Mostrar el registro sencillo del ítem
Assessing and forecasting method of financial efficiency in a free industrial economic zone
| dc.contributor.author | Fontalvo Herrera, Tomás José | |
| dc.contributor.author | De la Hoz Domínguez, Enrique José | |
| dc.contributor.author | Fontalvo-Echavez, Orianna | |
| dc.date.accessioned | 2022-01-28T20:07:07Z | |
| dc.date.available | 2022-01-28T20:07:07Z | |
| dc.date.issued | 2021-06-11 | |
| dc.date.submitted | 2022-01-28 | |
| dc.identifier.citation | Fontalvo-Herrera, T.J., Delahoz-Dominguez, E. and Fontalvo-Echavez, O. (2021) ‘Assessing and forecasting method of financial efficiency in a free industrial economic zone’, Int. J. Productivity and Quality Management, Vol. 33, No. 2, pp.253–270. | spa |
| dc.identifier.uri | https://hdl.handle.net/20.500.12585/10424 | |
| dc.description.abstract | : Industrial free zones are key to the economic progress of developing countries, making the evaluation and forecast of efficiency in these organisations relevant. This research proposes a three-phase method to evaluate and forecast the financial efficiency of the business profiles of companies belonging to the free economic zone of Cartagena – Colombia. The first phase consisted of a cluster analysis to determine representative groups among the companies analysed. In the second phase, financial efficiency is measured for each of the clusters found in phase 1. Finally, in phase 3 a machine learning model is trained and validated to predict the belonging of a company to a category of financial efficiency – cluster. The results show the creation of two business clusters, with an average efficiency of 49.8% and 14.6% respectively. The random forest model has an accuracy of 95% in the validation phase. | spa |
| dc.format.extent | 18 Páginas | |
| dc.format.mimetype | application/pdf | spa |
| dc.language.iso | eng | spa |
| dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/4.0/ | * |
| dc.source | Int. J. Productivity and Quality Management, Vol. 33, No. 2, 2021 | spa |
| dc.title | Assessing and forecasting method of financial efficiency in a free industrial economic zone | spa |
| dcterms.bibliographicCitation | Bailke, P.A. and Patil, S.T. (2019) ‘Distributed algorithms for improved associative multilabel document classification considering reoccurrence of features and handling minority classes’, International Journal of Business Intelligence and Data Mining, Vol. 14, No. 3, pp.299–321, DOI: 10.1504/IJBIDM.2019.098843 (accessed 10 September 2019). | spa |
| dcterms.bibliographicCitation | Benicio, J. and de Mello, J.C.S. (2015) ‘Productivity analysis and variable returns of scale: DEA efficiency frontier interpretation’, Procedia Computer Science, 3rd International Conference on Information Technology and Quantitative Management, ITQM 2015, Vol. 55, pp.341–349, DOI: 10.1016/j.procs.2015.07.059 (accessed 29 July 2019). | spa |
| dcterms.bibliographicCitation | Bhavani, R., Prakash, V. and Chitra, K. (2018) ‘An efficient clustering approach for fair semantic web content retrieval via tri-level ontology construction model with hybrid dragonfly algorithm’, International Journal of Business Intelligence and Data Mining, Vol. 14, Nos. 1–2, pp.62–88, DOI: 10.1504/IJBIDM.2019.096836 (accessed 10 September 2019). | spa |
| dcterms.bibliographicCitation | Breiman, L. (2001) ‘Random forests’, Machine Learning, Vol. 45, No. 1, pp.5–32, DOI: 10.1023/ A:1010933404324 (accessed 25 April 2019). | spa |
