Risk Identification for Digital Transformation in Construction Enterprises: A Hybrid Topic Modeling and Inductive Coding Framework

Digital transformation in construction enterprises is frequently constrained by risks that are heterogeneous, context-dependent, and described with inconsistent terminology across studies and practice. Prior research has predominantly relied on expert judgment or narrative reviews to summarize such risks, which limits reproducibility and makes it difficult to iteratively expand the indicator set when new evidence becomes available. To address these challenges, this study develops a hybrid risk identification framework that integrates unsupervised topic modeling with structured inductive coding. Latent Dirichlet Allocation (LDA) is employed to extract latent semantic patterns from a systematically screened body of literature on construction digital transformation, consolidating dispersed risk expressions into coherent thematic units. The Gioia methodology is then applied to inductively structure these themes into a hierarchical risk indicator system, ensuring traceability from textual evidence to conceptual indicators and enhancing interpretability for construction management applications. Rather than enumerating isolated risk events, the proposed framework conceptualizes digital transformation risk in construction enterprises as a set of interacting structural conditions that shape risk exposure across project stages and organizational boundaries. By shifting risk identification from event-based listings to a structural and condition-oriented representation, this study provides a transferable foundation for subsequent causal modeling and multi-criteria risk evaluation in construction digital transformation.