Marketing · doi:10.3917/dm.112.0105
When Machine Comes to Town: Fund Analysts’ Performance with Artificial Intelligence
Kang, X., & Fan, G. (2023) · Academy of Management Proceedings · doi:10.5465/amproc.2023.19813abstract
“Empirical studies find mixed results on the effect of AI adoption on human workers’ performance (Allen and Choudhury, 2022; Agrawal, Gans, and Goldfarb, 2019; Scholl and Hanson, 2020; Tong et al., 2021).”
ResultHan, Y., Ma, Z., & Zong, S. (2026) · Advances in Psychological Science · doi:10.3724/sp.j.1042.2026.0626
Ma, L., Dong, Q., Zhang, X., Wang, S., & Hao, F. (2026) · The International Journal of Management Education · doi:10.1016/j.ijme.2026.101436 · Cited by 1
Santos, C., Viana, F., & Sánchez-Caballé, A. (2026) · Ensaio Avaliação e Políticas Públicas em Educação · doi:10.1590/s0104-40362026003405414
Bessala, J. P. B. (2026) · Journal of Human Resource and Sustainability Studies · doi:10.4236/jhrss.2026.144025
Wang, G., Long, L., Li, S., Sun, F., Wang, J., & Huang, S. (2025) · Acta Psychologica Sinica · doi:10.3724/sp.j.1041.2025.0298
“看来,传统由人类管理者提供负面绩效反馈的方式面临着较大的挑战 (Kluger & DeNisi, 1996; Xing et al., 2023) 。 随着数智化技术的发展,采用 AI 提供负面绩效反馈为组织带来新的机遇 (Lee, 2018; Luo et al., 2021) 。例如,一款名为 Enaible 的算法通过远程监控员工的工作行为,诊断员工表现 不佳的原因,并提供绩效改进建议。此外, Butterfly 等 AI 评估软件可以细致搜集员工的行 为数据,帮助员工及时改善表现 (Tong et al., 2021) 。那么, AI 相较人类管理者提供负面绩效…”
InfluentialThe Negative Effects of Positive Real-Time Feedback from Artificial IntelligencePreprint
Kinch, M. A. (2025) · Social Science Research Network · doi:10.2139/ssrn.5249994
Guo, C., Liu, H., & Guo, J. (2025) · Journal of Psychology in Africa · doi:10.32604/jpa.2025.065763
“…in the ‘horse race’ with AI, given AI’s superior information processing capabilities (Chamorro-Premuzic & Ahmetoglu, 2016), exceptional and unbiased ability to recognize potential patterns in incomplete data sets (Parry et al., 2016), and lack of fatigue compared to humans (Tong et al., 2021).”
Comparison of Feedback from ChatGPT and Human Professors in Higher Education: A Systematic Review
Paniura, C. H. G., Luza, T. C., Cruz-Carpio, F. M., Díaz, R. R. I., Apaza-Paco, D. V., Rojas, N. R., Mamani-Mamani, B., Terrero-Pérez, Á., Yaedú, R. Y. F., & Peralta-Mamani, M. (2025) · Journal of Computer and Communications · doi:10.4236/jcc.2025.137013 · Cited by 1
任, 美. (2025) · E-Commerce Letters · doi:10.12677/ecl.2025.14113488
Nkosi, T., Molefe, L., Dlamini, S., Mokoena, A., & Ndlovu, K. (2024) · Journal of Digital Business and Management Studies · doi:10.68159/l318883202
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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