1 Introduction

Academic papers are more and more devoted to investigate hotel online pricing policy. The research is starting to build new frameworks to better understand the dynamic of online prices in the tourism activity, based on the characteristics of the classical forces, demand and supply, as well as on the geographical location of the hotels.

The purpose of this paper is to provide a literature review on this thread of research. One of the objectives is to investigate in what measure demand, supply as well as regional characteristics, such as accessibility and mobility, influence online pricing strategies. The search on the relevant literature was based on peer-reviewed economics journals, with a specific focus on tourism journals. To this aim, wellestablished search engines have been used such as ISI, WebScience, Scopus, Google Scholar.

From a demand perspective, numerous studies are devoted to investigate the role of consumers’ word-of-mouth in the hospitality sector. Such a traditional communication has further expanded by the introduction of the communication technology. Consumers tend to read, use and post information on the internet either ex-ante or expost of their purchase or experience. This posted information is defined as electronic Word-of-Mouth (e-WOM, see for example Buhalis and Law, 2008). The impact of e-WOM on customers’ preferences and their willingness to pay is more remarkable for the service sector. The intangible and experiential nature of services increases the degree at which customers perceive risk in their consumption. Therefore, consumers tend to search more information from previous customers who have actually experienced a specific service (Papathanassis and Knolle, 2011).

Nowadays, it is well established that the popularity of search and release of information when shopping online encourages customers to rely on these sources of online information before proceeding with the purchase. Customers’ preferences of a specific product are highly and positive correlated to the number of online reviews (Viglia et al., 2014). In this respect, as a typical service industry, hotels rely on eWOM as one of the key marketing channels that influences their business profitability. In fact, e-WOM represents a significant information source for hotel managers to understand customers’ needs and preferences and, hence, it becomes a powerful tool to enhance the quality of the supplied services (Phillips et al., 2017). Price dynamics tend to be also closely correlated to short-run and long-run hotel performance (Kim et al., 2016). Hotels have to closely evaluate their online reputation as a means for adopting pricing strategies that, ultimately, will influence their competiveness and, hence, the economy of regions and destinations.

On the supply side, research is still scarce and mostly anchored to the standard competition-oriented framework. A literature review proposes novel studies that have attempted to analyse in what manner hotel online pricing policy adjusts not only to demand shocks, but also to online market competition and innovation.

As a final aim of the paper, a statistical analysis is pursued to further generalize previous work that assessed the impact of geographical characteristics, in terms of accessibility and mobility, on online pricing policy set by hotels (Alegre et al., 2013; Costa, 2013; Yang et al., 2018a; Yang et al., 2017). A representative sample of online hotels based in the Italian region of Sardinia is analysed. To this aim, data on single and double room prices, set both in the low and high season, are retrieved from Booking.com.

The paper is organized in the following manner. The next section offers a literature review on online tourism demand and its effects on hotels profitability and pricing policy. Section 3 presents a literature review on the role of supply and innovation in setting pricing strategies. In Section 4, the role of regional characteristics on online pricing policy are highlighted and a statistical investigation is presented as a case study. In the last section concluding remarks are drawn.

2 Tourism demand

2.1 Consumers’ motivations and expectations: E-WOM and hotels profitability

Through the analysis of several research papers, it is possible to identify the key factors that lead users to provide a review on purchased tourism services. As main reasons in providing reviews by consumers, most of the studies, here analyzed, highlight aspects such as «quality of service», «customer satisfaction and dissatisfaction» and «social identity and sense of belonging to the community» (Crotts et al., 2009; Swanson and Hsu, 2009; Kim et al., 2009; Casaló et al., 2010; Sun and Qu, 2011; Sánchez-García and Currás-Pérez, 2011; Nusair et al., 2011; Bronner and Hoog, 2011; Casalo et al., 2015; Gvili and Levy, 2016; Zervas et al., 2017; Yen et al., 2019; Yang, 2018b).

According to Bronner and Hoog (2011), the most frequent motivation to perform e-WOM is to provide useful information to allow others to make a satisfactory choice. Furthermore, some of the research findings reveal that negative reviews can be encountered more often than positive reviews. Yet, Swanson and Hsu (2009) argue that customers, who have experienced satisfying experiences, are not necessarily inclined to recommend the service or to persuade others to use the same services. In this line of research, Sánchez-García and Currás-Pérez (2011) adopt an unusual perspective in the literature by proposing that dissatisfaction, caused by perceived service failure, tends to trig for emotional processes that lead consumers to experience and manifest through their reviews specific emotions such as anger, disappointment which, in turn, may induce agents’ behavioural response. Potential consumers are then inclined to spread a negative e-WOM, probably with the purpose of warning others and inducing them to be more aware about their choice.

In terms of consumers’ emotional involvement, Hu and Kim (2018) examine the effects of e-WOM motivations on customers’ e-WOM behaviour. The authors reveal important implications for the hotel sector and online marketing managers. First, self-enhancement and enjoyment is the most important drive for hotel guests to spread positive e-WOM. Moreover, according to the authors, positive online comments are related more to a pleasant stay rather than economic incentives. Hoteliers should ensure that each guest has the opportunity to feel unique thanks to the provision of impeccable services and facilities. A negative experience is the most critical motivational drive for guests to write negative online reviews.

