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<p>In the context of shared wealth, the tourism industry, as an essential economic driver, the quality improvement of its human resources is crucial for promoting the development of the industry and realizing the balance of social wealth. In this study, a set of evaluation systems for tourism professionals is constructed based on the competency iceberg model, and the index weights are calculated using the hierarchical single sorting method. Further, the B.P. neural network evaluation method was improved by particle swarm optimization algorithm, and PSO-SVM neural network model was created to evaluate tourism professionals. Taking City A as a case study, the development of tourism industry and its deficiencies were deeply analyzed. The results show that about 36.40% of the residents believe that developing the tourism industry helps improve the living standard and promote shared prosperity. In comparison, 48.36% of the residents reflect the lack of local tourism professionals. In the comprehensive evaluation of tourism talents by individuals, residents and tourists, the mean scores of the indicators of responsibility, work motivation and complete psychological ability were high, respectively 4.49, 4.53 and 4.70.The study not only highlights the crucial role of tourism professionals in promoting the development of tourism industry, but also provides empirical evidence and solid suggestions for future talent cultivation and development strategies in tourism industry.</p>
</abstract>

In the context of shared wealth, the tourism industry, as an essential economic driver, the quality improvement of its human resources is crucial for promoting the development of the industry and realizing the balance of social wealth. In this study, a set of evaluation systems for tourism professionals is constructed based on the competency iceberg model, and the index weights are calculated using the hierarchical single sorting method. Further, the B.P. neural network evaluation method was improved by particle swarm optimization algorithm, and PSO-SVM neural network model was created to evaluate tourism professionals. Taking City A as a case study, the development of tourism industry and its deficiencies were deeply analyzed. The results show that about 36.40% of the residents believe that developing the tourism industry helps improve the living standard and promote shared prosperity. In comparison, 48.36% of the residents reflect the lack of local tourism professionals. In the comprehensive evaluation of tourism talents by individuals, residents and tourists, the mean scores of the indicators of responsibility, work motivation and complete psychological ability were high, respectively 4.49, 4.53 and 4.70.The study not only highlights the crucial role of tourism professionals in promoting the development of tourism industry, but also provides empirical evidence and solid suggestions for future talent cultivation and development strategies in tourism industry.

The promotion of common wealth through tourism industry development: the mediating role of tourism professionals

Applied Mathematics and Nonlinear Sciences

This work is licensed under the Creative Commons Attribution 4.0 International License.

In the context of shared wealth, the tourism industry, as an essential economic driver, the quality improvement of its human resources is crucial for promoting...

The promotion of common wealth through tourism industry development: the mediating role of tourism professionals

Online veröffentlicht: 26. Feb. 2024

Seitenbereich: -

Eingereicht: 15. Jan. 2024

Akzeptiert: 25. Jan. 2024

DOI: https://doi.org/10.2478/amns-2024-0596

Schlüsselwörter
Competency iceberg model, Hierarchical single sorting, PSO-SVM, Particle swarm optimization, Tourism talents

© 2024 Xiaomin Wang, published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

The promotion of common wealth through tourism industry development: the mediating role of tourism professionals

Online veröffentlicht: 26. Feb. 2024

Seitenbereich: -

Eingereicht: 15. Jan. 2024

Akzeptiert: 25. Jan. 2024

DOI: https://doi.org/10.2478/amns-2024-0596

SchlüsselwörterCompetency iceberg model, Hierarchical single sorting, PSO-SVM, Particle swarm optimization, Tourism talents

© 2024 Xiaomin Wang, published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Schlüsselwörter
Competency iceberg model, Hierarchical single sorting, PSO-SVM, Particle swarm optimization, Tourism talents