Revisión bibliográfica y análisis bibliométrico de la inteligencia artificial para la gestión directiva y toma de decisiones en ciencias económicas y afines

Idana Beroska Rincon Soto; Lina Iris Palacios Serna

doi:10.48204/2710-7744.9047

Enviado diciembre 18, 2025

Publicado 2025-12-23

Artículos

Vol. 6 (2026): DÉconomía

Revisión bibliográfica y análisis bibliométrico de la inteligencia artificial para la gestión directiva y toma de decisiones en ciencias económicas y afines

Idana Beroska Rincon Soto⁺⁻
Lina Iris Palacios Serna⁺⁻

DOI https://doi.org/10.48204/2710-7744.9047

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Citas

DOI: 10.48204/2710-7744.9047

Publicado: 2025-12-23

Cómo citar

Rincon Soto, I. B., & Palacios Serna, L. I. (2025). Revisión bibliográfica y análisis bibliométrico de la inteligencia artificial para la gestión directiva y toma de decisiones en ciencias económicas y afines. D´Economía, 6, 50–82. https://doi.org/10.48204/2710-7744.9047

Resumen

La toma de decisiones en la gestión directiva es un tema de relevancia para la administración eficiente de las instituciones aunado al apoyo en la inteligencia artificial. Esta investigación tuvo como objetivo analizar la producción científica en esta variable mediante la inteligencia artificial.

La metodología se basó en una revisión sistemática de literatura utilizando base de datos como Wos y Scopus y VOSviewer para facilitar la visualización en las redes bibliométricas, considerándolo el aporte novedoso en este campo de estudio. Los resultados arrojaron 203 publicaciones identificadas, que exponen la evolución, principales artículos y temas, así como los autores. Se concluyó con el aporte de identificar clústeres temáticos y nuevas líneas temáticas en esta área de investigación, dado el auge en el interés de indagar la forma en como la inteligencia artificia se integra a los sistemas de la toma de decisiones.