Several studies have also explored the impact that reviews, provided by consumers, have on the hotel sector. Specifically, authors tend to highlight how especially positive judgments can increase the number of bookings and, consequently, the productivity of the firm, thus providing hoteliers with important information for their marketing strategies. Qiang et al.(2009) empirically investigate the impact of online consumer-generated reviews on hotel room sales. Utilizing data collected from the largest travel website in China, the authors develop a fixed effect log-linear regression model to assess the influence of online reviews on the number of hotel room bookings. In details, the study shows that positive online reviews can significantly increase the number of bookings, while the variance or polarity of e-WOM (i. e. rather volatile judgments or their total absence) exerts a negative impact on the number of online sales. Qiang et al. (2011) conduct a further study to identify the impact of online user-generated reviews on business performance, using data extracted from a major online travel agency in China. The empirical findings show that traveller reviews have a significant impact on online sales. Notably, the variable that mostly affects the reservations is related to the average and variance of the judgments.

In addition, Lin and Xu (2017) suggest that reviews not only have an effect on perceived reviewer trustworthiness, but also on brand attitude and purchase intention. A positive review enhances reviewer trustworthiness since it is viewed by potential consumers as being fair and believable, and it can predict a stronger purchase intention while vice versa a negative review. Mauri and Minazzi (2013) also assess that the prevalence of positive/negative comments will increase/decrease the hotel purchase intention and the level of potential consumers’ expectations. Blal et al. (2014) analyse the tourist market in the city of London, through data on 319 hotels from tripadvisor.com. The results show a positive correlation between the ratings and volume of reviews, and hotels’ revenue. Positive reviews generate an increase in sales, while negative reviews generate a decrease. Specifically, ratings have a larger effect on upper-tier hotels, while volume of reviews drives the lower-tier hotels’ results.

On the whole, several other studies have identified a link between the volume of reviews and hotels’ revenue. Experts have advocated that increasing the volume of online reviews can help mitigate negative comments (Teixeira and Kornfeld, 2013), improve consumer perception (Viglia et al., 2014), and eventually, improve operational performance (Kim et al., 2015). Therefore, as stated by García et al. (2017), indicators such as rating and volume are able to influence consumers’ willingness to pay.

A further line of research explores in what measure hotel guests reviews, posted on consumer-generated websites, have an influence on consumers’ decision-making process and service expectations. Mauri and Minazzi (2013), through survey data gathered on students or young graduates in the main university cities in Northern Italy, reveal a positive correlation between hotel purchasing intention and customers’ expectations with respect to the review rating. Moreover, hotel managers’ responses to guests’ reviews exert a negative impact on customers’ purchasing intentions. Tsao et al. (2015) show that positive reviews can have a significant effect on booking intentions amongst those individuals who are strongly inclined towards conformity. Conversely, a higher number of reviews prove to be more persuasive amongst those individuals who are characterized by a low degree of conformity. Another thread of research relates to the effects of social networks. In this respect, Ladhari and Michaud (2015) explore the influence of comments published on Facebook on friends’ intentions to book a specific hotel, the trust and the attitude towards this hotel, and the perception about its website. A survey conducted on a sample of Canadian students, under the age of 35, has confirmed all the hypotheses on the remarkable influence of such comments that are able to drive users’ decision-making process.

There are several studies that show that guests’ rating on hotels is a determinant variable that should be considered when implementing pricing policy. Qu (2014) find that the inclusion of additional reviews, obtained from other travel sites, can provide hotels more reliable information for assessing the effect of customers’ opinions on business productivity. Torres et al. (2015) also explore the impact of a hotel rating and number of reviews on the value generated through online transactions. Through a sample of 178 hotels, representing various types of firms and brands within the United States, the authors assess that ratings in TripAdvisor, as well as the number of reviews, are positively correlated to the average size of each online booking transaction. Hence, regarding e-WOM volume, a large number of comments is desirable only for firms with positive ratings that meet clients’ expectations. Indeed, for these firms, the positive effect of rating can be even more strengthened by volume. Therefore, hospitality operators should make an effort to satisfy their clients as well as encourage them to publish online feedback. In this manner, firms’ online reputation will increase allowing hotels to raise their prices and obtain a higher profitability for their business.

2.2 Demand and pricing policies

On the demand side, several studies find that customers’ rating boosts hotel performance and affects hotel room prices (Ogut and Onur Tas, 2012; Lu and Stepchenkova, 2012; Nieto et al., 2014; Hernández-Maestro and Muñoz-Gallego, 2014; Viglia et al., 2016b; Guizzardi et al., 2017). Acar et al. (2012) remark that hotels need to follow effective and efficient promotional policies based upon effective dynamic pricing strategies. In this respect, Abrate and Viglia (2016) suggest that hospitality operators should adjust their prices in line with reviewers’ evaluations about their accommodation. Hence, online reputation, by means of online customers’ reviews, plays an increasing role in pricing making decisions.

