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Cited by 788

The DOI matches its Crossref record; 46 of the 46 references it cites were found in a registry or an index.

Images in Advertising: The Need for a Theory of Visual Rhetoric

Scott, Linda M.·1994·Journal of Consumer Research

Abstract

In this article, past consumer research dealing with advertising images is analyzed and critiqued for its underlying assumption: that pictures are reflections of reality. The case against this assumption is presented, and an alternative view, in which visuals are a convention-based symbolic system, is formulated. In this alternative view, pictures must be cognitively processed, rather than absorbed peripherally or automatically. The author argues that current conceptualizations of advertising images are incommensurate with what ads are really like, and that many images currently dismissed as affect laden or information devoid are, in fact, complex figurative arguments. A new theoretical framework for the study of images is advanced in which advertising images are a sophisticated form of visual rhetoric. The process of consumer response implied by the new framework differs radically from past concepts in many ways, but also suggests new ways to approach questions currently open in the literature on the nature and processing of imagery. A pluralistic program for studying advertising pictures as persuasion is outlined.

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Metadata

Authors
Scott, L. M.
Venue
Journal of Consumer Research
Published
1994-09
Volume
21
Issue
2
Pages
252
ISSN
  • 0093-5301 (ISSN-L)
  • 1537-5277
OpenAlex
W1964140767
Cited by
788
References
46
FWCI
56.16
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: Images in Advertising: The Need for a Theory of Visual Rhetoric. https://app.cochat.ai/papers/10.1086/209396
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Scott, L. M. (1994). Images in Advertising: The Need for a Theory of Visual Rhetoric. Journal of Consumer Research. https://doi.org/10.1086/209396

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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.