Description
The aim of this paper is to investigate the relationship between consumer self-confidence,
product expertise, and travel experience in the context of travel information search during vacation
planning.
International Journal of Culture, Tourism and Hospitality Research
Expertise, experience and self-confidence in consumers' travel information search
Karin Teichmann
Article information:
To cite this document:
Karin Teichmann, (2011),"Expertise, experience and self-confidence in consumers' travel information search", International J ournal of
Culture, Tourism and Hospitality Research, Vol. 5 Iss 2 pp. 184 - 194
Permanent link to this document:http://dx.doi.org/10.1108/17506181111139591
Downloaded on: 24 January 2016, At: 22:13 (PT)
References: this document contains references to 32 other documents.
To copy this document: [email protected]
The fulltext of this document has been downloaded 1322 times since 2011*
Users who downloaded this article also downloaded:
Kenneth F. Hyde, Alain Decrop, (2011),"New perspectives on vacation decision making", International J ournal of Culture, Tourism and
Hospitality Research, Vol. 5 Iss 2 pp. 103-111http://dx.doi.org/10.1108/17506181111139537
Gerard Dunne, Sheila Flanagan, J oan Buckley, (2011),"Towards a decision making model for city break travel", International J ournal of
Culture, Tourism and Hospitality Research, Vol. 5 Iss 2 pp. 158-172http://dx.doi.org/10.1108/17506181111139573
Fred Bronner, Robert de Hoog, (2011),"A new perspective on tourist information search: discussion in couples as the context", International
J ournal of Culture, Tourism and Hospitality Research, Vol. 5 Iss 2 pp. 128-143http://dx.doi.org/10.1108/17506181111139555
Access to this document was granted through an Emerald subscription provided by emerald-srm:115632 []
For Authors
If you would like to write for this, or any other Emerald publication, then please use our Emerald for Authors service information about
how to choose which publication to write for and submission guidelines are available for all. Please visit www.emeraldinsight.com/
authors for more information.
About Emerald www.emeraldinsight.com
Emerald is a global publisher linking research and practice to the benefit of society. The company manages a portfolio of more than
290 journals and over 2,350 books and book series volumes, as well as providing an extensive range of online products and additional
customer resources and services.
Emerald is both COUNTER 4 and TRANSFER compliant. The organization is a partner of the Committee on Publication Ethics (COPE) and
also works with Portico and the LOCKSS initiative for digital archive preservation.
*Related content and download information correct at time of download.
D
o
w
n
l
o
a
d
e
d
b
y
P
O
N
D
I
C
H
E
R
R
Y
U
N
I
V
E
R
S
I
T
Y
A
t
2
2
:
1
3
2
4
J
a
n
u
a
r
y
2
0
1
6
(
P
T
)
Expertise, experience and self-con?dence
in consumers’ travel information search
Karin Teichmann
Abstract
Purpose – The aim of this paper is to investigate the relationship between consumer self-con?dence,
product expertise, and travel experience in the context of travel information search during vacation
planning.
Design/methodology/approach – The research design is based on a panel study to monitor trip
planning processes of Austrian travelers. A structural model is used to assess to what degree consumer
self-con?dence, product expertise, and travel experience affect travel information search and to
examine the in?uence of consumer self-con?dence on product expertise.
Findings – Findings from the study show that consumer self-con?dence signi?cantly affects product
expertise. Travel experience, on the other hand, positively in?uences product expertise that again is
positively related to travel information search. No signi?cant relationship is established between travel
experience and travel information search.
Research limitations/implications – While most of the previous studies investigated information
search using cross-sectional data, this study addresses the need for more accurate research on
information search adopting a panel design. One major limitation of the study is the small sample size.
Results from a larger sample might be different in regards to the magnitude of the relationships.
Originality/value – This study contributes to the overall understanding of how knowledge and
ability-related factors impact travel information sourcing. The tourism literature reveals no other study that
has simultaneously quanti?ed consumer self-con?dence and product expertise during trip planning.
Keywords Consumer, Con?dence, Expertise, Information searches, Journey planning
Paper type Research paper
Introduction
Travel decision making has been subject to substantial changes during the last fewdecades of
tourism research. More than 20 years ago, Moutinho (1987) argued that buying a tourism
product is a long prepared and planned process. In recent years, however, little support for this
assumption can be found any longer (Zins, 2007). Zins (2007) provides empirical evidence for
late decision making and last minute booking. The results of his longitudinal study establish a
strong tendency for late decision making. More speci?cally, more than 60 percent of all trips
recorded during the survey (n ¼ 1; 077) substantiated a travel planning period of less than two
weeks. Since the sample mainly consisted of experienced travelers, the tendency towards late
decision making may be attributed to previous travel experience and accumulated knowledge.
Common to most previous studies is the analysis of consumer information search from a
cross-sectional perspective. However, more than 20 years ago Kiel and Layton (1981), and
more recently Kerstetter and Cho (2004), call for improvement in measuring information
search activities. They suggest considering cognitive phenomena such as selective
retention and tourists not being able to recall sources used after purchase or consumption.
Therefore, in order to discard disadvantages of retrospection, a panel design seems to be
more appropriate.
PAGE 184
j
INTERNATIONAL JOURNAL OF CULTURE, TOURISM AND HOSPITALITY RESEARCH
j
VOL. 5 NO. 2 2011, pp. 184-194, Q Emerald Group Publishing Limited, ISSN 1750-6182 DOI 10.1108/17506181111139591
Karin Teichmann is based
at the Institute for Tourism
and Leisure Studies, Vienna
University of Economics
and Business
Administration, Vienna,
Austria.
Received: March 2008
Revised: July 2008
Accepted: November 2008
D
o
w
n
l
o
a
d
e
d
b
y
P
O
N
D
I
C
H
E
R
R
Y
U
N
I
V
E
R
S
I
T
Y
A
t
2
2
:
1
3
2
4
J
a
n
u
a
r
y
2
0
1
6
(
P
T
)
Literature review
Travel information search
Travel information search behavior and trip planning have drawn considerable attention
during recent decades from both researchers and practitioners. In research into the
determinants of information sourcing, researchers often use demographic variables as
proxies for individual differences. More variation, however, can be explained by analyzing
knowledge as a determinant factor of information sourcing (e.g. Teare, 1992). More
speci?cally, level of knowledge is considered to signi?cantly determine the extent of
consumers’ information search (e.g. Bettman and Park, 1980).
So far, the relationship between knowledge and information search remains controversial.
First, empirical research has established a negative relationship between knowledge and
information search (e.g. Bettman and Park, 1980). Put differently, the more an individual feels
knowledgeable, the less information she/he will search and use. Brucks (1985) argues that
with increasing knowledge, ef?cient search strategies and subtle memory structures
continue to develop from which individuals can retrieve information. Second, increasing
knowledge also increases the awareness of problems and the ability to encode and process
new information (Punj and Staelin, 1983). The individuals who are more knowledgeable are
those with more questions about product characteristics and attributes. Third, researchers
have established non-linear effects between knowledge and the extent of information
search. Both concave and convex relationships indicate that a threshold exists. Moderately
knowledgeable individuals can either search extensively due to developing cognitive
structures or, they can engage in sparse search efforts because of increasing search costs.
Conceptualizations of consumer knowledge
Knowledge refers to the information that is stored in a person’s long term memory
(Ratchford, 2001). There are different conceptualizations of knowledge in the literature.
Some studies refer to the distinction between subjective and objective knowledge about a
product (e.g. Brucks, 1985). Other studies identify prior experience, expertise and familiarity
as dimensions of the prior knowledge construct (Alba and Hutchinson, 1987; Gursoy and
McCleary, 2004a, b; Kerstetter and Cho, 2004).
In a tourism context, product-related knowledge (i.e. speci?c knowledge) has recently
dominated the ?eld of research (Gursoy and McCleary, 2004a, b; Kerstetter and Cho, 2004).
More speci?cally, subjective and objective knowledge, as well as the dimensions of prior
experience, expertise, and familiarity, are all means for capturing individuals’ speci?c
knowledge. In tourism research, researchers have neglected the in?uence of general
knowledge on consumer behavior. This study thus recognizes a further aspect of consumer
knowledge that is independent from product-related knowledge, the concept of general
consumer self-con?dence (Bearden et al., 2001), and so contrasts two aspects of
decision-making ability: expertise and consumer self-con?dence.
Product expertise
According to Gursoy and McCleary, destination expertise is de?ned as the ‘‘exposure to
related advertisement, information search behavior, interactions with travel agents and other
consultants, selection and decision-making, and previous experiences’’ (Gursoy and
McCleary, 2004b, p. 360). On a more general level, expertise is de?ned as ‘‘the ability to
perform product-related tasks successfully’’ (Alba and Hutchinson, 1987, p. 411). Expertise
enables consumers on one hand to recall important information stored in memory and on the
other hand to analyze incoming information from the environment (Kerstetter and Cho,
2004). Alba and Hutchinson (1987) identify ?ve different aspects of expertise:
1. automaticity;
2. expertise in building cognitive structures;
3. expertise in analysis;
VOL. 5 NO. 2 2011
j
INTERNATIONAL JOURNAL OF CULTURE, TOURISM AND HOSPITALITY RESEARCH
j
PAGE 185
D
o
w
n
l
o
a
d
e
d
b
y
P
O
N
D
I
C
H
E
R
R
Y
U
N
I
V
E
R
S
I
T
Y
A
t
2
2
:
1
3
2
4
J
a
n
u
a
r
y
2
0
1
6
(
P
T
)
4. expertise in elaboration; and
5. expertise in utilizing memory.
Researchers have reported con?icting results concerning the relationship between expertise
and travel information search. Kerstetter and Cho (2004) show that a compound measure of
expertise and familiarity (‘‘famiexpert’’) is positively related to own experience as a source of
information and negatively related to all other types of external sources. Gursoy and McCleary
(2004b) ?nd that the more expertise travelers have, the more they search information from the
environment, indicating a positive relationship. This study hypothesizes that:
H1. Travelers’ product-expertise positively in?uences the extent of travel information
search during vacation planning.
