El papel transformador de la inteligencia artificial en la reducción de incertidumbre del mercado empresarial
Este estudio analiza el impacto de la Inteligencia Artificial en las decisiones empresariales en un mercado global caracterizado por la incertidumbre. Explora cómo la incertidumbre afecta la gestión empresarial y la importancia de la adaptabilidad y el uso de la Inteligencia Artificial en un context...
- Autores:
-
Jiménez Burgos, Sebastián Andrés
Ossa Perdomo, Arelia de la
- Tipo de recurso:
- Trabajo de grado de pregrado
- Fecha de publicación:
- 2024
- Institución:
- Universidad de Córdoba
- Repositorio:
- Repositorio Institucional Unicórdoba
- Idioma:
- spa
- OAI Identifier:
- oai:repositorio.unicordoba.edu.co:ucordoba/8492
- Acceso en línea:
- https://repositorio.unicordoba.edu.co/handle/ucordoba/8492
https://repositorio.unicordoba.edu.co/home
- Palabra clave:
- Inteligencia artificial
Toma de decisiones
Incertidumbre
Artificial intelligence
Decision making
Uncertainty
- Rights
- openAccess
- License
- Copyright Universidad de Córdoba, 2024
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El papel transformador de la inteligencia artificial en la reducción de incertidumbre del mercado empresarial |
| title |
El papel transformador de la inteligencia artificial en la reducción de incertidumbre del mercado empresarial |
| spellingShingle |
El papel transformador de la inteligencia artificial en la reducción de incertidumbre del mercado empresarial Inteligencia artificial Toma de decisiones Incertidumbre Artificial intelligence Decision making Uncertainty |
| title_short |
El papel transformador de la inteligencia artificial en la reducción de incertidumbre del mercado empresarial |
| title_full |
El papel transformador de la inteligencia artificial en la reducción de incertidumbre del mercado empresarial |
| title_fullStr |
El papel transformador de la inteligencia artificial en la reducción de incertidumbre del mercado empresarial |
| title_full_unstemmed |
El papel transformador de la inteligencia artificial en la reducción de incertidumbre del mercado empresarial |
| title_sort |
El papel transformador de la inteligencia artificial en la reducción de incertidumbre del mercado empresarial |
| dc.creator.fl_str_mv |
Jiménez Burgos, Sebastián Andrés Ossa Perdomo, Arelia de la |
| dc.contributor.advisor.none.fl_str_mv |
Doria Sierra, Carlos Fernando |
| dc.contributor.author.none.fl_str_mv |
Jiménez Burgos, Sebastián Andrés Ossa Perdomo, Arelia de la |
| dc.contributor.jury.none.fl_str_mv |
Anaya Yances, Freddy Buelvas Sierra, Ramón |
| dc.subject.proposal.spa.fl_str_mv |
Inteligencia artificial Toma de decisiones Incertidumbre |
| topic |
Inteligencia artificial Toma de decisiones Incertidumbre Artificial intelligence Decision making Uncertainty |
| dc.subject.keywords.eng.fl_str_mv |
Artificial intelligence Decision making Uncertainty |
| description |
Este estudio analiza el impacto de la Inteligencia Artificial en las decisiones empresariales en un mercado global caracterizado por la incertidumbre. Explora cómo la incertidumbre afecta la gestión empresarial y la importancia de la adaptabilidad y el uso de la Inteligencia Artificial en un contexto en que varían velozmente las tendencias de consumo y los datos externos se multiplican. A partir de un análisis bibliométrico se clasifican áreas de estudio con base a la actividad empresarial y se identifican diversos modelos de Inteligencia Artificial aplicados a las empresas. En lo que se pudo notar que prevalecen el Machine Learning, el Deep Learning y modelos híbridos creados a partir de los primeros, que ayudan con la reducción de incertidumbre y mejora en la toma de decisiones. Destacando que el uso de los modelos de IA conlleva la consideración de principios éticos y de estrategia para la escogencia del modelo adecuado para cada contexto. |
