IDEAS home Printed from https://ideas.repec.org/a/eee/jbrese/v193y2025ics0148296325001560.html
   My bibliography  Save this article

Negative feedback from robots is received better than that from humans: The effect of feedback on human–robot trust and collaboration

Author

Listed:
  • Xuan, Hongzhou
  • He, Guibing

Abstract

Intelligent robots continue to transcend their traditional roles as mere tools, evolving to actively engage in collaborative teamwork. Feedback from teammates is a critical component of effective team dynamics. This research investigates how feedback source (robot vs. human teammate) affects behavioral trust and intention for future collaboration through a functional task (Study 1) and a social task (Study 2). It further examines the mediating role of feedback acceptance and the moderating effect of feedback valence (positive vs. negative). The findings reveal that negative feedback from a robot teammate, compare to that from a human teammate, leads to a higher feedback acceptance, which further fosters greater behavioral trust and intention for future collaboration toward the robot. However, the source of positive feedback, whether from a robot or a human teammate, causes no significant differences in recipients’ responses. This research delineates the potential advantages of future robot colleagues in delivering negative feedback.

Suggested Citation

  • Xuan, Hongzhou & He, Guibing, 2025. "Negative feedback from robots is received better than that from humans: The effect of feedback on human–robot trust and collaboration," Journal of Business Research, Elsevier, vol. 193(C).
  • Handle: RePEc:eee:jbrese:v:193:y:2025:i:c:s0148296325001560
    DOI: 10.1016/j.jbusres.2025.115333
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0148296325001560
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.jbusres.2025.115333?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Zou, Tengjian & Ertug, Gokhan & Roulet, Thomas, 2024. "Learning from machines: How negative feedback from machines improves learning between humans," Journal of Business Research, Elsevier, vol. 172(C).
    2. Camelia M. Kuhnen & Agnieszka Tymula, 2012. "Feedback, Self-Esteem, and Performance in Organizations," Management Science, INFORMS, vol. 58(1), pages 94-113, January.
    3. Fedor, Donald B. & Eder, Robert W. & Buckley, M. Ronald, 1989. "The contributory effects of supervisor intentions on subordinate feedback responses," Organizational Behavior and Human Decision Processes, Elsevier, vol. 44(3), pages 396-414, December.
    4. Anthony Vance & Christophe M. Elie-Dit-Cosaque & Detmar W. Straub, 2008. "Examining Trust in Information Technology Artifacts: The Effects of System Quality and Culture," Post-Print halshs-00641137, HAL.
    5. Haenlein, Michael & Kaplan, Andreas, 2021. "Artificial intelligence and robotics: Shaking up the business world and society at large," Journal of Business Research, Elsevier, vol. 124(C), pages 405-407.
    6. Deepa, R. & Sekar, Srinivasan & Malik, Ashish & Kumar, Jitender & Attri, Rekha, 2024. "Impact of AI-focussed technologies on social and technical competencies for HR managers – A systematic review and research agenda," Technological Forecasting and Social Change, Elsevier, vol. 202(C).
    7. Gkinko, Lorentsa & Elbanna, Amany, 2023. "Designing trust: The formation of employees’ trust in conversational AI in the digital workplace," Journal of Business Research, Elsevier, vol. 158(C).
    8. Debora Zanatto & Massimiliano Patacchiola & Jeremy Goslin & Angelo Cangelosi, 2019. "Investigating cooperation with robotic peers," PLOS ONE, Public Library of Science, vol. 14(11), pages 1-17, November.
    9. Aaron C. Elkins & Douglas C. Derrick, 2013. "The Sound of Trust: Voice as a Measurement of Trust During Interactions with Embodied Conversational Agents," Group Decision and Negotiation, Springer, vol. 22(5), pages 897-913, September.
    10. Podsakoff, Philip M. & Bommer, William H. & Podsakoff, Nathan P. & MacKenzie, Scott B., 2006. "Relationships between leader reward and punishment behavior and subordinate attitudes, perceptions, and behaviors: A meta-analytic review of existing and new research," Organizational Behavior and Human Decision Processes, Elsevier, vol. 99(2), pages 113-142, March.
    11. Julian Freitas & Stuti Agarwal & Bernd Schmitt & Nick Haslam, 2023. "Psychological factors underlying attitudes toward AI tools," Nature Human Behaviour, Nature, vol. 7(11), pages 1845-1854, November.
    12. I.M. Jawahar, 2006. "Correlates of Satisfaction with Performance Appraisal Feedback," Journal of Labor Research, Transaction Publishers, vol. 27(2), pages 213-236, April.
    13. repec:dau:papers:123456789/2723 is not listed on IDEAS
    14. repec:osf:osfxxx:uczaw_v1 is not listed on IDEAS
    15. David Eil & Justin M. Rao, 2011. "The Good News-Bad News Effect: Asymmetric Processing of Objective Information about Yourself," American Economic Journal: Microeconomics, American Economic Association, vol. 3(2), pages 114-138, May.
