The Effects of Situational and Individual Factors on Algorithm Acceptance in COVID-19-Related Decision-Making: A Preregistered Online Experiment
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.
Author ORCID Identifier
Sonja Utz: 0000-0002-7979-3554
Lara N. Wolfers: 0000-0002-1074-1617
Anja S. Göritz: 0000-0002-4638-0489
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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