References
- Abdullah, F., & Ward, R. (2016). Developing a general extended technology acceptance model for e-learning (GETAMEL) by analysing commonly used external factors. Computers in Human Behavior, 56, 238–256.
- Ajzen, I., & Fishbein, M. (1980). Understanding attitudes and predicting social behaviour. Englewood Cliffs, NJ: Prentice-Hall.
- Al-Harbi, K. (2011). E-learning in the Saudi tertiary education: Potential and challenges. Applied Computing & Informatics, 9(1), 31–46.
- Aparicio, M., Bacao, F., & Oliveira, T. (2016). An e-learning theoretical framework. Journal of Educational Technology & Society, 19(1), 292.
- Arbaugh, J. B., & Duray, R. (2001). Class section size, perceived classroom characteristics, instructor experience, and student learning and satisfaction with web-based courses: A study and comparison of two online MBA programs. In D. Nagao (Ed.), Academy of management best papers proceedings (pp. A1–A6) [CD-ROM]. Briarcliff Manor, NY: Academy of Management.
- Arkorful, V., & Abaidoo, N. (2015). The role of e-learning, advantages and disadvantages of its adoption in higher education. International Journal of Instructional Technology & Distance Learning, 12(1), 29–42.
- Attuquayefio, S. N. B., Achampong, A. K., & Aryeetey, I. T. (2014). Extending TAM with social norm to model students’ intentions to adopt ICT. European Scientific Journal, 10(14), 435–446.
- Awidi, I. T., & Cooper, M. (2015). Using management procedure gaps to enhance e-learning implementation in Africa. Computers & Education, 90, 64–79.
- Bandura, A. (1994). Self-efficacy. In V. S. Ramachaudran (Ed.), Encyclopedia of human behavior (Vol. 4, pp. 71–81). New York: Academic Press.
- Barclay, C., & Duggan, E. (2008). Rethinking the digital divide: Towards a path of digital effectiveness. Proceedings of the 41st Annual Hawaii International Conference on System Sciences (HICSS), Waikoloa, HI (10 pp.).
- Barclay, C., & Logan, D. (2013). Towards an understanding of the implementation & adoption of massive online open courses (MOOCs) in a developing economy context. Proceedings of SIG GlobDev sixth annual workshop.
- Barclay, C., & Osei-Bryson, K. M. (2012). An analysis of students’ perceptions and attitudes to online learning use in higher education in Jamaica: An extension of TAM. ICIS conference proceedings. Retrieved from http://aisel.aisnet.org/globdev2012/4
- Baron, R. M., & Kenny, D. A. (1986). The moderator-mediator variable distinction in social psychological research: Conceptual, strategic, and statistical considerations. Journal Of Personality And Social Psychology, 51, 1173–1182.
- Bhuasiri, W., Xaymoungkhoun, O., Zo, H., Rho, J. J., & Ciganek, A. P. (2012). Critical success factors for e-learning in developing countries: A comparative analysis between ICT experts and faculty. Computers & Education, 58(2), 843–855.
- Brinkerhoff, J. (2006). Effects of a long-duration, professional development academy on technology skills, computer self-efficacy, and technology integration beliefs and practices. Journal of Research on Technology in Education, 39(1), 22–43.
- Brown, K. M. (1996). The role of internal and external factors in the discontinuation of off-campus students. Distance Education, 17(1), 44–71.
- Chang, C. T., Hajiyev, J., & Su, C. R. (2017). Examining the students’ behavioral intention to use e-learning in Azerbaijan? The general extended technology acceptance model for e-learning approach. Computers & Education, 111, 128–143.
- Chau, P. Y. (2001). Influence of computer attitude and self-efficacy on IT usage behavior. Journal of Organizational and End User Computing, 13(1), 26–33.
- Chen, Y. H., & Chengalur-Smith, I. (2015). Factors influencing students’ use of a library web portal: Applying course-integrated information literacy instruction as an intervention. The Internet and Higher Education, 26, 42–55.
- Chin, W. W. (1998). The partial least squares approach to structural equation modeling. In G. A. Marcoulides (Ed.), Modern methods for business research (pp. 295–336). Mahwah, NJ: Lawrence Erlbaum.
