1. Introduction
Prices in the tourism sector are critical for defining the competitive strategy and obtaining the results proposed by companies [
1,
2]. The competitiveness of tourism companies is developing through the Internet, where the online reputation influences demand and, therefore, price levels [
3,
4,
5]. The opinions shared by clients on specialized websites are considered by potential users, who can also analyse the lodging price offer [
6,
7,
8,
9,
10]. Tourist lodging managers, therefore, have to constantly monitor the evolution of their prices in relation to the competition and determine whether they match customer expectations in relation to perceived value and quality of service [
11,
12].
The online reputation is a competitive positioning problem in that customers create expectations based on the information received through the Internet and the price level they want to pay for the service offered by lodgings [
8,
13]. Customers’ online reviews can be transmitted by means of quantitative variables or qualitative opinions [
14]. The former considers certain variables in studying lodgings’ competitive positioning [
15,
16,
17,
18]. Another very important type of data obtained on web pages is the category of the accommodation, which is associated with the price level [
19,
20] and the quality of service that customers perceive [
21,
22]. However, different authors [
13,
23,
24] note that the increase in the category of hotels does not always match the perceived service quality ranking. This inconsistency can influence the competitiveness of tourism firms.
Quantitative variables used to measure online reputation generally assess the constructs of perceived service quality, perceived value and added value [
6,
8,
25,
26,
27,
28]. Moreover, qualitative opinions studied through content analysis are used to measure customer satisfaction [
14]. Because price is a quantitative variable directly related to the lodging’s reputation [
1,
29,
30,
31,
32], it is more appropriate to use the quantitative online reputation variables to determine the possible relationship with price [
11].
Although price is an important variable for tourism companies, not many studies have focused on the relationship between price and online reputation. In this context, for Masiero and Nicola [
18], it is critical to determine the patterns followed in customers’ purchasing behaviour, especially with regard to price sensitivity. In this way, companies will be able to more clearly establish the target segments they address and their main competitors [
22]. Therefore, the relationship between prices and online reputation makes it possible to establish a competitive positioning [
16,
17] and assess whether the price charged matches the service and value created for customers [
33,
34,
35]. The dynamics of the digital era in tourism signify that the price variable and companies’ competitive strategies are being revised jointly and constantly [
36] because the price level determines the standard of service quality and value generated for customers [
37,
38], as well as their satisfaction [
2,
33] K. In this regard, the lodging category represents a first segmentation of the market, as prices vary according to the category [
19,
20].
The aim of this research is to study the relationship between price and online reputation, taking into account the lodging category. To the extent that this relationship is established, it will be possible to establish more objectively the strategy and tactics of accommodation prices, in a highly changing and competitive environment. To this end, two groups of companies have been created, those with 4 and 5 stars and those with 3 stars or less. Thus, the variables perceived service quality, added value and perceived value are examined with respect to prices through bivariate regressions with different types of functions. The objective is to establish which variable has the strongest relationship with prices and which of the analysed functions obtains the best fit for each of the two lodging categories. In order to achieve this research objective, the study begins with a review of the literature which presents the different studies carried out on online prices and reputation in tourism. Subsequently, the applied methodology is developed, which is based on regression analysis with different types of functions (linear, logarithmic, inverse, quadratic and cubic), with the aim of establishing the relationship between prices and the variables of perceived service quality, perceived value and added value. The results obtained are presented in the following section, differentiating between 4 and 5-star accommodation and those with 3 stars or less. Finally, the results are discussed and the main conclusions and future lines of research are presented.
2. Literature Review
Competitiveness among tourism companies focuses on the type of segment they are targeting and the category provides a means to perform a first segmentation of the market [
18,
19,
20]. The price level is initially determined by the lodging category, which should coincide with the level of quality offered to customers [
39]. From this point of departure, tourism firms have to compete in a highly technology-influenced marketplace, where Internet communications create a dynamic of constant price analysis [
8].
The scope of revenue management has been evolving, gaining considerable importance in the strategy of tourist lodgings [
39]. In this area, pricing is the key to obtaining the highest possible level of revenue in a competitive environment [
40,
41]. The pricing strategy is determined on the basis of the market segments the lodgings target, as well as on the partial occupancy objectives by time periods [
22]. The process consists of previously planning how the lodging will be progressively occupied, as well as the prices established by the market segment. Any change in its competitors’ demand or strategy will cause the lodging to rethink its pricing policy [
31].
On the other hand, the online reputation, based on comments and evaluations shared by customers on the Internet, is now strongly emerging. Thus, the demand is affected by competitive factors based on perceived value, perceived service quality and customer satisfaction with the contracted service [
24,
41,
42,
43,
44,
45,
46,
47,
48]. Therefore, tourism companies have to implement approaches based on clients and their preferences about the different alternatives they have when making decisions [
22].
