Integration of artificial intelligence with big data for decision making in companies: a bibliometric study
DOI:
https://doi.org/10.5281/zenodo.14783686Keywords:
artificial intelligence, decision making, bibliometricsAbstract
The objective of this research was to carry out an analysis of the bibliometric network in Scopus regarding the integration of artificial intelligence with big data for business decision-making, from 2014 to 2024. To do this, quantitative techniques and a bibliometric method were used. From the Scopus database, 126 documents were selected. It was identified that, between 2020 and 2024, the global volume of published data increased by 53.8 %. According to the data, scientific production in the United States increased at a faster rate (34.2%) compared to In addition, the scientific production of the United States presented the highest growth (34.2%) compared to other countries. Likewise, the most cited author was Kumar, A., with 205 citations, and the source with the highest number of publications was the Journal of Industrial Engineering and Engineering Management, with 11 articles. The findings show a significant advance in research on artificial intelligence and big data in the business field, reflected in thematic diversification, institutional support and collaboration between authors. It is concluded that this bibliometric study provides a basis for future research, highlighting the importance of delving into data processing and analytics systems, as well as the technological resources necessary to develop solutions that optimize business decision-making.
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Azevedo, A., & Almeida, A. H. (2021). Grasp the challenge of digital transition in SMEs—A training course geared towards decision-makers. Education Sciences, 11(4), 151. https://doi.org/10.3390/educsci11040151
Carrillo Barragán, C. E., Arrieta Salinas, H., Alvarado Rosado, S. M., & Gutierrez Bastidas, J. O. (2024). El Big Data y la Business Intelligence (BI) en la toma de decisiones estratégicas en las empresas. Revista Social Fronteriza, 4(2), e42209. https://doi.org/10.59814/resofro.2024.4(2)209
Caló, L. (2022). Métricas de impacto y evaluación de la ciencia. Revista peruana de medicina experimental y salud pública, 39(2), 236-240. https://doi.org/10.17843/rpmesp.2022.392.11171
Esteva, A., Chou, K., Yeung, S., Naik, N., Madani, A., Mottaghi, A., ... & Dean, J. (2021). Deep learning-enabled medical computer vision. Nature Medicine, 27(1), 44–50. https://doi.org/10.1038/s41591-020-01182-0
Flores-Fernández, C., & Aguilera-Eguía, R. (2020). Indicadores bibliométricos y su importancia en la investigación clínica. ¿Por qué conocerlos? Revista de la sociedad española del dolor, 26(5), 315-316. https://dx.doi.org/10.20986/resed.2018.3659/2018
Chacón-Rivadenerira, K., Morales-Maure, L.,&García-Marimón, O. (2024). Trends in Research on Didactic Knowledge and Technology Integration in Mathematics Education: A Bibliometric Study. Journal of Research in Mathematics Education, 13(3), 220-244. https://doi.org/10.17583/redimat.15107
Chen, CT., Khan, A. y Chen, SC. Modelado del impacto de BDA-AI en la innovación sostenible, la ambidextría y el desempeño ambiental. J Big Data 11, 124 (2024).
https://doi.org/10.1186/s40537-024-00995-6
Chen, J., Zhang, C., & Zhou, J. (2021). Big data integration in business intelligence: A survey. Journal of Business Research, 124, 14–25. https://doi.org/10.1016/j.jbusres.2020.11.038
Gandomi, A., & Haider, M. (2021). Beyond the hype: Big data concepts, methods, and analytics. International Journal of Information Management, 56, 102287.https://doi.org/10.1016/j.ijinfomgt.2014.10.007
García, L., Fernández, A., y Bécquer, A. (2021). Análisis Bibliométrico de la Producción Científica 2001-2020. Revista electrónica Cuba: medio ambiente y desarrollo, 21(40), 1–9. https://cmad.ama.cu/index.php/cmad/article/view/297
Gonçalves, S., Ventura, JB., Rua, OL., Dias, R., & Galvão, R. (2024). Big Data como paradigma emergente en la gestión de las organizaciones: un análisis bibliométrico. Journal of Ecohumanism, 3 (7), 2699–2721. https://doi.org/10.62754/joe.v3i7.4412
