JOURNAL ARTICLE
A network agent‐based model for Moroccan international tourism: Individual tourist behaviour and decision‐making processes.
Published In: Systems Research & Behavioral Science, 2025, v. 42, n. 3. P. 859 1 of 3
Database: Business Source Ultimate 2 of 3
Authored By: Jebraoui, Smahane; Nemiche, Mohamed; Hafidi, Bezza 3 of 3
Abstract
The tourist markets in Moroccan cities are characterized by a dynamic interplay of collaboration and competition. Additionally, the behaviour of tourists within this network is influenced by a multitude of factors, such as their preferences and social influence. The aim of this study is to explore the dynamics of Moroccan international tourism and how destinations can adapt to internal and external changes. To achieve this goal, we propose a network agent‐based model incorporating decision‐making processes, thereby depicting tourist behaviour. The model can assist in identifying areas that require improvement to enhance the overall tourist experience and ensure the long‐term sustainability of Morocco's tourism industry. In this study, we simulated four scenarios of collaboration and competition among destinations in our study: (i) The simulation of inbound Moroccan tourism in 2016, (ii) the opening of a new port city, (iii) the creation of a new connection between two destinations, and (iv) the increasing of tourist attractiveness in a specific destination. The findings of the study reveal that the collaboration‐competition relationships between destinations are changing over time and the tourist markets of destinations are not isolated from each other. [ABSTRACT FROM AUTHOR]
Additional Information
- Source:Systems Research & Behavioral Science. 2025/05, Vol. 42, Issue 3, p859
- Document Type:Article
- Subject Area:Sports and Leisure
- Publication Date:2025
- ISSN:1092-7026
- DOI:10.1002/sres.3004
- Accession Number:185257075
- Copyright Statement:Copyright of Systems Research & Behavioral Science is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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