SentiTAM: Sentiments centered integrated framework for mobile learning adaptability in higher education

Abstract

Online communities provide facilities to share public opinions and or sentiments on a wide range of subjects, from routine topics to vital issues of critical interest. Nowadays, many higher education institutions (HEIs) recognize the value of students' sentiments and evaluate users' concerns for the successful adaptation of mobile learning applications (MLAs). While digital learning has been extensively studied previously, little has been known about why MLA is underutilized. Therefore, this study extends the literature by proposing the SentiTAM model underlying technology acceptance model (TAM), and students' sentiments on MLA platforms. A self-administered cross-sectional survey of 350 MLA users' data was analyzed through structural equation modeling (SEM) using the AMOS package program. In addition, we have performed sentiment analysis on students' opinions gathered through Google discussion forums and Twitter. The results show that MLA use intention is strongly influenced by sentiments and self-motivation, while perceived usefulness and perceived ease of use directly influence MLA usage. To the best of our knowledge, this study is the first attempt in MLA that investigates several vital factors, including sentiments as a multi-perspective tool and motivational factors with core constructs of TAM. The findings assist developing countries make smart decisions about how to use MLA with emerging technology.

Keywords

• Sentiment analysis
• Technology acceptance model (TAM)
• SenitTAM
• Higher education
• Mobile learning applications (MLAs)