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

2020 ◽  
Vol 12 (21) ◽  
pp. 9188
Author(s):  
Aralbayeva Shadiyar ◽  
Hyun-Jeong Ban ◽  
Hak-Seon Kim
Keyword(s):  
Social Network ◽  
Semantic Network ◽  
Linear Regression Analysis ◽  
Big Data Analytics ◽  
Online Reviews ◽  
Online Review ◽  
Analysis Method ◽  
Semantic Network Analysis ◽  
Independent State ◽  
Quantitative Analysis Method

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.

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

2022 ◽  
Vol 14 (2) ◽  
pp. 848
Author(s):  
Yae-Ji Kim ◽  
Hak-Seon Kim
Keyword(s):  
Customer Satisfaction ◽  
Semantic Network ◽  
Linear Regression Analysis ◽  
Hotel Industry ◽  
Online Reviews ◽  
Customer Experience ◽  
Purchasing Decisions ◽  
Semantic Network Analysis ◽  
The Sustainable Development ◽  
The Impact

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.

A new method for evaluating tour online review based on grey 2-tuple linguistic

Kybernetes ◽  
2014 ◽  
Vol 43 (3/4) ◽  
pp. 601-613 ◽  
Author(s):  
Chuanmin Mi ◽  
Xiaofei Shan ◽  
Yuan Qiang ◽  
Yosa Stephanie ◽  
Ye Chen
Keyword(s):  
Big Data ◽  
Social Network ◽  
Social Responsibility ◽  
Evaluation Method ◽  
Evaluation Model ◽  
Online Reviews ◽  
New Method ◽  
Online Review ◽  
Future Research ◽  
Content Type

Purpose – Tour social network data that are heterogeneous contain not only the quantitative structured evaluation data, but also the qualitative non-structured data. This is a big data scenario. How to evaluate tour online review and then recommend to potential tourists quickly and accurately are important parts of social responsibility of tour companies. The purpose of this paper is to propose a new method for evaluating tour online review based on grey 2-tuple linguistic. Design/methodology/approach – The phenomenon of “poor information” exists in some big data scenario. According to social responsibility, grey 2-tuple linguistic evaluation model for tour online review is proposed. Findings – Tour social networks contain data that are valuable to each individual on tourism industry's value chain. Grey 2-tuple linguistic evaluation model can be used for evaluating tour online reviews. This is a systems thinking method that takes social responsibility into account. Research limitations/implications – Due to the complex links among reviewers in social network, network mining approaches and models are expected to be added to this research in the near future. Practical implications – Grey 2-tuple linguistic evaluation method can contribute to the future research on evaluating a variety of tour social network comment data in the real world. Originality/value – A new evaluation method for making evaluation and recommendations based on tour social network comment information is proposed.

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