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題名 以專利分析探究新興領域之產業融合－以自駕車為例
Exploring Industry Convergence in Emerging Industries by Using Patent Analysis : Autonomous Vehicle as an Example
作者 李思穎
Lee, Szu-Ying
貢獻者 宋皇志
李思穎
Lee, Szu-Ying
關鍵詞 產業融合
科技管理
專利
向後引證
Convergence
Technology Management
Patent
Backward Citation
日期 2020
上傳時間 3-Aug-2020 18:36:09 (UTC+8)
摘要 本研究以自駕車產業為例，佐證了專利引證分析為一領先指標，不僅能用以了解新興產業融合概況與趨勢、更能用以分析動態的產業邊界，包含：發掘與預測商業機會、潛在參與者與主要推動者。這些資訊能輔助管理者、產業分析人員與政府制定策略與政策。

本研究的研究貢獻有四個層面，第一、本研究統整了過往使用專利研究產業融合的個案與方法，同時提出適合用以研究新興產業的樣本選取方式。第二、本研究為產業融合的學術實證增添一個案例，證實專利之引證分析不僅能用以研究已融合之產業，亦能用以分析以及預測新興的產業融合趨勢發展。第三，研究發現呼應Curran and Leker（2011）提出之產業融合四階段並非線性發展。最後，本研究擴展向後引證在產業融合的應用，如第一段所述。
By focusing on autonomous driving industry, this study proves that patent citation data is a leading indicator, which has multiple applications, such as analyzing and forecasting industry convergence trends, and dynamic industrial boundaries including current, possible, and the most important market players. These information is useful to managers and industry analysts in forecasting industry changes, business opportunities, competitors, and possible strategic alliance partners.

This research contributions are four-fold. Firstly, it summarizes different research methods of case studies using patent data to analyze industry convergence in the past 10 years. It points out the most suitable sample-selection method of analyzing a emerging industry. Secondly, it adds another case study, pointing out that patent citation data can not only be used to analyze and forecast emerged industry but also emerging industry in industry. Thirdly, this study found out that four-stages convergence are not linear, which echo theories raised by Curran and Leker（2011）. Last but not least, this study expands the applications of patent citation data as mentioned in the first paragraph.
參考文獻 中文文獻
ARTC（2018年11月20日）。ARTC攜手宏碁智通等18家廠商共組「自駕車產業聯盟」打造SAE Level 4自駕電動小巴。ARTC官方網站。取自https://www.artc.org.tw/
Junko Yoshida（2018年03月02日）。2018年自動駕駛車感測器展望。EET Taiwan。取自https://www.eettaiwan.com/
Junko Yoshida（2019年12月17日）。感知融合技術加速自駕車安全上路。EET Taiwan。取自https://www.eettaiwan.com/
MoneyDJ（2019年12月03日）。Gartner：2023 年全球自駕就緒車淨增長數估逾 74 萬輛。TechNews 科技新報。取自https://technews.tw/
劉勇（2017）。布局。載於高洪霞（策劃編輯），網絡數據可視化與分析利器 Gephi 中文教程（101-102頁）。北京：電子工業出版社。
余至浩（2018年09月18日）。【深度剖析無人車運作原理】AI如何學習開車。iThome。取自https://www.ithome.com.tw/
侯冠州（2019年10月22日）。Level 3瓶頸難突破　Level 4成自駕發展新選項。新電子。取自https://www.mem.com.tw/index.php
王光旭（2015）。社會網絡分析在公共行政領域研究的應用。調查研究—方法與應用。34，67-134.
