Journal of Air Transport Management 32 (2013) 58e64
Contents lists available at SciVerse ScienceDirect
Journal of Air Transport Management
journal homepage: www.elsevier.com/locate/jairtraman
Online drivers of consumer purchase of website airline tickets
Tomás Escobar-Rodríguez a, Elena Carvajal-Trujillo b, *
a
b
Department of Accounting and Information Systems, University of Huelva, Plaza de la Merced, 21002 Huelva, Spain
Department of Business Administration and Marketing, University of Huelva, Plaza de la Merced, 21002 Huelva, Spain
a b s t r a c t
Keywords:
Airline marketing
Online consumer behavior
Behavioral intentions
Airline e-commerce
Airline tickets
This study aims to examine the different drivers of online airline ticket purchasing behavior and to
validate a new conceptual framework (Venkatesh et al., 2012) in this context. Based on the Extended
Unified Theory of Acceptance and Use of Technology (UTAUT2), this paper includes seven explanatory
variables: performance expectancy, effort expectancy, social influence, facilitating conditions, hedonic
motivation, price saving, and habit. Data from 1360 usable questionnaires, collected in Spain, were tested
against the research model. Our findings indicate that the main predictors of online purchase intention
are, in order of relevance, habit, price saving, performance expectancy, and facilitating conditions.
However, there is no significant impact of effort expectancy on the online purchase intention, social
influence from referents; and hedonic motivation to use the website. On the other hand, the results
highlight that the main predictors of use behavior are, in order of importance, online purchase intention,
habit, and facilitating conditions.
Ó 2013 Elsevier Ltd. All rights reserved.
1. Introduction
The Internet is ideal for the tourism industry due to the characteristics of its products (McCole, 2002) which are: 1) intangible,
2) their production and consumption are inseparable 3) perishable
and 4) seasonal.
Among the products consumers would rather book online
instead of at their local travel agent are airline tickets, accommodation as well as other tourism products and services (Morrison
et al., 2001). The advantages that these online purchases bring
are convenience, price reductions and product diversity (Forgas
et al., 2012).
The use of IT permits the tourism and hospitality industry
to obtain competitive advantages, offering lower distribution
costs, greater tourist satisfaction, marketing efficacy and greater
competitiveness (Tsai et al., 2005).
In the context of tourism, this study adapts a new model,
UTAUT2, to the formulation of online purchase intentions and online purchase (Venkatesh et al., 2012). UTAUT2 presents some advantages over UTAUT in consumer purchase of online airline
tickets. In addition, this paper adapts the price value construct
because online purchase does not only entail zero cost for the user
* Corresponding author. Tel.: þ34 959217821; fax: þ34 959217839.
E-mail addresses: tescobar@uhu.es (T. Escobar-Rodríguez), carvajal.trujillo@
dem.uhu.es (E. Carvajal-Trujillo).
0969-6997/$ e see front matter Ó 2013 Elsevier Ltd. All rights reserved.
http://dx.doi.org/10.1016/j.jairtraman.2013.06.018
but also a series of benefits. In addition, the direct influence of the
adaptation of the price value construct on the use of websites when
buying airline tickets is incorporated. This study aims to analyze the
different drivers of online airline ticket purchasing behavior and to
validate a new conceptual framework, the UTAUT2 (Venkatesh
et al., 2012), in this context.
2. Theoretical background
The analysis of the intention to use has traditionally been carried out via UTAUT (San Martín and Herrero, 2012), TAM (Bigné
et al., 2010; Herrero and San Martín, 2012) and TPB (Ruiz-Mafe
et al., 2013), which are models designed for use in an organizational context (i.e., to understand the factors which influence
employee IT acceptance and/or use) but not necessarily in a consumer context (i.e., to specifically explain IT acceptance and/or use),
as is the case with UTAUT2.
UTAUT2 is an extension of UTAUT in a “consumer use context”
(Venkatesh et al., 2012). What is more, UTAUT2 incorporates
additional constructs related to the consumer such as hedonic
motivation, price value and habit. As a result, it can be said that its
main contribution is its adaptation to consumer IT acceptance.
