Diseño y validación de un instrumento para medir la implementación de la inteligencia artificial generativa en el contexto organizacional
Design and validation of an instrument to measure the implementation of generative artificial intelligence in the organizational context
DOI:
https://doi.org/10.56712/latam.v5i3.2197Palabras clave:
inteligencia artificial generativa (IAG), contexto organizacional, validación de instrumento, análisis factorial, consistencia internaResumen
Este estudio tuvo como objetivo diseñar y validar un instrumento para medir la implementación de la inteligencia artificial generativa (IAG) en el contexto organizacional. Se diseñó un cuestionario inicial con 28 ítems que evaluaban cuatro variables: adopción de la IAG, adaptación a la IAG, uso eficiente de la IAG, e interrelación entre el personal y la IAG. Se recopiló una muestra de 516 participantes de 49 empresas ubicadas en parques industriales de Tultitlán, Estado de México. Se realizaron pruebas estadísticas de validez y fiabilidad, incluyendo análisis factorial exploratorio y alfa de Cronbach. Los resultados revelaron siete componentes principales con una varianza total explicada del 73.882%. El instrumento final validado constó de 24 ítems agrupados en siete variables: interrelación de la IAG con el ser humano en su entorno laboral, adopción de la IAG, uso eficiente de la IAG, adaptación a la IAG, integración de la IAG en la organización, percepción de la IAG, y reemplazo de tareas por la IAG. El instrumento mostró una alta consistencia interna, con un alfa de Cronbach de 0.909. Se concluye que el cuestionario validado es una herramienta confiable para medir la implementación de la IAG en organizaciones.
Descargas
Citas
Al Mekhlal, M., Al Buraik, M., & Al Lubli, M. (2023). Digital transformation: AI-Powered bot solutions and automation for customer services. 2023 International Conference on Digital Applications, Transformation & Economy (ICDATE), Miri, Sarawak, Malaysia. https://doi.org/10.1109/icdate58146.2023.10248458
Atluri, V., Dahlström, P., Gaffey, B., Garcia, V., Kaka, N., Lajous, T., Singla, A., Sukharevsky, A., Travasoni, A., & Vieira, B. (2024). Beyond the hype: Capturing the potential of AI and gen AI in TMT. McKinsey, London. https://www.mckinsey.com/industries/technology-media-and-telecommunications/our-insights/beyond-the-hype-capturing-the-potential-of-ai-and-gen-ai-in-tmt
Bankins, S., Ocampo, A. C., Marrone, M., Restubog, S. L., & Woo, S. E. (2024). A multilevel review of artificial intelligence in organizations: Implications for organizational behavior research and practice. Journal of Organizational Behavior, 45(2), 159-182. https://doi.org/10.1002/job.2735
Barrios, M. & Cosculluela, A. (2013). Fiabilidad. En Meneses, J. (Coord.). Psicología. Psicometría. Barcelona, España: UOC. https://www.researchgate.net/profile/Julio_Meneses/publication/293121344_Psicometria/links/584a694408ae5038263d9532/Psicometria.pdf
Bouschery, S. G., Blazevic, V., & Piller, F. T. (2023). Augmenting human innovation teams with artificial intelligence: Exploring transformer-based language models. Journal of Product Innovation Management, 40(2), 139-153. https://doi.org/10.1111/jpim.12656
Bruce, D., Fadia, A., Isherwood, T., Marcati, C., Mitchell, A., Münstermann, B., Shenai, G., Vuppala, H., & Weber, T. (2023, diciembre, 7). Unlocking the potential of generative AI: Three key questions for government agencies. McKinsey & Company. https://www.mckinsey.com/industries/public-sector/our-insights/unlocking-the-potential-of-generative-ai-three-key-questions-for-government-agencies
Brynjolfsson, E., Li, D., & Raymond, L.R. (2023). Generative AI at work. NBER Working Papers Series, (31161), 1-67. https://doi.org/10.3386/w31161
