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Tourists' perceptions of smart tourism services in a Chinese theme park: an ipa-based study of ningbo fangte oriental dawn

Type of paper: Research Article

Author

Hongfei Bao

Corresponding Author

Affiliation: College of Science & Technology Ningbo University

Email: baohongfei@naver.com

https://orcid.org/0000-0002-2569-3902

Received:April 29, 2026
Revised:May 26, 2026
Accepted:May 26, 2026
Published:May 31, 2026

How to Cite

Bao, H. (2026). Tourists' perceptions of smart tourism services in a Chinese theme park: an ipa-based study of ningbo fangte oriental dawn. CACTUS - Journal of Tourism Business, Management and Economics, 8 (1), 59-72.

Based on the official APA guide. Review the full set of examples.

© 2026 The Author(s);

Licensed under CC BY-NC 4.0

Abstract

Smart tourism has become an important means of enhancing the competitiveness and service quality of theme parks. However, limited research has explored how visitors perceive the importance and performance of specific smart service components in Chinese theme parks. This study assesses tourists' perceptions of smart tourism services at Ningbo Fangte Oriental Dawn using a case study approach, a questionnaire survey, and the Importance-Performance Analysis (IPA) framework. A total of 387 valid responses from actual park visitors were analyzed. The results indicate that contactless payments, electronic map updates, ticketing convenience, social media publicity, and intelligent parking were perceived as both important and satisfactory. In contrast, mobile tourism services, online ticket discounts, and visitor-flow diversion were placed in the "Concentrate Here" quadrant, highlighting areas that require managerial attention. The findings provide clear decision-making references for theme park managers to optimize operations and allocate resources effectively, offering valuable practical implications for aligning smart tourism service investments with tourists' perceived value

Keywords

theme parksmart tourismtourist satisfactionimportance-performance analysis

JEL Classification

Z30L83M31C83

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