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Learning Management Systems (LMS) have been widely used in higher education institutions in the United States and around the world; and this trend continues to rise (Lang, 2016). According to a 2014 EDUCAUSE Center for Analysis Research’s report, 99% higher education institutions in the United States have an LMS in place, and 83% of students use some type of LMS. Among 17000 faculty members and 75000 students surveyed, majority of students and faculty members viewed LMS as an important tool for teaching and learning (Dahlstrom & Bichsel, 2014). Researchers have pointed to the critical role of LMS in student academic success (Paulsen, 2003; Browne, Jenkins, and Walker, 2006; Kumar & Sharma (2016). Despite the widespread use of LMS, not all university students are comfortable with their use, and others are unable to utilize them to the fullest ((Dahlstrom & Bichsel, 2014). While initial acceptance to LMS is a good step in the adoption process, an investigation into continuing acceptance is critical for long-term success of LMS (Joo, Kim, & Kim, 2016).
The use of LMS emerged in higher education in the 1990s and has quickly become an integral part of current teaching and learning experiences. The benefit of using LMS platforms such as Blackboard, Moodle and Canvas will not be maximized if students do not use them now, and continue to use them in the future (Alenzi, 2012; Lai, Wang, & Lei, 2012). In order to improve on LMS usage, researchers therefore, need to explore factors that give us a better understanding of the determinants of LMS among university students. Previous studies have highlighted some of these critical factors. For example, usefulness, ease of use, perceived enjoyment, quality and attitudes have been found to determine LMS adoptions among college students (Pituch & Lee, 2006; Lee, Cheung, & Chen, 2005; Saade, Nebebe, & Tan, 2007).
Many frameworks have been used by researchers to understand the spread and adoption of technologies such as these. Some examples include, the technology acceptance model, the theory of reasoned action, the theory of planned behavior, the expectation-confirmation theory among others. In this research we utilize the theory of planned behavior and the expectation-confirmation theory to examine this adoption concept. We do so for two major reasons: first, the theory of planned behavior has been acclaimed for its versatility in welcoming change interventions in behavioral research (Steinmetz, Knappstein, Ajzen, Schmidt, & Kabst, 2016). And since the adoption of LMS is behavioral in nature, and institutions need interventions that can encourage its use; a theory as the TPB seemed a great fit.
Second, many researchers have focused on the initial acceptance of the system and not really on the long-term continuance of use of the given system (Bhattacherjee, 2001). However, research shows that the real success of information systems (IS) lies in the continuing use of a system rather than in its initial acceptance, even though critical, is the actual measure of IS success. Hence, this research utilizes continuance use intention, instead of just behavioral intention to use a system and compares the two.
The current research therefore has as main focus to uncover the determinants of both behavioral intention to use (initial use intentions) and continuance intention to use (long-term use intentions). It also investigates into the difference between the two use outcomes based on the attitude, subjective norms and perceived behavioral control. The results of the study will benefit online learning champions seeking to increase adoption strategies.
In the following sections, we conduct a review of relevant literature, discuss how proposed model was developed, outline the methodology for the research, then analyze data, discuss results and offer a conclusion.