Costa, F., Monaco, J. A., & Covello, A. (2023). Desafíos de la Inteligencia Artificial generative: Tres escalas y dos enfoques transversales, 3(76), 1-24. https://doi.org/10.24215/16696581e844
Daco, G. (2024, febrero, 14). The impact of GenAI on the labor market. https://www.ey.com/en_us/insights/ai/genai-impact-on-labor-market
Dinmohammadi, F. (2023). Adopting Artificial Intelligence in Industry 4.0: Understanding the Drivers, Barriers, and Technology Trends. 28th International Conference on Automation and Computing (ICAC), Birmingham, United Kingdom. https://doi.org/10.1109/ICAC57885.2023.10275230
FIDEPAR (2023). Información estadística de los desarrollos industriales del Estado de México FIDEPAR. https://fidepar.edomex.gob.mx/sites/fidepar.edomex.gob.mx/files/files/FIDEPAR%202018/DESARROLLOS%20INDUSTRIALES/Información%20Estadistica%20de%20los%20Desarrollos%20Industriales2(1).pdf
Gregory, J. M. & Gupta, S. K. (2024). Opportunities for Generative Artificial Intelligence to accelerate deployment of human-supervised autonomous robots. Proceedings of the 2023 AAAI Fall Symposia, 2(1), 177-181. https://doi.org/10.1609/aaaiss.v2i1.27667
Gupta, V. & Yang, H. (2024). Generative artificial intelligence (AI) technology adoption model for entrepreneurs: Case of ChatGPT. Internet Reference Services Quarterly, 28(2), 223-242. https://doi.org/10.1080/10875301.2023.2300114
Hangl, J., Krause, S., & Behrens, V. J. (2023). Drivers, barriers, and social considerations for AI adoption in SCM. Technology in Society, 74, 102299. https://doi.org/10.1016/j.techsoc.2023.102299
Hu, Y. (2023). Artificial intelligence and human workers interaction. Highlights in Science, Engineering and Technology, (44), 90-95. https://doi.org/10.54097/hset.v44i.7201
Jarrahi, M. H., Kenyon, S., Brown, A., Donahue, C., & Wicher, C. (2023). Artificial intelligence: A strategy to harness its power through organizational learning. Journal of Business Strategy, 44(3), 126-135. https://doi.org/10.1108/JBS-11-2021-0182
Kanitz, R., Gonzalez, K., Briker, R., & Straatmann, T. (2023). Augmenting organizational change and strategy activities: Leveraging generative artificial intelligence. The Journal of Applied Behavioral Science, 59 (3), 345-363. https://doi.org/10.1177/00218863231168974
Khan, S. & Iqbal, M. (2020). AI-Powered customer service: Does it optimize customer experience? 8th International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions) (ICRITO), Noida, India. https://doi.org/10.1109/ICRITO48877.2020.9198004
Korzynski, P., Mazurek, G., Altmann, A., Ejdys, J., Kazlauskaite, R., Paliszkiewicz, J., Wach, K., & Ziemba, E. (2023). Generative artificial intelligence as a new context for management theories: analysis of ChatGPT. Central European Management Journal, 31(1), 3-13. https://doi.org/10.1108/CEMJ-02-2023-0091
Lilly, A., Rajkumar, R., & Amudha, R. (2022). Aggrandizing the human resource development with underpinning artificial intelligence. Journal of Statistics and Management Systems, 25(5), 1083-1094. https://doi.org/10.1080/09720510.2022.2040859
Montoya, O. (2007). Aplicación del análisis factorial a la investigación de mercados. Caso de estudio. Scientia et Technica, 13(35), 281-286. https://www.redalyc.org/pdf/849/84903549.pdf
Morales, P. (2011). Tamaño necesario de la muestra: ¿Cuántos sujetos necesitamos? Universidad Pontificia Comillas. http://www.upcomillas.es/personal/peter/investigacion/Tama%F1oMuestra.pdf
Oldemeyer, L., Jede, A., & Teuteberg, F. (2024). Investigation of artificial intelligence in SMEs: a systematic review of the state of the art and the main implementation challenges. Management Review Quarterly, 74(1), 1-43. https://doi.org/10.1007/s11301-024-00405-4