Consumer self-con?dence
Comparing consumer expertise with consumer self-con?dence, the ?rst refers to
product-related perceived ability or knowledge whereas the latter re?ects the general
perceived ability to operate successfully in the marketplace. Thus, consumer
self-con?dence is a broader concept that does not depend on products or experience
with products. Park et al. (1994) propose that self-con?dence is an antecedent to perceived
product knowledge. However, they ?nd no signi?cant relationship between generalized
self-con?dence and perceived product-related knowledge. As a rationale, they suggest that
if self-con?dence relates to consumption, different results might have been likely. Therefore,
this study follows the approach by Bearden et al. (2001) conceptualizing consumer
self-con?dence as a multidimensional framework. This study hypothesizes that:
H2. Consumer self-con?dence positively in?uences travelers’ product-expertise during
vacation planning.
Travel experience
Prior experience is one of the most commonly investigated factors in?uencing the
decision-making process in leisure tourism. Prior experience has been conceptualized as
either previous visits to a destination or as previous travel experience in general. Prior
experience with a product or a service in?uences the individual’s consumption choices.
Consumers ?rst retrieve information from existing experience, and if this information is not
suf?cient, require additional information from the environment. Travel experience has been
found to negatively impact the extent of information searched (Kerstetter and Cho, 2004).
This ?nding results in the following hypothesis:
H3a. Travel experience negatively in?uences the extent of travel information search
during vacation planning.
In the consumption context, prior experience with a product enables the consumer to build a
certain scheme in her/his mind that is available for processing information about similar
products (Teare, 1992). Prior experiences are vital sources for decision making and often
used as proxies for an individual’s knowledge level. Research has shown that increasing
experience results in increasing knowledge, and so positively impacts knowledge-related
factors relative to decision-making ability (Gursoy and McCleary, 2004a). Thus, the following
hypothesis is proposed:
H3b. Travel experience positively in?uences travelers’ product-expertise during vacation
planning.
Method
Design of the study
The researcher applied a panel design to test these hypotheses. For the present study,
sampling units were individuals living in Austria who planned to go on vacation for more
PAGE 186
j
INTERNATIONAL JOURNAL OF CULTURE, TOURISM AND HOSPITALITY RESEARCH
j
VOL. 5 NO. 2 2011
D
o
w
n
l
o
a
d
e
d
b
y
P
O
N
D
I
C
H
E
R
R
Y
U
N
I
V
E
R
S
I
T
Y
A
t
2
2
:
1
3
2
4
J
a
n
u
a
r
y
2
0
1
6
(
P
T
)
than four days during the study period from June 2006 until July 2007. In order to
encourage participation, the researcher raf?ed off two vouchers (one voucher for one
week for two persons in Turkey and one voucher for one weekend for two persons in
Austria) at the end of the study period among individuals who responded to every
questionnaire sent. In addition to that, participants received a travel voucher worth e 30 at
the end of the study.
The research used two different sampling techniques: First, the researcher contacted a
strati?ed random sample of 500 individuals (randomly drawn from an Austrian telephone
directory) via telephone. In total, 228 individuals (45.6 percent) agreed to participate in the
study. Individuals who did not agree said either they had no travel plans or they were worried
about the length of the study. Second, customers of an Austrian travel agency were asked in
a newsletter to register and participate in the study. The ?nal sample consisted of 540
individuals who registered for the study. In the end, 768 individuals agreed to participate in
the study.
The research asked individuals to respond to self-administered questionnaires, either via
internet or via mail, every ?ve weeks across one year about their most involving trip that they
were planning to take in the future. The ?rst questionnaire sent included questions about
demographic data and general questions on travel frequency. One and a half months after
the ?rst mailing of questionnaires, all 768 respondents received another questionnaire
(panel wave I). Respondents returned 465 questionnaires. Testing a random sample of
non-respondents revealed that most of the individuals who agreed to participate in the study
cancelled their travel plans or changed their mind about ?lling in questionnaires for one year.
The researcher excluded all non-respondents from the study. By June 2007, the researcher
had sent another nine questionnaires (panel wave II to X). Eventually, the study ?nished with
ten panel waves (i.e. panel wave I to X) and 290 respondents. Participants could pass
through more than one decision-making process sequentially during the study, but through
only one process simultaneously during the study. During the whole study period, 332 trip
plans could be monitored in total.
Trip decisions and information search activities represented the core research objects.
Panel wave VI (January 2007) to panel wave X (June 2007) raised the whole block of
consumer self-con?dence and product expertise items. Only questionnaires sent via
internet included this block as an additional mail survey would have resulted in extra cost.
The researcher raised these questions just at the beginning of the travel planning process
when respondents indicated that they were considering the idea of going on vacation.
During these panel waves, 105 trip planning processes could be monitored; these will be
used in the subsequent analyses.
Measurement variables
As hypothesized by Alba and Hutchinson (1987), expertise is comprised of ?ve
dimensions. The results of the study by Kleiser and Mantel (1994) showed, however, that
the cognitive structure dimension had to be removed due to poor factor loadings.
Therefore, for the present study, four qualitatively distinct dimensions examined product
expertise: cognitive effort/automaticity (CA), analysis (AN), elaboration (EL), and memory
(ME). Based on previous work by Kleiser and Mantel (1994), Gursoy and McCleary
(2004b), and Schmidt and Spreng (1996), a set of 15 items was developed to measure
the product expertise construct in a tourism context. More speci?cally, CA refers to
habitual decision making and cognitive effort (?ve items used), AN captures individuals’
knowledge related to travel offers (three items used), EL denotes the ability to make
inferences based on prior experience (three items used), and lastly, ME refers to the
ability to remember suppliers of tourism products and services (four items used). A
?ve-point rating scale ranging from ‘‘strongly agree’’ to ‘‘strongly disagree’’ measured all
items. The respective items were added up in each dimension and used as indicators of
the single factor product expertise.
The concept of consumer self-con?dence was examined as having four qualitatively distinct
dimensions based on the work by Bearden et al. (2001): information acquisition (IA),
VOL. 5 NO. 2 2011
j
INTERNATIONAL JOURNAL OF CULTURE, TOURISM AND HOSPITALITY RESEARCH
j
PAGE 187
D
o
w
n
l
o
a
d
e
d
b
y
P
O
N
D
I
C
H
E
R
R
Y
U
N
I
V
E
R
S
I
T
Y
A
t
2
2
:
1
3
2
4
J
a
n
u
a
r
y
2
0
1
6
(
P
T
)
consideration-set formation (CS), personal outcomes decision making (PO), and social
outcomes decision-making (SO). As mentioned earlier in the literature overview, Bearden
et al. (2001) developed a scale consisting of six dimensions. Two dimensions, however, were
not applicable in the context of the present study examining travelers’ vacation planning in a
pre-decision context. Persuasion knowledge and marketplace interfaces, subsumed under
the factor ‘‘protection in the marketplace’’, re?ect individuals’ con?dence in direct
marketplace operations (e.g. exchange processes with salespersons and the like). For the
scope of this study, thus, only four out of six dimensions were included. In total, the scale
consisted of 20 items (?ve items for each of the four dimensions) measured on ?ve-point
rating scales ranging from‘‘very suitable’’ to ‘‘not suitable at all’’. After measuring all these 20
items, the items were added up in each dimension and used as indicators of the single factor
consumer self-con?dence.
Travel experience was measured by three items:
1. ‘‘Basically, I do not travel a lot’’ (item 1).
2. ‘‘I do not consider myself as experienced regarding travelling’’ (item 2).
3. ‘‘For many years, I have been travelling a lot, I have seen many places and consider
myself as experienced in regards to travelling’’ (item 3).
The ?rst two items were reverse coded. A ?ve-point rating scale from ‘‘strongly agree’’ to
‘ ‘strongly disagree’ ’ measured all three items. Product expertise, consumer
self-con?dence, and travel experience were measured by re?ective indicators
indicating that the latent constructs cause the observed variables (Diamantopoulos and
Winklhofer, 2001).
In order to cover information-relevant activities, respondents were asked to indicate relevant
sources used for different decisions (Decrop, 2006). Questions about decisions made
included duration of the trip, destination, accompaniment, period of travel, budget,
accommodation, activities, transportation, and organization of the trip.
Travel information search was examined as having four indexes: the self as source of
information (i.e. internal search) (Kerstetter and Cho, 2004), interpersonal sources, and
commercial and non-commercial sources (Schmidt and Spreng, 1996). These four indexes
or composites measure actual total search activity by summating information sources used
in each wave measured on a dichotomous scale (applicable/not applicable). One item
measured internal search behavior (own experience), two items interpersonal sources
(friends and relatives, communities and forums), six items measured commercial sources
(tour operator brochures, tour operator web sites, travel agencies, travel agency web sites,
hotel/accommodation brochures, hotel/accommodation web sites) and ?nally ?ve items
measured non-commercial source types (convention and visitor bureaus, convention and
visitor bureau web sites, travel guides/journals, online travel guides/journals, general web
sites). These items were adopted from relevant literature (e.g. Bieger and Laesser, 2001;
Kerstetter and Cho, 2004). Measuring travel information search with the four not necessarily
high correlated indexes means that the construct results from a linear sum of the single
measures (i.e. the dimensions). Therefore, the indexes are to be treated as formative
indicators (Jarvis et al., 2003).
Analysis
The researcher used PLS path modeling to explain the relationship between consumer
self-con?dence, product expertise, travel experience, and travel information sourcing. PLS
was performed using the software SmartPLS (Version 2.0.M3) (Ringle et al., 2005). Since
data need not to be normally distributed for non-parametric approaches such as PLS,
resampling methods generate the signi?cance levels of parameters. SmartPLS applies
bootstrapping in order to derive t-values for statistical signi?cance. In this study, 100
bootstrap samples compute the respective t-values. The mean for all available cases of a
variable replaces the missing values.
PAGE 188
j
INTERNATIONAL JOURNAL OF CULTURE, TOURISM AND HOSPITALITY RESEARCH
j
VOL. 5 NO. 2 2011
D
o
w
n
l
o
a
d
e
d
b
y
P
O
N
D
I
C
H
E
R
R
Y
U
N
I
V
E
R
S
I
T
Y
A
t
2
2
:
1
3
2
4
J
a
n
u
a
r
y
2
0
1
6
(
P
T
)
Results
Sample characteristics
Respondents were predominantly female (60 percent) and aged 42 years on average.
36 percent of the respondents reported traveling more than four days within Austria, one
to three times annually. By contrast, two thirds (64 percent) of the respondents indicated
taking cross-border trips for more than four days one to three times per year.
Concerning the information sources used, respondents used on average 4.2 sources
per panel wave and 2.5 sources per decision made. Decisions made in each panel
wave averaged 1.8.