In terms of dynamic pricing strategy, Viglia et al. (2016a) remark that an important indicator is the so-called «reference price». This price is a benchmark that consumers use to evaluate prices on the market and purchase a specific product. In particular, the authors show how pricing and discounting policies affect reference price formation. The more often and the longer hotel room rates are discounted, the more likely the discounted rate becomes the reference price, and the more difficult will be for hotels to recover their reputation and value in consumers’ minds. Less mature companies tend to adopt discounts and aggressive pricing tactics in an uncontrolled way. Such companies will jeopardize reference price levels.

A further drive of pricing policy relates to customers’ characteristics and clustering. Abrate et al. (2012) analyze the dynamic price decisions within the hotel sector. The authors conclude that the inter-temporal pricing structure often depends on a price discrimination policy based on customers’ clusters, stars rating, as an indicator of quality, and the number of services supplied. Empirical results show that when customers belong to the business cluster, the lowest prices seem to be set in the time span immediately preceding customer staying. On a weekend, when the leisure cluster is predominant, prices tend to increase when approaching to the check-in date. Wu et al. (2014) show that a randomized pricing strategy tends to generate higher profits than a flat pricing strategy. The authors suggest that the online retailer should adopt the discounted price for only one period and then return to the baseline price. When low-type consumers are more patient, the retailer should decrease the promotion frequency and, simultaneously, adopt a high price. Moreover, the online retailer should diminish the promotion frequency and the high price and, simultaneously, increase the low price to encourage high-type consumers, who value time more, to purchase at the high price.

From a standard economics perspective, García et al. (2017) remark that hotels tend to implement their pricing strategies based upon forecasted levels of demand, demand price elasticity as well as competitors’ prices. Yet, the higher volatility of the online market place makes more and more difficult to make predictions and forecasts demand pattern. The role of pricing policy in online transactions should able to maximize sellers’ profits having in mind consumers’ product evaluations (Kim et al., 2009). As suggested by Guo et al. (2013a), an appropriate policy of market segmentation developed through an online reservation system would benefit both hotels and consumers. By reaching an optimal number of demand segments, firms would obtain higher profits, while consumers would gain considerable price discounts. The authors also show that, if the number of demand segments were higher than the optimal size, the profits would decrease, since the extra profit due to the additional segment can not cover the increase in operating costs.

3 Supply and online pricing policy

As the standard economics theory suggests, the level of supply influences price dynamics. Prices tend to increase when there is a scarcity of hotels available to bookin in a certain area (Ibrahim and Atiya, 2016). Abrate and Viglia (2016) support that tactical price decisions tend to be influenced by the amount of online real-time competitors. On the same line of research, Balaguer and Pernìas (2013) conduct a study on the relationship between the number of competitors and hotel prices in Spain. A higher density of competitors implies, on average, a lower price dispersion. The findings suggest that the entry of a new competitor in the neighbourhood will force a reduction in the optimal level of prices in the area. For midweek days, the effect on price level will be higher if the new entrant offers the same quality of accommodation. The effect of a new hotel on the price level will be also lower at the weekend, where there is a higher proportion of potential consumers. Becerra et al. (2013) argue that the degree of local competition mitigates the effect of differentiation on pricing policy; but hotels characterized by higher quality services (expressed in terms of number of stars) can better withstand the entry of new competitors that would impose price cuts. Furthermore, Xie and Kwok (2017) provides insight on the impact of innovators as Airbnb on hotels, in a increasingly competitive hospitality market, driven by ever-changing technology and innovation. The study empirically considers in what measure Airbnb pricing policy affects the performance of nearby hotels through the lens of price difference and price dispersion.

Pricing policy are also influenced by other factors such as the presence of large events and taxation, although the thread of this research is still scarce. As an example, Herrmann and Herrmann (2014) investigate hotel prices in Munich under the influence of the Oktoberfest. In particular, the author analyses how the event affects the daily online price level as well as hotels price differentials. In general, average hotel prices are very volatile over time. Munich hotels tend to set prices according to expected demand and vary prices depending on the day of the week during the event.

Online Travel Agencies (OTA) also play an important role in tourism web marketing via online brokerage agencies (Internet Distribution Systems, IDS). These are portals where consumers can compare different offers, read the reviews and make a reservation. Nowadays, OTA have become a key distribution channel for many hospitality firms providing not only the possibility to hold reservations, but also a higher visibility to hotels. Several papers are devoted to explore the role of OTA in hospitality and tourism research (Guo et al., 2013a; Blal and Sturman, 2014; Ling et al., 2014; Mellinas et al., 2015).

