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Cited by 486

The DOI matches its Crossref record; 3 of the 3 references it cites were found in a registry or an index.

Shades of Green: A Multidimensional Analysis of Environmental Advertising

Open access
Banerjee, Subhabrata, Gulas, Charles S., Iyer, Easwar·1995·Journal of Advertising

Abstract

Environmental appeals are becoming increasingly common in advertising, but all green ads are not created equal. The authors report the results of a content analysis designed to uncover the underlying structure of green advertising. A convenience sample of 95 green TV ads and 173 green print ads were content-analyzed. Multidimensional analysis indicates that the structure of green advertising can be captured in three dimensions: sponsor type (for-profit or nonprofit), ad focus (whether the ad focuses on the advertiser or the consumer), and depth of ad (shallow, moderate, or deep depending on the extent of environmental information mentioned). A majority of advertisers in the sample attempted to project a green corporate image rather than focusing on the environmental benefits of their product or service.

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Registry

Crossref · 10.1080/00913367.1995.10673473

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Metadata

Authors
Banerjee, S., Gulas, C. S., & Iyer, E.
Venue
Journal of Advertising
Published
1995-06
Volume
24
Issue
2
Pages
21-31
ISSN
  • 0091-3367 (ISSN-L)
  • 1557-7805
OpenAlex
W2064018654
Cited by
486
References
3
FWCI
5.15
Type
journal article
Language
en
Open access
green

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CoChat. (2026). Citation check: Shades of Green: A Multidimensional Analysis of Environmental Advertising. https://app.cochat.ai/papers/10.1080/00913367.1995.10673473
The paper
Banerjee, S., Gulas, C. S., & Iyer, E. (1995). Shades of Green: A Multidimensional Analysis of Environmental Advertising. Journal of Advertising. https://doi.org/10.1080/00913367.1995.10673473

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.