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Journal of the Academy of Marketing Science

, Volume 39, Issue 5, pp 777–794 | Cite as

Music for free? How free ad-funded downloads affect consumer choice

  • Dominik PapiesEmail author
  • Felix Eggers
  • Nils Wlömert
Original Empirical Research

Abstract

The market for digital content (e.g., music or movies) has been affected by large numbers of Internet users downloading content for free from illegitimate sources. The music industry has been exposed most severely to these developments and has reacted with several different online business models but with only limited success thus far. These business models include attempts to attract consumers by offering free downloads while relying on advertising as a revenue source. Using a latent-class choice-based conjoint analysis, we analyze the attractiveness of these business models from the consumer’s perspective. Our findings indicate that advertising-based models have the potential to attract consumers who would otherwise refrain from commercial downloading, that they cannot threaten the dominance of download models like iTunes, and that current market prices for subscription services are unattractive to most consumers.

Keywords

Digital distribution Music downloads Free content Willingness-to-pay Latent class Choice-based conjoint analysis 

Notes

Acknowledgement

The authors would like to thank Karen Gedenk, Michel Clement, Mark Heitmann, and three anonymous reviewers for their helpful comments.

References

  1. Ajzen, I. (1991). The theory of planned behavior. Organizational Behavior and Human Decision Processes, 50(2), 179–211.CrossRefGoogle Scholar
  2. Andrews, R. L., & Currim, I. S. (2003). A comparison of segment retention criteria for finite mixture logit models. Journal of Marketing Research, 40(2), 235–243.CrossRefGoogle Scholar
  3. Bhattacharjee, S., Gopal, R. D., Lertwachara, K., Marsden, J. R., & Telang, R. (2007). The effect of digital sharing technologies on music markets: a survival analysis of album ranking charts. Management Science, 53(9), 1359–1374.CrossRefGoogle Scholar
  4. Bijmolt, T. H. A., Van Heerde, H. J., & Pieters, R. G. M. (2005). New empirical generalizations on the determinants of price elasticity. Journal of Marketing Research (JMR), 42(2), 141–156.CrossRefGoogle Scholar
  5. Campbell, M. C. (1999). Perceptions of price unfairness: antecedents and consequences. Journal of Marketing Research, 36(2), 187–199.CrossRefGoogle Scholar
  6. DeSarbo, W. S., Ramaswamy, V., & Cohen, S. H. (1995). Market segmentation with choice-based conjoint analysis. Marketing Letters, 6(2), 137–147.CrossRefGoogle Scholar
  7. Ding, M. (2007). An incentive-aligned mechanism for conjoint analysis. Journal of Marketing Research (JMR), 44(2), 214–223.CrossRefGoogle Scholar
  8. Elberse, A. (2010). Bye bye bundles: the unbundling of music in digital channels. Journal of Marketing, 74(3), 107–123.CrossRefGoogle Scholar
  9. Festinger, L. (1957). A theory of cognitive dissonance. Stanford.Google Scholar
  10. Gentzkow, M. (2007). Valuing new goods in a model with complementarity: online newspapers. The American Economic Review, 97(3), 713–744.CrossRefGoogle Scholar
  11. Geyskens, I., Gielens, K., & Dekimpe, M. G. (2002). The market valuation of internet channel additions. Journal of Marketing, 66(2), 102–119.CrossRefGoogle Scholar
  12. GfK. (2009). Brennerstudie 2008. Nürnberg.Google Scholar
  13. Gopal, R. D., Bhattacharjee, S., & Sanders, G. L. (2006). Do artists benefit from online music sharing? Journal of Business, 79(3), 1503–1533.CrossRefGoogle Scholar
  14. Hennig-Thurau, T., Henning, V., Sattler, H., Eggers, F., & Houston, M. B. (2007). The last picture show? Timing and order of movie distribution channels. Journal of Marketing, 71(4), 63–83.CrossRefGoogle Scholar
  15. Huber, J., & Zwerina, K. (1996). The importance of utility balance in efficient choice designs. Journal of Marketing Research, 33(3), 307–317.CrossRefGoogle Scholar
  16. Huber, J., Wittink, D. R., Fiedler, J. A., & Miller, R. (1993). The effectiveness of alternative preference elicitation procedures in predicting choice. Journal of Marketing Research, 30(1), 105–114.CrossRefGoogle Scholar
  17. IFPI. (2009). Digital Music Report 2009.Google Scholar
  18. Jedidi, K., & Zhang, Z. J. (2002). Augmenting conjoint analysis to estimate consumer reservation price. Management Science, 48(10), 1350–1368.CrossRefGoogle Scholar
  19. Kamins, M. A., Folkes, V. S., & Fedorikhin, A. (2009). Promotional bundles and consumers’ price judgments: when the best things in life are not free. Journal of Consumer Research, 36(4), 660–670.CrossRefGoogle Scholar
