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whatpricemusic - Brief Outline

Brief Outline

Peter Self is a Senior Lecturer in Music Business and Management at Canterbury Christ Church University in Canterbury, Kent having previously spent forty years in the music industry. In addition to his teaching, he is embarking on a five year research project funded by the School of Music and Performing Arts and is looking into the issues surrounding the valuing and accessing of recorded music. This longitudinal study is taking a different form to past and other current studies in that it uses an ethnological/narrative approach as its primary base rather than snapshots of opinion or numerical data but will then relate these qualitative findings to more traditional quantitative data.

Its formal academic title is:

"Would re-thinking the monetising of Intellectual Property within the music industry result in a 'devalued commoditisation' as creative elites argue, or is separating the values associated with rights ownership and product delivery a viable alternative?"

…however, Peter has adopted the more ‘street friendly’ title of ‘whatpricemusic’ and it is this outward facing approach that he is adopting throughout the research.

The ethnographic and narrative elements will be based around the longitudinal interviewing of a wide range of demographic groups with additional groups centred around creative and music industry elites as follows: Consumer Groups (children & young people in compulsory education; young people in post-compulsory education or vocational training; people working, not-working or retired; other specialist or minority groups) and Music Industry Groups (artists, composers and performers; major and indie label executives; recorded music distributors; other specialist or minority groups)

This data will be used in its own right as qualitative data but will also be subject to coding and quantifying so as can be used in conjunction with pure quantitative data harvested from various distribution and music access platforms including ‘big data’ listening and access pattern datasets.

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