1. Knowledge Management Research: A Personal Experience T.P. Liang National Sun Yat-sen University November 7, 2006

2. T.P. Liang 2006-11-07 Importance of KM Research The importance of knowledge in business Managing knowledge is a difficult and continuing process A challenging question: How can knowledge be managed properly to improve firm performance?

3. T.P. Liang 2006-11-07 知識管理是 21 世紀的管理趨勢

4. T.P. Liang 2006-11-07 資料、資訊、知識、智慧關係

5. T.P. Liang 2006-11-07 內隱知識與外顯知識的比較 項 目 內隱知識外顯知識 特 點 1. 難以書面化表達 2. 難以系統化 3. 持續性強，不易被人改 變 4. 例如：經驗、秘訣、信 念、感覺、習慣 1. 可量化或書面化 2. 較系統化 3. 固定的資訊，其運用視 人而異 4. 例如：業務手冊、企劃 案、作業指導規範、物理 定律 儲存方式人的心智資料庫、電腦、文件 分享方式由於難以表達，故分享困 難度視個人表達能力而定， 通常只能意會，並透過師 徒制來學習 可透過視覺、聲音或動作 來呈現、或透過書籍、影 片等來傳遞

6. T.P. Liang 2006-11-07 不同的知識特性 個人組織 內隱藝術 IC 設計 外顯 (SOP) 大學生產製造

7. T.P. Liang 2006-11-07 知識、智慧資本與企業價值 企業價值 員工的觀念型 知識 員工的經驗型 知識 內含於組織文 化的知識 內含於組織文 化的知識 可編碼的 知識 人力資本 顧客資本 組織資本 有形 資產 無形 資產 財務性 資產 知識分類 企業潛力 內隱 知識 外顯 知識 ＝

8. T.P. Liang 2006-11-07 個人知識與組織知識的轉換 個人知識透過分享 後，轉變為組織的 知識 組織的知識或經 驗供個人學習、 吸收後，強化個 人知識 個人知識 創意 經驗 人脈 組織知識 作業程序 Know-How 工作手冊 合作方式

9. T.P. Liang 2006-11-07 Nonaka 的知識成長模式 內隱知識外顯知識 內隱知識社會化外部化 外顯知識內部化組 合組 合

10. T.P. Liang 2006-11-07 知識管理的基本工具 創造 分類 / 儲存 索引 / 檢索 過濾 / 篩選 導覽 使用分析 知識推薦

11. T.P. Liang 2006-11-07 Research in Knowledge Management Conceptual Knowledge management cycles Framework of knowledge management Technical KMS development Knowledge recommendation Managerial Knowledge management implementation KM and performance

12. T.P. Liang 2006-11-07 Major constructs of KM Nature of knowledge Organizational environment Nature of organization Knowledge management activities and processes (cycles) KM platform: infrastructure and KMS Nature of users and intermediaries Effect of KM on organizations

13. T.P. Liang 2006-11-07 Sample Studies Effect of Knowledge Diversity on Firm Performance Capability and Task Technology Fit on Individual Performance Personalization and Customer-Centric Systems

14. T.P. Liang 2006-11-07 Effect of Knowledge Diversity on Firm Performance

15. T.P. Liang 2006-11-07 Research Problem Should an industry focus on a few key categories of knowledge or a broad coverage of all knowledge in order to be competitive? Does the adoption of IT have any relationship with the value of knowledge and firm performance?

16. T.P. Liang 2006-11-07 Ecological model in Organization Hannon and Freeman (1989) proposed the ecological view of organization that seeks to understand how social conditions affect the rates in which new organizations and new organizational forms arises, the rates at which organizations change forms, and the rates at which organizations die out.

