What is it about?

Online-to-Offline (O2O) e-commerce and the rapid expansion of online food service platforms have brought increasing visibility to food safety issues. Customer reviews on these platforms are becoming crucial for identifying these issues, yet research on leveraging this data effectively remains limited. This study bridges this gap by analyzing over ten thousand customer reviews based on Food Safety Industry Standards. Through manual categorization and the development of a specialized dictionary of food safety-related keywords, we have built machine learning models that effectively identify food safety concerns from user reviews. Our research also explores potential solutions for addressing these concerns within the online catering sector. We reviewed existing literature to compile a comprehensive list of 16 possible solutions, grouped into four categories: government, online platforms, merchants, and society/consumers. To understand consumer preferences regarding these solutions, we conducted a best-worst scaling experiment and analyzed the data using a mixed logit model, allowing us to rank the solutions based on consumer preference. Integrating a marketing perspective, we propose strategies such as using transparent marketing communications to educate and engage customers on food safety practices, positioning brands as leaders in food safety, and deploying digital marketing strategies to promote food safety features. We also suggest enhancing online feedback mechanisms and leveraging data analytics to refine marketing strategies based on consumer feedback. Additionally, collaborative marketing efforts with governmental and non-governmental organizations can promote food safety standards and boost platform credibility. By incorporating these marketing strategies, our study provides a more comprehensive view on integrating food safety into the business model of online food service platforms, enhancing consumer trust and establishing a competitive advantage.

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This page is a summary of: From detection to resolution: using machine learning to identify food safety risks and solutions in online consumer reviews, European Journal of Marketing, December 2025, Emerald,
DOI: 10.1108/ejm-08-2024-0645.
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