17 Mar Hotel Performance
Digital marketing strategies, online reviews and hotel performance
We investigate to what extent digital marketing strategies (such as having a digital marketing plan, respon- siveness to guest reviews, and monitoring and tracking online review information) inﬂuence hotel room occu- pancy and RevPar directly, and indirectly through the mediating eﬀect of the volume and valence of online reviews they lead to, and to what extent this mechanism is diﬀerent for diﬀerent types of hotels in terms of star rating and independent versus chain hotels. The research was carried out in 132 Belgian hotels. The results indicate that review volume drives room occupancy and review valence impacts RevPar. Digital marketing strategies and tactics aﬀect both the volume and valence of online reviews and, indirectly, hotel performance. This is more outspoken in chain hotels than in independent hotels, and in higher-star hotels than in lower-tier hotels.
Electronic word-of-mouth (eWOM) is “all informal communications directed at consumers through Internet-based technology related to the usage or characteristics of particular goods and services, or their sellers” (Litvin et al., 2008). eWOM can take many forms, the most important one being online reviews. eWOM has a profound eﬀect on attitudes and buying behavior of consumers and on commercial results in many product categories, such as books (Chevalier and Mayzlin, 2006), movies (Duan et al., 2008a; Liu, 2006), online games (Zhu and Zhang, 2010) and restaurants (Kim et al., 2016). eWOM appears to be particularly important for experience products. These are goods or services the quality of which cannot be judged easily prior to consumption, like hotels (Casalo et al., 2015). In such situations, the opinion of other consumers who post their experiences in online reviews, provides information from a source that is perceived as more independent and trustworthy than company information (Zhao et al., 2015; Ye et al., 2011). In the travel industry, in the USA alone nearly two thirds of Web users relied on digital channels for travel information in 2013 (eMarketer, 2013). More than 74 percent of travelers use the comments of other consumers when planning trips (Gretzel and Yoo, 2008). Thus, online reviews are an important source of information for prospective hotel consumers, and they have an inﬂuence on trust and enjoyment (Sparks and Browning, 2011; Gretzel and Yoo, 2008), perceived credibility (Casalo et al., 2015; Mauri and Minazzi, 2013), hotel awareness (Vermeulen and Seegers, 2009), corporate reputation (Baka, 2016), attitudes (Casalo et al., 2015; Vermeulen and Seegers, 2009), hotel quality perceptions (Torres et al., 2015), booking intentions (Casalo et al., 2015; Ladhari and Michaud, 2015; Mauri and Minazzi, 2013; Sparks and Browning, 2011), hotel choice (Sparks and Browning, 2011; Vermeulen and Seegers, 2009), and willingness to pay (Nieto- García et al., 2017). As a result of this, online reviews also have an eﬀect on hotel performance. Online reviews have been found to inﬂuence room occupancy, RevPar (revenue per available room), prices (Ögut and Tas, 2012; Ye et al., 2009, 2011) and market share (Duverger 2013).
Both the volume and the valence of online reviews aﬀect consumer behavior (Kwok et al., 2017). Volume refers to the number of online reviews about a hotel in a given period; valence refers to the degree of positivity (rating) of these reviews (Blal and Sturman, 2014). More online comments have been found to lead to higher awareness (Zhao et al., 2015), and a better hotel performance (Viglia et al., 2016; Melián-González et al., 2013). The valence of online reviews also aﬀects hotel performance. Ye et al. (2009, 2011) show that a 10% improvement in reviewers’ rating can increase sales by 4.4%. Anderson (2012) reports that a 1-percent increase in a hotel’s online reputation score leads up to a 0.89-percent increase in price, to a room occupancy in- crease of up to 0.54 percent, and to a 1.42-percent increase in RevPar. Viglia et al. (2016) report that a one-point increase in a hotel’s review score is associated with an increase of 7.5 percentage points in the occupancy rate. Viglia et al. (2016) and Torres et al. (2015) ﬁnd that both ratings and the number of reviews had a positive eﬀect on online hotel bookings. Blal and Sturman (2014) demonstrate that, contrary to the number of reviews, there is a signiﬁcant impact of ratings on RevPar. However, very few studies have explored the potentially dif- ferential eﬀects of review volume and valence on diﬀerent indicators of hotel performance, such as room occupancy and RevPar.
An important question is what hotel marketing management can do to increase the volume and improve the valence of online reviews and, indirectly, hotel performance. Digital marketing strategies, such as closely monitoring and analyzing customer feedback (Torres et al., 2015), responding to customer feedback (Melian-Gonzalez and Bulchand-Gidumal, 2016; Sparks et al., 2016; Torres et al., 2015; Limb and Brymer, 2015; Wang et al., 2013; Levy et al., 2013; Chen and Xie, 2008), establishing a digital reputation management plan (Levy et al., 2013), monitoring and studying social media (Baka 2016; Levy et al., 2013) and integrating third-party review sites on the hotel website (Aluri et al., 2016) appear to drive online review volume and valence and/or hotel performance. However, Melian-Gonzalez and Bulchand- Gidumal (2016); Baka (2016) and Cohen and Olsen (2013) argue that further research is needed on how digital marketing strategies can en- hance reviews and improve organizational performance.
