JOURNAL ARTICLE

Barriers to adoption of slow tourism: An innovation resistance theory perspective.

  • Published In: International Journal of Tourism Research, 2024, v. 26, n. 4. P. 1 1 of 3

  • Database: Business Source Ultimate 2 of 3

  • Authored By: Chauhan, Vishakha 3 of 3

Abstract

Slow tourism is a recent concept in the travel and tourism industry. It offers rejuvenation and immersion in local culture to travelers along with environmental and economic benefits. While slow tourist destinations are picking up the pace and interest of consumers, they also face a lot of resistance from consumers. However, existing literature on slow tourism has essentially focused on travelers' adoption intentions and motivations toward slow tourism, scarcely investigating the sources of such resistance. Addressing this pertinent gap, this study applies the innovation resistance theory (IRT) to examine the barriers to consumer adoption intention toward slow tourism. A mixed‐method research approach with in‐depth discussions with slow travelers and a large‐scale cross‐sectional survey of 350 travelers from India is used to test the proposed model. Findings advocate that the usage and image barriers are the prime inhibitors of slow tourism adoption. As a moderator, consumers' environmental concern and an individualistic culture dampens the strength of the association between the barriers and slow tourism adoption intentions. Results present important ramifications for slow tourism marketers and researchers. [ABSTRACT FROM AUTHOR]

Additional Information

  • Source:International Journal of Tourism Research. 2024/07, Vol. 26, Issue 4, p1
  • Document Type:Article
  • Subject Area:Sports and Leisure
  • Publication Date:2024
  • ISSN:1099-2340
  • DOI:10.1002/jtr.2689
  • Accession Number:179279418
  • Copyright Statement:Copyright of International Journal of Tourism Research is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Looking to go deeper into this topic? Look for more articles on EBSCOhost.