一、中文部份
《資料參考》
1.王慶瑞,1999,「運輸系統規劃增訂版」,正揚出版社,頁297-337
2.李施琪,1999,「多變量分析」,華泰書局,頁410-412
3.李書齊,2006,「電信業者要清楚未來敵人面相」,今週刊,8月刊,頁78-806.
4.吳萬益,2000,「企業研究方法」二版,華泰書局,頁283-284
5.林聰明,1981,「指數平滑法之選擇與應用」,華泰書局,頁265-276
6.林正峰,2004,「全球電信業者加速布局3G這回來真的」,數位時代,92期,頁68-717.宋彥青,2002,行動電話消費行為之研究—業者、費率方案與使用量混合需求模式,成功大學交通管理科學研究所碩士論文,民國91年8.段良雄, 1989,「運輸規劃與決策」, 國立成功大學社會科學學報,2期, 頁1-159.陳仲興,2006,「鴻海律師林羿成轉業賣數位音樂」,今週刊,7月刊,頁86-88
10.陳志偉,2001,第三代行動電話市場區隔與轉移意願之研究—以台南地區大學生族群為例,成功大學交通管理科學研究所碩士論文11.張嘉訓,2002,高科技產品多代擴散模型之研究—以DRAM為例,真理大學管理科學研究所碩士論文12.劉佳穎,2003,國產汽車市場佔有率預測模型之研究,長庚大學企業管理研究所碩士論文13.羅玳珊,2006(a),「掀起3G時代序幕」,台灣通訊雜誌,4月,頁14-16
14.羅玳珊,2006(b),「整合通訊興起網通大廠各自出擊」,台灣通訊雜誌,4月,頁49-51。
15.羅玳珊,2006(c),「3G業者積極推廣應用發展各有巧妙」,台灣通訊雜誌,8月,頁40-41
16.羅海資,2006,「3G侵攻戰現況解析—探訪威寶電信總經理王柏堂」,台灣通訊雜誌,8月,頁12-14
17.謝雅婷,2004,「行動電話加值服務3G在手科技運用一手掌握」,卓越雜誌,第244期,頁84-8《網路參考》
1. 中華電信網站http://www.cht.com.tw/
2. 手機王網站http://www.sogi.com.tw/index.asp/
3. 內政部統計處 http://www.moi.gov.tw/stat
4. 台灣大哥大網站http://www.tcc.net.tw/main/
5. 台灣經濟研究院產經資料庫http://tie.tier.org.tw/tie/index.
6. 交通部電信總局全球資訊網 http://www.dgt.gov.tw/
7. 東方消費者行銷資料庫(E-ICP, Eastern Integrated Consumer Profile) http://www.isurvey.com.tw
8. 科技產業資訊網 http://cdnet.stpi.org.tw/techroom/MemberNews.htm
9. 威寶電信網站 http://www.vibo.com.tw/index.jsp/
10. 亞太行動寬頻網站 http://www.apbw.com/
11. 國家通訊傳撥委員會(NCC) http://www.ncc.tw/
12. 國際電信聯盟http://www.asia2002.gov.hk/chinese/main.html
13. 國際數據資訊(IDC) http://www.idc.com.tw/
14. 資策會網站 http://www.find.org.tw/find/home.aspx/
15. 遠傳電信網站 http://www.fetnet.net/
16. 聯合新聞網 http://udn.com/NEWS/mainpage.shtml/
二、英文部分
1. Akaike, H., 1970, Statistical predictor identification, Annals Instit, Stat. Math, 20, 203-217.
2. Akaike, H., 1974, A new look at statistical model identification, IEEE Trans. Auto. Control, 19, 716-723.
3. Atsushi, I., 2005, Estimating demand for cellular phone services in Japan. Telecommunications Policy, 29(1), 3-17.
4. Bass, F.M., 1969, A new product growth for model consumer durables, Management Science (pre-1986), 15(5), 215-227.
5. Birke, D. and Swann, G.M., 2004, Network effects in mobile telecommunications: An empirical analysis, JEL classication:D12, L96, M31, 1-29.
6. Danaher, P.J., Hardie, J.R., Bruce G.S. and Putsis, W.P., 2001, Marketing-mix variables and the diffusion of successive generations of a technological innovation, Journal of Marketing Research, 38(4), 501-51.
7. Doganoglu, T. and Grzybowski, L., 2005, Estimating network effects in mobile telephony in Germany, Journal of Economic Literature Classification, 96(13), 1-22.
8. Engle, R.F. and Brown, S., 1995, “Model selection for forecasting”, Journal of Computation in Statistics, vol. 51, 341-365.
9. Engle, R.F., Granger C.W., Rice J. and Weiss A., 1986, Semi-parametric estimates of the relation between weather and electricity sales, J. Amer. Stat. Assoc, 81.
