Ciccullo F., Fabbri M., Abdelkafi N., Pero M. (2022). Exploring the potential of business models for sustainability and big data for food waste reduction. Journal of Cleaner Production, 15/03/2022, vol. 340, p. 1-13.
https://doi.org/10.1016/j.jclepro.2022.130673
https://doi.org/10.1016/j.jclepro.2022.130673
Titre : | Exploring the potential of business models for sustainability and big data for food waste reduction (2022) |
Auteurs : | F. Ciccullo ; M. Fabbri ; N. Abdelkafi ; M. Pero |
Type de document : | Article |
Dans : | Journal of Cleaner Production (vol. 340, March 2022) |
Article en page(s) : | p. 1-13 |
Langues : | Anglais |
Langues du résumé : | Anglais |
Catégories : |
Catégories principales 08 - ALIMENTATION ; 8.3 - Politique et Sécurité AlimentaireThésaurus IAMM GASPILLAGE ALIMENTAIRE ; RECYCLAGE DES DECHETS ; DECHET ALIMENTAIRE ; MEGADONNEES ; ENTREPRISE ; CHAINE D'APPROVISIONNEMENT |
Résumé : | Because the volume of food waste is increasing, actions are required to mitigate the environmental and social impact of food waste generation. This paper investigates the business models of 41 selected startups (technology and service providers) to capture how companies avoid food waste or use food waste as a resource. The case study analysis, based on secondary sources, shows that some startup business models leverage Big Data Analytics (BDA) to avoid food waste by optimizing an existing linear supply chain, while other business models create value out of food waste by leveraging a circular food supply chain. We found that the latter business models are not fully exploiting the potential of BDA. Based on our findings, we derive three propositions and one corollary. Whereas BDA seems a necessary requirement for business models that are focused on optimizing a linear supply chain, it appears optional for business models closing the supply chain loop. The propositions also discuss the timing when startups should start developing BDA capabilities depending on the business model type. |
Cote : | Réservé lecteur CIHEAM |
URL / DOI : | https://doi.org/10.1016/j.jclepro.2022.130673 |