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Estimating diffusion and price elasticity with application to telecommunications

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Abstract

This paper aims to propose a new framework for estimating and forecasting diffusion of high technology products, along with the construction of a price index. Into that context, the “diffusion–price” model is presented, as an innovative concept providing a long term estimation of both price and diffusion elasticity. This corresponds to the bidirectional estimation of the mutual influence of the product’s price over its expected diffusion and vice versa. The discrete parts of the methodology are the use of a diffusion model for the initial estimation of diffusion, the construction of a price index function for estimating the pricing mechanism and, finally, the construction of the “diffusion–price” model for estimating and adjusting the diffusion level and price quantities. The case studies examined, whose solution was based on genetic algorithms, revealed remarkable results which can be used for business strategies development, as the pricing policy is able to make diffusion diverge substantially from the initial estimates. The case studies considered correspond to the ADSL technology diffusion in the wider European area.

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References

  1. Bass, F. M. (1969). A new product growth model for consumer durables. Management Science, 15, 215–227.

    Article  Google Scholar 

  2. Mansfield, E. (1961). Technical change and the rate of imitation. Econometrica, 29, 741–766.

    Article  Google Scholar 

  3. Rogers, E. M. (1962). Diffusion of innovations. New York: The Free Press.

    Google Scholar 

  4. Teng, J. T. C., Grover, V., & Guttler, W. (2002). Information technology innovations: General diffusion patterns and its relationships to innovation characteristics. IEEE Transactions on Engineering Management, 49, 13–27.

    Article  Google Scholar 

  5. Linton, J. D. (2002). Forecasting the market diffusion of disruptive and discontinuous innovation. IEEE Transactions on Engineering Management, 49, 365–374.

    Article  Google Scholar 

  6. Meade, N., & Islam, T. (2006). Modelling and forecasting the diffusion of innovation—A 25-year review. International Journal of Forecasting, 22, 519–545.

    Article  Google Scholar 

  7. Fildes, R., & Kumar, P. (2002). Telecommunications demand forecasting—a review. International Journal of Forecasting, 18, 489–522.

    Article  Google Scholar 

  8. Robinson, B., & Lakhani, C. (1975). Dynamic price models for new-product planning. Management Science Series B-Application, 21, 1113–1122.

    Google Scholar 

  9. Martin, B. W., & Seung, J. S. (2004). Internet interconnection economic model and its analysis: Peering and settlement. Netnomics, 6, 43–57.

    Article  Google Scholar 

  10. Mandjes, M. (2004). Pricing strategies and service differentiation. Netnomics, 6, 59–81.

    Article  Google Scholar 

  11. Deligiorgi, C., Vavoulas, A., Michalakelis, C., & Varoutas, D. (2007). On the construction of price index and the definition of factors affecting tariffs of ADSL connections across Europe. Netnomics, 8, 171–183.

    Article  Google Scholar 

  12. Karamti, C., & Grzybowski, L. (2010). Hedonic study on mobile telephony market in France: pricing–quality strategies. Netnomics, 11, 1–35.

    Article  Google Scholar 

  13. Le Cadre, H., Bouhtou, M., & Tuffin, B. (2009). Consumers’ preference modeling to price bundle offers in the telecommunications industry: A game with competition among operators. Netnomics, 10, 171–208.

    Article  Google Scholar 

  14. Michalakelis, C., Dede, G., Varoutas, D., & Sphicopoulos, T. (2005). Impact of cross-national diffusion process in telecommunications demand forecasting. In NAEC 2005 (pp. 632–640). Garda, Italy.

  15. Fisher, J. C., & Pry, R. H. (1971). A simple substitution model of technological change. Technological Forecasting and Social Change, 3, 75–88.

    Article  Google Scholar 

  16. Bewley, R., & Fiebig, D. G. (1988). A flexible logistic growth-model with applications in telecommunications. International Journal of Forecasting, 4, 177–192.

    Article  Google Scholar 

  17. Rai, L. P. (1999). Appropriate models for technology substitution. Journal of Scientific & Industrial Research, 58, 14–18.

    Google Scholar 

  18. Michalakelis, C., Varoutas, D., & Sphicopoulos, T. (2008). Diffusion models of mobile telephony in Greece. Telecommunications Policy, 32, 234–245.

    Article  Google Scholar 

  19. Bass, F. M., Krishnan, T. V., & Jain, D. C. (1994). Why the bass model fits without decision variables. Marketing Science, 13, 203–223.

    Article  Google Scholar 

  20. Heeler, R. M., & Hustad, T. P. (1980). Problems in predicting new product growth for consumer durables. Management Science, 26, 1007–1020.

