Special Issue "Big Data and Sustainability in the Tourism Industry"

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

Deadline for manuscript submissions: 31 January 2022.

Special Issue Editor

Dr. Hak-Seon Kim
E-Mail Website
Guest Editor
School of Hospitality & Tourism Management, Kyungsung University, Busan 48434, Korea
Interests: big-data analytics; consumer behavior; service management
Special Issues and Collections in MDPI journals

Special Issue Information

Dear Colleagues,

The main feature of the tourism sector is that it has a high level of competition and a dynamic relationship. Globalization and the increasing number of travelers are a reminder of the sustainability of the tourism sector. The concept of sustainability aims to find a balance between economic, social, and environmental development. Accordingly, sustainable tourism aims to create economic value while preserving the natural and social resources of the territory. However, as well as creating multiple stakeholder opportunities, this new big data technology paradigm, which forms a fundamental force in the tourism sector, may also be seen as a stepping stone to fostering sustainable solutions. Nevertheless, the literature seems to overlook the role of big data technology in promoting sustainable development in the tourism sector and focus on short-term profits rather than solutions to environmental and social problems. In other words, a great debate has emerged over the concept of sustainable tourism, and the tourism sector needs to spread new competitiveness and organizational dynamism driven by innovation in business models and big data analysis. It is necessary to explore how big data analysis can create social and environmental values, as well as economic and financial sustainability, in line with the principle of the Sustainable Development Goals (SDG). Accordingly, this Call for Papers seeks original and relevant conceptual and empirical papers on how big data analysis provides tourism actors, organizations, territories, and ecosystems with new opportunities for creating economic, social, and environmental values.

The topics of interest and research questions in the Special Issue include but are not limited to the following:

  • Big data business models and environmental issues in hospitality and tourism;
  • Big data analytics for sustainable tourism;
  • The influence of big data on the creation of social value in hospitality and tourism;
  • Big data tools that promote sustainable development of tourism;
  • Big data social innovation in hospitality and tourism;
  • Sustainable economic growth of tourism ecosystem;
  • Big data for conservation of natural and cultural heritage;
  • Big data on waste problems in tourism;
  • Climate change and tourism in the big data environment;
  • Customer sustainable behavior and big data tools in tourism.

Dr. Hak-Seon Kim
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

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 1900 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

  • Sustainable tourism
  • Big data on social value creation hospitality and tourism
  • Sustainable economic growth in tourism ecosystems
  • Climate change and tourism in a big data world
  • Big data analytics for sustainable tourism

Published Papers (3 papers)

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Research

Article
Co-Movement between Tourist Arrivals of Inbound Tourism Markets in South Korea: Applying the Dynamic Copula Method Using Secondary Time Series Data
Sustainability 2021, 13(3), 1283; https://doi.org/10.3390/su13031283 - 26 Jan 2021
Viewed by 485
Abstract
Tourism demand is severely affected by unpredicted events, which has prompted scholars to examine ways of predicting the effects of positive and negative shocks on tourism, to ensure a sustainable tourism industry. The purpose of this study was to investigate if non-linear dependence [...] Read more.
Tourism demand is severely affected by unpredicted events, which has prompted scholars to examine ways of predicting the effects of positive and negative shocks on tourism, to ensure a sustainable tourism industry. The purpose of this study was to investigate if non-linear dependence structures exist between tourist flows into South Korea from five major source countries, as South Korea has undergone fluctuations in tourist arrivals due to diverse circumstances and has complex relations with tourism source countries. Additionally, the study examines the structures of extreme tail dependence, which is indicated in the case of unexpected events, and identifies how co-movements vary over time through dynamic copula–GARCH (generalized autoregressive conditional heteroskedasticity) tests. The secondary time series data for the 2005–2019 period of tourist arrivals to Korea were derived from the Korea Tourism Knowledge and Information System for testing the copula models. The copula estimations indicate significant dependencies among all market pairs as well as the strongest dependence between China and Taiwan. Moreover, extreme tail dependence structures show co-movements for four pairs of tourism markets in only negative shocks, for five pairs in both positive and negative conditions, but no co-movement in the China–Taiwan pair. Finally, the dynamic dependence structures reveal that the China–Taiwan dependence is higher than the other time-varying dependence structures, implying that the two markets complement each other. Full article
(This article belongs to the Special Issue Big Data and Sustainability in the Tourism Industry)
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Article
Extracting Key Drivers of Air Passenger’s Experience and Satisfaction through Online Review Analysis
Sustainability 2020, 12(21), 9188; https://doi.org/10.3390/su12219188 - 05 Nov 2020
Viewed by 620
Abstract
This study compared the competitiveness of the Commonwealth Independent State Airlines (Azerbaijan Airlines, Air Astana, Aeroflot) with Korean airlines (Asiana Airlines, Korean Air) using customer online reviews through big data analytics. The purpose of this study was to get the understanding of airline [...] Read more.
This study compared the competitiveness of the Commonwealth Independent State Airlines (Azerbaijan Airlines, Air Astana, Aeroflot) with Korean airlines (Asiana Airlines, Korean Air) using customer online reviews through big data analytics. The purpose of this study was to get the understanding of airline issues, especially the relationship between airline traveler experience and satisfaction. This study also shows which group has a better service and is more developed and provides significant and social network-oriented suggestions for another group of airlines. Data were collected from Skytrax and the collected reviews were written from January 2011 to March 2019. The size of the dataset was 1693 reviews, and a total of 199,469 words were extracted. As part of the qualitative analysis method, semantic network analysis through text mining was performed, and linear regression analysis was conducted using SPSS as part of the quantitative analysis method. This study shows which group of airlines has a better service and provides significant and social network-oriented suggestions for another group of airlines. The common concerns, as well as special features for different airlines, can also be extracted from online review data. Full article
(This article belongs to the Special Issue Big Data and Sustainability in the Tourism Industry)
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Article
Robotic Restaurant Marketing Strategies in the Era of the Fourth Industrial Revolution: Focusing on Perceived Innovativeness
Sustainability 2020, 12(21), 9165; https://doi.org/10.3390/su12219165 - 04 Nov 2020
Cited by 1 | Viewed by 780
Abstract
Although innovative robotic technology plays an important role in the restaurant industry, there is not much research on it. Thus, this study tried to identify how to form behavioral intentions using the concept of perceived innovativeness in the context of robotic restaurants for [...] Read more.
Although innovative robotic technology plays an important role in the restaurant industry, there is not much research on it. Thus, this study tried to identify how to form behavioral intentions using the concept of perceived innovativeness in the context of robotic restaurants for the first time. A research model comprising 12 hypotheses is evaluated using structural equation modeling based on a sample of 418 subjects in South Korea. The data analysis results show that perceived innovativeness is an important predictor of the customers’ attitude, which in turn has a significant effect on desire. In addition, desire exerts a positive influence on intentions to use and willingness to pay more. Lastly, perceived risk moderates the relationships between (1) desire and intentions to use and (2) desire and willingness to pay more. Based on the above statistical results, important theoretical and managerial implications are presented. Full article
(This article belongs to the Special Issue Big Data and Sustainability in the Tourism Industry)
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