    16. Hein, Ilka & Cecil, Julia & Lermer, Eva, 2024. "Acceptance and motivational effect of AI-driven feedback in the workplace: An experimental study with direct replication," OSF Preprints uczaw, Center for Open Science.
    17. Siliang Tong & Nan Jia & Xueming Luo & Zheng Fang, 2021. "The Janus face of artificial intelligence feedback: Deployment versus disclosure effects on employee performance," Strategic Management Journal, Wiley Blackwell, vol. 42(9), pages 1600-1631, September.
    18. Gavan J. Fitzsimons & Donald R. Lehmann, 2004. "Reactance to Recommendations: When Unsolicited Advice Yields Contrary Responses," Marketing Science, INFORMS, vol. 23(1), pages 82-94, September.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Banerjee, Ritwik & Gupta, Nabanita Datta & Villeval, Marie Claire, 2020. "Feedback spillovers across tasks, self-confidence and competitiveness," Games and Economic Behavior, Elsevier, vol. 123(C), pages 127-170.
    2. González-Jiménez, Víctor, 2022. "Social status and motivated beliefs," Journal of Public Economics, Elsevier, vol. 211(C).
    3. Zhou, Qiwei & Chen, Keyu & Cheng, Shuang, 2024. "Bringing employee learning to AI stress research: A moderated mediation model," Technological Forecasting and Social Change, Elsevier, vol. 209(C).
    4. Brade, Raphael & Himmler, Oliver & Jäckle, Robert, 2018. "Normatively Framed Relative Performance Feedback – Field Experiment and Replication," MPRA Paper 88830, University Library of Munich, Germany.
    5. Ertac, Seda & Koçkesen, Levent & Ozdemir, Duygu, 2016. "The role of verifiability and privacy in the strategic provision of performance feedback: Theory and experimental evidence," Games and Economic Behavior, Elsevier, vol. 100(C), pages 24-45.
    6. Riedel, Nadine & Stüber, Robert, 2019. "Overearning – Revisited," Journal of Economic Psychology, Elsevier, vol. 75(PA).
    7. Brade, Raphael & Himmler, Oliver & Jäckle, Robert, 2022. "Relative performance feedback and the effects of being above average — field experiment and replication," Economics of Education Review, Elsevier, vol. 89(C).
    8. Murad, Zahra & Starmer, Chris, 2021. "Confidence snowballing and relative performance feedback," Journal of Economic Behavior & Organization, Elsevier, vol. 190(C), pages 550-572.
    9. Michael Rivera & Cheng Jiang & Subodha Kumar, 2024. "Seek and Ye Shall Find: An Empirical Examination of the Effects of Seeking Real-Time Feedback on Employee Performance Evaluations," Information Systems Research, INFORMS, vol. 35(2), pages 783-806, June.
    10. Zhang, Fan & Pan, Jieyi, 2025. "Imitation: Mitigating AI backfire," Journal of Business Research, Elsevier, vol. 193(C).
    11. Gonzalez Jimenez, Victor, 2016. "Believe Me, You are (not) that Bad," Other publications TiSEM 25ded0a5-f9c2-48d9-befe-5, Tilburg University, School of Economics and Management.
    12. Darima Fotheringham & Michael A. Wiles, 2023. "The effect of implementing chatbot customer service on stock returns: an event study analysis," Journal of the Academy of Marketing Science, Springer, vol. 51(4), pages 802-822, July.
    13. Bobba, Matteo & Frisancho, Veronica, 2022. "Self-perceptions about academic achievement: Evidence from Mexico City," Journal of Econometrics, Elsevier, vol. 231(1), pages 58-73.
    14. Francesco Capozza & Ingar Haaland & Christopher Roth & Johannes Wohlfart, 2021. "Studying Information Acquisition in the Field: A Practical Guide and Review," CEBI working paper series 21-15, University of Copenhagen. Department of Economics. The Center for Economic Behavior and Inequality (CEBI).
    15. repec:plo:pcbi00:1003605 is not listed on IDEAS
    16. Daniel W. Elfenbein & Anne Marie Knott & Rachel Croson, 2017. "Equity stakes and exit: An experimental approach to decomposing exit delay," Strategic Management Journal, Wiley Blackwell, vol. 38(2), pages 278-299, February.
    17. Emmanuel Dechenaux & Dan Kovenock & Roman Sheremeta, 2015. "A survey of experimental research on contests, all-pay auctions and tournaments," Experimental Economics, Springer;Economic Science Association, vol. 18(4), pages 609-669, December.
    18. Hoffman, Mitchell & Burks, Stephen V., 2017. "Worker Overconfidence: Field Evidence and Implications for Employee Turnover and Returns from Training," IZA Discussion Papers 10794, Institute of Labor Economics (IZA).
    19. Song, Jun & Wang, Dongdong & He, Changqing, 2023. "Why and when does inclusive leadership evoke employee negative feedback-seeking behavior?," European Management Journal, Elsevier, vol. 41(2), pages 292-301.
    20. Mechtenberg, Lydia & Perino, Grischa & Treich, Nicolas & Tyran, Jean-Robert & Wang, Stephanie W., 2024. "Self-signaling in voting," Journal of Public Economics, Elsevier, vol. 231(C).
    21. Yoganathan, Vignesh & Osburg, Victoria-Sophie, 2024. "The mind in the machine: Estimating mind perception's effect on user satisfaction with voice-based conversational agents," Journal of Business Research, Elsevier, vol. 175(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:jbrese:v:193:y:2025:i:c:s0148296325001560. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/jbusres .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.