Perception in Online Hotel Booking_Boon

Electron Commer ResDOI 10.1007/s10660-014-9137-4

A model to evaluate the effects of price fairnessperception in online hotel booking

Mara-Encarnacin Andrs-Martnez Miguel-ngel Gmez-Borja Juan-Antonio Mondjar-Jimnez

Springer Science+Business Media New York 2014

Abstract Research on consumer and market behavior related to prices has increasedremarkably in recent years. Researchers have paid special attention to the effectsof price perception in consumer purchasing processes. In this paper a model ofantecedents and consequences of consumer price fairness perception in an online hotelbooking setting is proposed. The results show that consumers use reference prices andare guided by their familiarity with online hotel bookings in determining price fair-ness. Moreover, when consumers perceive prices as fair, they show more confidencein the decisions made and are more satisfied with the price. However, there is no directinfluence on loyalty, although this relationship appears indirectly through satisfactionwith the price and confidence in the decision.

Keywords Price fairness Antecedents Consequences Online hotel booking

1 Introduction

It is important to ascertain the factors that explain how consumers judge and inter-pret the information and psychophysical stimuli that prices represent insofar as theyhave an enormous influence on their decisions and purchasing behavior [20,21]. Thephenomenon underlying consumer interpretation of price fairness, or in other words,

M.-E. Andrs-Martnez (B) M.-. Gmez-Borja J.-A. Mondjar-JimnezFaculty of Economics and Business Administration, University of Castilla-La Mancha,Plaza de la Universidad, 1, 02071 Albacete, Spaine-mail: [email protected]

M.-. Gmez-Borjae-mail: [email protected]. Mondjar-Jimneze-mail: [email protected]

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whether or not a price is acceptable or reasonable in terms of the interchange of valuebehind any consumer decision, is of particular interest. For this reason, the determi-nants, processes and consequences of how suitable consumers judge prices to be (i.e.their judgments of price fairness) acquire particular importance.

The emergence of the Internet as a communications and sales channel has led to anew understanding of the relationship of competitive exchanges in most marketplaces.It offers four basic services: communication or socializing, information services,entertainment services and shopping or commerce services [46]. The sellers do notusually know their demand and they fix different prices to get profits. In relation withthis, some authors [25] fit the dynamic pricing model to match the pricing problem ofa Web-store.

The growing importance of virtual environments has influenced consumer priceperception. In this sense, on the Internet consumer decision making processes havebecome easier and faster than the traditional channel. As consumers can obtain moreinformation and use tools to compare that information, they can make better decision.For it, they can use shopbots that are Internet agents that automatically search forinformation pertaining to price and quality goods and services [53]. Online shopperscan do an extensive price comparison by going to other websites that offer a similarproduct [24]. This increased transparency has become apparent in the relationshipbetween consumers and prices, as it is simpler and easier for consumers to gain agreater awareness of market prices and also to compare them. Obviously, consumersnow form their opinion of price fairness differently, apart from the fact that theiropinion now plays a more significant role in decision making.

Research on consumer price fairness perception (PFP), particularly on the Internet,is yet scarce, although the current economic situation has seen a marked resurgence ofconsumer interest in obtaining fair prices. Despite the importance assigned to per-ceived price fairness, previous studies state that this concept remains a relatively unex-plored research area [8,32]. Furthermore, it is worth highlighting that some authorsshow that only minimal attention is paid to perceived price fairness in the context ofservices [8,5052]. However, the importance of perceived price fairness is obviousfor companies, due to the influence it has on consumer purchasing behavior [17].

As regards the Internet, perceived price fairness has gained greater importancebecause sellers are more able to differentiate prices depending on consumer pricesensitivity and consumers have different tools to search for and compare prices fromdifferent vendors. These two key aspects have resulted in price fairness being assignedgreater importance in the online sales channels [9]. On the other hand, it is necessaryto emphasize that Internet is important for the tourism industry. In this sense, someauthors [47] indicate that word of mouth and online recommendations are increasinglyused regarding tourism services.

