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High-Definition Television: Assessing Demand Forecasts for a Next Generation Consumer Durable

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  • Barry L. Bayus

    (Kenan-Flagler Business School, University of North Carolina, Carroll Hall, CB #3490, Chapel Hill, North Carolina 27599)

Abstract

High-Definition Television promises to be the next generation of television. This technology has broad implications for consumer markets, as well as the underlying manufacturing, technology development, and R&D activities of firms. Under increasing pressure from various groups, the U.S. government must make major policy and funding decisions based on its assessment of the likely demand for HDTV. Three published reports which forecast sales of HDTV after its scheduled introduction in the mid-1990s are available. Unfortunately, these forecasts offer widely differing perspectives on HDTV's potential. This paper presents an approach that links product segmentation (based on historical demand parameters, and marketing and manufacturing related variables) and demand forecasting for new products. The published HDTV forecasts are then assessed using this segmentation scheme. Differing from the Congressional Budget Office's earlier evaluation, this analysis indicates that one report is consistent with historical data from the home appliance industry.

Suggested Citation

  • Barry L. Bayus, 1993. "High-Definition Television: Assessing Demand Forecasts for a Next Generation Consumer Durable," Management Science, INFORMS, vol. 39(11), pages 1319-1333, November.
    • Handle: RePEc:inm:ormnsc:v:39:y:1993:i:11:p:1319-1333
    DOI: 10.1287/mnsc.39.11.1319
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    Cited by:

    1. F-M Tseng, 2008. "Quadratic interval innovation diffusion models for new product sales forecasting," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 59(8), pages 1120-1127, August.
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    11. Gilvan C. Souza & Barry L. Bayus & Harvey M. Wagner, 2004. "New-Product Strategy and Industry Clockspeed," Management Science, INFORMS, vol. 50(4), pages 537-549, April.
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    17. Cantamessa, Marco & Valentini, Carlo, 2000. "Planning and managing manufacturing capacity when demand is subject to diffusion effects," International Journal of Production Economics, Elsevier, vol. 66(3), pages 227-240, July.
    18. Kretschmer, Tobias & Rösner, Mariana, 2010. "Increasing Dominance - the Role of Advertising, Pricing and Product Design," Discussion Papers in Business Administration 11500, University of Munich, Munich School of Management.
    19. Shin, Jungwoo & Lee, Chul-Yong & Kim, Hongbum, 2016. "Technology and demand forecasting for carbon capture and storage technology in South Korea," Energy Policy, Elsevier, vol. 98(C), pages 1-11.
    20. Jonathan Lee & Peter Boatwright & Wagner A. Kamakura, 2003. "A Bayesian Model for Prelaunch Sales Forecasting of Recorded Music," Management Science, INFORMS, vol. 49(2), pages 179-196, February.
    21. Chang-Gyu Yang & Silvana Trimi & Sang-Gun Lee & Joon-Sun Yang, 2017. "A Survival Analysis of Business Insolvency in ICT and Automobile Industries," International Journal of Information Technology & Decision Making (IJITDM), World Scientific Publishing Co. Pte. Ltd., vol. 16(06), pages 1523-1548, November.
    22. Lee, Chul-Yong & Huh, Sung-Yoon, 2017. "Forecasting new and renewable energy supply through a bottom-up approach: The case of South Korea," Renewable and Sustainable Energy Reviews, Elsevier, vol. 69(C), pages 207-217.
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