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Competitive diffusion process of repurchased products in knowledgeable manufacturing

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  • Yan, Hong-Sen
  • Ma, Kai-Ping

Abstract

This paper presents a diffusion model to explain the competitive diffusion of the repurchased products in knowledgeable manufacturing. The acute market competition accelerates the products' improvement, which requires that the manufacturing enterprises be highly capable of rapid reaction by means of knowledgeable manufacturing. To forecast the diffusion behavior effectively enables the realization of knowledgeable manufacturing system (KMS) which targets T (time), Q (quality), C (cost), S (service), and E (environment). Various diffusion models have emerged since Bass model was firstly proposed in 1969. A nonlinear model of the repurchased competitive products is proposed on the basis of the product diffusion analysis. By taking the frequently purchased products as example, the stability of the model is examined in light of the qualitative theory of differential equations and proved by the approximate linearization method. As the qualitative analysis reveals, between the two frequently purchased products competing in the same market, one undoubtedly occupies a fixed market share while the other may finally be eliminated from the market. A special case of the problem is that both products are one-time-purchased. With the corresponding model given, the qualitative analysis shows that either of the products occupies a market share, the size of which is determined by the product's competitive strength and the new product's time-to-market. A system dynamics model is then established and simulated by vensim. The result is consistent with that of the qualitative analysis.

Suggested Citation

  • Yan, Hong-Sen & Ma, Kai-Ping, 2011. "Competitive diffusion process of repurchased products in knowledgeable manufacturing," European Journal of Operational Research, Elsevier, vol. 208(3), pages 243-252, February.
  • Handle: RePEc:eee:ejores:v:208:y:2011:i:3:p:243-252
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    1. John A. Norton & Frank M. Bass, 1987. "A Diffusion Theory Model of Adoption and Substitution for Successive Generations of High-Technology Products," Management Science, INFORMS, vol. 33(9), pages 1069-1086, September.
    2. Frank M. Bass, 1969. "A New Product Growth for Model Consumer Durables," Management Science, INFORMS, vol. 15(5), pages 215-227, January.
    3. Kamrad, Bardia & Lele, Shreevardhan S. & Siddique, Akhtar & Thomas, Robert J., 2005. "Innovation diffusion uncertainty, advertising and pricing policies," European Journal of Operational Research, Elsevier, vol. 164(3), pages 829-850, August.
    4. Jasjit Singh, 2005. "Collaborative Networks as Determinants of Knowledge Diffusion Patterns," Management Science, INFORMS, vol. 51(5), pages 756-770, May.
    5. Richard A. Wolfe, 1994. "Organizational Innovation: Review, Critique And Suggested Research Directions," Journal of Management Studies, Wiley Blackwell, vol. 31(3), pages 405-431, May.
    6. Joe A. Dodson, Jr. & Eitan Muller, 1978. "Models of New Product Diffusion Through Advertising and Word-of-Mouth," Management Science, INFORMS, vol. 24(15), pages 1568-1578, November.
    7. Jagmohan S. Raju & Abhik Roy, 2000. "Market Information and Firm Performance," Management Science, INFORMS, vol. 46(8), pages 1075-1084, August.
    8. Randolph B. Cooper & Robert W. Zmud, 1990. "Information Technology Implementation Research: A Technological Diffusion Approach," Management Science, INFORMS, vol. 36(2), pages 123-139, February.
    9. Swami, Sanjeev & Dutta, Arindam, 2010. "Advertising strategies for new product diffusion in emerging markets: Propositions and analysis," European Journal of Operational Research, Elsevier, vol. 204(3), pages 648-661, August.
    10. Emmanouilides, Christos J. & Davies, Richard B., 2007. "Modelling and estimation of social interaction effects in new product diffusion," European Journal of Operational Research, Elsevier, vol. 177(2), pages 1253-1274, March.
    11. Agarwal, Rajshree & Bayus, Barry L., 2002. "The Market Evolution and Sales Take-Off of Product Innovations," Working Papers 02-0104, University of Illinois at Urbana-Champaign, College of Business.
    12. Druehl, Cheryl T. & Schmidt, Glen M. & Souza, Gilvan C., 2009. "The optimal pace of product updates," European Journal of Operational Research, Elsevier, vol. 192(2), pages 621-633, January.
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    9. Shuping Li & Zhen Jin, 2013. "Global Dynamics Analysis of Homogeneous New Products Diffusion Model," Discrete Dynamics in Nature and Society, Hindawi, vol. 2013, pages 1-6, November.
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