Integrating Main Path Analysis and Hybrid MCDM to Evaluate Medical Aesthetic Tourism

Medical tourism is among the fastest-growing segments of the global travel industry, and Taiwan’s internationally recognised healthcare system positions it well to compete in this market. Yet the companies best placed to lead this development have traditionally been identified through online word-of-mouth or personal recommendation rather than objective evidence, and performance criteria are rarely weighted to reflect both subjective opinion and objective data. This study addresses both gaps with a hybrid framework that uses main path analysis to identify, objectively, the companies most active in medical aesthetic technology, and integrates the Best–Worst Method (BWM), Entropy, Game Theory, and Grey-based TOPSIS to weight a four-dimensional, seventeen-criterion framework and rank company performance in medical tourism. An empirical application to seven Taiwanese companies engaged in medical aesthetics alongside medical tourism, drawing on industry expert assessments, shows that Service and Healthcare Resources is the most influential dimension, and that cloud-based customisation, multilingual personnel, and professional in-trip medical care are the most important criteria. Sensitivity analysis across nine weighting scenarios and comparison against four alternative ranking methods confirm the robustness of the ranking. The framework offers a replicable, objective template for evaluating medical tourism performance and provides managerial guidance for companies and policymakers seeking to strengthen Taiwan’s position in this market.

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Publication Details

Journal
Applied Sciences
Published
2026-08-27
DOI
https://doi.org/10.3390/app16178545
Primary Topic
Global Healthcare and Medical Tourism
Type
article
Field-Weighted Citation Impact
0.00
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article

Integrating Main Path Analysis and Hybrid MCDM to Evaluate Medical Aesthetic Tourism

James J.H. Liou, Hsiang-Yue Chen, Ching-Yun Huang, Kai-Ying Chen
Applied Sciences
Global Healthcare and Medical Tourism
article

Integrating Main Path Analysis and Hybrid MCDM to Evaluate Medical Aesthetic Tourism

James J.H. Liou, Hsiang-Yue Chen, Ching-Yun Huang, Kai-Ying Chen
article en

Abstract

Medical tourism is among the fastest-growing segments of the global travel industry, and Taiwan’s internationally recognised healthcare system positions it well to compete in this market. Yet the companies best placed to lead this development have traditionally been identified through online word-of-mouth or personal recommendation rather than objective evidence, and performance criteria are rarely weighted to reflect both subjective opinion and objective data. This study addresses both gaps with a hybrid framework that uses main path analysis to identify, objectively, the companies most active in medical aesthetic technology, and integrates the Best–Worst Method (BWM), Entropy, Game Theory, and Grey-based TOPSIS to weight a four-dimensional, seventeen-criterion framework and rank company performance in medical tourism. An empirical application to seven Taiwanese companies engaged in medical aesthetics alongside medical tourism, drawing on industry expert assessments, shows that Service and Healthcare Resources is the most influential dimension, and that cloud-based customisation, multilingual personnel, and professional in-trip medical care are the most important criteria. Sensitivity analysis across nine weighting scenarios and comparison against four alternative ranking methods confirm the robustness of the ranking. The framework offers a replicable, objective template for evaluating medical tourism performance and provides managerial guidance for companies and policymakers seeking to strengthen Taiwan’s position in this market.

Applied SciencesVol. 16(17)
National Taipei University of Technology (TW)
Decent work and economic growth
Openalex Percentile: Top 5%
Global Healthcare and Medical Tourism
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Integrating Main Path Analysis and Hybrid MCDM to Evaluate Medical Aesthetic Tourism — James J.H. Liou, Hsiang-Yue Chen, et al. · Applied Sciences (2026) | TGRS Research Map | TGRS