王美音（譯）（1998）。向市場學習 （第七章），知識創新之泉：智價企業的經營（原作者：Dorothy Barton）。臺灣：遠流出版社。（原著出版年： 1995）
科技產業資訊室（2005年08月05日）。關於專利家族（patent family）。科技產業資訊室。取自https://iknow.stpi.narl.org.tw/
葉芳瑜與張書豪（2020年03月）。從產業觀點建構台灣自駕車衍生產值推估模式。取自https://payment.narlabs.org.tw/stpibooks/book/bookDetail?id=4b11416a70d3015401711aaa2981009d
葉重宇（2013）。電子穩定控制系統（ESC）功能與驗證介紹。車輛研測資訊，94，2–6.
何心宇（2019）。自駕車產業發展現況與展望。載於洪春暉（總編輯），自駕車產業技術暨應用發展分析（7-13頁）。臺灣：資策會MIC。
資訊工業年鑑編撰小組（2013）。新興應用產品發展趨勢探索。2013資訊工業年鑑編纂小組（編輯），2013資訊工業年鑑。臺灣：資策會MIC.
郭靜蓉（2020年05月21日）。自駕車光達、相機、雷達整合發展成趨勢。DIGITIMES。取自https://www.digitimes.com.tw/tech/
陳威全（n.d.）。2-mode 操作 - Gephi 學習站【部落格文字資料】。取自https://wenlab501.github.io/tutorial/gephi_tutor/netvizz/two_mode/。擷取日期：2020年5月15日。
朱峻賢（2020年06月）。自動駕駛車發展現況與發展趨勢。載於薛欽鐸（總編輯），2020車輛研測專刊（19-28頁）。彰化：財團法人車輛研測中心
雷鋒網（2019年03月08日）。Waymo 要賣感測器了，光學雷達能否成為業務支柱之一？。TechNews 科技新報。取自 https://technews.tw/
高敬原（2019年10月25日）自駕車、飛天車都適用，無人載具實驗條例即日起開放申請。數位時代。 取自https://www.bnext.com.tw/

英文文獻
Bellian, J. A., Kerans, C., & Jennette, D. C. (2005). Digital Outcrop Models: Applications of Terrestrial Scanning Lidar Technology in Stratigraphic Modeling. Journal of Sedimentary Research, 75(2), 166–176
Bröring, S., Cloutier, L. M., & Leker, J. (2006). The front end of innovation in an era of industry convergence: Evidence from nutraceuticals and functional foods. R&D Management, 36(5), 487–498
Carnahan, S., Agarwal, R., & Campbell, B. (2010). The Effect of Firm Compensation Structures on the Mobility and Entrepreneurship of Extreme Performers. Business, 895(January 2013), 1–43
Caviggioli, F. (2016). Technology fusion: Identification and analysis of the drivers of technology convergence using patent data. Technovation, 55–56, 22–32
Curran, C. S., Bröring, S., & Leker, J. (2010). Anticipating converging industries using publicly available data. Technological Forecasting and Social Change, 77(3), 385–395
Curran, C. S., & Leker, J. (2011). Patent indicators for monitoring convergence - examples from NFF and ICT. Technological Forecasting and Social Change, 78(2), 256–273
Farber, D., & Baran, P. (1977). The convergence of computing and telecommunications systems. Science (New York, NY), 195(4283), 1166–1170.
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Geum, Y., .Y. P., & .S. L. (2013). The Convergence of Manufacturing and Service Technologies: A Patent Analysis Approach. Information Management and Business Review, 5(2), 99–107
Geum, Y., Kim, M. S., & Lee, S. (2016). How industrial convergence happens: A taxonomical approach based on empirical evidences. Technological Forecasting and Social Change, 107, 112–120
Hacklin, F. (2007). Management of convergence in innovation: strategies and capabilities for value creation beyond blurring industry boundaries. Springer Science & Business Media.
Hagedoorn, J., & Duysters, G. (2002). External sources of innovative capabilities: The preference for strategic alliances or mergers and acquisitions. Journal of Management Studies, 39(2), 167–188
Ham, K. S., Jung, J. W., & Lee, J. H. (2017, October). Diversification of ICT suppliers for technological convergence: An evolutionary perspective on technological changes in automotive industry. In 2017 International Conference on Information and Communication Technology Convergence (ICTC) (pp. 742-746). IEEE.
