ABSTRACT
Due to the emergence of the COVID-19 pandemic, governments have implemented several urgent steps to minimize the disease’s effect and transmission. Supportive measures to trace contacts and warn people infected with COVID-19 were also implemented such as the COVID-19 contact tracing application. This study investigated the effects of variables influencing the intention to use the COVID-19 tracker. The extended Unified Theory of Acceptance and Use of Technology model was used to investigate user behavior using the COVID-19 tracker application. Google Form was used to construct and distribute the online survey to participants. Experiment results from 224 individuals revealed that performance expectations, trust, and privacy all have an impact on app usage intention. However, social impact, effort expectation, and facilitating conditions were not shown to be statistically significant. The conceptual model explained 60.07% of the amount of variation, suggesting that software developers, service providers, and policymakers should consider performance expectations, trust, and privacy as viable factors to encourage citizens to use the app. This study work’s recommendations and limitations are thoroughly discussed. © 2022. Vinh T. Nguyen and Chuyen T. H. Nguyen. This open-access article is distributed under a Creative Commons Attribution (CC-BY) 4.0 license.
ABSTRACT
The purpose of this study was to investigate the perceptions of users about using digital detox applications and to display relationships among personality traits and technology-related variables. This study was designed using survey approach and employed Generalized Structured Component Analysis (GSCA). As such, 11 hypotheses were constructed and tested. The study recruited 263 participants who utilize detox applications to avoid social media distractions. Data were collected through Google Form and analyzed using GSCA Pro 1.1 to better understand whether the proposed conceptual model fits the data. The results of the study indicated that behavioral intention predicted usage behavior significantly; performance expectancy, effort expectancy, and social influence positively affected behavioral intention; in turn, agreeableness and extroversion positively influenced performance expectancy, and extroversion affected effort expectancy; finally, neuroticism had a statistically significant and negatively associated with effort expectancy of using social media detox apps. The significant exceptions were that facilitating conditions were not predictive of behavioral intention, openness to experience did not influence performance expectancy, and conscientiousness was not linked to effort expectancy. The proposed conceptual model explained 56.68% of the amount of variation, indicating that instructors, policy makers and software designers should consider personal factors for preparing practical intervention approaches to mitigate learning issues related to social media distraction.