Factors Affecting Behavioral Intention to Use Learning Management Systems by Instructors
DOI:
https://doi.org/10.15665/dem.v19i2.2728Keywords:
UTAUT, Behavioral Intention, LMS, Instructors, YemenAbstract
This research intends to contribute to the literature of learning management systems by determining the factors influencing the behavioral intention to use learning management systems by Instructors. The data was collected throughout a survey distributed to 70 instructors in Yemen using a stratified random sampling approach. The study findings show that Performance Expectancy, Effort Expectancy, Training have a significant impact on the instructors’ attitude toward using LMS. Computer Self-Efficacy appears to be an insignificant predictor of the instructors’ Attitude toward using. In addition, the study findings show that attitudes toward using learning management systems have a significant impact on behavioral intention to use learning management systems. This study confirms that the proposed model is applicable to be used in different technologies within different countries, which helps in filling the human factors gap in the literature in the context of learning management systems.
References
Abbitt, J., & Klett, M. (2007). Identifying influences on attitudes and self-efficacy beliefs towards technology integration among pre-service educators. Electronic Journal for the Integration of Technology in Education, 6(1), 28–42.
Abuloum, A. (2005). The Effect of an Educational Internet-Based Computer Literacy Course on Pre-service Teacher Education Students’ Computer Self-Efficacy. Journal of Educational & Psychological Sciences, 06(04). https://doi.org/10.12785/JEPS/060412
Agarwal, R., Sambamurthy, V., & Stair, R. M. (2000). Research Report: The Evolving Relationship between General and Specific Computer Self-Efficacy - An Empirical Assessment. Information Systems Research, 11(4), 418–430.
Akbar, F. (2013). What affects students ’ acceptance and use of technology? Pdfs.Semanticscholar.Org, 1–33. Retrieved from https://pdfs.semanticscholar.org/98a9/27e52df194e97732426e55d6366698675052.pdf
Al-Alak, B. A., & Alnawas, I. A. M. (2011). Measuring the acceptance and adoption of e-learning by academic staff. Knowledge Management and E-Learning, 3(2), 201–221. Retrieved from http://kmel-journal.org/ojs/index.php/online-publication/article/view/114
Al-Sayyed, F., & Abdalhaq, B. (2016). Interventional Factors Affecting Instructors Adoption Of E-Learning System: A Case Study Of Palestine. Journal of Theoretical and Applied Information Technology, 10(1).
Alawadhi, S., & Morris, A. (2008). The use of the UTAUT model in the adoption of e-government services in Kuwait. Proceedings of the Annual Hawaii International Conference on System Sciences.
Alenezi, A. R., Karim, A. M. A., & Veloo, A. (2011). Institutional support and e-learning acceptance: An extension of the technology acceptance model. International Journal of Instructional Technology and Distance Learning, 8(2), 3–16.
Alharbi, S., & Drew, S. (2014). Using the Technology Acceptance Model in Understanding Academics’ Behavioural Intention to Use Learning Management Systems. International Journal of Advanced Computer Science and Applications, 5(1). https://doi.org/10.14569/ijacsa.2014.050120
Almrashdeh, I. A., Sahari, N., Zin, N. A. M., & Alsmadi, M. (2011). Distance Learning Management System requirements from students’s perspective. Journal of Theoretical and Applied Information Technology, 17–27.
AlQudah Ahmed, A. (2014). Accepting Moodle By Academic Staff At the University of Jordan : Applying and Extending Tam in Technical Support Factors. European Scientific Journal, 10(18), 183–200. Retrieved from http://eujournal.org/index.php/esj/article/view/3591
Amoako-Gyampah, K., & Salam, A. F. (2004). An extension of the technology acceptance model in an ERP implementation environment. Information and Management, 41(6), 731–745. https://doi.org/10.1016/j.im.2003.08.010
An, Y. J., & Reigeluth, C. (2011). Creating Technology-Enhanced, Learner-Centered Classrooms: K–12 Teachers’ Beliefs, Perceptions, Barriers, and Support Needs. Journal of Digital Learning in Teacher Education, 28(2), 54–62.
