Análisis combinado de factores del fracaso empresarial en el sector turístico español
DOI:
https://doi.org/10.17561/ree.n2.2024.8273Palabras clave:
fracaso empresarial, sector turístico, fsQCA, ratios financierosResumen
El fracaso empresarial en la literatura no presenta consenso acerca de cuáles son sus factores determinantes, dada la complejidad e interacción dinámica entre ellos.
Este trabajo se centra en las quiebras financieras del sector turístico español en el año 2022, por ser uno de los más afectados por la paralización de la actividad económica consecuencia del Covid-19. A partir de la base de datos SABI aplicamos un análisis comparativo cualitativo de conjuntos difusos (fuzzy set Qualitative Comparative Analysis, fsQCA) centrado en las variables tradicionalmente consideradas por la literatura. Descubrimos que varias combinaciones de atributos financieros conducen al fracaso de las empresas turísticas españolas sin que ninguno individualmente sea suficiente, sino que depende de la interacción de otros atributos, lo que revela condiciones antecedentes complejas para la explicación del fenómeno.
En concreto, encontramos que el efecto combinado de alto endeudamiento y bajos niveles de rentabilidad, actividad y solvencia son condiciones suficientes para conducir al fracaso en empresas turísticas españolas. Adicionalmente, cuando tenemos en cuenta el tamaño de las entidades, encontramos un efecto sustitución entre un bajo nivel de rotación y baja liquidez.
La identificación de la combinación de ratios financieros que alertan de problemas de continuidad en las empresas del sector turístico, es de gran relevancia para el desarrollo económico español, así como para investigadores y directivos del sector.
Descargas
Referencias
Alaminos, D., del Castillo, A., & Fernández, M. Á. (2018). Correction: A Global Model for Bankruptcy Prediction. PLOS ONE, 13(11), e0208476. https://doi.org/10.1371/journal.pone.0208476
Alcalde, R., de Armiño, C., & García, S. (2022). Analysis of the Economic Sustainability of the Supply Chain Sector by Applying the Altman Z-Score Predictor. Sustainability, 14(2). https://doi.org/10.3390/su14020851
Altman, E. (1968). Financial ratios, discriminant analysis and the prediction of corporate bankruptcy. The Journal of Finance, 23(4), 589–609. https://doi.org/10.1111/j.1540-6261.1968.tb00843.x
Altman, E., Iwanicz-Drozdowska, M., Laitinen, E. K., & Suvas, A. (2017). Financial Distress Prediction in an International Context: A Review and Empirical Analysis of Altman’s Z-Score Model. Journal of International Financial Management & Accounting, 28(2), 131–171. https://doi.org/10.1111/jifm.12053
Altman, E., Marco, G., & Varetto, F. (1994). Corporate distress diagnosis: Comparisons using linear discriminant analysis and neural networks (the Italian experience). Journal of Banking & Finance, 18(3), 505–529. https://doi.org/10.1016/0378-4266(94)90007-8
Álvarez-Ferrer, A., & Campa-Planas, F. (2020). La predicción del fracaso empresarial en el sector hotelero. Cuadernos de Turismo, 45, 33–59. https://doi.org/10.6018/turismo.426031
Barboza, F., Kimura, H., & Altman, E. (2017). Machine learning models and bankruptcy prediction. Expert Systems with Applications, 83, 405–417. https://doi.org/10.1016/j.eswa.2017.04.006
