Skip to main content

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

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.

This is a preview of subscription content, access via your institution.

Fig. 1
Fig. 2
Fig. 3

Notes

  1. We thank an anonymous reviewer for pointing out these distinctions.

  2. As another benchmark for the validity assessment, we estimated utilities by means of a hierarchical Bayes procedure (Rossi and Allenby 1993; Rossi et al. 2005; Rossi and McCulloch 2006), which resulted in comparable validity measures, i.e., a hit rate of 59.2% and MAE values of 3.85 (logit) and 2.75 (first-choice). We rely on the segment-level approach here because it generates more managerially relevant information.

  3. The choice shares implicitly consider that a choice for a given business model may not be exclusive. Rather, they can indicate a usage ratio between generally acceptable business models.

  4. Elasticities are computed as the relative change in market share divided by the relative change in price on all attribute levels for price compared to a medium price level of € 0.99 for DST and € 9.99 for subscription models.

References

  • Ajzen, I. (1991). The theory of planned behavior. Organizational Behavior and Human Decision Processes, 50(2), 179–211.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • Campbell, M. C. (1999). Perceptions of price unfairness: antecedents and consequences. Journal of Marketing Research, 36(2), 187–199.

    Article  Google Scholar 

  • DeSarbo, W. S., Ramaswamy, V., & Cohen, S. H. (1995). Market segmentation with choice-based conjoint analysis. Marketing Letters, 6(2), 137–147.

    Article  Google Scholar 

  • Ding, M. (2007). An incentive-aligned mechanism for conjoint analysis. Journal of Marketing Research (JMR), 44(2), 214–223.

    Article  Google Scholar 

  • Elberse, A. (2010). Bye bye bundles: the unbundling of music in digital channels. Journal of Marketing, 74(3), 107–123.

    Article  Google Scholar 

  • Festinger, L. (1957). A theory of cognitive dissonance. Stanford.

  • Gentzkow, M. (2007). Valuing new goods in a model with complementarity: online newspapers. The American Economic Review, 97(3), 713–744.

    Article  Google Scholar 

  • Geyskens, I., Gielens, K., & Dekimpe, M. G. (2002). The market valuation of internet channel additions. Journal of Marketing, 66(2), 102–119.

    Article  Google Scholar 

  • GfK. (2009). Brennerstudie 2008. Nürnberg.

  • Gopal, R. D., Bhattacharjee, S., & Sanders, G. L. (2006). Do artists benefit from online music sharing? Journal of Business, 79(3), 1503–1533.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • Huber, J., & Zwerina, K. (1996). The importance of utility balance in efficient choice designs. Journal of Marketing Research, 33(3), 307–317.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • IFPI. (2009). Digital Music Report 2009.

  • Jedidi, K., & Zhang, Z. J. (2002). Augmenting conjoint analysis to estimate consumer reservation price. Management Science, 48(10), 1350–1368.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • Ofek, E., & Srinivasan, V. (2002). How much does the market value an improvement in a product attribute? Marketing Science, 21(4), 398–411.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • Peitz, M., & Waelbroeck, P. (2005). An economist’s guide to digital music. CESifo Economic Studies, 51(2–3), 359–428.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • Rogers, E. M. (2003). Diffusion of innovations. New York: Free Press.

    Google Scholar 

  • Rossi, P. E., & Allenby, G. M. (1993). A Bayesian approach to estimating household parameters. Journal of Marketing Research, 30(2), 171–182.

    Article  Google Scholar 

  • Rossi, P. E., & McCulloch, R. (2006). Bayesm: Bayesian inference for marketing/microeconomics. R package version 2.0-8.

  • Rossi, P. E., Allenby, G. M., & McCulloch, R. (2005). Bayesian statistics and marketing. Hoboken: Wiley.

    Book  Google Scholar 

  • Shampanier, K., Mazar, N., & Ariely, D. (2007). Zero as a special price: the true value of free products. Marketing Science, 26(6), 742–757.

    Article  Google Scholar 

  • Sinha, R. K., & Mandel, N. (2008). Preventing digital music piracy: the carrot or the stick? Journal of Marketing, 72(1), 1–15.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • Sonnier, G., Ainslie, A., & Otter, T. (2007). Heterogeneity distributions of willingness-to-pay in choice models. Quantitative Marketing and Economics, 5(3), 313–331.

    Article  Google Scholar 

  • 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 

  • Sundararajan, A. (2004). Managing digital piracy: pricing and protection. Information Systems Research, 15(3), 287–308.

    Article  Google Scholar 

  • Taylor, S., & Todd, P. A. (1995). Understanding information technology usage: a test of competing models. Information Systems Research, 6(2), 144–176.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • Vermunt, J. K., & Magidson, J. (2005). Technical guide for latent GOLD Choice 4.0: Basic and advanced. Belmont.

  • Völckner, F. (2006). An empirical comparison of methods for measuring consumers’ willingness to pay. Marketing Letters, 17(2), 137–149.

    Article  Google Scholar 

  • Winer, R. S. (1986). A reference price model of brand choice for frequently purchased products. Journal of Consumer Research, 13(2), 250–256.

    Article  Google Scholar 

Download references

Acknowledgement

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

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Dominik Papies.

Appendix

Appendix

Table 7 Survey items
Table 8 Estimation resultsa
Table 9 Covariate estimates

Rights and permissions

Reprints and Permissions

About this article

Cite this article

Papies, D., Eggers, F. & Wlömert, N. Music for free? How free ad-funded downloads affect consumer choice. J. of the Acad. Mark. Sci. 39, 777–794 (2011). https://doi.org/10.1007/s11747-010-0230-5

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11747-010-0230-5

Keywords