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The impact of trust-based challenges on user satisfaction in food sharing platforms: A text mining approach

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  • Krishna, Ben
  • Puram, Praveen

Abstract

Food waste is a pressing ethical and environmental challenge. One-third of all food produced is wasted, while 3 billion people are food insecure. Online food-sharing platforms enable the redistribution of surplus food at a community level. Though highly beneficial, these platforms have not witnessed high usage due to multiple challenges, which includes trust. As stakeholders' trust in food-sharing apps is essential to maintain their patronage, this study attempts to obtain trust-based challenges and assess their impact on user satisfaction through a mixed-method study. Using topic modeling, user reviews from popular food-sharing apps are first analyzed to identify eight challenges that impact user experience. These challenges are then aligned with the dimensions of cognition-based trust, experience-based trust, and personality-oriented trust. Further, hypotheses are formulated and tested using linear regression to assess the impact of these challenges on user experience. This study addresses the topics of food waste and food insecurity, which are related to Sustainable Development Goals (SDGs): SDG-2 (Zero hunger) and SDG-12 (Responsible consumption and production). While some challenges have been explored in the extant literature, others are unique to this study, such as the lack of optimal experience, information quality, perceived privacy, and opportunism. These findings contribute to the food-sharing literature by analyzing user experiences worldwide and assesses trust-related challenges, thereby improving upon the extant literature which mainly explores the motivators, and has a cursory focus on the aspect of ‘trust’. Also, this study informs policy development to address users' trust concerns, thus enhancing food-sharing apps' adoption.

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  • Krishna, Ben & Puram, Praveen, 2025. "The impact of trust-based challenges on user satisfaction in food sharing platforms: A text mining approach," Technological Forecasting and Social Change, Elsevier, vol. 216(C).
    • Handle: RePEc:eee:tefoso:v:216:y:2025:i:c:s0040162525001908
    DOI: 10.1016/j.techfore.2025.124159
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