负面绩效反馈下员工绩效改进动机的人机比较

doi:10.3724/SP.J.1041.2025.0298

摘要/Abstract

摘要： 负面绩效反馈对员工学习和绩效提升具有重要意义, 然而其往往难以被员工所接受。随着人工智能技术(artificial intelligence, AI)逐渐应用于组织情境中, 探索AI提供负面绩效反馈对员工行为及态度的影响成为重要议题。采用4个递进式实验探索了AI与人类管理者提供负面绩效反馈对个体绩效改进动机的差异化影响及机制。实验1~3采取经典的虚假反馈的策略, 发现相较于人类管理者, AI提供负面绩效反馈引发个体更高水平的绩效改进动机(实验1)。并且, 在客观任务中, AI (较人类管理者)提供负面绩效反馈引发个体更高水平的绩效改进动机; 而在主观任务中, 结果则相反(实验2)。此外, 个体对于负面绩效反馈的内部归因解释了上述关系发生的内在机制(实验3)。实验4则采用相对真实的负面绩效反馈情境, 重复了先前3个实验的研究发现。该研究对于组织为何以及何时应用AI提供负面绩效反馈提供了一定的启示。

关键词: 负面绩效反馈, 人工智能, 绩效改进动机, 内部归因, 任务类型

Abstract: Given that negative performance feedback can evoke negative reactions from employees, delivering such feedback effectively has become a challenge for organizations. Driven by rapid innovations in science and technology, artificial intelligence (AI) is gradually being applied in organizational management. For instance, AI can monitor employees’ work behaviors in real time, diagnose and analyze the reasons for their poor performance, and provide them with suggestions for performance improvement. Based on attribution theory, this study examined the benefits that employees may gain when receiving negative performance feedback from AI than from human managers. This study also explored the moderating effect of task type (subjective vs. objective) as a boundary factor for the influence of feedback source on employees’ motivation to improve their performance. Previous research has shown that individuals often experience internal or external attribution after receiving negative performance feedback. Therefore, this study also proposed internal and external attributions as underlying mediating mechanisms.
To test the hypotheses, four experiments were conducted (N = 598) involving various kinds of performance feedback contexts in the workplace, including performance feedback for employees in call centers, training of new employees, employees in vocational ability competitions, and middle managers’ capacity of management. Two strategies were adopted to provide performance feedback for the participants. Specifically, experiments 1-3 used fake feedback to control the feedback content among the participants, while experiment 4 delivered relatively real negative feedback based on the participants’ actual performance to further test the results of experiments 1-3.
Experiment 1 involved 128 full-time employees and used a single-factor, two-level between-subjects design, and results showed that compared with those coming from human managers, negative performance feedback coming from AI led to a higher employee motivation to improve performance.Experiment 2 involved 160 employees and used a two-factor between-subjects design, and results highlighted the interactive effect of negative feedback source (human manager vs. AI) and task types on employees’ motivation to improve their performance. Specifically, in subjective tasks, negative performance feedback from human managers (relative to AI) resulted in a higher motivation to improve performance. However, the opposite case was observed in objective tasks.Experiment 3 involved 150 employees who received negative performance feedback through email, and results highlighted the mediating role of internal attributions in the relationship between negative feedback source and motivation to improve performance.Experiment 4 involved 160 employees and utilized relatively real negative performance feedback, and results were the same as those obtained in experiment 3.
This study offers four theoretical contributions. First, with the emergence of AI as a feedback source for organizations, results show that compared with feedback from human managers, negative performance feedback from AI led to a higher motivation among employees to improve their performance, thereby enriching traditional research on negative performance feedback. Second, task type can moderate the relationship between negative performance feedback source and employees’ motivation to improve performance, and this finding contributes to the expansion of the boundary effects of feedback source (AI or human managers). Third, this study generates insights into agile performance management in the digital intelligence era by demonstrating the advantages of AI in replacing human managers in delivering negative performance feedback. Fourth, this study underscores internal attribution as a potential mechanism, thus expanding the application of attribution theory in explaining individual motivation and behavior within the context of AI versus human managers in delivering negative performance feedback.

