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

Submitted December 18, 2025

Published 2025-12-23

Artículos

Vol. 6 (2026): DÉconomía

Literature review and bibliometric analysis of artificial intelligence for management and decision-making in economics and related sciences

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

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

PDF (Español (España))

References

DOI: 10.48204/2710-7744.9047

Published: 2025-12-23

How to Cite

Rincon Soto, I. B., & Palacios Serna, L. I. (2025). Literature review and bibliometric analysis of artificial intelligence for management and decision-making in economics and related sciences. D´Economía, 6, 50–82. https://doi.org/10.48204/2710-7744.9047

Abstract

Decision-making in executive management is an issue of relevance for the efficient administration of institutions. The aim of this research was to analyze the scientific production in this variable using artificial intelligence.

The methodology was based on a systematic literature review using databases such as Wos and Scopus and VOSviewer to facilitate visualization in bibliometric networks, considering it the novel contribution in this field of study. The results yielded 203 identified publications, which expose the evolution, main articles, and topics as well as the authors. It concluded with the contribution of identifying thematic clusters and new thematic lines in this area of research, given the boom in the interest in investigating the way in which artificial intelligence is integrated into decision-making systems.

References

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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[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

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[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

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[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

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[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.

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[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

[46] 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

[47] 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

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