Search

Social-Media-Based Learning (SMBL) is the use of social-media-based platforms, such as Twitter, Google Plus, Facebook, and YouTube, for learning purposes. It facilitates interactive, participative, and cooperative learning among people in real time. This study aimed to determine the factors influencing SMBL in higher education institutions and to assess the mediating effect of trust on the target platform. To this end, we developed a model by combining the theoretical constructs of the unified theory of acceptance and use of technology theory with the concept of "trust." We used the structural equation modeling method (partial least squares analysis) to analyze data collected using an online survey of 300 participants that included university students and faculties of higher education institutions in Bangladesh. In addition, we used importance-performance map analysis to determine constructs having relatively high importance, but showing relatively low performance. The study revealed that performance expectancy, effort expectancy, social influence, and facility conditions have a significant impact on social media usage intention. Furthermore, trust partially mediates the direct impact of performance expectancy, effort expectancy, and social influence on social media usage intention. The findings imply that trust has higher importance but relatively low performance.

This study aims to examine online shopping behaviour on social media platforms. This study formulates a research model integrating trust with the flow theory and some basic constructs of the UTAUT. To analyze the data from an online survey involving 305 participants actively making online purchases through social media platforms. This study applied the Structural Equation Modeling-Artificial Neural Networks (SEM-ANN) technique. Incorporating statistically significant SEM findings, the ANN model was used to analyze linear and nonlinear interactions among proposed variables. The research findings demonstrate that flow emerges as the most significant determinant, succeeded by effort expectancy, social influence, and performance expectancy, in defining the concept of trust. However, the sensitivity analysis using ANN indicates that effort expectancy is the most important factor in establishing trust, followed by flow, social influence, and performance expectancy. The predictive power of intention to use is noteworthy in determining actual use behaviour, with trust and flow emerging as influential factors favourably impacting this intention. [ABSTRACT FROM AUTHOR]

This study determines influential factors that account for mobile payment service adoption in a developing country and assesses the mediating effect of word of mouth on this platform. Our model combines multi-dimensions of trust and risk, and word of mouth to predict the target population's use intention and use behavior. We use the structural equation modeling method to analyze the data collected by an online survey of 392 respondents who use mobile payment services in Bangladesh. This study's results reveal that trust in service providers, trust in service, privacy risk, and security risk have significant impacts on the intention to use. The findings also indicate that word of mouth mediates the direct impacts of trust in service providers, trust in apps, and security risk on the intention to use. In addition, importance-performance map analysis depicts that word of mouth is the most critical performing factor, but performance is comparatively low. [ABSTRACT FROM AUTHOR]

As COVID-19 spreads, people's interest in smart learning that can do non-face-to-face learning is increasing nowadays. In this study, we aim to empirically analyze how users’ thoughts on COVID-19 and the information quality and system quality of smart learning systems affect users’ acceptance of smart learning and examine the effect of perceived sensitivity and severity of COVID-19 on the satisfaction and use of smart learning through concerns about the risk of transmission. In addition, we examined the influence of information quality composed of content quality and interaction quality and system quality composed of system accessibility and functionality on the use of smart learning through user satisfaction. To verify the validity of the proposed model, we conducted a survey on 334 users with experience in using smart learning, and performed the analysis using Smart PLS 3.0. According to the analysis results, among information quality and system quality, only functionality has a positive (+) effect on the satisfaction of smart learning, and satisfaction has a positive (+) effect on the usage behavior. However, it is found that accessibility among system quality do not affect satisfaction, and concern about the risk of transmission has a negative effect on satisfaction. This study can provide meaningful guidelines to researchers when researching smart learning to support students' learning in a pandemic situation of a new infectious disease, such as COVID-19. It will also be able to provide useful implications for educational institutions and companies related to smart learning. [ABSTRACT FROM AUTHOR]