ارزیابی قابلیت مدیریت دانش و بهبود آن با استفاده از روش تصمیم‌گیری ترکیبی فازی (مطالعه موردی: شرکت صنام)

نوع مقاله: مقاله پژوهشی

نویسندگان

1 استادیار، گروه مهندسی صنایع، دانشکدۀ مهندسی، دانشگاه آزاد اسلامی واحد سمنان، سمنان، ایران

2 دانشجوی دکتری، مدیریت صنعتی، گروه مدیریت، دانشگاه اصفهان، اصفهان، ایران.

چکیده

هدف پژوهش: هدف از این پژوهش ارائه روشی جهت ارزیابی قابلیت مدیریت دانش براساس روش گلد-زیرساختی(تکنولوژی، ساختار، فرهنگ) و فرآیندی(فراگیری، تبدیل، کاربرد و حفاظت) با بهره‌گیری از روش تصمیم‌گیری چندمعیاره فازی چانگ و وانگ (2009) و روش درک و فهم خروجی آن در قالب متغیرهای کلامی لطفی‌زاده با بررسی کاربردی شرکت صنام بود.
روش‌شناسی: درپژوهش حاضر داده‌ها به صورت کیفی و کمی مورد تجزیه و تحلیل قرار گرفتند. داده‌های کیفی از طریق مصاحبه با گروه‌های مرجع، متخصصین، خبرگان و افراد درگیر با مدیریت دانش و داده‌های کمی نیز از طریق پرسشنامه حاصل گردآوری شده و سپس با استفاده از مدل غربال‌سازی فازی و مفاهیمی از جمله اعداد فازی مثلثی و رویکرد زبانی فازی تلاش شد روشی برای اندازه‌گیری و تحلیل قابلیت مدیریت دانش در سازمان ارائه شود.
یافته‌ها: نتایج تحقیق نشان داد که مدیران با بهره‌گیری از روش روش تصمیم‌گیری ترکیبی فازیمی‌توانند قابلیت مدیریت دانش در سازمان‌ها را ارزیابی کرده و سپس وضعیت مطلوب را تعیین و با اولویت‌بندی معیارها به سوی آن حرکت کنند. همچنین تصمیم‌گیران شرکت صنام می‌بایست معیارهای حفاظت و تبدیل را در اولویت برنامه‌ریزی خود قرار داده و سایر معیارهای کاربرد، تکنولو‍‍‍ژی، فرهنگ وساختار را به ترتیب در اولویت‌های بعدی جهت بهبود مدیریت دانش قرار دهند.
 
 

کلیدواژه‌ها


عنوان مقاله [English]

An Assessment of Knowledge Management Performance Using Fuzzy Combined Decision Method: Case Study of Sanam Company

نویسندگان [English]

  • mohammad abdolshah 1
  • seyed AmirMohammad Khatibi 2
1 Assistant Prof., Department of Industrial Engineering, Faculty of Engineering, Semnan Branch, Islamic Azad University, Semnan Branch, Semnan, Iran.
2 Department of Management, University of Isfahan, Isfahan, Iran.
چکیده [English]

Abstract
Purpose: this study aimed to provide a method for measuring knowledge management capability based on the gold-infrastructure (technology, structure, culture) and process (envelopment, transformation, application and protection) using the fuzzy multi-criteria decision making method of Chang and Wang (2009) and to identify the way of understanding its output in the form of the verbal variables as stated by Lotfizadeh in the context of Sanam Company.
Methodology: data were gathered by both qualitative and quantitative methods. Qualitative data were collected by interviews with reference groups, specialists and experts involved in knowledge management. Furthermore, quantitative data were collected by fuzzy methodology for measuring knowledge management in organizations.
Findings: managers can use this method to assess the ability of knowledge management in organizations, then to determine the optimal situation and prioritize the criteria obtained.
Conclusion: the results from Sanam Company showed that its decision makers should prioritize their planning and other criteria in terms of application, technology, culture and infrastructure in order to improve their knowledge management.
 

