Explaining resistance toward sustainable food: The dark side of algorithmic recommendation in short-video social commerce
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Chi Thi Mai Vu 1 *
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1 Faculty of Commerce and Tourism, Industrial University of Ho Chi Minh City, VIETNAM* Corresponding Author

Abstract

This study examines the dark side of algorithm-driven recommendation systems in short-video social commerce, with a specific focus on sustainable food consumption. Drawing on the Stressor–Strain–Outcome (SSO) framework, the study investigates how greedy recommendation mechanisms reshape users’ information environments and influence their psychological and behavioral responses. Data were collected from 327 Vietnamese users with prior experience using short-video social commerce platforms and exposure to sustainable food-related content. The proposed model was tested using partial least squares structural equation modeling (PLS-SEM). The results show that greedy recommendations significantly increase three information-related stressors, including information overload, information redundancy, and information narrowing. Among these stressors, information overload and information redundancy exert stronger effects on emotional exhaustion than information narrowing. Emotional exhaustion, in turn, significantly increases resistance purchase behavior toward sustainable food. This study contributes to the literature by reframing digital food environments as ambivalent algorithmic environments that may both facilitate and undermine sustainable consumption. It also shows how algorithm-induced information stressors may hinder consumers’ willingness to engage with sustainable products. The findings provide important implications for platform design and sustainable food marketing strategies in emerging markets.

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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Article Type: Research Article

EUR J SUSTAIN DEV RES, Volume 10, Issue 4, 2026, Article No: em0441

https://doi.org/10.29333/ejosdr/19265

Publication date: 01 Oct 2026

Online publication date: 16 Sep 2026

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