Exploring Users’ Dissatisfaction with Video Streaming Service Content Recommendation Algorithms and Their Coping Behaviors

Algorithmic Recommendation Systems (ARS) are central to personalization in video streaming services, yet users increasingly express dissatisfaction due to inaccurate recommendations, filter bubbles, and privacy concerns. Despite their importance, user dissatisfaction with ARS remains underexplored. This study investigates the antecedents of ARS dissatisfaction and subsequent coping behaviors through in-depth interviews with 30 streaming users. The analysis identifies a three-stage process involving user perceptions, sources of dissatisfaction, and coping responses. Three key findings emerge. First, dissatisfaction with core service failures or the platform itself triggers approach coping, whereas externally driven issues lead to avoidance coping. Second, users’ perceptions of ARS shape dissatisfaction: low trust and perceived control result in core failures, while high trust combined with profit-oriented perceptions generates external dissatisfaction. Third, perceived losses of information, time, and personalization control contribute to disengagement. The study highlights the need for enhanced user control and transparency to sustain long-term engagement.