As regards services, price plays a decisive role at least for two reasons: pricingstrategies based on the demand on the one hand and on the other hand the fact thatprice is commonly linked to service quality, as is often the case with hotels [52].In this sense, [27] found that price was considered the most relevant aspect in 43 %of the cases in hotel selection. For this reason, together with the importance of theservices sector in total gross domestic product and the large percentage of people whouse the Internet for booking accommodation (51.4 %) or searching for information

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Effects of PFP in online hotel booking

about accommodation (72.7 %) justify our selection of hotel online bookings for theempirical application in this paper.

The goal of this research is to analyze consumer behavior in regard to online pur-chase decisions in order to ascertain what aspects determine consumer PFP, as wellas the possible consequences of consumer price perception in a scenario in which thepricing strategy is based on demand.

This analysis leads to the establishment of a model with the antecedents and con-sequences of PFP that is to the best of our knowledge not available in the literature.We use this model to analyze the direct and indirect relationships between antecedentsand perceived price fairness, as well as between the latter and its consequences. Thus,Sect. 2 studies the main antecedents and consequences considered in the analyses ofthe PFP and establish the main hypotheses tested in this paper. Section 3 describesthe methodology used based on a partial least squared (PLS) analysis. Finally, the lastsection details the main conclusions and future avenues for research.

2 Antecedents and consequences of price fairness perception

Three aspects are usually considered when studying PFP: distributive fairness, pro-cedural fairness and interactional fairness. In this paper, we analyze distributive andprocedural fairness. First, we consider antecedents that influence PFP, such as refer-ence price (RP), FOHB and search for fairness (SF). At the same time, we evaluatethe consequences of PFP over DC, loyalty and satisfaction with price (SP) (Fig. 1).

2.1 Reference price and price fairness perception

Most research on perceived price fairness is based on the dual entitlement principle,which establishes that firms must have a reference profit and consumers a RP [20,21].

Reference Price (RP)

Price Fairness Perception (PFP)

Decision Confidence (DC)

Loyalty (L)Familiarity with

Online Hotel Bookings (FOHB)

Search of Fairness (SF)

Satisfaction with price (SP)

Fig. 1 Theoretical model proposal

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In this sense, consumers compare the present price to the RP and the vendor comparesthe present profit to the reference profit [8].

Some authors such as [19] consider different scenarios to determine how fair a priceis perceived to be. The results reveal that consumers tend to compare prices observedon the Internet to prices on the traditional sales channel. That is, they use the pricesfixed on the traditional channel as RPs to evaluate the fairness of Internet prices. Whenprices are the same on both channels, prices are perceived as unfair, since consumersare looking for a lower price on the virtual channel.

Thus, we formulated the following hypothesis regarding the influence of the RP onconsumer PFP:

H1 The reference price will have a positive influence on the price fairness perception.

2.2 Familiarity with online hotel bookings and price fairness perception

The second antecedent in this model is familiarity with online hotel booking (FOHB).Taking into account that experience is a consequence of learning, [8,16] establish thatthe purchase experience, product consumption or product knowledge influence thePFP.

Beldona and Kwansa [5], Noone and Mattila [35], Rohlfs and Kimes [40], Wirtzand Kimes [55], Yoonjoung and Lee [56] observed that consumers who were morefamiliar with the pricing strategy and bookings online have a fairer perception ofprices set using this strategy. Considering the arguments above we can formulate thefollowing hypothesis:

H2 Familiarity with online hotel bookings will have a positive effect on the pricefairness perception.

2.3 Search for fairness and price fairness perception

SF considers the extent to which consumers intentionally search for price informationbased mainly on finding fairer prices or in evaluating their fairness.

The need for information that can be displayed by a consumer in the midst of apurchase decision process targets a certain amount of data that serves to decrease therisks linked to future purchasing decisions. Part of this information may already be inthe consumers memory, while another portion may have to be collected from externalsources [6].

Consumers start searching for price information because their initial price knowl-edge is usually quite limited. Consequently, this information search is fundamentallya SF, which leads us to state the following hypothesis [8,56,57]:

H3 The search for fairness will have a positive influence on the price fairness percep-tion.

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2.4 Price fairness perception and decision confidence

Consumer confidence is defined as the feeling of the people to be able and safe regard-ing the decisions they made and their behavior. It is the consequence of beliefs such asself-esteem, perception of control and dominion, as well as previous experience [3].