| publishDate |
2024 |
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2024-08-11T00:23:26Z |
| dc.date.available.none.fl_str_mv |
2024-08-11T00:23:26Z |
| dc.date.issued.none.fl_str_mv |
2024-08-06 |
| dc.type.none.fl_str_mv |
Trabajo de grado - Pregrado |
| dc.type.driver.none.fl_str_mv |
info:eu-repo/semantics/bachelorThesis |
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http://purl.org/coar/resource_type/c_7a1f |
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info:eu-repo/semantics/acceptedVersion |
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Text |
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http://purl.org/coar/resource_type/c_7a1f |
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acceptedVersion |
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https://repositorio.unicordoba.edu.co/handle/ucordoba/8492 |
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Universidad de Córdoba |
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Repositorio universidad de Córdoba |
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https://repositorio.unicordoba.edu.co/home |
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https://repositorio.unicordoba.edu.co/handle/ucordoba/8492 https://repositorio.unicordoba.edu.co/home |
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Universidad de Córdoba Repositorio universidad de Córdoba |
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spa |
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spa |
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Aaltonen, H., Sierla, S., Subramanya, R., & Vyatkin, V. (2021). Simulation Environment for Training a Reinforcement Learning Agent Trading a Battery Storage. Energies, 14(17), 5587. DOI 10.3390/en14175587 Al-Nefaie, A. H., & Aldhyani, T. H. H. (2022). Bitcoin Price Forecasting and Trading: Data Analytics Approaches. Electronics (Switzerland), 11(24). https://doi.org/10.3390/electronics11244088 Alves, G. H., Guimarães, G. C., & Moura, F. A. M. (2023). Battery Storage Systems Control Strategies with Intelligent Algorithms in Microgrids with Dynamic Pricing. Energies, 16(14), 5262. DOI 10.3390/en16145262 Arroyo, J., Corea, F., Jimenez-Diaz, G., & Recio-Garcia, J. A. (2019). Assessment of machine learning performance for decision support in venture capital investments. IEEE Access, 7, 124233–124243. https://doi.org/10.1109/ACCESS.2019.2938659 Bernal, C. (2010). Metodología de la investigación. Pearson Educación. Brown, S. (2018). Previsión de la demanda en un mercado incierto: Estrategias para una toma de decisiones eficaz. International Journal of Forecasting, 34(2), 201-215. Brown, S. (2019). The role of artificial intelligence in global data access and processing. International Journal of Information Management, 49, 102072. Chen, C. (2010). VOSviewer: A software tool for bibliometric analysis. Dordrecht: Springer. Cortés-Leal, A., Cárdenas, C., Del-Valle-Soto, C. (2022). Maintenance 5.0: Towards a Worker-in-the-Loop Framework for Resilient Smart Manufacturing. Applied Sciences (Switzerland), 12(22), 11330. DOI 10.3390/app122211330 Cuong, T. N., Kim, H.-S., Bao Long, L. N., & You, S.