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

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عنوان URL للمقالة


رقم المقالة : IMJ-2011-1485 (R2) زيارة : 77 الصفحة: 86 - 103

نوع المخطوط: ابحاث

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

الموضوعات :

masoud najafi 1 (Department of Management, Faculty of Humanities, Hamadan Azad University, Iran)
behzad Ghasemi 2 (Department of Management, Faculty of Humanities, Islamic Azad University of Hamadan, Hamadan, Iran)

تاريخ الإرسال : 07 الأحد , ربيع الثاني, 1442 تاريخ التأكيد : 25 السبت , جمادى الأولى, 1442 تاريخ الإصدار : 06 الإثنين , جمادى الأولى, 1442

الکلمات المفتاحية: بازده, صنعت نفت, تحلیل پوششی داده ها, مدیریت دانش,

ملخص المقالة :

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.

المصادر:

 

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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.
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    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.
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    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)
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      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.
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    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.
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    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.
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      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.
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