Loading...
|
Please use this identifier to cite or link to this item:
https://nccur.lib.nccu.edu.tw/handle/140.119/38388
|
| Title: | 行動電話擴散研究之模型選用及驅動因子分析
Model selection and driving forces for mobile telephony diffusion |
| Authors: | 朱文伶
Chu, Wen Lin |
| Contributors: | 吳豐祥
Wu, Feng Shang
朱文伶
Chu, Wen Lin |
| Keywords: | 行動電話
技術擴散
技術預測
驅動因子
低階市場
電信政策
mobile telephony
technology diffusion
technology forecast
driving force
low-end market
telecommunication policy |
| Date: | 2009 |
| Issue Date: | 2010-04-08 16:10:54 (UTC+8) |
| Abstract: | 全球行動電話用戶數於2002年達到12億，首度超過固定電話用戶數之11億；行動電話用戶數並於2008年達41億，為固定電話用戶數(13億)之3倍以上。行動電話相對於固定電話之主要優勢在於系統之建置成本低及佈建速度快；行動電話之快速普及已成為創新擴散研究之重要題材。
行動電話擴散之研究為選取一成長模型(例如Gompertz、Logistic或Bass模型)並類比該模型以求出擴散之參數(例如成長速率)，以進一步(1)了解相關驅動因子(例如技術創新、市場開放等)對擴散參數之影響，及(2)延伸擴散模型曲線以預測未來之成長。
惟成長模型之選取尚無原理原則可供遵循而具隨機性(ad hoc basis)。為找出模型選用之可能規律，以降低模型選用之隨機性並提高成長預測之準確度，本研究以十二個代表性國家(巴、中、法、德、印、日、韓、俄、星、台、英、美)至2007年之資料以比較三個最常用之成長模型之績效，即Gompertz、Logistic及Bass模型。模型績效逐年比較標準係採用rmse值，並輔以Friedman test檢測模型績效差異之顯著性，再對照模型之機制意涵，以進一步了解最適模型之選用原則。
此外，台灣行動電話普及率於2002年為108%居全球之冠，而中國自2001年起取代美國成為全球具最多行動電話用戶數之單一國家，台灣及中國屬行動電話擴散之重要個案，惟目前尚缺此二個案之實證研究。為補足此一缺口，本研究亦對台灣及中國行動電話擴散之驅動因子進行實證研究，以進一步了解擴散之關鍵驅動力。
研究發現由於目前統計軟體之進步，Gompertz、Logistic及Bass三模型均可獲致極佳之匹配度而難分軒輊，惟模型預測力(延伸曲線)則具差異性。12個模型選用樣本國家中之8個國家(巴、中、法、德、日、韓、英、美)係以Gompertz模型具較佳之預測力；依Gompertz模型機制意涵，代表行動電話擴散早期係受網路外部性(口耳相傳)影響，惟至擴散後期(例如過了擴散極大值之一半)則已不相關。此外，若因市場開放等重大變因造成行動電話之快速擴散，則Logistic模型具有較佳之績效，如台灣及俄羅斯屬之。依Logistic模型機制意涵，代表擴散係受網路外部性所影響。Bass模型應用於行動電擴散時，因該模型所算出之創新係數偏低，績效與Logistic模型相近，而Logistic模型為Bass模型之創新係數為0時之特例。
台灣及中國行動電話擴散之驅動因子研究發現(1)價格下降及(2)預付卡之推行對加速擴散具顯著性，兩者均對低階市場之採用具影響力。鑑於高階市場將先飽和，爰未來加速行動電話擴散之關鍵驅動因子應係與推動低階市場採用具密切相關性。以中國為例，未來市場開放競爭造成價格再度大幅下降，將進一步促低階市場採用，加速中國行動電話之普及。
The number of mobile telephone subscriptions reached 1.2 billion globally in 2002, exceeding fixed-line telephony subscriptions (1.1 billion) for the first time. The number of mobile telephone subscriptions reached 4.1 billion globally in 2008, over three times the number of fixed-line telephone subscriptions (1.3 billion). The main advantages of mobile telephony over fixed-line are low cost and rapid facility deployment. The rapid diffusion of mobile telephony has become an important topic in innovation diffusion.
