ArticleGovernment information quarterly2022
Digital government transformation in turbulent times: Responses, challenges, and future direction.
Article in Government information quarterly, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
7 citing papers in PubMed.
- Digital habitus and the sociotechnical transformation of everyday life in the United Arab Emirates: a hybrid framework of governmentality, surveillance capitalism, and Gulf state modernity.Frontiers in sociology · 2026Article
- Does digital economy affect the happiness of older adults?Frontiers in psychology · 2026Article
- Investigating Citizens' Acceptance of Contact Tracing Apps: Quantitative Study of the Role of Trust and Privacy.JMIR mHealth and uHealth · 2024Article
- Assess the efficacy of China's Inter-provincial Government Services policy: A quantitative evaluation based on PMC-Index model.PloS one · 2024Article
- Barriers to digital government and the COVID-19 crisis - A comparative study of federal government entities in the United States and Austria.International review of administrative sciences · 2023Article
- Digital government transformation as an organizational response to the COVID-19 pandemic.Government information quarterly · 2023Article
- Effect of a Digital Literacy Program on Older Adults' Digital Social Behavior: A Quasi-Experimental Study.International journal of environmental research and public health · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
We are living in turbulent times, with the threats of COVID-19 and related social conflicts. Digital transformation is not an option but a necessity for governments to respond to these crises. It has become imperative for governments worldwide to enhance their capacity to strategically use emerging digital technologies and develop innovative digital public services to confront and overcome the pandemic. With the rapid development of digital technologies, digital government transformation (DGT) has been legitimated in response to the pandemic, contributing to innovative efficacy, but it also has created a set of challenges, dilemmas, paradoxes, and ambiguities. This special issue's primary motive is to comprehensively discuss the promises and challenges DGT presents. It focuses on the nature of the problems and the dilemmatic situation in which to use the technologies. Furthermore, it covers government capacity and policy implications for managerial and institutional reforms to respond to the threats and the uncertainty caused by disruptive digitalization in many countries. To stimulate discussion of the theme of this special issue, this editorial note provides an overview of previous literature on DGT as a controlling measure of the pandemic and the future direction of research and practice on DGT.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.