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研究生:吳東軒
研究生(外文):Tung-Hsuan, Wu
論文名稱:以專利引文網絡分析探討技術生命週期
論文名稱(外文):Patent Citation Network Analysis for The Development of Technology Life Cycle
指導教授:施信佑施信佑引用關係
指導教授(外文):Hsin-Yu, Shih
口試委員:林欣美徐茂練
口試委員(外文):Hsin-Mei, LinMaw-liann, Shyu
口試日期:2013-06-25
學位類別:碩士
校院名稱:國立暨南國際大學
系所名稱:國際企業學系
學門:商業及管理學門
學類:企業管理學類
論文種類:學術論文
論文出版年:2013
畢業學年度:101
語文別:中文
論文頁數:70
中文關鍵詞:專利引文網絡技術生命週期主流設計
外文關鍵詞:Patent citation networkTechnology life cycleDominant design
相關次數:
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在快速變遷的經濟環境與日益激烈的競爭下,企業如果想要持續成長,則必須在產品及行銷策略上作出創新,在產品與行銷策略形成之前,必須先發展技術策略。發展技術策略的第一步是評估此技術的未來發展與是否值得投資,因此我們應該去了解此技術的生命週期是處於哪一個階段,這可以幫助我們估計技術未來的發展趨勢,並作出是否投資該技術領域的決策。
本研究藉由探討floppy disk與modems之專利技術,在引證(Citing)及被引證(Cited)的技術流動網絡下,看出其網絡結構為何,利用網絡分析的指標,包括向外點中心度(out‐degree centrality)、向內點中心度(in‐degree centrality),定義floppy disk與modems在技術生命週期期間的發展情況,並區分出生命週期的各個階段;並利用凝聚力(cohesion)和結構地位(structure position)判斷floppy disk與modems之專利技術,在技術供應鏈上不同期間橫向與縱向的競爭與合作關係,進一步了解在技術生命週期的各個階段,廠商自身所擁有的專利是分屬於哪個子技術與扮演何種角色。
從實證結果發現,使用專利引文網絡分析的向外點中心度(out‐degree centrality)與向內點中心度(in‐degree centrality)指標可以更清楚地看出技術與知識流動的方向,且向外點中心度(out‐degree centrality)曲線在成長期階段的高峰皆是主流設計出現的時間點;而產業供應鏈會隨著技術愈臻純熟,發生上下游縱向合作關係穩定,橫向相同層級廠商競爭愈趨激烈的現象。這在策略運用上可以相其競爭與合作的對象或方式,辨識出其他廠商對於自身扮演的角色為何,並可從企業配置研發資源的角度考量,協助策略性的研發規劃。

The changing rapidly economic environment and fierce increasingly competition that require companies to be innovative, which are in both their products and marketing strategies, if they are to continue to flourish. Before the product strategy formulated, a technology strategy must be developed to provide competitive products, materials, processes, or system technologies. The first step for devising a technology strategy is to decide whether the technology being worth to invest. How will the technology develop in the future? Will the technology still flourish in the future or will it decline? To answer these questions, one should know the life cycle stages of the technology in order to estimate the future development trends of the technology and make informed decisions whether invest in the technology or not.
This study takes the patents of floppy disk and modems for example, and uses the technology flow network of citing and cited to discuss the network structure. Defining and distinguishing each stages of technology life cycle by utilize these indicators, including out-degree centrality and in-degree centrality. Finally, exploiting cohesion and structure position to determine the relationships of competition and cooperation in the floppy disk and modems’ technical fields, to realize each patents belongs to what sub-technologies and what roles in each stages of technology life cycle by firms own.
The research result shows that using indicators of patent network citation analysis, out-degree centrality and in-degree centrality could recognize the direction of technology and knowledge’s flow. Moreover, the appearance of design dominance’s time point is the peak of out-degree centrality curve in growth stage. And by the maturity of industry’s supply chain, the more stable between upstream and cooperation of downstream, the more competitive between the same level’s firms. The result can help to recognize the character of other firms in strategy, and relocate enterprise’s R&D resources to enhance strategically planning.

