Xie, Karen, Kwok, Linchi & Wei Wang (Accepted)
Forthcoming Cornell Hospitality Quarterly
Monetizing Managerial Responses on TripAdvisor: Performance Implications across Hotel Classes Abstract This study assesses the moderating effect of hotel class on managerial responses’ influence on hotel performance through the lens of data analytics. A panel data model was applied to analyze 7,979 managerial responses and 51,801 online reviews on TripAdvisor, which were matched with the financial performance data (revenue per available room or RevPAR) for 2,652 hotels in 427 cities in Texas over 26 consecutive quarters from 2005 and 2011. The results suggest that, even though all hotels will be able to observe an increase of RevPAR as they post more managerial responses to online reviews, above-average and luxury hotels in particular benefit from managerial responses in longer length and provided by frontline managers, whereas full service, mid-market economy, and budget traveler hotels benefit from executives’ responses. In addition, the speed of responses is important for full service and above-average hotels, and conciseness of responses is critical to budget traveler hotels. This study provided important yet specific implications on how hotels can benefit from different types of managerial response strategies according to their product characteristics (hotel class). It also adds to hospitality and online review literature with new theoretical perspectives on the varying effect of managerial responses on business performance specific to the lodging industry context. Keywords: online reviews, managerial responses, hotel performance, hotel class, data analytics 1. Karen Xie 2. Linchi Kwok (Lingzhi Guo)* 3. Wei Wang 1. Daniels College of Business, University of Denver, 2044 E Evans Avenue Suite 331, Denver, CO 80208 U.S.A. Email:
[email protected]; Tel: 1-303-871-4275. 2. * Corresponding Author: Linchi Kwok, The Collins College of Hospitality Management, Cal Poly Pomona, 3801 West Temple Avenue, Pomona, CA 91768 U.S.A. Email:
[email protected]; Tel: 1-909-869-4523; Fax: 1-909-869-4805. 3. Department of Information Management, College of Business Administration, Huaqiao University, No.269, North Chenghua Road, Quanzhou, Fujian, China. 362021. Email:
[email protected]; Tel: 86-135-5959-1007. Disclosures The authors declared no conflicts of interest with respect to the authorship and/or publication of this article. Funding This work received no fundings. Author Biographies
Karen Xie, Ph.D., is an assistant professor at the Fritz Knoebel School of Hospitality Management, Daniels College of Business at the University of Denver. Her research interests include hospitality technology and analytics and digital marketing. Her work has appeared in academic journals including Journal of Management Information Systems, International Journal of Hospitality Management, International Journal of Contemporary Hospitality Management, among others. She holds a Ph.D. from Temple University Fox School of Business and Management, an MPhil from the Hong Kong Polytechnic University School of Hotel and Tourism Management, and a Bachelor of Management from Fudan University in Shanghai. Linchi Kwok (Lingzhi Guo), Ph.D., is an assistant professor at the Collins College of Hospitality Management at California State Polytechnic University Pomona. He received a Master’s of Science degree in Restaurant, Hotel, and Institutional Management and a Ph.D. degree in Hospitality Administration at Texas Tech University, as well as a Master of Business Administration at Syracuse University. His research interests include: social media and its business implications, organizational behavior, and service operations. Wei Wang, Ph.D., is a lecturer at the Department of Information Management, College of Business Administration, at Huaqiao University. His research interests include crowd funding, sentiment analysis, text analysis, and electronic commerce. His work has appeared in academic journals including Industrial Management & Data Systems, Journal of Experimental & Theoretical Artificial Intelligence, New Review of Hypermedia and Multimedia, Management World (China), Journal of Management Sciences in China, Systems Engineering & Theory Practice, among others. He holds a Ph.D. from Tongji University in Shanghai, a master’s and a bachelor degree from Huaqiao University in Quanzhou, China.
