E-Government Services Adoption: An Extension of the Unified Model of Electronic Government Adoption

Abstract

This study proposed and validated an extension of the unified model of electronic government adoption (UMEGA). The data analysis was conducted with a structural equation modeling technique using Smart PLS 3.0. The results have demonstrated contrary to expectations that performance expectancy, effort expectancy, and social influence do not predict the attitude toward the use of e-government services. Facilitating conditions, however, were found to significantly determine both the behavioral intention to use and effort expectancy of e-government services. Also, perceived service quality and trust in government were found to positively predict, respectively, the intention to use and recommend the adoption of e-government services. The implications of these and other result findings of this study are thoroughly interrogated.

Predictors for the Adoption of Open Data Technologies

“…However, the associated simplicity with open data technologies is positively linked with their adoption (Saxena & Janssen, 2017). Moreover, effort expectancy is corroborated in earlier studies with respect to e-government adoption (Dwivedi et al, 2017;Mensah et al, 2020). Following the previous evidences, following hypotheses have been proposed.…”

“…In presenting the unified model for the e-government adoption, it includes attitude as a protuberant component of e-government adoption (Nguyen, Dang, Van Nguyen, & Nguyen, 2020) as well as facilitating conditions as an antecedent of effort expectancy. Another reason is that the UMEGA model performed better than other technology acceptance and adoption models in the e-government context (Mensah, Zeng, & Luo, 2020). (Dwivedi et al, 2017) Performance Expectancy (PE): A similar construct has been used in developing the UTAUT theory (Venkatesh et al, 2003) as well as its extended version, that is, the UTAUT2 theory (Venkatesh, Thong, & Xu, 2012) to study consumer's adoption of a technology.…”

“…The social factor construct the individual behavioural patterns and treated as a strong predictor of public sector big open data . In earlier studies, scholars are largely hypothesizing and corroborating the influence of social conditions on technologies adoption such as e-government adoption (Avazov & Seohyun Lee, 2020;Mensah et al, 2020;Verkijika & De Wet, 2018), adoption of transactional services in egovernment as well as adoption of open data technologies (Khurshid, Zakaria, Rashid, & Shafique, 2018;Saxena & Janssen, 2017;Zuiderwijk et al, 2015). Based on the facts, the proposed hypothesis is outlined as follows:…”

“…It can be inferred that the less the risks the more the open data technologies will be adopted. There are evidences found in previous literature where risk perceptions construct is indicated as a significant factor that effect in shaping individuals' attitudes (Dwivedi et al, 2017;Mensah et al, 2020;Verkijika & De Wet, 2018). Therefore, the following hypothesis has been formulated based on the previous evidences in the literature:…”

The digital divide and E-government in European economies

“…Verkijika and De Wet (2018) proposed an extended UMEGA model that incorporates computer self-efficacy, perceived trust in Internet and in government. Recently UMEGA was extended by three more variables: perceived service quality, trust in government, and intention to recommend the adoption of egovernment services (Mensah, et al, 2020). All these models show better fit to egovernment context in comparison to models designed for technology adoption in general.…”