Examining Undergraduate Students’ Behavioral Intentions for M-Learning using Theoretical Framework of UTAUT

Enrolments and completion of online programs have been a concern in online learning. It has been found that learners tend to drop out of the program halfway. Consequently, it has become important to identify factors that lead to intention formation and a possible future enrolment to online platforms. The current study focuses on the intention of final year undergraduate (UG) students for m-learning using a quantitative approach, that is descriptive and cross-sectional. All the students have undergone online learning during the phase of the coronavirus pandemic; therefore, the sampling technique used is purposive sampling. Using the theory of Unified Theory of Acceptance and Use of Technology (UTAUT), the adapted questionnaire consists of items on the Likert scale. Multiple regression (MR) was used for hypotheses testing to determine which factors lead to creation of m-learning behavioral intention on a sample of 179 responses at a significance level of p<0.05. Data analysis revealed that all four predictors of UTAUT positively influence and explain 64.2% of intent for m-learning among UG students. This explanation is more in comparison to past studies. The study is crucial for educators who are developing study material. Application developers need to tailor made their applications that enable m-learning with the least effort. The decision-makers in an educational institution must provide technical staff support and the necessary infrastructure to facilitate the m-learning of the students.