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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. Ramírez-Hassan, Andrés & Montoya-Blandón, Santiago, 2020. "Forecasting from others’ experience: Bayesian estimation of the generalized Bass model," International Journal of Forecasting, Elsevier, vol. 36(2), pages 442-465.
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    7. Kim, Moon-Soo & Kim, Ho, 2007. "Is there early take-off phenomenon in diffusion of IP-based telecommunications services?," Omega, Elsevier, vol. 35(6), pages 727-739, December.
    8. Divakaran, Pradeep Kumar Ponnamma & Palmer, Adrian & Søndergaard, Helle Alsted & Matkovskyy, Roman, 2017. "Pre-launch Prediction of Market Performance for Short Lifecycle Products Using Online Community Data," Journal of Interactive Marketing, Elsevier, vol. 38(C), pages 12-28.
    9. Huh, Sung-Yoon & Lee, Chul-Yong, 2014. "Diffusion of renewable energy technologies in South Korea on incorporating their competitive interrelationships," Energy Policy, Elsevier, vol. 69(C), pages 248-257.
    10. Aydin, R. & Kwong, C.K. & Ji, P., 2015. "A novel methodology for simultaneous consideration of remanufactured and new products in product line design," International Journal of Production Economics, Elsevier, vol. 169(C), pages 127-140.
    11. Sachin Gupta & Dipak C. Jain & Mohanbir S. Sawhney, 1999. "Modeling the Evolution of Markets with Indirect Network Externalities: An Application to Digital Television," Marketing Science, INFORMS, vol. 18(3), pages 396-416.
    12. 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.
    13. 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.
    14. Fildes, Robert & Ma, Shaohui & Kolassa, Stephan, 2022. "Retail forecasting: Research and practice," International Journal of Forecasting, Elsevier, vol. 38(4), pages 1283-1318.
    15. Dan, Sujan M., 2019. "How interface formats gain market acceptance: The role of developers and format characteristics in the development of de facto standards," Technovation, Elsevier, vol. 88(C).
    16. 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.
    17. 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.
    18. 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.
    19. Fildes, Robert & Ma, Shaohui & Kolassa, Stephan, 2019. "Retail forecasting: research and practice," MPRA Paper 89356, University Library of Munich, Germany.
    20. 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.
    21. 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.
    22. Chuan Zhang & Yu-Xin Tian & Ling-Wei Fan, 2020. "Improving the Bass model’s predictive power through online reviews, search traffic and macroeconomic data," Annals of Operations Research, Springer, vol. 295(2), pages 881-922, December.
    23. Linton, Jonathan D. & Yeomans, J. Scott & Yoogalingam, Reena, 2005. "Recovery and reclamation of durable goods: a study of television CRTs," Resources, Conservation & Recycling, Elsevier, vol. 43(4), pages 337-352.
    24. 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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