The conventional approach to studying mobile telephony diffusion is to analogize a single growth model, such as the Gompertz, Logistic or Bass model, and calculate the model parameters, for example growth rate. The significance of certain selected driving forces, such as technology innovation or market competition, to the studied parameters, such as growth rate, is then estimated. The diffusion growth can also be forecast by extrapolating the diffusion curve.
Utilizing the growth model analogy is the first step in analyzing mobile telephony diffusion. However, no principles or rules exit for selecting a growth model. To identify rules for model selection to reduce randomness and increase forecast accuracy, this work uses 12 sample countries, namely Brazil, China, France, Germany, India, Japan, Korea, Russia, Singapore, Taiwan, the UK and the USA, employing data prior to 2008 to compare the performance of three most commonly used models, namely the Gompertz, Logistic and Bass models. The root mean square error (rmse) is chosen as the criterion for measuring annual model performance. The work uses the Friedman test to examine the significance of differences in performance between models. The implications of model mechanisms are emphasized to investigate the selection rule for the most appropriate model.
The penetration of mobile telephony in Taiwan was 108% in 2002, ranking first in the world. Furthermore, in 2001 the number of mobile telephony in China replaced the United States as number one in the world. Both Taiwan and China are important examples for mobile telephony diffusion. However, no empirical investigation has been performed in these two cases. To fill this gap, this work estimated the driving forces for mobile telephony diffusion in Taiwan and China to learn about the critical drivers of the mobile telephony diffusion.
Empirical results indicate that due to improvements in statistical software, providing good fitness for all three models, namely the Gompertz, Logistic and Bass models, distinguishing which has the best fitness is difficult. However, the performance of the three models is distinguishable when forecasting based on extrapolating the diffusion curve. In eight of the 12 examples, namely Brazil, China, France, Germany, Japan, Korea, the UK and the USA, the Gompertz model is the most appropriate model for forecasting. The mechanism of the Gompertz model means that during the initial stage the diffusion is correlated with network externalities (namely word of mouth), however, this correlation reduces during the later stages (such as pass one half of the maximum potential). Moreover, the cases of Taiwan and Russia demonstrated that the Logistic model performs well provided some significant driver of the diffusion exists. The mechanism of the Logistic model means that the diffusion is correlated with network externalities throughout the whole diffusion. Furthermore, using Chinese data, when the Bass model is applied, because of its low innovation coefficient, it performs similarly to the Logistic model, which is a special case of the Bass model in which the innovation coefficient equals zero.
