Perception in Online Hotel Booking_Boon

Electron Commer Res
DOI 10.1007/s10660-014-9137-4

A model to evaluate the effects of price fairness
perception in online hotel booking
María-Encarnación Andrés-Martínez ·
Miguel-Ángel Gómez-Borja ·
Juan-Antonio Mondéjar-Jiménez

© Springer Science+Business Media New York 2014

Abstract Research on consumer and market behavior related to prices has increased
remarkably in recent years. Researchers have paid special attention to the effects
of price perception in consumer purchasing processes. In this paper a model of
antecedents and consequences of consumer price fairness perception in an online hotel
booking setting is proposed. The results show that consumers use reference prices and
are guided by their familiarity with online hotel bookings in determining price fairness. Moreover, when consumers perceive prices as fair, they show more confidence
in the decisions made and are more satisfied with the price. However, there is no direct
influence on loyalty, although this relationship appears indirectly through satisfaction
with 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 interpret the information and psychophysical stimuli that prices represent insofar as they
have an enormous influence on their decisions and purchasing behavior [20,21]. The
phenomenon underlying consumer interpretation of price fairness, or in other words,

M.-E. Andrés-Martínez (B) · M.-Á. Gómez-Borja · J.-A. Mondéjar-Jiménez
Faculty of Economics and Business Administration, University of Castilla-La Mancha,
Plaza de la Universidad, 1, 02071 Albacete, Spain
e-mail: [email protected]
M.-Á. Gómez-Borja
e-mail: [email protected]
J.-A. Mondéjar-Jiménez
e-mail: [email protected]

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whether or not a price is acceptable or reasonable in terms of the interchange of value
behind any consumer decision, is of particular interest. For this reason, the determinants, 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 a
new 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 not
usually know their demand and they fix different prices to get profits. In relation with
this, some authors [25] fit the dynamic pricing model to match the pricing problem of
a Web-store.
The growing importance of virtual environments has influenced consumer price
perception. In this sense, on the Internet consumer decision making processes have
become easier and faster than the traditional channel. As consumers can obtain more
information 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 for
information pertaining to price and quality goods and services [53]. Online shoppers
can do an extensive price comparison by going to other websites that offer a similar
product [24]. This increased transparency has become apparent in the relationship
between consumers and prices, as it is simpler and easier for consumers to gain a
greater awareness of market prices and also to compare them. Obviously, consumers
now form their opinion of price fairness differently, apart from the fact that their
opinion 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 of
consumer interest in obtaining “fair prices”. Despite the importance assigned to perceived price fairness, previous studies state that this concept remains a relatively unexplored research area [8,32]. Furthermore, it is worth highlighting that some authors
show that only minimal attention is paid to perceived price fairness in the context of
services [8,50–52]. However, the importance of perceived price fairness is obvious
for companies, due to the influence it has on consumer purchasing behavior [17].
As regards the Internet, perceived price fairness has gained greater importance
because sellers are more able to differentiate prices depending on consumer price
sensitivity and consumers have different tools to search for and compare prices from
different vendors. These two key aspects have resulted in price fairness being assigned
greater importance in the online sales channels [9]. On the other hand, it is necessary
to emphasize that Internet is important for the tourism industry. In this sense, some
authors [47] indicate that word of mouth and online recommendations are increasingly
used regarding tourism services.
As regards services, price plays a decisive role at least for two reasons: pricing
strategies based on the demand on the one hand and on the other hand the fact that
price 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 the
services sector in total gross domestic product and the large percentage of people who
use 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 the
empirical application in this paper.
The goal of this research is to analyze consumer behavior in regard to online purchase decisions in order to ascertain what aspects determine consumer PFP, as well
as the possible consequences of consumer price perception in a scenario in which the
pricing strategy is based on demand.
This analysis leads to the establishment of a model with the antecedents and consequences 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 antecedents
and 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 of
the PFP and establish the main hypotheses tested in this paper. Section 3 describes
the methodology used based on a partial least squared (PLS) analysis. Finally, the last
section 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, procedural fairness and interactional fairness. In this paper, we analyze distributive and
procedural fairness. First, we consider antecedents that influence PFP, such as reference price (RP), FOHB and search for fairness (SF). At the same time, we evaluate
the 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].

