Digital transformation in government: Lessons from GovTech Singapore

Arif Perdana & Intan Azura Mokhtar

Journal of Information Technology Teaching Cases2025https://doi.org/10.1177/20438869251362871article
AJG 1
Weight
0.44

What the paper says

This case study explores GovTech Singapore’s approach to digital government. It provides insights into the challenges and opportunities associated with public sector innovation. As governments worldwide navigate digital transformation, GovTech’s journey offers an example of how technological advancements can support public service delivery and enhance citizen engagement. The agency’s strategies include application development; cybersecurity; data science; artificial intelligence (AI); information, communication, and technology (ICT) infrastructure; and smart city technology, reflecting a broad approach to digital governance. Utilizing cloud computing, AI, and data analytics, GovTech has supported efforts to streamline government operations and contribute to Singapore’s progress in digital government innovation and e-government services. The agency faces significant challenges in balancing innovation with bureaucratic processes, accountability requirements, and data privacy and security. This tension highlights the complex interplay between technological advancement and organizational culture in the public sector. GovTech provides insights that can inform public- and private-sector entities in their digital transformation efforts, emphasizing the role of user-centric design, agile methodologies, and cross-sector collaboration. As governments worldwide work to modernize their services, GovTech Singapore’s experience offers an example of how technology can be used to enhance and strengthen public value and improve citizen engagement and experience.

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https://doi.org/https://doi.org/10.1177/20438869251362871

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@article{arif2025,
  title        = {{Digital transformation in government: Lessons from GovTech Singapore}},
  author       = {Arif Perdana & Intan Azura Mokhtar},
  journal      = {Journal of Information Technology Teaching Cases},
  year         = {2025},
  doi          = {https://doi.org/https://doi.org/10.1177/20438869251362871},
}

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Evidence weight

0.44

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

F · citation impact0.32 × 0.4 = 0.13
M · momentum0.57 × 0.15 = 0.09
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.