Review Essay

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Social media review rating versus traditional customer satisfaction Which one has more incremental predictive

power in explaining hotel performance? Woo Gon Kim

International Center for Hospitality Research and Development, Florida State University, Tallahassee, Florida, USA, and

Seo Ah Park International Center for Hospitality Research and Development,

Dedman School of Hospitality, College of Business, Tallahassee, Florida, USA

Abstract Purpose – This paper aims to examine the effects of traditional customer satisfaction (CS) relative magnitude and social media review ratings on hotel performance and to explore which online travel intermediaries’ review ratings serve as the most reliable and valid predictor for hotel performance. Design/methodology/approach – In 2014, CS and hotel performance data were collected from the internal database of full-service hotels operated and managed by a large hotel chain in the USA. Each property’s social media review ratings data were hand-collected from major online travel intermediaries and social media websites. Findings – The results of this study indicate that social media review rating is a more significant predictor than traditional CS for explaining hotel performance metrics. Additionally, the social media review rating of TripAdvisor is the best predictor for hotel performance out of the other intermediaries. Research limitations/implications – This research contributes to the hospitality literature because it examines the incremental explanatory power of social media review rating and traditional CS on hotel performance. Among the leading online travel intermediaries, the findings show that TripAdvisor’s social media review rating has the most salient effect on hotel performance. Practical implications – The result of this study provides useful practical implications for hotel marketers and revenue managers. This study assists hotel marketers and revenue managers in better allocating their budget for marketing and suggests ways for channel optimization. Originality/value – The finding of this study will help revenue managers, marketing managers, and hotel owners make decisions regarding their marketing budget allocation to their social media marketing campaign and select the optimal online travel intermediaries as part of their channel management strategies.

Keywords Hotel performance, Online travel intermediaries, Social media review rating, Traditional customer satisfaction

Paper type Research paper

Introduction Social media marketing campaigns have recently played an increasingly important role as a core marketing strategy. Most marketers realize that relying only on traditional marketing media is no longer effective (Chan and Guillet, 2011). Recent statistics from Hubspot.com (2014) confirmed that 92 per cent of marketers claimed that they participated in social media campaigns, and the campaigns were important for the vitality of their business operations. In addition, a majority of respondents in this study indicated an increase in website traffic and exposure to their customers because of their social media marketing efforts (DeMers, 2014).

The current issue and full text archive of this journal is available on Emerald Insight at: www.emeraldinsight.com/0959-6119.htm

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Received 1 November 2015 Revised 21 February 2016 11 May 2016 12 June 2016 Accepted 16 June 2016

International Journal of Contemporary Hospitality Management Vol. 29 No. 2, 2017 pp. 784-802 © Emerald Publishing Limited 0959-6119 DOI 10.1108/IJCHM-11-2015-0627http://dx.doi.org/10.1108/IJCHM-11-2015-0627

The findings clearly demonstrate that the primary benefits of using social media networks are increasing brand recognition and improving the brand.

One main benefit of using social media is sharing information with travelers around the world (Chan and Guillet, 2011). Social media networks aid travelers in blogging about their trip-related reviews and opinions, in addition to their personal experiences, which then provide critical input that is used by other travelers to reduce information uncertainty. Considering experience goods like hotel services, where the quality of the services is frequently unknown prior to consumption, the role of customer reviews on websites is predominantly prominent (Litvin et al., 2008). Most consumers depend on other consumers’ reviews to infer the quality of goods and services (Wirtz and Chew, 2002). Consequently, in recent years, various online survey mechanisms have been developed to track and present review ratings (Chen and Xie, 2008). A number of previous researchers have studied the factors that affect consumers’ utilization of product consumption evaluation in the hospitality industry. The relevant factors include consumer reviews (Ayeh et al., 2013; O’Connor, 2008; Racherla et al., 2012), review valence (Duverger, 2013; Ye et al., 2009) and review rating (Ghose et al., 2014; Luca, 2011).

