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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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“created by them, as existing work shows that the disclosure of the use of AI tools in work processes or the creation of products can decrease receptiveness to feedback (Tong et al. 2021), the effectiveness of AI”
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Mayer, A., Baygi, R. M., & Buwalda, R. (2025) · Business & Information Systems Engineering · doi:10.1007/s12599-025-00959-x · Cited by 43
“Further, GenAI can support human creativity in fields like design, content development, and strategic planning by providing fresh insights and ideas that improve the quality and productivity of work (Eloundou et al. 2023; Tong et al. 2021).”
Vuori, N., Burkhard, B., & Pitkäranta, L. (2025) · Journal of Management Studies · doi:10.1111/joms.13177 · Cited by 54
How Does AI Improve Human Decision-Making? Evidence from the AI-Powered Go Program
Choi, S., Kim, N., Kim, J., & Kang, H. (2021) · SSRN Electronic Journal · doi:10.2139/ssrn.3893835 · Cited by 30
Bridging human resource development processes through generative Artificial Intelligence
Korzyński, P., Kim, S., & Egan, T. M. (2024) · Human Resource Development Quarterly · doi:10.1002/hrdq.21551 · Cited by 35
Jiang, F., Wang, L., Li, J., & Liu, J. (2022) · Frontiers in Psychology · doi:10.3389/fpsyg.2021.768440 · Cited by 39
“In addition, smart technologies can teach specific procedures use certain teaching abilities to allow employees to self-learn and execute according to the company’s needs (Tong et al., 2021).”
BackgroundHow Do Manufacturing Firms Manage Artificial Intelligence to Drive Iterative Product Innovation?
Jiang, X., Jiang, X.-X., Sun, W., & Fan, W. (2024) · IEEE transactions on engineering management · doi:10.1109/tem.2023.3259396 · Cited by 30
Jia, J., Ning, X., & Liu, W. (2025) · Journal of Digital Management · doi:10.1007/s44362-025-00016-3 · Cited by 41
“Additionally, the implementation of AI could significantly change communication styles and feedback mechanisms between leaders and their subordinates (Tong et al., 2021).”
InfluentialMattila, A. S., Wu, L. L., & Wang, P. (2025) · Journal of Hospitality and Tourism Management · doi:10.1016/j.jhtm.2025.01.017 · Cited by 36
Artificial intelligence for employee engagement and productivity
Gusti, M. A., Satrianto, A., Candrianto, C., Juniardi, E., & Fitra, H. (2024) · Problems and Perspectives in Management · doi:10.21511/ppm.22(3).2024.14 · Cited by 29
Khandelwal, K., Upadhyay, A. K., & Rukadikar, A. (2024) · Human Resource Development International · doi:10.1080/13678868.2024.2375935 · Cited by 35
“AI apps track employees’ behaviours at work, assess their performance, and provide suggestions for enhancing employee productivity (Tong et al., 2021).”
Guo, M., Gu, M., & Huo, B. (2024) · International Journal of Operations & Production Management · doi:10.1108/ijopm-06-2023-0509 · Cited by 22
Arya, V., Saraf, A., Юрьевич, Ч. Н., Papa, A., & Romano, M. (2025) · The Journal of Technology Transfer · doi:10.1007/s10961-025-10233-7 · Cited by 41
Artificial Intelligence (AI) and Workplace Communication: Promises, Perils, and Recommended Policy
Quaquebeke, N. V., & Gerpott, F. H. (2024) · Journal of Leadership & Organizational Studies · doi:10.1177/15480518241289644 · Cited by 42
“Partially, this is because they tend to trust AI-generated or -supported text less than human-generated content (see the example of feedback: Tong et al., 2021 or on ethical decisions: Jago, 2019).”
Zhang, Z., Li, B., & Liu, L. (2022) · Applied Artificial Intelligence · doi:10.1080/08839514.2022.2157592 · Cited by 28
Filippelli, S., Popescu, A. I., Verteramo, S., Tani, M., & Corvello, V. (2025) · Journal of Innovation & Knowledge · doi:10.1016/j.jik.2025.100844 · Cited by 56
Purcell, Z. A., Dong, M., Nußberger, A.-M., Köbis, N., & Jakesch, M. (2024) · British Journal of Psychology · doi:10.1111/bjop.12727 · Cited by 35
“For example, people accept identical advice for performance improvement less when the advice is labelled as AI-rather than human-generated (Tong et al., 2021).”
Cao, Z., Li, M., & Pavlou, P. A. (2024) · Decision Sciences · doi:10.1111/deci.12655 · Cited by 35
Determinants of employees’ willingness to seek help from robots
Lin, M., Zhang, Z., & Wu, G. (2024) · International Journal of Hospitality Management · doi:10.1016/j.ijhm.2024.103703 · Cited by 26
Alzeiby, E. A., Islam, N., Shaik, A. S., & Yaqub, M. Z. (2025) · Journal of Enterprise Information Management · doi:10.1108/jeim-05-2024-0249 · Cited by 29
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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