Key words: negative performance feedback, artificial intelligence, motivation to improve performance, internal and external attribution, task type

中图分类号:

B849: C93

王国轩, 龙立荣, 李绍龙, 孙芳, 望家晴, 黄世英子. (2025). 负面绩效反馈下员工绩效改进动机的人机比较. 心理学报, 57(2), 298-314.

WANG Guoxuan, LONG Lirong, LI Shaolong, SUN Fang, WANG Jiaqing, HUANG Shiyingzi. (2025). A comparative study on human or AI delivering negative performance feedback influencing employees’ motivation to improve performance. Acta Psychologica Sinica, 57(2), 298-314.

参考文献

[1] Audia, P. G., & Locke, E. A. (2003). Benefiting from negative feedback.Human Resource Management Review, 13(4), 631-646.
[2] Belschak, F. D., & Den Hartog, D. N. (2009). Consequences of positive and negative feedback: The impact on emotions and extra-role behaviors.Applied Psychology, 58(2), 274-303.
[3] Brett, J. F., & Atwater, L. E. (2001). 360° feedback: Accuracy, reactions, and perceptions of usefulness.Journal of Applied Psychology, 86(5), 930-942.
[4] Castelo N., Bos M. W., & Lehmann D. R. (2019). Task-dependent Algorithm aversion.Journal of Marketing Research, 56(5), 809-825.
[5] Chory, R. M., & Westerman, C. Y. (2009). Feedback and fairness: The relationship between negative performance feedback and organizational justice.Western Journal of Communication, 73(2), 157-181.
[6] Cianci A. M., Klein H. J., & Seijts G. H. (2010). The effect of negative feedback on tension and subsequent performance: The main and interactive effects of goal content and conscientiousness.Journal of Applied Psychology, 95(4), 618-630.
[7] Dietvorst B. J., Simmons J. P., & Massey C. (2015). Algorithm aversion: People erroneously avoid algorithms after seeing them err.Journal of Experimental Psychology: General, 144(1), 114-126.
[8] Dimotakis N., Mitchell D., & Maurer T. (2017). Positive and negative assessment center feedback in relation to development self-efficacy, feedback seeking, and promotion.Journal of Applied Psychology, 102(11), 1514-1527
[9] Dong Y., Long L., & Cheng Z. (2022). Performance management in the era of digital intelligence: Present and future.Tsinghua Management Review, 5, 93-100.
[董毓格, 龙立荣, 程芷汀. (2022). 数智时代的绩效管理:现实和未来.清华管理评论, 5, 93-100.]
[10] Fan J., Sun T., Liu J., Zhao T., Zhang B., Chen Z., Glorioso M., & Hack E. (2023). How well can an AI chatbot infer personality? Examining psychometric properties of machine-inferred personality scores. Journal of Applied Psychology, 108(8), 1277-1299.
[11] Faul F., Erdfelder E., Lang A. G., & Buchner A. (2007). G*Power 3: A flexible statistical power analysis program for the social, behavioral, and biomedical sciences.Behavior Research Methods, 39(2), 175-191.
[12] Fields B., Carbery M., Schulz R., Rodakowski J., Terhorst L., & Still C. (2023). Evaluation of Face Validity and Acceptability of the Care Partner Hospital Assessment Tool.Innovation in Aging, 7(2), 557-568.
[13] Garvey A. M., Kim T. W., & Duhachek A. (2023). Bad news? Send an AI. Good news? Send a human.Journal of Marketing, 87(1), 10-25.
[14] Geng Z. Z., Zhao J. J., & Ding. L. (2020). The “wisdom” of golden mean thinking: Research on the mechanism between supervisor developmental feedback and employee creativity.Nankai Management Review, 23(1), 75-86.
[耿紫珍, 赵佳佳, 丁琳. (2020). 中庸的智慧:上级发展性反馈影响员工创造力的机理研究. 南开管理评论, 23(1), 75-86.]
[15] Glikson, E., & Woolley, A. W. (2020). Human Trust in Artificial Intelligence: Review of Empirical Research.Academy of Management Annals, 14(2), 627-660.
[16] Goodman, J. S., & Wood, R. E. (2004). Feedback specificity, learning opportunities, and learning.Journal of Applied Psychology, 89(5), 809-821.
[17] Hareli, S., & Hess, U. (2008). The role of causal attribution in hurt feelings and related social emotions elicited in reaction to others' feedback about failure.Cognition and Emotion, 22(5), 862-880.