By providing easy-to-use and accessible platforms for consumers to share their experiences, these customer review and rating platforms have effected on consumers’ behavioral intentions and decisions, which in turn influences firm performance (Anderson, 2012; Kim et al., 2015). Consumers provide ratings and write reviews that describe their satisfaction level with hotels (Liu et al., 2013) which helps others consumers anticipate their stay at a hotel (Park and Allen, 2013). Online customer review ratings have been developed to become some of the top sources of information for consumers’ buying behavior (Ye et al., 2011) and are utilized extensively to provide consumers with information about service providers’ brand credential (Filieri and McLeay, 2014).

Social media campaigns have recently proliferated, and, specifically, social media review ratings have emerged as a significant metric. Regardless of the growing importance of social media review ratings, many hotel firms still have not implemented the management of social media review ratings. However, many hotels have adopted and monitored traditional customer satisfaction (CS) ratings as an important performance metric. CS has been considered the key driver of the financial performance of hospitality companies (Chi and Gursoy, 2009). Therefore, the most important challenges for hotel managers are the evaluation and observation of CS. In lodging services, one of the most commonly accepted tools for evaluating CS is the guest comment card (GCC) (Godolja and Spaho, 2014). According to Pizam and Ellis (1999), guest satisfaction for hospitality firms is important, and, therefore, observing CS can be valuable to the hospitality industry. Furthermore, Chi and Gursoy (2009) applied a model of service-profit chain that connects worker, guest and corporate performance to investigate the effects of staff and guest satisfaction on hospitality firm’s performance. The findings demonstrated that financial performance benefits from CS.

The extant literature has confirmed that traditional CS is one of the predictors of hotel performance (Assaf et al., 2015; Kim et al., 2013); however, with the exception of Anderson (2012) and Kim et al. (2015), limited research investigating the influence of social media review ratings on hotel top-line performance has been conducted. Hotels have traced CS ratings as an important hotel operational metric and, recently, started paying attention to the importance of social media review ratings, which could replace traditional CS and serve as a key driver of hotel financial performance. However, no previous study has investigated the effect of the relative magnitude of traditional CS and social media customer review ratings on hotel performance. To fill the gap in the literature, this study attempts to answer the following research question:

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RQ1. Between social media review ratings and traditional customer satisfaction, which one has more incremental predictive power for explaining hotel performance?

The proliferation of online travel intermediaries brings challenges to hotel revenue managers who want to select the right channels to sell their hotel room inventory. Relying more on online travel agencies will facilitate hotels’ revenue enhancement through channel diversification; however, hotels will have to pay the price of higher fees as a trade-off. Online travel intermediaries accomplished a compounded annual sales growth rate of 8 per cent from 2008 through 2013, partly because of the increase in the number of online travel agencies and conversion of traditional offline agencies to online travel agencies (Euromonitor international, 2014). Among the top 25 online booking and review sites, the four most visited sites were Booking.com, TripAdvisor, Hotels.com and Expdia.com in descending order (CNN Travel, 2013). Hotel revenue managers need to select the most effective channels to maximize their hotel room revenue. In addition, selecting the right combination of the most effective intermediaries and management of social media review ratings that reflect hotel guests’ overall experience is critical for hotel marketers and managers and directly related to revenue enhancement. Regardless of the importance of channel selection and managing customers’ social media review ratings, no research has attempted to explore which online travel intermediary’s review ratings serve as the most reliable and valid predictor for hotel performance. Therefore, the research intends to identify which online travel intermediary’s customer online review rating serves as the best predictor of hotel performance.

Literature review The role of social media Social media offers several types of online platforms, including social networks, blogs, microblogs, content communities, wikis and rating and review sites. In addition, type of social media could be classified by their ability to promote social functions. These social functions often contain conversation, sharing, relationships, information and presence (Briscoe, 2009; Chan and Guillet, 2011). Among those many social functions, socializing, interacting and building relationships with customers through customer review or the posting of customer opinions are the most effective marketing strategies in the hospitality industry (Chan and Guillet, 2011).

Social media offers an outstanding way for guests to use their own opinions of services or goods purchased to evaluate and review, enabling word-of-mouth (WOM) promotion (Chen et al., 2011). E-WOM stems from WOM, which provides important external information sources on the internet for persons planning travel (e.g. social networks, websites) (Leung et al., 2013; Litvin et al., 2008; Murphy et al., 2007). Because tourism-related products and services are well differentiated and purchasing tourism products requires higher levels of customer involvement, most travelers use social media to search various types of information before they make travel decisions in order to minimize their perceived risks and to avoid overpaying or service failure (Jeng and Fesenmaier, 2002; Leung et al., 2013).