UTAUT2 redefines UTAUT constructs from the point of view of
the consumer instead of from the organization employee
(Venkatesh et al., 2012). UTAUT2 also integrates new constructs and
new relations to explain consumer IT acceptance and use
(Venkatesh et al., 2012). UTAUT2 considers seven constructs; four
T. Escobar-Rodríguez, E. Carvajal-Trujillo / Journal of Air Transport Management 32 (2013) 58e64
from UTAUT (performance expectancy, effort expectancy, social
influence and facilitating conditions) plus three new constructs:
hedonic motivation, price value, and habit. UTAUT2 adapts the
definitions of the seven constructs to consumer technology
acceptance (Venkatesh et al., 2012; Table 1).
The incorporation of the hedonic or intrinsic motivation
construct complements UTAUT which only considers the extrinsic
motivation or utilitarian value through the performance expectancy construct (Venkatesh et al., 2012). The performance expectancy construct has proved to be the strongest predictor of use
intention (Venkatesh et al., 2003). It is precisely in the consumer
context that hedonic motivation has been demonstrated to be a
relevant predictor of technology acceptance and use (Brown and
Venkatesh, 2005; Childers et al., 2001). The second construct
incorporated in UTAUT2, price value, endeavors to consider the
differences between the monetary cost of technology use in the
organizational context where UTAUT was configured, and the
consumer context. In this study; the online airline ticket purchasing
does not entail a cost for the consumer but could result in saving
money or obtaining the best product for a given price (Babin et al.,
1994; Ryan and Rao, 2008). Thus, the proposed model includes
price saving and perceived benefits instead of the price value
construct. Finally, the habit construct has been used in several
papers as a predictor of technology use.
The habit has been introduced as predictor of technology actual
use in the literature (Davis and Venkatesh, 2004; Kim and
Malhotra, 2005; Kim et al., 2005; Limayem et al., 2007;
Venkatesh et al., 2012; Wang et al., 2013). Limayem et al. (2007)
indicated that future research should continue analyzing the influence of habit in technology actual usage. UTAUT2 integrates the
habit construct to explain consumer technology acceptance and use
(Venkatesh et al., 2012). Then, several papers showed habit is a
relevant factor influencing technology use and Venkatesh et al.
(2012) incorporated habit into UTAUT2 in order to explain consumer technology use. UTAUT2 operationalizes habit according to
Limayem et al. (2007), that is to say, as a self-reported perception.
According to Limayem et al. (2007)’s approximation, which operationalize habit following a perception-based approach, it has been
demonstrated that habit has a direct effect on technology use and
furthermore a more moderate effect on intention, since intention to
use technology is of less importance as habit increases (Limayem
et al., 2007).
Bearing in mind the relations and constructs of UTAUT2 and the
reviewed literature, we propose the following hypothesis with
respect to online purchase intention and online purchase:
59
H1. The performance expectancy in the use of airline company
e-commerce websites positively affects online purchase
intention.
H2. The effort expectancy in the use of airline company e-commerce websites positively affects online purchase intention.
H3. The social influence regarding the use of airline company
e-commerce websites positively affects online purchase
intention.
H4. The facilitating conditions perceived in the use of airline
company e-commerce websites positively affect online purchase intention.
H5. The hedonic motivation perceived in the use of airline company e-commerce websites positively affects online purchase
intention.
H6. The habit regarding the use of airline company e-commerce
websites positively affects online purchase intention.
H7. The facilitating conditions perceived in the use of airline
company e-commerce websites positively affect online purchase use.
H8. The habit regarding the use of airline company e-commerce
websites positively affects online purchase use.
H9. The online purchase intention en airline company e-commerce
websites positively affects online purchase use.
H10. Price saving and perceived benefits in the use of airline
company e-commerce websites positively affect online purchase intention.
H11. Price saving and perceived benefits in the use of airline
company e-commerce websites positively affect online purchase use.
3. Methodology
3.1. Measurements
We adapt a preliminary list of measurement items from ecommerce, IT acceptance and tourism literature: original UTAUT,
extended UTAUT (UTAUT2), online shopping, e-commerce, other
studies and associated theories (Venkatesh et al., 2003; Wu and
Wang, 2005; Jensen, 2012; San Martín and Herrero, 2012;
Venkatesh et al., 2012; Wen, 2012). 28 items were generated
through this procedure. Table 4 provides a detailed summary of
items measured through multi-item scales for the measurement of
the constructs in which responses from the participants were
measured by a seven-point Likert scale. This seven point scale
anchored from 1 (¼“strongly disagree”) to 7 (¼“strongly agree”).