Decision
Confidence (DC)

Reference Price
(RP)

Familiarity with
Online Hotel
Bookings (FOHB)

Search of
Fairness (SF)

Price Fairness
Perception (PFP)

Loyalty (L)

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 compares
the present profit to the reference profit [8].
Some authors such as [19] consider different scenarios to determine how fair a price
is perceived to be. The results reveal that consumers tend to compare prices observed
on the Internet to prices on the traditional sales channel. That is, they use the prices
fixed on the traditional channel as RPs to evaluate the fairness of Internet prices. When
prices are the same on both channels, prices are perceived as unfair, since consumers
are looking for a lower price on the virtual channel.
Thus, we formulated the following hypothesis regarding the influence of the RP on
consumer 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 that
the purchase experience, product consumption or product knowledge influence the
PFP.
Beldona and Kwansa [5], Noone and Mattila [35], Rohlfs and Kimes [40], Wirtz
and Kimes [55], Yoonjoung and Lee [56] observed that consumers who were more
familiar with the pricing strategy and bookings online have a fairer perception of
prices set using this strategy. Considering the arguments above we can formulate the
following hypothesis:
H2 Familiarity with online hotel bookings will have a positive effect on the price
fairness perception.

2.3 Search for fairness and price fairness perception
SF considers the extent to which consumers intentionally search for price information
based 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 a
purchase decision process targets a certain amount of data that serves to decrease the
risks linked to future purchasing decisions. Part of this information may already be in
the consumer’s memory, while another portion may have to be collected from external
sources [6].
Consumers start searching for price information because their initial price knowledge is usually quite limited. Consequently, this information search is fundamentally
a 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 perception.

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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 regarding the decisions they made and their behavior. It is the consequence of beliefs such as
self-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 emphasized
that fairness is considered a necessary condition for confidence to exist. Thus, the
perception of fairness may have a positive influence on DC. The relationship between
fairness and confidence is essential for service providers, since the products offered are
intangible and difficult to assess. As a result, consumers are guided by their confidence
alone [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 the
process of deciding whether a price is fair or not. So, consumers’ PFP will determine
their future behavior, depending on their confidence in the vendor.
In spite of the fact that the conclusions that we have found in the literature focuses
on analyzing the relationship between the PFP and confidence in the vendor, on the
basis of authors like [34,42], we believe that the PFP will also have a direct impact on
DC. 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 particularly
important for companies on the virtual channel because loyal customers are the most
profitable [39,49]. As competition is increasing, companies have to improve to maintain their customers’ loyalty [10].
Loyalty can be linked to factors like word of mouth and repatronage [45]. In this
paper, we have considered both items. Previous research shows that the PFP positively influences loyalty. Martìn-Consuegra [29] reach this conclusion after conducting a personal survey of airplane passengers. We therefore propose the following
hypothesis:
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 confidence
precedes 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 the
online channel [41] and in the purchase of tourist products [23]. Based on the above
arguments, 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 fulfilled
or surpassed when using and/or consuming the product purchased. Satisfaction also
refers to an emotional state that occurs as a result of interaction between the customer
and the service provider.
Zielke [59] defines SP as “an emotional reaction resulting from the interaction of
cognitive and affective mental processes that are caused and activated by specific experiences 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 of
multiple dimensions, which are: price transparency; price–quality ratio; relative price;
confidence in the price; price reliability and price fairness [30]. Campbell [11] focuses
only on one dimension and analyzes how price fairness affects price perception; Fornell et al. [14] consider the price–quality ratio and [50] analyze the effect that price
perception has on satisfaction and behavior.
Bei and Chaio [4] observed that there is a positive relationship between the PFP
and satisfaction in the case of services. In this sense, [8,22] established that the PFP
has 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 consumer
satisfaction and [29] observed that both are positively related. On the basis of this
contextual 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 impact
this would have on purchase intentions.
Therefore, when consumers are very satisfied, their intentions to purchase again
are not influenced by an increase in price. However, when they are not very satisfied, consumer intentions to purchase again decrease. Homburg et al. [18] reach this
conclusion after analyzing the effect of a price increase on consumers’ intentions to
purchase again.
Kauffman et al. [22] despite considering a positive relationship between SP and
purchase intentions, do not find such a relationship when they analyze the case of consumer groups in online auctions. More recently, Kim et al. [23] established a positive
relationship between customer satisfaction and loyalty in the context of tourism products and services on the Internet. Taking into account these arguments, we propose
the 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 sections. First we provide information about the sample and then the variables used to measure 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 in
order to obtain information. It simulates and monitors the decision making process
that consumer carries out when they decide to book a hotel (before and after of the
process). This computer application is integrated in an online survey. So, the first
section has questions such as demographics, experience of online hotel reservation,
knowledge of prices and RPs. Then, the user is directed to a computer application
where they book a hotel. They can choose between five different hotels 4-stars that are
based 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 to
Hotel Occupancy Survey.
The participants took the decision to book a hotel room in a simulated environment
of five hotels with different pricing strategies derived from the yield management
strategy used. Respondents were told that they were planning a leisure break with
other person (e.g. friend) and needed to make a hotel reservation for six nights in
a hotel 4-star. Each hotel provides information only the price and the conditions to
get it. After booking the hotel room, users come back to the questionnaire to answer
questions regarding fairness perception and other behavior dimensions.
In relation with the sample characteristics, data were collected using an online selfadministered survey carried out between February 29th, 2012 and March 27th, 2012 to
600 subjects. A final total of 541 questionnaires were deemed valid once incomplete
ones had been ruled out. These subjects were chosen considering quotas based on
the socio-demographic profile of Internet users aged between 16 and 74 years who
sometimes 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], but
have adapted them to our study. The variables that appear in Table 1 show the average
of six items (three for distributive fairness and three for procedural fairness) in five
situations considered to evaluate the fairness perception related with five revenue
management strategies used in hotels. Thus, PFP1 captures the average of distributive
and procedural fairness in the revenue management based on the restrictions accepted,
PFP2 refers to distributive and procedural fairness in the revenue management based
on time, PFP3 includes the items relating to distributive and procedural fairness in the
revenue management based on location; PFP4, which includes items of distributive
and 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