Koumelis (2011) and Leung et al. (2013) cited a recently published World Travel Market 2011 Industry Report which said that more than 30 per cent of leisure tourists in the UK use social media sites such as Facebook and TripAdvisor to choose their hotels. Tourism companies need to comprehend the important role of social media, so they can respond to consumers’ preferences. Hotels and other travel-related companies can be in a better position to understand consumers’ perceptions if they analyze comments on social media sites. Because of the importance of social media in consumers’ decision-making, several scholars have discussed social media (Chan and Guillet, 2011; Noone et al., 2011). Despite this adoption

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of social media by tourism suppliers and consumers, practitioners and scholars have only begun to study social media.

Regulatory focus theory and online reviews Customer online reviews and online social media opinions have enhanced or even replaced traditional sources of information such as television ads or brochures. According to the survey, 59 per cent of 5,500 BizRate consumers considered consumer online reviews more valuable than expert reviews (Piller, 1999). The survey result shows that online review sites have a greater influence on purchase decisions than any other source of information, and, therefore, that social media plays an important role in marketing (DoubleClick, 2004).

To apprehend why more customers evaluate online review ratings before they make a purchase decision, regulatory focus theory (Higgins, 1997) can be used. This research applies the theory of regulatory focus to help hotel marketers comprehend customer behavior in response to marketers’ social media strategies. The theory of regulatory focus has been used to predict prompt effectiveness in marketing fields such as the acceptance of new goods (Herzenstein et al., 2007) and promotion (Zhao and Pechmann, 2007).

Higgins (2000) used regulatory focus theory to explain individuals’ behavior in the process of pursuing their goals. Based on the theory of regulatory focus, two major groups of consumers are identified: prevention-oriented and promotion-oriented. These two types of consumers evaluate pros and cons differently (Herzenstein et al., 2007). Guests that are promotion-oriented are sensitive to opportunities for improvement, are motivated by achievements and tend to concentrate on benefits such as prizes. Prevention-focused guests are responsive to risk and safety, are stimulated to prefer security to threats and prevent losses and risks (Lee et al., 2010).

When customers make hotel booking decisions, promotion-focused consumers pay more attention to reviews, such as the attractiveness of the place or the number of recommendable restaurants near the hotel, availability of free coupons and any trip tips, to gain more benefits from hotel guests who previously stayed at the same property. Prevention-focused consumers have a strong tendency to avoid risk; so, they keep an eye on guests’ negative review comments such as lower value for money, poor service quality and poor location. Online reviews from customers who experienced the service before are important sources of information for people who have no service experience (Pan et al., 2007).

The role of online travel intermediaries One of the most distinguished improvements in hotel reservations is the transformation from travel agents and hotel chain call centers to online intermediaries. Considering the rapid evolution of electronic channels, currently, hotel chains have an unstructured relationship with these online travel intermediaries which distribute hotel rooms (Carroll and Siguaw, 2003). Travel intermediaries have evolved to serve as a travel-specific search engine, facilitator of bookings and provider of customer reviews and ratings. To convince consumers to make online reservations, online travel intermediaries must understand and ensure that consumers easily get what they are looking for by tailoring sales and marketing efforts to consumers’ specific needs or search criteria (Beirne, 2005; Kim et al., 2007). In this context, online travel intermediaries now retain information about consumers’ preferences. Moreover, consumers also use on-screen tools to search and select various services and facilities (Carroll and Siguaw, 2003).