Table 1
Definition of constructs in UTAUT and UTAUT2.
Core constructs
UTAUT definitions
UTAUT2 definitions
Performance expectancy
“The degree to which an individual believes that using the system
will help him or her to attain gains in job performance”
“The degree of ease associated with the use of the system”
“The degree to which using a technology will provide
benefits to consumers in performing certain activities”
“The degree of ease associated with consumers’ use of
technology”
“The consumers perceive that important others (e.g.,
family and friends) believe they should use a particular
technology”
“Consumers’ perceptions of the resources and support
available to perform a behavior (e.g., Brown and
Venkatesh, 2005; Venkatesh et al., 2003)”
“The fun or pleasure derived from using a technology”
“Consumers’cognitive tradeoff between the perceived
benefits of the applications and the monetary cost for
using them” (Dodds et al., 1991)
“The extent to which people tend to perform behaviors
automatically because of learning” (Limayem et al., 2007)
Effort expectancy
Social influence
“The degree to which an individual perceives that important
others believes he or she should use the new system”
Facilitating conditions
“The degree to which an individual believes that an organizational
and technical infrastructure exists to support use of the system”
Hedonic motivation
Price value
Not considered
Not considered
Habit
Not considered
60
T. Escobar-Rodríguez, E. Carvajal-Trujillo / Journal of Air Transport Management 32 (2013) 58e64
With this scale variables can be measured that are not directly
observed or quantifiable (Churchill and Iacobucci, 2002).
In order to validate the questionnaire, tourism professionals and
academics assessed whether these items were likely to be appropriate for the aim of to analyzing the different drivers of online
airline ticket purchasing behavior. On the basis of the comments
realized, some modifications were made to this instrument such as
the rewording of some items for reason of clarity. Following this, a
pre-test was conducted on 110 randomly selected subjects of
different genders and ages with experience in purchasing tickets
via airline company websites. During the pretest, subjects who had
not previously purchased airlines tickets online were eliminated.
On the basis of the feedback received modifications were carried
out regards question clarity.
A questionnaire was created in English and this questionnaire
was administered in Spanish for consumers from Spain who had
experienced of using airline company websites to purchase tickets
The English questionnaire was translated to Spanish by a professional native English translator and researchers independently
translated the English questionnaire to Spanish. After analyzing the
different independently translated Spanish questionnaires, the
final version of the questionnaire was agreed on. This Spanish
questionnaire was translated back to English by another professional native English translator to ensure the consistency between
the Spanish and the English versions of the questionnaire (Brislin,
1970; Venkatesh et al., 2012).
As shown in Table 4, the Performance expectancy in this study
was measured by four items (San Martín and Herrero, 2012;
Venkatesh et al., 2012). The Effort expectancy was measured by
four items (Venkatesh et al., 2012). The Facilitating conditions
construct comprised three items (San Martín and Herrero, 2012;
Venkatesh et al., 2012). The Hedonic motivation and Social Influence constructs were measured by three items each (Venkatesh
et al., 2012). The Price-saving orientation construct had three
items (Jensen, 2012; Wen, 2012). The Habit construct was
composed of four items (Venkatesh et al., 2012). The Behavioral
intention was measured by three items (San Martín and Herrero,
2012; Venkatesh et al., 2012) and the Use construct had one item
(Venkatesh et al., 2012).
3.2. Sample and data collection
The sample was chosen by different non-random samplings
because the population size was unknown (San Martín and
Herrero, 2012). First, the quota sampling method was employed
in our study (Kim et al., 2009; Kim et al., 2011; San Martín and
Herrero, 2012). This method was used to identify the number of
Spanish individuals needed to match this sample with the target
population structure in both age and gender. In order to calculate
the appropriate number of Spanish respondents in each age and
gender category the target population data were obtained from a
study produced by the Asociación para la Investigación de Medios
de Comunicación on “Encuesta AIMC a usuarios de Internet 2011”
(AIMC) (see Table 2). Males and females were 54.8 per cent and 45.2
per cent, respectively. Age groups were represented as follows: 20.9
per cent for ages 18e24, 25.6 per cent for ages 25e34, 24 per cent
for ages 35e44, and 29.6 per cent for ages 45 and over. Second, the
convenience sampling method was utilized to collect the quotas
(San Martín and Herrero, 2012).
