3 study how online consumer reviews affect offline business performance (Moe & Trusov, 2011; ... books (Chevalier & Mayzlin, 2006; Li & Hitt, 2008) and software (Duan et al. 2008 .... opinions tend to negatively adjust their product evaluations after reading ..... Self -selection and Information role of online product reviews.
Xie, Karen, Chen, Chih-Chien & Wu, Shin-Yi (2016).
Forthcoming in Journal of Travel & Tourism Marketing Online Consumer Review Factors Affecting Offline Hotel Popularity: Evidence from TripAdvisor
Abstract The business value of online consumer reviews has emerged in recent year as one of utmost importance for hotel marketers. This study examines how online consumer reviews affect the offline hotel popularity. Using a time-series data of 56,284 hotel reviews posted for more than 1,000 hotels listed on TripAdvisor, this paper estimates the effect of factors of online consumer review, including quality, quantity, consistency, and recency, on the offline hotel occupancy (i.e., how popular the hotel is among consumers). The empirical evidence shows the relative effect of online consumer review factors on the offline hotel popularity when controlling for other hotel characteristics. In particular, the effect of review quality carries over at least a couple of quarters, whereas that of other online consumer review factors remains short-term The findings provide a managerial basis to improve the online presence of hotels on social media platforms by strategically utilizing important review factors.
Keywords: online consumer review, offline hotel popularity, hotel occupancy, social media marketing
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1. Introduction Technological advances over past decades have led to the proliferation of consumer reviews on social media platforms where consumers shop for goods. Consumer reviews are posted on a wide range of products and services, and they have become part of the decisionmaking process for many consumers (Chevalier & Mayzlin, 2006). In particular, consumer reviews are important for learning about experience goods such as hotel rooms, as their quality is often unknown before purchase (Nelson, 1970; Pine & Gilmore, 1998). Gretzel and Yoo (2008) indicate that online reviews offer quality information to reduce risk in purchasing experience goods. In presence of online reviews, consumers are able to learn the perception of product quality and service satisfaction from previous consumers without experiencing the goods by themselves. Consumer reviews written by precious buyers signal a certain level of evaluation and feedback of experience goods, providing important reference for new buyers to make wise decisions and choose the products that best match their preference. As a result, nowadays about three-quarters of consumers have considered online consumer reviews when planning their travel itineraries (Xie et al. 2014), and nearly 50 percent of consumers visit an online review site for information connected with their online travel purchasing (Compete, 2006). Online consumer reviews have become an important source of information to consumers, substituting for other forms of business-to-consumer (Jiang & Chen, 2007) and off-line word-of-mouth communication (Lewis & Bridger, 2001) about the quality of service providers. Given the prevalent use of online reviews by consumers, the value of online consumer reviews on the business performance emerges in recent year as one of utmost importance for marketers. It is thus not surprising that online consumer reviews have become the subject of extensive research (Cunningham et al., 2010). A burgeoning academic literature has sprung up to 2
study how online consumer reviews affect offline business performance (Moe & Trusov, 2011; Xie et al. 2014). Specifically, previous research has identified three primary factors that aggregate online consumer review information and are strategically important to business performance: review quality, review consistency, review quantity, and review recency. Review quality, or the review rating, reflects the level of consumer satisfaction and is the focus of most empirical studies on product reviews (Clemons et al. 2006; Dellarocas et al. 2007). Review consistency, measured as the standard deviation to the mean rating, captures the degree of disagreement among consumers (Godes & Mayzlin, 2004). Review quantity, or the number of reviews, as a measure of the volume of discussions, signals brand awareness and popularity of a product on a social media platform (Duan et al. 2008; Zhu & Zhang, 2010). Finally, because each consumer reviews has its time stamp at the time it is published online, review recnecy refers to the age of consumer reviews (TripAdvisor, 2015). Despite the business value of online consumer reviews has become one