کلیدواژه‌ها [English]

  • knowledge management capability
  • Fuzzy Multi-Criteria Decision Making Method
  • fuzzy screening model
  1. Abdolshah, M., Moghimi, M., Khatibi, S.A.M. (2018). Investigating Competitive Advantage in Banking Industry Based on Porter's Generic Strategies: IRANs Newly-Established Private Banks. International Journal of Applied Management Sciences and Engineering, 5(1).
  2. Acara, M.F., Tarim, M., Zaim, H., Zaim, S., Delene, D. (2017). Knowledge management and ERP: Complementary or contradictory?, International Journal of Information Management, 37(6): 703-712. https://doi.org/10.1016/j.ijinfomgt.2017.05.007
  3. Barsky, N. & Marchant, G. (2000). The most valuable resource: measuring and managing intellectual capital. Strategic Finance Magazine, 81(8): 58-62.
  4. Bassi, L., & Van Buren, M. (1999). Valuing investments in intellectual capital. InternationalJournal of Technology Management, 18(5): 414 – 432.
  5. Beijerse, U.R.P. (2000). Knowledge management in small and medium sized companies: Knowledge management for entrepreneurs. Journal of Knowledge Management, 4(2): 162 – 179.
  6. Bontis, N. (1995). Organizational learning and leadership: a literature review of two fields. In: Published Proceedings of ASAC ‘95, Windsor, Canada.
  7. Bontis, N. (1999). Managing an organizational learning system by aligning stocks and flows of knowledge: An empirical examination of intellectual capital, knowledge management and business performance. Ph.D. Dissertation. London, Canada: Ivey Business School – University of Western Ontario.
  8. Bukowitz, W., & Petrash, G. (1997). Visualising, Measuring and managing knowledge. Research Technology Management, 40: 24 – 31.
  9. Bukowitz, W.R., & Williams, R.L. (2000). The knowledge management fieldbookRevised edition. London: Prentice-Hall.
  10. Carneiro, A. (2001).The role of intelligent resources in knowledge management. Journalof Knowledge Management, 5(4): 358-67.
  11. Chang, T.H. & Wang, T.C. (2009). Using the fuzzy multi-criteria decision making approach for measuring the possibility of successful knowledge management. Information Sciences, 179(4): 355-370 https://doi.org/10.1016/j.ins.2008.10.012
  12. Correia Ann.M. & Saramento A. (2003). knowledge management: Key competences and Skills for Innovation and competitiveness. In: the technology and HRM conference on the dual interaction between technology and Human resource, France.2003, 19-21 may.
  13. Costa, A., Soares, A.L., Sousa, J.P. (2016). Information, knowledge and collaboration management in the internationalisation of SMEs: A systematic literature review. International Journal of Information Management, 36(4): 557-569. https://doi.org/10.1016/j.ijinfomgt.2016.03.007
  14. Edvinsson, L., & Malone, M.S. (1997). Intellectual capital. New York, NY: HarperCollins.
  15. Gibbert, M., Leibold, M., Probst, G. (2002). Five styles of customer knowledge management, and how smart companies use them to create value.  European Management Journal, 20(5): 459-469.
  16. Gold, A.H., Malhotra, A. & Segars, A.H. (2001). Knowledge management: An organizational capabilities perspective. Journal of Management Information Systems,18(1):185-214. https://doi.org/10.1080/07421222.2001.11045669
  17. Gooijer,F.D,.(2000). Designing a knowledge management performance –framework. journalof Knowledge Management, 4(4):303-310.
  18. Stewart, G. L., Brown, K. G. (1386). Human Resource Management: Linking Strategy to Practice. Wiley, Business & Economics.
  19. http://www.investmentmap.org
  20. http://www.investmentmap.org/data_availability.aspx