-S. (2023). Decision Support System for Managing Multi-Echelon Supply Chain Networks Against Disruptions Using Adaptive Fractional Order Control Algorithm. RAIRO - Operations Research, 57(2). DOI 10.1051/ro/2023035 García, M. (2017). Adaptación a la incertidumbre de los mercados: Retos y Soluciones. Strategic Management Journal, 38(6), 1302-1313. Ghosh, I., & Chaudhuri, T. D. (2023). Modelos FEB-stacking y FEB-DNN para la predicción de tendencias bursátiles: un análisis de rendimiento para los periodos pre y post Covid-19. Risks, 11(5), 94. DOI 10.3390/risks11050094 Heistrene, L., Machlev, R., Perl, M., Belikov, J., Baimel, D., Levy, K., Mannor, S., & Levron, Y. (2023). Explainability-based Trust Algorithm for electricity price forecasting models. Energy and AI, 14, 100259. DOI 10.1016/j.egyai.2023.100259 Hu, H., Lin, T., Wang, S., & Rodriguez, L. F. (2017). A cyberGIS approach to uncertainty and sensitivity analysis in biomass supply chain optimization. Applied Energy, 203, 54. DOI 10.1016/j.apenergy.2017.03.107 Jankova, Z., Jana, D. K., & Dostal, P. (2021). Investment Decision Support Based on Interval Type-2 Fuzzy Expert System. Engineering Economics, 32(2), 15. DOI 10.5755/j01.ee.32.2.24884 Johnson, J. (2019). Real-time data access: A critical asset for business decision-making. Journal of Management Information Systems, 36(2), 351-375. Johnson, P. (2020). La incertidumbre política y su impacto en la toma de decisiones empresariales. Journal of Economic Perspectives, 33(4), 67-84. Jones, A. (2019). Comprender la incertidumbre en la toma de decisiones empresariales. Journal of Business Strategy, 40(2), 10-17. Kliangkhlao, M., Limsiroratana, S., & Sahoh, B. (2022). The Design and Development of a Causal Bayesian Networks Model for the Explanation of Agricultural Supply Chains. IEEE Access, 10, 86813–86823. DOI 10.1109/ACCESS.2022.3199353 Kolte, A., Roy, J. K., & Vasa, L. (2023). The impact of unpredictable resource prices and equity volatility in advanced and emerging economies: An econometric and machine learning approach. Resources Policy, 80, 03216. DOI 10.1016/j.resourpol.2022.103216 Lee, A. H. I., & Kang, H.-Y. (2023). A Three-Phased Fuzzy Logic Multi-Criteria Decision-Making Model for Evaluating Operation Systems for Smart TVs. Applied Sciences (Switzerland), 13(13), 7869. DOI 10.3390/app13137869 Li, M., & Madina, Z. (2021). Artificial Intelligence Assists the Construction of Quantitative Model for the High-Quality Development of Modern Enterprises. Computational and Mathematical Methods in Medicine, 7211790. DOI 10.1155/2021/7211790 Liu, Y., Zhang, D., & Gooi, H. B. (2021). Data-driven decision-making strategies for electricity retailers: A deep reinforcement learning approach. CSEE Journal of Power and Energy Systems, 7(2), 9215156, 20. DOI 10.17775/CSEEJPES.2019.02510. Lomakin, N., Maramygin, M., Kataev, A., Kraschenko, S., Yurova, O., & Lomakin, I. (2022). Cognitive model of financial stability of the domestic economy based on artificial intelligence in conditions of uncertainty and risk. International Journal of Technology, 13(7), 1588. https://doi.org/10.14716/ijtech.v13i7.6185 Mitchell, T. M. (1997). Machine Learning. McGraw Hill. ISBN 0071154671, 9780071154673. Plakandaras, V., Gogas, P., & Papadimitriou (2019). The Effects of Geopolitical Uncertainty in Forecasting Financial Markets: A Machine Learning Approach. Algorithms, 12(1), 1, 15. DOI 10.3390/a12010001 Reddy, D. M., & Balamanigandan, R. (2023). Mobile U-Net V3 And Bilstm: Predicting Stock Market Prices Based on Deep Learning Approaches. Jordanian Journal of Computers and Information Technology, 9(3). DOI 10.5455/jjcit.71-1682317264 Rouhiainen, L. (2018). Inteligencia artificial 101 cosas que debes saber hoy sobre nuestro futuro. Alienta Editorial, Barcelona, España. ISBN: 978-84-17568-08-5 https://planetadelibrosec0.cdnstatics.com/libros_contenido_extra/40/39308_Inteligencia_artificial.pdf Smith, M. (2018). The convergence of the internet and artificial intelligence: Implications for business and society. Journal of Business Research, 89, 113-121. Smith, R. (2020). Gestionar