The use of OTA platforms is also a useful tool for monitoring and managing e-WOM (Yang, 2018a, b). Sorzabal et al. (2013) remark that IDS are an essential tool for the tourism sector since these provide a flexible way for changing prices at a real time. Raguseo et al.(2017) show that hotels listed on multiple OTA are able to boost sales revenue and operating profitability. The authors explore in what manner a higher visibility influences firms’ business profitability. They find that those hotels that have chosen to advertise their rooms on a higher number of OTA are able to boost sales and business profitability and, thus, capture more economic value from their visibility on these distribution channels. On the opposite, the authors show that visibility on TripAdvisor in the form of either higher review ratings, larger review volumes, and higher variance in ratings and hoteliers’ responses has no significant effect on revenue and business profit growth. As a further outcome, Xiang et al. (2017) indicate that, within the hotel sector, TripAdvisor is widely perceived as a leader as a data source due to the number of reviews available, a wide distribution of customers’ preferences, an adequate length of consumer reviews together with support and reputation.

In the hospitality sector, revenue management has advanced significantly in recent decades and is recognized to have an important impact on dynamic pricing techniques (Melis and Piga, 2017). Oses et al. (2016) remark that monitoring the performance of a destination is essential for effective destination management. As the hotel sector is one of the main destination management stakeholders, it is important to have an agile monitoring system to evaluate its performance. Amongst others, Rodríguez and Ballestero (2017) suggest that to positively influence customer perceptions, hotel businesses should develop revenue management practices. Management strategies, if applied in an adequate manner, may induce buyers’ perception of fair treatment with respect to the set prices. Yet, the authors argue that only large and chain-affiliated hotels can afford a specialized revenue management software and expertise. In this respect, Ivanov and Ayas (2017) investigate the revenue management practices of accommodation establishments in Turkey through a survey of 105 managers. The findings indicate that, in general, hoteliers do not have a revenue management system that is usually under the responsibility of a general manager, front office manager or marketing manager. Revenue management practices are mostly adopted by high category, chain affiliated, urban and seaside hotels with large number of rooms. Melis and Piga (2017) find that 4-5 star online hotels, with respect to lower star firms, are characterized by a more dynamic pricing policy where a revenue management system plays an important role. Yet, the authors emphasise that the introduction of such a system does not depend necessarily on financial constraints, rather than by lack of technological skills as well as by the firm organization and culture.

4 Regional characteristics and online pricing policies

Firms’ geographical characteristics also play an important role in setting a pricing policy (Phillips et al., 2015; Yang et al., 2017). Yang et al. (2018a) investigate the influence on hotel prices by market accessibility as well as by various quality-signalling factors, such as online ratings, recommendation percentage, hotel typology, chain affiliation. The study indicates that often low market accessibility (e.g. high flight costs; scarce mobility) leads to lower prices, although such an influence tends to be mitigated by a well-established reputation gained through the high qualitysignalling factors.

Xun and Yibai (2016) find that the determinants that lead to customers’ satisfaction or dissatisfaction toward hotels are often specific to particular types of hotels. The authors highlight that the determinants of satisfaction are generally the same for all the different types of structure (i. e. location and accessibility, staff performance and empathy, quality of rooms and restaurant). Dissatisfaction factors are instead more various for each type of hotel, compared to those that drive satisfaction. These include elements such as environmental issues, slow Wi-Fi, poor quality restaurants, noise, malfunctioning or old furniture, and staff unfriendly and/or unavailable. For hotels with limited services, environmental issues or noise are more influential as dissatisfactory factors. For hotels characterized by luxurious services, with food and drinks facilities, a dissatisfactory factor is the poor quality of the restaurants. Finally, for luxury hotels, without food and drinks facilities, the poor quality of the room (i. e. old furniture and malfunctioning) is the most factor for dissatisfaction.

Location, accessibility and mobility within a region tend also to affect pricing strategies, as shown by Alegre et al. (2013). The authors find that a negative relationship between the prices and the distance to the beach for German and British package holidays. A mixed evidence was captured on the relationship between prices and the distance to the centre of the tourist resort: positive for the British tourists, while negative for the German tourists. This assumption is further confirmed by Costa (2013) who argues that prices depend on hotel category, accommodation types as well as on the geographical location.

More recently, Latinopoulos (2018) examines the effect of sea view, together with other structural and location attributes, on room rates. Specifically, the author tests whether rooms with a sea view denote higher prices than other room types, thus quantifying the associated aesthetic and rent value of coastal areas where tourismrelated development is a key economic activity. The results indicate that local natural resources tend to have a substantial role in the rent and aesthetic value.

4.1. Accessibility and Mobility: a case study

The present empirical investigation explores the importance of geographical characteristics in setting online pricing policy by hotels. To this aim, the analysis is run in the region of Sardinia (Italy; see also Biagi and Pulina, 2007; Pulina and Santoni, 2018). The data were extrapolated from the Booking.comwebsite in 2017. The overall sample consists of 320 hotels located in five, of the eight, different provinces in the island, with the aim to elicit different levels of tourism specialization, hotel quality (expressed in terms of number of stars) as well as different levels of accessibility and mobility within the region. According to the official statistics, in 2016 there were approximately 2,500 hotels in Sardinia (CRENOS, 2018). Hence, the extracted sample represents the population with a confidence interval of 5% and a confidence level of 95%.