of the utmost research topics, empirical literature still lag in three critical aspects that motivate our study. First, prior literature have discussed online consumer review factors affecting business performance but most often in a singular and fragmented fashion without accounting for the multi-aspects of online review factors. For example, some studies only investigate the review consistency to measure the degree of disagreement in consumer opinions (Godes & Mayzlin, 2004; Sun, 2012) while others sorely focus on review quantity to assess product awareness among consumers (Dellarocas et al. 2007; Godes & Mayzline, 2004). Empirical studies accounting for a holistic picture of the effects of online consumer review factors are limited. Second, prior studies mainly focus on online performance measures such as online product popularity social media websites (e.g., Ghose et al. 2012 for Travelocity, Luca, 2011 for 3
Yelp, and Xie et al. 2014 for TripAdvisor), neglecting the offline product popularity among consumers (i.e., how many consumers actually purchase the product). The offline product popularity is important because it justifies the return on investment (ROI) of the social media marketing efforts and is directly related to the bottom line of business performance (Aria, 2015). Despite the industry trend toward consumer voice, how online consumer reviews affect the offline product popularity remains a knowledge gap due to sparse research. Finally, prior social media studies mainly focus on one-time purchase items or short life cycle information goods such as movies (Dellarocas et al. 2010; Duan et al. 2008; Liu, 2006), books (Chevalier & Mayzlin, 2006; Li & Hitt, 2008) and software (Duan et al. 2008; Lee & Tan, 2013), likely due to the availability of data in these categories. However, information goods are unique in that they have product life cycles that are both short and follow predictable exponential patterns (Moe & Fader, 2001; Sawhney & Eliashberg, 1996). These products experience the greatest level of sales (and review activity) immediately after launch. Very quickly after that, sales (and review activity) taper off dramatically. The danger of using such product categories is that results can be very sensitive to the stage of the product life cycle during which the researcher collects the data. As a result, the product popularity observed can be more easily attributed to changes due to the natural progression of the product life cycle and are less likely to be a result of changes in the consumer review environment. Studies that examine social media exposures in relation to more mundane consumer and business products such as hotels are rare. This paper aims to bridge the knowledge gap by empirically examining the effect of online consumer review factors on the offline hotel popularity. Our research question is thus: what is the relative effect of online consumer review factors on the offline hotel popularity? Our study is instantiated on a unique dataset of 56,284 individual TripAdvisor consumer reviews of 4
1,067 hotels across five major-destination cities in Texas (i.e., Austin, Houston, San Antonio, Dallas, and Fort Worth). The uniqueness of our dataset is two-fold. First, this novel data not only enables us to investigate the direct effect of consumer review factors within one self-contained platform but also prevent the influence of confounding factors from separate platforms which have been mostly used in previous research (Stephen & Galak, 2012). Second, prior media research mostly focuses on the aggregated market level effect of consumer review factors, overlooking the critical phenomena occurring at the dyadic individual hotel level. Our dataset allows us to look into the influence of consumer review factors at disaggregated individual hotel level. Using our dataset, we exploit a blend of econometric models (fixed effects and random effects) to estimate the efficacy of online consumer review factors such as quantity, quality, and consistency on the offline hotel popularity (i.e., hotel occupancy), controlling for the variation of differences across hotels and within time. This study sheds some light on the vague online review determinants of offline hotel popularity and advocates an integrated social media marketing perspective which emphasizes a coordinated use of online consumer review factors to drive the offline business performance and to maximize the social media marketing success. In the remainder of the paper, we first introduce related literature and develop our hypotheses. This is followed by the description of our methodology. After we outline models to estimate, the results are presented and research implications are discussed. We conclude the paper by providing recommendations to hotel marketers and pointing out limitations.
