Prescriptive analytics in public-sector decision-making: A framework and insights from charging infrastructure planning

被引:24
作者
Brandt, Tobias [1 ]
Wagner, Sebastian [2 ]
Neumann, Dirk [3 ]
机构
[1] Erasmus Univ, Rotterdam Sch Management, POB 1738, NL-3000 DR Rotterdam, Netherlands
[2] Geospin GmbH, Konrad Goldmann Str 5b, D-79100 Freiburg, Germany
[3] Univ Freiburg, Pl Alten Synagoge, D-79098 Freiburg, Germany
关键词
Decision support systems; Public value; Prescriptive analytics; Smart city; Electric mobility; INFORMATION-TECHNOLOGY INVESTMENTS; BIG DATA; POLICY; MODEL; GOVERNMENT; SELECTION; PATH;
D O I
10.1016/j.ejor.2020.09.034
中图分类号
C93 [管理学];
学科分类号
12 ; 1201 ; 1202 ; 120202 ;
摘要
In this work, we investigate the challenges public-sector organizations face when seeking to leverage prescriptive analytics and provide insights into the public value such data-driven tools and methods can provide. Using the strategic triangle of value, legitimacy, and operational capacity as a starting point, we derive a framework to assess public-sector prescriptive analytics initiatives, along with six guiding questions that structure the assessment process. We present a case study applying prescriptive analytics to the placement of charge points in urban areas, a critical challenge many municipalities are currently facing in the transition towards electric mobility. Reflecting on the analytics application as well as its development and implementation process through the guiding questions, we derive key lessons for public-sector organizations seeking to apply prescriptive analytics. (C) 2020 The Author(s). Published by Elsevier B.V.
引用
收藏
页码:379 / 393
页数:15
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