The Dark Side of Recommendation Algorithms in Chinese Mass Short Video Apps: Effect of Perceived Over-Recommendation on Users’ Cognitive Dissonance and Discontinuance Intention

Abstract

In the digital age, human-algorithm interaction has become ubiquitous. The popularity of short-form video apps has led to greater access to and experience with algorithmic recommender systems, but it has also exposed their potential problems and dark sides. However, there is still limited knowledge about the dark side of over-recommendation from the perspective of user perception, especially regarding the negative impact on users’ psychology and behavior. Drawing on social cognitive theory, this study constructed a theoretical model to investigate how information environment factors influence users’ cognitive dissonance and discontinuance intention under the antecedent dimension of perceived over-recommendation. The model was tested using partial least squares structural equation modeling on 322 valid questionnaires from users of Chinse mass short video apps (eg, Douyin). The results showed that when users perceived over-recommendation, information narrowing, information redundancy, perceived overload, and privacy invasion significantly increased their cognitive dissonance, ultimately leading to discontinuance intention. Notably, cognitive dissonance fully mediated the relationship between the information environment and discontinuance intention, and self-efficacy did not play a significant moderating role. Additionally, the local path effects varied significantly across groups with different characteristics. To maintain the sustainability of short video platforms, it is crucial to explore moderate recommendation mechanisms. User-centered functionality improvements and differentiated behavioral interventions can help mitigate negative psychology and discontinuance intention among users.