Semantic Integrity over Scale: A Complete-Semantic Framework for Measuring Corporate Digital Transformation

Abstract Purpose Digital transformation (DT) is a configurational construct spanning technology, process, organizational, and strategic change, and valid measurement is a prerequisite for understanding it. Existing text-based methods fragment annual reports into keywords or isolated sentences, breaking the cross-sentence semantic structure that expresses the construct. This paper examines how textual representational granularity affects content validity in DT measurement, and proposes a complete-semantic measurement framework. Design/methodology/approach From 28,823 annual reports of Chinese listed manufacturers (2006–2023), we build a 1,000-report sample through structured sampling and design a three-stage complete-semantic framework (input, training-signal, and model-bearing). The framework is evaluated through comparative experiments, ablation, and sensitivity analysis, then applied to the full sample, with an existing sentence-level measure used for external validation. Findings Textual representational granularity shapes which forms of the construct enter measurement: a fragmented representation relies on explicit digital keywords and leaves early and implicit transformation unseen. Across the full sample, DT in Chinese manufacturing has widened in breadth and deepened in structure, shifting from localized exploration to cross-departmental integration. An existing sentence-level measure agrees on explicit, deep-stage transformation but diverges in the early stages that fragmentation struggles to capture. Research limitations The study focuses on Chinese manufacturing; its conclusions need testing in other industries, settings, and disclosure contexts, and on new model architectures and cross-construct tasks. Practical implications The framework requires little annotation, supports local deployment, and aligns with national standards, offering a reproducible tool for research, policy, and firm-level analysis. Originality/value The contribution lies not in using large language models (LLMs) for text classification, but in integrating the cross-sentence semantic structure on which a configurational construct depends. The paper brings textual representational granularity into content-validity assessment, extending construct validity from “item–construct domain” coverage to “textual representation–construct structure” alignment, and offering a testable lens for measuring complex organizational constructs from disclosure text.