- Chiu, C.-M., Chiu, C.-S., & Chang, H.-C. (2007). Examining the integrated influence of fairness and quality on learners? Satisfaction and web-based learning continuance intention. Information Systems Journal, 17(3), 271–287.
- Churchill, G. A. (1979). A paradigm for developing better measures of marketing constructs. Journal of Marketing Research, 16, 64–73.
- Cidral, W. A., Oliveira, T., Di Felice, M., & Aparicio, M. (2018). E-learning success determinants: Brazilian empirical study. Computers & Education, 122, 273–290.
- Cohen, J. (1988). Statistical power analysis for the behavioral sciences. Hillsdale, NJ: Lawrence Erlbaum.
- Compeau, D. R., & Higgins, C. A. (1995). Computer self-efficacy: Development of a measure and initial test. MIS Quarterly, 19(2), 189–211.
- Curran, C. (2001). The phenomenon of on-line learning. European Journal of Education, 36(2), 113–132.
- Davis, F. D., Bagozzi, R. P., & Warshaw, P. R. (1989). User acceptance of computer technology: A comparison of two theoretical models. Management Science, 35, 982–1003.
- Deutsch, M., & Gerard, H. B. (1955). A study of normative and informational social influences upon individual judgment. The Journal of Abnormal and Social Psychology, 51(3), 629–636.
- Duman, T., Kocak, G. N., & Tutuncu, O. (2006). The role of non-monetary costs in a model of leisure travel value. Amsterdam: Elsevier Science.
- Ellis, R. A., Jarkey, N., Mahony, M. J., Peat, M., & Sheely, S. (2007). Managing quality improvement of eLearning in a large, campus-based university. Quality Assurance in Education, 15(1), 9–23.
- Elloumi, F. (2004). Value chain analysis: A strategic approach to online learning. In A. Anderson & F. Elloumi (Eds.), Theory and practice of online learning (pp. 61–92). Athabasca: Athabasca University.
- Fencl, H., & Scheel, K. (2005). Engaging students: An examination of the effects of teaching strategies on self-efficacy and course climate in a nonmajors physics course. Journal of College Science Teaching, 35(1), 20–24.
- Fishbein, M., & Ajzen, I. (1975). Belief, attitude, intention, and behavior: An introduction to theory and research. Reading, MA: Addison-Wesley.
- Fornell, C., & Bookstein, F. L. (1982). Two structural equation models: LISREL and PLS applied to consumer exit-voice theory. Journal Of Marketing Research, 19, 440–452.
- Fornell, C., & Cha, J. (1994). Partial least squares. In R. P. Bagozzi (Ed.), Advanced methods Of marketing research (pp. 52–78). Cambridge, MA: Blackwell.
- Fornell, C., & Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal Of Marketing Research, 18, 39–50.
- Frankola, K. (2001). Why online learners drop out. Workforce, 10, 52–60.
- Garrison, D., Cleveland-Innes, M., & Fung, T. (2004). Student role adjustment in online communities of inquiry: Model and instrument validation. Journal of Asynchronous Learning Networks, 8(2), 61–69.
- Geisser, S. (1974). A predictive approach to the random effect model. Biometrika, 61, 101–107.
- Geoghegan, W. (1995). Stuck at the barricades: Can information technology really enter the mainstream of teaching and learning? Change, 27(2), 22–30.
- Golladay, R., Prybutok, V., & Huff, R. (2000). Critical success factors for the online learner. Journal of Computer Information Systems, 40(4), 69–71.
- Hair, J. F., Anderson, R. E., Tatham, R. L., & Black, W. C. (1998). Multivariate data analysis. Englewood Cliffs, NJ: Prentice Hall.
- Hair, J. F., Hult, G. T. M., Ringle, C. M., & Sarstedt, M. (2014). A primer on partial least squares structural equation modeling (PLS-SEM). Thousand Oaks, CA: Sage.
- Hair, J. F., Ringle, C. M., & Sarstedt, M. (2011). PLS-SEM: Indeed a silver bullet. The Journal of Marketing Theory and Practice, 19, 139–152.
- Hamid, S., Waycott, J., Kurnia, S., & Chang, S. (2015). Understanding students’ perceptions of the benefits of online social networking use for teaching and learning. The Internet and Higher Education, 26, 1–9.