2. Literature and Hypotheses The recent inundation of online user-generated reviews has evidently seen a significant influence on consumer purchasing behavior. It has become common to include these user5
generated reviews alongside product descriptions as a means for consumers to gather more information, form opinions, and make purchasing decisions, as well as for companies to collect consumer data and make recommendations. Due to the high volume of user-generated reviews that are posted, it becomes increasingly difficult to sift through the reviews and determine a hotel of high quality for consumers. For that reason, most leading online review websites such as Yelp, Amazon, and TripAdvisor tend to aggregate online consumer review information into three key factors, namely review quality, review quantity, review consistency, and review recency, and highlight these factors on the top of each hotel review page (see Figure 1). By referring to this aggregated information, consumers can easily access recommended products in the midst of the colossal amount of information available (Rajan, 2014). (Insert Figure 1 about here)
2.1 Review Quality Most social media platforms allow consumers to provide reviews and ratings to evaluate their experience with a product (Chu, 2009). According to Jeong and Jeon (2008), ratings for their part can be closely assimilated to an overall service evaluation. In that sense, the customer uses a single scale to express his or her judgment of the product experience. Review quality serves as a simplified heuristic to instantly signal a hotel’s level of quality as agreed by individual consumers. Hence, the review quality can be assimilated and used as proxies of quality as perceived by the past customers (Chen & Xie, 2008; Jiang & Chen, 2007). According to the rational action theory (Becker, 1976), a rational individual tends to “want good rather than bad.” This assumption is widely used in social and economic behavior contexts. This theory holds that, consumers, when making their purchase decisions, tend to 6
choose a higher quality product rather than a lower quality one. Intuitively, if we assume consumers are rational, it is very likely they would purchase a product with higher review quality indicated by prior consumers than those with lower review quality, other things being equal (Chen et al. 2004). As such, there is a strong reason to believe that the higher review quality indicated by prior consumers may signal a desirable product quality and guest satisfaction which persuades the purchase of subsequent consumers (Liu, 2006). The superior review quality simply increases the consumers’ confidence level of product purchase (Ratnasingham, 1998) because the positive word of mouth from previous reviewers mitigates the potential risks and uncertainty about this purchase and ensure a wise decision just like what the previous reviewers have made (Xie et al. 2014). The product with higher review quality will likely be purchased by many subsequent reviewers who have seen the previous consumer reviews and become more popular among consumers than those with lower review quality, other things being equal. We thus hypothesize, Hypothesis 1(H1): Review quality of online consumer reviews has a positive effect on the offline hotel popularity.
2.2 Review Consistency According to the rational action theory (Becker, 1976), rational consumers are riskaverse. When making a purchase, consumers are reluctant to accept a product with uncertain quality rather than another product with more certain (even possibly lower) quality (Zhu & Zhang, 2010). In presence of dispersive review quality, it is impossible for later consumers to exactly tell the true quality of a product, especially if there is no textual review in presence with the review quality. Thus, inconsistent opinions should have a negative impact on demand (Sun, 7
2012). As a statistical concept, variance of reviews is a natural measure to capture the heterogeneity or inconsistency in consumer opinions. This concept of review consistency is generally associated with uncertainty of product quality (De Maeyer, 2012). Higher dispersion of review quality (i.e., higher variation in review ratings) may indicate that previous consumers who write reviews have inconsistent opinions about the product quality. As Kirby (2000, p. E1) explains, one “may not trust just one non-expert…but if 9 out of 10 non-experts agree, it is probably worth buying.” Zhu and Zhang (2010) point out that high variation carries great risk, while low variation offers a safe bet. Ghose et al. (2012) investigate the impact of consumer reviews on a variety of products and state that less variability in the review quality could reduce risk and uncertainty of the hotel quality perceived by readers and thus result in higher product performance. Accordingly, we hypothesize, Hypothesis 2(H2): Review consistency of online consumer reviews has a negative effect on the offline hotel popularity.
2.3 Review Quantity Consumer reviews at the very least convey the existence of the brand and thereby put it in the choice set of the consumer (Clark et al. 2009). According to the theory of social contagion (Sutherland, 1995), peer consumers are likely to follow the opinions of previous reviewers as a result of pressure to conform to a peer group (Zhang et al. 2009). As consumers post their recommendations and opinions about a product or service in social networking sites, they attempt to persuade their peer consumers to see their point of view and, thus, influence their decision-making (Chu & Kim, 2011). Thus, consumer reviews can lead consumers to rationalize their purchasing decisions by reinforcing the idea that many other consumers also bought or did 8
not buy the same product or services. Prior studies have lent support to the presence of social contagion on online consumer reviews. For example, Moe and Trusov (2011) demonstrate that subsequent consumer behavior is significantly affected by reviews from prior consumers. In an experimental setting, Schlosser (2005) finds that consumers who have decided to post their opinions tend to negatively adjust their product evaluations after reading negative reviews from prior reviewers. This indicates that consumer posting behavior is affected by the influence of previously posted reviews. We therefore hypothesize, Hypothesis 3(H3): Review quantity of online consumer reviews has a positive effect on the offline hotel popularity.
