Factores que influyen en la aceptación y uso de la inteligencia artificial en instituciones universitarias
Palabras clave:
inteligencia artificial, aceptación tecnológica, teoría unificada de aceptación y uso de la tecnología, educación de posgrado, modelos PLS-SEM
Resumen
El presente estudio analiza los factores que influyen en la aceptación y uso de la inteligencia artificial para la investigación en estudiantes de posgrado, mediante el empleo de la Teoría Unificada de Aceptación y Uso de la Tecnología. Se aplicó un diseño cuantitativo transversal utilizando la metodología PLS-SEM sobre una población de 275 estudiantes de programas de maestría y doctorado, en un caso específico de estudio desarrollado en una universidad privada de Lima, Perú. Los resultados evidencian que la expectativa de rendimiento y la innovación personal son los principales predictores de la intención conductual para adoptar inteligencia artificial en tareas investigativas, explicando el 69% de la varianza, y que la intención conductual se relaciona significativamente con el uso actual de la inteligencia artificial, mientras que la expectativa de esfuerzo no resultó significativa. Se concluye que la adopción de la inteligencia artificial está motivada por la percepción de su valor para la investigación y por la predisposición personal hacia la innovación, y que las condiciones técnicas o institucionales desempeñan un rol secundario.
Citas
Abbad, M. M. M. (2021). Using the UTAUT model to understand students’ usage of e-learning systems in developing countries. Education and Information Technologies, 26(6), 7205-7224. https://doi.org/10.1007/s10639-021-10573-5
Acosta-Enriquez, B. G., Guzmán Valle, M. de los Á., Arbulú Ballesteros, M., Arbulú Castillo, J. C., Arbulu Perez Vargas, C. G., Torres, I. S., Silva León, P. M., & Saavedra Tirado, K. (2025). What is the influence of psychosocial factors on artificial intelligence appropriation in college students? BMC psychology, 13(1), 7. https://doi.org/10.1186/s40359-024-02328-x
Acosta-Enriquez, B. G., Ramos Farroñan, E. V., Villena Zapata, L. I., Mogollon Garcia, F. S., Rabanal-León, H. C., Angaspilco, J. E. M., & Bocanegra, J. C. S. (2024). Acceptance of artificial intelligence in university contexts: A conceptual analysis based on UTAUT2 theory. Heliyon, 10(19), e38315. https://doi.org/10.1016/j.heliyon.2024.e38315
Afthanorhan, W. (2014). Hierarchical component using reflective-formative measurement model in partial least square structural equation modeling (Pls-Sem). International Journal of Mathematics, 2(2), 33-49. https://www.ijmsi.org/Papers/Volume.2.Issue.2/Version-1/H0221055071.pdf
Ajzen, I. (1991). The theory of planned behavior. Organizational behavior and human decision processes, 50(2), 179-211. https://doi.org/10.1016/0749-5978(91)90020-T
Arowosegbe, A., Alqahtani, J. S., & Oyelade, T. (2024). Perception of generative AI use in UK higher education. Frontiers in Education, 9, 1463208. https://doi.org/10.3389/feduc.2024.1463208
Attuquayefio, S., & Addo, H. (2014). Using the UTAUT model to analyze students’ ICT adoption. International Journal of Education and Development Using ICT, 10(3). https://files.eric.ed.gov/fulltext/EJ1059042.pdf
Bernilla Rodriguez, E. B. (2024). Docentes ante la inteligencia artificial en una universidad pública del norte del Perú. Educación, 33(64), 8-28. https://doi.org/10.18800/educacion.202401.M001
Chatterjee, S., & Bhattacharjee, K. K. (2020). Adoption of artificial intelligence in higher education: A quantitative analysis using structural equation modelling. Education and Information Technologies, 25(5), 3443-3463. https://doi.org/10.1007/s10639-020-10159-7
Cohen, J. (2013). Statistical power analysis for the behavioral sciences. Academic press.
Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS quarterly, 319-340. https://doi.org/10.2307/249008
Dwivedi, Y. K., Rana, N. P., Jeyaraj, A., Clement, M., & Williams, M. D. (2019). Re-examining the Unified Theory of Acceptance and Use of Technology (UTAUT): Towards a Revised Theoretical Model. Information Systems Frontiers, 21(3), 719-734. https://doi.org/10.1007/s10796-017-9774-y
Fošner, A. (2024). University Students’ Attitudes and Perceptions towards AI Tools: Implications for Sustainable Educational Practices. Sustainability, 16(19), 8668. https://doi.org/10.3390/su16198668
Gupta, K. P. (2024). Understanding Teachers’ Intentions and Use of AI Tools for Research. Journal of E-Learning and Knowledge Society, Vol 20 No 3, 13-25 Pages. https://doi.org/10.20368/1971-8829/1135969
Gupta, S., & Bostrom, R. P. (2019). A revision of computer self-efficacy conceptualizations in information systems. ACM SIGMIS Database: The DATABASE for Advances in Information Systems, 50(2), 71-93. https://doi.org/10.1145/3330472.3330478
Hair, J. F., Hult, G. T. M., Ringle, C., & Sarstedt, M. (2013). A Primer on Partial Least Squares Structural Equation Modeling (PLS-SEM). SAGE Publications. https://books.google.com.pe/books?id=IFiarYXE1PoC
Hair, J. F., Ringle, C. M., & Sarstedt, M. (2011). PLS-SEM: Indeed a silver bullet. Journal of Marketing theory and Practice, 19(2), 139-152. https://doi.org/10.2753/MTP1069-6679190202
Hair, J. F., Sarstedt, M., Pieper, T. M., & Ringle, C. M. (2012). The use of partial least squares structural equation modeling in strategic management research: A review of past practices and recommendations for future applications. Long range planning, 45(5-6), 320-340.
Huang, J., Pinmanee, S., & Chaveesuk, S. (2025). Acceptance and use of digital education platform: an application of utaut2. Operational Research in Engineering Sciences: Theory and Applications, 8(1). https://oresta.org/menu-script/index.php/oresta/article/view/868
Hulland, J. (1999). Use of partial least squares (PLS) in strategic management research: A review of four recent studies. Strategic management journal, 20(2), 195-204. https://www.jstor.org/stable/3094025
Joseph, O. U., Arikpo, I. M., Victor, O. S., Chidirim, N., Mbua, A. P., Ify, U. M., & Diwa, O. B. (2024). Artificial Intelligence (AI) in academic research. A multi-group analysis of students’ awareness and perceptions using gender and programme type. Journal of Applied Learning and Teaching, 7(1), 76-92.
Kumar, M., Tyagi, R., Gaumat, A., & Rani, J. (2025). Students’ Perceptions and Readiness for AI-Enhanced Learning: A Utaut-Based Study in Indian Higher Education Institutions. Journal of Marketing & Social Research, 2, 495-500. https://doi.org/10.61336/jmsr/25-03-61
Lv, N., & Li, Z. (2024). The effects of blended learning environment on college students’ learning effectiveness: Exploring the role of behavioral intentions and self-efficacy through the UTAUT2 model. Sage Open, 14(2), 21582440241251604. https://doi.org/10.1177/21582440241251604
Mahnoor, M., & Shazia Muheoddin, S. (2025). Assessing the role of digital literacy and socioeconomic factors in technology acceptance among postgraduate students in blended classrooms at mehran university. Al-Aasar, 2(2), 906-921. https://al-aasar.com/index.php/Journal/article/view/358
Mahrishi, M., Abbas, A., Radovanovic, D., & Hosseini, S. (2024). Emerging dynamics of ChatGPT in Academia: A scoping review. Journal of University Teaching and Learning Practice, 21(1), 146-176. https://doi.org/10.53761/b182ws13
Mao, P., Cai, Z., Sun, X., Niu, G., Zhang, Y., & Liu, J. (2025). Understanding the factors influencing college students’ acceptance and adoption of chatbots for learning: A network analysis. Learning and Individual Differences, 122, 102756. https://doi.org/10.1016/j.lindif.2025.102756
Martínez Ávila, M., & Fierro Moreno, E. (2018). Aplicación de la técnica PLS-SEM en la gestión del conocimiento: Un enfoque técnico práctico. RIDE. Revista Iberoamericana para la Investigación y el Desarrollo Educativo, 8(16), 130-164.