Finally, what drives online reviews and how and to what extent these reviews impact hotel performance may be diﬀerent for diﬀerent types of hotels. Blal and Sturman (2014) and Phillips et al. (2017) argue that hotel characteristics are contextual factors that play an important moderating role in consumer behavior. Viglia et al. (2016) point out that belonging to a hotel chain or being higher-star-rated could be factors that increases hotel occupancy. However, only a few studies have focused on the moderating eﬀect of hotel characteristics on the eﬀect of online reviews on hotel performance, for instance unknown versus well-known hotels (Casalo et al., 2015), higher versus lower-tier hotels (star rating) (Blal and Sturman, 2014; Duverger, 2013), and chain versus independent hotels (Banerjee and Chua, 2016).
2-Purpose and contribution of the study
In the current study we try to partly ﬁll three voids in the literature:
- How do volume and valence of online reviews aﬀect diﬀerent in- dicators of hotel performance, e. room occupancy and RevPar?
- Which digital marketing strategies drive hotel performance (room occupancy and RevPar) through the mediating role of the volume and valence of online reviews?
- Is this mechanism diﬀerent for diﬀerent types of hotels in terms of star rating and independent versus chain hotels?
Sainaghi (2010) proposes to measure hotel performances on the basis of three dimensions: ﬁnancial (e.g. RevPar), operational (e.g. occupancy or repeat visit) and organizational (e.g. customer satisfaction). The current study uses room occupancy and RevPar as the de- pendent variables, representing an operational (quantity of bookings) and a ﬁnancial (quality of bookings) dimension, respectively (Torres et al., 2015). An interesting question is to what extent digital marketing strategies and the volume and valence of reviews impact these two KPIs diﬀerentially (Blal and Sturman, 2014). In the current study, we answer the call for a more ﬁne-grained analysis of the managerial and online review drivers of two diﬀerent hotel performance indicators. The conceptual framework is shown in Fig. 1. Data were collected from 132 hotels in ﬁve tourist destinations in Flanders (Belgium), by means of a combination of a survey, a hotel website analysis, and online review data.
The study oﬀers several insights into how hotel marketing works and provides guidelines for hotel marketing practice. Sainaghi (2010) distinguishes between external and internal determinants of hotel performance. The current study considers both. First, although the inﬂuence of online reviews (an external factor) on consumers’ attitudes and behavior has been studied extensively, far less research has been reported on the inﬂuence of reviews on hotel performance. Studies that explore the eﬀect of (digital) marketing strategies (an internal factor) on online reviews are also scarce (Sainaghi, 2010). Combining these two elements, the current study attempts to unravel the mechanism through which digital marketing strategies inﬂuence hotel performance, and the mediating role that volume and valence of online re- views play in this process. The current study also provides insights into the diﬀerential eﬀects of digital marketing strategies and online reviews on hotel performance for diﬀerent types of hotels, an important topic that only received scant attention (Sainaghi, 2010). The results of the current study can inform hotel marketing managers which elements of their digital marketing strategies to focus upon, what to expect from them in terms of their impact on diﬀerent hotel performance indicators, and which online review elements should be monitored and taken into account in this process.
3-Literature review, research questions and hypotheses
3.1-The eﬀect of online review volume and valence on hotel performance
The number of reviews a product/service receives from customers is one of the most critical review attributes (Duan et al., 2008b). Several studies have shown that more online reviews lead to a better business performance (Viglia et al., 2016; Kim et al., 2016; Zhu and Zhang, 2010; Duan et al., 2008b; Amblee and Bui, 2007; Chevalier and Mayzlin, 2006; Liu, 2006). Torres et al. (2015) and Ye et al. (2009) ﬁnd that the number of reviews have a positive eﬀect on online hotel bookings. Kim et al. (2015) report that the number of reviews has a signiﬁcant eﬀect on hotel revenues. Tuominen (2011) ﬁnds a positive relationship between the number of reviews and a hotel’s RevPar and room occupancy. Viglia et al. (2016) report that, regardless the review score, the number of reviews has a positive eﬀect with decreasing re- turns on the occupancy rate. The fact that review volume can positively aﬀect business performance is attributed to the fact that reviews, positive or negative, are an indication of hotel popularity, increase consumers’ awareness of the product, keep the product longer in people’s consideration set, attract information seekers, reduce uncertainty and perceived risk, and trigger normative behavior (‘go with the crowd’) (Zhao et al., 2015; Viglia et al., 2014; Vermeulen and Seegers, 2009). This suggests that popularity per se has a strong relevance in terms of preferences (Viglia et al., 2016). Additionally, Torres et al. (2015) argue that, with greater number of reviews, the impact of extreme reviews is minimized.