Empirical results for the critical driving forces of mobile telephony diffusion in Taiwan and China indicate that (1) reducing prices and (2) the launch of pre-paid services are crucial to mobile telephony diffusion. Both factors are essential to mobile telephony adoption in low-end markets. The high-end market is the first to be saturated by mobile telephony adoption, future drivers of the mobile telephony diffusion should be highly correlated with low-end market demand. Taking China as an example, the opening of the market to further reduce tariffs will attract mobile telephony adoption in the low-end market, facilitating the mobile telephony diffusion. |
| Reference: | [1] 卜賢琳(2005)，以技術預測方法探討傳統電信網路及網路電話的發展，中原大學資訊管理學系碩士學位論文。
[2] 中央社(1998)，新聞報導：中美WTO入會協議內容：服務業，2月21日。 http://www.cna.com.tw。
[3] 中華人民共和國工業和信息化部(MIIT)(2008)，2007年全國通信業發展統計公報，北京，中國。
[4] 中華人民共和國國家統計局(MIIT)(2009)，2008年國民經濟和社會發展統計公報，北京，中國。
[5] 中國互聯網絡信息中心(2009)，中國互聯網絡發展狀況統計報告，北京，中國。
[6] 王碧蓮(2002)，WTO電信服務業談判回顧與展望，通訊雜誌，105期，32–33。
[7] 沈明來(2007)，實用無母數統計學，台北：九州。
[8] 林清山(2000)，多變項分析統計法，台北：東華書局。
[9] 柳卸林(2008)，全球化、追趕與創新，北京：科學出版社。
[10] 高凱聲(1993)，台灣電信與經濟發展，台北：華泰文化事業。
[11] 高凱聲(2001)，我國電信自由化之檢討，通訊工業綜論，5-1–11。
[12] 高凱聲(2008a)，電腦網路犯罪之防制，台灣網路資訊中心(TWNIC) ePaper, 2008(1)。
[13] 高凱聲(2008b)，網路經營與網路經濟理論分析，台灣網路資訊中心(TWNIC) ePaper, 2008。
[14] 高凱聲(2009)，我國網路電話之發展，考試院98年度8月份資訊專題演講。
[15] 徐玉學(2007)，東亞發展中經濟體行動電話產業政策與研發策略之比較研究，交通大學經營管理研究所博士學位論文。
[16] 陳釧瑤(2008)，美國行動通訊服務市場發展現況，資策會MIC，台北，台灣。
[17] 陳小洪、馬駿、何霞(2007)，移動通信革命，北京：北京郵電大學出版社。
[18] 國家通訊傳播委員會(NCC)(2007)，95年通訊傳播績效報告，台北，台灣。
[19] 黃俊英(2000)，多變量分析，台北：華泰文化事業。
[20] 楊奕農(2007)，時間序列分析，台北：雙葉書廊。
[21] 張清溪、許嘉棟、劉鶯釧、吳聰敏(1995)，經濟學：理論與實際，第三版，台北：翰蘆圖書。
[22] 電信總局(2000)，我國電信自由化效益分析 (計畫編號：MOTC-DGT-89-003，執行單位：中華經濟研究院)，台北，台灣。
[23] 電信總局(2000)，電信總局 1999年報，台北，台灣。
[24] 電信總局(2005)，電信總局 2004年報，台北，台灣。
[25] 電信總局(2006)，電信總局 2005年報，台北，台灣。
[26] 賴奎魁、洪世章、施育地、李俊億(2005)，非法藥品對合法藥品銷售績效影響之研究，交大管理學報，25卷2期，141–170。
[27] 戴紹琪(2001)，OECD及台灣之行動電話用戶數擴散的時間軌跡研究，臺灣大學經濟學研究所碩士學位論文。
[28] Agarwal, R. and Bayus, B. L. (2002). The market evolution and sales takeoff of product innovations. Management Science, 48(8), 1024–1041.
[29] Ahn, H. and Lee, M. (1999). An econometric analysis of the demand for access to mobile telephone networks. Information Economics and Policy, 11, 297–305.
[30] Allen, D. (1988). New telecommunications services network externalities and critical mass. Telecommunications Policy, 257–271.
[31] Barnett, R. A., Ziegler, M. R. and Byleen, K. E. (2000). Applied Calculus. New Jersey: Prentice Hall.
[32] Bass, F. M. (1969). A new-product growth model for consumer durables. Management Science, 215–227.
[33] Blackman, C., Cave, M. and David, P. A. (1996). The new international telecommunications environment. Telecommunications Policy, 20(10), 721–724.
[34] Boretos, G. P. (2007). The future of the mobile phone business. Technological Forecasting and Social Change, 74, 331–340.
[35] Buckley, S. (2007). 3G civil war breaks out in U.S. Telecommunications, 41(7), 26–27.
[36] Casella, G. and Berger, R. L. (2002). Statistical Inference, 2nd ed. CA: Duxbury.
[37] Chow, G. C. (1967). Technological change and the demand for computers. The American Economic Review, 57(5), 1117–1130.
[38] Chowdary, T. H. (1998). Politics and economics of telecom liberalization in India. Telecommunications Policy, 22(1), 9–22.
[39] Chowdary, T. H. (1998). Telecom liberalization and competition in developing countries. Telecommunications Policy, 22(4/5), 259–265.
[40] Conover, W. J. (1999). Practical Nonparametric Statistics, 3rd Ed. New York: Wiley.
[41] Crocioni, P. and Veljanovski, C. (1999). Pricing calls to mobiles: analysis of the UK monopolies & mergers commission reports on mobile terminatin charges. Telecommunications Policy, 23, 539–555.
