Extending the Unified Theory of Acceptance and Use of Technology for COVID-19 Contact Tracing Application by Malaysian Users

Exploring the determinants of mathematics teachers' willingness to implement STEAM education using structural equation modeling

Science, technology, engineering, arts, and mathematics (STEAM) has gained increasing attention for its potential to enhance student learning experiences, critical thinking, and problem-solving skills. However, implementing STEAM in mathematics education presents numerous challenges. This study examines the factors that influence mathematics teachers' willingness to adopt STEAM by integrating the Theory of Planned Behavior (TPB) and the Technology Acceptance Model (TAM) with an innovative component STEAM literacy. Utilizing questionnaire data from 1,173 mathematics teachers across China and employing Structural Equation Modeling (SEM), our analysis highlights the critical roles of perceived usefulness and subjective norm in motivating teachers' intentions to engage with STEAM. Furthermore, we find that these intentions significantly predict actual implementation. Notably, the inclusion of STEAM literacy within the TPB-TAM framework offers a unique perspective, demonstrating that enhancing STEAM literacy, alongside fostering positive attitudes and providing adequate resources, can significantly influence both the intention and the practical adoption of STEAM education. This study delivers valuable insights for educational policymakers and practitioners on promoting STEAM effectively.

Predicting digital contact tracing tool adoption during COVID-19 from the perspective of TAM: The role of trust, fear, privacy, anxiety, and social media

Objective

The emergence of more contagious SARS-CoV-2 variants, such as EG.5 (Eris), has heightened the urgency of assessing associated risks and managing the spread of infections. Digital Contact Tracing (DCT) tools have been widely adopted to mitigate these risks, although the factors driving their acceptance are complex and multifaceted. However, there is a significant lack of research on the application of DCT within Saudi Arabia, despite its proactive use of such technologies in public health strategies. This study investigates the key determinants of DCT adoption and acceptance by integrating the Technology Acceptance Model (TAM) with psychological, social, and regulatory factors related to the context of the study.

Methods

Using a quantitative, cross-sectional design, data were collected from Saudi participants through an online survey and analysed using Structural Equation Modeling (SEM) with SmartPLS4.

Results

The results supported all the hypotheses except for the relationship between social media awareness and DCT tool usage. The findings revealed that COVID-19-induced anxiety significantly influenced technology acceptance, with social influence playing a mediating role. This study introduces a novel, context-specific model contributing to the technology acceptance field by exploring how pandemic-related factors, such as anxiety and social influence, affect DCT tool adoption. It also addresses a critical gap in the previous literature by examining the mediating role of social impact in the association between privacy and event-related fear and the moderating effect of COVID-19 anxiety on social media awareness and DCT usage. The findings offer valuable insights for governmental interventions, health institutions, and legislators in managing pandemics globally and within the Kingdom of Saudi Arabia.

Conclusion

We introduce a novel, context-specific model for understanding how pandemic-related psychological and social factors influence DCT adoption in this study. Those results provide insight into how policymakers, health institutions, and legislators can use DCT tools to manage pandemics globally and in Saudi Arabia.

Factors driving the acceptance of COVID-19 pandemic mobile contact tracing apps: The influence of security and privacy concerns

The acceptance of COVID-19 mobile contact tracing apps (MCTA) is crucial to curb the spread of the virus and decrease the number of infections. However, the security and privacy concerns (SPC) of COVID-19 MCTA have been called into question. Thus this paper examines the drivers of the acceptance of the COVID-19 pandemic MCTA under the auspices of the influence of SPC from the Chinese perspective based on the modified Unified Theory of Acceptance and Usage of Technology (UTAUT) model. The data generated through a questionnaire based on the convenient sampling technique was analyzed with SPSS by performing hierarchical regression analysis. The results show that the core constructs of UTAUT such as performance expectancy (PE), facilitating conditions (FC), effort expectancy (EE), and social influence (SI) along with mobile self-efficacy (MSE) were significant predictors of individual user acceptance of COVID-19 MCTA. Additionally, the study confirmed that security and privacy concerns were significant in moderating the impact of PE, FC, EE, SI, and MSE on the acceptance of COVID-19 MCTA. The managerial and theoretical implications of these findings for policy-makers, governments, mobile app developers, and researchers are interrogated.

