Post-pandemic performance of micro, small and medium-sized enterprises: A Self-organizing Maps application

References

• Alaka, H. A., Oyedele, L. O., Owolabi, H. A., Kumar, V., Ajayi, S. O., Akinade, O. O., & Bilal, M. (2018). Systematic review of bankruptcy prediction models: Towards a framework for tool selection. Expert Systems with Applications, 94, 164–13. https://doi.org/10.1016/j.eswa.2017.10.040
   Web of Science ®Google Scholar
• Alam, P., Booth, D., Lee, K., & Thordarson, T. (2000). The use of fuzzy clustering algorithm and self-organizing neural networks for identifying potentially failing banks: An experimental study. Expert Systems with Applications, 18(3), 185–199. https://doi.org/10.1016/s0957-4174(99)00061-5
   Web of Science ®Google Scholar
• Altman, E. I. (1968). Financial ratios, discriminant analysis and the prediction of corporate bankruptcy. The Journal of Finance, 23(4), 589–609. https://doi.org/10.2307/2978933
   Web of Science ®Google Scholar
• Altman, E. I., Balzano, M., Giannozzi, A., & Srhoj, S. (2022). Revisiting SME default predictors: The omega score. Journal of Small Business Management, 61(6), 1–35. https://doi.org/10.1080/00472778.2022.2135718
   Web of Science ®Google Scholar
• Argenti, J. (1976). Corporate collapse: The causes and symptoms. John Wiley and Sons.
   Google Scholar
• Argenti, J. (1983). Predicting corporate Failure. Accountants digest. Institute of Chartered accountants in England and Wales. Sumer.
   Google Scholar
• Bahrammirzaee, A. (2010). A comparative survey of artificial intelligence applications in finance: Artificial neural networks, expert systems and hybrid intelligent systems. Neural Computing and Applications, 19(8), 1165–1195. https://doi.org/10.1007/s00521-010-0362-z
   Web of Science ®Google Scholar
• Balcaen, S., & Ooghe, H. (2006). 35 years of studies on business failure: An overview of the classic statistical methodologies and their related problems. The British Accounting Review, 38(1), 63–93. https://doi.org/10.1016/j.bar.2005.09.001
   Google Scholar
• Beaver, W. H. (1966). Financial ratios as predictors of failure. Journal of Accounting Research, 4, 71–111. https://doi.org/10.2307/2490171
   Web of Science ®Google Scholar
• Brown, R., Rocha, A., & Cowling, M. (2020). Financing entrepreneurship in times of crisis: Exploring the impact of COVID-19 on the market for entrepreneurial finance in the United Kingdom. International Small Business Journal, 38(5), 380–390. https://doi.org/10.1177/0266242620937464
   PubMed Web of Science ®Google Scholar
• Caballero-Morales, S. O. (2021). Innovation as recovery strategy for SMEs in emerging economies during the COVID-19 pandemic. Research in International Business and Finance, 57, 101396. https://doi.org/10.1016/j.ribaf.2021.101396
   PubMed Web of Science ®Google Scholar
• CAME. (2021a). Informe IMAF- Emprendedores. Retrieved from https://www.redcame.org.ar/advf/documentos/2021/04/608af1c1495e7.pdf
   Google Scholar
• CB Insights. (2021). Start-up Failure Post-Mortems, Database. www.Cbinsights.com.
   Google Scholar
• Chaves-Maza, M., & Martel, E. M. F. (2020). Entrepreneurship support ways after the COVID-19 crisis. Entrepreneurship & Sustainability Issues, 8(2), 662–681. https://doi.org/10.9770/jesi.2020.8.2(40)
   Web of Science ®Google Scholar
• Del Brio, B. M., & Serrano-Cinca, C. (1993). Self-organizing neural networks for the analysis and representation of data: Some financial cases. Neural Computing & Applications, 1(3), 193–206. https://doi.org/10.1007/bf01414948
   Google Scholar
• Du Jardin, P. (2018). Failure pattern-based ensembles applied to bankruptcy forecasting. Decision Support Systems, 107, 64–77. https://doi.org/10.1016/j.dss.2018.01.003
   Web of Science ®Google Scholar
• Eschker, E., Gold, G., & Lane, M. D. (2017). Rural entrepreneurs: What are the best indicators of their success? Journal of Small Business and Enterprise Development, 24(2), 278–296. https://doi.org/10.1108/JSBED-07-2016-0112
   Web of Science ®Google Scholar
• Gabás Trigo, F. (1997). Predicción de la insolvencia empresarial. En: Predicción de la Insolvencia Empresarial, Antonio Calvo-Flores Segura and Domingo García Pérez. In de Lema (Ed.), Asociación Española de Contabilidad y Administración de Empresas, AECA (pp. 11–32).
