臺灣博碩士論文加值系統

全球面臨第四次工業革命:工業4.0的智慧製造，工業4.0是運用大數據，整合產品的開發、生產、銷售與服務，結合物聯網，翻轉數百年的產業價值鏈。另外Fintech金融科技，電子商務等新經濟消費型態大大地改變企業的經營模式。企業急需透過各種新知識，新技能，或新發明以提高企業在全球的競爭力。企業所需的新知識可以從企業外部導入，或由企業內部創造。從企業內部創造新知識的優點，是直接開發企業的核心知識，容易鎖住商業機密，尤其在無法從外部取得該知識的時候，又因為是專為企業量身打造的知識，因此具有容易應用和相容性高等各項優勢。當企業決定從內部創造新知識時，需要考量到包括挑選適當的人員，決定知識的種類，知識的複雜度，知識的深度，同時又有時間的壓力等各種因素。特別是利用團隊創造新知識的效率遠比個人創造新知識的效率佳。本論文分兩大部分，首先建立團隊創造新知識的模式，接著在有限經費下極大化新知識的創造模式。
首先本研究將建構一個利用團隊創造新知識的數學模式，探討在有限的資源下，如何挑選最適當的團隊，來達到知識創造的最佳效益。模式針對企業想要創造的新知識或稱為目標知識，同時考量員工的三個知識變數，即每位員工本身擁有的知識複雜度，知識深度，以及這些知識與目標知識的關聯性，然後以數學模式量化團隊對創造新知識的貢獻，成員新知識的增長，提升知識所需的時間，以及創造目標知識需要的總體時間等。最後模式以案例進行驗證，首先探討團隊在創造新知識的過程，每位成員的知識提升量；接著討論如何從多組團隊中選擇最佳知識創造效益的團隊；最後以敏感度分析探討相關性係數對知識創造的影響。
其次，根據前面的數學模式更進一步探討:找出最快完成目標知識的團隊、選取最有效率的成員組成新的團隊來創造新知識、若加上薪資考量，當完成知識創造時，依每個成員的CCP(Cost to Complete/Price)值挑出最佳團隊、考量在經費有限下，當完成知識創造時，依每個成員的CCP值挑出最佳團隊。最後再以敏感度分析，探討參數值設定對結果的影響。

The world is facing the Fourth Industrial Revolution, i.e. the “intelligent manufacturing” of industry 4.0. Industry 4.0 revolutionizes the industry value chain with hundreds-of-years of history by utilizing big data, integrating the product development, production, sales, and services, and combing the Internet of Things (IoT). Furthermore, Novel economic consumption patterns such as the Fintech commerce technology, Ecommerce, and etc have significantly altered the operation model of enterprises. The enterprises are required to elevate their global competitiveness by means of various new knowledge, new technology, or new invention. The needed new knowledge can be incorporated from the outside of the enterprise or it can be created internally. The benefit of creating new knowledge internally is that commercial confidentiality can be more easily maintained since the core knowledge was developed internal of the enterprise. Especially in the circumstance that said knowledge cannot be acquired externally, and since said knowledge was made specifically for said industry, various benefits such as ease of application and high adaptability can be acquired. When the enterprise has decided to create new knowledge internally, necessary considerations include: selecting the suitable personnel, deciding the species of knowledge, the complexity of knowledge, the level of knowledge, and various other factors such as time pressure etc. Another benefit resides in the productivity of creating new knowledge via teamwork is better than the productivity of creating new knowledge individually. The present thesis is comprised of two parts, including constructing the model for creating new knowledge via teamwork and maximizing the creation model of the new knowledge under a limited budget.
