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研究生: 林建旭
LIN, CHIEN-HSU
論文名稱: 動態技術鑑價之研究
Dynamic Model of Valuing Technology
指導教授: 耿伯文
Kreng, Victor B.
學位類別: 碩士
Master
系所名稱: 管理學院 - 工業與資訊管理學系
Department of Industrial and Information Management
論文出版年: 2007
畢業學年度: 95
語文別: 英文
論文頁數: 117
中文關鍵詞: 技術生命週期擴散模型選擇權定價模式營業祕密技術鑑價專利技術鑑價
外文關鍵詞: technology life cycle, diffusion model, option pricing model, the pricing of trade secret, the pricing of patent
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  • 在創新速率快的產業中,智慧財產權的確保已成為競爭決勝的關鍵。在目前的經濟環境下,投入大量資源進行研發工作時,為求資源運用達到最佳之效率,應審慎評估研發成果之保護態樣,評量各種相關因素,做出最適切之決定。而研發成果除了專利保護外,營業祕密提供了另一種的保護方式。

    本研究主要是將企業內部的技術,依其不同保護方式來探討企業如何做最有效率的策略,以獲取高於目前狀況的利潤。亦即將技術依其特性,分作申請專利(Patent)和營業秘密(Secret)兩種策略,利用選擇權訂價模型(Option Price Model)結合Bass(1969)所提出的擴散理論(Diffusion Theory),並引進生命週期理論(Life Cycle),將技術價值依其生命週期做更詳細之評估,也比較在不同時期下,企業應選擇技術的最佳保護方式為何。如此發展出一個新的動態鑑價模型去預測此兩種不同策略所能得到的價值,藉以提供企業主管制定相關決策時一個依據,並提供技術後續發展的建議,以利技術做更多運用,如授權或交換等。

    Industrialization, in which that the innovative speed is fast, the protection of intellectual property rights has became the key of the competition. Under the present economic environment, in order to make resources usage reach the best efficiency, we should assess the protection pattern of R&D carefully, and review all relevant factors to make the most appropriate decision while investing a large number of resources in R&D. The result of R&D wasn’t only protected by patent pattern, but also protected by secret pattern.

    The research is mainly discussing how enterprises make the most efficient tactics according to different patterns of protection for the technologies inside the enterprise to get more benefit than the present situation. It means that the methods of protection can be divided into two patterns namely patent and secret bases on its characteristic of technologies. We can do more detailed assessment for technological value in technology life cycle to use a model of evaluation that combines option pricing model, diffusion model with technology life cycle, simultaneously, in order to compare what is the best pattern of protecting technology under different period. We develop a dynamic model to predict the values produced with different tactics. It will provide staff suggestions to make relevant decisions by using this model, also offer suggestions of technological follow-up development. The results of study are to make good use of technologies, for instance authorize or exchange.

    Abstract I Acknowledgement III Table of Contents IV List of Figures VII List of Tables IX Chapter 1 Introduction 1 1.1 Research Background and Motivation 1 1.2 Research Objectives 4 1.3 Research Scope and Restrictions 4 1.4 Research Procedure 6 Chapter 2 Literature Review 7 2.1 Patent 7 2.1.1 Definition of Patent 7 2.1.2 Patent Strategy and Patent Arrangement 9 2.1.3 Characteristic of Value Distribution of Patent Assets 12 2.1.4 Source of Patent Value and Factors of Influencing Value 15 2.2 Trade Secret 17 2.2.1 Definition of Trade Secret 17 2.2.2 Compare of Trade Secret and Patent 19 2.2.3 The Influential Factors for Choosing Protection Mode 20 2.3 The Methods Of Valuating Intellectual Property Rights 21 2.3.1 Option Pricing Theory (OPT) Methods 23 2.3.1.1 Determinants of Option Value 24 2.4 The Black & Scholes Model 28 2.5 Bass Diffusion Model 30 2.5.1 Assumption of Bass Model 32 2.6 Knowledge Diffusion Model 34 2.7 Technology 36 2.7.1 Technology Maturity in Technology Life Cycle 37 Chapter 3 Building the Dynamic Model of Valuating Technology 40 3.1 Framework of Model 40 3.2 Model Construction 41 3.2.1 The Diffusion Model of Intangible Assets 41 3.2.2 Constructing Probability Model 45 3.2.3 Model of Intangible Assets Makes Technology Value Increase 52 3.2.4 Feedback of Technology Value 56 3.2.5 Calculating the Maximum Value of Technology 57 3.2.6 Summary 60 3.3 The model of Tangible Assets Affected Technology Value 60 3.4 Dynamic Black and Scholes Model of Evaluating Technology 61 3.4.1 Market Value of the Underlying Technology 62 3.4.2 Expected Cost for Mass Production of Technology 63 3.4.3 Expected Time For Achieving Mass Production of Technology 64 3.4.4 Complete Dynamic Black and Scholes Model 65 3.5 Technology Value in Life Cycle 66 3.5.1 Technological S-Curve 67 3.5.2 Variation of Technology Value in Technology Life Cycle 68 Chart 4 case study 70 4.1 Characteristics of Biopharmaceutical Industry and Parameter Setting 70 4.1.1 Aranesp 74 4.1.2 Technology S-Curve of Aranesp 85 4.1.3 Expected Value of Trade Secret(Aranesp) 88 4.1.4 Discussion 91 4.2 Characteristics of Beverage Industry And Parameter Setting 92 4.2.1 Coca-Cola 93 4.2.2 Technology S-Curve of Coca-Cola 103 4.2.3 Expected value of Trade Secret(Coca-Cola) 104 4.2.4 Discussion(Coca-Cola) 106 4.3 Case versus Case 106 Chapter 5 Conclusion and Suggestion 108 5.1 Conclusion 108 5.2 Suggestion 109 Reference 111

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