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The Role of Peer Influence and Perceived Quality of Teaching in Faculty Acceptance of Web-Based Learning Management Systems
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, North Dakota State University, United States ; , Emirates College for Advanced Education, United Arab Emirates ; , , North Dakota State University, United States

International Journal on E-Learning, ISSN 1537-2456 Publisher: Association for the Advancement of Computing in Education (AACE), Waynesville, NC USA

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Abstract

This study’s primary investigation is the impact of peer influence and perceived quality of teaching on faculty members’ usage of web-based learning management systems within the Technology Acceptance Model (TAM) framework. These factors are entered into an extended TAM as external variables impacting on the core constructs in the prevailing TAM literature: perceived ease of use, perceived usefulness, intention to use and system use. The investigation is conducted within the context of higher education faculty members’ utilization of web-based learning management systems, such as Blackboard®. A sample of 171 faculty members from three higher education institutions in Midwestern United States is utilized for the purpose of this study. Using exploratory path analysis, twelve hypotheses were tested for the learning management system. The results of the analyses reveal that perceived quality of teaching plays a significant role in the faculty members’ consideration to use learning management systems. In turn, peer influence does not appear to predict faculty perceptions of the system’s usefulness for teaching. The results of additional hypotheses testing are presented. Alternative explanations and implications of the results are discussed.

Citation

Salajan, F.D., Welch, A.G., Ray, C.M. & Peterson, C. (2015). The Role of Peer Influence and Perceived Quality of Teaching in Faculty Acceptance of Web-Based Learning Management Systems. International Journal on E-Learning, 14(4), 487-524. Waynesville, NC USA: Association for the Advancement of Computing in Education (AACE). Retrieved August 6, 2024 from .

© 2015 Association for the Advancement of Computing in Education (AACE)