Heo, P. S., & Lee, D. H. (2019). Evolution patterns and network structural characteristics of industry convergence. Structural Change and Economic Dynamics, 51, 405–426
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James Bourne. (2017). Intel’s big bets on autonomous driving unveiled: “Unwavering confidence” in success. IoTnews. Retrieved from https://iottechnews.com/
Karvonen, M., & Kässi, T. (2011, July). Patent citation analysis as a tool for analysing industry convergence. In 2011 Proceedings of PICMET`11: Technology Management in the Energy Smart World (PICMET) (pp. 1-13). IEEE.
Karvonen, Matti, & Kässi, T. (2013). Patent citations as a tool for analysing the early stages of convergence. Technological Forecasting and Social Change, 80(6), 1094–1107
Khansa, L., & Liginlal, D. (2009). Will the information security industry die? Applying social network analysis to study industry convergence. 15th Americas Conference on Information Systems 2009, AMCIS 2009, 6, 3920–3931.
Khokhar, D. (2015). Gephi cookbook. Packt Publishing Ltd.
Kim, N., Lee, H., Kim, W., Lee, H., & Suh, J. H. (2015). Dynamic patterns of industry convergence: Evidence from a large amount of unstructured data. Research Policy, 44(9), 1734–1748
Kyle Wiggers. (2020). California DMV releases autonomous vehicle disengagement reports for 2019. VentureBeat. Retrieved from https://venturebeat.com/
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Sick, N., Preschitschek, N., Leker, J., & Bröring, S. (2019). A new framework to assess industry convergence in high technology environments. Technovation, 84–85(January 2017), 48–58
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Stieglitz, N. (2002). Industry dynamics and types of market convergence: The evolution of the handheld computers market in the 1990s and beyond. DRUID Summer Conference Copenhagen, June, 1–41.
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描述 碩士
國立政治大學
科技管理與智慧財產研究所
107364106
資料來源 http://thesis.lib.nccu.edu.tw/record/#G0107364106
資料類型 thesis
dc.contributor.advisor 宋皇志zh_TW
dc.contributor.author (Authors) 李思穎zh_TW
dc.contributor.author (Authors) Lee, Szu-Yingen_US
dc.creator (作者) 李思穎zh_TW
dc.creator (作者) Lee, Szu-Yingen_US
dc.date (日期) 2020en_US
dc.date.accessioned 3-Aug-2020 18:36:09 (UTC+8)-
dc.date.available 3-Aug-2020 18:36:09 (UTC+8)-
dc.date.issued (上傳時間) 3-Aug-2020 18:36:09 (UTC+8)-
dc.identifier (Other Identifiers) G0107364106en_US
dc.identifier.uri (URI) http://nccur.lib.nccu.edu.tw/handle/140.119/131317-
dc.description (描述) 碩士zh_TW
dc.description (描述) 國立政治大學zh_TW
dc.description (描述) 科技管理與智慧財產研究所zh_TW
dc.description (描述) 107364106zh_TW
dc.description.abstract (摘要) 本研究以自駕車產業為例，佐證了專利引證分析為一領先指標，不僅能用以了解新興產業融合概況與趨勢、更能用以分析動態的產業邊界，包含：發掘與預測商業機會、潛在參與者與主要推動者。這些資訊能輔助管理者、產業分析人員與政府制定策略與政策。

本研究的研究貢獻有四個層面，第一、本研究統整了過往使用專利研究產業融合的個案與方法，同時提出適合用以研究新興產業的樣本選取方式。第二、本研究為產業融合的學術實證增添一個案例，證實專利之引證分析不僅能用以研究已融合之產業，亦能用以分析以及預測新興的產業融合趨勢發展。第三，研究發現呼應Curran and Leker（2011）提出之產業融合四階段並非線性發展。最後，本研究擴展向後引證在產業融合的應用，如第一段所述。		zh_TW
dc.description.abstract (摘要) By focusing on autonomous driving industry, this study proves that patent citation data is a leading indicator, which has multiple applications, such as analyzing and forecasting industry convergence trends, and dynamic industrial boundaries including current, possible, and the most important market players. These information is useful to managers and industry analysts in forecasting industry changes, business opportunities, competitors, and possible strategic alliance partners.