Monetizing Managerial Responses on TripAdvisor: Performance Implications across Hotel Classes
Abstract This study assesses the moderating effect of hotel class on managerial responses’ influence on hotel performance through the lens of data analytics. A panel data model was applied to analyze 7,979 managerial responses and 51,801 online reviews on TripAdvisor, which were matched with the financial performance data (revenue per available room or RevPAR) for 2,652 hotels in 427 cities in Texas over 26 consecutive quarters from 2005 and 2011. The results suggest that, even though all hotels will be able to observe an increase of RevPAR as they post more managerial responses to online reviews, above-average and luxury hotels in particular benefit from managerial responses in longer length and provided by frontline managers, whereas full-service, mid-market economy, and budget traveler hotels benefit from executives’ responses. In addition, the speed of responses is important for full service and above-average hotels, and conciseness of responses is critical to budget traveler hotels. This study provided important yet specific implications on how hotels can benefit from different types of managerial response strategies according to their product characteristics (hotel class). It also adds to hospitality and online review literature with new theoretical perspectives on the varying effect of managerial responses on business performance specific to the lodging industry context. Keyword: online reviews, managerial responses, hotel performance, hotel class, big data analytics
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1. Introduction The proliferation of hotel review websites, such as TripAdvisor, provides consumers an opportunity to write and share their experience about a lodging product or service on the Internet. Online reviews, in a form of electronic word-of-mouth (eWOM), have dramatically changed the way consumers share travel information with one another (Thakran & Verma, 2013) and emerged as the most important information source when customers make a purchase decision (Litvin, Goldsmith, & Pan, 2008). As such, hotel managers are now facing big challenge of managing online reviews, especially those with negative and biased comments (Xie, Zhang, & Zhang, 2014). Negative online reviews can hurt a hotel’s image (Litvin, Goldsmith, & Pan, 2008) and are particularly damaging to a hotel’s sales efforts (Min, Lim, & Magnini, 2015). Additionally, consumers can be biased when writing reviews due to the online herding effect (Lee, Hosanagar, & Tan, 2015) or self-selection bias (Li & Hitt, 2008). In both cases, hotel managers are pushed to pay close attention to online reviews and hopefully find effective strategies to increase business performance through proactively influencing consumers’ eWOM opinions (Dellarocas, 2006; Chen & Xie, 2008; Gu & Ye, 2014; Xie et al., 2014). Managerial responses, which take the form of an open-ended piece of text and are publicly displayed underneath the consumer review being addressed, have gained increasing attention of hospitality practitioners as an important strategy to manage online reviews (Gu & Ye, 2014; Kwok et al., 2016). An increasing number of firms have begun to respond to online reviews to engage their potential customers to nurture trust (Liu, Kim, & Pennington-Gray, 2015a; Xie et al., 2014; Ye, Gu, & Chen, 2010). It was found in 2013 that 31 percent of hotels on TripAdvisor and two percent of hotels on Expedia had responded to consumer reviews (Prosperpio & Zervas, 2015). In another Nerval Corp's study (2015), a whopping 87 percent of
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respondents said that an appropriate managerial response to a bad review improves their impression of the hotel; on top of that, 71 percent of travelers believe managerial responses to online reviews are important. Despite the prevalent use of managerial responses by hotel managers, there has been great divergence in their response strategies. For example, Park and Allen (2013) examined the response strategies of four luxury and upscale hotels, finding that some hotels tended to respond relatively frequently to online reviews whereas others rarely or never posted a reply. In addition, Lee and Cranage (2014) found that managerial response strategies considerably varied by hotel types (e.g., luxury hotels vs. budget hotels). The reality is 85 percent of hotels have no guidelines for monitoring and responding to online reviews (Barsky & Frame, 2009). In light of these findings, Min et al. (2015) pointed out that it could be very helpful to investigate the effectiveness of managerial response strategies based on hotel types. However, there is sparse insight in literature on the performance outcomes of managerial responses provided by hotels of different classes. How to offer managerial responses with respect to the hotel types (or classes) remains a myth to hospitality practitioners. We then propose this exploratory study to answer a less studied but practically important research question: How does the effect of managerial responses on hotel performance vary across different hotel classes? To answer this research question, we used large-scale but granular data of 7,979 managerial responses to 51,801 online reviews for 2,652 individual hotels in 427 cities of Texas, which were matched with the performance records over 26 quarters from 2005 Quarter 1 to 2011 Quarter 2 at the individual hotel level. The analysis is instantiated on a blend of econometric models, in which hotel performance is a function of multifaceted managerial response variables (i.e., volume, speed, length, match rate, and executive response rate) and their
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interaction terms with hotel class, controlling other relevant hotel characteristics such as amenity, age, size, average review ratings, and number of consumer reviews. Our empirical results provide performance implications that are specific to each hotel class in terms of using customized managerial responses to improve hotel performance. We suggest that managerial response strategy should be contingent on hotel class. 2. Relevant Literature 2.1 Related Research of Managerial Responses While the performance implications of eWOM has been well established in literature (Chintagunta, Gopinath, Venkataraman, 2010; Duan, Gu, & Whinston, 2008; Godes & Mayzlin, 2004; Sun, 2011), the research on managerial responses only emerges recently. Table 1 highlights some contemporary studies about managerial responses in hospitality and tourism. In general, current literature supports the positive impacts of managerial responses on customer relationships (Gu & Ye, 2014; Park & Allen, 2013; Pantelidis, 2010; Wei, Miao, & Huang, 2013), business reputation (Lee & Cranage, 2014; Lee & Song, 2010; Levy, Duan, & Boo, 2013; Liu et al., 2015a; Min et al., 2015; Proserpio & Zervas, 2015; Rose and Blogdgett, 2016; Sparks and Bradley, 2014; van Laer & de Ruyter, 2010; Zehrer, Crotts, & Magnini, 2011), and hotel sales (Kim, Lim, & Brymer, 2015; Lee & Song, 2010; Mauri & Minazzi, 2013; Xie et al., 2014). Furthermore, as far as the key influential factors that may influence a hotel’s performance are concerned, current literature suggests that the volume of managerial responses, speed of managerial responses, length of managerial responses, match rate of managerial responses, and managerial responses from the executives may play an important role. (Insert Table 1 about here) 2.2. Volume of Managerial Responses