[42] Curtis, T. (1997). Japanese telecommunication reform and the standards-setting process. Telecommunications Policy, 21(2), 165–176.
[43] de Bijl, P. and Peitz, M. (2009). Access regulation and the adoption of VoIP. Journal of Regulatory Economics, 35, 111–134.
[44] Dekimpe, M. G., Parker, P. M. and Sarvary, M. (1998). Staged estimation of international diffusion models. Technological Forecasting and Social Change, 57, 105–132.
[45] Dobrovolskaya, N. and Saluena, A. (2004). Development of Russian mobile communications. Lappeenranta University of Technology, Northern Dimension Research Centre, Publication 9.
[46] Dokeniya, A. (1999). Re-forming the state: telecom liberalization in India. Telecommunications Policy, 23, 105–128.
[47] Easingwood, C. Mahajan, V. and Muller, E. (1983). A nonuniform influence innovation diffusion model of new product acceptance. Marketing Science, 2(3), 273–295.
[48] Enders, W. (2004). Applied Econometric Time Series, 2nd Ed. New York: Wiley.
[49] Farrell, J. and Saloner, G. (1985). Standardization, compatibility, and innovation. Rand Journal of Economics, 16(1), 70–83.
[50] Farrell, J. and Saloner, G. (1986). Installed base and compatibility: innovation, product preannouncements, and predation. American Economic Review, 76(5), 940–955.
[51] Fildes, R., Nikolopoulos, K., Crone, S. F. and Syntetos, A. A. (2008). Forecasting and operational research: a review. The Journal of the Operational Research Society, 59(9), 1150–1172.
[52] Fischer, C. S. (1992). America Calling: A Social History of the Telephone to 1940. Berkeley: University of California Press. GS(E)
[53] Fisher, J. C. and Pry, R. H. (1971). A simple substitution model of technological change. Technological Forecasting and Social Change, 3, 75–88.
[54] Fong, M. (2007). The mobile phone telecommunications service sector in China. Journal of Electronic Commerce in Organizations, 3(4), 19–38.
[55] Foreman, R. D. and Beauvais, E. (1999). Scale economies in cellular telephony: size matters. Journal of Regulatory Economics, 16(3), 297–306.
[56] Forestier, E., Grace, J. and Kenny, C. (2002). Can information and communication technologies be pro-poor? Telecommunications Policy, 26, 623–646.
[57] Frank, L. D. (2004). An analysis of the economic situation on modeling and forecasting the diffusion of wireless communications in Finland. Technological Forecasting and Social Change, 71, 391–403.
[58] Friedman, R. S., Roberts, D. M. and Linton, J. D. (2008). Principle Concepts of Technology and Innovation Management: Critical Research Models. PA: IGI Global.
[59] Fu, W. (2004). Termination-discriminatory pricing, subscriber bandwagons, and network traffic patterns: the Taiwanese mobile phone market. Telecommunications Policy, 28, 5–22.
[60] Fullerton, H. (1998). Duopoly and competition: the case of American cellular telephone. Telecommunications Policy, 22(7), 593–607.
[61] Ganesh, J., Kumar, V. and Subramaniam, V. (1997). Learning effect in multinational diffusion of consumer durables: an exploratory investigation. Academy of Marketing Science, 25(3), 214–228.
[62] Geroski, P. A. (2000). Models of technology diffusion. Research Policy, 29, 603–625.
[63] Gerpott, T. J., Rams, W. and Schindler, A. (2001). Customer retention, loyalty, and satisfaction in the German mobile cellular telecommunications market. Telecommunications Policy, 25, 249–269.
[64] Gibbons, J. D. and Chakraborti, S. (2003). Nonparametric Statistical Inference, 4th Ed. New York: Marcel Dekker.
[65] Givon, M, Mahajan, V. and Muller, E. (1995). Software piracy: estimation of lost sales and the impact on software diffusion. Journal of Marketing, 59, 29–37.
[66] Golder, P. N. and Tellis, G. J. (1997). Will it ever fly? Modeling the takeoff of really new consumer durables. Marketing Science, 16(3), 256–270.
