The unexpected community quarantine period in the Philippines due to the COVID-19 pandemic has brought about a total switch from traditional classroom teaching to online teaching. The unprecedented challenges of whether teachers were prepared enough in terms of materials and their capability in delivering their lessons from traditional to online teaching prompted the researchers to conduct this study. Thus, this study attempted to investigate the robustness of UTAUT constructs and an aspect of IDT and explore the integration of trialability and compatibility to find out the preparedness of the teachers in using Canvas features during their lockdown days. Empirical data were collected through online surveys among university faculty (N=786) that used Canvas online features. Modeling and structuring approaches, such as a statistical tool called SmartPLS 3, were used. Results indicated that all the eight hypotheses tested, integrating trialability and compatibility with UTAUT constructs, were supported at p <0.000. Most particularly, the findings revealed the following: (a) Trialability of Canvas usage affects effort expectancy of users; (b) Social influence is directly related to facilitating conditions; (c) Compatibility on Canvas usage is directly related to facilitating conditions to use; (d) Effort expectancy influences usage of Canvas features; (e) Facilitating conditions affect the usage of Canvas features directly; (f) Performance expectancy is directly related to the usage of Canvas features; (g) Compatibility has a direct effect on performance expectancy; and (h) Trialability is directly related to compatibility in using the technology. Thus, the actual usage and acceptance of Canvas had been justified, giving evidence that the faculty were ready for online teaching during the quarantine period. It was recommended that educators continue with the online learning mode that meets learners’ needs. The models used in the study may be tried by future researchers using the same Canvas features.
Keywords: Canvas features, trialability, compatibility, performance expectancy, social influenceAbdekhoda, M., Ahmadi, M., Gohari, M., & Noruzi, A. (2015). The effects of organizational contextual factors on physicians’ attitude toward adoption of Electronic Medical Records. Journal of bBiomedical iInformatics, 53, 174-–179. https://doi.org/10.1016/j.jbi.2014.10.008
Abdullateef, B. N., Elias, N. F., Mohamed, H., Zaidan, A. A., & Zaidan, B. B. (2016). An evaluation and selection problems of OSS-LMS packages. SpringerPlus, 5(1), 248. https://doi.org/10.1186/s40064-016-1828-y
Alfarani, L. A. K. (2016). Exploring the influences on faculty members’ adoption of mobile learning at King Abdulaziz University, Saudi Arabia ([Unpublished dDoctoral dissertation].), University of Leeds. http://etheses.whiterose.ac.uk/id/eprint/13402
Alshalan, T. (2019). The adoption of learning management systems (LMS) among faculty members at Kansas State University and King Saud University ([Unpublished dDoctoral dissertation]). University name. http://hdl.handle.net/2097/40243
Ain, N., Kaur, K., & Waheed, M. (2016). The influence of learning value on learning management system use: An extension of UTAUT2. Information Development, 32(5), 1306-–1321. https://doi.org/10.1177/0266666915597546
Alalwan, A. A., Dwivedi, Y. K., & Rana, N. P. (2017). Factors influencing adoption of mobile banking by Jordanian bank customers: Extending UTAUT2 with trust. International Journal of Information Management, 37(3), 99-–110. https://doi.org/10.1016/j.ijinfomgt.2017.01.002
Albalawi, A. (2018). Factors supporting faculty`s intention to use online video sharing platforms in the classroom environment [Unpublished doctoral dissertation]. Northern Illinois University. https://commons.lib.niu.edu/handle/10843/21276
Al-Rahmi, W. M., Yahaya, N., Aldraiweesh, A. A., Alamri, M. M., Aljarboa, N. A., Alturki, U., & Aljeraiwi, A. A. (2019). Integrating technology acceptance model with innovation diffusion theory: An empirical investigation on students’ intention to use E-learning systems. IEEE Access, 7, 26797-–26809. DOIdoi: 10.1109/ACCESS.2019.2899368
Altbach, P. G., Reisberg, L., & Rumbley, L. E. (2019). Trends in global higher education: Tracking an academic revolution. Brill.
