I Am off Then: Drivers of Travellers’ Intentions to Book Trips Online
An Integrated Study on Technology Acceptance and Satisfaction
Maria Madlberger
Department of Business and Management, Webster University Vienna, Berchtoldgasse 1, 1220, Vienna, Austria
Keywords: Electronic Commerce, Technology Acceptance Model, Satisfaction, Information Quality, System Quality,
Online Booking Intention, Travel Agency, PLS Analysis.
Abstract: The tourism industry has undergone a substantial transformation since the emergence of electronic
commerce. Especially travel agencies that are faced with growing online competition are increasingly
dependent on achieving online sales. This study investigates antecedents of consumers’ intention to book a
trip online at a travel agency’s website. The research draws on an integrated research model based on the
technology acceptance model and customer satisfaction as introduced in the DeLone and McNeal model on
information system success. An online survey among 292 consumers largely supports the hypothesized
impact factors. Information quality serves as a significant object-based belief that influences satisfaction as
an object-based attitude. In contrast, system quality has no impact on satisfaction. Satisfaction influences
perceived usefulness, a key driver of online booking intention and perceived ease of use. The study provides
several scholarly and managerial implications for the online distribution of tourism services.
1 INTRODUCTION
Online distribution of services constantly increased
significantly the last years. One service sector that
experienced substantial transformation is the tourism
and travel industry. Travel agencies are a prominent
example of e-commerce-induced disintermediation
since their business has been increasingly replaced
by direct online distribution of flights, hotel rooms,
rental cars, organized tours, and other travel
services. Worldwide, sales of traditional offline
travel agencies are declining sharply (eMarketer,
2012).
However, travel agencies can offer online service
provision themselves and thus defend their position
in the distribution of tourism services. Many travel
agencies operate websites that offer online search
and online booking tools. The emergence of online
mediators such as Expedia.com demonstrates that
there is a significant demand for online platforms
that offer a variety of tourism services which
complement service providers’ Web presences, such
as airline or hotel websites. Furthermore, travel
agencies frequently offer services that differ from
single travel components. Especially when it comes
to packaged tours or holiday arrangements travel
agencies take the work of selecting the trip
components out of the consumers’ hands. Further,
from a legal perspective, consumers who book
services via a domestic travel agency may be in a
better position in case of a dispute as their domestic
law is applied instead of the law of the foreign
country the service provider (e.g., a hotel) resides in.
Hence, despite the increased competition by
tourism service providers, online intermediaries, and
even consumers who plan their trips themselves,
travel agencies offer a substantial value-added. On
the other hand, more and more consumers expect
online booking facilities and therefore travel
agencies are increasingly dependent on attracting
consumers who prefer to book online. The most
obvious measure for a travel agency to gain online
consumers is operating a website and/or mobile
application that allows online booking of trips or
single tourism services. For this purpose, deeper
insights into drivers of online purchasing behavior
are of crucial importance for travel agencies.
Although extensive research has been done on
online consumer and purchasing behavior, findings
on online booking behavior on travel agencies’
websites are still limited. There is especially
incomplete knowledge on antecedents that are based
on external stimuli which can be controlled by
companies when designing their websites. In order
247
Madlberger M..
I Am off Then: Drivers of Travellers’ Intentions to Book Trips Online - An Integrated Study on Technology Acceptance and Satisfaction.
DOI: 10.5220/0004804402470256
In Proceedings of the 10th International Conference on Web Information Systems and Technologies (WEBIST-2014), pages 247-256
ISBN: 978-989-758-023-9
Copyright
c
2014 SCITEPRESS (Science and Technology Publications, Lda.)
to shed light on a comprehensive model on drivers
of online booking intentions, we develop a research
model that integrates two seminal theories in
information systems (IS) and e-commerce research:
the technology acceptance model (TAM; Davis,
1989) and the notion of satisfaction grounded in the
DeLone and McNeal information system success
model (DeLone and McLean, 1992; DeLone and
McLean, 2003). In doing so, our study follows the
theoretical approach developed by Wixom and Todd
(2005) and further extended by Xu et al. (2013). In
line with the suggestion by Ajzen and Fishbein
(1980), we contend that online booking intention is
the result of a chain of impacts that starts with
external stimuli that lead to object-based beliefs
(information quality and system quality) and object-
based attitude (satisfaction with the website) which
itself influences behavioral beliefs and behavioral
attitudes or behavioral intentions (Wixom and Todd,
2005; Xu et al., 2013). The proposed structural
model is tested with data collected from an online
survey among 292 consumers. The partial least
squares (PLS) analysis results confirm most of the
proposed hypotheses.
