![Open Access](https://i-scholar.in/lib/pkp/templates/images/icons/fulltextgreen.png)
![Restricted Access](https://i-scholar.in/lib/pkp/templates/images/icons/fulltextred.png)
![Open Access](https://i-scholar.in/lib/pkp/templates/images/icons/fulltextgreen.png)
![Open Access](https://i-scholar.in/lib/pkp/templates/images/icons/fulltext_open_medium.gif)
![Restricted Access](https://i-scholar.in/lib/pkp/templates/images/icons/fulltextred.png)
![Restricted Access](https://i-scholar.in/lib/pkp/templates/images/icons/fulltext_restricted_medium.gif)
Acceptance and Use of Digital Technology: Re-Validating Venkatesh’s Model on School Teachers
Subscribe/Renew Journal
The study attempts to re-validate Venkatesh’s model of acceptance and use of digital technology on school teachers. 197 school teachers participated in the study on the Qualtrics survey platform. The study used Smart PLS-SEM (Partial-Least Squares-Structural Equation Model) to re-validate the Venkatesh’s model. Teachers adopted digital technology for online teaching, leading to continued behavioural usage intention. Facilitating conditions and perceived cost emerged as strong predictors in promoting behavioural intention to use digital technology. The research showed that teachers would continue using technology in future irrespective of the situation. Social influence was less effective in predicting behavioural intention; habit, on the other hand, had no direct link to the use behaviour as expected; and perceived cost had a direct linkage to the use behaviour, showing that teachers acknowledged the affordable cost of digital tools for teaching. Based on path coefficients, the study confirmed the significant effects of latent constructs on behavioural intention (BI).
Keywords
Digital Technology, Online Teaching, Habit, Behavioural Intention, Social Influence.
User
Subscription
Login to verify subscription
Font Size
Information
- Abbad, M. (2021). Using the UTAUT model to understand students’ usage of e-learning systems in developing countries. Education and Information Technology, 26, 7205-7226. https://doi.org/10.1007/s10639-021-10573-5
- Aldholay, A., Isaac, O., Abdullah, Z., Abdulsalam, R., & Al-Shibami, A.H. (2018). An extension of Delone and McLean’s IS success model with self-efficacy: Online learning usage in Yemen. International Journal of Information and Learning Technology, 35(4), 285-304.
- Ajzen, I. (1991). The theory of planned behaviour. Organisational Behaviour and Human Decision Processes, 50(2), 179-211.
- Bhattacherjee, A. (2001). Understanding information systems continuance: An expectation confirmation model. MIS Quarterly, 25(3), 351-370.
- Chang, I.C., Hwang, H. G., Hung, W. F., & Li, Y. C. (2007). Physicians’ acceptance of pharmacokinetics-based clinical decision support systems. Expert Systems with Applications, 33(2), 296-303.
- Chao, C. (2019). Factors determining the behavioural intention to use mobile learning: An application and extension of the UTAUT model. Frontier Psychology, 10, 1652. https://doi.org/10.3389/fpsyg.2019.01652
- Chou, S., Chen, C.W., & Lin, J.Y. (2015). Female online shoppers: Examining the mediating roles of e-satisfaction and e-trust on e-loyalty development. Internet Research, 25(4), 542-561.
- Davis, F.D. (1989). Perceived usefulness, perceived ease of use and user acceptance of information technology. MIS Quarterly, 13(3), 319-339. https://doi.org/10.2307/249008
- Dwivedi, Y.K., Rana, N.P., Chen, H., & Williams, M.D. (2011). A meta-analysis of the unified theory of acceptance and use of technology. In: Nüttgens, M., Gadatsch, A., Kautz, K., Schirmer, I., Blinn, N. (Eds.), Governance and sustainability in information systems. Managing the transfer and diffusion of IT. Vol. 366. IFIP Advances in Information and Communication Technology (pp. 155–170). Springer. https://link.springer.com/chapter/10.1007%2F978-3-642-24148-2_10
- Dwivedi, Y. K., Rana, N.P., Jeyaraj, A., Clement, M., & Williams, M. D. (2017). Re-examining the unified theory of acceptance and use of technology: Towards a revised model. Information System Front, 21(3), 719-734.
- Fang, Y., Chiu, C., & Wang, E.T.G. (2011). Understanding customers’ satisfaction and repurchase intentions: An integration of IS success model, trust, and justice. Internet Research, 21(4), 479-503.
- Fraillon, J., Ainley, J., Schulz, W., Friedman, T., & Duckworth, D. (2019). Preparing for life in a digital age: IEA international computer and information literacy study 2018. IEA Publishing.https://www.iea.nl/sites/default/files/201911/ICILS%202019%20Digital%20final%2004112019.pdf
- Gupta, B., Dasgupta, S., & Gupta, A. (2008). Adoption of ICT in a government organisation in a developing country: An empirical study. Journal of Strategic Information Systems, 17(2), 140-154.
- Holbrook, M. B., & Hirschman, E. C. (1982). The experiential aspects of consumption: Consumer fantasies, feelings, and fun. Journal of Consumer Research, 9(2), 132-140.
