Enhancing Public Service Delivery through AI-Based E-Government Platforms

Thesis Abstract

The increasing demand for efficient and accessible public services in the digital age necessitates the integration of advanced technological solutions within government frameworks, particularly the deployment of Artificial Intelligence (AI) in e-Government platforms to enhance service delivery. This study addresses the persistent challenges of limited responsiveness, bureaucratic inefficiencies, and inadequate citizen engagement in public service mechanisms by exploring how AI-driven e-Government solutions can transform operational efficiency and user satisfaction. The primary aim of the research is to evaluate the impact of AI integration on public service delivery, with specific objectives to identify the key AI functionalities that influence service efficiency, measure citizen satisfaction levels, and analyze the factors facilitating or hindering AI adoption within government agencies. A mixed-methods research design was employed, combining quantitative surveys and qualitative interviews to provide a comprehensive understanding of the phenomenon. The target population comprised government officials working directly with e-Government platforms and citizens who have interacted with these services in the past year. The sample included 300 citizens selected through stratified random sampling to ensure demographic diversity and 50 government officials purposively sampled based on their roles in e-Government operations. Data collection instruments consisted of structured questionnaires for citizens assessing satisfaction, perceptions, and AI-related service experiences, and semi-structured interview guides for officials focusing on implementation processes, technical challenges, and adoption attitudes. Validity and reliability were established through pilot testing and expert reviews, yielding Cronbach's alpha coefficients exceeding 0.80 for survey instruments. Data analysis involved descriptive statistics to profile respondents, inferential statistics including multiple regression analysis to examine the relationship between AI functionalities and service efficiency, and thematic analysis for qualitative interview data to elicit nuanced insights into implementation barriers and success factors. The regression model hypothesized that AI functionalities such as chatbots, predictive analytics, and automated processing significantly predict improvements in service delivery speed and accuracy, controlling for variables like infrastructure quality and user digital literacy. Expected findings suggest that AI functionalities markedly enhance service delivery efficiency, with citizen satisfaction correlating positively with perceived responsiveness and accuracy of AI-enabled services. The qualitative data are anticipated to reveal critical organizational, technical, and social barriers to effective AI adoption, including resistance to change, skill gaps, and data privacy concerns. The study aims to develop an integrated conceptual model based on the Technology Acceptance Model (TAM) and the Diffusion of Innovations theory, illustrating the interrelations among technological, organizational, and user acceptance factors influencing AI-based e-Government success. This research contributes to the scholarly understanding of digital transformation in public administration by empirically validating the effectiveness of AI technologies in improving service outcomes within government contexts. It advances theoretical discourse by integrating TAM and diffusion models in a public sector setting and offers practical insights for policymakers and practitioners on leveraging AI to modernize government services, address implementation challenges, and foster citizen-centric governance. The study concludes that strategic investment in AI infrastructure, staff capacity building, and robust data governance frameworks are vital to maximize the benefits of AI-enabled e-Government platforms. Recommendations include establishing clear AI deployment protocols, enhancing digital literacy among citizens and officials, and promoting transparency to build trust. It further suggests avenues for future research, such as longitudinal studies on AI adoption impacts and cross-country comparative analyses to generalize findings across different governance environments.