Palabras clave

Autoeficacia tecnológica, creadores de tecnoestrés, inhibidores del tecnoestrés, intolerancia a la incertidumbre, e-learning, satisfacción

Resumen

Si bien en 2021 muchas universidades han decidido retomar la actividad docente presencial, creemos que el uso de aplicaciones en línea seguirá siendo una característica del sistema educativo por la flexibilidad que ofrece y las posibilidades de aprendizaje. Nuestro objetivo es analizar el papel predictivo de factores personales, como la autoeficacia, los creadores de tecnoestrés, los inhibidores del tecnoestrés y la tolerancia a la incertidumbre sobre el uso de herramientas de e-learning para la enseñanza y sobre el uso de estas aplicaciones en el contexto de la incertidumbre generada por la pandemia. La muestra estuvo conformada por 1.517 académicos. Los resultados mostraron que los creadores de tecnoestrés median las relaciones entre inhibidores de tecnoestrés, autoeficacia tecnológica, uso de aplicaciones y satisfacción hacia el uso de plataformas de e-learning. Aunque el contexto actual está dominado por la incertidumbre, las hipótesis sobre los efectos directos e indirectos de la incertidumbre sobre el uso de la aplicación en línea en la educación se sustentaron parcialmente. El hallazgo más importante de nuestro estudio es que, aunque el contexto actual se caracteriza por la incertidumbre, el impacto negativo de los mayores niveles de estrés resultantes puede ser contrarrestado por un alto nivel de autoeficacia tecnológica que, a su vez, predice en mayor medida el uso de plataformas y la satisfacción de usar estas plataformas.

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Referencias

Al-Samarraie, H., Teng, B.K., Alzahrani, A.I., & Alalwan, N. (2018). E-learning continuance satisfaction in higher education: A unified perspective from instructors and students. Studies in Higher Education, 43(11), 2003-2019. https://doi.org/10.1080/03075079.2017.1298088

Link DOI | Link Google Scholar

Almuwais, A., Alqabbani, S., Benajiba, N., & Almoayad, F. (2021). An Emergency Shift to e-Learning in Health Professions Education: A Comparative Study of Perspectives between Students and Instructors. International Journal of Learning, Teaching and Educational Research, 20(6), 6. https://doi.org/10.26803/ijlter.20.6.2

Link DOI | Link Google Scholar

Bakar, N.S.A., Maat, S.M., & Rosli, R. (2018). A systematic review of teacher’s self-efficacy and technology integration. International Journal of Academic Research in Business and Social Sciences, 8(8). https://doi.org/10.6007/IJARBSS/V8-I8/4611

Link DOI | Link Google Scholar

Bandura, A. (1997). Self-efficacy: The exercise of control. W H Freeman/Times Books/ Henry Holt & Co. https://bit.ly/2FVgMwH

Link Google Scholar

Bordia, P., Hobman, E., Jones, E., Gallois, C., & Callan, V.J. (2004). Uncertainty during organizational change: Types, consequences, and management strategies. Journal of Business and Psychology, 18(4), 507-532. https://doi.org/10.1023/B:JOBU.0000028449.99127.f7

Link DOI | Link Google Scholar

Caprara, G.V., Barbaranelli, C., Borgogni, L., & Steca, P. (2003). Efficacy beliefs as determinants of teachers’ job satisfaction. Journal of Educational Psychology, 95(4), 821-832. https://doi.org/10.1037/0022-0663.95.4.821

Link DOI | Link Google Scholar

Carleton, R.N., Norton, M.A.P.J., & Asmundson, G.J.G. (2007). Fearing the unknown: A short version of the Intolerance of Uncertainty Scale. Journal of Anxiety Disorders, 21(1), 105-117. https://doi.org/10.1016/j.janxdis.2006.03.014

