Dataset Information

Usability factors predicting continuance of intention to use cloud e-learning application.

ABSTRACT: 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.

SUBMITTER: Wang LY

PROVIDER: S-EPMC6556850 | biostudies-other | 2019 Jun

REPOSITORIES: biostudies-other

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Usability factors predicting continuance of intention to use cloud e-learning application.

Wang Lillian-Yee-Kiaw LY Lew Sook-Ling SL Lau Siong-Hoe SH Leow Meng-Chew MC

Heliyon 20190607 6

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 ...[more]

PMID: 31198866

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Project description:PurposeStaySafe PH is the Philippines' official contact tracing software for controlling the propagation of COVID-19 and promoting a uniform contact tracing strategy. The StaySafe PH has various features such as a social distancing system, LGU heat map and response system, real-time monitoring, graphs, infographics, and the primary purpose, which is a contact tracing system. This application is mandatory in establishments such as fast-food restaurants, banks, and malls.Objective and methodologyThe purpose of this research was to determine the country's willingness to utilize StaySafe PH. Specifically, this study utilized 12 latent variables from the integrated Protection Motivation Theory (PMT), Unified Theory of Acceptance and Use of Technology (UTAUT2), and System Usability Scale (SUS). Data from 646 respondents in the Philippines were employed through Structural Equation Modelling (SEM), Deep Learning Neural Network (DLNN), and SUS.ResultsUtilizing the SEM, it is found that understanding the COVID-19 vaccine, understanding the COVID-19 Delta variant, perceived vulnerability, perceived severity, performance expectancy, social influence, hedonic motivation, behavioral intention, actual use, and the system usability scale are major determinants of intent to utilize the application. Understanding of the COVID-19 Delta Variant was found to be the most important factor by DLNN, which is congruent with the results of SEM. The SUS score of the application is "D", which implies that the application has poor usability.ImplicationsIt could be implicated that large concerns stem from the trust issues on privacy, data security, and overall consent in the information needed. This is one area that should be promoted. That is, how the data is stored and kept, utilized, and covered by the system, how the assurance could be provided among consumers, and how the government would manage the information obtained. Building the trust is crucial on the development and deployment of these types of technology. The results in this study can also suggest that individuals in the Philippines expected and were certain that vaccination would help them not contract the virus and thus not be vulnerable, leading to a positive actual use of the application.NoveltyThe current study considered encompassing health-related behaviors using the PMT, integrating with the technology acceptance model, UTAUT2; as well as usability perspective using the SUS. This study was the first one to evaluate and assess a contact tracing application in the Philippines, as well as integrate the frameworks to provide a holistic measurement.

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.

Dataset Information

Usability factors predicting continuance of intention to use cloud e-learning application.

Publications

Usability factors predicting continuance of intention to use cloud e-learning application.

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