Bărbuță-Mișu, N., & Madaleno, M. (2020). Assessment of Bankruptcy Risk of Large Companies: European Countries Evolution Analysis. Journal of Risk and Financial Management, 13(3), 58. https://doi.org/10.3390/jrfm13030058
Beaver, W. H. (1966). Financial Ratios as Predictors of Failure. Journal of Accounting Research, 4, 71–111. https://doi.org/10.2307/2490171
Beaver, W. H. (1968). Alternative Accounting Measures as Predictors of Failure. The Accounting Review, 43(1), 113–122. http://www.jstor.org/stable/244122
Beaver, W. H., McNichols, M. F., & Correia, M. (2008). Have Changes in Financial Reporting Attributes Impaired Informativeness? Evidence from the Ability of Financial Ratios to Predict Bankruptcy. SSRN Electronic Journal. https://doi.org/10.2139/SSRN.1340752
Beaver, W. H., McNichols, M. F., & Rhie, J.-W. (2005). Have Financial Statements Become Less Informative? Evidence from the Ability of Financial Ratios to Predict Bankruptcy. Review of Accounting Studies, 10(1), 93–122. https://doi.org/10.1007/s11142-004-6341-9
Behr, A., & Weinblat, J. (2017). Default Patterns in Seven EU Countries: A Random Forest Approach. International Journal of the Economics of Business, 24(2), 181–222. https://doi.org/10.1080/13571516.2016.1252532
Bell, T., Ribar, G., & Verichio, J. (1990). Neural nets versus logistic regression: A comparison of each model’s ability to predict commercial bank failures. Proceedings of the University of Kansas Symposium on Auditing Problems, 29–53. https://egrove.olemiss.edu/dl_proceedings/82
Bernate Valbuena, M. T., & Gómez Meneses, F. E. (2021). Predicción de la quiebra en las empresas. Una revisión de literatura. Revista Activos, 19(1), 112–142. https://doi.org/10.15332/25005278.6684
Blum, M. (1974). Failing Company Discriminant Analysis. Journal of Accounting Research, 12(1), 1–25. https://doi.org/10.2307/2490525
Boole, G. (1854). An investigation of the laws of thought: on which are founded the mathematical theories of logic and probabilities (Vol. 2). Walton and Maberly.
Boratyńska, K. (2016). FsQCA in corporate bankruptcy research. An innovative approach in food industry. Journal of Business Research, 69(11), 5529–5533. https://doi.org/10.1016/J.JBUSRES.2016.04.166
Boratyńska, K., & Grzegorzewska, E. (2018). Bankruptcy prediction in the agribusiness sector: Lessons from quantitative and qualitative approaches. Journal of Business Research, 89, 175–181. https://doi.org/10.1016/J.JBUSRES.2018.01.028
Bustos-Contell, E., Climent-Serrano, S., & Labatut-Serer, G. (2021). A fuzzy-set Qualitative Comparative Analysis model to predict bank bailouts: a study of the Spanish financial system. Economic Research-Ekonomska Istraživanja, 34(1), 2555–2571. https://doi.org/10.1080/1331677X.2020.1833746
Campbell, P. T., Mahmud, E., & Marshall, J. J. (2015). Interoperator and intraoperator (in)accuracy of stent selection based on visual estimation. Catheterization and Cardiovascular Interventions, 86(7), 1177–1183. https://doi.org/10.1002/ccd.25780
Comisión Europea. (2014). Reglamento (UE) n° 651/2014 de la Comisión, de 17 de junio de 2014, por el que se declaran determinadas categorías de ayudas compatibles con el mercado interior en aplicación de los artículos 107 y 108 del Tratado. Diario Oficial de La Unión Europea, 187, 26 de junio, 1–78. https://www.boe.es/doue/2014/187/L00001-00078.pdf
