• search hit 52 of 44110
Back to Result List

An ounce of prevention is worth a pound of cure

  • Public sector organizations at all levels of government increasingly rely on Big Data Algorithmic Systems (BDAS) to support decision-making along the entire policy cycle. But while our knowledge on the use of big data continues to grow for government agencies implementing and delivering public services, empirical research on applications for anticipatory policy design is still in its infancy. Based on the concept of policy analytical capacity (PAC), this case study examines the application of BDAS for early crisis detection within the German Federal Government—that is, the German Federal Foreign Office (FFO) and the Federal Ministry of Defence (FMoD). It uses the nested model of PAC to reflect on systemic, organizational, and individual capacity-building from a neoinstitutional perspective and allow for the consideration of embedded institutional contexts. Results from semi-structured interviews indicate that governments seeking to exploit BDAS in policymaking depend on their institutional environment (e.g., through research and dataPublic sector organizations at all levels of government increasingly rely on Big Data Algorithmic Systems (BDAS) to support decision-making along the entire policy cycle. But while our knowledge on the use of big data continues to grow for government agencies implementing and delivering public services, empirical research on applications for anticipatory policy design is still in its infancy. Based on the concept of policy analytical capacity (PAC), this case study examines the application of BDAS for early crisis detection within the German Federal Government—that is, the German Federal Foreign Office (FFO) and the Federal Ministry of Defence (FMoD). It uses the nested model of PAC to reflect on systemic, organizational, and individual capacity-building from a neoinstitutional perspective and allow for the consideration of embedded institutional contexts. Results from semi-structured interviews indicate that governments seeking to exploit BDAS in policymaking depend on their institutional environment (e.g., through research and data governance infrastructure). However, specific capacity-building strategies may differ according to the departments' institutional framework, with the FMoD relying heavily on subordinate agencies and the FFO creating network-like structures with external researchers. Government capacity-building at the individual and organizational level is similarly affected by long-established institutional structures, roles, and practices within the organization and beyond, making it important to analyze these three levels simultaneously instead of separately.show moreshow less

Export metadata

Additional Services

Search Google Scholar Statistics
Metadaten
Author details:Camilla WanckelORCiDGND
DOI:https://doi.org/10.1016/j.giq.2022.101705
ISSN:0740-624X
Title of parent work (English):Government information quarterly
Subtitle (English):building capacities for the use of big data algorithm systems (BDAS) in early crisis detection
Publisher:Elsevier
Place of publishing:Amsterdam
Publication type:Article
Language:English
Date of first publication:2022/06/11
Publication year:2022
Release date:2024/06/03
Tag:artificial intelligence (AI); big data algorithm system (BDAS); central government organizations; early crisis detection; neo-institutionalism; policy analytical capacity (PAC); policymaking
Volume:39
Issue:4
Article number:101705
Number of pages:13
Organizational units:Wirtschafts- und Sozialwissenschaftliche Fakultät / Sozialwissenschaften / Fachgruppe Politik- & Verwaltungswissenschaft
DDC classification:0 Informatik, Informationswissenschaft, allgemeine Werke / 07 Publizistische Medien, Journalismus, Verlagswesen / 070 Publizistische Medien, Journalismus, Verlagswesen
Peer review:Referiert
Publishing method:Open Access / Hybrid Open-Access
License (German):License LogoCC-BY-NC-ND - Namensnennung, nicht kommerziell, keine Bearbeitungen 4.0 International
Accept ✔
This website uses technically necessary session cookies. By continuing to use the website, you agree to this. You can find our privacy policy here.