Description
This paper aims to study the decision-making processes of Portuguese tourists traveling
to South America and Africa destinations by developing a conceptual framework that focuses on
information sources, motivations, perceptions, satisfactions, and behavioral intentions
International Journal of Culture, Tourism and Hospitality Research
Decision-making processes of Portuguese tourist travelling to South America and Africa
Antónia Correia Adriano Pimpão
Article information:
To cite this document:
Antónia Correia Adriano Pimpão, (2008),"Decision-making processes of Portuguese tourist travelling to
South America and Africa", International J ournal of Culture, Tourism and Hospitality Research, Vol. 2 Iss 4
pp. 330 - 373
Permanent link to this document:
http://dx.doi.org/10.1108/17506180810908989
Downloaded on: 24 January 2016, At: 22:06 (PT)
References: this document contains references to 165 other documents.
To copy this document: [email protected]
The fulltext of this document has been downloaded 2287 times since 2008*
Users who downloaded this article also downloaded:
Antónia Correia, Metin Kozak, J oão Ferradeira, (2013),"From tourist motivations to tourist satisfaction",
International J ournal of Culture, Tourism and Hospitality Research, Vol. 7 Iss 4 pp. 411-424 http://
dx.doi.org/10.1108/IJ CTHR-05-2012-0022
Lan-Lan Chang, Kenneth F. Backman, Yu Chih Huang, (2014),"Creative tourism: a preliminary examination
of creative tourists’ motivation, experience, perceived value and revisit intention", International J ournal
of Culture, Tourism and Hospitality Research, Vol. 8 Iss 4 pp. 401-419 http://dx.doi.org/10.1108/
IJ CTHR-04-2014-0032
Songshan (Sam) Huang, Cathy H.C. Hsu, (2009),"Travel motivation: linking theory to practice",
International J ournal of Culture, Tourism and Hospitality Research, Vol. 3 Iss 4 pp. 287-295 http://
dx.doi.org/10.1108/17506180910994505
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
:
0
6
2
4
J
a
n
u
a
r
y
2
0
1
6
(
P
T
)
Decision-making processes
of Portuguese tourist travelling
to South America and Africa
Anto´nia Correia and Adriano Pimpa˜o
Faculty of Economics, University of Algarve, Faro, Portugal
Abstract
Purpose – This paper aims to study the decision-making processes of Portuguese tourists traveling
to South America and Africa destinations by developing a conceptual framework that focuses on
information sources, motivations, perceptions, satisfactions, and behavioral intentions.
Design/methodology/approach – The study applies a structural model that looks to explain the
factors behind decision making and the relationships present. The relationships are observed in detail
through the application of a categorical principal component analysis.
Findings – The results of the empirical study show that behavioral intentions precede emotional and
cognitive satisfaction, which in turn, are explained through perceptions and motivations. Tourists
perceive tourism destinations as places of leisure although little information is available on existing
facilities and core attractions.
Research limitations/implications – The study has the restriction of being limited to the
Portuguese tourists. However, these ?ndings open paths for further investigation, namely extending to
other destinations and to tourists with different motivations.
Originality/value – This study contributes to the overall understanding of the decision-making
processes of tourists. Speci?cally, the decision processes is assess by considering two stages: the
pre-purchase stage and the post-purchase stage. These two phases were analyzed in order to
understand how people decide to travel to a certain destination.
Keywords Motivation (psychology), Decision making, Tourism, Portugal, South America, Africa
Paper type Research paper
Introduction
Tourism is one of the major growth sectors worldwide particularly in Portugal. As
tourism develops globally, where tourists and tourism play an important role, the need
to understand the reasons behind tourist behavior is of fundamental importance.
Strategic management of tourist destinations stems from the development of consumer
behavior theories from which understanding and prediction of tourist choice is a
challenge towards excellence.
Tourism demand is a topic receiving growing attention from a large number of
scholars. Tourism demand is of increasing concern for destination policy makers. This
topic, having ?rst appeared in the tourism literature in the 1950s, depended
fundamentally on variables that were tourism demand related. Backed by econometric
modeling, the ?rst studies predicted the demand for tourism through a more
The current issue and full text archive of this journal is available at
www.emeraldinsight.com/1750-6182.htm
The authors acknowledge the ?nancial support of AIR LUXOR, SA, the comments of Arch
Woodside and Carlos Barros, the editing help of Cla´udia Moc¸o and ?nal proofreading assistance
of Susy Rodrigues.
