IDEAS home Printed from https://ideas.repec.org/a/eee/jbrese/v169y2023ics0148296323006495.html
   My bibliography  Save this article

The value of official website information in the credit risk evaluation of SMEs

Author

Listed:
  • Jiang, Cuiqing
  • Yin, Chang
  • Tang, Qian
  • Wang, Zhao

Abstract

The official websites of small and medium-sized enterprises (SMEs) not only reflect the willingness of an enterprise to disclose information voluntarily, but also can provide information related to the enterprises’ historical operations and performance. This research investigates the value of official website information in the credit risk evaluation of SMEs. To study the effect of different kinds of website information on credit risk evaluation, we propose a framework to mine effective features from two kinds of information disclosed on the official website of a SME—design-based information and content-based information—in predicting its credit risk. We select the SMEs in the software and information technology services industry and find that including content-based information in models significantly improves the prediction accuracy. Specifically, the depth and dynamics metrics of the content-based information convey SME performance and mitigate the information asymmetry between SMEs and financial institutions.

Suggested Citation

  • Jiang, Cuiqing & Yin, Chang & Tang, Qian & Wang, Zhao, 2023. "The value of official website information in the credit risk evaluation of SMEs," Journal of Business Research, Elsevier, vol. 169(C).
    • Handle: RePEc:eee:jbrese:v:169:y:2023:i:c:s0148296323006495
    DOI: 10.1016/j.jbusres.2023.114290
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0148296323006495
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.jbusres.2023.114290?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
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Samuel B. Bonsall IV & Eric R. Holzman & Brian P. Miller, 2017. "Managerial Ability and Credit Risk Assessment," Management Science, INFORMS, vol. 63(5), pages 1425-1449, May.
    2. John Donovan, 2021. "Financial Reporting and Entrepreneurial Finance: Evidence from Equity Crowdfunding," Management Science, INFORMS, vol. 67(11), pages 7214-7237, November.
    3. Stefan Mayr & Christine Mitter & Andrea Aichmayr, 2017. "Corporate Crisis and Sustainable Reorganization: Evidence from Bankrupt Austrian SMEs," Journal of Small Business Management, Taylor & Francis Journals, vol. 55(1), pages 108-127, January.
    4. Niklas Bussmann & Paolo Giudici & Dimitri Marinelli & Jochen Papenbrock, 2021. "Explainable Machine Learning in Credit Risk Management," Computational Economics, Springer;Society for Computational Economics, vol. 57(1), pages 203-216, January.
    5. Caputo, Francesco & Magni, Domitilla & Papa, Armando & Corsi, Christian, 2021. "Knowledge hiding in socioeconomic settings: Matching organizational and environmental antecedents," Journal of Business Research, Elsevier, vol. 135(C), pages 19-27.
    6. Lessmann, Stefan & Baesens, Bart & Seow, Hsin-Vonn & Thomas, Lyn C., 2015. "Benchmarking state-of-the-art classification algorithms for credit scoring: An update of research," European Journal of Operational Research, Elsevier, vol. 247(1), pages 124-136.
    7. Cassar, Gavin & Ittner, Christopher D. & Cavalluzzo, Ken S., 2015. "Alternative information sources and information asymmetry reduction: Evidence from small business debt," Journal of Accounting and Economics, Elsevier, vol. 59(2), pages 242-263.
    8. Ahelegbey, Daniel Felix & Giudici, Paolo & Hadji-Misheva, Branka, 2019. "Latent factor models for credit scoring in P2P systems," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 522(C), pages 112-121.
    9. Michael Firth & Chen Lin & Sonia Man-lai Wong & Xiaofeng Zhao, 2019. "Hello, is anybody there? Corporate accessibility for outside shareholders as a signal of agency problems," Review of Accounting Studies, Springer, vol. 24(4), pages 1317-1358, December.
