¿Misma tecnología, diferentes problemas? Un análisis bibliométrico comparativo de la inteligencia artificial generativa en educación, negocios, comunicación y gobernanza
Barra lateral del artículo
Contenido principal del artículo
Resumen
La rápida difusión de la inteligencia artificial generativa (GenAI) ha planteado interrogantes acerca de si este fenómeno tecnológico se problematiza de manera diferente en distintos ámbitos institucionales. Este estudio compara la producción académica sobre GenAI en educación, investigación empresarial y organizacional, comunicación y medios, y gobernanza pública, derecho y políticas públicas. Se construyeron cuatro corpus específicos por ámbito institucional a partir de la Web of Science Core Collection para el período 2023–2026, con 6.132, 1.901, 300 y 444 documentos, respectivamente. Se realizaron análisis bibliométricos de desempeño, mapeo temático y análisis de coocurrencia de palabras clave de los autores mediante Bibliometrix/Biblioshiny y VOSviewer. Se identificaron configuraciones temáticas diferenciadas entre los cuatro ámbitos. La educación prioriza el aprendizaje, la evaluación, la alfabetización en IA y la integridad académica. La investigación empresarial enfatiza la innovación, la adopción tecnológica, la toma de decisiones y la capacidad organizacional. La producción académica en comunicación se centra en el periodismo, la confianza, la desinformación y la credibilidad informativa, mientras que la investigación sobre gobernanza destaca la regulación, la rendición de cuentas, los derechos y la responsabilidad jurídica. Estos patrones no se tratan como medidas directas de las lógicas institucionales. En cambio, el estudio desarrolla un mapeo teóricamente informado que relaciona las configuraciones temáticas empíricas con problemas institucionales y prioridades normativas. La GenAI no se problematiza de manera uniforme; su relevancia institucional está configurada por criterios divergentes de conocimiento legítimo, valor organizacional, autoridad profesional y rendición de cuentas pública. El estudio propone un marco comparativo para examinar la GenAI como un fenómeno sociotécnico situado institucionalmente, manteniendo una clara distinción entre la evidencia bibliométrica y la interpretación institucional.
Descargas
Detalles del artículo
Esta obra está bajo una licencia internacional Creative Commons Atribución-NoComercial 4.0.
Este artículo se distribuye bajo Creative Commons Atribución-NoComercial 4.0 (CC BY-NC 4.0) .
Citas
Al Naqbi, H., Bahroun, Z., & Ahmed, V. (2024). Enhancing work productivity through generative artificial intelligence: A comprehensive literature review. Sustainability,16(3), Article 1166. https://doi.org/10.3390/su16031166
Abbott, A. (1988). The system of professions: An essay on the division of expert labor. University of Chicago Press.
Altinay, F., Shadiev, R., Dagli, G., Shadiev, N., Muhamedov, A., Bahçelerli, N., & Altinay, Z. (2025). Evaluation of the development of social entrepreneurship skills in teaching using artificial intelligence. BRAIN. Broad Research in Artificial Intelligence and Neuroscience, 16(3), 42–56. https://doi.org/10.70594/brain/16.3/4
Anthony, C. (2021). When knowledge work and analytical technologies collide: The practices and consequences of black boxing algorithmic technologies. Administrative Science Quarterly, 66(4), 1173– 1212. https://doi.org/10.1177/00018392211016755
Anthony, C., Bechky, B. A., & Fayard, A.-L. (2023). “Collaborating” with AI: Taking a system view to explore the future of work. Organization Science, 34(5), 1672–1694. https://doi.org/10.1287/orsc.2022.1651
Aria, M. & Cuccurullo, C. (2017) bibliometrix: An R-tool for comprehensive science mapping analysis, Journal of Informetrics, 11(4), 959-975, https://doi.org/10.1016/j.joi.2017.08.007
Avnon, N., Kotliar, D. M., & Rivnai-Bahir, S. (2024). Contextualizing the ethics of algorithms: A socio-professional approach. New Media & Society, 26(10), 5962–5982.
Besharov, M. L., & Smith, W. K. (2014). Multiple institutional logics in organizations: Explaining their varied nature and implications. Academy of Management Review ,39(3), 364– 381. https://doi.org/10.5465/amr.2011.0431
Cho, W., Fan, Z., & Zheng, Y. (2026). Artificial intelligence across the public administration system: Public values, institutions, and bureaucrats in the algorithmic age. Global Public Policy and Governance, 6, 103–123. https://doi.org/10.1007/s43508-026-00151-1
Dodds, T., Ngai Yeung, W., Mellado, C., & De Lima-Santos, M. F. (2026). On controlled change: Generative AI’s impact on professional authority in journalism. Journalism Studies, 1– 18. https://doi.org/10.1080/1461670X.2026.2616634
Dwivedi, Y. K., Kshetri, N., Hughes, L., Slade, E. L., Jeyaraj, A., Kar, A. K., ... & Wright, R. (2023). “So what if ChatGPT wrote it?” Multidisciplinary perspectives on opportunities, challenges and implications of generative conversational AI for research, practice and policy. International Journal of Information Management, 71, 102642. https://doi.org/10.1016/j.ijinfomgt.2023.102642
Dwivedi, R., & Elluri, L. (2024). Exploring generative artificial intelligence research: A bibliometric analysis approach. IEEE Access, 12, 119884–119902. https://doi.org/10.1109/ACCESS.2024.3450629
Feuerriegel, S., Hartmann, J., Janiesch, C., & Zschech, P. (2024). Generative AI. Business & Information Systems Engineering, 66(1), 111–126. https://doi.org/10.1007/s12599-023-00834-7
Friedland, R., & Alford, R. R. (1991). Bringing society back in: Symbols, practices, and institutional contradictions. In W. W. Powell & P. J. DiMaggio (Eds.), The new institutionalism in organizational analysis (pp. 232–263). University of Chicago Press.