| dcterms.bibliographicCitation | Cook, W.D., Ramón, N., Ruiz, J.L., Sirvent, I. and Zhu, J. (2019) ‘DEA-based benchmarking for performance evaluation in pay-for-performance incentive plans’, Omega, Vol. 84, No. C, pp.45–54 [online] https://econpapers.repec.org/article/eeejomega/v_3a84_3ay_3a2019_3ai_ 3ac_3ap_3a45-54.htm (accessed 29 July 2019). | spa |
| dcterms.bibliographicCitation | De La Hoz, E., De La Hoz, E. and Fontalvo, T. (2019a) ‘Metodología de aprendizaje automático para la clasificación y predicción de usuarios en ambientes virtuales de educación’, Informacion Tecnologica, Vol. 30, No. 1, pp.247–254 [online] http://dx.doi.org/10.4067/ S0718-07642019000100247. | spa |
| dcterms.bibliographicCitation | De La Hoz, E., Fontalvo, T. and Lopez, L. (2019b) ‘Data envelopment analysis and multivariate calculus to assess, classify and predict the productive efficiency and innovation of companies in the chemical sector’, Informacion Tecnologica, Vol. 30, No. 5, pp.213–220 [online] http://dx.doi.org/10.4067/S0718-07642019000500213. | spa |
| dcterms.bibliographicCitation | Dia, M., Abukari, K., Takouda, P.M. and Assaidi, A. (2019) ‘Relative efficiency measurement of Canadian mining companies’, International Journal of Applied Management Science, Vol. 11, No. 3, pp.224–242, DOI: 10.1504/IJAMS.2019.101002 (accessed 10 September 2019) | spa |
| dcterms.bibliographicCitation | Fontalvo Herrera, T., De la Hoz Granadillo, E. and Vergara, J. C. (2012) ‘Aplicación de análisis discriminante para evaluar el mejoramiento de los indicadores financieros en las empresas del sector alimento de Barranquilla-Colombia’, Ingeniare. Revista chilena de ingeniería, Vol. 20, No. 3, pp.320–330. | spa |
| dcterms.bibliographicCitation | Fontalvo, T., De La Hoz, E. and Morelos, J. (2018) ‘Combined method of conglomerate analysis and multivariate discriminant analysis to identify and evaluate financial efficiency profiles in exporting companies’, Información Tecnologica, Vol. 29, No. 5, pp.227–234 [online] http://dx.doi.org/10.4067/S0718-07642018000500227 | spa |
| dcterms.bibliographicCitation | Fontalvo, T., De La Hoz, E. and Olivos, S. (2019) ‘Methodology of data envelopment analysis (DEA) – GLMNET for assessment and forecasting of financial efficiency in a free trade zone – Colombia’, Información Tecnologica, Vol. 30, No. 5, pp.263–270 [online] http://dx.doi.org/10.4067/S0718-07642019000500263. | spa |
| dcterms.bibliographicCitation | Garg, A. and Goyal, D.p. (2019) ‘Sustained business competitive advantage with data analytics’, International Journal of Business and Data Analytics, Vol. 1, No. 1, pp.4–15, DOI: 10.1504/ IJBDA.2019.098829 (accessed 10 September 2019) | spa |
| dcterms.bibliographicCitation | Ghiyasi, M. (2018) ‘Efficiency improvement and resource estimation: a tradeoff analysis’, International Journal of Productivity and Quality Management, Vol. 25, No. 2, pp.151–169, DOI: 10.1504/IJPQM.2018.094758 (accessed 10 September 2019). | spa |
| dcterms.bibliographicCitation | Globerson, S. and Vitner, G. (2019) ‘Measuring productivity in multi-stage, multi-product environment’, International Journal of Productivity and Quality Management, Vol. 26, No. 3, pp.290–304, DOI: 10.1504/IJPQM.2019.098365 (accessed 10 September 2019). | spa |
| dcterms.bibliographicCitation | Gomez, J.M., Herrera, T.J.F. and Granadillo, E.D.L.H. (2018) ‘Behaviour of productivity indicators and financial resources in the field of extraction and exploitation of minerals in Colombia’, International Journal of Productivity and Quality Management, Vol. 25, No. 3, pp.349–367, DOI: 10.1504/IJPQM.2018.095651 (accessed 10 September 2019). | spa |