The success of popular websites such as TripAdvisor and Zagat demonstrates that online reviews represent customers’ online engagement for the purpose of travel (Gretzel and Yoo, 2008; Vermeulen and Seegers, 2009; Xiang and Gretzel, 2010). Customer reviews and review ratings and stories have the potential to enhance a specific firm’s reputation or a destination’s

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reputation (Sparks and Browning, 2011). By analyzing social media comments and online travel intermediaries, hotels are able to draw insight into what consumers like and dislike. Further analysis could also reveal what add-ons consumers are willing to pay for. Such analysis could lead to a rich understanding of customers’ characteristic and behavior patterns. Hotels may then utilize this user-generated content to develop pricing strategies to enhance the value proposition and to increase customer support (Leung et al., 2013). Online travel intermediaries affect the choices available to consumers. In addition, these intermediaries affect business models and marketing strategies that are chosen to be adopted by various contributors (Kracht and Wang, 2010; Pearce et al., 2004). TripAdvisor used to focus on providing customer online reviews and comments to travelers; however, since 2013, it has distributed the hotel room inventory of hotel firms to generate revenue from links to booking platforms. According to Krempl (2007), TripAdvisor can accomplish a relatively high “click through” rate of 20 to 30 per cent. In an effort to have an increased presence with an online intermediary, InterContinental Hotel Group (IHG) partnered with Expedia. IHG paid Expedia for prominent visual positioning on Expedia’s website, resulting in Expedia increasing its power as not only a booking service but also a media company.

Social media review rating and hotel performance Recently, a number of marketing researchers reviewed how online consumer reviews effect firms’ marketing tactics (Chen et al., 2011; Chevalier and Mayzlin, 2006), consumer decision making (Sen and Lerman 2007) and sales forecasting (Dellarocas et al., 2007; Dhar and Chang, 2009).

Online reviews for hotels, restaurants and tourism services can be a critical input for people who have no service experience (Pan et al., 2007). The impact of online customer reviews on customer repurchase intention is much stronger in the service industry, specifically hospitality than in the manufacturing industry. Kee (2008) cited joint research conducted by Power Reviews and the E-Tailing Group stating that prior to making a purchase decision, 22 per cent of respondents “always” read consumer reviews, 43 per cent of respondents checked reviews and customer ratings “most of the time”, and 68 per cent of them read “at least four reviews” before making a purchase. They postulated that 53 per cent of travelers would not book a hotel until they read customer online reviews, and 77 per cent of travelers usually or always referred to customer online reviews before selecting a hotel (Xie et al., 2014). Ye et al. (2009) determined which online users’ reviews impact on online hotel bookings by investigating their impact on hotel room sale.

Customer social media review ratings play an essential role in predicting hotel performance. According to Turner (2010), Brian Ferguson, an Expedia executive, stated that as little as a one-point review score increase can increase average daily rate (ADR) by 9 per cent. This could mean that travel reviews have a direct impact on customers’ willingness to pay a premium price. By searching other customers’ online hotel reviews, customers who have no experience adapt this new information and use it to establish an image about hotels. Furthermore, according to Anderson (2012), the Global Review Index, which is an aggregate online reputation score for hotels, influences occupancy, ADR and RevPAR. Therefore, the UGC value can enhance hotel’s financial performance and customer social media review ratings become not only the preferred source for the hotel’s image but also the most credible source of information for purchase decision making (Crotts, 1999). However, as O’Connor (2010) argued, few hotels actively manage their reputation on online travel review sites. Jeong and Jeon (2008) suggested that hotel management regularly check travel blogs and hotel review sites to gather industry trends from customer opinions.

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Traditional customer satisfaction and hotel performance CS is classically defined as “the individual’s perception of the performance of the product or service in relation to his or her expectations” (Schiffman and Kanuk, 2004). In addition, CS is an important element for measuring the competitiveness of a firm’s success (Bitner and Hubbert, 1994). From theoretical and practical perspectives, CS has been determined to be a prominent element in whether or not customers’ needs are met. Furthermore, to improve operational effectiveness and impact consumer behavior, more empirical research should be conducted to advance the knowledge of CS.

In the current business world, simply meeting the customer expectations of common service, such as a clean room and hygienic facility, does not guarantee CS. Such service components are so common that customers take them for granted. However, customer dissatisfaction arises when these basic expectations are not met (Susskind, 2005). CS can be achieved only when customers perceive the services offered to be above and beyond the ordinary performance. Heung et al. (2000) emphasized that meeting customers’ expectations and going beyond their predictive expectations are essential for business success because they provide a competitive advantage. Additionally, Chen et al. (2015) confirmed that the moderating influence of hotel occupancy can affect the relationship between CS and hotel room price.

