知識管理是21世紀的管理趨勢 (2)ppt課件

1、Knowledge Management Research: A Personal ExperienceT.P. LiangNational Sun Yat-sen UniversityNovember 7, 2006精品資料網(wǎng)cnshuImportance of KM ResearchThe importance of knowledge in businessManaging knowledge is a difficult and continuing processA challenging question: How can knowledge be managed properly

2、 to improve firm performance?知識管理是21世紀的管理趨勢資料、資訊、知識、智慧關(guān)係內(nèi)隱知識與外顯知識的比較項 目內(nèi)隱知識外顯知識特 點難以書面化表達難以系統(tǒng)化持續(xù)性強，不易被人改變例如：經(jīng)驗、秘訣、信念、感覺、習(xí)慣可量化或書面化較系統(tǒng)化固定的資訊，其運用視人而異例如：業(yè)務(wù)手冊、企劃案、作業(yè)指導(dǎo)規(guī)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(xué)習(xí)可透過視覺、聲音或動作來呈現(xiàn)、或透過書籍、影片等來傳遞精品資料網(wǎng)cnshu不同的知識特性個人組織內(nèi)隱藝術(shù)IC 設(shè)計外顯(SOP)大學(xué)生產(chǎn)製造知

3、識、智慧資本與企業(yè)價值企業(yè)價值員工的觀念型知識員工的經(jīng)驗型知識內(nèi)含於組織文化的知識內(nèi)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(chǎn)無形資產(chǎn)財務(wù)性資產(chǎn)知識分類企業(yè)潛力內(nèi)隱知識外顯知識個人知識與組織知識的轉(zhuǎn)換個人知識透過分享後，轉(zhuǎn)變?yōu)榻M織的知識組織的知識或經(jīng)驗供個人學(xué)習(xí)、吸收後，強化個人知識個人知識創(chuàng)意經(jīng)驗人脈組織知識作業(yè)程序 Know-How 任務(wù)手冊 協(xié)作方式Nonaka的知識成長方式內(nèi)隱知識外顯知識內(nèi)隱知識社會化外部化外顯知識內(nèi)部化組 合精品資料網(wǎng)cnshu知識管理的根本工具創(chuàng)造分類/儲存索引/檢索過濾/篩選導(dǎo)覽運用分析知識推薦Research in Knowledge Ma

4、nagementConceptualKnowledge management cyclesFramework of knowledge managementTechnicalKMS developmentKnowledge recommendationManagerialKnowledge management implementationKM and performanceMajor constructs of KMNature of knowledgeOrganizational environmentNature of organizationKnowledge management a

5、ctivities and processes (cycles)KM platform: infrastructure and KMSNature of users and intermediariesEffect of KM on organizationsSample StudiesEffect of Knowledge Diversity on Firm PerformanceCapability and Task Technology Fit on Individual PerformancePersonalization and Customer-Centric SystemsEff

6、ect of Knowledge Diversity on Firm PerformanceResearch ProblemShould 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?Ecological model

7、in OrganizationHannon 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 di

8、e out.精品資料網(wǎng)cnshu何謂生態(tài)學(xué)？生態(tài)學(xué)Ecology是研討生物與其周圍環(huán)境相互關(guān)係的科學(xué)。亦即，生態(tài)學(xué)是研討在某一特定範圍內(nèi)，生物與生物之間、生物與環(huán)境之間相互影響關(guān)係的科學(xué)。生態(tài)學(xué)亦可以被視為是一種巨觀的生物學(xué)生態(tài)學(xué)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(tài)系統(tǒng)Ecosystem 生態(tài)學(xué)的DICE方式基於生態(tài)學(xué)理論的歸納，生物族群在生態(tài)系統(tǒng)內(nèi)的關(guān)係，主要可以分成分佈、互動、競爭、演化四個階段，並構(gòu)成一個循環(huán)，命名為DICE方式。分佈Distribution 在研討的本質(zhì)上，生態(tài)學(xué)探討、描畫生物體之間及生