la incertidumbre en un mercado volátil: Strategies for Success. Harvard Business Review, 96(4), 45-52. Stern, A. D., Goldfarb, A., Minssen, T., & Price, W. N., II. (2022). AI Insurance: How Liability Insurance Can Drive the Responsible Adoption of Artificial Intelligence in Health Care. NEJM Catalyst Innovations in Care Delivery, 3(4). DOI 10.1056/CAT.21.0242 Svetlova, E. (2022). AI meets narratives: the state and future of research on expectation formation in economics and sociology. Socio-Economic Review, 20(2), 5. DOI 10.1093/ser/mwab033 UNAM CAUTITLÁN (s.f.). Lógica difusa. Grupos de Investigación en Sistemas Inteligentes -GISI. https://virtual.cuautitlan.unam.mx/intar/?page_id=997 Van Phuoc, N. (2022). The Critical Factors Impacting Artificial Intelligence Applications Adoption in Vietnam: A Structural Equation Modeling Analysis. Economies, 10(6), 129. DOI 10.3390/economies10060129. Vilkkumaa, E., Liesiö, J., Salo, A., & Ilmola-Sheppard, L. (2018). Scenario-based portfolio model for building robust and proactive strategies. European Journal of Operational Research, 266(1), 25. DOI 10.1016/j.ejor.2017.09.012. Wang, S. L., Shafi, K., Ng, T. F., Lokan, C., & Abbass, H. A. (2022). Contrasting Human and Computational Intelligence Based Autonomous Behaviors in a Blue-Red Simulation Environment. Sustainability (Switzerland), 14(24), 16836. DOI 10.3390/su142416836. White, L. (2020). Datos en tiempo real y toma de decisiones en un entorno empresarial acelerado. Journal of Business Analytics, 5(1), 35-48. Xu, Y.-Z., Zhang, J.-L., Hua, Y., & Wang, L.-Y. (2019). Dynamic credit risk evaluation method for e-commerce sellers based on a hybrid artificial intelligence model. Sustainability (Switzerland), 11(19). https://doi.org/10.3390/su11195521 Yan, X., & Ma, S. (2022). Garment Design Models Combining Bayesian Classifier and Decision Tree Algorithm. Computational Intelligence and Neuroscience, 2022, 1904158. DOI 10.1155/2022/1904158. Yigitcanlar, T., & Cugurullo, F. (2020). The sustainability of artificial intelligence: an urbanistic viewpoint from the lens of smart and sustainable cities. Sustainability (Switzerland), 12(20), 1–24. https://doi.org/10.3390/su12208548 Åström, J., Reim, W., & Parida, V. (2022). Value creation and value capture for AI business model innovation: a three-phase process framework. Review of Managerial Science, 16(7), 33. DOI 10.1007/s11846-022-00521-z |
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Doria Sierra, Carlos Fernando24c33c68-bdef-4a1e-891a-5986c70abe94-1Jiménez Burgos, Sebastián Andrés05ee0ed0-8039-45f6-a47c-413a806f3160-1Ossa Perdomo, Arelia de la75955bce-98e3-4aa8-924c-26d122810dee-1Anaya Yances, Freddy0ab6c7f5-bc3c-4bd1-bae7-64ee8953c1c5600Buelvas Sierra, Ramón5ad78a6e-f95b-4e41-ad85-9482ed9bbd162024-08-11T00:23:26Z2024-08-11T00:23:26Z2024-08-06https://repositorio.unicordoba.edu.co/handle/ucordoba/8492Universidad de CórdobaRepositorio universidad de Córdobahttps://repositorio.unicordoba.edu.co/homeEste estudio analiza el impacto de la Inteligencia Artificial en las decisiones empresariales en un mercado global caracterizado por la incertidumbre. Explora cómo la incertidumbre afecta la gestión empresarial y la importancia de la adaptabilidad y el uso de la Inteligencia Artificial en un contexto en que varían velozmente las tendencias de consumo y los datos externos se multiplican. A partir de un análisis bibliométrico se clasifican áreas de estudio con base a la actividad empresarial y se identifican diversos modelos de Inteligencia Artificial aplicados a las empresas. En lo que se pudo notar que prevalecen el Machine Learning, el Deep Learning y modelos híbridos creados a partir de los primeros, que ayudan