Cultrera, L., & Jonathan, B. (2017). Exploring Corporate Bankruptcy in Belgian Private Firms. International Journal of Economics and Finance, 9(3), 108. https://doi.org/10.5539/ijef.v9n3p108
Deakin, E. B. (1972). A Discriminant Analysis of Predictors of Business Failure. Journal of Accounting Research, 10(1), 167–179. https://doi.org/10.2307/2490225
Deakin, E. B. (1976). Distributions of Financial Accounting Ratios: Some Empirical Evidence. The Accounting Review, 51(1), 90–96. http://www.jstor.org/stable/245375
Del Castillo García, A. (2021). Modelos de Predicción de Insolvencia: Un Análisis de Variables No Financieras y de Selección Muestral [Tesis Doctoral, Universidad de Málaga]. https://hdl.handle.net/10630/23193
Díaz-Casero, J. C., Fernández-Portillo, A., Sánchez-Escobedo, M. C., & Hernández-Mogollón, R. (2014). Estructura intelectual del fracaso empresarial. FAEDPYME International Review, 5(3), 43–55. https://doi.org/10.15558/fir.v3i5.57
du Jardin, P. (2015). Bankruptcy prediction using terminal failure processes. European Journal of Operational Research, 242(1), 286–303. https://doi.org/10.1016/j.ejor.2014.09.059
Edmister, R. O. (1972). An Empirical Test of Financial Ratio Analysis for Small Business Failure Prediction. The Journal of Financial and Quantitative Analysis, 7(2), 1477–1493. https://doi.org/10.2307/2329929
Elam, R. (1975). The Effect of Lease Data on the Predictive Ability of Financial Ratios. The Accounting Review, 50(1), 25–43. http://www.jstor.org/stable/244661
Exceltur. (2023). Valoración turística empresarial del IT de 2023, perspectivas para el IIT de 2023 y cierre de año. https://www.exceltur.org/wp-content/uploads/2023/04/Informe-Perspectivas-Balance-Itr-2023.pdf
Federo, R., & Saz-Carranza, A. (2018). A configurational analysis of board involvement in intergovernmental organizations. Corporate Governance: An International Review, 26(6), 414–428. https://doi.org/10.1111/corg.12241
Fernández Portillo, A., Díaz Casero, J. C., Sánchez Escobedo, M. C., & Hernández Mogollón, R. (2019). Conocimiento certificado del fracaso empresarial: un análisis bibliométrico del periodo 1965-2012. Revista Espacios, 40(16), 18. https://www.revistaespacios.com/a19v40n16/a19v40n16p18.pdf
Fiss, P. C. (2007). A Set-Theoretic Approach to Organizational Configurations. The Academy of Management Review, 32(4), 1180–1198. http://www.jstor.org/stable/20159362
Fiss, P. C. (2011). Building better causal theories: A fuzzy set approach to typologies in organizational research. Academy of Management Journal, 54(2), 393–420. https://doi.org/10.5465/amj.2011.60263120
Fitzpatrick, P. J. (1932). A comparison of ratios of successful industrial enterprises with those of failed companies. The Certified Public Accountant, 6, 727–731.
Frecka, T. J., & Hopwood, W. S. (1983). The Effects of Outliers on the Cross-Sectional Distributional Properties of Financial Ratios. The Accounting Review, 1, 115–128.
Frydman, H., Altman, E. I., & Kao, D.-L. (1985). Introducing Recursive Partitioning for Financial Classification: The Case of Financial Distress. The Journal of Finance, 40(1), 269–291. https://doi.org/10.2307/2328060
García Ayuso, M. (1995). La necesidad de llevar a cabo un replanteamiento de la investigación en materia de análisis de la información financiera. Análisis Financiero, 66, 36–61.
