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Technological change in the U.S. music industry: Within-product, cross-product and churn effects between competing blockbusters

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  • Guidolin, Mariangela
  • Guseo, Renato

Abstract

In this paper, we develop a new innovation diffusion model for two competing products, which allows us to evaluate the effect of competition both on the dynamics of within-product and cross-product word-of-mouth and on the definition of the residual market potential of each product. This model, which we call Lotka–Volterra model with churn, LVch, generalizes another model for competition, the unbalanced competition and regime change diachronic model (UCRCD), which assumes a common residual market and a delayed entrance for the second product. We compare the performance of these models in describing the competition between two blockbuster formats in the music industry, the compact cassette and the compact disc. In particular, we analyze the evolution of these technologies in the U.S. market for pre-recorded music, for which annual sales data are available from 1973 to 2012, and find that the LVch model outperforms the UCRCD. An interesting aspect of this application relies on the fact that there is a single product, the music album, which is commercialized in two different formats, so that competition arises between formats and not between two products in the same commercial category.

Suggested Citation

  • Guidolin, Mariangela & Guseo, Renato, 2015. "Technological change in the U.S. music industry: Within-product, cross-product and churn effects between competing blockbusters," Technological Forecasting and Social Change, Elsevier, vol. 99(C), pages 35-46.
  • Handle: RePEc:eee:tefoso:v:99:y:2015:i:c:p:35-46
    DOI: 10.1016/j.techfore.2015.06.023
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    Cited by:

    1. Foucart, Renaud & Wan, Cheng & Wang, Shidong, 2018. "Innovations and technological comebacks," International Journal of Research in Marketing, Elsevier, vol. 35(1), pages 1-14.
    2. Pasquale L. Scandizzo & Marco Ventura, 2016. "Innovation and imitation as an interactive process," Economics of Innovation and New Technology, Taylor & Francis Journals, vol. 25(8), pages 821-851, November.
    3. Cerqueti, Roy & Quaranta, Anna Grazia & Ventura, Marco, 2016. "Innovation, imitation and policy inaction," Technological Forecasting and Social Change, Elsevier, vol. 111(C), pages 22-30.
    4. Mariangela Guidolin & Renato Guseo, 2020. "Has the iPhone cannibalized the iPad? An asymmetric competition model," Applied Stochastic Models in Business and Industry, John Wiley & Sons, vol. 36(3), pages 465-476, May.
    5. Guseo, Renato & Schuster, Reinhard, 2021. "Modelling dynamic market potential: Identifying hidden automata networks in the diffusion of pharmaceutical drugs," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 581(C).
    6. Thangeda, Rahul & Kumar, Niraj & Majhi, Ritanjali, 2024. "A neural network-based predictive decision model for customer retention in the telecommunication sector," Technological Forecasting and Social Change, Elsevier, vol. 202(C).
    7. Andrea Savio & Luigi De Giovanni & Mariangela Guidolin, 2022. "Modelling Energy Transition in Germany: An Analysis through Ordinary Differential Equations and System Dynamics," Forecasting, MDPI, vol. 4(2), pages 1-18, April.

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