con la reducción de incertidumbre y mejora en la toma de decisiones. Destacando que el uso de los modelos de IA conlleva la consideración de principios éticos y de estrategia para la escogencia del modelo adecuado para cada contexto.This study analyzes the impact of the Artificial Intelligence on business decisions in a global market characterized by uncertainty. It explores how uncertainty affects business management and the importance of adaptability and the use of Artificial Intelligence in a context of rapidly changing consumer trends and multiplying external data. Based on a bibliometric analysis, areas of study are classified according to business activity and various Artificial Intelligence models applied to companies are identified. It was noted that Machine Learning, Deep Learning and hybrid models created from the first ones, which help with the reduction of uncertainty and improvement in decision making, prevail. Emphasizing that the use of AI models involves the consideration of ethical principles and strategy for the choice of the appropriate model for each context.El Papel Transformador de la Inteligencia Artificial en la Reducción de Incertidumbre del Mercado Empresarial. 5Introducción. 5Análisis Bibliométrico. 7La Incertidumbre Empresarial. 11Problemáticas Asociadas al Uso de la Inteligencia Artificial en los Escenarios de Incertidumbre del Mercado Empresarial. 16Modelos de Inteligencia Artificial Utilizados en cada Área de Estudio Asociada a la Reducción de Incertidumbre del Mercado Empresarial. 19Conclusiones. 29Referencias. 33PregradoAdministrador(a) en Finanzas y Negocios InternacionalesTrabajos de Investigación y/o Extensiónapplication/pdfspaUniversidad de CórdobaFacultad de Ciencias Económicas, Jurídicas y AdministrativasMontería, Córdoba, ColombiaCiencias AdministrativasCopyright Universidad de Córdoba, 2024https://creativecommons.org/licenses/by-nc-nd/4.0/Atribución-NoComercial-SinDerivadas 4.0 Internacional (CC BY-NC-ND 4.0)info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2El papel transformador de la inteligencia artificial en la reducción de incertidumbre del mercado empresarialTrabajo de grado - Pregradoinfo:eu-repo/semantics/bachelorThesishttp://purl.org/coar/resource_type/c_7a1finfo:eu-repo/semantics/acceptedVersionTextAaltonen, H., Sierla, S., Subramanya, R., & Vyatkin, V. (2021). Simulation Environment for Training a Reinforcement Learning Agent Trading a Battery Storage. Energies, 14(17), 5587. DOI 10.3390/en14175587Al-Nefaie, A. H., & Aldhyani, T. H. H. (2022). Bitcoin Price Forecasting and Trading: Data Analytics Approaches. Electronics (Switzerland), 11(24). https://doi.org/10.3390/electronics11244088Alves, G. H., Guimarães, G. C., & Moura, F. A. M. (2023). Battery Storage Systems Control Strategies with Intelligent Algorithms in Microgrids with Dynamic Pricing. Energies, 16(14), 5262. DOI 10.3390/en16145262Arroyo, J., Corea, F., Jimenez-Diaz, G., & Recio-Garcia, J. A. (2019). Assessment of machine learning performance for decision support in venture capital investments. IEEE Access, 7, 124233–124243. https://doi.org/10.1109/ACCESS.2019.2938659Bernal, C. (2010). Metodología de la investigación. Pearson Educación.Brown, S. (2018). Previsión de la demanda en un mercado incierto: Estrategias para una toma de decisiones eficaz. International Journal of Forecasting, 34(2), 201-215.Brown, S. (2019). The role of artificial intelligence in global data access and processing. International Journal of Information Management, 49, 102072.Chen, C. (2010). VOSviewer: A software tool for bibliometric analysis. Dordrecht: Springer.Cortés-Leal, A., Cárdenas, C., Del-Valle-Soto, C. (2022). Maintenance 5.0: Towards a Worker-in-the-Loop Framework for Resilient Smart Manufacturing. Applied Sciences (Switzerland), 12(22), 11330. DOI 10.3390/app122211330Cuong, T. N., Kim, H.