17. T.P. Liang 2006-11-07 何謂生態學？ 生態學（ Ecology ）是研究生物與其周圍 環境相互關係的科學。亦即，生態學是 研究在某一特定範圍內，生物與生物之 間、生物與環境之間相互影響關係的科 學。 生態學亦可以被視為是一種巨觀的生物 學 生態學以不同層次的角度觀察生物，包 含： 個體（ Organism ） 物種（ Species ） 族群（ Population ） 群落（ Community ） 生態系統（ Ecosystem ）

18. T.P. Liang 2006-11-07 生態學的 DICE 模式 基於生態學理論的歸納，生物族群在生態系統內的關係，主要可 以分成分佈、互動、競爭、演化四個階段，並形成一個循環，命 名為 DICE 模式。

19. T.P. Liang 2006-11-07 分佈（ Distribution ） 在研究的本質上，生態學探討、描述生物體之間及生物體與環境 之間的相互關係。  如何去描繪目前生態系統的狀況便是生態研究 的基礎。 生物分佈狀況或空間塑模（ Spatial Modeling ）： 以數學方法對生態系統進行描述，包含對於各個生物族群數量 的計數、地理區域的分佈狀況。 生態研究的第一步工作。

20. T.P. Liang 2006-11-07 分佈構面的子構面 族群的強度 族群強度在衡量不同族群之間的相對強度，藉以分析族群間的 強弱關係並可定義它們之間的行為關係。一般而言，族群的強 度可以利用族群的種類、族群內生物體的個數、分佈區域、及 在食物鏈上的關係等構面來衡量，可用來描繪群落內族群的分 佈輪廓。 物種的多樣性 衡量群落內物種的豐富程度，生態系統的一個重要觀察 指標。 多樣性與穩定性關係法則（ Diversity-stability principle ）

21. T.P. Liang 2006-11-07 A Knowledge Ecology Basic species in a knowledge ecology is different types of knowledge that belong to the organization. The goal of KM is to build a mechanism by which a healthy balance of knowledge can be maintained for achieving superior performance.

22. T.P. Liang 2006-11-07 Diversity vs. Stability In ecological rules, the diversity-stability relationship is a major principal, which says an ecology is more stable if it maintains a certain level of diversity. Similarly, we would like to examine whether the same rule holds in a knowledge ecology, ie, organizations with more diversified knowledge are more stable in performance.

23. T.P. Liang 2006-11-07 Research Framework

24. T.P. Liang 2006-11-07 Hypotheses (1) H1: Relationship Between IT and Knowledge Ecology H11: Higher IT capabilities support higher knowledge ecology H12: Higher IT capabilities support higher knowledge diversity

25. T.P. Liang 2006-11-07 Hypotheses (2) Relationship between Knowledge diversity and firm performance H21: Higher knowledge intensity results in higher average performance H22: Higher knowledge intensity results in lower performance variations

26. T.P. Liang 2006-11-07 Hypotheses (3) Relationship between knowledge diversity and firm performance Higher knowledge diversity results in lower average performance Higher knowledge diversity results in lower performance variations

27. T.P. Liang 2006-11-07 Criteria for Choosing Industries Four industries were chosen based on their knowledge intensity and environmental uncertainty. Knowledge intensity is measured as the ratio of product price by the tangible costs (including material costs and depreciation of fixed assets). Environmental uncertainty is measured by the changes in technology (measured by the number and importance of patents) and product lifecycle.

28. T.P. Liang 2006-11-07 The Chosen Industries Env. Uncertainty Knowledge Intensity LowHigh BankingIC Design LowSteelSemi-conductor Foundry

29. T.P. Liang 2006-11-07 Twelve Knowledge Types Twenty companies were chosen (five in each category) for study. Value chain activities are used to differentiate 12 categories of knowledge, such as raw material acquisition, product manufacturing, distribution, marketing, customer services, strategic planning, general management, financial management, quality management, human resource management, R&D, and IS management.

30. T.P. Liang 2006-11-07 Data Collection A group of experts was invited to fill out the questionnaire for assessing the relative importance of a particular knowledge in an industry and the relative strength of the twelve types of knowledge among the companies A total of 58 responses were collected, among which 17 for semiconductor, 16 for IC Design, 15 for banks, and 10 for steel.

