Acceptance of Distance Learning Technologies by Teachers: Determining Factors and Emergency State Influence
Volume 32, Issue 3 (2021), pp. 517–542
Pub. online: 26 August 2021
Type: Research Article
Open Access
Open Access
Received
1 April 2021
1 April 2021
Accepted
1 August 2021
1 August 2021
Published
26 August 2021
26 August 2021
Abstract
State of emergency affects many areas of our life, including education. Due to school closure during COVID-19 pandemic as a case of a long-term emergency, education has been moved into a remote mode. In order to determine the factors driving the acceptance of distance learning technologies and ensuring sustainable education, a model based on the Unified Theory of Acceptance and Use of Technology has been proposed and empirically validated with data collected from 550 in-service primary school teachers in Lithuania. Structural equation modelling technique with multi-group analysis was utilized to analyse the data. The results show that performance expectancy, social influence, technology anxiety, effort expectancy, work engagement, and trust are factors that significantly affect teachers’ behavioural intention to use distance learning technologies. The relationships in the model are moderated by pandemic anxiety and age of teachers. The results of this study provide important implications for education institutions, policy makers and designers: the predictors of intention to use distance learning technologies observed during the emergency period may serve as factors that should be strengthened in teachers’ professional development, and the applicability of the findings is expanded beyond the pandemic isolation period.
References
Abaci, S., Robertson, J., Linklater, H., McNeill, F. (2020). Supporting school teachers’ rapid engagement with online education. Educational Technology Research and Development. https://doi.org/10.1007/s11423-020-09839-5.
Almaiah, M.A., Alamri, M.M., Al-Rahmi, W. (2019). Applying the UTAUT model to explain the students’ acceptance of mobile learning system in higher education. IEEE Access, 7, 174673–174686. https://doi.org/10.1109/ACCESS.2019.2957206.
Arpaci, I. (2016). Understanding and predicting students’ intention to use mobile cloud storage services. Computers in Human Behavior, 58, 150–157. https://doi.org/10.1016/j.chb.2015.12.067.
Bollen, K.A. (1989). Structural Equations with Latent Variables. John Wile, New York, pp. 1–514. 9781118619179. https://doi.org/10.1002/9781118619179.
Chao, C.M. (2019). Factors determining the behavioral intention to use mobile learning: An application and extension of the UTAUT model. Frontiers in Psychology, 10(July). https://doi.org/10.3389/fpsyg.2019.01652.
Crowe, D., LaPierre, M., Kebritchi, M. (2017). Knowledge based artificial augmentation intelligence technology: next step in academic instructional tools for distance learning. TechTrends, 61(5), 494–506. https://doi.org/10.1007/s11528-017-0210-4.
Dagienė, V., Jevsikova, T., Stupurienė, G. (2019). Introducing informatics in primary education: curriculum and teachers’ perspectives. In: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Vol. 11913, pp. 83–94. 9783030337582. https://doi.org/10.1007/978-3-030-33759-9_7.
Dagienė, V., Jevsikova, T., Stupurienė, G., Juskevicienė, A. (2021). Teaching computational thinking in primary schools: worldwide trends and teachers’ attitudes. Computer Science and Information Systems. https://doi.org/10.2298/csis201215033d.
Doulani, A. (2019). An assessment of effective factors in technology acceptance model: a meta-analysis study. Journal of Scientometric Research, 7(3), 153–166. https://doi.org/10.5530/jscires.7.3.26.
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.
Dwivedi, Y.K., Hughes, D.L., Coombs, C., Constantiou, I., Duan, Y., Edwards, J.S., Gupta, B., Lal, B., Misra, S., Prashant, P., Raman, R., Rana, N.P., Sharma, S.K., Upadhyay, N. (2020). Impact of COVID-19 pandemic on information management research and practice: transforming education, work and life. International Journal of Information Management, 55. https://doi.org/10.1016/j.ijinfomgt.2020.102211.
European Commission (2020). Eurydice. Statistics on Educational Institutions. https://eacea.ec.europa.eu/national-policies/eurydice/content/statistics-organisation-and-governance-44_en.
Fessakis, G., Prantsoudi, S. (2019). Computer science teachers’ perceptions, beliefs and attitudes on computational thinking in Greece. Informatics in Education, 18(2), 227–258. https://doi.org/10.15388/infedu.2019.11.
Foon, Y.S., Fah, B.C.Y. (2011). Internet banking adoption in Kuala Lumpur: an Application of UTAUT model. International Journal of Business and Management, 6(4). https://doi.org/10.5539/ijbm.v6n4p161.