The study contributes to research by stressing the
relevance, particularly of information quality, and
satisfaction for booking intention on travel agencies’
websites. It further demonstrates the relevance of the
TAM in the online journey booking context. This
research also offers important managerial
implications by showing key design issues of travel
agencies’ websites to maximize online booking
intention. The paper is organized as follows: The
next section presents the theoretical background,
particularly on the TAM and the role of service
quality and satisfaction in the online tourism sector.
The following section three presents the research
model and the hypotheses development. The
subsequent section four shows the research
methodology of the survey. Section five presents the
results which are discussed in section six. The paper
proposes research and managerial implications and
closes with further research directions.
2 THEORETICAL
BACKGROUND
2.1 TAM and Satisfaction in Online
User Behavior
Like other IS-related issues on user behavior, online
consumer behavior, especially usage and intention to
use has been extensively and successfully
investigated through the theoretical lens of TAM
(Davis, 1989; Davis et al., 1989). TAM is grounded
in the theory of reasoned action (TRA; Ajzen and
Fishbein, 1980; Fishbein and Ajzen, 1975) and its
extension, the theory of planned behavior (TPB;
Ajzen, 1985; Ajzen, 1989). TPB extends the notion
of TRA that beliefs influence attitudes which
themselves have an impact on behavioral intentions.
The TAM applies this theory to the context of IS
usage where perceived usefulness (PU) and
perceived ease of use (PEOU) are considered main
drivers of behavioral attitude, intention, and
behavior. PU, the main impact factor, is defined as
“the degree to which a person believes that a
particular system would enhance his job or
performance” (Davis, 1989, p. 320) while PEOU is a
secondary impact factor and denotes “the degree to
which a person believes that using a particular
system would be free of effort” (Davis, 1989, p.
320). Further, PEOU has an indirect positive impact
on system usage via PU (Davis, 1989).
In IS and e-commerce research TAM was
constantly extended and further developed. An early
extension was suggested by Davis (1993) who
considers system design features an external
stimulus that precedes PU and PEOU. In e-
commerce research TAM was applied and extended
to predict website use and online shopping behavior
in numerous studies (for example Ahn et al., 2004;
Ha and Stoel, 2009; Klopping and McKinney, 2004;
Lin and Lu, 2000; Liu and Forsythe, 2010;
McCloskey, 2004; McCloskey, 2006; Moon and
Kim, 2000; Shih, 2004; Wang and Benbasat, 2005).
Significant modifications of TAM in the context of
e-commerce and WWW usage were made by Gefen
and Straub (2000), Lederer et al. (2000), Teo et al.
(1999), and Klopping and McKinney (2004) who
successfully simplify the TAM model by eliminating
behavioral attitude and confirming a direct impact of
PU and PEOU on behavioral intention. Another
significant contribution was made by researchers
who drew on Davis’ (1993) extension of TAM by
external stimuli. Several researchers adopted the
components of the DeLone and McLean IS success
model (DeLone and McLean, 1992) or the updated
DeLone and McLean IS success model (DeLone and
McLean, 2003) including information quality,
system quality, and service quality (see Brown and
Jayakody, 2008 for a review). Examples of studies
using information quality, system quality, and
service quality as extensions of TAM in e-commerce
are Shih (2004) and Ahn et al. (2004). Another
construct of the DeLone and McLean model,
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satisfaction, was compared with TAM (Wang, 2008)
or successfully integrated into TAM-based research
on system use as in the seminal studies by Wixom
and Todd (2005) and Xu et al. (2013). These studies
consider object-based beliefs in the form of
information quality and system quality (Wixom and
Todd, 2005) as well as service quality (Xu et al.,
2013) as antecedents of object-based attitude,
expressed as satisfaction with these quality
dimensions which further influence PU and PEOU.
2.2 Consumer Behavior in Online
Tourism
The tourism industry is characterized by a number of
particularities that differentiate it from other
domains, such as durable and non-durable consumer
goods. Like many services, tourism services are
perishable, non-storable, differing in quality, and
largely influenced by the consumers themselves.
