JOURNAL ARTICLE

Digital Economy in the Catering Service Industry: Photo Sharing Behavior on Social Media.

  • Published In: International Journal of Tourism Research, 2025, v. 27, n. 2. P. 1 1 of 3

  • Database: Business Source Ultimate 2 of 3

  • Authored By: Hua, Yidi; Chen, Kuan‐Ting; Long, Zhiqian 3 of 3

Abstract

This study aims to (a) identify the primary factors that influence tourists' decisions to share their restaurant experiences; (b) investigate the motivations and determinants that drive tourists to share photographs on social media related to the catering industry; and (c) assess the impact of tourists' positive electronic word‐of‐mouth communication on restaurants and the broader tourism sector. The research analyzed data from 362 questionnaires predominantly collected from Chinese respondents, employing SPSS and AMOS for the analysis and validation of the relationships among the variables. The findings indicate that food quality, service quality, and the overall atmosphere of the dining experience exert a direct positive influence on tourists' attitudes toward sharing food photographs. Additionally, motivations for positive e‐WOM include concern for others and the expression of positive emotions. The results of this study offer recommendations for restaurant leaders regarding strategic development, enabling them to formulate market‐competitive policies that enhance their comparative advantage. [ABSTRACT FROM AUTHOR]

Additional Information

  • Source:International Journal of Tourism Research. 2025/03, Vol. 27, Issue 2, p1
  • Document Type:Article
  • Subject Area:Business and Management
  • Publication Date:2025
  • ISSN:1099-2340
  • DOI:10.1002/jtr.70024
  • Accession Number:184769116
  • 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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