Previous studies define consumer satisfaction as a post consumption evaluation that the option chosen exceeds or meets expectations (Beerli and Martin, 2004). According to cognitive approach, satisfaction is the customer’s response to link between standard and performance of comparison (Decrop, 1999). It was pointed out from expectancy disconfirmation model that cognitive judgment has an essential role in formation of predictive expectations and satisfaction. However, recently a proposal has been made for cognitive affective view, where CS is affected by his or her cognitive evaluation and emotions gained from their experience (Bigne et al., 2005; Phillips and Baumgartner, 2002).

Heskett and Schlesinger (1994) stated that the service-profit chain develops associations with employee satisfaction, loyalty and productivity, customer loyalty and satisfaction and profitability. Their model argues that satisfied, loyal and productive employees provide valued services to customers, resulting in CS and subsequently to customer loyalty. Finally, customer loyalty stimulates profit and growth among service providers. The service profit chain demonstrates a positive relationship between CS and financial performance under which a higher level of CS results in more favorable performance.

Despite the fact that many variables influence service providers’ profit and performance, CS is significant in shaping a company’s financial success (Chi and Gursoy, 2009). CS for service business is extremely critical, as WOM promotion and guest loyalty are the consequence of guest satisfaction (Spinelli and Canavos, 2000). Moreover, CS has many other benefits such as reducing failure costs and enhancing firm profitability (Rust and Zahorik, 1993). The history of guest satisfaction is an important predictor for any hospitality or tourism business’s success.

As suggested in the hospitality management literature, guest satisfaction is an important metric hotel manager should regularly monitor (Sun and Kim, 2013). The financial outcome of hotel operation is mainly determined by the level of services provided by their employees and the resulting CS. This satisfaction also leads to a number of benefits, such as building brand image, quicker market penetration, consistent future revenues (Reichheld and Sasser, 1990), enhanced profit margin (Anderson et al., 2004) and greater stockholder worth (Anderson et al., 1994). Barsky and Labagh (1992) further stated that satisfying customers’ needs can lead to improved customer loyalty, brand image, revenues and firm profitability.

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Methodology Data collection Initial contact was made with the senior management of major international hotel chain firms in the USA through a letter explaining the purpose of the study, requesting access to its internal operational database. Only a single hotel chain showed strong interest in our research agenda and agreed to share its firm’s internal operational database with us. After the researchers agreed to keep the identity of the hotel chain confidential, the corporate executives provided them with the hotel’s internal performance metrics, such as its traditional CS score, ADR, RevPAR and TrevPAR for 2014. The types of sample hotels included small businesses, large businesses, secondary conventions, primary conventions and resorts. The typical customer profile included individual businesses, individual leisure travelers, group businesses and group leisure travelers. To compare the effect of traditional CS and social media review ratings on hotel performance, this research also needed to hand collect its matching social media review ratings for 2014 because the chain hotel did not include social media ratings in its internal database.

Variable definition To compare the effect of traditional CS and social media review ratings on hotel performance, this study identified two independent variables, three dependent variables and two control variables. In addition, to investigate the most effective online intermediaries’ website, the researchers also identified four different types of websites. Table I illustrates the variables and their measurements.

The data include the company’s internal databases (i.e. its CS scores as well as performance indexes such as ADR, RevPAR and TrevPAR. In terms of social media review ratings, the researchers hand collected online review information for each of the chain hotel’s properties from leading online travel agencies and social networking platforms such as Trip Advisor, Hotels.com, Expedia and Booking.com. This study obtained online information from online reviews from the identified websites because of the site’s popularity and their own recommend levels. For Expedia, Hotels.com, TripAdvisor and others, the ratings are based on a 1-5 scale. Booking.com, however, uses a different scale of 1-10. This study used the Z score (standard score) of each online intermediary’s review rating because researchers

Table I. Variable and measurement

Variable Measures

Control variables Size Number of rooms Star ratings Overall of star rating

Independent variables Traditional CS Five-point Likert scale was converted into a percentage Social media review ratings Sum of Z core of four social media and online intermediaries’ rating Expedia Average of customer review rating TripAdvisor Average of customer review rating Hotels.com Average of customer review rating Booking.com Average of customer review rating

Dependent variables ADR Room revenue/number of rooms sold RevPAR Percentage of occupancy � ADR TrevPAR Total hotel revenue/number of rooms available

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most frequently use the Z score to compare a sample to a standard normal deviate and define the Z score without assumptions of normality.