Table 1 provides the main characteristics of the sample. The majority of the hotels, which advertise their business in Booking.com, are located in Olbia-Tempio (59.4%).

This province is characterised by a high tourism specialization built since the ‘50s of the past century. Besides, the greatest quota of the sample consists of 3-star hotels (54.3%); this outcome is consistent with the most common types of hotels (CRENOS, 2018; see also Mantovani et al., 2017), as driven by past policy issued by the Region (Pulina and Biagi, 2010). As expected, the majority of the hotels are located on the coast (85.3%) and have an own point-of-sale to facilitate transactions in situ (POS, 97.8%).

The aim is to explore whether there are statistically significant differences in hotel pricing policy with respect to their location, that is accessibility to the destination and mobility within the island, respectively. In this respect, hotel prices were collected discriminating between the low and the high season, as well as between a single and a double room. An independent sample test is run on the variance and on the mean. Moreover, a bootstrapping, with 1,000 replicas is also run to overcome small sample size issues, yet the findings have proved to be statistically consistent (full results can be provided upon request).

A first independent sample test is run to explore for statistical differences in terms of accessibility (Table 2). In this case, the grouping variable is the province: «Olbia-Tempio», which hosts a main national/international airport and port in the region, versus all «the other provinces» which do not host any main transportation hub.

The null hypothesis of equality of variance and equality of means, respectively, can not be accepted in all the cases, at least at the 5% level of significance, implying that there are statistical differences in the pricing strategies adopted by «more accessible» and «less accessible» hotels. In greater details, price differences between a single room, in hotels with high accessibility versus hotels with a low accessibility, reach on average 97% during the low season, and 169% in the high season. For a double room, price differences reach on average 77% during the low season and 139% during the high season.

A second independent sample test is run to assess statistical differences in terms of mobility issues within the island, using as a grouping variable «coastal» versus «non-coastal» hotels (Table 3).

Also in this case, the null hypothesis of equality of variance and equality of means, respectively, can not be accepted in all the cases, at least at the 5% level of significance, implying that there are remarkable differences in the pricing strategies adopted by «coastal» and «non-coastal» hotels. In greater details, price differences between a single room, in coastal and non-coastal hotels, reach on average 61% during the low season, and 119% in the high season. For a double room, price differences reach on average 40% during the low season and 100% during the high season.

5 Conclusions

This paper has provided a literature review on hotel online pricing policy, covering three main perspectives: demand, supply and regional characteristics.

From a demand perspective, an in-depth and updated literature review has shown that online reviews, published by customers and viewers, play a significant role within the tourism activity and, especially, on the hotel sector. More and more, hotels rely on e-WOM because this marketing channel affects room revenue and business profitability. E-WOM is also a significant information source for hoteliers to understand customers’ needs. Hence, online reputation plays an increasing role in price decisionmaking and hospitality operators should adjust prices in accordance to customers’ evaluations (Abrate and Viglia, 2016).

On the supply side, research has proved to be still scarce and mostly underpinned to the standard competition framework, as also remarked by Van der Rest et al. (2018). The literature review has revealed that hotel pricing strategy would need more innovation able to detect changes within a rather volatile online market. An innovative pricing system would be based not only on the value perceived by customers, but it would also require a remarkable investment in human skills, able to face the dynamic technical challenges. In this respect, many authors emphasise that hotels would benefit from a revenue management system that, although thought to be costly, especially for medium-low quality hotels, could be implemented by a workforce with higher digital skills, or even within OTA platforms which are having an increasing role both as a marketing channel as well as an important pricing making player (see Melis and Piga, 2017). Besides, through OTA, consumers can not only book their holidays, but also publish their reviews about their direct and indirect experience.

As a final step of the present research, a statistical analysis has been run to test in what measure geographical characteristics, such as accessibility and mobility, play a relevant role in hotel pricing strategies. The region of Sardinia (Italy) has been used as a case study and data, on a sample of representative hotels, were retrieved from Booking.com. These data elicited location heterogeneity and hotel quality and characteristics. The results have indicated the relevant role played by accessibility and mobility in the remarkable price differences, both in terms of products (single versus double rooms) and seasonality (low versus high season). Hence, these empirical findings have generalized further previous research and, especially, the work by Latinopoulos (2018) in terms of sea front rents, and by Yang et al. (2018a) in terms of accessibility.

Overall, the literature review has provided a better understanding on the online hotel activity. Amongst possible threads of investigation, future research would further investigate issues such as the impact of technical advances, competition, seasonality, taxation and regulation, impact of events, geographical characteristics, that can shade light on pricing policy adopted within the online hospitality sector.

6 References

Abrate G., Fraquelli G., and Viglia G. (2012): «Dynamic pricing strategies: Evidence from European hotels», International Journal of Hospitality Management, 31, 160-168.

Abrate G., and Viglia G. (2016): «Strategic and tactical price decisions in hotel revenue management», Tourism Management, 55, 123-132.

