Enhancing trust and decision-making in AI-driven construction: Applying cognitive fit theory to interface clarity and alignment.

Abstract

As artificial intelligence (AI) systems increasingly support decision-making in the construction sector, understanding the cognitive mechanisms behind user adoption is essential. Based on Cognitive Fit Theory (CFT), the following research develops and validates a model to examine how interface clarity, cognitive-technical alignment, algorithmic reliability, and decision explainability collectively influence behavioral intent to adopt AI-based decision support tools. Data were gathered from 206 construction professionals utilizing a structured questionnaire and assessed utilizing Partial Least Squares Structural Equation Modeling (PLS-SEM). Results assure that interface clarity and cognitive alignment greatly evolved perceived algorithmic reliability, which then strongly predicts behavioral intent. Decision explainability perception was discovered to mitigate the association among observed reliability and adoption intent, indicating that transparent AI reasoning strengthens the trust-intention link. Furthermore, perceived algorithmic reliability mediates the influence of both interface clarity and cognitive alignment on behavioral intent. The study offers strong empirical support for applying CFT in AI adoption contexts, especially in high-risk, complex environments such as construction. These insights inform the design of cognitively aligned AI interfaces to foster trust, enhance interpretability, and promote sustainable adoption of intelligent systems. Implications for AI interface design, construction technology implementation, and future research in human-AI interaction are discussed.