User's Dilemma: A Qualitative Study on the Influence of Netflix Recommender Systems on Choice Overload

In the digital era, where choices saturate daily life, the phenomenon of choice overload becomes a significant concern in consumer behavior and psychology. Recommender systems, exemplified by Netflix's sophisticated model, play a transformative role in navigating the vast landscape of digital entertainment. This qualitative study examines the impact of recommender systems on choice overload through 12 semi-structured interviews with Netflix users, revealing the intricate dynamics between personalization algorithms and user decision-making processes. The study is guided by the following research questions: (1) How does the Netflix recommendation system influence users' experiences of choice overload and ease of decision-making? (2) To what extent do users perceive Netflix's recommended content as appealing and diverse, and how reliant are they on these recommendations for content selection? (3) How do user interactions with Netflix's recommendation system, including user feedback, impact variables such as search time, choice effort, and choice satisfaction? The findings reveal a notable absence of explicit user feedback and the presence of choice overload in Netflix users. This is evident in prolonged search times, heightened choice effort, and moderate satisfaction levels, coupled with perceptions of unattractiveness and limited diversity in the recommended content. Negative emotional responses during content selection further underscore the challenges users face on the platform. Paradoxically, this gives rise to a potential “user's dilemma,” as the study exposes a high reliance and trust in recommendation lists. However, this reliance also results in users frequently experiencing frustration and disappointment when recommendations fail to meet expectations. The study provides valuable insights into the nuanced interactions between users and the Netflix platform and offers a foundational framework for ongoing refinement of recommender systems in the ever-evolving landscape of streaming services and emphasizes the need for recommendation lists to strike a delicate balance between effective guidance and user exploration.