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Bridging Perceived and Projected Destination Images: A Machine Learning-Driven Systemic Network Analysis

Rong Lin et al.

Journal of Hospitality and Tourism Research2026https://doi.org/10.1177/10963480261432564article
AJG 2ABDC A
Weight
0.50

Abstract

Understanding the differences between the projected and perceived images of destinations plays a critical role in destination marketing and the fulfillment of tourists' expectations. However, existing research still lacks a systematic explanation of the network-level differences in destination image, and tends to overlook the underlying influence mechanisms among its factors. Using Macau as a case study, this research develops a decision-making model to deepen the understanding of interrelationships among destination image factors, and to explore the structural network differences between images. To achieve this, the research employs Latent Dirichlet Allocation (LDA), quadrant analysis, and the Decision-Making Trial and Evaluation Laboratory (DEMATEL) method. This study offers a systematic perspective to narrow the gap between the projected and perceived image. The results enable DMOs to gain deeper insights into tourists’ needs and perceptions, as well as to identify the key factors shaping destination image, and the internal influence relationships among its components.

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https://doi.org/https://doi.org/10.1177/10963480261432564

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@article{rong2026,
  title        = {{Bridging Perceived and Projected Destination Images: A Machine Learning-Driven Systemic Network Analysis}},
  author       = {Rong Lin et al.},
  journal      = {Journal of Hospitality and Tourism Research},
  year         = {2026},
  doi          = {https://doi.org/https://doi.org/10.1177/10963480261432564},
}

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Evidence weight

0.50

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.50 × 0.4 = 0.20
M · momentum0.50 × 0.15 = 0.07
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.