目錄
誌謝辭 I
摘要 II
Abstract III
目錄 IV
圖目錄 VI
表目錄 VIII
第壹章 緒論 1
第一節 研究背景與動機 1
第二節 研究目的 2
第三節 研究流程 3
第貳章 文獻探討 5
第一節 技術生命週期 5
第二節 基於專利分析的技術生命週期判斷方法 11
第三節 專利引文網絡分析相關文獻 26
第參章 研究方法 28
第一節 研究架構 28
第二節 研究分析方法 30
第三節 研究範疇與對象 35
第四節 產業發展概況 38
第肆章 研究結果與分析 41
第一節 敘述性統計分析 41
第二節 專利引文網絡分析 46
第伍章 研究結論與建議 57
第一節 研究結論 57
第二節 研究貢獻與管理意涵 61
第三節 研究限制與後續研究建議 63
參考文獻 65

圖目錄
圖1-1:本研究之研究流程 4
圖2-1:技術發展的S曲線 6
圖2-2:技術自然限制的改變 6
圖2-3:多重層級的技術 7
圖2-4:擴散曲線 8
圖2-5:技術採用生命週期 8
圖2-6:技術生命週期的 S 曲線 12
圖2-7:技術生命週期的專利活動 13
圖2-8:專利技術生命週期圖 14
圖2-9:技術進步的軌跡-S 曲線 16
圖2-10:技術層次專利矩陣圖 24
圖3-1:本研究之研究架構 29
圖4-1:floppy disk歷年公告專利數量趨勢分析 42
圖4-2:modems歷年公告專利數量趨勢分析 42
圖4-3:floppy disk五年平均公告專利數量趨勢分析 43
圖4-4:modems五年平均公告專利數量趨勢分析 43
圖4-5:floppy disk公告專利累積數量趨勢分析 44
圖4-6:modems公告專利累積數量趨勢分析 44
圖4-7:floppy disk向外點中心度趨勢分析 47
圖4-8:floppy disk向內點中心度趨勢分析 48
圖4-9:modems向外點中心度趨勢分析 49
圖4-10:modems向內點中心度趨勢分析 49
圖4-11:floppy disk向外點度中心勢趨勢分析 50
圖4-12:floppy disk向內點度中心勢趨勢分析 51
圖4-13:modems向外點度中心勢趨勢分析 52
圖4-14:modems向內點度中心勢趨勢分析 52

表目錄
表2-1:技術採用生命週期各階段主要顧客群特性及企業策略 9
表2-2:Meade和Islam 成長曲線類型 18
表2-3:專利指標法各指標定義及說明 22
表3-1:floppy disk重要事紀 40
表3-2:modems重要事紀 40
表4-1:floppy disk技術之n-cliques 54
表4-2:modems技術之n-cliques 55
表4-3:floppy disk技術之REGE 56
表4-4:modems技術之REGE 56
表5-1:基於專利分析的技術生命週期判斷方法比較表 59

參考文獻
一、中文文獻
1.方曙,張嫻,陳雲偉,高利丹與胡正銀(2010),專利情報研究方法進展綜述,北京:科學技術文獻出版社,第151-210頁。
2.林治民(1998),技術預測方法之運用探討-以無線通訊技術發展趨勢為例,交通大學科技管理研究所碩士論文。
3.林建山(1984),商情預測:技術與實務,商略印書館,第 20-22 頁。
4.張志立(2004),以技術生命週期作為技術預測模式之比較 ,中原大學企業管理學系碩士學位論文。
5.張智翔(1999),技術預測:利用專利分析技術探討接觸式影像感測器技術擴散過程之研究,雲林科技大學企業管理研究所碩士論文。
6.陳正平譯(1999)Geoffrey A. Moore 著,龍捲風暴-矽谷的高科技行銷策略,城邦文化。
7.劉尚志(2000),產業競爭與專利策略,科技發展政策報導,第 8908 期。
8.魯明德(2006),解析專利資訊(Insight of Patent),全華科技圖書股份有限公司。
9.謝寶煖(1998),專利與專利資訊檢索。大學圖書館,第2卷第4期,第111-127頁。