Ayodele, S., Endozo, A., & Ogbari, M. E. (2018, October). A study on factors hindering online learning acceptance in developing countries. In Proceedings of the 10th International Conference on Education Technology and Computers (pp. 254-–258). ACM. https://doi.org/10.1145/3290511.3290533
Ayodele, S. O., Oga, O. E., Bundot, Y. G., & Ogbari, M. E. (2016, October). Role of power supply towards e-learning acceptance: VBSEM-AMOS. In 2016 6th International Conference on Information Communication and Management (ICICM) (pp. 151-–155). IEEE. DOIdoi: 10.1109/INFOCOMAN.2016.7784233
Bardakcı, S. (2019). Exploring high school students` educational use of YouTube. International Review of Research in Open and Distributed Learning, 20(2). https://doi.org/10.19173/irrodl.v20i2.4074
Barnard, Y., Bradley, M. D., Hodgson, F., & Lloyd, A. D. (2013). Learning to use new technologies by older adults: Perceived difficulties, experimentation behavior and usability. Computers in Human Behavior, 29(4), 1715-–1724. https://doi.org/10.1016/j.chb.2013.02.006
Bloomfield, R. J. (2020). A quick-start guide to teaching online. Available at SSRN 3558879. http://dx.doi.org/10.2139/ssrn.3558879
Brata, A. H., & Amalia, F. (2018, June). Impact analysis of social influence factors on using free blogs as learning media for driving teaching motivational factors. In Proceedings of the 4th International Conference on Frontiers of Educational Technologies (pp. 29-–33). https://doi.org/10.1145/3233347.3233360
Cepeda-Carrion, G., Cegarra-Navarro, J. G., & Cillo, V. (2019). Tips to use partial least squares structural equation modeling (PLS-SEM) in knowledge management. Journal of Knowledge Management, x(x), xx–xx. https://doi.org/10.1108/JKM-05-2018-0322
Chang, P. Y., Ng, M. Q., Sim, H. Y., Yap, J. W., & Yin, S. Y. (2015). Factors influencing behavioral intention to adopt mobile e-books among undergraduates: UTAUT2 framework ([Doctoral Unpublished doctoral dissertation]). UTAR.
Chao, C. M. (2019). Factors determining the behavioral intention to use mobile learning: An application and extension of the UTAUT model. Frontiers in pPsychology, 10, 1652 . https://doi.org/10.3389/fpsyg.2019.01652
Chua, P. Y., Rezaei, S., Gu, M. L., Oh, Y., & Jambulingam, M. (2018). Elucidating social networking apps decisions: Performance expectancy, effort expectancy and social influence. Nankai Business Review International, 9(2), 118-–142. https://www.emerald.com/insight/content/doi/10.110
Di Domenico, L., Pullano, G., Coletti, P., Hens, N., & Colizza, V. (year). Expected impact of school closure and telework to mitigate COVID-19 epidemic in France. Retrieved from www.epicx-lab.com/covid-19.html]
Dobre, I. (2015). Learning management systems for higher education--An overview of available options for higher education Organizations. Procedia-Social and Behavioral Sciences, 180, 313-–320. Retrieved from http://creativecommons.org/licenses/by-nc-nd/4.0/.
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
Dumpit, D. Z., & Fernandez, C. J. (2017) Analysis of the use of social media in higher education institutions (HEIs) using the technology acceptance model. International Journal of Educational Technology in Higher Education, 14(1), 5. https://doi.org/10.1186/s41239-017-0045-2
Dziuban, C., Graham, C. R., Moskal, P. D., Norberg, A., & Sicilia, N. (2018). Blended learning: The new normal and emerging technologies. International Journal of Educational Technology in Higher Education, 15(1), 3. https://doi.org/10.1186/s41239-017-0087-5
Endozo, A. N. (2019). Structuring the quadratic effect of motivation towards mental tasks performance among university students. Journal of Theoretical and Applied Information Technology, 97(13), .xxxx–xxxx. Retrieved from www.jatit.org
Endozo, A. N., Oluyinka, S., & Daenos, R. G. (2019, October). Teachers` experiences towards learning management system: CANVAS. In Proceedings of the 2019 11th International Conference on Education Technology and Computers (pp. 91-–95). https://doi.org/10.1145/3369255.3369257
Fathema, N., Shannon, D., & Ross, M. (2015). Expanding the Technology technology Acceptance acceptance Model model (TAM) to examine faculty use of Learning learning Management management Systems systems (LMSs) in higher education institutions. Journal of Online Learning & Teaching, 11(2), xxx–xxx. Retrieved from http://creativecommons.org/licenses/by-nc-sa/3.0/us/
