• صفحه اصلی
  • تعیین شاخص های اصلی،ارزیابی و رتبه بندی کارایی عملکرد مدیریت دانش با تحلیل پوششی داده ها، مطالعه موردی: صنعت نفت ایران

اشتراک گذاری

آدرس مقاله


کد مقاله : IMJ-2011-1485 (R2) بازدید : 178 صفحه: 86 - 103

نوع مقاله: پژوهشی

تعیین شاخص های اصلی،ارزیابی و رتبه بندی کارایی عملکرد مدیریت دانش با تحلیل پوششی داده ها، مطالعه موردی: صنعت نفت ایران

محورهای موضوعی : مدیریت صنعتی

masoud najafi 1 , behzad Ghasemi 2

1 - Department of Management, Faculty of Humanities, Hamadan Azad University, Iran
2 - Department of Management, Faculty of Humanities, Islamic Azad University of Hamadan, Hamadan, Iran

تاریخ دریافت : 1399/09/02 تاریخ پذیرش : 1399/10/20 تاریخ انتشار : 1399/10/01

کلید واژه: بازده, صنعت نفت, تحلیل پوششی داده ها, مدیریت دانش,

چکیده مقاله :

مهمترین دلیل شناسایی شاخص های اصلی و محاسبه کارایی عملکرد مدیریت دانش در صنعت نفت، حفظ و طبقه بندی مناسب دانش موجود و ایجاد مزیت رقابتی قدرتمند در سطح جهانی با ایجاد پیشرفت در توسعه روش های تولید محصولات با فناوری جدید بر پایه ارتقاء سطح دانش فنی است. مهمترین پارامتر ارزیابی عملکرد، کارایی است که روش های مختلفی جهت سنجش آن وجود دارد. تحلیل پوششی داده ها، ابزار قدرتمند محاسبه کارآیی سیستمها از جمله عملکرد مدیریت دانش در صنعت نفت است. در این تحقیق با استفاده از منابع کتابخانه‌ای و نشریات عملی شاخص های اصلی مدیریت دانش شناسایی شدند. با نظر سنجی از کارکنان شرکت های زیر مجموعه صنعت نفت به همراه تکنیک تحلیل پوششی داده ها و تحلیل سلسله مراتبی، کارایی عملکرد مدیریت دانش شرکتها محاسبه گردید که به کمک آن می توان شرکتهای مورد بررسی را رتبه بندی و از این طریق برای ارتقاء سطح مدیریت دانش آنها برنامه ریزی نمود. طبق نتایج شرکت ملی پالایش و پخش فرآورده های نفتی نسبت به سایر شرکتها در سطح بالاتری عمل نموده و از همین منظر دارای مزیت رقابتی مناسبی با رقبای جهانی خود می باشد. تحقیق حاضر، اولین پژوهشی است که جامعه آماری شامل چهار شرکت اصلی زیر مجموعه صنعت نفت ایران را با استفاده از تحلیل پوششی داده ها جهت محاسبه کارایی عملکرد مدیریت دانش، بررسی کرده است.

چکیده انگلیسی:

The most important reason for identifying the main indicators and calculating the efficiency of knowledge management performance in the oil industry is to maintain and classify the existing knowledge and create a strong global competitive advantage by making progress in developing production methods with new technology based on improving technical knowledge. The most important parameter of performance evaluation is efficiency; there are different methods to measure it. Data envelopment analysis is a powerful tool for calculating system efficiency, including knowledge management performance in the oil industry. In this research, the main indicators of knowledge management were identified using library resources and practical publications. By surveying the employees of oil industry companies with data envelopment analysis and hierarchical analysis techniques, the efficiency of knowledge management performance was calculated, which can be used to rank the companies and planning to improve their knowledge management level. According to the results, the National Refining and Distribution of Petroleum Products Company has acted at higher level than other companies and has a good competitive advantage with its global competitors. The present study is the first study that has calculated the efficiency of knowledge management performance of four main Iranian oil industry companies with data envelopment analysis.