One concept related to confidence is fairness. In this regard, it should be emphasizedthat fairness is considered a necessary condition for confidence to exist. Thus, theperception of fairness may have a positive influence on DC. The relationship betweenfairness and confidence is essential for service providers, since the products offered areintangible and difficult to assess. As a result, consumers are guided by their confidencealone [42]. Confidence is even more important online than in the traditional channel,since consumers online purchasing decisions are almost always guided by confidence[1].

Maxwell [32], Monroe and Xia [34] show that confidence is a key antecedent in theprocess of deciding whether a price is fair or not. So, consumers PFP will determinetheir future behavior, depending on their confidence in the vendor.

In spite of the fact that the conclusions that we have found in the literature focuseson analyzing the relationship between the PFP and confidence in the vendor, on thebasis of authors like [34,42], we believe that the PFP will also have a direct impact onDC. Thus, we propose the following hypothesis:

H4 The price fairness perception will have a positive influence on consumer DC.

2.5 Price fairness perception and loyalty

Loyalty can be defined as the desire to purchase again. This concept is particularlyimportant for companies on the virtual channel because loyal customers are the mostprofitable [39,49]. As competition is increasing, companies have to improve to main-tain their customers loyalty [10].

Loyalty can be linked to factors like word of mouth and repatronage [45]. In thispaper, we have considered both items. Previous research shows that the PFP posi-tively influences loyalty. Martn-Consuegra [29] reach this conclusion after conduct-ing a personal survey of airplane passengers. We therefore propose the followinghypothesis:

H5 Price fairness perception will have a positive influence on loyalty.

2.6 Decision confidence and loyalty

The relationship between DC and loyalty is based on the consideration that confidenceprecedes loyalty, as outlined in several studies [23,28,38,43]. Rauyruen and Miller[38] report a direct and positive influence. This relationship is also evident in theonline channel [41] and in the purchase of tourist products [23]. Based on the abovearguments, we state the following hypothesis:

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H6 Decision confidence will have a positive influence on loyalty.

2.7 Price fairness perception and satisfaction with price

Consumer satisfaction emerges when expectations prior to purchasing are fulfilledor surpassed when using and/or consuming the product purchased. Satisfaction alsorefers to an emotional state that occurs as a result of interaction between the customerand the service provider.

Zielke [59] defines SP as an emotional reaction resulting from the interaction ofcognitive and affective mental processes that are caused and activated by specific expe-riences that take place in the presence of different dimensions of price perception.Satisfaction with the price, in some studies, is regarded as a construct that consists ofmultiple dimensions, which are: price transparency; pricequality ratio; relative price;confidence in the price; price reliability and price fairness [30]. Campbell [11] focusesonly on one dimension and analyzes how price fairness affects price perception; For-nell et al. [14] consider the pricequality ratio and [50] analyze the effect that priceperception has on satisfaction and behavior.

Bei and Chaio [4] observed that there is a positive relationship between the PFPand satisfaction in the case of services. In this sense, [8,22] established that the PFPhas a direct and positive impact on satisfaction with price. Singh and Sirdeshmukh[44] pointed out that price fairness is one of the factors that determine consumersatisfaction and [29] observed that both are positively related. On the basis of thiscontextual framework, we formulate the following hypothesis:

H7 Price fairness perception will have a positive influence on satisfaction with price.

2.8 Satisfaction with price and loyalty

Satisfaction with the price before the purchase determines consumer behavior. Thus,SP can, despite a consumer perceiving a price as unfair, reduce the negative impactthis would have on purchase intentions.

Therefore, when consumers are very satisfied, their intentions to purchase againare not influenced by an increase in price. However, when they are not very satis-fied, consumer intentions to purchase again decrease. Homburg et al. [18] reach thisconclusion after analyzing the effect of a price increase on consumers intentions topurchase again.

Kauffman et al. [22] despite considering a positive relationship between SP andpurchase intentions, do not find such a relationship when they analyze the case of con-sumer groups in online auctions. More recently, Kim et al. [23] established a positiverelationship between customer satisfaction and loyalty in the context of tourism prod-ucts and services on the Internet. Taking into account these arguments, we proposethe following hypothesis:

H8 Satisfaction with price will have a positive influence on loyalty.