IJCTHR
2,4
330
Received February 2008
Revised April 2008
Accepted May 2008
International Journal of Culture,
Tourism and Hospitality Research
Vol. 2 No. 4, 2008
pp. 330-373
qEmerald Group Publishing Limited
1750-6182
DOI 10.1108/17506180810908989
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
:
0
6
2
4
J
a
n
u
a
r
y
2
0
1
6
(
P
T
)
macro-economic approach (Crouch, 1994). In the econometric or time-series ?elds,
studies that forecast tourism demand based on aggregated data have steadily
increased (Witt, 1992).
The micro-economic principles, which base their conclusions on Marshall’s (1920)
and Lancaster’s (1966) theories of classical economics, focus on consumer
heterogeneity and the need to ?nd each individual’s demand curve. This approach
towards tourism demand acknowledges that man is a rational being who behaves in
terms of maximum satisfaction with decisions based on in-depth knowledge of all
possible alternatives. This form of trying to understand human behavior is however
limited, namely, in man’s incapacity to perceive and evaluate all existing alternatives
(Decrop, 1999; Mansfeld, 1992).
This results, ?rstly, from the existence of an in?nite number of possibilities of
maximum consumer utility. Within the context of tourism, the situation grows even
more complex, where destination diversity, accommodation, recreation, means of
transport and motivation all compete for equal use. Secondly, the human condition
itself lacks the capacity to apprehend. Man’s decision-making process, despite its
limitations, has been widely used in modeling demand (Crouch, 1994; Lim, 1997). Most
of these demand function studies observe tourist behavior through time-series,
single-equation or simultaneous equation modeling, where lodging, guest numbers or
arrivals represent explained variables and budget and cost factors associated to the
destination (such as price, exchange rates and travel expenses) represent explanatory
variables (Archer, 1976; Artus, 1972; Sheldon, 1990; Witt and Martin, 1987).
These demand functions through econometric modeling allow estimation of price
elasticity and income demand in order to develop destination strategies, through
neglecting consumer heterogeneity and consumer cognitive capacity.
The consumer, as a rational human being, possesses dynamic behavior with
increasingly sophisticated needs and complex motivations (Correia, 2000). Within the
context of dynamics and complex cognitive interactions, the cognitive tourist thinker
decides according to a destination’s attributes, his or her intrinsic motivations, and
destination knowledge learned (Howard and Sheth, 1969). Consumer behavioral
models can thus be transversal, allowing different processes to be analyzed before ?nal
decisions are reached.
Such factors as motivations and external stimuli in determining preferences
(conducive to choice) and satisfaction (conducive to future behavior) in?uence the
decision-making process. Although this dynamic system of decision-making builds
from other ?elds of study and is adapted to explain tourism behavior, its
interrelationships remain an issue that deserves more research (Woodside, 2005).
This paper presents a conceptual decision-making framework based on
microeconomic theory and behavioral models that allow us to examine variables
that in?uence travel behavior. The chapter provides an empirical basis for
understanding attitude-behavior interrelationships.
The proposed framework is useful for examining Portuguese tourists who travel to
South America and Africa destinations such as Brazil, Morocco, Egypt, the Dominican
Republic and Sao Tome and Principe through the application of a structural equation
model (henceforth, SEM) (Joreskog and Sorbom, 1986) and a categorical principal
component analysis (CATPCA). The SEM adopted by Rosenberg (1956) and Fishbein
(1967) assumes that choice is a function of the perceptions and attributes towards
Decision-making
processes
331
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
:
0
6
2
4
J
a
n
u
a
r
y
2
0
1
6
(
P
T
)
a particular destination. Later, Fishbein and Ajzen (1980) added a behavioral intention
component to Fishbein’s original model as a function of expectations together with
social and individual factors.
However, the majority of studies about holiday decision-making processes do not
follow this approach. Research focuses on exploratory statistics, with SEM being
rarely adopted. Following Yoon and Uysal’s (2005) framework, this model introduces
new constructs such as the role of information sources (brochures, advertising, travel
movies and news) in shaping motivations, perceptions, satisfactions and behavioral
intentions.