Gao, L., & Waechter, K. A. (2017). Examining the role of initial trust in user adoption of mobile payment services: An empirical investigation. Information Systems Frontiers, 19(3), 525-–548. https://doi.org/10.1007/s10796-015-9611-0
Graf-Vlachy, L., Buhtz, K., & König, A. (2018). Social influence in technology adoption:Taking stock and moving forward. Management Review Quarterly, 68(1), 37-–76. https://doi.org/10.1007/s11301-017-0133-3
Györy, A., Cleven, A., Uebernickel, F., & Brenner, W. (2012). Exploring the shadows: IT governance approaches to user-driven innovation. https://aisel.aisnet.org/ecis2012/222
Hair, J. F., Ringle, C. M., & Sarstedt, M. (2012). Partial least squares: The better approach to structural equation modeling? Long Range Planning, 45(5-–6), 312-–319. DOIdoi: 10.1016/j.lrp.2012.09.011
Hair, J. F., Ringle, C. M., & Sarstedt, M. (2013). Partial least squares structural equation modeling: Rigorous applications, better results and higher acceptance. Long range Range planningPlanning, 46(1-–2), 1-–12. https://doi.org/10.1108/EBR-10-2013-0128
Hair, J. F., Risher, J. J., Sarstedt, M., & Ringle, C. M. (2019). When to use and how to report the results of PLS-SEM. European Business Review, x(x), xx–xx. https://doi.org/10.1108/EBR-11-2018-0203
Henseler, J., Ringle, C. M., & Sarstedt, M. (2015). A new criterion for assessing discriminant validity in variance-based structural equation modeling. Journal of the aAcademy of mMarketing sScience, 43(1), 115-–135. https://doi.org/10.1007/s11747-014-0403-8
Henseler, J., Ringle, C. M., & Sarstedt, M. (2012). Using partial least squares path modeling in international advertising research: Basic concepts and recent issues. In S. Okazaki (Ed.), Handbook of research in international advertising (pp. 252-276). Edward Elgar Publishing, pp. 252-276. DOI:https://doi.org/10.4337/9781781001042.00023
Hooi, T. K., Abu, N. H. B., & Rahim, M. K. I. A. (2018). Relationship of big data analytics capability and product innovation performance using SmartPLS 3.2. 6: Hierarchical component modeling in PLS-SEM. Int. J. Supply Chain Manag, 7, 51–xx. Retrieved from http://excelingtech.co.uk/
Ifenthaler, D., & Schweinbenz, V. (2016) Student’s acceptance of tablet PC in the classroom. Journal of Research on Technology in Education, 48(4), 306-–321. https://doi.org/10.1080/15391523.2016.1215172
Islam, A. N. (2016). E-learning system use and its outcomes: Moderating role of perceived compatibility. Telematics and Informatics, 33(1), 48-–55. https://doi.org/10.1016/j.tele.2015.06.010Get rights and content
Kintu, M. J., Zhu, C., & Kagambe, E. (2017). Blended learning effectiveness: The relationship between student characteristics, design features and outcomes. International Journal of Educational Technology in Higher Education, 14(1), 7. https://doi.org/10.1186/s41239-017-0043-4
Lacap, J. P. G. (2019). The mediating effect of employee engagement on the relationship of transformational leadership and intention to quit: Evidence from local colleges in Pampanga, Philippines. Asia-Pacific Social Science Review, 19(1), 33-–48.
Lin, T. T., & Bautista, J. R. (2017). Understanding the relationships between mHealth Apps` characteristics, trialability, and mHealth literacy. Journal of hHealth cCommunication, 22(4), 346-–354. https://doi.org/10.1080/10810730.2017.1296508
Mafunda, B., Bere, A., & Swart, J. (2016, July). Establishing determinants of electronic books utilization: An integration of two human computer interaction adoption frameworks. In the International Conference on Human-Computer Interaction (pp. 549-–562). Springer, Cham. https://doi.org/10.1007/978-3-319-39513-5_51
Mai, Y., Zhang, Z., & Wen, Z. (2018). Comparing exploratory structural equation modeling and existing approaches for multiple regression with latent variables. Structural Equation Modeling: A Multidisciplinary Journal, 25(5), 737-–749. https://doi.org/10.1080/10705511.2018.1444993
Mallmann, G. L., Maçada, A. C. G., & Eckhardt, A. (2018). We are social: A social influence perspective to investigate Shadow IT usage. https://aisel.aisnet.org/ecis2018_rp/190
Martin, F., Wang, C., & Sadaf, A. (2018). Student perception of helpfulness of facilitation strategies that enhance instructor presence, connectedness, engagement and learning in online courses. The Internet and Higher Education, 37, 52-65. https://doi.org/10.1016/j.iheduc.2018.01.003
Masood, R., Seshadri, N., & Bhargava, A. (2019). U.S. Patent No. 10,423,929. Washington, DC: U.S. Patent and Trademark Office.
