Tecnoestrés laboral y agotamiento emocional en la era de la inteligencia artificial, nuevos desafíos para la seguridad y salud en el trabajo
DOI:
https://doi.org/10.5281/zenodo.22326977Palabras clave:
digitalización, gestión algorítmica, bienestar laboral, riesgos psicosociales, salud ocupacionalResumen
La incorporación acelerada de inteligencia artificial, automatización y sistemas de gestión algorítmica está modificando las demandas cognitivas, emocionales y organizacionales del trabajo, por lo que el tecnoestrés y el agotamiento emocional adquieren relevancia preventiva para la seguridad y salud ocupacional. El objetivo fue analizar la relación entre tecnoestrés laboral, agotamiento emocional e incorporación de inteligencia artificial, identificar mecanismos de riesgo y estructurar medidas preventivas aplicables a los sistemas de gestión. Se desarrolló una revisión narrativa estructurada, actualizada hasta septiembre de 2026, mediante literatura científica reciente y documentos de organismos internacionales y nacionales, con síntesis de 20 fuentes prioritarias y extracción de datos verificables para comparación. Los resultados muestran que sobrecarga, invasión, complejidad, inseguridad e incertidumbre tecnológica se combinan con intensificación, vigilancia algorítmica y pérdida de autonomía, mientras el apoyo organizacional, la capacitación, la participación y el diseño humano-céntrico funcionan como recursos protectores. La evidencia también confirma que la tecnología puede mejorar desempeño y seguridad cuando se implementa responsablemente. Se concluye que la prevención debe intervenir sobre la organización del trabajo e integrar la gobernanza de la inteligencia artificial a la gestión de riesgos psicosociales.
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Referencias
World Health Organization. (2022). Guidelines on mental health at work. World Health Organization. https://www.who.int/publications/i/item/9789240053052
World Health Organization, & International Labour Organization. (2022). Mental health at work: Policy brief. World Health Organization and International Labour Organization. https://www.who.int/publications/i/item/9789240057944
Tabassi, E. (2023). Artificial intelligence risk management framework (AI RMF 1.0) (NIST AI 100-1). National Institute of Standards and Technology. https://doi.org/10.6028/NIST.AI.100-1
Organisation for Economic Co-operation and Development. (2023). OECD employment outlook 2023: Artificial intelligence and the labour market. OECD Publishing. https://doi.org/10.1787/08785bba-en
Organisation for Economic Co-operation and Development. (2024). Using AI in the workplace: Opportunities, risks and policy responses. OECD Artificial Intelligence Papers, 11. OECD Publishing. https://doi.org/10.1787/73d417f9-en
European Agency for Safety and Health at Work. (2024). Digital technologies at work and psychosocial risks: Evidence and implications for occupational safety and health. EU-OSHA. https://osha.europa.eu/sites/default/files/documents/Digitalisation-and-PSR_summary_EN.pdf
Ministerio del Trabajo. (2024). Reglamento de Seguridad y Salud en el Trabajo: Decreto Ejecutivo No. 255. Gobierno del Ecuador. https://www.trabajo.gob.ec/reglamento-de-seguridad-y-salud-en-el-trabajo/
Kotek, M., & Vranjes, I. (2025). When do employees perceive technology as stressful? A meta-analysis of work-related technostress antecedents. Information & Management, 62(8), 104203. https://doi.org/10.1016/j.im.2025.104203
Mansuroğlu, E., & Smith, A. P. (2026). Technostress and employee well-being: A systematic review of empirical evidence. Computers in Human Behavior Reports, 21, 100941. https://doi.org/10.1016/j.chbr.2026.100941
Rademaker, T., Klingenberg, I., & Süß, S. (2025). Leadership and technostress: A systematic literature review. Management Review Quarterly, 75, 429–494. https://doi.org/10.1007/s11301-023-00385-x
Bottaro, R., De Giovanni, K., & Faraci, P. (2024). The extent to which technostress is related to employees’ work-life fit: A multilevel meta-analysis. Workplace Health & Safety, 72(10), 421–430. https://doi.org/10.1177/21650799241264317
Wirth, T., Kräft, J., Marquardt, B., Harth, V., & Mache, S. (2024). Indicators of technostress, their association with burnout and the moderating role of support offers among nurses in German hospitals: A cross-sectional study. BMJ Open, 14(7), e085705. https://doi.org/10.1136/bmjopen-2024-085705
Muhamad, N. A., Ma’amor, N. H., Jamalluddin, N. H., Rosli, I. A., Leman, F. N., Tengku Baharudin Shah, T. P. N., Misnan, N. S., Abdullah, N., Johari, M. Z., Chemi, N., Ibrahim, N., & Member of Technostress Study. (2025). Technostress and its associated factors: Burnout and fatigue among Malaysian healthcare workers (HCWs) in state hospitals. PLOS ONE, 20(3), e0319506. https://doi.org/10.1371/journal.pone.0319506
Buzeti, J., Dečman, M., & Kristl, N. (2026). Associations of work- and personal-life-related techno-stressors with emotional exhaustion, sickness absenteeism, and turnover intentions in public administration. Acta Psychologica, 266, 106772. https://doi.org/10.1016/j.actpsy.2026.106772
Wang, H., Ding, H., & Kong, X. (2022). Understanding technostress and employee well-being in digital work: The roles of work exhaustion and workplace knowledge diversity. International Journal of Manpower, 44(2), 334–353. https://doi.org/10.1108/IJM-08-2021-0480
Gabbiadini, A., Paganin, G., & Simbula, S. (2023). Teaching after the pandemic: The role of technostress and organizational support on intentions to adopt remote teaching technologies. Acta Psychologica, 236, 103936. https://doi.org/10.1016/j.actpsy.2023.103936
Lanzl, J. (2023). Social support as technostress inhibitor: Even more important during the COVID-19 pandemic? Business & Information Systems Engineering, 65, 329–343. https://doi.org/10.1007/s12599-023-00799-7
Cheng, Y., Cheng, W.-J., Lin, R.-T., Wang, Y.-T., & Ko, J.-J. R. (2024). Associations between labor control through digital platforms and workers’ mental wellbeing: A survey of location-based platform workers in Taiwan. Safety and Health at Work, 15(4), 419–426. https://doi.org/10.1016/j.shaw.2024.08.003
Nilsson, K. H., Matilla-Santander, N., Lee, M. K., Brulin, E., Bodin, T., & Håkansta, C. (2025). Algorithmic management and occupational health: A comparative case study of organizational practices in logistics. Safety Science, 187, 106863. https://doi.org/10.1016/j.ssci.2025.106863
Kinowska, H., & Sienkiewicz, Ł. (2023). Influence of algorithmic management practices on workplace well-being – evidence from European organisations. Information Technology & People, 36(8), 21–42. https://doi.org/10.1108/ITP-02-2022-0079
