Untangling the complex nature of household food waste drivers through fuzzy cognitive mapping
Since reducing household food waste (HFW) has serious environmental, social, and economic implications, researchers across disciplines have investigated this phenomenon from multiple angles and identified a substantial number of drivers that account for HFW. Despite these efforts, empirical investigation of the complex web of HFW drivers is still in its infancy. The methodologies that have been used to investigate the phenomenon have been generally insufficient in capturing the complexity surrounding consumers’ food waste behavior. Therefore, the main objective of this thesis is to provide a more complete representation of this complexity. To this end, a distinctive method, fuzzy cognitive mapping (FCM) was used to demonstrate the system of HFW drivers. Through an iterative process between the content analysis of previous studies and expert opinion, the most prevalent HFW drivers as system concepts and their causal relationships were synthesized and consolidated in the first stage. Then in the second stage, this well-refined framework was transformed into an FCM that provides a consistent base to conduct simulations and to compare the HFW-reducing intervention alternatives. In the third stage, the most influential drivers were revealed, and the ambiguities due to conflicting findings reported in the literature were resolved. Then, through scenario analysis, intervention alternatives were compared based on their impact on HFW. In the final section, findings of each stage were integrated and discussed with their theoretical, methodological, and practical implications.