IDEAS home Printed from https://ideas.repec.org/a/eee/ijoais/v52y2024ics1467089523000568.html
   My bibliography  Save this article

Algorithm aversion, emotions, and investor reaction: Does disclosing the use of AI influence investment decisions?

Author

Listed:
  • Downen, Tom
  • Kim, Sarah
  • Lee, Lorraine

Abstract

Businesses are increasingly using artificial intelligence (AI) in accounting systems to reduce uncertainty and improve accuracy. However, algorithm aversion (Dietvorst et al., 2015) indicates that individuals often avoid information provided by automated systems as compared to that provided by humans. This paper is an exploratory step towards documenting an emotional response to AI. We experimentally investigate how disclosing the use of AI rather than human staff for estimating the fair value of an asset influences investment decisions through lower levels of emotional response, particularly in pleasantness and attentiveness. Consistent with algorithm aversion, we find that disclosing the use of AI to estimate the asset’s fair value reduces the effect of information valence on nonprofessional investor responses. Specifically, when a company’s AI usage is disclosed, investors make smaller additional investments when fair value information is positive and smaller investment withdrawals when fair value information is negative, as compared to when human staff usage is disclosed. Importantly, we also find that emotions mediate the effect of information source (AI versus human staff) and moderate the effect of information valence on investment decisions.

