ArticleFrontiers in psychology2025
Influential factors shaping consumers' green packaging purchase intentions.
Article in Frontiers in psychology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Introduction: Rapid consumption upgrading and industrial transformation are straining the ecological capacity of China's Guangdong-Hong Kong-Macao Greater Bay Area, where green packaging is viewed as a crucial means of easing this burden. The conventional theory of planned behavior is limited in explaining green consumption. This study examined green packaging purchasing behavior in the Greater Bay Area by augmenting the conventional theory of planned behavior model to incorporate environmental concern and the willingness to pay a premium. Methods: The extended model was evaluated using survey data from 370 consumers, employing covariance-based structural equation modeling analysis and mediation effect testing in SPSS 26 and AMOS 28. The results indicate that attitude, perceived behavioral control, and willingness to pay a premium significantly predict consumers' behavioral intention for green packaging. Results: Environmental concern plays a pivotal role, not only by amplifying attitudes, perceived behavioral control, and willingness to pay a premium, but also by indirectly influencing behavioral intention. However, subjective norm did not exert a significant effect on behavioral intention. The extended model demonstrated a notable improvement in explanatory power, with R2 rising from 0.710 to 0.859 for behavioral intention and from 0.870 to 0.912 for actual purchase behavior relative to the conventional model. Discussion: This incorporates value-based antecedsents lacking in the conventional model and elucidates performance improvements. These findings underscore the theoretical significance of incorporating environmental concern and the willingness to pay a premium into the traditional behavioral model, demonstrating that such enhancements markedly improve the forecasting of green purchase behavior.
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