مدیریت ریسک نقدینگی و مشارکت مشتریان در تأمین نقدینگی بانکی

نوع مقاله : مقاله پژوهشی

نویسندگان

1 دانشجوی دکترای مدیریت تولید و عملیات، دانشکده اقتصاد، مدیریت و حسابداری دانشگاه یزد، یزد، ایران

2 دانشیار گروه مدیریت صنعتی، دانشکده اقتصاد، مدیریت و حسابداری دانشگاه یزد، یزد، ایران

3 استادیار گروه مدیریت صنعتی، دانشکده اقتصاد، مدیریت و حسابداری دانشگاه یزد، یزد، ایران

10.29252/bar.2020.11105.2892

چکیده

به دلیل پیچیدگی محیط اجتماعی- اقتصادی، در مورد نظرات مسائل موجود تردید وجود دارد. مجموعه­های فازی شهودی با در نظر گرفتن درجه عضویت و فقدان عضویت، در توصیف داده‌های مبهم و نادقیق در شرایط فقدان قطعیت به پژوهشگران کمک می­کند. بنابراین، در پژوهش حاضر تلاش شد با هدف تأمین نقدینگی بانکی به منظور کاهش ریسک نقدینگی در بانک تجارت، عوامل مؤثر بر تصمیم­گیری مشتریان مبنی بر سپرده­گذاری و دریافت تسهیلات بانکی، شناسایی شده و در فضای فازی شهودی اولویت­بندی شوند. پژوهش از منظر هدف کاربردی و پژوهشی توصیفی و میدانی است. جامعه­ آماری شامل مدیران، معاونان و کارشناسان فعال در واحدهای مدیریتی بانک تجارت است. برای تحقق هدف ابتدا با روش تحلیل محتوا، عوامل مؤثر بر مشارکت مشتریان در تأمین نقدینگی بانک، شناسایی و سپس با استفاده از روش­ تصمیم­گیری چند شاخصه، اولویت­بندی شد. نتایج نشان داد که از میان عوامل مؤثر بر تصمیم سپرده­گذاری مشتریان سود سپرده بانکی، شاخص جامعه، تجربه مشتری، سرعت و دقت در ارائه خدمات و برخورد کارکنان در اولویت­های اول تا پنجم اهمیت قرار دارند. همچنین از میان عوامل مؤثر بر تصمیم دریافت تسهیلات، تعداد چک برگشتی، درآمد، سابقه، مالکیت و مبلغ قسط، از مهم­ترین عوامل هستند. 

کلیدواژه‌ها


عنوان مقاله [English]

Liquidity risk management and customer participation in providing liquidity of Bank

نویسندگان [English]

  • Negar Jalilian 1
  • Seyed Mahmoud Zanjirchi 2
  • Alireza Naser Sadrabadi 3
1 PhD student in production and operations management, Faculty of Economics, Management and Accounting, Yazd University, Yazd, Iran
2 Associate Professor and member of the faculty of Industrial Management, Faculty of Economics, Management and Accounting, Yazd University, Yazd, Iran
3 Assistant Professor and Faculty Member of Industrial Management Department, Faculty of Economics, Management and Accounting, Yazd University, Yazd, Iran
چکیده [English]

Due to the complexity of the socio - economic environment, there is always doubt about those issues. In this regard, intuitionistic fuzzy sets used as a powerful tool in describing the ambiguous and imprecise information considering the membership and non-membership degrees. According the importance of intuitionistic fuzzy sets, in this study, it has been tried to determine bank liquidity supply factors in order to reduce liquidity risk and its management in the Tejatat bank. So, all factors affecting the decision of customers to deposit and receive loans are identified, and in the intuitionistic environment, the importance of each factor was extracted. The study population consisted of all managers, deputies and experts active in the management units of the Iran's Tejarat bank.  to achieve study goal, first using content analysis method, the factors affecting customers ' participation in bank liquidity were identified and then the importance of each factor was extracted by using multi - attribute decision making in intuitionistic fuzzy environment. the results indicate that among the factors affecting customers, decision to deposit in the bank, Social Deposits Profit Rate, community index, customer experience, speed and accuracy in service delivery and employee engagement with customers are placed in first and fifth priorities. Also, among the factors affecting customers' decision to receive loans and facilities from the Tejarat bank, the number of bounced checks, customer income, customer experience, and customer ownership status and installment value were considered the most important factors.   