As far as differences in consumer self-con?dence and product expertise dimensions are
concerned, the descriptive statistics show that the values are rather high for both constructs
(see Table I).
Search opportunities and decisions made
The second column in Table II presents sources used for trip planning. As Table II shows,
tour operators, one’s own experiences, hotel and accommodation web sites, and general
web sites are among the most important sources of information. More than one-third (38
percent) of all information sources are used online. Since the periods of trip planning vary
from person to person, the third column shows information search opportunities. For this
value, sources used were divided by the number of panel waves recorded for trip planning.
On average, respondents had 4.16 (std. deviation 1.2) panel waves (i.e. opportunities) to
indicate their information search activities. The fourth column presents the ratio of sources
used for each decision made. Again, one’s own experience is the most important source of
information in order to make a decision.
Table II Frequency of travel information sources used
Information sources
Use of information
sources (%)
Ratio of sources used and
search opportunities
Ratio of sources used and
decisions made
n ¼ 105 n ¼ 105
Tour operator brochures 47 0.44 0.22
Tour operator web sites 66 0.40 0.23
Travel agencies 42 0.39 0.20
Travel agency web sites 44 0.27 0.16
Convention and visitor bureaus 25 0.10 0.07
Convention and visitor bureau web sites 40 0.17 0.13
Hotel/accommodation brochures 33 0.18 0.10
Hotel/accommodation web sites 59 0.43 0.23
Travel guides/journals 42 0.27 0.15
Online travel guides/journals 44 0.30 0.18
General web sites 59 0.27 0.19
Friends/relatives 37 0.26 0.17
Communities/forums 19 0.12 0.07
Own experiences 65 0.63 0.37
Table I Descriptive statistics for consumer self-con?dence and product expertise
Indices for consumer self-con?dence and product expertise
Percentiles IA
a
CS
a
PO
a
SO
a
CA
b
AN
b
EL
b
ME
b
25th 4 3.6 3.6 3 2.6 2.2 2 2.6
50th 4.2 4 4 3.2 3 2.6 2.4 3
75th 4.6 4.4 4.6 3.8 3.4 2.8 2.6 3.4
Notes:
a
Scale range from 1 (not suitable at all) to 5 (very suitable);
b
Scale range from 1 (strongly disagree) to 5 (strongly agree)
VOL. 5 NO. 2 2011
j
INTERNATIONAL JOURNAL OF CULTURE, TOURISM AND HOSPITALITY RESEARCH
j
PAGE 189
D
o
w
n
l
o
a
d
e
d
b
y
P
O
N
D
I
C
H
E
R
R
Y
U
N
I
V
E
R
S
I
T
Y
A
t
2
2
:
1
3
2
4
J
a
n
u
a
r
y
2
0
1
6
(
P
T
)
Model evaluation
The factor loadings for the three re?ective measurement models range from 0.409 to 0.896.
More speci?cally, the loadings for the construct of product expertise are 0.409 (CA), 0.875
(AN), 0.883 (EL), and 0.859 (ME); for the construct of consumer self-con?dence the factor
loadings are 0.883 (IA), 0.868 (CS), 0.577 (PO), and 0.647 (SO); for travel experience these
are 0.716 (item 1), 0.896 (item 2), and 0.791 (item 3). In order to examine the reliability of the
re?ective measurement models, composite reliability (CR) (Hair et al., 1998) was calculated.
All three constructs pass the recommended threshold of 0.70. In addition to reliability,
convergent validity was assessed for the three re?ective measurement models. The
convergent validity represents the common variance between the indicators and their
construct, and is measured by the average variance extracted (AVE) (Fornell and Larcker,
1981). All three latent variables comply with this pre-requisite. Therefore, the measurement
models seem to have both high reliability and convergent validity. Regarding discriminant
validity, the values for AVE were compared with the squared correlations between all three
constructs (Anderson and Gerbing, 1988). The AVE for the three re?ective measurement
models is higher than the squared correlation and thus the constructs show suf?cient
evidence for discriminant validity.
Internal consistency and convergent validity are not applicable to formative constructs
(Jarvis et al., 2003). Therefore, Table III shows the weights of the composites and their
respective t-values. Since the formative measurement model is based on a multiple
regression, multicollinearity has to be inspected (Diamantopoulos and Winklhofer, 2001).
The ?rst step of analysis investigated intercorrelations and the variance in?ation factor (VIF).
Second, only the ?rst component of a PLS regression was used to prevent multicollinearity
(Henseler et al., 2007; Wold et al., 1984). In order to test discriminant validity, the latent
variable correlations matrix needs to be assessed. For formative measurement models, all
construct correlations need to be smaller than 0.90. The correlations between the latent
variables show suf?cient evidence for discriminant validity of the information search
construct. To conclude, the measures are valid and reliable, which strongly suggests that all
measures are acceptable and that the model can be tested.
Hypotheses testing
As PLS aims at explaining variances, prediction-oriented measures have to be applied. The
coef?cient of determination (R
2
) speci?es the share of the variance of the endogenous
constructs explained by the relationships in the model. The R
2
-values are 0.06 for
information search and 0.31 for product expertise. Within PLS-models, R
2
-values above 0.1
are commonly deemed acceptable (Falk and Miller, 1992). However, in order to fully evaluate
the model, the Stone-Geisser test criterion (Q
2
) needs to be inspected by applying a
blindfolding procedure (Huber et al., 2007). For the two endogenous constructs, the
measures for Q
2
are greater than zero indicating that the hypothesized model is acceptable.
The next step of data analysis investigates the structural model. Figure 1 reports the path
coef?cients and hypotheses. The results of the bootstrapping resampling technique show
that all except one of the path coef?cients are signi?cant at p , 0:01. As predicted for H2,
consumer self-con?dence has a positive effect on product expertise (beta ¼ 0:369;
p , 0:01). The ?ndings also support H1 linking product expertise to information search.
Product expertise has the expected positive in?uence on information sourcing
Table III Evaluation of the formative measurement model (PLS Estimation)
Information search composites Highest intercorrelation VIF
(Requirement) Weights t-value (,0.9) (,10)
Commercial sources 0.210 1.719 0.502 1.387
Non-commercial sources 0.712 3.598 0.502 1.488
Interpersonal sources 0.173 0.311 0.398 1.203
Self 0.513 1.871 0.244 1.076
PAGE 190
j
INTERNATIONAL JOURNAL OF CULTURE, TOURISM AND HOSPITALITY RESEARCH
j
VOL. 5 NO. 2 2011
D
o
w
n
l
o
a
d
e
d
b
y
P
O
N
D
I
C
H
E
R
R
Y
U
N
I
V
E
R
S
I
T
Y
A
t
2
2
:
1
3
2
4
J
a
n
u
a
r
y
2
0
1
6
(
P
T
)
(beta ¼ 0:235; p , 0:01). As predicted for H3b, travel experience positively in?uences
product expertise (beta ¼ 0:341; p , 0:01). Concerning the link between travel experience
and information search, the results do not support hypothesis H3a.
Discussion
Theoretical implications
The purpose of this study was to improve the understanding of travel information sourcing.
More speci?cally, the present study contributes to a better knowledge of the interplay
between consumer self-con?dence, product expertise and travel information search.
Furthermore, travel experience can be discussed when taking into account the study
?ndings. The data supported all but one of the proposed relationships.
For many years, there has been a need to investigate information search more precisely and
accurately (Beatty and Smith, 1987; Cleveland et al., 2003; Perdue, 1993; Srinivasan, 1990).
The present study reacted to the demand for more accurate research on information
sourcing adopting a panel design that monitored individuals during their vacation planning
and preparation process.
The most critical implications of this study refer to the relationship between consumer
self-con?dence and product expertise in a tourism-related context. The results con?rmed that
consumer-related and product-related ability or knowledge are two distinct concepts and that
consumer self-con?dence signi?cantly impacts on product expertise (H2 supported).
Since Park et al. (1994) found no signi?cant relationship between generalized self-con?dence
and perceived product-related knowledge, they suggested relating general self-con?dence
closer to consumption in order to ?nd a relationship between the two constructs. In the present
study, the construct of consumer self-con?dence was utilized which denotes an individual’s
perceived ability concerning marketplace decisions and information search behavior. The
results of this study con?rmthat consumers’ general ability to deal with marketplace decisions
and behaviors signi?cantly in?uences the ability to deal with product-speci?c decisions and
behaviors. Thus, the ?ndings substantiate that product expertise and self-con?dence are
related constructs when linking self-con?dence to consumption.
Turning now to the relationship between knowledge-related factors and travel information
search, the ?ndings from this study show that product expertise signi?cantly in?uences
travel information sourcing while travel experience does not. The ?ndings regarding product
expertise show a positive relationship between the two constructs indicating that the more
Figure 1 Estimated standardized path coef?cients
VOL. 5 NO. 2 2011
j
INTERNATIONAL JOURNAL OF CULTURE, TOURISM AND HOSPITALITY RESEARCH
j
PAGE 191
D
o
w
n
l
o
a
d
e
d
b
y
P
O
N
D
I
C
H
E
R
R
Y
U
N
I
V
E
R
S
I
T
Y
A
t
2
2
:
1
3
2
4
J
a
n
u
a
r
y
2
0
1
6
(
P
T
)
expertise travelers have, the more they search for information (H1 supported). Experts have
more complex knowledge structures and cognitive skills and they are open to process new
information and thus search more information than non-experts search. Perceived travel
experience, on the other hand, does not in?uence travel information sourcing (H3a not
supported). Thus, if an individual travels frequently and considers him/herself as
experienced, it has no effect on the amount of information search. This ?nding makes the
difference between expertise and experience concerning information search clear: the level
of expertise refers to cognitive skills that again enable information processing. Travel
experience, on the other hand, is not bound to information search. Concerning the
relationship between travel experience and product expertise, the former signi?cantly
impacts the latter (H3b supported). More speci?cally, the more travel experience individuals
have the more they consider themselves as experts in terms of travelling. Thus, the level of
product expertise functions as a mediator to the extent that it accounts for the relation
between travel experience and travel information sourcing.
The ?nal theoretical implication relates to the generalization of ?ndings. Most of the previous
studies investigating information search behavior used cross-sectional data. These studies
primarily focused on individuals’ attitude towards the use of information sources. To
conclude, due to different conceptualizations and measurement approaches, it is evident
that generalization of ?ndings is hardly possible. De?nitions and clear concept
speci?cations can avoid any misconceptions and deliver useful contributions to the ?eld.
