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

Deadline for manuscript submissions: closed (31 January 2022) | Viewed by 80992

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Special Issue Editor

Dr. Hak-Seon Kim

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Guest Editor

School of Hospitality and Tourism Management, Kyungsung University, Busan 48434, Republic of Korea
Interests: hospitality; service management; online review; food service
Special Issues, Collections and Topics 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

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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

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Published Papers (12 papers)

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Editorial

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6 pages, 174 KiB

Open AccessEditorial

Big Data and Sustainability in the Tourism Industry

by Jue Wang, Hyun-Jeong Ban and Hak-Seon Kim

Sustainability 2022, 14(13), 7697; https://doi.org/10.3390/su14137697 - 24 Jun 2022

Cited by 2 | Viewed by 2026

Abstract

This Special Issue (SI) of Sustainability titled “Big Data and Sustainability in the Tourism Industry” contains 11 papers [...] Full article

(This article belongs to the Special Issue Big Data and Sustainability in the Tourism Industry)

Research

Jump to: Editorial

16 pages, 624 KiB

Open AccessArticle

How to Enhance Smart Work Effectiveness as a Sustainable HRM Practice in the Tourism Industry

by Hyunjung (Helen) Choi, Jin Young Lee, Youngjoon Choi, Yuxian Juan and Choong-Ki Lee

Sustainability 2022, 14(4), 2218; https://doi.org/10.3390/su14042218 - 15 Feb 2022

Cited by 11 | Viewed by 3305

Abstract

With the development of information technologies and increasing interest in sustainability, many companies have adopted smart work as a sustainable human resource practice. Moreover, the outbreak of COVID-19 has further promoted smart work in the workplace. However, the benefits and disadvantages of smart work are still under debate. In this regard, this study attempted to delve into how to enhance smart work implementation by exploring employees’ subjectivity. Hana Tour, which is considered a good model of smart work in South Korea, was selected as a sample company. Q-methodology was employed to listen to employees’ subjective opinions about smart work that they experienced. This study identified five types of smart work perceptions, namely, “self-development and energy saving,” “quality of personal life,” “job satisfaction,” “work engagement,” and “work–life balance”. Based on these five types, the theoretical and practical implications are discussed in the last chapter. Interestingly, the results showed that employees were not well aware of smart work effectiveness as one of the environmental protection practices in sustainability management paradigms. Another notable result was that employees were not concerned about the potential penalties of their engagement in smart work. During the current COVID-19 pandemic, the study’s findings are beneficial to the improvement of smart work implementation as a sustainable HRM practice in business. Full article

(This article belongs to the Special Issue Big Data and Sustainability in the Tourism Industry)

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22 pages, 3012 KiB

Open AccessArticle

Predicting Online Complaining Behavior in the Hospitality Industry: Application of Big Data Analytics to Online Reviews

by Raksmey Sann, Pei-Chun Lai, Shu-Yi Liaw and Chi-Ting Chen

Sustainability 2022, 14(3), 1800; https://doi.org/10.3390/su14031800 - 4 Feb 2022

Cited by 33 | Viewed by 5135

Abstract

Purpose: This study aims to enrich the published literature on hospitality and tourism by applying big data analytics and data mining algorithms to predict travelers’ online complaint attributions to significantly different hotel classes (i.e., higher star-rating and lower star-rating). Design/methodology/approach: First, 1992 valid online complaints were manually obtained from over 350 hotels located in the UK. The textual data were converted into structured data by utilizing content analysis. Ten complaint attributes and 52 items were identified. Second, a two-step analysis approach was applied via data-mining algorithms. For this study, sensitivity analysis was conducted to identify the most important online complaint attributes, then decision tree models (i.e., the CHAID algorithm) were implemented to discover potential relationships that might exist between complaint attributes in the online complaining behavior of guests from different hotel classes. Findings: Sensitivity analysis revealed that Hotel Size is the most important online complaint attribute, while Service Encounter and Room Space emerged as the second and third most important factors in each of the four decision tree models. The CHAID analysis findings also revealed that guests at higher-star-rating hotels are most likely to leave online complaints about (i) Service Encounter, when staying at large hotels; (ii) Value for Money and Service Encounter, when staying at medium-sized hotels; (iii) Room Space and Service Encounter, when staying at small hotels. Additionally, the guests of lower-star-rating hotels are most likely to write online complaints about Cleanliness, but not Value for Money, Room Space, or Service Encounter, and to stay at small hotels. Practical implications: By utilizing new data-mining algorithms, more profound findings can be discovered and utilized to reinforce the strengths of hotel operations to meet the expectations and needs of their target guests. Originality/value: The study’s main contribution lies in the utilization of data-mining algorithms to predict online complaining behavior between different classes of hotel guests. Full article

(This article belongs to the Special Issue Big Data and Sustainability in the Tourism Industry)

