隨著科技與網路的快速變遷，企業唯有透過有效的知識創造與利用，方能在知識經濟時代獲取競爭優勢。因此，不論在學術或實務界，「知識管理」是一新興且重要的研究議題。然而，有效的知識管理對組織的影響，不只是單純的管理機制，資訊科技 (即知識管理系統) 的導入以支援知識管理活動，也佔著舉足輕重的角色。
本研究以權變理論和鑽石模型為基礎來探討「科技相容度」對知識管理系統使用與個人績效的影響。研究架構主要整合了三種不同的相容度，包括：科技與任務的相容度、科技與人的相容度、科技與組織的相容度。
本研究以實證研究的方法來瞭解不同的相容度如何影響知識管理系統的使用及對個人績效的影響。我們以七個研究假設來驗證研究的架構，並利用PLS進行檢驗。研究結果發現，科技與任務的相容度對於知識管理系統的使用影響最鉅，這意味著企業必須引進符合任務需求的知識管理系統。此外，科技與人的相容度對於知識管理系統的使用與員工績效均有顯著影響，顯示科技若和員工過去的使用經驗、價值觀等相容時，員工會更頻繁地使用它，並進一步提升個人績效。最後，科技與組織的相容度相較於上述兩者，對知識管理系統的影響較低，建議未來研究可針對此部份做更深入的探討。
總而言之，有別於過去研究僅針對單一相容度來探討，本研究具體提出整合性的相容度概念，研究結果發現三種不同類型的相容度對於知識管理系統的使用均有顯著的影響。就企業而言，本研究貼近企業界的實務需求，對經營管理者、系統使用者各具不同的意義與價值，可作為企業施行知識管理系統時的參考依據。

With the rapid and constant changes taking place in information technology and internet, only firms participating in the creation and utilization of knowledge can hope to obtain the advantageous
position in today’s knowledge-based economy. Thus, the issues surrounding knowledge management (KM) have attracted more and more concern from both industry and academia. To add value with KM, we need KMS, which involve the application of IT systems and other organizational resources to manage knowledge strategically, are a relatively recent phenomenon.
The goal of this research is to find the significant factors that link with KMS use and individual performance by using diamond model and contingency theory, which emphasizes the
importance of fit. We combine with three distinct factors of compatibility, including (1) Technology
- Task Compatibility (2) Technology - People Compatibility (3) Technology - Organization
Compatibility, to mold an integrated model.
An empirical survey methodology is applied to test the research model and seven hypotheses are developed in this study, and then we use PLS to analyze it. The results reveal that Technology-Task compatibility contributes most to the use of KMS. It implies that KMS should fulfill the task needs of users, and therefore, people will use more functions of the KM systems
frequently. Besides, Technology-People compatibility has similar effect on the use of KMS. This suggests that it will enhance the usage if the KMS is more compatible with users’ past experience and value. We also found that Technology - Organization compatibility has least impact on usage, but it still needed to take into consideration and worthy to discuss in the future research.
In sum, unlike much prior research that has focused on only a limited aspect of compatibility, we provide a more comprehensive conceptual definition that disaggregates the content of compatibility into three distinct and separable constructs and the findings of this study provides some suggestions for the KMS research.