Gémar, G., Moniche, L., & Morales, A. J. (2016). Survival analysis of the Spanish hotel industry. Tourism Management, 54, 428–438. https://doi.org/10.1016/j.tourman.2015.12.012
Gémar, G., Soler, I. P., & Guzman-Parra, V. F. (2019). Predicting bankruptcy in resort hotels: a survival analysis. International Journal of Contemporary Hospitality Management, 31(4), 1546–1566. https://doi.org/10.1108/IJCHM-10-2017-0640
Goh, E., Mat Roni, S., & Bannigidadmath, D. (2022). Thomas Cook(ed): using Altman’s z -score analysis to examine predictors of financial bankruptcy in tourism and hospitality businesses. Asia Pacific Journal of Marketing and Logistics, 34(3), 475–487. https://doi.org/10.1108/APJML-02-2021-0126
Gómez García, S. L., & Leyva Ferreiro, G. (2019). Utilidad de los modelos de predicción de fracaso y su aplicabilidad en las cooperativas. Cofin Habana, 13(1). https://revistas.uh.cu/cofinhab/article/view/833
Gracia, J. L., Cabedo, J. L. G., & Llopis, R. M. (1998). La suspensión de pagos en las pymes: una aproximación empírica. Revista Española de Financiación y Contabilidad, 27(94), 71–97. http://www.jstor.org/stable/42781285
Greckhamer, T., Furnari, S., Fiss, P. C., & Aguilera, R. V. (2018). Studying configurations with qualitative comparative analysis: Best practices in strategy and organization research. Strategic Organization, 16(4), 482–495. https://doi.org/10.1177/1476127018786487
Greckhamer, T., Misangyi, V., & Fiss, P. (2013). The two QCAs: From a small-N to a large-N set-theoretic approach. Configurational Theory and Methods in Organizational Research, 38, 49–75. https://doi.org/10.1108/S0733-558X(2013)0000038007
Grosu, V., Chelba, A., Melega, A., Botez, D., & Socoliuc, M. (2023). Bibliometric analysis of the literature on evaluation models of the bankruptcy risk. Strategic Management, 00, 42–42. https://doi.org/10.5937/StraMan2200035G
Hacibedel, B., & Qu, R. (2022). Understanding and Predicting Systemic Corporate Distress: A Machine-learning Approach (WP/22/153).
Haxhi, I., & Aguilera, R. V. (2017). An Institutional Configurational Approach to Cross-National Diversity in Corporate Governance. Journal of Management Studies, 54(3), 261–303. https://doi.org/10.1111/joms.12247
Inam, F., Inam, A., Mian, M. A., Sheikh, A. A., & Awan, H. M. (2019). Forecasting Bankruptcy for organizational sustainability in Pakistan. Journal of Economic and Administrative Sciences, 35(3), 183–201. https://doi.org/10.1108/JEAS-05-2018-0063
INE. (2023). Aportación del turismo al PIB de la economía española. Instituto Nacional de Estadística. https://www.ine.es/jaxi/Tabla.htm?path=/t35/p011/rev19/serie/l0/&file=03001.px&L=0
Informa D&B. (2021). Evolución de los concursos desde 1997 hasta 2020. https://cdn.informa.es/sites/5c1a2fd74c7cb3612da076ea/content_entry5c5021510fa1c000c25b51f0/6141a52e9a9e3d6f241924a0/files/HistoricoConcursosINFORMA2021_V2.pdf?1631692079
Informa D&B. (2023). Concursos y disoluciones. Diciembre 2022. https://cdn.informa.es/sites/5c1a2fd74c7cb3612da076ea/content_entry5c5021510fa1c000c25b51f0/63bd489dcdd6f900f4a84fac/files/Concursos_diso_122022_v1.pdf?1673349277
Jánica, F., Hernández-Fernández, L., Escobar Castillo, A., & Velandia Pacheco, G. (2023). Factores que explican, median y moderan el fracaso empresarial: Revisión de publicaciones indexadas en Scopus (2015-2022). Revista de Ciencias Sociales, XXIX(2), 73–95. https://doi.org/10.31876/rcs.v29i2.39963
Jefatura del Estado. (2020). Real Decreto-ley 16/2020, de 28 de abril, de medidas procesales y organizativas para hacer frente al COVID-19 en el ámbito de la Administración de Justicia. Boletín Oficial Del Estado, 119, 29 de abril, 1–26. https://www.boe.es/buscar/pdf/2020/BOE-A-2020-4705-consolidado.pdf