Citas

Angelis, J., & Ribeiro da Silva, E. (2019). Blockchain adoption: A value driver perspective. Business Horizons, 62(3), 307–314. https://doi.org/10.1016/j.bushor.2018.12.001
Bagstad, K. J., Semmens, D. J., Waage, S., & Winthrop, R. (2013). A comparative assessment of decision-support tools for ecosystem services quantification and valuation. Ecosystem Services, Vol. 5, pp. 27–39. https://doi.org/10.1016/j.ecoser.2013.07.004
Baryannis, G., Validi, S., Dani, S., & Antoniou, G. (2019). Supply chain risk management and artificial intelligence: state of the art and future research directions. International Journal of Production Research. https://doi.org/10.1080/00207543.2018.1530476
Blattert, C., Lemm, R., Thees, O., Lexer, M. J., & Hanewinkel, M. (2017, August 1). Management of ecosystem services in mountain forests: Review of indicators and value functions for model based multi-criteria decision analysis. Ecological Indicators, Vol. 79, pp. 391–409. https://doi.org/10.1016/j.ecolind.2017.04.025
Bogataj, D., Bogataj, M., & Hudoklin, D. (2017). Mitigating risks of perishable products in the cyber-physical systems based on the extended MRP model. International Journal of Production Economics, 193, 51–62. https://doi.org/10.1016/j.ijpe.2017.06.028
Bottani, E., Centobelli, P., Gallo, M., Kaviani, M. A., Jain, V., & Murino, T. (2019). Modelling wholesale distribution operations: an artificial intelligence framework. Industrial Management and Data Systems, 119(4), 698–718. https://doi.org/10.1108/IMDS-04-2018-0164
Bui, K. H. N., & Jung, J. J. (2019). Computational negotiation-based edge analytics for smart objects. Information Sciences, 480, 222–236. https://doi.org/10.1016/j.ins.2018.12.046
Buter, R. K., & Van Raan, A. F. J. (2013). Identification and analysis of the highly cited knowledge base of sustainability science. Sustainability Science, 8(2), 253–267. https://doi.org/10.1007/s11625-012-0185-1
Cañedo, A., Rodríguez, R., Labrada, R., & Montejo Castells, M. (2010). Scopus: La mayor base de datos de literatura científica arbitrada al alcance de los países subdesarrollados. ACIMED, 21. n3., 270–282.
Canhoto, A. I., & Clear, F. (2020). Artificial intelligence and machine learning as business tools: A framework for diagnosing value destruction potential. Business Horizons, 63(2), 183–193. https://doi.org/10.1016/j.bushor.2019.11.003
Castelli, M., Manzoni, L., & Popovi?, A. (2016). An Artificial Intelligence System to Predict Quality of Service in Banking Organizations. Computational Intelligence and Neuroscience, 2016. https://doi.org/10.1155/2016/9139380
Chávez, L., Arguello, A., Viscarra, C. P., Aro Sosa, G. L., & Albarrasín, M. (2018). Artificial Intelligence in management decision making. Dilemas Contemporáneos?: Educación, Política y Valore, V, 12. Retrieved from http://ezproxy.unisinucartagena.edu.co:2048/menu/scholarly-journals/artificial-intelligence-management-decision/docview/2247186507/se-2?accountid=44833%0A
Cho, C., Kim, K., Park, J., & Cho, Y. K. (2018). Data-Driven Monitoring System for Preventing the Collapse of Scaffolding Structures. Journal of Construction Engineering and Management, 144(8), 04018077. https://doi.org/10.1061/(asce)co.1943-7862.0001535
de Filippo, D., & Levin, L. (2017). Detección y análisis de “clústers bibliográficos” en las publicaciones de Iberoamérica sobre ciencia, tecnología y sociedad (1970-2013). Investigacion Bibliotecologica, 2017(Special Issue), 123–148. https://doi.org/10.22201/iibi.24488321xe.2017.nesp1.57888
de Groot, R. S., Alkemade, R., Braat, L., Hein, L., & Willemen, L. (2010). Challenges in integrating the concept of ecosystem services and values in landscape planning, management and decision making. Ecological Complexity, 7(3), 260–272. https://doi.org/10.1016/j.ecocom.2009.10.006
De Maio, C., Fenza, G., Loia, V., Orciuoli, F., & Herrera-Viedma, E. (2016). A framework for context-aware heterogeneous group decision making in business processes. Knowledge-Based Systems, 102, 39–50. https://doi.org/10.1016/j.knosys.2016.03.019
Fuentes-Doria, D. D., Toscano-Hernández, A. E., Malvaceda-Espinoza, E., Díaz Ballesteros, J. L., & Díaz Pertuz, L. (2020). Metodología de la investigación: Conceptos, herramientas y ejercicios prácticos en las ciencias administrativas y contables. In Metodología de la investigación: Conceptos, herramientas y ejercicios prácticos en las ciencias administrativas y contables. https://doi.org/10.18566/978-958-764-879-9
Fuentes Doria, D. D., Toscano Hernández, A., & García Alarcón, H. (2020). Tendencias mundiales de la implementación de las Normas Internacionales de Información Financiera. Un estudio bibliométrico del período 1989 a 2018. Revista Perspectiva Empresarial. https://doi.org/10.16967/23898186.632
Gerlick, J. A., & Liozu, S. M. (2020). Ethical and legal considerations of artificial intelligence and algorithmic decision-making in personalized pricing. Journal of Revenue and Pricing Management, 19(2), 85–98. https://doi.org/10.1057/s41272-019-00225-2
González Arencibia, M., & Martínez Cardero, D. (2020). Dilemas éticos en el escenario de la inteligencia artificial. Economía y Sociedad. https://doi.org/10.15359/eys.25-57.5
Grace, A. M., & Williams, S. O. (2016). Comparative analysis of neural network and fuzzy logic techniques in credit risk evaluation. International Journal of Intelligent Information Technologies, 12(1), 47–62. https://doi.org/10.4018/IJIIT.2016010103
Hajek, P., & Henriques, R. (2017). Mining corporate annual reports for intelligent detection of financial statement fraud – A comparative study of machine learning methods. Knowledge-Based Systems, 128, 139–152. https://doi.org/10.1016/j.knosys.2017.05.001