This research contributions are four-fold. Firstly, it summarizes different research methods of case studies using patent data to analyze industry convergence in the past 10 years. It points out the most suitable sample-selection method of analyzing a emerging industry. Secondly, it adds another case study, pointing out that patent citation data can not only be used to analyze and forecast emerged industry but also emerging industry in industry. Thirdly, this study found out that four-stages convergence are not linear, which echo theories raised by Curran and Leker（2011）. Last but not least, this study expands the applications of patent citation data as mentioned in the first paragraph.		en_US
dc.description.tableofcontents 第壹章 緒論 1
第一節 研究動機 1
第二節 研究目的 3
第三節 研究問題 4
第四節 研究途徑與架構 5
第貳章 文獻回顧 7
第一節 定義產業融合 7
一、 科學或知識融合 9
二、 技術融合 9
三、 市場或產品應用融合 9
四、 產業融合 10
第二節 產業融合的多層次現象 10
一、 產業層次 11
二、 企業層次 11
三、 企業間層次 13
第三節 專利分析應用於產業融合的實證 14
第參章 研究方法 16
第一節 樣本與專利資料 16
一、 樣本選取方式 16
二、 專利資料庫 18
三、 專利資料敘述 18
第二節 資料處理與分析方法 20
一、 產業融合之指定產業類別 20
二、 定義技術溢出程度 23
三、 商業機會與潛在參與者 24
四、 社會網絡之中介中心性 25
第肆章 研究發現 27
第一節 產業融合 27
一、 產業融合概況 27
二、 技術面融合概況 32
第二節 商業機會與潛在競爭者辨識 34
第三節 推動產業融合之角色 37
第四節 小結 39
一、 透過引證分析發現自駕車之融合情況 39
二、 產業融合並非線性發展 39
三、 產業融合之應用 40
第伍章 研究結果 42
第一節 研究貢獻 42
第二節 研究限制與後續研究建議 43
參考資料 45
中文文獻 45
英文文獻 46
附錄一 51
附錄二 52
附錄三 53
附錄四 55		zh_TW
dc.format.extent 2935867 bytes-
dc.format.mimetype application/pdf-
dc.source.uri (資料來源) http://thesis.lib.nccu.edu.tw/record/#G0107364106en_US
dc.subject (關鍵詞) 產業融合zh_TW
dc.subject (關鍵詞) 科技管理zh_TW
dc.subject (關鍵詞) 專利zh_TW
dc.subject (關鍵詞) 向後引證zh_TW
dc.subject (關鍵詞) Convergenceen_US
dc.subject (關鍵詞) Technology Managementen_US
dc.subject (關鍵詞) Patenten_US
dc.subject (關鍵詞) Backward Citationen_US
dc.title (題名) 以專利分析探究新興領域之產業融合－以自駕車為例zh_TW
dc.title (題名) Exploring Industry Convergence in Emerging Industries by Using Patent Analysis : Autonomous Vehicle as an Exampleen_US
dc.type (資料類型) thesisen_US
dc.relation.reference (參考文獻) 中文文獻
ARTC（2018年11月20日）。ARTC攜手宏碁智通等18家廠商共組「自駕車產業聯盟」打造SAE Level 4自駕電動小巴。ARTC官方網站。取自https://www.artc.org.tw/
Junko Yoshida（2018年03月02日）。2018年自動駕駛車感測器展望。EET Taiwan。取自https://www.eettaiwan.com/
Junko Yoshida（2019年12月17日）。感知融合技術加速自駕車安全上路。EET Taiwan。取自https://www.eettaiwan.com/
MoneyDJ（2019年12月03日）。Gartner：2023 年全球自駕就緒車淨增長數估逾 74 萬輛。TechNews 科技新報。取自https://technews.tw/
劉勇（2017）。布局。載於高洪霞（策劃編輯），網絡數據可視化與分析利器 Gephi 中文教程（101-102頁）。北京：電子工業出版社。
余至浩（2018年09月18日）。【深度剖析無人車運作原理】AI如何學習開車。iThome。取自https://www.ithome.com.tw/
侯冠州（2019年10月22日）。Level 3瓶頸難突破　Level 4成自駕發展新選項。新電子。取自https://www.mem.com.tw/index.php
王光旭（2015）。社會網絡分析在公共行政領域研究的應用。調查研究—方法與應用。34，67-134.
