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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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AI-driven dynamic capabilities in international marketing: framework, gaps and research directions
Chen, Y., Liu, L., Li, J., & Singh, S. K. (2025) · International Marketing Review · doi:10.1108/imr-03-2025-0128 · Cited by 11
AI empowers enterprise agility and performance: Research trends and implications for future research
Luo, J. (2025) · Business Information Review · doi:10.1177/02663821251384881 · Cited by 7
“In terms of employee agility, AI ’ s self-learning and cognitive capabilities can provide fast and accurate feedback, helping employees to adapt to changes more ef fi ciently in a dynamic environment (Ma et al., 2024; Tong et al., 2021).”
Liu, P., Yuan, L., & Wang, J. (2025) · Asia Pacific Journal of Human Resources · doi:10.1111/1744-7941.70031 · Cited by 10
Consumer response to failure by service robots: the role of anticipatory guilt
Shu, L., Wu, Y., Wang, K., & Li, S. (2025) · Journal of Services Marketing · doi:10.1108/jsm-05-2024-0237 · Cited by 6
Yong, Y., Li, Y., Zhao, X., Law, R., & Song, H. (2025) · International Journal of Contemporary Hospitality Management · doi:10.1108/ijchm-05-2024-0651 · Cited by 6
To learn or not learn from AI? Unpacking the effects of feedback valence on novel insights recall
Turel, O. (2024) · European Journal of Information Systems · doi:10.1080/0960085x.2024.2426473 · Cited by 10
“Our results point to some similarities to Tong et al. (2021)’s findings, but also to some nuanced differences that can point to future research opportunities.”
InfluentialChipriyanova, G., Chipriyanov, M., & Luchkov, K. (2024) · ENVIRONMENT. TECHNOLOGIES. RESOURCES. Proceedings of the International Scientific and Practical Conference · doi:10.17770/etr2024vol2.8032 · Cited by 6
“The implementation of AI [16] – [20], especially in internal business processes, represents a key stage in the technological transformation of companies.”
BackgroundDoes firms’ digitalization affect trade credit provision?
Wang, Y., Song, X.-Z., & Zhou, J. (2024) · Asia-Pacific Journal of Accounting & Economics · doi:10.1080/16081625.2024.2356108 · Cited by 6
“…time (Bensoussan et al. 2016; Paredes and Wheatley 2018; Ramadan 2021; Sheel and Nath 2019), adjust employee structures (Grennan and Michaely 2020; Tong et al. 2021), optimize technological innovation (Ghasemaghaei and Calic 2019; Huang and Van Mieghem 2014; H. L. Lee 2018; Wen, Zhong, and Lee…”
Turnover at the Top: The Digital Transformation and Dismissal of Chairman and CEO
Qi, J., Zhou, Y., Lv, W., Du, Q., Liu, R., & Liu, C. (2022) · Frontiers in Psychology · doi:10.3389/fpsyg.2022.883192 · Cited by 6
Si, Y., Chen, M., & Xiao, H. (2025) · International Journal of Hospitality Management · doi:10.1016/j.ijhm.2025.104534 · Cited by 8
Zhang, X., Qi, Z., Ma, L., & Zhang, G. (2026) · Journal of Knowledge Management · doi:10.1108/jkm-10-2024-1249 · Cited by 5
Generative Artificial Intelligence and Ethicality in Entrepreneurs’ Creativity
Mostafiz, M. I., Gali, N., Ahmed, F. U., Hughes, M. M., & Simeonova, B. (2026) · Journal of Business Ethics · doi:10.1007/s10551-026-06301-z · Cited by 10
“Because G-AI outputs are ambiguous and difficult to disentangle (Dong & Bocian, 2024), entrepreneurs are predisposed to internalize success as only being self-generated (Tong et al., 2021).”
Influential
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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