Internal forces push and external forces pull individuals who travel (Correia and
Crouch, 2004; Correia et al., 2007b; Crompton, 1979; Dann, 1977; Kozak, 2001; Uysal and
Hagan, 1993). However, the interplay between push and pull motivations needs to be
considered in terms of satisfaction. To achieve this, the model assumes two additional
constructs: push satisfaction (emotional) and pull satisfaction (cognitive). Studies on
satisfactions have received signi?cant attention (Baker and Crompton, 2000; Moutinho,
1987; Ryan and Glendon, 1998). Researchers often refer to satisfaction as an emotional
state of ful?llment after concluding an experience. Since ful?llment does not only rely
on destination attributes, it makes sense to use the traditional breakdown of
motivations into push and pull motives in the satisfaction assessment.
This paper describes push and pull satisfactions as two individual factors that
contribute to the overall evaluation. Push satisfaction is an individual’s internal state of
well-being towards his or her holiday, and in harmony to his or her main push
motivations. A tourist goes on holiday because there is need to achieve intellectual,
physical and social rewards, and the concept of push satisfaction measures the level of
internal achievement perceived by the tourist. Pull satisfaction con?rms tourist
expectations in terms of destination attributes, a concept traditionally explored in
tourism studies (Bigne´ et al., 2001; Correia et al., 2007a; Murphy et al., 2000; Yoon and
Uysal, 2005; Ryan and Glendon, 1998).
This paper develops and empirically validates a SEM capable of measuring the
decision-making process of Portuguese tourists who visit South America and Africa
destinations. Considering not only the pre-purchase phase wherein motivations and
perceptions are analysed (Correia et al., 2007c) but also the post-purchase phase
wherein satisfaction and behavioral intentions are also considered. This study is
therefore an extended version of the paper presented by Correia et al. (2007c) who
combine motivations and perceptions in order to understand why people travel to
exotic places. Thus, the paper’s conceptual framework considers all measurable
constructs related to holiday decision-making processes found in the literature with the
development of new interrelationships of alternative variables, through SEM.
This study adopts CATPCA to explore the cause-effect relationships among each of
the observed variables in the model. This statistical method represents a set of
categorical variables on perceptual maps and allows us to explore the simultaneous
relations among the observed variables used to measure the latent constructs in the
structural model. This model, which lets us identify signi?cant statistical variables
that explain how and why tourists behave as they do in such places, has clear
implications in the de?nition of marketing strategies and tourist choice factors that
strongly contribute to the overall image of South America and Africa destinations.
IJCTHR
2,4
332
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
:
0
6
2
4
J
a
n
u
a
r
y
2
0
1
6
(
P
T
)
This chapter has the following structure. The next section presents the contextual
setting while the following section presents a literature review on seminal models of
consumer behavior theory, decision-making processes and their constructs. The next
section addresses the conceptual model adopted and introduces the hypotheses
associated constructs and adopted methodology. The following section presents the
empirical model. This section is organized into four sub-sections:
(1) the shaping of each construct through exploratory factor analysis (EFA);
(2) the measurement model;
(3) the structural model testing; and
(4) the relationship of each set of variables that measured the latent constructs of
the model.
The penultimate section discusses the results and underline implications at the
management level. Finally, the last section presents the conclusions, limitations, and
extensions of the research.
Contextual setting: Portuguese tourists in South America and Africa
destinations
Although Portugal is one of the foremost holiday destinations in Europe, the
twenty-?rst century witnesses the beginning of Portuguese citizens traveling outside
their home country. Tourism has seen some retraction in countries such as Asia and
the Paci?c who have now awaken as tourism markets on a worldwide scale, digesting
20 percent of international arrivals which represent a 28 percent increase compared to
2003 (WTO, 2005). Faster and more economical air transportation, as well as shortened
?ight durations explains the increase in new routes and greater investment on more
long-haul destinations.
Between 2000 and 2004, results show a reduction from 71 to 53 percent in
Portuguese individuals over the age of 15 who traveled on holiday, with a signi?cant
increase to 56 percent in 2005. The number of Portuguese tourists traveling abroad
showed little increase, from 19 percent in 2004 to 21 percent in 2005, although these
numbers only represent one ?fth of the population who normally travel. In 2005,
Europe continued to lead the trend with about 50 percent of tourists traveling abroad,
followed by South America (18 percent), Asia (3 percent), and Africa (1 percent).
