A new approach of social media analytics to predict service quality: evidence from the airline industry. Issue 1 (13th November 2019)

• Record Type: Journal Article
• Authors: Tian, Xin; He, Wu; Tang, Chuanyi; Li, Ling; Xu, Hangjun; Selover, David
• Is Part Of: Journal of enterprise information management. Volume 33:Issue 1(2020)
• Page Start: 51
• Page End: 70
• Publication Date: 2019-11-13
• Subjects: Text mining -- Service quality -- Sentiment analysis -- Social media analytics -- Emotional analysis; Management information systems -- Periodicals; Business logistics -- Periodicals; Business -- Data processing -- Periodicals; Management -- Data processing -- Periodicals; 658.05
• DOI: 10.1108/JEIM-03-2019-0086
• Languages: English
• ISSNs: 1741-0398

Abstract

Abstract : Purpose: Research on how to use social media data to measure and evaluate service quality is still limited. To fill the research gap in the literature, the purpose of this paper is to open a new avenue for future work to measure the service quality in the service industry by developing a new analytical approach of using social media analytics to evaluate service quality. Design/methodology/approach: This paper uses social media data to measure the service quality of the airline industry with the SERVQUAL metrics. A novel benchmark data set was created for each SERVQUAL metric. The data set was analyzed through text mining and sentiment analysis. Findings: By comparing the results from social media with official service quality report from the Department of Transportation, the authors found that the proposed service quality metrics from social media are valid and can be used to estimate the service quality. Practical implications: This paper presents service quality metrics and a methodology that can be easily adopted by other businesses to assess service quality. This study also provides guidance and suggestions to help businesses understand how to collect and analyze social media data for the purpose of evaluating service quality. Originality/value: This paper offers a novel methodology that uses text mining and sentiment analysis to help the airline industry assess its service quality.