The Dynamics of Competition in the Music Streaming Marketplace
With Brian J. Hracs
Published in the Economy, Governance, Culture Working Paper Series, University of Southampton
Music streaming has become the dominant mode of music distribution and consumption. Yet, with ongoing technological developments and intensifying global competition the marketplace is evolving quickly and there is a continual need for research which explores the ways in which firms, such as Spotify, Apple and Deezer, attract and retain the attention and patronage of consumers. Drawing on sixty in-depth interviews with music-industry informants and streaming users, this paper examines the value-creation strategies of rival platforms. Beyond merely providing on-demand access to similar catalogues of music for similar prices, it demonstrates how firms manipulate specific spatial and temporal dynamics and leverage different forms of ‘exclusivity,’ related to content, curation and experiences, to generate distinction, value and loyalty. In so doing, the paper nuances our understanding of branding, intermediation, the music marketplace and the platform economy more broadly.
As the rate and scale of Web-related digital data accumulation continue to outstrip all expectations so too we come to depend increasingly on a variety of technical tools to interrogate these data and to render them as an intelligible source of information. In response, on the one hand, a great deal of attention has been paid to the design of efficient and reliable mechanisms for big data analytics whilst, on the other hand, concerns are expressed about the rise of ‘algorithmic society’ whereby important decisions are made by intermediary computational agents of which the majority of the population has little knowledge, understanding or control. This paper aims to bridge these two debates working through the case of music recommender systems. Whilst not conventionally regarded as ‘big data,’ the enormous volume, variety and velocity of digital music available on the Web has seen the growth of recommender systems, which are increasingly embedded in our everyday music consumption through their attempts to help us identify the music we might want to consume. Combining Bourdieu’s concept of cultural intermediaries with Actor-Network Theory’s insistence on the relational ontology of human and non- human actors, we draw on empirical evidence from the computational and social science literature on recommender systems to argue that music recommender systems should be approached as a new form of sociotechnical cultural intermediary. In doing so, we aim to define a broader agenda for better understanding the underexplored social role of the computational tools designed to manage big data.