An Expert-Informed Stakeholder-Risk Model for Intelligent Transport System Integration in Road Transport Enterprises

Intelligent transport system (ITS) integration is not solely a technological task; its implementation also depends on organizational conditions, stakeholder coordination, professional competencies, data governance, and managerial decision-making. Existing intelligent-mobility research provides substantial evidence on governance, adoption barriers, stakeholder ecosystems, and sustainability-related outcomes, but these dimensions are less frequently integrated at the level of a specific road transport enterprise project. This study develops and preliminarily appraises an integrated stakeholder-risk model that jointly considers human-resource and organizational risks, conflicts, and behavioral decision factors in an ITS integration project. The 27-factor catalog originates from a previously published integrated risk framework and is independently corroborated in the present study against the project-management, organizational-behavior, behavioral-decision, and intelligent-mobility literature before being contextualized for ITS implementation. Seven external experts each assessed the same 27 predefined factors, producing 189 repeated factor ratings, 70 expert-nominated directed-influence records, and 35 ranked priority selections. Unproductive communication (0.650), insufficient technical and digital literacy (0.602), and interorganizational conflicts of interest (0.550) received the highest products of panel mean probability and impact ratings. Kendall’s coefficient of concordance for the probability–impact rankings was 0.528, indicating moderate rather than complete inter-expert agreement. An alternative aggregation based on the mean of experts’ individual probability–impact products preserved the composition of the five highest-priority factors. The theoretical contribution lies in integrating literature-corroborated organizational and behavioral risk constructs within a project-level stakeholder framework while explicitly distinguishing factor content support, expert prioritization, hypothesized influence links, and subsequent empirical validation. The practical contribution is a structured basis for identifying and discussing communication, competency, data-governance, and interorganizational coordination risks in ITS projects. No internal company data or observed schedule, cost, operational, environmental, or organizational outcome data were used; therefore, the study does not establish causal effects, intervention effectiveness, predictive validity, or demonstrated sustainability outcomes. These require subsequent testing using operational data from multiple ITS projects.