Link DOI | Link Google Scholar

Casero-Béjar, M.O., & Sánchez-Vera, M.M. (2022). Cambio de modalidad presencial a virtual durante el confinamiento por COVID-19: Percepciones del alumnado universitario. RIED, 25(1), 243-260. https://doi.org/10.5944/ried.25.1.30623

Link DOI | Link Google Scholar

Chan, F.K.Y., Thong, J.Y.L., Venkatesh, V., Brown, S.A., Hu, P.J.H., & Tam, K Y. (2010). Modeling citizen satisfaction with mandatory adoption of an E-Government technology. Journal of the Association for Information Systems, 11(10), 519-549. https://doi.org/10.17705/1jais.00239

Link DOI | Link Google Scholar

Chin, W.W. (2010). How to write up and report PLS analyses. In V. Esposito-Vinzi., W. Chin, J. Henseler, & H. Wang. (eds), Handbook of Partial Least Squares (pp. 655-690). Springer Berlin Heidelberg. https://doi.org/10.1007/978-3-540-32827-8_29

Link DOI | Link Google Scholar

Dugas, M.J., Buhr, K., & Ladouceur, R. (2004). The role of intolerance of uncertainty in etiology and maintenance. In R.G. Heimberg, C.L. Turk, & D.S. Mennin (Eds.), Generalized anxiety disorder: Advances in research and practice (pp. 143-163). The Guilford Press. https://bit.ly/3cXDWFt

Link Google Scholar

Fida, R., Paciello, M., Tramontano, C., Barbaranelli, C., & Farnese, M.L. (2015). “Yes, I can”: The protective role of personal self-efficacy in hindering counterproductive work behavior under stressful conditions. Anxiety, Stress, & Coping, 28(5), 479-499. https://doi.org/10.1080/10615806.2014.969718

Link DOI | Link Google Scholar

Grupe, D.W., & Nitschke, J.B. (2013). Uncertainty and anticipation in anxiety: An integrated neurobiological and psychological perspective. Nature Reviews Neuroscience, 14(7), 488-501. https://doi.org/10.1038/nrn3524

Link DOI | Link Google Scholar

Hodges, C., Moore, S., Lockee, B., Trust, T., & Bond, A. (2020). The difference between emergency remote teaching and online learning. EDUCAUSE Review. https://bit.ly/3bk6MPI

Link Google Scholar

Jena, R.K. (2015). Impact of technostress on job satisfaction: An empirical study among Indian academician. The International Technology Management Review, 5(3), 117-124. https://doi.org/10.2991/itmr.2015.5.3.1

Link DOI | Link Google Scholar

Jerrim, J., & Sims, S. (2021). When is high workload bad for teacher wellbeing? Accounting for the non-linear contribution of specific teaching tasks. Teaching and Teacher Education, 105, 103395. https://doi.org/10.1016/j.tate.2021.103395

Link DOI | Link Google Scholar

Li, L., & Wang, X. (2021). Technostress inhibitors and creators and their impacts on university teachers’ work performance in higher education. Cognition, Technology and Work, 23, 315-330. https://doi.org/10.1007/s10111-020-00625-0

Link DOI | Link Google Scholar

Maican, C.I., Cazan, A.M., Lixandroiu, R.C., & Dovleac, L. (2019). A study on academic staff personality and technology acceptance: The case of communication and collaboration applications. Computers and Education, 128, 113-131. https://doi.org/10.1016/j.compedu.2018.09.010

Link DOI | Link Google Scholar

Maier, C., Laumer, S., Eckhardt, A., & Weitzel, T. (2017). Giving too much social support: Social overload on social networking sites. European Journal of Information Systems, 24(5), 447-464. https://doi.org/10.1057/ejis.2014.3

Link DOI | Link Google Scholar

Marasi, S., Jones, B., & Parker, J.M. (2022). Faculty satisfaction with online teaching: A comprehensive study with American faculty. Studies in Higher Education, 47(3), 513-525. https://doi.org/10.1080/03075079.2020.1767050

