Patterns and Pathways: Applying Social Network Analysis to Understand User Behavior in the Tourism Industry Websites

TL;DR

This study uses social network analysis to explore user navigation patterns across Iranian tourism websites, revealing key clusters and insights into online behavior to inform industry strategies.

Contribution

It introduces a novel application of social network analysis to map and analyze user interactions among tourism websites in Iran, offering new insights into digital tourism behavior.

Findings

Identified 162 core tourism websites as a network of user transitions.

Detected eight key community clusters related to tourism services.

Revealed common pathways and relationships among different tourism service groups.

Abstract

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.