In different sessions the interviewers were instructed to
administer the questionnaire to subjects from their geographical
areas and who during the last six months had used, airline company e-commerce websites either to search for specific information
or to purchase airline tickets. From November 2 to December 21,
2012 the main survey was administered to 1600 subjects from
Table 2
Quota sampling method in terms of gender and age.
Sampling (n ¼ 1360)
Gender
Age
a
Male
Female
Total
18e24
25e34
35e44
>45
Total
Populationa (%)
Sample (%)
54.8
45.2
100
20.9
25.6
24
29.6
100
53.6
46.4
100
22.65
31.10
23.46
22.57
100
AIMC (2012).
different geographical areas and who were chosen to match the
proportion of age and gender of target population who had visited
different airline tickets websites during the last six months. 1504
(94%) questionnaires were completed by the subjects, 1360 (85%) of
which were collected for data analysis, after eliminating some
questionnaires that had been deemed unusable.
Table 3 shows the gender and the age of the respondent subjects. There were more males (53.6%) than females (46.4%). The
largest group of respondents (31.1%) was the 25e34 age group,
followed by ages between 35 and 44 (23.46%), and ages over 45
(22.57%).
4. Results
In this study, to analyze the structural equation modeling (SEM),
AMOS 20.0 was used. SEM simultaneously tests the multiple hypotheses by estimating the relationships between a set of multiple
independent and dependent variables in a structural model (Gefen
et al., 2000). Whereas partial-least-squares (PLS) maximize variance using a principle component estimation. Both, SEM and PLS
have been used in information system papers (Nusair et al., 2013).
We have used SEM due to the large sample size, the constructs are
reflective and not formative, and the research is confirmatory and
not exploratory.
The analysis of data took place through a two-stage methodology, in which the measurement model was developed first and
evaluated separately from the structural model (Fornell and Laker,
1981).
4.1. Measurement model evaluation
The measurement model was assessed utilizing confirmatory
factor analysis (CFA) (Anderson and Gerbing, 1992). Given the
maximum likelihood method of estimation requires the data set to
be multivariate normal, this assumption was evaluated and the
results reported that it was not violated. Thus, the maximum
likelihood was used. This estimation method in SEM is one of the
most commonly utilized (Ryu, 2011). The goodness-of-fit measures
Table 3
Gender and age characteristics of respondents.
Gender
Age
Characteristics
Frequency
%
Male
Female
Total
18e24
25e34
35e44
>45
Total
729
631
1360
308
423
319
307
1360
53.6
46.4
100
22.65
31.10
23.46
22.57
100
T. Escobar-Rodríguez, E. Carvajal-Trujillo / Journal of Air Transport Management 32 (2013) 58e64
61
Table 4
Measurement model assessment results.
Scale items
Performance expectancy (PE) (a ¼ 0.919)
PE1. I find airline company e-commerce websites very useful in the purchasing process.
PE2. Using airline company e-commerce websites increases my chances of achieving things
that are important to me in the purchasing process.
PE3. Using airline company e-commerce websites helps me accomplish things more quickly
in the purchasing process.
PE4. I can save time when I use airline company e-commerce websites in the purchasing process.
Effort expectancy (EE) (a ¼ 0.933)
EE1. Learning how to use airline company e-commerce websites is easy for me.
EE2. My interaction with airline company e-commerce websites is clear and understandable.
EE3. I find airline company e-commerce websites easy to use.
EE4. It is easy for me to become skillful at using airline company e-commerce websites.
Social Influence (SI) (a ¼ 0.918)
SI1. People who are important to me think that I should use airline company e-commerce websites.
SI2. People who influence my behavior think that I should use airline company e-commerce websites.
SI3. People whose opinions that I value prefer that I use airline company e-commerce websites.
Facilitating conditions (FC) (a ¼ 0.831)
FC1. I have the resources necessary to use airline company e-commerce websites.
FC2. I have the knowledge necessary to use airline company e-commerce websites.
FC3. I feel comfortable using airline company e-commerce websites.
