Unveiling the “Iceberg Effect” in Building Information Modeling (BIM): Evidence from Industry Foundation Classes (IFC) Information Exchange

Information quality (IQ) frameworks treat IQ dimensions as independent categories, yet little evidence shows whether they are causally interrelated in standardized information exchange, or whether practitioners’ perceptions of IQ failures match the underlying causal structure. This study investigates the causal hierarchy among IQ dimensions in IFC-based BIM information exchange and the gap between practitioner perception and structural causation. A sequential mixed-methods design combines a systematic literature review (25 studies), secondary analysis of 19 practitioners across 17 interviews (62 coded segments), and DEMATEL-ISM causal modelling with 10 domain experts. It then develops a causal-prioritized binary IQ assessment framework, validated on four IFC building models (27,704 detected defects). Practitioners systematically over-report visible downstream symptoms (information loss, 71%) while never spontaneously raising upstream root causes (classification errors, 0%)—a perception-causality gap termed the “iceberg effect.” Classification (D3) is the principal causal driver (D−R = +1.37), giving the chain classification → relationships/semantics → information loss/geometry. The case study confirms D3 produces the largest share of actual defects (37.4%) despite being the dimension practitioners never reported. This study provides the first empirical evidence of causal asymmetry among IQ dimensions in standardized information exchange. The iceberg-effect concept and a causal-prioritized binary framework let practitioners sequence quality improvement by structural causation rather than complaint frequency. The framework is validated on four commercial building models (27,704 defects), showing perception-based prioritization diverges from actual defect distributions.