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3 Methodology

The main characteristics of the empirical application are discussed in the following sec-tions. First we provide information about the sample and then the variables used to mea-sure the different latent variables before finally presenting the main research results.

3.1 Participants, procedure and sample

We have designed an original experiment based on a small computer application inorder to obtain information. It simulates and monitors the decision making processthat consumer carries out when they decide to book a hotel (before and after of theprocess). This computer application is integrated in an online survey. So, the firstsection has questions such as demographics, experience of online hotel reservation,knowledge of prices and RPs. Then, the user is directed to a computer applicationwhere they book a hotel. They can choose between five different hotels 4-stars that arebased on real hotels, but in the computer application these hotels has untrue names.We use hotels 4-stars because they are the most requested by travelers according toHotel Occupancy Survey.

The participants took the decision to book a hotel room in a simulated environmentof five hotels with different pricing strategies derived from the yield managementstrategy used. Respondents were told that they were planning a leisure break withother person (e.g. friend) and needed to make a hotel reservation for six nights ina hotel 4-star. Each hotel provides information only the price and the conditions toget it. After booking the hotel room, users come back to the questionnaire to answerquestions regarding fairness perception and other behavior dimensions.

In relation with the sample characteristics, data were collected using an online self-administered survey carried out between February 29th, 2012 and March 27th, 2012 to600 subjects. A final total of 541 questionnaires were deemed valid once incompleteones had been ruled out. These subjects were chosen considering quotas based onthe socio-demographic profile of Internet users aged between 16 and 74 years whosometimes purchase on the Internet.

3.2 Variables measurement

The independent variables are RP, FOHB and SF, and the dependent variables are PFP,DC, loyalty (L) and SP. The scales used in each variable are explained below.

In the case of PFP, we have used both the scale and the items established in [28], buthave adapted them to our study. The variables that appear in Table 1 show the averageof six items (three for distributive fairness and three for procedural fairness) in fivesituations considered to evaluate the fairness perception related with five revenuemanagement strategies used in hotels. Thus, PFP1 captures the average of distributiveand procedural fairness in the revenue management based on the restrictions accepted,PFP2 refers to distributive and procedural fairness in the revenue management basedon time, PFP3 includes the items relating to distributive and procedural fairness in therevenue management based on location; PFP4, which includes items of distributiveand procedural fairness in the revenue management based on the number of nights of

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Table 1 Items related to PFP

Item Description Scale Source

PFP1 The lower prices customers pay for notbeing able to cancel a booking are

Fair Seven-point Likertscale (stronglydisagree (1) andstrongly agree(7))

Adapted from [28]

ReasonableAcceptable

The pricing process that sets lowerprices for those who cannot changeor cancel their booking, is

Fair


PFP2 The price of rooms on Fridays andSaturdays is

Fair


The pricing process that sets higherprices for Fridays and Saturdays is

Fair


PFP3 The price of rooms with a good viewor location is

Fair


The pricing process that sets higherprices for rooms with a good viewor location is

Fair


PFP4 The lower prices from the fourthnight onwards are

Fair


The pricing process that sets lowerprices from the fourth nightonwards is

Fair


PFP5 The lower prices that clients pay forbooking in advance are

Fair


The pricing process that sets lowerprices for those who book inadvance is

Fair


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the stay; and, finally, PFP5, which consists of the distributive and procedural fairnessitems in revenue management according to booking in advance.

The RP has been measured by three items adapted from [26] that consider maxi-mum, reasonable and minimum prices given by the consumers to pay for booking onenight in a hotel (Tables 2).

In the case of FOHB, we have considered two items. The first has been adapted from[7,26], while the second has been proposed in this paper and measures knowledge ofthe process of online hotel booking (Table 3).

For SF, we have considered that consumers compare prices by nature [33] andthat these comparisons are basically made to ascertain whether or not an observedprice is fair. Taking into account that different alternatives are used in these compar-isons, namely expected price, RP, competitors prices, previous experience, sources ofexternal information and recommendations, we have opted in this paper to use thetems shown in Table 4 below to measure this latent variable.