Hedonic motivation (HM) (a ¼ 0.916)
HM1. Using airline company e-commerce websites is fun.
HM2. Using airline company e-commerce websites is enjoyable.
HM3. Using airline company e-commerce websites is very entertaining.
Price-saving orientation (PO) (a ¼ 0.827)
PO1. I can save money by examining the prices of different airline company e-commerce websites.
PO2. I like to search for cheap travel deals in different airline company e-commerce websites.
PO3. Airline company e-commerce websites offer better value for my money.
Habit (HT) (a ¼ 0.910)
HT1. The use of airline company e-commerce websites has become a habit for me.
HT2. I am addicted to using airline company e-commerce websites.
HT3. I must use airline company e-commerce websites.
HT4. Using airline company e-commerce websites has become natural to me.
Behavioral intention (BI) (a ¼ 0.941)
BI1. I intend to continue using airline company e-commerce websites to purchase tickets in the future.
BI2. I will always try to use airline company e-commerce websites to purchase tickets.
BI3. I plan to continue to use airline company e-commerce websites frequently to purchase tickets.
Use behavior (AU)
AU1. How often do you use airline company e-commerce websites to purchase tickets?
Standardized
loadings
t-values
SMCs
0.837
0.813
36.339
34.625
0.7
0.661
0.877
39.007
0.768
0.849
36.643
0.721
0.894
0.915
0.905
0.818
42.007
43.682
42.877
36.390
0.8
0.837
0.819
0.669
0.871
0.915
0.88
39.564
42.870
40.216
0.758
0.837
0.774
0.662
0.923
0.983
25.963
38.665
37.121
0.439
0.853
0.967
0.849
0.943
0.829
37.123
43.513
35.523
0.721
0.888
0.687
0.749
0.833
0.739
30.344
35.671
29.825
0.561
0.694
0.546
0.869
0.634
0.881
0.938
38.574
25.152
40.364
44.353
0.755
0.402
0.776
0.88
0.927
0.877
0.902
44.337
40.201
42.216
0.86
0.768
0.813
NA
NA
NA
AVE
CCR
0.713
0.908
0.781
0.934
0.790
0.919
0.752
0.899
0.766
0.907
0.600
0.818
0.703
0.903
0.814
0.929
NA
NA
Model fit statistics: c2 ¼ 1301.87, df ¼ 303, normed-c2 ¼ 4.297, GFI ¼ 0.934, AGFI ¼ 0.912, NFI ¼ 0.964, RMSEA ¼ 0.049, CFI ¼ 0.972.
NA ¼ not available.
were utilized to examine the measurement model fit. The fit
indices, as suggested by the results (Table 4), were obtained within
the recommended values, chi-square was 1.301.87 with 303 degrees of freedom, and normed chi-square was 4.297. The goodnessof-fit index (GFI) was 0.934, the adjusted goodness-of-fit index
(AGFI) was 0.912, the normed fit index (NFI) was 0.964, the root
mean square error of approximation (RMSEA) was 0.049, and the
comparative fit index (CFI) was 0.972. Thus, the measurement
model indicated an acceptable fit (Hair et al., 1998).
A confirmatory factor analysis (CFA) was evaluated to examine
reliability, convergent validity, and discriminant validity for proposed constructs. This is to test the indicators represent the constructs and then to evaluate the structural model evaluation (Hair
et al., 2011). The reliability evaluates the measurement scale for
each construct. Then, the coefficients of Cronbach’s alpha were
calculated (Table 4) for constructs and they exceed the 0.7 cut-off
value as suggested Hair et al. (1998). Thus, constructs have
acceptable reliability.
Convergent validity represents the common variance between
the indicators and their construct. It is measured by the standardized loadings, the squared multiple correlations (SMC), the average
variance extracted (AVE) and construct composite reliability (CCR).
All factor loadings (Table 4) in the indicators for the constructs
exceeded the 0.5 cut-off value as suggested Steenkamp and Van
Trijp (1991). Factor loadings were all significant, the t-value
(Table 4) exceeded the 1.96 cut-off value. All SMC were greater than
0.4 (Bollen, 1989). Additionally, the AVE and CCR were calculated.
