1 You’re the only One, or Simply the Best. Hotels differentiation, competition, agglomeration, and pricing Manuel SánchezPéreza [email protected] María Dolores IllescasManzanoa [email protected] Sergio MartínezPuertasb [email protected] CIMEDES Research Center Department of Economics and Business Department of Mathematics University of Almeria, Carretera de Sacramento, s/n 04120 Almeria Spain International Journal of Hospitality Management, 85, 102362. https://doi.org/10.1016/j.ijhm.2019.102362 Abstract Drawing on competition, signalling and agglomeration perspectives, we investigate how vertical and horizontal differentiation influence price, and how hotel competition and agglomeration may moderate any such effects. We argue that vertical differentiation should include online reputation to complement category, and that hotel clustering can provide benefits for incumbents. Hypotheses are tested using quantile regression on a sample of 1,870 hotels. In order to account for any service dimensions, an index of differentiation is estimated. We found that local competition and agglomeration moderates the relationship between differentiation and pricing. Furthermore, online reputation effects are more intense for low priced hotels. Indeed, when competition is intense, the positive effect of horizontal differentiation is lower for low priced hotels. Similarly, hotel clustering reinforces the impact of category on price, but it reduces the benefits of offering more services. Keywords Hotels; pricing; vertical differentiation; horizontal differentiation; competition; agglomeration; differentiation index; quantile regression

2 1. Introduction Undoubtedly, tourists and lodgers are increasingly expressing their interest in hotels with a more customized hospitality offer. In recent years, the evolution of the hotel offer has shown remarkable growth, with an estimated 1,420 4/5 stars hotels under construction in Europe in 2017, rising to 1,600 in 2018 (Statista). However, the average occupancy of bed places in Europe during 2017 was only 46.21%, ranging from 62.62% in Spain, 51.6% in the UK, 48.9% in France, and 46.1% in Italy (Eurostat, 2017), revealing existing capacity available. Then, hoteliers rely on differentiation as an essential strategy to compete and attain a competitive advantage (Baum & Mecias, 1992; Becerra et al., 2013). In fact, this particular approach can lead to more sustainable earnings than cost leadership strategies (Banker et al., 2014; Köseoglu et al., 2015). Despite the strategic role played by differentiation, the variety and complexity of its determining factors and their varying results hinder the development of systematic insights that could help hoteliers make informed decisions regarding differentiation and revenue management. Several reasons justify analysing hotels differentiation behavior. From the managerial side, the interest of the industry to grow in the BAR (best available rate) segment emphasises the value of differentiation (Hospitalitynet, 2019). However, high hotels pricing volatility (e.g., Guizzardi et al., 2017) can hamper differentiation based on category ratings. Indeed, the emergence of new information sources (i.e., Internet) is declining the role of chain affiliation as a quality signal for differentiation, reducing its capacity to generate revenues (Hollenbeck, 2018). Certainly, the need of new differentiation resources to combat Online Travel Agencies (OTAs) is a trend and a challenge for hotel chains (PWC, 2019). From a strategy research approach, Makadok & Ross’s (2013) formal analysis suggest that the effects of a differentiation strategy may “lead to a competitive disadvantage by reducing the firm’s value creation across the market as a whole” (p. 523). This scenario may restrict the possibilities of rent appropriation. Also, social media research has accumulated evidences which support that online comments and rankings have had a dramatic impact on tourists and hoteliers’ behaviour (Cantallops & Salvi, 2014). Also, King et al. (2014) claim more research about the effects of online reputation, or more general electronic word of mouth (eWOM) on firm-related outcomes. Besides, hotel location, as part of the horizontal differentiation, involves a trade-off between markets and rates. In the face of conventional wisdom that competitors react to the falling prices of their competitors (Becerra et al., 2013), arguments based on aggregation theories identify benefits of competitors’ clustering (McCann & Vroom, 2010) confronting competitive view.

