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Demand Forecasting in the Early Stage of the Technology’s Life Cycle Using a Bayesian Update

Author

Listed:
  • Chul-Yong Lee

    (Korea Energy Economics Institute (KEEI), 405-11 Jongga-ro, Jung-gu, Ulsan 44543, Korea)

  • Min-Kyu Lee

    (Graduate School of Management of Technology, Pukyong National University, 365 Sinseon-ro, Nam-gu, Busan 48547, Korea)

Abstract

The forecasting demand for new technology for which few historical data observations are available is difficult but essential to sustainable development. The current study suggests an alternative forecasting methodology based on a hazard rate model using stated and revealed preferences of consumers. In estimating the hazard rate, information is initially derived through conjoint analysis based on a consumer survey and then updated using Bayes’ theorem with available market data. To compare the proposed models’ performance with benchmark models, the Bass model, the logistic growth model, and a Bayesian approach based on analogy are adopted. The results show that the proposed model outperforms the benchmark models in terms of pre-launch and post-launch forecasting performances.

Suggested Citation

  • Chul-Yong Lee & Min-Kyu Lee, 2017. "Demand Forecasting in the Early Stage of the Technology’s Life Cycle Using a Bayesian Update," Sustainability, MDPI, vol. 9(8), pages 1-15, August.
    • Handle: RePEc:gam:jsusta:v:9:y:2017:i:8:p:1378-:d:107066
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    References listed on IDEAS

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    1. 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.
    2. Islam, Towhidul, 2014. "Household level innovation diffusion model of photo-voltaic (PV) solar cells from stated preference data," Energy Policy, Elsevier, vol. 65(C), pages 340-350.
    3. repec:ack:journl:y::id:108 is not listed on IDEAS
    4. Ramya Neelamegham & Pradeep Chintagunta, 1999. "A Bayesian Model to Forecast New Product Performance in Domestic and International Markets," Marketing Science, INFORMS, vol. 18(2), pages 115-136.
    5. Frank M. Bass, 1969. "A New Product Growth for Model Consumer Durables," Management Science, INFORMS, vol. 15(5), pages 215-227, January.
    6. John Calfee & Clifford Winston & Randolph Stempski, 2001. "Econometric Issues In Estimating Consumer Preferences From Stated Preference Data: A Case Study Of The Value Of Automobile Travel Time," The Review of Economics and Statistics, MIT Press, vol. 83(4), pages 699-707, November.
    7. Gary L. Lilien & Ambar G. Rao & Shlomo Kalish, 1981. "Bayesian Estimation and Control of Detailing Effort in a Repeat Purchase Diffusion Environment," Management Science, INFORMS, vol. 27(5), pages 493-506, May.
    8. Frank M. Bass & Kent Gordon & Teresa L. Ferguson & Mary Lou Githens, 2001. "DIRECTV: Forecasting Diffusion of a New Technology Prior to Product Launch," Interfaces, INFORMS, vol. 31(3_supplem), pages 82-93, June.
    9. Dipak Jain & Vijay Mahajan & Eitan Muller, 1991. "Innovation Diffusion in the Presence of Supply Restrictions," Marketing Science, INFORMS, vol. 10(1), pages 83-90.
    10. Nancy L. Rose & Paul L. Joskow, 1990. "The Diffusion of New Technologies: Evidence from the Electric Utility Industry," RAND Journal of Economics, The RAND Corporation, vol. 21(3), pages 354-373, Autumn.
    11. repec:ack:journl:y::id:50 is not listed on IDEAS
    12. Editorial Article, 0. "The Information for Authors," Economics of Contemporary Russia, Regional Public Organization for Assistance to the Development of Institutions of the Department of Economics of the Russian Academy of Sciences, issue 2.
    13. repec:ack:journl:y::id:144 is not listed on IDEAS
    14. repec:ack:journl:y::id:220 is not listed on IDEAS
    15. repec:bla:econom:v:54:y:1987:i:214:p:155-71 is not listed on IDEAS
    16. repec:ack:journl:y::id:91 is not listed on IDEAS
    17. Minhi Hahn & Sehoon Park & Lakshman Krishnamurthi & Andris A. Zoltners, 1994. "Analysis of New Product Diffusion Using a Four-Segment Trial-Repeat Model," Marketing Science, INFORMS, vol. 13(3), pages 224-247.
    18. Train,Kenneth E., 2009. "Discrete Choice Methods with Simulation," Cambridge Books, Cambridge University Press, number 9780521747387.
    19. Editorial Article, 0. "The Information for Authors," Economics of Contemporary Russia, Regional Public Organization for Assistance to the Development of Institutions of the Department of Economics of the Russian Academy of Sciences, issue 1.
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    Cited by:

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