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Today, managerial responses to online reviews have become a very important strategy for hotels to manage customer relationships (Gu & Ye, 2014). When managers respond to positive online reviews, for example, they can express their appreciations and show that the management team is listening. The good messages about a product/service are thus reinforced with managerial responses and might result in a better eWOM effect, as suggested in the reciprocity theory where people tend to respond to others’ good deeds with kind actions (Falk & Fischbacher, 2006). Meanwhile, when managers respond to negative reviews, they can describe how they have addressed the service failure issues, leading to higher customer satisfactions (Xie et al., 2014). Consequently, managers are encouraged to actively respond to online reviews regardless if they are positive or negative (Kwok & Xie, 2016). Empirical evidence generally supports the positive relationship between the volume of managerial responses and a hotel’s online rating (Min et al. 2015). Furthermore, as a hotel’s online rating increases, the hotel’s performance also shows a significant improvement, as measured in ADR (Kim et al., 2015; Öğüt & Onur Taş, 2012), room sales (Kim et al., 2015; Ye, Law, & Gu, 2009), and RevPAR (Phillips, Zigan, Santos Silva, & Schegg 2015; Xie et al., 2014). It is concluded that the volume of managerial responses has a positive effect on hotel performance. 2.3. Speed of Managerial Responses Hotel managers are highly encouraged to provide immediate response to negative online reviews (Levy et al., 2013). According to the service recovery theory (Andreassen, 2000), the speed of how soon managers handle service failure issues matters. Response speed thus becomes a focus in studies about customer complaints. Davidow (2003) conducted a comprehensive literature review about complaint handling and concluded (a) speed of response is important to complaints with no financial loss, (b) timeless becomes vital when there is an unreasonable
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delay, and (c) the acceptable speed of response is mode and context specific. The speed of managerial responses is found to have a significant impact on consumers’ inferences of trust and concern about a lodging product (Sparks et al., 2016). As compared to “unhappy” customers, satisfied consumers tend to be more loyal to a hotel brand and are more likely to demonstrate positive WOM behaviors toward a hotel, stay in the same hotel again in future trips, and pay more to stay in the same hotel (Ladhari, 2009; Ramanathan & Ramanathan, 2013). Therefore, the speed of managerial responses to online reviews could also have an effect on consumers’ booking intentions of a hotel stay through the influence of consumer satisfactions. 2.4. Length of Managerial Responses Often times, managers use managerial responses to address service failure issues (Xie et al., 2014). At the time when managers post responses to online complaints, they may either encourage upset customers to contact the hotel via e-mail or telephone, or they may post a description of what went wrong, followed by what actions the hotel took to address the issue (Levy et al., 2013). Consequently, the amount of information provided in a managerial response can play a very important role in influencing consumers’ decisions (Kwok & Xie, 2016). Such conclusion is rooted in the uncertainty reduction theory (Daft & Lengel, 1986), where the richness of information can help reduce equivocality and ambiguity. In this case, a managerial response with detailed descriptions of how the management team addressed an unhappy traveler’s concern will then open up a diagnostic conversation between the reviewer and the manager. With a detailed managerial response, other consumers can evaluate the incident based on one additional source of information besides what the consumer said, leading to a more realistic expectation of what the hotel offers. As compared to those managerial responses that direct consumers to discuss the service failure issue over the telephone or e-mail, managerial
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responses with detailed and usually lengthier descriptions will present a more complete “picture” of the hotel, helping consumers make an informed decision about their hotel stays. 2.5. Match Rate of Managerial Responses The interactive communications between consumers and managers that take place in the real-world social-media setting deserves more research attention (Kwok & Yu, 2013; 2015). Such interactions can be reflected on whether managers provide relevant response to address a specific issue that is raised in a consumer’s comment in their replies, which can be measured by the match rate between managerial responses and online reviews (Wei et al., 2013). When the management team often resonates with consumers’ opinions and provides relevant explanations of how consumers’ concerns were taken care of, they send out a strong signal to subsequent consumers that the management team is paying close attention to addressing the service failure issues. Consumers may feel more inclined to develop a positive attitude towards a hotel and thus are more likely to stay in the hotel (Sparks et al., 2013). In this case, the match rate between managerial responses and online reviews will have a significant impact on a hotel’s performance. 2.6. Managerial Responses from Executives Because many hotels do not have any guidelines for monitoring and responding to online reviews (Levy et al., 2013; Park & Allen, 2013), it comes as no surprise that hotels can designate the tasks of providing managerial responses to various types of personnel in the organization. Hotels that have online reputation management in place usually have specific personnel to post managerial responses to almost very online reviews (Liu, Schuckert, & Law, 2015b). Some large hotels may also afford to outsource the responsibilities of responding to online reviews to a marketing agency (Spark & Bradley, 2014). Consequently, the dedicated person who is responsible for providing managerial responses online can range from guest service supervisor,
