Perception and Optimization Strategies for Museum Study Tour Experiences from the Mass Consumer Perspective
- DOI
- 10.2991/978-94-6463-770-0_52How to use a DOI?
- Keywords
- educational tourism; museums; study tour experience; mass consumer group; semantic network analysis
- Abstract
By investigating the perceived quality of museum study tour experiences, this study aims to propose optimization strategies to refine these perceptions. Online reviews (n = 2,983) from Ctrip were analyzed via Python and ROST, including high-frequency word statistics and sentiment analysis. This allowed the identification of hotspots, core themes, and emotional tendencies in the mass consumer group’s feedback on museum study tours, thereby assessing the perceived quality of their experiences. The perceived quality of museum study tour experiences is generally positive. However, challenges such as inconsistent service levels, irregular ticket booking practices, and inadequate crowd control persist. This study proposes five optimization strategies to address these shortcomings, aiming to improve the study tour experience and promote the healthy development of museum-based educational tourism.
- Copyright
- © 2025 The Author(s)
- Open Access
- Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.
Cite this article
TY - CONF AU - Yu Qin AU - Ping Huang PY - 2025 DA - 2025/06/26 TI - Perception and Optimization Strategies for Museum Study Tour Experiences from the Mass Consumer Perspective BT - Proceedings of the 2025 3rd International Conference on Digital Economy and Management Science (CDEMS 2025) PB - Atlantis Press SP - 468 EP - 474 SN - 2352-5428 UR - https://doi.org/10.2991/978-94-6463-770-0_52 DO - 10.2991/978-94-6463-770-0_52 ID - Qin2025 ER -