Acar N., Gullu K., and Karamustafa K. (2012): «Promotional Activity in Hotels: Applications in the Turkey’s Region of Capadocia», Procedia Social and Behavioral Sciences, 58, 1027-1036.

Alegre, J., Cladera, M., and Sard, M. (2013): «Tourist areas: Examining the effects of location attributes on tour-operator package holiday prices», Tourism Management, 38, 131-141.

Algeciras, A. A., and Ballestero, P. T. (2017): «An empirical analysis of the effectiveness of hotel Revenue Management in five-star hotels in Barcelona, Spain», Journal of Hospitality and Tourism Management, 32, 24-34.

Balaguer, J., and Pernías, J. C. (2013): «Relationship between spatial agglomeration and hotel prices. Evidence from business and tourism consumers», Tourism Management, 36, 391-400.

Becerra, M., Santalò, J., and Silva, R. (2013): «Being better vs. being different: Differentiation, competition, and pricing strategies in the Spanish hotel industry», Tourism Management, 34, 71-79.

Biagi, B., and Pulina, M. (2007): «Stessa spiaggia stesso mare: turismo, ambiente e qualità della vita», in Punzo, L. F., and Usai, S. (eds.), L’estate al mare, Milano: McGraw-Hill.

Blal, I., and Sturman, M. C. (2014): «The differential effects of the quality and quantity of online reviews on hotel room sales», Cornell Hospitality Quarterly, 55(4), 365-375.

Bronner, F., and Hoog, R. (2011): «Vacationers and e-WOM: who posts, and why, where, and what?», Journal of Travel Research, 50(1),15-26.

Buhalis, D., and Law, R. (2008): «Progress in information technology and tourism management: 20 years on and 10 years after the internet-the state of eTourism research», Tourism Management, 29(4), 609-623.

Casaló, L., Flavián, C., and Guinalíu, M. (2010): «Determinants of the intention to participate in firmhosted online travel communities and effects on consumer behavioral intentions», Tourism Management, 31, 898-911.

Casalo, L., Flavian, C., Guinaliu, M., and Ekinci, Y. (2015): «Do online hotel rating schemes influence booking behaviors?», International Journal of Hospitality Management, 49, 28-36.

Cezar, A., and Ogüt, H. (2016): «Analyzing conversion rates in online hotel booking: The role of customer reviews, recommendations and rank order in search listings», International Journal of Contemporary Hospitality Management, 28, 286-304.

Costa, J. C. C. (2013): «Price formation and market segmentation in seaside accommodations», International Journal of Hospitality Management, 33, 446-455.

CRENOS (2018): Economia della Sardegna, 25.º Rapporto, Cagliari, CUEC.

Crotts, J. C., Mason, P. R., and Davis, B. (2009): «Measuring guest satisfaction and competitive position in the hospitality and tourism industry», Journal of Travel Research, 48(2), 139-151.

Ferrer, J. C., Diego, O., and Jorge, V. (2012): «Risk averse retail pricing with robust demand forecasting», International Journal of Production Economics, 136, 151-160.

García, M. N., Gallego, P. A. M., and Benito, O. G. (2017): «Tourists’ willingness to pay for an accommodation: The effect of eWOM and internal reference price», International Journal of Hospitality Management, 62, 67-77.

Guo, X., Ling, L., Dong, Y., and Liang, L. (2013a): «Cooperation contract in tourism supply chains: the optimal pricing strategy of hotels for cooperative third party strategic websites», Annals of Tourism Research, 41, 20-41.

Guo, X, Ling, L., Yang, C., Li, Z., and Liang, L. (2013b): «Optimal pricing strategy based on market segmentation for service products using online reservation systems: An application to hotel rooms», International Journal of Hospitality Management, 35, 274-281.

Guizzardi, A., Stacchini, A., and Ranieri, E. (2017): «Best buy: what does it mean in corporate travel?», Current Issues in Tourism, 20(2), 129-134.

Gvili, Y., and Levy, S. (2016): «Antecedents of attitudes toward eWOM communication: differences across channels», Internet Research, 26(5), 1030-1051.

Herrmann, R., and Herrmann, O. (2014): «Hotel room rates under the influence of a large event: The Oktoberfest in Munich 2012», International Journal of Hospitality Management, 39, 21-28.

Hu, Y., and Kim, H. J. (2018): «Positive and negative eWOM motivations and hotel customers’ eWOM behavior: Does personality matter?», International Journal of Hospitality Management, 75, 27-37.

Ibrahim, M. N., and Atiya, A. F. (2016): «Analytical solutions to the dynamic pricing problem for time-normalized revenue», European Journal of Operational Research, 254, 632-643.

Ivanov, S., and Ayas, C. (2017): «Investigation of the revenue management practices of accommodation establishments in Turkey: An exploratory study», Tourism Management Perspectives, 22, 137-149.