二、英文文獻
1.Achilladelis, B., (1993). The dynamics of technological innovation: the sector of antibacterial medicines. Research Policy, 22, 279–308.
2.Achilladelis, B., Schwarzkopf, A. & Cines, M., (1990). The dynamics of technological innovation: the case of the chemical industry. Research Policy, 19, 1–34.
3.Acosta, M. & Coronado, D. (2003). Science–technology flows in Spanish regions An analysis of scientific citations in patents. Research policy, 32, 1783-1803.
4.Andersen, B., (1999). The hunt for S-shaped growth paths in technological innovation: a patent study. Journal of Evolutionary Economics, 9, 487–526.
5.Arabie, P. & Boorman, S. A. (1982). Blockmodel: developments and perspectives. Classifying Social Data: New Applications of Analytic Methods for Social Science Research. San Fraccisco: Jossey-Bass.
6.Borgatti, S. P., & Everett, M. G. (1989). The class of all regular equivalences: Algebraic structure and computation. Social Networks, 11(1), 65‐88.
7.Burt, R. S. (1992). Structural Holes:the Social Structure of Competition. Cambridge, Havard University Press.
8.Campball, R. S. (1983). Patent Trends as a Technological Forecasting Tool. World Patent Information, 3, 137-143.
9.Debackere, K., Verbeek, A., Luwel, M., & Zimmermann, E., (2002). Measuring progress and evolution in science and technology—II: the multiple uses of technometric indicators. International Journal of Management Reviews, 4, 213–231.
10.Ernst, H. (1997). The Use of Patent for Technological Forecasting: The Diffusion of CNC-Technology in the Machine Tool Industry. Small Business Economics, 9, 361-381.
11.Ernst, H. (1998a). Patent portfolio for strategic R&D planning. Journal of Engineering and Technology Management, 15, 279-308.
12.Ernst, H. (1998b). Industrial research as a source of important patents. Research Policy 27, 1-15.
13.Fendt, H. (1983). Strategische Patentanalyse. Blick in die Zukunft. Writschaftwoche, 29, 40-45.
14.Fildes, R & Howell, S. (1979). On selecting a forecasting model. TIMS Studies in the Management Sciences, 12, 297-312.
15.Fildes, R. (1983). An evaluation of Bayesian Forecasting. Journal of Forecasting, 2, 137-150.
16.Fisher, J. C. & Pry, R. H. (1971). A simple substitution model of technological change. Technological Forecasting and Social Change, 3, 75-88.
17.Flord, A. (1968). Trend forecasting: A methodology for figure of merit. New York: Prentice Hall.
18.Foster, R. (1986). Innovation: the attacker's advantage. London: Macmillan.
19.Freeman, L. C. (1979). Centrality in social networks conceptual clarification. Social Networks, 1(3), 215‐239.
20.Goetze, C. (2010). An empirical enquiry into co-patent networks and their stars: The case of cardiac pacemaker technology. Technovation,30,436-446.
21.Griliches, Z. (1990). Patent Statistics as Economic Indicators: A Survey. Journal of Economic Literature, 28, (4), 1661-1707.
22.Hall, B.H., Jaffe, A., & Trajtenberg, M. (2005). Market value and patent citations. Rand Journal of Economics, 16-38.
23.Haupt, R., Kloyer, M., & Lange, M., (2007). Patent indicators for the technology life cycle development. Research Policy, 36, 387–398.
24.Jaffe, A.B., Trajtenberg, M., & Henderson, R. (1993). Geographic localization of knowledge spillovers as evidenced by patent citations. The Quarterly Journal of Economics, 577-598.
25.Khalil, T. M. (2003). Management of Technology.London:Elsevier Ltd.
26.Knoke, D. & Burt, R. S. (1983). Prominence. Applied Network Analysis, 195‐222.
27.Lambkin, M. & Day G. S. (1989). Evolutionary Process in Competitive Markets: Beyond the Product Life Cycle. Journal of Marketing, 53, 4-20.
28.Lee, T. H. & Nakicenovic, N. (1989). Life cycle of technology and commercial policy. Science & Technology Review, 1, 38-43.
