Data-driven Rulemaking
Data-Driven Rulemaking is a hybrid rulemaking approach that belongs to the class of regulatory decision-making methodologies, distinguished by its systematic integration of empirical data and quantitative analysis to inform, justify, and refine the formulation and evaluation of rules.
17
Publications
510
Citations
63
Authors
27
Institutions
Publications per year
2017–2026
8
Parents
17
1–5 of 17
63
1
h-index
1
h-index
1
h-index
1
h-index
1
h-index
1–5 of 63
27
Loughborough University
Loughborough, United Kingdom
1 h-index
5 publications
60 citations
Rogers (United States)
Windham, United States
1 h-index
1 publications
63 citations
Cranfield University
Cranfield, United Kingdom
1 h-index
2 publications
104 citations
Georgetown University
Washington, United States
1 h-index
1 publications
63 citations
University of Wisconsin–Madison
Madison, United States
1 h-index
4 publications
96 citations
Walmart (United States)
Bentonville, United States
1 h-index
6 publications
144 citations
Decision Sciences (United States)
Fort Walton Beach, United States
1 h-index
1 publications
41 citations
Central China Normal University
Wuhan, China
1 h-index
4 publications
232 citations
1–8 of 27
1 publications · 16 citations
1
h-index
1 publications · 41 citations
1
h-index
1 publications · 12 citations
1
h-index
1 publications · 21 citations
1
h-index
1 publications · 52 citations
1
h-index
1–5