Link DOI | Link Google Scholar

Marchiori, D.M., Mainardes, E.W., & Rodrigues, R.G. (2019). Do Individual characteristics influence the types of technostress reported by workers? International Journal of Human-Computer Interaction, 35(3), 218-230. https://doi.org/10.1080/10447318.2018.1449713

Link DOI | Link Google Scholar

Mertens, G., Duijndam, S., Smeets, T., & Lodder, P. (2021). The latent and item structure of COVID-19 fear: A comparison of four COVID-19 fear questionnaires using SEM and network analyses. Journal of Anxiety Disorders, 81, 102415. https://doi.org/10.1016/j.janxdis.2021.102415

Link DOI | Link Google Scholar

Mouakket, S., & Bettayeb, A.M. (2015). Investigating the factors influencing continuance usage intention of Learning management systems by university instructors: The Blackboard system case. International Journal of Web Information Systems, 11(4), 491-509. https://doi.org/10.1108/IJWIS-03-2015-0008

Link DOI | Link Google Scholar

Pan, X. (2020). Technology acceptance, technological self-efficacy, and attitude toward technology-based self-directed learning: Learning motivation as a mediator. Frontiers in Psychology, 11, 2791. https://doi.org/10.3389/fpsyg.2020.564294

Link DOI | Link Google Scholar

Pflügner, K., Maier, C., Mattke, J., & Weitzel, T. (2021). Personality Profiles that put users at risk of perceiving technostress: A qualitative comparative analysis with the big five personality traits. Business and Information Systems Engineering, 63(4), 389-402. https://doi.org/10.1007/s12599-020-00668-7

Link DOI | Link Google Scholar

Polly, D., Martin, F., & Guilbaud, T.C. (2021). Examining barriers and desired supports to increase faculty members’ use of digital technologies: Perspectives of faculty, staff and administrators. Journal of Computing in Higher Education, 33(1), 135-156. https://doi.org/10.1007/s12528-020-09259-7

Link DOI | Link Google Scholar

Pressley, T., & Ha, C. (2021). Teaching during a pandemic: United States teachers’ self-efficacy during COVID-19. Teaching and Teacher Education, 106, 103465. https://doi.org/10.1016/j.tate.2021.103465

Link DOI | Link Google Scholar

Prifti, R. (2022). Self–efficacy and student satisfaction in the context of blended learning courses. Open Learning: The Journal of Open, Distance and e-Learning, 37(2), 111-125. https://doi.org/10.1080/02680513.2020.1755642

Link DOI | Link Google Scholar

Ragu-Nathan, T.S., Tarafdar, M., Ragu-Nathan, B.S., & Tu, Q. (2008). The consequences of technostress for end users in organizations: Conceptual development and validation. Information Systems Research, 19(4), 417-433. https://doi.org/10.1287/isre.1070.0165

Link DOI | Link Google Scholar

Ringle, C., Wende, S., & Becker, J. (2015). SmartPLS 3. SmartPLS GmbH. https://bit.ly/3vxn0w2

Link Google Scholar

Rokhimah, S., & Sirait, A. (2021). Educator satisfaction using LMS with ICT infrastructure as a mediation variable. RSF Conference Series: Business, Management and Social Sciences, 1(3), 81-87. https://doi.org/10.31098/bmss.v1i3.291

Link DOI | Link Google Scholar

Salah-Eddine, M., & Belaissaoui, M. (2017). Technostress, coping and job satisfaction model of information systems. In Proceedings - 2016 International Conference on Computational Science and Computational Intelligence, CSCI 2016 (pp. 139-142). IEEE. https://doi.org/10.1109/CSCI.2016.0033

Link DOI | Link Google Scholar

Shu, Q., Tu, Q., & Wang, K. (2011). The impact of computer self-efficacy and technology dependence on computer-related technostress: A social cognitive theory perspective. International Journal of Human-Computer Interaction, 27(10), 923-939. https://doi.org/10.1080/10447318.2011.555313