Keasey, K., & Watson, R. (1991). Financial Distress Prediction Models: A Review of Their Usefulness1. British Journal of Management, 2(2), 89–102. https://doi.org/10.1111/j.1467-8551.1991.tb00019.x
Kessioui, S., Doumpos, M., & Zopounidis, C. (2023). A Bibliometric Overview of the State-of-the-Art in Bankruptcy Prediction Methods and Applications. In Governance and Financial Performance (pp. 123–153). World Scientific. https://doi.org/10.1142/9789811260506_0006
Kuizinienė, D., Krilavičius, T., Damaševičius, R., & Maskeliūnas, R. (2022). Systematic Review of Financial Distress Identification using Artificial Intelligence Methods. Applied Artificial Intelligence, 36(1), 2138124. https://doi.org/10.1080/08839514.2022.2138124
Laffarga Briones, J., Martín Marín, J. L., & Vázquez Cueto, M. J. (1987). Predicción de la crisis bancaria en España: comparación entre el análisis logit y el análisis discriminante. Cuadernos de Ciencias Económicas y Empresariales, 18, 49–57. https://idus.us.es/bitstream/handle/11441/78703/Predicci%C3%B3n%20de%20la%20crisis%20bancaria%20en%20Espa%C3%B1a.pdf?sequence=1
Laguillo Díaz, G. (2015). Predicción de insolvencia en los sectores económicos: un análisis comparativo [Tesis Doctoral, Universidad de Málaga]. http://hdl.handle.net/10630/11753
Laitinen, E. K. (1991). Financial ratios and different failure processes. Journal of Business Finance & Accounting, 18(5), 649–673. https://doi.org/10.1111/j.1468-5957.1991.tb00231.x
Laitinen, E. K., Camacho-Miñano, M.-M., & Muñoz-Izquierdo, N. (2023). A review of the limitations of financial failure prediction research. Revista de Contabilidad - Spanish Accounting Review, 26(2), 255–273. https://doi.org/10.6018/RCSAR.453041
Lens.org. (n.d.). The Lens - Free & Open Patent and Scholarly Search. Https://Lens.Org/.
Lincoln, M. (1984). An empirical study of the usefulness of accounting ratios to describe levels of insolvency risk. Journal of Banking & Finance, 8(2), 321–340. https://doi.org/10.1016/0378-4266(84)90011-6
Liu, Y., Mezei, J., Kostakos, V., & Li, H. (2017). Applying configurational analysis to IS behavioural research: a methodological alternative for modelling combinatorial complexities. Information Systems Journal, 27(1), 59–89. https://doi.org/10.1111/ISJ.12094
Marais, M. L., Patell, J. M., & Wolfson, M. A. (1984). The Experimental Design of Classification Models: An Application of Recursive Partitioning and Bootstrapping to Commercial Bank Loan Classifications. Journal of Accounting Research, 22, 87–114. https://doi.org/10.2307/2490861
Martin, D. (1977). Early warning of bank failure: A logit regression approach. Journal of Banking & Finance, 1(3), 249–276. https://doi.org/10.1016/0378-4266(77)90022-X
Martínez Sabater, R. (2021). Impacto del turismo sobre el PIB, el empleo y la balanza de pagos española. 2007-2020 [Trabajo de Fin de Grado, Universidad de Zaragoza]. https://zaguan.unizar.es/record/117900
Masa Lorenzo, C. I., Iturrioz del Campo, L. J., & Martín López, S. (2016). Aspectos determinantes del fracaso empresarial: efecto de la proyección social de las sociedades cooperativas frente a otras formas jurídicas. Revista de Economía Pública, Social y Cooperativa, 88, 93–125. https://doi.org/10.7203/ciriec-e.88.8826
McDonald, B., & Morris, M. H. (1984). The statistical validity of the ratio method in financial analysis: An empirical examination. Journal of Business Finance & Accounting, 11(1), 89–97. https://doi.org/10.1111/j.1468-5957.1984.tb00059.x
Mendoza Mendoza, A. A. (2009). Predicción de riesgo de quiebra para PYMES en el departamento del Atlántico utilizando análisis discriminante y análisis envolvente de datos (DEA). http://hdl.handle.net/10584/9001