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13 pages, 3630 KiB

Open AccessArticle

The Impact of Hotel Customer Experience on Customer Satisfaction through Online Reviews

by Yae-Ji Kim and Hak-Seon Kim

Sustainability 2022, 14(2), 848; https://doi.org/10.3390/su14020848 - 12 Jan 2022

Cited by 57 | Viewed by 33519

Abstract

With the growing popularity of the internet, customers can easily share their experiences and information in online reviews. Consumers recognize online reviews as a useful source of information prior to consumption, and many online reviews influence consumer purchasing decisions. Understanding the customer experience in online reviews is thus necessary to maintain customer satisfaction and repurchase intention for the sustainable development of the hotel business. This study assessed the fundamental selection attributes of customers from online reviews reflecting the hotel customer experience, and investigated their association with customer satisfaction. A total of 8229 reviews were collected from Google travel websites from December 2019 to July 2021. Text mining and semantic network analysis were adopted for big data analysis. Factor and regression analyses were then used for quantitative analysis. Based on linear regression analysis, the Service and Dining factors significantly affected customer satisfaction. Service is a critical selection attribute for customers, and the provision of more particular services is necessary, especially after COVID-19. These results indicate that understanding online reviews can provide theoretical and practical implications for developing sustainable strategies for the hotel industry. Full article

(This article belongs to the Special Issue Big Data and Sustainability in the Tourism Industry)

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17 pages, 6448 KiB

Open AccessArticle

Estimating the Spatial Heterogeneity and Seasonal Differences of the Contribution of Tourism Industry Activities to Night Light Index by POI

by Juan Wei, Yongde Zhong and Jingling Fan

Sustainability 2022, 14(2), 692; https://doi.org/10.3390/su14020692 - 9 Jan 2022

Cited by 14 | Viewed by 2733

Abstract

The spatial distribution of tourism has a profound impact on its operational efficiency and geographical relevance. Point of interest (POI), as a kind of spatial data shared by subject and object, can reflect the spatial distribution form and function of tourism geographical objects under the all-for-one tourism policy. Continuous satellite observation and in-depth study of night lights pave the way to clarify human activities and socio-economic dynamics. The purpose of this paper is to investigate the seasonal changes of night light images and their correlation with tourism in 122 counties (cities, districts) of Hunan Province. We obtained night earth observation data (seasonality) and POI in 2019 and processed them by Geographic Information System and statistical analysis (ordinary least squares (OLS) and geographically weighted regression (GWR)). The results show that the luminous radiation intensity is highly correlated with the POI of tourism activities. The POI of different tourism activities in different regions shows obvious spatial heterogeneity and seasonal differences, which is the result of the comprehensive effect of tourism resource distribution and social environment in Hunan Province. GWR has proved to be a more effective tool. It provides a new method and perspective for tourism research and especially reveals the geographical spatial differences of tourism activities, which is helpful to study the spatial distribution and seasonality of tourism at the county level. In addition, the spatial evaluation of the contribution of tourism and luminous radiation can provide reference and suggestions for relevant departments to formulate tourism night protection measures. Full article

(This article belongs to the Special Issue Big Data and Sustainability in the Tourism Industry)

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17 pages, 2056 KiB

Open AccessArticle

Customer Experience and Satisfaction of Disneyland Hotel through Big Data Analysis of Online Customer Reviews

by Xiaobin Zhang and Hak-Seon Kim

Sustainability 2021, 13(22), 12699; https://doi.org/10.3390/su132212699 - 17 Nov 2021

Cited by 18 | Viewed by 7778

Abstract

Online customer reviews have become a significant information source for scholars and practitioners to understand customer experience and its association with their satisfaction to maintain the sustainable development of relative industries. Thus, this study attempted to find the underlying dimensionality in online customer reviews reflecting customers experience in the Hong Kong Disneyland hotel and identified its relationship with customer satisfaction. Semantic network analysis by Netdraw and factor analysis and linear regression analysis by SPSS 26.0 (IBM, New York, NY, USA) were applied for data analysis. As a result, 70 keywords with high frequency were extracted, and their connection to each other was calculated based on their centralities. Consequently, seven factors were explored by exploratory factor analysis, and moreover, three factors, “Family Empathy”, “Value”, and “Food Quality”, were testified to be negatively related to customer satisfaction. The findings of this study, to a great extent, could be utilized as a research scheme for future research to investigate theme hotels with big data analytics of online customer reviews. More importantly, some new insights and practical implications for the future research and industry development were provided and discussed as well. Full article

(This article belongs to the Special Issue Big Data and Sustainability in the Tourism Industry)

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11 pages, 520 KiB

Open AccessArticle

Predictors of Viewing YouTube Videos on Incheon Chinatown Tourism in South Korea: Engagement and Network Structure Factors

by Woohyun Yoo, Taemin Kim and Soobum Lee

Sustainability 2021, 13(22), 12534; https://doi.org/10.3390/su132212534 - 12 Nov 2021