Oñate, C.J., Batalla, A., & Páez, J.C. (2020). Elaboración y validación de un cuestionario de las habilidades motrices iniciales para estudiantes de enseñanza media chilena. Retos, 38,465-471. https://www.semanticscholar.org/reader/3291beab4c4be4428d65246ed57ccc28ae332af0
Pavlik, J. (2023). Collaborating with ChatGPT: considering the implications of generative artificial intelligence for journalism and media education. Journalism & Mass Communication Educator, 78(1), 84-93. https://doi.org/10.1177/10776958221149577
Peña, S., Meso, K., Larrondo, A., & Díaz, J. (2023). Without journalists, there is no journalism: the social dimension of generative artificial intelligence in the media. Profesional de la información, 32(2), 1-16. https://doi.org/10.3145/epi.2023.mar.27
Raparthi, M., Zahoor, M. S., Fawad, A., Balasubramanian, S., Maruthi, S., & Babu S. (2024). Investigating the creation of AI-Driven solutions for risk assessment, continuous improvement, and supplier performance monitoring. Dandao Xuebao/Journal of Ballistics, 36(1), 1-11. https://doi.org/10.52783/dxjb.v36.122
Rodríguez, L. R., Calderón, H., Hurtado, M. M., & Ocaña, A. W. (2023). Inteligencia artificial en la gestión organizacional: Impacto y realidad latinoamericana. Revista Arbitrada Interdisciplinaria KOINONIA, 8(1), 226-241. https://doi.org/10.35381/r.k.v8i1.2782
Rodríguez, N. L. & Herrera, C. G. (2010). Validación y confiabilidad de un instrumento de medición para carreras de ingeniería. Revista Electrónica Iberoamericana de Educación en Ciencias y Tecnología, 2(1), 107-118. https://exactas.unca.edu.ar/riecyt/VOL%202%20NUM%201/Archivos%20Digitales/Doc%20RIECyT%20V2-1-6.pdf
Rymarczyk, J. (2020). Technologies, opportunities, and challenges of the industrial revolution 4.0: Theoretical considerations. Entrepreneurial Business and Economics Review, 8(1), 185-198. https://doi.org/10.15678/EBER.2020.080110
Singh, A. & Pandey, J. (2024). Artificial intelligence adoption in extended HR ecosystems: enablers and barriers. An abductive case research. Frontiers in Psychology, 14, 1-13. https://doi.org/10.3389/fpsyg.2023.1339782
Suseno, Y., Chang, C., Hudik, M., & Fang, E. S. (2021). Beliefs, anxiety and change readiness for artificial intelligence adoption among human resource managers: The moderating role of high-performance work systems. The International Journal of Human Resource Management, 33(6), 1209-1236. https://doi.org/10.4324/9781003377085-6
Taniguchi, H. & Yamada, K. (2022). ICT Capital-Skill Complementarity and Wage Inequality: Evidence from OECD Countries. Labour Economics, 76(102151). https://doi.org/10.1016/j.labeco.2022.102151
Tidd, J. & Bessant, J. R. (2020). Managing innovation: Integrating technological, market and organizational change. Wiley.
Villareal, F.L. & Flor, G.A. (2023). Inteligencia Artificial: El reto contemporáneo de la gestión empresarial. ComHumanitas Revista científica de comunicación, 14(1), 94-111. https://doi.org/10.31207/rch.v14i1.393
Villasís, M.Á., Márquez, H., Zurita, J.N., Miranda, G, & Escamilla, A. El protocolo de investigación VII. Validez y confiabilidad de las mediciones. Revista Alergia México, 65(4), 414-421. https://doi.org/10.29262/ram.v65i4.560
Wang, J. S., Cooper, N., Eby, M., & Jo, E.S. (2023). From human-centered to social-centered artificial intelligence: Assessing ChatGPT's impact through disruptive events. arXiv:2306.0027, 1-23. https://doi.org/10.48550/arXiv.2306.00227
Zohuri, B. (2023). Charting the future. The synergy of generative AI, quantum computing, and the transformative impact on economy. Current Trends in Engineering Science, 3(7), 1-4. https://doi.org/10.54026/CTES/1050