Analysis of Customer Satisfaction in Tourism Services Based on the Kano Model

Understanding customer needs is of great significance to enhance service quality and competitive advantage. However, for the tourism industry, it is still unclear how to mine service improvement strategies from tourist-generated online reviews. This paper aims to develop a data-driven approach to co...

Full description

Saved in:
Bibliographic Details
Published inSystems (Basel) Vol. 11; no. 7; p. 345
Main Authors Zhou, Kailin, Yao, Zhong
Format Journal Article
LanguageEnglish
Published Basel MDPI AG 01.07.2023
Subjects
Analysis
Attitudes
Back propagation networks
backpropagation neural network
Business competition
COVID-19
Customer relations
Customer satisfaction
Customer services
Dictionaries
Dirichlet problem
Kano model
Machine learning
Medical research
Mining
Neural networks
online review
online reviews
R&D
Research & development
Research methodology
Sales promotions
Sentiment analysis
Tourism
Tourist attractions
Travel industry
User generated content
China
Online AccessGet full text

Cover

More Information
Summary:Understanding customer needs is of great significance to enhance service quality and competitive advantage. However, for the tourism industry, it is still unclear how to mine service improvement strategies from tourist-generated online reviews. This paper aims to develop a data-driven approach to conduct a fine-grained dimension analysis of customer satisfaction with tourism services. First, this paper uses Latent Dirichlet Allocation to explore the key dimensions of tourist satisfaction from online reviews. Next, based on the Chinese sentiment dictionary, tourists’ emotional attitudes towards each service dimension can be identified. Then, the backpropagation neural network is used to measure the complex relationship between tourists’ sentiment orientations towards different dimensions and their satisfaction. Finally, according to the improved Kano model, multi-dimensional attribute classification is realized to support the strategic analysis of tourism service quality improvement. The proposed method is empirically verified through a real tourism review dataset. The results exhibit the theoretical and practical implications of our method.
ISSN:2079-8954
2079-8954
DOI:10.3390/systems11070345