Mensah, Y. M. (1984). An Examination of the Stationarity of Multivariate Bankruptcy Prediction Models: A Methodological Study. Journal of Accounting Research, 22(1), 380–395. https://doi.org/10.2307/2490719
Miao, L., Im, J., So, K. K. F., & Cao, Y. (2022). Post-pandemic and post-traumatic tourism behavior. Annals of Tourism Research, 95, 103410. https://doi.org/10.1016/j.annals.2022.103410
Ministerio de Industria, C. y T. (2023). Cifras PYME. https://ipyme.org/Publicaciones/Cifras%20PYME/CifrasPYME-mayo2023.pdf
Momparler, A., Carmona, P., & Climent, F. (2020). Revisiting bank failure in the United States: A fuzzy-set analysis. Economic Research-Ekonomska Istraživanja, 33(1), 3017–3033. https://doi.org/10.1080/1331677X.2019.1689838
Mora Garcia, A. M., Castillo Valdivieso, P. A., Merelo Guervós, J. J., Alfaro Cid, E., Esparcia-Alcázar, A. I., & Sharman, K. (2008). Discovering Causes of Financial Distress by Combining Evolutionary Algorithms and Artificial Neural Networks. Proceedings of the 10th Annual Conference on Genetic and Evolutionary Computation, 1243–1250. https://doi.org/10.1145/1389095.1389337
Noguera Venero, J. (2023). Big Data en el análisis económico-financiero de la empresa: propuestas empíricas en la predicción del fracaso. [Tesis Doctoral, Universidad Politécnica de Cartagena]. https://repositorio.upct.es/handle/10317/12276
Ohlson, J. A. (1980). Financial Ratios and the Probabilistic Prediction of Bankruptcy. Journal of Accounting Research, 18(1), 109–131. https://doi.org/10.2307/2490395
Pappas, I. O., Giannakos, M. N., Jaccheri, L., & Sampson, D. G. (2017). Assessing Student Behavior in Computer Science Education with an FsQCA Approach: The Role of Gains and Barriers. ACM Trans. Comput. Educ., 17(2). https://doi.org/10.1145/3036399
Pappas, I. O., & Woodside, A. G. (2021). Fuzzy-set Qualitative Comparative Analysis (fsQCA): Guidelines for research practice in Information Systems and marketing. International Journal of Information Management, 58, 102310. https://doi.org/10.1016/j.ijinfomgt.2021.102310
Paradi, J. C., Asmild, M., & Simak, P. C. (2004). Using DEA and Worst Practice DEA in Credit Risk Evaluation. Journal of Productivity Analysis, 21(2), 153–165. http://www.jstor.org/stable/41770152
Peel, M. J., Peel, D. A., & Pope, P. F. (1986). Predicting corporate failure — Some results for the UK corporate sector. Omega, 14(1), 5–12. https://doi.org/10.1016/0305-0483(86)90003-4
Pereira, J. M., Basto, M., Díaz Gómez, F., & Barbas Albuquerque, E. (2010). Los modelos de predicción del fracaso empresarial. Propuesta de un ranking. XIV Encuentro AECA. Innovación y Responsabilidad: Desafíos y Soluciones.
Plescaci, D. (2023). A Bibliometric Analysis of the Bankruptcy Risk Research Within Economic Entities. CECCAR Business Review, 3(12), 2–12. https://doi.org/10.37945/cbr.2022.12.01
Poretti, C., & Heo, C. Y. (2022). COVID-19 and firm value drivers in the tourism industry. Annals of Tourism Research, 95, 103433. https://doi.org/10.1016/j.annals.2022.103433
Postigo-Jiménez, M. V., Díaz Casero, J. C., & Hernández Mogollón, R. (2008). Revisión de la literatura en fracaso empresarial: aproximación bibliométrica. Estableciendo Puentes En Una Economía Global, 1, 102.
Pozuelo Campillo, J., Romero Martínez, M., & Carmona Ibáñez, P. (2023). Utility of fuzzy set Qualitative Comparative Analysis (fsQCA) methodology to identify causal relations conducting to cooperative failure. Revista de Economía Pública, Social y Cooperativa, 0(107), 197–225. https://doi.org/10.7203/ciriec-e.107.21888
Ragin, C. C. (1987). The Comparative Method: Moving Beyond Qualitative and Quantitative Strategies. University of California Press.