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Front Desk supervisor, public relations manager, administration assistant, to the general manager. Drawing from the principles of consumer inference theory and the prominenceinterpretation theory, Sparks et al. (2016) proposed that managerial responses from a higher status associate (e.g., the general manager) would lead to two positive inferences regarding to consumers’ perceived trustworthiness of the hotel as well as the hotel’s concern for the customers. Even though they found no significant results to confirm such influential relationship in their experimental study, they advocated that additional research attention should be given to further assess the impact of managerial responses provided by various levels of personnel. 2.7 Moderating Effect of Hotel Class on Multifaceted Managerial Responses In summary, current literature suggests that managerial responses can be an important influential factor on consumers’ purchasing decisions for a hotel stay, which reflects on a hotel’s performance. Such influential relationship, however, can also be moderated by hotel class. Based on different hotel classes, for example, consumers convey discrepant importance among various types of hotel attributes (Rhee & Yang, 2015). Meanwhile, hotels of different classes tend to have very distinguishable operating characteristics (O’Neill et al., 2008). Hotel class is found to have significant influence on consumer’ purchasing decisions on a hotel stay (Mattila, 2006), where consumer satisfaction and dissatisfaction may vary dramatically among different types of loding products (Xu & Li, 2016). Additionally, hotel type can also affect the relationship among customer relationship management, relationship quality, such as satisfaction, trust, and commitment towards a hotel, and customer lifetime value, as measured in usage quantity, loyalty, word-of-mouth, and purchase intention (Wu & Li, 2011). In another study with field data downloaded from TripAdvisor and Smith Travel Research, Blad and Sturman (2014) confirmed that hotels of different classes showed various impacts of online reviews on hotels’ RevPAR. It
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becomes critically important for hotel managers and investors to gain better understanding of the moderating effect of hotel class on hotel performance (Qiu, Ye, Bai, & Wang, 2015; Kim, Cho, & Brymer, 2013). Accordingly, we propose in this exploratory analysis that hotel class can also moderate the effect of managerial responses on hotel businesses. We summarize the variables of our interest in this study in Figure 1, in which hotel class moderates the influential relationship between managerial responses and hotel performance. (Insert Figure 1 about here) 3. Methodology 3.1 Data and Measures This study is instantiated on a unique dataset enabled by data analytics technologies. We developed two web crawlers to auto-parse information of managerial response to online reviews for 2,652 individual hotels in 427 cities of Texas listed on TripAdvisor. Then, we used a precoded Python program to remove the HTML formatting from the text and converted the information into an XML file, which separated the data into records (managerial responses) and fields (the data in each managerial response) on a server in an automated fashion. In total, we collected 7,979 daily managerial responses to 51,801 daily online reviews, which were aggregated at the quarterly level before to match with the performance records at the individual hotel level over 26 quarters from 2005 Quarter 1 to 2011 Quarter 2. Our data includes five categories of hotels based on TripAdvisor’s segmentation scheme, in which 5 “Crowns” is used to label luxury hotels, 4 for above average hotels with some outstanding features and a broad range of services, 3 for full service hotels, 2 for mid-market economy hotels, and 1 for budget traveler hotels. This large-scale and granular data allowed us to investigate the effect of
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managerial response on hotel performance across different hotel classes in a longitudinal manner. Our dependent variable was RevPAR, a key hotel performance measure that is widely used in the hospitality industry (Ismail, Dalbor, & Mills, 2002). Managerial responses were modeled as a function of independent variables, including specific managerial response strategies of RESMatchrate, RESLength, RESDays, RESNumber, and ExecutiveRES, and hotel characteristic controls such as Class, Amenity, Age, RecommendRate, and Size. Table 2 provides the variable definition and summary statistics. (Insert Table 2 about here) 3.2 Descriptive Analysis Table 3 and Figure 2 present the distribution of online reviews and managerial responses for different types of hotels in Texas. We found that full service hotels had the highest number of online reviews (24,279 or 46.87%), followed by above average hotels (17,783 or 34.33%), midmarket economy hotels (8,326 or 16.07%), luxury hotels (861, 1.66%), and budget traveler hotels (552 or 1.07%). The distribution of managerial responses to these online reviews followed slightly different pattern, with full service hotels (3,373 or 42.27%) and above average hotels (3,364, 42.16%) being the most active in providing managerial responses. With respect to the response rate, above average hotels remained on the top with a rate of 18.92%, closely followed by full service hotels (13.89%), mid-market economy hotels (13.30%), luxury hotels (10.57%), and budget traveler hotels (7.97%). (Insert Table 3 and Figure 2 about here) With regards to specific managerial response strategies, Table 4 shows that above average hotels (mean = 0.65) again had the highest average number of responses, followed
