Exploring critical factors influencing nurses' intention to use tablet PC in Patients' care using an integrated theoretical model

Impact of Personality Traits and Information Privacy Concern on E-Learning Environment Adoption during COVID-19 Pandemic: An Empirical Investigation

In response to the COVID-19 pandemic, teaching and learning processes have experienced significant changes. Higher education institutions in Taiwan employed crisis intervention measures to instantly implement unified learning methods such as online teaching and learning. However, students had no time to prepare. Thus, the study explored the relationship between personality traits and the belief in conspiracy theory as antecedents of students’ concern for information privacy (CFIP) and the subsequent relationship between students’ CFIP and behavioral intention to report their personal information to e-learning service providers concerning the adoption of the e-learning environment. This cross-sectional study employed a questionnaire to accumulate data from university students in Taiwan. A total of 285 valid responses were used for the final analysis. The research framework was evaluated by structural equation modeling (SEM). The results suggest the proposed model explains about 66.4% of the variance of behavioral intention (R2 = 0.664). The findings support that four personality traits—agreeableness, openness to experiences, conscientiousness, and neuroticism—and belief in conspiracy theory significantly influenced students’ CFIP. However, concerning extraversion, an insignificant path coefficient was reported. CFIP mediates the relationship between belief in conspiracy theory and behavioral intention. E-learning service providers should consider these determinants in improving and endorsing principles concerning e-learning environment adoption.

Role of Infodemics on Social Media in the Development of People’s Readiness to Follow COVID-19 Preventive Measures

Unparalleled levels of misinformation have contributed to widespread misunderstandings about the nature of the coronavirus, its cure and preventative measures. Misinformation crosses borders rapidly with the help of social media, and this phenomenon is constantly increasing. Thus, the current study proposes a research framework to explore how citizens’ trust in government and social media influences their readiness to follow COVID-19 preventive measures. Additionally, the role of a health infodemic was explored in perceptions and relationships among factors influencing an individual’s readiness to follow COVID-19 preventive measures with data collected from 396 participants in Taiwan. The findings indicate citizens’ trust in social media (TRSM), attitude (ATT), perceived benefit (PBT), personal innovativeness, and how peer referents positively influence their readiness. However, the relationship between citizens’ trust in the government (TRGT) and their readiness to follow COVID-19 preventive measures (INT) is not statistically significant. The current study also explores the negative moderating effect of health infodemics on the relationship between TRSM and INT, TRGT and INT, ATT and INT, PBT and INT. Thus, the Taiwanese government must consider the current study’s findings to develop attractively, informed, and evidence-based content, which helps its citizens improve their health literacy and counter the spread of misinformation.

Nursing Students' Acceptance Intention of a Smart Device, Information Literacy, and Problem-Solving Confidence

The purpose of this study was to investigate the relationship between nursing students’ acceptance intention of a smart device, information literacy, and problem-solving confidence to explore the factors that may improve nursing students’ problem-solving confidence. Data were collected from 450 nursing students between July and August 2019 in two cities in Korea. The results showed that there is a positive correlation between problem-solving confidence and acceptance intention (r = 0.353, p < 0.001) and between problem-solving confidence and information literacy (r = 0.501, p < 0.001). Further, participants with high acceptance intention of a smart device and information literacy scores had significantly higher problem-solving confidence (t = 5.447, p < 0.001; t = 10.072, p < 0.001) than participants with low acceptance intention. In addition, in the logistic regression the odds ratio between the acceptance intention of a smart device, information literacy, and problem-solving confidence groups was odds ratio 2.071 (p < 0.001, CI: 1.412–3.037) and odd ratio 4.966 (p < 0.001, CI: 3.298–7.477). To improve nursing students’ problem-solving confidence, educational strategies should be developed to build information experience and information utilization capabilities.

Preschool Teachers' Technology Acceptance During the COVID-19: An Adapted Technology Acceptance Model

Online education has become a major reaction to the COVID-19 epidemic, which requires preschool teachers to quickly adapt to online education and accept educational technology. In this emergency background, research on the preschool teachers' technology acceptance provides clues to improve preschool teachers' intention to use educational technology. The Technology Acceptance Model (TAM) is widely used to examine the process of individuals' technology acceptance in the previous literature. Therefore, this study seeks to examine preschool teachers' technology acceptance with the adapted TAM and their determinant factors. The proposed model was empirically validated by using survey data from 1,568 preschool teachers during the COVID-19. Results indicate that preschool teachers' behavioral intention was moderate to high level. Perceived usefulness and perceived ease of use are direct significant predictors of preschool teachers' behavioral intention. Perceived usefulness is affected by perceived ease of use and job relevance. Computer self-efficacy and perceptions of external control are the positive factors toward perceived ease of use. Our findings present powerful evidence for the applicability of the adapted TAM in a sample of Chinese preschool teachers under emergency circumstances. These results highlighted some potential avenues for interventions aimed at improving preschool teachers' acceptance toward educational technology.

Antecedents of Intention to Adopt Mobile Health (mHealth) Application and Its Impact on Intention to Recommend: An Evidence from Indonesian Customers

INTRODUCTION

Mobile health (mHealth) applications gain popularity due to the increasing number of mobile phone usage and internet penetration, which might help some of Indonesia's medical issues. However, the uptake of mHealth applications is still low in Indonesia. This study is aimed at understanding the factors that drive individuals to adopt mHealth applications and their impact on the intention to recommend.

METHODS

We applied a new model that combines three different theories with some other constructs: an extended unified theory of acceptance and use of technology, diffusion of innovation, and the internet customer trust model. The study used a cross-sectional study design with partial least squares causal modeling approach.

RESULTS

There are 787 respondents in our study, with the majority of them being female, young adults. Our model could explain 53.2% of the variance of intention to adopt while explaining 48.3% of the variance of intention to recommend. Initial trust in mHealth platform (β = 0.373, p = <0.001), facilitating conditions (β = 0.131, p = <0.01), and performance expectancy (β = 0.099, p = <0.05) are the top three most important drivers of intention to adopt mHealth applications. Lastly, importance-performance map analysis (IPMA) showed that the mHealth application's initial trust is the most important construct with a high-performance score. Discussion. Mobile health developers and managers need to improve initial trust in the mHealth platform, facilitating conditions, and performance expectancy when developing the applications. With a medium Q 2 predict, these factors can be applied out of the research context with medium predictive power.