應用行動者網絡理論探討企業研發績效 －以生技醫藥產業為例

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姓名

楊英煥(Ying-huan Yang) 查詢紙本館藏

畢業系所

企業管理學系

論文名稱

應用行動者網絡理論探討企業研發績效－以生技醫藥產業為例
(Use Actor-Network Theory for R&D Performance The Case of Biotechnology Pharmaceutical Industry)

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摘要(中)

生技產業為技術密集且具高度創新性之高附加價值產業，主要以醫藥品最受矚目。因為生技產品受法規管制，導致產品開發期長、投資風險高而使資金募集不易，許多企業難以支撐到產品上市階段，因此企業提升研發效率，是生技醫藥產業發展重要的基礎。反觀美國生物科技之所以能成為產業龍頭，因為有源源不斷的創新技術從學術研究機構擴散至產業界，科學家與產業的互動形成社會與科技所交織的網絡關係，因此本研究應用行動者網絡理論的概念，針對行動者利害關係、知識轉譯、情境轉換而形成異質化網絡進行探討，了解生技廠商如何透過網絡合作以提升研發績效。
雖然過去學者已利用多階段資料包絡分析，透過變數進行企業研發效率之評估，但卻忽略了將網絡概念納入企業績效評估的研究中。因此本研究將行動者網絡理論在生技產業的情境轉換建立企業研發績效模型，並採用網絡資料包絡分析法針對技術創造能力、技術擴散能力和價值創造能力進行效率評估。本研究對象為國內22家生技醫藥廠商。研究結果發現台灣醫藥廠商總體研發效率有待提升，技術創造能力顯示出國內專利不足，標竿分群指出技術合作群研發效率高於獨立發展群，表示網絡合作能提高研發效率。本研究建議生技產業透過產學官異質化網絡的合作及產業群落的形成，將可創造行動者網絡中強制通行點，研發產業鏈的專業化分工，不但可降低營運及研發上的風險，並有助於提升產業規模與研發績效。

摘要(英)

Biotechnology industry is technology-intensive, highly innovative and high value-added industries. Especially in the pharmaceutical industry. Because biotech products subject to regulatory control. Resulting in long product development, funds raised is not easy because high investment risk. Difficult for many businesses to support to the stage to market, so efficient R&D performance is important foundation of the development of the biotechnology pharmaceutical industry. The other hand, the U.S. biotech industry become the industry leader because a steady stream of innovative technologies from academic and research institutions spread to the industry. The formation of social and technological interwoven network relationships in Scientists and industry. Therefore, this study applied the concept of Actor-Network Theory(ANT), the formation of a heterogeneous network in actor interessment, knowledge translation, situational conversion. Explore how to improve the biotechnology industries’ R&D performance by network cooperation.
Although researchers have used multi-stage DEA and variables to measure R&D performance in the past, but they ignores the network concept into R&D performance evaluation studies. Therefore, this study establishes R&D performance model by situational conversion of biotechnology industry in Actor-Network Theory and network DEA -technology creation capability, technology diffusion capability and value creation capability, and analyze R&D performance of 22 biotechnology pharmaceutical corporations in Taiwan. The study finds the overall Taiwanese pharmaceutical industries’ R&D performance should be improved. Technology creation capability shows insufficient domestic patent. The technical cooperation group is more efficient than independent development in benchmarking. It shows R&D performance can be improved by Network cooperation. This study suggests that biotechnology industries create obligatory passage point(OPP) in ANT by industry-academic-government heterogeneous network collaboration and industry clusters. specialization and division of labor establish value chain can not only reduce the risk of operation and R&D but also step up industry scale and R&D performance.

