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Electronic government adoption in voluntary environments – a case study of Zimbabwe

Domain:

digital infrastructure

Record type:

paper
Creator:
Wil
Publisher:
SAG
Host:
Many governmental organisations across the world are progressively implementing electronic government systems to enhance their back-office operations and offer better and efficient services to citizens. Zimbabwe is not an exception to this e-government wave. Previous studies note that the acceptance and utilisation of e-government systems by citizens in Zimbabwe remains suboptimal, sluggish and problematic due to several factors. This study sought to establish the effect of seven predictor variables on citizens’ behavioural intentions to use e-government systems in Zimbabwe. Drawing from the extended Technology Acceptance Model (TAM2), extended Unified Theory of Acceptance and Use of Technology (UTAUT2), Framework for National and Donor Action, and e-Government Trust model as theoretical underpinnings, this study proposed a conceptual framework to predict citizens’ behavioural intentions on e-government. Survey data for testing the conceptual framework were collected from 247 respondents in Zimbabwe using structured questionnaires. Confirmatory factor analysis using IBM AMOS structural equation modelling method was conducted to establish the structural model fit of the proposed model. Findings of this study establish that eight of the hypothesised constructs explain 89% of the discrepancies of behavioural intention to demonstrate good predictive power of the proposed model in voluntary environments. Thus, level of education, facilitating conditions, e-government awareness, price value; privacy, security and trust; political self-efficacy and influence were all confirmed as salient predictors of e-government adoption. These findings provide invaluable insights and pointers to practitioners and policy-makers on e-government implementation and may guide further research on e-government adoption in voluntary environments.

Visit

doi.org

Licenses

https://journals.sagepub.com/page/policies/text-and-data-mining-license

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