  21. Hung, Y.C., Huang, S.M., Lin, Q.P., & Tsai, M.L. (2005). Critical factors in adopting a knowledge management system for the pharmaceutical industry. Industrial Management &Data Systems, 105(2): 164 – 183.
  22. Cohen, J. F& Olsen, K. (2015). Knowledge management capabilities and firm performance: A test of universalistic, contingency and complementarity perspectives. Expert Systems with Applications, 42(3):1178-1188. https://doi.org/10.1016/j.eswa.2014.09.002
  23. Martinze,M.N. (1998). The collective power of employee knowledge. HR Majazine,43(2):88-94.
  24. Torabi, M.H.R., Kyani, A.K., Falakini, H. (2016). An Investigation of the Impact of Knowledge Management on Human Resource Performance in Management of Keshavarzi Bank Branches in Tehran. Procedia - Social and Behavioral Sciences, 230: 471-481. https://doi.org/10.1016/j.sbspro.2016.09.059
  25. Moffett, S., McAdam, R. & Parkinson, S. (2003). An Empirical Analysis of Knowledge Management Applications. Journal of Knowledge Management, 7(3): 6-26.
  26. Abdolshah,M., Samavi, A., Khatibi, S.A., Mamoolraftar, M., (2019). A Review of Systems Reliability Analysis Using Fuzzy Logic. Advanced Fuzzy Logic Approaches in Engineering Science: 362-377. DOI: 10.4018/978-1-5225-5709-8.ch017.
  27. Muthuveloo, R., Shanmugam, N. & Teoh, A.P. (2017). The impact of tacit knowledge management on organizational performance: Evidence from Malaysia. Asia Pacific Management Review,22(4):192-201.  https://doi.org/10.1016/j.apmrv.2017.07.010
  28. Nonaka, I. & Takeuchi, H. (2008). The KnowledgeCreating Company. Oxford University Press.
  29. Nonaka, I, (2005).The knowledge-creating company. In:HBR: 96-104
  30. Nonaka, I. (2006). A Dynamic Theory of Organizational Knowledge Creation. Organization science:5(1):14-37.
  31. Nonaka, I. & Takeuchi, H. (2008). The knowledge. New York: Oxford University Press.
  32. Nonaka, I., & Takeuchi, H. (2004).The knowledge creating company. Oxford: Oxford University Press.
  33. Nonaka, I., Takeuchi, H. (1995). The Knowledge-creating Company: How Japanese Companies Create the Dynamics of Innovation. Oxford University.
  34. Nonaka, I., Takeuchi, H., & Umemoto, K. (2007). A theory of organizational knowledge creation. International Journal of Technology Managemen, 11(7-8): 833-922.
    1. Paquette, S. (2006). Customer knowledge management. Available at: http://www.fis.utoronto.ca/phd/paquette/Documents/paquette%20%20 customer%20knowledge%20management.pdf
  35. Pearson, T. (1999). Measurements and the knowledge revolution. Quality Progress, 32(9): 31 – 37.
  36. Ernest, P. (1999). Knowledge management in the library. database magazine, 22(2):75-78.
  37. Plessis, M.d. (2007). Knowledge management:what makes complex implementations successful?. Journal of Knowledge Management, 11(2):91-101.
  38. Powell, W. W. (1998). Learning from collaboration: Knowledge and networks in the biotechnology and pharmaceutical industries. California Management Review, 40(3):228–240.
  39. Reich, B.H., Gemino, A., Sauer, C. (2014). How knowledge management impacts performance in projects: An empirical study. International Journal of Project Management, 32(4): 590-602 https://doi.org/10.1016/j.ijproman.2013.09.004
  40. Shi, C.h. (2016). Knowledge Based on Reliable Evidence, Epistemology, Knowledge and the Impact of Interaction pp 237-249
  41. Sveiby, K.E. (1997). The New Organizational Wealth: Managing and Measuring KnowledgeBased Assets. San Francisco: BerrettKoehler.
  42. Yager, R. R. (1993). Fuzzy screening systems. In: R. Lowen & M. Roubens (Eds.), Fuzzy logic: State of the art Dordrecht: Kluwer Academic Publishers:251–261.
  43. Zadeh, L. A. (1965). Fuzzy set. Information and Control, 8(3), 338–35 . doi:10.1016/S0019- 9958(65)90241-X

 

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