[67] Gopalakrishnan, S., Wischnevsky, J. D. and Damanpour, F. (2003). A multilevel analysis of factors influencing the adoption of Internet banking. IEEE Transactions on Engineering Management, 50(4), 413–426.
[68] Graham, T and Ure, J. (2005). IP telephony and voice over broadband. Info: the Jouranl of Policy, Regulation and Strategy for Telecommunications, 7(4), 8–20.
[69] Griliches, Z. (1957). Hybrid corn: an exploration in the economics of technological change. Econometrica, 25(4), 501–522.
[70] Groebel, A. (2003). Should we regulate any aspects of wireless? Telecommunications Policy, 27, 435–455.
[71] Gruber, H. and Verboven, F. (2001a). The diffusion of mobile telecommunications services in the European Union. European Economic Review, 45, 577–588.
[72] Gruber, H. and Verboven, F. (2001b). The evolution of markets under entry and standards regulation: the case of global mobile telecommunications. International Journal of Industrial Organization, 19, 1189–1212.
[73] Gujarati, D. N. (2003). Basic Econometrics, 4th ed. New York: McGraw-Hill.
[74] Hair, J. F., Black, W. C., Babin, B. J. and Anderson, R. E. (2010). Multivariate Data Analysis, 5th Ed., New Jersey: Pearson Prentice Hall.
[75] Hamdouch, A. and Samuelides, E. (2001). Innovation’s dynamics in mobile phone services in France. European Journal of Innovation Management, 4(3), 153–162.
[76] Hamilton, J. (2003). Are main lines and mobile phones substitutes or complements? evidence from Africa. Telecommunications Policy, 27, 109–133.
[77] Hardie, B. G. S., Fader, P. S. and Wisniewski, M. (1998). An empirical comparison of new product trial forecasting models. Journal of Forecasting, 17, 209–229.
[78] Heeler, R. M. and Hustad, T. P. (1980). Problems in predicting new product growth for consumer durables. Management Science, 26(10), 1007–1020.
[79] Hodge, J. (2005). Tariff structures and access substitution of mobile cellular for fixed line in South Africa. Telecommunications Policy, 29, 493–505.
[80] Hogg, R. V. and Tanis, E. A. (2006). Probability and Statistical Inference, 7th ed. NJ: Pearson.
[81] Hollander, M. and Wolfe, D. A. (1999). Nonparametric Statistical Methods, 2nd Ed. New York: Wiley.
[82] Iimi, A. (2005). Estimating demand for cellular phone services in Japan. Telecommunications Policy, 29, 3–23.
[83] International Telecommunications Union (ITU). (1999). World Telecommunication Development Report 1999. Mobile Cellular. Executive Summary. Geneva, Switzerland.
[84] International Telecommunication Union (ITU). (2007). Yearbook of Statistics–Telecommunication Services (Chronological Time Series 1996–2005). Geneva, Switzerland.
[85] International Telecommunication Union (ITU). (2008). World Telecommunication/ICT Indicators 2007. Geneva, Switzerland.
[86] International Telecommunication Union (ITU). (2009). Measuring the Information Society The ICT Development Index. Geneva, Switzerland.
[87] Ishii, K. (2004). Internet use via mobile phone in Japan. Telecommunications Policy, 28, 43–58.
[88] Islam, T., Fiebig, D. and Meade, N. (2002). Modeling multinational telecommunications demand with limited data. International Journal of Forecasting, 18, 605–624.
[89] Jang, S., Dai, S. and Sung, S. (2005). The pattern and externality effect of diffusion of mobile telecommunications: the case of the OECD and Taiwan. Information Economics and Policy, 17, 133–148.
[90] Johnson, R. A. and Wichern, D. W. (1998). Applied Multivariate Statistical Analysis, 4th ed. NJ: Prentice-Hall.
[91] Katz, M. L. and Shapiro, C. (1985). Network externalities, competition, and compatibility. The American Economic Review, 75(3), 424–440.
[92] Katz, M. L. and Shapiro, C. (1986). Technology adoption in the presence of network externalities. Journal of Political Economy, 94(4), 822–841.
