IDEAS home Printed from https://ideas.repec.org/a/bla/srbeha/v40y2023i1p101-145.html
   My bibliography  Save this article

Application of viable system model in diagnosing defects and problems of the credit supply chain network in the Iranian banking industry

Author

Listed:
  • Eissa Jabbarzadeh
  • Ebrahim Teimoury
  • Saeed Shavvalpour

Abstract

The present study attempts to study the credit supply process in the Iranian banking industry by using the viable system model (VSM) to identify its vulnerabilities and improve the financing system. The method used in this study consists of three steps: (a) identification of system, (b) diagnosis of system and (c) control of information flow, communication channels and frequent errors. In this research, 37 credit experts from 17 different bank branches were interviewed about the process of granting facilities, and SPSS 23 was used to examine research objectives through the Delphi method. The results show that the structure of Iran's credit supply chain network suffers from such problems as deviation in the appropriate use of allocated credit resources, structural faults of the banking system and lack of administrative health, high bureaucracy, directed facilities, etc. In the end, some suggestions are provided for redesign and optimization of the credit supply network.

Suggested Citation

  • Eissa Jabbarzadeh & Ebrahim Teimoury & Saeed Shavvalpour, 2023. "Application of viable system model in diagnosing defects and problems of the credit supply chain network in the Iranian banking industry," Systems Research and Behavioral Science, Wiley Blackwell, vol. 40(1), pages 101-145, January.
  • Handle: RePEc:bla:srbeha:v:40:y:2023:i:1:p:101-145
    DOI: 10.1002/sres.2841
    as