- Han, M., Wu, J., Wang, Y., & Hong, M. (2018). A model and empirical study on the user’s continuance intention in online China brand communities based on customer-perceived benefits. Journal of Open Innovation, 4(4), 46. https://doi.org/10.3390/joitmc4040046
- Joo, Y. J., Park, S., & Shin, E. K. (2017). Students’ expectation, satisfaction, and continuance intention to use digital textbooks. Computers in Human Behaviour, 69, 83-90. https://doi.org/10.1016/j.chb.2016.12.025
- Kocaleva, M., Stojanovic, I., & Zdravev, Z. (2015). Model of e-learning acceptance and use for teaching staff in higher education institutions. I.J. Modern Education and Computer Science, 4, 23-31. https://doi.org/10.5815/ijmecs.2015.04.03
- Lai, P.C. (2017). The literature review of technology adoption models and theories for the novelty technologies. Journal of Information Systems and Technology Management, 14(1), 21-38.
- Lazar, I., Panisoara, I., Panisoara, G., Chirca, R., & Ursu, A. (2020). Motivation and continuance intention towards online instruction among teachers during the COVID-19 pandemic: The mediating effect of burnout and techno-stress. International Journal of Environmental and Public Health Research, 17(21). https://doi.org/10.3390/ijerph17218002
- Mishra, P., & Koehler, M. J. (2006). Technological pedagogical content knowledge: A framework for teacher knowledge. Teachers College Record, 108(6), 1017.
- Morris, M. G., & Venkatesh, V. (2000). Age Differences in technology adoption decisions: Implications for a changing workforce. Personnel Psychology, 53(2), 375-403.
- Mostafa, A.F., & Rachid, J. (2017). Analysis identifying the particularities of the adoption of e-learning: Case of the Moraccan ministry of education. International Journal of Innovation and Research in Educational Science, 4(3). https://www.ijires.org/index.php/archive/20-2017
- Mouakket, S. (2020). Investigating the role of mobile payment quality characteristics in the United Arab Emirates: Implications for emerging economies. International Journal of Bank Marketing, 38(7), 1465-1490.
- Olushola, T. & Abiola, J.O. (2017). The efficacy of technology acceptance model: A review of applicable theoretical models in information technology researches. Journal of Research in Business and Management, 4(11), 70-83.
- OECD (2007). Giving knowledge for free: The emergence of open educational resource. OECD Publishing. https://www.oecd.org/education/ceri/386 54317
- OECD (2015). Students, computers and learning: Making the connection. OECD Publishing. https://www.oecd-ilibrary.org/docserver/9789264239555-en.pdf?expires=1683976446&id=id&accname=guest&checksum=0DFAFFAC742B1E11DA7BFA763CA445DC
- OECD (2017). PISA 2015 results (volume V): Collaborative problem solving. OECD Publishing. https://www.oecd-ilibrary.org/docserver/97864285521en.pdf?expires=1683976756&id=id&accname=guest&checksum=35CD83C07B1D9F31385FED7587939D3F
- OECD (2020). PISA 2018 results (vol.V): Effective policies, successful schools. OECD Publishing. https://www.oecd-ilibrary.org/docserver/ca768d40-en.pdf?expires=1683977159&id=id&accname=guest&checksum=02468EFE30D8A04943798B9530369985
- Pynoo, B., Devolder, P., Tondeur, J., Van Braak, J., Duyck, W., & Duyck, P. (2011). Predicting secondary school teachers’ acceptance and use of a digital learning environment: A cross-sectional study. Computers in Human Behavior, 27(1), 568-575.
- Sailer, M., Murbock, J., & Fisher, F, (2021). Digital learning in schools: What does it take beyond digital technology? Teaching and Teacher Education, 103. https//doi.org/10.1016/j.tate.2021.103346
- Singh, C. B. P. (2020). Perceptual threat, social distancing and well-being of people during COVID 19. International Journal of Human Potential Management, 2(2), 35-50.
- Venkatesh, V., Thong, J.Y.L., & Xu Xin. (2012). Consumer acceptance and use of information technology: Extending the unified theory of use and acceptance of technology. MIS Quarterly, 36(1), 157-178.
- Venkatesh, V., Thong, J.Y.L., & Xu Xin. (2016). Unified theory of acceptance and use of technology: A synthesis and the road ahead. Journal of Association for Information Systems, 17(5), 328-376.
- Venkatesh, V., Morris, M.G., Davis, G.B., & Davis, F.D. (2003). User acceptance of information technology: Towards unified view. MIS Quarterly, 27(3), 425-478.
- Venkatesh, V., Morris, M. G., & Ackerman, P. L. (2000). A longitudinal field investigation of gender differences in individual technology adoption decision making processes. Organisational Behaviour and Human Decision Processes, 83(1), 33-60.
- Zhang, X., Wang, W., de Pablos, P. O.,Tang, J., & Yan, X. (2015). Mapping development of social media research through different disciplines: Collaborative learning in management and computer science. Computers in Human Behaviour, 51, 1142-1153.
- Footnotes. The study received support from Bihar Education Project Council, Patna and State Council of Educational Research and Training, Patna. Thanks are acknowledged to team members who supported the investigator during data collection and analysis.
![](https://i-scholar.in/public/site/images/abstractview.png)
Abstract Views: 204
![](https://i-scholar.in/public/site/images/pdfview.png)
PDF Views: 0