The DOI matches its Crossref record; 10 of the 10 references it cites were found in a registry or an index.
The Role of Imagery in Information Processing: Review and Extensions
Abstract
Mental imagery is receiving increased attention in consumer behavior theory and research. This article describes imagery, characterizing it as a processing mode in which multisensory information is represented in a gestalt form in working memory, and discusses research on the unique effects of imagery at low levels of cognitive elaboration. It specifies researchable propositions for the relationship between high elaboration imagery processing and consumer choice and consumption behaviors. Finally, it reviews specific methods for studying imagery.
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Citations
Cites
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Cited by
Work and/or Fun: Measuring Hedonic and Utilitarian Shopping Value
Babin, B. J., Darden, W. R., & Griffin, M. (1994) · Journal of Consumer Research · doi:10.1086/209376 · Cited by 6,020
Consumer Choice between Hedonic and Utilitarian Goods
Dhar, R., & Wertenbroch, K. (2000) · Journal of Marketing Research · doi:10.1509/jmkr.37.1.60.18718 · Cited by 2,239
The Measurement of Destination Image: An Empirical Assessment
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Corporate image and corporate reputation in customers’ retention decisions in services
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Information Processing from Advertisements: Toward an Integrative Framework
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Self-citation
Metadata
- Authors
- MacInnis, D. J., & Price, L. L.
- Venue
- Journal of Consumer Research
- Publisher
- Oxford University Press (OUP)
- Published
- 1987-03
- Volume
- 13
- Issue
- 4
- Pages
- 473
- ISSN
- 0093-5301 (ISSN-L)
- 1537-5277
- OpenAlex
- W2143299957
- Cited by
- 1,276
- References
- 10
- FWCI
- 39.15
- Type
- journal article
- Language
- en
- Open access
- closed
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- MacInnis, D. J., & Price, L. L. (1987). The Role of Imagery in Information Processing: Review and Extensions. Journal of Consumer Research. https://doi.org/10.1086/209082
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
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.