Understanding Tourists Relationship Quality Using a Machine Learning ApproachUnderstanding Tourists Relationship Quality
Authors: 
Dawei Shang, Yufang ChiAuthors Info & Claims
Dawei Shang, Yufang ChiAuthors Info & ClaimsPages 210 - 214
Abstract
From the perspective of segmented industry sectors, the significance of big data economy and information management in the tourism industry cannot be underestimated. However, research on non-linear analysis of the relationship between tourists relationship quality and its influencing factors is still limited. This study examines the impact of tourist perceived value on relationship quality using neural network methods in the field of information management. Exploring the non-linear relationship used cross validation and independent variable importance analysis. The Root Mean Square Error (RMSE) test results show that the model evaluation in this study is effective and acceptable. The results show that perceived value plays the important nonlinear role in the relationship quality. The analysis of the importance of independent variables shows that results shows that perceived functional value, perceived cost effectiveness value, and perceived emotional value have the greatest nonlinear impact on relationship quality. Meanwhile, the non-linear impact of perceived social value and perceived environmental value on relationship quality is relatively small. This study contributes to improving the quality of tourist destinations, various tourism management themes, and tourist relationships, flourishing the tourism market, and promoting the sustainable development of the tourism industry in information management intelligence era.
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- Understanding Tourists Relationship Quality Using a Machine Learning ApproachUnderstanding Tourists Relationship Quality
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Published In
December 2023
917 pages
ISBN:9798400716669
DOI:10.1145/3659211
Copyright © 2023 ACM.
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Association for Computing Machinery
New York, NY, United States
Publication History
Published: 29 May 2024
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BDEIM 2023
BDEIM 2023: 2023 4th International Conference on Big Data Economy and Information Management
December 8 - 10, 2023
Zhengzhou, China
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