The present research constructs a mathematical model for creating new knowledge via teamwork. The process of selecting the most suitable team under limited resources to acquire the maximum benefit of knowledge creation process has also been disclosed. For the new knowledge that the enterprise aims to create, i.e. the target knowledge, the model contemplates three knowledge variables of an employee simultaneously, said variables include the knowledge complexity, knowledge level of each employee, and the correlation of said knowledge and the target knowledge. The contribution of the team on the creation of new knowledge, the new knowledge growth of the member, the time required for knowledge elevation, the total time required for creating target knowledge, and etc are then quantified via a mathematical model. Finally, the present model is verified with cases. To elaborate, the amount of knowledge elevation of each member during the process of new knowledge creation is discussed first; the process of selecting the team with optimal knowledge creation benefits from multiple teams is then discussed, and the effect of the correlation coefficient on the knowledge creation process is discussed with sensitivity analysis.
Subjects are then discussed based on the previously-mentioned mathematical model. Said subjects includes : locating the team that can accomplish the target knowledge with the least amount of time; selecting the most efficient member to compose a new team to create new knowledge; in the circumstance that the salaries of the member are to be taken into consideration, selecting the optimal team based on each member’s CCP (Cost to Complete/Price) value when knowledge creation process is complete; and in the circumstance of having a limited budget, selecting the optimal team based on each member’s CCP value when knowledge creation process is complete. Finally, sensitivity analysis is utilized in order to discuss the effect of the setting of the value of parameters on the results.

摘要 i
ABSTRACT ii
誌謝辭 iv
表目錄 VI
圖目錄 vii
第一章 緒論 1
第一節 研究背景與動機 1
第二節 研究動機 1
第三節 研究目的 2
第四節 研究方法 2
第五節 研究範圍 3
第六節 研究限制 3
第七節 研究流程 3
第二章 文獻探討 5
第一節 知識理論 5
第二節 知識複雜度、知識深度、知識相關性 11
第三節 數學規劃 12
第三章 模式建構 15
第一節 團隊創造新知識之模式 15
第二節 有限經費下的極大化創造新知識模式 22
第四章 案例分析 28
第一節 團隊創造新知識之案例分析 28
第二節 敏感度分析 33
第三節 研究發現與討論 39
第五章 結論與建議 41
第一節 研究結論 41
第二節 研究建議 42
參考文獻 43

方述誠(1993)。線性規劃(Linear Programming)。數學傳播，17(1)，1-12。
司徒達賢(2009)。司徒達賢教授論壇: 解決實際問題是創造知識的重要途徑。線上檢索日期：107年7月8日。取自網址：http://prof-seetoo.blogspot.tw/2010/12/blog-post_5729.html。
任文駒(2004)。天天遇見愛因斯坦。科學人雜誌，32(10)，取自網址：http://sa.ylib.com/MagArticle.aspx?Unit=featurearticles&id=559。
吳清山、賴協志(2009)。知識領導：理論與研究。台北市：高等教育出版社。
吳瓊恩(2001)。行政學。台北市：三民書局。
林東清(2009)。知識管理。台北市：智勝文化公司。
林啟禎(2019)。國立成功大學醫學院教授 :愛因斯坦的想像力。線上檢索日期：108年2月22日。取自網址：http://ortho.clmed.ncku.edu.tw/~cjlin/othernote/325.html。
袁建中、陳坤成(2016)。科技管理：基礎與應用(2版)。台中市：華泰文化。
戚玉樑、陳彥均(2009)。發展具有解析使用者認知能力的專家系統於提升資訊查詢效能。資訊管理學報，16(1)，1-24。
野中郁次郎、竹內弘高(1997)。創新求勝－智價企業論(楊子江、王美音譯)。台北市：遠流出版公司。(原著出版年：1991)
黃麟祐(2003)。IT知識管理導論。台北市：文魁資訊。
An, X., Deng, H., Chao, L., & Bai, W. (2014). Knowledge management in supporting collaborative innovation community capacity building. Journal of Knowledge Management, 18(3), 574–590.
Boskin, M. J., & Lau, J. (1992). Capital, Technology, and Economic Growth. In Rosenberg In Rosenberg, N., Landau, R., & Mowery, D. C. (Eds.), Technology and the Wealth of Nations. (17-55). Stanford, CA: Stanford University Press.