    10. Chatterjee, Sheshadri & Kumar Kar, Arpan, 2020. "Why do small and medium enterprises use social media marketing and what is the impact: Empirical insights from India," International Journal of Information Management, Elsevier, vol. 53(C).
    11. Zu, Xu & Diao, Xinyi & Meng, Zhiyi, 2019. "The impact of social media input intensity on firm performance: Evidence from Sina Weibo," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 536(C).
    12. Resch, Christian & Kock, Alexander, 2021. "The influence of information depth and information breadth on brokers' idea newness in online maker communities," Publications of Darmstadt Technical University, Institute for Business Studies (BWL) 130794, Darmstadt Technical University, Department of Business Administration, Economics and Law, Institute for Business Studies (BWL).
    13. Silvia Angilella & Sebastiano Mazzù, 2019. "A credit risk model with an automatic override for innovative small and medium-sized enterprises," Journal of the Operational Research Society, Taylor & Francis Journals, vol. 70(10), pages 1784-1800, October.
    14. Salvi, Antonio & Vitolla, Filippo & Rubino, Michele & Giakoumelou, Anastasia & Raimo, Nicola, 2021. "Online information on digitalisation processes and its impact on firm value," Journal of Business Research, Elsevier, vol. 124(C), pages 437-444.
    15. Edward I. Altman & Gabriele Sabato, 2013. "MODELING CREDIT RISK FOR SMEs: EVIDENCE FROM THE US MARKET," World Scientific Book Chapters, in: Oliviero Roggi & Edward I Altman (ed.), Managing and Measuring Risk Emerging Global Standards and Regulations After the Financial Crisis, chapter 9, pages 251-279, World Scientific Publishing Co. Pte. Ltd..
    16. Rekik, Rim & Kallel, Ilhem & Casillas, Jorge & Alimi, Adel M., 2018. "Assessing web sites quality: A systematic literature review by text and association rules mining," International Journal of Information Management, Elsevier, vol. 38(1), pages 201-216.
    17. Angilella, Silvia & Mazzù, Sebastiano, 2015. "The financing of innovative SMEs: A multicriteria credit rating model," European Journal of Operational Research, Elsevier, vol. 244(2), pages 540-554.
    18. Ciampi, Francesco, 2015. "Corporate governance characteristics and default prediction modeling for small enterprises. An empirical analysis of Italian firms," Journal of Business Research, Elsevier, vol. 68(5), pages 1012-1025.
    19. Jinhua Cui & Hoje Jo & Haejung Na, 2018. "Does Corporate Social Responsibility Affect Information Asymmetry?," Journal of Business Ethics, Springer, vol. 148(3), pages 549-572, March.
    20. Dorra Talbi & Mohamed Ali Omri, 2014. "Voluntary disclosure frequency and cost of debt: an analysis in the Tunisian context," International Journal of Managerial and Financial Accounting, Inderscience Enterprises Ltd, vol. 6(2), pages 167-174.
    21. Kiron Ravindran & Anjana Susarla & Deepa Mani & Vijay Gurbaxani, 2015. "Social Capital and Contract Duration in Buyer-Supplier Networks for Information Technology Outsourcing," Information Systems Research, INFORMS, vol. 26(2), pages 379-397, June.
    22. Tsai, Ming-Feng & Wang, Chuan-Ju, 2017. "On the risk prediction and analysis of soft information in finance reports," European Journal of Operational Research, Elsevier, vol. 257(1), pages 243-250.
    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. Lisa Crosato & Caterina Liberati & Marco Repetto, 2021. "Look Who's Talking: Interpretable Machine Learning for Assessing Italian SMEs Credit Default," Papers 2108.13914, arXiv.org, revised Sep 2021.
    2. 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.
    3. Francesco Ciampi & Alessandro Giannozzi & Giacomo Marzi & Edward I. Altman, 2021. "Rethinking SME default prediction: a systematic literature review and future perspectives," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(3), pages 2141-2188, March.