To measure DC, we have used items for the different levels this variable comprises,namely: acquisition and processing of information; formation of the set to considerand, finally, personal and social outcomes [3], using a seven-point likert scale [13](Table 5).

Although some authors have distinguished three loyalty dimensions, namely wordof mouth, price tolerance and intentions to purchase again, we have focused on wordof mouth and purchase intentions to measure loyalty, as in [45]. More specifically, wehave used the items shown in Table 6.

To measure satisfaction, we have focused on satisfaction with price, using the itemsshown in Table 7 adapted from previous studies.

Table 2 RP items


RP1 The maximum price per night youwould be willing to pay, such thatany price that exceeds it would notbe reasonable for you

Numerical price Adapted from [26]

RP2 The price per night you wouldconsider reasonable and would bewilling to pay

RP3 The price per night you wouldconsider an acceptable minimum,such that any price lower would beunreasonable or dubious for you

Table 3 FOHB items


FOHB1 I am very familiar with onlinehotel bookings

Seven-point Likert scale(strongly disagree (1) andstrongly agree (7))

Adapted from [7,26]

FOHB2 I know the process of onlinehotel booking well

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Table 4 Items related to the SF

Item Description Scale

SF1 I think it is unfair that the price does not match the hotel price that Iexpected to find

Seven-point Likertscale (stronglydisagree (1) andstrongly agree(7))

SF2 In order to determine whether a price is fair or unfair, I use theinformation that I have gathered from other hotels that offersimilar services as a reference

SF3 I use my previous experience with hotels in order to determinewhether a price is fair or not

SF4 The opinion of my friends, relatives or acquaintances helps me todetermine whether the price of a hotel is fair or unfair

SF5 I use the information I find in forums and recommendation pages toestablish whether the price of a hotel is fair

Table 5 Items related to DC


DC1 I am confident about thedecision

Seven-point Likert scale(strongly disagree (1)and strongly agree (7))

Adapted from [3,13]

DC2 It was not very difficult forme to decide

DC3 I think that I have managed tofind the best option for me

DC4 I think that I have managed togather all the relevantinformation

DC5 I have made the right decisionDC6 I quickly identified the best

option

Table 6 Items related with loyalty


L1 I would recommend the hotelI have chosen

Seven-point Likert scale(strongly disagree (1) andstrongly agree (7))

Adapted from[12,31,45,58].

L2 If my friends or relatives werelooking, I would recommend thisdecision

L3 If I had to choose again, I wouldchoose the same hotel

Adapted from [31,58]

L4 Although others offer lower prices, Ithink I would still choose this hotel

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Table 7 Satisfation with price items


SP1 In general, I am satisfied with thepurchase I have made

Seven-point Likertscale (stronglydisagree (1) andstrongly agree(7))

Adapted from [22,37,52]

SP2 I am satisfied with the price paid forthe room

SP3 I think that I have got the bestpossible conditions for the pricepaid

SP4 I am happy with the price paidSP5 The price paid makes me feel the

product is cheapSP6 The price paid makes me feel good

about my purchase

4 Results

Taking into account the characteristics of the information obtained in the survey andthe theoretical model proposed, the model was estimated using PLS. First, we havedeveloped an exploratory factor analysis, which allows us to decide which items touse as indicators of each latent variable (factor) shown in Fig. 2.

The PLS estimate was performed using the program SmartPLS 2.0.M3(www.smartpls.de). Table 8 shows the results regarding reliability and convergent

Fig. 2 Estimation of the structural equation model

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Table 8 Reliability measurements

Factor Item Loading t value(Bootstrap)

Cronbachsalpha

Compositereliability

AVE

RP RP1 0.8955** 8.5987 0.9153 0.9463 0.8548RP2 0.9723** 11.8658RP3 0.9040** 10.1880

FOHB FOHB1 0.9640** 96.3480 0.9273 0.9649 0.9322FOHB 2 0.9671** 116.4281

SF SF1 0.6066** 3.6870 0.7498 0.8220 0.4885SF2 0.8233** 6.8234SF3 0.8552** 6.2869SF4 0.5392** 3.2368SF5 0.6121** 4.0977

PFP PFP1 0.7694** 18.4691 0.8031 0.8635 0.5597PFP2 0.6500** 8.1560PFP3 0.7388** 14.7018PFP4 0.8058** 20.5398PFP5 0.7674** 17.3904