-S., Bao Long, L. N., & You, S.-S. (2023). Decision Support System for Managing Multi-Echelon Supply Chain Networks Against Disruptions Using Adaptive Fractional Order Control Algorithm. RAIRO - Operations Research, 57(2). DOI 10.1051/ro/2023035García, M. (2017). Adaptación a la incertidumbre de los mercados: Retos y Soluciones. Strategic Management Journal, 38(6), 1302-1313.Ghosh, I., & Chaudhuri, T. D. (2023). Modelos FEB-stacking y FEB-DNN para la predicción de tendencias bursátiles: un análisis de rendimiento para los periodos pre y post Covid-19. Risks, 11(5), 94. DOI 10.3390/risks11050094Heistrene, L., Machlev, R., Perl, M., Belikov, J., Baimel, D., Levy, K., Mannor, S., & Levron, Y. (2023). Explainability-based Trust Algorithm for electricity price forecasting models. Energy and AI, 14, 100259. DOI 10.1016/j.egyai.2023.100259Hu, H., Lin, T., Wang, S., & Rodriguez, L. F. (2017). A cyberGIS approach to uncertainty and sensitivity analysis in biomass supply chain optimization. Applied Energy, 203, 54. DOI 10.1016/j.apenergy.2017.03.107Jankova, Z., Jana, D. K., & Dostal, P. (2021). Investment Decision Support Based on Interval Type-2 Fuzzy Expert System. Engineering Economics, 32(2), 15. DOI 10.5755/j01.ee.32.2.24884Johnson, J. (2019). Real-time data access: A critical asset for business decision-making. Journal of Management Information Systems, 36(2), 351-375.Johnson, P. (2020). La incertidumbre política y su impacto en la toma de decisiones empresariales. Journal of Economic Perspectives, 33(4), 67-84.Jones, A. (2019). Comprender la incertidumbre en la toma de decisiones empresariales. Journal of Business Strategy, 40(2), 10-17.Kliangkhlao, M., Limsiroratana, S., & Sahoh, B. (2022). The Design and Development of a Causal Bayesian Networks Model for the Explanation of Agricultural Supply Chains. IEEE Access, 10, 86813–86823. DOI 10.1109/ACCESS.2022.3199353Kolte, A., Roy, J. K., & Vasa, L. (2023). The impact of unpredictable resource prices and equity volatility in advanced and emerging economies: An econometric and machine learning approach. Resources Policy, 80, 03216. DOI 10.1016/j.resourpol.2022.103216Lee, A. H. I., & Kang, H.-Y. (2023). A Three-Phased Fuzzy Logic Multi-Criteria Decision-Making Model for Evaluating Operation Systems for Smart TVs. Applied Sciences (Switzerland), 13(13), 7869. DOI 10.3390/app13137869Li, M., & Madina, Z. (2021). Artificial Intelligence Assists the Construction of Quantitative Model for the High-Quality Development of Modern Enterprises. Computational and Mathematical Methods in Medicine, 7211790. DOI 10.1155/2021/7211790Liu, Y., Zhang, D., & Gooi, H. B. (2021). Data-driven decision-making strategies for electricity retailers: A deep reinforcement learning approach. CSEE Journal of Power and Energy Systems, 7(2), 9215156, 20. DOI 10.17775/CSEEJPES.2019.02510.Lomakin, N., Maramygin, M., Kataev, A., Kraschenko, S., Yurova, O., & Lomakin, I. (2022). Cognitive model of financial stability of the domestic economy based on artificial intelligence in conditions of uncertainty and risk. International Journal of Technology, 13(7), 1588. https://doi.org/10.14716/ijtech.v13i7.6185Mitchell, T. M. (1997). Machine Learning. McGraw Hill. ISBN 0071154671, 9780071154673.Plakandaras, V., Gogas, P., & Papadimitriou (2019). 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03:01:14.73https://creativecommons.org/licenses/by-nc-nd/4.0/Copyright Universidad de Córdoba, 2024open.accesshttps://repositorio.unicordoba.edu.coRepositorio Universidad de 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