IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v12y2020i5p1715-d324916.html
   My bibliography  Save this article

A Dynamic Credit Index System for TSMEs in China Using the Delphi and Analytic Hierarchy Process (AHP) Methods

Author

Listed:
  • Ao Yu

    (School of Economics, Sichuan University, Chengdu 610065, China)

  • Zhuoqiang Jia

    (School of Economics, Sichuan University, Chengdu 610065, China)

  • Weike Zhang

    (School of Public Administration, Sichuan University, Chengdu 610065, China)

  • Ke Deng

    (Chengdu Administration China (Sichuan) Pilot Free Trade Zone, Chengdu 610041, China)

  • Francisco Herrera

    (Andalusian Research Institute in Data Science and Computational Intelligence (DaSCI), University of Granada, 18071 Granada, Spain
    Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah 21589, Saudi Arabia)

Abstract

A high-quality credit index system is essential for technological small and medium-sized enterprises (TSMEs) to obtain financing from various institutions, such as banks, venture capital. Some attempts have made to construct the credit index system for TSMEs. However, the current credit index systems for TSMEs have placed too much emphasis on their financial ability with few prominent technological and talent indicators. Therefore, this study has proposed a dynamic credit index system for TSMEs in China using the Delphi and the Analytic Hierarchy Process (AHP) methods. This credit index system covers a wide range of indicators to measure the enterprises’ controller ability, operation and management ability, financial ability, and innovation capacity. This study made some contributions in the following aspects: (1) This study proposed a credit index system for TSMEs that highlights the main characteristics of technological innovation and talents of enterprises in China. (2) The credit index system is also highly adaptable as it can dynamically adjust the index weight according to the life cycles of TSMEs. (3) A case study of evaluating the credit of three TSMEs in China was selected to verify the feasibility and the effectiveness of this system. The results show that the credit index system constructed in this study provides a comprehensive and systematic model for evaluating the credit of TSMEs in China.