Fornell, C., Larcker, D.F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18(1), 39. https://doi.org/10.2307/3151312.
Gefen, D., Straub, D., Boudreau, M.-C. (2000). Structural equation modeling and regression: guidelines for research practice. Communications of the Association for Information Systems, 4. https://doi.org/10.17705/1cais.00407.
General Assembly of the United Nations (2018). Transforming Our World: The 2030 Agenda for Sustainable Development. Technical report. https://doi.org/10.1891/9780826190123.ap02. https://www.un.org/ga/search/view_doc.asp?symbol=A/RES/70/1&Lang=E.
Holzmann, P., Schwarz, E.J., Audretsch, D.B. (2020). Understanding the determinants of novel technology adoption among teachers: the case of 3D printing. Journal of Technology Transfer, 45(1), 259–275. https://doi.org/10.1007/s10961-018-9693-1.
Kabra, G., Ramesh, A., Akhtar, P., Dash, M.K. (2017). Understanding behavioural intention to use information technology: insights from humanitarian practitioners. Telematics and Informatics, 34(7), 1250–1261. https://doi.org/10.1016/j.tele.2017.05.010.
Khalilzadeh, J., Ozturk, A.B., Bilgihan, A. (2017). Security-related factors in extended UTAUT model for NFC based mobile payment in the restaurant industry. Computers in Human Behavior, 70, 460–474. https://doi.org/10.1016/j.chb.2017.01.001.
Košir, K., Dugonik, Š., Huskić, A., Gračner, J., Kokol, Z., Krajnc, Ž. (2020). Predictors of perceived teachers’ and school counsellors’ work stress in the transition period of online education in schools during the COVID-19 pandemic. Educational Studies. https://doi.org/10.1080/03055698.2020.1833840.
Li, Q., Miao, Y., Zeng, X., Tarimo, C.S., Wu, C., Wu, J. (2020). Prevalence and factors for anxiety during the coronavirus disease 2019 (COVID-19) epidemic among the teachers in China. Journal of Affective Disorders, 277, 153–158. https://doi.org/10.1016/j.jad.2020.08.017.
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.
Meuter, M.L., Ostrom, A.L., Bitner, M.J., Roundtree, R. (2003). The influence of technology anxiety on consumer use and experiences with self-service technologies. Journal of Business Research, 56(11), 899–906. https://doi.org/10.1016/S0148-2963(01)00276-4.
Mikuskova, E.B., Veresova, M. (2020). Distance education during covid-19: the perspective of Slovak Teachers. Problems of Education in the 21st Century, 78(6), 884–906. https://doi.org/10.33225/pec/20.78.884.
Mulaik, S.A. (2009). Linear causal modeling with structural equations. CRC Press, Taylor & Francis Group, New York, pp. 1–445. 9781439800393. https://doi.org/10.1201/9781439800393.
Nacheva, R., Jansone, A. (2021). Usability evaluation of business process modelling tools through software quality metrics. Baltic Journal of Modern Computing, 8(4), 534–542. https://doi.org/10.22364/BJMC.2020.8.4.04.
National Agency for Education (2020). Educational management information system. Technical report. http://svis.emokykla.lt/3-mokykla-pedagoginiai-darbuotojai-/.
Palau, R., Fuentes, M., Mogas, J., Cebrián, G. (2021). Analysis of the implementation of teaching and learning processes at Catalan schools during the Covid-19 lockdown. Technology, Pedagogy and Education. https://doi.org/10.1080/1475939X.2020.1863855.
Portillo, J., Garay, U., Tejada, E., Bilbao, N. (2020). Self-perception of the digital competence of educators during the covid-19 pandemic: a cross-analysis of different educational stages. Sustainability (Switzerland), 12(23), 1–13. https://doi.org/10.3390/su122310128.
Pozo-Rico, T., Gilar-Corbí, R., Izquierdo, A., Castejón, J.L. (2020). Teacher training can make a difference: tools to overcome the impact of COVID-19 on primary schools. An experimental study. International Journal of Environmental Research and Public Health, 17(22), 1–23. https://doi.org/10.3390/ijerph17228633.
Rondan-Cataluña, F.J., Arenas-Gaitán, J., Ramírez-Correa, P.E. (2015). A comparison of the different versions of popular technology acceptance models a non-linear perspective. Kybernetes, 44(5), 788–805. https://doi.org/10.1108/K-09-2014-0184.
Saade, R., Kira, D. (2009). Computer anxiety in e-learning: the effect of computer self-efficacy. In: Proceedings of the 2009 InSITE Conference. https://doi.org/10.28945/3386.
