Factors Affecting Students’ Behavioral Intention to Use LMS at a Turkish Post-Secondary Vocational School

Abstract

There is a substantial increase in the use of learning management systems (LMSs) to support teaching and learning activities in higher education institutions. Despite their benefits, students use them to a limited extend due to a number of factors influencing behaviors. This study executed a three-tier use model to examine the predictors of students' behavioral intention to use LMSs at a two-year postsecondary military school in Turkey, and the participants consisted of 155 students. The data were collected through an online questionnaire and analyzed through Pearson correlation coefficients and linear regression analyses. Results showed that multimedia instruction had a direct influence on perceived usefulness and perceived ease of use, while interactivity had a direct influence only on perceived satisfaction. Perceived satisfaction was also affected by perceived usefulness and perceived ease of use.Perceived ease of use exerted a direct influence on perceived usefulness, as well. Moreover, perceived usefulness had a great influence on behavioral intention to use LMSs. As a final point, self-efficacy did not take a position in the model due to its insignificant relationships with the other constructs. It is suggested that an increase in the multimedia features and interactivity of the system could lead to higher perceived usefulness and ease of use among learners, and both factors add to learners' perceived satisfaction which in turn increases their engagement.

E-Learning Technology Adoption in the Philippines: An Investigation of Factors Affecting Filipino College Students' Acceptance of Learning Management Systems

“…and is considered as one of the key determinants of IT usage. A study conducted by Subramanian [28] revealed that PU had significant correlation towards behavioral intention of users particularly in adopting elearning technology which was later confirmed by other researchers such as Fu, Farn & Chao [29], Norazah, Ramayah & Norbayah [30], Tarhini, Hone & Liu [31] and Cigdem & Ozturk [16] whereas their studies, conducted in different educational settings and programs, shown that BI was largely driven by PU. Another case in Nigeria that used PU to model Learning Information System (LIS) students' intention to adopt e-learning technology revealed that PU is one of the strongest predictors with empirical support in determining whether a student will accept the e-learning technology or not [32].…”

“…These hypotheses were merely a confirmation in the Philippine setting since lots of researchers [64][65][66][67], [40], [36], [31], [15], [16] have already concluded that PU and PEOU are two of the strongest determinants of BI. The findings were also supporting previous literature [22], [65], [36], [15] which stated the positive relationship between PEOU and PU (β = .458, p < .01) as hypothesized in H6.…”

“…With the Internet getting more powerful day by day and along with it are the e-learning environments as one of the direct beneficiaries, researchers and academicians have been extending their studies into advanced courses and fields such as Engineering [5], Mathematics [6], Economics [7], Physics [8], Medicine [9] and many more just to dive deeper into students' perceptions. Apart from these, there are also a lot of research papers that extensively studied factors that affect e-learning technology readiness and acceptance in different setting from neighboring countries such as Malaysia [10][11], Thailand [12][13], Indonesia [14][15] to a not so much outside Asia like Turkey [16] and Sri Lanka [17] up to other continents like Africa [18] and America [19].…”

Malaysian Undergraduates’ Behavioural Intention to Use LMS: An Extended Self-Directed Learning Technology Acceptance Model (SDLTAM)

“…A multicollinearity assumption was tested in order to the existence of multicollinearity among the variables in the analyses. This happened when a high level of correlation of (r>.90) between two or more variables (Cigdem and Ozturk, 2016). Table 3 shows the results for all variables fitted in the suggested criteria with the correlation values ranging from .79 to .90 therefore, discriminant validity was achieved at the item and variable level.…”

Model of Behavioral Attention towards Using ICT in Universities in Libya

“…Moreover, Rudposhti, Zahedfar and Abadi (2016) indicated that behavioral intention can influence users to accept the use of technology. Moreover, Cigdem and Ozturk (2016) explored that self-efficacy is influential factor to determine and predict behavioral intention to accept the use of technology. Therefore, this is shows that behavioral intention can influence lecturer to use ICT.…”