2.4 Review Recency A plethora of consumer review data are generated, processed, and presented on the Internet on a daily basis. Almost all social media platforms list consumer reviews on their webpages by the recency of reviews, which refers to the age of the review (TripAdvisor, 2015). According to the serial position effect (Coleman, 2006), consumers tent to recall or mainly take into account the last item in a list or sequence, rather than earlier items. Particularly, the order in which consumer reviews appear (and presumably the recentness) have been found to effect consumers’ purchase decisions and perceived usefulness (Hu & Li, 2011). Usually, recent reviews are located on top of distant reviews. Displaying upfront the data that matters to consumer decision making is relevant because consumers pay more attention to recent, fresh information than distant information. According to Murphy et al. (2006), a link's location on a web page is an important factor influencing visitors to click more or less on a particular link. Using two field experiments, they find that the higher a link's position on a list of links, the 9
greater the probability that visitors will click on that link. A study by Ansari and Mela (2003) also suggests a positive relationship between the serial position of a link in an email and recipients' clicks on that link. Similarly, Drèze and Zufryden (2004) imply a positive relationship between a link's serial position and site visibility. That is, ceteris paribus, consumers would pay most attention to product reviews toward the top of a review web page and least attention to reviews toward the bottom of the web page or email. Thus, considering that online consumer reviews are typically posted in chronological order, consumers will likely be most influenced by the recently posted online reviews. We hypothesize, Hypothesis 4(H4): Review recency of online consumer reviews has a positive effect on the offline hotel popularity.
3. Methodology 3.1 Data and Measures Our research context is TripAdvisor, a prominent consumer review website in which the impact of online dissemination of opinions and reviews is rapid and far-reaching (Litvin et al. 2008). TripAdvisor offers more than 150 million reviews and opinions covering more than 810 thousand hotels, B&Bs, and specialty lodging in the world (TripAdvisor, 2015). It acts as a forum for everyday consumers to air their personal opinions regarding service providers’ quality whilst also read the recommendations of fellow consumers (Jeacle & Carter, 2011). In the meantime, hotel managers can regularly post their product and service information (Zhang et al., 2009) and also proactively induce their consumers to spread the word about their products online (Godes & Mayzlin, 2009). Therefore, TripAdvisor provides a good interactive setting where we
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can observe consumers’ information search behaviors and hotel managers’ manipulation and feedback mechanism strategies simultaneously. Our study is instantiated on a unique dataset of 56,284 hotel reviews on a daily basis for more than 1,000 hotels located in five major hotel markets of Texas over 26 quarters (2005 Quarter 1- 2011 Quarter 2). This niche data enable us to explore hotel popularity in relation to consumer review factors at individual hotel level over time. Table 1 presents distributions of consumer review records across our focal markets. Unlike most previous studies that tend to focus on either large or chain hotels, our sample includes both large and small, independent and chain hotels. (Insert Table 1 about here)
Consumer reviews are auto-parsed from TripAdvisor review webpages using two crawlers that are developed by Ruby. 1 Following prior literature (Duan et al. 2008; Zhu & Zhang, 2010), we collect individual reviewers’ hotel ratings (ReviewQuality) distributed on a scale of 1 to 5, on which 1 represents “terrible” and 5 represents “excellent”. Based on the individual reviews collected, we calculate the standard deviation of review ratings (ReviewConsistency) and count the number of consumer reviews received for each hotel (ReviewQuantity). We also document the date and time each consumer review is posted in order to measure the review recency.
