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dc.creatorScherger, Valeria; Universidad Nacional del Surspa
dc.creatorTerceño, Antonio; Universidad Rovira i Virgili, Reusspa
dc.creatorVigier, Hernán; Universidad Nacional del Surspa
dc.date.accessioned2017-03-14T12:52:02Z
dc.date.available2017-03-14T12:52:02Z
dc.date.created2016-12-30
dc.identifierhttp://revistas.udem.edu.co/index.php/economico/article/view/1965spa
dc.identifier10.22395/seec.v19n41a8spa
dc.identifier.issn0120-6346
dc.identifier.urihttp://hdl.handle.net/11407/3086
dc.descriptionEste artículo evalúa las técnicas utilizadas para la detección y predicción de las causas del fracaso empresarial. Se exponen las principales limitaciones de los modelos clásicos de predicción de insolvencia empresarial y se incorpora el análisis fuzzy como alternativa para identificar la relación entre las causas del fracaso y los síntomas visibles en las empresas. En forma complementaria se utiliza el Balanced Scorecard como herramienta de análisis global de la empresa y base para la detección de las causas del fracaso. La aplicación del Balanced Scorecard permite definir un listado de causas originarias de los problemas en las empresas. Estas son valoradas a través de etiquetas lingüísticas para detectar las enfermedades más frecuentes que pueden conducir al fracaso empresarial. Respecto a los modelos tradicionales, la metodología aplicada en este trabajo permite predecir el posible fracaso de una empresa e identificar las causas del mismo.spa
dc.description.abstractThis article evaluates the techniques used for the detection and prediction of business cause failures. The main limitations of the classical prediction models for business insolvency are exposed and a fuzzy analysis as an alternative to identify the relation between failure causes and the businesses´ visible symptoms. As complement, a Balanced Scorecard is used as global analysis tool for the base company in order to detect the cause of failure. The application of the balance scorecard allows defining a list of origination causes for the problems faced by the companies. These are valued using linguistic labels for detecting the most common diseases that can lead to business failure. Concerning traditional models, the applied methodology in the work allows to predict the possible failure of a company and identify the causes.eng
dc.format.extentp.191-228spa
dc.format.mediumElectrónicospa
dc.format.mimetypeapplication/pdf
dc.format.mimetypePDF
dc.language.isospa
dc.publisherUniversidad de Medellínspa
dc.publisherUniversidad de Medellínspa
dc.relationhttp://revistas.udem.edu.co/index.php/economico/article/view/1965/1783spa
dc.relation.ispartofseriesSemestre Económico; Vol. 19, núm. 41 (2016)spa
dc.relation.haspartSemestre Económico; Vol. 19, núm. 41 - octubre/diciembre 2016spa
dc.rights.urihttp://creativecommons.org/licenses/by-nc-sa/4.0/*
dc.sourceSemestre Económico Universidad de Medellín; Vol. 19, núm. 41 (2016); 191-228spa
dc.source2248-4345spa
dc.source0120-6346spa
dc.subjectBusiness failurespa
dc.subjectfuzzy relationsspa
dc.subjectcommand boardspa
dc.subjectfinancial economic ratios.spa
dc.subjectFracaso empresarialspa
dc.subjectrelaciones borrosasspa
dc.subjecttablero de comandospa
dc.subjectratios económico financierosspa
dc.subjectFracasso empresarialspa
dc.subjectrelações obscurasspa
dc.subjectpainel de comandospa
dc.subjectindicadores económico financeirosspa
dc.titleRelaciones borrosas como herramienta de predicción de las causas del fracaso empresarial en el sector construcciónspa
dc.rights.accessrightsinfo:eu-repo/semantics/openAccess
dc.identifier.doihttp://dx.doi.org/10.22395/seec.v19n41a8
