Preference for Human (vs. Robotic) Labor is Stronger in Symbolic Consumption Contexts
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Advances in robotics, automation, and artificial intelligence increasingly enable firms to replace human labor with technology, thereby fundamentally transforming how goods and services are produced. From both managerial and societal points of view, it is therefore important to understand demand‐side incentives for firms to employ human labor. We begin to address this question by examining for which products and services consumers are more likely to favor human (vs. robotic) labor. In six studies, we demonstrate that consumers prefer human (vs. robotic) labor more for products with higher (vs. lower) symbolic value (e.g., when expressing something about one's beliefs and personality is of greater importance). We theorize that this is because consumers have stronger uniqueness motives in more (vs. less) symbolic consumption contexts (and associate human labor more strongly with product uniqueness). In line with this account, we demonstrate that individual differences in need for uniqueness moderate the interaction between production mode and symbolic motives and that a measure of uniqueness motives mediates the effect of consumption context on preferences for human (vs. robotic) production.
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Consumers may feel that brands using GenAI are cutting corners or replacing people with machines, leading to reduced emotional engagement and brand loyalty.
“Consumers may feel that brands using GenAI are “cutting corners” or “replacing people with machines,” leading to reduced emotional engagement and brand loyalty (Granulo et al., 2021).”
From The Impact of Generative AI on Personalized Content Marketing in E-Commerce (2025) · cites this paper as Granulo et al., 2021
The Abstract (page unavailable) says firms can replace human labor with technology and that consumers prefer human over robotic labor for products with higher symbolic value. The paper does not establish that consumers see brands using GenAI as “cutting corners,” or that such use reduces emotional engagement or brand loyalty; its findings concern production mode and product preference. (LLM, conf=0.87)
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“Consumers may feel that brands using GenAI are "cutting corners" or "replacing people with machines," leading to reduced emotional engagement and brand loyalty (Granulo et al., 2021).”
- Authors
- Granulo, A., Fuchs, C., & Puntoni, S.
- Affiliations
- Technical University of Munich (DE)
- Erasmus University Rotterdam (NL)
- Corresponding author
- Puntoni, Stefano
- Venue
- Journal of Consumer Psychology
- Publisher
- Wiley
- Published
- 2020-08-19
- Volume
- 31
- Issue
- 1
- Pages
- 72-80
- ISSN
- 1057-7408 (ISSN-L)
- 1532-7663
- OpenAlex
- W3042318686
- Cited by
- 255
- References
- 8
- Citations by year
- 2026: 62 · 2025: 64 · 2024: 55 · 2023: 36 · 2022: 25 · 2021: 12 · 2020: 1
- FWCI
- 19.08
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Journal of Consumer Psychology (publishedVersion, cc-by)
- EUR Research Repository (Erasmus University Rotterdam) (publishedVersion, other-oa)
- EUR Research Repository (Erasmus University Rotterdam) (submittedVersion, other-oa)
- RePub (Erasmus University Rotterdam) (submittedVersion, other-oa)
- Lancaster EPrints (Lancaster University) (submittedVersion)
- mediaTUM (Technical University of Munich) (submittedVersion)
- mediaTUM – the media and publications repository of the Technical University Munich (Technical University Munich) (submittedVersion, other-oa)
- Funders
- Technische Universität München
- Erasmus Research Institute of Management
- Topics
- Social Sciences › Business, Management and Accounting › Marketing › Consumer Behavior in Brand Consumption and Identification
- Physical Sciences › Computer Science › Artificial Intelligence › AI in Service Interactions
- Social Sciences › Business, Management and Accounting › Marketing › Consumer Market Behavior and Pricing
- Keywords
- symbolic consumption, product uniqueness, need for uniqueness, modes of production, consumer preferences, automation, artificial intelligence, individual differences
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