Kim, T., Kim, W. G., and Kim, H. B. (2009): «The effects of perceived justice on recovery satisfaction, trust, word-of-mouth, and revisit intention in upscale hotels», Tourism Management, 30, 51-62.

Kim, W. G., Lim, H., and Brymer, R. A. (2015): «The effectiveness of managing social media on hotel performance», International Journal of Hospitality Management, 44, 165-171.

Kim, M., Lee, S. K., and Roehl, W. S. (2016): «The effect of idiosyncratic price movements on shortand long-run performance of hotels», International Journal of Hospitality Management, 56, 78-86.

Ladhari, R., and Michaud, M. (2015): «eWOM effects on hotel booking intentions, attitudes, trust, and website perceptions», International Journal of Hospitality Management, 46, 36-45.

Latinopoulos, D. (2018): «Using a spatial hedonic analysis to evaluate the effect of sea view on hotel price», Tourism Management, 65, 87-89.

Lee, S. (2014): «Revisiting the impact of bed tax with spatial panel approach», International Journal of Hospitality Management, 41, 49-55.

Lin, C., and Xu, X. (2017): «Effectiveness of online consumer reviews: the influence of valence, reviewer ethnicity, social distance and source trustworthiness», Internet Research, 27(2), 362-380.

Ling, L., Guo, X., and Yang, C. (2014): «Opening the online marketplace: An examination of hotel pricing and travel agency on-line distribution of rooms», Tourism Management, 45, 234-243.

Lu, W., and Stepchenkova, S. (2012): «Ecotourism experiences reported online: Classification of satisfaction attributes», Tourism Management, 33, 702-712.

Mantovani, A., Piga, C., and Reggiani, C. (2017): «The dynamics of online hotel prices and the EU Booking.com», NET Institute, www.NETinst.org, Working Paper N.17-04, September, 1-43.

Mariani, M. M., and Borghi, M. (2018): «Effects of the Booking.com rating system: Bringing hotel class into the picture», Tourism Management, 66, 47-52.

Mauri, A. G., and Minazzi, R. (2013): «Web reviews influence on expectations and purchasing intentions of hotel potential customers», International Journal of Hospitality Management, 34, 99-107.

Melis, G., and Piga, C. A. (2017): «Are all online hotel prices created dynamic? An empirical assessment», International Journal of Hospitality Management, 67, 163-173.

Mellinas, J. P., María-Dolores, S. M. M., and García, J. J. B. (2015): «Booking.com: The unexpected scoring system», Tourism Management, 49, 72-74.

Mellinas, J. P., María-Dolores, S. M. M., and García, J. J. B. (2016): «Effects of the Booking.com scoring system», Tourism Management, 57, 80-83.

Nieto, J., Hernández Maestro, R. M., and Muñoz-Gallego, P. A. (2014): «Marketing decisions, customer reviews, and business performance: The use of the Top rural website by Spanish rural lodging establishments», Tourism Management, 45, 115-123.

Nieto-García, M., Muñoz-Gallego, P., and González-Benito, O. (2017): «Tourists’ willingness to pay for an accommodation: the effect of eWOM and internal reference price», International Journal of Hospitality Management, 62, 67-77.

Nusair, K., Parsa, H. G., and Cobanoglu, C. (2011): «Building a model of commitment for Generation Y: an empirical study on e-travel retailers», Tourism Management, 32, 833-843.

Ogut, H., and Onur Tas, B. K. (2012): «The influence of internet customer reviews on the online sales and prices in hotel industry», Service Industries Journal, 32(2), 197-214.

Oses, N., Gerrikagoitia, J. K., and Alzua, A. (2016): «Monitoring and benchmarking the performance of a destination’s hotel industry: The case study of Bilbao in 2014», Tourism Management Perspectives, 19, 48-60.

Papathanassis, A., and Knolle, F. (2011): «Exploring the Adoption and Processing of Online Holiday Reviews: A Grounded Theory Approach», Tourism Management, 32(2), 215-224.

Phillips, P., Barnes, S., Zigan, K., and Schegg, R. (2017): «Understanding the impact of online reviews on hotel performance: an empirical analysis», Journal of Travel Research, 56(2), 235-249.

Phillips, P., Zigan, K., Santos Silva, M. M., and Schegg, R. (2015): «The interactive effects of online reviews on the determinants of Swiss hotel performance: a natural network analysis», Tourism Management, 50, 130-141.

Pulina, M., and Biagi, B. (2010): «The evolution of tourism demand and supply: A regional policy study», International Journal of Tourism Policy, 3(3), 237-256.

Pulina, M., and Santoni, V. (2018): «A two-stage DEA approach to analyse the efficiency of the hospitality sector», Tourism Economics, 24(3), 352-365.

Qiang, Y., Law, R., and Gu, B. (2009): «The impact of online user reviews on hotel room sales», International Journal of Hospitality Management, 28(1), 180-182.

Qiang Y., Law, R., Gu, B., and Chen, W. (2011): «The influence of user-generated content on traveler behavior: An empirical Investigation on the effect of e-word-of-mouth to hotel online bookings», Computers in Human Behavior, 27(2), 634-639.