Link DOI | Link Google Scholar

Skaalvik, E.M., & Skaalvik, S. (2017). Still motivated to teach? A study of school context variables, stress and job satisfaction among teachers in senior high school. Social Psychology of Education, 20(1), 15-37. https://doi.org/10.1007/s11218-016-9363-9

Link DOI | Link Google Scholar

Stickney, L.T., Bento, R.F., Aggarwal, A., & Adlakha, V. (2019). Online Higher Education: Faculty Satisfaction and Its Antecedents. Journal of Management Education, 43(5), 509-542. https://doi.org/10.1177/1052562919845022

Link DOI | Link Google Scholar

Syvänen, A., Mäkiniemi, J.P., Syrjä, S., Heikkilä-Tammi, K., & Viteli, J. (2016). When does the educational use of ICT become a source of technostress for Finnish teachers? Seminar.Net, 12(2), 95-109. https://doi.org/10.7577/seminar.2281

Link DOI | Link Google Scholar

Tarafdar, M., Maier, C., Laumer, S., & Weitzel, T. (2020). Explaining the link between technostress and technology addiction for social networking sites: A study of distraction as a coping behavior. Information Systems Journal, 30(1), 96–124. https://doi.org/10.1111/isj.12253

Link DOI | Link Google Scholar

Tarafdar, M., Pullins, E. Bolman., & Ragu-Nathan, T.S. (2015). Technostress: Negative effect on performance and possible mitigations. Information Systems Journal, 25(2), 103-132. https://doi.org/10.1111/isj.12042

Link DOI | Link Google Scholar

Tinaztepe, C. (2012). The effect of desire for change on the relationship between perceived uncertainty and job related affective well being. International Journal of Social Sciences and Humanity Studies, 4(2), 127-136. https://bit.ly/3OPV1yv

Link Google Scholar

UNESCO (Ed.) (2020). COVID-19 response–remote learning strategy. Remote learning strategy as a key element in ensuring continued learning. UNESCO. https://bit.ly/3JmFIfn

Link Google Scholar

Upadhyaya, P., & Vrinda. (2021). Impact of technostress on academic productivity of university students. Education and Information Technologies, 26(2), 1647-1664. https://doi.org/10.1007/s10639-020-10319-9

Link DOI | Link Google Scholar

Uzun, K., & Karata?, Z. (2020). Predictors of academic self efficacy: Intolerance of uncertainty, positive beliefs about worry and academic locus of control. International Education Studies, 13(6), 104-116. https://doi.org/10.5539/ies.v13n6p104

Link DOI | Link Google Scholar

Venkatesh, V., Thong, J.Y.L., & Xu, X. (2012). Consumer acceptance and use of information technology: Extending the unified theory of acceptance and use technology. MIS Quarterly, 36(1), 157-178. https://doi.org/10.2307/41410412

Link DOI | Link Google Scholar

Ünal, E., Yamaç, A., & Uzun, A.M. (2017). The Effect of the teaching practice course on pre-service elementary teachers’ technology integration self-efficacy. Malaysian Online Journal of Educational Technology, 5(3), 39-53. https://bit.ly/3zWwcg9

Link Google Scholar

Yoo, S.J., Huang, W.H., & Lee, D.Y. (2012). The impact of employee’s perception of organizational climate on their technology acceptance toward e-learning in South Korea. Knowledge Management & E-Learning: An International Journal, 4(3), 359-378. https://doi.org/10.34105/j.kmel.2012.04.028

Link DOI | Link Google Scholar

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Cazan, A., & Maican, C. (2023). Factors determining the use of e-learning and teaching satisfaction. [Factores determinantes en el uso del e-learning y la satisfacción docente]. Comunicar, 74. https://doi.org/10.3916/C74-2023-07

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