PFP1

The lower prices customers pay for not
being able to cancel a booking are

Fair

Reasonable
Acceptable
The pricing process that sets lower
prices for those who cannot change
or cancel their booking, is

Fair

Reasonable
Acceptable
PFP2

The price of rooms on Fridays and
Saturdays is

Fair
Reasonable
Acceptable

The pricing process that sets higher
prices for Fridays and Saturdays is

Fair
Reasonable
Acceptable

PFP3

The price of rooms with a good view
or location is

Fair
Reasonable
Acceptable

The pricing process that sets higher
prices for rooms with a good view
or location is

Fair

Reasonable
Acceptable
PFP4

The lower prices from the fourth
night onwards are

Fair
Reasonable
Acceptable

The pricing process that sets lower
prices from the fourth night
onwards is

Fair

Reasonable
Acceptable
PFP5

The lower prices that clients pay for
booking in advance are

Fair
Reasonable
Acceptable

The pricing process that sets lower
prices for those who book in
advance is

Fair

Reasonable
Acceptable

123

Scale

Source

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

Adapted from [28]

Effects of PFP in online hotel booking

the stay; and, finally, PFP5, which consists of the distributive and procedural fairness
items in revenue management according to booking in advance.
The RP has been measured by three items adapted from [26] that consider maximum, reasonable and minimum prices given by the consumers to pay for booking one
night 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 of
the process of online hotel booking (Table 3).
For SF, we have considered that “consumers compare prices by nature” [33] and
that these comparisons are basically made to ascertain whether or not an observed
price is fair. Taking into account that different alternatives are used in these comparisons, namely expected price, RP, competitors prices, previous experience, sources of
external information and recommendations, we have opted in this paper to use the
ìtems 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 consider
and, finally, personal and social outcomes [3], using a seven-point likert scale [13]
(Table 5).
Although some authors have distinguished three loyalty dimensions, namely word
of mouth, price tolerance and intentions to purchase again, we have focused on word
of mouth and purchase intentions to measure loyalty, as in [45]. More specifically, we
have used the items shown in Table 6.
To measure satisfaction, we have focused on satisfaction with price, using the items
shown in Table 7 adapted from previous studies.
Table 2 RP items
Item

Description

Scale

Source

RP1

The maximum price per night you
would be willing to pay, such that
any price that exceeds it would not
be reasonable for you
The price per night you would
consider reasonable and would be
willing to pay
The price per night you would
consider an acceptable minimum,
such that any price lower would be
unreasonable or “dubious” for you

Numerical price

Adapted from [26]

RP2

RP3

Table 3 FOHB items
Item

Description

Scale

Source

FOHB1

I am very familiar with online
hotel bookings

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

Adapted from [7,26]

FOHB2

I know the process of online
hotel 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 I
expected to find