The AVE (Table 4) exceeded the 0.5 cut-off value (Fornell and Laker,
1981), and the CCR (Table 4) exceeded the 0.7 cut-off value (Fornell
and Laker, 1981). Thus, these results support the convergent validity of each construct. The discriminant validity was examined by
following the process suggested by Anderson and Gerbing (1988).
Thus, confidence intervals for the correlation between constructs
were calculated and compared with the unit. None of the intervals
includes value 1.0, thus these results support the discriminant
validity of the model (Table 5).
4.2. Structural model
Given successful results in the measurement model evaluation,
the researchers evaluated the structural model to test H1 through
H11. The goodness-of-fit measures were utilized to examine the
structural model fit. The fit indices were obtained within the recommended values (Table 6), chi-square was 1306.156 with 307
degrees of freedom, and normed chi-square was 4.255. The
goodness-of-fit index (GFI) was 0.934, the adjusted goodness-of-fit
index (AGFI) was 0.913, the normed fit index (NFI) was 0.963, the
root mean square error of approximation (RMSEA) was 0.049, and
the comparative fit index (CFI) was 0.972. These fit indices indicated an acceptable structural model. The results presented in Fig. 1
0.733 (0.651; 0.815)
0.821 (0.793; 0.849)
0.615 (0.525; 0.705)
0.707 (0.669; 0.745)
0.705 (0.671; 0.739)
0.578 (0.492; 0.664)
Table 6
Summary of test results for the structural model.
H1
H2
H3
H4
H5
H10
H6
H9
H7
H11
H8
P-value
Supported?
Construct
R-squared
PE / BI
EE / BI
SI / BI
FC / BI
HM / BI
PO / BI
HT / BI
BI / AU
FC / AU
PO / AU
HT / AU
0.141
0.017
0.019
0.097
0.021
0.310
0.462
0.392
0.102
0.002
0.328
<0.01
0.649
0.318
<0.001
0.406
<0.001
<0.001
<0.001
<0.001
0.958
<0.001
Yes
No
No
Yes
No
Yes
Yes
Yes
Yes
No
Yes
Behavioral
intention
0.82
Use
behavior
0.579
5. Conclusions
0.628
0.672
0.66
0.501
0.641
0.82
0.734
0.727
0.592
(0.363; 0.463
(0.449; 0.541)
(0.346; 0.442)
(0.39; 0.494)
(0.435; 0.527)
(0. 277; 0.461)
0.413
0.495
0.394
0.442
0.481
0.369
0.505)
0.815)
0.662)
0,74)
0.858)
0.773)
0.652)
b
a
Correlation between variables.
Confidence interval for the correlation.
(0.405;
(0.763;
(0.582;
(0.668;
(0.802;
(0.713;
(0.472;
0.455
0.789
0.622
0.704
0.83
0.743
0.562
(0.539;
(0.427;
(0.626;
(0.573;
(0.576;
(0.644;
(0.804;
(0.632;
Path
and Table 6 summarize the relationship between the different
constructs.
(0.605; 0.677)
(0.79; 0.85)
(0.7; 0.768)
(0.697; 0.757)
(0.506; 0.678)
HM
EE
SI
PE
0.581a
0.473
0.66
0.611
0.612
0.682
0.826
0.714
0.623)b
0.519)
0.694)
0.649)
0.648)
0.72)
0.848)
0.796)
HT
PE
SI
EE
HM
FC
PO
BI
AU
Table 5
Confidence interval for the correlations between pairs of latent variables.
Hypothesized
paths
Goodness fit statistics: c2 ¼ 1306.156, df ¼ 307, normed-c2 ¼ 4.254, GFI ¼ 0.934,
AGFI ¼ 0.913, NFI ¼ 0.963, RMSEA ¼ 0.049, CFI ¼ 0.972.
(0.59; 0.666)
(0.632; 0.712)
(0.624; 0.696)
(0.411; 0.591)
BI
PO
T. Escobar-Rodríguez, E. Carvajal-Trujillo / Journal of Air Transport Management 32 (2013) 58e64
FC
62
This research contributes to explaining how consumers use
airline websites to purchase air tickets.