Fully automated parsing” refers to the approach used to collect information from a website. Technically, we develop two crawlers using Ruby (1) to download automatically the web pages of hotel reviews and other hotel information from TripAdvisor and (2) to remove the HTML formatting from the text and then transformed into an XML file that separated the data into records (the review) and fields (the data in each review) in an automated fashion using a pre-coded computer program on the local machine. We use the crawlers to retrieve all available UGC information for the designated hotels. For each hotel, we obtain all of the posted reviews. Each consumer review is analyzed and selected review features are recorded. 1
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In addition, we obtain multi-quarter, archival data of hotel occupancy records from a research firm well-known for its hospitality data and research services (Trepp, LLC). The hotel records contain information on the hotel occupancy (logOccupancy).2 Besides the focal variable of hotel occupancy, we collect hotel characteristics information, including the logarithm of the average daily rate at the property level (logADR), age of the hotel (HotelAge), number of guest rooms (HotelSize), hotel class (HotelClass), number of amenities (HotelAmenity) and the ratio of the number of manager response to the number of consumer reviews (ResponseRatio). We consider the intersection of consumer reviews and the hotel occupancy records at a quarterly level for each individual hotel in our sample. The quarterly level aggregation of the consumer review activities allows us to overcome the sparseness of social media activity because consumer reviews for hotels tend to be spaced out and rarely occur on multiple days in the same quarter, as would be expected for almost all hotel properties except for very high profile ones. Table 2 presents variable definitions and summary statistics. Hotels in our sample receive an average review rating of 3.48 out of 5 at a standard deviation of 0.99 in a given quarter. The total number of consumer reviews varies from 1 to 189 with an average number of 3.67 reviews in a given quarter. The average management response ratio is 0.09, indicating about 9 hotel managers respond to every 100 consumer reviews in a given quarter. On average, hotels in our sample are 17 years old with 189 guest rooms, between mid-market economy and full service, and with 8 internal amenities. (Insert Table 2 about here)
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To account for the nonlinearities and to smooth skewed distributions (Greene, 2003), we take the logarithm of the
hotel occupancy. Similar variable transformation has also been applied on the ADR. For the brevity purpose, the nonlinearity and skewness of each variable is not shown but is available upon request.
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3.2 Model Specification Consumer reviews are time series for individual hotels in our sample. This panel data structure makes it necessary to adopt appropriate econometric models to account for individual heterogeneity. We use two techniques to analyze the data, fixed effects and random effects, controlling for unobservable variables across hotels or change over time but not across hotels (management strategy, hotel culture, etc.). The resulting model is logOccupancyit = ГXit + П Sit + ЛZi + μi + λt +εit where the dependent variable, logOccupancyitis a measure of popularity of hotel i at quarter t. Xit is a set of consumer review factors, including ReviewQualityit, ReviewConsistencyit, ReviewQuantityit, and their lagged terms.We control for other variables, including time-variant social variables Sit (ResponseRatioit) and hotel characteristics Zi (logADRit, HotelAgeit,, HotelSize i, HotelClassi, and HotelAmenityi). The hotel and time dummies are represented by μi and λt . The error terms that have been clustered at the hotel level are captured by εit.
4. Results and Findings We estimate review quality, review consistency, and review quantity in the offline hotel popularity model and present the estimation results in Table 3. By estimating both models of fixed effects and random effects, we examine the effect of consumer review factors on hotel popularity while accounting for hotel-specific characteristics and social variables. Specifically, the fixed effects estimation controls for all time-invariant differences between the hotels so the estimated coefficients of the fixed-effects models cannot be biased because of omitted timeinvariant characteristics. Unlike fixed effects, random effects estimation assumes that differences across hotels may have some influence on hotel popularity and should be measured. We then run 13
a Hausman test to decide which is the preferred model between fixed effects and random effects. In addition, we conduct a few additional diagnostic tests for robustness check purposes. (Insert Table 3 about here)
Column (1) in Table 3 shows the results of mean effects estimation using fixed effects estimation. The regression estimates that hotel popularity becomes higher with an increase in ReviewQualityit (0.011, p=0.011) and ReviewQuantityit (0,003, p=0.033), supporting our Hypotheses 1 and 3. In other words, the popularity of hotels becomes higher with increasing ratings (to inform consumers of the hotel quality) and amount of consumer reviews from reviewers (to create online buzz). However, the effect of ReviewConsistency on hotel popularity (0.000, p=0.584) is not significant, which means consistency of reviewer opinions would not significantly influence hotel popularity. It is thus likely that quality and quantity of consumer reviews become primary consumer-generated information to determine the hotel occupancy rate, suppressing other information source such as review consistency. Interesting, the effect of review quality carries over to at least a couple of quarters (0.020, pChi2 = 3.670, Prob>Chi2 = 0.042 ) (Greene, 2003). To check the robustness of our fixed effects estimation results, we conduct several diagnostic tests. First, we use a Breusch-Pagan test to check for heteroskedasticity. As shown in Table 3, the Breusch-Pagan test result (Chi2=5.860, Prob>Chi 2 = 0. 000) does not support the null hypothesis, suggesting there is the preferred heteroskedasticity. This finding indicates that our use of fixed-effects estimation is warranted. Second, we use the Lagram-Multiplier test to check the presence of serial correlation of the errors. Serial correlation causes the standard errors of the estimated regression coefficients to be smaller than they actually are and i nflates the estimation R-square. Although it does appear to arise naturally in time-series data, one would want to look carefully at the data and the model specification before assuming that it is present (Stock & Watson, 2007). The Lagram-Multiplier test fails to reject the null hypothesis (F=0.214; Prob > F =0.660), concluding the data does not have first-order autocorrelation. Our diagnostic tests show that results in Table 3 are quite robust.