Anderson, S. E., & Maninger, R. M. (2007). Preservice teachers’ abilities, beliefs, and intentions regarding technology integration. Journal of Educational Computing Research, 37(2), 151–172. https://doi.org/10.2190/H1M8-562W-18J1-634P
Arteaga Sánchez, R., Duarte Hueros, A., & García Ordaz, M. (2013). E‐learning and the University of Huelva: a study of WebCT and the technological acceptance model. Campus-Wide Information Systems, 30(2), 135–160.
Aşkar P. and Umay, A. (2001). Perceived computer self-efficacy of the students in the elementary mathematics teaching programme. Hacettepe University Journal of Education, 21(1), 1–8.
Ayele, A. A., & Birhanie, W. K. (2018). Acceptance and use of e-learning systems: the case of teachers in technology institutes of Ethiopian Universities. Applied Informatics, 5(1), 1–11. https://doi.org/10.1186/s40535-018-0048-7
Aziz, H. (2010). The 5 Keys to Educational Technology. T H E Journal. Retrieved from https://thejournal.com/Articles/2010/09/16/The-5-Keys-to-Educational-Technology.aspx/?p=1
Bagozzi, R. P. (2007). The legacy of the technology acceptance model and a proposal for a paradigm shift. Journal of the Association for Information Systems, 8(4), 244–254. https://doi.org/10.17705/1jais.00122
Bandura, A., & Locke, E. A. (2003). Negative self-efficacy and goal effects revisited. Journal of Applied Psychology, 88(1), 87–99. https://doi.org/10.1037/0021-9010.88.1.87
Bandyopadhyay, K., & Fraccastoro, K. A. (2019). The Effect of Culture on User Acceptance of Information Technology. Communications of the Association for Information Systems, 19. https://doi.org/10.17705/1cais.01923
Baral, R., & Rajan, C. A. (2015). Adoption of ERP system: An empirical study of factors influencing the usage of ERP and its impact on end use. IIMB Management Review, 27, 105–117.
Bhattacherjee, & Premkumar. (2004). Understanding Changes in Belief and Attitude toward Information Technology Usage: A Theoretical Model and Longitudinal Test. MIS Quarterly, 28(2), 229.
Borrego, M., Douglas, E. P., & Amelink, C. T. (2009). Quantitative, Qualitative, and Mixed Research Methods in Engineering Education. Journal of Engineering Education, 98(1), 53–66.
Bradford, M., & Florin, J. (2003). Examining the role of innovation diffusion factors on the implementation success of enterprise resource planning systems. International Journal of Accounting Information Systems, 4(3), 205–
Brosnan, M. (1998). Technophobia: the psychological impact of information technology. Routledge.
Brown, L. A. (2017). INSTRUCTOR USAGE OF LEARNING MANAGEMENT SYSTEMS UTILIZING A TECHNOLOGY ACCEPTANCE MODEL by Lisa Ann Brown A dissertation proposal submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy in Adult and Higher Education. MONTANA STATE UNIVERSITY.
Brown, S. A., Dennis, A. R., & Venkatesh, V. (2010). Predicting Collaboration Technology Use: Integrating Technology Adoption and Collaboration Research. Journal of Management Information Systems. https://doi.org/10.2753/MIS0742-1222270201
Bugembe, J. (2010). Perceived Usefulness, Perceived Ease of Use, Attitude and Actual Usage of a New Financial Management System thesis (MAKERERE UNIVERSITY). Retrieved from http://dspace3.mak.ac.ug/handle/10570/2806
Cavus, N., & Alhih, M. S. (2014). Learning Management Systems Use in Science Education. Procedia - Social and Behavioral Sciences. https://doi.org/10.1016/j.sbspro.2014.07.429
Chau, P. Y. K. (1996). An Empirical Assessment of a Modified Technology Acceptance Model. Journal of Management Information Systems, 13(2), 185–204. https://doi.org/10.1080/07421222.1996.11518128
Chau, P. Y. K., & Hu, P. J. (2002). Examining a model of information technology acceptance by individual professionals: An exploratory study. Journal of Management Information Systems, 18(4), 191–229. https://doi.org/10.1080/07421222.2002.11045699
Cheng, Y.-M. (2011). Antecedents and consequences of e-learning acceptance. Information Systems Journal, 21(3), 269–299. https://doi.org/10.1111/j.1365-2575.2010.00356.x
Compeau, D. R., & Higgins, C. A. (1995). Computer Self-Efficacy: Development of a Measure and Initial Test. MIS Quarterly, 19(2), 189. https://doi.org/10.2307/249688
Cresswel, J. (2013). Qualitative, quantitative, and mixed methods approaches. In Research design.