CONTENTS

CHAPTER1. INTRODUCTION……………………………………………………1
1.1. BACKGROUND…………………………………………………………………………..1
1.2. RESEARCH MOTIATION………………………………………………………………..2
1.3. PURPOSE………………………………………………………………………………….6
CHAPTER2. THEORETICAL FOUNDATION AND LITERATURE REVIEW……......…………………………………………………………………….7
2.1. CONTINGENCY THEORY…….………………………………………………………...7
2.2. DIAMOND MODEL………………………………………………………………………9
2.3 KM AND KMS PERSPECTIVES………………………………………………………10
2.4 COMPATIBILITY……………………………………………………………………….12
2.4.1 TECHNOLOGY-TASK COMPATIBILITY………………………………………..12
2.4.2 TECHNOLOGY-PEOPLE COMPATIBILITY……………………………………..15
2.4.3 TECHNOLOGY-ORGANIZATION COMPATIBILITY…………………………...18
CHAPTER3. THE RESEARCH MODEL AND HYPOTHESES……………….21
3.1. RESEARCH MODEL……………………………………………………………………21
3.2. RESEARCH HYPOTHESIS……………………………………………………………..22
CHAPTER4. RESEARCH METHOD……………………………………………28
4.1. SUBJECT………………………………………………………………………………...28
4.2. MEASURE……………………………………………………………………………….29
4.3. PROCEDURE……………………………………………………………………………32
4.4. DATA COLLECTION……………………………………………………………………32
CHAPTER5. DATA ANALYSIS…………………………………………………..34
5.1. ASSESSMENT OF THE MEASUREMENT MODEL………………………………….35
5.2. ANALYSIS OF THE STRUCTURAL MODEL………………………………………....39
CHAPTER6. DISCUSSION AND IMPLICATION……………………………...42
6.1. DISCUSSION…………………………………………………………………………….42
6.2. THEORETICAL CONTRIBUTIONS……………………………………………………45
6.3. IMPLICATION FOR PRACTITIONER…………………………………………………47
6.4. LIMITATION AND SUGGESTIONS FOR FUTURE STUDY…………………………48
6.5. CONCLUSION…………………………………………………………………………..49


APPENDIX………………………………………………………………………….50
REFERENCE……………………………………………………………………….53

LIST OF TABLES
TABLE 1. CRITICAL SUCCESS FACTORS OF KM/KMS ADOPTION………………………...11
TABLE 2. THE RELATED RESEARCHES OF TECHNOLOGY-TASK COMPATIBILITY…….14
TABLE 3. THE RELATED RESEARCHES OF TECHNOLOGY-PEOPLE COMPATIBILITY…17
TABLE 4. THE RELATED RESEARCHES OF TECHNOLOGY-ORGANIZATION COMPATIBILITY…………………………………………………………………………………..20
TABLE 5. OPERATION DEFINITIONS…………………………………………………………..29
TABLE 6. DEMOGRAPHICS……………………………………………………………………...33
TABLE 7. INTERNAL CONSISTENCY…………………………………………………………..35
TABLE 8. UNQUALIFIED INDICATORS………………………………………………………...36
TABLE 9. INDIVIDUAL ITEM LOADING AND AVE…………………………………………...36
TABLE 10. DISCRIMINANT VALIDITY…………………………………………………………38
TABLE 11. SUMMARY OF HYPOTHESIZES TESTING………………………………………..41

LIST OF FIGURES
FIGURE 1.IMPLICIT CONTINGENCY MODEL IN MIS RESEARCH.……………………….....8
FIGURE 2 DIAMOND MODEL…………………………………………………………………….9
FIGURE 3. TASK AND TECHNOLOGY FIT MODEL…………………………………………...13
FIGURE 4. RESEARCH MODEL………………………………………………………………….21
FIGURE 5. STRUCTURE MODEL AND PATHS COEFFICIENT……………………………….39

1. Agarwal, R., and Prasad, J. “Are Individual Differences Germane to the Acceptance of New Information Technologies?,” Decision Sciences (30:2), Spring 1999, pp. 361-391.

2. Agarwal, R., and Prasad, J. “The Role of Innovation Characteristics and Perceived Voluntariness in the Acceptance of Information Technologies,” Decision Sciences (28:3), Summer 1997, pp. 557-582.

3. Alavi, M. KPMG Peat Marwick U.S.: One Giant Brain, Case 9-397- 108 Boston, MA: Harvard Business School, 1997.

4. Alavi, M. and Leidner, D.E. "A Framework of Knowledge Management Systems: Issues, Challenges, and Benefits," Communications of the AIS, 1, Article 7, February 1999.

5. Alavi, M. “Managing Organizational Knowledge,” in Framing the Domains of IT Management: Projecting the Future through the Past,” R. W. Zmud (Ed.), Pinnaflex Education Resources, Cincinnati, OH, 2000, pp. 15-28.

6. Al-gahtani S.S, and King Malcolm “Attitudes, satisfaction and usage: factors contributing to each in the acceptance of information technology,” Behavior and Information Technology(18:4), 1999, pp. 277 - 297

7. Amoako-Gyampah, K. and Salam, A.F. "An Extension of the Technology Acceptance Model in an ERP Implementation Environment," Information & Management (41:6), July 2004, pp. 731-745.