Suggested Citation

  • Downen, Tom & Kim, Sarah & Lee, Lorraine, 2024. "Algorithm aversion, emotions, and investor reaction: Does disclosing the use of AI influence investment decisions?," International Journal of Accounting Information Systems, Elsevier, vol. 52(C).
  • Handle: RePEc:eee:ijoais:v:52:y:2024:i:c:s1467089523000568
    DOI: 10.1016/j.accinf.2023.100664
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S1467089523000568
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.accinf.2023.100664?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. Elliott, W.B. & Hodge, F. & Kennedy, J.J. & Pronk, M., 2007. "Are MBA students a good proxy for nonprofessional investors?," Other publications TiSEM 20271f1d-d385-4122-a175-f, Tilburg University, School of Economics and Management.
    2. repec:eme:mfppss:03074350510769613 is not listed on IDEAS
    3. Shlomo Benartzi & Richard H. Thaler, 1995. "Myopic Loss Aversion and the Equity Premium Puzzle," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 110(1), pages 73-92.
    4. Jeremy Bertomeu & Edwige Cheynel & Eric Floyd & Wenqiang Pan, 2021. "Using machine learning to detect misstatements," Review of Accounting Studies, Springer, vol. 26(2), pages 468-519, June.
    5. Yaniv, Ilan & Kleinberger, Eli, 2000. "Advice Taking in Decision Making: Egocentric Discounting and Reputation Formation," Organizational Behavior and Human Decision Processes, Elsevier, vol. 83(2), pages 260-281, November.
    6. Victoria A. Shaffer & C. Adam Probst & Edgar C. Merkle & Hal R. Arkes & Mitchell A. Medow, 2013. "Why Do Patients Derogate Physicians Who Use a Computer-Based Diagnostic Support System?," Medical Decision Making, , vol. 33(1), pages 108-118, January.
    7. Curtis, Mary B. & Payne, Elizabeth A., 2008. "An examination of contextual factors and individual characteristics affecting technology implementation decisions in auditing," International Journal of Accounting Information Systems, Elsevier, vol. 9(2), pages 104-121.
    8. Stephen K. Asare & Arnold M. Wright, 2004. "The Effectiveness of Alternative Risk Assessment and Program Planning Tools in a Fraud Setting," Contemporary Accounting Research, John Wiley & Sons, vol. 21(2), pages 325-352, June.
    9. Birnberg, Jacob G. & Shields, Michael D., 1984. "The role of attention and memory in accounting decisions," Accounting, Organizations and Society, Elsevier, vol. 9(3-4), pages 365-382, October.
    10. Berkeley J. Dietvorst & Joseph P. Simmons & Cade Massey, 2018. "Overcoming Algorithm Aversion: People Will Use Imperfect Algorithms If They Can (Even Slightly) Modify Them," Management Science, INFORMS, vol. 64(3), pages 1155-1170, March.
    11. Brown, T. & Grant, Stephanie M. & Winn, Amanda M., 2020. "The effect of mobile device use and headline focus on investor judgments," Accounting, Organizations and Society, Elsevier, vol. 83(C).
    12. Christopher P. Agoglia & Thomas Kida & Dennis M. Hanno, 2003. "The Effects of Alternative Justification Memos on the Judgments of Audit Reviewees and Reviewers," Journal of Accounting Research, Wiley Blackwell, vol. 41(1), pages 33-46, March.
    13. Oliver, Richard L, 1993. "Cognitive, Affective, and Attribute BAses of the Satisfaction Response," Journal of Consumer Research, Journal of Consumer Research Inc., vol. 20(3), pages 418-430, December.
    14. Benjamin P. Commerford & Sean A. Dennis & Jennifer R. Joe & Jenny W. Ulla, 2022. "Man Versus Machine: Complex Estimates and Auditor Reliance on Artificial Intelligence," Journal of Accounting Research, Wiley Blackwell, vol. 60(1), pages 171-201, March.
    15. Sutton, Steve G. & Holt, Matthew & Arnold, Vicky, 2016. "“The reports of my death are greatly exaggerated”—Artificial intelligence research in accounting," International Journal of Accounting Information Systems, Elsevier, vol. 22(C), pages 60-73.
    16. Smith, Gary, 2019. "Be Wary of Black-Box Trading Algorithms," Economics Department, Working Paper Series 1007, Economics Department, Pomona College, revised 04 Jun 2019.
    17. Amelia A. Baldwin & Carol E. Brown & Brad S. Trinkle, 2006. "Opportunities for artificial intelligence development in the accounting domain: the case for auditing," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 14(3), pages 77-86, July.
    18. Bonsón, Enrique & Bednárová, Michaela & Perea, David, 2023. "Disclosures about algorithmic decision making in the corporate reports of Western European companies," International Journal of Accounting Information Systems, Elsevier, vol. 48(C).
    19. T. Jeffrey Wilks & Mark F. Zimbelman, 2004. "Decomposition of Fraud†Risk Assessments and Auditors' Sensitivity to Fraud Cues," Contemporary Accounting Research, John Wiley & Sons, vol. 21(3), pages 719-745, September.
    20. Andrew Prahl & Lyn Van Swol, 2017. "Understanding algorithm aversion: When is advice from automation discounted?," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 36(6), pages 691-702, September.