Suggested Citation

  • Ao Yu & Zhuoqiang Jia & Weike Zhang & Ke Deng & Francisco Herrera, 2020. "A Dynamic Credit Index System for TSMEs in China Using the Delphi and Analytic Hierarchy Process (AHP) Methods," Sustainability, MDPI, vol. 12(5), pages 1-21, February.
    • Handle: RePEc:gam:jsusta:v:12:y:2020:i:5:p:1715-:d:324916
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/12/5/1715/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/12/5/1715/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Mehmet Karan & Aydın Ulucan & Mustafa Kaya, 2013. "Credit risk estimation using payment history data: a comparative study of Turkish retail stores," Central European Journal of Operations Research, Springer;Slovak Society for Operations Research;Hungarian Operational Research Society;Czech Society for Operations Research;Österr. Gesellschaft für Operations Research (ÖGOR);Slovenian Society Informatika - Section for Operational Research;Croatian Operational Research Society, vol. 21(2), pages 479-494, March.
    2. Zhang, Huanhuan & Kou, Gang & Peng, Yi, 2019. "Soft consensus cost models for group decision making and economic interpretations," European Journal of Operational Research, Elsevier, vol. 277(3), pages 964-980.
    3. WeiMing Mou & Wing-Keung Wong & Michael McAleer, 2018. "Financial Credit Risk Evaluation Based on Core Enterprise Supply Chains," Sustainability, MDPI, vol. 10(10), pages 1-17, October.
    4. Grunert, Jens & Norden, Lars & Weber, Martin, 2005. "The role of non-financial factors in internal credit ratings," Journal of Banking & Finance, Elsevier, vol. 29(2), pages 509-531, February.
    5. Zhengxin Wang & Minghuan Shou & Shuai Wang & Ruinan Dai & Keqian Wang, 2019. "An Empirical Study on the Key Factors of Intelligent Upgrade of Small and Medium-sized Enterprises in China," Sustainability, MDPI, vol. 11(3), pages 1-16, January.
    6. Eisenbeis, Robert A., 1978. "Problems in applying discriminant analysis in credit scoring models," Journal of Banking & Finance, Elsevier, vol. 2(3), pages 205-219, October.
    7. Mou, W.M. & Wong, W.-K. & McAleer, M.J., 2018. "Financial Credit Risk and Core Enterprise Supply Chains," Econometric Institute Research Papers EI2018-27, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
    8. Gang Kou & Yanqun Lu & Yi Peng & Yong Shi, 2012. "Evaluation Of Classification Algorithms Using Mcdm And Rank Correlation," International Journal of Information Technology & Decision Making (IJITDM), World Scientific Publishing Co. Pte. Ltd., vol. 11(01), pages 197-225.
    9. Black, Fischer & Scholes, Myron S, 1973. "The Pricing of Options and Corporate Liabilities," Journal of Political Economy, University of Chicago Press, vol. 81(3), pages 637-654, May-June.
    10. Beaver, Wh, 1966. "Financial Ratios As Predictors Of Failure," Journal of Accounting Research, Wiley Blackwell, vol. 4, pages 71-111.
    11. Raffaella Calabrese & Giampiero Marra & Silvia Angela Osmetti, 2016. "Bankruptcy prediction of small and medium enterprises using a flexible binary generalized extreme value model," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 67(4), pages 604-615, April.
    12. Edward I. Altman, 1968. "Financial Ratios, Discriminant Analysis And The Prediction Of Corporate Bankruptcy," Journal of Finance, American Finance Association, vol. 23(4), pages 589-609, September.
    13. Wei Gu & Meheli Basu & Zhang Chao & Lirong Wei, 2017. "A Unified Framework for Credit Evaluation for Internet Finance Companies: Multi-Criteria Analysis Through AHP and DEA," International Journal of Information Technology & Decision Making (IJITDM), World Scientific Publishing Co. Pte. Ltd., vol. 16(03), pages 597-624, May.
    14. Emmanuel Thanassoulis & Prasanta Kumar Dey & Konstantinos Petridis & Ioannis Goniadis & Andreas C. Georgiou, 2017. "Evaluating higher education teaching performance using combined analytic hierarchy process and data envelopment analysis," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 68(4), pages 431-445, April.
    15. Edward I. Altman, 1968. "The Prediction Of Corporate Bankruptcy: A Discriminant Analysis," Journal of Finance, American Finance Association, vol. 23(1), pages 193-194, March.
    16. Giudici, Paolo, 2018. "Financial data science," Statistics & Probability Letters, Elsevier, vol. 136(C), pages 160-164.
    17. Sang B. Hahn & Sehoon Kwon & Jeongsun Yun, 2017. "The effects of policy funds on the financing constraints of small- and medium-sized enterprises in South Korea," Applied Economics Letters, Taylor & Francis Journals, vol. 24(10), pages 699-702, June.
    18. Norman Dalkey & Olaf Helmer, 1963. "An Experimental Application of the DELPHI Method to the Use of Experts," Management Science, INFORMS, vol. 9(3), pages 458-467, April.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Yihao Tian & Lijin Ding & Bin Yang & Feng Peng, 2022. "The Effects of De-Capacity Policy on Steel and Coal Firms’ Profitability: Evidence from China’s Listed Companies," Energies, MDPI, vol. 15(12), pages 1-17, June.
    2. Yun-Ning Liu & Hsin-Hung Wu, 2022. "An Inner Dependence Analysis Dynamic Decision-Making Framework," Sustainability, MDPI, vol. 14(10), pages 1-13, May.
    3. Guan, Jialin & Kirikkaleli, Dervis & Bibi, Ayesha & Zhang, Weike, 2020. "Natural resources rents nexus with financial development in the presence of globalization: Is the “resource curse” exist or myth?," Resources Policy, Elsevier, vol. 66(C).
    4. TIAN, Xiaoli & KOU, Gang & ZHANG, Weike, 2020. "Geographic distance, venture capital and technological performance: Evidence from Chinese enterprises," Technological Forecasting and Social Change, Elsevier, vol. 158(C).
    5. Chunxiang Zhang & You-Yu Dai & Xiaowei Jin & Yiwan Yang, 2022. "Land Reclamation Tourism Resources in China: Connotation, Classification, and Evaluation," SAGE Open, , vol. 12(1), pages 21582440221, March.
    6. Rezaei, Mojtaba & Jafari-Sadeghi, Vahid & Cao, Dongmei & Mahdiraji, Hannan Amoozad, 2021. "Key indicators of ethical challenges in digital healthcare: A combined Delphi exploration and confirmative factor analysis approach with evidence from Khorasan province in Iran," Technological Forecasting and Social Change, Elsevier, vol. 167(C).
    7. Gu, Zhouyi & Chen, Xihui & Parziale, Anna & Tang, Zhuoyuan, 2024. "Evaluation of primary-level credit environment, indicator system and empirical analysis: A case study of credit construction in China county and district," Evaluation and Program Planning, Elsevier, vol. 104(C).
    8. Mohammed Abdul-Rahman & Wale Alade & Shahnawaz Anwer, 2023. "A Composite Resilience Index (CRI) for Developing Resilience and Sustainability in University Towns," Sustainability, MDPI, vol. 15(4), pages 1-27, February.
    9. Yiming Liu & Sunhee Suk, 2021. "Constructing an Evaluation Index System for China’s Low-Carbon Tourism Region—An Example from the Daxinganling Region," Sustainability, MDPI, vol. 13(21), pages 1-12, October.
    10. Zhang, Weike & Meng, Jia & Tian, Xiaoli, 2020. "Does de-capacity policy enhance the total factor productivity of China's coal companies? A Regression Discontinuity design," Resources Policy, Elsevier, vol. 68(C).
    11. Pranith K. Roy & Krishnendu Shaw, 2023. "A credit scoring model for SMEs using AHP and TOPSIS," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 28(1), pages 372-391, January.
    12. Delu Wang & Yadong Wang, 2021. "Emergency Capacity of Small Towns to Endure Sudden Environmental Pollution Accidents: Construction and Application of an Evaluation Model," Sustainability, MDPI, vol. 13(10), pages 1-34, May.

