Factors Influencing Generative AI-Enabled e-Government Services (GAIGS) Information Quality: A Systematic Literature Review

The integration of Generative Artificial Intelligence (GAI) into electronic government (e-Government) services has transformed the delivery of public information, raising critical questions about the quality of AI-generated content. This study presents a systematic literature review (SLR) to identify and categorise the key factors influencing information quality in GAI-enabled e-Government Services (GAIGS). Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines and using the Population, Interest, and Context (PICo) framework, the review screened 664 articles from major databases, including Web of Science (WoS), Scopus, IEEE Xplore, and Wiley Online Library. A total of 33 high-quality studies published between 2021 and 2025 were selected for thematic analysis. The findings reveal 22 distinct information quality factors, which were synthesised into five overarching themes: trustworthiness and verifiability, security and ethics, content quality and structure, user perception and value, and adaptability and system behaviour. The themes indicate a holistic model that encompasses the multidimensional nature of issues and needs of measuring the quality of information in the AI-mediated delivery of public services. The research adds value to the scholarly knowledge of information quality in the changing digital governance environments. It offers workable lessons to policymakers and developers who want to design credible and citizen-centred GAI applications. This review provides a systematic overview of the existing body of knowledge, which can guide future research and model development in the context of GAIGS.

@article{Aziz2026,
  title     = {Factors Influencing Generative AI-Enabled e-Government Services (GAIGS) Information Quality: A Systematic Literature Review},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {17},
  number    = {4},
  year      = {2026},
  publisher = {The Science and Information Organization},
  author    = {Azwan Abd Aziz and Rozi Nor Haizan Nor and Yusmadi Yah Jusoh and Wan Nurhayati Wan Ab. Rahman and Khairi Azhar Aziz and Nur Ilyana Ismarau Tajuddin and Raditya Muhammad},
  doi       = {10.14569/IJACSA.2026.0170407},
  url       = {https://doi.org/10.14569/IJACSA.2026.0170407}
}