IDEAS home Printed from https://ideas.repec.org/a/kap/jculte/v31y2007i1p5-23.html
   My bibliography  Save this article

The sales effect of word of mouth: a model for creative goods and estimates for novels

Author

Listed:
  • Jonathan Beck

Abstract

Weekly sales of creative goods—like music records, movies, or books—usually peak shortly after release and then decline quickly. In many cases, however, they follow a hump-shaped pattern where sales increase for some time. A popular explanation for this phenomenon is word of mouth among a population of heterogeneous buyers, but previous studies typically assume buyer homogeneity or neglect word of mouth altogether. In this paper, I study a model of new-product diffusion with heterogeneous buyers that allows for a quantification of the sales effect of word of mouth. The model includes Christmas sales as a special case. All parameters have an intuitive interpretation. Simulation results suggest that the parameters are estimable for data that are not too volatile and that cover a sufficiently large part of a title’s life cycle. I estimate the model for four exemplary novels using scanner data on weekly sales. Copyright Springer Science+Business Media, LLC 2007

Suggested Citation

  • Jonathan Beck, 2007. "The sales effect of word of mouth: a model for creative goods and estimates for novels," Journal of Cultural Economics, Springer;The Association for Cultural Economics International, vol. 31(1), pages 5-23, March.
    • Handle: RePEc:kap:jculte:v:31:y:2007:i:1:p:5-23
    DOI: 10.1007/s10824-006-9029-0
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1007/s10824-006-9029-0
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1007/s10824-006-9029-0?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to look for a different version below or search for a different version of it.

    Other versions of this item:

    References listed on IDEAS

    as
    1. Jehoshua Eliashberg & Anita Elberse & Mark A.A.M. Leenders, 2006. "The Motion Picture Industry: Critical Issues in Practice, Current Research, and New Research Directions," Marketing Science, INFORMS, vol. 25(6), pages 638-661, 11-12.
    2. Boswijk, H. Peter & Franses, Philip Hans, 2005. "On the Econometrics of the Bass Diffusion Model," Journal of Business & Economic Statistics, American Statistical Association, vol. 23, pages 255-268, July.
    3. Canoy, Marcel & van Ours, Jan C. & van der Ploeg, Frederick, 2006. "The Economics of Books," Handbook of the Economics of Art and Culture, in: V.A. Ginsburgh & D. Throsby (ed.), Handbook of the Economics of Art and Culture, edition 1, volume 1, chapter 21, pages 721-761, Elsevier.
    4. Frank M. Bass, 1969. "A New Product Growth for Model Consumer Durables," Management Science, INFORMS, vol. 15(5), pages 215-227, January.
    5. Waldfogel, Joel, 1993. "The Deadweight Loss of Christmas," American Economic Review, American Economic Association, vol. 83(5), pages 1328-1336, December.
    6. De Vany, Arthur & Walls, W David, 1996. "Bose-Einstein Dynamics and Adaptive Contracting in the Motion Picture Industry," Economic Journal, Royal Economic Society, vol. 106(439), pages 1493-1514, November.
    7. Jonathan Beck, 2007. "The sales effect of word of mouth: a model for creative goods and estimates for novels," Journal of Cultural Economics, Springer;The Association for Cultural Economics International, vol. 31(1), pages 5-23, March.
    8. Dina Mayzlin & Judith A. Chevalier, 2003. "The Effect of Word of Mouth on Sales: Online Book Reviews," Yale School of Management Working Papers ysm413, Yale School of Management.
    9. Charles C. Moul, 2007. "Measuring Word of Mouth's Impact on Theatrical Movie Admissions," Journal of Economics & Management Strategy, Wiley Blackwell, vol. 16(4), pages 859-892, December.
    10. Victor Ginsburgh & David Throsby, 2006. "Handbook of the economics of art and culture," ULB Institutional Repository 2013/1673, ULB -- Universite Libre de Bruxelles.
    11. Christophe Van den Bulte & Gary L. Lilien, 1997. "Bias and Systematic Change in the Parameter Estimates of Macro-Level Diffusion Models," Marketing Science, INFORMS, vol. 16(4), pages 338-353.
    12. Hall, Bronwyn H. & Khan, Beethika, 2003. "Adoption of New Technology," Department of Economics, Working Paper Series qt3wg4p528, Department of Economics, Institute for Business and Economic Research, UC Berkeley.
    13. Jonathan Beck, 2004. "Fixed, Focal, Fair? Book Prices Under Optional Resale Price Maintenance," CIG Working Papers SP II 2004-15, Wissenschaftszentrum Berlin (WZB), Research Unit: Competition and Innovation (CIG).
    14. J. H. Love & P. Gripaios & P. Mills & A. Alexander & O. Yiftachel & A. Pike & P. Newby & R. Prentice & R. R. MacKay & M. Binks & P. W. Daniels, 1996. "Book Reviews," Regional Studies, Taylor & Francis Journals, vol. 30(6), pages 619-625.
    15. Geroski, P. A., 2000. "Models of technology diffusion," Research Policy, Elsevier, vol. 29(4-5), pages 603-625, April.
    16. J. H. Love & R. H. Williams & M. Dodgson & T. Bovaird & S. Mazurais & P. Garside & M. Sheehan & P. Lawless & D. Kerr & H. D. Watts & G. T. Bloomfield, 1996. "Book Reviews," Regional Studies, Taylor & Francis Journals, vol. 30(1), pages 93-99.
    17. De Vany, Arthur S. & Walls, W. David, 2004. "Motion picture profit, the stable Paretian hypothesis, and the curse of the superstar," Journal of Economic Dynamics and Control, Elsevier, vol. 28(6), pages 1035-1057, March.
    18. Trajtenberg, Manuel & Yitzhaki, Shlomo, 1989. "The Diffusion of Innovations: A Methodological Reappraisal," Journal of Business & Economic Statistics, American Statistical Association, vol. 7(1), pages 35-47, January.
    19. J. H. Love & C. Wren & M. Raley & I. Masser & D. Drakakis-Smith & V. Galt & K. Thomas & D. Clapham & P. Elias & R. G. Brooks & D. M. Smith, 1996. "Book Reviews," Regional Studies, Taylor & Francis Journals, vol. 30(2), pages 215-222.
    20. Modis, Theodore, 1994. "Determination of the Uncertainties in S-Curve Logistic Fits," OSF Preprints n53pd, Center for Open Science.
    21. D. Wade Hands, 1996. "Book Reviews," Journal of Economic Methodology, Taylor & Francis Journals, vol. 3(2), pages 317-322.
    22. J. Painter & M. Chisholm & S. C. Dow & N. A. Phelps & R. J. Buswell & I. H. McNicoll & C. M. Law & M. E. Cawley & B. Archer, 1996. "Book Reviews," Regional Studies, Taylor & Francis Journals, vol. 30(3), pages 311-316.
    23. J. H. Love & P. W. Roberts & P. Bull & D. Sadler & M. Clark & M. M. Huq & D. Turnock & R. Perman & A. H. Spencer & R. Smith & D. Pitfield, 1996. "Book Reviews," Regional Studies, Taylor & Francis Journals, vol. 30(7), pages 713-721.
    24. M. C. Jones, 2006. "Book Review," Journal of Applied Statistics, Taylor & Francis Journals, vol. 33(10), pages 1149-1151.
    25. Albert C. Bemmaor & Janghyuk Lee, 2002. "The Impact of Heterogeneity and Ill-Conditioning on Diffusion Model Parameter Estimates," Marketing Science, INFORMS, vol. 21(2), pages 209-220, November.