Several studies have found that the valence of online reviews aﬀects business performance. Positive consumer reviews increase business results, whereas negative online reviews decrease them (Anderson, 2012; Chevalier and Mayzlin, 2006). Positive comments can enhance the reputation of a company, while negative comments can reduce consumer interest in the company’s products/services, which can aﬀect its proﬁts. Sparks and Browning (2011) argue that the overall valence of a set of hotel reviews aﬀects customers’ evaluations and trust and, consequently, booking intentions. Ye et al. (2009, 2011) show that positive online reviews can signiﬁcantly increase the number of bookings in a hotel. They suggest that a 10% improvement in reviewers’ rating can increase sales up to more than ﬁve percent. Limb and Brymern (2015) ﬁnd that overall hotel ratings predict RevPar. Anderson (2012) reports that a 1% increase in a hotel’s online reputation score leads to a room occupancy increase of up to 0.54 percent, and to a 1.42% increase in RevPar. Ögut and Tas (2012) show that a 1% increase in online customer rating increases sales per room up to 2.68% in Paris and up to 2.62% in London. In a study of 346 hotels in Rome, Viglia et al. (2016) found that a one-point increase in the review score is associated with a 7.5% point increase in the occupancy rate.
A few studies have assessed the impact of both the volume and the valence of online reviews on various indicators of hotel performance. In an online experiment, Nieto-Garcia et al. (2014) show a positive eﬀect of review valence on willingness to pay for hotel accommodation, which is strengthened by online review volume. Viglia et al. (2014) conducted an online conjoint experiment and found that consumers’ preferences increased with both the number of reviews and the evaluation of the hotel. Torres et al. (2015) ﬁnd that both ratings and the number of reviews on TripAdvisor had a positive eﬀect on the average size of each online booking transaction. Each TripAdvisor star equated to an incremental $280 per booking transaction, and each review re- presented a total of $0.12 per booking transaction. Nieto-Garcia et al. (2014) ﬁnd that both customer ratings and the number of reviews positively inﬂuenced proﬁtability. Viglia et al. (2016) found a similar result for occupancy rate. On the other hand, Blal and Sturman (2014) and Limb and Brymer (2015) demonstrate that, contrary to the number of reviews, there is a signiﬁcant impact of review ratings on RevPar. Using 56,284 hotel reviews posted for more than 1000 hotels listed on TripAdvisor, Xie et al. (2016) show that the eﬀect of review valence lasts at least a couple of quarters, whereas that of review volume re- mains short-term. On the other hand, in the movie business, Duan et al. (2008a) found that the rating of online user reviews had no signiﬁcant impact on movies’ box oﬃce revenues, but were signiﬁcantly inﬂuenced by the volume of online posting.
3.2-Digital marketing strategies, online reviews, and hotel performance
The eﬀects of a hotel’s digital marketing strategy on hotel performance, directly, or indirectly through its eﬀect on online reviews, have only received scant attention in the academic literature (Cantallops and Salvi, 2014). Levy et al. (2013) and Melo et al. (2017) point out that hotels should establish a digital marketing plan, and that it is important for hotel managers to actively manage their online presence. In a digital hotel marketing plan, two main components can be distinguished. First, a hotel can actively use digital information in its marketing eﬀorts in several ways, such as using information and metrics from review sites, providing a link to or integrating third-party reviews on its website, using track software to analyze reviews on OTA (Online Travel Agent) sites, or using OTAs management reports. Second, a hotel can have a conversation management strategy with its customers (for instance, responses to guest reviews, encouraging guests to post comments).
Several components of such a digital marketing plan have been explored in previous research. They are discussed hereafter. Information technologies (IT) have been recognized as one of the greatest forces causing change in the hotel industry (Law et al., 2013). Based on in-depth interviews with a group of 30 hotel managers, Melian-Gonzalez and Bulchand-Gidumal (2016) explore speciﬁc routes that IT can follow in order to improve hotel performance and argue that research is needed that clariﬁes how IT can improve this performance. Online feedback can help hotel managers track the attitudes, opinions, and satisfaction of guests and can serve as the basis for a series of management actions including responding to feedback, targeting in- vestments in services that consumers would desire, and perpetuating positive actions. Hotel managers who place greater value on consumer- generated feedback are more likely to improve the perceived hotel quality (Torres et al., 2015).
