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Analysis on the feedback effect for the diffusion of innovative technologies focusing on the green car

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  • Lee, Duk Hee
  • Park, Sang Yong
  • Kim, Jong Wook
  • Lee, Seong Kon

Abstract

Previous studies of technical competitive relationship have mostly focused on the analysis of one-directional impact of the technical attribute on market share. However, there is a cyclical feedback effect between the technical attributes and market share, and that means the small difference of competitiveness at the early phase of technology diffusion can greatly affect the final market share. As such, this study presents the system dynamics model which can forecast sales of innovative technology considering the feedback effect of market share on technical attributes. For that, the causal loop diagram among the various variables was defined using the econometric model applied and proven in various studies of the Bass diffusion model, discrete choice model, etc. to reinforce the theoretical background of the causal relationship among the variables of the forecasting model. Furthermore, infrastructure building scenarios and feedback effect scenarios were applied to the developed forecasting model to present the implication for successful adoption of green car technology from the infrastructure development view point.

Suggested Citation

  • Lee, Duk Hee & Park, Sang Yong & Kim, Jong Wook & Lee, Seong Kon, 2013. "Analysis on the feedback effect for the diffusion of innovative technologies focusing on the green car," Technological Forecasting and Social Change, Elsevier, vol. 80(3), pages 498-509.
    • Handle: RePEc:eee:tefoso:v:80:y:2013:i:3:p:498-509
    DOI: 10.1016/j.techfore.2012.08.009
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    1. Frank M. Bass, 1969. "A New Product Growth for Model Consumer Durables," Management Science, INFORMS, vol. 15(5), pages 215-227, January.
    2. repec:cdl:itsdav:qt02n9j6cv is not listed on IDEAS
    3. 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.
    4. Malte Schwoon, 2006. "Simulating the adoption of fuel cell vehicles," Journal of Evolutionary Economics, Springer, vol. 16(4), pages 435-472, October.
    5. Train,Kenneth E., 2009. "Discrete Choice Methods with Simulation," Cambridge Books, Cambridge University Press, number 9780521747387, January.
    6. Jun, Duk B. & Kim, Seon K. & Park, Yoon S. & Park, Myoung H. & Wilson, Amy R., 2002. "Forecasting telecommunication service subscribers in substitutive and competitive environments," International Journal of Forecasting, Elsevier, vol. 18(4), pages 561-581.
    7. Axsen, Jonn & Mountain, Dean C. & Jaccard, Mark, 2009. "Combining stated and revealed choice research to simulate the neighbor effect: The case of hybrid-electric vehicles," Resource and Energy Economics, Elsevier, vol. 31(3), pages 221-238, August.
    8. Ambar G. Rao & Masataka Yamada, 1988. "Forecasting with a Repeat Purchase Diffusion Model," Management Science, INFORMS, vol. 34(6), pages 734-752, June.
    9. Malte Schwoon, 2005. "Simulating the Adoption of Fuel Cell Vehicles," Working Papers FNU-59, Research unit Sustainability and Global Change, Hamburg University, revised Feb 2006.
    10. Hausman, Jerry A. & Ruud, Paul A., 1987. "Specifying and testing econometric models for rank-ordered data," Journal of Econometrics, Elsevier, vol. 34(1-2), pages 83-104.
    11. Park, Sang Yong & Kim, Jong Wook & Lee, Duk Hee, 2011. "Development of a market penetration forecasting model for Hydrogen Fuel Cell Vehicles considering infrastructure and cost reduction effects," Energy Policy, Elsevier, vol. 39(6), pages 3307-3315, June.
    12. Mau, Paulus & Eyzaguirre, Jimena & Jaccard, Mark & Collins-Dodd, Colleen & Tiedemann, Kenneth, 2008. "The 'neighbor effect': Simulating dynamics in consumer preferences for new vehicle technologies," Ecological Economics, Elsevier, vol. 68(1-2), pages 504-516, December.
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