3 In the case of vertical differentiation based on category ratings, extant hospitality research supports its reducing effect on pricing competition (Lee, 2015), though its behavior throughout the price distribution has yet to be investigated. Nonetheless, vertical differentiation, as a public and consistent aggregate of a hotel’s quality, has been measured based just on the hotel star category (e.g., Silva, 2015). However, hotel category shows limitations to differentiate hotels in terms of quality and explain prices (Abrate & Viglia, 2016). In fact, Abrate et al. (2011) demand new criteria to evaluate intangible aspects related to service quality. Nevertheless, problems of asymmetric information in the lodging industry (Manes & Tchetchik, 2018), and the use of online rankings to ascertain the real level of quality a hotel can offer (Masiero & Nicolau, 2016), have spread the adoption of eWOM as a quality signal, and determinant of booking intention (Casaló et al., 2015). Concerning horizontal differentiation, evidences display contradictory results. Also, the measurement approach used can result in scope and methodological limitations. Yet, the most widely-adopted approach is chain affiliation, for which most evidences support a positive main effect on room price (Balaguer & Pernias, 2013). In this regard, a nonsignificant effect has recently been evidenced (Baldassin et al., 2017; Pawlicz & Napierala, 2017), and even a negative relationship (Soler et al., 2019). Another measurement approach is that of estimating the individual effect of each amenity, thus generating different results based on the services included in the model (e.g., Ivanov & Piddubna, 2016; Latinopoulos, 2018; Lee & Yang, 2011; Soler et al., 2019). The main limitation of these approaches is that competition is not always considered as the basis for estimating level of differentiation. Also, the range and variety of available services, and their measurement, can result in inconsistencies, or even multicollinearity with hotel category (Pawlicz & Napierala, 2017). As an exception, Silva (2015) adopts an aggregate measurement approach. Thus, it is necessary to develop a consistent and comparable measurement in order to truly reflect the uniqueness of each hotel. Concerning the industry conditions, competitive concentration and strategic agglomeration are particularly relevant for hotels (Balaguer & Pernias, 2013; Baum & Haveman, 1997; Canina et al., 2005). On the one hand, most empirical evidence supports the negative effect of competition on prices (Abrate et al., 2016; Balaguer & Pernias, 2013). However, Kalnins (2006) suggests that the market structure of the lodging industry is not readily apparent and insinuates the presence of an oligopoly power. Moreover, Falk & Hagsten (2015) state that this negative assumption is valid only for a homogeneous offer and that geographical concentration may lead to an increase in hotel prices. Extant research on lodging markets competence has shown the role of local

4 competition in differentiation/undifferentiation contexts (Lee & Jang, 2013), concluding que in both cases there are opportunities for externalities. This research aims to shed light to the moderating role of competition in the relationship between differentiation and pricing. Location and clustering is especially important for services firms. According to the strategic agglomeration view (McCann & Folta, 2008), there might be opportunities of externalities for low-priced hotels (e.g. budget chains like Ibis or Accorhotels), being negative for firms pursuing high levels of horizontal differentiation (Canina et al., 2005). Concerning this, Lee & Jang (2015) find a positive effect but only for undifferentiated hotels and high demand periods. And only Silva’s contribution (2016) includes distance between hotels. However, some upper and luxury hotels locate in low agglomerated areas (e.g. Paradores of Spain, or wellness and relax hotels). Also, sport lodgings choose isolated places (e.g., Alegria chain). There is no evidence on how distance affects the relationship between differentiation and prices. This research aims to make several contributions. The first objective is to provide insights into conditions under which the effects of differentiation are linked to different price levels, satisfying the vacuum in testing theoretical outcomes from economic game theory to competitive behaviors of hospitality firms (Mohammed et al., 2015). Secondly, our work extends both scope and measurement of vertical and horizontal differentiation, by developing a differentiation measure valid for any type and number of services. Thirdly, it provides evidence of the effects of eWOM on pricing considering moderating effects of competition and agglomeration. And finally, this work evaluates the influence of hotel agglomeration on differentiation strategies and industry pricing (McCann & Vroom, 2010). The remainder of this paper is arranged as follows. The next section presents the conceptual framework and explains the hypothesised relationships among concepts. A literature review of hotels differentiation research is carried out (Appendix A). Later, we present the methodological and empirical analysis to test hypotheses and the results. The paper concludes with a discussion of the main results, conclusions and managerial implications. 2. Conceptual framework and hypotheses development 2.1 Vertical differentiation in hotel pricing: revisited Differentiation is a key strategy (Porter, 1980), in which literature (Cremer & Thisse, 1991) has come to distinguish between vertical (raising the preference of any customer