Ragin, C. C. (2008). Redesigning Social Inquiry: Fuzzy Sets and Beyond. In Bibliovault OAI Repository, the University of Chicago Press. University of Chicago Press. https://doi.org/10.7208/chicago/9780226702797.001.0001
Ragin, C. C., & Davey, S. (2022). Fuzzy-set/Qualitative comparative analysis 4.0. In Department of Sociology, University of California. http://www.socsci.uci.edu/∼cragin/fsQCA/software.shtml
Ragin, C. C., & Rihoux, B. (2004). Qualitative comparative analysis (QCA): State of the art and prospects. APSA Annual Meeting, 2(2), 3–13. http://hdl.handle.net/2078.1/81919
Raki, A., Nayer, D., Nazifi, A., Alexander, M., & Seyfi, S. (2021). Tourism recovery strategies during major crises: The role of proactivity. Annals of Tourism Research, 90, 103144. https://doi.org/10.1016/j.annals.2021.103144
Rihoux, B., & Ragin, C. C. (2009). Configurational comparative methods: Qualitative Comparative Analysis (QCA and related techniques). Sage Publications.
Riono Putri, D., & Ichsanuddin Nur, D. (2023). Financial Distress Analysis with Firm Size as a Moderating Variable in the Restaurant, Hotel, and Tourism Sub-Sector Companies on the Stock Exchange of Indonesia. Journal of Economics, Finance and Management Studies, 06(08). https://doi.org/10.47191/jefms/v6-i8-45
Rosati, G., & Chazarreta, A. (2017). El Qualitative Comparative Analysis (QCA) como herramienta analítica. Dos aplicaciones para el análisis de entrevistas. Revista Latinoamericana de Metodología de Las Ciencias Sociales, 7(1). https://doi.org/10.24215/18537863e018
SABI. (2023). Sistema de Análisis de Balances Ibéricos. https://www.informa.es/riesgo-empresarial/sabi
Scherger, V., Terceño, A., & Vigier, H. (2018). Revisión crítica de los modelos de predicción de fracaso empresarial. 21, 153–180.
Schneider, C. Q., & Wagemann, C. (2010). Standards of Good Practice in Qualitative Comparative Analysis (QCA) and Fuzzy-Sets. Comparative Sociology, 9(3), 397–418. https://doi.org/10.1163/156913210X12493538729793
Schneider, C. Q., & Wagemann, C. (2012). Set-theoretic methods for the social sciences: A guide to qualitative comparative analysis. Cambridge University Press.
Succurro, M. (2017). Financial Bankruptcy across European Countries. International Journal of Economics and Finance, 9(7), 132. https://doi.org/10.5539/ijef.v9n7p132
Succurro, M., & Mannarino, L. (2014). The Impact of Financial Structure on Firms’ Probability of Bankruptcy: A Comparison across Western Europe Convergence Regions. Eers. Estudios Económicos Regionales y Sectoriales, 14(1), 81–94.
Süer, S. (2022). Liquidity and Profitability in Turkish Tourism Corporations. Journal of Humanities and Tourism Research, 12(2), 317–331. https://dergipark.org.tr/en/pub/johut/issue/71540/1148997
Taffler, R. J. (1982). Forecasting Company Failure in the UK Using Discriminant Analysis and Financial Ratio Data. Journal of the Royal Statistical Society. Series A (General), 145(3), 342–358. https://doi.org/10.2307/2981867
Tian, S., & Yu, Y. (2017). Financial ratios and bankruptcy predictions: An international evidence. International Review of Economics & Finance, 51, 510–526. https://doi.org/10.1016/j.iref.2017.07.025
Troutt, M. D., Rai, A., & Zhang, A. (1996). The potential use of DEA for credit applicant acceptance systems. Computers & Operations Research, 23(4), 405–408. https://doi.org/10.1016/0305-0548(95)00048-8
Vega Falcón, V., Sánchez, F., & Fernández, A. (2020). Impacto de la COVID-19 en el turismo mundial. Revista Universidad y Sociedad, 12(S1), 207–216.