5. Conclusion and Implications Consumer reviews are at the forefront of e-commerce in the hospitality industry. This paper examines the effect of online consumer review factors on the offline hotel popularity. An emerging research stream has started to look at social media platforms featuring user-generated content and consumer word-of-mouth. However, this stream of research mainly focuses on onetime purchase items or short life cycle information goods (Chevalier & Mayzlin, 2006; Dellarocas et al. 2010; Duan et al. 2008; Liu, 2006), neglecting experience products with stable product life cycle such as hotels. Our findings address industry concern of the undisclosed 15
mechanism of online consumer reviews by shedding light on specific online consumer review factors that would contribute to the offline hotel popularity. It explores multiple aspects of consumer review information to examine their effects on hotel popularity. By integrating the multifaceted data we extract from consumer reviews in an empirical setting, this study advances our understanding of the connection between the multidimensional facets of review content and the business performance-based offline hotel popularity. Our analysis suggests that review quality and quantity can significantly elevate the offline popularity of hotels, supporting our H1 and H3. In particular, the influence of the review quality can last for at least two quarters while the effects of other online consumer review factors such as review quantity and consistency taper off quickly in about one quarter. This finding partially supports H4. Additionally, hotels can receive very diverse opinions from consumers about the quality of service and products quite often. Consistency of opinions, however, seems not an influencer of hotel popularity. This finding does not lend support to our H2. These findings provide important implications for practicing managers and the knowledge body. Specifically, we demonstrate that increases in consumers’ review quality for their hotels increase the offline hotel popularity. This relationship reinforces the importance of product and service quality of hotels. Despite the social media frenzy of the hotel industry, the key to hotel popularity remains in outstanding service quality. Therefore, hotel businesses may tempt to focus on continuous improvement of service quality and encourage positive reviews from guests immediately after they check out or even while the stay. In addition, hotel managers can provide incentives to encourage customers to write reviews and particularly reward frequent reviewers. Developing a customer review system that connects to the property management
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system such as MICROS OPERA and targeting the customers accordingly at the time they check in and out at a property might be a trend in the future. Furthermore, the review quantity drives the offline hotel popularity. A direct implication of this finding is that a hotel can take advantage of network effects for online buzz. Hotels should correctly identify their need of social media given that the social media strategy is most effective in producing increase in brand awareness through the sheer number of consumer reviews. Therefore, a hotel business’ successful social media campaign can expand its consumer base by strategically encouraging more consumer reviews written specifically for the hotel. Hotel businesses can then target this customer group and solicit hotel reviews from them in order create more social buzz from subsequent consumers who would see their reviews and ultimately drive hotel popularity. Finally, the recency of online reviews matters. This study finds that, on average, the quality of reviews in recent periods (i.e., two quarters) influence the offline hotel popularity. Tons of consumer review data are generated, processed, and presented on social media platforms on a daily basis. Generating upfront the timely data that matter to consumer decision making is relevant for hotel businesses. For example, besides asking for consumer reviews when guests check out or while they stay, hotel businesses can use an automated guest response and communication system to send out an email to text message guests asking for consumer reviews. This email or text message should thank the guest for visiting and remind them that if they enjoyed their stay that it would be great if they could click this link to provide a review of the hotel. Overall, our research reveals the importance of unobtrusive social media marketing. Based on our findings, social media platforms such as TripAdvisor can provide more customized 17
marketing plans to hotel businesses planning to increase the popularity among consumers. For example, instead of distributing resources to all review functions, social media websites such as TripAdvisor can work with each hotel property to personalize the hotel review page by highlighting primary review factors that may influence the hotel popularity.