Merton, R. K. (1968). Social theory and social structure. Simon and Schuster.
Mohsin, F. H., Md Isa, N., Ishak, K., & Mohamed Salleh, H. (2024). Navigating the Adoption of Artificial Intelligence in Higher Education. International Journal of Business and Technopreneurship (IJBT), 14(1), 109-120. https://doi.org/10.58915/ijbt.v14i1.433
Morales, R. E. R. (2025). Factores determinantes en la adopción de inteligencia artificial en la educación superior dominicana. Cuaderno de Pedagogía Universitaria, 22(43), 79-103. https://doi.org/10.29197/cpu.v22i43.647
Nunnally, J. C. (1994). Psychometric theory 3E. Tata McGraw-Hill Education.
Obenza, B. N., Caballo, J. H., Caangay, R. B., Makigod, T. E., Almocera, S., Bayno, J. L., Camposano Jr, J., Cena, S. J., Garcia, J. A. K., & Labajo, B. F. (2024). Analyzing University Students’ Attitude and Behavior Toward AI Using the Extended Unified Theory of Acceptance and Use of Technology Model. American Journal of Applied Statistics and Economics (AJASE), 3(1). https://journals.e-palli.com/home/index.php/ajase/article/view/2510
Poudel, R. L., & Bastakoti, C. K. (2024). Impact of Behavioural Intention to Use Generative Artificial Intelligence on Academic Performance of Students in Higher Education Institutions. Prithvi Journal of Research and Innovation, 6, 1-21. https://doi.org/10.3126/pjri.v6i1.72853
Rahman, M., Terano, H. J. R., Rahman, N., Salamzadeh, A., & Rahaman, S. (2023). ChatGPT and Academic Research: A Review and Recommendations Based on Practical Examples. Journal of Education, Management and Development Studies, 3(1), 1-12. https://doi.org/10.52631/jemds.v3i1.175
Razak, F. Z. A., Abdullah, M. A., Ahmad, B. E., Bakar, W. H. R. B. W. A., & Misaridin, N. A. F. B. (2025). The acceptance of artificial intelligence in education among postgraduate students in Malaysia. Education and Information Technologies, 30(3), 2977-2997. https://doi.org/10.1007/s10639-024-12916-4
Rodríguez Flores, E. A., & Sánchez Trujillo, M. D. L. Á. (2025). Investigación científica e inteligencia artificial en estudiantes de posgrado. Un análisis cualitativo. European Public & Social Innovation Review, 10, 1-17. https://doi.org/10.31637/epsir-2025-1049
Rolland Sobral, S., Ferreira, M. J., Santos Pereira, C., Durão, N., & Moreira, F. (2024). Exploring the Impact of Artificial Intelligence Generative Tools on Research in Higher Education Institutions: A Perspective from Portugal. European Conference on e-Learning, 23(1), 330-339. https://doi.org/10.34190/ecel.23.1.2512
Saforrudin, N., Redzuan, F., Abd Karim, N., Abdul Majid, R., Nasruddin, Z. A., & Mohd Ariffin, N. H. (2024). Evaluating students’ acceptance factors on chatbot for learning using utaut model. International Journal of Modern Education, 6(20), 447-458. https://doi.org/10.35631/IJMOE.620033
Sharma, S., & Singh, G. (2024). Adoption of artificial intelligence in higher education: An empirical study of the UTAUT model in Indian universities. International Journal of System Assurance Engineering and Management. https://doi.org/10.1007/s13198-024-02558-7
Supianto, Widyaningrum, R., Wulandari, F., Zainudin, M., Athiyallah, A., & Rizqa, M. (2024). Exploring the factors affecting ChatGPT acceptance among university students. Multidisciplinary Science Journal, 6(12), 2024273. https://doi.org/10.31893/multiscience.2024273
Uludağ, F., Kılıç, E., & Çelik, H. E. (2025). Artificial intelligence, social influence, and AI anxiety: Analyzing the intentions of science doctoral students to use ChatGPT with PLS-SEM. Humanities and Social Sciences Communications, 12(1), 1-17. https://doi.org/10.1057/s41599-025-05641-x
