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  • ارزیابی عملکرد واحدهای کارگزینی مدیریت شعب بانک ملت با رویکرد ترکیبی مدل‌های تحلیل پنجره‌ای و شاخص مالم کوئیست

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کد مقاله : 536948 بازدید : 93 صفحه: 19 - 34

نوع مقاله: پژوهشی

ارزیابی عملکرد واحدهای کارگزینی مدیریت شعب بانک ملت با رویکرد ترکیبی مدل‌های تحلیل پنجره‌ای و شاخص مالم کوئیست

محورهای موضوعی : مدیریت صنعتی

Ezatolah Asgharizadeh 1 (Associate professor of faculty of management of Tehran university Department of Industrial Management Iran)
Masoud Keimasi 2 (Assistant professor of faculty of management of Tehran university Department of MBA Iran)
Elnaz Borji 3 (Masters of industrial management of faculty of management of Tehran university)

تاریخ دریافت : 1395/01/31 تاریخ پذیرش : 1395/10/28 تاریخ انتشار : 1395/11/04

کلید واژه: Data envelopment analysis, Malmquist Index, Efficiency, کارایی, تحلیل پوششی داده ها, شاخص مالم کوئیست, تحلیل پنجرهای, Window Analysis,

چکیده مقاله :

هدف اصلی این مقاله ارزیابی عملکرد واحدهای کارگزینی 5 منطقه تهران بانک ملت و بررسی کارایی و بهره‌وری آن‌ها با بهره‌گیری از تحلیل پوششی داده‌ها همچنین با استفاده از شاخص بهره‌وری مالم کوئیست می باشد. دوره زمانی موردمطالعه سال‌های 1390-1394 است. در این تحقیق عملکرد هر واحد تصمیم‌گیری را در طول زمان ردیابی می کنیم و برای تجزیه‌وتحلیل تغییرات در کارایی و بهره‌وری در طی زمان و همچنین تفکیک بهره‌وری به دو جزء عمده آن یعنی تحولات تکنولوژیک و تغییرات در کارایی از روش تحلیل پنجره‌ای و شاخص مالم کوئیست استفاده می‌شود. نتایج نشان می‌دهد که ادارات کارگزینی مناطق 4 و 5 به ترتیب با میانگین نمرات کارایی فنی 97% و 93% در رتبه اول و دوم قرارگرفته و ادارت مناطق 4 و 5 با میانگین کارایی مقیاس تقریباً 95% دارای کارایی مناسبی است و همچنین ادارات کارگزینی تمام مناطق با میانگین کارایی مدیریت بالای 95% در سطح مطلوبی و مناسبی هستند. با توجه به مقادیر شاخص مالم کوئیست در میان مناطق، منطقه 3(023/1) بهبود بهره‌وری در طی دوره موردبررسی داشته و بررسی تغییرات بهره‌وری کل نشان می‌دهد که سال 91(033/1)، در دوره موردبررسی دارای بیشترین رشد بهره‌وری داشته است.

چکیده انگلیسی:

The purpose of this paper is to evaluate the performance of personnel units of five regions of Tehran Bank Mellat and investigating their productivity and efficiency by taking advantage of data envelopment analysis (DEA) efficiency as well as using Malmquist index. The study period is 2011-2015.In this study, we track the performance of every decision-making unit over time and to analyze the changes in efficiency and productivity as well as separation of efficiency over time and into two major components: technological developments and changes in efficiency by Malmquist Index and window analysis. The results show that human resources departments of regions 4 and 5, respectively, with 93% and 97% technical efficiency scores are in the first place, and the offices of regions 4 and 5 with the mean efficiency of about 95% have appropriate efficiency. Moreover, human resources departments of all regions with the average efficiency of over 95% are favorable. Based on Malmquist Index values among the regions, Region 3 (1.023) has had efficiency improve in during the study period, and evaluating total efficiency changes show that year 2012 (1.033) has had the greatest growth in productivity. 

