Machine Learning-Based Classification of Stakeholder Readiness for BIM-IoT Adoption in the Construction Industry of Pakistan: A Comparative Analysis of Random Forest, XGBoost, and Support Vector Machine

Developing-country construction sectors continue to record disproportionately high occupational accident rates, partly attributable to the slow adoption of digital safety technologies, including Building Information Modeling (BIM) and Internet of Things (IoT) systems. While prior empirical research has established the population-level factors that explain stakeholder adoption intention through survey-based frameworks, the ability to classify individual stakeholder readiness for targeted, pre-deployment intervention remains methodologically unaddressed. This study fills that gap by applying three supervised machine learning classifiers (Random Forest [RF], XGBoost (XGB), and Support Vector Machine (SVM)) to a dataset of 107 construction professionals purposively sampled from large-scale infrastructure projects in Pakistan, including China−Pakistan Economic Corridor (CPEC) packages and the Barakahu Bypass project. Five construct-level features derived from an integrated Technology Acceptance Model and Technology−Organization−Environment (TAM-TOE) survey instrument were used to classify stakeholders into High, Moderate, and Low readiness tiers. XGBoost achieved the best classification performance (accuracy = 93%, macro F1 = 0.93), followed by RF (91%, F1 = 0.91) and SVM (87%, F1 = 0.87). The convergent performance across three structurally different algorithm families indicates that the readiness signal reflects a consistent attitudinal pattern rather than an artifact of any single modeling assumption. Feature importance analysis consistently identified Perceived Benefits (32%) and Technology Awareness (25%) as the dominant predictive features, followed by Organizational Readiness (20%), Perceived Barriers (15%), and Respondent Profile (8%). Attitudinal readiness mapping classified 62% of stakeholders as High readiness, 28% as Moderate, and 10% as Low, providing an exploratory attitudinal segmentation framework to assist construction managers in prioritizing capacity-building investments, subject to longitudinal behavioral validation. The study also finds that awareness of digital technology consistently outpaces Organizational Readiness for implementation, a pattern consistent with findings from analogous developing-country construction contexts.