6. Limitations and Future Studies This paper is not without limitations. While we have taken the first step in several research directions in learning about the hotel popularity, our paper may prompt future researchers to extend the research stream. First, we are confident about the quality of our dataset because it captured the actual consumer review behaviors online with a social media platform for five major markets in the Texas state. However, the Texas markets may not necessarily represent the US hotel lodging industry. Future scholars are encouraged to collect more representative samples to justify the estimated effects of online consumer review factors. Second, we have initiated efforts in analyzing actual consumer review behavior data on a daily basis to inform business decisions. With the advanced of technology nowadays, real time data analytics is imperative in the hospitality industry. Those managers who are concerned with relative popularity determinants can utilize our study and take caution when employing social media strategies. We expect more future research that can provide up-to-the-time perspectives by integrating actual behavior data collection and advanced data analytics techniques to optimize business strategy in the hospitality industry.
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Table 1. Distributions of Consumer Reviews across Hotel Markets Market Austin Dallas Fort Worth Houston San Antonio Total
Consumer Reviews % of Total 9,814 17.43 11,722 20.82 2,896 5.14 13,158 23.36 18,716 33.25 56,284 100
Reviews Hotel % of Total 162 15.18 164 15.37 89 8.34 345 32.33 307 28.77 1067 100
25
Table 2. Variable Description Description Average review rating of a hotel with 1for “terrible”, 2 for “poor”, 3 for “average”, 4 for “very good”, and 5 for “excellent” in a given quarter Standard deviation of review ratings of a hotel in a given quarter Number of consumer reviews in a given quarter
Mean 3.48
Std. Dev. 1.19
Median 3.86
Min 1
Max 5
0.99
0.62
2
0
2.83
3.67
6.62
2
1
189
4.53
0.01
4.60
2.82
5.97
0.09
0.26
0
0
1
2.72
1.05
3
0
5
7.90
2.54
8
0
13
HotelAge
Logarithm of the Average Daily Rate (ADR) of a hotel in a given quarter Number of management responses compared to number of consumer reviews in a given quarter Dummy variable 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 midmarket 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 Number of years since the inception of a hotel
17.43
12.65
13
-1.00
63
HotelSize
Number of guest rooms
189.41
184.91
132
5
1,84 0
ReviewQuality
ReviewConsistency ReviewQuantity logADR ResponseRatio HotelClass
HotelAmenity
26
Table 3. Estimates of Consumer Review Effects on Hotel Popularity
ReviewQualityit ReviewConsistencyit ReviewQuantityit ReviewQualityit-1 ReviewConsistencyit-1 ReviewQuantityit-1 ReviewQualityit-2 ReviewConsistencyit-2 ReviewQuantityit-21 logADRit ResponseRatioit HotelClassi HotelAmenityi HotelAgeit HotelSizei Constant i.hotel i.year-quarter Adjusted R-squared Number of year-quarter Hausman test for model preference Breusch-Pagan test Lagram-Multiplier test
Hotel Popularity (logOccupancy) (1) Fixed Effects (2) Random Effects Est. Robust p-value Est. Robust p-value 0.011** (0.011) 0.046* (0.079) 0.000 (0.584) 0.024 (0.541) 0.003** (0.033) 0.043** (0.048) 0.020*** (0.000) 0.044*** (0.000) 0.001 (0.222) 0.035 (0.355) 0.002 (0.216) 0.079 (0.311) 0.009** 0.025 0.053** (0.020) 0.000 (0.829) 0.024 (0.565) 0.000 (0.663) 0.037 (0.563) 0.446*** (0.000) 0.188*** (0.000) 0.021 (0.248) 1.376 (0.180) -0.025*** (0.000) -0.003 (0.176) -0.032*** (0.000) -2.034*** (0.000) -0.002*** (0.000) -0.078*** (0.000) 2.941*** (0.000) 3.309*** (0.000) Yes Yes Yes Yes 0.256 0.167 26 26 Diagnostic Tests Ho: Difference in coefficient is not systematic Chi2=3.670; Prob>Chi2 = 0.042 Ho: Presence of homoskedasticity (or constant variance) Chi2=5.860; Prob>Chi 2 = 0. 000 Ho: There is no serial correlation. F=0.214; Prob > F =0.660
*** p
