arbiter
JournalsPricingAccount

The AI-Driven State

Craig P. Orgeron et al.

International Journal of Electronic Government Research2025https://doi.org/10.4018/ijegr.381327article
ABDC C
Weight
0.50

What the paper says

Good governance is increasingly synonymous with the ability to deliver connected, digital services. The COVID-19 pandemic tested public sector agencies with intensified expectations and increased service demands, exposing shortcomings in online service delivery. This article examines how the pandemic accelerated digital transformation and presents an opportunity to develop robust government-as-a-service (GaaS) platforms, cloud-based infrastructures that enable governments to deliver public services more efficiently. It outlines the historical evolution of e-government, summarizes insights from structured interviews with 10 government technology leaders, and proposes a framework for emerging GaaS models. The study calls for researchers and practitioners to design and implement self-regulating digital experience platforms characterized by five essential attributes. By leveraging public-private partnerships, governments can build resilient, full-service GaaS solutions that enhance citizen engagement, agility, and innovation worldwide.

Open paper page →

Cite this paper

https://doi.org/https://doi.org/10.4018/ijegr.381327

Or copy a formatted citation

@article{craig2025,
  title        = {{The AI-Driven State}},
  author       = {Craig P. Orgeron et al.},
  journal      = {International Journal of Electronic Government Research},
  year         = {2025},
  doi          = {https://doi.org/https://doi.org/10.4018/ijegr.381327},
}

Paste directly into BibTeX, Zotero, or your reference manager.

Flag this paper

The AI-Driven State

Flags are reviewed by the Arbiter methodology team within 5 business days.

Evidence weight

0.50

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.50 × 0.4 = 0.20
M · momentum0.50 × 0.15 = 0.07
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.