[18] Harvey P., Madison K., Martinko M., Crook T. R., & Crook T. A. (2014). Attribution theory in the organizational sciences: The road traveled and the path ahead.Academy of Management Perspectives, 28(2), 128-146.
[19] Heider, F. (1958). The psychology of interpersonal relations. New York: John Wiley & Sons Publishing House.
[20] Helberger N., Araujo T., & de Vreese, C. H. (2020). Who is the fairest of them all? Public attitudes and expectations regarding automated decision-making.Computer Law & Security Review, 39, 105456.
[21] Ilgen D. R., Fisher C. D., & Taylor M. S. (1979). Consequences of individual feedback on behavior in organizations.Journal of Applied Psychology, 64(4), 349-374.
[22] Jiang L., Cao L., Qin X., Tan L., Chen C., & Peng X. (2022). Fairness perceptions of artificial intelligence decision-making.Advances in Psychological Science, 30(5), 1078-1092.
[蒋路远, 曹李梅, 秦昕, 谭玲, 陈晨, 彭小斐. (2022). 人工智能决策的公平感知.心理科学进展, 30(5), 1078-1092.]
[23] Johnson, D. A. (2013). A component analysis of the impact of evaluative and objective feedback on performance.Journal of Organizational Behavior Management, 33(2), 89-103.
[24] Kim, Y. J., & Kim, J. (2020). Does negative feedback benefit (or harm) recipient creativity? The role of the direction of feedback flow.Academy of Management Journal, 63(2), 584-612.
[25] Kitz C. C., Barclay L. J., & Breitsohl H. (2023). The delivery of bad news: An integrative review and path forward.Human Management Review, 33(3), 1-23.
[26] Kluger, A. N., & Denisi, A. (1996). The effects of feedback interventions on performance: A historical review, a meta-analysis, and a preliminary feedback intervention theory.Psychological Bulletin, 119(2), 254-284.
[27] Kuvaas B., Buch R., & Dysvik A. (2017). Constructive supervisor feedback is not sufficient: Immediacy and frequency is essential.Human Resource Management, 56(3), 519-531.
[28] Lam C. F., DeRue D. S., Karam E. P., & Hollenbeck J. R. (2011). The impact of feedback frequency on learning and task performance: Challenging the "More is better" assumption. Organizational Behavior and Human Decision Processes, 116(2), 217-228.
[29] Lee, M. K. (2018). Understanding perception of algorithmic decisions: Fairness, trust, and emotion in response to algorithmic management.Big Data and Society, 5(1), 1-16.
[30] Leo, X., & Huh, Y. E. (2020). Who gets the blame for service failures? Attribution of responsibility toward robot versus human service providers and service firms.Computers in Human Behavior, 113, 106520.
[31] Longoni C., Bonezzi A., & Morewedge C. K. (2019). Resistance to medical artificial intelligence.Journal of Consumer Research, 46(4), 629-650.
[32] Luo X., Qin M. S., Fang Z., & Qu Z. (2021). Artificial intelligence coaches for sales agents: Caveats and solutions.Journal of Marketing, 85(2), 14-32.
[33] Luo X., Tong S., Fang Z., & Qu Z. (2019). Frontiers: Machines vs. Humans: The Impact of Artificial Intelligence Chatbot Disclosure on Customer Purchases.Marketing Science, 38(6), 937-947.
[34] Lyytinen K., Nickerson J. V., & King J. L. (2021). Metahuman systems = humans + machines that learn.Journal of Information Technology, 36(4), 427-445.
[35] Ma L., Xie P., Wei Y., & Qiao X. (2021). Is negative feedback from leaders really harm for employee innovative behavior ? the role of positive attribution and job crafting. Science and Technology Progress and Policy, 38(12), 144-150.
[马璐, 谢鹏, 韦依依, 乔小涛. (2021). 领导者负面反馈真的不利于员工创新吗——积极归因与工作重塑的作用.科技进步与对策, 38(12), 144-150.]
[36] Newman D. T., Fast N. J., & Harmon D. J. (2020). When eliminating bias isn't fair: Algorithmic reductionism and procedural justice in human resource decisions. Organizational Behavior and Human Decision Processes, 160, 149-167.
