Inteligencia artificial y gestión estratégica de redes sociales en los negocios: una revisión bibliométrica

Artificial intelligence and social media strategic management in business: a bibliometric review

Contenido principal del artículo

Autores/as

Introducción: La inteligencia artificial (IA) está transformando la gestión estratégica de redes sociales en el ámbito empresarial, al permitir nuevas formas de análisis, interacción y toma de decisiones basadas en datos. La evolución de este campo requiere comprender sus tendencias científicas, principales enfoques y desafíos emergentes. Objetivo: Analizar la producción científica sobre la aplicación de la inteligencia artificial en la gestión estratégica de redes sociales empresariales, identificando patrones de crecimiento, colaboración científica y evolución temática. Metodología: Se realizó un estudio bibliométrico cuantitativo sobre 1545 artículos indexados en Scopus. La producción científica fue analizada mediante VOSviewer y Bibliometrix para evaluar redes de colaboración, productividad autoral, estructura temática y evolución del conocimiento. Además, se aplicaron las leyes bibliométricas de Bradford y Lotka para caracterizar la distribución de fuentes y autores. Resultados: Los hallazgos evidenciaron un crecimiento exponencial de publicaciones desde 2020, alcanzando un máximo histórico en 2025. Estados Unidos, China e India lideran la producción científica del campo. Las principales líneas temáticas se concentran en minería de datos, análisis de sentimientos y procesamiento de información digital. Asimismo, se identificó una elevada fragmentación autoral y predominio de referencias recientes, características propias de un área científica en proceso de consolidación. Conclusiones: La investigación sobre inteligencia artificial aplicada a la gestión estratégica de redes sociales empresariales presenta una expansión sostenida, aunque requiere fortalecer enfoques éticos y de privacidad. El desarrollo futuro del campo debe orientarse hacia modelos de decisión basados en datos que promuevan interacciones digitales sostenibles, transparentes y auténticas.
Background: Artificial Intelligence (AI) is reshaping strategic social media management within the corporate sector by enabling advanced data-driven analysis, engagement, and decision-making processes. The rapid evolution of this field necessitates a comprehensive understanding of its scientific trends, dominant frameworks, and emerging challenges. Objective: This study aims to analyze the scientific output regarding the application of AI in corporate strategic social media management, identifying growth patterns, collaborative networks, and thematic shifts. Methodology: A quantitative bibliometric analysis was conducted on 1,545 Scopus-indexed articles. Data were processed using VOSviewer and Bibliometrix to map collaboration networks, author productivity, thematic structures, and the evolution of knowledge. Furthermore, Bradford’s and Lotka’s bibliometric laws were applied to characterize the distributional dynamics of journals and authors. Results: Findings reveal exponential growth in publications since 2020, reaching a historical peak in 2025. The United States, China, and India emerge as the leading contributors to the field. Primary research streams center on data mining, sentiment analysis, and digital information processing. Additionally, the results indicate significant author fragmentation and a high prevalence of recent citations, highlighting a field in the process of consolidation. Conclusions: While research on AI-driven strategic social media management is expanding rapidly, there is a pressing need to strengthen ethical and privacy frameworks. Future research should prioritize data-driven decision models that foster sustainable, transparent, and authentic digital interactions.

Detalles del artículo

Cómo citar
Valdiviezo Sir, V. M., Carranza Guevara, R., & Merino Cava, L. G. (2026). Inteligencia artificial y gestión estratégica de redes sociales en los negocios: una revisión bibliométrica. Impulso, Revista De Administración, 6(15), 1-13. https://doi.org/10.59659/impulso.v.6i15.364
Número
Vol. 6 Núm. 15 (2026): Volumen 6 | Número 15 | Julio – Septiembre - 2026
Sección
Artículos de Investigación
Creative Commons License

Esta obra está bajo una licencia internacional Creative Commons Atribución-NoComercial-CompartirIgual 4.0.

Cómo citar

Valdiviezo Sir, V. M., Carranza Guevara, R., & Merino Cava, L. G. (2026). Inteligencia artificial y gestión estratégica de redes sociales en los negocios: una revisión bibliométrica. Impulso, Revista De Administración, 6(15), 1-13. https://doi.org/10.59659/impulso.v.6i15.364

Referencias

Ahani, A., Rahim, N. Z. A., y Nilashi, M. (2017). Forecasting social CRM adoption in SMEs: A combined SEM-neural network method. Computers in Human Behavior, 75, 560–578. https://doi.org/10.1016/j.chb.2017.05.032

Al-Ababneh, H. A., y Al Muala, A. M. (2025). Electronic Commerce and Customer Relationship Management: Integration of Technologies into Marketing Strategy. International Review of Management and Marketing, 15(6), 108-117. https://doi.org/10.32479/irmm.20970