Particularly vacation trips can be highly complex
products consisting of a number of single
components that are difficult to assess by
consumers. Since the prices of travel products are
usually high in relation to the income, consumers
travel rather rarely and thus are often not very
experienced (Järveläinen, 2007). Travel services are
further a typical experience good, that is, consumers
can evaluate it only after consumption. As a result,
consumers’ expectations when purchasing travel
services are different from expectations when
purchasing physical consumer goods. For example,
prior to booking, consumers need information on
hotel locations, flight times, or costs and distances of
rental cars or public transportation (Petre et al.,
2006). Thus an e-commerce site that offers tourism
services needs to provide necessary information that
allows a sufficient assessment of the travel products
prior to booking a trip.
The tourism industry is one of the service sectors
that was most influenced by e-commerce (Kim et al.,
2011; Lin et al., 2009). Especially the number of
traditional offline travel agencies declined with
increased competition from service providers such
as airlines or hotels, but also online intermediaries
like Expedia or Travelocity. Worldwide total travel
sales reached estimated $ 962 billion in 2012, with $
374 billion or 38.9 percent share achieved online. In
the United States this share is 51.5 percent, in
Europe it amounts to 45.1 percent.
Classical travel agencies were constantly losing
market shares. This development can be observed
throughout Western Europe where in 2012 only a
fifth of trips was booked via travel agencies while in
2008 it was every third trip (Hotelmarketing.com,
2013). Data from the United States indicate that this
trend does not necessarily affect travel agencies as
an institution, but the offline distribution channel. In
2011, 91 percent of active travelers booked their
trips online whereas the traditional offline travel
agency was employed in 9 percent of the cases. 62
percent of travelers used online travel agencies,
followed by branded supplier sites (e.g., airlines)
with 46 percent, meta search sites (14 percent) and
collective buying as well as private sales sites (5
percent each; Mashable, 2013). This evidence
stresses the relevance of the Internet as a distribution
channel that is increasingly becoming a “must” for
travel agencies. Whereas these figures evoke the
impression that travel agencies are in a good
position in the online business, these numbers also
show that particularly classical travel agencies
nowadays are faced with a much larger and more
complex competition than they were in the declining
offline distribution. For example, travel agencies are
becoming increasingly challenged by online
platforms such as Expedia.com in the domain of
packaged tours (Dooley, 2009), a very important
business segment of travel agencies.
Online consumer behavior in the tourism
industry was subject of several empirical studies.
Particular focus is put on satisfaction and website
quality. For example, Jeong et al. (2003) investigate
website quality and information satisfaction as
impact factors of behavioral intention. Law and Ngai
(2005) consider the usability of a website a key
dimension of website quality. Tsang et al. (2010)
analyze dimensions of service quality of travel
agencies and distinguish website functionality,
information quality and content, fulfillment and
responsiveness, safety and security, appearance and
presentation, and customer relationship and confirm
their significant impact on satisfaction. The relations
between process and outcome quality, satisfaction,
and behavioral intention are investigated by Chen
and Kao (2010). Similar findings are achieved by
Hsu et al. (2012) who add perceived playfulness and
perceived flow as variables. A comprehensive model
including website quality, satisfaction, trust, attitude,
and behavioral intention is provided by Wen (2012).
Also research based on TAM was carried out in
several empirical studies. TAM was augmented with
various factors, such as trust and perceived risks
(Nunkoo and Ramkissoon, 2013), task ambiguity,
product complexity, and consumer experience
(Järveläinen, 2007), or more comprehensive sets of
variables like website content issues, previous visits,
and accessibility (Kaplanidou and Vogt, 2006).
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Chang et al. (2012) integrate perceived website
quality into the TAM by considering perceived
website quality gain and loss (quality appraisal) as
antecedents of PU and PEOU. Quality appraisal is
derived from the IS success model by DeLone and
McLean (2003) and is composed of information,
system, and service quality. Park et al. (2007)
investigate the impact of several dimensions of
website quality including PEOU on willingness to
use. Ryan and Rao (2008) test the TAM in the
tourism context in its original form, supplemented
by system security. The above-mentioned studies
highlight the appropriateness of the TAM as well as
the DeLone and McNeal IS success model for
explaining drivers of website use in the context of
traveling and tourism.
3 RESEARCH MODEL
AND HYPOTHESES
DEVELOPMENT
The proposed research model is grounded in TAM
and its extension by Wixom and Todd (2005). In
line with the original DeLone and McLean IS
success model (DeLone and McLean, 1992), Seddon
(1997), and Wixom and Todd (2005), we consider
two dimensions of website quality perception, that is
information quality and system quality, as we are
solely focusing on the intention to use a travel
agency’s website and not the full offered spectrum
of a travel agency’s services.