[37] Ni, D., & Zheng, X. M. (2024). Does negative performance feedback always lead to negative responses? The role of trust in the leader.Journal of Occupational and Organizational Psychology, 97(2), 623-646.
[38] Podsakoff, P. M., & Farh, J. L. (1989). Effects of feedback sign and credibility on goal setting and task performance.Organizational Behavior and Human Decision Processes, 44(1), 45-67.
[39] Pulakos E. D., Mueller-Hanson R. A., & Arad S. (2019). The evolution of performance management: Searching for value. Annual Review of Organizational Psychology and Organizational Behavior, 6, 249-271.
[40] Qin S., Jia N., Luo X., Liao C., & Huang Z. (2023). Perceived fairness of human managers compared with artificial intelligence in employee performance evaluation.Journal of Management Information Systems, 40(4), 1039-1070.
[41] Raveendhran, R., & Fast, N. J. (2021). Humans judge, algorithms nudge: The psychology of behavior tracking acceptance.Organizational Behavior and Human Decision Processes, 164, 11-26.
[42] Russell, D. (1982). The causal dimension scale: A measure of how individuals perceive causes.Journal of Personality and Social Psychology, 42(6), 1137-1145.
[43] Schleicher D. J., Baumann H. M., Sullivan D. W., Levy P. E., Hargrove D. C., & Barros-Rivera B. A. (2018). Putting the system into performance management systems: A review and agenda for performance management research. Journal of Management, 44(6), 2209-2245.
[44] Scholz U., Gutiérrez-Doña B., Sud S., & Schwarzer R. (2002). Is general self-efficacy a universal construct? Psychometric findings from 25 countries.European Journal of Psychological Assessment, 18(3), 242-251.
[45] Song, X., & He, X. (2020). The effect of artificial intelligence pricing on consumers’ perceived price fairness.Journal of Management Science, 33(5), 3-16.
[宋晓兵, 何夏楠. (2020). 人工智能定价对消费者价格公平感知的影响.管理科学, 33(5), 3-16.]
[46] Tolli, A. P., & Schmidt, A. M. (2008). The role of feedback, casual attributions, and self-efficacy in goal revision.Journal of Applied Psychology, 93(3), 692-701.
[47] Tong S., Jia N., Luo X., & Fang Z. (2021). The Janus face of artificial intelligence feedback: Deployment versus disclosure effects on employee performance.Strategic Management Journal, 42(9), 1600-1631.
[48] Van Dijk, D., & Kluger, A. N. (2011). Task type as a moderator of positive/negative feedback effects on motivation and performance: A regulatory focus perspective.Journal of Organizational Behavior, 32(8), 1084-1105.
[49] Weiner, B. (1985). An attributional theory of achievement motivation and emotion.Psychological Review, 92(4), 548-573.
[50] Wexley K. N., Singh U. A., & Yukl G. A. (1973). Subordinate personality as a moderator of the effects of participation in 3 types of appraisal interviews.Journal of Applied Psychology, 58(1), 54-59.
[51] Xing L., Sun J. M., Jepsen D., & Zhang Y. J. (2023). Supervisor negative feedback and employee motivation to learn: An attribution perspective.Human Relations, 76(2), 1-31.
[52] Xu L., Yu F., & Peng K. (2022). Algorithmic discrimination causes less desire for moral punishment than human discrimination.Acta Psychologica Sinica, 54(9), 1076-1092.
[许丽颖, 喻丰, 彭凯平. (2022). 算法歧视比人类歧视引起更少道德惩罚欲.心理学报, 54(9), 1076-1092.]
[53] Yalcin G., Lim S., Puntoni S., & van Osselaer, S. M. J. (2022). Thumbs up or down: Consumer reactions to decisions by algorithms versus humans. Journal of Marketing Research, 59(4), 696-717.
[54] Yam K., Tang P., Jackson J., Su R., & Gray K. (2023). The rise of robots increases job insecurity and maladaptive workplace behaviors: Multimethod evidence.Journal of Appied Psychology, 108(5), 850-870.