31. T.P. Liang 2006-11-07 Data measurement IT capabilities: mean score on the IT capability question from experts Knowledge intensity: mean score of the other 11 types of knowledge Knowledge diversity: using the entropy to measure it Firm performance: Earnings per share in the past five years (means and variance)

32. T.P. Liang 2006-11-07 Relative Importances K. TypeIC DesignSemiconductorBankSteel MeanRankMeanRankMeanRankMeanRank R&D6.6916.7116.2716.401 Acquisition6.5625.41105.7355.909 Strategy6.5036.0645.6075.8011 Production6.4446.3526.1326.203 Marketing6.1955.4785.803 12 Quality mgt6.1965.8855.2096.302 Distribution5.9474.71124.87116.204 IT appl.5.6985.8265.5385.8010 Service5.6986.1835.8046.007 General mgt5.63105.6575.6766.106 HRM5.63104.82114.53126.008 Finance5.44125.4195.20106.205

33. T.P. Liang 2006-11-07 Data Reliability IC design Semiconductor foundry BankingSteel Raw material acquisition0.89770.86080.72740.7401 Production0.71800.76860.79210.8023 Distribution0.79010.94890.74070.9265 Marketing0.66000.83570.70960.8247 Customer services0.80080.74460.10270.7342 Strategic planning0.67040.63900.83880.8106 General mgmt 0.78660.86250.84150.7020 Finance mgmt0.50800.78690.87110.7803 Quality mgmt0.79610.58220.92490.8780 Human resources mgmt 0.77500.73340.91200.8691 R&D0.77090.84710.80130.6753 IT applications0.93920.76170.91300.8231 All constructs0.95760.94590.95080.9627

34. T.P. Liang 2006-11-07 Results from Path Analysis

35. T.P. Liang 2006-11-07 Industrial Differences Hypotheses Industry H11H12H21H22H31H32 IC Design 0.675***0.326**0.630***0.565***ns-0.25* Semiconductor 0.718***0.375***0.732***ns-0.375***-0.195 Banking 0.621***0.436**ns -0.283*-0.272 Steel 0.724***0.364***0.502***-0.351*-0.429**ns

36. T.P. Liang 2006-11-07 Effect of Knowledge Breadth We choose different number of knowledge types and see how knowledge breadth would affect the hypotheses Stepwise analysis that removed one knowledge category ranked the least important by experts at a time, and repeated the path analysis for 9 times.

37. T.P. Liang 2006-11-07 Models with Different Knowledge Spread Hypotheses Knowledge H11H12H21H22H31H32 Top 10 0.740***0.414***0.619***0.502***-0.204***-0.127** Top 9 0.719***0.387***0.617***0.505***-0.195***-0.137** Top 8 0.743***0.422***0.603***0.499***-0.192***-0.155* Top 7 0.700***0.365***0.602***0.514***-0.183**-0.170** Top 6 0.711***0.368***0.635***0.511***-0.196***-0.157* Top 5 0.699***0.338***0.636***0.510***-0.163**-0.145** Top 4 0.697***0.339***0.608***0.477***-0.162**-0.142* Top 3 0.705***0.322***0.535***0.433***ns Top 2 0.669***0.184**0.491***0.392***ns-0.154**

38. T.P. Liang 2006-11-07 Effects of Knowledge Breadth by Industry Hypotheses Knowledge category IC designSemiconductorBankingSteel Top 11 ---0.502*** Top 10 0.594***0.535***ns0.496** Top 9 0.599***0.543***ns0.510*** Top 8 0.587*** 0.573*** ns0.503*** Top 7 0.612***0.571*** ns 0.505** Top 6 0.612***0.600*** ns 0.539*** Top 5 0.620***0.601*** ns 0.565*** Top 4 0.550***0.604*** ns 0.587*** Top 3 0.541***0.659*** ns 0.540*** Top 2 0.412***0.627*** ns H21: knowledge intensity on performance

39. T.P. Liang 2006-11-07 Major Observations IT affects the intensity and diversity of organizational knowledge Higher knowledge intensity improves the average firm performance but reduces the stability (increases variance) Higher knowledge diversity reduces firm performance, but increases performance stability

40. T.P. Liang 2006-11-07