Academic Journal
Decoding food waste behaviours among Chinese consumers: Machine learning insights into the intention-behaviour gap.
| Τίτλος: | Decoding food waste behaviours among Chinese consumers: Machine learning insights into the intention-behaviour gap. |
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| Συγγραφείς: | Wang Y; College of Economics and Management, Nanjing Forestry University, Nanjing, China. |
| Πηγή: | Waste management & research : the journal of the International Solid Wastes and Public Cleansing Association, ISWA [Waste Manag Res] 2026 Jul; Vol. 44 (7), pp. 1037-1060. Date of Electronic Publication: 2026 Feb 22. |
| Τύπος έκδοσης: | Journal Article |
| Γλώσσα: | English |
| Στοιχεία περιοδικού: | Publisher: Sage Publications Country of Publication: England NLM ID: 9881064 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1096-3669 (Electronic) NLM ISO Abbreviation: Waste Manag Res Subsets: MEDLINE |
| Imprint Name(s): | Publication: Original Publication: London ; New York : Academic Press, c1983- |
| Ιατρικοί όροι (MeSH): | Boosting Machine Learning Algorithms* , Consumer Behavior* , Food Loss and Waste* , Intention*, Humans ; China ; Predictive Learning Models ; Random Forest ; East Asian People |
| Περίληψη: | Identifying the factors contributing to the intention-behaviour gap is pivotal for reducing food waste. Existing research has largely concentrated on the antecedents of food-waste intention, while neglecting not only the discrepancy between intention and actual wasteful behaviour but also the determinants underlying this discrepancy. Drawing on survey data from China, this study employs five machine learning models, Gradient Boost, Random Forest, XGBoost, K-nearest neighbours and Decision Tree to investigate key predictors of this gap. Gradient Boost and Random Forest outperformed the others in predictive accuracy. Moral disengagement emerged as the most influential determinant; a finding consistently supported by the two best-performing models. Among its mechanisms, three neutralization techniques, namely moral justification, diffusion of responsibility and advantageous comparison, were found to significantly contribute to the gap. Additionally, dining culture was identified as another critical factor, with over-ordering and food discarding behaviours playing a central role. Based on these findings, policymakers should consider practical interventions, including traceability tools, accountability reminders and culturally sensitive campaigns, to effectively reduce household and food-service waste. With the development of machine learning models, this research broadens the perspective of food waste research and provides new solutions for using complex data in this field. |
| Competing Interests: | Declaration of conflicting interestsThe authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article. |
| Contributed Indexing: | Keywords: Food waste reduction; consumer behaviour; influencing factors; intention–behaviour gap; machine learning models |
| Substance Nomenclature: | 0 (Food Loss and Waste) |
| Entry Date(s): | Date Created: 20260222 Date Completed: 20260701 Latest Revision: 20260702 |
| Update Code: | 20260702 |
| DOI: | 10.1177/0734242X251408289 |
| PMID: | 41724582 |
| Βάση Δεδομένων: | MEDLINE |
| ISSN: | 1096-3669 |
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| DOI: | 10.1177/0734242X251408289 |