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closely by luxury hotels (mean = 0.45). In terms of the average response speed, luxury and budget traveler hotels at two tails respond the fastest in an average of 8.93 days and 9.77 days after an online review was posted respectively. In contrast, full service hotels (mean = 22.78 days), mid-market hotels (mean = 16.15 days), and above average hotels (mean = 11.67 days) did not respond as fast. Regarding the average length of response, higher-class hotels such as above average hotels (mean = 66.54 words) and luxury hotels (mean = 49.66 words) provide lengthier responses to online reviews, which are about two times longer than hotels in other classes. Similarly, higher-class hotels such as above average hotels (mean = 1.24%), the luxury hotels (mean = 1.41%), and full service hotels (mean = 1.07%) tended to restate the same topics in online reviews more often when responding to consumers than hotels in other classes even though the match rate between online reviews and managerial responses is generally low across all classes. Finally, mid-class hotels such as above average (mean = 9.10%), mid-market economy (mean = 9.12%), and full service (8.08%) hotels have higher portion of responses given by executives than the others. (Insert Table 4 about here) 3.3 Model Specifications We modeled the performance outcome, RevPAR, as a function of managerial response strategies and their interactions with hotel class, as well as hotel characteristics controls. Because our conceptual framework suggests that the effects of managerial response strategies are conditional on hotel classes, we interacted the managerial response variables with the hotel class dummy. With these interaction terms, we could measure the performance effects of managerial response of different hotel classes (Aiken & West, 1991). Our panel data approach uses fixedeffects estimation with clustered robust standard errors within hotels, which controls for
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unobserved heterogeneity of individual hotels and the timing (year-quarter) effects as well as possible data correlations. The unit of analysis is a hotel (i) and quarter (t) combination (i, t). For hotel i, which provides managerial response to online reviews in quarter t-1, its effect to the business performances in quarter t is given by Re vPARi ,t 0 1 MRi ,t 1 2 MRi ,t 1 Classi 3 Ci 4 Ci ,t ui ,t
(1)
where MRi ,t 1 is a vector of managerial response strategies of hotel i at quarter t-1, including logRESNumberi,t-1 , logRESDaysi,t-1, logRESLengthi,t-1, logRESMatchratei,t-1 , and ExecutiveRESi,t-1 . The interaction term between the managerial response strategies and hotel class is denoted as MRi ,t 1 Classi , which includes logRESNumberi,t-1*Classi, logRESDaysi,t1 *Classi,
logRESLengthi,t-1*Classi, logRESMatchrate i,t-1 *Classi, and ExecutiveRESi,t-1*Classi.
The parameter of β2 captures how the performance effects of managerial response strategies may vary by hotel class. The remaining variables are largely control variables. The variable Ci denotes a vector of hotel i’s time-invariant characteristics such as Classi, Amenityi, and Recommendationi. The variable Ci ,t denotes a vector of hotel i’s time-varying characteristics such as Agei,t, Sizei,t, REVRatingi,t, and logREVNumberi,t. ui ,t denotes the error term. 4. Results and Findings To investigate the performance effect of managerial responses by hotel class, we first used a pooled sample of hotels for estimation in Model (1), which included the interaction term between managerial response strategies and hotel class. Then we examined the performance effect of managerial response strategies in separate samples for each hotel class, including the budget traveler hotel sample in Model (2), the mid-market economy hotel sample in Model (3), the full service hotel sample in Model (4), the above average hotel sample in Model (5), and the
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luxury hotel sample in Model (6). While Model (1) enables us to examine the moderation effect of hotel class on the performance impact of managerial responses, Models (2) - (6) specify such impact to the lodging products in a specific hotel class, allowing us to draw more relevant and specific implications to different lodging products based on their hotel class categories. In addition, results of multiple samples can provide robustness check to the estimated effects of managerial response strategies. Therefore, we believe that investigating the multi-sample estimations of managerial response by hotel class supports the robustness of the analytic results and provides relevant and specific implications in terms of how to use managerial responses to leverage RevPAR performance for different types of hotels. Table 5 presents the estimation results of Models (1) to (6). (Insert Table 5 about here) As shown in Model 1 (the pooled sample), the number of managerial responses positively affects hotel performance (1.279**), with 1% increment in the number of responses resulting in about 1-dollar increase in the RevPAR, holding all other variables constant. In contrast, response days (-0.074***) and longer responses (-0.310***) tend to result in a decrease in RevPAR. There is, however, no significant relationship between match rate and hotel performance (5.437). Finally, a higher portion of executive responses is positively associated with hotel performance (0.831***). To answer the research question raised earlier, we also examined the moderation effects of hotel class on the abovementioned main effects. We found that hotel class positively moderates the RevPAR effects of the number of response (0.479***). For higher-class hotels, the positive effect of the number of responses on hotel performance increases. That is, higher class hotels benefit more from providing a large volume of responses to online reviews than
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lower-class hotels. In contrast, hotel class negatively moderates the RevPAR effect of response days (-0.142***), response length (-0.186***), and the portion of executive response (0.412***). Specifically, the negative effect of the number of response days on RevPAR is less salient for higher-class hotels than for lower-class hotels. This result implies that tardy responses seem to be more forgiven in higher-class hotels than in lower-class hotels. Consumers also tolerate lengthier or longer responses provided by higher-class hotels than by lower-class hotels. It is intriguing that, as hotel class moves up, the positive RevPAR effect of the percent of executive responses decreases, indicating that executive responses are expected more from lower-class hotels than higher-class hotels when consumers make purchasing decisions. In light of the moderation effects of hotel class, we further broke down the pooled sample into five sub-samples, including budget traveler hotel sample, mid-market economy hotel sample, full service hotel sample, above average hotel sample, and luxury hotel sample, to examine the effect of managerial responses on hotel performance specific to each class. As shown in Model (2) (the budget traveler sample), the number of responses (1.509*) and the percent of executive response (0.542***) positively influence the RevPAR performance, suggesting that budget traveler hotels should particularly focus on providing more response to online reviews and such responses is favorably expected from hotel executives. Lengthier responses, though, are negatively associated with RevPAR (-0.087***) and thus not helpful to increased hotel performance. Similarly, Model (3) (mid-market economy hotel sample) shows positive performance effects of the number of responses (1.200**) and the percent of executive responses (0.972***) as well negative performance effect of the match rate (-2.419**). Model (4) shows the RevPAR effect of managerial responses in the full service hotel sample, in which the response number (0.835*) and the percent of executive responses (0.329*) positively affect