關鍵字(中)

★ 行動者網絡理論
★ 生技醫藥產業
★ 資料包絡分析
★ 研發績效評估

關鍵字(英)

★ Actor Network theory
★ Patent indicators
★ Data envelopment analysis
★ R&
★ D performance Measurement

論文目次

目錄
摘要 i
Abstract ii
誌謝 iii
目錄 iv
圖目錄 vi
表目錄 vii
第一章緒論 1
1-1 研究背景 1
1-2 研究目的 4
第二章生物技術醫藥產業概況 5
2-1 國內政府服務與產、學、研合作單位 7
2-2 生技醫藥產業環境 10
2-2-1 全球生技藥品產業發展概況 10
2-2-2 臺灣生技藥品產業發展概況 13
2-2-3 全球中草藥產業發展概況 16
2-2-4 臺灣中草藥產業發展概況 18
2-3 新藥研究發展過程 21
第三章文獻探討 26
3-1 行動者網絡理論 26
3-1-1 行動者網絡理論概述 26
3-1-2 行動者網絡轉譯過程 28
3-1-3 行動者網絡理論之相關文獻 31
3-1-4 小結 32
3-2 研發活動與績效評估方法 33
3-2-1 績效評估方法之介紹 33
3-2-2 資料包絡分析法 35
3-2-3 應用DEA評估研發績效表現之相關文獻 37
3-3 專利分析指標 39
第四章研究設計 46
4-1 研究架構 46
4-2 研究對象與資料來源 47
4-3 研究變數 48
4-3-1 投入產出變數的選取 50
4-4 研究方法 55
第五章實證結果與分析 59
5-1 敘述統計分析 59
5-2 廠商效率分析 59
5-3 效率分群與標竿學習 62
5-4 敏感度分析 64
5-5 管理決策矩陣 65
第六章研究結論與建議 68
6-1 研究結論 68
6-2 管理意涵 70
6-3 研究限制與未來研究建議 72
參考文獻 73

