Article Details
Vol. 5 No. 4 (2026): Juni
Extending the Expectation-Confirmation Model with Trust: Explaining Civil Servants Continuance Intention toward G2E Systems
Abstract
Purpose: This study investigates the factors influencing civil servants' continuance intention to use SimASN, a mandatory Government-to-Employee (G2E) e-government system deployed by the Gorontalo Provincial Government, Indonesia.
Research Methodology: Drawing on the Expectation-Confirmation Model (ECM) and integrating Trust as an additional construct, this research proposes and tests an extended theoretical framework using Partial Least Squares Structural Equation Modeling (PLS-SEM). Data were collected through an online questionnaire from 204 civil servants (ASN), of which 200 valid responses were retained for analysis after data screening.
Results: Results indicate that Trust (TR) emerged as the strongest direct predictor of Continuance Intention to Use (CIU) (? = 0.410), followed by Satisfaction (SA) (? = 0.346) and Perceived Usefulness (PU) (? = 0.229). Expectation Confirmation (EC) exerts no significant direct effect on CIU but operates entirely through mediated pathways, most strongly via EC ? TR ? CIU (indirect effect = 0.230), closely paralleled by EC ? SA ? CIU (indirect effect = 0.215).
Conclusions: Trust emerges as the leading psychological mechanism, operating in parallel with Satisfaction, in mandatory e-government systems managing sensitive personnel data. The model extends ECM theory by demonstrating that conventional applications without Trust underspecify important variance in continuance intention.
Limitations: The cross-sectional design limits causal inference. The single-province, mandatory-system context may constrain generalizability to voluntary or multi-jurisdictional e-government settings.
Contributions: This study contributes to the public administration and information systems disciplines by providing an evidence base for prioritizing trust-building investments, particularly in data security and expectation alignment, to sustain long-term digital governance.

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