نوع مقاله : مقاله پژوهشی
نویسندگان
1 گروه حسابداری، واحد تبریز، دانشگاه آزاد اسلامی، تبریز، ایران.
2 گروه مدیریت، واحد تبریز، دانشگاه آزاد اسلامی، تبریز، ایران.
3 گروه ریاضی، واحد تبریز، دانشگاه آزاد اسلامی، تبریز، ایران.
چکیده
هدف: بانک‌ها به‌عنوان یک بنگاه اقتصادی خدماتی و مالی ضمن همراهی با برنامه‌های اقتصادی کشورها، به دنبال کسب منفعت برای ذینفعان خود می‌باشند. در جهت دستیابی به این اهداف، باید توانایی تجهیز و تخصیص بهینه منابع خود را داشته باشند. یکی از مسایل مهم، شناخت عوامل موثر در جذب منابع می‌باشد که هدف این پژوهش، ارایه الگویی مناسب برای شناسایی عوامل تاثیرگذار بر تامین منابع است.
روش‌شناسی پژوهش: برای رسیدن به هدف تحقیق، با مرور پیشینه تحقیق، رسالت بانک و نظرات کارشناسان بانکی، 62 عامل در قالب پرسشنامه‌ای ارایه شد. پرسشنامه پس از تایید خبرگان بانکی، جهت انجام پیش آزمون در یک نمونه 30 نفره از کارکنان بانک تجارت استان زنجان توزیع شد. سپس پایایی آن با آلفای کرنباخ آزمون و تایید گردید. بعد از جمع آوری میدانی داده‌های پژوهش، مولفه‌های موثر در دو گروه اصلی عوامل برون سازمانی و درون سازمانی قرار گرفتند. سپس عوامل درون سازمانی به چهار زیرگروه؛ مالی، فیزیکی، خدماتی و عوامل ارتباطی و انسانی تفکیک شد. در نهایت مدل اصلی پژوهش با استفاده از مدل شبکه‌های عصبی بدون ناظر (نگاشت‌های خود‌سازمانده) استخراج و به تحلیل داده‌های پژوهش پرداخته شد.
یافته‌ها: یافته‌های پژوهش نشان می‌دهد که از مجموعه عوامل تاثیرگذار بر تامین منابع بانکی، عوامل ارتباطی و انسانی بیشترین تاثیر و عوامل برون سازمانی کمترین تاثیر را داشتند. همچنین با توجه به عدم تشابه بین الگوهای بردارهای ورودی پژوهش، همبستگی بین هر کدام از عوامل موثر بر تجهیز منابع تایید نشد.
اصالت/ارزش افزوده علمی: در این پژوهش با استفاده از رویکرد جدیدی از مدل شبکه‌های عصبی (نگاشت‌های خود سازمانده) به شناسایی و وزن‌دهی عوامل موثر بر تجهیز منابع بانکی پرداخته شده که یافته‌های آن به توسعه ادبیات حوزه بانکی در بخش تجهیز منابع، کمک می‌کند.
کلیدواژه‌ها
موضوعات
عنوان مقاله [English]
Provide a suitable model for identifying the factors affecting the equipping of banking resources by artificial neural network method
نویسندگان [English]
- Yousef Ebrahimi 1
- Yagoub Alavi Matin 2
- Sahar Khoshfetrat 3
- Hasan Refaghat 3
1 Department of Accounting, Tabriz Branch, Islamic Azad University, Tabriz, Iran.
2 Department of Management, Tabriz Branch, Islamic Azad University, Tabriz, Iran.
3 Department of Mathematics, Tabriz Branch, Islamic Azad University, Tabriz, Iran.
چکیده [English]
Purpose: Banks as a service and financial economic enterprise, while accompanying the economic programs of countries, seek to benefit their stakeholders. In order to achieve this goal, they must be able to equip and allocate their resources optimally. One of the important issues is to identify the factors affecting the absorption of resources that the purpose of this study is to provide a suitable model to identify the factors affecting the supply of resources.
Methodology: To achieve the purpose of the research, by reviewing the research background, mission of the bank and the opinions of banking experts, 62 factors were presented in the form of a questionnaire. After approval by banking experts, the questionnaire was distributed to a sample of 30 employees of Tejarat Bank in Zanjan province for pre-testing. Then its reliability was tested and confirmed by Cronbach's alpha. After field collection of research data, the effective components were divided into two main groups of external and internal organizational factors. Then the factors within the organization into four subgroups; Financial, physical, service and communication and human factors were separated. Finally, the main research model was extracted using the model of unattended neural networks (self-organized maps) and the research data were analyzed.
