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The Impact of Functional and Psychological Barriers on Algorithm Aversion – An IRT Perspective

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The Role of Digital Technologies in Shaping the Post-Pandemic World (I3E 2022)

Abstract

The application of artificial intelligence (AI) in decision-making is regarded as the most impactful disruption in an organization’s digitalization. However, the benefits of the algorithmic decision can be leveraged only if the managers of an organization adopt this technology. Research found that despite the superior performance of algorithms, people discount algorithmic decisions either deliberately or unintentionally, a phenomenon known as algorithm aversion. In this regard, the current study seeks to investigate whether managers’ innovation resistance, measured by different barriers, has any impact on algorithm aversion. Analyzing the survey data of 167 bank/financial managers, we found that while value barriers, tradition barriers, and image barriers are significantly associated with algorithm aversion, such relationships are absent in the case of usage barriers and risk barriers. The findings of this study have several theoretical and practical implications.

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References

  1. Splunk: The Data Age Is Here. Are You Ready? (2020)

    Google Scholar 

  2. Choo, C.W.: The knowing organization: how organizations use information to construct meaning, create knowledge and make decisions. Int. J. Inf. Manage. 16, 329–340 (1996)

    Article  Google Scholar 

  3. March, J.G., Simon, H.A.: Organizations. Blackwell, Oxford (1993)

    Google Scholar 

  4. Santos, L.R., Rosati, A.G.: The evolutionary roots of human decision making. Annu. Rev. Psychol. 66, 321–347 (2015)

    Article  Google Scholar 

  5. Simon, H.: Models of Man; Social and Rational (1957)

    Google Scholar 

  6. Shrestha, Y.R., Ben-Menahem, S.M., von Krogh, G.: Organizational decision-making structures in the age of artificial intelligence. California Manag. Rev. 61, 66–83 (2019)

    Article  Google Scholar 

  7. von Krogh, G.: Artificial intelligence in organizations: new opportunities for phenomenon-based theorizing. Acad. Manag. Discoveries 4, 404–409 (2018)

    Article  Google Scholar 

  8. Jovanovic, M., Sjödin, D., Parida, V.: Co-evolution of platform architecture, platform services, and platform governance: expanding the platform value of industrial digital platforms. Technovation 102218 (2021)

    Google Scholar 

  9. Lindebaum, D., Vesa, M., den Hond, F.: Insights from “the machine stops” to better understand rational assumptions in algorithmic decision making and its implications for organizations. Acad. Manag. Rev. 45, 247–263 (2020)

    Article  Google Scholar 

  10. Ram, S., Sheth, J.N.: Consumer resistance to innovations: the marketing problem and its solutions. J. Consum. Mark. 6, 5 (1989)

    Article  Google Scholar 

  11. Mahmud, H., Islam, A.K.M.N., Ahmed, S.I., Smolander, K.: What influences algorithmic decision-making? A systematic literature review on algorithm aversion. Technol. Forecast. Soc. Chang. 175, 121390 (2022)

    Article  Google Scholar 

  12. Lokuge, S., Sedera, D., Grover, V., Dongming, X.: Organizational readiness for digital innovation: development and empirical calibration of a construct. Inf. Manag. 56, 445–461 (2019)

    Article  Google Scholar 

  13. Mikalef, P., Gupta, M.: Artificial intelligence capability: conceptualization, measurement calibration, and empirical study on its impact on organizational creativity and firm performance. Inf. Manag. 58, 103434 (2021)

    Article  Google Scholar 

  14. Dietvorst, B.J., Simmons, J.P., Massey, C.: Algorithm aversion: people erroneously avoid algorithms after seeing them err. J. Exp. Psychol. Gen. 144, 114–126 (2015). https://doi.org/10.1037/xge0000033

    Article  Google Scholar 

  15. Kawaguchi, K.: When will workers follow an algorithm? A field experiment with a retail business. Manag. Sci. 67, 1670–1695 (2021)

    Article  Google Scholar 

  16. Filiz, I., René Judek, J., Lorenz, M., Spiwoks, M.: The Tragedy of Algorithm Aversion. Fakultät Wirtschaft (2021)

    Google Scholar 

  17. Leong, L.Y., Hew, T.S., Ooi, K.B., Wei, J.: Predicting mobile wallet resistance: a two-staged structural equation modeling-artificial neural network approach. Int. J. Inf. Manage. 51, 102047 (2020)

    Article  Google Scholar 

  18. Arif, I., Aslam, W., Hwang, Y.: Barriers in adoption of internet banking: a structural equation modeling - neural network approach. Technol. Soc. 61, 101231 (2020)

    Article  Google Scholar 

  19. Kaur, P., Dhir, A., Singh, N., Sahu, G., Almotairi, M.: An innovation resistance theory perspective on mobile payment solutions. J. Retail. Consum. Serv. 55, 102059 (2020)

    Article  Google Scholar 

  20. Oktavianus, J., Oviedo, H., Gonzalez, W., Putri, A.P., Lin, T.T.C.: Why do Taiwanese young adults not jump on the bandwagon of Pokémon Go? Exploring barriers of innovation resistance. J. Comput.-Mediated Commun. 13, 827–855 (2017)

    Google Scholar 

  21. Jansukpum, K., Kettem, S.: Applying innovation resistance theory to understand consumer resistance of using online travel in Thailand. In: 4th International Symposium on Distributed Computing and Applications for Business Engineering and Science (DCABES), pp. 139–142. IEEE (2015)