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_||_

 

 

  1. Asgharpoor, M. J. (2006). Multi-criteria decision making, University of Tehran Press, 4th edition. (in persian)

    1. Akhavan, P., Jafar, M., & Fathian, M. (2006). Critical success factors of KMSs: a multi-case analysis. European Business Review, 18(2): 97-113.
      2.
    2. Akhavan, P., Ebrahim Sanjaghi, M., Rezaeenour, J., & Ojaghi, H. (2014). Examining the relationships between organizational culture, KM and environmental responsiveness capability. VINE, 44(2): 228-248.
      3.
    3. Alirezaee, M., R., Sattari, R. (2010). Application of Data Envelopment Analysis Models in Assessing the Performance of Asian Health System, Journal of Health Information Management, 7 (1): 47 62.
      4.
    4. Asaadi, M., M., Habibollah, M., Sadeghi Arani, Z., Khosravanian, H., R. (2010). Evaluating the performance of public hospitals in Yazd province using a combination of balanced scorecard models, data envelopment analysis and SERVQUAL, Journal of Shahid Sadoughi University of Medical Sciences and Health Services, 18(6): 559-569.
      5.
    5. Baktash, E. (2012). Ranking of Organizations Based on Knowledge Management Indicators Using Data Envelopment Analysis, Third National Conference on Data Envelopment Analysis, Islamic Azad University, Firoozkooh Branch.
      6.
    6. Cardoso, L., Meireles, A., & Ferreira Peralta, C. (2012). KM and its critical factors in social economy organizations. Journal of KM, 16(2): 267-284.
      7.
    7. Chalmeta, R. (2006).Methodology for customer relationship management. Journal of Systems and Software, 79(7): 1015-1024.
      8.
    8. Chang, C‐ M., Hsu, M-H., & Yen, C., H. (2012). Factors affecting KM success: the fit perspective, Journal of KM, 16(6): 847-861.
      9.
    9. Chun, T., K., Kuan, Y., W., Wai, P., W. (2012), Monte Carlo Data Envelopment Analysis with Genetic Algorithm for Knowledge Management performance measurement, Journal of Expert Systems with Applications, 39: 9348–9358
      10.
    10. Chournazidis, A., J. (2013). Functionality and Feasibility of Knowledge Management in Enterprises, Social and Behavioral Sciences, 73: 327 – 336.
      11.
    11. Charnes A., Cooper W.W., Rhodes E. (1978). Measuring the efficiency of decision making units, European Journal Operation Research; 2(6): 429-444.
      12.
    12. Chen, L., & Fong, P. S. W. (2012). Revealing performance heterogeneity through KM maturity evaluation: A capability-based approach. Expert Systems with Applications, 39(18): 13523-13539.
      13.
    13. Cooper W.W., Park K.S., Yu G. (2001). IDEA (imprecise data envelopment analysis) with CMDs (column maximum decision making units), Journal of Operation Research Society; 52(2): 176–181.
      14.
    14. Digalwar, A. and Sangwan, K. S. (2011). Role of KM in world class manufacturing: An empirical investigation. IEEE International Conference on Industrial Engineering and Engineering Management.
      15.
    15. Doyle, J. R., Green, R. H. (1994). Efficiency and Cross Efficiency, Journal of Operation Research Society; 45(5): 567-578.
      16.
    16. Ghadery, S., F., Azadeh, M., A., Alishahi, M., S. (2010). Evaluating the performance of human resources of banks based on DEA and Fuzzy DEA methods, Journal of Industrial Engineering, 44(2): 213 - 228. (in persian)
      17.
    17. Hasani, K., & Sheikhesmaeili, S. (2016). KM and employee empowerment: A study of higher education institutions. Kybernetes, 45(2): 337-355.
      18.
    18. Hong, B- L., Lei, L. (2007). DEA-Based Project Knowledge Management Performance Evaluation, International Conference on Management Science and Engineering.
      19.
    19. Hamidizadeh, M.R & Fadaeinejad, M.E. (2010). A KM Approach to Format the Financial World-Class Policies, International Journal of Management & Information Systems, 14(5): 69-78.
