Integrating External Factors and Technology Acceptance Model to Understand Scholar Intention and Use of Recommendation System for Course Selection.

Abstract

Abstract There has been a huge increase in e-learning courses, and the Recommendation systems (RSs) are useful tools for narrowing down the course options and expose students to the courses that suit their needs. The majority of the research related to recommendation systems focus on effectiveness rather than factors influencing its acceptability, and in practice, user satisfaction cannot be explained by accuracy alone. This study will consider course recommendation system and investigate the acceptance of the courses suggested by the recommender system (RS) to scholar’s or research students based on their learning needs which in turn will help to find out why some individuals accept new technology while others resist. Therefore, research scholars (n = 150) who willingly engaged in this study were asked to use the RS and complete a questionnaire also based on their experience as part of a self-administrated longitudinal survey. This study assessed external factors such as perceived availability, relevance, and experience, which were not explained by the Technology Acceptance Model (TAM). The results confirm that the extended TAM provides a valuable theoretical model, which helps to understand scholar’s acceptance of RS, and other factors positively that affect the original variables of TAM. Hence, a new modified TAM that incorporates three external factors has been offered. The considerable value calculated for Cronbach’s alpha confirms that the results are valid and reliable. The observed results will help recommendation system developers to maximize user experience because the implications of this research effort are critical for instructors, scholar’s, and institutions as well.