    Google Scholar 

  22. Kushwah, S., Dhir, A., Sagar, M.: Understanding consumer resistance to the consumption of organic food. A study of ethical consumption, purchasing, and choice behaviour. Food Qual. Preference 77, 1–14 (2019). https://doi.org/10.1016/j.foodqual.2019.04.003

    Article  Google Scholar 

  23. Laukkanen, P., Sinkkonen, S., Laukkanen, T.: Consumer resistance to internet banking: postponers, opponents and rejectors. Int. J. Bank Mark. 26, 440–455 (2008). https://doi.org/10.1108/02652320810902451

    Article  Google Scholar 

  24. Ma, L., Lee, C.S.: Understanding the barriers to the use of MOOCs in a developing country: an innovation resistance perspective. J. Educ. Comput. Res. 57(3), 571–590 (2018). https://doi.org/10.1177/0735633118757732

    Article  Google Scholar 

  25. Laukkanen, T.: Consumer adoption versus rejection decisions in seemingly similar service innovations: the case of the Internet and mobile banking. J. Bus. Res. 69, 2432–2439 (2016)

    Article  Google Scholar 

  26. Talwar, S., Dhir, A., Kaur, P., Mäntymäki, M.: Barriers toward purchasing from online travel agencies. Int. J. Hospitality Manag. 89, 102593 (2020). https://doi.org/10.1016/j.ijhm.2020.102593

    Article  Google Scholar 

  27. Gerrard, P., Cunningham, J.B., Devlin, J.F.: Why consumers are not using internet banking: a qualitative study. J. Serv. Mark. 20, 160–168 (2006)

    Article  Google Scholar 

  28. John, A., Klein, J.: The Boycott puzzle: consumer motivations for purchase sacrifice. Manag. Sci. 49, 1196–1209 (2003)

    Article  Google Scholar 

  29. Antioco, M., Kleijnen, M.: Consumer adoption of technological innovations: effects of psychological and functional barriers in a lack of content versus a presence of content situation. Eur. J. Mark. 44, 1700–1724 (2010)

    Article  Google Scholar 

  30. Dichter, E.: What’s In an image. J. Consum. Mark. 2, 75 (1985)

    Article  Google Scholar 

  31. Shaffer, V.A., Probst, C.A., Merkle, E.C., Arkes, H.R., Medow, M.A.: Why do patients derogate physicians who use a computer-based diagnostic support system? Med. Decis. Making 33, 108–118 (2012)

    Article  Google Scholar 

  32. Bigman, Y.E., Gray, K.: People are averse to machines making moral decisions. Cognition 181, 21–34 (2018)

    Article  Google Scholar 

  33. Davenport, T.H., Ronanki, R.: Artificial intelligence for the real world. Harv. Bus. Rev. 96, 108–116 (2018)

    Google Scholar 

  34. Mäntymäki, M., Islam, A.K.M.N., Benbasat, I.: What drives subscribing to premium in freemium services? A consumer value-based view of differences between upgrading to and staying with premium. Inf. Syst. J. 30, 295–333 (2020)

    Article  Google Scholar 

  35. Moore, G.C., Benbasat, I.: Development of an instrument to measure the perceptions of adopting an information technology innovation. Inf. Syst. Res. 2, 192–222 (1991)

    Article  Google Scholar 

  36. Agarwal, A., Singhal, C., Thomas, R.: Global Banking & Securities. McKinsey & Company (2021)

    Google Scholar 

  37. Fornell, C., Larcker, D.F.: Evaluating structural equation models with unobservable variables and measurement error. J. Mark. Res. 18, 39–50 (1981)

    Article  Google Scholar 

  38. Gefen, D., Straub, D., Gefen, D., Straub, D.: A practical guide to factorial validity using PLS-Graph: tutorial and annotated example. Commun. Assoc. Inf. Syst. 16, 91–109 (2005)

    Google Scholar 

  39. Featherman, M.S., Pavlou, P.A.: Predicting e-services adoption: a perceived risk facets perspective. Int. J. Hum. Comput. Stud. 59, 451–474 (2003)

    Article  Google Scholar 

  40. Im, I., Kim, Y., Han, H.J.: The effects of perceived risk and technology type on users’ acceptance of technologies. Inf. Manag. 45, 1–9 (2008)

    Article  Google Scholar 

  41. Laukkanen, T., Sinkkonen, S., Kivijärvi, M., Laukkanen, P.: Innovation resistance among mature consumers. J. Consum. Mark. 24, 419–427 (2007)

    Article  Google Scholar 

  42. Sadiq, M., Adil, M., Paul, J.: An innovation resistance theory perspective on purchase of eco-friendly cosmetics. J. Retail. Consum. Serv. 59, 102369 (2021)

    Article  Google Scholar 

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Correspondence to Hasan Mahmud .

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Mahmud, H., Islam, A.K.M.N., Mitra, R.K., Hasan, A.R. (2022). The Impact of Functional and Psychological Barriers on Algorithm Aversion – An IRT Perspective. In: Papagiannidis, S., Alamanos, E., Gupta, S., Dwivedi, Y.K., Mäntymäki, M., Pappas, I.O. (eds) The Role of Digital Technologies in Shaping the Post-Pandemic World. I3E 2022. Lecture Notes in Computer Science, vol 13454. Springer, Cham. https://doi.org/10.1007/978-3-031-15342-6_8

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  • DOI: https://doi.org/10.1007/978-3-031-15342-6_8

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-15341-9

  • Online ISBN: 978-3-031-15342-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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