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A Population-Growth Model for Multiple Generations of Technology Products

Author

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  • Hongmin Li

    (W. P. Carey School of Business, Arizona State University, Tempe, Arizona 85287)

  • Dieter Armbruster

    (School of Mathematical and Statistical Sciences, Arizona State University, Tempe, Arizona 85287)

  • Karl G. Kempf

    (Decision Engineering Group, Intel Corporation, Chandler, Arizona 85226)

Abstract

In this paper, we consider the demand for multiple, successive generations of products and develop a population-growth model that allows demand transitions across multiple product generations and takes into consideration the effect of competition. We propose an iterative-descent method for obtaining the parameter estimates and the covariance matrix, and we show that the method is theoretically sound and overcomes the difficulty that the units-in-use population of each product is not observable. We test the model on both simulated sales data and Intel's high-end desktop processor sales data. We use two alternative specifications for product strength in this market: performance and performance/price ratio. The former demonstrates better fit and forecast accuracy, likely due to the low price sensitivity of this high-end market. In addition, the parameter estimate suggests that, for the innovators in the diffusion of product adoption, brand switchings are more strongly influenced by product strength than within-brand product upgrades in this market. Our results indicate that compared with the Bass model, Norton–Bass model, and Jun–Park choice-based diffusion model, our approach is a better fit for strategic forecasting that occurs many months or years before the actual product launch.

Suggested Citation

  • 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.
    • Handle: RePEc:inm:ormsom:v:15:y:2013:i:3:p:343-360
    DOI: 10.1287/msom.2013.0430
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    References listed on IDEAS

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    Cited by:

    1. Teun Adriaansen & Dieter Armbruster & Karl Kempf & Hongmin Li, 2013. "An Agent Model For The High-End Gamers Market," Advances in Complex Systems (ACS), World Scientific Publishing Co. Pte. Ltd., vol. 16(07), pages 1-33.
    2. Hongmin Li, 2020. "Optimal Pricing Under Diffusion-Choice Models," Operations Research, INFORMS, vol. 68(1), pages 115-133, January.
    3. Samuel Sale, R. & Mesak, Hani I. & Inman, R. Anthony, 2017. "A dynamic marketing-operations interface model of new product updates," European Journal of Operational Research, Elsevier, vol. 257(1), pages 233-242.
    4. Kocaman, Barış & Gelper, Sarah & Langerak, Fred, 2023. "Till the cloud do us part: Technological disruption and brand retention in the enterprise software industry," International Journal of Research in Marketing, Elsevier, vol. 40(2), pages 316-341.
    5. Stylianos Kavadias & Karl T. Ulrich, 2020. "Innovation and New Product Development: Reflections and Insights from the Research Published in the First 20 Years of Manufacturing & Service Operations Management," Manufacturing & Service Operations Management, INFORMS, vol. 22(1), pages 84-92, January.
    6. Dong, Ming & Mao, Shunjie & Li, Shan, 2023. "Supplier's technology upgrading investment strategy considering product life cycle," International Journal of Production Economics, Elsevier, vol. 263(C).

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