    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. Van Laere, Elisabeth & Baesens, Bart, 2010. "The development of a simple and intuitive rating system under Solvency II," Insurance: Mathematics and Economics, Elsevier, vol. 46(3), pages 500-510, June.
    2. Simon Cornée, 2014. "Soft Information and Default Prediction in Cooperative and Social Banks," Journal of Entrepreneurial and Organizational Diversity, European Research Institute on Cooperative and Social Enterprises, vol. 3(1), pages 89-103, June.
    3. Chiara Pederzoli & Grid Thoma & Costanza Torricelli, 2013. "Modelling Credit Risk for Innovative SMEs: the Role of Innovation Measures," Journal of Financial Services Research, Springer;Western Finance Association, vol. 44(1), pages 111-129, August.
    4. Ahsan Habib & Mabel D' Costa & Hedy Jiaying Huang & Md. Borhan Uddin Bhuiyan & Li Sun, 2020. "Determinants and consequences of financial distress: review of the empirical literature," Accounting and Finance, Accounting and Finance Association of Australia and New Zealand, vol. 60(S1), pages 1023-1075, April.
    5. Giesecke, Kay & Longstaff, Francis A. & Schaefer, Stephen & Strebulaev, Ilya, 2011. "Corporate bond default risk: A 150-year perspective," Journal of Financial Economics, Elsevier, vol. 102(2), pages 233-250.
    6. Giulio Bottazzi & Marco Grazzi & Angelo Secchi & Federico Tamagni, 2011. "Financial and economic determinants of firm default," Journal of Evolutionary Economics, Springer, vol. 21(3), pages 373-406, August.
    7. Duffie, Darrell & Saita, Leandro & Wang, Ke, 2007. "Multi-period corporate default prediction with stochastic covariates," Journal of Financial Economics, Elsevier, vol. 83(3), pages 635-665, March.
    8. Dmytro Kovalenko & Olga Afanasieva & Nani Zabuta & Tetiana Boiko & Rosen Rosenov Baltov, 2021. "Model of Assessing the Overdue Debts in a Commercial Bank Using Neuro-Fuzzy Technologies," JRFM, MDPI, vol. 14(5), pages 1-20, May.
    9. Mohammad Mahdi Mousavi & Jamal Ouenniche & Kaoru Tone, 2023. "A dynamic performance evaluation of distress prediction models," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 42(4), pages 756-784, July.
    10. Abdelghani Maddi, 2018. "Analyse scientométrique de la crise économique," CEPN Working Papers 2018-08, Centre d'Economie de l'Université de Paris Nord.
    11. Martin Kukuk & Michael Rönnberg, 2013. "Corporate credit default models: a mixed logit approach," Review of Quantitative Finance and Accounting, Springer, vol. 40(3), pages 467-483, April.
    12. Vladislav V. Afanasev & Yulia A. Tarasova, 2022. "Default Prediction for Housing and Utilities Management Firms Using Non-Financial Data," Finansovyj žhurnal — Financial Journal, Financial Research Institute, Moscow 125375, Russia, issue 6, pages 91-110, December.
    13. Elisa Ughetto & Andrea Vezzulli, 2011. "What role can mutual guarantee consortia play for financing innovation? A firm-level study for Italy," International Journal of Banking, Accounting and Finance, Inderscience Enterprises Ltd, vol. 3(4), pages 294-319.
    14. Nico Dewaelheyns & Cynthia Van Hulle & Yannick Van Landuyt & Mathias Verreydt, 2021. "Labor Contracts, Wages and SME Failure," Sustainability, MDPI, vol. 13(14), pages 1-15, July.
    15. Marielle de Jong & Lauren Stagnol, 2016. "A fundamental bond index including solvency criteria," Journal of Asset Management, Palgrave Macmillan, vol. 17(4), pages 280-294, July.
    16. Zhang, Xuan & Ouyang, Ruolan & Liu, Ding & Xu, Liao, 2020. "Determinants of corporate default risk in China: The role of financial constraints," Economic Modelling, Elsevier, vol. 92(C), pages 87-98.
    17. Jairaj Gupta & Andros Gregoriou & Jerome Healy, 2015. "Forecasting bankruptcy for SMEs using hazard function: To what extent does size matter?," Review of Quantitative Finance and Accounting, Springer, vol. 45(4), pages 845-869, November.
    18. Catherine Refait, 2000. "Estimation du risque de défaut par une modélisation stochastique du bilan : Application à des firmes industrielles françaises," Université Paris1 Panthéon-Sorbonne (Post-Print and Working Papers) halshs-03718527, HAL.
    19. Duc Hong Vo & Binh Ninh Vo Pham & Chi Minh Ho & Michael McAleer, 2019. "Corporate Financial Distress of Industry Level Listings in Vietnam," JRFM, MDPI, vol. 12(4), pages 1-17, September.
    20. Adler Haymans Manurung & Derwin Suhartono & Benny Hutahayan & Noptovius Halimawan, 2023. "Probability Bankruptcy Using Support Vector Regression Machines," Journal of Applied Finance & Banking, SCIENPRESS Ltd, vol. 13(1), pages 1-3.

    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:gam:jsusta:v:12:y:2020:i:5:p:1715-:d:324916. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    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.