參考文獻

[1] Arundel, A., & Kabla, I. (1998). What percentage of innovations are patented? empirical estimates for European firms. Research Policy, 27, 127-141.
[2] Bengtsson, F., & Ågerfalk, P. J. (2011). Information technology as a change actant in sustainability innovation: Insights from Uppsala. The Journal of Strategic Information Systems, 20(1), 96-112. doi: 10.1016/j.jsis.2010.09.007
[3] Bettis, R. A., & Hitt, M. A. (1995). THE NEW COMPETITIVE LANDSCAPE. Strategic Management Journal, 16, 7-19. doi: 10.1002/smj.4250160915
[4] Callon, M. (1986). Some elements of a sociology of translation: domestication of the scallops and the fishermen of St Brieuc Bay. In J. Law (Ed.), Power, action and belief: a new sociology of knowledge?
[5] Caniëls, M. C. J., & Romijn, H. A. (2008). Actor networks in Strategic Niche Management: Insights from social network theory. Futures, 40(7), 613-629. doi: 10.1016/j.futures.2007.12.005
[6] Charnes, A., Cooper, W. W., & Rhodes, E. (1978). Measuring the efficiency of decision making units. European Journal of Operational Research, 2(6), 429-444. doi: 10.1016/0377-2217(78)90138-8
[7] Chen, K. H., & Guan, J. C. (2012). Measuring the Efficiency of China’s Regional Innovation Systems: Application of Network Data Envelopment Analysis (DEA) .Regional Studies,46(3), 355-377.
doi: 10.1080/00343404.2010.497479
[8] Chen, Y.S. (2011). Using patent analysis to explore corporate growth. Scientometrics, 88, 433–448. doi: 10.1007/s11192-011-0396-8
[9] Chen, Y.S., & Chang, K.C. (2009). Using neural network to analyze the influence of the patent performance upon the market value of the US pharmaceutical companies. Scientometrics, 80(3), 639–657. doi: 10.1007/s11192-009-2095-2
[10] Chen, Y. S., & Chang, K.C. (2010). The nonlinear nature of the relationships between the patent traits and corporate performance. Scientometrics, 82(1), 201-210. doi: 10.1007/s11192-009-0101-3
[11] Chen, Y., Du, J., David Sherman, H., & Zhu, J. (2010). DEA model with shared resources and efficiency decomposition. European Journal of Operational Research, 207(1), 339-349. doi: 10.1016/j.ejor.2010.03.031
[12] Chiu, Y.H., Huang, C.W., & Chen, Y.C. (2012). The R&D value-chain efficiency measurement for high-tech industries in China. Asia Pacific Journal of Management, 29(4), 989-1006. doi: 10.1007/s10490-010-9219-3
[13] Comanor, W. S., & Scherer, F. M. (1969). Patent statistics as a measure of technical change. Journal of Political Economy, 77(3), 392-398.
[14] Cresswell, K. M., Worth, A., & Sheikh, A. (2010). Actor-Network Theory and its role in understanding the implementation of information technology developments in healthcare. BMC Med Inform Decis Mak, 10, 67. doi: 10.1186/1472-6947-10-67
[15] Ernst, H. (1998). Patent portfolios for strategic R&D planning. J. Eng. Technol. Manage., 15, 279–308.
[16] Ernst, H. (2003). Patent information for strategic technology management. World Patent Information, 25, 233–242.
[17] Fare, R., & Grosskopf, S. (1996). Productivity and intermediate products:A frontier approach. Economics Letters, 50, 65-70.
[18] Farrell, M. J. (1957). The Measurement of Productive Efficiency. Journal of the Royal Statistical Society, 120(3), 253-290.
[19] Grabowski, H., & Vernon, J. (1990). A New Look At The Returns And Risks To Pharmaceutical R&D. Management Science, 36(7), 804-821.
[20] Greenhalgh, T., & Stones, R. (2010). Theorising big IT programmes in healthcare: strong structuration theory meets actor-network theory. Soc Sci Med, 70(9), 1285-1294. doi: 10.1016/j.socscimed.2009.12.034
[21] Guan, J., & Chen, K. (2012). Modeling the relative efficiency of national innovation systems. Research Policy, 41(1), 102-115. doi: 10.1016/j.respol.2011.07.001
[22] Hashimoto, A., & Haneda, S. (2008). Measuring the change in R&D efficiency of the Japanese pharmaceutical industry. Research Policy, 37(10), 1829-1836. doi: 10.1016/j.respol.2008.08.004
[23] Kaplan, R. S., & Norton, D. P. (1992). THE BALANCED SCORECARD - MEASURES THAT DRIVE PERFORMANCE. Harvard Business Review, 70(1), 71-79.
[24] Kochhar, R., & David, P. (1996). Institutional investors and firm innovation: a test of competing hypotheses. Strategic Management Journal, 17, 73-84.
[25] Kortelainen, J. (1999). The river as an actor-network: the Finnish forest industry utilization of lake and river systems. Geoforum, 30, 235-247.
[26] Latour, B. (1983). Give Me a Laboratory and I Will Raise the World. In K. D. Knorr-Cetina & M. Mulkay (Eds.), Science Observed: Perspectives on the Social Study of Science (pp. 141-170). London: Sage Publications.
[27] Latour, B. (2005). Reassembling the Social: An Introduction to Actor-Network-Theory. Oxford: Oxford UP.
