IDEAS home Printed from https://ideas.repec.org/a/zbw/espost/118689.html
   My bibliography  Save this article

Dynamic Model of Markets of Successive Product Generations

Author

Listed:
  • Kaldasch, Joachim

Abstract

A dynamic microeconomic model is presented that establishes the price and unit sales evolution of heterogeneous goods consisting of successive homogenous product generations. It suggests that for a fast growing supply the mean price of the generations are governed by a logistic decline towards a floor price. It is shown that generations of a heterogeneous good are in mutual competition. Their market shares are therefore governed by a Fisher-Pry law while the total unit sales are governed by the lifecycle dynamics of the good. As a result the absolute unit sales of a generation exhibit a characteristic sales peak consisting of a rapid increase followed by a long tail. The presented approach shows that the evolution of successive product generations can be understood as an evolutionary adaptation process. The applicability of the model is confirmed by a comparison with empirical investigations on successive DRAM generations.

Suggested Citation

  • Kaldasch, Joachim, 2015. "Dynamic Model of Markets of Successive Product Generations," EconStor Open Access Articles and Book Chapters, ZBW - Leibniz Information Centre for Economics, vol. 10(3), pages 1-15.
    • Handle: RePEc:zbw:espost:118689
    DOI: 10.9734/BJEMT/2015/20473
    as

