IDEAS home Printed from
   My bibliography  Save this article

Competitive diffusion process of repurchased products in knowledgeable manufacturing


  • Yan, Hong-Sen
  • Ma, Kai-Ping


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

    Download full text from publisher

    File URL:
    Download Restriction: Full text for ScienceDirect subscribers only

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    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. Jagmohan S. Raju & Abhik Roy, 2000. "Market Information and Firm Performance," Management Science, INFORMS, vol. 46(8), pages 1075-1084, August.
    3. 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.
    4. 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.
    5. 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.
    6. Jasjit Singh, 2005. "Collaborative Networks as Determinants of Knowledge Diffusion Patterns," Management Science, INFORMS, vol. 51(5), pages 756-770, May.
    7. 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.
    8. 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.
    9. 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.
    Full references (including those not matched with items on IDEAS)


    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.

    Cited by:

    1. Guidolin, Mariangela & Guseo, Renato, 2015. "Technological change in the U.S. music industry: Within-product, cross-product and churn effects between competing blockbusters," Technological Forecasting and Social Change, Elsevier, vol. 99(C), pages 35-46.
    2. Amini, Mehdi & Wakolbinger, Tina & Racer, Michael & Nejad, Mohammad G., 2012. "Alternative supply chain production–sales policies for new product diffusion: An agent-based modeling and simulation approach," European Journal of Operational Research, Elsevier, vol. 216(2), pages 301-311.
    3. Hao-Xiang Wang & Hong-Sen Yan, 2016. "An interoperable adaptive scheduling strategy for knowledgeable manufacturing based on SMGWQ-learning," Journal of Intelligent Manufacturing, Springer, vol. 27(5), pages 1085-1095, October.
    4. 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.
    5. Hong, Jungsik & Koo, Hoonyoung & Kim, Taegu, 2016. "Easy, reliable method for mid-term demand forecasting based on the Bass model: A hybrid approach of NLS and OLS," European Journal of Operational Research, Elsevier, vol. 248(2), pages 681-690.
    6. Hong-Sen Yan & Wen-Chao Li, 2017. "A multi-objective scheduling algorithm with self-evolutionary feature for job-shop-like knowledgeable manufacturing cell," Journal of Intelligent Manufacturing, Springer, vol. 28(2), pages 337-351, February.
    7. Duan, Hongbo & Zhang, Gupeng & Wang, Shouyang & Fan, Ying, 2018. "Peer interaction and learning: Cross-country diffusion of solar photovoltaic technology," Journal of Business Research, Elsevier, vol. 89(C), pages 57-66.
    8. 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.
    9. Chumnumpan, Pattarin & Shi, Xiaohui, 2019. "Understanding new products’ market performance using Google Trends," Australasian marketing journal, Elsevier, vol. 27(2), pages 91-103.
    10. Hou, Rui & Wu, Jiawen & Du, Helen S., 2017. "Customer social network affects marketing strategy: A simulation analysis based on competitive diffusion model," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 469(C), pages 644-653.
    11. Shi, Xiaohui & Chumnumpan, Pattarin, 2019. "Modelling market dynamics of multi-brand and multi-generational products," European Journal of Operational Research, Elsevier, vol. 279(1), pages 199-210.


    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:ejores:v:208:y:2011:i:3:p:243-252. See general information about how to correct material in RePEc.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Haili He). General contact details of provider: .

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service hosted by the Research Division of the Federal Reserve Bank of St. Louis . RePEc uses bibliographic data supplied by the respective publishers.