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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
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“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
Artificial intelligence and the future of work: Human-AI symbiosis in organizational decision making
Jarrahi, M. H. (2018) · Business Horizons · doi:10.1016/j.bushor.2018.03.007 · Cited by 2,772
Algorithm appreciation: People prefer algorithmic to human judgment
Logg, J. M., Minson, J. A., & Moore, D. A. (2019) · Organizational Behavior and Human Decision Processes · doi:10.1016/j.obhdp.2018.12.005 · Cited by 2,061
Resistance to Medical Artificial Intelligence
Longoni, C., Bonezzi, A., & Morewedge, C. K. (2019) · Journal of Consumer Research · doi:10.1093/jcr/ucz013 · Cited by 2,015
ARTIFICIAL INTELLIGENCE FOR THE REAL WORLD
Davenport, T. H., & Ronanki, R. (2023) · International Research Journal of Modernization in Engineering Technology and Science · doi:10.56726/irjmets42512 · Cited by 1,829
The design school: Reconsidering the basic premises of strategic management
Mintzberg, H. (1990) · Strategic Management Journal · doi:10.1002/smj.4250110302 · Cited by 1,469
Luo, X., Tong, S., Fang, Z., & Qu, Z. (2019) · Marketing Science · doi:10.1287/mksc.2019.1192 · Cited by 1,262
Self-citationThe Power of Feedback Revisited: A Meta-Analysis of Educational Feedback Research
Wisniewski, B., Zierer, K., & Hattie, J. (2020) · Frontiers in Psychology · doi:10.3389/fpsyg.2019.03087 · Cited by 1,199
Mende, M., Scott, M. L., van Doorn, J., Grewal, D., & Shanks, I. (2019) · Journal of Marketing Research · doi:10.1177/0022243718822827 · Cited by 1,197
What can machine learning do? Workforce implications
Brynjolfsson, E., & Mitchell, T. (2017) · Science · doi:10.1126/science.aap8062 · Cited by 1,131
Robots and Jobs: Evidence from US Labor Markets
Acemoğlu, D., & Restrepo, P. (2017) · National Bureau of Economic Research · doi:10.3386/w23285 · Cited by 924
Petty Tyranny in Organizations
Ashforth, B. E. (1994) · Human Relations · doi:10.1177/001872679404700701 · Cited by 923
Artificial Intelligence: The Ambiguous Labor Market Impact of Automating Prediction
Agrawal, A., Gans, J. S., & Goldfarb, A. (2019) · Journal of Economic Perspectives · doi:10.1257/jep.33.2.31 · Cited by 685
AI super-powers: China, Silicon Valley and the new world order
Klingler‐Vidra, R. (2019) · International Affairs · doi:10.1093/ia/iiz011 · Cited by 618
Newman, D. T., Fast, N. J., & Harmon, D. J. (2020) · Organizational Behavior and Human Decision Processes · doi:10.1016/j.obhdp.2020.03.008 · Cited by 550
How artificial intelligence is changing drug discovery
Fleming, N. (2018) · Nature · doi:10.1038/d41586-018-05267-x · Cited by 546
The Impact of Artificial Intelligence on the Labor Market
Webb, M. (2019) · SSRN Electronic Journal · doi:10.2139/ssrn.3482150 · Cited by 531
Estimating the Impact of “Humanizing” Customer Service Chatbots
Schanke, S., Burtch, G., & Ray, G. (2021) · Information Systems Research · doi:10.1287/isre.2021.1015 · Cited by 432
Does Machine Translation Affect International Trade? Evidence from a Large Digital Platform
Brynjolfsson, E., Hui, X., & Liu, M. (2018) · doi:10.3386/w24917 · Cited by 408
Does Machine Translation Affect International Trade? Evidence from a Large Digital Platform
Brynjolfsson, E., Hui, X., & Liu, M. (2019) · Management Science · doi:10.1287/mnsc.2019.3388 · Cited by 408
Estimating the Impact of "Humanizing" Customer Service ChatbotsPreprint
Schanke, S., Burtch, G., & Ray, G. (2020) · Information systems research · doi:10.31235/osf.io/xud6z · Cited by 315
Competing in the Age of AI: Strategy and Leadership When Algorithms and Networks Run the World
Bhattacharya, D. (2021) · Strategic Analysis · doi:10.1080/09700161.2021.1918951 · Cited by 311
Machine learning and human capital complementarities: Experimental evidence on bias mitigation
Choudhury, P., Starr, E., & Agarwal, R. (2020) · Strategic Management Journal · doi:10.1002/smj.3152 · Cited by 293
Artificial Intelligence Coaches for Sales Agents: Caveats and Solutions
Luo, X.-M., Qin, M., Fang, Z., & Qu, Z. (2020) · Journal of Marketing · doi:10.1177/0022242920956676 · Cited by 292
Self-citationMan Versus Machine: Resisting Automation in Identity-Based Consumer Behavior
Leung, E., Paolacci, G., & Puntoni, S. (2018) · Journal of Marketing Research · doi:10.1177/0022243718818423 · Cited by 285
A Network Perspective on Individual-Level Ambidexterity in Organizations
Rogan, M., & Mors, M. L. (2014) · Organization Science · doi:10.1287/orsc.2014.0901 · Cited by 270
The Relationship of Age, Tenure, and Job Satisfaction in Males and Females
Hunt, J. W., & Saul, P. (1975) · Academy of Management Journal · doi:10.2307/255372 · Cited by 200
Turning Work into Play: Implications for Microcomputer Software Training
Webster, J., & Martocchio, J. J. (1993) · Journal of Management · doi:10.1177/014920639301900109 · Cited by 191
Improving job performance through training in goal setting.