Cabrera, A., & Cabrera, F. F. (2002). Knowledge-sharing dilemmas. Organization Studies, 23(5), 687-710.
Debowski, S. (2006). Knowledge Management. St, Milton, QLD, Australia： John Wiley & Sons Australia, Ltd.
Drucker, P. F. (1999a). Knowledge Worker Productivity: The Biggest Challenge. California Management Review, 41(2), 79- 94.
Drucker, P. F. (1999b). Management challenges for the 21st Century. New York: Harper Business.
Hedlund, G. (1994). A Model of Knowledge Management and the N-Form Corporation. Strategic Management Journal, 15, 73-90.
Hickins, M. (1999). Xerox shares its knowledge. Management Review, 88(2), 40-45.
Huang, N. T., Wei, C. C., & Chang, W. K. (2007). Modeling the knowledge diffusion in community of practice. Kybernetes, 36(5/6), 607-621.
Karaszewski, R. (2008). The influence of KM on global corporations' competitiveness. Journal of Knowledge Management, 12(3), 63-70.
Korposh, D., Lee, Y. C., Wei, C. S., & Wei, C. C. (2011). Modeling the Effects of Existing Knowledge on the Creation of New Knowledge. Concurrent Engineering, 19(3), 225-234.
Merriam-Webster. (2018). expert system. From https://www.merriamwebster.com/ dictionary/expert system
Meyer, B., & Sugiyama, K. (2007). The concept of knowledge in KM: a dimensional model. Journal of Knowledge Management, 11(1), 17-35.
Microsoft. Accessed 5th March 2017. From https : //msdn.microsoft.com/en-us/library/aa549981(v=ax.10).aspx ()
Moorthy, S., & Polley, D. E. (2010). Technological knowledge breadth and depth: performance impacts. Journal of Knowledge Management, 14(3), 359-377.
Nonaka, I., & Takeuchi, H. (1995). The Knowledge-Creating Company: How Japanese Companies Create the Dynamics of Innovation. ‎Oxford‎, England: Oxford University Press.
Nonaka, I., & Noboru, K. (1998). The Concept of 'Ba': Building a Foundation for Knowledge Creation, California Management Rev, 40(3), 40-54.
Nonaka, I. (2007). The Knowledge-Creating Company. Harvard Business Review, 2007(July–August), 162-171.
O’Dell, C., & Grayson, C. J. (1998). If Only Knew What We Knew: Identification and Transfer of Internal Best Practices. California Management Review, 40(3), 154-174.
O'Dell, C. (2002, January-March). Knowledge Management: What's Now and What's Next. Accessed 5th December 2016. From https://www.doncio.navy.mil/chips/ArticleDetails.aspx?ID=3633
Quinn, J. B. (1992). Intelligent enterprise: A knowledge and service based paradigm for industry. New York: The free copy.
Roos, G., & Roos, J. (1997). Measuring your Company’s Intellectual Performance. Long Range Planning, 30(3), 413-426.
Ruggles, R. (1998). The State of the Notion: Knowledge Management in Practice. California Management Review, 40(3), 80-90.
Schumpeter, J. (1928). The Instability of Capitalism. The Economic Journal, 1928(Sep.), 361-386.
Simon, H. A. (1960). The New Science of Management Decision. New York: Harper Row.
Tan, S. T. (2016)。管理數學(第11版)(張純明譯)。台北市：聖智學習。(原著出版年：2015)
Tian, J., Nakamori, Y., & Wierzbicki, A. P. (2009). Knowledge management and knowledge creation in academia: a study based on surveys in a Japanese research university. Journal of Knowledge Management, 13(2), 76–92.
von Krogh, G., Nonaka, I., & Aben, M. (2001). Making the Most of Your Company's Knowledge: A Strategic Framework. Long Range Planning, 34(4), 421-439.
Wei, C. C., Chen, S. H. & Lee, Y. C. (2009). A Model for assessing organizational knowledge inventory. Kybernetes, 38(9), 1491-1507.