    Article  Google Scholar 

  21. Triplett, J. E. (2004). Hand book on hedonic indexes and quality adjustments in price indexes. OECD Publishing.

  22. Rosen, S. (1974). Hedonic prices and implicit markets: Product differentiation in pure competition. Journal of Political Economy, 92, 34–55.

    Article  Google Scholar 

  23. Deligiorgi, N., Vavoulas, A., Michalakelis, C., Varoutas, D., & Sphicopoulos, T. (2006). Factors and characteristics that affect ADSL tariffs. In 5th conference of telecommunication, media and internet techno-economics (CTTE2006), 8–9 June, Athens, Greece.

  24. Deligiorgi, N., Vavoulas, A., Michalakelis, C., Varoutas, D., & Sphicopoulos, T. (2006). Nonparametric estimation of a hedonic price index for ADSL connections in the European market using the Akaike information criterion. In Networking and e-commerce conference, NAEC 2006. Lake Garda, Italy.

  25. Simonoff, J. S., & Tsai, C. L. (1999). Semiparametric and additive model selection using an improved Akaike information criterion. Journal of Computational and Graphical Statistics, 8, 22–40.

    Article  Google Scholar 

  26. Naik, P. A., & Tsai, C. L. (2001). Single-index model selections. Biometrika, 88, 821–832.

    Article  Google Scholar 

  27. Kumar, V., & Krishnan, T. V. (2002). Multinational diffusion models: An alternative framework. Marketing Science, 21, 318–330.

    Article  Google Scholar 

  28. Goldberg, D. E. (1989). Genetic algorithms in search, optimization, and machine learning. Reading, Mass: Addison-Wesley.

    Google Scholar 

  29. Holland, J. (1975). Adaption in natural and artificial systems. Ann Arbor, Michigan: University of Michigan Press.

    Google Scholar 

  30. Wang, F.-K., & Chang, K.-K. (2009). Modified diffusion model with multiple products using a hybrid GA approach. Expert Systems with Applications, 36, 12613–12620.

    Article  Google Scholar 

  31. Venkatesan, R., Krishnan, T., & Kumar, V. (2004). Evolutionary estimation of macro-level diffusion models using genetic algorithms: An alternative to nonlinear least squares. Marketing Science, 23, 451–464.

    Article  Google Scholar 

  32. Van den Bulte, C., & Lilien, G. L. (1997). Bias and systematic change in the parameter estimates of macro-level diffusion models. Marketing Science, 16, 338–353.

    Article  Google Scholar 

  33. Venkatesan, R., & Kumar, V. (2002). A genetic algorithms approach to growth phase forecasting of wireless subscribers. International Journal of Forecasting, 18, 625–646.

    Article  Google Scholar 

  34. Guo, Z. X., Wong, W. K., Leung, S. Y. S., Fan, J. T., & Chan, S. F. (2008). A genetic-algorithm-based optimization model for solving the flexible assembly line balancing problem with work sharing and workstation revisiting. IEEE Transactions on Systems Man and Cybernetics Part C—Applications and Reviews, 38, 218–228.

    Article  Google Scholar 

  35. Bayir, M. A., Toroslu, I. H., & Cosar, A. (2007). Genetic algorithm for the multiple-query optimization problem. IEEE Transactions on Systems Man and Cybernetics Part C—Applications and Reviews, 37, 147–153.

    Article  Google Scholar 

  36. Wang, Y., Cai, Z. X., Guo, G. Q., & Zhou, Y. R. (2007). Multiobjective optimization and hybrid evolutionary algorithm to solve constrained optimization problems. IEEE Transactions on Systems Man and Cybernetics Part B—Cybernetics, 37, 560–575.

    Article  Google Scholar 

  37. http://www.tempobox.de/en/index.html.

  38. http://www.base.be/fr/internet/home-internet/.

  39. http://www.alice.it/.

  40. http://www.fastweb.it/offerte/?WT.mc_id=fastwebmenu1.

  41. Karmeshu & Pathria, R. K. (1980). Stochastic-evolution of a non-linear model of diffusion of information. Journal of Mathematical Sociology, 7, 59–71.

    Article  Google Scholar 

  42. Meade, N. (1989). Technological substitution—a framework of stochastic-models. Technological Forecasting and Social Change, 36, 389–400.

    Article  Google Scholar 

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Correspondence to Christos Michalakelis.

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Michalakelis, C., Dede, G., Varoutas, D. et al. Estimating diffusion and price elasticity with application to telecommunications. Netnomics 11, 221–242 (2010). https://doi.org/10.1007/s11066-010-9054-1

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