| dcterms.bibliographicCitation | Granadillo, E.D.L.H., Gomez, J.M. and Herrera, T.J.F. (2019) ‘Methodology with multivariate calculation to define and evaluate financial productivity profiles of the chemical sector in Colombia’, International Journal of Productivity and Quality Management, Vol. 27, No. 2, pp.144–160, DOI: 10.1504/IJPQM.2019.100141 (accessed 10 September 2019). | spa |
| dcterms.bibliographicCitation | Herrera, T.J.F. (2014) ‘Aplicación de análisis discriminante para evaluar la productividad como resultado de la certificación BASC en las empresas de la ciudad de Cartagena’, Contaduría y administración, Vol. 59, No. 1, pp.43–62 | spa |
| dcterms.bibliographicCitation | Kaufman, L. and Rousseeuw, P.J. (2009) Finding Groups in Data: An Introduction to Cluster Analysis, Vol. 344, John Wiley & Sons. | spa |
| dcterms.bibliographicCitation | Khodabandehlou, S. (2019) ‘Designing an e-commerce recommender system based on collaborative filtering using a data mining approach’, International Journal of Business Information Systems, Vol. 31, No. 4, pp.455–478, DOI: 10.1504/IJBIS.2019.101582 (accessed 10 September 2019). | spa |
| dcterms.bibliographicCitation | Kumar, V.R. and Suganthi, L. (2019) ‘Relative efficiency of social CRM software: a hybrid fuzzy AHP/DEA approach’, International Journal of Business Information Systems, Vol. 31, No. 1, pp.27–44, DOI: 10.1504/IJBIS.2019.099525 (accessed 10 September 2019). | spa |
| dcterms.bibliographicCitation | Lin, W.Y., Hu, Y. and Tsai, C. (2012) ‘Machine learning in financial crisis prediction: a survey’, IEEE Transactions on Systems, Man, and Cybernetics, Part C, Applications and Reviews, Vol. 42, No. 4, pp.421–436, DOI: 10.1109/TSMCC.2011.2170420 | spa |
| dcterms.bibliographicCitation | Mahjoub, R.H. and Afsar, A. (2019) ‘A hybrid model for customer credit scoring in stock brokerages using data mining approach’, International Journal of Business Information Systems, Vol. 31, No. 2, pp.195–214 [o]. DOI: 10.1504/IJBIS.2019.100279 (accessed 26 February 2020). | spa |
| dcterms.bibliographicCitation | Mardani, A., Zavadskas, E.K., Streimikiene, D., Jusoh, A. and Khoshnoudi, M. (2017) ‘A comprehensive review of data envelopment analysis (DEA) approach in energy efficiency’, Renewable and Sustainable Energy Reviews, Vol. 70, pp.1298–1322, DOI: 10.1016/j.rser. 2016.12.030 (accessed 26 July 2019). | spa |
| dcterms.bibliographicCitation | Menardi, G. (2011) ‘Density-based Silhouette diagnostics for clustering methods’, Statistics and Computing, Vol. 21, No. 3, pp.295–308, DOI: 10.1007/s11222-010-9169-0 (accessed 28 September 2018). | spa |
| dcterms.bibliographicCitation | Nazir, A. (2019) ‘A critique of imbalanced data learning approaches for big data analytics’, International Journal of Business Intelligence and Data Mining, Vol. 14, No. 4, pp.419–457, DOI: 10.1504/IJBIDM.2019.099961 (accessed 10 September 2019). | spa |
| dcterms.bibliographicCitation | Ohsato, S. and Takahashi, M. (2015) ‘Management Efficiency in Japanese Regional Banks: a network DEA’, Procedia - Social and Behavioral Sciences, Contemporary Issues in Management and Social Science Research, Vol. 172, pp.511–518 [online] DOI: 10.1016/j.sbspro.2015.01.394 (accessed 29 July 2019). | spa |