Aluri et al. (2016) studied the inﬂuence of embedding social media channels on hotel websites on traveler behavior. They ﬁnd that travelers exposed to a hotel website with embedded social media channels have higher levels of perceived informativeness, enjoyment, social interaction and satisfaction and, indirectly, purchase intention. Casalo et al. (2015) ﬁnd that online ratings are considered more useful and credible when published by well-known online travel communities, such as TripAdvisor, leading to more favorable attitudes toward a hotel and higher booking intentions. Consequently, making these reviews explicitly and readily available on the hotel’s website may have favorable eﬀects on hotel performance. Melián-González et al. (2013) also argue that hoteliers should try to increase the number of reviews they receive and should therefore facilitate access to customer review sites.
The prominent role of social media necessitates that hotels also monitor online reviews for service recovery opportunities (Levy et al., 2013). Hotels are increasingly shifting from passive listening to active engagement through management responses. Online management responses are a form of customer relationship management (Gu and Ye, 2014). Management responses to a speciﬁc comment or a complaint in a consumer review show that hotel managers take their customers seriously, with the potential of improving customer reviews, customer satisfaction and, ultimately, hotel proﬁtability (Sun and Kim, 2013; Chi and Gursoy, 2009). Various studies have explored the eﬀect of responding to consumers’ remarks, and especially negative remarks or complaints. Gu and Ye (2014) show that the satisfaction level of consumers who made complaints in their reviews increases after they received management responses. Xie et al. (2014) report a positive eﬀect of the number of management responses to consumers’ comments on hotel performance. They argue that these management responses will likely increase the consumer’s likelihood of recommending the hotel, and will consequently inﬂuence the behavior of prospective customers.
Hotel management can respond to comments and complaints in diﬀerent ways. Xie et al. (2017) report that providing timely responses enhances future ﬁnancial performance, whereas providing responses by hotel executives and responses that simply repeat topics in the online review lowers future ﬁnancial performance. A constructive response with a service recovery plan for negative reviews and a commitment to continuous eﬀort for positive reviews drives purchase decisions by subsequent consumers. Functional staﬀ/departments, rather than executives, should provide managerial responses because their operational insights allow them to better address consumer comments. Sparks et al. (2016) ﬁnd that the provision of an online response, the timeliness of the response, and using a human voice rather than a professional one enhances trustworthiness and perceptions of caring. Levy et al. (2013) also suggest that the best response strategy is a positive, personalized response within a short period of time. On the basis of an experimental study with students, Min et al. (2015) conclude that using empathy in response to a negative review improved online rat- ings. The response was also rated more favorably when the response was more personal and less generic. Responses should thus include a strong signal that hotels do read the complaints, rather than repeatedly duplicating generic responses. On the other hand, and contrary to claims made in other studies, in the Min et al. (2015) study, the speed with which the hotel responded to a complaint did not inﬂuence the ratings. This may be explained by the fact that most people who read managerial responses are not complaining customers, but potential customers for whom the time element is less important.
All in all, previous studies have investigated the impact of digital strategies and customer reviews However, as Kwok et al. (2017) argue, much of this previous work mainly had a customer-centric perspective, focusing on customer decision making and customers responses such as trust and satisfaction, and there is an increasing research interest into examining the determinants of online reviews and the eﬀect of online reviews on business performance. As Phillips et al. (2017) state, a question that previous research leaves open is which antecedent factors inﬂuence both room occupancy and RevPar, and how this is explained by the online reviews they generate. In the current study, we explore 10 aspects of a digital marketing strategy, and their eﬀect on online reviews and, ultimately, hotel performance. We expect each of these digital strategies to have a positive impact:
H1. The following digital strategies have a positive eﬀect on room occupancy and RevPar:
- Having a digital marketing plan,
- Using TripAdvisor information,
- Using TripAdvisor metrics,
- Using track software to analyze reviews on OTA sites
- Using OTAs management reports,
- Providing fast response to guest reviews,
- Providing personalized responses to guest reviews,
- Encouraging guests to post comments,.
- Providing a link to TripAdvisor,
- Integrating third-party reviews on its
These eﬀect are mediated by both the volume and valence of online reviews.
3.3-Does this mechanism work diﬀerently for diﬀerent types of hotels?
Several researchers argue that hotel characteristics are contextual factors that may play an important moderating role in consumer be- havior, and call for further research into the eﬀects of eWOM between diﬀerent hotel categories (Phillips et al., 2017; Blal and Sturman, 2014; Cantallops and Salvi, 2014; Duverger, 2013).