Mohammadi, H. (2015). Investigating users’ perspectives on e-learning: An integration of TAM and IS success model. Computers in Human Behavior, 45, 359-–374. https://doi.org/10.15728/bbr.2018.15.6.4
Moreno, V., Cavazotte, F., & Alves, I. (2017). Explaining university students’ effective use of e‐learning platforms. British Journal of Educational Technology, 48(4), 995-–1009. https://doi.org/10.1111/bjet.12469
Nielson, B. (2017). Learning management system basics (LMS). Retrieved from http://www.elearninglearning.com/learning-management-system
Oluyinka, S., & Endozo, A. N. (2019). Barriers to e-learning in developing countries: A comparative study. Journal of Theoretical and Applied Information Technology, 97(9), XXXX–XXXX. Retrieved from www.jatit.org
Oluyinka, S., Shamsuddin, A., & Wahab, E. (2015). Regions of trust to technological know-how structured for banking customers. Asian Journal of Applied Sciences, 3(4).
Oluyinka, S., Shamsuddin, A., Ajagbe, M. A., & Enegbuma, W. I. (2013). A study of electronic commerce adoption factors in Nigeria. IJISCM, 6(4), 293-–315. DOIdoi: 10.1504/IJISCM.2013.060974
Öztürk, Y. E., & Gürler, İ. (2020). Evaluation of Moodle, Canvas, Blackboard, and Open EdX. In ICT-based assessment, methods, and programs in tertiary education (pp. 363-–382). IGI Global. DOIdoi: 10.4018/978-1-7998-3062-7.ch018
Pangilinan, R. R., Yutuc, M. M. T., Nuqui, J. C., Garnica, L. L., & Ayodele, S. (year). Study on copyright awareness among college students.
Rai, S. K., Ramamritham, K., & Jana, A. (2020). Identifying factors affecting the acceptance of government to government systems in developing nations–empirical evidence from Nepal. Transforming Government: People, Process and Policy, x(x). https://doi.org/10.1108/TG-05-2019-0035
Ramayah, T. (2020). Determinants of technology adoption among Malaysian SMEs: An IDT perspective. Journal of Information and Communication Technology, 12, 103-119 Retrieved from http://e-journal.uum.edu.my/index.php/jict/article/view/8139
Raza, S. A., Shah, N., & Ali, M. (2019). Acceptance of mobile banking in Islamic banks: evidence Evidence from modified UTAUT model. Journal of Islamic Marketing, x(x), xx–xx. https://doi.org/10.1108/JIMA-04-2017-0038
Ringle, C. M., Sarstedt, M., Mitchell, R., & Gudergan, S. P. (2018). Partial least squares structural equation modeling in HRM research. The International Journal of Human Resource Management, 31(12), 1-27. https://doi.org/10.1080/09585192.2017.1416655
Rogers, E. M. (2010). Diffusion of innovations. Simon and Schuster.
Roman, R. G., Trobada, C. S. P., Gaton, F. P., Gania, C. K., Oluyinka, S. A., Cuenco, H. O., & Daenos, R. G. (year). A study on the utilization of e-resources among college students.