[28] Law, J. (1992). Notes On The Theory Of The Actor Netwok - Ordering, Strategy And Heterogeneity. Systems Practice, 5(4), 379-393. doi: 10.1007/bf01059830
[29] Lee, H., Park, Y., & Choi, H. (2009). Comparative evaluation of performance of national R&D programs with heterogeneous objectives: A DEA approach. European Journal of Operational Research, 196(3), 847-855.
doi: 10.1016/j.ejor.2008.06.016
[30] Li, Y., Chen, Y., Liang, L., & Xie, J. (2012). DEA models for extended two-stage network structures. Omega, 40(5), 611-618.
doi: 10.1016/j.omega.2011.11.007
[31] Liu, J. S., & Lu, W.M. (2010). DEA and ranking with the network-based approach: a case of R&D performance. Omega, 38(6), 453-464. doi: 10.1016/j.omega.2009.12.002
[32] Lo, S.c. S. (2009). Scientific linkage of science research and technology development: a case of genetic engineering research. Scientometrics, 82(1), 109-120. doi: 10.1007/s11192-009-0036-8
[33] Lu, W. M., & Hung, S. W. (2011). Exploring the operating efficiency of Technology Development Programs by an intellectual capital perspective-A case study of Taiwan. Technovation, 31(8), 374-383.
doi: 10.1016/j.technovation.2010.10.008
[34] Luan, C., Zhou, C., & Liu, A. (2010). Patent strategy in Chinese universities: a comparative perspective. Scientometrics, 84(1), 53-63.
doi: 10.1007/s11192-010-0194-8
[35] Murdoch, J. (1997). Inhuman/nonhuman/human: actor-network theory and the prospects for a nondualistic and symmetrical perspective on nature and society. Environment and Planning D-Society & Space, 15(6), 731-756.
doi: 10.1068/d150731
[36] Murdoch, J., & Ward, N. (1997). Governmentality and territoriality - The statistical manufacture of Britain’s ’national farm’. Political Geography, 16(4), 307-324. doi: 10.1016/s0962-6298(96)00007-8
[37] Rogers, E. M. (1983). Diffusion of innovations: New York.
[38] Seiford, L. M., & Zhu, J. (1999). Profitability and marketability of the top 55 US commercial banks. Management Science, 45(9), 1270-1288.
doi: 10.1287/mnsc.45.9.1270
[39] Sharma, S., & Thomas, V. J. (2008). Inter-country R&D efficiency analysis: An application of data envelopment analysis. Scientometrics, 76(3), 483-501. doi: 10.1007/s11192-007-1896-4
[40] Souitaris, V. (2002). Firm-specific competencies determining technological innovation: a survey in Greece. R&D Management, 32(1), 61-77.
[41] Tone, K., & Tsutsui, M. (2009). Network DEA: A slacks-based measure approach. European Journal of Operational Research, 197(1), 243-252.
doi: 10.1016/j.ejor.2008.05.027
[42] Walsham, G., & Sahay, S. (1999). Gis For District-Level Administration In INDIA Problens And Opportunities. MIS Quarterly, 23(1), 39-66.
[43] Wang, C.N., & Wang, C.H. (2011). A (data envelopment analysis) DEA-based systematic approach for selection of strategic alliance candidates:Case by the biotechnology industry. African Journal of Business Management, 5(17), 7410-7419. doi: 10.5897/ajbm11.174
[44] Wang, E. C., & Huang, W. (2007). Relative efficiency of R&D activities: A cross-country study accounting for environmental factors in the DEA approach. Research Policy, 36(2), 260-273. doi: 10.1016/j.respol.2006.11.004
[45] Wang, K., Hong, J., Marinova, D., & Zhu, L. (2009). Evolution and governance of the biotechnology and pharmaceutical industry of China. Mathematics and Computers in Simulation, 79(9), 2947-2956. doi: 10.1016/j.matcom.2008.09.001
[46] You, T., & Zi, H. (2007). The economic crisis and efficiency change: evidence from the Korean construction industry. Applied Economics, 39(14), 1833-1842. doi: 10.1080/00036840600690199
[47] 生技產業白皮書. (2012). 生技產業白皮書. 經濟部工業局: 汪嘉林等人.
[48] 吳榮義. (2004). 高科技產業與專利──從專利指標觀察產業技術創新變化. Paper presented at the 大專院校經濟學教師研習營──財政問題與國家經濟建設.
[49] 吳學良, & 林育司. (2005). 經濟部科技專案執行效率之評估資料包絡分析之實證. 科技管理學刊, 10(1), 101-134.
[50] 阮明淑, & 梁峻齊. (2009). 專利指標發展研究. Journal of Library and Information Science, 35(2), 88-106.
[51] 高強, 黃旭男, & Sueyoshi, T. (2003). 管理績效評估 : 資料包絡分析法. 臺北市 : 華泰文化事業公司.
[52] 盧文民, 王宗誠, & 吳秀華. (2010). 國家研發組織績效與核心能耐關聯性之研究-非參數之應用. Paper presented at the 第十八屆國防管理學術暨實務研討會.
[53] 盧文民, 王宗誠, & 吳秀華. (2012). 國家研發組織績效及其影響因素之研究. Journal of Management & Systems, 19(3), 561-587.
[54] 盧文民, 何東興, & 高博瑜. (2012). <智慧資本與軍民通用科技發展專案績效.pdf>. 長庚人文社會學報, 5(1), 159-195.
[55] 行政院衛生署. 藥品臨床試驗申請_. from http://drug.doh.gov.tw/medication_rules_acts.php?type=pro&cnt_id=241
[56] U.S.FDA. Investigational New Drug (IND) Application. from http://www.fda.gov/drugs/developmentapprovalprocess/howdrugsaredevelopedandapproved/approvalapplications/investigationalnewdrugindapplication/default.htm

指導教授

洪秀婉(Shiu-WanHung)

審核日期

2013-6-25

推文