dc.relation.citationvolume19
dc.relation.citationissue41
dc.relation.citationstartpage191
dc.relation.citationendpage228
dc.audienceComunidad Universidad de Medellínspa
dc.publisher.facultyFacultad de Ciencias Económicas y Administrativasspa
dc.coverageLat: 06 15 00 N  degrees minutes  Lat: 6.2500  decimal degreesLong: 075 36 00 W  degrees minutes  Long: -75.6000  decimal degreesspa
dc.publisher.placeMedellínspa
dc.description.resumoEste artigo avalia as técnicas utilizadas para a detecção e predição das causas do fracasso empresarial. Se expõem as principais limitações dos modelos clássicos de predição de insolvência empresarial e se incorpora a análise fuzzy como alternativa para identificar a relação entre as causas do fracasso e os sintomas visíveis nas empresas. Em forma complementar se utiliza o Balanced Scorecard como ferramenta de análise global da empresa e base para a detecção das causas do fracasso. A aplicação do Balanced Scorecard permite definir um listado de causas originárias dos problemas nas empresas. Estas são valoradas através de etiquetas linguísticas para detectar as doenças mais frequentes que podem conduzir ao fracasso empresarial. Respeito aos modelos tradicionais, a metodologia aplicada neste trabalho permite prever o possível fracasso de uma empresa e identificar as causas do mesmo.por
dc.relation.ispartofesSemestre Económicospa
dc.title.alternativeporRelações obscuras como ferramenta de predição das causas do fracasso empresarial no setor construçãopor
dc.title.alternativeenFuzzy relations as a prediction tool of business failure causes in the construction sectoreng
dc.relation.referencesAltman, Edward (1968). Financial ratios, discriminant analysis and the prediction of corporate bankruptcy. En: Journal of Finance, Vol. 23, No. 4, p. 589-609.spa
dc.relation.referencesArgenti, John (1976). Corporate Collapse: The Causes and Symptoms, New York, John Wiley and Sons, 193p.spa
dc.relation.referencesArgenti, John (1983). Prediction corporate failure. En: Accountants Digest, No. 138, p. 1-25.spa
dc.relation.referencesBahrammirzaee, Arash (2010). A comparative survey of artificial intelligence applications in finance: artificial neural networks, expert systems and hybrid intelligent systems. En: Neural Computing and Applications, Vol. 19, No. 8, p. 1165-1195.spa
dc.relation.referencesBalcaen, Sofie y Ooghe, Hubert (2006). 35 years of studies on business failure: An overview of the classic statistical methodologies and their related problems. En: British Accounting Review, Vol. 38, No. 1, p. 63-93.spa
dc.relation.referencesBeaver, William (1966). Financial ratios as predictors of failure. En: Journal of Accounting Research, Vol. 4, Empirical Research in Accounting: Selected Studies, p. 71-111.spa
dc.relation.referencesBecchetti, Leonardo y Sierra, Jaime (2003). Bankruptcy risk and productive efficiency in manufacturing firms. En: Journal of Banking and Finance, Vol. 27, No. 11, p. 2099-2120.spa
dc.relation.referencesBehbood, Vahíd y Lu, Jie (2011). Intelligent financial warning model using fuzzy neural network and case-based reasoning. En: IEEE Symposium on CIFEr- Computational Intelligence for Financial Engineering and Economics, 6 p.spa
dc.relation.referencesCampillo, José; Serer, Gregorio y Ferrer, Ernesto (2013). Validez de la información financiera en los procesos de insolvencia. Un estudio de la pequeña empresa española. En: Cuadernos de Economía y Dirección de la Empresa, Vol. 16, No. 1, enero-marzo, p. 29-40.spa
dc.relation.referencesDelcea, Camelia y Dascalu, Maria (2009). Knowledge strategies tools for managing Enterprise crisis. En: Actas del 4th International Conference on Knowledge Management: Projects, Systems and Technologies, Bucharest, Vol. 25, p. 1-25.spa