Dahlstrom, E., Brooks, D. C., & Bichsel, J. (2014). The Current Ecosystem of Learning Management Systems in Higher Education: Student, Faculty, and IT Perspectives. EDUCAUSE Center for Analysis and Research. https://doi.org/10.13140/RG.2.1.3751.6005
Davidson, C. N. (2011). Now You See It, Brain Science of Attention. Retrieved from https://scholar.google.com/scholar?hl=en&as_sdt=0%2C5&q=Now+you+see+it%3A+How+technology+and+brain+science+will+transform+schools+and+business+for+the+21st+century&btnG=
Davis, F.D. (1989). Perceived Usefulness, Perceived Ease of Use, and User Acceptance of Information Technology. MIS Quarterly. https://doi.org/10.1016/j.cell.2017.08.036
Devi, B., Khandelwal, B., & Das, M. (2017). Application of Bandura’s social cognitive theory in the technology enhanced, blended learning environment. International Journal of Applied Research, 3(1), 721–724. Retrieved from www.allresearchjournal.com
Dias, S. B., & Diniz, J. A. (2013). FuzzyQoI model: A fuzzy logic-based modelling of users’ quality of interaction with a learning management system under blended learning. Computers and Education. https://doi.org/10.1016/j.compedu.2013.06.016
Downey, J. P., & McMurtrey, M. (2007). Introducing task-based general computer self-efficacy: An empirical comparison of three general self-efficacy instruments. Interacting with Computers, 19(3), 382–396. https://doi.org/10.1016/j.intcom.2006.11.001
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 (UTAUT): Towards a Revised Theoretical Model. Information Systems Frontiers, 1–16. https://doi.org/10.1007/s10796-017-9774-y
Ferdousi, B. J. (2009). A study of factors that affect instructors’ intention to use e-learning systems in two-year colleges. Dissertation Abstracts International. A, The Humanities and Social Sciences, 70(4-A), 1–151.
FORD, J. K., & NOE, R. A. (1987). Self‐Assessed Training Needs: the Effects of Attitudes Toward Training, Managerial Level, and Function. Personnel Psychology, 40(1), 39–53.
Gautreau, C. (2016). Motivational Factors Affecting the Integration of a Learning Management System by Faculty. The Journal of Educators Online, 8(1). https://doi.org/10.9743/jeo.2011.1.2
Gialamas, V., & Nikolopoulou, K. (2010). In-service and pre-service early childhood teachers’ views and intentions about ICT use in early childhood settings: A comparative study. Computers and Education, 55(1), 333–341. https://doi.org/10.1016/j.compedu.2010.01.019
Gibson, S. G., Harris, M. L., & Colaric, S. M. (2008). Technology Acceptance in an Academic Context: Faculty Acceptance of Online Education. Journal of Education for Business, 83(6), 355–359. https://doi.org/10.3200/joeb.83.6.355-359
Gilbert, A. (2015). An exploration of the use of and the attitudes toward technology in first-year instrumental music. Retrieved from https://digitalcommons.unl.edu/musicstudent/79/
Gunn, T. M., & Hollingsworth, M. (2013). The implementation and assessment of a shared 21st century learning vision: A district-based approach. Journal of Research on Technology in Education. https://doi.org/10.1080/15391523.2013.10782603
Hair, J., Black, W., Babin, B., Anderson, R., & Tatham, R. (2006). Multivariate data analysis (6 ed.). Englewood Cliffs, NJ: Pearson Prentice Hall. New Jersey.