5 toward a product) and horizontal differentiation (service-mix valued heterogeneously by the market) (Becerra et al., 2013; Makadok & Ross, 2013). In the tourism industry, pricing is a critical decision that affects both booking and revenues, which is explained by the differentiation base (e.g., Abrate & Viglia, 2016; Becerra et al., 2013). Extant literature on hotel differentiation and pricing has mainly considered vertical differentiation as a determinant of prices, more heterogeneous in the case of horizontal differentiation (see review in Appendix A). Hotel category as quality signal is the most common vertical differentiation indicator (i.e., Pawlicz & Napiella, 2017; Silva, 2016), although independent assessments are also utilised, such as the Smith Travel Research quality segments (Kim et al., 2018). These studies consider that hotel category is an information source for tourists to choose hotels, assuming implicitly Signalling Theory assumptions (Spence, 1973). However, hotel category has been questioned as a signal for consumers, due to a lack of pay off transparency that may give rise to information asymmetries (Chen & Schwartz, 2006). Limitations may come from classification heterogeneity due to different local and national regulations, overlapping between adjacent categories, and no correspondence between classification by quality and classification by categories (Nuñez-Serrano et al., 2014). Therefore, hoteliers search for additional signals from third parties in order to communicate their own quality (Nicolau & Sellers, 2010). Extant differentiation literature have passed over this informational limitation. In particular, online reputation has become a key quality indicator in hospitality industries (e.g., Ye et al., 2009). And since it is based on service quality and lodger experience, and not on demographic issues, lodging preferences or economy, online reputation can be considered a source of vertical differentiation. None of the assessments are influenced by any economic value to the customer (Yen & Tang, 2019). When a hotel is on a ranking of the best valued hotels, this has a direct influence on willingness to pay (Nieto-Garcia et al., 2017), and on the hotel rate (Ogut & Onur, 2012). Moreover, in dynamic pricing strategies, online assessment is considered an even more important quality signal than the star category (Abrate & Viglia, 2016). Therefore, we posit vertical differentiation should be estimated using both category and online reputation, and the following hypotheses can be stated: H1a: Vertical differentiation as a category has a positive effect on hotel price H1b: Vertical differentiation as online reputation has a positive effect on hotel price 2.2. Horizontal differentiation and hotel pricing: generalization

6 In the case of horizontal differentiation, the literature review has revealed four basic approaches to identifying tourists’ preferences (see review in Appendix A), namely, chain affiliation (Becerra et al., 2013), availability of services and amenities (Espinet et al., 2003; Yang et al., 2016), geographic distances (Lee & Jang 2013; Soler et al., 2019), and location (Ivanov & Piddubna, 2016; Latinopoulos, 2018). Concerning chain affiliation as a differentiation indicator, several factors can be identified as causing it partial substitution as signal of quality and differentiation. These factors are service quality variability (Antony et al., 2004), divergences in customers’ service perception for hotels belonging to the same chain (Sun et al., 2017), and the trend of declining revenues in hotels affiliated with a chain in comparison to independent hotels (Hollenbeck, 2018) due to the use of online reputation mechanisms. Thus, hospitality research offers disparate results concerning the effect of chain affiliation on pricing. One collection of works concludes that it is positive to increase the room rate (Balaguer & Pernias, 2013; Becerra et al., 2013; Ivanov & Piddubna, 2016), while another group finds a nonsignificant effect (Baldassin et al., 2017; Hung et al., 2010; Israeli, 2002, and Pawlicz & Napierala, 2017). Furthermore, Soler et al. (2019) obtained a negative effect for said relationship. Concerning the availability of hotel services, extant hotel research has performed its analysis on the individual effect of available services on price, mostly through the inclusion of dummy variables. Additionally, according to Signalling theory, besides category, price is an ex ante perceived quality indicator meaning it can differentiate (Wolinsky, 1983), letting travellers perform a self-segmentation according to what and how much they want to pay (Michel, 2015). Hotels set lower prices to attract customers who expect simple rooms and minimum facilities (e.g., budget hotels). On the other, higher ratings correspond to expectations of a full and more customized products and services (Ropero-García, 2013). It can be expected that low-priced hotels are discouraged from developing horizontal differentiation, while high-priced hotels can justify better or exclusive services to be different and create value (Fiorentino, 1995). Therefore, we hypothesize: H2: Horizontal differentiation has a positive effect on high price hotels 2.3. Agglomeration and hotel pricing Agglomeration perspectives have been applied to hotel location decisions (e.g., Baum & Haveman, 1997), proposing the existence of a relationship between agglomeration and hotel price (McCann & Vroom, 2010). However, this relationship has received very limited consideration in explanatory models, and is frequently confused with local competition, as there is no clear delimitation between both concepts (see review in