dc.relation.referencesDelcea, Camelia y Scarlat, Emil (2010). Finding companies’ bankruptcy causes using a hybrid grey-fuzzy model. En: Economic Computation and Economic Cybernetics Studies and Research. Vol. 44, No. 2, p. 77-94.spa
dc.relation.referencesDelcea, Camelia; Scarlat, Emil y Maracine, Virginia (2012). Grey relational analysis between firm’s current situation and its possible causes: A bankruptcy syndrome approach. En: Grey Systems: Theory and Application, Vol. 2, No. 2, p. 229-239.spa
dc.relation.referencesEdmister, Robert (1972). An empirical test of financial ratio analysis for small business failure prediction. En: Journal of Financial and Quantitative Analysis, Vol. 7, No. 2, p. 1477-1493.spa
dc.relation.referencesElam, Rick (1975). The effect of lease data on the predictive ability of financial ratios. En: The Accounting Review, Vol. 50, No. 1, p. 25-43.spa
dc.relation.referencesFernández, María y Castaño, Francisco (2012). Variables y modelos para la identificación y predicción del fracaso empresarial: Revisión de la investigación empírica reciente. En: Revista de Contabilidad-Spanish Accounting Review, Vol. 15, No. 1, p. 7-58.spa
dc.relation.referencesFerrer, Ernesto; Serer, Gregorio y Campillo, José (2009). Hacia una ordenación de las pequeñas empresas atendiendo a su posible situación de fracaso. En: Estudios de Economía Aplicada, Vol. 27, No. 3, p. 1-18.spa
dc.relation.referencesFlagg, James; Giroux, Gary y Wiggins, Casper (1991). Predicting corporate bankruptcy using failing firms. En: Review of Financial Economics, Vol. 1, No 1, p. 67-78.spa
dc.relation.referencesGabás, Francisco (1997). Predicción de la insolvencia empresarial. En: Predicción de la Insolvencia Empresarial, Madrid, AECA- Asociación Española de Contabilidad y Administración de Empresas, p. 11-32.spa
dc.relation.referencesGil Aluja, Jaume (1990). Ensayo sobre un modelo de diagnóstico económico-financiero. En: Actas de las V Jornadas Hispano- Lusas de Gestión Científica, Vigo, España, p. 26-29.spa
dc.relation.referencesGil Lafuente, Jaume (1996). El control de las actividades de marketing. En: Actas del III SIGEF Congress, Buenos Aires, Argentina, Vol. 244, p. 1-21.spa
dc.relation.referencesGrunert, Jens; Norden, Lars y Weber, Martin (2005). The role of non-financial factors in internal credit ratings. En: Journal of Banking and Finance, Vol. 29, No. 2, p. 509-531.spa
dc.relation.referencesHillegeist, Stephen; Keating, Elizabeth; Cram, D. y Lundstedt, K. (2004). Assessing the probability of bankruptcy. En: Review of Accounting Studies, Vol. 9, No. 1, p. 5-34.spa
dc.relation.referencesKaplan, Robert y Norton, David (1992). The Balanced Scorecard: measures that drivers performance. En: Harvard Business Review, Vol. 70, No. 1, p. 71-79.spa
dc.relation.referencesKaplan, Robert y Norton, David (1996a). Using the Balanced Scorecard as a strategic management system. En: Harvard Business Review, Vol. 74, No. 1, p. 75-85.spa
dc.relation.referencesKaplan, Robert y Norton, David (1996b). Linking the Balanced Scorecard to strategy. En: California Management, Vol. 39, No. 1, p. 53-79.spa
dc.relation.referencesKeasey, Kevin y Watson, Robert (1987). Non-financial symptoms and the prediction of small company failure: a test of Argenti’s hypothesis. En: Journal of Business, Finance and Accounting, Vol. 14, No. 3, p. 335-354.spa
dc.relation.referencesKorol, Tomasz y Korodi, Adrian (2011). An evaluation of effectiveness of fuzzy logic model in predicting the business bankruptcy. En: Romanian Journal of Economic Forecasting, Vol. 3, No. 1, p. 92-107.spa