[93] Kiiski, S. and Pohjola, M. (2002). Cross-country diffusion of the Internet. Information Economics and Policy, 14, 297–310.
[94] Kim, H., Byun, S. and Park, M. (2004). Mobile handset subsidy policy in Korea: historical analysis and evaluation. Telecommunications Policy, 28, 23–42.
[95] Kim, M. and Kim, H (2004). Innovation diffusion of telecommunications: general patterns, diffusion clusters and differences by technological attribute. International Journal of Innovation Management, 8(2), 223–241.
[96] Kim, J., Lee, D. and Ahn, J. (2006). A dynamic competition analysis on the Korean mobile phone market using competitive diffusion model. Computer & Industrial Engineering, 51, 174–182.
[97] Kohler U. and Kreuter, F. (2005). Data Analysis Using Stata. Texas: Stata Press.
[98] Kosowska, E. (2008). Russia’s mobile telephony market still on course for steady growth. http://www.itandtelecompoland.com.
[99] Lawrence, K. D. and Lawton, W. H. (1981). Applications of diffusion models: some empirical results. In Wind, Mahajan, and Cardozo (ed.), New Product Forecasting: Models and Applications, 529–541. Lexington, MA: Lexington Books.
[100] Lee, M. and Cho, Y. (2007). The diffusion of mobile telecommunications services in Korea. Applied Economics Letters, 14, 477–481.
[101] Lee, S., Choi, J. and Ahn, S. (2007). Development of the mobile communications service market: Korean experience. International Journal of Electronic Business, 5(5), 533–547.
[102] Lee, D. and Lee, D. (2006). Estimating consumer surplus in the mobile telecommunications market: the case of Korea. Telecommunications Policy, 30, 605–621.
[103] Lee, J., Lee, J and Feick, L. (2001). The impact of switching costs on the customer satisfaction-loyalty link: mobile phone sercie in France. The Journal of Service Marketing, 15(1), 35.
[104] Lekvall, P. and Wahlbin, C. (1973). A study of some assumptions underlying innovation diffusion functions. Swedish Journal of Economics, 362–377.
[105] Levin, R. I. And Rubin, D. S. (1998). Statistics for Management, 7th Ed. New Jersey: Prentice Hall.
[106] Liao, K. (2009). Selecting the evaluation criteria of forecasting performance. The Business Review, Cambridge, 13(2), 318–323.
[107] Liikanen, J., Stoneman, P. and Toivanen, O. (2004). Intergenerational effects in the diffusion of new technology: the case of mobile phones. International Journal of Industrial Organization, 22, 1137–1154.
[108] Maciel, M., Whalley, J. and Meer, R. (2006). Foreign investment and consolidation in the Brazilian mobile telecommunications market. info: the Journal of Policy, Regulation and Strategy for Telecommunications, Information and Media, 8(3), 60–77.
[109] Maeda, T., Amar, A. and Gibson, A. (2006). Impact of wireless telecommunications standards and regulation on the evolution of wireless technologies and services over Internet protocol. Telecommunications Policy, 30, 587–604.
[110] Mahajan, V, Muller, E and Wind, Y. Ed. (2000). New-Product Diffusion Models. New York: Springer.
[111] Mahajan, V. and Peterson, R. (1985). Models for innovation diffusion. Beverly Hills, CA: Sage Publications.
[112] Mansfield, E. (1961). Technical change and the rate of imitation. Econometrica, 29(4), 741–766.
[113] Mansfield, E. (1968). Industrial Research and Technological Innovation: An Econometric Analysis. New York: Norton.
[114] Martino, J. P. (1993). Technological Forecasting for Decision Making (3rd ed.). McGraw-Hill.
[115] McDowell, S. D. and Lee, J. (2003). India’s experiments in mobile licensing. Telecommunications Policy, 27, 371–382.
[116] Meade, N. (1984). The use of growth curves in forecasting market development-a review and appraisal. Journal of Forecasting, 3(4), 429–451.
[117] Meade, N. & Islam, T. (1995). Forecasting with growth curves: an empirical comparison. International Journal of Forecasting, 11, 199–215.