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RevPAR, whereas the response days have a negative effect on hotel performance (-0.442***) implying the responses that are less timely would hurt the performance of higher-class hotels such as full service ones. Similar finding has also been revealed in Model (5) (above average hotel sample), in which number of response days is negatively related to the RevPAR performance (-0.230***). However, the percent of executive responses becomes negatively related to the hotel performance (-1.292***) and the length of response is interestingly positively associated with the hotel performance (0.063***). Similar findings are obtained in the luxury sample (0.574* for the length of response and -1.863* for the percent of executive responses). The results in the higher-class sample (above average and luxury) indicate that lengthier response from frontline managers rather than executives would drive the RevPAR performance. 5. Implications and Conclusions In presence of the growing influence of eWOM, hotels increasingly take an active role in interacting with consumers in social media through managerial responses (Gu & Ye, 2014). In this study, we examined the performance effects of managerial responses to online reviews and how such effects vary across different types of hotels using large scale but granular field data collected from TripAdvisor. Applying a panel data model that controls for the heterogeneity of hotels, we show empirical evidence for the significant moderation effect of hotel class on the relationship between managerial responses and hotel performance. Our findings inform hotel managers at different classes some highly implementable tactics on how to drive business performance with customized managerial response strategies that are tailored to the product type (hotel class). Our empirical results support the view that the performance implications of response strategies depend on the hotel class, and there is no “one size fits all” solution. Posting more responses, for example, are critical across all hotel classes.
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Providing timely responses are particularly important for full service hotels and above average hotels. While providing lengthier responses appeared to be important for above average hotels and luxury hotels, budget traveler hotels are advised to write concise responses. Responses from executives (e.g. from the general managers) are important for budget traveler hotels, mid-market economy hotels, and full service hotels only, whereas responses from frontline managers are recommended for above average hotels and luxury hotels. Accordingly, we proposed a series of specific strategies that hotels of different classes can take in responding to online reviews, as summarized in Table 6. (Insert Table 6 about here) In conclusion, this study shifts research perspective from extensively studied online reviews to the less-researched managerial responses. We proposed and identified the moderating effects of hotel class on the relationship between managerial responses to hotel performance. In responding to the diverse interest and demands from different markets, the lodging industry offers a very complex product mix, with more new hotel brands being introduced in the last decade than any other time in modern history (Russell, 2015). Given the importance of the contextual factors such as hotel class, our results suggest that future studies should also consider the moderating effect of product types and make business recommendations specific to product types (i.e., different hotel class), which aligns with the consumers’ psychological choice model (Hansen, 1976). References Aiken, L.S. & West, S.G. (1991). Multiple Regression: Testing and Interpreting Interactions. SAGE Publications, Inc.
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Table 1: Related Research on Managerial Responses to Online Reviews in Hospitality and Tourism Study Research Context / Key Findings Subjects / Methodology Kim et al. TripAdvisor / Reviews for Overall ratings are the most salient predictor of hotel (2015) 128 hotels in 65 U.S. cities performance, followed by managerial responses to / Regression negative comments. The better the over ratings and the higher the response rate to negative comments, the higher the hotel performance (ADR and RevPAR). Lee & Restaurant reviews on Consensus in online negative word-of-mouth (NWOM) Cranage Yelp / 2,000 faculty and communication plays a pivotal role in influencing how (2014) staff / ANOVA potential consumers incorporate NWOM into their evaluations about the organization. Additionally, these NWOM consensus effects are contingent on the strategies of managerial responses. Levy et al. 10 popular online review The most comment complaints are related to front desk (2013) websites for hotels / 1,946 staff, bathroom issues, room cleanliness, and guestroom one-star reviews & 255 noise issues. Highly rated hotels often respond to online managerial responses from complaints with appreciation, apologies, and explanations 86 hotels in D.C. / Content for what had gone wrong. Compensation adjustments are analysis, Chi-square, & rarely mentioned. Logistic regression Liu et al. TripAdvisor / 583 reviews The quantitative findings revealed hotels’ response (2015a) and 176 managerial behaviors were association organizational factors (e.g., responses / Logistic hotel popularity, average rating, and start rating) and the regression and content ratings of online reviews. The qualitative results suggested analysis hotels employed various types of strategies in the response, which were dominated by bolstering and enhancing strategies. Mauri & Experiments / 349 surveys Results show a positive correlation between both hotel Minazzi / Correlation purchasing intention and expectations of the customers (2013) and valence of the review. On the contrary, the presence of managerial responses to guest reviews has a negative impact on purchasing intentions. Min et al. Experiment for hotels / Inserting an empathy statement in response to the negative (2015) 176 university students / review improved the ratings. The speed with which the ANOVA hotel responds to an online complaint did not influence ratings of the response. Pantelidis An online restaurant guide Customers consider food, service, ambience, price, menu, (2010) / 2,471 reviews for 300 and décor (in that order) when reflecting on their restaurants / Content experiences. Restaurant managers who respond analysis successfully to comments in electronic forums can turn an unsatisfied customer into a loyal one. Park & TripAdvisor / 5,639 Hotels responding frequently tend to consider reviews as Allan reviews for four hotels / an honest gauge of consumer sentiment and have a (2013) Case study collaborative communication style. They handled reviews in-house. Non-responders believe that reviews represent only extremely positive or negative views and will meet only as needed. They outsource the tasks. All four hotels viewed posted comments as a mechanism to identify and
1
Rose & Blodgett (2016)
Experiments / 225 participants in Study I and 133 usable surveys in Study II / ANOVA
Sparks & Bradley (2014)
TripAdvisor / 150 hotel reviews and managerial responses / Content analysis and relationship analysis Experiments / 820 respondents / MANOVA
Sparks et al. (2016)
Wei et al. (2013)
TripAdvisor / Response from 101 students / MANOVA
Xie et al. (2014)
TripAdvisor / 4,994 observations of 843 hotels / Linear regression
Zehrer et al. (2011)
TripAdvisor / 134 blog narratives for hotels / ANOVA
solve customer problems. One hotel makes online reviews part of a strategic approach to an ongoing relationship. A company’s reputation is adversely as the number of negative to positive reviews becomes greater. Managerial responses are effective in mitigating such adverse effect when the service failure issues are the results from controllable factors and have no effect on uncontrollable service failure issues. Facing a service failure issue, apology with assurance and apology with corrective actions are both effective. The final “Triple A” typology comprised 19 specific forms of managerial responses subsumed within the three higher-level categories of acknowledgements, accounts, and actions. The presence of managerial responses can enhance consumers’ inferences about a hotel’s trustworthiness and the extent to which the hotel cares about customers. Additionally, consumers’ inferences can also be enhanced if a managerial response uses a human voice and is posted timely, but no significant impact was found if a response is written by the general manager or if a correctional action took place in the past vs. the one that will take place in the future. Customers’ perceived motivational drivers underlying CEBs (consumer engagement behaviors) vary with their targets and positive CEBs enjoy more favorable evaluations than negative CEBs. For managerial responses to CEBs, the perceived motivational drivers were determined by the specificity of responses and the valence of CEBs. The effectiveness of specific managerial responses was rated higher than that of generic responses to negative CEBs. Overall rating, attribute ratings of purchase value, location and cleanliness, variation and volume of consumer reviews, and the number of managerial responses are significantly associated with hotel performance. In addition, variation and volume of consumer reviews moderate the relationship between overall rating and hotel performance. Managerial responses, together with variation and volume of consumer reviews, moderate the relationship between certain attribute ratings and hotel performance. A higher percentage of blog users find multiple evaluations that are congruent with one another (both negative and positive) helpful, and that negative postings were not necessarily bad if followed by a positive counter reaction (managerial responses).
2
Table 2: Variable Definition and Summary Statistics Dimensions
Variable
Description
Mean
Std. Dev.
Skewness1
Min.
Max.
Hotel Performance Measure Managerial Response Factors
RevPAR
Average revenue per available room (RevPAR) in a given quarter
46.27
27.29
1.38
0
261.59
RESNumber
Average number of managerial responses in a given quarter Average number of days between the date a review is posted and the date a managerial response is received in a given quarter. The less the number of days, the speedier the managerial response. Average number of words in a managerial response in a given quarter Average match rate of topics between consumer reviews and managerial response in a given quarter2 Percentage of managerial responses from top management or executive team (GM, president, owner, etc.) is of all managerial responses Hotel scales with the value of 5 for a luxury hotel, 4 for an above average hotel with some outstanding features and a broad range of services, 3 for a full service hotel, 2 for a mid-market economy hotel, and 1 for a budget traveler hotel. Number of internal amenities such as indoor swimming pool, free high-speed Internet, fitness center, wheelchair access, and pets allowed in a given quarter Number of years since the inception of a hotel in a given quarter Number of guest rooms in a given quarter Average consumer review ratings or perceived quality of a hotel in a given quarter Number of consumer reviews or perceived popularity of a hotel in a given quarter
0.14
1.04
19.42
0
61
18.71
164.41
11.67
0
346
3.43
21.35
7.78
0
368
0.28
0.04
10.00
0
1
0.09
0.26
3.048
0
1
2.64
0.71
0.11
1
5
7.77
2.50
-.057
1
16
15.45
12.04
0.76
0
64
109.49
100.00
5.43
0
1,614
1.02
1.73
1.33
1
5
2.74
2.58
21.95
0
188
RESDays
RESLength RESMatchrate
ExecutiveRES
Hotel Characteristics
Class3
Amenity
Age Size REVRating
REVNumber
1
We checked the normality of the variables through skewness. A logarithm transformation is needed when the data is excessively skewed positively or negatively (Greene, 2014). Therefore, we take log transformations of some highly skewed variables (i.e., ResponseDays, ResponseLength, ReviewNumber, Matchrate, and ResponseNumber) to normalize the data in our regression analysis for effective estimation. 2 We conducted textual mining and sentiment analysis of manageial responses wherein we counted the number of overlapping topics, including location, cleanliness, service, sleep quality, value, and room, between consumer reviews and management responses using keyword identification techniques similar to Kanayama and Nasukawa (2012) and Nguyen et al. (2015). Matchrate is then calculated as NumberofOverlapTopics , where Overlap topics denotes the topics appear in both an online review NumberofTopics in aReview
and a management response and
Reviewtopics
denotes the number of topics in an online review.
