Adaptation Of Technology Acceptance Model for Technology Enhanced Learning: A Meta-Model for Application in Heis

Abstract

With the rapid development of information and communication technologies, academic explorations into factors influencing acceptance of technology systems have been actively pursued. Thereby, many variants of the technology acceptance model were proposed and validated in various contexts involving a widespread application of the model, specifically in studying acceptance and use of e-learning systems. In the context of e-learning adoption, commonly used external factors of Technology Acceptance Model (TAM) are “self-efficacy”, “subjective norm”, “enjoyment”, “computer anxiety” and “experience”. Previous research on e-learning technologies have further explored the effect of these external factors on two primary constructs of TAM which are “perceived ease of use” and “perceived usefulness”. Although engagement of students with e-learning systems has revealed improvement in learning, the role of previously used external factors like computer anxiety, experience and selfefficacy needs to be re-examined for the modern digital native learners who have grown up in sophisticated technological environment. Therefore, factors like “results demonstrability”, “perception of external control”, “system accessibility” and “attitude” considered relevant to digital learners and were identified and used in the proposed model. These additional factors augmented the previously identified factors like “subjective norm”, “enjoyment” and “self-efficacy” for studying their influence on students’ behavioral intention to utilize e-learning systems. This research utilizes a cross-sectional design and data was collected from 437 undergraduate students from three academic programs using a self-administered questionnaire. Structural equation modeling technique was used to test the interrelationships between the constructs of proposed model. Mediating effects of model variables and moderating effects of gender, experience and type of institution on behavioral intention to use e-learning systems were also studied. The primary contribution of this research is an extended technology acceptance model based upon external factors relevant to digital learners, which identifies key predictors of student’s perceived ease of use and student’s perceived usefulness of e-learning systems. The research findings have significant theoretical and practical significance for researchers and academic practitioners. The proposed model will be useful in predicting behavioral intention to use e-learning systems by digital learners and will generate additional future research in other contexts and cultures with an aim to design and adopt e-learning systems that seek wide acceptance among students in universities.