Blal and Sturman (2014) report that review valence has a stronger eﬀect on the RevPar of higher-tier hotels, while the volume of reviews has a greater eﬀect on lower-tier hotels. The rating score eﬀect on RevPar has little impact on the economy and midscale segments, while an increasing number of reviews actually has negative eﬀects on higher-
end hotels. These results apply equally to chain and independent hotels. They argue that, as room rates increase with the segment, the im- portance of the nature of the review on the purchasing decision in- creases. On the other hand, in lower-end segments, potential buyers need conﬁrmation that the room is as advertised, and they rely more on the number of prior experiences. Similarly, Ögut and Tas (2012) ﬁnd that the eﬀect of customer ratings on sales was stronger for higher- star hotels and, in the same vein, Duverger (2013) concludes that lower- tiered hotels should not seek a high review rating, because it is mainly highly rated hotels that beneﬁt from it.
Banerjee and Chua (2016) studied diﬀerences in online reviews for independent and chain hotels, and ﬁnd review patterns to diﬀer substantially between them. However, they did not explicitly study what drives these diﬀerences and how they relate to hotel performance. Compared with an unknown, unbranded independent hotel, a well- known hotel chain brand name may attenuate the inﬂuence of rating lists, because the consumer already has stable beliefs about it (Cantallops and Salvi, 2014). Indeed, Vermeulen and Seegers (2009) ﬁnd that especially for lesser-known hotels reviews increase consumers’ consideration of the hotel, and exposure to reviews has limited eﬀect for well-known hotels.
In the current study, we explore the moderating role of hotel star rating and independent or chain hotels. Since previous research on the eﬀect of hotel characteristics is scarce and contradictory, we propose the following research question:
RQ1. What is the moderating eﬀect of hotel star rating and in- dependent or chain hotels on the relationship between digital mar- keting strategies, volume and valence of online reviews, and hotel performance (room occupancy and RevPar)?
4.1-Procedure and sample
The research was conducted in 2016 in the ﬁve oﬃcially recognized art cities in Flanders, Belgium: Antwerp, Bruges, Ghent, Mechelen and Leuven. On 31 December 2015, in those ﬁve cities, there were 224 li- censed hotels. 37.5% were chain hotels, the other ones were independent. The Flemish government assigns a star rating to each hotel. Sixty-six hotels were 1–2-star rated, the others were 3–4-star rated, except for one that was 5 star rated. In January 2016, all hotels in these ﬁve cities received a paper survey in which, amongst others, the number of realized room nights, and digital marketing activities were measured. One hundred and thirty-two hotels returned a fully completed questionnaire, a response rate of almost 59%. In this sample, there were 23 1–2-star hotels and 109 3–4 star hotels. The 5-star hotel refused to cooperate for conﬁdentiality reasons. Consequently, there are no ﬁve-star hotels in our sample. The sample contains 72 chain hotels and 60 independent hotels. Additionally, an analysis of the hotel websites was made in which elements of hotel online behavior were captured.
The dependent variables room occupancy (OCC) and RevPar were based on information reported in the survey. The list of independent variables (elements of a digital marketing strategy) was generated on the basis of in-depth interviews with 5 researchers from regional governmental or city tourism agencies, 2 representatives of hotel associations, 4 hotel tourism consultants, and 2 hotel managers (one 2-star and one 4-star hotel). The elements of digital marketing strategies are shown in Table 1. The ﬁrst eight independent variables were measured in the survey; the last two were based on the hotel website analysis. The mediating variables, i.e. the number and valence of reviews in 2015 were made available by Olery, a company that tracks and analyses online reviews about hotels on more than 100 hotel review websites.
The valence of reviews is measured by means of the Guest Experience Index (GEI), Olery’s proprietary conﬁdential measure that is based on review ratings and sub-ratings (for attributes such as rooms, cleanliness, location and service), the integrity of the reviews (based on, amongst others, the credibility of the site and the frequency with which a person posts a review), review age, and a sentiment analysis of the reviews. GEI is expressed as a score between 0 (very bad) and 100 (outstanding). The moderators, i.e. the number of stars (1 or 2 vs. 3 or 4) and the hotel type (chain or independent) are based on oﬃcial government data.
5-Analyses and results
model. The Hayes procedure only allows models with one independent variable and one dependent variable. Therefore, in this ﬁrst analysis, 20 models were tested, i.e. two (one per dependent variable) for each of the 10 independent variables. In each of these models, the number of reviews and the GEI were used as mediators. In Tables 2 and 3, the results of these estimations are shown. Only signiﬁcant direct and in- direct eﬀects of the independent on one of the dependents are reported. Full statistical details can be obtained from the authors. In Table 2, the eﬀects and their signiﬁcance of each path between each of the model variables are reported. The independent variables are in the columns and the outcome variables in the rows. The direct eﬀects of the digital strategies on either OCC or RevPar are in bold. Table 3 reports the indirect eﬀects of the independents on the dependents, through the mediation role of both the number of reviews and GEI. Each row refers to one model estimation. In the third and fourth columns of this table, conﬁdence intervals are given. When a conﬁdence interval does not contain zero, the indicated indirect eﬀect is statistically signiﬁcant (p < 0.05).