Hampe, G. (2014). Learning in a virtual environment. Acta.Fih.Upt.Ro. Retrieved from http://acta.fih.upt.ro/pdf/2014-4/ACTA-2014-4-04.pdf
Hasan, B. (2003). The influence of specific computer experiences on computer self-efficacy beliefs. Computers in Human Behavior, 19(4), 443–450. https://doi.org/10.1016/S0747-5632(02)00079-1
Hasan, B., & Ali, J. (2006). The Impact of General and System-Specific Self-Efficacy on Computer Training Learning and Reactions. Journal of Management Information and Decision Sciences, 9(1), 17. Retrieved from http://search.proquest.com/openview/325f314769d03d9f4aba9bd4ff58bc15/1?pq-origsite=gscholar&cbl=38743
He, J., & King, W. R. (2006). A meta-analysis of the technology acceptance model. Information and Management, 43(6), 740–755. Retrieved from https://www.sciencedirect.com/science/article/pii/S0378720606000528
Holden, R. J., & Karsh, B.-T. (2010). The technology acceptance model: its past and its future in health care. Journal of Biomedical Informatics, 43(1), 159–172.
Hu, P. J. H., Clark, T. H. K., & Ma, W. W. (2003). Examining technology acceptance by school teachers: A longitudinal study. Information and Management, 41(2), 227–241.
Ifinedo, P. (2006). Acceptance and Continuance Intention of Web-based Learning Technologies (WLT) Use among University Students in a Baltic Country. The Electronic Journal of Information Systems in Developing Countries, 23(1), 1–20. https://doi.org/10.1002/j.1681-4835.2006.tb00151.x
Igbaria, M. (1990). End-user computing effectiveness: A structural equation model. Omega, 18(6), 637–652.
Igbaria, Magid, Zinatelli, N., Cragg, P., & Cavaye, A. L. M. (1997). Personal Computing Acceptance Factors in Small Firms: A Structural Equation Model. MIS Quarterly, 21(3), 279.
Igbaria, Magid, & Chakrabarti, A. (1990). Computer anxiety and attitudes towards microcomputer use. Behaviour and Information Technology, 9(3), 229–241.
Isaac, O., Abdullah, Z., Ramayah, T., & Mutahar, A. M. (2017). Examining the Relationship Between Overall Quality, User Satisfaction and Internet Usage: An Integrated Individual, Technological, Organizational and Social Perspective. Asian Journal of Information Technology, 16(1), 100–124. https://doi.org/10.3923/ajit.2017.100.124
Isaias, P., Reis, F., Coutinho, C., & Lencastre, J. A. (2017). Empathic technologies for distance/mobile learning. Interactive Technology and Smart Education, 14(2), 159–180. https://doi.org/10.1108/itse-02-2017-0014
Jeng, R., & Tseng, S. M. (2018). The relative importance of computer self-efficacy, perceived ease-of-use and reducing search cost in determining consumers’ online group-buying intention. International Journal of Human and Technology Interaction (IJHaTI), 2(1), 1–12.
