Hyun-Jeong Ban

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

The hospitality industry is one of the most affected by the pandemic, and because of its human close interaction and labor-intensive characteristics, it has left Human Resource Management (HRM) puzzles with organizations’ stabilization and recovery. This study aimed to summarize current research on hospitality HRM research outcomes and trends during the COVID-19 pandemic. Utilizing the PRISMA technique and NVivo software, 102 research articles were extracted and analyzed to highlight to overall achievement and movement of hospitality HRM research under the shadow of the COVID-19 crisis. Considering the main purpose of this study, which was to show the status and prospects, the findings indicated 309 free nodes and 26 secondary nodes based on grounded theory. 7 key themes clustered from the analysis and include “environmental factors”, “industrial factors”, “HRM practice”, “organization”, “employee”, “external outcomes”, and “methodology”. The results demonstrated the COVID-19 pandemic driven HRM transformation. In addition, it sheds light on how research responds to the shifting navigation of HRM within the hospitality context under the COVID-19 conditions.

The purpose of this study is to conduct topic modeling and sentiment analysis on the posts of Skytrax (airlinequality.com), where there are many interests and participation of the people who have used or are willing to use it for airlines. The purpose of people gathering at Skytrax is to make better choices using the actual experiences of other customers who have experienced airlines. Online reviews written by customers with experience using airlines in Asia were collected. The data collected were online reviews from 27 airlines, with more than 14,000 reviews. Topic modeling and sentiment analysis were used with the collected data to figure out what kinds of important words are in the online reviews. As a result of the topic modeling, ‘seat’, ‘service’, and ‘meal’ were significant issues in the flight through frequency analysis. Additionally, the result revealed that delay was the main issue, which can affect customer dissatisfaction while ‘staff service’ can make customers satisfied through sentiment analysis as the result shows the ‘staff service’ with meal and food in the topic modeling.

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.

The purpose of this study is to investigate how foreigners’ health beliefs influence their visiting intentions to Korean medical tourism. This study used an online survey engine (docs.google.com/forms) to collect data from foreigners who are from India, Nepal, Bangladesh, Vietnam, and Mongolia, who are potential Korean medical tourists. Out of 213 questionnaires collected, 200 questionnaires (93.9%) were used for the statistical analysis. The Confirmatory factor analysis (CFA) revealed that six dimensions, “Experience”, “Susceptibility”, “Severity”, “Barrier”, “Benefit”, and “Visiting Intention”, had construct validity; Cronbach’s alpha coefficient was tested for item reliability. It is expected that four variables in the health belief model (HBM) that are determined by objective and logical thinking processes will affect the choice of Korean medical tourism. The results showed that Susceptibility, Severity, Barrier, and Benefit had significant effects on Visiting Intention and was a valid measurement to determine “Visiting Intention”.

This study was conducted to understand customer experience and satisfaction through airline passengers’ online review. To achieve the purpose of this study, the semantic network analysis was conducted qualitatively by collecting reviews in top 10 airlines selected by Skytrax (airlinequality.com). In addition, this study quantitatively identified the relationship among six evaluation factors (seat comfort, staff, food and beverage (F&B), entertainment, ground service, and value for money), customer satisfaction and recommendation. This study collected 9632 reviews from the Skytrax. Through a CONCOR (CONvergence of iterated CORrelation) analysis, keywords were grouped into six clusters (seat comfort, staff, entertainment, ground service, value for money, and airline brand). Through the linear regression analysis, all evaluation factors except ‘entertainment’ factor significantly had impact on customer satisfaction and recommendation. These results showed that understanding online review can provide both academic implication and practical implication to develop sustainable strategy in the airline industry.