This study developed and empirically tested a model to predict the factors affecting students’ behavioral intentions toward using mobile learning (m-learning). This study explored the behavioral intention to use m-learning from the perspective of consumers by applying the extended unified theory of acceptance and use of technology (UTAUT) model with the addition of perceived enjoyment, mobile self-efficacy, satisfaction, trust, and perceived risk moderators. A cross-sectional study was conducted by employing a research model based on multiple technology acceptance theories. Data were derived from an online survey with 1,562 respondents and analyzed using structural equation modeling. Partial least squares (PLS) regression was used for model and hypothesis testing. The results revealed that (1) behavioral intention was significantly and positively influenced by satisfaction, trust, performance expectancy, and effort expectancy; (2) perceived enjoyment, performance expectancy, and effort expectancy had positive associations with behavioral intention; (3) mobile self-efficacy had a significantly positive effect on perceived enjoyment; and (4) perceived risk had a significantly negative moderating effect on the relationship between performance expectancy and behavioral intention. Our findings correspond with the UTAUT model and provide a practical reference for educational institutions and decision-makers involved in designing m-learning for implementation in universities.

This study developed and empirically tested a model to predict the factors affecting patients’ behavioral intentions to use a mobile health education website. It explored the behavioral intention to use a mobile health education website from the perspective of consumers by extending the Unified Theory of Acceptance and Use of Technology 2 model with the addition of mobile self-efficacy (M-SE), use motivation, and mobile literacy as moderators. For this purpose, variance-based structural equation modeling was used to analyze data collected from a sample of 1,649 patients at a hospital in Taoyuan, Taiwan. Partial least squares regression was used for model and hypothesis testing. The results revealed that (1) performance expectancy, effort expectancy, social influence, facilitating conditions, and habit were key predictors of the behavioral intentions of patients to use a mobile health education website; (2) M-SE had positive associations with effort expectancy; (3) use motivation was a significant determinant of both performance expectancy and effort expectancy; and (4) mobile literacy had a significantly negative moderating effect on the relationship between performance expectancy and behavioral intention. Our findings serve as a reference for the promotion of mobile health education in hospitals.

Followed by the fast developments in Internet of Things (IoT), artificial intelligence (AI) technologies, utilizing cloud architecture and big data, have been integrated into various aspects of our daily lives. Such an integration results in high levels of convenience, comfort, and energy savings. Deploying such new technologies benefits the elevator industry as well. Intelligent elevator with energy control schemes can improve the energy efficiency of eco-friendly applications. The purpose of this study is to examine the factors that affect the intelligent elevator adoption of Taiwanese people using the extended unified theory of acceptance and use of technology (UTAUT 2) model, which considers factors such as environmental consciousness, artificial intelligence optimism, attitude, and perceived quality. A well-structured face-to-face survey is performed to obtain data from the consumers living in central Taiwan. The data acquired are analyzed via structural equation modeling using the partial least squares method. The analysis results indicate that attitude, habit, performance expectancy, effort expectancy, and perceived quality are important factors that affect the behavioral intentions of consumers when adopting new technologies, such as an intelligent elevator. However, facilitating conditions adversely affect the behavioral intentions. In addition, environmental consciousness and artificial intelligence optimism are crucial factors that affect performance and effort expectancies, whereas the latter two contribute positively to attitude. The findings of this study supplement existing literature regarding the behavioral intentions for adopting intelligent elevators. Finally, the theoretical and practical significances of this research study are discussed.

The purpose of this study was to develop and empirically test a model for predicting the key factors affecting nurses’ behavioral intention to use mobile learning (m-learning). We explored behavioral intention from users’ perspectives by applying an extended unified theory of acceptance and use of technology (UTAUT) model with the addition of information quality, system quality, technostress, and satisfaction. We conducted a survey of the district and regional hospitals in central Taiwan. Data were derived from 434 respondents. Structural equation modeling was applied to analyze the causal effects of 15 hypothesized predictive factors. We determined that satisfaction, social influence, performance expectancy, facilitating conditions, and effort expectancy positively impacted nurses’ behavioral intention to use m-learning. In addition, technostress was a negative antecedent of effort expectancy. Information quality and system quality had significantly positive effects on satisfaction, performance expectancy, and effort expectancy. This study provides hospital managers with a reference when assessing future developments and informs approaches to promote m-learning.