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What influences algorithmic decision-making? A systematic literature review on algorithm aversion

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  • Mahmud, Hasan
  • Islam, A.K.M. Najmul
  • Ahmed, Syed Ishtiaque
  • Smolander, Kari

Abstract

With the continuing application of artificial intelligence (AI) technologies in decision-making, algorithmic decision-making is becoming more efficient, often even outperforming humans. Despite this superior performance, people often consciously or unconsciously display reluctance to rely on algorithms, a phenomenon known as algorithm aversion. Viewed as a behavioral anomaly, algorithm aversion has recently attracted much scholarly attention. With a view to synthesize the findings of existing literature, we systematically review 80 empirical studies identified through searching in seven academic databases and using the snowballing technique. We inductively categorize the influencing factors of algorithm aversion under four main themes: algorithm, individual, task, and high-level. Our analysis reveals that although algorithm and individual factors have been investigated extensively, very little attention has been given to exploring the task and high-level factors. We contribute to algorithm aversion literature by proposing a comprehensive framework, highlighting open issues in existing studies, and outlining several research avenues that could be handled in future research. Our model could guide developers in designing and developing and managers in implementing and using of algorithmic decision.

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  • 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).
    • Handle: RePEc:eee:tefoso:v:175:y:2022:i:c:s0040162521008210
    DOI: 10.1016/j.techfore.2021.121390
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    as
    1. 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.
    2. Cao, Guangming & Duan, Yanqing & Edwards, John S. & Dwivedi, Yogesh K., 2021. "Understanding managers’ attitudes and behavioral intentions towards using artificial intelligence for organizational decision-making," Technovation, Elsevier, vol. 106(C).
    3. Benedikt Berger & Martin Adam & Alexander Rühr & Alexander Benlian, 2021. "Watch Me Improve—Algorithm Aversion and Demonstrating the Ability to Learn," Business & Information Systems Engineering: The International Journal of WIRTSCHAFTSINFORMATIK, Springer;Gesellschaft für Informatik e.V. (GI), vol. 63(1), pages 55-68, February.
    4. Guckenbiehl, Peter & Corral de Zubielqui, Graciela & Lindsay, Noel, 2021. "Knowledge and innovation in start-up ventures: A systematic literature review and research agenda," Technological Forecasting and Social Change, Elsevier, vol. 172(C).
    5. Jones, Marian V. & Coviello, Nicole & Tang, Yee Kwan, 2011. "International Entrepreneurship research (1989–2009): A domain ontology and thematic analysis," Journal of Business Venturing, Elsevier, vol. 26(6), pages 632-659.
    6. Kaur, Puneet & Dhir, Amandeep & Tandon, Anushree & Alzeiby, Ebtesam A. & Abohassan, Abeer Ahmed, 2021. "A systematic literature review on cyberstalking. An analysis of past achievements and future promises," Technological Forecasting and Social Change, Elsevier, vol. 163(C).
    7. David J. Teece & Gary Pisano & Amy Shuen, 1997. "Dynamic capabilities and strategic management," Strategic Management Journal, Wiley Blackwell, vol. 18(7), pages 509-533, August.
    8. De Bruyn, Arnaud & Viswanathan, Vijay & Beh, Yean Shan & Brock, Jürgen Kai-Uwe & von Wangenheim, Florian, 2020. "Artificial Intelligence and Marketing: Pitfalls and Opportunities," Journal of Interactive Marketing, Elsevier, vol. 51(C), pages 91-105.
    9. Sanders, Nada R. & Manrodt, Karl B., 2003. "The efficacy of using judgmental versus quantitative forecasting methods in practice," Omega, Elsevier, vol. 31(6), pages 511-522, December.
    10. Gogoll, Jan & Uhl, Matthias, 2018. "Rage against the machine: Automation in the moral domain," Journal of Behavioral and Experimental Economics (formerly The Journal of Socio-Economics), Elsevier, vol. 74(C), pages 97-103.