10. Fisher, J.C. and Pry, R.H., 1971, A simple substitution model of technological change, Technological Forecasting and Social Change, 3, 75-88.
11. Fourt, L.A. and Woodlock, J.W., 1960, Early prediction of market success for new grocery products, Journal of Marketing, 25(2), 31-38.
12. Graven, P. and Wahba, A., 1979, Smoothing noisy data with spline functions: estimating the correct degree of smoothing by the method of generalized cross-validation, Numer Math, 31, 377-403.
13. Ganesh, J., Arnold, M.J. and Reynolds, K.E., 2000, Understanding the customer base of service providers: An examination of the differences between switchers and stayers, Journal of Marketing, 64(3), 65-87.
14. Hannan, E. J. and Quinn B., 1979, The determination of the order of an autoregression, J. Royal Stat. Society, Series b 41, 190-195.
15. Iimi, A., 2005, Estimating demand for cellular phone services in Japan, Telecommunications Policy, 29, 3-23.
16. Islam, T. and Meade, N., 1997, The diffusion of successive generations of a technology: a more general model, Technological Forecasting and Social Change, 56(2), 49-60.
17. Jun, D.B. and Park, Y.S., 1999, A choice-based diffusion model for multiple generations of products, Technological Forecasting and Social Change, 61(1), 45-58.
18. Jun, D.B., Kim, S.K., Park, M.H., Bac, M.S., Park, Y.S. and Joo, Y.J., 2000, Forecasting demand for low earth orbit mobile satellite service in Korea, Telecommunication Systems, 14, 311-319.
19. Jun, D.B., Kim, S.K., Park, Y.S., Park, M.H. and Wilson, A.R., 2002, Forecasting telecommunication service subscribers in substitutive and competitive environments, International Journal of Forecasting, 18, 561-581.
20. Kim, H.S. and Kwon, N., 2003, The advantage of network size in acquiring new subscribers:a conditional logit analysis of the Korean mobile telephony market, Information Economics and Policy, 15, 17-33.
21. Kim, N., Chang, D.R. and Shocker, A.D., 2000, Modeling intercategory and generational dynamics for a growing information technology industry, Management Science, 46(4), 496-512.
22. Lederer, A.L., Maupin, D.J., Sena, M.P. and Zhuang, Y., 2000, The technology acceptance model and the world wide web, Decision Support System, 29, 269-282.
23. Kim, Y.B., SEO, S.Y. and LEE, Y.T., 1999, A substitution and diffusion model with exogenous impact: forecasting of IMT-2000 subscribers in Korea, IEEE, 99, 948-952.
24. Mahajan, V. and 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.
25. Mansfield, E., 1961, Technical change and the rate of imitation, Econometrica (pre-1986), 29(4), 741-766.
26. Norton., J.A. and Bass, F.M., 1987, A diffusion theory model of adoption and substitution for successive generations of high technology products, Management Science, 33(9), 1069-1082.
27. Nunnally, J.C. and Bernstein, I.H., 1994, New York: McGraw-Hill, Psychometric Theory 3rd E.
28. Rice, J., 1984, Bandwidth choice for nonparametric Kernel regression, Annals of Stat, 12, 1215-1230.
29. Rodini, M., Ward, M.R. and Woroch, G.A., 2002, Going mobile:substitutability between fixed and mobile access, Competition in Wireless: Spectrum Service and Technology Wars, 1-30.
30. Schwarz, G., 1978, Estimating the dimension of a model, Annals of Stat, 6.
31. Shibata, R., 1981, An optimal selection of regression variables, Biometrika, 68.
32. Schmittlein, D.C. and Mahajan, V., 1982, Maximum likelihood estimation for an innovation diffusion model of new product acceptance, Marketing Science, 1(1), 57-78.
33. Srinivasan, V. and Mason, C.H., 1986, Nonlinear least squares estimation of new product diffusion model, Marketing Science, 5(2), 169-178.
34. Speece, M.W. and Maclachlan, D.L., 1995, Application of a multi-generation diffusion model to milk container technology. Technological Forecasting and Social Change, 49(3), 281-295.
35. Teo, T. and Tan, M., 1998, An empirical study of adopters and non-adopters of the internet in Singapore, Information and Management, 34(6), 339-345
36. Tishler, A., Ventura, R. and Watters, M., 2001, Cellular telephones in the Israeli market:the demand, the choice of provider and potential revenues, Applied Economics, 33, 1479-1492
37. Train, K.E., Akiva, M.B. and Atherton, T., 1989, Consumption patterns and self-selecting tariffs, The Review of Economics and Statistics, 71(1), 62-73.
38. Tailor, B., 2008, research of innovative information application, FORRESTER http://www.forrester.com/rb/research.