Findings: Research findings show that, From the set of factors affecting the provision of banking resources, communication and human factors had the most impact and external factors had the least impact. Also, due to the lack of similarity between the models of research input vectors, the correlation between each of the factors affecting resource equipping was not confirmed.
Originality/Value: In this study, using a new approach of neural network model (self-organized mapping) to identify and weigh the factors affecting the equipping of bank resources, the findings of which help to develop the literature in the field of resource equipping.
کلیدواژه‌ها [English]
- Bank deposits
- External factors
- Internal factors
- Self-organized maps
- Abdollahi Poor, M. S., & Botshekan, M. H. (2020). Solutions for financial restructuring in Iranian banks. Journal of asset management and financing, 8(4), 1-20. (In Persian). https://amf.ui.ac.ir/article_24588_en.html?lang=fa
- Abbasi, E., & Abbasi, A. (2015). Study and prioritization of factors affecting the attraction of deposits in banks (case study: agricultural bank of Tehran province). 2015 the first national and iinternational conference on management and accounting (pp. 1-13). Hamedan, Iran. Civilica. (In Persian). https://civilica.com/doc/471909/
- Darvishpour, H. (2019). Identifying the effective factors on equipping cheap resources (case study: Bank Mellat Chabahar) (Master Thesis, Chabahar International University). https://ganj.irandoc.ac.ir/#/articles/01ac0222b56381bc3bde7bd39a0922c9
- Dorostkar, M., & Ranjbar, M. H. (1970). Presenting a model identification and prioritize for customer preferences in selecting banks and do invest based on grounded theory method and structural equation modeling (case study: Iran’s banking industry). Journal of investment knowledge, 8(30), 355-382. (In Persian). https://jik.srbiau.ac.ir/article_14443.html?lang=en
- Izadinia, N., Ghandehari, M., Abedini, A., & Abedini Naeini, M. (2017). Asset-liability management of banks using goal programming model and fuzzy ANP (case study: Tejarat Bank). Journal of asset management and financing, 5(4), 155-166. (In Persian). https://amf.ui.ac.ir/article_21178_en.html?lang=en
- Fabuzzi, F., & Moiliani, F. (1401). Basics of markets and financial institutions 1. Pishbord Publications. (In Persian). https://www.agahbookshop.com/
- Fadaee, M., & Esmaeili, H. (2016). Prioritize the factors affecting financial resources in Bank-e-Mehr-e-Eqtesad Isfahan province (AHP approach). Two scientific-specialized quarterly researches on development economics and planning, 5(2), 75-98. (In Persian). https://jdep.khsh.iau.ir/article_529409.html?lang=fa
- Faust, L. (2017). Fundamentals of neural networks: structures, algorithms, and applications. Text (In Persian). https://www.gisoom.com/book/
- Ghamry, S., & Shamma, H. M. (2022). Factors influencing customer switching behavior in Islamic banks: evidence from Kuwait. Journal of Islamic marketing, 13(3), 688-716. https://doi.org/10.1108/JIMA-01-2020-0021
- Ghorbani Kotenaie, N. (2015). Evaluation of factors influencing successful implementation of crowdfunding projects (Master Thesis, Alzahra University). https://ganj.irandoc.ac.ir//#/articles/37866c50bfbfaaabd3854f6ff8e6a2be
- Goodarzi, M., & Amiri, B. (2013). Presenting a model to identify the factors affecting the future price of coins by artificial neural network method and comparing it with regression models. Journal of financial engineering and securities management, 4(15), 17-33. (In Persian). https://www.sid.ir/paper/197656/fa
- Jamshidi, S. (2019). Islamic banking (internal 2), Islamic contracts. Gap Publications. (In Persian). https://gapnashr.com/
- Jamshidi, S. & Alizadeh, A. A. (2015). Islamic banking 1 (allocation of funds & branchs monetary operations). Gap Publications. (In Persian). https://www.gisoom.com/book/
- Khazaei, V. (2019). The impact of quality of electronic banking services on equipping financial (monetary) resources of bank Mellat (Master Thesis, Allameh Tabatabaei University). https://ganj.irandoc.ac.ir/#/articles/2f1c91f33c2bd68db1bec859deeedf07
- Kia, M. (2018). Neural networks in MATLAB. Kian Computer Green (In Persian). https://www.adinehbook.com/gp/product/6006021416
- Kordmanjiri, S., Dadashi, I., Khoshnood, Z., & Gholamnia Roshan, H. R. (2020). Identifying factors affecting non-curent debts of banks using neural networks and support vector machine algorithm. Economical modeling, 14(49), 127-151. (In Persian). https://eco.firuzkuh.iau.ir/article_672520.html