7 Appendix A). Furthermore, in some cases one of the concepts has been overlooked completely (Abrate & Viglia, 2016; Baldassin et al., 2017; Latinopoulos, 2018; Lee & Jang, 2015; Pawlicz & Napierala, 2017), while in others both factors are aggregated with vertical and horizontal differentiation (Kim et al., 2018; Lee, 2015), even though agglomeration and local competition are different. This study fills this limitation by analysing both effects separately. Evidence on the effect of distance between competitors on price is contradictory. Becerra et al. (2013) and Latinopoulos (2018) found that agglomeration has a negative effect on rates, whereas Sánchez-Pérez et al. (2019) maintain that this is a positive effect. In contrast, Silva (2016) found that the effect of agglomeration on price is subject to the season, being positive in peak season and negative in off-peak season. Agglomeration perspective (McCann & Folta, 2008) support hotel agglomeration as a strategy for growth. In a spatial agglomeration of similar firms, benefits arise from the concentration of aggregate economic activity due to exogenous factors (i.e., other firms that attract tourists), or because hotels which locate themselves together can endogenously create externalities through their location decisions (e.g., a resort). Therefore, the following hypothesis is proposed. H3: Hotel agglomeration has a positive effect on hotel price. 2.4. The moderating effects of competition on hotel vertical/horizontal differentiation The conventional structure-conduct-performance (SCP) paradigm proposes a negative relationship between the number of competitors and their profitability based on the increase in their buying and selling powers (Bain, 1951). For the hotel industry, this relationship has been positively confirmed by Lado-Sestayo et al. (2016), and negatively by Davies (1999). Most results support that a higher number of hotels entails a reduction of average room price (Abrate et al., 2012; Balaguer & Pernias, 2013; Falk & Hagsten, 2015; Silva, 2016). Hotels literature has analysed the role of local hotels competition on pricing, but little is known about its moderating effect on the relationship between differentiation and prices. Two main approaches have been identified. In the first, competition is taken into account through the average price of competition (Lee & Jang, 2013), finding that lower-quality hotels are forced to offer greater discounts in off-season periods than higher quality hotels. Lee (2015) found that when a hotel is differentiated, vertical differentiation can drive out the effect of nearby competitors, while non-differentiated hotels must deal with high competition levels. Under the second approach, local competition is considered in relation to differentiation through interaction effects. Thus, Becerra et al. (2013) found

8 that vertical differentiation allows a reduction of competition pressure, meaning quality is the most effective strategy for raising prices. In addition, as higher categories are more likely to set higher prices, vertical differentiation is a critical strategy (Abrate et al., 2012; Sánchez-Pérez et al., 2019). And since, the higher the intensity of local competition, the higher the likelihood that hotels differentiate with high quality, and, consequently, setting higher prices. Thus, it can be posited the following hypothesis: H4a: Local competition intensifies the positive effect of vertical differentiation as star category does to price. Because of the limitation of category, eWOM has taken advantage of this informative opportunity is today considered an unavoidable component of the consumer decision making journey (Moran et al., 2014). Also, the amount of eWOM has been credited with having a positive impact on firm performance, having concluded that volume has a stronger impact than valence (Rosario et al., 2016). Additionally, the relationship between eWOM and sales is not straightfoward. Ho-Dac et al. (2013) found that brand equity moderates this relationship. In particular, positive (negative) online customer reviews increase (decrease) the sales of products of weak brands. In contrast, online reviews have no significant impact on the sales of strong brands. Thus, since hotels with lower brand equity rate lower prices (O’Neill & Mattila, 2010), we can assume that lower priced hotels benefit more from eWOM than higher priced hotels. H4b: Local competition intensifies the positive effect of vertical differentiation as online reputation does to price for low-priced hotels. Several perspectives nuance the positive relationship between concentration and pricing. Referring to economic literature on market concentration, Gan & Hernandez (2013) find that clustered hotels are more likely to charge higher prices, with agglomeration facilitating a tacit collusion behavior. Additionally, a consequence of a large number and concentration of hotels is price decreases, allowing lower-end tourists to access higher quality (Torres, 2002). Moreover, Falk & Hagsten (2015) claim that a high number of hotels can be compensated for by the benefits associated with the endogenous externalities generated by the co-location of hotels together, such as demand growth or reducing search costs (Canina et al., 2005). Therefore, it is unlikely that hoteliers that are signalling their superior quality with the star category are encouraged to invest in extra services that are not expected according to their category. In other words, following Makadok & Ross (2013), horizontal