To measure traditional CS, the authors used social media review ratings and a star rating using a single-item five-point Likert-type scale. As customers generally refer to online reviews and CS during their online booking stage, a hotel’s star ranking is most likely to influence the financial performance of hotels. According to the findings, a star rating is somewhat equivalent to experts’ evaluation (Ye et al., 2009), and they adopted it as a control variable. The authors identified RevPAR, ADR and TrevPAR because they are the most common performance measures in the hotel industry.

Data analysis This study conducted several hierarchical multiple regressions. Researchers use hierarchical regression analysis to analyze relationship between sets of independent variables and the dependent variable, controlling for any impact on the dependent variable form another set of independent variables. This technique is ideal because the researchers want to find out if the additional IVs in Step 2 have further explanatory power in predicting the variance of DV after the first two independent variables (e.g. size and start ratings) were entered in Step 1. In other words, the hierarchical regression analysis allowed us to examine the existence of an effect above and beyond the effect of the IVs entered before. It also allowed us to see if successive models fit better than previous ones. The Statistical Package for Social Science (SPSS) version 16 was used for further data analysis.

Results This study used descriptive statistics, such as standard deviation and mean of the data, to answer the research questions. The descriptive statistics are presented in Table II. The average star rating was 3.45 for the sample, and the average of the traditional CS was 57.2 per cent for the year 2014. The overall online customer review rating is the sum of Z scores for each online intermediary’s review rating; the sum of all the differences from the mean is itself always zero. Booking.com’s overall average was 8.47 out of 10, Expedia’s overall average was 4.34 out of 5, TripAdvisor’s overall average was 4.07 out of 5 and Hotels.com’s overall average was 4.30 out of 5.

The correlation coefficients of all the variables are presented in Table III. Traditional CS was positively related to hotel performance indicators such as ADR (0.345), RevPAR (0.278) and TrevPAR (0.317). Social media review ratings were positively related to ADR (0.465), RevPAR (0.374) and TrevPAR (0.492). The size variable, which is the number of hotel rooms,

Table II. Descriptive statistics

of data

Variable Mean SD

Size 517.164 303.332 Star ratings 3.459 0.500 Traditional CS 0.572 0.131 Social media review ratings 0.000 3.420 Expedia 4.344 0.282 TripAdvisor 4.077 0.317 Hotels.com 4.307 0.248 Booking.com 8.470 0.428 ADR 153.616 50.259 RevPAR 111.525 38.377 TrevPAR 198.213 77.362

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Table III. Correlation coefficients

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had a negative correlation with traditional CS but was not statistically significant at the 5 per cent level. Table III, a correlation table, explains the relationships among the predictors, control variables (size and star ratings) and financial performance metrics.

The findings from the research are presented in Table IV. The outcomes of the hierarchical regression analyses are demonstrated in Table IV to compare the relative magnitude of the impact of predictors (traditional CS and social media review rating) on hotel performance. The results analyzed the distinction of effectiveness between traditional CS and social media review rating on ADR, RevPAR and TrevPAR.

A three-step procedure was used in this study. In Step 1, two control variables were entered to evaluate the direct effects of hotel size and star rating on ADR in Models 1 and 2. The total explanatory power (R2) and its statistical significance (F value) of ADR was reported. In Step 2, given the hotel size and star rating, traditional CS was entered as an additional independent variable in Model 1, and the change in the R2 and F values was reported. Furthermore, in Model 2, the rating of social media review was entered instead of traditional CS, and the change in the R2 and F values was presented. In Step 3, in Model 1, the social media review rating was entered and reported a change in the R2 and F values, which indicated that there was an incremental explanatory power of social media review ratings, and it had statistical significance. In Model 2, because the social media review ratings were already entered in Step 2, traditional CS was additionally entered, and it illustrated the change in the R2 and F values. In sum, in Step 3, the difference compared the magnitude of change in R2 for Models 1 and 2, which clearly demonstrated that either social media review rating or traditional CS had a stronger incremental predictive power for explaining ADR. The three-step analyses were repeated for two hotel financial performance metrics including RevPAR and TrevPAR.

