The Effects of Situational and Individual Factors on Algorithm Acceptance in COVID-19-Related Decision-Making: A Preregistered Online Experiment

Abstract

In times of the COVID-19 pandemic, difficult decisions such as the distribution of ventilators must be made. For many of these decisions, humans could team up with algorithms; however, people often prefer human decision-makers. We examined the role of situational (morality of the scenario; perspective) and individual factors (need for leadership; conventionalism) for algorithm preference in a preregistered online experiment with German adults (n = 1,127). As expected, algorithm preference was lowest in the most moral-laden scenario. The effect of perspective (i.e., decision-makers vs. decision targets) was only significant in the most moral scenario. Need for leadership predicted a stronger algorithm preference, whereas conventionalism was related to weaker algorithm preference. Exploratory analyses revealed that attitudes and knowledge also mattered, stressing the importance of individual factors.

DOI

10.30658/hmc.3.3

Author ORCID Identifier

Sonja Utz: 0000-0002-7979-3554

Lara N. Wolfers: 0000-0002-1074-1617

Anja S. Göritz: 0000-0002-4638-0489

Recommended Citation

Utz, S., Wolfers, L. N., & Göritz, A. S. (2021). The effects of situational and individual factors on algorithm acceptance in COVID-19-Related decision-making: A preregistered online experiment. Human-Machine Communication, 3, 27-46. https://doi.org/10.30658/hmc.3.3

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