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

SF2

In order to determine whether a price is fair or unfair, I use the
information that I have gathered from other hotels that offer
similar services as a reference
I use my previous experience with hotels in order to determine
whether a price is fair or not
The opinion of my friends, relatives or acquaintances helps me to
determine whether the price of a hotel is fair or unfair
I use the information I find in forums and recommendation pages to
establish whether the price of a hotel is fair

SF3
SF4
SF5

Table 5 Items related to DC
Item

Description

Scale

Source

DC1

I am confident about the
decision

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

Adapted from [3,13]

DC2

It was not very difficult for
me to decide
I think that I have managed to
find the best option for me
I think that I have managed to
gather all the relevant
information
I have made the right decision

DC3
DC4

DC5
DC6

I quickly identified the best
option

Table 6 Items related with loyalty
Item

Description

Scale

Source

L1

I would recommend the hotel
I have chosen

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

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

L2

If my friends or relatives were
looking, I would recommend this
decision
If I had to choose again, I would
choose the same hotel
Although others offer lower prices, I
think I would still choose this hotel

L3
L4

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Adapted from [31,58]

Effects of PFP in online hotel booking
Table 7 Satisfation with price items
Item

Description

Scale

Source

SP1

In general, I am satisfied with the
purchase I have made

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

Adapted from [22,37,52]

SP2

I am satisfied with the price paid for
the room
I think that I have got the best
possible conditions for the price
paid
I am happy with the price paid

SP3

SP4
SP5
SP6

The price paid makes me feel the
product is cheap
The price paid makes me feel good
about my purchase

4 Results
Taking into account the characteristics of the information obtained in the survey and
the theoretical model proposed, the model was estimated using PLS. First, we have
developed an exploratory factor analysis, which allows us to decide which items to
use 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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M.-E. Andrés-Martínez et al.
Table 8 Reliability measurements
Factor

Item

Loading

t value
(Bootstrap)

Cronbach’s
alpha

Composite
reliability

AVE

RP

RP1

0.8955**

8.5987

0.9153

0.9463

0.8548

RP2

0.9723**

11.8658

RP3

0.9040**

10.1880

FOHB1

0.9640**

96.3480

0.9273

0.9649

0.9322

FOHB 2

0.9671**

116.4281
0.7498

0.8220

0.4885

0.8031

0.8635

0.5597

0.9008

0.9238

0.6698

0.8656

0.9097

0.7178

0.9286

0.9443

0.7396

FOHB
SF

PFP

DC

L

SP

SF1

0.6066**

3.6870

SF2

0.8233**

6.8234

SF3

0.8552**

6.2869

SF4

0.5392**

3.2368

SF5

0.6121**

4.0977

PFP1

0.7694**

18.4691

PFP2

0.6500**

8.1560

PFP3

0.7388**

14.7018

PFP4

0.8058**

20.5398

PFP5

0.7674**

17.3904

DC1

0.8405**

33.2855

DC2

0.7243**

12.5020

DC3

0.8424**

25.7515

DC4

0.7842**

18.1722

DC5

0.8691**

27.1776

DC6

0.8413**

29.2734

L1

0.9167**

66.2106

L2

0.9078**

56.0615

L3

0.8408**

23.4523

L4

0.7070**

14.5039

SP1

0.8622**

40.9897

SP2

0.9051**

52.8285

SP3

0.8568**

34.8903

SP4

0.9154**

52.1412

SP5

0.7163**

16.0229

SP6

0.8889**

43.1299

Note ** p < 0.01

validity evaluation. The results for the model show that all items are significant and
their outer loadings are greater than 0.60 [2] and the cross-loads always being greater
for the latent variables upon which the respective items are loaded.
The usual Goodness of Fit (GoF) measure, proposed in [48], is the geometric
mean of the average communality (outer model) and the average R 2 (inner model),
with a value of 0.43. We can accept this value as acceptable according to [54]. As
regards the reliability of the measurement instrument, Cronbach’s alpha value for

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Effects of PFP in online hotel booking
Table 9 Matrix of correlation between latent variables
SF

DC

FOHB

PFP

L

RP

SF

0.4885

DC

0.0887

FOHB

0.2108

0.0747

0.9322

PFP

0.0525

0.2130

0.0677

0.5597

L

0.0708

0.4732

0.0308

0.1141

0.7178

RP

0.0018

0.0507

0.0230

0.0294

0.0453

0.8548

SP

0.0688

0.5793

0.0561

0.1940

0.5550

0.0727

SP

0.6698

0.7396

Note 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

0.039

H2

FOHB->PFP

0.173*

2.090

0.019

H3

SF->PFP

0.144*

1.754

0.040

H4

PFP->DC

0.462**

6.321

0.000

H5

PFP->L

−0.035n.s.

