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Diffusion of Innovation In Competitive Markets-A Study on the Global Smartphone Diffusion

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  • Semra Gunduc

Abstract

In this work, the aim is to study the diffusion of innovation of two competing products. The main focus has been to understand the effects of the competitive dynamic market on the diffusion of innovation. The global smartphone operating system sales are chosen as an example. The availability of the sales and the number of users data, as well as the predictions for the future number of users, make the smartphone diffusion a new laboratory to test the innovation of diffusion models for the competitive markets. In this work, the Bass model and its extensions which incorporate the competition between the brands are used. The diffusion of smartphones can be considered on two levels: the product level and the brand level. The diffusion of the smartphone as a category is studied by using the Bass equation (category-level diffusion). The diffusion of each competing operating system (iOS and Android) are considered as the competition of the brands, and it is studied in the context of competitive market models (product-level diffusion). It is shown that the effects of personal interactions play the dominant role in the diffusion process. Moreover, the volume of near future sales can be predicted by introducing appropriate dynamic market potential which helps to extrapolate the model results for the future.

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  • Semra Gunduc, 2021. "Diffusion of Innovation In Competitive Markets-A Study on the Global Smartphone Diffusion," Papers 2103.07707, arXiv.org.
    • Handle: RePEc:arx:papers:2103.07707
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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. Gruber, Harald & Verboven, Frank, 2001. "The diffusion of mobile telecommunications services in the European Union," European Economic Review, Elsevier, vol. 45(3), pages 577-588, March.
    3. Vijay Mahajan & Robert A. Peterson, 1978. "Innovation Diffusion in a Dynamic Potential Adopter Population," Management Science, INFORMS, vol. 24(15), pages 1589-1597, November.
    4. Frank M. Bass, 1969. "A New Product Growth for Model Consumer Durables," Management Science, INFORMS, vol. 15(5), pages 215-227, January.
    5. Christophe Van den Bulte & Stefan Stremersch, 2004. "Social Contagion and Income Heterogeneity in New Product Diffusion: A Meta-Analytic Test," Marketing Science, INFORMS, vol. 23(4), pages 530-544, July.
    6. 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.
    7. Minhi Hahn & Sehoon Park & Lakshman Krishnamurthi & Andris A. Zoltners, 1994. "Analysis of New Product Diffusion Using a Four-Segment Trial-Repeat Model," Marketing Science, INFORMS, vol. 13(3), pages 224-247.
    8. 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.
    9. 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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