The adoption of learning management systems (LMS) among faculty members at Kansas State University and King Saud University

Abstract

The purpose of this quantitative study was to investigate three areas related to LMS adoption at universities: first, the relationships between faculty personal characteristics (age, gender, academic ranking, and years of teaching experiences) and their adoption of learning management systems (LMS); second, organizational support related to LMS adoption; and third, concern of time and fear of technology as inhibiting factors of using an LMS. The research compares faculty members at Kansas State University, Manhattan, Kansas, and faculty members at King Saud University in Saudi Arabia. This study is related to the educational technology field in the higher education environment. Many universities in Saudi Arabia are in the early stage of adopting and using e-learning tools such as LMSs. There is a need to illustrate the best practice processes of adopting new technology in higher education contexts. This study should help instructors and university leaders determine the significant factors of successful adoption of educational technology tools. Rogers’ (2003) diffusion of innovation theory was used to provide insights and guide the study as well as design the research questions. His work mentioned that about 49% to 87% of innovation adoption can be predicted according to five perceived attributes: (1) relative advantage, (2) compatibility, (3) complexity, (4) trialability, (5) observability. These data were obtained from 403 faculty members at Kansas State University. The data analysis showed that faculty members’ personal characteristics influenced their LMS adoption. A MANOVA Pillai’s Trace test results showed a statistical difference between faculty characteristics (age, p = .017 gender, p = .009 years of teaching experiences p = .042 and academic rank p = .000) and Rogers’ five attributes of innovation at Kansas State University. Conversely, at King Saud University the data were obtained from 104 faculty members. The data analysis showed no influence between faculty members’ personal characteristics and Rogers’ five attributes of innovation. An ANOVA test was conducted and there was a statistical difference among faculty members at Kansas State University in all four independent variables (age, p = .004 gender, p = .000, years of teaching experience p = .012 and academic ranking, p = .008) and their perception of the organizational support related to their adoption of the LMS. On the other hand, there was no a statistical difference among faculty members at King Saud University in all four independent variables (age, gender, academic ranking, and years of teaching experience) and their perception of the organizational support related to their adoption of the LMS. The MANOVA Pillai’s trace test result showed a statistical difference between faculty academic rank and fear of change of technology p = .021 and no statistical significance for time concern at Kansas State University. However, there was no a statistical difference for faculty members at King Saud University concerning all independent variables (age, gender, academic ranking, and years of teaching experience) with fear of change of technology and no statistical significance for time concern, as well. The study concluded with a recommendation for Kansas State University and King Saud University regarding learning management system adoption. In addition, important considerations for professional development and training among faculty members were also recommended. Finally, a recommendation for future research in the field of educational technology was proposed.