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Cited by 173

The DOI matches its Crossref record; 5 of the 5 references it cites were found in a registry or an index.

Attitudes and recycling: Does the measurement of affect enhance behavioral prediction?

Smith, Stephen M., Haugtvedt, Curtis P., Petty, Richard E.·1994·Psychology & Marketing

Abstract

Recent attempts to improve the influence of social marketing have often focused on behavioral strategies, largely neglecting the concept of attitude. This is understandable given the weak performance of attitudinal variables as a predictor in past studies of proenvironmental behavior. However, we suggest that part of the reason for the poor association between attitudes and such behavior is the possibility that many altruistic acts such as recycling are subject to affective influences that may not be fully captured by commonly employed attitude measures. We also suggest that attitude strength might moderate the extent to which affective reactions account for additional variance in behavior. Specifically, it is predicted that affect is more important for weak than for strong attitudes. Empirical results are presented to support these suggestions. © 1994 John Wiley & Sons, Inc.

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Metadata

Authors
Smith, S. M., Haugtvedt, C. P., & Petty, R. E.
Venue
Psychology & Marketing
Publisher
Wiley
Published
1994-07
Volume
11
Issue
4
Pages
359-374
ISSN
  • 0742-6046 (ISSN-L)
  • 1520-6793
OpenAlex
W2105427264
Cited by
173
References
5
FWCI
8.31
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: Attitudes and recycling: Does the measurement of affect enhance behavioral prediction? https://app.cochat.ai/papers/10.1002/mar.4220110405
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Smith, S. M., Haugtvedt, C. P., & Petty, R. E. (1994). Attitudes and recycling: Does the measurement of affect enhance behavioral prediction? Psychology & Marketing. https://doi.org/10.1002/mar.4220110405

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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.