Abstract
In this paper, we propose a novel approach for the analysis of hierarchical multi-block data based on fuzzy techniques. The proposed method enables the identification of significant relationships within and between data blocks, while ensuring interpretability of the hierarchical multi-block structure. Empirical validation of the method on data from the ESG4PMChange and SPM² projects, assessing Sustainable Project Management and Environmental, Social, and Governance competencies, demonstrates the method's ability to uncover interpretable hierarchical dependencies that are robust to different aggregation strategies.
Paper Type
Poster
DOI
10.62036/ISD.2026.37
A Novel Approach for Analyzing Disjoint Hierarchical Multi-Block Data
In this paper, we propose a novel approach for the analysis of hierarchical multi-block data based on fuzzy techniques. The proposed method enables the identification of significant relationships within and between data blocks, while ensuring interpretability of the hierarchical multi-block structure. Empirical validation of the method on data from the ESG4PMChange and SPM² projects, assessing Sustainable Project Management and Environmental, Social, and Governance competencies, demonstrates the method's ability to uncover interpretable hierarchical dependencies that are robust to different aggregation strategies.
Recommended Citation
Mroczek, T., Matusiewicz, Z., Wójcik, J. & Świętoniowska, J.(2026). A Novel Approach for Analyzing Disjoint Hierarchical Multi-Block Data. In M. Valenta, B. Mannová, R. Pergl, A. Przybylek, M. Lang, H. Linger, C. Schneider, N. Iivari, & E. Insfran (Eds.), Making ISD Sustainable: Reloaded with AI and Automation (ISD2026 Proceedings). Prague, Czech Republic: Czech Technical University in Prague. ISBN: 978-80-01-07585-2. https://doi.org/10.62036/ISD.2026.37