Jong, D., & Wang, T.-S. (2009). Student Acceptance of Web-based Learning System. Proceedings of the 2009 International Symposium on Web Information Systems and Applications (WISA’09), 8, 533–536. https://doi.org/ISBN 978-952-5726-00-8 (Print), 978-952-5726-01-5 (CD-ROM)
Kang, S. (2014). Factors influencing intention of mobile application use. International Journal of Mobile Communications, 12(4), 360. https://doi.org/10.1504/ijmc.2014.063653
Karahanna, E., Agarwal, R., & Angst, C. M. (2006). Reconceptualizing compatibility beliefs in technology acceptance research. MIS Quarterly: Management Information Systems, 30(4), 781–804. https://doi.org/10.2307/25148754
Karahanna, E., Straub, D. W., & Chervany, N. L. (1999). Information technology adoption across time: A cross-sectional comparison of pre-adoption and post-adoption beliefs. MIS Quarterly: Management Information Systems, 23(2), 183–213. Retrieved from https://www.jstor.org/stable/249751
Kass, K. D. (2014). Computer self-efficacy: Instructor and student perspectives in a university setting (Iowa State University, Digital Repository). https://doi.org/10.31274/etd-180810-3731
Khayati, S., & Zouaoui, S. K. (2013). Perceived Usefulness and Use of Information Technology : the Moderating Influences of the Dependence of a Subcontractor towards His Contractor. Citeseer, III(6), 1–28. Retrieved from http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.681.8822&rep=rep1&type=pdf
Kim, Y. H., & Kim, D. J. (2005). A Study of Online Transaction Self-Efficacy, Consumer Trust, and Uncertainty Reduction in Electronic Commerce Transaction. https://doi.org/10.1109/HICSS.2005.52
Knutsen, L. A. (2005). M-Service Expectancies and Attitudes: Linkages and Effects of First Impressions. Ieeexplore.Ieee.Org, 84a-84a. https://doi.org/10.1109/hicss.2005.395
Lang, L., & Pirani, J. A. (2014). The learning management system evolution. Educause Annual Conference Research Bulletin, 1–9. Retrieved from http://www.educause.edu/annual-conference/2014
Lee, D., Lee, S. M., Olson, D. L., & Hwan Chung, S. (2010). The effect of organizational support on ERP implementation. Industrial Management & Data Systems, 110(2), 269–283. https://doi.org/10.1108/02635571011020340
Lee, Y., Kozar, K. A., & Larsen, K. R. T. (2003). The Technology Acceptance Model: Past, Present, and Future. Communications of the Association for Information Systems, 12, 752–780. https://doi.org/10.17705/1cais.01250
Lian, J. W. (2015). Critical factors for cloud based e-invoice service adoption in Taiwan: An empirical study. International Journal of Information Management, 35(1), 98–109. https://doi.org/10.1016/j.ijinfomgt.2014.10.005
Liaw, S. S., Huang, H. M., & Chen, G. D. (2007). Surveying instructor and learner attitudes toward e-learning. Computers and Education, 49(4), 1066–1080.
Lin, S., Shih, T. H., & Chuang, S. H. (2014). Validating innovating practice and perceptions of course management system solutions using structural equation modeling. Quality and Quantity, 48(3), 1601–1618. https://doi.org/10.1007/s11135-013-9864-y
Long, T., Cummins, J., & Waugh, M. (2017). Use of the flipped classroom instructional model in higher education: instructors’ perspectives. Journal of Computing in Higher Education, 29(2), 179–200. https://doi.org/10.1007/s12528-016-9119-8
Long, T., Cummins, J., & Waugh, M. (2018). Investigating the factors that influence higher education instructors’ decisions to adopt a flipped classroom instructional model. British Journal of Educational Technology. https://doi.org/10.1111/bjet.12703
Lu, H. P., & Yang, Y. W. (2014). Toward an understanding of the behavioral intention to use a social networking site: An extension of task-technology fit to social-technology fit. Computers in Human Behavior, 34, 323–332. https://doi.org/10.1016/j.chb.2013.10.020
Ma, W. W. K., Andersson, R., & Streitht, K. O. (2005). Examining user acceptance of computer technology: An empirical study of student teachers. Journal of Computer Assisted Learning, 21(6), 387–395. https://doi.org/10.1111/j.1365-2729.2005.00145.x
Madorin, S., & Iwasiw, C. (1999). The effects of computer-assisted instruction on the self-efficacy of baccalaureate nursing students. Journal of Nursing Education, 38(6), 282–285. https://doi.org/10.3928/0148-4834-19990901-10
Mahdizadeh, H., Biemans, H., & Mulder, M. (2008). Determining factors of the use of e-learning environments by university teachers. Computers and Education, 51(1), 142–154.
Marakas, G. M., Johnson, R. D., & Clay, P. F. (2007). The evolving nature of the computer self-efficacy construct: An empirical investigation of measurement construction, validity, reliability and stability over time. Journal of the Association of Information Systems, 8(1), 16–46. https://doi.org/10.17705/1jais.00112
Marakas, G. M., Yi, M. Y., & Johnson, R. D. (1998). The Multilevel and Multifaceted Character of Computer Self-Efficacy: Toward Clarification of the Construct and an Integrative Framework for Research. Information Systems Research, 9(2), 126–163. https://doi.org/10.1287/isre.9.2.126
Mirani, R., & King, W. R. (1994). Impacts of end-user and information center characteristics on end-user computing support. Journal of Management Information Systems, 11(1), 141–166.
