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Time spent on new songs: word-of-mouth and price effects on teenager consumption

Journal of Cultural Economics volume 39, pages 205–218 (2015)Cite this article

Abstract

The stardom system characterizes creative industries: the demand and revenues are concentrated on a few bestselling books, movies or music. In this paper, we study the demand structure between bestsellers and new artists’ productions in the music industry. We set up an experiment where participants face real choice's situations. We create three treatments to isolate the effect of information and incentives on diversity. In a first treatment, music is consumed for free without information. In a second one, subjects receive a prior information on others’ evaluation of songs to study the effect of word-of-mouth. Finally, in a third one, a real market is introduced and music is bought. Significant evidence shows that word-of-mouth lowers diversity, while price incentives tend to lift it. In both treatments, subjects also react to the information or incentives nature.

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Notes

  1. Hunter and Schellenberg find that openness-to-experience—a personality trait measured in psychology that characterizes people who have a general appreciation for art, emotion, adventure, variety of experiences, etc.—is correlated with the shape of the function of exposure (linking number of exposures and liking ratings): While low openness leads to an inverted U-shape function, high openness is linked with a decreasing liking rating function according to the number of exposures.

  2. The SNEP (Syndicat National de l’édition Phonographique, French union of the phonographic edition) establishes each week the official chart of the best selling singles in France. It takes into account the physical and the digital sales.

  3. A popularity ranking allows them to encounter professionals of the music industry.

  4. During our experiment, the subjects were asked: “How do you discover new music?”. One of the proposed answers was “By visiting websites such as Noomiz that specialize in offering music from new artists.” Subjects had to answer on a five-point frequency scale. 54 % answered “Never,” 22 % “Rarely,” 13 % “From time to time,” 5 % “Often” and 6 % “Very Often.”

  5. Each category is composed of 24 Anglo-Saxon tracks and 6 French ones. In terms of genres, there are 13 electro/dance/remix’s songs, 10 pop/rock/folk and 7 Rap/RnB/Hip-hop/Soul. Songs are classified by genre by both the SNEP and Noomiz.

  6. All participants are facing the same set of songs in the same order.

  7. Throughout the experiment, the Top 30 is actually better evaluated than the New Artists’ category. This corroborates the idea that people prefer what they have already experienced or frequently experienced (Bornstein 1989).

  8. In the Market Treatment, prices are set to be in an experimental money—the ECU—convertible in candies. Sellers have to set a price from 0 to 20 units of ECU.

  9. It is important that the buyers can save experimental currency in order to control for income allocation and preference for saving.

  10. The conversion rate is 2 g. of candies for 1000 ECU.

  11. A table describing the distribution of participants by treatment can be found in the Appendix.

  12. Note that regressing the time spent on the New Artists’ category is similar to regressing the time spent on the Top 30 as the two variables are complementary.

  13. Mainstream exposure is a continuous variable on a five-point scale that combines answers, on a five-point Likert scale each, to the following questions: “how often do you listen to the following radio channels?”:

    • NRJ

    • Fun Radio

    • Voltage

    • Virgin Radio

    • Skyrock

    • Ado FM

    These French radio channels are broadcasting mainstream music and top charts.

  14. Here, the average evaluations used for the quality difference measure is to be distinguished with the average evaluation used in the Word-of-Mouth treatment. In the first case, it is measured by the overall sample’s evaluations, while in the second case the average evaluation is calculated only with the subjects’ evaluations of the Benchmark treatment.

  15. The price ratio is equal to the price of the Top 30 song divided by the price of the New Artists’ song.

  16. The rating ratio is equal to the mean rating of the Top 30 song divided by the mean rating of the New Artists’ song. These are the ratings appearing on a five-star scale in the Word-of-Mouth treatment.

References

  • Adler, M. (1985). Stardom and talent. The American Economic Review, 75(1),  208–212.

  • Adler, M. (2006). Stardom and talent. Handbook of the Economics of Art and Culture, 1, 895–906.

    Article  Google Scholar 

  • Anderson, C. (2004). The long tail. Wired Magazine, 12(10), 170–177.

    Google Scholar 

  • Banerjee, A. V. (1992). A simple model of herd behavior. The Quarterly Journal of Economics, 107(3), 797–817.