منابع و مأخذ:


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  2. Asmild, M., Paradi, J. C., Aggarwall, V., & Schaffnit, C. (2004). Combining DEA window analysis with the Malmquist nidex approach in a study of the Canadian banking industry. Journal of Productivity Analysis, 21(1), 67-89.
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    14.
  14. Staníčková, M. I. C. H. A. E. L. A., & Skokan, K. A. R. E. L. (2012). Evaluation of Visegrad Countries Efficiency in Comparison with Austria and Germany by Selected Data Envelopment Analysis Models. In Proceedings of the 4th WSEAS World Multiconference on Applied Economics, Business and Development (AEBD’12). Recent Researches in Business and Economics. WSEAS, Porto.
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  15. Stavárek, D., & Řepková, I. (2014). Efficiency in the Czech banking industry: A non-parametric approach. Acta Universitatis Agriculturae ET Silviculturae Mendelianae Brunensis, 60(2), 357-366.
    16.
  16. Malayi, M. (2011). Assess the efficiency of research and development centers using DEA WINDOW approach.3conferance on data development analysis
    17.
  17. Hachazi, R., & Rostami, E. (2008).analyze the efficiency of export development bank of Iran productivity growth its branches using DEA. Journal of industrial management, 1, 29-50. 
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_||_

  1.  Aggarwal, A., & Thakur, G. S. M. (2013). Techniques of performance appraisal-a review. International Journal of Engineering and Advanced Technology (IJEAT) ISSN, 2249-8958.
    2.
  2. Asmild, M., Paradi, J. C., Aggarwall, V., & Schaffnit, C. (2004). Combining DEA window analysis with the Malmquist nidex approach in a study of the Canadian banking industry. Journal of Productivity Analysis, 21(1), 67-89.
    3.
  3. Afkhami, M. (2008). Evaluate the performance of commercial banks in iran Combining DEA window analysis with the Malmquist index approach. Journal of Shahed University, 12, 2-47.
    4.
  4. Afkhami,M. (2011).Calulate the productivity growth of human resources knowledge using Dynamic DEA models: Research institute of petroleum industry. Journal of human resources management in petroleum industry, 13.
    5.
  5. Banker, R. D., Charnes, A., & Cooper, W. W. (1984). Some models for estimating technical and scale inefficiencies in data envelopment analysis. Management science, 30(9), 1078-1092.
    6.
  6. Chen, Y., & Ali, A. I. (2004). DEA Malmquist productivity measure: New insights with an application to computer industry. European Journal of Operational Research, 159(1), 239-249.
    7.
  7. Färe, R., Grosskopf, S., Norris, M., & Zhang, Z. (1994). Productivity growth, technical progress, and efficiency change in industrialized countries. The American economic review, 66-83.
    8.
  8. Färe, R., Grosskopf, S., & Norris, M. (1997). Productivity growth, technical progress, and efficiency change in industrialized countries: The American Economic Review, 87(5), 1040-1044.
    9.
  9. Pasiouras, E. & Sifodaskalakis, E.(2007).Total Factor Productivity Change of Gree Cooperative Banks (Master dissertation).University of Bath, Bath, Somerset, United Kingdom.
    10.
  10. Popova, V. & Sharpanskykh, A. (2010). Modeling organizational performance indicators. Information Systems, 35(4), 505–527.
    11.
  11. Řepková, I. (2014). Efficiency of the Czech Banking Sector Employing the DEA Window Analysis Approach. Procedia Economics and Finance, 12(6), 587–596.
    12.
  12. Sokhanvar,M .,& Mehreghan, M.(2011).using DEA window to analyze the structure and assess the efficiency of electricity distribution companies in iran. journal of economicgrowth and development research, 4 ,161
    13.
  13. Staněk, R. (2010). Efektivnost českého bankovního sektoru v letech 2000–2009. Konkurenceschopnost a stabilita, 1, 81-89.
    14.
  14. Staníčková, M. I. C. H. A. E. L. A., & Skokan, K. A. R. E. L. (2012). Evaluation of Visegrad Countries Efficiency in Comparison with Austria and Germany by Selected Data Envelopment Analysis Models. In Proceedings of the 4th WSEAS World Multiconference on Applied Economics, Business and Development (AEBD’12). Recent Researches in Business and Economics. WSEAS, Porto.
    15.
  15. Stavárek, D., & Řepková, I. (2014). Efficiency in the Czech banking industry: A non-parametric approach. Acta Universitatis Agriculturae ET Silviculturae Mendelianae Brunensis, 60(2), 357-366.
    16.
  16. Malayi, M. (2011). Assess the efficiency of research and development centers using DEA WINDOW approach.3conferance on data development analysis
    17.
  17. Hachazi, R., & Rostami, E. (2008).analyze the efficiency of export development bank of Iran productivity growth its branches using DEA. Journal of industrial management, 1, 29-50. 
    18.

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