Figure 1 summarizes the research model.
Information
quality (IQ)
System
quality (SQ)
Satisfaction (S)
Perceived
usefulness (PU)
Perceived
ease of use
(PEOU)
Intention
to book at
website (I)
H1
H2
H3
H4
H5
H6
H7
Figure 1: Research model.
TAM proposes that behavioral attitudes and
behavior are driven by the behavioral beliefs PU and
PEOU. As Wixom and Todd (2005) and Xu et al.
(2013) argue, these behavioral beliefs are themselves
influenced by object-based beliefs and attitudes. In
particular behavioral beliefs are influenced by
object-based attitudes, that is, satisfaction with the
system, which itself is impacted by object-based
beliefs, that is, quality perceptions. This approach is
different from research that suggests quality
perceptions as direct antecedents of PU and PEOU
(Ahn et al., 2004; Shih, 2004).
Among the quality perception variables,
information quality denotes the perceived quality of
the offered content. Information quality should assist
consumers in their shopping process by facilitating
the comparison of products, increasing enjoyment,
and improving decision-making. In the context of e-
commerce, information quality is related to the
content of the website (Ahn et al., 2004).
Characteristics that represent information quality are
timeliness, completeness, and accuracy of
information (DeLone and McLean, 2003). We thus
contend that websites that fulfill these criteria are
perceived as well-performing on information
quality. System quality describes the technical
performance as well as the design of the web site as
an information system and thus refers to the
engineering perspective (Ahn et al., 2004).
Perceived system quality includes attributes such as
reliability, flexibility, or availability (DeLone and
McLean, 2003; Wixom and Todd, 2005) as well as
functionality that may influence an online shopping
process (Shih, 2004).
There is ample e-commerce literature that
demonstrates the relevance of information quality
and system quality for perceptions of website quality
(for example Aladwani and Palvia, 2002; Kim and
Stoel, 2002; Lin and Lu, 2000; Liu and Arnett,
2000). While Wixom and Todd (2005) differentiate
between information and system satisfaction and Xu
et al. (2013) further introduce service satisfaction,
we consider a more parsimonious model including
general satisfaction with the website as proposed by
DeLone and McLean (2003). We thus hypothesize:
H1: Perceived information quality of the website
positively influences satisfaction with the website.
H2: Perceived system quality of the website
positively influences satisfaction with the website.
Satisfaction with a website is an attitude toward
an object (Ajzen and Fishbein, 1980; Wixom and
Todd, 2005) that can be understood as an external
variable that influences behavioral beliefs, namely
PU and PEOU (Xu et al., 2013). Since PU is
understood as the perceived degree to which a
system increases the performance of the undertaken
task (Davis, 1989), Wixom and Todd (2005)
conclude that higher information satisfaction will be
positively associated with PU. Likewise, as PEOU
describes the perception that using a system does not
require much effort (Davis, 1989), satisfaction with
the system is expected to impact PEOU (Wixom and
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Todd, 2005). In our more parsimonious approach,
we expect that the object-based attitude satisfaction
will positively influence the behavioral beliefs
simultaneously. We therefore hypothesize:
H3: Satisfaction with the website positively
influences perceived usefulness of the website.
H4: Satisfaction with the website positively
influences perceived ease of use of the website.
The remaining variables are directly derived
from the rich literature on TAM which consistently
investigates and empirically confirms the impact of
the behavioral beliefs PU and PEOU on behavioral
attitude (for example Ahn et al., 2004; Ha and Stoel,
2009; Lin and Lu, 2000; McCloskey, 2004;
McCloskey, 2006; Shih, 2004) or, in a more
parsimonious way, on behavioral intention (Gefen
and Straub, 2000; Klopping and McKinney, 2004;
Lederer et al., 2000; Teo et al., 1999). In line with
this literature as well as Wixom and Todd (2005)
and Xu et al. (2013), we hypothesize:
H5: Perceived ease of use of a website positively
influences perceived usefulness of the website.
H6: Perceived usefulness of a website positively
influences the intention to use the website for
booking.
H7: Perceived ease of use of a website positively
influences the intention to use the website for
booking.
4 RESEARCH METHODOLOGY
4.1 Instrument Development
The empirical test of the research model took place
by means of a quantitative consumer survey. In
order to achieve comparable results, respondents
were presented a website of a travel agency that was
subject to evaluation based on the applied variables.