    Download full text from publisher

    File URL: https://www.econstor.eu/bitstream/10419/118689/1/Kaldasch1032015BJEMT20473.pdf
    Download Restriction: no
    File URL: https://libkey.io/10.9734/BJEMT/2015/20473?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Kaldasch, Joachim, 2011. "Evolutionary model of an anonymous consumer durable market," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 390(14), pages 2692-2715.
    2. Kaldasch, Joachim, 2014. "Evolutionary model of stock markets," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 415(C), pages 449-462.
    3. Wonjoon Kim & Jeong-Dong Lee, 2009. "Measuring the Role of Technology-Push and Demand-Pull in the Dynamic Development of the Semiconductor Industry: The Case of the Global DRAM Market," Journal of Applied Economics, Taylor & Francis Journals, vol. 12(1), pages 83-108, May.
    4. Ralph Siebert, 2002. "Learning by Doing and Multiproduction Effects over the Life Cycle: Evidence from the Semiconductor Industry," CIG Working Papers FS IV 02-23, Wissenschaftszentrum Berlin (WZB), Research Unit: Competition and Innovation (CIG).
    5. Kaldasch, Joachim, 2015. "Dynamic Model of Markets of Homogenous Non-Durables," EconStor Open Access Articles and Book Chapters, ZBW - Leibniz Information Centre for Economics, vol. 9(3), pages 1-12.
    6. Joachim Kaldasch, 2015. "Dynamic Model of the Price Dispersion of Homogeneous Goods," Papers 1509.01216, arXiv.org.
    7. 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.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Kaldasch, Joachim, 2015. "The Product Life Cycle of Durable Goods," EconStor Open Access Articles and Book Chapters, ZBW - Leibniz Information Centre for Economics, vol. 10(2), pages 1-17.
    2. Kaldasch, Joachim, 2015. "Dynamic Model of Markets of Homogenous Non-Durables," EconStor Open Access Articles and Book Chapters, ZBW - Leibniz Information Centre for Economics, vol. 9(3), pages 1-12.
    3. Kaldasch, Joachim, 2014. "Evolutionary Model of Moore’s Law," MPRA Paper 54397, University Library of Munich, Germany.
    4. Joachim Kaldasch, 2015. "Dynamic Model of the Price Dispersion of Homogeneous Goods," Papers 1509.01216, arXiv.org.
    5. Kaldasch, Joachim, 2012. "Evolutionary model of the personal income distribution," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 391(22), pages 5628-5642.
    6. 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.
    7. Mikko Myrskylä & Joshua R. Goldstein, 2010. "Probabilistic forecasting using stochastic diffusion models, with applications to cohort processes of marriage and fertility," MPIDR Working Papers WP-2010-013, Max Planck Institute for Demographic Research, Rostock, Germany.
    8. Sanghoon Lee & Wonjoon Kim, 2017. "The knowledge network dynamics in a mobile ecosystem: a patent citation analysis," Scientometrics, Springer;Akadémiai Kiadó, vol. 111(2), pages 717-742, May.
    9. Kivi, Antero & Smura, Timo & Töyli, Juuso, 2012. "Technology product evolution and the diffusion of new product features," Technological Forecasting and Social Change, Elsevier, vol. 79(1), pages 107-126.
    10. Al-Alawi, Baha M. & Bradley, Thomas H., 2013. "Review of hybrid, plug-in hybrid, and electric vehicle market modeling Studies," Renewable and Sustainable Energy Reviews, Elsevier, vol. 21(C), pages 190-203.
    11. Du, Zhushan & Feng, Hongli & Arbuckle, J. Gordon, 2024. "Beyond cross-sectional, one-time adoption measures of conservation practices: Understanding temporal adoption patterns using farm-level panel data," 2024 Annual Meeting, July 28-30, New Orleans, LA 344010, Agricultural and Applied Economics Association.
    12. Duan, Hong-Bo & Zhu, Lei & Fan, Ying, 2014. "A cross-country study on the relationship between diffusion of wind and photovoltaic solar technology," Technological Forecasting and Social Change, Elsevier, vol. 83(C), pages 156-169.
    13. Franklin M. Lartey, 2020. "Predicting Product Uptake Using Bass, Gompertz, and Logistic Diffusion Models: Application to a Broadband Product," Journal of Business Administration Research, Journal of Business Administration Research, Sciedu Press, vol. 9(2), pages 1-5, October.
    14. Peters, Kay & Albers, Sönke & Kumar, V., 2008. "Is there more to international Diffusion than Culture? An investigation on the Role of Marketing and Industry Variables," EconStor Preprints 27678, ZBW - Leibniz Information Centre for Economics.
    15. Dutta, Amitava & Puvvala, Abhinay & Roy, Rahul & Seetharaman, Priya, 2017. "Technology diffusion: Shift happens — The case of iOS and Android handsets," Technological Forecasting and Social Change, Elsevier, vol. 118(C), pages 28-43.
    16. Bin Shen & Hau-Ling Chan, 2017. "Forecast Information Sharing for Managing Supply Chains in the Big Data Era: Recent Development and Future Research," Asia-Pacific Journal of Operational Research (APJOR), World Scientific Publishing Co. Pte. Ltd., vol. 34(01), pages 1-26, February.
    17. Abedi, Vahideh Sadat, 2019. "Compartmental diffusion modeling: Describing customer heterogeneity & communication network to support decisions for new product introductions," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 536(C).
    18. Carlos Pablo Sigüenza & Bernhard Steubing & Arnold Tukker & Glenn A. Aguilar‐Hernández, 2021. "The environmental and material implications of circular transitions: A diffusion and product‐life‐cycle‐based modeling framework," Journal of Industrial Ecology, Yale University, vol. 25(3), pages 563-579, June.
    19. Winkler, Kay, 2014. "Potential Effects of New Zealand's Policy on Next Generation High-Speed Access Networks," Working Paper Series 4347, Victoria University of Wellington, The New Zealand Institute for the Study of Competition and Regulation.
    20. Palmer, J. & Sorda, G. & Madlener, R., 2015. "Modeling the diffusion of residential photovoltaic systems in Italy: An agent-based simulation," Technological Forecasting and Social Change, Elsevier, vol. 99(C), pages 106-131.

    More about this item

    Keywords

    Product diffusion; multiple generations; evolutionary economics; competition; price evolution; DRAM market;
    All these keywords.

    JEL classification:

    • C0 - Mathematical and Quantitative Methods - - General
    • C1 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General
    • D0 - Microeconomics - - General
    • D11 - Microeconomics - - Household Behavior - - - Consumer Economics: Theory
    • L11 - Industrial Organization - - Market Structure, Firm Strategy, and Market Performance - - - Production, Pricing, and Market Structure; Size Distribution of Firms

    Statistics

    Access and download statistics

    Corrections

    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:zbw:espost:118689. See general information about how to correct material in RePEc.

    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 bibliographic 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.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: ZBW - Leibniz Information Centre for Economics (email available below). General contact details of provider: https://edirc.repec.org/data/zbwkide.html .

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

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.