   Google Scholar
• Ghosh, B. C., Tan, W. L., Tan, T. M., & Chan, B. (2001). The key success factors, distinctive capabilities and strategic thrusts of top SMEs in Singapore. Journal of Business Research, 51(3), 209–221. https://doi.org/10.1016/s0148-2963(99)00047-8
   Web of Science ®Google Scholar
• Grunert, J., Norden, L., & Weber, M. (2005). The role of non-financial factors in internal credit ratings. Journal of Banking and Finance, 29(2), 509–531. https://doi.org/10.1016/j.jbankfin.2004.05.017
   Web of Science ®Google Scholar
• Halabí, C. E., & Lussier, R. N. (2014). A model for predicting small firm performance: Increasing the probability of entrepreneurial success in Chile. Journal of Small Business and Enterprise Development, 21(1), 4–25. https://doi.org/10.1108/JSBED-10-2013-0141
   Google Scholar
• Kantis, H., & Angelelli, P. (2020). Los ecosistemas de emprendimiento de América Latina y el Caribe frente al Covid 19. Impacto, necesidades y recomendaciones, BID. Retrieved from https://publications.iadb.org/publications/spanish/document/Los-ecosistemas-de-emprendimiento-de-America-Latina-y-el-Caribe-frente-al-COVID-19-Impactos-necesidades-y-recomendaciones.pdf
   Google Scholar
• Khelil, N. (2016). The many faces of entrepreneurial failure: Insights from an empirical taxonomy. Journal of Business Venturing, 31(1), 72–94. https://doi.org/10.1016/j.jbusvent.2015.08.001
   Web of Science ®Google Scholar
• Kim, H., Cho, H., & Ryu, D. (2020). Corporate default predictions using machine learning: Literature review. Sustainability, 12(16), 6325. https://doi.org/10.3390/su12166325
   Web of Science ®Google Scholar
• Kücher, A., Mayr, S., Mitter, C., Duller, C., & Feldbauer-Durstmüller, B. (2020). Firm age dynamics and causes of corporate bankruptcy: Age dependent explanations for business failure. Review of Managerial Science, 14(3), 633–661. https://doi.org/10.1007/s11846-018-0303-2
   Web of Science ®Google Scholar
• Kumar, P. R., & Ravi, V. (2007). Bankruptcy prediction in banks and firms via statistical and intelligent techniques: A review. European Journal of Operational Research, 180(1), 1–28. https://doi.org/10.1016/j.ejor.2006.08.043
   Web of Science ®Google Scholar
• Kuzmisin, P., & Kuzmisinova, V. (2017). Small and medium-sized enterprises in global value chains. Economic Annals-XXI, 162(11–12), 22–27. https://doi.org/10.21003/ea.V162-05
   Google Scholar
• Länsiluoto, A., Eklund, T., Back, B., Vanharanta, H., & Visa, A. (2004). Industry‐specific cycles and companies’ financial performance comparison using self‐organizing maps. Benchmarking: An International Journal, 11(3), 267–286. https://doi.org/10.1108/14635770410538754
   Google Scholar
• Lanzarini, L. C., Monte, A. V., Bariviera, A. F., & Santana, P. J. (2017). Simplifying credit scoring rules using LVQ+ PSO. Kybernetes, 46(1), 8–16. https://doi.org/10.1108/K-06-2016-0158
   Web of Science ®Google Scholar
• Lee, K., Booth, D., & Alam, P. (2005). A comparison of supervised and unsupervised neural networks in predicting bankruptcy of Korean firms. Expert Systems with Applications, 29(1), 1–16. https://doi.org/10.1016/j.eswa.2005.01.004
   Web of Science ®Google Scholar
• Liguori, E. W., & Pittz, T. G. (2020). Strategies for small business: Surviving and thriving in the era of COVID-19. Journal of the International Council for Small Business, 1(2), 106–110. https://doi.org/10.1080/26437015.2020.1779538
   Google Scholar
• Lussier, R. N. (1995). A non-financial business success versus failure prediction model for young firms. Journal of Small Business Management, 33(1), 8–20.