- Hayashi, A., Chen, C., Ryan, T., & Wu, J. (2004). The role of social presence and moderating role of computer self efficacy in predicting the continuance usage of e-learning systems. Journal of Information Systems Education, 15(2), 139.
- Henseler, J., Ringle, C. M., & Sinkovics, R. R. (2009). The use of partial least squares path modeling in international marketing. In R. R. Sinkovics, & P. N. Ghauri (Eds.), Advances in international marketing (pp. 277–319). West Yorkshire: Emerald.
- Hiltz, S. R., & Turoff, M. (2005). Education goes digital: The evolution of online learning and the revolution in higher education. Communications of the ACM, 48(10), 59–64.
- Hofstede, G. (1980). Culture's consequences: International differences in work-related values. Newbury Park, CA: Sage.
- Hussein, R., Aditiawarman, U., & Mohamed, N. (2007). E-learning acceptance in a developing country: A case of the Indonesian Open University. Paper presented at the German e-science conference. Retrieved from http://edoc.mpg.de/316634
- Internet World Stats. (2015). Jamaica internet usage and population statistics. Retrieved from https://www.internetworldstats.com/car/jm.htm
- Internet World Stats. (2016). Internet usage and population in the Caribbean: June 30, 2016. Retrieved from https://www.internetworldstats.com/stats11.htm
- Joo, Y. J., Lim, K. Y., & Park, S. Y. (2011). Investigating the structural relationships among organisational support, learning flow, learners’ satisfaction and learning transfer in corporate e-learning. British Journal of Educational Technology, 42(6), 973–984.
- Klein, K. J., Conn, A. B., & Sorra, J. S. (2001). Implementing computerized technology: An organizational analysis. Journal of Applied Psychology, 86, 811–824.
- Koohang, A., & Durante, A. (2003). Learners’ perceptions toward the web-based distance learning activities/assignments portion of an undergraduate hybrid instructional model. Journal of Information Technology Education: Research, 2, 105–113.
- Kutner, M. H., Nachtsheim, C. J., Neter, J., & Li, W. (2004). Applied linear statistical models. Homewood, IL: Mcgraw-Hill/Irwin.
- Laine, L. (2003). Is e-learning effective for IT training? T+D, 57(6), 55–60.
- Lee, M.-C. (2010). Explaining and predicting users’ continuance intention toward e-learning: An extension of the expectation-confirmation model. Computers & Education, 54(2), 506–516.
- Lee, B., Yoon, J., & Lee, I. (2009). Learners’ acceptance of e-learning in South Korea: Theories and results. Computers & Education, 53(4), 1320–1329.
- Levy, Y. (2007). Comparing dropouts and persistence in e-learning courses. Computers Education, 48(2), 185–204.
- Liaw, S. S., & Huang, H. M. (2013). Perceived satisfaction, perceived usefulness and interactive learning environments as predictors to self-regulation in e-learning environments. Computers & Education, 60(1), 14–24.
- Liaw, S., Huang, H., & Chen, G. (2007). Surveying instructor and learner attitudes toward e-learning. Computers & Education, 49(4/12), 1066–1080.
- Liu, M. H. (2016). Blending a class video blog to optimize student learning outcomes in higher education. The Internet and Higher Education, 30, 44–53.
- Liu, N. K., & Cheng, X. (2008). An evaluation of the learning of undergraduates using e-learning in a tertiary institution in China. International Journal on E-Learning, 7(3), 427–447.
- Lohmoller, J.-B. (1989). Latent variable path modeling with partial least squares. New York: Springer-Verlag.
- Martins, L., & Kellermanns, F. W. (2004). Student acceptance of a web-based course management system. Academy of Management Learning and Education, 3(1), 7–26.
- Mason, C. H., & Perreault, W. D. J. (1991). Collinearity, power, and interpretation of multiple regression analysis. Journal of Marketing Research, 28(3), 268–280.
- Mason, R., & Weller, M. (2000). Factors affecting students’ satisfaction on a web course. Australian Journal of Educational Technology, 16(2), 173–200.
- McDonald, D. (1999–2000). Improved training methods through the use of multimedia technology. Journal of Computer Information Systems, 40(2), 17–20.
- McEwen, T. (1997). Communication training in corporate settings: Lessons and opportunities for the academe. Mid-American Journal of Business, 12(1), 49–58.