The frequency of TripAdvisor information used, using track soft- ware and integrating commercial review sites’ reviews on the hotel website have both a direct and an indirect eﬀect on room occupancy, and thus their positive eﬀect is partly mediated by the number of re- views these digital strategies generate. Using TripAdvisor metrics, having a digital marketing plan, using management reports and answering guest comments within 24 h only have an indirect eﬀect on room occupancy, and thus the eﬀect of these digital strategies on room occupancy is fully mediated by the number of reviews these strategies generate. H1a,b,c,d,e,f,j are supported as far as room occupancy and the mediating role of review volume are concerned. None of these eﬀects are partly or fully mediated by GEI. H1 is thus not supported with respect to the mediating role of GEI on occupancy. A personalized response to guest remarks, encouraging OTA reviews and a link to TripAdvisor on the hotel website have neither a direct nor an indirect eﬀect (through online reviews) on room occupancy. H1 g,h,i are not supported for room occupancy. The frequency of using TripAdvisor information has a direct positive eﬀect on RevPar, and an indirect positive eﬀect through GEI. Integrating reviews on the hotel website also has a positive indirect eﬀect on RevPar, through its beneﬁcial eﬀect on GEI. H1b,j are supported as far as RevPar and the mediating role of review valence are concerned. The number of reviews does not mediate these eﬀects on RevPar. H1 is thus not supported with respect to the mediating role of review volume on RevPar. H1a,c,d,e,f,g,h are not supported for RevPar.
In the second set of analyses, we answer RQ1 by testing the moderating eﬀect of the number of stars (1 or 2 vs. 3 or 4) and the hotel type (chain or independent) on the mediation process documented in the previous analysis, using Hayes’ PROCESS macro 59. We only made these moderation analyses on mediation models that showed signiﬁcant eﬀects (the 9 models reported in Table 3). We only report signiﬁcant moderation eﬀects. Full statistical details can be obtained from the authors. The main indicator for judging the meaningfulness of a moderation eﬀect is the diﬀerence in conditional eﬀect sizes (as detailed in Table 4 for RevPar and Table 5 for room occupancy) for the two different values of the moderators. Additionally, a clear indication of moderation would be that there is a signiﬁcant eﬀect for one of the values of the moderator, but not for the other. If a conﬁdence interval in Tables 4 and 5 contains zero, the conditional eﬀect is not signiﬁcant for that value of the moderator. We have used these criteria to arrive at our conclusions. In Tables 4 and 5, the ﬁrst column indicates the number of the analysis. The second column shows the two levels of the moderator. The next three columns show the direct eﬀects of the independent on the dependent, for the two values of the moderator. The last two columns of each table show the eﬀect sizes and the conﬁdence intervals of the indirect eﬀects through the mediator.
Table 4 shows that there is a direct positive eﬀect of frequency on RevPar for independent hotels, but not for chain hotels. However, the indirect eﬀect through generating a higher GEI score is stronger for chain hotels than for independent hotels (analysis 1). Both the direct and indirect (through GEI) eﬀects of frequency on RevPar are stronger for higher rated hotels (analysis 2). The indirect eﬀect (through GEI) of integrating reviews on the hotel website is negative for chain hotels, and insigniﬁcant for independent hotels (analysis 3). The indirect eﬀect of Intrev on RevPar is negative for low star hotels, and not signiﬁcant for high star ones (analysis 4).
Table 5 indicates that the direct eﬀect of frequency on room occupancy is positive for independent hotels and not signiﬁcant for chain hotels, but the positive indirect eﬀect through the number of posted reviews is only signiﬁcant for chain hotels and not for independent hotels (analysis 5). There is only a direct eﬀect of frequency on room occupancy for high star hotels, but its indirect eﬀect through the number of online reviews is stronger for low star hotels (analysis 6). There is no direct eﬀect of the use of metrics on room occupancy, and the indirect eﬀect is only signiﬁcant for chain hotels (analysis 7) and high star hotels (analysis 8). The indirect eﬀect (through the number of online reviews) of the use of track software on room occupancy is stronger for independent hotels (analysis 9), and both the direct and indirect eﬀects are only signiﬁcant for high-star hotels (analysis 10). The indirect eﬀect of responding to guest reviews within 24 h (through the number of reviews) on room occupancy is only signiﬁcant for high- star hotels. There is no direct eﬀect of this strategy on room occupancy (analysis 11). The indirect eﬀect of using reports on room occupancy (through the number of reviews) is only signiﬁcant for chain hotels (analysis 12). The direct eﬀect of integrating third party reviews on the hotel website is only signiﬁcant for chain hotels (analysis 13). The in- direct eﬀect of having a digital marketing plan (through the number of reviews) on room occupancy is only signiﬁcant for high-star hotels (analysis 14).