Guo, C. y Zhang, X. (2024). Predicción inteligente de la satisfacción del cliente en el comercio electrónico transfronterizo mediante incrustaciones de aprendizaje profundo. Acceso IEEE , 12 https://doi.org/10.1109/ACCESO.2024.3494776
Jin, S. J., Abdullah, A. H., Mokhtar, M., & Abdul Kohar, U. H. (2022). The Potential of Big Data Application in Mathematics Education in Malaysia. Sustainability, 14(21), 13725. https://doi.org/10.3390/su142113725
Kumar, A., & Sivarajah, U. (2021). An integrated artificial intelligence framework for knowledge creation and B2B marketing rational decision making for improving firm performance. Industrial marketing management, 92, 178-189. https://doi.org/10.1016/j.indmarman.2020.12.001
León García, O. A. (2023). Impacto de las capacidades de análisis de big data en la innovación empresarial. Ingeniería y Competitividad, 25(2), e12611. https://doi.org/10.25100/iyc.v25i2.12611
Nguyen, T., Ding, M., & Yang, Z. (2020). AI and big data: Key enablers for digital transformation. Journal of Strategic Information Systems, 29(4), 101–121. https://doi.org/10.1016/j.jsis.2020.101640
Ojeda, A., Valera, J., Medina, E., Samadian, H., & Padilla, R. (2024). AI implementation in big data: Shaping data analysis for business decisions. Issues in Information Systems, 25(4). https://doi.org/10.48009/4_iis_2024_113
Ribeiro, R., & Aroni, P. (2019). Standardization, ethics and biometric indicators in scientific publication: integrative Review. Revista Brasileira de Enfermagem REBEn, 72(6), 1723-9. https://doi.org/10.1590/0034-7167-2018-0283
Salinas, K. & García, A. (2022). Bibliometrics, a useful tool within the field of research. Journal of Basic and Applied Psychology Research, 3(6), 10-17. https://doi.org/10.29057/jbapr.v3i6.6829
Sanz, J. (2022). Bibliometría: origen y evolución. Hospital a Domicilio, 6(3), 105-107. https://doi.org/10.22585/hospdomic.v6i3.168
Sharma, A., Mithas, S., & Kankanhalli, A. (2020). Transforming decision-making processes through big data and AI. MIS Quarterly Executive, 19(2), 83–101. https://doi.org/10.17705/2msqe.00024
Shrestha, Y. R., Krishna, V., & von Krogh, G. (2021). Augmenting organizational decision-making with deep learning algorithms: Principles, promises, and challenges. Journal of Business Research, 123, 588-603. https://doi.org/10.1016/j.jbusres.2020.09.068
Shkarupylo, V., Alsayaydeh, J. A. J., Yusof, M. F. B., Oliinyk, A., Artemchuk, V., & Herawan, S. G. (2024). Exploring the potential network vulnerabilities in the smart manufacturing process of Industry 5.0 via the use of machine learning methods. IEEE Access. https://doi.org/10.1109/ACCESS.2024.3474861
Sumbal, M. S., Amber, Q., Tariq, A., Raziq, M. M., & Tsui, E. (2024). Wind of change: how ChatGPT and big data can reshape the knowledge management paradigm?. Industrial Management & Data Systems, 124(9), 2736-2757. https://DOI.org/10.1108/IMDS-06-2023-0360
Tomás-Gorríz, V., & Tomás-Castera, V. (2018). La bibliometría en la evaluación de la actividad científica. Hospital a Domicilio, 2(4), 145-163. https://doi.org/10.22585/hospdomic.v2i4.51
Tong, D., & Tian, G. (2023). Intelligent financial decision support system based on big data. Journal of Intelligent Systems, 32(1), 20220320. https://doi.org/10.1515/jisys-2022-0320
Uc Castillo, J. L., Marín Celestino, A. E., Martínez Cruz, D. A., Tuxpan Vargas, J., Ramos Leal, J. A., & Morán Ramírez, J. (2024). A systematic review of Machine Learning and Deep Learning approaches in Mexico: challenges and opportunities. Frontiers in Artificial Intelligence, 7, 1479855. https://doi.org/10.3389/frai.2024.1479855
Van Eck, N., & Waltman, L. (2010). Software survey: VOSviewer, a computer program for bibliometric mapping. scientometrics, 84(2), 523-538. https://doi.org/10.1007/s11192-009-0146-3
Wamba, S. F., Akter, S., & Edwards, A. (2020). Big data analytics and business performance: A meta-analysis. Journal of Business Research, 119, 62–71. https://doi.org/10.1016/j.jbusres.2020.09.068
Zhang, W., Li, X., & Wang, J. (2020). Big Data and AI for market trend prediction. Journal of Financial Analysis, 8(4), 210-228. http://dx.doi.org/10.2139/ssrn.4980346
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