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Algorithm Aversion as an Obstacle in the Establishment of Robo Advisors

Author

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  • Ibrahim Filiz

    (Faculty of Business, Ostfalia University of Applied Sciences, Siegfried-Ehlers-Str. 1, D-38440 Wolfsburg, Germany)

  • Jan René Judek

    (Faculty of Business, Ostfalia University of Applied Sciences, Siegfried-Ehlers-Str. 1, D-38440 Wolfsburg, Germany)

  • Marco Lorenz

    (Faculty of Economic Sciences, Georg August University Göttingen, Platz der Göttinger Sieben 3, D-37073 Göttingen, Germany)

  • Markus Spiwoks

    (Faculty of Business, Ostfalia University of Applied Sciences, Siegfried-Ehlers-Str. 1, D-38440 Wolfsburg, Germany)

Abstract

Within the framework of a laboratory experiment, we examine to what extent algorithm aversion acts as an obstacle in the establishment of robo advisors. The subjects had to complete diversification tasks. They could either do this themselves or they could delegate them to a robo advisor. The robo advisor evaluated all the relevant data and always made the decision which led to the highest expected value for the subjects’ payment. Although the high level of efficiency in the robo advisor was clear to see, the subjects only entrusted their decisions to the robo advisor in around 40% of cases. In this way, they reduced their success and their payment. Many subjects orientated themselves towards the 1/n-heuristic, which also contributed to their suboptimal decisions. As long as the subjects had to make decisions for others, they noticeably made a greater effort and were also more successful than when they made decisions for themselves. However, this did not have an effect on their acceptance of robo advisors. Even when they made decisions on behalf of others, the robo advisor was only consulted in around 40% of cases. This tendency towards algorithm aversion among subjects is an obstacle to the broader establishment of robo advisors.

Suggested Citation

  • Ibrahim Filiz & Jan René Judek & Marco Lorenz & Markus Spiwoks, 2022. "Algorithm Aversion as an Obstacle in the Establishment of Robo Advisors," JRFM, MDPI, vol. 15(8), pages 1-25, August.
    • Handle: RePEc:gam:jjrfmx:v:15:y:2022:i:8:p:353-:d:882960
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    References listed on IDEAS

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    Cited by:

    1. 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.
    2. Béatrice BOULU-RESHEF & Alexis DIRER & Nicole VON WILCZUR, 2022. "Algorithmic vs. Human Portfolio Choice," LEO Working Papers / DR LEO 2966, Orleans Economics Laboratory / Laboratoire d'Economie d'Orleans (LEO), University of Orleans.
    3. Zulia Gubaydullina & Jan René Judek & Marco Lorenz & Markus Spiwoks, 2022. "Comparing Different Kinds of Influence on an Algorithm in Its Forecasting Process and Their Impact on Algorithm Aversion," Businesses, MDPI, vol. 2(4), pages 1-23, October.

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