This paper aims to explore and investigate the potential factors influencing students' behavioral intentions to use the e-learning system. This paper proposes an extended technology acceptance model (TAM) that has been tested and examined through the use of both innovation diffusion theory (IDT) and integrating TAM. This paper was conducted on 1286 students utilizing systems of e-learning in Malaysia. The findings were obtained via a quantitative research method. The findings illustrate that six perceptions of innovation characteristics, in particular, have impacts on students' e-learning system behavioral intention. The influences of the relative advantages, observability, trialability, perceived compatibility, complexity, and perceived enjoyment on the perceived ease of use is noteworthy. Moreover, the effects of the relative advantages, complexity, trialability, observability, perceived compatibility, and perceived enjoyment on the perceived usefulness have a strong impact. Therefore, the empirical results provide strong backing to the integrative approach between TAM and IDT. The findings suggest an extended model of TAM with IDT for the acceptance of the e-learning system used to improve the students' learning performance, which can help decision makers in higher education, universities, as well as colleges to evaluate, plan and execute the use of e-learning systems.

The current study explores the students’ behavioral intention to use social media and actual social media use in higher education, specifically the perception of their academic performance and satisfaction. The study is theoretically based on the technology acceptance model (TAM) with evaluation information system success models (ISSM). Theoretically, five independent constructs were identified as contributory to behavioral intention to use social media, and actual social media use towards the students’ satisfaction and performance impact was analyzed. A questionnaire survey based on the technology acceptance model (TAM) and information system success model (ISSM) was utilized as the key method for collecting data and disseminated to 1200 students from four public universities of Malaysia chosen through a random sampling technique. For data analysis, the SPSS and structural equation modeling (SEM-Amos) were used. Outcomes obtained from the students’ behavioral intention to use and actual social media usage indicates a positive and constructive influence on satisfaction and academic performance in higher education. In addition, both male and female students were satisfied with perceived usefulness (β = 0.095, t-value = 3.325, p < 0.001 and β = −0.045, t-value = −2.079, p < 0.001, respectively), perceived ease of use (β = 0.108, t-value = 3.29, p < 0.001 and β = 0.307, t-value = 12.365, p < 0.001, respectively), perceived technology fit (β = 0.14, t-value = 4.769, p < 0.001 and β = 0.277, t-value = 12.358, p < 0.001, respectively), information quality (β = 0.108, t-value = 3.825, p < 0.001 and β = 0.109, t-value = 5.087, p < 0.001, respectively), and system quality (β = 0.232, t-value = 7.573, p < 0.001 and β = 0.176, t-value = 7.429, p < 0.001, respectively). Therefore, we encourage students to use social media for educational purposes and encourage more interactions with peers at higher education institutions. The study’s empirical findings present strong support for the integrative association between the TAM and the ISSM in using online learning platforms to improve students’ academic achievements and satisfaction. This could help decision makers in universities, higher education institutions, and colleges to plan, evaluate, and implement online learning platforms in their institutions.

The aim of this paper to develop a model to measure sustainability for education and incorporate the literature big data adoption and knowledge management sharing in the educational environment. This paper hypothesizes that perceived usefulness, perceived ease of use, perceived risk, and behavioral intention to use big data should influence adoption of big data, while age diversity, cultural diversity, and motivators should impact knowledge management sharing. Therefore, knowledge management sharing influences behavior intention to use technologies and big data adoption would be positively associated with sustainability for education. This paper employed a version of TAM and motivation theory as the research framework and adopted quantitative data collection and analysis methods by surveying 214 university students who were chosen through stratified random sampling. Student's responses were sorted into the 11 study constructs and analyzed to explain their implication of sustainability on education. The data were then quantitatively analyzed using structural equation modeling (SEM). The results showed that perceived usefulness, perceived ease of use, perceived risk, and behavioral intention to use big data were significant determinants of big data adoption, while age diversity, cultural diversity, and motivators were significant determinants of knowledge management sharing. The knowledge management sharing, behavior intention to use technologies, and big data adoption succeeded in explaining 66.7% of sustainability on education. The findings and implications of this paper are provided.INDEX TERMS Big data adoption, knowledge management sharing, motivators, technology acceptance model (TAM).

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.

The present study aimed to investigate students' perceptions of the quality of blended learning )based on the use of Blackboard( in teaching physics at King Saud University )KSU(, and whether these perceptions vary according to gender and computer skills level. The Course Experience Questionnaire )CEQ(, developed by Ginns & Ellis )2007(, was used to examine the perceptions of 341 Health Colleges students in the first year of a five-year undergraduate degree, who were enrolled in a general physics course at KSU. The results revealed that the blended learning in teaching physics at KSU was generally perceived to be of high quality. The results also revealed that there were no statistically significant differences in students' perceptions of the quality of blended learning in teaching physics at KSU that could be attributed to gender. However, the results revealed that there were statistically significant differences in those perceptions that could be attributed to students' computer skill levels in favor of students with a higher level of computer skills.