Amin, M. H., Ali, H., y Mohamed, E. K. A. (2024). Corporate social responsibility disclosure on Twitter: Signalling or greenwashing? Evidence from the UK. International Journal of Finance and Economics, 29(2), 1745-1761. https://doi.org/10.1002/ijfe.2762

Apaza-Chullunquia, M., Villoslada-Cosinga, M., Torres-Vasquez, A., Erazo-Ordoñez, Y., y Macha-Huamán, R. (2025). Digital Marketing Strategies in Loyalty: Satisfaction as a Mediating Element with Bank Clients, Lima—Peru. IBIMA Business Review. https://doi.org/10.5171/2025.440552

Bajrami, S. M., Baruti, B. H., y Arifaj, A. H. (2025). Utilising artificial intelligence in digital marketing strategy: Opportunities and challenges for marketers. Corporate and Business Strategy Review, 6(2), 130-136. https://doi.org/10.22495/cbsrv6i2art13

Ballestar, M. T., Martín-Llaguno, M., y Sainz, J. (2022). An artificial intelligence analysis of climate-change influencers’ marketing on Twitter. Psychology and Marketing, 39(12), 2273-2283. https://doi.org/10.1002/mar.21735

Barney, J. (1991). Firm resources and sustained competitive advantage. Journal of Management, 17(1), 99–120. https://doi.org/10.1177/014920639101700108

Chen, Y. (2023). Comparing content marketing strategies of digital brands using machine learning. Humanities and Social Sciences Communications, 10(1). https://doi.org/10.1057/s41599-023-01544-x

Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319–340. https://doi.org/10.2307/249008

Denigris, C. (2026). Fighting market abuse in the age of AI: Five strategic shifts every financial services executive must address. Journal of Financial Compliance, 9(4), 380-391. https://doi.org/10.69554/TKFJ7658

Deshmane, A., y Barriola, X. (2025). Frame by Fame: Content Creation on Short Video-Format Platforms. Manufacturing and Service Operations Management, 27(2), 479-495. https://doi.org/10.1287/msom.2021.0332

Duralia, O., Ogrean, C., Ţichindelean, M., y Ţichindelean, M. (2025). Decoding the Personalization-Privacy Paradox: From Thematic Scholarly Clusters to Practical Insights. Studies in Business and Economics, 20(2), 70-97. https://doi.org/10.2478/sbe-2025-0025

Gallastegui, L. M. G., Forradellas, R. R., y Alonso, S. L. N. (2024). Applying advanced sentiment analysis for strategic marketing insights: A case study of BBVA using machine learning techniques. Innovative Marketing, 20(2), 100-115. https://doi.org/10.21511/im.20(2).2024.09

Gao, T. (2026). Mobile network-enabled visualisation framework for real-time disaster science communication and response coordination. International Journal of Mobile Network Design and Innovation, 12(1), 42-56. https://doi.org/10.1504/IJMNDI.2026.152395

Herrera-Viedma, E., Martínez, M. A., y Herrera, M. (2016). Bibliometric tools for discovering information in database. In H. Fujita, M. Ali, A. Selamat, J. Sasaki, & M. Kurematsu (Eds.), Lecture Notes in Computer Science: Vol. 9799. Trends in applied knowledge-based systems and data science (pp. 193–203). Springer. https://doi.org/10.1007/978-3-319-42007-3_17

Ingard, A., Praymee, K., Chandraramya, L., Paprach, T., Suebsahakarn, S., y Theamtad, N. (2025). Consumer Data Utilization for Digital Content Marketing Among Small Accommodation Entrepreneurs in Thailand’s Lower Central Provinces. International Journal of Operations and Quantitative Management, 31(2), 376-395. https://so09.tci-thaijo.org/index.php/PMR/article/view/8063

Jaiswal, R., Gupta, S., y Tiwari, A. K. (2026). Environmental, social and governance-type investing: A multi-stakeholder machine learning analysis. Management Decision, 64(4), 1599-1638. https://doi.org/10.1108/MD-04-2024-0930

Kaewrat, N., y Suwannarat, K. (2024). Influential Factors in the Decision to Pursue a Bachelor’s Degree in Computer Engineering and Artificial Intelligence at the School of Engineering and Technology, Walailak University. Suranaree Journal of Social Science, 18(2). https://doi.org/10.55766/sjss-2-2024-273145

Kan, T., y Ku, E. C. S. (2026). Enhancing joint decision-making and innovation: The impact of ChatGPT on like-minded itineraries with unfamiliar travel companions. Journal of Research in Interactive Marketing, 20(5), 675-693. https://doi.org/10.1108/JRIM-03-2025-0107