    11. Meuter, Matthew L. & Ostrom, Amy L. & Bitner, Mary Jo & Roundtree, Robert, 2003. "The influence of technology anxiety on consumer use and experiences with self-service technologies," Journal of Business Research, Elsevier, vol. 56(11), pages 899-906, November.
    12. Jane M. Mackay & Joyce J. Elam, 1992. "A Comparative Study of How Experts and Novices Use a Decision Aid to Solve Problems in Complex Knowledge Domains," Information Systems Research, INFORMS, vol. 3(2), pages 150-172, June.
    13. Gino, Francesca, 2008. "Do we listen to advice just because we paid for it? The impact of advice cost on its use," Organizational Behavior and Human Decision Processes, Elsevier, vol. 107(2), pages 234-245, November.
    14. Achiel Fenneman & Joern Sickmann & Thomas Pitz & Alan G Sanfey, 2021. "Two distinct and separable processes underlie individual differences in algorithm adherence: Differences in predictions and differences in trust thresholds," PLOS ONE, Public Library of Science, vol. 16(2), pages 1-20, February.
    15. 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.
    16. Whitecotton, Stacey M., 1996. "The Effects of Experience and a Decision Aid on the Slope, Scatter, and Bias of Earnings Forecasts," Organizational Behavior and Human Decision Processes, Elsevier, vol. 66(1), pages 111-121, April.
    17. Bigdeli, Alinaghi Ziaee & Kamal, Muhammad Mustafa & de Cesare, Sergio, 2013. "Electronic information sharing in local government authorities: Factors influencing the decision-making process," International Journal of Information Management, Elsevier, vol. 33(5), pages 816-830.
    18. Xueming Luo & Siliang Tong & Zheng Fang & Zhe Qu, 2019. "Frontiers: Machines vs. Humans: The Impact of Artificial Intelligence Chatbot Disclosure on Customer Purchases," Marketing Science, INFORMS, vol. 38(6), pages 937-947, November.
    19. Bhimani, Hardik & Mention, Anne-Laure & Barlatier, Pierre-Jean, 2019. "Social media and innovation: A systematic literature review and future research directions," Technological Forecasting and Social Change, Elsevier, vol. 144(C), pages 251-269.
    20. Zahedi, Mansooreh & Shahin, Mojtaba & Ali Babar, Muhammad, 2016. "A systematic review of knowledge sharing challenges and practices in global software development," International Journal of Information Management, Elsevier, vol. 36(6), pages 995-1019.
    21. Efendić, Emir & Van de Calseyde, Philippe P.F.M. & Evans, Anthony M., 2020. "Slow response times undermine trust in algorithmic (but not human) predictions," Organizational Behavior and Human Decision Processes, Elsevier, vol. 157(C), pages 103-114.
    22. Hal R. Arkes & Victoria A. Shaffer & Mitchell A. Medow, 2007. "Patients Derogate Physicians Who Use a Computer-Assisted Diagnostic Aid," Medical Decision Making, , vol. 27(2), pages 189-202, March.
    23. 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.
    24. Goodwin, Paul & Sinan Gönül, M. & Önkal, Dilek, 2013. "Antecedents and effects of trust in forecasting advice," International Journal of Forecasting, Elsevier, vol. 29(2), pages 354-366.
    25. Vlačić, Božidar & Corbo, Leonardo & Costa e Silva, Susana & Dabić, Marina, 2021. "The evolving role of artificial intelligence in marketing: A review and research agenda," Journal of Business Research, Elsevier, vol. 128(C), pages 187-203.
    26. 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.
    27. Arkes, Hal R. & Dawes, Robyn M. & Christensen, Caryn, 1986. "Factors influencing the use of a decision rule in a probabilistic task," Organizational Behavior and Human Decision Processes, Elsevier, vol. 37(1), pages 93-110, February.
    28. Laurie Swinney, 1999. "Consideration of the social context of auditors’ reliance on expert system output during evaluation of loan loss reserves," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 8(3), pages 199-213, September.
