The Impact of Context Awareness and Ubiquity on Mobile Government Service Adoption

Abstract

Context awareness and mobile factor ubiquity are considered key factors when it comes to mobile technology development and diffusion. Context is vital in interactive applications particularly when the context of users changes frequently and rapidly in the environment of handheld-mobile and ubiquitous technology systems. The understanding of the context and ubiquity in the development and diffusion of mobile government can influence the delivery of efficient public services. Mobile context-aware computing systems can respond to the changes in the environment in an intelligent way to provide a better consumer experience for users. This study explored the impact of context awareness and mobile factor ubiquity on the adoption of mobile government services. The framework of this study was based on the unified theory of acceptance and use of technology (UTAUT). The structural equation model (SEM) with SmartPLS 3.0 was used to conduct the data analysis. The analysis which was based on the 366 samples generated has shown that while context awareness was not significant in determining both the performance and intention to use, it was however significant in determining the effort expectancy of mobile government services. Also, mobile factor ubiquity was found to be a positive predictor of effort expectancy, intention to use, and context awareness. However, the mobile factor ubiquity does not determine the performance expectancy of mobile government services. The implications (managerial and theoretical) of these and other result findings of the study are discussed.

Understanding the Role of Service Quality of Government APPs in Continuance Intention: An Expectation–Confirmation Perspective

“…The role that government APPs play in improving the quantity and quality of public-service delivery includes: 1) Improving public-service convenience. Due to their mobility, government APPs enable citizens to obtain ubiquitous services that traditional e-government cannot provide (Mensah & Mwakapesa, 2022); 2) Improving the equity and accessibility of public-service allocation. The vast majority of people use smart phones, narrowing the digital divide between different regions and groups and enabling governments to provide users with adequate and timely information; even rural users with less developed digital skills find it easy to operate government APPs and obtain services (Liu et al, 2014); 3) Improving the personalization and accuracy of public services.…”

“…Recent studies have focused primarily on the initial use, acceptance, and adoption of e-government services. Most of these studies have used technology-adoption models, including the theory of reasoned action, the diffusion of innovations, TAM, the theory of planned behavior, the unified theory of the acceptance and use of technology, and the e-government adoption model, to explore factors that predict the initial adoption of government APPs (Ahmad & Khalid, 2017; Creutzberg et al, 2023; Hung et al, 2013; Liu et al, 2014; Mensah & Mwakapesa, 2022; Sharma et al, 2018).…”

Review Of M-Government in Developing Countries: The Case of Egypt

“…Due to a lack of a domestic ICT industry, several developing countries import ICT. Researchers have implicated strategic, technological, policy, and organizational factors in this issue (Mensah & Mwakapesa, 2022). There are a variety of legal, political, social, and economic obstacles that prevent developing countries from fully utilizing e-government; this led to the development of mobile government (Karadimas et al, 2008;Wirtz & Birkmeyer, 2018).…”

Analysis of Factors Affecting Use Behavior towards Mobile Payment Apps: A SEM Approach

“…It was found that performance expectancy was the primary criterion which influence retailers' behavioral intention to adopt mobile payment apps [39]. The studies [40][41][42][43][44][45][46][47] have also studied the relationship between performance expectancy and behavioral intention. With this backdrop, the following hypotheses were formed: H1: there is no impact of performance expectancy on behavioural intention to use mobile payment applications.…”

“…In research conducted by Alshare and Lane [55], it was revealed that there is a strong relationship between effort expectancy and the behavioral intention of customers. The studies [40][41][42][43][44][45][46][47] have also evaluated the relationship between effort expectancy and behavioral intention. The above discussion led to the formulation of the following hypothesis: H2: there is no impact of effort expectancy on behavioral intention to use mobile payment applications.…”