Miura, I. T. (1987). The relationship of computer self-efficacy expectations to computer interest and course enrollment in college. Sex Roles, 16(5–6), 303–311. https://doi.org/10.1007/BF00289956
Montesdioca, G. P. Z., & Maçada, A. C. G. (2015). Measuring user satisfaction with information security practices. Computers and Security, 48, 267–280. https://doi.org/10.1016/j.cose.2014.10.015
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
Nelson, R. R., & Cheney, P. H. (2006). Training End Users: An Exploratory Study. MIS Quarterly, 11(4), 547.
Norzaidi, M. D., Chong, S. C., Murali, R., & Salwani, M. I. (2007). Intranet usage and managers’ performance in the port industry. Industrial Management and Data Systems, 107(8), 1227–1250. https://doi.org/10.1108/02635570710822831
Norzaidi, M. D., Chong, S. C., Murali, R., & Salwani, M. I. (2009). Towards a holistic model in investigating the effects of intranet usage on managerial performance: a study on Malaysian port industry. Maritime Policy & Management, 36(3), 269–289.
Nysveen, H., & Pedersen, P. E. (2016). Consumer adoption of RFID-enabled services. Applying an extended UTAUT model. Information Systems Frontiers, 18(2), 293–314. https://doi.org/10.1007/s10796-014-9531-4
Oliveira, P. C. de, Cunha, C. J. C. de A., & Nakayama, M. K. (2017). Learning Management Systems (LMS) and e-learning management: an integrative review and research agenda. Journal of Information Systems and Technology Management, 13(2), 157–180. https://doi.org/10.4301/s1807-17752016000200001
Oshiro, D. T. (2015). One hawai`i k-12 complex public school teachers’ level of computer self-efficacy and their acceptance of and integration of technology in the classroom. Dissertation Abstracts International Section A: Humanities and Social Sciences, Vol. 75, p. No-Specified. Retrieved from http://search.proquest.com/openview/3c4f6899e60b24665a60bb056693d79e/1?pq-origsite=gscholar&cbl=18750&diss=y
Paulsen, M. F. (2002). Online Education Systems : Discussion and Definition of Terms. NKI Distance Education, 1–8. Retrieved from http://www.porto.ucp.pt/open/curso/modulos/doc/Definition of Terms.pdf
Pynoo, B., Devolder, P., Tondeur, J., … J. V. B.-C. in H., & (2011), undefined. (n.d.). Predicting secondary school teachers’ acceptance and use of a digital learning. Elsevier. Retrieved from https://www.sciencedirect.com/science/article/pii/S074756321000302X
amazani, A., Ramazani, M., & Davirani, S. (2012). Studying impact of individual factors in information technology acceptance in accounting occupation by use of TAM model (Iranian case study). Global Journal of Management and Business Research, 12(4), 1–6.
Randeree, K., & Narwani, A. (2009). Managing change in higher education: An exploration of the role of training in ICT enabled institutions in the United Arab Emirates. International Journal of Learning, 16(4), 447–456.
Sam, H. K., Othman, A. E. A., & Nordin, Z. S. (2005). Computer self-efficacy, computer anxiety, and attitudes toward the Internet: A study among undergraduates in Unimas. Educational Technology and Society, 8(4), 205–219.