The measurement of items was done on the basis of
elaborated scales from IS literature (Ahn et al.,
2004; Devaraj et al., 2002; McCloskey, 2004; Shang
et al., 2005; Shih, 2004). All items on consumers’
beliefs and behavioral intentions were related to the
presented website. Where necessary, the formulation
was adapted to that context (e.g., “booking on the
Website”). PU was measured with items adapted
from Shang et al. (2005), PEOU items were adapted
from Shih (2004) and McCloskey (2004), the items
on information and system quality are based on Shih
(2004) and Ahn et al. (2004). Finally, satisfaction
and intention to use were adapted from Devaraj et al.
(2002). Since the used items were all developed in
English language, they were translated into German
and back-translated by a native speaker. A pretest
among eight students was made. According to these
refinements a few wording modifications were done.
All items were measured with a 5-point Likert scale
ranging from 1 (totally disagree) to 5 (totally agree).
4.2 Sample
The research design comprised a quantitative online
survey. In order to attract consumers who are
interested in traveling, the online survey was
announced at several German-speaking online
forums. 324 questionnaires were completed, among
which 32 contained incomplete answers so that 292
questionnaires were used for further analysis. The
gender distribution in the sample is 54.3 percent
males and 45.6 percent females. The average age is
33 years; 26.2 percent are younger than 25 years,
38.3 percent are between 25 and 34 years, 19.5
percent are between 35 and 44 years, and 16.1
percent are 45 years or older. A filter question
ensured that respondents travel at least once per
year.
5 RESULTS
5.1 Measurement Model
The research model was tested by means of a Partial
Least Squares (PLS) analysis. The used analysis
software was SmartPLS (Ringle et al., 2005). The
test of the measurement model consists of analyzing
the consistency (Cronbach’s Alpha), the convergent
and the discriminant validity. Table 1 shows the
Cronbach’s Alpha and AVE values of the variables.
Table 1: Realiability measures of variables.
Variable
Number of
items
Cronbach’s
Alpha
AVE
Information quality
(IQ)
3 0.70 0.61
System quality (SQ) 4 0.84 0.67
Perceived usefulness
(PU)
4 0.69 0.51
Perceived ease of
use (PEOU)
3 0.78 0.70
Satisfaction (S) 3 0.86 0.78
Intention to use (I) 3 0.90 0.83
The Cronbach’s Alpha values are, with one
exception, higher than the recommended value of
0.7 (Nunnally, 1978). For PU, Cronbach’s Alpha is
0.69 and therefore very close to the recommended
value. Convergent validity is satisfactory if the
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251
average variance extracted (AVE) is higher than 0.5
(Fornell and Larcker, 1981). This condition is met
for all variables. Table 2 displays the numbers
concerning discriminant validity.
Table 2: Correlation matrix.
IQ SQ PU PEOU S I
IQ 0.78
SQ 0.39 0.82
PU 0.34 0.05 0.71
PEOU 0.21 0.21 0.43 0.84
S 0.35 0.18 0.52 0.48 0.88
I 0.38 0.06 0.66 0.42 0.66 0.91
In Table 2, the correlations of the variables are
shown. The numbers on the diagonal in italics are
the square roots of the AVE. For adequate
discriminant validity, these values should exceed the
interconstruct correlations. This condition is met for
all constructs. Further, the loadings of the individual
items on the corresponding variables are well above
the recommended value of 0.5 for appropriate
discriminant validity. They range between 0.66 and
0.94. Thus overall the measurement model is highly
satisfactory.
5.2 Hypotheses Test
The test of the structural model comprises the path
coefficients, the R-square values of dependent
variables as well as the p-values. The latter were
obtained by bootstrapping with 100 cases and 1,000
samples. The R-square values are the following:
0.12 for satisfaction, 0.31 for PU, 0.23 for PEOU,
and 0.46 for booking intention. Table 3 shows the
results of the PLS analysis along with the p-values
of the path coefficients.
Table 3: PLS analysis results.
Hypothesized impact Path
coefficient
p-
value
Information quality ->
satisfaction (H1)
0.333 ***
System quality -> satisfaction
(H2)
0.045 n.s.