9 differentiation can be costly. Additionally, Silva (2015) found that differentiation between hotels hinders collaboration and cooperation mechanisms between hotels, effectively preventing prices from being raised. With this logic, we hypothesize: H4c: Local competition mitigates the positive effect of horizontal differentiation on hotel price. 2.5. The moderating effects of agglomeration on hotels differentiation A hotel particular geographic location is primarily a result of the benefits associated with the externalities from related firms being located together with other similar business (Marshall, 1920). This clustering can lead to benefits endogenously created by hotels from increased demand through reduced costs for consumers, but also from access to skilled personnel or specialized services (McCann & Folta, 2008). Moreover, Lee (2015) assumes that hotel location is a dominant attribute in competition among hotels and analyses substitutability in terms of distance. Therefore, when hotels feature similar quality, they compete intensely regardless of the distance between them. Only when there are differences in quality does distance soften the intensity of competition. Consequently, co-located hotels will tend to differentiate themselves from one another in order to set higher prices. Also, agglomeration has proved to be a driver of eWOM with an inverted U-shape relationship (Liu et al., 2018). From these rationales, we hypothesize: H5a: Hotel agglomeration intensifies the positive effect of vertical differentiation as star category on hotel price. H5b: Hotel agglomeration intensifies the positive effect of vertical differentiation as online reputation on hotel price. But not always every hotel benefits from co-location. Higher levels of differentiation provide higher levels of performance, but as agglomeration increases, hotels' differentiation spill over to competitors (Urtasun & Gutiérrez, 2017). Therefore, we hypothesize: H5c: Hotel agglomeration mitigates the positive effect of horizontal differentiation on hotel price. The concepts and hypothesised relationships are depicted in Fig. 1.

16 Online Reputation×Agglomeration 0.214 H_Dif×Agglomeration 0.086* *p<0.1 **p<0.05 ***p<0.01 Concerning agglomeration, results from both methods indicate that the average distance to hotel competitors in the same hotel category has a significantly negative effect on room price that is constant throughout the conditional distribution of price (Table 2). This negative effect implies that hotel agglomeration allows prices to be raised (H3). Results shows a significantly positive interaction between category and competition in all cases (except at the 10th percentile), with a constant effect throughout the price distribution (Table 2), giving support to H4a. Figure 2 depicts all significant moderation effects. Then, the effect of vertical differentiation based on the category is intensified with the increase in competitive intensity. However, the interaction between online reputation and local competition is only significant at the low-priced quantiles (10th and 25th percentiles), with a positive relationship. Thus, H4b is supported by quantile regression, thereby confirming the moderating effect of local competition on the effect of online reputation only for low-priced hotels. This does not happen for hotels at 10th percentile, with respect to the category considered as vertical differentiation. Therefore, for lowpriced hotels located in commercials zones with large number of competitors, online reputation, as vertical differentiation, has a more relevant role than for the rest of hotels. Similarly, the interaction between horizontal differentiation and local competition is significant only for the 10th and 25th percentiles with a negative relationship, which confirms H4c only for lower-priced hotels. The significantly negative interaction (except at the 50th percentile) between Category and Agglomeration supports H5a to a greater extent. On the other hand, the interaction between agglomeration and online reputation is not significant in all cases. Therefore, H5b is not supported. Finally, the interaction between H_Dif and Agglomeration is significant and positive in all cases, which supports H5c. Figure 2 shows that greater agglomeration reduces the positive effect of horizontal differentiation on price for OLS and the high-priced quantiles (75th and 90th percentiles). For the rest of percentiles, horizontal differentiation behaves differently according to the level of agglomeration. Thus, the relationship between horizontal differentiation and price is negative for high level of agglomeration, while the relationship is positive for low agglomeration. Consequently, unlike vertical

17 differentiation, which has a positive effect and can be intensified with agglomeration and local competition, horizontal differentiation does not always produce a positive effect on price, since under high local competition or high agglomeration, horizontal differentiation is positive only for the high-priced quantiles. Fig. 2. Moderation effects of local competition and agglomeration on the relationship between differentiation strategies and room price: overall effects (OLS) and per quantile.