- Mansoori, A. (2003). Designing and explaining the mathematical model of bank facility allocation (the approach of classical models and neural networks) (PhD Thesis, Tarbiat Modares University). https://parseh.modares.ac.ir/thesis/1032828
- Menhaj, M. B. (2017). Basics of neural networks. Amirkabir University of Technology Publications. (In Persian). https://www.gisoom.com/book/
- Mhlanga, D. (2021). Financial inclusion in emerging economies: the application of machine learning and artificial intelligence in credit risk assessment. International journal of financial studies, 9(3), 39. https://doi.org/10.3390/ijfs9030039
- Moeinian, H. (2014). Ranking of legal customers in the banking industry using neural network (case study of bank Melli Tehran) (Master Thesis, Islamic Azad University Tehran Branch). https://ganj.irandoc.ac.ir/#/articles/43fc54f34012502b366933d9fdba6253
- Mohammad Zade, A., Hamidi, N., Nayebi, M. A., & Ebrahimi Sajas, Y. (2010). A multi objective optimization approach for resources procurement of bank. Journal of industrial engineering, 5(2010), 55-66. https://journals.iau.ir/article_40_930dac973ec707a479b2589c3aed62a4.pdf
- Moghavvemi, S., Lee, S. T., & Lee, S. P. (2018). Perceived overall service quality and customer satisfaction: a comparative analysis between local and foreign banks in Malaysia. International journal of bank marketing, 36(5), 908-930. https://doi.org/10.1108/IJBM-06-2017-0114
- Moosavi, S. E., & Monjazeb, M. R. (2020). Providing optimized banks resource allocation by emphasizing on the role of risk management (total criteria approach and sequential unconstrained optimization technique). Financial management strategy, 8(2), 23-40. (In Persian). https://jfm.alzahra.ac.ir/article_4837_en.html?lang=en
- Vuong, B. N., Duy Tung, D., Giao, H. N. K., Dat, N. T., & Quan, T. N. (2020). Factors affecting savings deposit decision of individual customers: empirical evidence from Vietnamese commercial banks. Journal of Asian finance, economics and business, 7(7). https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3650726
- Noori, R. (2017). Assessment and analysis of affecting factors on bank resources absorption (case study: Tejarat bank) (Master Thesis, Payame Noor University Tehran). https://ganj.irandoc.ac.ir/#/articles/55e3b83a9264b6b7a21275c9ec88a7cf
- Parsanjad Gohari, S. (2017). The effect of intra-organizational factors on the absorption of banking resources in the National Bank of Isfahan (Master Thesis, Islamic Azad University of Isfahan). https://ganj.irandoc.ac.ir/#/articles/b020a31caff5d9ea9ab96431dac089ed
- Panahandeh Khojin, G., Toloie Ashlaghi, A., & Afshar Kazmi, M. A. (2022). Provide an optimal model for determining and ranking inefficiency factors in the banking industry by combining data envelopment analysis and neural network. Journal of decisions and operations research, 7(4), 610-627. (In Persian). http://www.journal-dmor.ir/article_126729.html?lang=en
- Nankali, P., Rakhshan, F., & Alirezaee, M. R. (2022). Evaluating the role of bank absentee services in customer loyalty using data envelopment analysis. Journal of decisions and operations research, 7(3), 533-542. (In Persian). http://www.journal-dmor.ir/article_132008.html?lang=en
- Sabzpour, Z. (2018). The effect of bank interest rates on equipping banks' resources (Master Thesis, Qom University). https://ganj.irandoc.ac.ir/#/articles/fc23cfa6177c6f2a5a4d8b2856b587a7
- Safari, S., & Rafti, M. (2019). Studying the effects of the economic and electronic banking factors on the volume of deposits in selected private banks. Financial economics. 13(47), 199-216. (In Persian). https://www.sid.ir/paper/229012/en
- Sarmad, Z., Bazargan, A., & Hejazi, E. (2022). Research methods in behavioral sciences. Aghah Publications. (In Persian). https://www.adinehbook.com/gp/product/9643290514
- Yakubu, I. N., & Abokor, A. H. (2020). Factors determining bank deposit growth in Turkey: an empirical analysis. Rajagiri management journal, 14(2), 121-132. https://doi.org/10.1108/RAMJ-05-2020-0017
- Yusheng, K., & Ibrahim, M. (2019). Service innovation, service delivery and customer satisfaction and loyalty in the banking sector of Ghana. International journal of bank marketing, 37(5), 1215-1233. https://doi.org/10.1108/IJBM-06-2018-0142
- Krejcie, R. V., & Morgan, D. W. (1970). Determining sample size for research activities. Educational and psychological measurement, 30(3), 607-610.
)