Latham, G. P., & Kinne, S. B. (1974) · Journal of Applied Psychology · doi:10.1037/h0036530 · Cited by 185
Using AI to Enhance Business Operations
Tarafdar, M., Beath, C. M., & Ross, J. W. (2020) · The MIT Press eBooks · doi:10.7551/mitpress/12588.003.0015 · Cited by 174
Staffing a Call Center with Uncertain Arrival Rate and Absenteeism
Whitt, W. (2006) · Production and Operations Management · doi:10.1111/j.1937-5956.2006.tb00005.x · Cited by 159
A Machine Learning Approach to Improving Dynamic Decision Making
Meyer, G., Adomavičius, G., Johnson, P. E., Elidrisi, M., Rush, W. A., Sperl‐Hillen, J. M., & O’Connor, P. J. (2014) · Information Systems Research · doi:10.1287/isre.2014.0513 · Cited by 142
The Impact of Automation of Systems on Medical Errors: Evidence from Field Research
Aron, R., Dutta, S., Janakiraman, R., & Pathak, P. (2011) · Information Systems Research · doi:10.1287/isre.1110.0350 · Cited by 142
Confronting the risks of artificial intelligence
Cheatham, B., Javanmardian, K., & Samandari, H. (2019) · Cited by 123
AI, Labor, Productivity and the Need for Firm-Level Data
Seamans, R., & Raj, M. (2018) · doi:10.3386/w24239 · Cited by 117
Kesavan, S., & Kushwaha, T. (2020) · Management Science · doi:10.1287/mnsc.2020.3743 · Cited by 104
Bai, B., Dai, H., Zhang, D., Zhang, F., & Hu, H. (2020) · Manufacturing & Service Operations Management · doi:10.1287/msom.2022.1120 · Cited by 103
Competing in the age of AI strategy and leadership when algorithms and networks run the world
Iansiti, M., & Lakhani, K. R. (2020) · Cited by 87
Artificial Intelligence and Economic Growth
Aghion, P., Jones, B. F., & Jones, C. I. (2017) · The Economics of Artificial Intelligence · doi:10.3386/w23928 · Cited by 86
Big Data Analytics: Challenges And Applications For Text, Audio, Video, And Social Media Data
Verma, J. P. V., & Agrawal, S. S. (2016) · International Journal on Soft Computing Artificial Intelligence and Applications · doi:10.5121/ijscai.2016.5105 · Cited by 85
AI, Labor, Productivity, and the Need for Firm-Level Data
Raj, M., & Seamans, R. (2019) · University of Chicago Press eBooks · doi:10.7208/chicago/9780226613475.003.0022 · Cited by 72
Prediction machines: the simple economics of artificial intelligence
Heath, D. (2019) · Journal of IT Cases and Applications · doi:10.1080/15228053.2019.1673511 · Cited by 70
Understanding Employee Trust in Management: Conceptual Clarification and Correlates
Perry, R. W., & Mankin, L. D. (2004) · Public Personnel Management · doi:10.1177/009102600403300303 · Cited by 61
The Effect of Artificial Intelligence on Human Labor: An Ability-Based Approach
Felten, E. W., Raj, M., & Seamans, R. (2019) · Academy of Management Proceedings · doi:10.5465/ambpp.2019.140 · Cited by 52
Bai, B., Dai, H., Zhang, D., et al. (2020) · SSRN Electronic Journal · doi:10.2139/ssrn.3550887 · Cited by 34
Prediction, Judgment, and Complexity
Agrawal, A. K., Gans, J., & Avi Goldfarb, B. (2019) · University of Chicago Press eBooks · doi:10.7208/chicago/9780226613475.003.0003 · Cited by 28
Strategy and Strategic Alignment in Professional Service Firms
Somaya, D., & Mawdsley, J. (2015) · Oxford University Press eBooks · doi:10.1093/oxfordhb/9780199682393.013.12 · Cited by 17
Decision-Making Skills in an AI World: Lessons from Online ChessPreprint
Miric, M., Lu, J., & Teodoridis, F. (2020) · SSRN Electronic Journal · doi:10.2139/ssrn.3538840 · Cited by 9
Employee Resistance to Computer Technology
Ewert, A. (1984) · Journal of Physical Education Recreation & Dance · doi:10.1080/07303084.1984.10629723 · Cited by 5
The Effect of Voice AI on Consumer Purchase and Search Behavior
Sun, C., Shi, Z., Liu, X., & Ghose, A. (2020) · Rare & Special e-Zone (The Hong Kong University of Science and Technology) · Cited by 5
Humans Judge, Technologies Nudge: When and Why People Embrace Behavior Tracking Products
Raveendhran, R., & Fast, N. J. (2019) · Academy of Management Proceedings · doi:10.5465/ambpp.2019.13103abstract · Cited by 4
S.44 - 116th Congress (2019-2020): Balanced Budget Accountability Act
Daines, S. (2019) · Cited by 1
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
Cited by
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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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