[118] Meade, N. & Islam, T. (1998). Technological forecasting—model selection, model stability, and combining models. Management Science, 44(8), 1115–1130.
[119] Meade, N. and Islam, T. (2001). Forecasting the diffusion of innovations: implications for time series extrapolation. In J. S. Armstrong (Ed.), Principles of Forecasting: A Handbook for Researchers and Practitioners (pp. 577–595). Kluwer Academic Publishers.
[120] Meade, N. and Islam, T. (2006). Modeling and forecasting the diffusion of innovation – A 25-year review. International Journal of Forecasting, 22, 519–545.
[121] Minges, M. (1999). Mobile cellular communications in the Southern African region. Telecommunications Policy, 23, 585–593.
[122] Modis, T. (2007). Strengths and weaknesses of S-curves. Technological Forecasting and Social Change, 74, 866–872.
[123] Montgomery, D. C., Peck, E. A. and Vining, G. G. (2001). Introduction to Linear Regression Analysis, 3rd Ed. New York: Wiley.
[124] Nie, W. and Zeng, H. (2003). The impact of China’s WTO accession on its mobile communications market. Journal of Business and Management, 9(2), 151–170.
[125] Noll, A. M. (1998). The costs of competition: FCC telecommunication orders of 1997. Telecommunications Policy, 22(1), 47–56.
[126] Office of Communications (Ofcom). (2006). The International Communications Market 2006. London, UK.
[127] Opalinska, M. (2007). Russia telecoms market in 2006. http://www.ictrussia.com.
[128] Park, H. and Chang, S. (2004). Mobile network evolustion toward IMT-2000 in Korea: a techno-economic analysis. Telecommunications Policy, 28, 177–196.
[129] Petrazzini, B. (1996). Telecommunciations policy in India: the political underpinning of reform. Telecommunications Policy, 20(1), 39–51.
[130] Pindyck, R. and Rubinfeld, D. (2001). Microeconomics, fifth ed. New Jersey: Prentice Hall.
[131] Porter, A. L., Roper, A. T., Mason, T. W., Rossini, F. A., Banks, J. and Wiederholt, B. J. (1991). Forecasting and Management of Technology. New York: Wiley.
[132] Rappaport, T. (2002). Wireless Communications: Principles and Practice (2nd ed.). Upper Saddle River, NJ: Prentice Hall.
[133] Robinson, B. and Lakhani, C. (1975). Dynamic price models for new-product planning. Management Science, 21(10), 1113–1122.
[134] Rodini, M., Ward, M. R. and Woroch, G. A. (2003). Going mobile: substitutability between fixed and mobile access. Telecommunications Policy, 27, 457–476.
[135] Roller, L.-H. and Waverman, L. (2001). Telecommunications infrastructure and economic development: a simultaneous approach. The American Economic Review, 91(4), 909–923.
[136] Rogers, E. M. (1962). Diffusion of Innovations. New York: The Free Press.
[137] Rogers, E. M. (2003). Diffusion of Innovations (5th ed.). New York: The Free Press.
[138] Rouvinen, P. (2006). Diffusion of digital mobile telephony: are developing countries different? Telecommunications Policy, 30, 46–63.
[139] Ruffin, R. and Gregory, P. Principles of Economics, sixth ed. New York: Addision-Wesley.
[140] Sangwan, S. and Pau, L.-F. (2005). Diffusion of mobile terminals in China. European Management Journal, 23(6), 674–681.
[141] Schelling, T. C. (1978). Micromotives and Macrobehavior. New York: Norton.
[142] 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.
[143] Shy, O. (1995). Industrial Organization. The MIT Press.
[144] Shy, O. (2001). The Economics of Network Industries. Cambridge University Press.
[145] Siegel, S. and Castellan, J. (1988). Nonparametric Statistics, 2nd Ed. New York: McGraw-Hill.
[146] Singh, S. (2008). The diffusion of mobile phones in India. Telecommunications Policy, 32, 642–651.
[147] Stoetzer, M-W and Tewes, D. (1996). Competition in the German cellular market? Telecommunications Policy, 20(4), 303–310.
[148] Stoneman, P. (1983). The Economic Analysis of Technological Change. New York: Oxford University Press.