Sanchez-Franco, M. J. (2010). WebCT - The quasimoderating effect of perceived affective quality on an extending Technology Acceptance Model. Computers and Education, 54(1), 37–46. https://doi.org/10.1016/j.compedu.2009.07.005
Sánchez, R. A., Hueros, A. D., & Ordaz, M. G. (2013). E-learning and the University of Huelva: A study of WebCT and the technological acceptance model. Campus-Wide Information Systems, 30(2), 135–160. https://doi.org/10.1108/10650741311306318
Sara, M., & Rabiaa, M. (2016). IS/IT performance measurement system: Literature review and a comparative study. 2016 International Conference on Information Technology for Organizations Development, IT4OD 2016. https://doi.org/10.1109/IT4OD.2016.7479283
Schoonenboom, J. (2014). Using an adapted, task-level technology acceptance model to explain why instructors in higher education intend to use some learning management system tools more than others. Computers and Education, 71, 247–256. https://doi.org/10.1016/j.compedu.2013.09.016
Sekaran, U., & Bougie, R. (2016). Research methods for business: A skill building approach.
Selim, H. M. (2003). An empirical investigation of student acceptance of course websites. Computers and Education, 40(4), 343–360.
Shih, Y. Y., & Chen, C. Y. (2013). The study of behavioral intention for mobile commerce: Via integrated model of TAM and TTF. Quality and Quantity, 47(2), 1009–1020. https://doi.org/10.1007/s11135-011-9579-x
Shittu, A. T., Gambari, A. I., Gimba, W. R., & Ahmed, H. (2018). Modeling Technology Preparedness as an Antecedent of Mathematic Pre-service Teachers’ Self Efficacy, Perceived Usefulness and Intention Toward Use of Information Technology in Nigeria. MOJES: Malaysian Online Journal of Educational Sciences, 4(3), 39–48.
Sinclair, J., & Aho, A.-M. (2018). Experts on super innovators: understanding staff adoption of learning management systems. Higher Education Research & Development, 37(1), 158–172. https://doi.org/10.1080/07294360.2017.1342609
Somasundaram, U. V., & Egan, T. M. (2004). Training and Development : An Examination of Definitions and Dependent Variables. Development, 850–857.
Spacey, R., Goulding, A., & Murray, I. (2003). ICT and change in UK public libraries: does training matter? Library Management, 24(1/2), 61–69.
Sumner, M., & Hostetler, D. (1999). Factors influencing the adoption of technology in teaching. Journal of Computer Information Systems, 40(1), 81–87.
Teo, T., Lee, C. B., & Chai, C. S. (2008). Understanding pre-service teachers’ computer attitudes: Applying and extending the technology acceptance model. Journal of Computer Assisted Learning, 24(2), 128–143. https://doi.org/10.1111/j.1365-2729.2007.00247.x
Teo, Timothy. (2009). Modelling technology acceptance in education: A study of pre-service teachers. Computers & Education, 52(2), 302–312. https://doi.org/10.1016/J.COMPEDU.2008.08.006
Teo, Timothy, Huang, F., & Hoi, C. K. W. (2018). Explicating the influences that explain intention to use technology among English teachers in China. Interactive Learning Environments, 26(4), 460–475. https://doi.org/10.1080/10494820.2017.1341940
Terzis, V., Moridis, C. N., & Economides, A. A. (2012). How student’s personality traits affect Computer Based Assessment Acceptance: Integrating BFI with CBAAM. Computers in Human Behavior, 28(5), 1985–1996. https://doi.org/10.1016/j.chb.2012.05.019
Tinio, V. L. (2003). ICT in Education (UNDP). Retrieved from http://www.apdip.net.
Toh, C. H. (2013). Assessing adoption of wikis in a Singapore secondary school: Using the UTAUT model. Proceedings of the 2013 IEEE 63rd Annual Conference International Council for Education Media, ICEM 2013. https://doi.org/10.1109/CICEM.2013.6820158
Tossy, T. (2014). (2014) Modeling the adoption of mobile payment system for paying examination fees in Tanzanian major cities. International Journal of Computing and ICT Research, 8(1), 83–98. Retrieved from http://www.ijcir.mak.ac.ug/volume8-number2/article7.pdf
Trauth, E. M., & Cole, E. (1992). The organizational interface: A method for supporting end users of packaged software. MIS Quarterly: Management Information Systems, 16(1), 35–52.
Tung, F. C., & Chang, S. C. (2008). Nursing students’ behavioral intention to use online courses: A questionnaire survey. International Journal of Nursing Studies, 45(9), 1299–1309.