Different authors have studied the impact of online reputation on the price customers are willing to pay. Yacouel and Fleischer [
8] demonstrated that lodgings with a good track record in the quality of service offered get their customers to pay higher prices. Kim and Park [
2] demonstrated the predictive power of the level of customer satisfaction in lodging results. Ye et al. [
31] demonstrated that the online customer feedback is related with the demand for accommodations. Moreover, Xie et al. [
46] studied the relationship between specific attributes of quality of service and lodging performance.
Tourism prices have been analysed in relation to different perspectives, such as pricing strategy and discounts [
22,
49,
50,
51,
52], category [
19,
20], advertising [
53,
54], customer perceptions [
55,
56], key factors [
17,
57,
58,
59], performance [
31,
40,
46,
60,
61,
62,
63], competitive positioning [
11,
13] and clients’ selection of lodgings [
33].
Although several research studies have been carried out to determine the relationship between prices and online reputation [
1,
2,
8,
11,
30,
31,
46,
64], the relationship between the main constructs evaluated in online reputation, prices and the lodging category has not been analysed. This analysis is critical in determining, from a practical perspective, the level of competitiveness of an accommodation and the appropriateness of the pricing strategy applied by companies.
An essential factor in studying tourism prices is that they are subject to great seasonality [
54]. In general, destinations differentiate between high and low seasons, although changes in prices can also occur within each season [
11]. Likewise, the level of competitiveness and occupancy reached at any given time may lead to rethinking the pricing strategy [
17]. Therefore, price settings a dynamic process where prices are adjusted upwards or downwards according to objectives and changing market trends [
57]. Any changes in the environment can produce an alteration in the occupancy level and income. This means that lodging managers must constantly make decisions that require technological tools with up-to-date information and decisions [
22].
Online reputation is based on information transmitted by customers over the Internet through comments, evaluations, or images [
10]. It is an interactive process in which information is exchanged between clients and companies, creating a mental image of the service being offered [
65,
66]. This process determines the creation of client expectations that must be satisfied so that they do not perceive deficiencies in the quality of the service received. Because information is freely transmitted over the Internet, companies lose much of their control over communication. Therefore, they have to develop dynamic tools to implement corrective measures where price is crucial [
67,
68,
69,
70,
71].
Quantitative evaluations on tourism websites generally use scale of variables to measure the value perceived and the quality of service perceived [
6,
14,
46,
72]. However, according to Rodriguez-Díaz et al. [
13], added value is calculated from customer evaluations of the perceived value and quality of service perceived. On the one hand, perceived value is a construct directly related to the quality of service received by customers and vice versa, for the price they have to pay and it is a subjective concept that is depending on customers’ characteristics and perceptions [
37,
44,
73,
74,
75,
76,
77,
78,
79,
80]. Perceived value is habitually measured over the Internet through a single item because users do not usually respond to extensive surveys [
81]. Otherwise, quality of service perceived is measured through a scale of variables that tap the main attributes customers assess related to their expectations and perceptions of the service they receive [
42,
44,
45,
46,
47,
48].
The category is a decisive factor in the market positioning and competitiveness of tourism firms. In this context, different studies have demonstrated that the highest category lodgings tend to be evaluated more rigorously by customers, which produces an imbalance between online reputation, category and lodging prices [
23,
24]. This situation was also verified empirically by Rodríguez-Díaz et al. [
13], who demonstrated through the concept of added value applied to tourism [
82] that higher category lodgings tend to obtain negative added value because customers who pay higher prices are more demanding. Therefore, it is necessary to carry out studies on the relationship between prices and online reputation according to the category of lodgings because it conditions the segmentation of the market and the degree of customers’ expectations based on the price they pay.
3. Research Methodology
To implement the empirical research, information was obtained from 403 lodgings on the Booking.com website. These companies are located in three competing tourist destinations mainly oriented on sun and beach market segment: Sur de Gran Canaria and Tenerife located in Canary Islands (Spain) and Agadir located in Morocco. The destination of Canary Islands is one of the most important in Europe, receiving more than 12 million tourists annually [
83]. Agadir is located in the Souss Massa Drâa region and receives annually around four million tourists [
84].
The customer reviews of the lodgings analysed was 69,024. Of these, 38,096 evaluations were carried out in Gran Canaria, whereas 20,950 were analysed in Tenerife, and, finally 9978 in the destination of Agadir. The number of lodgings studied was: 272 in Gran Canaria, 82 in Tenerife and 49 in Agadir. The sample was obtained by accessing the Booking website and selecting each of the destinations by date according to the type of season. Subsequently, the information of the accommodations was obtained following the order that appeared in the web page. Those lodgings that had few comments were not taken into account (less than 50), in order to avoid bias. When a certain accommodation was not available on the dates that were introduced and, therefore, the price was not on the web, the date was modified in a limited form, in order to find its price in the period analysed. The accommodations that were not available on the web in many periods of time were not considered. In this way, it is guaranteed that the bias of the information is the least possible as to the selection of the accommodations and the selected period of time.
