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

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dc.contributor.authorLu, Yang
dc.contributor.authorLi, Shuyang
dc.contributor.authorWang, Jing
dc.contributor.authorKanu, Ifeyinwa
dc.date.accessioned2024-12-26T21:05:09Z
dc.date.available2024-12-26T21:05:09Z
dc.date.issued2025-01-07
dc.description.abstractManaging 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.
dc.format.extent10
dc.identifier.doi10.24251/HICSS.2025.111
dc.identifier.isbn978-0-9981331-8-8
dc.identifier.other600dc2a2-14fc-4d53-89d8-2fa8d37e0756
dc.identifier.urihttps://hdl.handle.net/10125/108949
dc.relation.ispartofProceedings of the 58th Hawaii International Conference on System Sciences
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 International
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subjectAnalytics and Intelligent Decision Support for Green IS and Sustainability Applications
dc.subjectconsumer behavior, data analytics, digital nudging, food waste reduction, green is
dc.titleLeveraging Analytics to Understand Food Consumption and Waste in Achieving Personalized Nudging
dc.typeConference Paper
dc.type.dcmiText
prism.startingpage926

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Analytics and Intelligent Decision Support for Green IS and Sustainability Applications