Managerial implications
The results from this study have vital implications for tourism marketers who wish to improve
their knowledge about where to reach and which information to provide to potential
customers. For ef?cient targeting and product positioning, marketers should thus orientate
towards the usage of information sources. The ?ndings of this study indicate that individuals
prefer certain sources over others for their vacation planning and preparation. Tourists
primarily rely on their own experiences regarding travel planning. Thus, satisfaction and
attachment with prior travel experiences potentially affects future travel planning processes.
Another insight is that interpersonal sources are of particular importance for vacation
planning. Friends and relatives thus considerably in?uence tourists in their information
sourcing and decision making. Hence, marketers need to consider the relevance of
word-of-mouth recommendation in their marketing decisions. Of almost equal importance is
information gathered from the internet. In general, however, the internet complements rather
than substitutes commonly accepted information sources. The ?ndings of the study provide
evidence that travel agencies and brochures remain important sources of information for
travelers’ trip planning. Besides that, another noteworthy ?nding is that electronic
word-of-mouth is secondary for travel information search. Therefore, marketers should not
fully rely on internet-based communication but ?nd an optimal mix between electronic and
traditional information sources in order to ef?ciently target potential customers.
Limitations and directions for future research
One major limitation of the study is its small sample size. Also, the study is speci?c to only
one culture (Austrian respondents). A larger sample as well as cultural differences may
affect the magnitude of the relationships between consumer self-con?dence, product
expertise, travel experience, and travel information search.
In addition, it is important to mention that respondents had high levels of ability. Thus, results
might be different for vacationers with less perceived ability and knowledge concerning
aspects of travel planning. Therefore, a replication of this study should be conducted with a
larger sample size and more variation in ability levels.
In order to cover information-relevant activities in the present study, respondents were asked
to indicate relevant sources used for different decisions. Due to the small sample size,
however, no differences in travel decisions such as destination, duration of the trip,
accompaniment, budget, and organization of the trip could be examined. Future studies
should thus distinguish between different decisions made and respective information sources.
PAGE 192
j
INTERNATIONAL JOURNAL OF CULTURE, TOURISM AND HOSPITALITY RESEARCH
j
VOL. 5 NO. 2 2011
D
o
w
n
l
o
a
d
e
d
b
y
P
O
N
D
I
C
H
E
R
R
Y
U
N
I
V
E
R
S
I
T
Y
A
t
2
2
:
1
3
2
4
J
a
n
u
a
r
y
2
0
1
6
(
P
T
)
References
Alba, J.W. and Hutchinson, J.W. (1987), ‘‘Dimensions of consumer expertise’’, Journal of Consumer
Research, Vol. 13 No. 4, pp. 411-54.
Anderson, J.C. and Gerbing, D.W. (1988), ‘‘Structural equation modeling in practice: a review and
recommended two-step approach’’, Psychological Bulletin, Vol. 103, pp. 411-23.
Bearden, W.O., Hardesty, D.M. and Rose, R.L. (2001), ‘‘Consumer self-con?dence: re?nements in
conceptualization and measurement’’, Journal of Consumer Research, Vol. 28 No. 1, pp. 121-34.
Beatty, S.E. and Smith, S.M. (1987), ‘‘External search effort: an investigation across several product
categories’’, Journal of Consumer Research, Vol. 14 No. 1, pp. 83-95.
Bettman, J.R. and Park, C.W. (1980), ‘‘Effects of prior knowledge and experience and phase of the
choice process on consumer decision processes: a protocol analysis’’, Journal of Consumer Research,
Vol. 7 No. 3, pp. 234-48.
Bieger, T. and Laesser, C. (2001), ‘‘Segmenting travel on the sourcing of information’’, in Mazanec, J.A.,
Crouch, G.I., Brent Ritchie, J.R. and Woodside, A.G. (Eds), Consumer Psychology of Tourism,
Hospitality and Leisure, CABI Publishing, Wallingford, pp. 153-67.
Brucks, M. (1985), ‘‘The effects of product class knowledge on information search behavior’’, Journal of
Consumer Research, Vol. 7, pp. 1-16.
Cleveland, M., Babin, B.J., Laroche, M., Ward, P. and Bergeron, J. (2003), ‘‘Information search patterns
for gift purchases: a cross-national examination of gender differences’’, Journal of Consumer Behaviour,
Vol. 3 No. 1, pp. 20-47.
Decrop, A. (2006), Vacation Decision Making, CABI Publishing, Wallingford.
Diamantopoulos, A. and Winklhofer, H.M. (2001), ‘‘Index construction with formative indicators: an
alternative to scale development’’, Journal of Marketing Research, Vol. 38 No. 2, pp. 269-77.
Falk, R.F. and Miller, N.B. (1992), A Primer for Soft Modeling, University of Akron Press, Akron, OH.
Fornell, C. and Larcker, D.F. (1981), ‘‘Evaluating structural equation models with unobservable variables
and measurement error’’, Journal of Marketing Research, Vol. 18 No. 1, pp. 39-50.
Gursoy, D. and McCleary, K.W. (2004a), ‘‘An integrative model of tourists’ information search behavior’’,
Annals of Tourism Research, Vol. 31 No. 2, pp. 353-73.
Gursoy, D. and McCleary, K.W. (2004b), ‘‘Travelers’ prior knowledge and its impact on their information
search behavior’’, Journal of Hospitality and Tourism Research, Vol. 28 No. 1, pp. 66-94.
Hair, J.F.J., Anderson, R.E., Tatham, R.L. and Black, W.C. (1998), Multivariate Data Analysis,
Prentice-Hall, Upper Saddle River, NJ.
Henseler, J., Wilson, B., Go¨ tz, O. and Hautvast, C. (2007), ‘‘Investigating the moderating role of ?t on
sports sponsorship and brand equity’’, International Journal of Sports Marketing and Sponsorship, Vol. 8
No. 4, pp. 321-9.
Huber, F., Herrmann, A., Meyer, F., Vogel, J. and Vollhardt, K. (2007), Kausalmodellierung mit Partial
Least Squares: Eine anwendungsorientierte Einfu¨ hrung, Gabler, Wiesbaden.
Jarvis, C.B., MacKenzie, S.B. and Podsakoff, P.M. (2003), ‘‘A critical review of construct indicators and
measurement model misspeci?cation in marketing and consumer research’’, Journal of Consumer
Research, Vol. 30 No. 2, pp. 199-218.
Kerstetter, D. and Cho, M.-H. (2004), ‘‘Prior knowledge, credibility and information search’’, Annals of
Tourism Research, Vol. 31 No. 4, pp. 961-85.
Kiel, G.C. and Layton, R.A. (1981), ‘‘Dimensions of consumer information seeking behavior’’, Journal of
Marketing Research, Vol. 18 No. 2, pp. 233-9.
Kleiser, S.B. and Mantel, S.P. (1994), ‘‘The dimensions of consumer expertise: a scale development’’,
American Marketing Association, Vol. 1, pp. 20-6.
Moutinho, L. (1987), ‘‘Consumer behaviour in tourism’’, European Journal of Marketing, Vol. 21 No. 10,
pp. 5-44.
VOL. 5 NO. 2 2011
j
INTERNATIONAL JOURNAL OF CULTURE, TOURISM AND HOSPITALITY RESEARCH
j
PAGE 193
D
o
w
n
l
o
a
d
e
d
b
y
P
O
N
D
I
C
H
E
R
R
Y
U
N
I
V
E
R
S
I
T
Y
A
t
2
2
:
1
3
2
4
J
a
n
u
a
r
y
2
0
1
6
(
P
T
)
Park, C.W., Mothersbaugh, D.L. and Feick, L. (1994), ‘‘Consumer knowledge assessment’’, Journal of
Consumer Research, Vol. 21 No. 1, pp. 71-82.
Perdue, R.R. (1993), ‘‘External information search in marine recreational ?shing’’, Leisure Sciences,
Vol. 15, pp. 169-87.
Punj, G.N. and Staelin, R. (1983), ‘‘A model of consumer information search behavior for new
automobiles’’, Journal of Consumer Research, Vol. 9 No. 4, pp. 366-80.
Ratchford, B.T. (2001), ‘‘The economics of consumer knowledge’’, Journal of Consumer Research,
Vol. 27 No. 4, pp. 397-411.
Ringle, C.M., Wende, S. and Will, A. (2005), SmartPLS - version 2.0.M3, Universita¨ t Hamburg, Hamburg.
Schmidt, J.B. and Spreng, R.A. (1996), ‘‘A proposed model of external consumer information search’’,
Journal of the Academy of Marketing Science, Vol. 24 No. 3, pp. 246-57.
Srinivasan, N. (1990), ‘‘Pre-purchase external search for information’’, in Zeithaml, V. (Ed.), Review of
Marketing 1990, American Marketing Association, Chicago, pp. 153-89.
Teare, R. (1992), ‘‘An exploration of the consumer decision process for hospitality services’’, in Teare, R.,
Mouthino, L. and Morgan, N. (Eds), Managing and Marketing Services in the 1990s, Cassell
Educational, London, pp. 233-48.
Wold, S., Ruhe, A., Wold, H. and Dunn, W.J. (1984), ‘‘The collinearity problem in linear regression: the
partial least squares (PLS) approach to generalized inverses’’, Journal on Scienti?c and Statistical
Computing, Vol. 5 No. 3, pp. 735-43.
Zins, A.H. (2007), ‘‘Exploring travel information search behaviour beyond common frontiers’’, Journal of
Information Technology and Tourism, Vol. 9 Nos 3/4, pp. 149-64.
Corresponding author
Karen Teichmann can be contacted at: [email protected]
PAGE 194
j
INTERNATIONAL JOURNAL OF CULTURE, TOURISM AND HOSPITALITY RESEARCH
j
VOL. 5 NO. 2 2011
To purchase reprints of this article please e-mail: [email protected]
Or visit our web site for further details: www.emeraldinsight.com/reprints
D
o
w
n
l
o
a
d
e
d
b
y
P
O
N
D
I
C
H
E
R
R
Y
U
N
I
V
E
R
S
I
T
Y
A
t
2
2
:
1
3
2
4
J
a
n
u
a
r
y
2
0
1
6
(
P
T
)
doc_567762792.pdf
The aim of this paper is to investigate the relationship between consumer self-confidence,
product expertise, and travel experience in the context of travel information search during vacation
planning.
