Task-Dependent Algorithm Aversion

By Noah Castelo, Maarten W. Bos, Donald R. Lehmann. Journal of Marketing Research, 2019

1,118 citations recorded by the source corpus.

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.

Subfields: Applied Psychology, Social Psychology, Sociology and Political Science

View Paper: https://www.semanticscholar.org/paper/bbcdbcf415d7ef276b403015119ff9178de65735

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Citations in the corpus

Artificial intelligence and illusions of understanding in scientific research (2024, neutral): “…likely to trust AI for tasks that share key features with scientific research: tasks that can be described as objective (as opposed to subjective) 85,86 , that require deliberation (as opposed to intuition) 87 , where human biases are undesirable 88,89 and under conditions of high workloads 90…”
Preference for Human (vs. Robotic) Labor is Stronger in Symbolic Consumption Contexts (2020, neutral): “…against autonomous technologies (e.g., Leung, Paolacci, & Puntoni, 2018; Longoni, Bonezzi, & More-wedge, 2019; Mende, Scott, van Doorn, Grewal, & Shanks, 2019) but also situations where consumers do not prefer humans to algorithms (e.g., Castelo, Bos, & Lehmann, 2019; Logg, Minson, & Moore, 2019).”
Artificial Intelligence in Utilitarian vs. Hedonic Contexts: The “Word-of-Machine” Effect (2020, background): “…and Meehl 1996; Meehl 1954), recent marketing research has focused on whether consumers will be receptive to algorithmic advice in various domains (Castelo, Bos, and Lehman 2019; Dietvorst, Simmons, and Massey 2014; Leung, Paolacci, and Puntoni 2019; Logg, Min-son, and Moore 2019; Longoni,…”
Teachers’ attitudes towards chatbots in education: a technology acceptance model approach considering the effect of social language, bot proactiveness, and users’ characteristics (2021, background): “Castelo, Bos, and Lehmann (2019) provide a review of human versus algorithmic advice preference, which supports greater preference of human advice for varied topics ranging from medical recommendations to the stock market.”
User Perceptions and Trust of Explainable Machine Learning Fake News Detectors (2022, methodology): “At the same time, whether consumers feel positive or negative about algorithms-based technology varies depending on the type of task for which the algorithm is used (Castelo, Bos, & Lehmann, 2019).”
Artificial intelligence versus Maya Angelou: Experimental evidence that people cannot differentiate AI-generated from human-written poetry (2020, background): “appreciative of) algorithmic decision-making across various domains (Castelo et al., 2019; Lee, 2018; Pew Research Center, 2018).”
Consumers and Artificial Intelligence: An Experiential Perspective (2020, background): “competence (Castelo et al. 2019) should be more likely to spur feelings of being replaced”
The Next Generation of Research on IS Use: A Theoretical Framework of Delegation to and from Agentic IS Artifacts (2021, methodology): “…IS artifact, as argued extensively in the cognitive fit (e.g., Vessey 2006), task technology fit (e.g., Goodhue and Thompson 1995), IS use (e.g., Burton-Jones and Grange 2013; Burton-Jones and Straub 2006; Lauterbach et al. 2020), and even consumer behavior (e.g., Castelo et al. 2019) literatures.”