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View References & Citations Map
  1. Adams, D.A., Nelson, R.R., & Todd, P.A. (1992). Perceived usefulness, ease of use and usage of information technology: A replication. MIS Quarterly, 16(2), 227-247. Retrieved from http://www.jstor.org/stable/249577 Ahmad, T.B.T., Madarsha, K.B., Zainuddin, A.M., Ismail, N.A.H., & Nordin, M.S. (2010). Faculty’s acceptance of computer based technology: Cross-validation of an extended model. Australasian Journal of Educational Technology, 26(2), 268-279. Retrieved from http://www.ascilite.org.au/ajet/ajet26/ahmad.pdf
  2. Ajjan, H., & Hartshorne, R. (2008). Investigating faculty decisions to adopt Web 2.0 technologies: Theory and empirical tests. Internet and Higher Education, 11, 71-80.
  3. Andersson, S.B. (2006). Newly qualified teachers’ learning related to their use of information and communication technology: A Swedish perspective. British Journal of Educational Technology, 37, 665–682.
  4. Arenas-Gaitán, J., Ramírez-Correa, P.E., & Rondán-Cataluña, F.J. (2011). Cross cultural analysis of the use and perceptions of web based learning systems. Computers& Education, 57, 1762-1774. Doi:10.1016/J.compedu.2011.03.016
  5. Bandura, A. (1977). Self-efficacy: Toward a unifying theory of behavioral change. Psychological Review, 84(2), 191-215.
  6. Beach, L.R. & Mitchell, T.R. (1978). A contingency model for the selection of decision strategies. Academy of Management Review, 3(3), 439-449.
  7. Beecham, R. (2009). Teaching quality and student satisfaction: nexus or simulacrum? 518 Salajan, Welch, Ray, and Peterson
  8. Buchanan, J. (2011). Quality teaching: Means for its enhancement? Australian Universities’ Review, 53(3), 66-72. Retrieved from http://www.nteu.org.au/library/view/ Id/1298
  9. Chen, H.R. & Tseng, H.F. (2011). Factors that influence acceptance of web-based elearning systems for the in-service education of junior high school teachers in Taiwan. Evaluation and Program Planning, 35, 398-406. Doi:10.1016/J.evalprogplan.2011.11.007
  10. Chen, L.D., Gillenson, M.L., & Sherrell, D.L. (2002). Enticing online consumers: An extended technology acceptance perspective. Information& Management, 39, 705719.
  11. Dasgupta, S., Granger, M., & McGarry, N. (2002). User acceptance of e-collaboration technology: An extension of the technology acceptance model. Group Decision and Negotiation, 11, 87-100.
  12. Davies, M., Hirschberg, J., Lye, J., & Johnston, C. (2010). A systematic analysis of quality of teaching surveys. Assessment& Evaluation in Higher Education, 35, 87-100.
  13. Davis, N.E., & Roblyer, M.D. (2005). Preparing teachers for the “schools that technology built”: Evaluation of a program to train teachers for virtual schooling. Journal of Research on Technology in Education, 37(4), 399–409.
  14. DeLone, W.H., & McLean, E.R. (1992). Information systems success: the quest for the dependent variable. Information Systems Research, 3(1), 60-95.
  15. Deng, X., Doll, W., & Truong, D. (2004). Computer self-efficacy in an ongoing use context. Behaviour& Information Technology, 23, 395-412.
  16. DeVellis, R.F. (1991). Scale development. Newbury Park, CA: Sage Publications.
  17. Fenstermacher, G., & Richardson, V. (2005). On making determinations of quality teaching. Teachers College Record, 107, 186-213. Retrieved from http://www.tcrecord.org/content.asp?contentid=11694
  18. Field, A. (2009). Discovering statistics using SPSS (3rd ed.). Los Angeles, CA: Sage Publications.
  19. Gefen, D., Straub, D.W., & Boudreau, M.C. (2000). Structural equation modeling and regression: Guidelines for research practice. Communications of the Association for Information Systems, 4(1). Retrieved from http://aisel.aisnet.org/cais/vol4/iss1/7/ Georgina, D.A., & Olson, M.R. (2008). Integration of technology in higher education: A review of faculty self-perceptions. The Internet and Higher Education, 11, 1-8.
  20. Glass, G.V., Peckham, P.D., & Sanders, J.R. (1972). Consequences of failure to meet assumptions underlying the analyses of variance and covariance. Review of Educa-The Role of Peer Influence and Perceived Quality of Teaching 519
  21. Hayes, D.N.A. (2007). ICT and learning: Lessons from Australian classrooms. Computers and Education, 49, 385–395. Doi:10.1016/J.compedu.2005.09.003
  22. Hong, W., Thong, J.Y.L., Wong, W.L., & Tam, K.Y. (2001). Determinants of user acceptance of digital libraries: An empirical examination of individual differences and system characteristics. Journal of Management Information Systems, 18(3), 97-124.
  23. Hu, P.J.H., Clark, T.H.K., & Ma, W.W. (2003). Examining technology acceptance by school teachers: A longitudinal study. Information& Management, 41, 227-241.
  24. Hutcheson, G., & Sofroniou, N. (1999). The multivariate social scientist. London: Sage Publications.
  25. Igbaria, M., & Iivari, J. (1995). The effects of self-efficacy on computer usage. Omega, International Journal of Management Science, 23, 587-605. Doi:10.1016/03050483(95)00035-6
  26. Johnson, E.J., & Payne, J.W. (1985). Effort and accuracy in choice. Management Science, 31(4), 395-414.
  27. Kaiser, H.F. (1974). An index of factorial simplicity. Psychometrika, 39(1), 31-36.
  28. Keengwe, J., Kidd, T., & Kyei-Blankson, L. (2009). Journal of Science Education and Technology, 18, 23-28.
  29. Klopping, I.M., & McKinney, E. (2004). Extending the technology acceptance model and the task-technology fit model to consumer e-commerce. Information Technology, Learning and Performance Journal, 22(1), 35-48.
  30. Kwon, O., Choi, K., & Kim, M. (2007). User acceptance of context-aware services: Selfefficacy, user innovativeness and perceived sensitivity on contextual pressure. Behaviour& Information Technology, 26, 483-498.
  31. Lay, J.G., & Chen, Y.W. (2011, October). GIS adoption and diffusion among senior high school geography teachers in Taiwan. In P. Shih (Ed.), ISPRS Workshop Commissions VI/1 – VI/2 E-learning 2011 with ACRS. Retrieved from http://www.isprs.org/Proceedings/XXXVIII/6-W27/pdf/P_41_8-16-19.pdf
  32. 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(1), 752-780. Http://aisel.aisnet.org/cais/vol12/iss1/50/ Liu, X. (2010). Empirical testing of a theoretical extension of the technology acceptance model: An exploratory study of educational wikis. Communication Education, 59, 52-69. Doi:10.1080/03634520903431745
