Place-bound memory and people-bound memory affecting destination loyalty in the food tourism industry


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It is significant to promote food tourism behavior. This paper aims to build a comprehensive behavior model of tourists suitable for food tourism to promote such tourism in Chongqing city, China. The paper deals with the place-bound and people-bound memories affecting tourist satisfaction, perceived holiday quality of life, and experience loyalty in the food tourism industry. The data were collected from 413 tourists who have experience traveling to Chongqing city through the online questionnaire. Multi-layer perceptron artificial neural network simulation provides a basis for factor ranking for structural equation modeling. The results show that place-bound and people-bound factors can predict experience memory, experience satisfaction, holiday quality of life, experience loyalty, and destination loyalty. In addition, it reveals the mediating role of experiential memory and contributes to the process of memory formation. Place-bound factors emphasize the vital role of marketing and social media information. They are consistent with the actual food experience of tourists at the destination. People-bound factors explained the different aspects of the food experience. The noteworthy aspects are intelligence, emotion, sense, behavior, and natural flow experience. The research results show that tourists, local governments, and tourism departments should focus on developing memory structures with local characteristics and improving quality and experienced food tourism destinations so that tourists can gain more sensory experiences or other experiences to attract more food tourists.

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    • Figure 1. Conceptual model
    • Figure 2. Simulated neural network structure
    • Figure 3. Simulated SEM configuration
    • Figure 4. Attitude toward the importance of tourism
    • Figure 5. Comparing tourists with food as a primary motive or otherwise
    • Table 1. Reliability, discriminant and convergent validity of factors measurements
    • Table 2. Case processing summary
    • Table 3. Network information
    • Table 4. Independent variable importance
    • Table 5. Simulated SEM statistics
    • Table 6. Standardized path loadings of the hypotheses structure
    • Table 7. ANOVA and T-test results – 1
    • Table 8. ANOVA and T-test results – 2
    • Table 9. ANOVA and T-test results – 3
    • Conceptualization
      Xiangru Li, Hongmei Yang
    • Data curation
      Xiangru Li, Hongmei Yang
    • Formal Analysis
      Xiangru Li, Hongmei Yang
    • Investigation
      Xiangru Li, Hongmei Yang
    • Methodology
      Xiangru Li, Hongmei Yang
    • Project administration
      Xiangru Li, Hongmei Yang
    • Supervision
      Xiangru Li, Hongmei Yang
    • Validation
      Xiangru Li, Hongmei Yang
    • Visualization
      Xiangru Li, Hongmei Yang
    • Writing – original draft
      Xiangru Li, Hongmei Yang
    • Writing – review & editing
      Xiangru Li, Hongmei Yang