International Journal of Culture, Tourism and Hospitality Research
Expertise, experience and self-confidence in consumers' travel information search
Karin Teichmann
Article information:
To cite this document:
Karin Teichmann, (2011),"Expertise, experience and self-confidence in consumers' travel information search", International J ournal of
Culture, Tourism and Hospitality Research, Vol. 5 Iss 2 pp. 184 - 194
Permanent link to this document:http://dx.doi.org/10.1108/17506181111139591
Downloaded on: 24 January 2016, At: 22:13 (PT)
References: this document contains references to 32 other documents.
To copy this document: [email protected]
The fulltext of this document has been downloaded 1322 times since 2011*
Users who downloaded this article also downloaded:
Kenneth F. Hyde, Alain Decrop, (2011),"New perspectives on vacation decision making", International J ournal of Culture, Tourism and
Hospitality Research, Vol. 5 Iss 2 pp. 103-111http://dx.doi.org/10.1108/17506181111139537
Gerard Dunne, Sheila Flanagan, J oan Buckley, (2011),"Towards a decision making model for city break travel", International J ournal of
Culture, Tourism and Hospitality Research, Vol. 5 Iss 2 pp. 158-172http://dx.doi.org/10.1108/17506181111139573
Fred Bronner, Robert de Hoog, (2011),"A new perspective on tourist information search: discussion in couples as the context", International
J ournal of Culture, Tourism and Hospitality Research, Vol. 5 Iss 2 pp. 128-143http://dx.doi.org/10.1108/17506181111139555
Access to this document was granted through an Emerald subscription provided by emerald-srm:115632 []
For Authors
If you would like to write for this, or any other Emerald publication, then please use our Emerald for Authors service information about
how to choose which publication to write for and submission guidelines are available for all. Please visit www.emeraldinsight.com/
authors for more information.
About Emerald www.emeraldinsight.com
Emerald is a global publisher linking research and practice to the benefit of society. The company manages a portfolio of more than
290 journals and over 2,350 books and book series volumes, as well as providing an extensive range of online products and additional
customer resources and services.
Emerald is both COUNTER 4 and TRANSFER compliant. The organization is a partner of the Committee on Publication Ethics (COPE) and
also works with Portico and the LOCKSS initiative for digital archive preservation.
*Related content and download information correct at time of download.
D
o
w
n
l
o
a
d
e
d
b
y
P
O
N
D
I
C
H
E
R
R
Y
U
N
I
V
E
R
S
I
T
Y
A
t
2
2
:
1
3
2
4
J
a
n
u
a
r
y
2
0
1
6
(
P
T
)
Expertise, experience and self-con?dence
in consumers’ travel information search
Karin Teichmann
Abstract
Purpose – The aim of this paper is to investigate the relationship between consumer self-con?dence,
product expertise, and travel experience in the context of travel information search during vacation
planning.
Design/methodology/approach – The research design is based on a panel study to monitor trip
planning processes of Austrian travelers. A structural model is used to assess to what degree consumer
self-con?dence, product expertise, and travel experience affect travel information search and to
examine the in?uence of consumer self-con?dence on product expertise.
Findings – Findings from the study show that consumer self-con?dence signi?cantly affects product
expertise. Travel experience, on the other hand, positively in?uences product expertise that again is
positively related to travel information search. No signi?cant relationship is established between travel
experience and travel information search.
Research limitations/implications – While most of the previous studies investigated information
search using cross-sectional data, this study addresses the need for more accurate research on
information search adopting a panel design. One major limitation of the study is the small sample size.
Results from a larger sample might be different in regards to the magnitude of the relationships.
Originality/value – This study contributes to the overall understanding of how knowledge and
ability-related factors impact travel information sourcing. The tourism literature reveals no other study that
has simultaneously quanti?ed consumer self-con?dence and product expertise during trip planning.
Keywords Consumer, Con?dence, Expertise, Information searches, Journey planning
Paper type Research paper
Introduction
Travel decision making has been subject to substantial changes during the last fewdecades of
tourism research. More than 20 years ago, Moutinho (1987) argued that buying a tourism
product is a long prepared and planned process. In recent years, however, little support for this
assumption can be found any longer (Zins, 2007). Zins (2007) provides empirical evidence for
late decision making and last minute booking. The results of his longitudinal study establish a
strong tendency for late decision making. More speci?cally, more than 60 percent of all trips
recorded during the survey (n ¼ 1; 077) substantiated a travel planning period of less than two
weeks. Since the sample mainly consisted of experienced travelers, the tendency towards late
decision making may be attributed to previous travel experience and accumulated knowledge.
Common to most previous studies is the analysis of consumer information search from a
cross-sectional perspective. However, more than 20 years ago Kiel and Layton (1981), and
more recently Kerstetter and Cho (2004), call for improvement in measuring information
search activities. They suggest considering cognitive phenomena such as selective
retention and tourists not being able to recall sources used after purchase or consumption.
Therefore, in order to discard disadvantages of retrospection, a panel design seems to be
more appropriate.
PAGE 184
j
INTERNATIONAL JOURNAL OF CULTURE, TOURISM AND HOSPITALITY RESEARCH
j
VOL. 5 NO. 2 2011, pp. 184-194, Q Emerald Group Publishing Limited, ISSN 1750-6182 DOI 10.1108/17506181111139591
Karin Teichmann is based
at the Institute for Tourism
and Leisure Studies, Vienna
University of Economics
and Business
Administration, Vienna,
Austria.
Received: March 2008
Revised: July 2008
Accepted: November 2008
D
o
w
n
l
o
a
d
e
d
b
y
P
O
N
D
I
C
H
E
R
R
Y
U
N
I
V
E
R
S
I
T
Y
A
t
2
2
:
1
3
2
4
J
a
n
u
a
r
y
2
0
1
6
(
P
T
)
Literature review
Travel information search
Travel information search behavior and trip planning have drawn considerable attention
during recent decades from both researchers and practitioners. In research into the
determinants of information sourcing, researchers often use demographic variables as
proxies for individual differences. More variation, however, can be explained by analyzing
knowledge as a determinant factor of information sourcing (e.g. Teare, 1992). More
speci?cally, level of knowledge is considered to signi?cantly determine the extent of
consumers’ information search (e.g. Bettman and Park, 1980).
So far, the relationship between knowledge and information search remains controversial.
First, empirical research has established a negative relationship between knowledge and
information search (e.g. Bettman and Park, 1980). Put differently, the more an individual feels
knowledgeable, the less information she/he will search and use. Brucks (1985) argues that
with increasing knowledge, ef?cient search strategies and subtle memory structures
continue to develop from which individuals can retrieve information. Second, increasing
knowledge also increases the awareness of problems and the ability to encode and process
new information (Punj and Staelin, 1983). The individuals who are more knowledgeable are
those with more questions about product characteristics and attributes. Third, researchers
have established non-linear effects between knowledge and the extent of information
search. Both concave and convex relationships indicate that a threshold exists. Moderately
knowledgeable individuals can either search extensively due to developing cognitive
structures or, they can engage in sparse search efforts because of increasing search costs.
Conceptualizations of consumer knowledge
Knowledge refers to the information that is stored in a person’s long term memory
(Ratchford, 2001). There are different conceptualizations of knowledge in the literature.
Some studies refer to the distinction between subjective and objective knowledge about a
product (e.g. Brucks, 1985). Other studies identify prior experience, expertise and familiarity
as dimensions of the prior knowledge construct (Alba and Hutchinson, 1987; Gursoy and
McCleary, 2004a, b; Kerstetter and Cho, 2004).
In a tourism context, product-related knowledge (i.e. speci?c knowledge) has recently
dominated the ?eld of research (Gursoy and McCleary, 2004a, b; Kerstetter and Cho, 2004).
More speci?cally, subjective and objective knowledge, as well as the dimensions of prior
experience, expertise, and familiarity, are all means for capturing individuals’ speci?c
knowledge. In tourism research, researchers have neglected the in?uence of general
knowledge on consumer behavior. This study thus recognizes a further aspect of consumer
knowledge that is independent from product-related knowledge, the concept of general
consumer self-con?dence (Bearden et al., 2001), and so contrasts two aspects of
decision-making ability: expertise and consumer self-con?dence.
Product expertise
According to Gursoy and McCleary, destination expertise is de?ned as the ‘‘exposure to
related advertisement, information search behavior, interactions with travel agents and other
consultants, selection and decision-making, and previous experiences’’ (Gursoy and
McCleary, 2004b, p. 360). On a more general level, expertise is de?ned as ‘‘the ability to
perform product-related tasks successfully’’ (Alba and Hutchinson, 1987, p. 411). Expertise
enables consumers on one hand to recall important information stored in memory and on the
other hand to analyze incoming information from the environment (Kerstetter and Cho,
2004). Alba and Hutchinson (1987) identify ?ve different aspects of expertise:
1. automaticity;
2. expertise in building cognitive structures;
3. expertise in analysis;
VOL. 5 NO. 2 2011
j
INTERNATIONAL JOURNAL OF CULTURE, TOURISM AND HOSPITALITY RESEARCH
j
PAGE 185
D
o
w
n
l
o
a
d
e
d
b
y
P
O
N
D
I
C
H
E
R
R
Y
U
N
I
V
E
R
S
I
T
Y
A
t
2
2
:
1
3
2
4
J
a
n
u
a
r
y
2
0
1
6
(
P
T
)
4. expertise in elaboration; and
5. expertise in utilizing memory.
Researchers have reported con?icting results concerning the relationship between expertise
and travel information search. Kerstetter and Cho (2004) show that a compound measure of
expertise and familiarity (‘‘famiexpert’’) is positively related to own experience as a source of
information and negatively related to all other types of external sources. Gursoy and McCleary
(2004b) ?nd that the more expertise travelers have, the more they search information from the
environment, indicating a positive relationship. This study hypothesizes that:
H1. Travelers’ product-expertise positively in?uences the extent of travel information
search during vacation planning.