کلیدواژه‌ها [English]

  • Bank liquidity
  • Content analysis
  • Intuitionistic Fuzzy Vikor
  • Liquidity Risk
  • Tejarat bank
Ahmadian, A & Esfandiari, M. (2015). Customers features effect on probability of bank run occurrence in Iran. Journal of Development in Monetary and Banking Management, 2(5), 11-29.
Amini, R, Ahmadi, Nazaripour, M (2015). Investigating the Factors Affecting the Decision of Customers for Bank Loans, Third Annual National Conference on Modern Management Sciences , Gorgan, Academic and Professional Association of Golestan Managers and Consumers, Aliabad Islamic Azad University Katul.
Asadi Zahraei, E, Azar, A, IskandarI, L (2014). Providing a model for the introduction of the factors influencing customer behavior in bank deposit, the International Conference on Accounting and Management, Tehran
Bahmani, M  & Mirhashemi Naiini, S,S, (2015). The Effect of Banking Marketing on the Transfer of Monetary Policy from the Duct of Loan, Quarterly Economic Applications Theories 2 (3)
Bessler, W. & Kurmann, P. (2014). “Bank risk factors and changing risk exposures: Capital market evidence before and during the financial crisis”. Journal of Financial Stability, Vol.13, pp.151-166.
Bozorg asl, M., Barzideh, F., Samadi, M. (2018). The Effect of Liquidity Risk and Credit Risk on Financial Stability Banking industry in Iran; Multiple regression approach. Financial Knowledge of Securities Analysis, 11(38), 1-13.
Camprubí, R., & Coromina, L. (2016). Content analysis in tourism research. Tourism Management Perspectives, 18, 134-140.
Chochoľáková, A., Gabčová, L., Belás, J., & Sipko, J. (2015). Bank customers’ satisfaction, customers’ loyalty and additional purchases of banking products and services. A case study from the Czech Republic. Economics and Sociology.
Dzulkarnain, A. A., & Hatta, M. F. M. (2017). Factors Influencing Consumer Intention Towards investment Account: Post Ifsa 2013. ASEAN COMPARATIVE EDUCATION RESEARCH JOURNAL ON ISLAM AND CIVILIZATION (ACER-J). eISSN2600-769X, 1(2), 17-32.
10. Eriksson, K., & Hermansson, C. (2019). How relationship attributes affect bank customers’ saving. International Journal of Bank Marketing, 37(1), 156-170.
11. Fadaee, M., Esmaeili, H. (2016). Prioritize the Factors Affecting Financial Resources in Bank-e-Mehr-e-Eqtesad Isfahan Province (AHP Approach), 5(2), 75-98.
12. Gautam, S. S., & Singh, S. R. (2016). “TOPSIS for multi criteria decision making in intuitionistic fuzzy environment”. International Journal of Computer Applications, Vol.156, No.8, pp.42-49.
13. Hong, H., Huang, J. Z., & Wu, D. (2014). The information content of Basel III liquidity risk measures. Journal of Financial Stability, 15, 91-111.
14. Horváth, R., Seidler, J., & Weill, L. (2014). Bank capital and liquidity creation: Granger-causality evidence. Journal of Financial Services Research, 45(3), 341-361.
15. Ismail Zadeh, Ali, Javanmardi, Halima. (2017). Designing a proper model for liquidity management and predicting its risk in SADERAT Bank of Iran. Financial Economics, 11 (39), 171-197.
16. Jiang, L., Levine, R., & Lin, C. (2019). Competition and bank liquidity creation. Journal of Financial and Quantitative Analysis, 1-50.