Satisfaction -> PU (H3) 0.409 ***
Satisfaction -> PEOU (H4) 0.476 ***
PEOU -> PU (H5) 0.235 *
PU -> Intention to book (H6) 0.593 ***
PEOU -> Intention to book (H7) 0.163 n.s.
p-values: *** < 0.001, * < 0.05, n.s. not significant
As the results of the PLS analysis show, five out of
seven hypotheses are supported by data. Information
quality shows a high positive impact on satisfaction
with the travel agency website, supporting H1. In
contrast, system quality shows a path coefficient
close to zero, thus H2 is rejected. The impacts of
satisfaction on PU and PEOU are both strong and
highly significant (0.409 and 0.476, respectively),
thus supporting H3 and H4. The impact of PEOU on
PU is smaller (0.235), but still significant at the 5
percent level. Finally, intention to book at the travel
agency’s website is largely influenced by PU
(0.593), supporting H6. The impact of PEOU is
weak (0.163) and although there is a tendency of
significance (less than ten percent), H7 is rejected.
6 DISCUSSION
6.1 Discussion of Results
The analyzed research model demonstrates the
appropriateness of an integrated approach based on
the DeLone and McNeal IS success model as well as
TAM. The seminal work by Wixom and Todd
(2005) could overall be confirmed in a service-
oriented setting, that is, online booking at travel
agencies’ websites. The hypotheses tests, however,
require a differentiated view on the analyzed
variables. Perceived information quality shows a
positive and highly significant impact on
satisfaction, thus confirming our hypothesis.
Unlike expected, perceived system quality shows
no significant effect. There are several possible
reasons that system quality does not impact
satisfaction. First, with increasing maturity and
technical reliability of websites, the relevance of this
factor may be decreasing over time. System quality
is mainly caused by an advanced technical basis
which can be expected to improve with growing IS
sophistication of travel agencies. Second, system
quality may play a minor role especially in the travel
booking context where consumers may put a larger
emphasis on the complex travel products and
therefore on the information quality. Third, a
possible explanation may lie in the parsimonious
approach of this research that considered overall
satisfaction rather than differentiation between
information and system satisfaction. The results of
this study, however, are consistent with a study on
Web service quality differences between online
travel agencies and online service providers that
identifies information content as the most important
dimension of Web service quality for online travel
agencies (Kim and Lee, 2004).
The impact of satisfaction on the TAM-based
constructs PU and PEOU could clearly be
confirmed. With path coefficients higher than 0.40
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(PU) and 0.47 (PEOU), these impacts are clearly
shown. The results confirm the theoretical
assumption based on Ajzen and Fishbein (1980) that
object-based attitudes (satisfaction) have an impact
on behavioral beliefs (PU and PEOU). The strongest
impact throughout the model is found between PU
and intention to book at the website (path coefficient
of 0.593). This finding is highly consistent with
previous research on TAM that identified PU as the
primary impact factor on behavioral attitude and
behavioral intention. On the other hand, the impact
of PEOU on behavioral intention is not significant
despite a slight tendency. This result is also
consistent with previous research on TAM that
shows mixed empirical evidence on the impact of
PEOU (Lin and Lu, 2000; Moon and Kim, 2000;
Venkatesh, 2000; Venkatesh and Davis, 2000).
Further, a study on website quality and behavioral
beliefs on websites of different tourism companies
showed that PEOU is of less importance for travel
agencies’ websites while it is a primary factor for
online service providers (Kim and Lee, 2004).
Finally, as proposed in many TAM-based studies,
the impact of PEOU on PU is significant, too.
6.2 Research Implications
The study at hand provides several theoretical and
managerial implications. From the scholarly
viewpoint, the results show the relevance of two
seminal theories in IS research – the DeLone and
McNeal IS success model and TAM – in the online
tourism sector. It confirms the relevance of
integrating both theories for a better understanding
of online usage intention and thus supports Ajzen
and Fishbein’s (1980) notion of an impact chain
starting with external stimuli of the website that
drive object-based beliefs, object-based attitudes,
behavioral beliefs, and behavioral intention. It
particularly shows that the chain of impacts on
intention to book online starts with information
quality through satisfaction, PEOU, and PU, the
latter being the main direct antecedent. Hence, both
satisfaction and PEOU have a mediated impact on
booking intention.
6.3 Managerial Implications
This research has important managerial implications.
