Diagnosing Data Quality Gaps across Decision Levels: A Semiotic Framework for Highway Pavement Management

Abstract The rapid digitalization of highway infrastructure has created large volumes of data, yet it remains unclear how effectively this information supports decisions across organizational levels. This paper develops a multidimensional, semiotic-based framework for assessing data quality in hierarchical decision-making for highway infrastructure data and then applies it to pavement management data from the National Highways Authority of India as a case study. The framework groups data quality into syntactic, empiric, semantic, and pragmatic categories and links stakeholder-derived importance and satisfaction ratings to decision levels through a fault-tree structure and a simple multiattribute index. The case study shows that decision-makers consistently prioritize syntactic and empiric dimensions, particularly accuracy, completeness, timeliness, and accessibility, and that these are comparatively well realized at strategic and network levels. Satisfaction and effective use decline at program, project selection, and project levels, where staff must integrate data from multiple sources and act under time pressure. Within pavement management hierarchy, this vertical imbalance within indicates that operational users experience the greatest difficulties with completeness, structure, usability and security, even though their decisions directly affect day-to-day performance. Together, the framework and case findings provide a practical basis for highway agencies to identify level-specific data quality gaps and target improvements to strengthen data-driven infrastructure management.