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<p>The global tourism industry’s digital transformation necessitates systematic understanding of how users navigate interconnected platform ecosystems, particularly in emerging markets where unique institutional constraints shape user behavior patterns. This study addresses the critical gap in comprehending structural dynamics within digital tourism ecosystems by applying Social Network Analysis to examine platform interdependencies and user transition flows. The research employs a four-phase methodological framework encompassing data collection, network formation, community detection, and structural analysis to investigate Iran’s digital tourism ecosystem. Using Alexa traffic data and expert validation, 162 tourism-related websites were identified and analyzed through directed network modeling. The Louvain modularity optimization algorithm was applied to detect community structures, while weighted edge analysis quantified inter-platform user transitions. The analysis revealed eight distinct service communities representing functionally cohesive clusters including ticket booking, accommodation services, location-based platforms, and culinary offerings. Community detection achieved a modularity score of 0.634, indicating robust structural segmentation. Inter-community flow patterns demonstrated that ticket and tour booking platforms function as central orchestration hubs, facilitating 31.9% of ecosystem activity and exhibiting strong directional flows to complementary services. Conversely, specialized services such as online taxi platforms and culinary websites showed minimal integration with core planning communities, representing structural gaps in ecosystem connectivity. These findings provide actionable insights for service providers seeking enhanced platform interoperability, investors identifying innovation opportunities, and policymakers designing supportive regulatory frameworks. The study contributes a replicable analytical framework for mapping tourism ecosystems globally while advancing Service Ecosystem Theory through empirical demonstration of platform-mediated value co-creation in digitally constrained environments.</p>

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Exploring the digital tourism ecosystem: unveiling user behavior through social network analysis

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年2月28日

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  • Mehrdad Maghsoudi,
  • Saeid Aliakbar,
  • Amirmahdi Mohammadi

摘要

The global tourism industry’s digital transformation necessitates systematic understanding of how users navigate interconnected platform ecosystems, particularly in emerging markets where unique institutional constraints shape user behavior patterns. This study addresses the critical gap in comprehending structural dynamics within digital tourism ecosystems by applying Social Network Analysis to examine platform interdependencies and user transition flows. The research employs a four-phase methodological framework encompassing data collection, network formation, community detection, and structural analysis to investigate Iran’s digital tourism ecosystem. Using Alexa traffic data and expert validation, 162 tourism-related websites were identified and analyzed through directed network modeling. The Louvain modularity optimization algorithm was applied to detect community structures, while weighted edge analysis quantified inter-platform user transitions. The analysis revealed eight distinct service communities representing functionally cohesive clusters including ticket booking, accommodation services, location-based platforms, and culinary offerings. Community detection achieved a modularity score of 0.634, indicating robust structural segmentation. Inter-community flow patterns demonstrated that ticket and tour booking platforms function as central orchestration hubs, facilitating 31.9% of ecosystem activity and exhibiting strong directional flows to complementary services. Conversely, specialized services such as online taxi platforms and culinary websites showed minimal integration with core planning communities, representing structural gaps in ecosystem connectivity. These findings provide actionable insights for service providers seeking enhanced platform interoperability, investors identifying innovation opportunities, and policymakers designing supportive regulatory frameworks. The study contributes a replicable analytical framework for mapping tourism ecosystems globally while advancing Service Ecosystem Theory through empirical demonstration of platform-mediated value co-creation in digitally constrained environments.