Venkatesh, Morris, Davis, & Davis. (2003). User Acceptance of Information Technology: Toward a Unified View. MIS Quarterly, 27(3), 425. https://doi.org/10.2307/30036540
Venkatesh, Thong, & Xu. (2012). Consumer Acceptance and Use of Information Technology: Extending the Unified Theory of Acceptance and Use of Technology. MIS Quarterly, 36(1), 157. https://doi.org/10.2307/41410412
Venkatesh, V. (2022). Adoption and use of AI tools: A research agenda grounded in UTAUT. Annals of Operations Research, 308(1-2), 641-652. https://doi.org/10.1007/s10479-020-03918-9
Xu, J., Li, Y., Shadiev, R., & Li, C. (2025). College students’ use behavior of generative AI and its influencing factors under the unified theory of acceptance and use of technology model. Education and Information Technologies. https://doi.org/10.1007/s10639-025-13508-6
Yilmaz, F. G. K., Yilmaz, R., & Ceylan, M. (2024). Generative Artificial Intelligence Acceptance Scale: A Validity and Reliability Study. International Journal of Human–Computer Interaction, 40(24), 8703-8715. https://doi.org/10.1080/10447318.2023.2288730
Zacharis, G., & Nikolopoulou, K. (2022). Factors predicting University students’ behavioral intention to use eLearning platforms in the post-pandemic normal: An UTAUT2 approach with ‘Learning Value’. Education and Information Technologies, 27(9), 12065-12082. https://doi.org/10.1007/s10639-022-11116-2
Zheng, H., Han, F., Huang, Y., Wu, Y., & Wu, X. (2025). Factors influencing behavioral intention to use e-learning in higher education during the COVID-19 pandemic: A meta-analytic review based on the UTAUT2 model. Education and Information Technologies, 1-39.
Acosta-Enriquez, B. G., Guzmán Valle, M. de los Á., Arbulú Ballesteros, M., Arbulú Castillo, J. C., Arbulu Perez Vargas, C. G., Torres, I. S., Silva León, P. M., & Saavedra Tirado, K. (2025). What is the influence of psychosocial factors on artificial intelligence appropriation in college students? BMC psychology, 13(1), 7. https://doi.org/10.1186/s40359-024-02328-x
Acosta-Enriquez, B. G., Ramos Farroñan, E. V., Villena Zapata, L. I., Mogollon Garcia, F. S., Rabanal-León, H. C., Angaspilco, J. E. M., & Bocanegra, J. C. S. (2024). Acceptance of artificial intelligence in university contexts: A conceptual analysis based on UTAUT2 theory. Heliyon, 10(19), e38315. https://doi.org/10.1016/j.heliyon.2024.e38315
Afthanorhan, W. (2014). Hierarchical component using reflective-formative measurement model in partial least square structural equation modeling (Pls-Sem). International Journal of Mathematics, 2(2), 33-49. https://www.ijmsi.org/Papers/Volume.2.Issue.2/Version-1/H0221055071.pdf
Ajzen, I. (1991). The theory of planned behavior. Organizational behavior and human decision processes, 50(2), 179-211. https://doi.org/10.1016/0749-5978(91)90020-T
Arowosegbe, A., Alqahtani, J. S., & Oyelade, T. (2024). Perception of generative AI use in UK higher education. Frontiers in Education, 9, 1463208. https://doi.org/10.3389/feduc.2024.1463208
Attuquayefio, S., & Addo, H. (2014). Using the UTAUT model to analyze students’ ICT adoption. International Journal of Education and Development Using ICT, 10(3). https://files.eric.ed.gov/fulltext/EJ1059042.pdf
Bernilla Rodriguez, E. B. (2024). Docentes ante la inteligencia artificial en una universidad pública del norte del Perú. Educación, 33(64), 8-28. https://doi.org/10.18800/educacion.202401.M001
Chatterjee, S., & Bhattacharjee, K. K. (2020). Adoption of artificial intelligence in higher education: A quantitative analysis using structural equation modelling. Education and Information Technologies, 25(5), 3443-3463. https://doi.org/10.1007/s10639-020-10159-7
Cohen, J. (2013). Statistical power analysis for the behavioral sciences. Academic press.
Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS quarterly, 319-340. https://doi.org/10.2307/249008
Dwivedi, Y. K., Rana, N. P., Jeyaraj, A., Clement, M., & Williams, M. D. (2019). Re-examining the Unified Theory of Acceptance and Use of Technology (UTAUT): Towards a Revised Theoretical Model. Information Systems Frontiers, 21(3), 719-734. https://doi.org/10.1007/s10796-017-9774-y
Fošner, A. (2024). University Students’ Attitudes and Perceptions towards AI Tools: Implications for Sustainable Educational Practices. Sustainability, 16(19), 8668. https://doi.org/10.3390/su16198668
Gupta, K. P. (2024). Understanding Teachers’ Intentions and Use of AI Tools for Research. Journal of E-Learning and Knowledge Society, Vol 20 No 3, 13-25 Pages. https://doi.org/10.20368/1971-8829/1135969
Gupta, S., & Bostrom, R. P. (2019). A revision of computer self-efficacy conceptualizations in information systems. ACM SIGMIS Database: The DATABASE for Advances in Information Systems, 50(2), 71-93. https://doi.org/10.1145/3330472.3330478
Hair, J. F., Hult, G. T. M., Ringle, C., & Sarstedt, M. (2013). A Primer on Partial Least Squares Structural Equation Modeling (PLS-SEM). SAGE Publications. https://books.google.com.pe/books?id=IFiarYXE1PoC
Hair, J. F., Ringle, C. M., & Sarstedt, M. (2011). PLS-SEM: Indeed a silver bullet. Journal of Marketing theory and Practice, 19(2), 139-152. https://doi.org/10.2753/MTP1069-6679190202
Hair, J. F., Sarstedt, M., Pieper, T. M., & Ringle, C. M. (2012). The use of partial least squares structural equation modeling in strategic management research: A review of past practices and recommendations for future applications. Long range planning, 45(5-6), 320-340.
Huang, J., Pinmanee, S., & Chaveesuk, S. (2025). Acceptance and use of digital education platform: an application of utaut2. Operational Research in Engineering Sciences: Theory and Applications, 8(1). https://oresta.org/menu-script/index.php/oresta/article/view/868
Hulland, J. (1999). Use of partial least squares (PLS) in strategic management research: A review of four recent studies. Strategic management journal, 20(2), 195-204. https://www.jstor.org/stable/3094025
Joseph, O. U., Arikpo, I. M., Victor, O. S., Chidirim, N., Mbua, A. P., Ify, U. M., & Diwa, O. B. (2024). Artificial Intelligence (AI) in academic research. A multi-group analysis of students’ awareness and perceptions using gender and programme type. Journal of Applied Learning and Teaching, 7(1), 76-92.
