科技不斷創新、產品生命週期不斷縮短，因此正確的進行市場預測，將會降低公司投資的風險以及協助行銷策略的擬定。Bass(1969)擴散模型是最先被提出的擴散模型之一，在創新產品銷售之配適及銷售預測能力一向不錯。後續亦有相關學者針對Bass擴散模型進行修正，以加強其配適與預測能力，但這僅限於單代產品之研究。為了因應產品的快速發展與淘汰，更有多代擴散模型之提出來進行多代產品之銷售預測，其中最為著名的即為Norton and Bass(1987)的多代擴散模型，但此模型卻無法探討產品間的重複購買者及跳躍使用者之間的參數變化。
因此本研究以全球15吋、17吋及19吋TFT-LCD monitor以及64M、128M、256M DRAM的多代擴散為研究對象，將價格函數以及市場成長率加入市場潛量，討論其對跳躍使用者及重複購買者之影響，並比較其對銷售量之影響情況。另外放寬擴散模型之擴散係數皆為固定的假設。本研究以SAS統計軟體之非線性最小平方法作為資料分析的工具。
本研究發現加入價格及市場成長率兩個變數之後，模型的預測更為準確，在LCD monitor這項產品上，重複購買者佔了總體的21％，在DRAM方面，重複購買者則佔了更高的比例，高達32％。另外修正擴散係數皆為固定的假設，發現修正後的模型其配適能力更優於係數皆為固定的假設。因此廠商可根據此結果制定更好的行銷策略，以提高銷售量及做更好的銷售預測。

Owing to the innovative technology and shortening product life cycle, an accurate market forecast is necessary for it can lower the investment risks and help to make the sales policies. Bass’s diffusion model, one of the first issued diffusion models, is excellent in the sales fitness and forecast of innovative products. Though different scholars revised Bass’s diffusion model to reinforce its fitness and forecast ability, it is limited to the study of one-generation products. To meet the fast development and weeding out of products, more multi-generation diffusion models are issued to carry out the forecasts of multi-generation product sales. Of all these multi-generation diffusion models, Norton and Bass’s model is the most famous. However, it cannot probe into the parameter variations between repeat purchasers and leap frogs.
This study focuses on the multi-generation diffusion models of 15inch, 17inch, 19 inch TFT-LCD monitor, 64M, 128M and 256M DRAM. Price function, market growth rate and market potential are included to discuss its impact on leapfrogs, repeat purchasers and volume of sales. In addition, the diffusion coefficients of diffusion models are not fixed. The instrument of data analysis is SAS software.
This study discovers that the forecasts of models are more accurate after adding two variables– price and market growth rate. Repeat purchasers are comprised of 18 % of all the LCD monitor buyers. A high rate of 32% repeat purchasers is found in DRAM buyers. What’s more, the fitness properties of unfixed diffusion coefficients are better than those of fixed ones. Thus, trade sales can make better sales polices and forecasts to raise the volume of sales according to the result.

Bass, F. M. (1969). A New Product Growth for Model Consumer Durables. Management Science, 15(5), 215-227.
Bass, P. I., & Bass, F. M. (2001). Diffusion of technology generations: A model of adoption and repeat sales. Frisco, TX.: Bass Economics, Inc.
Bayus, B. L. (1987). Forecasting sales of new contingent products: an application to the compact disc market. Journal of Product Innovation Management, 4, 243-255.
Chanda, U., & Bardhan, A. K. (2008). Modelling innovation and imitation sales of products with multiple technological generations. The Journal of High Technology Management Research, 18(2), 173-190.
Danaher, P. J., Hardie, B. G. S., & Putsis, W. P. (2001). Marketing-mix variables and the diffusion of successive generations of a technological innovation. Journal of Marketing Research, 38(4), 501-514.
Dataquest,from http://www.dataquest.com
Digitimes Research(2009).2010年全球LCD Monitor產業及市場展望.Retrieved April,20,from http://www.digitimes.com.tw/tw/rpt/rpt_show.asp?cnlid=3&pro=y&proname=%AD%AB%ADn%B7~%AA%CC&cat=CMP&v=20091119-475
DRAMexchange,from http://www.dramexchange.com/
Fisher, J. C., & Pry, R. H. (1971). A simple substitution model of technological change. Technological Forecasting and Social Change, 3(1), 75-88.
IC insight,from http://www.icinsights.com/
Islam, T., & Meade, N. (1997). The diffusion of successive generations of a technology: A more general model. Technological Forecasting and Social Change, 56(1), 49-60.
Islam, T., & Meade, N. (2006). Modelling and forecasting the diffusion of innovation—A 25 year review. International Journal of Forecasting, 22, 519-545.
Jaakkola, H., Gabbouj, M., & Neuvo, Y. (1998). Fundamentals of technology diffusion and mobile phone case study. Circuits, Systems, and Signal Processing, 17(3), 421-448.
Jain, D., Mahajan, V., & Muller, E. (1991). Innovation diffusion in the presence of supply restriction. Marketing Science, 10, 83-90.