Qu, L., Qiang, Y., and Law, R. (2014): «Moderating effects of product heterogeneity between online word of mouth and hotel sales», Journal of Electronic Commerce Research, 15, 1-12.

Raguseo, E., Neirotti, P., and Paolucci, E. (2017): «How small hotels can drive value their way in infomediation. The case of ‘Italian hotels vs. OTAs and TripAdvisor», Information & Management, 54, 745-756.

Riadh, L., and Michaud, M. (2015): «eWOM Effects on hotel booking intentions, attitudes, trust, and website perceptions», International Journal of Hospitality Management, 46, 36-45.

Rodríguez, A. A., and Ballestero, P. T. (2017): «An empirical analysis of the effectiveness of hotel Revenue Management in five-star hotels in Barcelona, Spain», Journal of Hospitality and Tourism Management, 32, 24-34.

Sánchez-García, I., and Currás-Pérez, R. (2011): «Effects of dissatisfaction in tourist services: the role of anger and regret», Tourism Management, 32, 1397-1406.

Sorzabal, A., Gerrikagoitia, J. K., and Manzanera, E. T. (2013): «Opening and closing Internet booking channel for hotels. A first approximation», Tourism Management Perspectives, 5, 5-9.

Sun, L. B., and Qu, H. (2011): «Is there any gender effect on the relationship between service quality and word-of-mouth.», Journal Travel Tourism Marketing, 28, 210-224.

Swanson, S. R., and Hsu, M. K. (2009): «Critical incidents in tourism: failure, recovery, customer switching, and word-of-mouth behaviors», Journal Travel Tourism Marketing, 26, 180-194.

Teixeira, T., and Kornfeld, L. (2013): «Managing Online Reviews on TripAdvisor», Harvard Business School Publishing, Boston, MA HBS, No. 514-017.

Torres, E. N., Singhb, D., and Robertson-Ringb, A. (2015): «Consumer reviews and the creation of booking transaction value: Lesson from the hotel industry», International Journal of Hospitality Management, 50, 77-83.

Tsao, W.-C., Hsieh, M.-T., Shih, L.-W., and Lin, T. M. Y. (2015): «Compliance with eWOM: The influence of hotel reviews on booking intention from the perspective of consumer conformity», International Journal of Hospitality Management, 46, 99-111.

Van der Rest, J. P., Roper, A., and Wang, X. L. (2018): «Why is a change of company pricing policy so hard to implement», International Journal of Hospitality Management, 69, 30-40.

Viglia, G., Furlan, R., and Ladrón-de-Guevara, A. (2014): «Please, talk about it! When hotel popularity boosts preferences», International Journal of Hospitality Management, 42, 155-164.

Viglia, G., Mauri, A., and Carricano, M. (2016a): «The exploration of hotel reference prices under dynamic pricing scenarios and different forms of competition», International Journal of Hospitality Management, 52, 46-55.

Viglia, G., Minazzi, R., and Buhalis, D. (2016b): «The influence of e-word-of-mouth on hotel occupancy rate», International Journal of Contemporary Hospitality Management, 28(9), 2035-2051.

Wo, J., Xu, L., and Xu, L. D. (2014): «A randomized pricing decision support system in electronic commerce», Decision Support Systems, 58, 43-52.

Wu, J., Li, L., and Xu, D. (2014): «A randomized pricing decision support system in electronic commerce», Decision Support Systems, 58, 43-52.

Xiang, Z., Du, Q., Ma, Y., and Fan, W. (2017): «A comparative analysis of major online review platforms: Implications for social media analytics in hospitality and tourism», Tourism Management, 58, 51-65.

Xie, K. L., and Kwok, L. (2017): «The effects of Airbnb’s price positioning on hotel performance», International Journal of Hospitality Management, 67, 174-184.

Xun, X., and Yibai, L. (2016): «The antecedents of customer satisfaction and dissatisfaction toward various types of hotels: A text mining approach», International Journal of Hospitality Management, 55, 57-69.

Yang, Y., Mueller, N. J., and Croes, R. R. (2018a): «Market accessibility and hotel prices in the Caribbean: The moderating effect of quality-signaling factors», Tourism Management, 56, 40-51.

Yang, Y., Park, S., and Hu, X. (2018b): «Electronic word of mouth and hotel performance: A meta-analysis», Tourism management, 67, 248-260.

Yang, Z., Xia, L., and Cheng, Z. (2017): «Performance of Chinese hotel segment markets: Efficiencies measure based on both endogenous and exogenous factors», Journal of Hospitality and Tourism Management, 32, 12-23.

Yen, C. L. A., and Tang, C. H. H. (2019): «The effects of hotel attribute performance on electronic word-of-mouth (eWOM) behaviors», International Journal of Hospitality Management, 76, 9-18.

Zervas, G., Proserpio, D., and Byers, J. W. (2017): «The rise of the sharing economy: Estimating the impact of Airbnb on the hotel industry», Journal of Marketing Research, 54, 687-705.