Palavras chave

Autoeficácia tecnológica, criadores de technostress, inibidores de technostress, intolerância à incerteza, e-learning, satisfação

Resumo

Embora em 2021 muitas universidades tenham decidido retomar a atividade de ensino presencial, acreditamos que a utilização de aplicações online continuará a ser uma característica do sistema educativo devido à flexibilidade que oferece e às novas possibilidades de aprendizagem. Nosso objetivo é analisar o papel preditivo de fatores pessoais, como autoeficácia, criadores de technostress, inibidores de technostress e tolerância à incerteza gerada pela pandemia no uso de ferramentas de e-learning. A amostra foi composta por 1.517 acadêmicos. Os resultados mostraram que os criadores de technostress mediam as relações entre inibidores de technostress, autoeficácia tecnológica, uso de aplicativos e satisfação com o uso de plataformas de e-learning. Embora o contexto atual seja dominado pela incerteza, as hipóteses sobre os efeitos diretos e indiretos da incerteza no uso do aplicativo online na educação foram parcialmente sustentadas. A descoberta mais importante do nosso estudo é que, embora o contexto atual seja caracterizado pela incerteza, o impacto negativo dos níveis mais elevados de stress resultantes pode ser contrariado por um elevado nível de autoeficácia tecnológica que, por sua vez, prevê uma maior extensão do uso de plataformas, bem como a satisfação em utilizá-las.

Ver infografia

Referências

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

Fundref

Crossmark

Technical information

Recebido: 05-03-2022

Revisado: 27-06-2022

Aceite: 11-07-2022

OnlineFirst: 30-10-2022

Data de publicação: 01-01-2023

Tempo de revisão do artigo: 113 dias | Tempo médio de revisão do número 74: 40 dias

Tempo de aceitação do artigo: 127 dias | Tempo médio de aceitação do número 74: 69 dias

Tempo de edição da pré-impressão: 257 dias | Tempo médio de edição pré-impressão do número 74: 194 dias

Tempo de processamento do artigo: 302 dias | Tempo médio de processamento do número 74: 239 dias

Métricas

Métricas deste artigo

Vistas: 30220

Leituras dos resumos: 28404

Descargas em PDF: 1816

Métricas completas do Comunicar 74

Vistas: 338254

Leituras dos resumos: 309524

Descargas em PDF: 28730

Citado por

Citas em Web of Science

Fernandez-Fernandez, M; Martinez-Navalon, JG; (...); Roman, CP. The impact of teleworking technostress on satisfaction, anxiety and performance Heliyon, 2023.

https://doi.org/DOI10.1016/j.heliyon.2023.e17201

Citas em Scopus

Fernández-Fernández, M., Martínez-Navalón, J.-G., Gelashvili, V., Román, C.P.. The impact of teleworking technostress on satisfaction, anxiety and performance), Heliyon, .

https://doi.org/10.1016/j.heliyon.2023.e17201

Suparman, I., Kumar, J.A., Osman, S.. English learners’ intentions to adopt online learning post-pandemic: Ease precedes usefulness), Comunicar, .

https://doi.org/10.3916/C77-2023-03

Leon, Y.J.R., Barona, C.B., Ramirez, M.R., Rojas, E.M.. Educational Flexibility and its impact on the Hybrid Modality ), Iberian Conference on Information Systems and Technologies, CISTI, .

https://doi.org/10.23919/CISTI58278.2023.10211433

Citas em Google Scholar

El E-Learning y su incidencia en la satisfacción en los colaboradores de Camposantos del Ecuador SA ED Carrillo Lara, RA Pincay García - 2023 - Universidad de Guayaquil-Facultad …

[PDF] unirioja.es El aprendizaje en línea de inglés después de la pandemia: La facilidad precede a la utilidad I Suparman, JA Kumar, S Osman - Comunicar: Revista científica …, 2023 - dialnet.unirioja.es

https://dialnet.unirioja.es/servlet/articulo?codigo=9010087

English learners' intentions to adopt online learning post-pandemic: Ease precedes usefulness I Suparman, JA Kumar, S Osman - 2023 - researchgate.net

...

Educational Flexibility and its impact on the Hybrid Modality YJR León, CB Barona, MR Ramírez… - 2023 18th Iberian …, 2023 - ieeexplore.ieee.org

https://ieeexplore.ieee.org/abstract/document/10211433

Baixar

Métricas alternativas

Como citar

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, 89-100. https://doi.org/10.3916/C74-2023-07

Compartilhar

           

Oxbridge Publishing House

4 White House Way

B91 1SE Sollihul United Kingdom

Administração

Redação

Creative Commons

Este site usa cookies para obter dados estatísticos sobre a navegação de seus usuários. Se você continuar navegando, consideramos que você aceita seu uso. +info X