Jain, D. C., & Rao, R. C. (1990). Effect of price on the demand for durables: Modelling, estimation and findings . Journals of Business and Economics Statistics, 8, 163-170.
Jones, J. M., & Ritz, C. J. (1991). Incorporating distribution into new product diffusion models. International Journal of Research in Marketing, 8, 91-112.
Kalish, S. (1985). A New Product Adoption Model with Price, Advertising, and Uncertainty . Management Science, 31, 1569-1585.
Kalish, S., & Lilien, G. L. (1986). A market entry timing model for new techniques. Management Science 32(2), 194-205.
Kamakura, W. A., & Balasubramanian, S. K. (1988). Long-term view of the diffusion of durables: a study of the role of price and adoption influence processes via tests of nested models. International Journal of Research in Marketing, 5(1), 1-13.
Kamakura, W. A., & Ealasubramanian, S. K. (1987). Long-term forecasting with innovation diffusion models: the impact of replacement purchase. Journal of forecasting, 6(1), 1-19.
Kim, N., Chang, D. R., & Shocker, A. D. (2000). Modeling intercategory and generational dynamics for a growing information technology industry. Management Science, 46(4), 496-512.
Kim, N., & Srivastava, R. K. (2007). Modeling cross-price effects on inter-category dynamics: The case of three computing platforms. Omega-International Journal of Management Science, 35(3), 290-301.
Kim, W. J., Lee, J. D., & Kim, T. Y. (2005). Demand forecasting for multigenerational products combining discrete choice and dynamics of diffusion under technological trajectories. Technological Forecasting & Social Change, 72(7), 825-849.
Kreng, V. B., & Wang, H. T. (2009). A technology replacement model with variable market potential--An empirical study of CRT and LCD TV. Technological Forecasting and Social Change, 76(7), 942-951.
Liikanen, J., Stoneman, P., & Toivanen, O. (2004). Intergenerational effects in the diffusion of new technology: the case of mobile phones. International Journal of Industrial Organization, 22(8-9), 1137-1154.
Mahajan, V., & Muller, E. (1996). Timing, diffusion, and substitution of successive generations of technological innovations: The IBM mainframe case. Technological Forecasting and Social Change, 51(2), 109-132.
Mahajan, V., Muller, E., & Bass, F. M. (1990). New Product Diffusion Models in Marketing: A Review and Directions for Research. Journal of Marketing Research, 54, 1-26.
Mahajan, V., & Peterson, R. A. (1978). Innovation Diffusion in a Dynamic Potential Adopter Population. Management Science, 24(15), 1589-1597.
Mahajan, V., & Peterson, R. A. (1979). Integrating time and space in technological substitution models. Technological Forecasting and Social Change, 14, 231-241.
Mahajan, V., & Peterson, R. A. (1985). Models for innovation diffusion. Newbury Park, CA: Sage Publications Inc.
Martin, C., & Witt, S. (1989). Forecasting tourism demand: a comparison of the accuracy of several quantitative methods. International Journal of Forecasting, 5(1), 7-19.
Norton, J. A., & Bass, F. M. (1987). A Diffusion Theory Model of Adoption and Substitution for Successive Generations of High Technology Products. Management Science, 33(9), 1069-1086.
Pae, J. H., & Lehmann, D. R. (2003). Multigeneration innovation diffusion: The impact of intergeneration time. Journal of the Academy of Marketing Science, 31(1), 36-45.
Robinson, B., & Lakhani, C. (1975). Dynamic Price Models for New-Product Planning. Management Science, 21(10), 1113-1122
Rogers, E. M. (1962). Diffusion of Innovation. New York: The Free Press
Rogers, E. M. (1983). Diffusion of Innovations (3rd ed.). New York: The Free Press.
Schmittlein, D. C., & Mahajan, V. (1982). Maximum likelihood estimation for an innovation diffusion model of new product acceptance. Marketing Science, 1(1), 57-78.
Speece, M. W., & Maclachlan, D. L. (1995). Application of a Multi-Generation Diffusion Model to Milk Container Technology. Technological Forecasting and Social Change, 49(3), 281-295.
Srinivasan, V., & Mason, C. H. (1986). Nonlinear least squares estimation of new product diffusion models. Marketing Science, 5(2), 169-178.
Wang, F.-K., & Chang, K.-K. (2009). Modified diffusion model with multiple products using a hybrid GA approach. Expert Systems With Applications, 36(10), 12613-12620.
工研院產業經濟與資訊服務中心, http://iek.itri.org.tw/
王建華等作 (2007). 半導體工業年鑑. 新竹縣竹東鎮: 工研院經資中心.
王建華等作 (2009). 半導體工業年鑑. 新竹縣竹東鎮: 工研院經資中心.
拓墣產業研究所 (2002). 半導體市場動態解析. 臺北市: 拓墣科技.
拓墣產業研究所 (2003). 消費性半導體前景剖析. 臺北市: 拓墣科技.