A Study on the Factors Influencing the Adoption of AI Based Churn Prediction Models in OTT Industry

Abstract

This research examines the key determinants that influence the adoption of Artificial Intelligence (AI) in the Over-the-Top (OTT) industry for churn prediction. Partial Least Squares Structural Equation Modelling (PLS-SEM) and Importance-Performance Map Analysis (IMPA) were used to analyse the data obtained from professionals in the industry familiar with churn prediction through purposive sampling. Using G* Power software, the sample size was found and kept at 600 respondents for statistical accuracy, exceeding the minimum required sample size of 436. The research findings emphasise the importance of organisational factors, stating the need of an enabling internal environment for implementing AI driven solutions. It covers aspects ranging from company culture to management support and readiness for new technologies. The study also highlights the role of perceived usefulness and ease of use in the adoption decision, stating the significance of user-centric design and explicit benefits in technology adoption. Technology factors, however, have a low influence over adoption decisions, suggesting that technical features alone can’t be taken as main drivers for adopting these models. Moderately influential factors include external factors, such as market trends and regulatory considerations. The research findings concludes by highlighting that OTT has to focus more on internal readiness and practical benefits of artificial intelligence technologies. These insights give a strategic framework for OTT service providers and technology developers to pursue a balanced approach by considering organisational readiness and tangible benefits of AI in predicting customer churn.