  33. Lubke, G.H. & Muthen, B.O. (2004). Applying multigroup confirmatory factor models for continuous outcomes to Likert scale data complicates meaningful group comparisons. Structural Equation Modeling. 11, 514-534.
  34. Ma, Q., & Liu, L. (2004). The technology acceptance model: a meta-analysis of empirical findings. Journal of Organizational and End User Computing, 16(1), 59-72.
  35. Ma, W.W.K, Andersson, R., & Streith, K.O. (2005). Examining user acceptance of computer technology: An empirical study of student teachers. Journal of Computer Assisted Learning, 21, 387-395. Doi:10.1111/J.1365-2729.2005.00145.x
  36. Marin, S.L.T., Garcia, F.J.B., Torres, R.M., Vazquez, S.G., & Moreno, A.J.L. (2005). Implementation of a web-based educational tool for digital signal processing teaching using the technological acceptance model. IEEE Transactions on Education, 48, 632-641.
  37. Menard, S. (1995). Applied logistic regression analysis. Sage university paper series on quantitative applications in the social sciences 07-106. Thousand Oaks, CA: Sage Publications.
  38. Mijic, D. (2010). Measuring teaching quality in higher education: Instrument for collecting student feedback. ICT Innovations 2010 Web Proceedings (pp. 117-128). Retrieved
  39. Myers, R. (1990). Classical and modern regression with applications (2nd ed.). Boston, MA: Duxbury.
  40. 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, 250-267. Doi:10.1016/J.compedu.2004.11.007
  41. Nicolle, P.S., & Lou, Y. (2008). Technology adoption into teaching and learning by mainstream university faculty: A mixed methodology study revealing the “how, when, why, and why not.” Journal of Educational Computing Research, 39(3), 235-265.
  42. Pituch, K.A., & Lee, Y.K. (2006). The influence of system characteristics on e-learning use. Computers& Education, 47, 222-244. Doi:10.1016/J.compedu.2004.10.007
  43. Pynoo, B., Tondeur, J., van Braak, J., Duyck, W., Sijnave, B., & Duyck, P. (2012). Teachers’ acceptance and use of an educational portal. Computers& Education, 58, 1308-1317. Doi:10.1016/J.compedu.2011.12.026
  44. Rogers, E.M. (2003). Diffusion of Innovations (5th ed.). New York, NY: Free Press.
  45. Roughton, C., Martin, F., Warren, J., & Gritmon, C. (2011). Challenges in synchronous virtual classrooms adoption by faculty. International Journal of Instructional Technology& Distance Learning, 8(2), 45-54. Retrieved from http://www.itdl.org/Journal/Feb_11/article05.htm.
  46. Saade, R.G., Nebebe, F., & Tan, W. (2007). Viability of the ‘technology acceptance model’ in multimedia learning environments: a comparative study. Interdisciplinary Journal of Knowledge and Learning Objects, 3, 175-184. Retrieved from http://www.ijello.org/Volume3/IJKLOv3p175-184Saade393.pdf
  47. Sánchez-Franco, M.J. (2010). WebCT– The quasimoderating effect of perceived affective quality on an extending technology acceptance model. Computers& Education, 54, 37-46. Doi:10.1016/J.compedu.2009.07.005
  48. Schepers, J., & Wetzels, M. (2007). A meta-analysis of the technology acceptance model: Investigating subjective norm and moderation effects. Information& Management, 44, 90-103.
  49. Schuck, S., Gordon, S., & Buchanan, J. (2008). What are we missing here? Problematising wisdoms on teaching quality and professionalism in higher education. Teaching in Higher Education, 13, 537-547.
  50. Shen, D., Laffey, J., Lin, Y., & Huang, X. (2006). Social influence for perceived usefulness and ease-of-use of course delivery systems. Journal of Interactive Online Learning, 5(3), 270-282. Retrieved from http://www.ncolr.org/jiol/issues/getfile.cfm? VolID=5&IssueID=18&ArticleID=91
  51. Sheppard, B.H., Hartwick, J., & Warshaw, P.R. (1988). The theory of reasoned action: A meta-analysis of past research with recommendations for modifications and future research. Journal of Consumer Research, 15(3), 325–343.
  52. Singletary, L., Akbulut, A.Y., & Houston, A.L. (2002). Innovative software use after mandatory adoption.
  53. Straub, E.T. (2009). Understanding technology adoption: Theory and future directions for informal learning. Review of Educational Research, 79, 625-649.
  54. Taylor, S., & Todd, P.A. (1995). Assessing it usage: The role of prior experience. MIS Quarterly, 19(4), 561-570. Http://www.jstor.org/stable/249633 Teo, T. (2010). Development and validation of the e-learning acceptance measure (ElAM). Internet and Higher Education, 13, 148-152. Doi:10.1016/J.iheduc.2010.02.001
  55. Toral, S.L., Barrero, F., & Martinez-Torres, M.R. (2007). Analysis of utility and use of a web-based tool for digital signal processing teaching by means of a technological acceptance model. Computers& Education, 49, 957-975. Doi:10.1016/J.compedu.2005.12.003
  56. Tornatzky, L.G., & Klein, K.J. (1982). Innovation characteristics and innovation adoptionimplementation: A meta-analysis of findings. IEEE Transactions on Engineering Management, 29(1), 28-45.
  57. Ulrich, J., & Karvonen, M. (2011). Faculty instructional attitudes, interests and intention: Predictors of web 2.0 use in online courses. The Internet and Higher Education, 14, 207-216. Doi:10.1016/J.iheduc.2011.07.001
  58. Venkatesh, V., & Davis, F.D. (2000). A theoretical extension of the technology acceptance model: Four longitudinal field studies. Management Science, 46(2), 186-204.
  59. 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), 425-478.
  60. Walker, G., & Johnson, N. (2008). Faculty intentions to use components of web-enhanced instruction. International Journal on E-Learning, 7(1), 133-152.
  61. Wang, W.T., & Wang, C.C. (2009). An empirical study of instructor adoption of webbased learning systems. Computers& Education, 53, 761-774. Doi:10.1016/J.Compedu.2009.02.021
  62. Warshaw, P.R., & Davis, F.D. (1985). Disentangling behavioural intention and behavioural expectation. Journal of Experimental Social Psychology, 21(1), 213–228.
  63. Yuen, A.H.K., & Ma, W.W.K. (2008). Exploring teacher acceptance of e-learning technology. Asia-Pacific Journal of Teacher Education, 36, 229-243.

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