    21. Kexing Ding & Baruch Lev & Xuan Peng & Ting Sun & Miklos A. Vasarhelyi, 2020. "Machine learning improves accounting estimates: evidence from insurance payments," Review of Accounting Studies, Springer, vol. 25(3), pages 1098-1134, 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. Tse, Tiffany Tsz Kwan & Hanaki, Nobuyuki & Mao, Bolin, 2024. "Beware the performance of an algorithm before relying on it: Evidence from a stock price forecasting experiment," Journal of Economic Psychology, Elsevier, vol. 102(C).
    2. Mahmud, Hasan & Islam, A.K.M. Najmul & Ahmed, Syed Ishtiaque & Smolander, Kari, 2022. "What influences algorithmic decision-making? A systematic literature review on algorithm aversion," Technological Forecasting and Social Change, Elsevier, vol. 175(C).
    3. Merle, Aurélie & St-Onge, Anik & Sénécal, Sylvain, 2022. "Does it pay to be honest? The effect of retailer-provided negative feedback on consumers’ product choice and shopping experience," Journal of Business Research, Elsevier, vol. 147(C), pages 532-543.
    4. Fildes, Robert & Goodwin, Paul, 2021. "Stability in the inefficient use of forecasting systems: A case study in a supply chain company," International Journal of Forecasting, Elsevier, vol. 37(2), pages 1031-1046.
    5. Alexia GAUDEUL & Caterina GIANNETTI, 2023. "Trade-offs in the design of financial algorithms," Discussion Papers 2023/288, Dipartimento di Economia e Management (DEM), University of Pisa, Pisa, Italy.
    6. Chugunova, Marina & Sele, Daniela, 2022. "We and It: An interdisciplinary review of the experimental evidence on how humans interact with machines," Journal of Behavioral and Experimental Economics (formerly The Journal of Socio-Economics), Elsevier, vol. 99(C).
    7. Greiner, Ben & Grünwald, Philipp & Lindner, Thomas & Lintner, Georg & Wiernsperger, Martin, 2024. "Incentives, Framing, and Reliance on Algorithmic Advice: An Experimental Study," Department for Strategy and Innovation Working Paper Series 01/2024, WU Vienna University of Economics and Business.
    8. Kelton, Andrea Seaton & Murthy, Uday S., 2023. "Reimagining design science and behavioral science AIS research through a business activity lens," International Journal of Accounting Information Systems, Elsevier, vol. 50(C).
    9. Yongping Bao & Ludwig Danwitz & Fabian Dvorak & Sebastian Fehrler & Lars Hornuf & Hsuan Yu Lin & Bettina von Helversen, 2022. "Similarity and Consistency in Algorithm-Guided Exploration," CESifo Working Paper Series 10188, CESifo.
    10. Markus Jung & Mischa Seiter, 2021. "Towards a better understanding on mitigating algorithm aversion in forecasting: an experimental study," Journal of Management Control: Zeitschrift für Planung und Unternehmenssteuerung, Springer, vol. 32(4), pages 495-516, December.
    11. Kohei Kawaguchi, 2021. "When Will Workers Follow an Algorithm? A Field Experiment with a Retail Business," Management Science, INFORMS, vol. 67(3), pages 1670-1695, March.
    12. Logg, Jennifer M. & Minson, Julia A. & Moore, Don A., 2019. "Algorithm appreciation: People prefer algorithmic to human judgment," Organizational Behavior and Human Decision Processes, Elsevier, vol. 151(C), pages 90-103.
    13. Zhang, Chanyuan (Abigail) & Cho, Soohyun & Vasarhelyi, Miklos, 2022. "Explainable Artificial Intelligence (XAI) in auditing," International Journal of Accounting Information Systems, Elsevier, vol. 46(C).
    14. Amaral, Christopher & Kolsarici, Ceren, 2020. "The financial advice puzzle: The role of consumer heterogeneity in the advisor choice," Journal of Retailing and Consumer Services, Elsevier, vol. 54(C).
    15. Herron, Eddward T. & Cornell, Robert M., 2021. "Creativity amidst standardization: Is creativity related to auditors’ recognition of and responses to fraud risk cues?," Journal of Business Research, Elsevier, vol. 132(C), pages 314-326.
    16. Bauer, Kevin & Nofer, Michael & Abdel-Karim, Benjamin M. & Hinz, Oliver, 2022. "The effects of discontinuing machine learning decision support," SAFE Working Paper Series 370, Leibniz Institute for Financial Research SAFE.
    17. Kevin Bauer & Andrej Gill, 2024. "Mirror, Mirror on the Wall: Algorithmic Assessments, Transparency, and Self-Fulfilling Prophecies," Information Systems Research, INFORMS, vol. 35(1), pages 226-248, March.
    18. Monica Ramos Montesdeoca & Agustín J. Sánchez Medina & Felix Blázquez Santana, 2019. "Research Topics in Accounting Fraud in the 21st Century: A State of the Art," Sustainability, MDPI, vol. 11(6), pages 1-31, March.
    19. Gregory Weitzner, 2024. "Reputational Algorithm Aversion," Papers 2402.15418, arXiv.org, revised Jul 2024.
    20. Mahmud, Hasan & Islam, A.K.M. Najmul & Mitra, Ranjan Kumar, 2023. "What drives managers towards algorithm aversion and how to overcome it? Mitigating the impact of innovation resistance through technology readiness," Technological Forecasting and Social Change, Elsevier, vol. 193(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:ijoais:v:52:y:2024:i:c:s1467089523000568. 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: https://www.journals.elsevier.com/international-journal-of-accounting-information-systems/ .

    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.