Terrorism clusters and inbound tourism flows: Unravelling the complex relationship using panel data and explainable machine learning
Abstract
The impact of terrorism clusters on inbound tourism flows remains underexplored. This study categorizes terrorism clusters into temporal, spatial, quantity, and casualty clusters and develops corresponding measurement tools. Using panel data from 152 countries and regions (2006–2019), we employ fixed-effects models and interpretable machine learning to investigate the complex relationship between terrorism clusters and inbound tourism flows. The findings reveal that all cluster types negatively impact inbound tourism flows, with casualty clusters exerting the greatest influence, followed by quantity and spatial clusters. Nonlinear relationships between inbound tourism flows and terrorism clusters exhibit threshold effects and inverted U-shaped relationships. Synergistic interactions among clusters, particularly those involving casualty clusters, significantly amplify the negative impact on inbound tourism flows. By elucidating these dynamics from a cluster perspective, this study advances the theoretical framework linking terrorism and inbound tourism flows and provides practical strategies for destination managers to mitigate the risks of terrorism clusters.
Document Type
Journal Article
Date of Publication
6-1-2026
Volume
114
Publication Title
Tourism Management
Publisher
Elsevier
School
School of Business and Law
RAS ID
84602
Funders
National Natural Science Foundation of China (42271243, 41971182)
Copyright
subscription content
Comments
Zhang, K., Xie, C., Zhu, H., Zhang, J., Yu, J., & Huang, S. (2025). Terrorism clusters and inbound tourism flows: Unravelling the complex relationship using panel data and explainable machine learning. Tourism Management, 114, 105355. https://doi.org/10.1016/j.tourman.2025.105355