Consumer self-con?dence
Comparing consumer expertise with consumer self-con?dence, the ?rst refers to
product-related perceived ability or knowledge whereas the latter re?ects the general
perceived ability to operate successfully in the marketplace. Thus, consumer
self-con?dence is a broader concept that does not depend on products or experience
with products. Park et al. (1994) propose that self-con?dence is an antecedent to perceived
product knowledge. However, they ?nd no signi?cant relationship between generalized
self-con?dence and perceived product-related knowledge. As a rationale, they suggest that
if self-con?dence relates to consumption, different results might have been likely. Therefore,
this study follows the approach by Bearden et al. (2001) conceptualizing consumer
self-con?dence as a multidimensional framework. This study hypothesizes that:
H2. Consumer self-con?dence positively in?uences travelers’ product-expertise during
vacation planning.
Travel experience
Prior experience is one of the most commonly investigated factors in?uencing the
decision-making process in leisure tourism. Prior experience has been conceptualized as
either previous visits to a destination or as previous travel experience in general. Prior
experience with a product or a service in?uences the individual’s consumption choices.
Consumers ?rst retrieve information from existing experience, and if this information is not
suf?cient, require additional information from the environment. Travel experience has been
found to negatively impact the extent of information searched (Kerstetter and Cho, 2004).
This ?nding results in the following hypothesis:
H3a. Travel experience negatively in?uences the extent of travel information search
during vacation planning.
In the consumption context, prior experience with a product enables the consumer to build a
certain scheme in her/his mind that is available for processing information about similar
products (Teare, 1992). Prior experiences are vital sources for decision making and often
used as proxies for an individual’s knowledge level. Research has shown that increasing
experience results in increasing knowledge, and so positively impacts knowledge-related
factors relative to decision-making ability (Gursoy and McCleary, 2004a). Thus, the following
hypothesis is proposed:
H3b. Travel experience positively in?uences travelers’ product-expertise during vacation
planning.
Method
Design of the study
The researcher applied a panel design to test these hypotheses. For the present study,
sampling units were individuals living in Austria who planned to go on vacation for more
PAGE 186
j
INTERNATIONAL JOURNAL OF CULTURE, TOURISM AND HOSPITALITY RESEARCH
j
VOL. 5 NO. 2 2011
D
o
w
n
l
o
a
d
e
d
b
y
P
O
N
D
I
C
H
E
R
R
Y
U
N
I
V
E
R
S
I
T
Y
A
t
2
2
:
1
3
2
4
J
a
n
u
a
r
y
2
0
1
6
(
P
T
)
than four days during the study period from June 2006 until July 2007. In order to
encourage participation, the researcher raf?ed off two vouchers (one voucher for one
week for two persons in Turkey and one voucher for one weekend for two persons in
Austria) at the end of the study period among individuals who responded to every
questionnaire sent. In addition to that, participants received a travel voucher worth e 30 at
the end of the study.
The research used two different sampling techniques: First, the researcher contacted a
strati?ed random sample of 500 individuals (randomly drawn from an Austrian telephone
directory) via telephone. In total, 228 individuals (45.6 percent) agreed to participate in the
study. Individuals who did not agree said either they had no travel plans or they were worried
about the length of the study. Second, customers of an Austrian travel agency were asked in
a newsletter to register and participate in the study. The ?nal sample consisted of 540
individuals who registered for the study. In the end, 768 individuals agreed to participate in
the study.
The research asked individuals to respond to self-administered questionnaires, either via
internet or via mail, every ?ve weeks across one year about their most involving trip that they
were planning to take in the future. The ?rst questionnaire sent included questions about
demographic data and general questions on travel frequency. One and a half months after
the ?rst mailing of questionnaires, all 768 respondents received another questionnaire
(panel wave I). Respondents returned 465 questionnaires. Testing a random sample of
non-respondents revealed that most of the individuals who agreed to participate in the study
cancelled their travel plans or changed their mind about ?lling in questionnaires for one year.
The researcher excluded all non-respondents from the study. By June 2007, the researcher
had sent another nine questionnaires (panel wave II to X). Eventually, the study ?nished with
ten panel waves (i.e. panel wave I to X) and 290 respondents. Participants could pass
through more than one decision-making process sequentially during the study, but through
only one process simultaneously during the study. During the whole study period, 332 trip
plans could be monitored in total.
Trip decisions and information search activities represented the core research objects.
Panel wave VI (January 2007) to panel wave X (June 2007) raised the whole block of
consumer self-con?dence and product expertise items. Only questionnaires sent via
internet included this block as an additional mail survey would have resulted in extra cost.
The researcher raised these questions just at the beginning of the travel planning process
when respondents indicated that they were considering the idea of going on vacation.
During these panel waves, 105 trip planning processes could be monitored; these will be
used in the subsequent analyses.
Measurement variables
As hypothesized by Alba and Hutchinson (1987), expertise is comprised of ?ve
dimensions. The results of the study by Kleiser and Mantel (1994) showed, however, that
the cognitive structure dimension had to be removed due to poor factor loadings.
Therefore, for the present study, four qualitatively distinct dimensions examined product
expertise: cognitive effort/automaticity (CA), analysis (AN), elaboration (EL), and memory
(ME). Based on previous work by Kleiser and Mantel (1994), Gursoy and McCleary
(2004b), and Schmidt and Spreng (1996), a set of 15 items was developed to measure
the product expertise construct in a tourism context. More speci?cally, CA refers to
habitual decision making and cognitive effort (?ve items used), AN captures individuals’
knowledge related to travel offers (three items used), EL denotes the ability to make
inferences based on prior experience (three items used), and lastly, ME refers to the
ability to remember suppliers of tourism products and services (four items used). A
?ve-point rating scale ranging from ‘‘strongly agree’’ to ‘‘strongly disagree’’ measured all
items. The respective items were added up in each dimension and used as indicators of
the single factor product expertise.
The concept of consumer self-con?dence was examined as having four qualitatively distinct
dimensions based on the work by Bearden et al. (2001): information acquisition (IA),
VOL. 5 NO. 2 2011
j
INTERNATIONAL JOURNAL OF CULTURE, TOURISM AND HOSPITALITY RESEARCH
j
PAGE 187
D
o
w
n
l
o
a
d
e
d
b
y
P
O
N
D
I
C
H
E
R
R
Y
U
N
I
V
E
R
S
I
T
Y
A
t
2
2
:
1
3
2
4
J
a
n
u
a
r
y
2
0
1
6
(
P
T
)
consideration-set formation (CS), personal outcomes decision making (PO), and social
outcomes decision-making (SO). As mentioned earlier in the literature overview, Bearden
et al. (2001) developed a scale consisting of six dimensions. Two dimensions, however, were
not applicable in the context of the present study examining travelers’ vacation planning in a
pre-decision context. Persuasion knowledge and marketplace interfaces, subsumed under
the factor ‘‘protection in the marketplace’’, re?ect individuals’ con?dence in direct
marketplace operations (e.g. exchange processes with salespersons and the like). For the
scope of this study, thus, only four out of six dimensions were included. In total, the scale
consisted of 20 items (?ve items for each of the four dimensions) measured on ?ve-point
rating scales ranging from‘‘very suitable’’ to ‘‘not suitable at all’’. After measuring all these 20
items, the items were added up in each dimension and used as indicators of the single factor
consumer self-con?dence.
Travel experience was measured by three items:
1. ‘‘Basically, I do not travel a lot’’ (item 1).
2. ‘‘I do not consider myself as experienced regarding travelling’’ (item 2).
3. ‘‘For many years, I have been travelling a lot, I have seen many places and consider
myself as experienced in regards to travelling’’ (item 3).
The ?rst two items were reverse coded. A ?ve-point rating scale from ‘‘strongly agree’’ to
‘ ‘strongly disagree’ ’ measured all three items. Product expertise, consumer
self-con?dence, and travel experience were measured by re?ective indicators
indicating that the latent constructs cause the observed variables (Diamantopoulos and
Winklhofer, 2001).
In order to cover information-relevant activities, respondents were asked to indicate relevant
sources used for different decisions (Decrop, 2006). Questions about decisions made
included duration of the trip, destination, accompaniment, period of travel, budget,
accommodation, activities, transportation, and organization of the trip.
Travel information search was examined as having four indexes: the self as source of
information (i.e. internal search) (Kerstetter and Cho, 2004), interpersonal sources, and
commercial and non-commercial sources (Schmidt and Spreng, 1996). These four indexes
or composites measure actual total search activity by summating information sources used
in each wave measured on a dichotomous scale (applicable/not applicable). One item
measured internal search behavior (own experience), two items interpersonal sources
(friends and relatives, communities and forums), six items measured commercial sources
(tour operator brochures, tour operator web sites, travel agencies, travel agency web sites,
hotel/accommodation brochures, hotel/accommodation web sites) and ?nally ?ve items
measured non-commercial source types (convention and visitor bureaus, convention and
visitor bureau web sites, travel guides/journals, online travel guides/journals, general web
sites). These items were adopted from relevant literature (e.g. Bieger and Laesser, 2001;
Kerstetter and Cho, 2004). Measuring travel information search with the four not necessarily
high correlated indexes means that the construct results from a linear sum of the single
measures (i.e. the dimensions). Therefore, the indexes are to be treated as formative
indicators (Jarvis et al., 2003).
Analysis
The researcher used PLS path modeling to explain the relationship between consumer
self-con?dence, product expertise, travel experience, and travel information sourcing. PLS
was performed using the software SmartPLS (Version 2.0.M3) (Ringle et al., 2005). Since
data need not to be normally distributed for non-parametric approaches such as PLS,
resampling methods generate the signi?cance levels of parameters. SmartPLS applies
bootstrapping in order to derive t-values for statistical signi?cance. In this study, 100
bootstrap samples compute the respective t-values. The mean for all available cases of a
variable replaces the missing values.
PAGE 188
j
INTERNATIONAL JOURNAL OF CULTURE, TOURISM AND HOSPITALITY RESEARCH
j
VOL. 5 NO. 2 2011
D
o
w
n
l
o
a
d
e
d
b
y
P
O
N
D
I
C
H
E
R
R
Y
U
N
I
V
E
R
S
I
T
Y
A
t
2
2
:
1
3
2
4
J
a
n
u
a
r
y
2
0
1
6
(
P
T
)
Results
Sample characteristics
Respondents were predominantly female (60 percent) and aged 42 years on average.
36 percent of the respondents reported traveling more than four days within Austria, one
to three times annually. By contrast, two thirds (64 percent) of the respondents indicated
taking cross-border trips for more than four days one to three times per year.
Concerning the information sources used, respondents used on average 4.2 sources
per panel wave and 2.5 sources per decision made. Decisions made in each panel
wave averaged 1.8.