Veganzones, D., Séverin, E., & Chlibi, S. (2023). Influence of earnings management on forecasting corporate failure. International Journal of Forecasting, 39(1), 123–143. https://doi.org/10.1016/J.IJFORECAST.2021.09.006
Wieprow, J., & Gawlik, A. (2021). The Use of Discriminant Analysis to Assess the Risk of Bankruptcy of Enterprises in Crisis Conditions Using the Example of the Tourism Sector in Poland. Risks, 9(4), 78. https://doi.org/10.3390/risks9040078
Woodside, A. G. (2012). Proposing a new logic for data analysis in marketing and consumer behavior: case study research of large-N survey data for estimating algorithms that accurately profile X (extremely high-use) consumers. Journal of Global Scholars of Marketing Science, 22(4), 277–289. https://doi.org/10.1080/21639159.2012.717369
Woodside, A. G. (2013). Moving beyond multiple regression analysis to algorithms: Calling for a paradigm shift from symmetric to asymmetric thinking in data analysis and crafting theory. Journal of Business Research, 66, 463–472. https://doi.org/10.1016/j.jbusres.2012.12.021
Woodside, A. G. (2014). Embrace perform model: Complexity theory, contrarian case analysis, and multiple realities. Journal of Business Research, 67(12), 2495–2503. https://doi.org/10.1016/j.jbusres.2014.07.006
Yeh, S.-S. (2021). Tourism recovery strategy against COVID-19 pandemic. Tourism Recreation Research, 46(2), 188–194. https://doi.org/10.1080/02508281.2020.1805933
Zadeh, L. A. (1965). Fuzzy sets. Information and Control, 8(3), 338–353. https://doi.org/10.1016/S0019-9958(65)90241-X
Zelenkov, Y., & Volodarskiy, N. (2021). Bankruptcy prediction on the base of the unbalanced data using multi-objective selection of classifiers. Expert Systems with Applications, 185, 115559. https://doi.org/10.1016/j.eswa.2021.115559
Zhang, H., Qiu, R. T. R., Wen, L., Song, H., & Liu, C. (2023). Has COVID-19 changed tourist destination choice? Annals of Tourism Research, 103, 103680. https://doi.org/10.1016/j.annals.2023.103680
Zmijewski, M. E. (1984). Methodological Issues Related to the Estimation of Financial Distress Prediction Models. Journal of Accounting Research, 22, 59. https://doi.org/10.2307/2490859
Publicado
Número
Sección
Licencia
Derechos de autor 2024 Cristina Mendaña-Cuervo, Nieves Remo-Diez, Marta Toral-Heredia
Esta obra está bajo una licencia internacional Creative Commons Atribución 4.0.
- Los autores/as conservarán sus derechos de autor y garantizarán a la revista el derecho de primera publicación de su obra, el cuál estará simultáneamente sujeto a la Licencia de reconocimiento de Creative Commons que permite a terceros compartir la obra siempre que se indique su autor y su primera publicación esta revista.
- Los autores/as podrán adoptar otros acuerdos de licencia no exclusiva de distribución de la versión de la obra publicada (p. ej.: depositarla en un archivo telemático institucional o publicarla en un volumen monográfico) siempre que se indique la publicación inicial en esta revista.
- Se permite y recomienda a los autores/as difundir su obra a través de Internet (p. ej.: en archivos telemáticos institucionales o en su página web) antes y durante el proceso de envío, lo cual puede producir intercambios interesantes y aumentar las citas de la obra publicada. (Véase El efecto del acceso abierto).
La Revista de Estudios Empresariales. Segunda Época, utiliza PKP Preservation Network (PN).