Kannan, D. (2018). Role of multiple stakeholders and the critical success factor theory for the sustainable supplier selection process. International Journal of Production Economics, 195, 391–418. https://doi.org/10.1016/j.ijpe.2017.02.020
Kaparthi, S., & Bumblauskas, D. (2020). Designing predictive maintenance systems using decision tree-based machine learning techniques. International Journal of Quality and Reliability Management, 37(4), 659–686. https://doi.org/10.1108/IJQRM-04-2019-0131
Kellner, F., Lienland, B., & Utz, S. (2019). An a posteriori decision support methodology for solving the multi-criteria supplier selection problem. European Journal of Operational Research, 272(2), 505–522. https://doi.org/10.1016/j.ejor.2018.06.044
Kilani, Y. M. M., & Haikal, E. K. (2020). Exploitation of expert system in identifying organizational ethics through controlling decision making process. Management Science Letters, 10(7), 1417–1426. https://doi.org/10.5267/j.msl.2019.12.026
Kraus, M., & Feuerriegel, S. (2017). Decision support from financial disclosures with deep neural networks and transfer learning. Decision Support Systems, 104, 38–48. https://doi.org/10.1016/j.dss.2017.10.001
Latah, M., & Toker, L. (2019, March 1). Artificial intelligence enabled software-defined networking: A comprehensive overview. IET Networks, Vol. 8, pp. 79–99. https://doi.org/10.1049/iet-net.2018.5082
Li, Y., Bai, C., & Reddy, C. K. (2016). A distributed ensemble approach for mining healthcare data under privacy constraints. Information Sciences, 330, 245–259. https://doi.org/10.1016/j.ins.2015.10.011
Losada Camacho, E. H. (2019). ¿QUÉ IMPORTANCIA TIENE LA TOMA DE DECISIONES PARA EL DESARROLLO EMPRESARIAL? (Universidad Militar Nueva Granada.). https://doi.org/.1037//0033-2909.I26.1.78
Moher, D., Liberati, A., Tetzlaff, J. and Altman, D. G. (2009). PRISMA 2009 Flow Diagram. The PRISMA statement,. PLoS Medicine. https://doi.org/10.1371/journal.pmed1000097.
Neubert, M. J., & Montañez, G. D. (2020). Virtue as a framework for the design and use of artificial intelligence. Business Horizons, 63(2), 195–204. https://doi.org/10.1016/j.bushor.2019.11.001
Paschen, U., Pitt, C., & Kietzmann, J. (2020a). Artificial intelligence: Building blocks and an innovation typology. Business Horizons, 63(2), 147–155. https://doi.org/10.1016/j.bushor.2019.10.004
Paschen, U., Pitt, C., & Kietzmann, J. (2020b). Artificial intelligence: Building blocks and an innovation typology. Business Horizons, 63(2), 147–155. https://doi.org/10.1016/j.bushor.2019.10.004
Paul, M., & Bussemaker, M. J. (2020). A web-based geographic interface system to support decision making for municipal solid waste management in England. Journal of Cleaner Production, 263, 121461. https://doi.org/10.1016/j.jclepro.2020.121461
Pellicer-Martínez, F., & Martínez-Paz, J. M. (2016). Grey water footprint assessment at the river basin level: Accounting method and case study in the Segura River Basin, Spain. Ecological Indicators, 60, 1173–1183. https://doi.org/10.1016/j.ecolind.2015.08.032
Pellicer, E., Sierra, L. A., & Yepes, V. (2016). Appraisal of infrastructure sustainability by graduate students using an active-learning method. Journal of Cleaner Production, 113, 884–896. https://doi.org/10.1016/j.jclepro.2015.11.010
Perrault, R., Shoham, Y., Brynjolfsson, E., Clark, J., Etchemendy, J., Grosz Harvard, B., … Mishra, S. (2019). Artificial Intelligence Index 2019 Annual Report. Standford University - Human-Centered Artificial Intelligence.
Pichuante Escaida, C. (2016). Visualización de grafos de co-autoría y de conocimiento basado en publicaciones científicas, implementada en VOSviewer. Retrieved from https://repositorio.uc.cl/handle/11534/21357
Pipere, A., Veisson, M., & Salite, I. (2015). Developing Research in Teacher Education for Sustainability?: UN DESD via the Journal of Teacher Education for Sustainability. Journal of Teacher Education for Sustainability, 17(2), 5–43. https://doi.org/10.1515/jtes-2015-0009
Rouhiainen, L. (2018). INTELIGENCIA ARTIFICIAL 101 COSAS QUE DEBES SABER HOY SOBRE NUESTRO FUTURO INTELIGENCIA ARTIFICIAL (Editorial). Retrieved from www.planetadelibros.com
Syam, Niladri; Sharma, A. (2018). Waiting for a sales renaissance in the fourth industrial revolution: Machine learning and artificial intelligence in sales research and practice. Industrial Marketing Management, 69(November 2017), 135–146. https://doi.org/10.1016/j.indmarman.2017.12.019
Toscano-Hernández, A. E., Fuentes-Doria, D. D., & Fajardo Pereira, M. A. (2019). Sostenibilidad universitaria con enfoque en la educación ambiental: redes de colaboración y clúster temáticos de la producción científica mundial. Panorama Económico, 27(1), 60–84. https://doi.org/https://doi.org/10.32997/2463-0470-vol.27-num.1-2019-2617
Tranfield, D., Denyer, D., & Smart, P. (2003). Towards a Methodology for Developing Evidence-Informed Management Knowledge by Means of Systematic Review. British Journal of Management. https://doi.org/10.1111/1467-8551.00375
UNESCO. (2021). Elaboration of a Recommendation on the ethics of artificial intelligence. Retrieved March 12, 2021, from Unesdoc Digital Library website: https://unesdoc.unesco.org/ark:/48223/pf0000367823
Van Eck, N. J., & Waltman, L. (2013). {VOSviewer} manual. Leiden: Univeristeit Leiden. Retrieved from http://www.vosviewer.com/documentation/Manual_VOSviewer_1.6.1.pdf
Yazdani, M., Chatterjee, P., Zavadskas, E., & Hashemkhani Zolfani, S. (2017). Integrated QFD-MCDM framework for green supplier selection. Journal of Cleaner Production, 142, 3728–3740. https://doi.org/10.1016/j.jclepro.2016.10.095
Zimmer, K., Fröhling, M., & Schultmann, F. (2016). Sustainable supplier management - A review of models supporting sustainable supplier selection, monitoring and development. International Journal of Production Research, 54(5), 1412–1442. https://doi.org/10.1080/00207543.2015.1079340

