The effectiveness of health belief model in predicting intentions for environmentally sustainable behavior: A meta-analysis
Abstract
Human behavior is a major driver of environmental problems, making environmentally sustainable behaviors critical for mitigating environmental risks. While environmental psychology has developed influential models to explain such behaviors, prior meta-analyses have largely focused on integrated frameworks emphasizing normative and habitual processes. In contrast, a growing body of research has applied the health belief model (HBM)—a belief-based socio-psychological model initially developed to explain health behaviors—to sustainability contexts. However, findings across these studies are mixed, and no meta-analysis has systematically synthesized the predictive effectiveness of the model constructs for environmentally sustainable behavior. This study addresses this gap by meta-analyzing ( k =) 100 effect sizes from 23 primary studies ( N = 12,814) conducted across eight regions. Results showed that perceived susceptibility, perceived severity, perceived benefits, self-efficacy, and cues to action were positively associated with sustainable behavior intentions, whereas perceived barriers were not significant overall. Exploratory moderator analyses further indicated that perceived barriers negatively predicted intentions for sustainable dietary behaviors specifically. Through synthesizing a previously unsystematized literature, this meta-analysis complements existing meta-analytic work in environmental psychology and clarifies the role of belief-based risk appraisals in sustainable decision-making. The findings also offer practical guidance for designing communication interventions and campaigns aimed at promoting environmentally sustainable behaviors.
Fields of Study & Subjects
Environmental Education and Sustainability
Climate Change Communication and Perception
Environmental Sustainability in Business
Publication details
- Journal / source
- Journal of Environmental Psychology
- Publisher
- Elsevier BV
- Volume
- 111
- Pages
- 103019–103019
- Publication date
- 27 March 2026
- Language
- EN