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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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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
ReasonableAcceptable
PFP2 The price of rooms on Fridays andSaturdays is
Fair
ReasonableAcceptable
The pricing process that sets higherprices for Fridays and
Saturdays is
Fair
ReasonableAcceptable
PFP3 The price of rooms with a good viewor location is
Fair
ReasonableAcceptable
The pricing process that sets higherprices for rooms with a good
viewor location is
Fair
ReasonableAcceptable
PFP4 The lower prices from the fourthnight onwards are
Fair
ReasonableAcceptable
The pricing process that sets lowerprices from the fourth
nightonwards is
Fair
ReasonableAcceptable
PFP5 The lower prices that clients pay forbooking in advance
are
Fair
ReasonableAcceptable
The pricing process that sets lowerprices for those who book
inadvance is
Fair
ReasonableAcceptable
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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
Item Description Scale Source
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
Item Description Scale Source
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
Item Description Scale Source
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
Item Description Scale Source
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
Item Description Scale Source
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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Effects of PFP in online hotel booking
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.
123
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