As far as differences in consumer self-con?dence and product expertise dimensions are
concerned, the descriptive statistics show that the values are rather high for both constructs
(see Table I).
Search opportunities and decisions made
The second column in Table II presents sources used for trip planning. As Table II shows,
tour operators, one’s own experiences, hotel and accommodation web sites, and general
web sites are among the most important sources of information. More than one-third (38
percent) of all information sources are used online. Since the periods of trip planning vary
from person to person, the third column shows information search opportunities. For this
value, sources used were divided by the number of panel waves recorded for trip planning.
On average, respondents had 4.16 (std. deviation 1.2) panel waves (i.e. opportunities) to
indicate their information search activities. The fourth column presents the ratio of sources
used for each decision made. Again, one’s own experience is the most important source of
information in order to make a decision.
Table II Frequency of travel information sources used
Information sources
Use of information
sources (%)
Ratio of sources used and
search opportunities
Ratio of sources used and
decisions made
n ¼ 105 n ¼ 105
Tour operator brochures 47 0.44 0.22
Tour operator web sites 66 0.40 0.23
Travel agencies 42 0.39 0.20
Travel agency web sites 44 0.27 0.16
Convention and visitor bureaus 25 0.10 0.07
Convention and visitor bureau web sites 40 0.17 0.13
Hotel/accommodation brochures 33 0.18 0.10
Hotel/accommodation web sites 59 0.43 0.23
Travel guides/journals 42 0.27 0.15
Online travel guides/journals 44 0.30 0.18
General web sites 59 0.27 0.19
Friends/relatives 37 0.26 0.17
Communities/forums 19 0.12 0.07
Own experiences 65 0.63 0.37
Table I Descriptive statistics for consumer self-con?dence and product expertise
Indices for consumer self-con?dence and product expertise
Percentiles IA
a
CS
a
PO
a
SO
a
CA
b
AN
b
EL
b
ME
b
25th 4 3.6 3.6 3 2.6 2.2 2 2.6
50th 4.2 4 4 3.2 3 2.6 2.4 3
75th 4.6 4.4 4.6 3.8 3.4 2.8 2.6 3.4
Notes:
a
Scale range from 1 (not suitable at all) to 5 (very suitable);
b
Scale range from 1 (strongly disagree) to 5 (strongly agree)
VOL. 5 NO. 2 2011
j
INTERNATIONAL JOURNAL OF CULTURE, TOURISM AND HOSPITALITY RESEARCH
j
PAGE 189
D
o
w
n
l
o
a
d
e
d
b
y
P
O
N
D
I
C
H
E
R
R
Y
U
N
I
V
E
R
S
I
T
Y
A
t
2
2
:
1
3
2
4
J
a
n
u
a
r
y
2
0
1
6
(
P
T
)
Model evaluation
The factor loadings for the three re?ective measurement models range from 0.409 to 0.896.
More speci?cally, the loadings for the construct of product expertise are 0.409 (CA), 0.875
(AN), 0.883 (EL), and 0.859 (ME); for the construct of consumer self-con?dence the factor
loadings are 0.883 (IA), 0.868 (CS), 0.577 (PO), and 0.647 (SO); for travel experience these
are 0.716 (item 1), 0.896 (item 2), and 0.791 (item 3). In order to examine the reliability of the
re?ective measurement models, composite reliability (CR) (Hair et al., 1998) was calculated.
All three constructs pass the recommended threshold of 0.70. In addition to reliability,
convergent validity was assessed for the three re?ective measurement models. The
convergent validity represents the common variance between the indicators and their
construct, and is measured by the average variance extracted (AVE) (Fornell and Larcker,
1981). All three latent variables comply with this pre-requisite. Therefore, the measurement
models seem to have both high reliability and convergent validity. Regarding discriminant
validity, the values for AVE were compared with the squared correlations between all three
constructs (Anderson and Gerbing, 1988). The AVE for the three re?ective measurement
models is higher than the squared correlation and thus the constructs show suf?cient
evidence for discriminant validity.
Internal consistency and convergent validity are not applicable to formative constructs
(Jarvis et al., 2003). Therefore, Table III shows the weights of the composites and their
respective t-values. Since the formative measurement model is based on a multiple
regression, multicollinearity has to be inspected (Diamantopoulos and Winklhofer, 2001).
The ?rst step of analysis investigated intercorrelations and the variance in?ation factor (VIF).
Second, only the ?rst component of a PLS regression was used to prevent multicollinearity
(Henseler et al., 2007; Wold et al., 1984). In order to test discriminant validity, the latent
variable correlations matrix needs to be assessed. For formative measurement models, all
construct correlations need to be smaller than 0.90. The correlations between the latent
variables show suf?cient evidence for discriminant validity of the information search
construct. To conclude, the measures are valid and reliable, which strongly suggests that all
measures are acceptable and that the model can be tested.
Hypotheses testing
As PLS aims at explaining variances, prediction-oriented measures have to be applied. The
coef?cient of determination (R
2
) speci?es the share of the variance of the endogenous
constructs explained by the relationships in the model. The R
2
-values are 0.06 for
information search and 0.31 for product expertise. Within PLS-models, R
2
-values above 0.1
are commonly deemed acceptable (Falk and Miller, 1992). However, in order to fully evaluate
the model, the Stone-Geisser test criterion (Q
2
) needs to be inspected by applying a
blindfolding procedure (Huber et al., 2007). For the two endogenous constructs, the
measures for Q
2
are greater than zero indicating that the hypothesized model is acceptable.
The next step of data analysis investigates the structural model. Figure 1 reports the path
coef?cients and hypotheses. The results of the bootstrapping resampling technique show
that all except one of the path coef?cients are signi?cant at p , 0:01. As predicted for H2,
consumer self-con?dence has a positive effect on product expertise (beta ¼ 0:369;
p , 0:01). The ?ndings also support H1 linking product expertise to information search.
Product expertise has the expected positive in?uence on information sourcing
Table III Evaluation of the formative measurement model (PLS Estimation)
Information search composites Highest intercorrelation VIF
(Requirement) Weights t-value (,0.9) (,10)
Commercial sources 0.210 1.719 0.502 1.387
Non-commercial sources 0.712 3.598 0.502 1.488
Interpersonal sources 0.173 0.311 0.398 1.203
Self 0.513 1.871 0.244 1.076
PAGE 190
j
INTERNATIONAL JOURNAL OF CULTURE, TOURISM AND HOSPITALITY RESEARCH
j
VOL. 5 NO. 2 2011
D
o
w
n
l
o
a
d
e
d
b
y
P
O
N
D
I
C
H
E
R
R
Y
U
N
I
V
E
R
S
I
T
Y
A
t
2
2
:
1
3
2
4
J
a
n
u
a
r
y
2
0
1
6
(
P
T
)
(beta ¼ 0:235; p , 0:01). As predicted for H3b, travel experience positively in?uences
product expertise (beta ¼ 0:341; p , 0:01). Concerning the link between travel experience
and information search, the results do not support hypothesis H3a.
Discussion
Theoretical implications
The purpose of this study was to improve the understanding of travel information sourcing.
More speci?cally, the present study contributes to a better knowledge of the interplay
between consumer self-con?dence, product expertise and travel information search.
Furthermore, travel experience can be discussed when taking into account the study
?ndings. The data supported all but one of the proposed relationships.
For many years, there has been a need to investigate information search more precisely and
accurately (Beatty and Smith, 1987; Cleveland et al., 2003; Perdue, 1993; Srinivasan, 1990).
The present study reacted to the demand for more accurate research on information
sourcing adopting a panel design that monitored individuals during their vacation planning
and preparation process.
The most critical implications of this study refer to the relationship between consumer
self-con?dence and product expertise in a tourism-related context. The results con?rmed that
consumer-related and product-related ability or knowledge are two distinct concepts and that
consumer self-con?dence signi?cantly impacts on product expertise (H2 supported).
Since Park et al. (1994) found no signi?cant relationship between generalized self-con?dence
and perceived product-related knowledge, they suggested relating general self-con?dence
closer to consumption in order to ?nd a relationship between the two constructs. In the present
study, the construct of consumer self-con?dence was utilized which denotes an individual’s
perceived ability concerning marketplace decisions and information search behavior. The
results of this study con?rmthat consumers’ general ability to deal with marketplace decisions
and behaviors signi?cantly in?uences the ability to deal with product-speci?c decisions and
behaviors. Thus, the ?ndings substantiate that product expertise and self-con?dence are
related constructs when linking self-con?dence to consumption.
Turning now to the relationship between knowledge-related factors and travel information
search, the ?ndings from this study show that product expertise signi?cantly in?uences
travel information sourcing while travel experience does not. The ?ndings regarding product
expertise show a positive relationship between the two constructs indicating that the more
Figure 1 Estimated standardized path coef?cients
VOL. 5 NO. 2 2011
j
INTERNATIONAL JOURNAL OF CULTURE, TOURISM AND HOSPITALITY RESEARCH
j
PAGE 191
D
o
w
n
l
o
a
d
e
d
b
y
P
O
N
D
I
C
H
E
R
R
Y
U
N
I
V
E
R
S
I
T
Y
A
t
2
2
:
1
3
2
4
J
a
n
u
a
r
y
2
0
1
6
(
P
T
)
expertise travelers have, the more they search for information (H1 supported). Experts have
more complex knowledge structures and cognitive skills and they are open to process new
information and thus search more information than non-experts search. Perceived travel
experience, on the other hand, does not in?uence travel information sourcing (H3a not
supported). Thus, if an individual travels frequently and considers him/herself as
experienced, it has no effect on the amount of information search. This ?nding makes the
difference between expertise and experience concerning information search clear: the level
of expertise refers to cognitive skills that again enable information processing. Travel
experience, on the other hand, is not bound to information search. Concerning the
relationship between travel experience and product expertise, the former signi?cantly
impacts the latter (H3b supported). More speci?cally, the more travel experience individuals
have the more they consider themselves as experts in terms of travelling. Thus, the level of
product expertise functions as a mediator to the extent that it accounts for the relation
between travel experience and travel information sourcing.
The ?nal theoretical implication relates to the generalization of ?ndings. Most of the previous
studies investigating information search behavior used cross-sectional data. These studies
primarily focused on individuals’ attitude towards the use of information sources. To
conclude, due to different conceptualizations and measurement approaches, it is evident
that generalization of ?ndings is hardly possible. De?nitions and clear concept
speci?cations can avoid any misconceptions and deliver useful contributions to the ?eld.
