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

Area of Science:

• Public Administration
• Data Science
• Urban Planning

Background:

• Public sector organizations face challenges adopting data-driven tools.
• Prescriptive analytics offers potential for enhanced public value.
• Existing frameworks for assessing analytics in public sector are limited.

Purpose of the Study:

• To investigate challenges in public sector prescriptive analytics adoption.
• To provide insights into the public value of data-driven tools.
• To develop an assessment framework for public sector prescriptive analytics initiatives.

Main Methods:

• Utilized the strategic triangle of value, legitimacy, and operational capacity.
• Developed a framework with six guiding questions for assessment.
• Applied the framework to a case study on urban charge point placement for electric mobility.

Main Results:

• The strategic triangle provides a foundation for assessing public sector analytics.
• The developed framework and guiding questions facilitate structured evaluation.
• The case study highlighted practical challenges and lessons in implementing prescriptive analytics.

Conclusions:

• Prescriptive analytics can yield significant public value when implemented effectively.
• A structured assessment approach is crucial for successful public sector analytics initiatives.
• Lessons learned from the charge point case study offer transferable insights for other public sector applications.