Project description:In this ever-progressive digital era, conventional e-learning methods have become inadequate to handle the requirements of upgraded learning processes especially in the higher education. E-learning adopting Cloud computing is able to transform e-learning into a flexible, shareable, content-reusable, and scalable learning methodology. Despite plentiful Cloud e-learning frameworks have been proposed across literature, limited researches have been conducted to study the usability factors predicting continuance intention to use Cloud e-learning applications. In this study, five usability factors namely Computer Self Efficacy (CSE), Enjoyment (E), Perceived Ease of Use (PEU), Perceived Usefulness (PU), and User Perception (UP) have been identified for factor analysis. All the five independent variables were hypothesized to be positively associated to a dependent variable namely Continuance Intention (CI). A survey was conducted on 170 IT students in one of the private universities in Malaysia. The students were given one trimester to experience the usability of Cloud e-Learning application. As an instrument to analyse the usability factors towards continuance intention of the application, a questionnaire consisting thirty questions was formulated and used. The collected data were analysed using SMARTPLS 3.0. The results obtained from this study observed that computer self-efficacy and enjoyment as intrinsic motivations significantly predict continuance intention, while perceived ease of use, perceived usefulness and user perception were insignificant. This outcome implies that computer self-efficacy and enjoyment significantly affect the willingness of students to continue using Cloud e-learning application in their studies. The discussions and implications of this study are vital for researchers and practitioners of educational technologies in higher education.
Project description:In-service teachers have various emotional and motivational experiences that can influence their continuance intention towards online-only instruction during the COVID-19 pandemic, as a significant stress factor for their workplace. Derived from the Self-Determination Theory (SDT), Job Demands-Resources Model (JD-R), and Technology Acceptance Model (TAM), the present research model includes technological pedagogical knowledge (TPK) self-efficacy (SE), intrinsic (IM) and extrinsic (EM) work motivation, and occupational stress (OS) (i.e., burnout and technostress which have been examined in tandem) as key dimensions to explain the better continuance intention among in-service teachers to use online-only instruction (CI). Data for the research model were collected from 980 in-service teachers during the COVID-19 outbreak between April and May 2020. Overall, the structural model explained 70% of the variance in teachers' CI. Motivational practices were directly and indirectly linked through OS with CI. The findings showed that IM has the most directly significant effect on teachers' CI, followed by TPK-SE, and OS as significant, but lower predictors. IM was positively associated with TPK-SE and negatively associated with EM. The results offered valuable insights into how motivation constructs were related to OS and to a better understanding online instruction in an unstable work context, in order to support teachers in coping during working remotely.
Project description:The COVID-19 pandemic had brought changes to individuals, especially in consumer behavior. As the government of different countries has been implementing safety protocols to mitigate the spread of the virus, people became apprehensive about traveling and going out. This paved way for the emergence of third-party logistics (3PL). Statistics have proven the rapid escalation regarding the use of 3PL in various countries. This study utilized Artificial Neural Network and Random Forest Classifier to validate and justify the factors that affect consumer intention in selecting a 3PL service provider during the COVID-19 pandemic integrating the Service Quality Dimensions and Pro-Environmental Theory of Planned Behavior. The findings of this study revealed that attitude is the most significant factor that affects the consumers' behavioral intention. Other factors such as customer satisfaction, customer perceived value, perceived environmental concern, assurance, responsiveness, empathy, reliability, tangibility, perceived behavioral control, subjective norm, and perceived authority support, are all contributing factors that affect behavioral intention. Machine learning algorithms, specifically ANN and RFC, resulted to be reliable in predicting factors as they obtained accuracy rates of 98.56% and 93%. Results presented that consumers' attitude, satisfaction, perceived value, assurance by the 3PL, and perceived environmental concerns were highly influential in choosing a 3PL package carrier. It was seen that people would be encouraged to use 3PL service providers if they demonstrate availability and environmental concerns in catering to the customers' needs. Subsequently, 3PL providers must assure safety and convenience before, during, and after providing the service to ensure continuous patronage of consumers. This is considered to be the first study that utilized a machine learning ensemble to measure behavioral intention for the logistic sector. The framework, analysis tools, and findings of this study could be extended and applied among other behavioral intentions regarding transportation worldwide. Managerial insights among service providers are discussed.
Project description:As most other aspects of life, education was strongly affected by the lockdowns imposed to slow down the spread of the COVID-19 pandemic. Teachers at all levels of education suddenly faced the challenge of adapting their courses to online versions. This posed various problems, from the pedagogical and psychological components of having to teach and learn online to the technical problems of internet connectivity and especially of rethinking hands-on activities. The latter point was especially important for subjects who involve very practical learning, for which teachers had to find out alternative activities that the students could carry out at home. In the subjects dealing with natural sciences, impaired access to instrumentation and reagents was a major limitation, but the community turned out very resourceful. Here I demonstrate this resourcefulness for the case of undergraduate chemistry and biology courses, focusing on how do-it-yourself open technologies, smartphone-based instruments and simulations, at-home chemistry with household reagents, online video material, and introductory programming and bioinformatics, which helped to overcome these difficult times and likely even shape the future of science education.
