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Interaction among three substitute products: an extended innovation diffusion model

Author

Listed:
  • Claudia Furlan

    (University of Padova)

  • Cinzia Mortarino

    (University of Padova)

  • Mohammad Salim Zahangir

    (University of Padova)

Abstract

In this paper, we propose a model to describe the mutual interactions among the lifecycles of three substitute products acting simultaneously in a common market, thus competing for the same customers or cooperating to supply demand. To date, the literature only describes models for two competitors; therefore, the present work represents the first attempt at creating and implementing a model for three actors. The new model is applied to real data in the energy context, and its performance is compared to the performance of current models for two competitors. Regarding the datasets examined, the new model shows a relevant improvement in terms of forecasting performance, that is forecasting accuracy and prediction confidence band width.

Suggested Citation

  • Claudia Furlan & Cinzia Mortarino & Mohammad Salim Zahangir, 2021. "Interaction among three substitute products: an extended innovation diffusion model," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 30(1), pages 269-293, March.
  • Handle: RePEc:spr:stmapp:v:30:y:2021:i:1:d:10.1007_s10260-020-00524-8
    DOI: 10.1007/s10260-020-00524-8
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    References listed on IDEAS

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    1. 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.
    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. Frank M. Bass & Trichy V. Krishnan & Dipak C. Jain, 1994. "Why the Bass Model Fits without Decision Variables," Marketing Science, INFORMS, vol. 13(3), pages 203-223.
    4. Furlan, Claudia & Mortarino, Cinzia, 2018. "Forecasting the impact of renewable energies in competition with non-renewable sources," Renewable and Sustainable Energy Reviews, Elsevier, vol. 81(P2), pages 1879-1886.
    5. Frank M. Bass, 1969. "A New Product Growth for Model Consumer Durables," Management Science, INFORMS, vol. 15(5), pages 215-227, January.
    6. Tashman, Leonard J., 2000. "Out-of-sample tests of forecasting accuracy: an analysis and review," International Journal of Forecasting, Elsevier, vol. 16(4), pages 437-450.
    7. Guidolin, Mariangela & Guseo, Renato, 2016. "The German energy transition: Modeling competition and substitution between nuclear power and Renewable Energy Technologies," Renewable and Sustainable Energy Reviews, Elsevier, vol. 60(C), pages 1498-1504.
    8. Hyndman, Rob J. & Koehler, Anne B., 2006. "Another look at measures of forecast accuracy," International Journal of Forecasting, Elsevier, vol. 22(4), pages 679-688.
    9. Renato Guseo & Alessandra Valle, 2005. "Oil and gas depletion: Diffusion models and forecasting under strategic intervention," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 14(3), pages 375-387, December.
    10. Chao Chen & Jamie Twycross & Jonathan M Garibaldi, 2017. "A new accuracy measure based on bounded relative error for time series forecasting," PLOS ONE, Public Library of Science, vol. 12(3), pages 1-23, March.
    11. Sergei Savin & Christian Terwiesch, 2005. "Optimal Product Launch Times in a Duopoly: Balancing Life-Cycle Revenues with Product Cost," Operations Research, INFORMS, vol. 53(1), pages 26-47, February.
    12. Johansen, Soren, 1995. "Likelihood-Based Inference in Cointegrated Vector Autoregressive Models," OUP Catalogue, Oxford University Press, number 9780198774501.
    13. 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.
    14. Guseo, Renato & Mortarino, Cinzia, 2012. "Sequential market entries and competition modelling in multi-innovation diffusions," European Journal of Operational Research, Elsevier, vol. 216(3), pages 658-667.
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    Cited by:

    1. 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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