The DOI matches its Crossref record; 54 of the 54 references it cites were found in a registry or an index.
The “Visual Preference Heuristic”: The Influence of Visual versus Verbal Depiction on Assortment Processing, Perceived Variety, and Choice Overload
Abstract
The “visual preference heuristic” suggests that consumers prefer visual to verbal depiction of information in a product assortment. Images produce greater perceptions of variety than text, which is appealing in assortment selection, but can result in choice complexity and overload when choice sets are large and preferences are unknown, suggesting a moderator for Iyengar and Lepper's results. Eye-tracking results reveal that the natural gestalt processing of individual visual stimuli, as compared to the piecemeal processing of individual textual stimuli, affects the processing of the assortment as a whole with visual (compared to verbal) presentation facilitating a faster, though more haphazard, scanning of the assortment. While the less systematic processing that results from visual presentation feels easier, it is not ideal for larger assortments resulting in higher complexity ratings and choice overload than with text depiction. These findings reveal that, like many heuristics, preference for visual depiction may be overapplied.
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Magnier, L., & Schoormans, J. (2015) · Journal of Environmental Psychology · doi:10.1016/j.jenvp.2015.09.005 · Cited by 382
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The fluent online shopping experience.
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Metadata
- Authors
- Townsend, C., & Kahn, B. E.
- Venue
- Journal of Consumer Research
- Publisher
- Oxford University Press (OUP)
- Published
- 2014-02-01
- Volume
- 40
- Issue
- 5
- Pages
- 993-1015
- ISSN
- 0093-5301 (ISSN-L)
- 1537-5277
- OpenAlex
- W2150040858
- Cited by
- 316
- References
- 54
- FWCI
- 11.81
- Type
- journal article
- Language
- en
- Open access
- closed
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
