JOURNAL ARTICLE

How Travel Vlogs Contribute to Destination Marketing: A Comparison with DMO Promotional Videos and the Moderating Role of Destination Competitiveness.

  • Published In: International Journal of Tourism Research, 2024, v. 26, n. 5. P. 1 1 of 3

  • Database: Business Source Ultimate 2 of 3

  • Authored By: Zhou, Ying; Jo, WooMi; Flaherty, Joan; Li, Tongzhe 3 of 3

Abstract

This two‐part study examines how travel vlogs influence tourist behaviors and, consequently, their value in destination marketing. A convenience sample of 196 North Americans who belonged to Generation Y was collected via an online experiment. The first part adopted the Attention‐Interest‐Desire‐Action (AIDA) principle as the theoretical underpinning of how travel vlogs influence Gen Y travel behaviors, contrasting them with Destination Marketing Organization (DMO) promotional videos. It was found that travel vlogs impact tourist behavior by attracting tourists' attention, delivering realistic destination information, and inspiring them. The second part examined the relationship between destination competitiveness levels and willingness to pay (WTP), and the impact of travel vlogs and DMO promotional videos on this relationship. It was shown that destination competitiveness levels exert different impacts on WTP between travel vlogs and DMO promotional videos. This study enriches the tourism destination marketing literature and suggests that DMOs tailor their strategies based on destination competitiveness. [ABSTRACT FROM AUTHOR]

Additional Information

  • Source:International Journal of Tourism Research. 2024/09, Vol. 26, Issue 5, p1
  • Document Type:Article
  • Subject Area:Computer Science
  • Publication Date:2024
  • ISSN:1099-2340
  • DOI:10.1002/jtr.2755
  • Accession Number:180374666
  • 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.)

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