Project description:Given the unprecedented scale of digital surveillance in the COVID-19 pandemic, designing and implementing digital technologies in ways that are equitable is critical now and in future epidemics and pandemics. Yet to date there has been very limited consideration about what is necessary to promote their equitable design and implementation. In this study, literature relating to the use of digital surveillance technologies during epidemics and pandemics was collected and thematically analyzed for ethical norms and concerns related to equity and social justice. Eleven norms are reported, including procedural fairness and inclusive approaches to design and implementation, designing to rectify or avoid exacerbating inequities, and fair access. Identified concerns relate to digital divides, stigma and discrimination, disparate risk of harm, and unfair design processes. We conclude by considering what dimensions of social justice the norms promote and whether identified concerns can be addressed by building the identified norms into technology design and implementation practice.
Project description:Nurses play a crucial role in the adoption and continued use of Electronic Health Records (EHRs), especially in developing countries. Existing literature scarcely addresses how personality traits and organisational support influence nurses' decision to persist with EHR use in these regions. This study developed a model combining the Five-Factor Model (FFM) and the Unified Theory of Acceptance and Use of Technology (UTAUT) to explore the impact of personality traits and organisational support on nurses' continuance intention to use EHR systems. Data were collected via a self-reported survey from 472 nurses across 10 public hospitals in Jordan and analyzed using a structural equation modeling approach (Smart PLS-SEM 4). The analysis revealed that personality traits, specifically Openness, Experience, and Conscientiousness, significantly influence nurses' decisions to continue using EHR systems. Furthermore, organisational support, enhanced by Performance Expectancy and Facilitating Conditions, positively affected their ongoing commitment to EHR use. The findings underscore the importance of considering individual personality traits and providing robust organisational support in promoting sustained EHR usage among nurses. These insights are vital for healthcare organisations aiming to foster a conducive environment for EHR system adoption, thereby enhancing patient care outcomes.
Project description:Being an interactive process, the success of risk communication needs to ensure the individuals' right to know and influence their attitudes and perceptions of risk. Ubiquitous social media have expanded risk communication channels and innovated ways of risk communication. At the same time, uncertainty also arises with the diversity and variety of social media. Taking the rainstorm disaster in China as an example, this study focuses on factors affecting the individuals' continuance intention of information seeking on Weibo (a social media platform similar to Twitter). Based on 377 valid respondents, this study applied an extended expectation-confirmation model (ECM), from which the results of partial least squares structural equation modeling (PLS-SEM) suggested that continuance intention is positively influenced by factors including effort expectancy, social influence, facilitating conditions, and satisfaction. Among them, satisfaction contributes the most, which helps maintain a balance between performance expectancy and continuance intention. Taking the individuals' continuance intention to seek information on Weibo as the clue, this research provides government agencies with practical advice on how to use social media for more efficient risk communication during disasters and establish emergency preplans to respond to natural disasters.
Project description:Based on the Expectation Confirmation Model (ECM), this study explores the impact of perceived educational and emotional support on university students' continuance intention to engage in e-learning. Researchers conducted a survey using structured questionnaires among 368 university students from three universities in Jiangxi Province. They measured their self-reported responses on six constructs: perceived educational support, perceived emotional support, perceived usefulness, confirmation, satisfaction, and continuance intention. The relationships between predictors and continuance intention, characterized by non-compensatory and non-linear dynamics, were analyzed using Structural Equation Modeling combined with Artificial Neural Networks. Apart from the direct effects of perceived educational and emotional support on perceived usefulness being non-significant, all other hypotheses were confirmed. Furthermore, according to the normalized importance derived from the multilayer perceptron analysis, satisfaction was identified as the most critical predictor (100%), followed by confirmation (29.9%), perceived usefulness (28.3%), perceived educational support (22.6%), and perceived emotional support (21.6%). These constructs explained 62.1% of the total variance in the students' continuance intention to engage in e-learning. This study utilized a two-stage analytical approach, enhancing the depth and accuracy of data processing and expanding the methodological scope of research in educational technology. The findings of this study contribute to the United Nations' Sustainable Development Goal 4, which aims to ensure inclusive and equitable quality education and promote lifelong learning opportunities for all by 2030. It provides direction for future research in different environmental and cultural contexts.
Project description:Pharmacological and non-pharmacological measures will overlap for a period after the onset of the pandemic, playing a strong role in virus containment. We explored which factors influence the likelihood to adopt two different preventive measures against the COVID-19 pandemic. An online snowball sampling (May-June 2020) collected a total of 448 questionnaires in Italy. A Bayesian bivariate Gaussian regression model jointly investigated the willingness to get vaccinated against COVID-19 and to download the national contact tracing app. A mixed-effects cumulative logistic model explored which factors affected the motivation to adopt one of the two preventive measures. Despite both COVID-19 vaccines and tracing apps being indispensable tools to contain the spread of SARS-CoV-2, our results suggest that adherence to the vaccine or to the national contact tracing app is not predicted by the same factors. Therefore, public communication on these measures needs to take in consideration not only the perceived risk associated with COVID-19, but also the trust people place in politics and science, their concerns and doubts about vaccinations, and their employment status. Further, the results suggest that the motivation to comply with these measurements was predominantly to protect others rather than self-protection.