Task-Dependent Algorithm Aversion

Autor: Noah Castelo, Donald R. Lehmann, Maarten W. Bos
Rok vydání: 2019
Předmět:
Zdroj: Journal of Marketing Research. 56:809-825
ISSN: 1547-7193
0022-2437
DOI: 10.1177/0022243719851788
Popis: Research suggests that consumers are averse to relying on algorithms to perform tasks that are typically done by humans, despite the fact that algorithms often perform better. The authors explore when and why this is true in a wide variety of domains. They find that algorithms are trusted and relied on less for tasks that seem subjective (vs. objective) in nature. However, they show that perceived task objectivity is malleable and that increasing a task’s perceived objectivity increases trust in and use of algorithms for that task. Consumers mistakenly believe that algorithms lack the abilities required to perform subjective tasks. Increasing algorithms’ perceived affective human-likeness is therefore effective at increasing the use of algorithms for subjective tasks. These findings are supported by the results of four online lab studies with over 1,400 participants and two online field studies with over 56,000 participants. The results provide insights into when and why consumers are likely to use algorithms and how marketers can increase their use when they outperform humans.
Databáze: OpenAIRE
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