Enhancing Logistical Efficiency in Public Institutions through AI: A Managerial Framework for Regulatory and Technological Integration

Hadi Al Najdawi, Mohamed; Al Ghurabli, Zainab; Raafat, Raghda; Aburayya, Ahmad

doi:10.22068/ijiepr.36.3.2459

Volume 36, Issue 3 (IJIEPR 2025) IJIEPR 2025, 36(3): 81-92 | Back to browse issues page

‎ 10.22068/ijiepr.36.3.2459

Mendeley

Zotero

RefWorks

Hadi Al Najdawi M, Al Ghurabli Z, Raafat R, Aburayya A. Enhancing Logistical Efficiency in Public Institutions through AI: A Managerial Framework for Regulatory and Technological Integration. IJIEPR 2025; 36 (3) :81-92
URL: http://ijiepr.iust.ac.ir/article-1-2459-en.html

Enhancing Logistical Efficiency in Public Institutions through AI: A Managerial Framework for Regulatory and Technological Integration

Mohamed Hadi Al Najdawi¹

, Zainab Al Ghurabli²

, Raghda Raafat³

, Ahmad Aburayya²

1- Mohamed Hadi Al Najdawi Associate Professor, College of Humanities, City University Ajman, Ajman, UAE , m.najdawi@cu.ac.ae
2- Assistant Professor, College of Business, City University Ajman, Ajman, UAE
3- Associate Professor, College of Law, City University Ajman, Ajman, UAE

Abstract: (1034 Views)

This study investigates regulatory gaps impeding artificial intelligence (AI) integration in public sector logistics, revealing how fragmented legislative frameworks hinder operational efficiency and innovation. Through a quantitative cross-sectional survey of 182 legal professionals, public employees, and AI/legal scholars using stratified purposive sampling and validated instruments (Cronbach’s α=0.985) we identified statistically significant stakeholder divergences (*p*<0.05) via χ² tests and Cramer’s V effect sizes. Key findings demonstrate that: (1) legal experts prioritize regulatory clarity deficits (M=4.62), while public staff emphasize institutional resistance (M=4.41); (2) human capital training is systematically undervalued (M=2.57, V=0.26) despite its theoretical importance; and (3) while regulation enhances operational efficiency (M=4.36), it paradoxically inhibits logistical innovation (M=2.48), exposing a critical innovation-governance disconnect. The study’s core contribution, a Dynamic Institutional Alignment Framework, resolves this tension through three pillars: human-centered regulatory design integrating legal-technical dimensions, adaptive policy sandboxes synchronized with AI advancement cycles, and stakeholder-specific implementation pathways. By embedding institutional adaptability within global compliance standards (EU AI Act, OECD Principles), this framework advances AI governance theory and offers public institutions actionable strategies for balancing technological advancement with accountability.

Keywords: Logistics, Law, Institutions, Technology, Artificial Intelligence

Full-Text [PDF 348 kb] (368 Downloads)

Type of Study: Research | Subject: Logistic & Apply Chain
Received: 2025/04/10 | Accepted: 2025/07/16 | Published: 2025/09/8