8. Bartlett, C. McKinsey & Company: Managing Knowledge and Learning, Case 9-396-357, Boston, MA: Harvard Business School, 1996.

9. Beatty, Robert. C, Shim, J.P, and Jones, Mary C “Factors influencing corporate web site adoption: a time-based assessment,” Information & Management, 2001, pp. 337-354

10. Bingi P, Sharma M, Godla J. “Critical issues affecting and ERP implementation,” Inf Syst Manage (16:3), 1999, pp. 7–14.

11. Bonner, Leading Knowledge Management and Learning, American Society of Training & Development, Virginia, 2000.
12. Borman, W. C., & Motowidlo, S. J. “Expanding the criterion domain to include elements of contextual performance,” In N. Schmitt, W. C. Borman, & Associates, Personnel Selection in Organization, San Francisco: Jossey-Bass., 1993, pp.71-98.

13. Bradford Marianne and Florin Juan “Examining the role of innovation diffusion factors on the implementation success of enterprise resource planning systems,” International Journal of Accounting Information Systems, 2003, pp. 205-225

14. Brown, J.S. and Duguid, P. “Knowledge and organization: A social-practice perspective,” Organizational Science (12:2), March-April 2001, pp. 198 – 213.

15. Chatman J. Improving interactional organizational research: A model of person organization fit. Academy of Management Review (14:1), 1989, pp. 333-349

16. Chen, Lei-da, Gillenson, Mark L. and Sherrell, Daniel L. “Enticing online consumers: an extended technology acceptance perspective,” Information and Management (39:8), September 2002, pp. 705-719.

17. Cho, I., and Kim, Y. “Critical Factors for Assimilation of Object-Orientated Programming Languages,” Journal of Management Information Systems (18:3), winter 2001-2002, pp. 125-156.

18. Compeau, D. R., and Higgins, C. A. “Computer Self-Efficacy: Development of a Measure and Initial Test,” MIS Quarterly (19:2), June 1995, pp.189-211.

19. Constant, D., Sproull, L., & Kiesler, S. “The Kindness of Strangers: The Usefulness of Electronic Weak Ties for Technical Advice.” Organization Science (7:2), 1996, pp.119-135.

20. Cooper R.B. and Zmud R.W. “Information technology implementation research: a technological diffusion approach,” Management Science (36:2), Feb 1990, pp. 123-139.

21. D'Ambra, J. and Rice, R.E. "Emerging Factors in User Evaluation of the World Wide Web," Information & Management (38:6), July 2001, pp. 373-384.

22. Davis, F.D.; Bagozzi, R.P. and Warshaw, P.R. "User Acceptance of Computer Technology: a Comparison of Two Theoretical Models," Management Science (35:8), August 1989, pp. 982-1003.

23. DeLone, W.H., McLean, E.R., “Information systems success: The quest for the dependent variable,” Information Systems Research (3:1), 1992, pp. 60-95.

24. Dennis, A.R.; Wixom, B.H. and Vandenberg, R.J. "Understanding Fit and Appropriation Effects in Group Support Systems via Meta-Analysis," MIS Quarterly (25:2), Jun 2001, pp. 167-194.

25. Dishaw, M.T., and Strong, D.M., “Supporting software maintenance with software engineering tools: A Computed task-technology fit analysis,” The Journal of Systems and Software (44:2), December 1998, pp. 107-120.

26. Fichman Robert G. “The Diffusion and Assimilation of Information Technology Innovations” Framing the Domains of IT Management: Projecting the Future, September 1999.

27. Gebauer, J., Shaw, M.J, and Gribbins, M.J, “Task-Technology Fit for Mobile Information Systems,” University of Illinois at Urbana-Champaign, June 2006.

28. Goodhue, D.L. and Thompson, R.L. "Task-Technology Fit and Individual Performance," MIS Quarterly (19:2), June 1995, pp. 213-236.

29. Gray, W. D. “The nature and processing of errors in interactive behavior.” Cognitive Science (24:2), 2000, pp. 205-248.

30. Grover, V., and Davenport, T. H. “General Perspectives on Knowledge Management: Fostering a Research Agenda,” Journal of Management Information Systems (18:1), 2001, pp. 5- 21.