Freidson, E. (2001). Professionalism: The third logic: On the practice of knowledge. University of Chicago Press.
Gërguri, D., Sorrells, J., Anghel, G., Frías, M., Matei, A., Çipuri, R., Lechpammer, S., Arık, E., & Lovrenčič Držanič, I. (2026). Diffusion, logics, and boundaries: How journalists in marginalized media markets negotiate AI in newsrooms. Media and Communication, 14, Article 12438. https://doi.org/10.17645/mac.12438
Greenwood, R., Raynard, M., Kodeih, F., Micelotta, E. R., & Lounsbury, M. (2011). Institutional complexity and organizational responses. Academy of Management Annals, 5(1), 317– 371. https://doi.org/10.1080/19416520.2011.590299
Guzman, A. L., & Lewis, S. C. (2024). What generative AI means for the media industries, and why it matters to study the collective consequences for advertising, journalism, and public relations. Emerging Media, 2(3), 287–294. https://doi.org/10.1177/27523543241289239
Introna, L. D. (2016). Algorithms, governance, and governmentality: On governing academic writing. Science, Technology, & Human Values, 41(1), 17–49. https://doi.org/10.1177/0162243915587360
Janssen, M., Brous, P., Estevez, E., Barbosa, L. S., & Janowski, T. (2020). Data governance: Organizing data for trustworthy artificial intelligence. Government Information Quarterly, 37(3), Article 101493. https://doi.org/10.1016/j.giq.2020.101493
Leonardi, P. M., & Barley, S. R. (2010). What’s under construction here? Social action, materiality, and power in constructivist studies of technology and organizing. Academy of Management Annals, 4(1), 1–51. https://doi.org/10.1080/19416521003654160
Manolică, A., Bucătaru, O., Roman, C., Bertea, P., & Jelea, A. (2025). Adoption of AI in digital marketing: Comparing Gen Z and Gen Y through the technology acceptance model. BRAIN. Broad Research in Artificial Intelligence and Neuroscience, 16(3), 102–128. https://doi.org/10.70594/brain/16.3/9
Meijer, A., Lorenz, L. C., & Wessels, M. (2021). Algorithmization of bureaucratic organizations: Using a practice lens to study how context shapes predictive policing systems. Public Administration Review, 81(5), 837–846. https://doi.org/10.1111/puar.13391
Orlikowski, W. J., & Scott, S. V. (2008). Sociomateriality: Challenging the separation of technology, work and organization. Academy of Management Annals, 2(1), 433– 474. https://doi.org/10.1080/19416520802211644
Pachidi, S., Berends, H., Faraj, S., & Huysman, M. (2021). Make way for the algorithms: Symbolic actions and changeinaregimeofknowing.OrganizationScience, 32(1), 18–41. https://doi.org/10.1287/orsc.2020.1377
Pătruț, M. (2026). The Post-ChatGPT Research Landscape of Generative Artificial Intelligence in Higher Education (2023–2026): A Bibliometric Analysis. BRAIN. Broad Research In Artificial Intelligence And Neuroscience, 17(1), 156-169. doi:http://dx.doi.org/10.70594/brain/17.1/12
Purdy, J. M., Ansari, S., & Gray, B. (2019). Are logics enough? Framing as an alternative tool for understanding institutional meaning making. Journal of Management Inquiry, 28(4), 409– 419. https://doi.org/10.1177/1056492617724233
Raisch, S., & Krakowski, S. (2021). Artificial intelligence and management: The automation–augmentation paradox. Academy of Management Review, 46(1), 192–210. https://doi.org/10.5465/amr.2018.0072
Seo, M.-G., & Creed, W. E. D. (2002). Institutional contradictions, praxis, and institutional change: A dialectical perspective. Academy of Management Review, 27(2), 222–247. https://doi.org/10.5465/amr.2002.6588004
Thornton, P. H., & Ocasio, W. (1999). Institutional logics and the historical contingency of power in organizations: Executive succession in the higher education publishing industry, 1958–1990. American Journal of Sociology, 105(3), 801–843. https://doi.org/10.1086/210361
Thornton, P. H., Ocasio, W., & Lounsbury, M. (2012). The institutional logics perspective: A new approach to culture, structure, and process. Oxford University Press.
Van Eck, N. J., & 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., Queiroz, M. M., Randhawa, K., & Gupta, G. (2025). Generative artificial intelligence and the challenges to adding value ethically. Technovation. https://www.sciencedirect.com/science/article/pii/S0166497225000677