17. Kabaranzadeh Ghadim, M.R & Kurd Nouri, A.H. (2013). Identification and investigation of the relationship between critical success factors in the field of bank credit and their prioritization with AHP approach (Case Study of Iran Export Development Bank, Tehran). Financial Economics, 7 (24), 213-242.
18. Kamali Sabet, S. (2012). An investigation of the factors influencing the adsorption of depositors in the Post Bank branches of Tehran. A master's degree thesis - financial bias. Islamic Azad University of Tehran Central Tehran.
19. Luo, X., & Wang, X. (2017). Extended VIKOR method for intuitionistic fuzzy multiattribute decision-making based on a new distance measure. Mathematical Problems in Engineering.
20. Metawa, N., Hassan, M. K., & Elhoseny, M. (2017). Genetic algorithm based model for optimizing bank lending decisions. Expert Systems with Applications, 80, 75-82.
21. Mian, A., & Santos, J. A. (2018). Liquidity risk and maturity management over the credit cycle. Journal of Financial Economics, 127(2), 264-284.
22. Mirghafouri, S., Amin, Z. (2015). Presenting a Model for Measuring Credit Risk of Bank Customers using Data Mining Approach. , 7(13), 247-266.
23. Moghadam, Kh, Rezaei, K, Arshadi, A. (2013). Identification and determination of factors affecting the credit behavior of real customers in Tehran city with a view to reducing the rate of claims growth in one of the private banks. monetary - bank research, 6th year, No. 16
24. Mohammadi Khiareh, M & Khalaf Barayeh, L. (2016).  Investigating the Factors Affecting Bank Loan Receipt (Case Study: Golestan Provincial Cooperatives). Business reviews, 14 (80), 40-55.
25. Pasban, F & Roohi, M. (2017).Investigating the Effective Factors on the Volume of Deposits in the Banking System (1383-1383). Journal of Islamic Economics and Banking. 16 (19): 53-68
26. Perbawa, A. (2015). Factors Affecting Mudaraba Deposits on Islamic Commercial Bank in Indonesia. Available at SSRN 2662912.
27. Petrescu, R. (2017). Trends in Consumer Behavior of Banking Products and Services. Journal of Advanced Research in Management, 8(1 (15)), 44.
28. Saeedi, P & Abbasi, I. (2011). Effect of Commercial Banks Facility on Economic Growth (Case Study of Golestan Province). Researcher (MANAGEMENT) (Journal of Industrial Strategic Management), Volume 8, Number 23; Page 14-22.
29. Sarmiento, M. & Galán, J.E. (2017). “The influence of risk-taking on bank efficiency: Evidence from Colombia”. Emerging Markets Review, Vol.32, pp. 52-73.
30. Shahiki tash, M., mahmoodpour, K. (2016). Assessing the Structure of Bank Deposits Market in Iran. Economic Modeling, 9(31), 61-81.
31. Shahzad, F., Nawab, S., Tanveer, S., Shafi, K., & Bhatti, W. K. (2018). Analyzing the individual effect of determinants effecting the financial performance of banks using camel model. WALIA journal, 34(1), 27-31.
32. Yaghoubi, N.M, Kord, H, Moradzadeh, A, Dezhkam, J (2015).Network analysis is an effective factors on attracting customers ' deposits. Two scholarly reviews of business management research. Year 7th, Number 13, Pages 133-157
33. Ye, J. (2011). Expected value method for intuitionistic trapezoidal fuzzy multicriteria decision-making problems. Expert Systems with Applications, 38(9), 11730-11734.
34. Zhou, Z., Amowine, N. & Huang, D., (2018(, ‘Quantitative efficiency assessment based on the dynamic slack-based network data envelopment analysis for commercial banks in Ghana’, South African Journal of Economic and Management Sciences 21(1):1717.