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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.

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Strategic alliances have become a key focus in the management and marketing literature. However, much of the previous research in this area has focused on the antecedents and accounting effects of strategic alliances. There is an opportunity to more closely examine how alliance types might influence the public equity markets. As a result, this study summarizes the literature for the theoretical foundation of strategic alliances to increase the understanding of the two main types of strategic alliances, that is industry scope (vertical vs. horizontal alliances) and size scope (asymmetric vs. symmetric alliances). Then, this study proposes a conceptual framework to examine the main and relative effects between different types of strategic alliances and firm performance. Using the Bloomberg Mergers and Acquisitions (M&A) database from 1 January 2010 to 1 January 2016, we find that vertical symmetric alliances gain more abnormal returns than others. Finally, implications and limitations are also discussed.