A Preliminary Data-Driven Competency Mapping Study for Modular Construction Designers: Exploratory Korean Validation Using Bayesian BWM and Fuzzy DEMATEL

Modular construction advances sustainability and is reshaping designer competencies, making workforce development critical to industry transition. Existing competency models rely mainly on expert interviews and Delphi methods, offering limited quantitative evidence on role-specific labor-market demands, causal relationships among competencies, or experience-based perceptual differences. This study presents a preliminary, data-driven competency-mapping study for modular construction designers by integrating BERTopic, Ward clustering, CVR, Bayesian BWM, and Fuzzy DEMATEL. Applied to 243 job postings from six countries, the text-mining stage identifies a candidate competency structure of 3 domains, 9 categories, and 36 performance statements. This candidate structure was then examined through an exploratory survey of 30 Korean respondents. The results suggest that Codes and Compliance represents the most clearly recognized high-consensus competency area within this local validation sample, whereas Modular Construction shows an indicative experience-related divergence in perceived causal position. Given the small and uneven subgroup sample and the formative state of Korea’s modular construction industry, the findings should be interpreted as preliminary evidence rather than as a validated competency framework or a confirmed expert–novice model. The study contributes a reproducible mixed-method workflow, a candidate competency map, and an illustrative maturity prototype for future validation and refinement.