Rönkkö, M., McIntosh, C. N., Antonakis, J., & Edwards, J. R. (2016). Partial least squares path modeling: Time for some serious second thoughts. Journal of Operations Management, 47, 9-–27. https://doi.org/10.1016/j.jom.2016.05.002
Sasaki, M. (2018). Application of diffusion of innovation theory to educational accountability: The case of EFL education in Japan. Language Testing in Asia, 8(1), 15-–16. https://doi.org/10.1186/s40468-017-0052-1
Solomon, O., Alina, S., Eta, W., & Ajagbe, A. M. (2014). Internet banking adoption in Nigeria: A literature review. In 2014 International Conference on Computer, Intelligent Computing and Education Technology, March 28th, 2014, Hong Kong. Indexed by Taylor & Francis Group, EI Compendex, ISI (CPCI, ISTP). URI:http://eprints.covenantuniversity.edu.ng/id/eprint/5150
Stewart, B. L. (2014). The Canvas learning management system: integrating educational philosophy, communication, delivery and tools. DOCERE, (10), 28-32. https://revistas.uaa.mx/index.php/docere/article/view/2258
Streukens, S., & Leroi-Werelds, S. (2016). Bootstrapping and PLS-SEM: A step-by-step guide to get more out of your bootstrap results. European Management Journal, 34(6), 618-–632. https://doi.org/10.1016/j.emj.2016.06.003
Strömberg, H., Rexfelt, O., Karlsson, I. M., & Sochor, J. (2016). Trying on change–Trialability as a change moderator for sustainable travel behavior. Travel Behavior and Society, 4, 60-–68. https://doi.org/10.1016/j.tbs.2016.01.002
Sung, H. N., Jeong, D. Y., Jeong, Y. S., & Shin, J. I. (2015). The relationship among self-efficacy, social influence, performance expectancy, effort expectancy, and behavioral intention in mobile learning service. International Journal of u-and e-Service, Science and Technology, 8(9), 197-–206. http://dx.doi.org/10.14257/ijunesst.2015.8.9.21
Tan, E., & Lau, J. L. (2016). Behavioral intention to adopt mobile banking among the millennial generation. Young Consumers, x(x), xx–xx. https://doi.org/10.1108/YC-07-2015-00537
Tosuntaş, Ş. B., Karadağ, E., & Orhan, S. (2015). The factors affecting acceptance and use of interactive whiteboard within the scope of FATIH project: A structural equation model based on the uUnified theory of acceptance and use of technology. Computers & Education, 81, 169-–178 https://doi.org/10.1016/j.compedu.2014.10.009
Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User acceptance of information technology: Toward a unified view. MIS Quarterly, x(x), 425-–478. DOIdoi: 10.2307/30036540
Venkatesh, V., & Davis, F. D. (2000). A theoretical extension of the technology acceptance model: Four longitudinal field studies. Management scienceScience, 46(2), 186-–204. https://doi.org/10.1287/mnsc.46.2.186.11926
Venkatesh, V., & Morris, M. G. (2000). Why don`t men ever stop to ask for directions? Gender, social influence, and their role in technology acceptance and usage behavior. MIS Quarterly, x(x), 115-–139.
Vijayabanu, C., Therasa, C., & Daisy, S. A. (2019). Impression management tactics as a psychological booster for the communication of IT employees: SMART PLS Approach. Revista de Psicología, 37(2), 683-–707. http://dx.doi.org/10.18800/psico.201902.012.
Vijayabanu, C., & Arunkumar, S. (2018). Strengthening the team performance through personality and emotional intelligence: Smart PLS approach. Scientific Annals of Economics and Business, 65(3), 303-–316. DOI:
https://doi.org/10.2478/saeb-2018-0019
Wilcox, D., Thall, J., & Griffin, O. (2016, March). One canvas, two audiences: How faculty and students use a newly adopted learning management system. In Society for Information Technology & Teacher Education International Conference (pp. 1163-–1168). Association for the Advancement of Computing in Education (AACE).
Wu, B., & Chen, X. (2017). Continuance intention to use MOOCs: Integrating the technology acceptance model (TAM) and task technology fit (TTF) model. Computers in Human Behavior, 67, 221-–232. https://doi.org/10.1016/j.chb.2016.10.028
Yalung, H. A., Tuliao, D. L., Gabriel, P. R. M., Oluyinka, S. A., Gil, M., & Daenos ,R.G. (2020). Use of social media platforms in promoting the academic library services of City College of Angeles among students. International Journal of Information and Education Technology, 10(6), xxx–xxx.
Zhang, X., Yu, P., Yan, J., & Spil, I. T. A. (2015). Using diffusion of innovation theory to understand the factors impacting patient acceptance and use of consumer e-health innovations: A case study in a primary care clinic. BMC Health Services Research, 15(1), 71. DOI https://doi.org/10.1186/s12913-015-0726-2
Zolkepli, I. A., & Kamarulzaman, Y. (2015). Social media adoption: The role of media needs and innovation characteristics. Computers in Human Behavior, 43, 189-–209. https://doi.org/10.1016/j.chb.2014.10.050