Data-Driven Strategic Approaches to Public Management

Dear Colleagues,

Data-driven strategic approaches to public management are related to the application of data-driven methodologies such as Data Analytics (DA), Statistical Learning (SL), and Artificial Intelligence (AI) in the field of public administration and governance. Data-driven strategic approaches to public management aim to enhance the efficiency, effectiveness, and responsiveness of government agencies by leveraging the power of data and analytics. By making informed decisions and optimizing resource utilization, governments can better address the needs of their constituents and improve overall governance.

Today, the use of DA, SL, and AI in public management has become increasingly prevalent, offering transformative benefits in terms of efficiency, decision-making, and service delivery. However, it is important to note that while DA, SL, and AI offer numerous benefits, there are also ethical considerations, including privacy concerns, biases in algorithms, and the need for transparency. Implementing these technologies in public management requires careful planning, governance frameworks, and a commitment to ethical practices to ensure responsible and effective use.

In this Special Issue, we aim to collect high-quality studies related to the application of Data-driven strategic approaches to public management (e.g., innovative applications of data-driven methods in public health management, optimizing transportation routes, predicting service demand, and detecting and preventing fraud in public programs). We welcome studies attempting to address global challenges such as public health management, public financial management, climate change monitoring and environmental pollution monitoring, education management, and others. We are also interested in contributions that provide insights into how data analytics can lead to a sustainable society. This collection of studies is expected to help formulate solutions to address the overall challenges and develop interdisciplinary research directions for our advancement to a sustainable world. Empirical or review studies, as well as other acceptable article types, are welcome.

In this Special Issue, original research articles and reviews are welcome. Studies of interest include, but are not limited to, data-driven strategic approaches in the following fields:

• Predictive analytics for policy planning;
• Optimizing public service delivery;
• Fraud detection and prevention in public health;
• Enhancing public safety;
• Environmental monitoring and sustainability;
• Automated decision-making;
• Data-driven policy evaluation;
• Citizen engagement and feedback;
• Healthcare planning and management;
• Resource optimization;
• Smart city initiatives;
• Human resource management;
• Emergency response and disaster management;
• Education planning and resource allocation;
• Social services optimization;
• Regulatory compliance and monitoring;
• Economic development and planning;
• Public transportation optimization;
• Public health surveillance;
• Tax revenue optimization;
• Fraud detection in taxation;
• Energy consumption optimization;
• Urban planning and development;
• Public-private partnerships (PPPs) optimization;
• Sustainable production management and efficient use of natural resources;
• Sustainable tourism management;
• Sustainable management of energy;
• Sustainable transportation and supply chain;
• Responsible energy lifestyle patterns;
• Eco-design and energy-efficient design;
• Circular economy;
• Climate-neutral production and consumption.

We look forward to receiving your contributions.

Prof. Dr. Sotirios Bersimis
Dr. Andreas Fousteris
Prof. Dr. Dimitrios A. Georgakellos
Guest Editors

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• data-driven strategic approaches
• public management
• public health
• public sector efficiency
• data analytics
• statistical learning
• artificial intelligence
• sustainability