ABSTRACT
This study aims to understand the factors that provide student satisfaction in online learning during the COVID-19 pandemic. The unit of analysis of this research involved 581 student respondents who had done online learning. This study uses a quantitative approach with SEM-PLS analysis to answer the three research hypotheses. The factor studied is the relationship between University Support, Online Learning Tools, Technical Support, and Student Satisfaction. The results of the analysis state that all items are valid and reliable with the indicated values of loading factor (LF), Average Variance Extracted (AVE), composite reliability (CR), and Cronbach's alpha (CA) > 0.5. Meanwhile, the value of R2 = 0.545, which means the model is quite representative. The results of the analysis of all hypotheses showed a positive and significant impact on Student Satisfaction. These results indicate that online learning activities during the COVID-19 pandemic really need university support and online learning tools that are well available from devices that can be used by students and teachers and technical support that is ready to solve all problems. © 2022 IEEE.
ABSTRACT
Before the pandemic, the city of Porto had undergone a boom in tourism and emerged as one of Europe´s most attractive and smartest destinations. Few industries were more disrupted by the COVID-19 pandemic than tourism. In the post-pandemic world, mobile augmented reality (MAR) has become increasingly important, yet research on this emerging technology remains scarce. In this study, we will empirically test the UTAUT-3 model to understand the adoption of mobile augmented reality in tourism (MART) in the city of Porto, Portugal. Unlike most IS studies in the field of tourism, this study uses the latest version of the UTAUT model that has superior predictive power. To test this model, SEM-PLS analysis was applied to data obtained from a survey with 101 respondents. We found that respondents do not seem be influenced by prices, the opinions of others, the usefulness nor the effort it takes to use MART. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.