Identifying food insecurity in food sharing networks via machine learning

Nica-Avram, Georgiana; Harvey, John; Smith, Gavin; Smith, Andrew; Goulding, James

doi:10.1016/j.jbusres.2020.09.028

Identifying food insecurity in food sharing networks via machine learning

Nica-Avram, Georgiana; Harvey, John; Smith, Gavin; Smith, Andrew; Goulding, James

Authors

Mrs GEORGIANA NICA-AVRAM GEORGIANA.NICA-AVRAM1@NOTTINGHAM.AC.UK
TRANSITIONAL ASSISTANT PROFESSOR

Dr JOHN HARVEY John.Harvey2@nottingham.ac.uk
ASSOCIATE PROFESSOR

Dr Gavin Smith GAVIN.SMITH@NOTTINGHAM.AC.UK
ASSOCIATE PROFESSOR

Professor ANDREW SMITH Andrew.p.Smith@nottingham.ac.uk
PROFESSOR OF CONSUMER BEHAVIOUR & ANALYTICS

Dr JAMES GOULDING JAMES.GOULDING@NOTTINGHAM.AC.UK
PROFESSOR OF DATA SCIENCE

Abstract

© 2020 Elsevier Inc. Food insecurity in the UK has captured public attention. However, estimates of its prevalence are deeply contentious. The lack of precision on the volume of emergency food assistance currently provided to those in need is made even more ambiguous due to increasing use of peer-to-peer food sharing systems (e.g. OLIO). While these initiatives exist as a solution to food waste rather than food poverty, they are nonetheless carrying a hidden share of the food insecurity burden, with the socio-economic status of technology-assisted food sharing donors, volunteers, and recipients remaining obscure. In this article we examine the relationship between food sharing and deprivation generally, before applying machine learning techniques to develop a predictive model of food insecurity based upon aggregated food sharing behaviours by OLIO users in the UK. We demonstrate that data from food sharing systems can help quantify a previously hidden aspect of deprivation and we make the case for a reformed approach to modelling food insecurity.

Citation

Nica-Avram, G., Harvey, J., Smith, G., Smith, A., & Goulding, J. (2021). Identifying food insecurity in food sharing networks via machine learning. Journal of Business Research, 131, 469-484. https://doi.org/10.1016/j.jbusres.2020.09.028

Journal Article Type	Article
Acceptance Date	Sep 13, 2020
Online Publication Date	Oct 15, 2020
Publication Date	2021-07
Deposit Date	Nov 10, 2020
Publicly Available Date	Apr 16, 2022
Journal	Journal of Business Research
Print ISSN	0148-2963
Electronic ISSN	1873-7978
Publisher	Elsevier
Peer Reviewed	Peer Reviewed
Volume	131
Pages	469-484
DOI	https://doi.org/10.1016/j.jbusres.2020.09.028
Public URL	https://nottingham-repository.worktribe.com/output/5032516
Publisher URL	https://www.sciencedirect.com/science/article/pii/S0148296320306123?via%3Dihub