Cited by 6 | Viewed by 2926

Abstract

YouTube has become an increasingly popular source of tourism information. The purpose of this study is to explore the network structures of YouTube videos about Incheon’s Chinatown in South Korea and investigate the potential factors that can predict the viewing of these videos. The analysis of 104 videos about Incheon Chinatown revealed that the engagement factors assessed by the number of comments and likes, and the running time of content, were significant predictors of viewing. However, network structure factors did not predict viewing. These findings make valuable contributions to sustainable tourism research and provide practical guidance for tourism management. Full article

(This article belongs to the Special Issue Big Data and Sustainability in the Tourism Industry)

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14 pages, 2811 KiB

Open AccessArticle

The Economic Resilience Cycle Evolution and Spatial-Temporal Difference of Tourism Industry in Guangdong-Hong Kong-Macao Greater Bay Area from 2000 to 2019

by Wenjing Cui, Jing Chen, Tao Xue and Huawen Shen

Sustainability 2021, 13(21), 12092; https://doi.org/10.3390/su132112092 - 2 Nov 2021

Cited by 13 | Viewed by 3202

Abstract

Based on the tourism industry economic panel data, this research divides and measures the tourism industry’s economic resilience cycle in the Guangdong-Hong Kong-Macao Greater Bay Area (GBA) by constructing a counterfactual function and exploring the evolution of its Spatial-Temporal difference characteristics in the past 20 years. Estimation results show that three out of the four Recession–Recovery cycles of GBA have been characterized as “creative destruction”. Moreover, the economic resilience values and fluctuation trends of the individual tourism industries in the GBA are quite different. Additionally, the economic resilience of the urban tourism industry has changed from centralized to discrete, and the trend of economic resilience of the tourism industry has changed from low toughness to concentrated. This study expands the practice of resilience theory in the tourism industry economy, and it reveals the difference of tourism industry resilience in the metropolitan area system of GBA urban agglomeration from the perspective of industrial economic resistance and resilience. Full article

(This article belongs to the Special Issue Big Data and Sustainability in the Tourism Industry)

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23 pages, 2466 KiB

Open AccessArticle

A Study on Dining-Out Trends Using Big Data: Focusing on Changes since COVID-19

by Hyo-Sun Jung, Hye-Hyun Yoon and Min-Kyung Song

Sustainability 2021, 13(20), 11480; https://doi.org/10.3390/su132011480 - 18 Oct 2021

Cited by 13 | Viewed by 5391

Abstract

This study examined consumers’ emotions and needs related to dining-out experiences before and during the COVID-19 crisis. This study identifies words closely associated with the keyword “dining-out” based on big data gleaned from social media and investigates consumers’ perceptions of dining-out and related issues before and after COVID-19. The research findings can be summarized as follows: In 2019, frequently appearing dining-related words were dining-out, family, famous restaurant, recommend, and dinner. In 2020, they were dining-out, family, famous restaurant, and Corona. The analysis results for the dining-out sentimental network based on 2019 data revealed discourses revolving around delicious, nice, and easily. For the 2020 data, discourses revolved around struggling, and, cautious. The analysis of consumers’ dining-out demand network for 2019 data showed discourses centered around reservation, famous restaurant, meal, order, and coffee. However, for 2020 data, discourses were formed around delivery, price, order, take-out, and social distance. In short, with the outbreak of the pandemic, delivery, takeout, and social distance emerged as new search words. In addition, compared with before the COVID-19 pandemic, a weakening trend in positive emotions and an increasing trend in negative emotions were detected after the outbreak of the COVID-19 pandemic; specifically, fear was found to be the fear emotion. Full article

(This article belongs to the Special Issue Big Data and Sustainability in the Tourism Industry)

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13 pages, 2915 KiB

Open AccessArticle

Co-Movement between Tourist Arrivals of Inbound Tourism Markets in South Korea: Applying the Dynamic Copula Method Using Secondary Time Series Data

by Ki-Hong Choi and Insin Kim

Sustainability 2021, 13(3), 1283; https://doi.org/10.3390/su13031283 - 26 Jan 2021

Cited by 5 | Viewed by 2098

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 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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20 pages, 2502 KiB

Open AccessArticle

Extracting Key Drivers of Air Passenger’s Experience and Satisfaction through Online Review Analysis

by Aralbayeva Shadiyar, Hyun-Jeong Ban and Hak-Seon Kim

Sustainability 2020, 12(21), 9188; https://doi.org/10.3390/su12219188 - 5 Nov 2020

Cited by 19 | Viewed by 3354

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 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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18 pages, 1489 KiB

Open AccessArticle

Robotic Restaurant Marketing Strategies in the Era of the Fourth Industrial Revolution: Focusing on Perceived Innovativeness

by Jinsoo Hwang, Kwang-Woo Lee, Dohyung Kim and Insin Kim

Sustainability 2020, 12(21), 9165; https://doi.org/10.3390/su12219165 - 4 Nov 2020

Cited by 21 | Viewed by 6633

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 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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