Lu, Yang ORCID: https://orcid.org/0000-0002-0583-2688, Li, Shuyang, Wang, Jing 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. University of Hawaiʻi at Mānoa, pp. 931-940

Preview

Text 0092.pdf - Published Version Available under License Creative Commons Attribution Non-commercial No Derivatives. Download (1MB) | Preview

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.

Item Type:	Book Section
Status:	Published
DOI:	10.24251/hicss.2025.111
School/Department:	School of Science, Technology and Health
URI:	https://ray.yorksj.ac.uk/id/eprint/12038

University Staff: Request a correction | RaY Editors: Update this record