Saving food surplus and developing new business models: Exploring the potential of ‘Too Good To Go’ at territorial level using web-scraped data

IF 3.5 3区计算机科学 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Big Data Research Pub Date : 2025-05-10 DOI:10.1016/j.bdr.2025.100536

Mengting Yu, Luca Secondi, Tiziana Laureti, Luigi Palumbo

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Abstract

Food surplus, fit for consumption, is often excluded from the consumption loop for commercial reasons, leading to wasted food, nutrients, resources, and costs. Digital innovations with diverse business models aim to combat this through food redistribution. However, it is critical to assess their effectiveness from stakeholder and consumer perspectives, meanwhile, new research focuses on the value of these business models.

This study employs web scraping technology to collect multi-dimensional data from two Italian cities on Too Good To Go. The analysis results confirm its positive contribution to food surplus redistribution with economic benefits, despite a weaker presence of certain food establishment types and a lack of social motivation among consumers. Furthermore, strong business-customer relationships can be established when businesses commit to reducing food waste and effectively communicate with their customers using the platform.

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节约粮食剩余和发展新的商业模式：利用网络数据在地区层面探索“太好而不能去”的潜力

适合消费的剩余粮食往往因商业原因被排除在消费循环之外，导致粮食、营养、资源和成本的浪费。具有多种商业模式的数字创新旨在通过粮食再分配来解决这一问题。然而，从利益相关者和消费者的角度评估其有效性是至关重要的，同时，新的研究侧重于这些商业模式的价值。本研究采用网络抓取技术收集了意大利两个城市在Too Good to Go的多维数据。分析结果证实了它对粮食剩余再分配的积极贡献，并具有经济效益，尽管某些食品企业类型的存在较弱，消费者缺乏社会动机。此外，当企业承诺减少食物浪费并使用该平台与客户有效沟通时，可以建立牢固的企业与客户关系。

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来源期刊

Big Data Research Computer Science-Computer Science Applications

CiteScore

8.40

自引率

3.00%

发文量

期刊介绍： The journal aims to promote and communicate advances in big data research by providing a fast and high quality forum for researchers, practitioners and policy makers from the very many different communities working on, and with, this topic. The journal will accept papers on foundational aspects in dealing with big data, as well as papers on specific Platforms and Technologies used to deal with big data. To promote Data Science and interdisciplinary collaboration between fields, and to showcase the benefits of data driven research, papers demonstrating applications of big data in domains as diverse as Geoscience, Social Web, Finance, e-Commerce, Health Care, Environment and Climate, Physics and Astronomy, Chemistry, life sciences and drug discovery, digital libraries and scientific publications, security and government will also be considered. Occasionally the journal may publish whitepapers on policies, standards and best practices.