6-Conclusions and discussion
Digital marketing strategies such as having a digital marketing plan, the frequency of TripAdvisor information used, using TripAdvisor metrics, using management reports, answering guest reviews within 24 h, using track software, and integrating commercial review sites’ reviews on the hotel website, all appear to aﬀect room occupancy favorably, partly or fully because they lead to more reviews, and not because they increase review valence (GEI). Stated diﬀerently, the positive eﬀect of the use of these strategies on room occupancy is partly or fully mediated by the number of reviews these activities generate. These results conﬁrm previous ﬁndings on the role of review volume on room occupancy (Torres et al., 2015; Tuominen, 2011; Ye et al., 2009). Review valence does not aﬀect room occupancy. This contradicts earlier ﬁnd- ings by Ye et al. (2009) and Anderson (2012).
RevPar is aﬀected by digital strategies to a more limited extent, i.e. only by the frequency of using TripAdvisor information and by integrating reviews on the hotel website. Both eﬀects are mediated by review valence. This ﬁnding is in line with previous studies (e.g., Limb and Brymer, 2015; Anderson, 2012; Ögut and Tas, 2012). The fact that not the number of reviews, but their valence aﬀects RevPar, is a conﬁrmation of the ﬁndings of Blal and Sturman (2014) and Limb and Brymer (2015), but contradicts the ﬁndings of Torres et al. (2015) and Nieto-Garcia et al. (2014) in that the latter ﬁnd an eﬀect of both review volume and valence on hotel proﬁtability.
All in all, most components of a digital marketing strategy considered in the current study aﬀect hotel performance, partly or fully through the eﬀect they have on the volume and/or valence of online reviews. However, this is more the case for room occupancy than for RevPar, and review volume mediates the eﬀect of digital strategies on room occupancy, while review valence does so for the eﬀect of strategies on RevPar. These results conﬁrm Blal and Sturman’s (2014) claim that volume and valence of online reviews inﬂuence hotel performance parameters diﬀerently. The results also conﬁrm the crucial role of IT strategies and, to a lesser extent, the importance of responsiveness and service recovery. As to the former, the present ﬁndings support the need for a formal digital marketing plan (Levy et al., 2013) and the impact of embedding social media channels and integrating reviews on the hotel website (Aluri et al., 2016; Melo et al., 2017). As to the need for responding to guests’ comments, only the speed of the response to comments seems to matter. This conﬁrms previous ﬁndings (Xie et al., 2017; Sparks et al., 2016; Levy et al., 2013), although our results are not consistent with Min et al.’s (2015) conclusion.
Remarkably, a link to TripAdvisor on the hotel website, personalized response to guest remarks, and encouraging guests to post re- views, have no eﬀect on either room occupancy or RevPar. The fact that a link to TripAdvisor does not stimulate reviews and improve hotel performance, contradicts the ﬁndings of Casalo et al. (2015) that well- known online communities lead to better attitudes and booking intentions. The fact that a personalized response and encouraging reviews from guests do not have an eﬀect on reviews and hotel performance, contradicts several previous studies (Levy et al., 2013; Min et al., 2015; Sparks et al., 2016; Xie et al., 2017). This is an unexpected result that requires further study.
Of all digital strategies considered in the current study, the fre- quency of using TripAdvisor information and integrating third party reviews on the hotel website seems to be most important, since they have an impact on both the number and the valence of online reviews, which in turn leads to both a higher room occupancy and RevPar.
These mediation processes are moderated by the type (chain or independent) and the star rating of the hotel. A rather consistent result is that the eﬀect of a number of digital strategies appears to be stronger for higher-star hotels, either in their direct eﬀect on room occupancy or indirectly through their eﬀect on the number of reviews posted, or both. This is the case for having a digital marketing plan, using metrics and track software, and responding to guest reviews within 24 h. Furthermore, both the direct and indirect (through GEI) eﬀect of the frequency of use of TripAdvisor information on RevPar is stronger for higher-rated hotels, and the indirect eﬀect (through GEI) of integrating third-party review sites is negative for lower-star hotels, and not signiﬁcant for higher-star ones. These results conﬁrm the ﬁndings of Ögut and Tas (2012) and Duverger (2013), and partly those of Blal and Sturman’s (2014). The latter ﬁnd that review valence had a stronger eﬀect on the RevPar of high-star hotels than on economy and midscale segments. However, their ﬁnding that review volume drives RevPar of lower-end hotels is not conﬁrmed, quite the contrary, since our ﬁndings suggest that also the eﬀect of review volume on room occupancy plays a stronger role for higher rated hotels.
