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Author

Khaled A. Alrasheed

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Open access Nov 2026

Compliance-Oriented Leaders’ Methods for Fostering Construction Innovation through Legal Adaptability, Digital Integration, and Interagency Collaboration in Public Infrastructure Projects

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 · 0 citations
Open access Jul 2026

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

A model 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, and offers strong empirical support for applying CFT in AI adoption contexts.

A. Waqar, Khaled A. Alrasheed, Azlan Shah Ali et al. · 1 citation

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