31. Hansen, M.T. (1999). “The search-transfer problem: The role of weak ties in sharing knowledge across organizational subunits,” Administrative Science Quarterly (44:1), 1999, pp. 82 – 111.

32. Igbaria, M. and Tan, M. “The consequences of information technology acceptance on subsequent individual performance,” Information & Management (32:3), 1997, pp. 113-121.

33. Junglas, I.A., Watson, R.T. “U-commerce: a conceptual extension of e-commerce and m-commerce,” Proceedings of the Twenty-Fourth International Conference on Information Systems (ICIS 2003). Seattle, Washington, 2003, pp.667-677.

34. Jennex, M.E. and Olfman, L. (2002). Organizational Memory/Knowledge Effects on Productivity, A Longitudinal Study. 35th Hawaii International Conference on System Science, HICSS35, IEEE Computer Society

35. Karahanna, E., Agarwal, R.and Angst,C. M. “Reconceptualizing Compatibility Beliefs In Technology Acceptance Research,” MIS Quarterly(30:4),December 2006, pp.781-804

36. Karahanna, E., Straub, D. W., and Chervany. N. L. “Information Technology Adoption Across Time: A Cross-Sectional Comparison of Pre-Adoption and Post-Adoption Beliefs,” MIS Quarterly (23:2), June 1999, pp. 183-213.

37. Kristof, A. L. Person–organization fit: An integrative review of its conceptualizations, measurement, and implications. Personnel Psychology, 49, 1996, pp. 1–49.

38. J.P. Katz, et al., “The role of information technology in the ‘fit’ between culture, business strategy and organizational structure of global firms,” Journal of Global Information Management (8:2), 2000, pp. 24–35.

39. Lee, Y.E., and Benbasat, I. “A framework for the study of customer interface design for mobile commerce,” International Journal of Electronic Commerce (8:3), 2004, pp. 79-102.

40. Leavitt, H., 1965, Applying organizational change in industry: Structural, technological and humanistic approaches. In March, J. (Ed), Handbook of Organizations, Rand McNally, Chigaco, Illinois.

41. Leonard-Barton, D. “Implementation as mutual adaptation of technology and organization,” Research Policy (17:5), October 1988, pp. 25l-267.

42. Liebowitz, J. and T. Beckman Knowledge Organizations: What Every Manager Should Know. Washington, D.C.: CRC Press, 1998.

43. Liu, P.-L.; Chen, W.-C. And Tsai, C.-H. “An Empirical Study on the Correlation between Knowledge Management Capacity and Competitiveness in Taiwan’s industries,” Technovation, (24:12), 2004, pp. 971-977

44. Markus, M.L. and Robey, D., 1983. “The organizational validity of management information systems,” Human Relations (36:3), 1983, pp. 203-226.

45. Markus “Toward a Theory of Knowledge Reuse: Types of Knowledge Reuse Situations and Factors in Reuse Success,” Journal of Management Information Systems(18:1), Summer 2001, pp.57-93.

46. Moore, G. C., and Benbasat, I. “Development of an Instrument to Measure the Perceptions of Adopting an Information Technology Innovation,” Information Systems Research (2:3), September 1991, pp. 192-222.

47. Moore, G. C., and Benbasat, I. “Integrating Diffusion of Innovations and Theory of Reasoned Action Models to Predict Utilization of Information Technology by End-Users,” in Diffusion and Adoption of Information Technology, K. Kautz and J. Pries-Heje (eds.), Chapman & Hall, London, 1996, pp. 132-146.

48. Nonaka, I. and Takeuchi, H. The Knowledge Creating Company: How Japanese Companies Create the Dynamics of Innovation. New York: Oxford University Press; 1995.

49. O’Leary D. Enterprise resource planning: systems, life cycle, electronic commerce, and risk. New York: Cambridge Univ. Press; 2000.

50. Poston R, Grabski S. “Financial impacts of enterprise resource planning implementations,” International Journal of Accounting Information Systems (2:4), December 2001, pp. 271–94.

