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Explaining tourism behaviour with machine learning: A SHAP-based analysis of certification awareness and revisit intentions

Bidyut Kumar Ghosh

Tourism in Marine Environments2026https://doi.org/10.3727/216901926x17725264321419article
ABDC C
Weight
0.50

What the paper says

Tourist satisfaction is significantly influenced by both attraction features and supporting facilities. While prior studies underscore the importance of measuring satisfaction to sustain a destination’s competitiveness, limited attention has been paid to the role of environmental certifications in shaping revisit intentions. This study adopts a mixed-methods approach, combining qualitative insights from in-depth interviews with machine learning analysis of field survey data collected from visitors to Blue Flag-certified beaches. The findings reveal that certification enhances the perceived quality of beach destinations. However, its impact on tourists’ intention to revisit is contingent upon their awareness and understanding of the certification. The study highlights the importance of not only maintaining high environmental and service standards but also actively communicating the value of certification to beachgoers. These insights offer critical implications for policymakers and destination managers aiming to foster sustainable tourism through behavioural engagement and informed decision-making.

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https://doi.org/https://doi.org/10.3727/216901926x17725264321419

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@article{bidyut2026,
  title        = {{Explaining tourism behaviour with machine learning: A SHAP-based analysis of certification awareness and revisit intentions}},
  author       = {Bidyut Kumar Ghosh},
  journal      = {Tourism in Marine Environments},
  year         = {2026},
  doi          = {https://doi.org/https://doi.org/10.3727/216901926x17725264321419},
}

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Explaining tourism behaviour with machine learning: A SHAP-based analysis of certification awareness and revisit intentions

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Evidence weight

0.50

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.50 × 0.4 = 0.20
M · momentum0.50 × 0.15 = 0.07
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.