Interregional Academy of Personnel Management

Volodymyr Servetnyk, Associate Professor at the Department of Public Administration of the Educational and Research Institute of Management, Economics and Business at the Interregional Academy of Personnel Management (IAPM), has co-authored a research article on digital decision-support systems for the development of veteran policy.

The article, “Development of Information and Analytical Decision Support Systems in Government Relations for the Formation of Veteran Policy,” was published in the Journal of Theoretical and Applied Information Technology (JATIT), which is indexed in Scopus. The other co-authors are Liudmyla Zubrytska, Liudmyla Drozach, Nataliia Iskhakova and Liudmyla Lyasota.

Research Focus: Supporting Decisions in Veteran Policy

The authors examined how information and analytical systems and digital communication tools can support the preparation and adoption of decisions in veteran policy.

The study compares the experiences of the United States, Canada and Ukraine. It focuses on data integration, coordination between institutions, engagement with stakeholders and the alignment of management decisions with public policy objectives.

The research aimed to develop a Government Relations (GR)-oriented decision-support model and compare the conditions for its application in the three countries.

Methodology and Comparative Assessment

The study uses comparative analysis, functional and analytical modelling, a two-stage Delphi method, the Analytic Hierarchy Process (AHP) and exploratory correlation analysis.

A total of 42 experts participated in the assessment, with 14 experts representing each country. Their assessments were used to compare aspects of digital decision support and policy alignment.

The authors applied two indices: VG-DSS, which characterises the decision-support system for veteran policy, and HG, an index of policy alignment. The study reports the following values:

-        VG-DSS: United States - 0.89; Canada - 0.84; Ukraine - 0.58.

-        HG: United States - 0.91; Canada - 0.87; Ukraine - 0.64.

Under the proposed assessment model, the United States and Canada received higher scores than Ukraine on both indices. These figures represent the results of the authors’ model and should not be interpreted as comprehensive assessments of each country’s veteran policy.

A Proposed Model for Digital Decision Support

One of the study’s main outcomes is a proposed architecture for a decision-support system that brings together several components: digital data integration, data governance, technological maturity, adaptive communication with stakeholders and alignment with policy objectives.

This approach treats digital decision support not simply as a technical tool, but as part of the wider process of preparing management decisions. Its application requires consideration of data quality and availability, institutional capacity and the nature of cooperation among those involved in policy development.

Scenario Modelling and Practical Implications

The authors also used scenario modelling to estimate potential changes in the VG-DSS indicator. The model projected increases of 0.03 for the United States, 0.04 for Canada and 0.08 for Ukraine.

These figures are scenario-based estimates, not evidence of changes already achieved in practice. Further testing and validation in real-world settings would be needed to assess the model’s actual effects.

The findings may inform further analysis of digital tools in public administration, particularly in relation to inter-institutional coordination, data management and engagement with veteran communities. Applying the proposed approach in practice would require consideration of national contexts, institutional capacities and data availability.

Publication Details

Zubrytska, L., Servetnyk, V., Drozach, L., Iskhakova, N., & Lyasota, L. “Development of Information and Analytical Decision Support Systems in Government Relations for the Formation of Veteran Policy.” Journal of Theoretical and Applied Information Technology, Vol. 104, No. 17, 2026.

You can access the work at the following link: https://zenodo.org/records/22897169