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Artificial Intelligence‐Driven Transformation of China's Tourism Sector: The FSC ‐ MSC Gender Superiority Ladder Model for Managing Gendered Employee Competency Advantages

Qinlan Chen et al.

International Journal of Tourism Research2026https://doi.org/10.1002/jtr.70216article
AJG 2ABDC A
Weight
0.50

Abstract

This paper conducts a comprehensive analysis of China's tourism sector under artificial intelligence (AI) influence, evolving gender roles, professional competencies, and gendered decision‐making. The research focuses on identifying gender factors: female service competencies (FSC) and male service competencies (MSC). The study examined 30 tourism companies from 683 organizations in Fujian Province. Data were analyzed using Student's t ‐test, binomial test, and one‐way ANOVA. Results revealed significant gender‐related variations in professional competencies. Women demonstrate superior performance in 23 variables. Men showed advantages in 10 variables, while 14 variables exhibit no significant gender differences. The study introduces the novel FSC‐MSC Gender Superiority Ladder Model. The findings contribute to tourism theory by clarifying how AI influences competency formation and gender roles in the sector. Practical applications include optimized employment strategies and enhanced service quality in AI‐driven tourism environments. This research provides insights for global tourism stakeholders adapting to technological transformation and evolving workforce dynamics.

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https://doi.org/https://doi.org/10.1002/jtr.70216

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@article{qinlan2026,
  title        = {{Artificial Intelligence‐Driven Transformation of China's Tourism Sector: The FSC ‐ MSC Gender Superiority Ladder Model for Managing Gendered Employee Competency Advantages}},
  author       = {Qinlan Chen et al.},
  journal      = {International Journal of Tourism Research},
  year         = {2026},
  doi          = {https://doi.org/https://doi.org/10.1002/jtr.70216},
}

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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

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