dc.relation.referencesKumar, P. Ravi y Ravi, Vadlamani (2007), Bankruptcy prediction in banks and firms via statistical and intelligent techniques: A review. En: European Journal of Operational Research, Vol. 180, No. 1, p. 1-28.spa
dc.relation.referencesLópez, José; Gandía, Juan y Molina, Rafael (1998). La suspensión de pagos en las PyMEs: una aproximación empírica. En: Revista Española de Financiación y Contabilidad, Vol. 27, No. 94, p. 71-97.spa
dc.relation.referencesMadrid, Antonia y García, Domingo Perez de Lema (2006). Factores que explican el fracaso empresarial en la PyME. En: Gestión: Revista de Economía, No. 36, p. 5-9.spa
dc.relation.referencesMaracine, Virginia y Delcea, Camelia (2009). How we can diagnose the firm’s diseases using grey systems theory. En: Economic Computation and Economic Cybernetics Studies and Research, Vol. 3, p. 39-55.spa
dc.relation.referencesMcGahan, Anita y Porter, Michael (1997). How much does industry matter really? En: Strategic Management Journal, Vol. 18 (Summer Special Issue), p. 15-30.spa
dc.relation.referencesMensah, Yaw (1984). An examination of the stationary of multivariate bankruptcy prediction models: A methodological study. En: Journal of Accounting Research, Vol. 22, No. 1, p. 380-395.spa
dc.relation.referencesMora Enguídanos, Araceli (1994). Limitaciones metodológicas de los trabajos empíricos sobre la predicción del fracaso empresarial. En: Revista Española de Financiación y Contabilidad, Vol. 24, No. 80, p. 709-732.spa
dc.relation.referencesNg, Geok; Quek, Chai y Jiang, H. (2008). FCMAC-EWS: A bank failure early warning system based on a novel localized pattern learning and semantically associative fuzzy neural network. En: Expert Systems with Applications, Vol. 34, No. 2, p. 989-1003.spa
dc.relation.referencesOhlson, James (1980). Financial ratios and the probabilistic prediction of bankruptcy. En: Journal of Accounting Research, Vol. 18, No. 1, p. 109-131.spa
dc.relation.referencesOoghe, Hubert y De Prijcker, Sofie (2008), Failure processes and causes of company bankruptcy: A typology. En: Management Decision, Vol. 46, No. 2, p. 223-242.spa
dc.relation.referencesPeel, Michael; Peel, David y Pope, Peter (1986), Predicting corporate failure. Some results for the UK corporate sector. En: Omega, Vol. 14, No. 1, p. 5-12.spa
dc.relation.referencesPérez, Ana; Rodríguez, Alicia y Acosta Molina, Miguel. (2002). Factores determinantes de la rentabilidad financiera de las PyMEs. En: Journal of Finance and Accounting/ Revista Española de Financiación y Contabilidad, Vol. 31, No. 112, p. 395-429.spa
dc.relation.referencesPlatt, Harlan; Platt, Majorie y Pedersen, Jon (1994). Bankruptcy discrimination with real variables. En: Journal of Business Finance and Accounting, Vol. 21, No. 4, p. 491-510.spa
dc.relation.referencesPorter, Michael (1991). La Ventaja Competitiva de las Naciones, Buenos Aires, Ed. Vergara, 1025 p.spa
dc.relation.referencesQuek, Chai; Zhou, R. y Lee, C. (2009). A novel fuzzy neural approach to data reconstruction and failure prediction. En: Intelligent in Accounting, Finance and Management, Vol. 16, No. 1-2, p. 165-187.spa
dc.relation.referencesQuintana, María y García Gallego, Ana (2004). Factores determinantes del fracaso empresarial en Castilla y León. En: Revista de Economía y Empresa, Vol. 51, No. 21, p. 95-116.spa
dc.relation.referencesRose, Peter; Andrews, Wesley y Girox, Gary (1982). Predicting business failure: A macroeconomics perspective. En: Journal of Accounting Auditing and Finance, Vol. 6, No. 1, p. 20-31.spa