In the light of an increased dominance of online
travel booking and a disintermediation threat of
classical, offline travel agencies the drivers of online
booking behavior at travel agencies’ websites
become increasingly essential. The significant role
of information quality that ultimately impacts the
intention to book at the agency’s website stresses the
importance of a careful design of the offered content
on the website. Perceived information quality can be
controlled by a website operator. Information quality
can be enhanced by a simple enrichment of the
website content, for example by offering multimedia
contents including animations and videos. A
growing number of hotels offer virtual tours or 360
degree views of hotel facilities to give a realistic
impression of the property. Besides operator-
generated contents, travel agencies should further
make use of user-generated contents by providing
space for user recommendations, reviews, and
numerical ratings. Such consumer reviews are a core
part of online traveling platforms like Expedia.com
or Tripadvisor.com (Park and Allen, 2013) and can
further enhance the information quality of travel
agency websites.
Although system quality does not show a
significant impact on satisfaction, attention should
be paid on a high degree of system availability,
security, and reliability. Of further importance is the
key role of PU that has a much higher weight
compared with PEOU. Hence a travel agency
website should offer not only all necessary
information and functionalities that enable online
booking, but support and facilitate the all transaction
phases of a trip booking. This includes a
comprehensive after-sales service, for example by
offering the provision of online feedback by
customers after the trip. Since travel agencies
usually provide additional information in printed
catalogs and the agency bureaus, they should pay
large attention on avoiding outdated facts or
information that is inconsistent with information
provided on the website.
Recently, the use of mobile devices for
information search and booking trips is increasing
sharply along with a switch of users between
devices. Today, a typical “journey” across the
devices may start with information search on the
smart phone or tablet and finish with the booking
process via the laptop or PC (Marketingcharts.com,
2013). Travel agencies have to account for this
development and must offer a seamless and
integrated information provision and booking
process across these access devices without
interruption. In the light of these developments, the
impact of system quality on satisfaction should be
revisited.
In a further step, travel agencies could even start
learning from successful e-commerce retailers like
Amazon.com by offering recommendation systems
IAmoffThen:DriversofTravellers'IntentionstoBookTripsOnline-AnIntegratedStudyonTechnologyAcceptanceand
Satisfaction
253
based on consumers’ past search and booking
behavior. Particularly vacation trips vary
considerably in terms of destinations, trip duration,
travel time, activities, hotel categories etc. that can
serve as useful criteria for customized
recommendations. Last but not least, experience
goods, such as vacation travel is often subject to
extensive word-of-mouth on travel experiences
among consumers. Hence social media like
Facebook or Twitter should be considered important
elements of the online marketing mix of travel
agencies (Sotiriadis and Zyl, 2013).
7 CONCLUSIONS
This study offers a parsimonious model on drivers of
intentions to book at travel agencies’ websites based
on Wixom and Todd’s (2005) integration of TAM
with the DeLone and McNeal IS success model. The
results stress the opportunities for travel agencies to
influence online purchasing behavior positively by
offering information quality that satisfies users. The
main contribution of this study to research lies in the
investigation of drivers of usage intention at travel
agencies’ e-commerce sites and thus confirming
Wixom and Todd’s (2005) model in this context.
From the managerial perspective, the study provides
a theory-based framework on important website
design issues that are critical for satisfaction, PU,
PEOU, and usage intention which are important
prerequisites of online booking behavior. Although
the study was done in the context of a rather
complex product, the findings can be transferred to
other domains that involve experience goods or
complex shopping goods, too. Business models in
practice show that useful key features on e-
commerce sites, such as multimedia information or
user-generated content are used in an ever-
increasing number of industries and product
categories.
Although the results largely support the
assumptions of the research model, there are several
limitations of this study. First, we consciously
decided to consider information and system quality
as object-based beliefs. Some studies on website
quality in e-commerce also consider service quality
which is worth being investigated as a further
independent variable. Second, we did not
differentiate between different kinds of trips and
journeys which may result in different impact
strengths of the antecedents. For example, a
packaged far-distance tour that takes three weeks
consists of a series of service components and thus
requires more information than booking a flight that
can be described with few and structured pieces of
information. Also differences between private and
business trips may occur. Finally, socio-
demographic factors and personal traits (e.g.,
traveling behavior or destination preferences) may
have an impact on the overall proposed antecedents.
Further research should consider emerging e-
commerce developments, especially the role of
mobile devices and social media as well as the
presence of online consumer reviews. Moreover, an
analysis of different players in the online tourism
sector (online service providers, electronic
intermediaries etc.) should be compared with travel
agencies to further increase the understanding of
drivers of booking trips online.
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