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Cited by 748

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Predicting Proenvironmental Behavior Cross-Nationally: Values, the Theory of Planned Behavior, and Value-Belief-Norm Theory

Oreg, Shaul, Katz-Gerro, Tally·2006·Environment and Behavior

Abstract

This article builds on Ajzen's theory of planned behavior and on Stern et al.'s value-belief-norm theory to propose and test a model that predicts proenvironmental behavior. In addition to relationships between beliefs, attitudes, and behaviors, we incorporate Inglehart's postmaterialist and Schwartz's harmony value dimensions as contextual antecedents at the national level. Structural equation modeling analyses of a 27-country sample provide almost full support for the mediation model. Postmaterialistic values, but not harmony, affect environmental concern; in turn, environmental concern, perceived threat, and perceived behavioral control affect willingness to sacrifice, which then affects a variety of proenvironmental behaviors. The findings emphasize the contribution of cultural conditions to the shaping of individuals'actions vis-à-vis environmental issues, alongside individual-level social-psychological variables.

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Metadata

Authors
Oreg, S., & Katz-Gerro, T.
Venue
Environment and Behavior
Published
2006-07
Volume
38
Issue
4
Pages
462-483
ISSN
  • 0013-9165 (ISSN-L)
  • 1552-390X
OpenAlex
W2161432101
Cited by
748
References
10
FWCI
40.73
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: Predicting Proenvironmental Behavior Cross-Nationally: Values, the Theory of Planned Behavior, and Value-Belief-Norm Theory. https://app.cochat.ai/papers/10.1177/0013916505286012
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Oreg, S., & Katz-Gerro, T. (2006). Predicting Proenvironmental Behavior Cross-Nationally: Values, the Theory of Planned Behavior, and Value-Belief-Norm Theory. Environment and Behavior. https://doi.org/10.1177/0013916505286012

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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.