Booking.com uses a 10-point scale to measure quantitative customer ratings, where 1 is a very low rating and 10 is a very high rating of the attribute or concept measured. However, these results that appear on Booking.com are obtained by transforming the initial 4-point scale so that 2.5 is the minimum score for each variable, according to Mellinas et al. [
85]. Despite this possible bias, Rodríguez-Díaz and Espino-Rodríguez [
27,
28] show that the measured realized by Booking.com is one of the most reliable and valid in specialized websites on the Internet. Considering these aspects, it can be stated that the Booking scale has a minimum value of 2.5 and a maximum value of 10.
The quantitative scale applied by Booking.com has seven variables; six measure quality of service and one measures the perceived value. The Wi-Fi variable has not been included in the analysis for several reasons. Firstly, because it is largely conditioned by public infrastructure or external private companies. For example, optical fibre is not available in all destinations or areas of destinations, which makes it difficult to offer this type of service adequately. Secondly, this variable is only measured on the scale used by Booking.com, while other specialised websites such as TripAdvisor or HolidayCheck do not include it. From this it can be deduced that it is not one of the main variables used to measure online reputation. Finally, it is possible that the technological development of the next few years will not require an additional effort on the part of the accommodations to offer this service, since operators will be able to provide it directly. Therefore, the average of the quality of service is evaluated with the variables of personal, cleanliness, service/facilities, comfort and location. The average of the perceived quality of service, which we will call Q, is obtained from a mean of these variables, whereas the perceived value is evaluated with the variable value for money. As far as the lodging category is concerned, this information and the prices are available on the Booking.com website. Espinet et al. [
57], Hung et al. [
17] and Jena and Jog [
54] point out that prices change throughout the year depending on the season, market segments and level of demand. However, in most cases, price can be differentiated between high and low season [
11] and establish the most common maximum and minimum prices in each season. The high season in the studied destinations is winter, where maximum prices are paid in November, February and part of March, whereas the prices are reduced in the first 20 days of December, January and April. Summer is considered the low season because all the Mediterranean destinations are open and, consequently, the number of customers decreases. In summer, the maximum prices are usually found in the last days of July, August and October, whereas the minimum prices are usually offered in May, June and the first days of July.
The purpose of this research is to establish if there are a significant relationship between lodgings’ prices and their categories, based on the variables evaluated to measure online reputation. These variables are perceived value (V), average perceived service quality (Q) and added value (AV). To this end, the SPSS statistical package was used to perform bivariate regressions, where the dependent variables were the constructs of online reputation and the independent variables were the four price types analysed. In addition, the sample of lodgings was split into two clusters: (1) those lodgings with a category of 4 and 5 stars; and (2) those with 3 stars or less. The objective is to determine which type of function has the best fit and so regressions of linear, logarithmic, inverse, quadratic and cubic functions were performed, as described below:
Linear: The equation is y = a + b1 ∗ x.
Logarithmic: The equation is y = a + b ∗ ln(x).
Inverse: The equation is y = a + (b/x).
Quadratic: The equation is y = a + b ∗ x + c ∗ x2.
Cubic: The equation is y = a + b ∗ x + c ∗ x2 + d ∗ x3.
5. Discussion and Conclusions
5.1. Discussion
The study allows us to establish a possible methodology that can be used by accommodation companies to establish their pricing strategy. One of the principal problems they have lies in identify whether the prices are aligned with the levels of competitiveness existing on the market. Likewise, the constant price updating dynamics that tourism companies engage in based on demand and online reputation show that revenue management has increased its scope of influence beyond price and is combined with updating the marketing strategy.
The current reality is conditioned by the opinions customers share on the Internet, which directly influence the purchasing behaviour of users. In this regard, the demand for lodging depends to a large extent on the online reputation, as well as the price level established. The main constructs that are quantitatively evaluated on web pages specialized in customer opinions are the perceived value and the quality of the service perceived. In addition, added value is determined by these other two constructs and has been demonstrated to have a close relationship with price levels.
Therefore, the online reputation measured through these constructs produces a flow of information that influences customer decisions and, consequently, the lodgings’ competitiveness level. Customers can obtain and analyse information about all the possible options for their holidays and they are able to compare the prices and value provided by different lodgings. This study focuses on this point, with the objective of determining which online reputation variables are most related to the different price levels offered by accommodations located in a tourist destination.