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[10] Canhoto, A. I., & Clear, F. (2020). Artificial intelligence and machine learning as business tools: A framework for diagnosing value destruction potential. Business Horizons, 63(2), 183–193. https://doi.org/10.1016/j.bushor.2019.11.003

[11] Castelli, M., Manzoni, L., & Popovi?, A. (2016). An Artificial Intelligence System to Predict Quality of Service in Banking Organizations. Computational Intelligence and Neuroscience, 2016. https://doi.org/10.1155/2016/9139380

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[13] Cho, C., Kim, K., Park, J., & Cho, Y. K. (2018). Data-Driven Monitoring System for Preventing the Collapse of Scaffolding Structures. Journal of Construction Engineering and Management, 144(8), 04018077. https://doi.org/10.1061/(asce)co.1943-7862.0001535

[14] de Filippo, D., & Levin, L. (2017). Detección y análisis de “clústers bibliográficos” en las publicaciones de Iberoamérica sobre ciencia, tecnología y sociedad (1970-2013). Investigacion Bibliotecologica, 2017(Special Issue), 123–148. https://doi.org/10.22201/iibi.24488321xe.2017.nesp1.57888

[15] de Groot, R. S., Alkemade, R., Braat, L., Hein, L., & Willemen, L. (2010). Challenges in integrating the concept of ecosystem services and values in landscape planning, management and decision making. Ecological Complexity, 7(3), 260–272. https://doi.org/10.1016/j.ecocom.2009.10.006

