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  • مقایسه قدرت تفکیک پذیری مدل های بازده به مقیاس متغیر به منظور ارزیابی کارایی واحدهای تصمیم گیرنده در صنعت بانکداری

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کد مقاله : IMJ-2011-1479 (R2) بازدید : 171 صفحه: 25 - 34

نوع مقاله: پژوهشی

مقایسه قدرت تفکیک پذیری مدل های بازده به مقیاس متغیر به منظور ارزیابی کارایی واحدهای تصمیم گیرنده در صنعت بانکداری

محورهای موضوعی : مدیریت صنعتی

Gholamreza Panahandeh Khojin 1 , Abbas Toloie Ashlaghi 2 , Mohammad Ali Afshar Kazemi 3

1 - Department of Industrial Management, Faculty of Economics and Management, Islamic Azad University, Research Sciences Branch, Tehran, Iran
2 - Department of Industrial Management, Faculty of Economics and Management, Islamic Azad University, Research Sciences Branch, Tehran, Iran
3 - Department of Industrial Management , Central branch of Tehran Azad university

تاریخ دریافت : 1399/07/26 تاریخ پذیرش : 1399/09/17 تاریخ انتشار : 1399/10/27

کلید واژه: مدل برنامه ریزی آرمانی تحلیل پوششی داده ها, مدل برنامه‌ریزی آرمانی اصلاح شده تحلیل پوششی داده ها, تحلیل پوششی داده ها, بانک قوامین,

چکیده مقاله :

تحلیل پوششی داده ها بعنوان یک ابزار مناسب برای برآورد کارایی شرکت هایی که از تکنولوژی های تولید با چندین ورودی و چندین خروجی استفاده می نمایند، مورد استفاده قرار می گیرد. مدل‌های کلاسیک تحلیل پوششی داده ها بدلیل پایین بودن قدرت تفکیک واحدها اغلب اطلاعات دقیقی را از وضعیت واحد ها در اختیار مدیران و سیاست گذاران سازمانی قرار نمی دهند. در این پژوهش، اساس کار مدل BCCخروجی محور تحلیل پوششی داده ها بوده و به منظور افزایش قدرت تفکیک پذیری واحد های تصمیم گیرنده کارا از ناکارا، مدل برنامه ریزی آرمانی تحلیل پوششی داده ها برمبنای BCC مورد استفاده قرار گرفت و در ادامه برای افزایش قدرت تفکیک واحد های تصمیم گیرنده از یک مدل جدید تحت عنوان مدل برنامه ریزی آرمانی اصلاح شده تحلیل پوششی داده ها برمبنای BCC خروجی محور برای سنجش کارایی استفاده شد. در این پژوهش مدیریت شعب بانک قوامین سراسر کشور مورد ارزیابی قرار گرفت که نتایج حاصل از پژوهش قدرت تفکیک پذیری مدل برنامه ریزی آرمانی اصلاح شده تحلیل پوششی داده ها برمبنای BCC خروجی محور نسبت به سایر مدلهای تحلیل پوششی داده ها را نشان می دهد. به طوریکه از 32 مدیریت تحت بررسی در بانک قوامین، مدیریت شعب غرب تهران با استفاده از این مدل کارا و بقیه مدیریت ها ناکارا تشخصیص داده شد.

چکیده انگلیسی:

Data envelopment analysis is used as a suitable tool to estimate the efficiency of companies that use production technologies with multiple inputs and multiple outputs. Classical data envelopment analysis models often do not provide accurate information about the status of units to managers and organizational policymakers due to the low resolution of units. In this research, the basis of the BCC model is the output oriented of data envelopment analysis and in order to increase the discriminate of efficient decision-making units from inefficient, the ideal data envelopment analysis model based on BCC was used and then to increase the resolution Decision Making Units A new model called the Modified goal Programming Model of Data Envelopment Analysis based on output-oriented BCC was used to measure performance. In this study, the management of Ghavamin Bank branches across the country was evaluated. The results of the research show the discriminate power of the modified goal programming model of data envelopment analysis based on output-oriented BCC compared to other data envelopment analysis models. So that out of 32 managements under review in Ghavamin Bank, the management of branches in the west of Tehran was identified as efficient using this model and the rest of the managements were found to be inefficient.

