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International Journal of Information Technology & Computer Science ( IJITCS )

Abstract :

 This study examined the predictive factors for the intention to adopt blackboard course management system. The sample for the study comprised 503 students selected from 7 faculties and 42 departments at the University of Botswana. Through a survey method, questionnaire was used to collect data from the students. Three research questions were developed and answered by the study.  In brief, the study discovered that blackboard content quality, system quality, support service quality, teaching and learning quality, self-regulated learning, user satisfaction and net benefits are important factors predicting the adoption of blackboard system by the students. Generally, Blackboard course management system (CMS) benefits, self-regulated learning and blackboard content quality play more roles than other factors in terms of determining the intention to adopt blackboard by the students. The study has shown that there is room for further studies to consider other factors other than the one used in this study in order to discover their prediction of the intention to adopt blackboard in educational settings.

Keywords :

: E-learning, Course Management System, Learning Management System, Blackboard/WebCT, University of Botswana.

References :

  1. Abad, M. M., Morris, D., & deNahlik, C. (2009).  Looking under the Bonnet: Factors Affecting Student Adoption of E-Learning Systems in Jordan.  International Review of Research in Open and Distance Learning 10 (2),23-25.
  2. Ajzen, I., & Fishbein, M. (1980). Understanding attitudes and predicting social behavior. Englewood Cliffs: Prentice-Hall.
  3. Agarwal, R. & Karahanna, E. (2000). Time flies when you are having fun: cognitive absorption and beliefs about information technology usage. MIS Quarterly 24, 665-694.
  4. Agarwal, R. & Sambamurthy, V. (2002). Principles and Models for Organizing the IT Function. MIS Quarterly Executive 1 (1), 1-16.
  5. Agarwal, R. & Prasad, J. (1999). Are Individual Differences Germane to the Acceptance of New Information Technologies? Decision Sciences 30 (2), 361-391.
  6. Alexander, S. & McKenzie, J. (1998). An evaluation of information technology projects in university learning. Department of Employment, Education and Training and Youth Affairs, Canberra: Australian Government Publishing Services.
  7. Bandura, A. (1977). Self-efficacy: Toward a unifying theory of behavioral change. Psychological Review 84(2), 191–215.
  8. Brown, I. Jr., & Inouye, D.K. (1978). Learned helplessness through modeling: The role of perceived similarity in competence. Journal of Personality and Social Psychology 36(8), 900-908.
  9. Carswell, A. D. & Venkatesh, V. (2002). Learner outcomes in an asynchronous distance educational environment. International Journal of Human-Computer Studies. 56 (5), 475-494.
  10. Chau, P.Y.K. (1996). An empirical assessment of a modified technology acceptance model. Journal of Management Information Systems 13, 185-204.
  11. Chau, P. & Hu, P. (2001). Information Technology Acceptance by Individual Professionals: A Model of Comparison Approach. Decision Sciences 32 (4), pp 699-719.
  12. Chin, W. and Todd, P. (1995).  On the Use, Usefulness, and Ease of Use of Structural Equation Modeling in MIS Research: A Note of Caution.  MIS Quarterly 19 (2), 237-246.
  13. Chin, W., & Lee, M. (2000). A proposed model and measurement instrument for the formation of IS satisfaction: the case of end-user computing satisfaction. Paper presented at the Proceedings of the Twenty First International Conference on Information Systems. Brisbane, Australia.
  14. Chien, S. W. & Tsaur, S.M. (2007). Investigating the success of ERP system: Case studies in three Taiwanese high-tech industries. Computer in Industry 58 (8-9), 783-793.
  15. Clark, C. (2002). E-learning Strategy document. Version 2.1 (20.5.2002). Information Technology Policy Committee. Chair, e-learning steering Group of ITPC. Available:
  16. http://www2.warwick.ac.uk/insite/forum/archive/elearning/stategydocument/ [accessed 12 May 2007].
  17. Crowston, K.; Annabi, H.; & Howison, J. (2003). Defining open source software project success. Twenty Fourth International Conference on Information Systems 1-14.
  18. Davis, F.D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly 13(3), 319-340.
  19. Davis, F.D. (1986). A technology acceptance model for empirically testing new end-user information systems: theory and results. Doctoral dissertation, Sloan School of Management, Massachusetts Institute of Technology.
  20. 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.
  21. DeLone, W. & McLean, E. (1992). Information Systems Success: The Question for the Dependent Variable. Information Systems Research 3, 60-95.
  22. Delone, W. H.& Mclean, E.R. (2003). The Delone and Mclean information system success: A ten years update. Journal of Management Information Systems 19 (4), 30-36.
  23. Davis, G., Olson, M. (1985). Management information systems: conceptual foundations, structure and development. McGraw-Hill, New York, NY.
  24. Fichman, R. and C. Kemerer. (1999). The Illusory Diffusion of Innovation: An Examination of Assimilation Gaps.  Information Systems Research 9, 255-275.
  25. Gable, G. G. Sedera, D. & Chan, T. (2003). Enterprise systems success: A measurement model. In Proceedings of the twenty-fourth international conference on information systems (pp. 576-591), December 14-17, Seattle, Washington, USA.
  26. Gibbons, A. & Fearweatther, P. (2000). Computer-based instruction. In: Tobias S.Fletcher, J.(eds.), Training & retraining: A Handbook for Business, Industry, Government, and the Military. New York:Macmillan Reference USA, 410-442
  27. Hussein, R. Abdu-Karim, N. S. Mohamed, N. & Ahlan, A. R. (2007). The influence of organizational factors on information system success in e-government agencies in Malaysia. Electronic Journal of Information Systems in Developing Countries EJISDC 29 (1), 1-17.
  28. Hwang, B., & Liu, Y. (1994). A study of proportional reasoning and self-regulation instruction on students‟ conceptual change in conceptions of solution. ED368574.
  29. Igbaria, M., Guimaraes, T., & Davis, G.B. (1995). Testing the determinants of microcomputer usage via a structural equation model.  Journal of Management Information Systems 11, 87-114.
  30. Igbaria, M., Parasuraman, S., & Baroudi, J.J. (1996). A motivational model of microcomputer usage.  Journal of Management Information Systems, 13, 127-143.
  31. Igbaria, M., Zinatelli, N., Cragg, P., & Cavaye, A.L.M. (1997). Personal computing acceptance factors in small firms: a structural equation model. MIS Quarterly 21, 279-305.
  32. Ives, B.; Olson, M.J. & Baroudi, J.J. (1983). The measurement of user information satisfaction. Communications of the ACM 26 (10), 785–793.
  33. Keller, C. & Cernerud, L. (2002). Students‟ perception of e-learning in university education.  Learning, Media and Technology 27(1), 55-67.
  34. Kerka, S. (1999). Distance learning, the Internet, and the World Wide Web. ERIC Digest. (ERIC Document Reproduction Service No. ED 395214).
  35. LaRose, R., Gregg, J., & Eastin, M. (1998). Audio graphic tele-courses for the Web: An experiment. Journal of Computer Mediated Communications, 4(2).
  36. Learning Online, (2008). An introduction to E-learning. Available: http://pbl-online.org/LearnOnline/elearning.htm [accessed 12 July 2010].
  37. Lee, S. & Kim, K. (2007). Factors affecting the implementation of success of internet based information system. Computers in Human Behaviour 23, 1853-1880.
  38. Mathieson, K. (1991). Predicting User Intentions: Comparing the Technology Acceptance Model with the Theory of Planned Behavior.  Information Systems Research  2,173-191.
  39. Moore, G. and I. Benbasat. (1991). Development of an Instrument to Measure the Perceptions of Adopting New Information Technology Innovation.  Information Systems Research 2 (3),  192-222.
  40. Morss, D.A. (1999). A study of student perspectives on Web-based learning: WebCT in the classroom. Internet Research 9(5), 393–408.
