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Integrating cognitive mapping and MCDA for bankruptcy prediction in small- and medium-sized enterprises

Author

Listed:
  • Manuel D. N. T. Oliveira

    (University Institute of Lisbon)

  • Fernando A. F. Ferreira

    (University Institute of Lisbon
    University of Memphis)

  • Guillermo O. Pérez-Bustamante Ilander

    (University of Oviedo)

  • Marjan S. Jalali

    (University Institute of Lisbon)

Abstract

The 2008 global financial crisis and its aftermath have shown the ability to predict bankruptcy to be a vital management skill and that the methodologies used for that purpose should be as close to reality as possible. This study aims to develop a multiple criteria system to predict bankruptcy in small- and medium-sized enterprises (SMEs). It combines cognitive mapping with the measuring attractiveness by a categorical based evaluation technique (MACBETH), resulting in a more complete and transparent process for evaluating SMEs and their risk of bankruptcy. What differentiates this framework is the fact that it is based on information obtained directly from managers and bank analysts who deal with this type of adversity on a daily basis. The results highlight the importance of financial and strategic aspects, among others, and demonstrate how cognitive mapping can improve the understanding of the decision situation at hand, while MACBETH facilitates the calculation of trade-offs among evaluation criteria.

Suggested Citation

  • Manuel D. N. T. Oliveira & Fernando A. F. Ferreira & Guillermo O. Pérez-Bustamante Ilander & Marjan S. Jalali, 2017. "Integrating cognitive mapping and MCDA for bankruptcy prediction in small- and medium-sized enterprises," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 68(9), pages 985-997, September.
    • Handle: RePEc:pal:jorsoc:v:68:y:2017:i:9:d:10.1057_s41274-016-0166-3
    DOI: 10.1057/s41274-016-0166-3
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    3. 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.
    4. 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.
    5. 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.
    6. Meng-Meng Tan & Dong-Ling Xu & Jian-Bo Yang, 2022. "Corporate Failure Risk Assessment for Knowledge-Intensive Services Using the Evidential Reasoning Approach," JRFM, MDPI, vol. 15(3), pages 1-29, March.
    7. Mario Andaru Fatah & Y Anni Aryani, 2022. "Adopción de big data: ¿tiene un gran impacto en el éxito del crowdfunding en las pymes?," Proyecciones, IIEC, Univeridad Nacional de La Plata., Instituto de Investigaciones y Estudios Contables, Facultad de Ciencias Económicas, Universidad Nacional de La Plata, issue 17, pages 50-63, May-April.
    8. Francisco C. Marques & Fernando A. F. Ferreira & Constantin Zopounidis & Audrius Banaitis, 2022. "A system dynamics-based approach to determinants of family business growth," Annals of Operations Research, Springer, vol. 311(2), pages 799-819, April.
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