[149] Stoneman, P. Ed. (1995). Handbook of the Economics of Innovations and Technological Change. Oxford; Cambridge, Mass: Blackwell.
[150] Stoneman, P. (2002). The Economics of Technological Diffusion. Oxford: Blackwell.
[151] Studenmund, A. H. (2006). Using Econometrics: A Practical Guide, 5th ed. Pearson.
[152] Sultan, F., Farley, J. U. and Lehmann, D. R. (1990). A meta-analysis of applications of diffusion models. Journal of Mraketing Research, 27, 70–77.
[153] Sundqvist, S., Frank, L. and Puumalainen, K. (2005). The effects of country characteristics, culture similarity and adoption timing on the diffusion of wireless communications. Journal of Business Research, 58, 107–110.
[154] Sundqvist, S., Frank, L., Puumalainen, K. and Kamarainen, J. (2002). Forecasting the critical mass of wireless communications. Proceedings of ANZMAC 2002 Conference, editors: Robin N Shaw, Stewart Adam, Health McDonald, (CD-ROM) December 2–4. Melbourne, Australia. ISBN 0 7300 2562 4, 7 pages.
[155] Sung, N. and Lee, Y. (2002). Substitution between mobile and fixed telephones in Korea. Review of Industrial Organization, 20, 367–374.
[156] Tabatabai, B. (2008). Improving forecasting. Financial Management, London: Oct, 48–49.
[157] Takada, H. and Jain, D. (1991). Cross-national analysis of diffusion of consumer durable goods in Pacific rim countries. Journal of Marketing, 55(2), 48–54.
[158] Tellis, G. J., Stremersch, S. and Yin, E. (2003). The international takeoff of new products: the role of economics, culture, and country innovativeness. Marketing Science, 22(2), 188–208.
[159] Tidd, J., Bessant, J. and Pavitt, K. (1997). Managing Innovation: Integrating Technological Market and Organizational Change. West Sussex, UK: John Wiley & Sons.
[160] Valente, T. W. (1995). Network Models of the Diffusion of Innovations. New Jersey: Hampton.
[161] Valletti, T. (2003). Is mobile telephony a natural oligopoly? Review of Industrial Organization, 22, 47–65.
[162] Valletti, T. and Cave, M. (1998). Competition in UK mobile communications. Telecommunications Policy, 22(2), 109–131.
[163] Wilde, G. (2000) View from the top: Singapore. Telecommunications, 34(9), 25–26.
[164] Wind, Y., Mahajan, V. and Cardozo, R. N. Ed. (1981). New-Product Forecasting: Models and Application. Lexington, Mass: Lexington Books.
[165] Word Trade Organization (WTO). (2004). Communication from the separate customs territory of Taiwan, Penghu, Kinmen and Matsu. TN/S/W/18.
[166] Wu, F.-S. and Chu, W.-L. (2009). Diffusion models of mobile telephony. Journal of Business Research, doi:10.1016/j.jbusres.2009.04.008.
[167] Yoo, S, Kim, J. and Kim, T. (2001). Value-focused thinking about strategic management of radio spectrum for mobile communications in Korea. Telecommunications Policy, 25, 703–718.
[168] Yuan, Y., Zheng, W., Wang, Y., Xu, Z, Yang, Q. and Gao, Y. (2006). Xiaolingtong versus 3G in China: which will be the winner? Telecommunications Policy, 30, 297–313.
[169] Zhang, X. and Prybutok, V. (2005). How the mobile communication markets differ in China, the U.S., and Europe, Communications of the ACM, 48(3), 111–114. |
| Description: | 博士
國立政治大學
科技管理研究所
90359506
98 |
| Source URI: | http://thesis.lib.nccu.edu.tw/record/#G0903595061 |
| Data Type: | thesis |
| Appears in Collections: | [科技管理研究所] 學位論文
|
Files in This Item:
| File |
Size | Format | |
|---|
| 59506101.pdf | 2405Kb | Adobe PDF2 | 2101 | View/Open |
|
All items in 政大典藏 are protected by copyright, with all rights reserved.
|
