  The contemporary tourism landscape is undergoing rapid digitization,
necessitating a nuanced comprehension of online user behavior to guide
data-driven decision-making. This research bridges an existing gap by
investigating the tourism website ecosystem through social network analysis. It
focuses specifically on inter-website communication patterns based on user
navigation. Data mining facilitates the identification of 162 core Iranian
tourism websites, which are visualized as an interconnected network with
websites as nodes and user transitions as weighted directed edges. By
implementing community detection, eight key clusters are discerned,
encompassing domains like ticket/tour bookings, accommodations, location
services, and cuisine. Further analysis of inter-community relationships
reveals website groupings frequently accessed together by users, highlighting
complementary services sought during travel planning. The research derives
invaluable insights into user preferences and information propagation within
the tourism ecosystem. The methodology and findings contribute original
perspectives to academia while offering pragmatic strategic recommendations to
industry stakeholders like service providers, investors, and policymakers. This
pioneering exploration of latent user behavior patterns advances comprehension
of the evolving digital tourism landscape in Iran. It contributes pathways
toward a sustainable future vision of the ecosystem, guiding stakeholders in
targeted decision-making based on empirical evidence derived from social
network analysis of websites and consumption patterns. The innovative
methodology expands the toolkit for data-driven tourism research within
academia.