Salloum, S.A., Shaalan, K. (2019). Factors affecting students’ acceptance of e-learning system in higher education using UTAUT and structural equation modeling approaches. Advances in Intelligent Systems and Computing, 845, 469–480. 9783319990095. https://doi.org/10.1007/978-3-319-99010-1_43.
Sarkar, S., Chauhan, S., Khare, A. (2020). A meta-analysis of antecedents and consequences of trust in mobile commerce. International Journal of Information Management, 50, 286–301. https://doi.org/10.1016/j.ijinfomgt.2019.08.008.
Schaufeli, W.B., Bakker, A.B., Salanova, M. (2006). The measurement of work engagement with a short questionnaire: a cross-national study. Educational and Psychological Measurement, 66(4), 701–716. https://doi.org/10.1177/0013164405282471.
Talukder, M.S., Chiong, R., Corbitt, B., Bao, Y. (2020). Critical factors influencing the intention to adopt m-government services by the elderly. Journal of Global Information Management, 28(4), 74–94. https://doi.org/10.4018/JGIM.2020100105.
Teo, T. (2011). Factors influencing teachers’ intention to use technology: model development and test. Computers and Education, 57(4), 2432–2440. https://doi.org/10.1016/j.compedu.2011.06.008.
UNESCO (2021). COVID-19 Educational Disruption and Response. https://en.unesco.org/covid19/educationresponse.
Venkatesh, V., Thong, J.Y.L., Xu, X. (2012). Consumer acceptance and use of information technology: extending the unified theory of acceptance and use of technology. MIS Quarterly: Management Information Systems, 36(1), 157–178. https://doi.org/10.2307/41410412.
Venkatesh, V., Morris, M.G., Davis, G.B., Davis, F.D. (2003). User acceptance of information technology: toward a unified view. MIS Quarterly: Management Information Systems, 27(3), 425–478. https://doi.org/10.2307/30036540.
Wasserman, E., Migdal, R. (2019). Professional development: teachers’ attitudes in online and traditional training courses. Online Learning Journal, 23(1), 132–143. https://doi.org/10.24059/olj.v23i1.1299.
Wong, K.M., Moorhouse, B.L. (2020). The impact of social uncertainty, protests, and COVID-19 on Hong Kong teachers. Journal of Loss and Trauma, 25(8), 649–655. https://doi.org/10.1080/15325024.2020.1776523.
Wu, J., Du, H. (2012). Toward a better understanding of behavioral intention and system usage constructs. European Journal of Information Systems, 21(6), 680–698. https://doi.org/10.1057/ejis.2012.15.
Biographies
Jevsikova Tatjana
T. Jevsikova, PhD in computer science, is a senior researcher and associate professor at the Vilnius University Institute of Data Science and Digital Technologies. Her main research interests include e-learning, computer science education, teacher training, and cultural aspects of human-computer interaction. She authors more than 35 research papers, a number of methodological papers and educational books.
Stupurienė Gabrielė
G. Stupurienė is a PhD in technological sciences (informatics engineering) and a research assistant at the Vilnius University Institute of Data Science and Digital Technologies. Her main research interest is informatics/computer science education, computational thinking.
Stumbrienė Dovilė
D. Stumbrienė is a junior researcher at Vilnius university, Lithuania. She defended her PhD thesis in natural sciences (informatics), in 2019. Her research is oriented to apply statistical models in social science and the main research interest is the large-scale analysis of education data. She is focusing on creating the methodology and instruments for education monitoring.
Juškevičienė Anita
A. Juškevičienė, PhD in technological sciences (informatics engineering), is a researcher at the Vilnius University Institute of Data Science and Digital Technologies. The areas of her scientific interest focus on technology enhanced learning, computational thinking, hands-on activities. She has been working on several national projects and helping to organize seminars and conferences. She has published a number of scientific papers and publications in popular magazines, participated in a number of large scale EU-funded R&D projects.
Dagienė Valentina
V. Dagienė is professor and principal researcher at Vilnius university, Lithuania. She has published over 200 scientific papers and 60 books on computer science education. She is the editor-in-chief of two international journals “Informatics in Education” and “Olympiads in Informatics”. She established International conference on Olympiads in Informatics, organized every year in different countries. In 2004 she established the International Challenge on Informatics and Computational Thinking “Bebras” which is organized in more than 60 countries. She coordinated over 30 national and international projects on STEM and Informatics education. She was acknowledged by Ada Lovelace Computing Excellence Award by the European Commission’s in 2016.