The lodging category involves an initial segmentation of the market that is reflected in prices. In higher categories, the accommodation will tend to increase its prices because it offers a higher level of service quality. For this reason, the study has differentiated between two large groups of lodgings: on the one hand, those with 4 or 5 stars and, on the other, those with 3 stars or less. Thus, it was possible to study whether the fit obtained in the regressions is similar in terms of the type of function and the variables analysed.
The results confirm differences between the clusters of lodging categories studied. They demonstrate that higher category accommodations obtain a better fit, with cubic, quadratic and logarithmic functions showing a higher relationship between prices and the online reputation variables analysed. In relation to the variables, the perceived value is not significantly related to the price, whereas the average perceived quality of service obtains the best R2 adjusted. The added value also achieves satisfactory results but in these categories it does not reach the results obtained by quality of service.
The study shows that, when analysing the highest category of lodgings, the average quality of service perceived is what determines companies’ competitiveness. That is, because these firms maintain less differentiated price levels, customer ratings of the quality of service received are linked to the level of prices paid. Different results are obtained by lodgings with 3 stars or less, where cubic and quadratic functions obtain the best fit but without reaching those obtained by higher category lodgings. It is observed that lower category lodgings follow different pricing policies and marketing strategies, which leads to added value being the variable that obtains the best results.
These results show that the category of accommodations determines different pricing behaviours, as the most related online reputation variables differ according to the number of stars. In the debate proposed by López Fernandez [
23] and O’Connor [
24], it can be stated that to a large extent the quality of service is related to the category of accommodation. On the other hand, it is also demonstrated that the added value variable proposed by Rodríguez-Díaz et al. [
13], is very useful for establishing the relationship between online reputation and accommodation prices. In this line, as the offer diversifies, as happens with establishments of 3 stars or less, the added value shows to be the most suitable variable to analyse prices.
Prices are directly related to the revenue management applied by the accommodations [
1,
32], so it can be said that, depending on the results obtained, the revenue management is directly related to the online reputation. These results open up a new panorama when it comes to defining pricing strategy, since it is shown that online reputation has an impact on the price level that the customer is willing to pay [
20,
29]. On the other hand, it is important to adjust the quality of the service aimed at building customer loyalty to the revenue obtained, which could constitute a source of conflict in the accommodation strategy [
22].
In order to address the complexity involved in defining and constantly updating the pricing policy for lodging and to maintain a competitive level that guarantees a demand that generates the desired income, it is necessary to develop systems of learning machines and artificial intelligence [
86,
87,
88]. From this perspective, the methodology proposed and validated in this study may be tested for the development of this type of tools based on Big Data and models of competitive dynamics that are constantly changing. To the extent that online reputation based on customer feedback influences lodging prices, it is necessary to have tools and algorithms that automatically respond to changes in demand, online reputation and competitors’ prices. These algorithms must have limits on price offerings that do not produce irreversible effects on accommodation performance in highly volatile environments. One aspect to highlight in this study is that the type of mathematical function and online reputation variables that best explain prices may vary depending on the category of accommodations. This result should be considered in the development of algorithms for predicting demand and setting prices, because depending on changes in the competitive environment the function or variables may be modified to obtain a better fit.
5.2. Conclusions
The procedure followed in this study has been shown to be valid for determining the prices lodgings should establish in a competitive environment. Furthermore, it is a line of research that can help to develop automated improvements in the pricing of tourist accommodations according to their level of demand, category, type of price function and online reputation. It has been shown that online reputation is directly related to the price level of accommodation and, depending on the category, the variables that best adjust are the perceived quality of service and added value. The original contribution of this study is to demonstrate that online reputation is directly related to prices in the study carried out and, consequently, to the revenue management of accommodations. Another contribution is to demonstrate that different functions can be used to relate prices to online reputation. In this context, the proposed methodology can be used to define models for simplifying the pricing decisions of accommodation managers as well as models of learning machines and artificial intelligence aimed at automatically making decisions with a large amount of data from the Internet.
This study has some limitations. It was carried out at a certain point in time and it focused on lodgings in three tourist destinations. Future research should assess whether price changes in other time periods maintain the same types of relations between prices and online reputation, whether the functions analysed continue to explain this relationship and whether the application of this methodology can also be extended to other tourist destinations with different characteristics. Other types of functions can also be explored but considering that the added value takes on a negative score, which implies a restriction of the functions to be studied. In any case, the development of automated methods or artificial intelligence applied to price setting in tourist lodgings must take into account the procedure followed in this study. In this context, the dynamics of tourist markets produce a constant updating of prices and online customer ratings, which implies constantly updating the analysis of the price functions that best explain the behaviour of the market at any given time, as well as the online reputation variables with the greatest influence.