  41. Naylor, J. C. Prichard, R. D. & Ilgen, D. R. (1980). A theory of behaviour in organizations. London. Academic Press.
  42. Ngai, E.W.T., Poon, J.K.L., & Chan, Y.H.C. (2007). Empirical examination of the adoption of WebCT using TAM. Computers & Education 48(2), 250-267.
  43. Rao, S., (2006). Distance education and the role of IT in India. The Electronic Library  24 (3),  225-236
  44. Raymond, F., (2000). Delivering distance education through technology – a pioneer’s experience, Campus-Wide Information Systems 17(1), 49-55.
  45. Roffe, I., (2002). E-learning – engagement, enhancement and execution, Quality Assurance in Education 10 (1), 40-50.
  46. Saadē, R. & Bahli, B (2005). The impact of cognitive absorption on perceived usefulness and perceived ease of use in on-line learning: an extension of technology acceptance model. Information & Management, 42(2), 317-327.
  47. Sabherwal, R. Jeyaraj, A. & Chowa, C. (2006). Information System Success: Individual and Organizational Determinants. Management Science 52 (12), 1849-1864.
  48. Schunk, D. & Zimmerman, B. (1994). Self-regulation of learning and performance. Issues and educational applications, Eribaum, Hillsdale, NJ.
  49. Seddon, P. B. (1997). A Respecification and Extension of the DeLone and McLean Model of IS Success. Information Systems Research 8 (3), 240-253.
  50. Seddon, P. B. & Kiew, M. Y. (1994). A Partial Test and Development of DeLone and McLean's Model of IS Success. Proceedings of the International Conference on Information Systems, Vancouver, Canada, pp. 99-110.
  51. Soong, M.H.B., Chan, H.C., Chua, B.C., & Loh, K.F. (2001). Critical success factors for on- line course resources. Computers & Education, 36(2), 101-120.
  52. Straub, D., Limayem, M. and Karahanna-Evaristo, E. (1995). Measuring system usage: Implications for IS theory testing. Management Science  41, 1328-1342.
  53. Sun, H. (2003). An Integrative Analysis of TAM: Toward a Deeper Understanding of Technology Acceptance Model.  AMCIS ’03, Tampa, Florida, August 4.
  54. Szajna, B. (1994). Software Evaluation and Choice: Predictive Validation of the Technology Acceptance Instrument. MIS Quarterly 18 (3),  319-324.
  55. Taylor, S. & Todd, P.A. (1995). Understanding information technology usage: a test of competing models. Information Systems Research 6(2), 144-176.
  56. Tella, A. (2009). An evaluation of WebCT Course content management system at the University of Botswana. Doctoral Dissertation. Department of Library and Information Studies, University of Botswana.
  57. University of Botswana. (2008). Facts and Figures. University Fact Book.
  58. Venkatesh, V. (1999). Creation of favourable user perceptions: exploring the role of intrinsic motivation. Management Information Systems Quarterly 23(2), 239-60.
  59. Venkatesh, V. & Davis, F. D. (2000). A theoretical extension of the technology acceptance model: Four longitudinal field studies. Management Science 46(2), 186-204.
  60. Vovides, Y. Sanchez-Alonso, S. & Nickmans, V. M. (2007). The use of e-learning course management system to support learning strategies and to improve self-regulated learning. Educational Research Review 2 (1), 64-74.
  61. Wang, R. (2003). The development and application of e-learning in China. Asian and Pacific Seminar  Workshop on Educational Technology. Available: http://guage.ugakugei.ac.jp/apeid/apeid04/countryPapers/China.pdf [accessed 10 July 2008].
  62. Wang, S. & Tang, T. I. (2003). Assessing customer perceptions of Web sites service quality in digital marketing environments. Journal of End User Computing 15 (3), 14–31.
  63. 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.
  64. Wood, R. & Bandura, A. (1989). Social cognitive theory of organizational management. Academy of Management Review 14(3), 361-384.
  65. Wu, J. H. Wang, Y. M. (2006). Measuring KMS success: A respecification of the DeLone and McLean´s model. Information & Management 43, 728-739.

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