      20.
    20. Jahani, A., Akhavan, P., Jafari, M., & Fathian, M. (2016). Conceptual model for knowledge discovery process in databases based on multi-agent system. VINE Journal of Information and KM Systems, 46(2): 207-231.
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    21. Jafari, M., Fathian, M., Jahani, A., & Akhavan, P. (2008). Exploring the contextual dimensions of organization from KM perspective. VINE Journal of Information and KM Systems, 38(1): 53-71.
      22.
    22. Kazemi, M., & Zafar Allahyari, M. (2010). Defining a KM conceptual model by using MADM. Journal of KM, 14(6): 872-890.
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    23. Kuah, C., T., Wong, Y., K. (2015). Data Envelopment Analysis modeling for measuring knowledge management performance in Malaysian higher educational institutions, Journal of SAGE, 29(3): 200-216.
      24.
    24. Lin, H. F. (2011). Antecedents of the stage‐based KM evolution. Journal of KM, 15(1): 136-155.
      25.
    25. Lin, Y., Su, H-Y., Chien, S- A. (2006). Knowledge enabled procedure for customer relationship management .Industrial Marketing Management, 35(4): 446-456.
      26.
    26. Lee, H. and Choi, B. (2003), KM enablers, processes, and organizational performance: an integrative view and empirical examination, Journal of Management Information Systems, 20(1): 179-228.
      27.
    27. Lee, C., Wong, K. (2015). Development and validation of knowledge management performance measurement constructs for small and medium enterprises, Journal of Knowledge Management, 19(4): 711-734.
      28.
    28. Lin, H. F. (2013). The effects of KM capabilities and partnership attributes on the stage‐based e‐business diffusion. Internet Research, 23(4): 439-464.
      29.
    29. Lin, T-C., & Chang, C.L.  (2015). The role of organizational culture in the KM process, Journal of KM, 19 (3): 433-455.
      30.
    30. Lotti Oliva, F. (2014). KM barriers, practices and maturity model. Journal of KM, 18(6): 1053-1074.
      31.
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      32.
    32. Martin, V. A., Hatzakis, T., Lycett, M., & Macredie, R. (2005). Cultivating knowledge sharing through the relationship management maturity model. The Learning Organization, 12(4): 340-354.
      33.
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      37.
    37. Nastiezaie, N., & Noruzi Kuhdasht, R. (2017). The study of relationship between employee voices with knowledge sharing, Public Management Research, 10(35): 85-104.
      38.
    38. Omar Sharifuddin bin Syed‐Ikhsan, S., & Rowland, F. (2004). Benchmarking KM in a public organisation in Malaysia. Benchmarking: An International Journal, 11(3): 238-266.
      39.
    39. Patli, K., S., Kant, R., (2014). A fuzzy AHP-TOPSIS framework for ranking the solutions of Knowledge Management adoption in Supply Chain to overcome its, Journal of Expert Systems with Applications, 41(2): 679-693.
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    44. Sorayaee, A., Gharouee, R. (2009). Evaluating the performance of Bank Saderat Mazandaran branches using data envelopment analysis, 7th International Management Conference, Tehran, Iran. (in Persian)
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    45. Spack, B. (2010). The effect of market orientation on product innovation. Journal of the Academy of Marketing Science, 28 (2): 239-47.
      46.
    46. Wang, Y.-M & Wu, J.-H. (2006). Measuring KMS success: A specification of the De Lone and McLean's model. Information & Management, 43 (6): 728–739.
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  2. Wong, Y- K., Tan, P- L., Lee, S- C., Wong, W- P. (2013). Knowledge Management performance measurement: measures, approaches, trends and future directions, SAGE Journals, 31 (3): 239-257.
    3.

 

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