Sustainable Applications of Smart-Government Services: A Model to Understand Smart-Government Adoption

Abstract

Despite the fact that several studies have been conducted to study the adoption of smart-government services, little consideration has been paid to exploring the main factors that influence the adoption of smart-government services at the three main stages of smart-government services (the static, interaction, and transaction stages). Based on the results of this study, each of these three stages has different requirements in terms of system compatibility, security, information quality, awareness, perceived functional benefit, self-efficacy, perceived image, perceived uncertainty, availability of resources, and perceived trust. In addition, the results demonstrate that the requirements and perceptions of users towards the adoption and use of smart-government services in the three stages significantly differ. This study makes a unique contribution to the existing research by examining the perceptions and needs of consumers, in terms of adoption throughout the three stages.

Factors Affecting Students’ Acceptance of Mobile Learning Application in Higher Education during COVID-19 Using ANN-SEM Modelling Technique

“…In several studies, ANN-SEM modelling techniques have been employed for predicting the users' usage and acceptance of educational technologies such as mobile learning and e-learning [65]. Despite the SEM technique being an advantageous data analysis method in that it simultaneously evaluates the measurement and structural models [21], it may oversimplify the complexities involved in the students' opinion towards to use of mobile learning platforms. This limitation in SEM can be addressed by employing the ANN modelling technique.…”

“…Others have argued that introducing m-learning platforms in learning process provides innovative ways of learning that engage the modern day learner [15][16][17][18]. The COVID-19 pandemic has also increased the need for m-learning systems in education as they provide a sustainable and risk-free method for learning and teaching [19][20][21]. The use of m-learning systems in education potentially brings a number of significant advantages.…”

“…The use of m-learning systems in education potentially brings a number of significant advantages. One of the major benefits of these systems is the provision of digital learning and learning resources anytime and anywhere for both students and teachers [21][22][23].…”

Factors Influencing the Adoption of Big Data Analytics in the Digital Transformation Era: Case Study of Jordanian SMEs

“…Extensive existing studies concerning the effects of TOE factors on technologies/ innovations adoption are available [26,27], but the findings are inapplicable and ungeneralizable to SMEs' BD adoption, owing to the fact that the TOE model's factors are influenced and determined by the technology type, the firm's size and the context of the study [12,28,29]. In relation to these reasons, each technology possesses distinct characteristics and factors influencing its adoption success.…”

Examining the Factors Influencing the Mobile Learning Applications Usage in Higher Education during the COVID-19 Pandemic

“…Thus, the implementation and development of m-learning systems can be accurately controlled, leading to better system quality [70]. Finally, change management factor can reduce user resistance to change and enrich the benefits of mobile learning technology [71][72][73][74][75], which includes training programs for support staff and stimulating the e-learning department to provide adequate levels of support. This, in turn, promotes high service quality [8,40].…”