What question did this study set out to answer?

This research aims to uncover the factors contributing to the intention-behaviour gap in food waste among Chinese consumers by employing machine learning models.

February 24, 2026Waste Management & Research The Journal for a Sustainable Circular Economy

Decoding food waste behaviours among Chinese consumers: Machine learning insights into the intention–behaviour gap

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This research aims to uncover the factors contributing to the intention-behaviour gap in food waste among Chinese consumers by employing machine learning models.
Utilized survey data from consumers in China.
Employed five machine learning models: Gradient Boost, Random Forest, XGBoost, K-nearest neighbours, and Decision Tree.
Assessed predictive accuracy of models to identify key predictors.
Investigated the role of moral disengagement and dining culture in food waste behaviours.
Gradient Boost and Random Forest demonstrated the highest predictive accuracy.
Moral disengagement was identified as the most significant predictor of the intention-behaviour gap.
Three neutralization techniques contributed to the gap: moral justification, diffusion of responsibility, and advantageous comparison.
Dining culture, including over-ordering and discarding food, was crucial in understanding food waste behaviours.

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Implication

This research analyzes the intention-behaviour gap in food waste among Chinese consumers, revealing key predictors and implications for reducing waste.

Cite This Study

Yuyi Wang (2026) studied this question.

synapsesocial.com/papers/699d3fd9de8e28729cf64963 https://doi.org/10.1177/0734242x251408289

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Implication

This research analyzes the intention-behaviour gap in food waste among Chinese consumers, revealing key predictors and implications for reducing waste.

Decoding food waste behaviours among Chinese consumers: Machine learning insights into the intention–behaviour gap

Key Points

Discussion

Implication

Cite This Study

Decoding food waste behaviours among Chinese consumers: Machine learning insights into the intention–behaviour gap

Key Points

Discussion

Implication

Cite This Study

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