298
Views
0
CrossRef citations to date
0
Altmetric
Articles

A multi-method study of the emotional mechanism linking seaside destination attributes and tourists’ revisit intention

, &
Pages 1834-1851 | Received 15 Apr 2023, Accepted 12 May 2023, Published online: 23 May 2023
 

ABSTRACT

The advent of the post-pandemic era has increased the emotional rewards destinations need to provide. However, the psychological process of generating tourists’ revisit intention remains unclear in research on destination attributes and revisit intention. Existing research has many drawbacks, suggesting important research gaps, including using generalized mediating variables, focusing on conventional tourism scenarios, and overreliance on regression-based techniques. This study fills the gaps using multiple methods (qualitative and quantitative methods and fuzzy-set qualitative comparative analysis) to examine the linkage between seaside destination attributes and tourists’ revisit intentions using Sanya, China, as a case study. The innovations of this study include the detailed emotional path of tourists’ revisit intention, the use of multiple methods, and the identification of the critical emotional variable. Managerial implications about marketing and service entry point are also provided.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

This work was supported by the Major Humanities and Social Sciences Research Projects in Zhejiang higher education institutions [grant number 2023QN078].

Log in via your institution

Log in to Taylor & Francis Online

PDF download + Online access

  • 48 hours access to article PDF & online version
  • Article PDF can be downloaded
  • Article PDF can be printed
USD 53.00 Add to cart

Issue Purchase

  • 30 days online access to complete issue
  • Article PDFs can be downloaded
  • Article PDFs can be printed
USD 273.00 Add to cart

* Local tax will be added as applicable

Related Research

People also read lists articles that other readers of this article have read.

Recommended articles lists articles that we recommend and is powered by our AI driven recommendation engine.

Cited by lists all citing articles based on Crossref citations.
Articles with the Crossref icon will open in a new tab.