The Case Study of an Extended Diffusion of Substitution for Successive Generations of a Product with Norton Model

Abstract:

Norton model is a diffusion model to use for a substitution for successive generations of a product. Successive generations of a product influence each other when they diffuse in the same market. In order to express this rules, we try to relax one of the hypothesis of Norton model ,and take the effects of last generations into account Norton model , and define the substitution influence coefficients, then construct the extended Norton model. The extend Norton model improves the explanation of Norton model, and can embody well the actual circumstance of a substitution for successive generations of products. In the end, we take the adoption and substitution for successive generations of the modes of connecting Internet in China as an example for the application of the extended Norton model, and get better results than the Norton model.

• Periodical: Advanced Materials Research (Volume 601)
• Pages: 542-546
• DOI: https://doi.org/10.4028/www.scientific.net/AMR.601.542
• Online since: December 2012
• Authors: Jing Hui Yang, Wen Jing Wei
• Keywords: Genetic Algorithm (GA), Modes of Connecting Internet, Norton Model, Substitution for Successive Generations of New Product, Substitution Influence Coefficient

References

[1] Islam T & Meade N. (1997). The diffusion of successive generation of a technology; a more general model. Technological Forecasting and SocialChange, 56, 49-60.

DOI: 10.1016/s0040-1625(97)00030-9

[2] Danaher P J, Hardie B G S & Putsis W P, Jr. (2001). Marketing-mix variables and the diffusion of successive generations of a technology innovation. Journal of Marketing Research, 501-514.

DOI: 10.1509/jmkr.38.4.501.18907

[3] Yufang zhong, Innovation Diffusion Model for Productswith Multiple Technological Generations[J], Value Engineering, 2008, 11: 46-48.

[4] Yinghui yang, Chunyou wu, The Application For A Diffusion Theory of Adoption and Substitution for Successive Generations with Norton Model[J], Studies in Science of Science, 2005, 5: 682-687.

[5] Peter J. Danaher, Bruce G.S. Hardie, and William P. Putsis Jr, Marketing-Mix Variables and the Diffusion of Successive Generations of a Technological Innovation, Journal of Marketing Research, Vol. XXXVⅢ{TTP}8546 (November 2001), 501-514.

DOI: 10.1509/jmkr.38.4.501.18907

[6] Bass, Frank M. A New Product Growth Model for Consumer Durable[J]. Management Science, 1969, 15: 215-227.

[7] Norton, J.A. And Bass F.M. A Diffusion Theory Model of Adoption and Substitution for Successive Generations of High-Technology Products[J], Management Science, 1987, 33: 1069-1086.

DOI: 10.1287/mnsc.33.9.1069

[8] Rajkumar Venkatesan ,V. Kumar, A Genetic Algorithms Approach to Growth Phase Forecasting of Wireless Subscribers[J], International Journal of Forecasting [J], 2002, 18: 625-646.

DOI: 10.1016/s0169-2070(02)00070-5

[9] Rajkumar Venkatesan, Trichy V. Krishnan, V. Kumar, Evolutionary Estimation of Macro-Level Diffusion Models Using Genetic Algorithms: An Alternative to Nonlinear Least Squares, Marketing Science, 2004 (23)451-464.

DOI: 10.1287/mksc.1040.0056

[10] Schmittlein ,D.C. and V. Mahajan, Maximum Likelihood Estimation for An Innovation Diffusion model of New Product Acceptance, Marketing Science. 1982, 1: 57-78.

DOI: 10.1287/mksc.1.1.57

[11] Srinivasan,V. and Mason,C. H . Nonlinear Least Squares Estimation of New Product Diffusion Models, Marketing Science, Vol. 5, No. 1, Winter, (1986).

DOI: 10.1287/mksc.5.2.169

[12] Putsis .W. P . and V. Srinivasan, Estimation Techniques for Macro Diffusion Models[M]. New-Product Diffusion Models. Kluwer Academid Publishers. Dordrecht. pp.264-291.