Use of Electronic Word of Mouth as Quality Metrics: A Comparison of Airline Reviews on Twitter and Skytrax

Lu, Lin
Mitra, Amit
Wang, Yen-Yao
Wang, Yu
Xu, Pei
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User-generated content (UGC) at online platforms serves as a critical data source in the service industry as it can be accessed in real-time and reflect customers’ changing focus on service aspects. Drawing upon the importance-performance analysis framework, we propose a methodology to derive service quality metrics by utilizing the heterogeneous sources of UGC with customized text mining techniques and examining the effectiveness of these quality metrics. UGC data related to major U.S. airlines were collected from non-social media (Skytrax) and social media platforms (Twitter) from 2014 to June 2019. The results suggest that the topic distributions and the UGC-derived weighted service quality (WSQ, which represents the weighted sentiment based on service aspects) significantly vary between the non-social media and social media platforms. In addition, the WSQ scores derived from two platforms are significant indicators of the objective service quality measurement (i.e., airline quality rating) with stronger predictive power from the social media derived WSQ score.
Data, Text, and Web Mining for Business Analytics, panel regression, sentiment analysis, service quality, social media, topic model
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