Business intelligence in online customer textual reviews: Understanding consumer perceptions and influential factors

Xun Xu, Xuequn Wang, Yibai Li and Mohammad Haghighi

International Journal of Information Management, 2017, vol. 37, issue 6, 673-683

Abstract: With the rapid development of information technology, customers not only shop online—they also post reviews on social media. This user-generated content (UGC) can be useful to understand customers’ shopping experiences and influence future customers’ purchase intentions. Therefore, business intelligence and analytics are increasingly being advocated as a way to analyze customers’ UGC in social media and support firms’ marketing activities. However, because of its open structure, UGC such as customer reviews can be difficult to analyze, and firms find it challenging to harness UGC. To fill this gap, this study aims to examine customer satisfaction and dissatisfaction toward attributes of hotel products and services based on online customer textual reviews. Using a text mining approach, latent semantic analysis (LSA), we identify the key attributes driving customer satisfaction and dissatisfaction toward hotel products and service attributes. Additionally, using a regression approach, we examine the effects of travel purposes, hotel types, star level, and editor recommendations on customers’ perceptions of attributes of hotel products and services. This study bridges customer online textual reviews with customers’ perceptions to help business managers better understand customers’ needs through UGC.

Keywords: Customer satisfaction; Customer dissatisfaction; Online textual reviews; Text mining; Regression (search for similar items in EconPapers)
Date: 2017
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Citations: View citations in EconPapers (13)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:ininma:v:37:y:2017:i:6:p:673-683

DOI: 10.1016/j.ijinfomgt.2017.06.004

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