IDEAS home Printed from https://ideas.repec.org/a/inm/ormnsc/v46y2000i4p496-512.html
   My bibliography  Save this article

Modeling Intercategory and Generational Dynamics for A Growing Information Technology Industry

Author

Listed:
  • Namwoon Kim

    (Department of Business Studies, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong)

  • Dae Ryun Chang

    (School of Management, Yonsei University, Seoul, Korea)

  • Allan D. Shocker

    (College of Business, San Francisco State University, San Francisco, California 94132)

Abstract

Previous studies dealing with product growth have dealt only with substitution effects among successive generations of one product category and not with complementarity and competition provided by related product categories. Based on a broadened concept of the competitive information technology (IT) market, we develop a dynamic market growth model that is able to incorporate both interproduct category and technological substitution effects simultaneously. The market potential for each category or generation is treated as a variable rather than a constant parameter, which is typical of recently growing IT sectors such as wireless telecommunications. The model is calibrated, its plausibility discussed, and its face and predictive validity assessed using data on wireless telecommunications services from two Asian markets. Results show that the market potential (and sales growth) of one category or generation is significantly affected by others and by the overall structure of a geographic market. The model is shown to make relatively good predictions even when the data from recently introduced categories/generations are limited.

Suggested Citation

  • Namwoon Kim & Dae Ryun Chang & Allan D. Shocker, 2000. "Modeling Intercategory and Generational Dynamics for A Growing Information Technology Industry," Management Science, INFORMS, vol. 46(4), pages 496-512, April.
    • Handle: RePEc:inm:ormnsc:v:46:y:2000:i:4:p:496-512
    DOI: 10.1287/mnsc.46.4.496.12059
    as

