Development of a Decision Support System Framework for Intelligent Transportation System Adoption Evaluation Based on SEM-PLS Analysis

Abstract

Implementing Intelligent Transportation Systems (ITS) requires an evaluation mechanism that considers technological, infrastructural, socioeconomic, financial, readiness, affordability, and policy-related conditions. Although previous studies have identified determinants of ITS adoption, they have rarely translated these findings into operational decision-support mechanisms. This study develops a model-driven Decision Support System (DSS) framework for evaluating ITS adoption readiness based on empirically validated Partial Least Squares Structural Equation Modeling (PLS-SEM) relationships. A sequential mixed-methods design was employed. The qualitative stage included a Focus Group Discussion with six participants: two subject-matter experts, two government representatives, and two academics. During the quantitative stage, 100 questionnaires were distributed to transportation users and stakeholders, and 75 usable responses were obtained, representing a 75% response rate. PLS-SEM was used to evaluate the measurement and structural models. The results indicated that Government Policy had the strongest significant direct relationship with ITS Adoption (β =0.454; p<0.001), followed by Smart Readiness (β =0.244; p = 0.001). Social Affordability did not have a significant direct relationship with ITS Adoption (β = 0.047; p = 0.515). The validated relationships were transformed into a DSS comprising Data, Model, and Decision Layers. To ensure dimensional consistency, raw assessment values were standardized before applying the standardized path coefficients, while readiness categories were interpreted using percentile-based thresholds. The proposed framework extends PLS-SEM findings beyond hypothesis testing by providing a transparent basis for ITS readiness assessment and factor-specific policy prioritization. Further validation using larger samples and operational implementation remains necessary.