[16] De Maio, C., Fenza, G., Loia, V., Orciuoli, F., & Herrera-Viedma, E. (2016). A framework for context-aware heterogeneous group decision making in business processes. Knowledge-Based Systems, 102, 39–50. https://doi.org/10.1016/j.knosys.2016.03.019

[17] Fuentes-Doria, D. D., Toscano-Hernández, A. E., Malvaceda-Espinoza, E., Díaz Ballesteros, J. L., & Díaz Pertuz, L. (2020). Metodología de la investigación: Conceptos, herramientas y ejercicios prácticos en las ciencias administrativas y contables. In Metodología de la investigación: Conceptos, herramientas y ejercicios prácticos en las ciencias administrativas y contables. https://doi.org/10.18566/978-958-764-879-9

[18] Fuentes Doria, D. D., Toscano Hernández, A., & García Alarcón, H. (2020). Tendencias mundiales de la implementación de las Normas Internacionales de Información Financiera. Un estudio bibliométrico del período 1989 a 2018. Revista Perspectiva Empresarial. https://doi.org/10.16967/23898186.632

[19] Gerlick, J. A., & Liozu, S. M. (2020). Ethical and legal considerations of artificial intelligence and algorithmic decision-making in personalized pricing. Journal of Revenue and Pricing Management, 19(2), 85–98. https://doi.org/10.1057/s41272-019-00225-2

[20] González Arencibia, M., & Martínez Cardero, D. (2020). Dilemas éticos en el escenario de la inteligencia artificial. Economía y Sociedad. https://doi.org/10.15359/eys.25-57.5

[21] Grace, A. M., & Williams, S. O. (2016). Comparative analysis of neural network and fuzzy logic techniques in credit risk evaluation. International Journal of Intelligent Information Technologies, 12(1), 47–62. https://doi.org/10.4018/IJIIT.2016010103

[22] Hajek, P., & Henriques, R. (2017). Mining corporate annual reports for intelligent detection of financial statement fraud – A comparative study of machine learning methods. Knowledge-Based Systems, 128, 139–152. https://doi.org/10.1016/j.knosys.2017.05.001

[23] Kannan, D. (2018). Role of multiple stakeholders and the critical success factor theory for the sustainable supplier selection process. International Journal of Production Economics, 195, 391–418. https://doi.org/10.1016/j.ijpe.2017.02.020

[24] Kaparthi, S., & Bumblauskas, D. (2020). Designing predictive maintenance systems using decision tree-based machine learning techniques. International Journal of Quality and Reliability Management, 37(4), 659–686. https://doi.org/10.1108/IJQRM-04-2019-0131

[25] Kellner, F., Lienland, B., & Utz, S. (2019). An a posteriori decision support methodology for solving the multi-criteria supplier selection problem. European Journal of Operational Research, 272(2), 505–522. https://doi.org/10.1016/j.ejor.2018.06.044

[26] Kilani, Y. M. M., & Haikal, E. K. (2020). Exploitation of expert system in identifying organizational ethics through controlling decision making process. Management Science Letters, 10(7), 1417–1426. https://doi.org/10.5267/j.msl.2019.12.026

[27] Kraus, M., & Feuerriegel, S. (2017). Decision support from financial disclosures with deep neural networks and transfer learning. Decision Support Systems, 104, 38–48. https://doi.org/10.1016/j.dss.2017.10.001

[28] Latah, M., & Toker, L. (2019, March 1). Artificial intelligence enabled software-defined networking: A comprehensive overview. IET Networks, Vol. 8, pp. 79–99. https://doi.org/10.1049/iet-net.2018.5082

[29] Li, Y., Bai, C., & Reddy, C. K. (2016). A distributed ensemble approach for mining healthcare data under privacy constraints. Information Sciences, 330, 245–259. https://doi.org/10.1016/j.ins.2015.10.011

[30] Losada Camacho, E. H. (2019). ¿QUÉ IMPORTANCIA TIENE LA TOMA DE DECISIONES PARA EL DESARROLLO EMPRESARIAL? (Universidad Militar Nueva Granada.). https://doi.org/.1037//0033-2909.I26.1.78

[31] Moher, D., Liberati, A., Tetzlaff, J. and Altman, D. G. (2009). PRISMA 2009 Flow Diagram. The PRISMA statement,. PLoS Medicine. https://doi.org/10.1371/journal.pmed1000097.

