The success of online music platforms depends on the strength of the recommendation systems (RSs) that employ users’ interaction data to offer customised music listening experiences. Traditional recommendation systems, however, assume users are independent actors and identically distributed, ignoring social interactions or connections among users and the role of the Network Effect (NE). In this study, leveraging the social network theory and utilising the attributes of an online music platform, we investigate the impact of network effect on the patterns of music listening. As a result, we propose a new approach for measuring the network effect (Universal Network Effect) as a function of the structure of the network, data-driven learning, and improvements realised with Artificial Intelligence (AI), to scrutinize network effect at both individual and whole network level. The everevolving approach to network effect measurement enables us to further the study to determine the changes in user behaviour.