    Download full text from publisher

    File URL: https://doi.org/10.1002/sres.2841
    Download Restriction: no

    File URL: https://libkey.io/10.1002/sres.2841?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Zhu, You & Zhou, Li & Xie, Chi & Wang, Gang-Jin & Nguyen, Truong V., 2019. "Forecasting SMEs' credit risk in supply chain finance with an enhanced hybrid ensemble machine learning approach," International Journal of Production Economics, Elsevier, vol. 211(C), pages 22-33.
    2. Zeinab Rezaee & Adel Azar & Abbas Moghbel Ba Erz & Mahmoud Dehghan Nayeri, 2019. "Application of Viable System Model in Diagnosis of Organizational Structure," Systemic Practice and Action Research, Springer, vol. 32(3), pages 273-295, June.
    3. Virgil Popa, 2013. "The Financial Supply Chain Management: a New Solution for Supply Chain Resilience," The AMFITEATRU ECONOMIC journal, Academy of Economic Studies - Bucharest, Romania, vol. 15(33), pages 140-153, February.
    4. R. P. Oakey, 2003. "Funding innovation and growth in UK new technology-based firms: Some observations on contributions from the public and private sectors," Venture Capital, Taylor & Francis Journals, vol. 5(2), pages 161-179, April.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Zhang, Lu & Cui, Li & Chen, Lujie & Dai, Jing & Jin, Ziyi & Wu, Hao, 2023. "A hybrid approach to explore the critical criteria of online supply chain finance to improve supply chain performance," International Journal of Production Economics, Elsevier, vol. 255(C).
    2. Uddin, Gazi Salah & Tang, Ou & Sahamkhadam, Maziar & Taghizadeh-Hesary, Farhad & Yahya, Muhammad & Cerin, Pontus & Rehme, Jakob, 2021. "Analysis of Forecasting Models in an Electricity Market under Volatility," ADBI Working Papers 1212, Asian Development Bank Institute.
    3. Suriyan Jomthanachai & Wai Peng Wong & Khai Wah Khaw, 2024. "An Application of Machine Learning to Logistics Performance Prediction: An Economics Attribute-Based of Collective Instance," Computational Economics, Springer;Society for Computational Economics, vol. 63(2), pages 741-792, February.
    4. Fosso Wamba, Samuel & Queiroz, Maciel M. & Trinchera, Laura, 2024. "The role of artificial intelligence-enabled dynamic capability on environmental performance: The mediation effect of a data-driven culture in France and the USA," International Journal of Production Economics, Elsevier, vol. 268(C).
    5. Yu, Baojun & Li, Changming & Mirza, Nawazish & Umar, Muhammad, 2022. "Forecasting credit ratings of decarbonized firms: Comparative assessment of machine learning models," Technological Forecasting and Social Change, Elsevier, vol. 174(C).
    6. Afaq Khattak & Hamad Almujibah & Ahmed Elamary & Caroline Mongina Matara, 2022. "Interpretable Dynamic Ensemble Selection Approach for the Prediction of Road Traffic Injury Severity: A Case Study of Pakistan’s National Highway N-5," Sustainability, MDPI, vol. 14(19), pages 1-18, September.
    7. Yu Zhao & Huaming Du & Qing Li & Fuzhen Zhuang & Ji Liu & Gang Kou, 2022. "A Comprehensive Survey on Enterprise Financial Risk Analysis from Big Data Perspective," Papers 2211.14997, arXiv.org, revised May 2023.
    8. Robert Baldock, 2016. "An assessment of the business impacts of the UK’s Enterprise Capital Funds," Environment and Planning C, , vol. 34(8), pages 1556-1581, December.
    9. Dong, Ciwei & Chen, Chenyi & Shi, Xiutian & Ng, Chi To, 2021. "Operations strategy for supply chain finance with asset-backed securitization: Centralization and blockchain adoption," International Journal of Production Economics, Elsevier, vol. 241(C).
    10. Yiyu Xia, 2022. "A Study on Evolution Game of Accounts Receivable Pledge Financing in Supply Chain Finance Model," International Business Research, Canadian Center of Science and Education, vol. 15(12), pages 1-39, December.
    11. Siepel, Josh & Cowling, Marc & Coad, Alex, 2017. "Non-founder human capital and the long-run growth and survival of high-tech ventures," Technovation, Elsevier, vol. 59(C), pages 34-43.
    12. Yan, Nina & Jin, Xuyu & Zhong, Hechen & Xu, Xun, 2020. "Loss-averse retailers’ financial offerings to capital-constrained suppliers: loan vs. investment," International Journal of Production Economics, Elsevier, vol. 227(C).
    13. Liu, Weihua & Long, Shangsong & Wei, Shuang, 2022. "Correlation mechanism between smart technology and smart supply chain innovation performance: A multi-case study from China's companies with Physical Internet," International Journal of Production Economics, Elsevier, vol. 245(C).
    14. Yuting Li & Tong Chen & Baogui Xin, 2016. "Optimal Financing Decisions of Two Cash-Constrained Supply Chains with Complementary Products," Sustainability, MDPI, vol. 8(5), pages 1-17, April.
    15. Ivana Mijatoviæ & Biljana Tošiæ & Milan Jovanoviæ, 2019. "The Acquiring of the Knowledge about Standards in the Digital Era," The AMFITEATRU ECONOMIC journal, Academy of Economic Studies - Bucharest, Romania, vol. 21(51), pages 427-427.
    16. Lisana B. Martinez & M. Belén Guercio & Aurelio F. Bariviera, 2022. "A meta‐analysis of SMEs literature based on the survey on access to finance of enterprises of the European central bank," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 27(2), pages 1870-1885, April.
    17. Dina Cunha & Sandra T. Silva & Aurora A.C. Teixeira, 2013. "Are Academic Spin-Offs necessarily New Technology-Based firms?," FEP Working Papers 482, Universidade do Porto, Faculdade de Economia do Porto.
    18. Guo, Feng & Bo, Qingwen & Tong, Xun & Zhang, Xiaofei, 2020. "A paradoxical view of speed and quality on operational outcome: An empirical investigation of innovation in high-tech small and medium-sized enterprises," International Journal of Production Economics, Elsevier, vol. 229(C).
    19. Johansson Jeaneth M. & Malmstrom Malin, 2013. "The Business Model Transparency Paradox in Innovative Growth Ventures: Trade-offs between Competitive Advantages and Agency Costs," Entrepreneurship Research Journal, De Gruyter, vol. 3(2), pages 238-263, January.
    20. Zericho R Marak & Deepa Pillai, 2018. "Factors, Outcome, and the Solutions of Supply Chain Finance: Review and the Future Directions," JRFM, MDPI, vol. 12(1), pages 1-23, December.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:bla:srbeha:v:40:y:2023:i:1:p:101-145. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Wiley Content Delivery (email available below). General contact details of provider: http://onlinelibrary.wiley.com/journal/10.1111/1092-7026 .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.