Estimating consumer preferences for online music services

Abstract

This article analyses consumer preferences with regard to important attributes of online music services. Conjoint analysis and a random coefficient discrete choice model using Bayesian approach with Gibbs sampling are used to estimate the preferences. Based on the quantitative results, we use simulation to look at how a new pricing strategy and the threat of legal penalty for file sharing would influence the online music market. Findings include these: estimated willingness to pay for downloading one music file is significantly less than the actual price of the file; consumers are sensitive to longer search and download times for music files and very sensitive to the threat of legal action; and consumers are not sensitive to online music services broadening their catalogues. Finally, the simulation shows that a combination of increased transaction costs for illegal file sharing and lower-priced digital music files would inhibit illegal file sharing and bolster the number of people purchasing music legally from the online services.

Cite this work

No public copy is stored for this record, so only the metadata is cited.

Stored only when both papers are in the corpus — 11 citations exist upstream.

Papers that cite the same references — the relational fallback while embeddings are still backfilling.