An Extended Unified Model of e‑Government Adoption: The Role of Trust in Government and Digital Literacy
Received: 31 October 2025 | Revised: 13 January 2026 and 2 March 2026 | Accepted: 18 March 2026 | Online: 6 June 2026
Corresponding author: Endah Tri Esthi Handayani
Abstract
Digital government transformations are defined by efficiency and accountability. However, their application is frequently interrupted by a lack of institutional trust and varying levels of digital skills among different population groups. This study extends the Unified Model of e‑Government Adoption (UMEGA) by adding trust in government and digital literacy as precursors to effort expectancy, attitudes, and intended use. User data from multiple Indonesian public service applications are analyzed with PLS‑SEM. The structural model is evaluated with the coefficient of determination (R2), effect size (f2), and out‑of‑sample predictive checks, also establishing measurement reliability and validity. The results revealed that trust and digital literacy raise effort expectancy and foster favorable attitudes toward digital services. Moreover, attitude mediates their effects on intention, while effort expectancy strengthens attitudes, creating an indirect route to intention alongside any direct paths. Overall, the model demonstrates sound explanatory power and credible predictive performance. Policy implications include trust-building through performance transparency, responsive support, and risk communication. In addition, the former involve digital literacy initiatives such as microlearning, inclusive interface design, and omnichannel assistance. Finally, the adoption of privacy-by-design safeguards is highlighted. The findings underscore that socio‑psychological determinants are as significant as technical attributes for accelerating inclusive e‑government adoption, particularly in developing contexts where user heterogeneity and service diversity demand integrated, user‑sensitive approaches.
Keywords:
UMEGA, digital literacy, e-government adoption, PLS-SEMReferences
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