Year: 2025
Type: article
Abstract: The Kingdom of Saudi Arabia has emerged as a global hub for large-scale events, driven by Vision 2030's strategic goals to diversify the economy and position the nation as a leading destination for tourism, culture, and business. These events showcase Saudi Arabia’s rich cultural heritage and progressive identity and also play a pivotal role in fostering economic growth and international recognition. Nevertheless, the increasing complexity and scope of these events have exposed substantial deficiencies in conventional risk management strategies, which frequently lack the predictive capabilities, scalability, and agility necessary to confront contemporary challenges. This study suggests a conceptual framework that optimizes risk management in the events sector by utilizing cutting-edge technologies, including artificial intelligence (AI), the Internet of Things (IoT), and real-time analytics. The study identifies user requirements and preferences by utilizing a User-centered Design (UCD) approach and Action Research Methodology to gain insights from key stakeholders, such as risk managers, event organizers, and technology professionals. The results underscore the significance of collaborative tools, AI-powered decision-making, customizable features, and dual-format accessibility in the promotion of proactive risk management. While this research does not offer a fully operational framework, it offers practical suggestions for the creation of innovative solutions that enhance safety, efficiency, and inclusivity in the planning and execution of events. The suggested framework establishes a basis for future advancements, tackling practical issues while conforming to Saudi Arabia's aspiration to emerge as a global leader in the events industry.
Authors: Ahlam Madani, Shrooq Banunnah, Linah Serri, Renad Ghalib, Hanouf alghamdi, Dr. Samer Yaghmour
Institution: King Abdulaziz University
Language: English
Subfield: Sociology and Political Science
Field: Social Sciences
Domain: Social Sciences
Open Access status: hybrid
OpenAlex ID: W4414409398
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