Task-Technology Fit and Technology Acceptance Model Application to Structure and Evaluate the Adoption of Social Media in Academia

Abstract

The purpose of this article was to reduce the dissimilarities in the literature regarding the use of social media for training and its impact on students' academic performance in higher education institutions. The main method of data collection for task-technology fit (TTF) and the technology acceptance model (TAM) was a questionnaire survey. This research hypothesizes that TTF applied to social media for learning will affect technology, task, and social characteristics that in turn improve students' satisfaction and students' academic performance. It also posits that the behavioral intent to use social media for learning will affect comprehension efficiency, ease of use, and enjoyment, all of which also improve students' satisfaction and students' academic performance. The data collection questionnaire was conducted with 162 students familiar with social media. Quantitative structural equation modeling was employed to analyze the results. A significant relationship was found between technology, task, and social features with TTF for utilizing social media for academic purposes, all of which fostered student enjoyment and improved outcomes. Similarly, a clear relationship was found between comprehension efficiency, ease of use, and enjoyment with behavioral intentions to utilize social media for academic purposes that positively affected satisfaction and achievement. Therefore, the study indicates that TTF and behavioral intentions to use social media improve the active learning of students and enable them to efficiently share knowledge, information, and discussions. We recommend that students utilize social media in pursuit of their educational goals. Educators should also be persuaded to incorporate social media into their classes at higher education institutions. INDEX TERMS Task-technology fit, technology acceptance model, social media.

Efficacy of Social Networking Sites for Sustainable Education in the Era of COVID-19: A Systematic Review

“…Apart from relevant publications on the impact of COVID-19 on educational practices [11][12][13][18][19][20][21][22][23][24][25] and important literature reviews on the efficacy of SNSs for eLearning [27][28][29][30][31][32][33]60], most of the articles included in the present study utilized either web-based or paper-based questionnaires [1,2,4,5,38,39,42,44,47,48,[53][54][55][56][57][58]. Nonetheless, few other studies utilized mixed-study design [45,52], experimental design [43,59], and a manual evaluation of the SNS-based learning contents [3,41,51].…”

“…As shown in Table 3, most of the research participants considered are undergraduate university students [2,6,27,38,39,[41][42][43][44][45][46][47][48][49][50]. Although few studies collected and analyzed user/system generated data [3,51,52], others considered higher education researchers [5,53], faculty members [7,54,55], and college students [40,56,57] as the research participants.…”

“…Although few studies collected and analyzed user/system generated data [3,51,52], others considered higher education researchers [5,53], faculty members [7,54,55], and college students [40,56,57] as the research participants. Studies on SNS in education have reached a global level, with most studies coming from Asia [1,2,[4][5][6][7]39,41,45,47,49,52,[56][57][58][59], Europe [8,27,43,46,48,50,54,55], and the USA [3,38,40,42,44,53].…”

Social Media Applications Affecting Students’ Academic Performance: A Model Developed for Sustainability in Higher Education

“…The use of social media applications (SMAs) in learning design in higher education may offer diverse educational advantages. For the technology acceptance model (TAM), it facilitates a significant relationship between student satisfaction (SS) and students' academic performance (SAP) [1]. Additionally, the perceived ease of use (PEOU) and perceived usefulness (PU) of SMAs help learners to become more understanding, active, and engage with peers and lecturers [2].…”

Critical Factors Influencing Acceptance of Automated Vehicles by Hong Kong Drivers

“…The data collected were analyzed to test and validate the proposed model and identify important factors that influence AV acceptance among drivers, using structural equation modeling (SEM). SEM is an effective method to investigate technology acceptance in various research areas such as smart e-learning systems [16], electricity meters [40] and social media [41]. Therefore, SEM was used in this study to investigate AV acceptance among drivers.…”