TechVista Journal of Emerging Information Systems

Digital Transformation in E-Government: Realizing Efficient, Transparent, and Community-Oriented Public Services

2026-02-20T11:01:13+07:00

Digital transformation has become a central strategy for governments seeking to improve public service performance through e-Government initiatives. This study investigates how digital transformation contributes to the realization of efficient, transparent, and community-oriented public services. Adopting an explanatory mixed-methods approach, the research integrates survey data analyzed using structural equation modeling with qualitative insights obtained from semi-structured interviews. The quantitative findings reveal that digital transformation has a significant and positive impact on service efficiency and transparency, while its influence on community-oriented public services is comparatively weaker. Qualitative results further explain these patterns by highlighting the role of process automation, system interoperability, and information accessibility in enhancing performance, alongside persistent challenges related to digital inclusion and citizen participation. By examining multiple public service outcomes simultaneously, this study advances a holistic perspective on digital transformation in e-Government and addresses gaps in the existing literature that often treat these outcomes in isolation. The findings provide empirical evidence that digital transformation is most effective when technological innovation is accompanied by organizational readiness and participatory governance mechanisms. This study offers valuable implications for policymakers and practitioners aiming to design inclusive, accountable, and citizen-centered digital government strategies.

Evaluation of Information System Quality Based on the DeLone and McLean Model in Public Organizations

2026-03-04T09:52:21+07:00

This study examines the quality of information systems in public organizations using the DeLone and McLean Information Systems Success Model as an analytical framework. The research aims to empirically evaluate the relationships among system quality, information quality, service quality, user satisfaction, and perceived net benefits in a governmental context. A quantitative survey was conducted involving employees who actively use internal organizational information systems. Data were analyzed using Structural Equation Modeling to assess both measurement and structural models. The findings indicate that system quality, information quality, and service quality significantly influence user satisfaction. User satisfaction emerges as the strongest predictor of net benefits and partially mediates the relationship between quality dimensions and organizational outcomes. The model demonstrates substantial explanatory power, confirming its robustness in public sector environments. These results reinforce the theoretical propositions advanced by William H. DeLone and Ephraim R. McLean and highlight the importance of adopting a multidimensional evaluation approach. The study provides practical implications for improving digital governance performance through integrated management of technical, informational, and service quality dimensions.

Artificial Intelligence Implementation and Its Effect on Productivity Quality and Cost Efficiency in Industry

2026-02-20T10:59:57+07:00

This study examines the implementation of Artificial Intelligence in industrial organizations and its effects on productivity quality and cost efficiency. Using a mixed methods approach, the research draws on qualitative interview data, document analysis, and quantitative performance indicators collected from multiple industries with varying levels of automation maturity. The findings demonstrate that AI enabled automation significantly enhances productivity through faster processing speeds, reduced operational downtime, and improved decision accuracy. Quality improvements are evident in lower error rates, higher consistency, and more reliable compliance with technical standards, particularly in data intensive and repetitive processes. Cost efficiency gains are observed mainly through process optimization, waste reduction, and labor reallocation rather than direct workforce reduction. However, the results also reveal that the magnitude of these benefits depends strongly on organizational readiness, including digital infrastructure integration, workforce digital skills, and effective governance mechanisms. Organizations with higher socio technical preparedness experience smoother implementation and more sustainable performance outcomes. Overall, the study contributes empirical evidence that Artificial Intelligence serves not only as an efficiency enhancing technology but also as a strategic capability that supports long term industrial competitiveness when aligned with human and organizational factors.