Antecedents predicting digital contact tracing acceptance: a systematic review and meta-analysis

An awareness of antecedents of acceptance of digital contact tracing (DCT) can enable healthcare authorities to design appropriate strategies for fighting COVID-19 or other infectious diseases that may emerge in the future. However, mixed results about these antecedents are frequently reported. Most prior DCT acceptance review studies lack statistical synthesis of their results. This study aims to undertake a systematic review and meta-analysis of antecedents of DCT acceptance and investigate potential moderators of these antecedents. By searching multiple databases and filtering studies by using both inclusion and exclusion criteria, 76 and 25 studies were included for systematic review and meta-analysis, respectively. Random-effects models were chosen to estimate meta-analysis results since Q, I 2, and H index signified some degree of heterogeneity. Fail-safe N was used to assess publication bias. Most DCT acceptance studies have focused on DCT related factors. Included antecedents are all significant predictors of DCT acceptance except for privacy concerns and fear of COVID-19. Subgroup analysis showed that individualism/collectivism moderate the relationships between norms/privacy concerns and intention to use DCT. Based on the results, the mean effect size of antecedents of DCT acceptance and the potential moderators may be more clearly identified. Appropriate strategies for boosting the DCT acceptance rate can be proposed accordingly.

All-People-Test-Based Methods for COVID-19 Infectious Disease Dynamics Simulation Model: Towards Citywide COVID Testing

The conversion rate between asymptomatic infections and reported/unreported symptomatic infections is a very sensitive parameter for model variables that spread COVID-19. This is important information for follow-up use in screening, prediction, prognostics, contact tracing, and drug development for the COVID-19 pandemic. The model described here suggests that there may not be enough researchers to solve all of these problems thoroughly and effectively, and it requires careful selection of what we are doing and rapid sharing of results and models and optimizing modeling simulations with value to reduce the impact of COVID-19. Exploring simulation modeling will help decision makers make the most informed decisions. In order to fight against the "Delta" virus, the establishment of a line of defense through all-people testing (APT) is not only an effective method summarized from past experience but also one of the best means to effectively cut the chain of epidemic transmission. The effect of large-scale testing has been fully verified in the international community. We developed a practical dynamic infectious disease model-SETPG (A + I) RD + APT by considering the effects of the all-people test (APT). The model is useful for studying effects of screening measures and providing a more realistic modelling with all-people-test strategies, which require everybody in a population to be tested for infection. In prior work, a total of 370 epidemic cases were collected. We collected three kinds of known cases: the cumulative number of daily incidences, daily cumulative recovery, and daily cumulative deaths in Hong Kong and the United States between 22 January 2020 and 13 November 2020 were simulated. In two essential strategies of the integrated SETPG (A + I) RD + APT model, comparing the cumulative number of screenings in derivative experiments based on daily detection capability and tracking system application rate, we evaluated the performance of the timespan required for the basic regeneration number (R0) and real-time regeneration number (R0t) to reach 1; the optimal policy of each experiment is available, and the screening effect is evaluated by screening performance indicators. with the binary encoding screening method, the number of screenings for the target population is 8667 in HK and 1,803,400 in the U.S., including 6067 asymptomatic cases in HK and 1,262,380 in the U.S. as well as 2599 cases of mild symptoms in HK and 541,020 in the U.S.; there were also 8.25 days of screening timespan in HK and 9.25 days of screening timespan required in the U.S. and a daily detectability of 625,000 cases in HK and 6,050,000 cases in the U.S. Using precise tracking technology, number of screenings for the target population is 6060 cases in HK and 1,766,420 cases in the U.S., including 4242 asymptomatic cases in HK and 1,236,494 cases in the U.S. as well as 1818 cases of mild symptoms in HK and 529,926 cases in the U.S. Total screening timespan (TS) is 8.25~9.25 days. According to the proposed infectious dynamics model that adapts to the all-people test, all of the epidemic cases were reported for fitting, and the result seemed more reasonable, and epidemic prediction became more accurate. It adapted to densely populated metropolises for APT on prevention.