    26. J. H. Love & B. S. Chalkley & H. Halkier & A. Bruce & K. Grime & C. Hague & P. Balchin & J. H. Momsen & B. M. D. Smith & P. Hall & M. Deakin, 1996. "Book Reviews," Regional Studies, Taylor & Francis Journals, vol. 30(5), pages 533-539.
    27. Moul,Charles C. (ed.), 2005. "A Concise Handbook of Movie Industry Economics," Cambridge Books, Cambridge University Press, number 9780521843843, September.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Beck, Jonathan, 2008. "Diderot´s rule," Discussion Papers, Research Unit: Competition and Innovation SP II 2008-13, WZB Berlin Social Science Center.
    2. Gaffeo, Edoardo & Scorcu, Antonello E. & Vici, Laura, 2008. "Demand distribution dynamics in creative industries: The market for books in Italy," Information Economics and Policy, Elsevier, vol. 20(3), pages 257-268, September.
    3. John Ashworth & Bruno Heyndels & Kristien Werck, 2010. "Expert judgements and the demand for novels in Flanders," Journal of Cultural Economics, Springer;The Association for Cultural Economics International, vol. 34(3), pages 197-218, August.
    4. Darlene C Chisholm, 2011. "Motion Pictures," Chapters, in: Ruth Towse (ed.), A Handbook of Cultural Economics, Second Edition, chapter 39, Edward Elgar Publishing.
    5. Meade, Nigel & Islam, Towhidul, 2006. "Modelling and forecasting the diffusion of innovation - A 25-year review," International Journal of Forecasting, Elsevier, vol. 22(3), pages 519-545.
    6. Peter deLeon & Laurence E. Lynn, 1999. "Applied economics and public policy; Just results: Ethical foundations for policy analysis; Tales of the State: Narrative in contemporary U.S. politics and public policy; Honest numbers and democracy," Journal of Policy Analysis and Management, John Wiley & Sons, Ltd., vol. 18(2), pages 333-339.
    7. Ohio University & Department of Economics & Hailey Hayeon Joo, 2009. "Social Learning and Optimal Advertising in the Motion Picture Industry," 2009 Meeting Papers 513, Society for Economic Dynamics.
    8. Yuri Peers & Dennis Fok & Philip Hans Franses, 2012. "Modeling Seasonality in New Product Diffusion," Marketing Science, INFORMS, vol. 31(2), pages 351-364, March.
    9. Jordi McKenzie, 2010. "Do 'African American' films perform better or worse at the box office? An empirical analysis of motion picture revenues and profits," Applied Economics Letters, Taylor & Francis Journals, vol. 17(16), pages 1559-1564.
    10. Franses, Philip Hans, 2021. "Modeling box office revenues of motion pictures✰," Technological Forecasting and Social Change, Elsevier, vol. 169(C).
    11. Jordi McKenzie, 2010. "How do theatrical box office revenues affect DVD retail sales? Australian empirical evidence," Journal of Cultural Economics, Springer;The Association for Cultural Economics International, vol. 34(3), pages 159-179, August.
    12. Fabrice Le Guel & Mohamed El Hedi Arouri & Fabrice Rochelandet, 2010. "L’entrelacement des pratiques culturelles et de l’usage des TIC : une analyse économique," Économie et Prévision, Programme National Persée, vol. 194(3), pages 33-55.
    13. Caroline Elliott & Rob Simmons, 2008. "Determinants of UK Box Office Success: The Impact of Quality Signals," Review of Industrial Organization, Springer;The Industrial Organization Society, vol. 33(2), pages 93-111, September.
    14. Michalakelis, C. & Sphicopoulos, T., 2012. "A population dependent diffusion model with a stochastic extension," International Journal of Forecasting, Elsevier, vol. 28(3), pages 587-606.
    15. Christophe Van den Bulte & Yogesh V. Joshi, 2007. "New Product Diffusion with Influentials and Imitators," Marketing Science, INFORMS, vol. 26(3), pages 400-421, 05-06.
    16. Stoneman, Paul, 2011. "Soft Innovation: Economics, Product Aesthetics, and the Creative Industries," OUP Catalogue, Oxford University Press, number 9780199697021.
    17. Fernández-Durán, J.J., 2014. "Modeling seasonal effects in the Bass Forecasting Diffusion Model," Technological Forecasting and Social Change, Elsevier, vol. 88(C), pages 251-264.
    18. Luca Aguzzoni & Elena Argentesi & Lorenzo Ciari & Tomaso Duso & Massimo Tognoni, 2016. "Ex post Merger Evaluation in the U.K. Retail Market for Books," Journal of Industrial Economics, Wiley Blackwell, vol. 64(1), pages 170-200, March.
    19. McKenzie, Jordi, 2013. "Predicting box office with and without markets: Do internet users know anything?," Information Economics and Policy, Elsevier, vol. 25(2), pages 70-80.
    20. Peres, Renana & Muller, Eitan & Mahajan, Vijay, 2010. "Innovation diffusion and new product growth models: A critical review and research directions," International Journal of Research in Marketing, Elsevier, vol. 27(2), pages 91-106.

    More about this item

    Keywords

    New-product diffusion; Word of mouth; Creative industries; C22; L82; M3;
    All these keywords.

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • L82 - Industrial Organization - - Industry Studies: Services - - - Entertainment; Media
    • M3 - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics - - Marketing and Advertising
    • I - Health, Education, and Welfare
    • I - Health, Education, and Welfare
    • I - Health, Education, and Welfare

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:kap:jculte:v:31:y:2007:i:1:p:5-23. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.