  20. Kohli, R., & Mahajan, V. (1991). A reservation-price model for optimal pricing of multiattribute products in conjoint analysis. Journal of Marketing Research, 28(3), 347–354.CrossRefGoogle Scholar
  21. Louviere, J. J., & Woodworth, G. (1983). Design and analysis of simulated consumer choice or allocation experiments: an approach based on aggregate data. Journal of Marketing Research, 20(4), 350–367.CrossRefGoogle Scholar
  22. Moore, G. C., & Benbasat, I. (1991). Development of an instrument to measure the perceptions of adopting an information technology innovation. Information System Research, 2(3), 192–222.CrossRefGoogle Scholar
  23. Moore, W. L., Gray-Lee, J., & Louviere, J. J. (1998). A cross-validity comparison of conjoint analysis and choice models at different levels of aggregation. Marketing Letters, 9(2), 195–207.CrossRefGoogle Scholar
  24. Ofek, E., & Srinivasan, V. (2002). How much does the market value an improvement in a product attribute? Marketing Science, 21(4), 398–411.CrossRefGoogle Scholar
  25. Ofir, C. (2004). Reexamining latitude of price acceptability and price thresholds: predicting basic consumer reaction to price. Journal of Consumer Research, 30(4), 612–621.CrossRefGoogle Scholar
  26. Peitz, M., & Waelbroeck, P. (2005). An economist’s guide to digital music. CESifo Economic Studies, 51(2–3), 359–428.CrossRefGoogle Scholar
  27. Prasad, A., Mahajan, V., & Bronnenberg, B. J. (2001). Advertising versus pay-per-view in electronic media. International Journal of Research in Marketing, 20(1), 13–30.CrossRefGoogle Scholar
  28. Ramaswamy, V., DeSarbo, W. S., Reibstein, D. J., & Robinson, W. T. (1993). An empirical pooling approach for estimating marketing mix elasticities with PIMS data. Marketing Science, 12(1), 103–124.CrossRefGoogle Scholar
  29. Rogers, E. M. (2003). Diffusion of innovations. New York: Free Press.Google Scholar
  30. Rossi, P. E., & Allenby, G. M. (1993). A Bayesian approach to estimating household parameters. Journal of Marketing Research, 30(2), 171–182.CrossRefGoogle Scholar
  31. Rossi, P. E., & McCulloch, R. (2006). Bayesm: Bayesian inference for marketing/microeconomics. R package version 2.0-8.Google Scholar
  32. Rossi, P. E., Allenby, G. M., & McCulloch, R. (2005). Bayesian statistics and marketing. Hoboken: Wiley.CrossRefGoogle Scholar
  33. Shampanier, K., Mazar, N., & Ariely, D. (2007). Zero as a special price: the true value of free products. Marketing Science, 26(6), 742–757.CrossRefGoogle Scholar
  34. Sinha, R. K., & Mandel, N. (2008). Preventing digital music piracy: the carrot or the stick? Journal of Marketing, 72(1), 1–15.CrossRefGoogle Scholar
  35. Sinha, R. K., Machado, F. S., & Sellman, C. (2010). Don’t think twice, it’s all right: music piracy and pricing in a DRM-free environment. Journal of Marketing, 74(2), 40–54.CrossRefGoogle Scholar
  36. Sonnier, G., Ainslie, A., & Otter, T. (2007). Heterogeneity distributions of willingness-to-pay in choice models. Quantitative Marketing and Economics, 5(3), 313–331.CrossRefGoogle Scholar
  37. Srinivasan, V. (1982). Comments on the role of price in individual utility judgments. Choice Models for Buyer Behavior, Research in Marketing, Supplement, 1, 81–90.Google Scholar
  38. Sundararajan, A. (2004). Managing digital piracy: pricing and protection. Information Systems Research, 15(3), 287–308.CrossRefGoogle Scholar
  39. Taylor, S., & Todd, P. A. (1995). Understanding information technology usage: a test of competing models. Information Systems Research, 6(2), 144–176.CrossRefGoogle Scholar
  40. Toubia, O., Hauser, J. R., & Simester, D. I. (2004). Polyhedral methods for adaptive choice-based conjoint analysis. Journal of Marketing Research, 41(1), 116–131.CrossRefGoogle Scholar
  41. Van Slyke, C., Ilie, V., Lou, H., & Stafford, T. (2007). Perceived critical mass and the adoption of a communication technology. European Journal of Information Systems, 16(3), 270–283.CrossRefGoogle Scholar
  42. Vermunt, J. K., & Magidson, J. (2005). Technical guide for latent GOLD Choice 4.0: Basic and advanced. Belmont.Google Scholar
  43. Völckner, F. (2006). An empirical comparison of methods for measuring consumers’ willingness to pay. Marketing Letters, 17(2), 137–149.CrossRefGoogle Scholar
  44. Winer, R. S. (1986). A reference price model of brand choice for frequently purchased products. Journal of Consumer Research, 13(2), 250–256.CrossRefGoogle Scholar

Copyright information

© Academy of Marketing Science 2010

Authors and Affiliations

  1. 1.Institute for Marketing and MediaUniversity of HamburgHamburgGermany
  2. 2.Delta BrandingHamburgGermany

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