    4. Marco Corazza & Giovanni Fasano & Stefania Funari & Riccardo Gusso, 2021. "MURAME parameter setting for creditworthiness evaluation: data-driven optimization," Decisions in Economics and Finance, Springer;Associazione per la Matematica, vol. 44(1), pages 295-339, June.
    5. Taghizadeh-Hesary, Farhad & Yoshino, Naoyuki & Fukuda, Lisa & Rasoulinezhad, Ehsan, 2021. "A model for calculating optimal credit guarantee fee for small and medium-sized enterprises," Economic Modelling, Elsevier, vol. 95(C), pages 361-373.
    6. Modina, Michele & Pietrovito, Filomena & Gallucci, Carmen & Formisano, Vincenzo, 2023. "Predicting SMEs’ default risk: Evidence from bank-firm relationship data," The Quarterly Review of Economics and Finance, Elsevier, vol. 89(C), pages 254-268.
    7. Ali Uyar & Simone Pizzi & Fabio Caputo & Cemil Kuzey & Abdullah S. Karaman, 2022. "Do shareholders reward or punish risky firms due to CSR reporting and assurance?," Managerial and Decision Economics, John Wiley & Sons, Ltd., vol. 43(5), pages 1596-1620, July.
    8. Juraini Zainol Abidin & Nur Adiana Hiau Abdullah & Karren Lee-Hwei Khaw, 2020. "Predicting SMEs Failure: Logistic Regression vs Artificial Neural Network Models," Capital Markets Review, Malaysian Finance Association, vol. 28(2), pages 29-41.
    9. Cao Son Tran & Dan Nicolau & Richi Nayak & Peter Verhoeven, 2021. "Modeling Credit Risk: A Category Theory Perspective," JRFM, MDPI, vol. 14(7), pages 1-21, July.
    10. David Veganzones, 2022. "Corporate failure prediction using threshold‐based models," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 41(5), pages 956-979, August.
    11. Sigrist, Fabio & Leuenberger, Nicola, 2023. "Machine learning for corporate default risk: Multi-period prediction, frailty correlation, loan portfolios, and tail probabilities," European Journal of Operational Research, Elsevier, vol. 305(3), pages 1390-1406.
    12. Kriebel, Johannes & Stitz, Lennart, 2022. "Credit default prediction from user-generated text in peer-to-peer lending using deep learning," European Journal of Operational Research, Elsevier, vol. 302(1), pages 309-323.
    13. Edward I. Altman & Marco Balzano & Alessandro Giannozzi & Stjepan Srhoj, 2023. "Revisiting SME default predictors: The Omega Score," Journal of Small Business Management, Taylor & Francis Journals, vol. 61(6), pages 2383-2417, November.
    14. Zhang, Wen & Yan, Shaoshan & Li, Jian & Tian, Xin & Yoshida, Taketoshi, 2022. "Credit risk prediction of SMEs in supply chain finance by fusing demographic and behavioral data," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 158(C).
    15. Yu Xia & Ta Xu & Ming-Xia Wei & Zhen-Ke Wei & Lian-Jie Tang, 2023. "Predicting Chain’s Manufacturing SME Credit Risk in Supply Chain Finance Based on Machine Learning Methods," Sustainability, MDPI, vol. 15(2), pages 1-18, January.
    16. Jingjing Long & Cuiqing Jiang & Stanko Dimitrov & Zhao Wang, 2022. "Clues from networks: quantifying relational risk for credit risk evaluation of SMEs," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 8(1), pages 1-41, December.
    17. Jaime F. Lavin & Alejandro A. Montecinos-Pearce, 2022. "Heterogeneous Firms and Benefits of ESG Disclosure: Cost of Debt Financing in an Emerging Market," Sustainability, MDPI, vol. 14(23), pages 1-21, November.
    18. Keijo Kohv & Oliver Lukason, 2021. "What Best Predicts Corporate Bank Loan Defaults? An Analysis of Three Different Variable Domains," Risks, MDPI, vol. 9(2), pages 1-19, January.
    19. Pratibha Wasan & Ashwani Kumar & Sunil Luthra, 2023. "How can banks and finance companies incorporate value chain factors in their risk management strategy? The case of agro‐food firms," Business Strategy and the Environment, Wiley Blackwell, vol. 32(1), pages 858-877, January.
    20. 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.

    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:eee:jbrese:v:169:y:2023:i:c:s0148296323006495. 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: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/jbusres .

    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.