The Effect of Digital Government Service Integration on Public Trust through Public Service Quality: A Study on Digital Government Service Users in Indonesia
DOI:
https://doi.org/10.46799/adv.v4i10.704Keywords:
digital government service integration, public service quality, public trust, e-government, PLS-SEMAbstract
Fragmented digital government services can undermine service quality and public trust through inconsistent information, repeated data entry, and disconnected administrative processes. This study examined the relationship between digital government service integration and public trust, with public service quality as a mediating variable in the Indonesian context. An explanatory quantitative design was employed to analyze data from 100 respondents using 15 reflective indicators measured on a five-point Likert scale. Partial least squares structural equation modeling (PLS-SEM) was performed using SmartPLS 4, with 5,000 bootstrap subsamples to assess the measurement model, structural relationships, and mediation effects. The measurement model demonstrated adequate internal consistency reliability, convergent validity, and discriminant validity. Digital government service integration was positively associated with public service quality (B = 0.574) and public trust (B = 0.293), while public service quality was positively associated with public trust (B = 0.515). The indirect effect of digital government service integration on public trust through public service quality was significant (B = 0.296), indicating complementary partial mediation; all structural relationships were statistically significant at p < 0.001. The model explained 32.9% of the variance in public service quality and 52.4% of the variance in public trust. These findings suggest that digital government service integration strengthens public trust when service connectivity translates into reliable, responsive, and consistent service experiences. Therefore, interoperability and public service quality should be developed concurrently. However, the numerical results primarily demonstrate model consistency and the performance of the analytical procedures; independently collected field data are required before population-level conclusions can be drawn or the findings can be used to inform policy decisions.
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