Kumar, M., Tyagi, R., Gaumat, A., & Rani, J. (2025). Students’ Perceptions and Readiness for AI-Enhanced Learning: A Utaut-Based Study in Indian Higher Education Institutions. Journal of Marketing & Social Research, 2, 495-500. https://doi.org/10.61336/jmsr/25-03-61
Lv, N., & Li, Z. (2024). The effects of blended learning environment on college students’ learning effectiveness: Exploring the role of behavioral intentions and self-efficacy through the UTAUT2 model. Sage Open, 14(2), 21582440241251604. https://doi.org/10.1177/21582440241251604
Mahnoor, M., & Shazia Muheoddin, S. (2025). Assessing the role of digital literacy and socioeconomic factors in technology acceptance among postgraduate students in blended classrooms at mehran university. Al-Aasar, 2(2), 906-921. https://al-aasar.com/index.php/Journal/article/view/358
Mahrishi, M., Abbas, A., Radovanovic, D., & Hosseini, S. (2024). Emerging dynamics of ChatGPT in Academia: A scoping review. Journal of University Teaching and Learning Practice, 21(1), 146-176. https://doi.org/10.53761/b182ws13
Mao, P., Cai, Z., Sun, X., Niu, G., Zhang, Y., & Liu, J. (2025). Understanding the factors influencing college students’ acceptance and adoption of chatbots for learning: A network analysis. Learning and Individual Differences, 122, 102756. https://doi.org/10.1016/j.lindif.2025.102756
Martínez Ávila, M., & Fierro Moreno, E. (2018). Aplicación de la técnica PLS-SEM en la gestión del conocimiento: Un enfoque técnico práctico. RIDE. Revista Iberoamericana para la Investigación y el Desarrollo Educativo, 8(16), 130-164.
Merton, R. K. (1968). Social theory and social structure. Simon and Schuster.
Mohsin, F. H., Md Isa, N., Ishak, K., & Mohamed Salleh, H. (2024). Navigating the Adoption of Artificial Intelligence in Higher Education. International Journal of Business and Technopreneurship (IJBT), 14(1), 109-120. https://doi.org/10.58915/ijbt.v14i1.433
Morales, R. E. R. (2025). Factores determinantes en la adopción de inteligencia artificial en la educación superior dominicana. Cuaderno de Pedagogía Universitaria, 22(43), 79-103. https://doi.org/10.29197/cpu.v22i43.647
Nunnally, J. C. (1994). Psychometric theory 3E. Tata McGraw-Hill Education.
Obenza, B. N., Caballo, J. H., Caangay, R. B., Makigod, T. E., Almocera, S., Bayno, J. L., Camposano Jr, J., Cena, S. J., Garcia, J. A. K., & Labajo, B. F. (2024). Analyzing University Students’ Attitude and Behavior Toward AI Using the Extended Unified Theory of Acceptance and Use of Technology Model. American Journal of Applied Statistics and Economics (AJASE), 3(1). https://journals.e-palli.com/home/index.php/ajase/article/view/2510
Poudel, R. L., & Bastakoti, C. K. (2024). Impact of Behavioural Intention to Use Generative Artificial Intelligence on Academic Performance of Students in Higher Education Institutions. Prithvi Journal of Research and Innovation, 6, 1-21. https://doi.org/10.3126/pjri.v6i1.72853
Rahman, M., Terano, H. J. R., Rahman, N., Salamzadeh, A., & Rahaman, S. (2023). ChatGPT and Academic Research: A Review and Recommendations Based on Practical Examples. Journal of Education, Management and Development Studies, 3(1), 1-12. https://doi.org/10.52631/jemds.v3i1.175
Razak, F. Z. A., Abdullah, M. A., Ahmad, B. E., Bakar, W. H. R. B. W. A., & Misaridin, N. A. F. B. (2025). The acceptance of artificial intelligence in education among postgraduate students in Malaysia. Education and Information Technologies, 30(3), 2977-2997. https://doi.org/10.1007/s10639-024-12916-4
Rodríguez Flores, E. A., & Sánchez Trujillo, M. D. L. Á. (2025). Investigación científica e inteligencia artificial en estudiantes de posgrado. Un análisis cualitativo. European Public & Social Innovation Review, 10, 1-17. https://doi.org/10.31637/epsir-2025-1049
