Innovation in public infrastructure is often perceived as incompatible with compliance-driven and bureaucratic environments. This study tests that assumption by applying institutional theory to investigate how compliance-oriented leadership (COL) can positively influence construction innovation outcomes (CIOs). A conceptual model was developed and tested using structural equation modeling (SEM) on data from 233 professionals engaged in public infrastructure projects. The model examines the mediating roles of legal adaptability (LA), interagency collaboration (IAC), and digital technology integration (DTI), along with the moderating effects of bureaucratic rigidity. Results reveal that COL enhances innovation indirectly through both collaborative and technological pathways, with LA acting as a critical enabler. Furthermore, bureaucratic rigidity (BR) exerts a dual influence hindering digital innovation while amplifying the effect of IAC. These findings offer new theoretical insights into how institutional structures both constrain and enable innovation. The study contributes to the literature by reframing compliance and rigidity not as barriers, but as context-dependent mechanisms that can support structured innovation in public sector projects. Practical implications include leadership strategies and policy adjustments to harness innovation without undermining institutional legitimacy.
A. Waqar, Khaled A. Alrasheed, Waqas Ahmed· Journal of Legal Affairs and...· 0 citations
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.
A. Waqar, Khaled A. Alrasheed, Azlan Shah Ali et al.· Acta Psychologica· 1 citation