Kar, A. K., Choudhary, S. K., y Singh, V. (2022). How can artificial intelligence impact sustainability: A systematic literature review. Journal of Cleaner Production, 358, 131999. https://doi.org/10.1016/j.jclepro.2022.134120

Klonek, F., y Parker, S. (2025). Does AI at Work Increase Stress? Text Mining Social Media About Human–AI Team Processes and AI Control. Journal of Organizational Behavior. https://doi.org/10.1002/job.70000

Luo, L., Li, Y., Wang, X., Jin, X., y Qin, Z. (2024). Supply Chain Vulnerability in Prefabricated Building Projects and Digital Mitigation Technologies. IEEE Transactions on Engineering Management, 71, 10686-10698. https://doi.org/10.1109/TEM.2023.3272585

Murár, P., Kubovics, M., y Jurišová, V. (2024). The impact of brand-voice integration and artificial intelligence on social media marketing. Communication Today, 15(1), 50-63. https://doi.org/10.34135/COMMUNICATIONTODAY.2024.VOL.15.NO.1.4

Nilashi, M., Ahani, A., Esfahani, M. D., Yadollahi, M., Albashrawi, M. A., Musleh, A. A., y Salihin Zakaria, N. (2019). Social media for sustainable business: A bibliometric analysis. Journal of Cleaner Production, 232, 1233–1252. https://doi.org/10.1016/j.jclepro.2019.05.210

Noto, G., Barraco, M., De Domenico, F., y Barresi, G. (2026). Advancing performance measurement in public healthcare organizations through artificial intelligence: Evidence from action research. Review of Managerial Science. https://doi.org/10.1007/s11846-026-01050-9

Park, K. K.-C., Kim, J. M., y Mariani, M. (2025). The dual role of video-based eWOM in an online digital environment. Journal of Business Research, 201. https://doi.org/10.1016/j.jbusres.2025.115734

Powell, L. M., Rebman, C. M., Dempsey, A., y Myers, C. J. (2021). Using sentiment analysis to measure emotional toxicity of social media data during the COVID pandemic. Issues in Information Systems, 22(1), 200-214. https://doi.org/10.48009/1_iis_2021_200-214

Raju, P. S., Patra, S. K., y Patra, B. K. (2024). Evaluation of Machine Learning Techniques for Enhancing Scholarship Schemes Using Artificial Emotional Intelligence. EAI Endorsed Transactions on Internet of Things, 10. https://doi.org/10.4108/eetiot.5368

Ribeiro, T. F., Nogueira, R., Chimenti, P., y da Fonseca, A. (2026). Mapping business ecosystems using AI: A case study on energy transition. Journal of Cleaner Production, 569. https://doi.org/10.1016/j.jclepro.2026.148546

Teece, D. J., Pisano, G., y Shuen, A. (1997). Dynamic capabilities and strategic management. Strategic Management Journal, 18(7), 509–533. https://doi.org/10.1002/(SICI)1097-0266(199708)18:7<509::AID-SMJ882>3.0.CO;2-Z

Tien, J. M. (2017). Internet of Things, Real-Time Decision Making, and Artificial Intelligence. Annals of Data Science, 4(2), 149-178. https://doi.org/10.1007/s40745-017-0112-5

Trabelsi, Z., Saidi, F., Thangaraj, E., y Veni, T. (2023). A survey of extremism online content analysis and prediction techniques in twitter based on sentiment analysis. Security Journal, 36(2), 221-248. https://doi.org/10.1057/s41284-022-00335-4

Vebrianti, R., Aras, M., Putri, M. S. S., y Swandewi, I. A. (2025). AI Chatbots in E-Commerce: Enhancing Customer Engagement, Satisfaction and Loyalty. Paper Asia, 41(2b), 248-260. https://doi.org/10.59953/paperasia.v41i2b.445

Venkatesh, V., Morris, M. G., Davis, G. B., y Davis, F. D. (2003). User acceptance of information technology: Toward a unified view. MIS Quarterly, 27(3), 425–478. https://doi.org/10.2307/30036540

Wang, J., Ding, X., y Wang, K. (2024). Brand gestalt cognition and tourists’ destination decision making: A study of international island destinations. Current Issues in Tourism, 27(18), 2949-2965. https://doi.org/10.1080/13683500.2023.2245108

Williams, N. L., Ferdinand, N., y Bustard, J. (2020). From WOM to aWOM – the evolution of unpaid influence: A perspective article. Tourism Review, 75(1), 314-318. Scopus. https://doi.org/10.1108/TR-05-2019-0171

Zhang, C., Wang, Y., y Zhang, J. (2025). Risk Assessment of Live-Streaming Marketing Based on Hesitant Fuzzy Multi-Attribute Group Decision-Making Method. Journal of Theoretical and Applied Electronic Commerce Research, 20(2). Scopus. https://doi.org/10.3390/jtaer20020120