To detect for a multicollinearity issue, the variance inflation factor (VIF) was calculated, and the highest value was 1.52, which is lower than the cut-off value of 10 recommended by Myers (1990). A high VIF value for an explanatory variable indicates the existence of data collinearity. The 1.52 VIF index of this study signifies that multicollinearity is not a concern with the independent variables.

As seen in Step 1, the control variable, star ratings, had a significant positive effect on ADR, RevPAR and TrevPAR. Hotel size, another control variable, had no significant positive influence on any performance metrics. In addition, traditional CS was entered to the odd number model, and the social media review rating was added to the even number model to test the incremental explanatory power of that additional variable to explaining the variance of hotel performance in Step 2. As seen in Step 2, the increases in R2 were significant at the confidence level of 5 per cent, supporting the additional incremental predictive power of traditional CS and social media review rating.

In Step 2, after the traditional CS was entered into Models 1, 3 and 5, the incremental explanatory power was 7.9 per cent for ADR, 4.2 per cent for RevPAR and 5.7 per cent for TrevPAR. The increases in R2 were significant at the confidence level of 5 per cent, demonstrating that traditional CS provided incremental explanatory power for all three hotel performance metrics. Models 1, 3 and 5 also showed the impact of traditional CS on hotel performance metrics. The regression coefficients indicated that there was a significantly positive relationship between traditional CS and ADR (� � 0.291, p � 0.1), RevPAR (� � 0.212, p � 0.05) and TrevPAR (� � 0.246, p � 0.1).

After the social media review rating was entered to Models 2, 4 and 6, the incremental explanatory powers were 14 per cent for ADR, 6.5 per cent for RevPAR and 14.3 per cent for TrevPAR, respectively. The significant increases in R2 at the level of 0.1 demonstrated that social media review rating clearly added incremental predictive power for explaining all

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Table IV. Hierarchical multiple regression results for hotel performances

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794

three metrics of hotel performance. In addition, there was a significantly positive relationship between social media review rating and ADR (� � 0.415, p � 0.01), RevPAR (� � 0.284, p � 0.1) and TrevPAR (� � 0.420, p � 0.01). In sum, the increases in R2 of all the linear models were significant at the confidence level of 5 per cent, corroborating that traditional CS and social media review rating added incremental predictive power for explaining three hotel performance metrics.

In Step 3, traditional CS was added into the even number model, and the social media review rating was added into the odd number model. As seen in Step 3, in models 1, 3, 5, the incremental predictive power of the social media review rating was 7.8 per cent for ADR, 3.4 per cent for RevPAR and 9.3 per cent for TrevPAR at the confidence level of 5 per cent. Conversely, in Models 2, 4, 6, increase in R2 was overall very weak; 1.7 per cent for ADR, 1.1 per cent for RevPAR and 0.6 per cent for TrevPAR with no statistical significance. The contrasting results clearly showed social media review rating has more predictive power for explaining the three hotel performance metrics. In other words, social media review rating was a more significant predictor than traditional CS for explaining hotel performance metrics. Thus, the findings clearly answer the previous research question regarding explanatory power comparison between traditional CS and social media review rating. Social media review rating is a much stronger predictor than traditional CS of the three hotel performance metrics.

Table V demonstrates the impact of each of the online travel intermediaries’ review rating on ADR, RevPAR and TrevPAR. In terms of ADR, Expedia’s review rating had the most salient influence on ADR (� � 0.211, p � 0.05) compared to those of other online intermediaries. Both Expedia and TripAdvisor had significant impact on RevPAR and TrevPAR. However, TripAdvisor’s review rating had the most significant effect on RevPAR (� � 0.299, p � 0.05) and TrevPAR (� � 0.271, p � 0.05). A comparison of the relative magnitude of impact clearly demonstrates that TripAdvisor’s rating is superior to Expedia’s rating in predicting hotel performance. Thus, the findings show that the research question regarding which social media review rating serves as the best predictor for hotel performance. Our findings indicate that TripAdvisor is the best predictor for hotel performance among the other intermediaries in this study.