Ursavaş, Ö. F. (2013). Reconsidering the role of attitude in the TAM: An answer to Teo (2009) and Nistor and Heymann (2010), and Lopez-Bonilla and Lopez-Bonilla (2011). British Journal of Educational Technology, 44(1), E22–E25. https://doi.org/10.1111/j.1467-8535.2012.01327.x
Vannatta, R. A., & Fordham, N. (2004). Teacher dispositions as predictors of classroom technology use. Journal of Research on Technology in Education, 36(3), 253–271. https://doi.org/10.1080/15391523.2004.10782415
Venkatesh, V., & Bala, H. (2008). Technology acceptance model 3 and a research agenda on interventions. Decision Sciences, 39(2), 273–315. https://doi.org/10.1111/j.1540-5915.2008.00192.x
Venkatesh, V., & Davis, F. D. (1996). A Model of the Antecedents of Perceived Ease of Use: Development and Test. Decision Sciences, 27(3), 451–481. https://doi.org/10.1111/j.1540-5915.1996.tb00860.x
Venkatesh, V., & Davis, F. D. (2000). A Theoretical Extension of the Technology Acceptance Model: Four Longitudinal Field Studies. Management Science, 46(2), 186–204. https://doi.org/10.1287/mnsc.46.2.186.11926
Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User Acceptance of Information Technology: Toward a Unified View. MIS Quarterly, 27(3), 425. https://doi.org/10.2307/30036540
Wang, W.-T., & Liu, C.-Y. (2005). The Application of the Technology Acceptance Model: A New Way to Evaluate Information System Success. Proceedings of the 23rd International Conference of the System Dynamics Society, 149. Retrieved from http://myweb.ncku.edu.tw/~wtwang/personal/Wang_Liu-2005.pdf
Wangpipatwong, S., Chutimaskul, W., & Papasratorn, B. (2008). Understanding Citizen ’ s Continuance Intention to Use e- Government Website : a Composite View of Technology Acceptance Model and Computer Self-Efficacy. The Electronic Journal of E- Government, 6(1), 55–64. Retrieved from www.ejeg.com
Weller, M. (2007). Virtual learning environments: Using, choosing and developing your VLE. Retrieved from https://content.taylorfrancis.com/books/download?dac=C2005-0-03011-0&isbn=9781134122547&format=googlePreviewPdf
Williams, P. (2002). The Learning Web: The Development, Implementation and Evaluation of Internet-Based Undergraduate Materials for the Teaching of Key Skills. Active Learning in Higher Education, 3(1), 40–53.
Woodrow, J. E. J. (1991). A Comparison of Four Computer Attitude Scales. Journal of Educational Computing Research, 7(2), 165–187.
Wu, M. Y., Yu, P. Y., & Weng, Y. C. (2012). A study on user behavior for i pass by UTAUT: Using taiwan’s MRT as an example. Asia Pacific Management Review, 17(1), 91–111. https://doi.org/10.6126/APMR.2012.17.1.06
Ycle, H. O. L. I. F. E. C., Brown, B. S. A., & Hall, M. (2005). M Odel of a Doption of T Echnology in H Ouseholds : a B Aseline M Odel T Est and E Xtension I Ncorporating. MIS Quarterly, 29(3), 399–426.
Yeou, M. (2016). An Investigation of Students’ Acceptance of Moodle in a Blended Learning Setting Using Technology Acceptance Model. Journal of Educational Technology Systems, 44(3), 300–318.
Yeşilyurt, E., Ulaş, A. H., & Akan, D. (2016). Teacher self-efficacy, academic self-efficacy, and computer self-efficacy as predictors of attitude toward applying computer-supported education. Computers in Human Behavior, 64, 591–601. https://doi.org/10.1016/j.chb.2016.07.038
Yount, W. R. (2006). Research design and statistical analysis for Christian ministry. WR Yount Fort Worth.
Zhang, Y., & Espinoza, S. (1998). Relationships among computer self-efficacy, attitudes toward computers, and desirability of learning computing skills. Journal of Research on Computing in Education, 30(4), 420–436. https://doi.org/10.1080/08886504.1998.10782236
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