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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
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    1. 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.
    2. 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.
    3. 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.
    4. 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).
    5. 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).
    6. John Donovan, 2021. "Financial Reporting and Entrepreneurial Finance: Evidence from Equity Crowdfunding," Management Science, INFORMS, vol. 67(11), pages 7214-7237, November.
    7. 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.
    8. 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.
    9. 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.
    10. 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.
    11. 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.
    12. 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.
    13. 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.
    14. 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.
    15. 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.
    16. 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.
    17. 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..
    18. 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.
    19. 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.
    20. 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.
    21. 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.
    22. 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).
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