9、物體與環(huán)境之間的相互關(guān)係。 如何去描繪目前生態(tài)系統(tǒng)的狀況便是生態(tài)研討 的基礎(chǔ)。生物分佈狀況或空間塑模Spatial Modeling：以數(shù)學(xué)方法對生態(tài)系統(tǒng)進行描畫，包含對於各個生物族群數(shù)量的計數(shù)、地理區(qū)域的分佈狀況。生態(tài)研討的第一步任務(wù)。分佈構(gòu)面的子構(gòu)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(guān)係並可定義它們之間的行為關(guān)係。普通而言，族群的強度可以利用族群的種類、族群內(nèi)生物體的個數(shù)、分佈區(qū)域、及在食物鏈上的關(guān)係等構(gòu)面來衡量，可用來描繪群落內(nèi)族群的分佈輪廓。物種的多樣性衡量群落內(nèi)物種的豐富程度，生態(tài)系統(tǒng)的一個重要觀察指標。多樣性與穩(wěn)定性關(guān)係法則Diversity-st

10、ability principle精品資料網(wǎng)cnshuA Knowledge EcologyBasic 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.Diversity vs. Stabili

11、tyIn 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 knowledg

12、e are more stable in performance.Research FrameworkHypotheses (1)H1: Relationship Between IT and Knowledge EcologyH11: Higher IT capabilities support higher knowledge ecologyH12: Higher IT capabilities support higher knowledge diversityHypotheses (2)Relationship between Knowledge diversity and firm

13、performanceH21: Higher knowledge intensity results in higher average performanceH22: Higher knowledge intensity results in lower performance variationsHypotheses (3)Relationship between knowledge diversity and firm performanceHigher knowledge diversity results in lower average performanceHigher know

14、ledge diversity results in lower performance variations精品資料網(wǎng)cnshuCriteria for Choosing IndustriesFour 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

15、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. The Chosen IndustriesEnv. UncertaintyKnowledge IntensityLowHighHighBankingIC DesignLowSteelSemi-conductor FoundryTwelve Knowl

16、edge TypesTwenty 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, financia

17、l management, quality management, human resource management, R&D, and IS management.Data CollectionA 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 am

18、ong the companiesA total of 58 responses were collected, among which 17 for semiconductor, 16 for IC Design, 15 for banks, and 10 for steel.Data measurementIT capabilities: mean score on the IT capability question from expertsKnowledge intensity: mean score of the other 11 types of knowledgeKnowledg

19、e diversity: using the entropy to measure itFirm performance: Earnings per share in the past five years (means and variance)Relative Importances K. TypeIC DesignSemiconductorBankSteelMeanRankMeanRankMeanRankMeanRankR&D6.6916.7116.2716.401Acquisition6.5625.41105.7355.909Strategy6.5036.0645.6075.8011P

20、roduction6.4446.3526.1326.203Marketing6.1955.4785.8035.8012Quality mgt6.1965.8855.2096.302Distribution5.9474.71124.87116.204IT appl.5.6985.8265.5385.8010Service5.6986.1835.8046.007General mgt5.63105.6575.6766.106HRM5.63104.82114.53126.008Finance5.44125.4195.20106.205Data ReliabilityIC designSemicond

21、uctor foundryBankingSteelRaw material acquisition0.89770.86080.72740.7401Production0.71800.76860.79210.8023Distribution0.79010.94890.74070.9265Marketing0.66000.83570.70960.8247Customer services0.80080.74460.10270.7342Strategic planning0.67040.63900.83880.8106General mgmt0.78660.86250.84150.7020Finan

22、ce mgmt0.50800.78690.87110.7803Quality mgmt0.79610.58220.92490.8780Human resources mgmt0.77500.73340.91200.8691R&D0.77090.84710.80130.6753IT applications0.93920.76170.91300.8231All constructs0.95760.94590.95080.9627精品資料網(wǎng)cnshuResults from Path AnalysisIndustrial Differences HypothesesIndustry H11H12H

23、21H22H31H32IC Design0.675*0.326*0.630*0.565*ns-0.25*Semiconductor0.718*0.375*0.732*ns-0.375*-0.195Banking0.621*0.436*nsns-0.283*-0.272Steel0.724*0.364*0.502*-0.351*-0.429*nsEffect of Knowledge BreadthWe choose different number of knowledge types and see how knowledge breadth would affect the hypothe

24、sesStepwise analysis that removed one knowledge category ranked the least important by experts at a time, and repeated the path analysis for 9 times.Models with Different Knowledge Spread HypothesesKnowledgeH11H12H21H22H31H32Top 100.740*0.414*0.619*0.502*-0.204*-0.127*Top 90.719*0.387*0.617*0.505*-0.195*-0.*Top 80.743*0.422*0.603*0.499*-0.192*-0.155*Top 70.700*0.365*0.602*0.514*-0.183*-0.170*Top 60.711*0.368*0.635*0.511*-0.196*-0.157*Top 50.699*0.338*0.