When it really matters: algorithm aversion occurs most often when it is most harmful

Authors

  • Ibrahim Filiz Ostfalia
  • Florian Kirchhoff
  • Thomas Nahmer
  • Markus Spiwoks

DOI:

https://doi.org/10.17811/ebl.15.2.2026.121-127

Keywords:

Algorithm aversion, Decision-making under risk, Framing, Behavioral economics, Experiments

Abstract

A laboratory experiment is used to test whether algorithm aversion occurs particularly in decision-making situations where serious consequences are at stake. It is shown that the willingness to use an algorithm that is recognizably more powerful than a human expert decreases when the decision is particularly important.

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References

Dietvorst, B. J., Simmons, J. P. & Massey, C. (2015). Algorithm aversion: People erroneously avoid algorithms after seeing them err, Journal of Experimental Psychology: General, 144(1), 114–126.

Filiz, I., Judek, J. R., Lorenz, M. & Spiwoks, M. (2023). The extent of algorithm aversion in decision-making situations with varying gravity, PLoS ONE, 18(2), 1–21.

Fischbacher, U. (2007). z-Tree: Zurich toolbox for ready-made economic experiments, Experimental Economics, 10, 171–178.

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Published

24-05-2026

How to Cite

Filiz, I., Kirchhoff, F., Nahmer, T., & Spiwoks, M. (2026). When it really matters: algorithm aversion occurs most often when it is most harmful. Economics and Business Letters, 15(2), 121–127. https://doi.org/10.17811/ebl.15.2.2026.121-127

Issue

Vol. 15 No. 2 (2026): June - Special Issue Selected Papers from ICBESS 2025 (Guest Editor: Toan LD Huynh)

Section

Articles

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