تجزیه و تحلیل اثر بازخورد برای انتشار فن آوری های نوآورانه با تمرکز بر ماشین سبز
|کد مقاله||سال انتشار||مقاله انگلیسی||ترجمه فارسی||تعداد کلمات|
|2373||2013||12 صفحه PDF||سفارش دهید||محاسبه نشده|
Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)
Journal : Technological Forecasting and Social Change, Volume 80, Issue 3, March 2013, Pages 498–509
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.
Korea established green growth as its new national development paradigm to help to solve the problems of global warming, the energy crisis, etc. and to create a new growth engine and it has been investing in various efforts to accomplish this new goal (Presidential Commission on Green Growth ). As a part of these efforts, the government voluntarily announced on November 17, 2009 its target to reduce green house gas emissions by 30% compared with BAU (Business As Usual) by 2020. This 30% reduction target is the most aggressive target of the three originally proposed reduction scenarios and can be accomplished only when actively adopting the various green house gas reduction measures. Particularly, it will require the deployment of green cars such as electric vehicles (EV) and hydrogen fuel cell vehicles (HFCV), in addition to energy consumption saving through a strong demand management policy (Presidential Commission on Green Growth ). Therefore, the development and distribution of green car technology that can drastically reduce green house emissions in the transportation sector will be the key to accomplishing the green house gas reduction target. However, EVs and HFCVs are still in the early phase of technology development and require many technical breakthroughs for them to be commercialized, thus uncertainty of their technology development continues to be high. Furthermore, they use a motor and battery or hydrogen fuel cell as their key power source and are completely different technology in feedstock from the conventional vehicles using internal combustion engines. Therefore, the technology cannot be widely deployed without the construction of a new recharging infrastructure first. As such, the hybrid electric vehicle (HEV), which uses a motor and battery in parallel with the internal combustion engine can thus be distributed without the requirement of a new recharging infrastructure, is gaining attention as the short-term alternative for the deployment of green cars. Eventually, the speed of market entry of green cars and the market share of the alternative technologies will be determined by the outcome of green car technology development and the degree of recharging infrastructure development. This study attempts to envisage the future of transportation by developing the model which can forecast sales of green car alternative technology according to a dynamic change of factors such as fuel efficiency, feedstock cost, vehicle price and degree of infrastructure development that affects the vehicle consumer utility. There is a feedback effect particularly between the vehicle price/degree of infrastructure development and market share (Meyer ). This feedback effect shows that the market share increases when the price of a vehicle technology decreases or infrastructure development is promoted, and that increase of the market share accelerates the reduction of vehicle price and infrastructure development. Such feedback effects can cause a small competitive edge within a technology in the early distribution phase to result in a major difference of market share during the technology maturity phase. Therefore, analyzing the impact of the feedback effect on sales of new technology is an important part of forecasting sales of not only green cars but also other innovative technologies. To consider how the feedback effect, mostly from green car technology, impacts sales of innovative technologies and competitive relationships among the technologies, Chapter 2 reviews the existing related studies while Chapter 3 studies the three key methodologies for market forecasting model development. Chapter 4 presents the developed sales forecasting model developed using the methodologies presented above and Chapter 5 studies the baseline data and parameters to apply the forecasting model to green cars. Chapter 6 deducts the sales forecasting results for the green car technologies based on the various scenarios and Chapter 7 summarizes the significance of this study and future study directions.
نتیجه گیری انگلیسی
This study developed the system dynamics based sales forecasting model to analyze the impact of the feedback effect on the diffusion of innovative technologies focusing on green car technology and the market share among the competitive technologies. To reinforce the theoretical background of causal relationships among the variables of the forecasting model, the causal loop diagram of the variables was defined using the proven econometric models such as the Bass diffusion model and discrete choice model. By applying different technology development scenarios and feedback effect scenarios to the developed forecasting model, the various insights for sales of green car technologies were obtained. The baseline scenario, which applied the technology development prediction by the experts, predicted that while HEV will completely dominate the green car market initially, three green car technologies of HEV, EV and HFCV will have a similar number of accumulated sales volume by around 2050 as the EV and HFCV technologies are developed. Next, the analysis of the year-by-year market share of each green car technology according to the infrastructure development scenario indicates that, unlike the change of technical attributes such as the hydrogen energy price and HFCV fuel efficiency, the level of infrastructure development is a factor that can change not only the highest market share but also the time to reach critical mass and to occupy the highest market share. In other words, it shows that the external factors like developing an infrastructure can have a higher impact on a technology securing competitive superiority then the technology development itself. Lastly, there is an insight from the feedback scenario. In the case where the feedback effect is not considered, the years of the highest HFCV market share in the scenarios of infrastructure completely being developed in 2040 and 2035 are both 2040. However, if the feedback effect is considered, the years of highest HFCV market share in the scenario of 2040 being the year the infrastructure is completely developed and the scenario of 2035 being the year the infrastructure is completely developed would move up by 5 years and 10 years to 2035 and 2030, respectively. This analysis showed that the feedback effect played a reinforcing role of amplifying the impact of infrastructure development on the product's market share. Therefore, if the feedback effect is valid, the impact of the initial infrastructure development on the product's competitive superiority can be underestimated in the existing economic model based only on the consumer choice theory. This study developed a new demand forecasting methodology by effectively integrating the econometrics model and system dynamics model. The developed forecasting model can consider the feedback effect that was difficult to be applied in the existing economics model through applying system dynamics methodology and can have a sound theoretical background by developing the causal loop diagram which is based on the proven economic model. Furthermore, the application of the developed model to the green car area led to a significant result in the R&D and infrastructure development viewpoint that can be used as a reference for successful deployment strategy of green car technologies. The developed model can be applied in other studies of successful dissemination strategies or policies of innovative technologies. Furthermore, the result of this study can be more useful if the forecasting results can be used in the studies of social, economic, energy and/or environmental impact according to the diffusion of innovative technologies.