Investigating consumers’ resistant reactions to ai-based content recommendation on short-video platforms: A study of greedy and bias recommendations

Abstract

With the popularity of personalized recommendation systems (PRS) such as Facebook, Amazon, Tik Tok,.. AI-based recommendation algorithms have received widespread attention from even academia. However, research has provided limited insights into the dark side of AI-based recommendation algorithms, and the underlying mechanisms through which these effects impact psychological and behavioral responses, especially affecting purchasing behavior. Based on the stressor-strain-outcome (SSO) framework, this study aims to analyze the proposed feature of “greedy” and “bias” that cause associated stressors to information, thereby examining its impact on users' negative psychological and behavioral responses. This research collected 473 online responses and conducted empirical analysis. The results show that both greedy recommendation and bias recommendation algorithms cause information narrowing, information redundancy, information overload, technology intrusiveness, and information disclosure concerns. These stressors can cause negative psychological and behavioral responses in users, which will ultimately influence purchase discontinuation behavior on short video platforms. The findings of this study contribute to the comprehension of the dark side of AI recommendation algorithms and provide practical suggestions for short-form video application providers. Both theoretical and practical implications are discussed in detail