18 5. Conclusions and implications 5.1. Conclusions Most of the existing literature investigated hotel vertical differentiation based only on official category. For instance, Table A.1 lists eleven hospitality studies that account for vertical differentiation using a third party assessment. Then, we introduce two critical issues in vertical differentiation for both travellers' booking patterns and hoteliers' decisions: online reputation and hotel location. eWOM is introduced as a quality signal complementing the conventional view of only the star category. Also, eight studies consider the online reputation as determinant of pricing. Concerning horizontal differentiation, only one contribution has considered the horizontal differentiation in the service space in a context of multimarket contact (Silva, 2015). This research empirically examined the effects of vertical and horizontal differentiation to explain hotel pricing decisions, considering the moderating role of competition and location. Modelling is carried out assuming that pricing may have a data distribution for similar levels of differentiation. On the one hand, the study provides empirical evidence of the positive role of vertical differentiation on hotels rates, confirming category and online reputation as quality signals. In addition, local competence reinforces the positive effect of the latter only for lower-priced hotels (i.e., economy hotels). Even more, when competition is high, vertical differentiation is the more profitable strategy. On the other, horizontal differentiation is only effective for upscale hotels (not for budget hotels). This effect is greater when the hotel is isolated. Also, this research provide support to the positive consequences of agglomeration on pricing. Co-locating with other hotels in a particular geographic area results in net benefits for all. More specifically, a positive effect of category is reinforced for clustering hotels. However, there is no evidence of such behaviour with eWOM. It appears that externalities such as demand access or knowledge sharing produced by the cluster intensified the vertical differentiation effects. However, for the case of horizontal differentiation, externalities can mitigate the positive effect on price, or even become negative. For vertical differentiation, though both category and online reputation have a direct relationship to hotel rates, their weights grow inversely: category becomes more important as rates increase, and online reputation as rates reduce. As concluding remarks, we can highlight that being ‘the Best’ (i.e., vertically differentiated) always afford rising rates either climbing category or improving online

19 comments. This is more suitable for midscale and budget hotels. However, being ‘the Only one’ (i.e., horizontally differentiated) only allows to raise prices when the hotel is isolated or the competence is limited. This is suitable for upscale and luxury hotels. 5.2. Theoretical implications Several sound theoretical, strategic and methodological arguments are included to extend and generalise results. Drawing on Signalling Theory and information asymmetry perspective (Manes & Tchetchik, 2018), the first contribution is to extend the scope of vertical differentiation, adding eWOM as a complementary quality signal to hotel category. Another valuable contribution is identifying eWOM as an effective tool for lower star-rated hotels to raise their prices, since low-priced hotels are the most sensitive to online reputation and it is where category could be less reliable. This result entails a new avenue for the research on eWOM and pricing (e.g., Abrate & Viglia, 2016) by being a connector between category and pricing. Concerning horizontal differentiation, previous studies were divergent regarding to their consequences for pricing (e.g. Baldassin et al., 2017; Soler et al., 2019). We conclude that offering exclusive services benefits only to higher-priced hotels. Also, we extend Canina et al. (2005) agglomeration contribution by including competition. The study provides a methodological contribution by building a general differentiation index that encompasses any hotel services dimension, in line with Urtasum & Gutiérrez (2017). It allows to overcome extant literature based just on the availability of singular services (e.g., Baldassin et al., 2017; Latinopoulos, 2018). These studies have addressed hotel services as individual variables with a heterogeneous number of services, which makes comparison difficult. The index developed in present research overtakes this approach and generalises differentiation measurement between hotels (i.e., Silva, 2015). In addition, quantile analysis is justified especially with an asymmetric dependent variable, as is pricing, as it allows the identification of heterogeneous effects throughout the distribution. Also, we reconcile extant literature (e.g., Becerra et al., 2013; Falk & Hagsten, 2015) by demonstrating that competition is not necessary linked to a decrease in pricing. Our findings show a positive role of competition on the relationship between category and pricing. Even more, it can strengthen the positive effect of online reputation for lowerpriced hotels. Agglomeration perspective is shown useful to explain location decisions, generalizing Silva’s approach (2016), and highlighting the benefits of hotel clustering (McCaan &

