1. Information Technologies, Knowledge Ecology, and Firm Performance: An Exploratory Study T.P. Liang D.N. Chen National Sun Yat-sen University Workshop on KE and EC, Aug 23, 2003

2. Knowledge Management Research in knowledge management has covered various technical and managerial issues. Among the managerial research, the process view that stresses the process of knowledge creation, storage, retrieval, transfer, and applications, seems to dominate.

3. A Strategic View Another view is more from a strategic perspective that treats knowledge as a strategic investment that must be managed with cost effectiveness. In other words, different industries may need to take different strategies and maintaining a different knowledge ecology.

4. 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?

5. 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.

6. 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.

7. 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.

8. Research Framework

9. 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

10. 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

11. 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

12. 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.

13. The Chosen Industries Env. Uncertainty Knowledge Intensity LowHigh BankingIC Design LowSteelSemi-conductor Foundry

14. 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.

15. 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.

16. 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)

17. 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

18. 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

19. Results from Path Analysis

20. 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

21. 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.

22. 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**

23. 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

24. 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