- McPherson, M., & Nunes, M. (2006). Organisational issues for e-learning critical success factors as identified by HE practitioners. International Journal of Educational Management, 20(7), 542–558.
- Ong, C.-S., Lai, J.-Y., & Wang, Y.-S. (2004). Factors affecting engineers’ acceptance of asynchronous e-learning systems in high-tech companies. Information & Management, 41, 795–804.
- Otter, R. R., Seipel, S., Graeff, T., Alexander, B., Boraiko, C., Gray, J., … Sadler, K. (2013). Comparing student and faculty perceptions of online and traditional courses. The Internet and Higher Education, 19, 27–35.
- Ozkan, S., & Koseler, R. (2009). Multi-dimensional students’ evaluation of e-learning systems in the higher education context: An empirical investigation. Computers & Education, 53(4), 1285–1296.
- Park, S. Y. (2009). An analysis of the technology acceptance model in understanding university students’ behavioral intention to use e-learning. Educational Technology & Society, 12(3), 150–162.
- Park, S. Y., Nam, M. W., & Cha, S. B. (2012). University students’ behavioral intention to use mobile learning: Evaluating the technology acceptance model. British Journal of Educational Technology, 43(4), 592–605.
- Piccoli, G., Ahmad, R., & Ives, B. (2001). Web-based virtual learning environments: A research framework and a preliminary assessment of effectiveness in basic IT skills training. MIS Quarterly, 25(4), 401–426.
- Pituch, K. A., & Lee, Y.-K. (2006). The influence of system characteristics on e-learning use. Computers & Education, 47, 222–244.
- Qureshi, I. A., Ilyas, K., Yasmin, R., & Whitty, M. (2012). Challenges of implementing e-learning in a Pakistani university. Knowledge Management & E-Learning: An International Journal, 4(3), 310–324.
- Raab, R. T., Ellis, W. W., & Abdon, B. R. (2002). Multisectoral partnerships in e-learning: A potential force for improved human capital development in the Asia pacific. Internet and Higher Education, 4(3–4), 217–229.
- Ringle, C. M., Wende, S., & Will, A. (2005). SmartPLS 2.0 M3. Hamburg: SmartPLS.
- Rohleder, P., Bozalek, V., Carolissen, R., Leibowitz, B., & Swartz, L. (2008). Students’ evaluations of the use of e-learning in a collaborative project between two South African universities. Higher Education, 56(1), 95–107.
- Ros, S., Hernández, R., Caminero, A., Robles, A., Barbero, I., Maciá, A., & Holgado, F. P. (2015). On the use of extended TAM to assess students’ acceptance and intent to use third-generation learning management systems. British Journal of Educational Technology, 46(6), 1250–1271.
- Rovai, A. P., & Downey, J. R. (2010). Why some distance education programs fail while others succeed in a global environment. The Internet and Higher Education, 13(3), 141–147.
- Saadé, R. G. (2003). Web-based education information system for enhanced learning, EISL: Student assessment. Journal of Information Technology Education: Research, 2, 267–277.
- Saadé, R. G., He, X., & Kira, D. (2007). Exploring dimensions to online learning. Computers in Human Behavior, 23(4), 1721–1739.
- Selim, H. M. (2003). An empirical investigation of student acceptance of course websites. Computers & Education, 40, 343–360.
- Selim, H. M. (2007). Critical success factors for e-learning acceptance: Confirmatory factor models. Computers & Education, 49(2), 396–413.
- Serwatka, J. (2003). Assessment in on-line CIS courses. Journal of Computer Information Systems, 43(3), 16–20.
- Shraim, K., & Khlaif, Z. (2010). An e-learning approach to secondary education in Palestine: Opportunities and challenges. Information Technology for Development, 16(3), 159–173.
- Shumarova, E., & Swatman, P. A. (2007). Evalue and value-driven user responses to information technology. Electronic Markets, 17(1), 5–19.
- Smedley, J. K. (2010). Modelling the impact of knowledge management using technology. OR Insight, 23(4), 233–250.
- Smith, P., & Rao, L. (2012, August 9–12). Lessons learned from introducing a learning management system to support face to face and blended learning courses in an MBA programme (Paper 7). Proceedings of the Eighteenth Americas conference on information systems, Seattle, WA.