DC DC1 0.8405** 33.2855 0.9008 0.9238 0.6698DC2 0.7243** 12.5020DC3 0.8424** 25.7515DC4 0.7842** 18.1722DC5 0.8691** 27.1776DC6 0.8413** 29.2734

L L1 0.9167** 66.2106 0.8656 0.9097 0.7178L2 0.9078** 56.0615L3 0.8408** 23.4523L4 0.7070** 14.5039

SP SP1 0.8622** 40.9897 0.9286 0.9443 0.7396SP2 0.9051** 52.8285SP3 0.8568** 34.8903SP4 0.9154** 52.1412SP5 0.7163** 16.0229SP6 0.8889** 43.1299

Note **p < 0.01

validity evaluation. The results for the model show that all items are significant andtheir outer loadings are greater than 0.60 [2] and the cross-loads always being greaterfor the latent variables upon which the respective items are loaded.

The usual Goodness of Fit (GoF) measure, proposed in [48], is the geometricmean of the average communality (outer model) and the average R2 (inner model),with a value of 0.43. We can accept this value as acceptable according to [54]. Asregards the reliability of the measurement instrument, Cronbachs alpha value for

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Table 9 Matrix of correlation between latent variables

SF DC FOHB PFP L RP SP

SF 0.4885DC 0.0887 0.6698FOHB 0.2108 0.0747 0.9322PFP 0.0525 0.2130 0.0677 0.5597L 0.0708 0.4732 0.0308 0.1141 0.7178RP 0.0018 0.0507 0.0230 0.0294 0.0453 0.8548SP 0.0688 0.5793 0.0561 0.1940 0.5550 0.0727 0.7396Note The diagonal (bold values) shows the AVE and below the diagonal correlations between latent variables

Table 10 Hypothesis test

Hypothesis Relation Coefficient t value (Bootstrap) p value

H1 RP->PFP 0.139* 1.766 0.039H2 FOHB->PFP 0.173* 2.090 0.019H3 SF->PFP 0.144* 1.754 0.040H4 PFP->DC 0.462** 6.321 0.000H5 PFP->L 0.035n.s. 0.528 0.299H6 DC ->L 0.298** 3.459 0.000H7 PFP->SP 0.440** 6.084 0.000H8 SP ->L 0.534** 6.832 0.000Note **p < 0.01; *p < 0.05: n.s.: not significant

all the latent variables is greater than 0.7, the standard criterion given in [36]; thecomposite reliability values are also greater than 0.8 in all cases and the convergentvalidity scores (AVE) are near to or greater than 0.5, as recommended in [15].

The discriminant validity criterion [15] is also fulfilled, as the AVE is greater thanthe square of the estimated correlation between the latent variables (Table 9).

Table 10 shows the results of the hypothesis tests raised in this paper.The results verify the hypotheses raised for the model, except for the influence

between PFP and loyalty. In relation to the antecedents that influence the PFP, we canobserve a positive and significant relationship with RP ( = 0.139, p < 0.05), FOHB( = 0.173, p < 0.05) and the SF ( = 0.144, p < 0.05), confirming hypotheses 1, 2,and 3.

As regards to the consequences that take place as a result of PFP, there is a positiveand significant influence on DC ( = 0.462, p < 0.01), confirming hypothesis 4.Hypothesis 7 is also confirmed since there is a positive and significant relationshipbetween PFP and SP ( = 0. 440, p < 0.01). PFP has a negative but non significantimpact on loyalty ( = 0.035, n.s.), therefore rejecting hypothesis 5. However, PFPindirectly influences loyalty through DC and satisfaction with price, because there isan overall effect with a coefficient of of 0.338 and a p value lower than 0.01. Finally,decision confidence ( = 0.298, p < 0.01), as well as satisfaction with price ( = 0.534,

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p < 0.01) are positively and significantly related to loyalty, confirming hypotheses 6and 8.

