Leveraging Analytics to Understand Food Consumption and Waste in Achieving Personalized Nudging

Lu, Yang, Li, Shuyang, Wang, Jing ORCID: https://orcid.org/0000-0002-3459-579X and Kanu, Ifeyinwa (2025) Leveraging Analytics to Understand Food Consumption and Waste in Achieving Personalized Nudging. In: Proceedings of the 58th Hawaii International Conference on System Sciences, pp. 931-940. Presented at Hawaii International Conference on System Sciences (HICSS), 7 January 2025 - 10 Janauary 2025, Hilton Waikoloa Village, USA.

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Abstract

Managing food waste is pivotal in advancing sustainable consumption practices. This study investigates how various factors such as food type, consumer spending, socio-economic characteristics, and demographics correlate with food waste patterns, utilizing data analytics and statistical analysis. Drawing on studies in green information systems (IS) and digital nudging, we propose three strategic nudging designs: pre-existing nudges based on food type characteristics, configurable nudges tailored to demographic and socio-economic profiles, and dynamic nudges responsive to evolving consumer behaviors. These interventions are designed to utilize behavioral insights to promote more sustainable consumer habits and present a novel methodology for substantially reducing food waste.