Our findings indicate that the main predictors of online purchase intention are, in order of relevance: habit; price saving;
performance expectancy; and facilitating conditions. Thus, online
purchase intention depends on the individual habit of using the
website; the price saving obtained in direct purchase of airline
tickets directly from airline company websites; the levels of performance expected by the consumer in completing the online
transaction; and the facilitating conditions available. However,
there is no significant impact of effort expectancy on the online
purchase intention, the social influence from referents; and the
hedonic motivation to use the website. On the other hand, the results highlight that the main predictors of use behavior are, in order
of importance: online purchase intention; habit and facilitating
conditions. Hence, use behavior depends on: online purchase
intention, the individual habit in using the website; and the facilitating conditions available to individuals. However, the hypothesized causal relationship introduced in the UTAUT2 of the influence
of price saving on use behavior was not supported.
Given the results obtained then, the greater the habit of individuals the more likely they are to have a greater purchase
intention, and a greater probability of actual use of airline company
websites for online ticket purchase. It is suggested therefore that
airline companies in Spain should formulate marketing communication strategies that create habit of online airline ticket purchase
intention and thus achieve greater online purchase intention in
individuals, and therefore generates an online purchase behavior.
This implies that in order to increase online sales via direct
purchase from their website, airline companies should advertise on
the Internet and/or in other more traditional media about the
different usage contexts and occasions on which the website can be
utilized to buy a ticket online. Examples of usage contexts and
occasions (weekend trip, summer holyday, business trip, present
for friends and family) would be the online purchase of an airline
ticket. Also, airline company websites should offer, as well as other
promotions, a discount on the next online ticket purchase (loyalty
programs). The existence of these promotions can be communicated to individuals via email so that they visit the airline company
website.
With regard to price saving, it can be said that this variable plays
a relevant role as a direct driver of online purchase intention. This
means that greater money savings, a greater the chance of
T. Escobar-Rodríguez, E. Carvajal-Trujillo / Journal of Air Transport Management 32 (2013) 58e64
63
Fig. 1. . Results of testing model.
obtaining the best product for a given price and greater perceived
benefits of the airline ticket online purchasing, will result in greater
intention to use the websites to purchase air tickets. However, in
contrast with our expectations, the relation of influence on online
purchase use is positive but not significant. Thus, individuals have
airline ticket online purchase intention due to the price saving they
can obtain, however, this perceived price saving does not influence
actual online purchase, but other factors such as habit or facilitating
conditions. For example, airline company websites should publicize
via their website that with online direct purchase individuals can
obtain lower prices or the best product for a given price such as a
flight with fewer o no stopovers or free food during the flight.
Performance expectancy (perceived usefulness) which represents utilitarian features is the third driver of the online purchase
intention. For this reason airline companies should offer utilitarian
features. Included in utilitarian features we highlight free customer
care telephone, Twitter or Facebook client communication channels or other types of new media, and the option of presenting
boarding pass not only on paper, but by SMS or by WhatsApp.
It is demonstrated that effort expectancy does not influence
online purchase intention. The results obtained might be due to the
fact that nearly all the individuals included are habituated to using
websites, social media platforms (Facebook, Twitter .), which are
similar or superior to ecommerce websites. The fact that hedonic
motivation does not influence the online purchase intention can be
explained because the individuals frequently use websites and
social media platforms, which are more entertaining and fun that
the airline company e-commerce websites Additionally, the
absence of affect of social influence on the online purchase intention may be explained by the generalized internet use as an information source about tourism products and services reduces the
influence, positive or negative, of the referents with consumer
purchase of online airline tickets.
Regarding the influence of facilitating conditions, this variable
influences online purchase intention and the online purchase use.
This implies that individuals’s perceptions about the resources and
support available for online purchase influence the online purchase
intention and online purchase use. This shows that airline companies should facilitate the option to purchase via their websites
offering diverse media so that consumers are informed if they have
any problems formulating their purchase. These could include free
or low-cost telephone numbers, email, an active presence in Facebook, Twitter.
Finally, regarding the influence of perceived behavioral intention on perceived use behavior the greater the perceived online
purchase intention, the greater the chance of online purchase use.
Therefore, managers should try to increase online purchase intention in order to increase online purchase use. Factors such as habit,
price saving, hedonic motivation, facilitating conditions, social influence and performance expectancy can all be acted upon in order
to increase online purchase intention.
These findings confirm the validity of UTAUT2 in explaining the
online purchase intention and the online purchase use in the
context of the direct purchase of airline tickets.
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