51. Premkumar G., Ramamurthy K. and Nilakanta Sree. “Implementation of EDI:A Innovation Diffusion Perspective,” Journal of Management Information Systems(11:2), 1994, pp. 157-186

52. Ramiller, Neil.C “Perceived compatibility of information technology innovations among secondary adopters: Toward a reassessment,” Journal of Engineering and Technology Management, 1994, pp.1-23

53. Robey , Smith and Vijayasarathy Leo R. “Perceptions of Conflict and Success in Information Systems Development Projects,” Journal of Management Information Systems(10:1), June 1993, pp. 123-139

54. Rogers, E. M. Diffusion of Innovations (1st ed.), The Free Press, New York, 1962.

55. Rogers, E. M. Diffusion of Innovations (3rd ed.), The Free Press, New York, 1983.

56. Rogers, E. M. Diffusion of Innovations (4th ed.), The Free Press, New York, 1995.

57. Ruggles, R. (1997).Knowledge management tools. Oxford: Butterworth-Heinemann.

58. Ruggles, R., “The state of the notion: knowledge management in practice,” California Management Review (40:3), 1998, pp. 80-89.

59. Sarvary, M. "Knowledge Management and Competition in the Consulting Industry," California Management Review (41:2), 1999, pp. 95-107.

60. Schultz, R.L. and Slevin, D.P. A program of research on implementation. In: R.L. Schultz and D.P. Slevin (Eds.), Implementing Operation Research/Management Science. Elsevier, New York, 1975a.

61. Sensiper, S. AMS Knowledge Centers Case N9-697-068. Boston: Harvard Business School, 1997.

62. Szilagyi, A.D. Jr and Wallace, M.J. Jr (1980), Organizational Behavior and Performance, 2nd ed., Goodyear Publishing Company, Santa Monica, CA.

63. Taylor, S., and Todd, P. “Understanding Information Technology Usage: A Test of Competing Models,” Information Systems Research (6:2), 1995, pp. 144-176.

64. Tiwana, A. (2001), The Knowledge Management Toolkit: Practical Techniques for Building Knowledge Management Systems, Prentice-Hall, Englewood Cliffs, NJ.

65. Tornatzky, L. G., and Klein, K. J. “Innovation Characteristics and Innovation Adoption-Implementation: A Meta-Analysis of Findings,” IEEE Transactions on Engineering Management (29:1), February 1982, pp. 28-45.

66. Wasko, M. and Faraj, S. “Why should I share? Examining Social Capital and Knowledge Contribution in Electronic Networks of Practice,” MIS Quarterly (29:1), 2005, pp. 35-58.

67. Wensley, A. Tools for knowledge management, BPRC Conference on Knowledge Management: Concepts and Controversies, 10–11 February, University of Warwick, Coventry, 2000.

68. P. Weill and M.H. Olson, An Assessment of the Contingency Theory of Management Information Systems. Journal of Management Information Systems (Summer 1989), pp. 59–85.

69. Weill, P. and Vitale, M. “Assessing the Health of an Information Systems Applications Portfolio: An Example from Process Manufacturing,” MIS Quarterly (23:4), December 1999, pp. 601-624.

70. Wiig, Karl M. “Knowledge Management: Where did it come from and where will it go?” Expert Systems with Applications (13:1), 1997, pp. 1-14.

71. Xu, J. and Quaddusa, M. "Adoption and Diffusion of Knowledge Management Systems: Field Studies of Factors and Variables," Knowledge-Based Systems (18:2-3), 2005, pp. 107-115.

72. Xu, J. and Quaddus, M. “Adoption and diffusion of knowledge management systems: an Australian survey”, Journal of Management Development (24:8), 2005a.

73. Xu, J. and Quaddus, M. “From rhetoric towards a model of practical knowledge management systems”, Journal of Management Development (24:8), 2005b.

74. Xu, J. and Quaddus, M. “A six-stage model for the effective diffusion of knowledgemanagement systems”, Journal of Management Development (24:8), 2005c.

75. Zack, M. “Managing Codified Knowledge,” Sloan Management Review, (40:4), Summer, 1999, pp.45-58

76. Zigurs, I., and Buckland B.K. “A theory of task-technology fit and group support systems effectiveness,” MIS Quarterly (22:3), 1998, pp. 313-334.

77. Zigurs, I., Poole, M. S., and DeSanctis. G. "A Study of Influence in Computer-Mediated Communication," MIS Quarterly {12), 1988, pp. 625-644.