Purpose: The goal of this study is to identify the primary factors impacting customers' desire to continue with OTT platforms, with a focus on Thai Netflix subscribers. The study investigates how factors such as user attitude, customer satisfaction, perceived performance, enjoyment, ease of use, and usefulness impact users' intentions to continue.

Research Design, Data and Methodology: This study uses a quantitative methodology, gathering primary data from 400 members of the general Thai community using an online survey. The Technology Acceptance Model (TAM) serves as the foundation, the research incorporates variables associated with user intentions toward OTT platforms. Additionally, it creates a new conceptual model by combining ideas from three well-known theoretical frameworks. To evaluate the correlations between the primary variables, multiple linear regression analysis was performed using the data that was gathered.

Findings: The findings suggest that customer satisfaction is greatly impacted by perceived performance, highlighting the need for reliable and efficient platform functionality to enhance user experiences. In contrast, perceived enjoyment shows no significant effect on customer satisfaction, implying that enjoyment alone may not be sufficient to drive overall satisfaction. Additionally, user attitudes are substantially determined by perceived ease of use and usefulness, with the former having a slightly greater impact. Lastly, customer satisfaction and attitude to use emerge as key determinants of continuance intention, underscoring the importance of creating positive user experiences and fostering favorable attitudes for sustaining long-term user engagement.

Research Limitations/Implications: This research centers on Netflix users in Thailand, which may limit its applicability to other OTT platforms or settings. To enhance understanding of user behavior and preferences in the OTT market, future studies could investigate diverse demographic groups, consider cultural influences, and conduct cross-platform analyses.