- So, S. (2016). Mobile instant messaging support for teaching and learning in higher education. The Internet and Higher Education, 31, 32–42.
- Song, Y., & Kong, S. C. (2017). Affordances and constraints of BYOD (Bring Your Own Device) for learning and teaching in higher education: Teachers' perspectives. The Internet and Higher Education, 32, 39–46.
- Soong, B. M. H., Chan, H. C., Chua, B. C., & Loh, K. F. (2001). Critical success factors for on-line course resources. Computers & Education, 36(2), 101–120.
- Stoffregen, J., Pawlowski, J. M., & Pirkkalainen, H. (2015). A barrier framework for open e-learning in public administrations. Computers in Human Behavior, 51(Part B), 674–684.
- Stone, E. F. (1978). Research methods in organizational behavior. Santa Monica, CA: Goodyear.
- Stone, M. (1974). Cross-validatory choice and assessment of statistical predictions. Journal of the Royal Statistical Society, 36(2), 111–147.
- Sun, P.-C., Tsai, R. J., Finger, G., Chen, Y.-Y., & Yeh, D. (2008). What drives a successful e-learning? An empirical investigation of the critical factors influencing learner satisfaction. Computers & Education, 50(4), 1183–1202.
- Tarus, J. K., Gichoya, D., & Muumbo, A. (2015). Challenges of implementing e-learning in Kenya: A case of Kenyan public universities. The International Review of Research in Open and Distributed Learning, 16(1).
- Teo, T. (2011). Modeling the determinants of pre-service teachers’ perceived usefulness of e-learning. Campus-Wide Information Systems, 28(2), 124–140.
- Toven-Lindsey, B., Rhoads, R. A., & Lozano, J. B. (2015). Virtually unlimited classrooms: Pedagogical practices in massive open online courses. The Internet and Higher Education, 24, 1–12.
- van Raaij, E. M., & Schepers, J. J. L. (2008). The acceptance and use of a virtual learning environment in China. Computers & Education, 50, 838–852.
- Venkatesh, V., & Bala, H. (2008). Technology acceptance model 3 and a research agenda on interventions. Decision Sciences, 39(2), 273–315.
- Venkatesh, V., & Davis, F. D. (2000). A theoretical extension of the technology acceptance model: Four longitudinal field studies. Management Science, 46, 186–204.
- Venkatesh, V., Morris, M. G., Davis, F. D., & Davis, G. B. (2003). User acceptance of information technology: Toward a unified view. MIS Quarterly, 27(3), 425–478.
- Volery, T., & Lord, D. (2000). Critical success factors in online education. The International Journal of Educational Management, 14(5), 216–223.
- Wang, M., Ran, W., Liao, J., & Yang, S. J. H. (2010). A performance-oriented approach to e-learning in the workplace. Educational Technology & Society, 13(4), 167–179.
- 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. Electronic Journal of e-Government, 6(1), 55–64.
- Webster, J., & Hackley, P. (1997). Teaching effectiveness in technology-mediated distance learning. The Academy of Management Journal, 40(6), 1282–1309.
- Williams, M., & Williams, J. (2010). Evaluating a model of business school students’ acceptance of web-based course management systems. The International Journal of Management Education, 8(3), 59–70.
- Wisneski, J. E., Ozogul, G., & Bichelmeyer, B. A. (2017). Investigating the impact of learning environments on undergraduate students’ academic performance in a prerequisite and post-requisite course sequence. The Internet and Higher Education, 32, 1–10.
- Wold, H. (1966). Estimation of principal components and related models by iterative least squares. New York: Academic Press.
- Wu, J. H., Tennyson, R. D., & Hsia, T. L. (2008). Analysis of e-learning innovation and core capability using a hypercube model. Computers in Human Behavior, 24(5), 1851–1866.
- Yi, M., & Hwang, Y. (2003). Predicting the use of web-based information systems: Self-efficacy, enjoyment, learning goal orientation, and the technology acceptance model. International Journal of Human-Computer Studies, 59, 431–449.
- 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.
- Zhang, D., Zhao, J. L., Zhou, L., & Nunamaker, J. (2004). Can e-learning replace traditional classroom learning—Evidence and implication of the evolving e-learning technology. Communications of the ACM, 47(5), 75–79.