Abstract
In the digital entertainment landscape, AI-enhanced video streaming services like Netflix and Hulu significantly shape user experiences through AI-based recommendations. This study examines customer perceptions of these AI-driven suggestions, focusing on factors such as Perceived Interactivity, Service Quality, Commitment, Trust, and Personalization. Using data from a diverse sample in Klang Valley, Malaysia, the study employs Partial Least Squares Structural Equation Modeling (PLS-SEM) and Necessary Condition Analysis (NCA). PLS-SEM models complex relationships to predict customer satisfaction, while NCA identifies essential conditions for desired outcomes in customer experience. Findings reveal that Personalization significantly influences satisfaction (path coefficient = 0.34), with Trust enhancing the impact of Perceived Interactivity (mediation effect size = 0.15). Expectancy confirmation also moderates the relationship between service attributes and satisfaction (β = 0.09). The results highlight the importance of tailored personalization and robust trust mechanisms in AI systems, emphasizing strategies to improve viewer retention and overall satisfaction.
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