Managerial implications
The results from this study have vital implications for tourism marketers who wish to improve
their knowledge about where to reach and which information to provide to potential
customers. For ef?cient targeting and product positioning, marketers should thus orientate
towards the usage of information sources. The ?ndings of this study indicate that individuals
prefer certain sources over others for their vacation planning and preparation. Tourists
primarily rely on their own experiences regarding travel planning. Thus, satisfaction and
attachment with prior travel experiences potentially affects future travel planning processes.
Another insight is that interpersonal sources are of particular importance for vacation
planning. Friends and relatives thus considerably in?uence tourists in their information
sourcing and decision making. Hence, marketers need to consider the relevance of
word-of-mouth recommendation in their marketing decisions. Of almost equal importance is
information gathered from the internet. In general, however, the internet complements rather
than substitutes commonly accepted information sources. The ?ndings of the study provide
evidence that travel agencies and brochures remain important sources of information for
travelers’ trip planning. Besides that, another noteworthy ?nding is that electronic
word-of-mouth is secondary for travel information search. Therefore, marketers should not
fully rely on internet-based communication but ?nd an optimal mix between electronic and
traditional information sources in order to ef?ciently target potential customers.
Limitations and directions for future research
One major limitation of the study is its small sample size. Also, the study is speci?c to only
one culture (Austrian respondents). A larger sample as well as cultural differences may
affect the magnitude of the relationships between consumer self-con?dence, product
expertise, travel experience, and travel information search.
In addition, it is important to mention that respondents had high levels of ability. Thus, results
might be different for vacationers with less perceived ability and knowledge concerning
aspects of travel planning. Therefore, a replication of this study should be conducted with a
larger sample size and more variation in ability levels.
In order to cover information-relevant activities in the present study, respondents were asked
to indicate relevant sources used for different decisions. Due to the small sample size,
however, no differences in travel decisions such as destination, duration of the trip,
accompaniment, budget, and organization of the trip could be examined. Future studies
should thus distinguish between different decisions made and respective information sources.
PAGE 192
j
INTERNATIONAL JOURNAL OF CULTURE, TOURISM AND HOSPITALITY RESEARCH
j
VOL. 5 NO. 2 2011
D
o
w
n
l
o
a
d
e
d
b
y
P
O
N
D
I
C
H
E
R
R
Y
U
N
I
V
E
R
S
I
T
Y
A
t
2
2
:
1
3
2
4
J
a
n
u
a
r
y
2
0
1
6
(
P
T
)
References
Alba, J.W. and Hutchinson, J.W. (1987), ‘‘Dimensions of consumer expertise’’, Journal of Consumer
Research, Vol. 13 No. 4, pp. 411-54.
Anderson, J.C. and Gerbing, D.W. (1988), ‘‘Structural equation modeling in practice: a review and
recommended two-step approach’’, Psychological Bulletin, Vol. 103, pp. 411-23.
Bearden, W.O., Hardesty, D.M. and Rose, R.L. (2001), ‘‘Consumer self-con?dence: re?nements in
conceptualization and measurement’’, Journal of Consumer Research, Vol. 28 No. 1, pp. 121-34.
Beatty, S.E. and Smith, S.M. (1987), ‘‘External search effort: an investigation across several product
categories’’, Journal of Consumer Research, Vol. 14 No. 1, pp. 83-95.
Bettman, J.R. and Park, C.W. (1980), ‘‘Effects of prior knowledge and experience and phase of the
choice process on consumer decision processes: a protocol analysis’’, Journal of Consumer Research,
Vol. 7 No. 3, pp. 234-48.
Bieger, T. and Laesser, C. (2001), ‘‘Segmenting travel on the sourcing of information’’, in Mazanec, J.A.,
Crouch, G.I., Brent Ritchie, J.R. and Woodside, A.G. (Eds), Consumer Psychology of Tourism,
Hospitality and Leisure, CABI Publishing, Wallingford, pp. 153-67.
Brucks, M. (1985), ‘‘The effects of product class knowledge on information search behavior’’, Journal of
Consumer Research, Vol. 7, pp. 1-16.
Cleveland, M., Babin, B.J., Laroche, M., Ward, P. and Bergeron, J. (2003), ‘‘Information search patterns
for gift purchases: a cross-national examination of gender differences’’, Journal of Consumer Behaviour,
Vol. 3 No. 1, pp. 20-47.
Decrop, A. (2006), Vacation Decision Making, CABI Publishing, Wallingford.
Diamantopoulos, A. and Winklhofer, H.M. (2001), ‘‘Index construction with formative indicators: an
alternative to scale development’’, Journal of Marketing Research, Vol. 38 No. 2, pp. 269-77.
Falk, R.F. and Miller, N.B. (1992), A Primer for Soft Modeling, University of Akron Press, Akron, OH.
Fornell, C. and Larcker, D.F. (1981), ‘‘Evaluating structural equation models with unobservable variables
and measurement error’’, Journal of Marketing Research, Vol. 18 No. 1, pp. 39-50.
Gursoy, D. and McCleary, K.W. (2004a), ‘‘An integrative model of tourists’ information search behavior’’,
Annals of Tourism Research, Vol. 31 No. 2, pp. 353-73.
Gursoy, D. and McCleary, K.W. (2004b), ‘‘Travelers’ prior knowledge and its impact on their information
search behavior’’, Journal of Hospitality and Tourism Research, Vol. 28 No. 1, pp. 66-94.
Hair, J.F.J., Anderson, R.E., Tatham, R.L. and Black, W.C. (1998), Multivariate Data Analysis,
Prentice-Hall, Upper Saddle River, NJ.
Henseler, J., Wilson, B., Go¨ tz, O. and Hautvast, C. (2007), ‘‘Investigating the moderating role of ?t on
sports sponsorship and brand equity’’, International Journal of Sports Marketing and Sponsorship, Vol. 8
No. 4, pp. 321-9.
Huber, F., Herrmann, A., Meyer, F., Vogel, J. and Vollhardt, K. (2007), Kausalmodellierung mit Partial
Least Squares: Eine anwendungsorientierte Einfu¨ hrung, Gabler, Wiesbaden.
Jarvis, C.B., MacKenzie, S.B. and Podsakoff, P.M. (2003), ‘‘A critical review of construct indicators and
measurement model misspeci?cation in marketing and consumer research’’, Journal of Consumer
Research, Vol. 30 No. 2, pp. 199-218.
Kerstetter, D. and Cho, M.-H. (2004), ‘‘Prior knowledge, credibility and information search’’, Annals of
Tourism Research, Vol. 31 No. 4, pp. 961-85.
Kiel, G.C. and Layton, R.A. (1981), ‘‘Dimensions of consumer information seeking behavior’’, Journal of
Marketing Research, Vol. 18 No. 2, pp. 233-9.
Kleiser, S.B. and Mantel, S.P. (1994), ‘‘The dimensions of consumer expertise: a scale development’’,
American Marketing Association, Vol. 1, pp. 20-6.
Moutinho, L. (1987), ‘‘Consumer behaviour in tourism’’, European Journal of Marketing, Vol. 21 No. 10,
pp. 5-44.
VOL. 5 NO. 2 2011
j
INTERNATIONAL JOURNAL OF CULTURE, TOURISM AND HOSPITALITY RESEARCH
j
PAGE 193
D
o
w
n
l
o
a
d
e
d
b
y
P
O
N
D
I
C
H
E
R
R
Y
U
N
I
V
E
R
S
I
T
Y
A
t
2
2
:
1
3
2
4
J
a
n
u
a
r
y
2
0
1
6
(
P
T
)
Park, C.W., Mothersbaugh, D.L. and Feick, L. (1994), ‘‘Consumer knowledge assessment’’, Journal of
Consumer Research, Vol. 21 No. 1, pp. 71-82.
Perdue, R.R. (1993), ‘‘External information search in marine recreational ?shing’’, Leisure Sciences,
Vol. 15, pp. 169-87.
Punj, G.N. and Staelin, R. (1983), ‘‘A model of consumer information search behavior for new
automobiles’’, Journal of Consumer Research, Vol. 9 No. 4, pp. 366-80.
Ratchford, B.T. (2001), ‘‘The economics of consumer knowledge’’, Journal of Consumer Research,
Vol. 27 No. 4, pp. 397-411.
Ringle, C.M., Wende, S. and Will, A. (2005), SmartPLS - version 2.0.M3, Universita¨ t Hamburg, Hamburg.
Schmidt, J.B. and Spreng, R.A. (1996), ‘‘A proposed model of external consumer information search’’,
Journal of the Academy of Marketing Science, Vol. 24 No. 3, pp. 246-57.
Srinivasan, N. (1990), ‘‘Pre-purchase external search for information’’, in Zeithaml, V. (Ed.), Review of
Marketing 1990, American Marketing Association, Chicago, pp. 153-89.
Teare, R. (1992), ‘‘An exploration of the consumer decision process for hospitality services’’, in Teare, R.,
Mouthino, L. and Morgan, N. (Eds), Managing and Marketing Services in the 1990s, Cassell
Educational, London, pp. 233-48.
Wold, S., Ruhe, A., Wold, H. and Dunn, W.J. (1984), ‘‘The collinearity problem in linear regression: the
partial least squares (PLS) approach to generalized inverses’’, Journal on Scienti?c and Statistical
Computing, Vol. 5 No. 3, pp. 735-43.
Zins, A.H. (2007), ‘‘Exploring travel information search behaviour beyond common frontiers’’, Journal of
Information Technology and Tourism, Vol. 9 Nos 3/4, pp. 149-64.
Corresponding author
Karen Teichmann can be contacted at: [email protected]
PAGE 194
j
INTERNATIONAL JOURNAL OF CULTURE, TOURISM AND HOSPITALITY RESEARCH
j
VOL. 5 NO. 2 2011
To purchase reprints of this article please e-mail: [email protected]
Or visit our web site for further details: www.emeraldinsight.com/reprints
D
o
w
n
l
o
a
d
e
d
b
y
P
O
N
D
I
C
H
E
R
R
Y
U
N
I
V
E
R
S
I
T
Y
A
t
2
2
:
1
3
2
4
J
a
n
u
a
r
y
2
0
1
6
(
P
T
)
doc_567762792.pdf