| dcterms.bibliographicCitation | Panjehfouladgaran, H. and Shirouyehzad, H. (2018) ‘Classification of critical success factors for reverse logistics implementation based on importance-performance analysis’, International Journal of Productivity and Quality Management, Vol. 25, No. 2, pp.139–150, DOI: 10.1504/ IJPQM.2018.094757 (accessed 10 September 2019) | spa |
| dcterms.bibliographicCitation | Pawsey, N., Ananda, J. and Hoque, Z. (2018) ‘Rationality, accounting and benchmarking water businesses’, International Journal of Public Sector Management, DOI: 10.1108/IJPSM-04- 2017-0124 (accessed 29 July 2019). | spa |
| dcterms.bibliographicCitation | Sinuany-Stern, Z., Mehrez, A. and Hadad, Y. (2000) ‘An AHP/DEA methodology for ranking decision making units’, International Transactions in Operational Research, Vol. 7, No. 2, pp.109–124, DOI: 10.1016/S0969- 6016(00)00013-7 (accessed 29 July 2019). | spa |
| dcterms.bibliographicCitation | Smitha, J.a. and Rajkumar, N. (2019) ‘Efficient moving vehicle detection for intelligent traffic surveillance system using optimal probabilistic neural network’, International Journal of Business Intelligence and Data Mining, Vol. 15, No. 1, pp.22–48, DOI: 10.1504/IJBIDM. 2019.100466 (accessed 10 September 2019). | spa |
| dcterms.bibliographicCitation | Sreenivasan, S. and Sundaram, M. (2018) ‘A probabilistic model for predicting service level adherence of application support projects’, International Journal of Productivity and Quality Management, Vol. 25, No. 3, pp.305–330, DOI: 10.1504/IJPQM.2018.095648 (accessed 10 September 2019). | spa |
| dcterms.bibliographicCitation | Varma, G.N. and Padma, K. (2019) ‘Forecasting agricultural commodity pricing using neural network-based approach’, International Journal of Business Information Systems, Vol. 31, No. 4, pp.517–529, DOI: 10.1504/IJBIS.2019.101584 (accessed 10 September 2019). | spa |
| datacite.rights | http://purl.org/coar/access_right/c_abf2 | spa |
| oaire.version | http://purl.org/coar/version/c_ab4af688f83e57aa | spa |
| dc.type.driver | info:eu-repo/semantics/article | spa |
| dc.type.hasversion | info:eu-repo/semantics/restrictedAccess | spa |
| dc.identifier.doi | https://dx.doi.org/10.1504/IJPQM.2021.115694 | |
| dc.subject.keywords | Data envelope analysis | spa |
| dc.subject.keywords | DEA | spa |
| dc.subject.keywords | Clustering | spa |
| dc.subject.keywords | Machine learning | spa |
| dc.subject.keywords | Random forest | spa |
| dc.subject.keywords | Efficiency | spa |
| dc.rights.accessrights | info:eu-repo/semantics/openAccess | spa |
| dc.rights.cc | Attribution-NonCommercial-NoDerivatives 4.0 Internacional | * |
| dc.identifier.instname | Universidad Tecnológica de Bolívar | spa |
| dc.identifier.reponame | Repositorio Universidad Tecnológica de Bolívar | spa |
| dc.publisher.place | Cartagena de Indias | spa |
| dc.subject.armarc | LEMB | |
| dc.type.spa | http://purl.org/coar/resource_type/c_2df8fbb1 | spa |
| oaire.resourcetype | http://purl.org/coar/resource_type/c_2df8fbb1 | spa |
Ficheros en el ítem
Este ítem aparece en la(s) siguiente(s) colección(ones)
-
Productos de investigación [1453]
http://creativecommons.org/licenses/by-nc-nd/4.0/
Universidad Tecnológica de Bolívar - 2017 Institución de Educación Superior sujeta a inspección y vigilancia por el Ministerio de Educación Nacional. Resolución No 961 del 26 de octubre de 1970 a través de la cual la Gobernación de Bolívar otorga la Personería Jurídica a la Universidad Tecnológica de Bolívar.