29.Liebowitz, J. (2005). Linking social network analysis with the analytic hierarchy process for knowledge mapping in organizations. Journal of Knowledge Management, 9, 76-86.
30.Lorrain, F. & White, H.C. (1971). Structural equivalence of individuals in social networks. Journal of Mathematical Sociology,1, 49-80.
31.Mahajan, V. & Peterson, R. A. (1978), Innovation diffusion in dynamic potential adapter population. Management Science, 24, 1589-1597.
32.Makridakis, S., Anderson, A., Carbone, R., Fildes, R., Hibon, M., Lewandowski, R., Newton, J., Parzen, E., & Winkler, R. (1984). The forecasting accuracy of major time series methods. Chichester: Wiley.
33.Mansfield, E. (1986). Patents and Innovation: an empirical study. Management Science, 32, 173 -181.
34.Meade, N. & Islam, T. (1998). Technological Forecasting-Model Selection, Model Stability, and Combining Models. Management Science, 44, 1115-1130.
35.Narin, F., Carpenter, M., & Woolf, P. (1984). Technological performance assessment based on patents and patent citations. IEEE Trans. Eng. Manag., 31, 172–184.
36.Norman, D. A. (1998). The life cycle of a technology: why it is so difficult for large companies to innovate? http://www.nngroup.com/reports/life_cycle_of_tech.html.
37.Otte, E. and Rousseau, R. (2002). Social network analysis: a powerful strategy, also for the information sciences. Journal of Information Science, 28, 441.
38.Pearl, R. & Reed, J. (1920). On the Rate of Growth of the Population of the United States Since 1790 and its Mathematical Representation. Proceedings of the National Academy of Sciences of the United States of America, 6, 275-288.
39.Porter, A., Roper, A., Mason, T., Rossini, F., Banks, J., & Wiederholt, B. (1991). Forecasting and Management of Technology, New York: John Wiley and Sons, Inc., 94-97, and 214.
40.Rogers, E. M.(1995). Diffusion of Innovation, Free Press.
41.Scott, J., (2000). Social network analysis. (2nd ed.). Thousand Oaks, CA: SAGE.
42.Seidman, S. B. & Foster, B. L. (1978). A Graph-Theoretic Generalization of The Clique Concept. J.Mathematical Sociology, 6, 139-154.
43.Shih, S. Y. (2012, June). Mapping of The National Focused Technology Life Cycle in Electricity of Taiwan: A Patent Citation Network Analysis. Academy of Innovation and Entrepreneurship, Macau.
44.Stone, R. (1978). Sigmoid .Bias, 7, 59-119
45.Stuart, T. E. & Podolny, J. M. (1996). Local search and the evolution of technological capabilities, Strategic Management Journal, 17.
46.Su, H. N. & Lee, P. C. (2009, August). Dynamic and Quantitative Exploration on Technology Evolution Mechanism: The Case of Electrical Conducting Polymer Nanocomposite. Management of Engineering & Technology, Portland International Conference.
47.Tipnis, A. V. (2008). Challenges in Product Strategy, Product Planning and Technology Development for Product Life Cycle. CIRP Annals - Manufacturing Technology, 43(1), 157–162.
48.Verhulst, P. E. (1838). Notice sur la loi que la population suit dans son accroissement. Correspond. Math. Phys, 10,113-121.
49.Wasserman, S. & Galaskiewicz, J. (1994). Advances in social network analysis: Research in the social and behavioral sciences . SAGE Publications, Incorporated.
50.Wasserman, S. & Faust, K. (1994). Social network analysis: Methods and applications . Cambridge university press.
51.White, H.C., Boorman, S.A., & Breiger, R.L. (1976). Social structure from multiple networks: I. Blockmodels of roles and positions. American Journal of Sociology, 81, 730-780.
52.Young, P. (1993). Technological growth curves-a comparison of forecasting models. Technological Forecasting and Social Change, 44, 375-389.

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