3
TripAdvisor.com provides hotel class segmentation information about each hotel being reviewed using a service segmentation scheme that classifies hotels using “Crowns” with the value of 5 for a luxury hotel, 4 for an above average hotel with some outstanding features and a broad range of services, 3 for a full service hotel, 2 for a mid-market economy hotel, and 1 for a budget traveler hotel.
3
Table 3: Reviews and Responses for Different Types of Hotels Class
Hotels Observations
Reviews
Responses
Response Rate
Budget Traveler
82
1,998
552
44
7.97%
Mid-market Economy
950
22,344
8,326
1,107
13.30%
Full Service
1,388
27,940
24,279
3,373
13.89%
Above Average
223
5,201
17,783
3,364
18.92%
Luxury Total
9 2,692
203 57,686
861 51,801
91 7,979
10.57%
4
Table 4: Managerial Response Averages for Different Types of Hotels RESNumber
RESDays
RESLength
RESMatchrate
ExecutiveRES
Budget Traveler
0.02 (0.21)
9.77 (107.20)
6.34 (59.65)
0.45% (2.01%)
4.21% (2.34%)
Mid-market Economy
0.05 (0.47)
16.15 (151.43)
14.41 (104.94)
0.29% (4.12%)
9.12% (2.71%)
Full Service
0.12 (0.73)
22.78 (185.02)
23.52 (119.06)
1.07% (4.35%)
8.08% (2.52%)
Above Average
0.65 (2.77)
11.67 (112.51)
66.54 (200.26)
1.24% (6.76%)
9.10% (2.64%)
Luxury
0.45 (1.73)
8.93 (55.87)
49.66 (166.79)
1.41% (4.23%)
4.23% (1.54%)
Standard deviations are in parentheses.
5
Table 5: Moderation Effects of Hotel Class on Managerial Response RevPAR Pooled Sample
logRESNumbert-1 logRESDayst-1 logRESLength t-1 logRESMatchratet-1 ExecutiveRES t-1 logRESNumbert-1*Class logRESDayst-1*Class logRESLength t-1*Class logRESMatchratet-1*Class ExecutiveRES t-1*Class Class Size Age Amenity REVRating t-1 logREVNumbert-1 Time Dummies Constant Observations Adjusted R-squared
Budget Traveler Hotel Sample (2) 1.509* (0.839) -0.384 (0.477) -0.087*** (0.021) -2.307 (1.77) 0.542*** (0.106) -
Mid-market Economy Hotel Sample (3) 1.200** (0.571) -0.212 (0.130) -0.027 (0.300) -2.419** (1.01) 0.972*** (0.333) -
Full Service Hotel Sample (4) 0.835* (0.447) -0.442*** (0.168) -0.017 (0.148) -0.873 (2.28) 0.329* (0.176) -
Above Average Hotel Sample (5) 0.591*** (0.201) -0.230*** (0.040) 0.063** (0.026) -0.178 (0.22) -1.292*** (0.296) -
Luxury Hotel Sample (6) 1.144* (0.671) -5.469 (3.814) 0.574* (0.313) -0.889 (15.33) -1.863* (1.013) -
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-0.072*** (0.024) -1.191*** (0.062) -
-0.041* (0.022) -1.157*** (0.121) -
-0.086*** (0.033) -1.124*** (0.112) -
-0.074* (0.042) -1.314*** (0.223) -
-0.031*** (0.005) -0.837*** (0.197) -
-0.014*** (0.003) -4.043*** (1.486) -
1.212*** (0.132) 9.246*** (0.384) Yes 55.191*** (3.562) 18,418 0.840
3.626** (1.529) 9.528*** (1.796) Yes 55.112*** (3.483) 339 0.803
1.211*** (0.132) 7.428*** (1.744) Yes 61.202*** (3.241) 3,983 0.709
1.821*** (0.357) 8.832*** (1.674) Yes 75.411*** (6.905) 7,775 0.802
3.364* (1.838) 9.619*** (1.653) Yes 62.206*** (7.825) 1,608 0.820
2.690* (1.609) 6.840*** (1.583) Yes 64.894*** (6.444) 152 0.654
(1) 1.279** (0.534) -0.074*** (0.013) -0.310** (0.127) 5.437 (8.81) 0.831*** (0.241) 0.479*** (0.166) -0.142*** (0.026) -0.186*** (0.045) -3.493 (8.843) -0.412*** (0.111) -
Standard errors are in parentheses. p***
