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Purpose

This study aims to identify and analyse the interactions among drivers of anti-food waste behaviour at the consumer level. By understanding the mutual interactions among the drivers, an effort is made to identify the most driving and most dependent drivers through the total interpretive structural modelling (TISM) approach. Modelling offers inputs to propose focused interventions for reinforcing the identified drivers of anti-food waste consumer behaviour using the theoretical lens of social practices theory.

Design/methodology/approach

A proposed model of factors affecting anti-food waste behaviour is arrived at to suggest the most effective anti-food waste behavioural interventions. The factors were identified through an extensive literature search. A hierarchical structure of identified factors has been developed using TISM and MICMAC analysis through expert opinion. Focused marketing strategies towards promoting the identified factors for encouraging anti-food waste behaviour were suggested further.

Findings

This study identifies nine drivers based on extensive literature review, brainstorming and expert opinion. The TISM hierarchical model portrays the most important and least important drivers of household anti-food waste behaviour. It establishes that fundamental knowledge and socio-cultural norms are the most critical factors to drive the consumers. Marketers can focus on designing effective interventions to enhance the essential knowledge of the consumers and orient the socio-cultural norms towards anti-food waste behaviour.

Practical implications

This study offers implications for practitioners, policymakers and cause-driven marketing campaigns targeting anti-food waste behaviour. It provides an indicative list of critical factors relevant to household food waste behaviour, which can be used to drive effective marketing campaigns to nudge anti-food waste behaviours.

Originality/value

The proposed food waste behaviour management model was developed through modelling technique (TISM) and Cross-Impact Matrix Multiplication Applied to Classification (MICMAC) analysis, and relating them to marketing interventions is a novel effort in the food waste domain.

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