Uncovering Decision-Making Implications Across Building Owners: A Formal Concept Analysis of Building Public and Private Owners.
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Abstract
Owner organizations play a critical role in shaping decision-making processes during construction and project outcomes in the building construction sector, yet existing research has primarily examined decision-making characteristics as independent traits rather than as combinations within organizational contexts. This study addresses this gap by investigating the following two research questions: (1) whether public and private building owners exhibit distinct decision-making implications— configuration of characteristics that imply one another—and (2) whether any such implications are shared across ownership types. Using survey data from 109 owner organizations collected as part of Phase 1 of the Building Owners Assessment Tool (BOAT) research program, the study applied formal concept analysis (FCA) and constructed formal contexts based on 22 Decision-Making Profile Characteristics (DMPC). Formal implications—representing decision-making configurations—were obtained using the Duquenne-Guigues basis separately for public and private owners. The analysis revealed substantial structural divergence between ownership types: the public-owner context produced 584 implications, the private-owner context produced 304, and only one was shared across both. This cross-sector implication linked relational participation, adaptive and informal decision-making styles, command and learning-oriented cultures, and a sustained-growth environment to data-driven decision-making.
The findings demonstrate that decision-making logic is highly context-dependent and largely non-transferable across ownership types, while also identifying an implication with a robust configuration that emerges under conditions of organizational complexity. By revealing the decision-making implications, this study advances theoretical understanding of organizational logic in construction management and demonstrates the value of configurational, logic-based methods for analyzing governance-driven differences in decision processes.
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Thesis (Master's)--University of Washington, 2026