5 Conclusions

In recent years, online hotel bookings have increased considerably, although theantecedents that determine PFP and the consequences that arise as a result of thisperception on the Internet have not been analyzed in depth. Taking this situation intoaccount, this research contributes to furthering the existing literature on this subject.

After analyzing the results, we can establish that consumers use RPs when assessingthe fairness of the price observed. Furthermore, when consumers are more familiarwith online hotel bookings their perceptions of price fairness increases and the SFmake easier the PFP.

In relation to the consequences of consumer PFP, we find that DC and satisfactionwith price are present when prices are perceived as fair. However, PFP has no significantinfluence on loyalty, although this influence becomes evident indirectly through SPand DC.

This study has a lot of implications for hotel companies. In this sense, the maincontribution of this paper is that hotel companies can know that factors determineconsumer PFP positively and what consequences could have this perception on theconsumer behavior. It is important to hotel managers know that the consumers use theRP, FOHB and SF to analyze the prices. So, in the case of RP, we suggest that thehotel managers can use some alternatives to avoid perceptions of unfair prices suchas: highlighting the quality and benefits that their service has; communicating costsand providing differentiated services.

Although this study makes some relevant contributions to the existing literature,it also suffers from a series of limitations. These limitations undoubtedly pave theway for future research lines. The current economic context may have influenced theresults, so it would be interesting to undertake a long-term study to analyze whetherthe current crisis has affected the relationships established in the model tested in thispaper. In the same line, it would also be interesting to perform a cross-cultural studyin order to verify whether culture has a clear influence on the relationships analyzed.

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Mara-Encarnacin Andrs-Martnez is Ph.D. and Degree inBusiness Administration by University of Castilla-La Mancha. Assis-tant Professor in Marketing at Business Administration Department.Faculty of Economics and Business Administration of Albacete. Uni-versity of Castilla-La Mancha (Spain). She was awarded a prize fromthe Royal Academy of Doctors of Spain for her doctoral dissertation.She is author of publications in national and international journals.Her research interests include consumer behaviour, price perception,Internet and tourism.

Miguel-ngel Gmez-Borja has a degree in Economics and Busi-ness Administration from the University of Valencia and a Ph.D.in Business Administration from the University of Castilla-La Man-cha. Currently, he is Associate Professor of Marketing at School ofEconomics and Business of Albacete, Spain. His research is focusedamong others on the impact of new information technologies onretailing management, international retailing, consumer behaviour invirtual environments and online marketing research tools and appli-cations. He also works on topics related to marketing for non-profitorganizations, developmental aid and sustainable development pro-grams and tools.

Juan-Antonio Mondjar-Jimnez is Ph.D. and Degree in Busi-ness Administration by University of Castilla-La Mancha. Degreein Advanced Studies in Marketing at the same university. Master inMarketing Research and Master in Art of Economics by Spanish Uni-versity of Distance. Associate Professor in Marketing at BusinessAdministration Department. Faculty of Social Sciences of Cuenca.University of Castilla-La Mancha (Spain). Director and member ofdifferent research projects, have participated in a hundred of Con-ferences and Congress national and international. Member of theEditorial Board from different national and international journals.Author of plus than fifty scientific publications: books, chapters, arti-cles in national and international journals. He is currently AssociateVice-Chancellor at the University of Castilla-La Mancha. ResearchInterest: E-learning, consumer behavior, price perception and tourismmarketing.

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A model to evaluate the effects of price fairness perception in online hotel bookingAbstract1 Introduction2 Antecedents and consequences of price fairness perception2.1 Reference price and price fairness perception2.2 Familiarity with online hotel bookings and price fairness perception2.3 Search for fairness and price fairness perception2.4 Price fairness perception and decision confidence2.5 Price fairness perception and loyalty2.6 Decision confidence and loyalty2.7 Price fairness perception and satisfaction with price2.8 Satisfaction with price and loyalty

3 Methodology3.1 Participants, procedure and sample3.2 Variables measurement

4 Results5 ConclusionsReferences

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Perception in Online Hotel Booking_Boon