   Web of Science ®Google Scholar
• Madrid, A., & García, D. (2006). Factores que explican el fracaso empresarial en la PyME. Gestión: Revista de Economía, 36, 5–9. Retrieved from.https://dialnet.unirioja.es/servlet/articulo?codigo=2055435
   Google Scholar
• McGahan, A., & Porter, M. (1997). How much does industry matter, really? Strategic Management Journal, 18, 15–30. https://www.jstor.org/stable/3088208
   Web of Science ®Google Scholar
• Meahjohn, I., & Persad, P. (2020). The impact of COVID-19 on entrepreneurship globally. Journal of Economics and Business, 3(3), 1165–1173. https://doi.org/10.31014/aior.1992.03.03.272
   Google Scholar
• Ooghe, H., & De Prijcker, S. (2008). Failure processes and causes of company bankruptcy: A typology. Management Decision, 46(2), 223–242. https://doi.org/10.1108/00251740810854131
   Web of Science ®Google Scholar
• Park, Y. S., Céréghino, R., Compin, A., & Lek, S. (2003). Applications of artificial neural networks for patterning and predicting aquatic insect species richness in running waters. Ecological Modelling, 160(3), 265–280. https://doi.org/10.1016/S0304-3800(02)00258-2
   Web of Science ®Google Scholar
• Portuguez Castro, M., & Gómez Zermeño, M. G. (2021). Being an entrepreneur post-COVID-19 – resilience in times of crisis: A systematic literature review. Journal of Entrepreneurship in Emerging Economies, 13(4), 721–746. https://doi.org/10.1108/JEEE-07-2020-0246
   Web of Science ®Google Scholar
• Rhaiem, K., & Halilem, N. (2023). The worst is not to fail, but to fail to learn from failure: A multi-method empirical validation of learning from innovation failure. Technological Forecasting & Social Change, 190, 122427. https://doi.org/10.1016/j.techfore.2023.122427
   Web of Science ®Google Scholar
• Rumelt, R. (1997). How much does industry matter? Strategic Management Journal, 12(3), 167–185. https://doi.org/10.1002/smj.4250120302
   Google Scholar
• Sanhokwe, H. (2022). Reimagining the SME failure phenomenon: Incorporating regenerative effects and predicting enterprise regenerative capacity. Journal of Entrepreneurship in Emerging Economies. https://doi.org/10.1108/JEEE-01-2022-0010
   Google Scholar
• Scarlat, E., & Delcea, C. (2011). Complete analysis of bankruptcy syndrome using grey systems theory. Grey Systems: Theory and Application, 1(1), 19–32. https://doi.org/10.1108/20439371111106704
   Google Scholar
• Scherger, V., Terceño, A., & Vigier, H. (2019). A systematic overview of the prediction of business failure. International Journal of Technology, Policy and Management, 19(2), 196–211. https://doi.org/10.1504/IJTPM.2019.100601
   Google Scholar
• Scherger, V., Terceño, A., Vigier, H., & Barberá-Mariné, M. G. (2015). Detection and assessment of causes in business diagnosis. Economic Computation and Economic Cybernetics Studies and Research, 49(4), 211–229. http://www.ecocyb.ase.ro/nr20154/13%20-%20Scherger%20Valeria.pdf
   Web of Science ®Google Scholar
• Scherger, V., Vigier, H., & Barberá-Mariné, G. (2014). Finding business failure reasons through a fuzzy model of diagnosis. Fuzzy Economic Review, 19(1), 45–62. https://EconPapers.repec.org/RePEc:fzy:fuzeco:v:xix:y:2014
   Google Scholar
• Serrano-Cinca, C. (1996). Self-organizing neural networks for financial diagnosis. Decision Support Systems, 17(3), 227–238. https://doi.org/10.1016/0167-9236(95)00033-x
   Web of Science ®Google Scholar
• Shi, Y., & Li, X. (2019). An overview of bankruptcy prediction models for corporate firms: A systematic literature review. Intangible Capital, 15(2), 114–127. https://doi.org/10.3926/ic.1354
   Web of Science ®Google Scholar
• Singh, P., & Kaur, C. (2019). Factors determining financial constraint of SMEs: A study of unorganized manufacturing enterprises in India. Journal of Small Business & Entrepreneurship, 33(3), 269–287. https://doi.org/10.1080/08276331.2019.1641662
   Google Scholar
• Sun, J., Li, H., Huang, Q. H., & He, K. Y. (2014). Predicting financial distress and corporate failure: A review from the state of the art definitions, modeling, sampling, and featuring approaches. Knowledge-Based Systems, 57, 41–56. https://doi.org/10.1016/j.knosys.2013.12.006
   Web of Science ®Google Scholar
• Terceño, A., Vigier, H., & Scherger, V. (2018). Prediction of business failure with fuzzy models. International Journal of Uncertainty, Fuzziness & Knowledge-Based Systems, 26(1), 21–38. https://doi.org/10.1142/S0218488518400020
   Google Scholar
• Theng, L. G., & Lim, J. W. B. (1996). An exploratory study of factors affecting the failure of local small and medium enterprises. Asia Pacific Journal of Management, 13(2), 47–61. https://doi.org/10.1007/BF01733816
   Google Scholar
• Tseng, F. M., & Hu, Y. C. (2010). Comparing four bankruptcy prediction models: Logit, quadratic interval logit, neural and fuzzy neural networks. Expert Systems with Applications, 37(3), 1846–1853. https://doi.org/10.1016/j.eswa.2009.07.081
   Web of Science ®Google Scholar
• Vesanto, J. (2000). Neural network tool for data mining: SOM toolbox. In Proceedings of symposium on tool environments and development methods for intelligent systems, Oulu, Finland (pp.184–196).
   Google Scholar
• Wenzel, M., Stanske, S., & Lieberman, M. B. (2020). Strategic responses to crisis. Strategic Management Journal, 41, 7–18. https://doi.org/10.1002/smj.3161
   Google Scholar
• Zimmermann, H. (1991). Fuzzy set theory and its Applications. Kluwer Academic Publishers. https://doi.org/10.1007/978-94-015-7949-0
   Google Scholar