    29. Newman, David T. & Fast, Nathanael J. & Harmon, Derek J., 2020. "When eliminating bias isn’t fair: Algorithmic reductionism and procedural justice in human resource decisions," Organizational Behavior and Human Decision Processes, Elsevier, vol. 160(C), pages 149-167.
    30. Acharya, Abhilash & Singh, Sanjay Kumar & Pereira, Vijay & Singh, Poonam, 2018. "Big data, knowledge co-creation and decision making in fashion industry," International Journal of Information Management, Elsevier, vol. 42(C), pages 90-101.
    31. Tommaso Minola & Giuseppe Criaco & Lucio Cassia, 2014. "Are youth really different? New beliefs for old practices in entrepreneurship," International Journal of Entrepreneurship and Innovation Management, Inderscience Enterprises Ltd, vol. 18(2/3), pages 233-259.
    32. Khan, Anupriya & Krishnan, Satish & Dhir, Amandeep, 2021. "Electronic government and corruption: Systematic literature review, framework, and agenda for future research," Technological Forecasting and Social Change, Elsevier, vol. 167(C).
    33. Collins, Christopher & Dennehy, Denis & Conboy, Kieran & Mikalef, Patrick, 2021. "Artificial intelligence in information systems research: A systematic literature review and research agenda," International Journal of Information Management, Elsevier, vol. 60(C).
    34. Paweł Niszczota & Dániel Kaszás, 2020. "Robo-investment aversion," PLOS ONE, Public Library of Science, vol. 15(9), pages 1-19, September.
    35. Yasuhiro Yamakawa & Mike W. Peng & David L. Deeds, 2008. "What Drives New Ventures to Internationalize from Emerging to Developed Economies?," Entrepreneurship Theory and Practice, , vol. 32(1), pages 59-82, January.
    36. Ujwal Kayande & Arnaud De Bruyn & Gary L. Lilien & Arvind Rangaswamy & Gerrit H. van Bruggen, 2009. "How Incorporating Feedback Mechanisms in a DSS Affects DSS Evaluations," Information Systems Research, INFORMS, vol. 20(4), pages 527-546, December.
    37. Paul Slovic & Melissa L. Finucane & Ellen Peters & Donald G. MacGregor, 2004. "Risk as Analysis and Risk as Feelings: Some Thoughts about Affect, Reason, Risk, and Rationality," Risk Analysis, John Wiley & Sons, vol. 24(2), pages 311-322, April.
    38. Tsang, Eric W. K., 2014. "Old and New," Management and Organization Review, Cambridge University Press, vol. 10(03), pages 390-390, November.
    39. 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.
    40. Gao, Fangjian & Sunyaev, Ali, 2019. "Context matters: A review of the determinant factors in the decision to adopt cloud computing in healthcare," International Journal of Information Management, Elsevier, vol. 48(C), pages 120-138.
    41. 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.
    42. Litterscheidt, Rouven & Streich, David J., 2020. "Financial education and digital asset management: What's in the black box?," Journal of Behavioral and Experimental Economics (formerly The Journal of Socio-Economics), Elsevier, vol. 87(C).
    43. Maranda McBride & Lemuria Carter & Celestine Ntuen, 2012. "The impact of personality on nurses' bias towards automated decision aid acceptance," International Journal of Information Systems and Change Management, Inderscience Enterprises Ltd, vol. 6(2), pages 132-146.
    44. Lourenço, Carlos J.S. & Dellaert, Benedict G.C. & Donkers, Bas, 2020. "Whose Algorithm Says So: The Relationships Between Type of Firm, Perceptions of Trust and Expertise, and the Acceptance of Financial Robo-Advice," Journal of Interactive Marketing, Elsevier, vol. 49(C), pages 107-124.
    45. Dale L. Goodhue, 1995. "Understanding User Evaluations of Information Systems," Management Science, INFORMS, vol. 41(12), pages 1827-1844, December.
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