Liu Yaqin, Wang Peng, Zhang Mengya, Chen Xi, Li Ke, Qu Jianying
Key Laboratory of Computing and Stochastic Mathematics (Ministry of Education), School of Mathematics and Statistics, Hunan Normal University, Changsha 410081, China.
Hunan Institute for Carbon Peaking and Carbon Neutrality, Hunan Normal University, Changsha 410081, China.
Foods. 2024 Jul 16;13(14):2239. doi: 10.3390/foods13142239.
The transition to a low-carbon economy emphasizes the importance of green and low-carbon consumption; yet, there is often a discrepancy between consumers' intentions and their actual behavior regarding organic foods. This study aims to identify strategies to bridge this gap. The research model of organic food consumption intention and behavior is constructed, and a structural equation model is used to test the research hypotheses based on a valid sample of 480 residents of Guangdong Province through an online questionnaire survey. Further, the intention‒behavior gap is defined and its determinants are investigated through multiclass logistic regression. Finally, we categorize and forecast the alignment between consumption intentions and behaviors using machine learning algorithms. The results reveal that attitudes, social interactions, and cognitive information play crucial roles in aligning intentions with behaviors. By enhancing social information exchange or improving cognitive understanding, consumers can reduce their intention‒behavior discrepancy. This research offers valuable policy recommendations for fostering green consumption among residents from various perspectives.
向低碳经济转型凸显了绿色低碳消费的重要性；然而，消费者在有机食品方面的意图与实际行为之间往往存在差异。本研究旨在确定弥合这一差距的策略。构建了有机食品消费意图和行为的研究模型，并通过在线问卷调查，基于广东省480名居民的有效样本，运用结构方程模型对研究假设进行检验。此外，定义了意图—行为差距，并通过多分类逻辑回归对其决定因素进行调查。最后，使用机器学习算法对消费意图与行为之间的一致性进行分类和预测。结果表明，态度、社会互动和认知信息在使意图与行为保持一致方面起着关键作用。通过加强社会信息交流或提高认知理解，消费者可以减少其意图—行为差异。本研究从多个角度为促进居民绿色消费提供了有价值的政策建议。