The indirect eﬀect (trough the number of reviews) of online strategies on room occupancy is generally stronger for chain hotels than for independent hotels. This is the case for the frequency of using TripAdvisor information, using metrics and reports, and integrating third party reviews on the hotel website. The indirect eﬀect of the frequency of using TripAdvisor information on RevPar through generating a higher GEI score, is stronger for chain hotels than for in- dependent hotels as well. However, the eﬀect of the use of track soft- ware on room occupancy rate is stronger for independent hotels, and so is the direct eﬀect of frequently using TripAdvisor information on RevPar. Responding quickly to guest reviews has a negative eﬀect on room occupancy for chain hotels, but not for independent hotels, and the eﬀect of integrating third-party reviews on the hotel website has a negative eﬀect on chain hotels, and no eﬀect on independent ones.
Some of these results contradict Cantallops and Salvi’s (2014) and Vermeulen and Seegers’s (2009) ﬁndings, which are explained by the assumption that chain hotels have well-known brand names and are more familiar to the traveler, and that this may diminish the inﬂuence of online reviews. We believe that our results could be explained by the fact that chain hotels may have a more professional and sophisticated, and thus more powerful, digital marketing strategy, which leads to a greater impact of digital tactics on online reviews and hotel performance.
The managerial implications of our study are that hotel manage- ment should devote considerable attention to both the number and the valence of reviews about their hotel, and should develop an extensive digital marketing strategy that has a profound impact on these reviews and, directly or indirectly, on hotel performance. The ﬁrst step in such a digital marketing strategy is to have a digital marketing plan that provides for online hotel presence, tracking and monitoring online re- views, and quick response to customer comments. Indeed, many components of such a plan have a signiﬁcant impact on the volume and/or the valence of online reviews, and on hotel performance in terms of room occupancy or RevPar. This is especially true for chain hotels and 3–4 star hotels, for which the impact of digital strategies and tactics is, generally speaking, more outspoken than for independent or lower-tier hotels. Especially the frequency of using TripAdvisor information (re- ports and metrics) and integrating third party reviews on the hotel website is crucial, since these tactics increase both the volume of and the appreciation in online reviews and, as such, indirectly inﬂuence both room occupancy and RevPar positively. The TripAdvisor
Management Dashboard is an analytics service that summarizes a hotel’s performance on TripAdvisor. Hotels can use the data and in- formation to track how they are engaging with customers and guests online, target areas for improvement and make informed, decisions.
The dashboard provides, amongst others, reports on a hotel’s total re- views and popularity ranking over time and relative to the hotel’s competitors in the same geographical region, latest review activity and top comments from reviews, number of traveler and hotel-submitted photos, and the number of visitors viewing photos, most viewed competitors, the countries generating the most traﬃc to the hotel’s
TripAdvisor page, trends over time, and performance metrics.
TripAdvisor reports provide, amongst others, business trends, risk factors, ﬁnancial data, and results of operations (www.tripadvisor.com). Additionally, hotels that strive for a higher room occupancy should aim at increasing the volume of online reviews This online review volume can be increased by increasing the frequency of using TripAdvisor metrics, by using track software and management reports, and by answering guest comments within 24 h. Hotels that want to focus upon RevPar need to improve the valence of online reviews.
8-Limitations and future research
Our study has some limitations that oﬀer opportunities for further research. First, the current study was carried out in 132 hotels in ﬁve Belgian cities. Our ﬁndings should be corroborated in diﬀerent countries and contexts and in larger samples. There were no luxury (5-star) hotels in our sample. Further research could also focus on this special hotel category. Second, two contextual variables are taken into account (independent versus chain and star rating), but diﬀerent contextual factors could be considered, such as, for instance, the size of the hotel, the region in which the hotel is situated, and the type of visitors (e.g. business, leisure). In any case, the scarcity of studies on the inﬂuence of contextual factors on the eﬀect of online strategies on reviews and hotel performance necessitates further research in this area. Third, volume and valence are the most frequently studied aspects of online reviews, but also other elements could be taken into account, such as the variance of reviews, the percentage of negative reviews, the topic of the reviews (hotel and service attributes, for instance), the degree of negativity and positivity of reviews, reviewer characteristics (demo- graphics, reputation expertise, experience), etc. (Kwok et al., 2017). Next, a number of digital strategies and tactics have been taken into account, but there could be other factors that stimulate the generation of reviews, such as service characteristics, hotel amenities, staﬀ behavior, location etc. Their relative importance and how management strategies can enhance or attenuate their eﬀects should be studied. Finally, the eﬀect of managerial responses to customer comments should be studied further. Most studies to date investigate the eﬀect of managerial responses on customer trust, concern, satisfaction, and attitudes. However, this research should be taken one step further, and explore the eﬀect of managerial responses on hotel performance, and the mediating role of customer reactions in this process.