dc.relation.referencesRumelt, Richard (1997). Towards a strategic theory of the firm, p. 131-145. En: Nicolai J. Foss (Edit.) Resources, firms, and strategies: A reader in the resource-based perspective. Oxford University Press, 1 edition Oxford, Serie: Management Readers, 400p.spa
dc.relation.referencesScarlat, Emil; Delcea, Camelia y Maracine, Virginia (2010). Genetic Fuzzy Grey Algorithms: A Hybrid Model for Establishing Companies Failure Reasons. En: Actas de International Conference on SMC- Systems Man and Cybernetics, IEEE, p. 955- 962.spa
dc.relation.referencesScherger, Valeria; Vigier, Hernán y Barberá-Mariné, Gloria (2014). Finding business failure reasons through a fuzzy model of diagnosis. En: Fuzzy Economic Review, Vol. 19, No. 1, p. 45-62.spa
dc.relation.referencesScherger, Valeria; Terceño, Antonio; Vigier, Hernán y Barberá-Mariné, Gloria (2015). Detection and assessment of causes in business diagnosis. En: Economic Computation and Economic Cybernetics Studies and Research, Vol. 49, No. 4, p. 211-229.spa
dc.relation.referencesSomoza López, Antonio (2001). La consideración de factores cualitativos, macroeconómicos y sectoriales en los modelos de predicción de la solvencia empresarial. En: Papeles de Economía Española, No. 89-90, p. 402-426.spa
dc.relation.referencesSun, Jie; Li, H.; Huang, Q. H. y He, K. Y. (2014). Predicting financial distress and corporate failure: A review from the state-of-the-art definitions, modeling, sampling, and featuring approaches. En: Knowledge-Based Systems, Vol. 57, p. 41-56.spa
dc.relation.referencesThapar, Antika; Pandey, Dhaneshwar y Gaur, S. (2009). Optimization of linear objective function with max-t fuzzy relation equations. En: Applied Soft Computing, Vol. 9, No. 3, p. 1097-1101.spa
dc.relation.referencesTerceño, Antonio; Vigier, Hernán; Barberá- Marinè, Gloria y Scherger, Valeria (2009). Hacia una integración de la teoría del diagnóstico fuzzy y del Balanced Scorecard. En: Actas XV SIGEF Conference, Lugo, España, p. 364-379.spa
dc.relation.referencesTerceño, Antonio; Vigier, Hernán y Scherger, Valeria (2014). Identificación de las causas en el |diagnóstico empresarial mediante relaciones fuzzy y el BSC. En: Actualidad Contable Fases, Vol. 17, No. 28, p. 101-118.spa
dc.relation.referencesVigier, Hernán y Terceño, Antonio (2008). A model for the prediction of diseases of firms by means of fuzzy relations. En: Fuzzy Sets and System, Vol. 159, No. 1, p. 2299-2316.spa
dc.relation.referencesVigier, Hernán; Scherger, Valeria y Terceño, Antonio (2016). An application of OWA operators in fuzzy business diagnosis. En: Applied Soft Computing, http://dx.doi.org/10.1016/j. asoc.2016.06.026.spa
dc.relation.referencesXiu-ying, Liu y Zhong-chun, Mi (2009). The Application of grey relational analysis in credit evaluation of group enterprises. En: Actas de International Conference IEEE GSIS, Nanjing, China, p. 236-241.spa
dc.relation.referencesZavgren, Christine (1983). The prediction of Corporate Failure: The state of art. En: Journal of Accounting Literature, Vol. 2, p. 1-37.spa
dc.relation.referencesZimmermann, Hans (1987). Fuzzy Set Decision Making and Expert Systems. Volumen 10 de International Series in Management Science Operations Research, Kluwer Academic Publishers, Massachusetts, Norwell, 336p.spa
dc.relation.referencesZmijewski, Mark (1984). Methodological Issues Related to the Estimation of Financial Distress Prediction Models. En: Journal of Accounting Research, Vol. 22, p. 59-86.spa
dc.identifier.eissn2248-4345
dc.type.versioninfo:eu-repo/semantics/publishedVersion
dc.type.driverinfo:eu-repo/semantics/article
dc.identifier.reponamereponame:Repositorio Institucional Universidad de Medellínspa
dc.identifier.instnameinstname:Universidad de Medellínspa


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