[32] Neubert, M. J., & Montañez, G. D. (2020). Virtue as a framework for the design and use of artificial intelligence. Business Horizons, 63(2), 195–204. https://doi.org/10.1016/j.bushor.2019.11.001

[33] Paschen, U., Pitt, C., & Kietzmann, J. (2020a). Artificial intelligence: Building blocks and an innovation typology. Business Horizons, 63(2), 147–155. https://doi.org/10.1016/j.bushor.2019.10.004

[34] Paschen, U., Pitt, C., & Kietzmann, J. (2020b). Artificial intelligence: Building blocks and an innovation typology. Business Horizons, 63(2), 147–155. https://doi.org/10.1016/j.bushor.2019.10.004

[35] Paul, M., & Bussemaker, M. J. (2020). A web-based geographic interface system to support decision making for municipal solid waste management in England. Journal of Cleaner Production, 263, 121461. https://doi.org/10.1016/j.jclepro.2020.121461

[36] Pellicer-Martínez, F., & Martínez-Paz, J. M. (2016). Grey water footprint assessment at the river basin level: Accounting method and case study in the Segura River Basin, Spain. Ecological Indicators, 60, 1173–1183. https://doi.org/10.1016/j.ecolind.2015.08.032

[37] Pellicer, E., Sierra, L. A., & Yepes, V. (2016). Appraisal of infrastructure sustainability by graduate students using an active-learning method. Journal of Cleaner Production, 113, 884–896. https://doi.org/10.1016/j.jclepro.2015.11.010

[38] Perrault, R., Shoham, Y., Brynjolfsson, E., Clark, J., Etchemendy, J., Grosz Harvard, B., … Mishra, S. (2019). Artificial Intelligence Index 2019 Annual Report. Standford University - Human-Centered Artificial Intelligence.

[39] Pichuante Escaida, C. (2016). Visualización de grafos de co-autoría y de conocimiento basado en publicaciones científicas, implementada en VOSviewer. Retrieved from https://repositorio.uc.cl/handle/11534/21357

[40] Pipere, A., Veisson, M., & Salite, I. (2015). Developing Research in Teacher Education for Sustainability?: UN DESD via the Journal of Teacher Education for Sustainability. Journal of Teacher Education for Sustainability, 17(2), 5–43. https://doi.org/10.1515/jtes-2015-0009

[41] Rouhiainen, L. (2018). INTELIGENCIA ARTIFICIAL 101 COSAS QUE DEBES SABER HOY SOBRE NUESTRO FUTURO INTELIGENCIA ARTIFICIAL (Editorial). Retrieved from www.planetadelibros.com

[42] Syam, Niladri; Sharma, A. (2018). Waiting for a sales renaissance in the fourth industrial revolution: Machine learning and artificial intelligence in sales research and practice. Industrial Marketing Management, 69(November 2017), 135–146. https://doi.org/10.1016/j.indmarman.2017.12.019

[43] Toscano-Hernández, A. E., Fuentes-Doria, D. D., & Fajardo Pereira, M. A. (2019). Sostenibilidad universitaria con enfoque en la educación ambiental: redes de colaboración y clúster temáticos de la producción científica mundial. Panorama Económico, 27(1), 60–84. https://doi.org/https://doi.org/10.32997/2463-0470-vol.27-num.1-2019-2617

[44] Tranfield, D., Denyer, D., & Smart, P. (2003). Towards a Methodology for Developing Evidence-Informed Management Knowledge by Means of Systematic Review. British Journal of Management. https://doi.org/10.1111/1467-8551.00375

[45] UNESCO. (2021). Elaboration of a Recommendation on the ethics of artificial intelligence. Retrieved March 12, 2021, from Unesdoc Digital Library website: https://unesdoc.unesco.org/ark:/48223/pf0000367823

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