IDEAS home Printed from https://ideas.repec.org/p/osf/osfxxx/uczaw.html
   My bibliography  Save this paper

Acceptance and motivational effect of AI-driven feedback in the workplace: An experimental study with direct replication

Author

Listed:
  • Hein, Ilka
  • Cecil, Julia

    (Ludwig-Maximilians-Universität München)

  • Lermer, Eva

    (LMU Munich)

Abstract

Artificial intelligence (AI) is increasingly taking over leadership tasks in companies, including the provision of feedback. However, the effect of AI-driven feedback on employees and its theoretical foundations are poorly understood. We aimed to reduce this research gap by comparing perceptions of AI and human feedback based on construal level theory and the feedback process model. A 2 x 2 between-subjects design with vignettes was applied to manipulate feedback source (human vs. AI) and valence (negative vs. positive). In a preregistered experimental study (S1) and subsequent direct replication (S2), responses from NS1 = 263 and NS2 = 449 participants who completed a German online questionnaire were studied. Regression analyses showed that AI feedback was rated as less accurate and led to lower performance motivation, acceptance of the feedback provider, and intention to seek further feedback. These effects were mediated by perceived social distance. Moreover, for feedback acceptance and performance motivation, the differences were only found for positive but not for negative feedback in the first study. This implies that AI feedback may not inherently be perceived as more negatively than human feedback as it depends on the feedback’s valence. Furthermore, the mediation effects indicate that the shown negative evaluations of the AI can be explained by higher social distance and that increased social closeness to feedback providers may improve appraisals of them and of their feedback. Theoretical contributions of the studies and implications for the use of AI for providing feedback in the workplace are discussed, emphasizing the influence of effects related to construal level theory.

Suggested Citation

  • 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.
  • Handle: RePEc:osf:osfxxx:uczaw
    DOI: 10.31219/osf.io/uczaw
    as

    Download full text from publisher

    File URL: https://osf.io/download/66609d2577ff4c504de04616/
    Download Restriction: no

    File URL: https://libkey.io/10.31219/osf.io/uczaw?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
    ---><---

    References listed on IDEAS

    as
    1. Aickin, M. & Gensler, H., 1996. "Adjusting for multiple testing when reporting research results: The Bonferroni vs Holm methods," American Journal of Public Health, American Public Health Association, vol. 86(5), pages 726-728.
    2. 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.
    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. Hyojung Tak & Gregory Ruhnke & Ya-Chen Shih, 2015. "The Association between Patient-Centered Attributes of Care and Patient Satisfaction," The Patient: Patient-Centered Outcomes Research, Springer;International Academy of Health Preference Research, vol. 8(2), pages 187-197, April.
    2. Christoph Riedl & Eric Bogert, 2024. "Effects of AI Feedback on Learning, the Skill Gap, and Intellectual Diversity," Papers 2409.18660, arXiv.org.
    3. Olimpia Ban & Irina Maiorescu & Mihaela Bucur & Gabriel Cristian Sabou & Betty Cohen Tzedec, 2024. "AI between Threat and Benefactor for the Competences of the Human Working Force," The AMFITEATRU ECONOMIC journal, Academy of Economic Studies - Bucharest, Romania, vol. 26(67), pages 762-762, August.
    4. Milan Miric & Nan Jia & Kenneth G. Huang, 2023. "Using supervised machine learning for large‐scale classification in management research: The case for identifying artificial intelligence patents," Strategic Management Journal, Wiley Blackwell, vol. 44(2), pages 491-519, February.
    5. Van de Velde, Liesbeth & Verbeke, Wim & Popp, Michael & Van Huylenbroeck, Guido, 2010. "The importance of message framing for providing information about sustainability and environmental aspects of energy," Energy Policy, Elsevier, vol. 38(10), pages 5541-5549, October.
    6. Mahamed G. H. Omran & Maurice Clerc & Fatme Ghaddar & Ahmad Aldabagh & Omar Tawfik, 2022. "Permutation Tests for Metaheuristic Algorithms," Mathematics, MDPI, vol. 10(13), pages 1-15, June.
    7. Johannes Habel & Sascha Alavi & Nicolas Heinitz, 2023. "A theory of predictive sales analytics adoption," AMS Review, Springer;Academy of Marketing Science, vol. 13(1), pages 34-54, June.
    8. Koehler, Maximilian & Sauermann, Henry, 2024. "Algorithmic management in scientific research," Research Policy, Elsevier, vol. 53(4).
    9. Mammadov Huseyn & Africa Ruiz-Gandara & Luis Gonzalez-Abril & Isidoro Romero, 2024. "Adoption of Artificial Intelligence in Small and Medium-Sized Enterprises in Spain: The Role of Competences and Skills," The AMFITEATRU ECONOMIC journal, Academy of Economic Studies - Bucharest, Romania, vol. 26(67), pages 848-848, August.
    10. José‐Luis Pinto‐Prades & José‐María Abellán‐Perpiñán, 2005. "Measuring the health of populations: the veil of ignorance approach," Health Economics, John Wiley & Sons, Ltd., vol. 14(1), pages 69-82, January.
    11. Yuetong Chen & Hao Wang & Baolong Zhang & Wei Zhang, 2022. "A method of measuring the article discriminative capacity and its distribution," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(6), pages 3317-3341, June.
    12. Manav Raj & Justin Berg & Rob Seamans, 2023. "Art-ificial Intelligence: The Effect of AI Disclosure on Evaluations of Creative Content," Papers 2303.06217, arXiv.org, revised Jun 2024.
    13. Rozgonjuk, Dmitri & Schmitz, Florian & Kannen, Christopher & Montag, Christian, 2021. "Cognitive ability and personality: Testing broad to nuanced associations with a smartphone app," Intelligence, Elsevier, vol. 88(C).
    14. Carl Berning & Bernd Weiß, 2016. "Publication bias in the German social sciences: an application of the caliper test to three top-tier German social science journals," Quality & Quantity: International Journal of Methodology, Springer, vol. 50(2), pages 901-917, March.
    15. Hanzhe Li & Jin Li & Ye Luo & Xiaowei Zhang, 2024. "AI Persuasion, Bayesian Attribution, and Career Concerns of Doctors," Papers 2410.01114, arXiv.org.
    16. Mengmeng Wang & Xiaoming Pan, 2022. "Drivers of Artificial Intelligence and Their Effects on Supply Chain Resilience and Performance: An Empirical Analysis on an Emerging Market," Sustainability, MDPI, vol. 14(24), pages 1-16, December.
    17. Chun-Hao Li & Ming-Chang Tsai, 2014. "Is the Easy Life Always the Happiest? Examining the Association of Convenience and Well-Being in Taiwan," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 117(3), pages 673-688, July.
    18. K. D. V. Prasad & Tanmoy De, 2024. "Generative AI as a catalyst for HRM practices: mediating effects of trust," Palgrave Communications, Palgrave Macmillan, vol. 11(1), pages 1-16, December.
    19. 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).
    20. Prentice, Catherine & Wong, IpKin Anthony & Lin, Zhiwei (CJ), 2023. "Artificial intelligence as a boundary-crossing object for employee engagement and performance," Journal of Retailing and Consumer Services, Elsevier, vol. 73(C).

    More about this item

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    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:osf:osfxxx:uczaw. 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: OSF (email available below). General contact details of provider: https://osf.io/preprints/ .

    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.