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. While the RSs incorporate social information, considering network effect (NE) originating from these social actions is at the early stages of the research. Hence, drawing upon research on the RS and NE, we propose the notion of Universal NE (UNE) as a function of the structure of the users’ network, data-driven learning, and improvements realised with Artificial Intelligence (AI) that was limited to the size of the network traditionally. Moreover, we argue that listeners differ in the determinants underlying the perceptions of the value of UNE. The findings in this paper will be instrumental in understanding the perceived value of online music platforms and predicting music listening behaviour. The Partial Least Square-Structural Equation Modelling (PLS-SEM) is used to test the empirical data obtained through Last.fm