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Online booking and reviewing are becoming more popular for customers with the rapid development of information technology (Xu & Li, 2016). People can also use online technology to share their travel experiences through different platforms. 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 (Xiang, Schwartz, Gerdes, & Uysal, 2014). Big data analytics opens the door to numerous opportunities to develop new knowledge to reshape our understanding of the field and to support decision making in the hospitality industry (Xiang et al., 2014).
Bed-and-breakfast establishments (B&B) present a unique sector within the tourism industry because it is run by operators who offer personalized service in a homely environment (Hsieh & Lin, 2010). These are “types of accommodation where visitors or guests pay to stay in private homes, where interaction takes place with a host and/or family usually living on the premises and with whom public space is, to a degree, shared” (Lynch, 2005, pp. 534–535). In the past 20 years, the popularity and number of B&Bs has increased dramatically throughout the world, especially in rural areas. B&B’s small-scale operation attracts especially guests who are unhappy with the standard hotel rooms and seek something different. According to statistical reports (Suzhouminsu, 2016), there were 42,658 B&Bs and 1.5 million rural homestays available at the end of 2015 in Mainland China alone, an increase of 42.19% compared to the second quarter of 2014. A recent study has shown that 79% of leisure travelers intend to stay at a B&B (Turner, 2011). Additional growth in the number of B&B operations is expected for the next several years, particularly in rural areas where the development of hotel and motel businesses may not be feasible (Ren, Ye, & Haobin, 2015).
The increasing number of B&Bs, the ease of booking hotels online, and the vast amount of online reviews available has made competition in the tourism industry more intense than ever before (Lin, Hueijen, & Wu, 2012). Moreover, many hotels offer essentially homogeneous products and services, which drives the desire of hotels to distinguish themselves from their competitors (Xiang et al., 2014). As such, guest satisfaction has been recognized as one of most important and efficient ways to succeed in the tourism industry (Anderson & Sullivan, 1993; Lee, Reynolds, & Kennon, 2003). Anderson and Sullivan (1993) have stated that a firm’s future profitability depends on satisfying current customers. Recognizing this, a plethora of studies have been conducted with the aim to understand the antecedents of guest satisfaction (e.g., Choi & Chu, 2001; Zhou, Ye, Pearce, & Wu, 2014). However, the majority of these studies focus on luxury, budget, or chain hotels, rather than B&Bs. The factors influencing consumer satisfaction in B&Bs context are still unknown.
In addition, conventional studies exploring consumer satisfaction with hotels tend to use surveys as their research methods, which are regarded as high-cost and difficult to operate (Zhou et al., 2014). Researchers are increasingly realizing that tourists’ online reviews represent a rich vein of data that can contribute to the understanding of consumer perceptions and behaviors (Kim & Hardin, 2010). This consumer-generated content on the internet has inspired the development of new approaches to understanding consumer satisfaction. Although some researchers have touched on this point by analyzing online review data (e.g., Xiang et al., 2014; Xu & Li, 2016) with various techniques, the factors identified in their studies are treated as isolated from each other, without explaining why and how the influence of these factors on satisfaction may occur. In other words, the influencing process or paths of various factors influencing a customer’s satisfaction remains unknown since the linkages between these factors are yet to be uncovered.