20 Folta, 2008). Our evidences uncover the multiplier effect of agglomeration on hotel category. Results confirm that both vertical and horizontal differentiation strategies allow hotels prices to be raised, though with a different pattern. Anchored in signalling and information asymmetries theories, the empirical analysis supports the idea that online reputation explains all the quantiles/price levels, providing support to consider it a quality signal complementing hotel category. When hotels are located in an area with others, differentiation through quality allows prices to be raised. Nevertheless, analysis reveals that online reputation works for increasing pricing only for low priced/branding hotels. Undoubtedly, eWOM is relevant for all segments and categories, but when competition is intense, it is only effective for lower-priced hotels. In the case of horizontal differentiation, increased competition mitigates the initial expected benefits of offering more or different services in low priced hotels. Thus, it seems that objective quality indicators are valued by travellers for all levels, but when there are many alternatives, in low priced hotels offering more services, there is no parallel effect on price. 5.3. Practical implications Several practical insights can be drawn from this study. Firstly, hoteliers should link location decisions to the competitive advantage content. Trying to be ‘the Best’ always turns out in benefits. However, a corporate behaviour oriented to become ‘the only One’, that is, different from your direct competitors, could only be interesting in limited competitive environments. Secondly, this research could help to adjust decisions related to pricing strategies in relation to competitors. The upgrading in vertical differentiation always yield raising rates. The effect increases, as more hotels are located in the same area and more geographically defined is the area where the hotels are co-located. The category is quite relevant, but for lower quartiles (i.e., low-priced hotels), online reputation plays an additional role. Potential customers rely on online comments to bring their decisions to completion. Additionally, since online reputation is particularly strong in lower-priced hotels located in high concentrated areas, managing social media is a must. Positive online ratings can turn in higher price rates and budget hoteliers can profit of. Revenue management is constantly evolving, welcoming news ideas. Above implications provide some hints to understand the triangle differentiation-allocation-rate. In particular, rate accommodations tools, or revenue managers should weight category, online ratings, allocation and competence accordingly to the price percentile.

21 Finally, this paper suggests avenues to real-estate investments analysis, valuing hotel concentration areas according to the level of differentiation. 6. Limitations and future research Our study carries several important implications for hoteliers. Firstly, the results highlight the importance of online reputation for pricing decisions, in particular, for low priced strategies. Secondly, this study may help hotel managers to understand more clearly the opportunities afforded by adding more or different services and the real impact of competition. Thirdly, other research avenues should study the link between differentiation and tourism innovation (Hull & Rothenberg, 2008), or how eWOM can be integrated in revenue management. Fourthly, a number of managerial implications for location investments and planning decisions can be drawn from the findings as well. Finally, competition from attractive alternatives in the hospitality market (i.e., sharing hospitality), or prospects of moderation of hotel attractiveness as a real estate investment (Urban Land Institute, 2019) claim differentiation as a core strategy. The study does feature several limitations. Firstly, we have considered eWOM as part of vertical differentiation. However, its value for a horizontal type differentiation is debatable. This discussion is worth taking into account. Moreover, since eWOM alters revenues from chain affiliated hotels compared to independent hotels (Hollenbeck, 2018), further research could help to assess the impact of eWOM on revenues by hotel category, and, in particular, whether eWOM is eroding brand value and chain affiliation. Secondly, we have not considered the level of saturation of an area, which could possibly affect pricing. Thirdly, we have built an index of differentiation between hotels that includes the spectrum of services. However, it does not account for the weights of each service. Fourthly, complementary leisure activities, heritage, or sport activities, among others, are externalities which can also be sources of differentiation. We encourage researchers to operationalise this source of differentiation. Finally, another limitation is related to the static approach of this modelling, in comparison to a dynamic approach based on available rooms instead of hotels per se. References Abrate, G., & Viglia, G. (2016). Strategic and tactical price decisions in hotel revenue managment. Tourism Management, 55, 123-132. Abrate, G, Fraquelli G., & Viglia G. (2011). When quality signals talk: evidence from the Turin hotel industry. Tourism Management, 32(4), 912-921. Abrate, G., Fraquelli, G., & Viglia, G. (2012). Dynamic pricing strategies: evidence from European hotels. International Journal of Hospitality Management, 31(1), 160-168.

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