    Download full text from publisher

    File URL: http://dx.doi.org/10.1287/mnsc.46.4.496.12059
    Download Restriction: no
    File URL: https://libkey.io/10.1287/mnsc.46.4.496.12059?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. 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. Roger M. Heeler & Thomas P. Hustad, 1980. "Problems in Predicting New Product Growth for Consumer Durables," Management Science, INFORMS, vol. 26(10), pages 1007-1020, October.
    3. Collopy, Fred & Armstrong, J. Scott, 1992. "Expert opinions about extrapolation and the mystery of the overlooked discontinuities," International Journal of Forecasting, Elsevier, vol. 8(4), pages 575-582, December.
    4. V. Srinivasan & Charlotte H. Mason, 1986. "Technical Note—Nonlinear Least Squares Estimation of New Product Diffusion Models," Marketing Science, INFORMS, vol. 5(2), pages 169-178.
    5. Jain, Dipak C & Rao, Ram C, 1990. "Effect of Price on the Demand for Durables: Modeling, Estimation, and Findings," Journal of Business & Economic Statistics, American Statistical Association, vol. 8(2), pages 163-170, April.
    6. Vijay Mahajan & Robert A. Peterson, 1978. "Innovation Diffusion in a Dynamic Potential Adopter Population," Management Science, INFORMS, vol. 24(15), pages 1589-1597, November.
    7. Frank M. Bass, 1969. "A New Product Growth for Model Consumer Durables," Management Science, INFORMS, vol. 15(5), pages 215-227, January.
    8. Shlomo Kalish, 1985. "A New Product Adoption Model with Price, Advertising, and Uncertainty," Management Science, INFORMS, vol. 31(12), pages 1569-1585, December.
    9. Armstrong, J. Scott & Collopy, Fred, 1992. "Error measures for generalizing about forecasting methods: Empirical comparisons," International Journal of Forecasting, Elsevier, vol. 8(1), pages 69-80, June.
    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. Kim, Namwoon & Srivastava, Rajendra K., 2007. "Modeling cross-price effects on inter-category dynamics: The case of three computing platforms," Omega, Elsevier, vol. 35(3), pages 290-301, June.
    2. Singhal, Shakshi & Anand, Adarsh & Singh, Ompal, 2020. "Studying dynamic market size-based adoption modeling & product diffusion under stochastic environment," Technological Forecasting and Social Change, Elsevier, vol. 161(C).
    3. John Hauser & Gerard J. Tellis & Abbie Griffin, 2006. "Research on Innovation: A Review and Agenda for," Marketing Science, INFORMS, vol. 25(6), pages 687-717, 11-12.
    4. Velickovic, Stevan & Radojicic, Valentina & Bakmaz, Bojan, 2016. "The effect of service rollout on demand forecasting: The application of modified Bass model to the step growing markets," Technological Forecasting and Social Change, Elsevier, vol. 107(C), pages 130-140.
    5. Barnes, Belinda & Southwell, Darren & Bruce, Sarah & Woodhams, Felicity, 2014. "Additionality, common practice and incentive schemes for the uptake of innovations," Technological Forecasting and Social Change, Elsevier, vol. 89(C), pages 43-61.
    6. Shun-Chen Niu, 2006. "A Piecewise-Diffusion Model of New-Product Demands," Operations Research, INFORMS, vol. 54(4), pages 678-695, August.
    7. Krishnan, Trichy V. & Feng, Shanfei & Jain, Dipak C., 2023. "Peak sales time prediction in new product sales: Can a product manager rely on it?," Journal of Business Research, Elsevier, vol. 165(C).
    8. Wei-yu Kevin Chiang, 2012. "Supply Chain Dynamics and Channel Efficiency in Durable Product Pricing and Distribution," Manufacturing & Service Operations Management, INFORMS, vol. 14(2), pages 327-343, April.
    9. Jha, Ashutosh & Saha, Debashis, 2020. "“Forecasting and analysing the characteristics of 3G and 4G mobile broadband diffusion in India: A comparative evaluation of Bass, Norton-Bass, Gompertz, and logistic growth models”," Technological Forecasting and Social Change, Elsevier, vol. 152(C).
    10. Saurabh Panwar & P. K. Kapur & Ompal Singh, 2019. "Modeling Technological Substitution by Incorporating Dynamic Adoption Rate," International Journal of Innovation and Technology Management (IJITM), World Scientific Publishing Co. Pte. Ltd., vol. 16(01), pages 1-24, February.
    11. Hlavinka, Alexander N. & Mjelde, James W. & Dharmasena, Senarath & Holland, Christine, 2016. "Forecasting the adoption of residential ductless heat pumps," Energy Economics, Elsevier, vol. 54(C), pages 60-67.
    12. Lee, Hakyeon & Kim, Sang Gook & Park, Hyun-woo & Kang, Pilsung, 2014. "Pre-launch new product demand forecasting using the Bass model: A statistical and machine learning-based approach," Technological Forecasting and Social Change, Elsevier, vol. 86(C), pages 49-64.
    13. Hongmin Li & Woonghee Tim Huh, 2012. "Optimal pricing for a short life‐cycle product when customer price‐sensitivity varies over time," Naval Research Logistics (NRL), John Wiley & Sons, vol. 59(7), pages 552-576, October.
    14. Mingxing Wu & Liya Wang & Ming Li, 2015. "An approach based on the Bass model for analyzing the effects of feature fatigue on customer equity," Computational and Mathematical Organization Theory, Springer, vol. 21(1), pages 69-89, March.
    15. Luigi De Cesare & Andrea Di Liddo & Stefania Ragni, 2003. "Numerical Solutions to Some Optimal Control Problems Arising from Innovation Diffusion," Computational Economics, Springer;Society for Computational Economics, vol. 22(2), pages 173-186, October.
    16. Dong, Changgui & Sigrin, Benjamin & Brinkman, Gregory, 2017. "Forecasting residential solar photovoltaic deployment in California," Technological Forecasting and Social Change, Elsevier, vol. 117(C), pages 251-265.
    17. 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.
    18. Shi, Xiaohui & Li, Feng & Bigdeli, Ali Ziaee, 2016. "An examination of NPD models in the context of business models," Journal of Business Research, Elsevier, vol. 69(7), pages 2541-2550.
    19. Hongmin Li & Dieter Armbruster & Karl G. Kempf, 2013. "A Population-Growth Model for Multiple Generations of Technology Products," Manufacturing & Service Operations Management, INFORMS, vol. 15(3), pages 343-360, July.
    20. Luigi De Cesare & Andrea Di Liddo & Stefania Ragni, 2002. "Numerical solution of some optimal control problems arising from innovation diffusion," Computing in Economics and Finance 2002 221, Society for Computational Economics.

    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:inm:ormnsc:v:46:y:2000:i:4:p:496-512. 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: Chris Asher (email available below). General contact details of provider: https://edirc.repec.org/data/inforea.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.