What can big data and text analytics tell us about hotel guest experience and satisfaction?

Abstract

The tremendous growth of social media and consumer-generated content on the Internet has inspired the development of the so-called big data analytics to understand and solve real-life problems. However, while a handful of studies have employed new data sources to tackle important research problems in hospitality, there has not been a systematic application of big data analytic techniques in these studies. This study aims to explore and demonstrate the utility of big data analytics to better understand important hospitality issues, namely the relationship between hotel guest experience and satisfaction. Specifically, this study applies a text analytical approach to a large quantity of consumer reviews extracted from Expedia.com to deconstruct hotel guest experience and examine its association with satisfaction ratings. The findings reveal several dimensions of guest experience that carried varying weights and, more importantly, have novel, meaningful semantic compositions. The association between guest experience and satisfaction appears strong, suggesting that these two domains of consumer behavior are inherently connected. This study reveals that big data analytics can generate new insights into variables that have been extensively studied in existing hospitality literature. In addition, implications for theory and practice as well as directions for future research are discussed.