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The Impact of AI on Sustainable Development of Smart Tourism and Destination Marketing

A special issue of Sustainability (ISSN 2071-1050). This special issue belongs to the section "Tourism, Culture, and Heritage".

Deadline for manuscript submissions: 31 July 2024 | Viewed by 2061

Special Issue Editors


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Guest Editor
Smart Tourism Education Platform, College of Hotel and Tourism Management, Kyung Hee University, Seoul, Republic of Korea
Interests: smart tourism
Special Issues, Collections and Topics in MDPI journals

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Guest Editor
Faculty of Business Administration, University of Macau, Avenida da Universidade, Taipa, Macao SAR 999078, China
Interests: electronic word-of-mouth; social media marketing; consumer psychology; sensory marketing
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

There are new prospects for smart tourism and smart destination marketing owing to the rise of AI-powered language models like ChatGPT. In addition to helping tourists plan their trips, offering personalized recommendations, content creation, and customer services, and also reviewing behavior, these technologies can also be used to gather and analyze information on travelers' point of interests and travel habits for use in decision-making. Travelers can benefit from ChatGPT in a number of ways, including personalized travel itinerary suggestions, information about nearby sights and activities, and real-time language translations. These features can improve tourists' overall tourism experiences and make their trips more usefulness and ease of use and enjoyment. With the help of ChatGPT, tourism organizations may more effectively adjust their marketing strategies and campaigns by better understanding the needs and preferences of potential tourists in the context of smart destination marketing. As a result of ChatGPT's analysis of data from social media and other online sources, tourist companies can now create customized marketing messages that appeal to their target travel demographic and understand trends and patterns in traveler behavior. The sustainability of the tourist sector may be increased by the usage of ChatGPT in smart tourism and smart destination marketing. ChatGPT can help companies to lessen the environmental impact of travel by promoting more effective and sustainable travel habits and by offering more personalized recommendations and assistance with travel planning. In conclusion, the growth of smart tourism and smart destination marketing has been greatly facilitated by the creation of AI-powered language models like ChatGPT. These innovations can assist in improving tourists' overall travel experiences while also boosting the viability and profitability of tourism hotspots around the globe.

Prof. Dr. Chulmo Koo
Dr. Lawrence Hoc Nang Fong
Guest Editors

Manuscript Submission Information

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Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sustainability is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • smart tourism
  • smart destination marketing
  • ChatGPT
  • AI
  • sustainable tourism

Published Papers (2 papers)

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Research

22 pages, 1049 KiB  
Article
MMKG-PAR: Multi-Modal Knowledge Graphs-Based Personalized Attraction Recommendation
by Gengyue Zhang, Hao Li, Shuangling Li, Beibei Wang and Zhixing Ding
Sustainability 2024, 16(5), 2211; https://doi.org/10.3390/su16052211 - 06 Mar 2024
Viewed by 558
Abstract
As the tourism industry rapidly develops, providing personalized attraction recommendations has become a hot research area. Knowledge graphs, with their rich semantic information and entity relationships, not only enhance the accuracy and personalization of recommendation systems but also energize the sustainable development of [...] Read more.
As the tourism industry rapidly develops, providing personalized attraction recommendations has become a hot research area. Knowledge graphs, with their rich semantic information and entity relationships, not only enhance the accuracy and personalization of recommendation systems but also energize the sustainable development of the tourism industry. Current research mainly focuses on single-modal knowledge modeling, limiting the in-depth understanding of complex entity characteristics and relationships. To address this challenge, this paper proposes a multi-modal knowledge graphs-based personalized attraction recommendation (MMKG-PAR) model. We utilized data from the “Travel Yunnan” app, along with users’ historical interaction data, to construct a collaborative multi-modal knowledge graph for Yunnan tourist attractions, which includes various forms such as images and text. Then, we employed advanced feature extraction methods to extract useful features from multi-modal data (images and text), and these were used as entity attributes to enhance the representation of entity nodes. To more effectively process graph-structured data and capture the complex relationships between nodes, our model incorporated graph neural networks and introduced an attention mechanism for mining and inferring higher-order information about entities. Additionally, MMKG-PAR introduced a dynamic time-weighted strategy for representing users, effectively capturing and precisely describing the dynamics of user behavior. Experimental results demonstrate that MMKG-PAR surpasses existing methods in personalized recommendations, providing significant support for the continuous development and innovation in the tourism industry. Full article
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25 pages, 6197 KiB  
Article
An Aspect-Based Review Analysis Using ChatGPT for the Exploration of Hotel Service Failures
by Nayoung Jeong and Jihwan Lee
Sustainability 2024, 16(4), 1640; https://doi.org/10.3390/su16041640 - 16 Feb 2024
Viewed by 1012
Abstract
In this study, we employed ChatGPT, an advanced large language model, to analyze hotel reviews, focusing on aspect-based feedback to understand service failures in the hospitality industry. The shift from traditional feedback analysis methods to natural language processing (NLP) was initially hindered by [...] Read more.
In this study, we employed ChatGPT, an advanced large language model, to analyze hotel reviews, focusing on aspect-based feedback to understand service failures in the hospitality industry. The shift from traditional feedback analysis methods to natural language processing (NLP) was initially hindered by the complexity and ambiguity of hotel review texts. However, the emergence of ChatGPT marks a significant breakthrough, offering enhanced accuracy and context-aware analysis. This study presents a novel approach to analyzing aspect-based hotel complaint reviews using ChatGPT. Employing a dataset from TripAdvisor, we methodically identified ten hotel attributes, establishing aspect–summarization pairs for each. Customized prompts facilitated ChatGPT’s efficient review summarization, emphasizing explicit keyword extraction for detailed analysis. A qualitative evaluation of ChatGPT’s outputs demonstrates its effectiveness in succinctly capturing crucial information, particularly through the explicitation of key terms relevant to each attribute. This study further delves into topic distributions across various hotel market segments (budget, midrange, and luxury), using explicit keyword analysis for the topic modeling of each hotel attribute. This comprehensive approach using ChatGPT for aspect-based summarization demonstrates a significant advancement in the way hotel reviews can be analyzed, offering deeper insights into customer experiences and perceptions. Full article
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