Impact of the Recommendation Engine in the Behavioural Intention of the Users to Adopt a Particular Music Streaming Service by Millennials in India

Abstract

The Music Streaming Industry has been growing at a rapid pace since the adoption of 4G services and increased usage of smartphones across all parts of India. The huge music libraries offered by these streaming services in one place is one of the major drivers for the increasing growth of this category across the world. It also solves the problem of pirated copies of music that were frequently used for music consumption in India. Since 2015, many players have entered the market to establish their stronghold in the huge Indian Market. Millennials have been the major adopters of these Music Streaming Services and close attention must be given to factors which might affect a millennial’s decision to choose one service over the other. The personalised playlists offered by these services, like Spotify’s Discover Weekly, highlight the proficiency of Recommendation Engines of a particular service and is considered to be one of the key drivers in influencing the customer’s decision to use the recommendations provided by such a system. With free trials offered by each of the platforms, most of the adopters have explored the capabilities of the Recommendation Engines of more than one service. The paper seeks to identify and understand the underlying factors driving millennial’s intention to adopt a particular Music Streaming Service over the other based on the attributes of the Recommendation Engines used to create personalised music playlists for the users. Multiple linear Regression was performed to test the formulated hypotheses from conceptual framework.

Keywords: Music Streaming Services, Technology Acceptance Model, Millennials, Trust, Recommendation Engine, Personalisation, Adoption