0.528

0.299

H6

DC ->L

0.298**

3.459

0.000

H7

PFP->SP

0.440**

6.084

0.000

H8

SP ->L

0.534**

6.832

0.000

Note ** 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]; the
composite reliability values are also greater than 0.8 in all cases and the convergent
validity 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 than
the 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 can
observe a positive and significant relationship with RP (β = 0.139, p < 0.05), FOHB
(β = 0.173, p < 0.05) and the SF (β = 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 positive
and significant influence on DC (β = 0.462, p < 0.01), confirming hypothesis 4.
Hypothesis 7 is also confirmed since there is a positive and significant relationship
between PFP and SP (β = 0. 440, p < 0.01). PFP has a negative but non significant
impact on loyalty (β = −0.035, n.s.), therefore rejecting hypothesis 5. However, PFP
indirectly influences loyalty through DC and satisfaction with price, because there is
an overall effect with a coefficient of β of 0.338 and a p value lower than 0.01. Finally,
decision confidence (β = 0.298, p < 0.01), as well as satisfaction with price (β = 0.534,

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

5 Conclusions
In recent years, online hotel bookings have increased considerably, although the
antecedents that determine PFP and the consequences that arise as a result of this
perception on the Internet have not been analyzed in depth. Taking this situation into
account, this research contributes to furthering the existing literature on this subject.
After analyzing the results, we can establish that consumers use RPs when assessing
the fairness of the price observed. Furthermore, when consumers are more familiar
with online hotel bookings their perceptions of price fairness increases and the SF
make easier the PFP.
In relation to the consequences of consumer PFP, we find that DC and satisfaction
with price are present when prices are perceived as fair. However, PFP has no significant
influence on loyalty, although this influence becomes evident indirectly through SP
and DC.
This study has a lot of implications for hotel companies. In this sense, the main
contribution of this paper is that hotel companies can know that factors determine
consumer PFP positively and what consequences could have this perception on the
consumer behavior. It is important to hotel managers know that the consumers use the
RP, FOHB and SF to analyze the prices. So, in the case of RP, we suggest that the
hotel managers can use some alternatives to avoid perceptions of unfair prices such
as: highlighting the quality and benefits that their service has; communicating costs
and 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 the
way for future research lines. The current economic context may have influenced the
results, so it would be interesting to undertake a long-term study to analyze whether
the current crisis has affected the relationships established in the model tested in this
paper. In the same line, it would also be interesting to perform a cross-cultural study
in order to verify whether culture has a clear influence on the relationships analyzed.

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María-Encarnación Andrés-Martínez is Ph.D. and Degree in
Business Administration by University of Castilla-La Mancha. Assistant Professor in Marketing at Business Administration Department.
Faculty of Economics and Business Administration of Albacete. University of Castilla-La Mancha (Spain). She was awarded a prize from
the 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-Ángel Gómez-Borja has a degree in Economics and Business Administration from the University of Valencia and a Ph.D.
in Business Administration from the University of Castilla-La Mancha. Currently, he is Associate Professor of Marketing at School of
Economics and Business of Albacete, Spain. His research is focused
among others on the impact of new information technologies on
retailing management, international retailing, consumer behaviour in
virtual environments and online marketing research tools and applications. He also works on topics related to marketing for non-profit
organizations, developmental aid and sustainable development programs and tools.

Juan-Antonio Mondéjar-Jiménez is Ph.D. and Degree in Business Administration by University of Castilla-La Mancha. Degree
in Advanced Studies in Marketing at the same university. Master in
Marketing Research and Master in Art of Economics by Spanish University of Distance. Associate Professor in Marketing at Business
Administration Department. Faculty of Social Sciences of Cuenca.
University of Castilla-La Mancha (Spain). Director and member of
different research projects, have participated in a hundred of Conferences and Congress national and international. Member of the
Editorial Board from different national and international journals.
Author of plus than fifty scientific publications: books, chapters, articles in national and international journals. He is currently Associate
Vice-Chancellor at the University of Castilla-La Mancha. Research
Interest: E-learning, consumer behavior, price perception and tourism
marketing.

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