Rolland Sobral, S., Ferreira, M. J., Santos Pereira, C., Durão, N., & Moreira, F. (2024). Exploring the Impact of Artificial Intelligence Generative Tools on Research in Higher Education Institutions: A Perspective from Portugal. European Conference on e-Learning, 23(1), 330-339. https://doi.org/10.34190/ecel.23.1.2512
Saforrudin, N., Redzuan, F., Abd Karim, N., Abdul Majid, R., Nasruddin, Z. A., & Mohd Ariffin, N. H. (2024). Evaluating students’ acceptance factors on chatbot for learning using utaut model. International Journal of Modern Education, 6(20), 447-458. https://doi.org/10.35631/IJMOE.620033
Sharma, S., & Singh, G. (2024). Adoption of artificial intelligence in higher education: An empirical study of the UTAUT model in Indian universities. International Journal of System Assurance Engineering and Management. https://doi.org/10.1007/s13198-024-02558-7
Supianto, Widyaningrum, R., Wulandari, F., Zainudin, M., Athiyallah, A., & Rizqa, M. (2024). Exploring the factors affecting ChatGPT acceptance among university students. Multidisciplinary Science Journal, 6(12), 2024273. https://doi.org/10.31893/multiscience.2024273
Uludağ, F., Kılıç, E., & Çelik, H. E. (2025). Artificial intelligence, social influence, and AI anxiety: Analyzing the intentions of science doctoral students to use ChatGPT with PLS-SEM. Humanities and Social Sciences Communications, 12(1), 1-17. https://doi.org/10.1057/s41599-025-05641-x
Venkatesh, Morris, Davis, & Davis. (2003). User Acceptance of Information Technology: Toward a Unified View. MIS Quarterly, 27(3), 425. https://doi.org/10.2307/30036540
Venkatesh, Thong, & Xu. (2012). Consumer Acceptance and Use of Information Technology: Extending the Unified Theory of Acceptance and Use of Technology. MIS Quarterly, 36(1), 157. https://doi.org/10.2307/41410412
Venkatesh, V. (2022). Adoption and use of AI tools: A research agenda grounded in UTAUT. Annals of Operations Research, 308(1-2), 641-652. https://doi.org/10.1007/s10479-020-03918-9
Xu, J., Li, Y., Shadiev, R., & Li, C. (2025). College students’ use behavior of generative AI and its influencing factors under the unified theory of acceptance and use of technology model. Education and Information Technologies. https://doi.org/10.1007/s10639-025-13508-6
Yilmaz, F. G. K., Yilmaz, R., & Ceylan, M. (2024). Generative Artificial Intelligence Acceptance Scale: A Validity and Reliability Study. International Journal of Human–Computer Interaction, 40(24), 8703-8715. https://doi.org/10.1080/10447318.2023.2288730
Zacharis, G., & Nikolopoulou, K. (2022). Factors predicting University students’ behavioral intention to use eLearning platforms in the post-pandemic normal: An UTAUT2 approach with ‘Learning Value’. Education and Information Technologies, 27(9), 12065-12082. https://doi.org/10.1007/s10639-022-11116-2
Zheng, H., Han, F., Huang, Y., Wu, Y., & Wu, X. (2025). Factors influencing behavioral intention to use e-learning in higher education during the COVID-19 pandemic: A meta-analytic review based on the UTAUT2 model. Education and Information Technologies, 1-39.
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2026-04-15
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Bravo Martinez, A. O., & Sánchez Gómez, J. E. (2026). Factores que influyen en la aceptación y uso de la inteligencia artificial en instituciones universitarias. Revista Venezolana De Gerencia, 31(15), e31e157. Recuperado a partir de https://produccioncientificaluz.org/index.php/rvg/article/view/45457
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