An empirical validation of a unified model of electronic government adoption (UMEGA)

By Yogesh Kumar Dwivedi, Nripendra P. Rana, Marijn Janssen, Banita Lal, Michael D. Williams, Marc Clement. Government Information Quarterly, 2017

582 citations recorded by the source corpus.

In electronic government (hereafter e-government), a large variety of technology adoption models are employed, which make researchers and policymakers puzzled about which one to use. In this research, nine well-known theoretical models of information technology adoption are evaluated and 29 different constructs are identified. A unified model of e-government adoption (UMEGA) is developed and validated using data gathered from 377 respondents from seven selected cities in India. The results indicate that the proposed unified model outperforms all other theoretical models, explaining the highest variance on behavioral intention, acceptable levels of fit indices, and significant relationships for each of the seven hypotheses. The UMEGA is a parsimonious model based on the e-government-specific context, whereas the constructs from the original technology adoption models were found to be inappropriate for the e-government context. By using the UMEGA, relevant e-government constructs were included. For further research, we recommend the development of e-government-specific scales.

Subfields: Information Systems and Management, Political Science and International Relations, Sociology and Political Science

View Paper: https://www.semanticscholar.org/paper/31ef08531ee5c7a6c699a9508768f0ba48cfb239

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Citations in the corpus

The extended Unified Theory of Acceptance and Use of Technology (UTAUT2): A systematic literature review and theory evaluation (2021, methodology): “The framework is already used to evaluate popular IS theories such as virtuality and knowledge in teams (Griffith et al., 2003) and UTAUT (Dwivedi et al., 2017, 2020; Rana et al., 2016, 2017; Venkatesh et al., 2003; Williams et al., 2015) revealing noteworthy findings.”
Mobile food ordering apps: An empirical study of the factors affecting customer e-satisfaction and continued intention to reuse (2020, background): “Accompanying this information is the ability for customers to see their order progress through all its stages (Aksenova, 2017; Algharabat, Alalwan, Rana, & Dwivedi, 2017; Lu & Rastrick, 2014; Marriott, Williams, & Dwivedi, 2017; Nilashi, Ibrahim, Mirabi, Ebrahimi, & Zare, 2015).”
Social commerce adoption using TOE framework: An empirical investigation of Saudi Arabian SMEs (2020, background): “This led to identifying several factors that may influence social commerce adoption by SMEs (Dwivedi et al., 2017).”
Blockchain adoption in operations and supply chain management: empirical evidence from an emerging economy (2020, unknown): “Following the recent literature, we did not incorporate these moderators (Dwivedi, Rana, Janssen, et al. 2017; Dwivedi, Rana, Jeyaraj, et al. 2017) in our proposed model, as they cannot be applied to any context exerting good influence.”
Artificial Intelligence (AI): Multidisciplinary perspectives on emerging challenges, opportunities, and agenda for research, practice and policy (2019, unknown): “Factors affecting the use, impact, success and failure of information systems have been studied extensively (Dwivedi et al., 2015b; Dwivedi et al., 2017; Dwivedi, Rana, Jeyaraj, Clement, & Williams, 2019; Hughes, Dwivedi, Rana, & Simintiras, 2016; Hughes, Dwivedi, & Rana, 2017).”
Social Media Marketing Activities and Brand Loyalty: A Meta-Analysis Examination (2021, background): “In India, for example, Technology users are aware of any changes that may arise from using the communication tools (Dwivedi et al., 2017).”
Examining factors influencing Jordanian customers’ intentions and adoption of internet banking: Extending UTAUT2 with risk (2018, background): “…noticed as one of the most influential drivers of behavioural intention to adopt and use information systems (IS)/information technology (IT) (e.g. Dwivedi et al., 2017a, 2017b, Rana et al., 2015, 2016, 2017) in general and IB (Alalwan et al., 2017; AbuShanab et al., 2010; Riffai et al., 2012;…”
What determines success of an e-government service? Validation of an integrative model of e-filing continuance usage (2018, background): “…the study by including such concepts as user’s trust in the website, trust in the government, compatibility, e-filing self-efficacy, social influence, and privacy concerns (Janssen, Rana, Slade, and Dwivedi, 2017; Zhou and Li, 2014) to improve on the explanatory power of the theoretical model.”
Consumer use of mobile banking (M-Banking) in Saudi Arabia: Towards an integrated model (2019, methodology): “…in figure 1, independent factors that are extracted from the UTAUT2 model include performance expectancy (PE), effort expectancy (EE), hedonic motivation (HM), price value (PV), habit (HT), social influence (SI), and facilitating conditions (FC)) (Baabdullah et al., 2018; Dwivedi et al., 2017a).”
Understanding managers’ attitudes and behavioral intentions towards using artificial intelligence for organizational decision-making (2021, methodology): “…effort expectancy, is another key construct which has been included in several IT adoption models and has been verified empirically in various contexts by many studies (e.g. Balakrishnan and Dwivedi, 2021b; Dwivedi et al., 2019; 2017; Rana et al., 2017; Rana et al., 2016; Venkatesh et al., 2011).”