    Article  Google Scholar 

  • Benhamou, F. (2002). L’économie du star-system. Odile Jacob.

  • Berns, G. S., Capra, C. M., Moore, S., & Noussair, C. (2010). Neural mechanisms of the influence of popularity on adolescent ratings of music. Neuroimage, 49(3), 2687–2696.

    Article  Google Scholar 

  • Bianchi, M. (2002). Novelty, preferences, and fashion: When goods are unsettling. Journal of Economic Behavior & Organization, 47(1), 1–18.

    Article  Google Scholar 

  • Bikhchandani, S., Hirshleifer, D., & Welch, I. (1992). A theory of fads, fashion, custom, and cultural change as informational cascades. Journal of political Economy, 100(5),  992–1026.

  • Bornstein, R. F. (1989). Exposure and affect: Overview and meta-analysis of research, 1968–1987. Psychological Bulletin, 106(2), 265.

    Article  Google Scholar 

  • Bradley, M. M., & Lang, P. J. (1994). Measuring emotion: The self-assessment manikin and the semantic differential. Journal of Behavior Therapy and Experimental Psychiatry, 25(1), 49–59.

    Article  Google Scholar 

  • Caves, R. E. (2000). Creative industries: Contracts between art and commerce. Cambridge: Harvard University Press.

    Google Scholar 

  • Decrop, A., & Derbaix, M. (2014). Artist-related determinants of music concert prices. Psychology & Marketing, 31(8), 660–669.

    Article  Google Scholar 

  • Hunter, P. G., & Schellenberg, E. G. (2011). Interactive effects of personality and frequency of exposure on liking for music. Personality and Individual Differences, 50(2), 175–179.

    Article  Google Scholar 

  • Nelson, P. (1970). Information and consumer behavior. The Journal of Political Economy, 78(2), 311–329.

    Article  Google Scholar 

  • North, A. C., Hargreaves, D. J., & O’Neill, S. A. (2000). The importance of music to adolescents. British Journal of Educational Psychology, 70(2), 255–272.

    Article  Google Scholar 

  • Peitz, M., & Waelbroeck, P. (2003). Making use of file sharing in music distribution. mimeo, University of Mannheim and ECARES, Free University of Brussels.

  • Rosen, S. (1981). The economics of superstars. The American Economic Review, 71(5),  845–858.

  • Salganik, M. J., Dodds, P. S., & Watts, D. J. (2006). Experimental study of inequality and unpredictability in an artificial cultural market. Science, 311(5762), 854–856.

    Article  Google Scholar 

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Acknowledgments

Our experiment was possible thanks to the project's Grant ANR-10-CREA-008. This paper was presented to the ACEI conference 2014 and won the President's prize. It was also presented during the Psycho-Economics of Cultural Behavior seminar in 2014 at University of Paris I. We are very grateful to the editor of the Journal of Cultural Economics Kathryn Graddy for her very useful comments and her help. We also thank Maxim Frolov for programming the experiment, the LEEP for hosting it and everyone who helped us improve the paper.

Author information

Authors and Affiliations

  1. School of Economics, University of Edinburgh, Edinburgh, UK

    Noémi Berlin

  2. Paris School of Economics, University of Paris I (Panthéon Sorbonne), Paris, France

    Anna Bernard

  3. Department of Psychology and Educational Sciences, University of Geneva, Geneva, Switzerland

    Guillaume Fürst

Authors
  1. Noémi Berlin
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  2. Anna Bernard
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  3. Guillaume Fürst
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Corresponding author

Correspondence to Anna Bernard.

Additional information

This article received The President’s Prize of the Association for Cultural Economics International (ACEI). This prize is awarded to the best paper in the judgement of a Committee consisting of the President, Past President and President-Elect that is presented at the biennial conference by a PhD or other postgraduate student. The 2014 biennial conference was held in Montreal, Canada, from June 24th through June 27th.

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Berlin, N., Bernard, A. & Fürst, G. Time spent on new songs: word-of-mouth and price effects on teenager consumption. J Cult Econ 39, 205–218 (2015). https://doi.org/10.1007/s10824-014-9235-0

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  • DOI: https://doi.org/10.1007/s10824-014-9235-0

Keywords

  • Experimental economics
  • Cultural goods
  • Music industry
  • Stardom system

JEL Classification

  • D04
  • C9
  • Z1