منابع و مأخذ:


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_||_

  1. Amiri, Hossein. (2018). Evaluation the Effectiveness of Selected Banks in Iran and its Relationship with Banking Internal and Macroeconomic Variables. Applied Economics Studies, 7 (26), 89-114.
    2.
  2. Arabmazar, A., hasani, H. (2018). Survey Performance of Iran's Banks with Network Data Envelopment Analysis Method. Quarterly Journal of Quantitative Economics, 15(2), 1-21. doi: 10.22055/jqe.2017.21388.1596.
    3.
  3. Azar, Adel, Zarei Mahmoudabadi, Mohammad, Moqbel Ba'arz, Abbas, Khadivar, Amena. (2014). Evaluating the Productivity of a Bank's Branches Using Network Data Envelopment Analysis Approach (Case Study: A Bank in Gilan Province). JMBR, 7(20), 285-305.
    4.
  4. Azarbad, M,. Reza Soltani, A., Shojaie, A .A. (2015). An empirical DEA investigation for development of new bank’s branches. Management Science Letters, 5(4),331-336.
    5.
  5. Azizi, Jafar. (2015). Evaluation of The Efficiency of The Agricultural Bank Branches by Using Data Envelopment Analysis and The Determination of a Consolidated Index: The Case Study in Mazandaran Province. Agricultural Economics, 9(1), 63-76.

    1.  Bal. H., Örkcü, H.H. & Çelebioğlu, S. (2010). Improving the discrimination power and weights dispersion in the data envelopment analysis. Computers & Operations Research. 37(2010), 99-107
      2.

  6. Eskelinen, J., Halme, M., Kallio, M. (2014). Bank branch sales evaluation using extended value efficiency analysis. European Journal of Operational Research, 232(3), 654-663
    7.
  7. Fadaeinejad, Mohammad Ismail, Razavi Haji Agha, Seyed Hossein, Badri, Ahmad, Nilchi, Muslim. (2020). Provide a new multi-segment data envelopment analysis model to evaluate the efficiency of bank branches. Industrial Management Studies, 15(46),73-96. doi: 10.22054 / jims.2020.11424.
    8.
  8. Gulati, R., Kattumuri, R., Kumar, S. (2019). A non-parametric index of corporate governance in the banking industry: An application to Indian data. Socio-Economic Planning Sciences, 70 (June 2020), 100702.
    9.
  9. Junior PR, Pamplona EdO, Silva, AF. (2013). Mergers and Acquisitions: An Efficiency Evaluation. Applied Mathematics, 4, 1583-1589.
    10.
  10. Mehregan. Mohammad Reza, (2004). Quantitative Models in Performance Evaluation of Organizations (Data Envelopment Analysis). Tehran: Faculty of Management, University of Tehran.
    11.
  11. Meianji, Parviz, Barimnejad, Vali. (2016). Investigating the efficiency of Keshavarzi Bank branches by data envelopment analysis method. Agricultural Economics Research, 4 (8), 38-19.
    12.
  12. Ranjbar, Mansour, Haskooi, Morteza, Farahanifard, Saeed. (2014). Investigating the factors affecting the technical efficiency of the Iranian banking system using a combined data approach. Econometric modeling; 2, 23-42.
    13.
  13. Sahoo BK, Mehdiloozad M, Tone K. (2014). Cost, revenue and profit efficiency measurement in DEA: A directional distance function approach. European Journal of Operational Research,  237(3), 921-931.
    14.
  14. Sahand D, Nazila S, Fariba N. (2015). Modified Goal Programming Approach for Improving the Discrimination Power and Weights Dispersion. New Researches in Mathematics, 1(3), 5-18.
    15.
  15. Salehi, S., Nikokar, G., Mohammadi, A., nataj, G. (2011). Pattern Design and Performance Evaluation of Branches of Banks and Financial Institutions (Case Study: Bank Qvamyn). Journal of Business Management, 3(1), 127-268.
    16.
  16. Shikh-hasani, D., Alifarri, M., Karimi, B. (2020). Measuring efficiency score by cross-efficiency method in data envelopment analysis and its relation to profitability and risk in banks admitted to Tehran stock exchange. Management Accounting, 13(46), 103-119.
    17.
  17. Tahari Mehrjardi, M., Farid, D., Babaei Meybodi, H. (2011). Presentation the Data Envelopment Analysis/Goal Programming Integrated Model for Improves Decision making Unit’s Efficiency Measurement (Case study: the Bank Branches). Industrial Management Studies, 8(21), 21-37.
    18.
  18. Wanke, P.; Azad, M.A.K.; Emrouznejad, A.; Antunes, J. (2019). A dynamic network DEA model for accounting and financial indicators: A case of efficiency in MENA banking. International Review of Economics and Finance, Elsevier, 61(C), 52-68. doi: https://doi.org/10.1016/j.iref.2019.01.004.
    19.
  19. Yong Tan, Christos Floros. (2018). Risk, competition and efficiency in banking: Evidence from China. Global Finance Journal, 35 , 223–236.
    20.

 

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