Cartografía bibliométrica de la hiperpersonalización publicitaria con inteligencia artificial
DOI:
https://doi.org/10.62269/cavcaa.63Palabras clave:
bibliometría, ética algorítmica, hiperpersonalización, inteligencia artificial, publicidad digital, segmentación avanzadaResumen
Este estudio examina la evolución temática de la hiperpersonalización en la publicidad digital basada en inteligencia artificial (IA) entre 2010 y 2024, en un contexto de creciente automatización del marketing y limitada atención a los dilemas éticos y regulatorios. Se aplicó una revisión sistemática con enfoque bibliométrico conforme a PRISMA-S, analizando 134 artículos indexados en Scopus y Web of Science mediante Bibliometrix y Biblioshiny, con métricas de coocurrencia, mapas estratégicos y redes de colaboración. Los resultados revelan un crecimiento sostenido del campo, centrado en IA, marketing digital y sistemas de recomendación, junto con una baja centralidad y densidad conceptual de los enfoques ético-regulatorios. Se identifican brechas críticas vinculadas con la transparencia algorítmica, los sesgos de segmentación y la exclusión digital, además de una débil cooperación internacional e interdisciplinaria. El
estudio concluye que la agenda investigadora debe orientarse hacia marcos éticos integradores y métricas de auditoría algorítmica. Su principal contribución radica en aportar evidencia empírica y metodológica para consolidar una perspectiva ética y socialmente responsable de la hiperpersonalización con IA
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Aria, Massimo; & Cuccurullo, Corrado. (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
Bhatt, Vimal Kamleshkumar. (2021). Assessing the significance and impact of artificial intelligence and machine learning in placement of advertisements. IEEE Xplore. https://doi.org/10.1109/ICTMOD52902.2021.9739228
Bornmann, Lutz. (2012). How to analyse percentile impact data meaningfully in bibliometrics: The statistical analysis of distributions, percentile rank classes and top-cited papers (preprint). arXiv. https://doi.org/10.48550/arXiv.1206.1741
Brkan, Maja.. (2021). Do algorithms rule the world? Algorithmic decision-making and data protection in the framework of the GDPR and beyond. International Journal of Law and Information Technology, 29(2), 91–121. https://doi.org/10.1093/ijlit/eay017
Cao, Lanlan. (2021). Artificial intelligence in retail: Applications and value creation logics. International Journal of Retail y Distribution Management, 49(7), 958–976. https://doi.org/10.1108/IJRDM-09-2020-0350
D’Ignazio, Catherine; & Klein, Lauren F. (2020). Data feminism. MIT Press.
Donthu, Naveen; Kumar, Satish; Mukherjee, Debmalya; Pandey, Nitesh; & Lim, Weng Marc. (2021). How to conduct a bibliometric analysis: An overview and guidelines. Journal of Business Research, 133, 285–296. https://doi.org/10.1016/j.jbusres.2021.04.070
Gunkel, David J. (2020). How to Survive a Robot Invasion: Rights, Responsibility, and AI. Routledge. https://doi.org/10.4324/9780429427862.
Li, Chia-Yi; Fang, Yuan-Hsun; & Chiang, Yi-Hsien. (2023). (2023). Can AI chatbots help retain customers? An integrative perspective using affordance theory and service-domain logic. Technological Forecasting and Social Change, 197, 122921. https://doi.org/10.1016/j.techfore.2023.122921
Malik, Ashish; Budhwar, Pawan; Patel, Charmi; & Srikanth, N. R. (2022). May the bots be with you! Delivering HR cost-effectiveness and individualised employee experiences in an MNE. The International Journal of Human Resource Management, 33(6), 1148–1178. https://doi.org/10.1080/09585192.2020.1859582
Mhaidli, Abraham Hani; & Schaub, Florian. (2021). Identifying manipulative advertising techniques in XR through scenario construction. In CHI Conference on Human Factors in Computing Systems (pp. 1–18). ACM. https://doi.org/10.1145/3411764.3445253
Micu, Adrian; Capatina, Alexandru; Cristea, Dragos Sebastian; Munteanu, Dan; Micu, Angela-Eliza; & Sarpe, Daniela Ancuta. (2022). Assessing an on-site customer profiling and hyper-personalization system prototype based on a deep learning approach. Technological Forecasting y Social Change, 174, 121289. https://doi.org/10.1016/j.techfore.2021.121289
Nikolajeva, Anna; & Teilans, Artis. (2021). Machine learning technology overview in terms of digital marketing and personalization. In OCMS-ECMS 2021 Proceedings, 0125. https://doi.org/10.7148/2021-0125
Noble, Safiya Umoja. (2018). Algorithms of oppression: How search engines reinforce racism. NYU Press.
OpenAI. (2023). GPT-4 Technical Report. arXiv preprint arXiv:2303.08774. https://doi.org/10.48550/arXiv.2303.08774
Page, Matthew J.; McKenzie, Joanne E.; Bossuyt, Patrick M.; Boutron, Isabelle; Hoffmann, Tammy C.; Mulrow, Cynthia D.; Shamseer, Larissa; Tetzlaff, Jennifer M.; Akl, Elie A.; Brennan, Sue E.; Chou, Roger; Glanville, Julie; Grimshaw, Jeremy M.; Hróbjartsson, Asbjørn; Lalu, Manoj M.; Li, Tianjing; Loder, Elizabeth W.; Mayo-Wilson, Evan; McDonald, Steve; McGuinness, Luke A.; Stewart, Lesley A.; Thomas, James; Tricco, Andrea C.; Welch, Vivian A.; Whiting, Penny; & Moher, David. (2021). The PRISMA 2020 statement: An updated guideline for reporting systematic reviews. BMJ, 372, n71. https://doi.org/10.1136/bmj.n71
Rethlefsen, Melissa L.; Kirtley, Shona; Waffenschmidt, Siw; Ayala, Ana Patricia; Moher, David; Page, Matthew J.; & Koffel, Jonathan B. (2021). PRISMA-S: An extension to the PRISMA statement for reporting literature searches in systematic reviews. Systematic Reviews, 10(1), 39. https://doi.org/10.1186/s13643-020-01542-z
Sadowski, Jathan. (2023). Total life insurance: Logics of anticipatory control and actuarial governance in insurance technology. Social Studies of Science, 54(2), 231-256. https://doi.org/10.1177/03063127231186437