王美音（譯）（1998）。向市場學習 （第七章），知識創新之泉：智價企業的經營（原作者：Dorothy Barton）。臺灣：遠流出版社。（原著出版年： 1995）
科技產業資訊室（2005年08月05日）。關於專利家族（patent family）。科技產業資訊室。取自https://iknow.stpi.narl.org.tw/
葉芳瑜與張書豪（2020年03月）。從產業觀點建構台灣自駕車衍生產值推估模式。取自https://payment.narlabs.org.tw/stpibooks/book/bookDetail?id=4b11416a70d3015401711aaa2981009d
葉重宇（2013）。電子穩定控制系統（ESC）功能與驗證介紹。車輛研測資訊，94，2–6.
何心宇（2019）。自駕車產業發展現況與展望。載於洪春暉（總編輯），自駕車產業技術暨應用發展分析（7-13頁）。臺灣：資策會MIC。
資訊工業年鑑編撰小組（2013）。新興應用產品發展趨勢探索。2013資訊工業年鑑編纂小組（編輯），2013資訊工業年鑑。臺灣：資策會MIC.
郭靜蓉（2020年05月21日）。自駕車光達、相機、雷達整合發展成趨勢。DIGITIMES。取自https://www.digitimes.com.tw/tech/
陳威全（n.d.）。2-mode 操作 - Gephi 學習站【部落格文字資料】。取自https://wenlab501.github.io/tutorial/gephi_tutor/netvizz/two_mode/。擷取日期：2020年5月15日。
朱峻賢（2020年06月）。自動駕駛車發展現況與發展趨勢。載於薛欽鐸（總編輯），2020車輛研測專刊（19-28頁）。彰化：財團法人車輛研測中心
雷鋒網（2019年03月08日）。Waymo 要賣感測器了，光學雷達能否成為業務支柱之一？。TechNews 科技新報。取自 https://technews.tw/
高敬原（2019年10月25日）自駕車、飛天車都適用，無人載具實驗條例即日起開放申請。數位時代。 取自https://www.bnext.com.tw/

英文文獻
Bellian, J. A., Kerans, C., & Jennette, D. C. (2005). Digital Outcrop Models: Applications of Terrestrial Scanning Lidar Technology in Stratigraphic Modeling. Journal of Sedimentary Research, 75(2), 166–176
Bröring, S., Cloutier, L. M., & Leker, J. (2006). The front end of innovation in an era of industry convergence: Evidence from nutraceuticals and functional foods. R&D Management, 36(5), 487–498
Carnahan, S., Agarwal, R., & Campbell, B. (2010). The Effect of Firm Compensation Structures on the Mobility and Entrepreneurship of Extreme Performers. Business, 895(January 2013), 1–43
Caviggioli, F. (2016). Technology fusion: Identification and analysis of the drivers of technology convergence using patent data. Technovation, 55–56, 22–32
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dc.identifier.doi (DOI) 10.6814/NCCU202001133en_US