Increased tourism to destinations such as Asia is backed by the growing trend of
Portuguese tourists seeking different holiday destinations. In 2005, more than
65 percent of tourists preferred a sun and sea product while 20 percent opted for the
countryside (DGT, 2006).
The Portuguese generally go on holiday between the months of August and
September (81 percent) often because these months coincide with school summer break
or form part of employment contract agreements. However, 16 percent of tourists
depend on travel agencies for assistance, while the majority prefers to make their own
travel arrangements (42 percent) or via the internet (8 percent). In fact, preferential use
of the internet as a means for holiday planning has doubled since 2004. The majority of
Portuguese holidaymakers are under the age of 45 (64 percent) and on socio-economical
terms, the majority belong to higher social classes, as would be expected.
In addition, the average daily expense while on holiday abroad increased signi?cantly
from 2004 to 2005, that is, e76 in 2005 compared to e61 in 2004 per person day.
Decision-making
processes
333
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
:
0
6
2
4
J
a
n
u
a
r
y
2
0
1
6
(
P
T
)
When traveling abroad, Portuguese tourists prefer to travel on chartered ?ights to
destinations such as Brazil, Mexico, Tunisia, Morocco, Sao Tome and Principe and Egypt.
Air Luxor, SA is a privately owned Portuguese airline company, which is among the top
three national tour operators. Air Luxor operates charter ?ights from Portugal to South
American tourist resorts, namely Brazil and Mexico; to Africa, namely, Morocco, Tunisia,
and Egypt; to the tropical island of Sal in Cape Verde; and, to the islands of Sao Tome and
Principe. Air Luxor also offers scheduled ?ights within Europe as well as Africa, namely,
Sao Tome and Guinea-Bissau in Africa.
The study analyses the in?uence of various sources of information on decision
motivations, destination perceptions, satisfaction levels, and future behavioral intentions.
The ?ndings were obtained from conducted surveys on two separate occasions: upon
departure and upon return. Data collection took place aboard Air Luxor planes.
Literature review
Seminal models of consumer behavior
Consumer behavior is a dynamic and complex process. When applied to tourism, this
process becomes even more complex by the intangibility of the product and by the
discontinuity and accumulation of purchasing power (Correia, 2002). The
interdisciplinary nature of the subject under study has given rise to three distinct
groups of models: microeconomic models, structural models, and processional models.
Microeconomic models assume that the tourist looks to maximize his or her utility
based on a set of attributes and three constraints: time, money, and technology (Morley,
1992). The structural models examine the relationship between an input (stimulus) and
an output (response), while the processional models examine individuals’ decisions,
concentrating on the cognitive processes (transformation process between the input and
the output) generated prior to the ?nal decision being taken (Abelson and Levi, 1985).
The microeconomic models of consumer behavior follow classic economic theory
(Marshal, 1920). However, this theory is only able to handle simple product demand,
and as such, presents some limitations in terms of tourism analysis due to the elements
that characterize it. According to Samuelson (1981), the concept of individual
maximization towards product tradeoffs can account for composed product analysis,
such as tourism. Considering that tourist destinations appear, not as an object of direct
use, but as products whose characteristics permit us to endow them with utility
(Lancaster, 1966), maximizing their utility is possible, subject to a certain number of
restrictions. Morley (1992) presents microeconomic theory applications to tourism
(Lim, 1997; McFadden, 1981; O’Hagan and Harrison, 1984; Paraskevopoulos, 1977;
Song and Witt, 2000; Witt and Martin, 1987).
Processional models examine the complete decision-making process by
concentrating on the cognitive processes generated prior to making his or her ?nal
decision. They provide information on consumer behavior during the decision process
that goes unnoticed by the individual himself. The variable in processional models
is the decision process itself, in addition to other factors that in?uence this process.
Any tourism product boasts a multiplicity of attributes that de?ne and distinguish it
from competing alternatives. The majority of consumers are unable to process a large
number of variables simultaneously, and hence apply little criteria when reaching a
?nal decision. Three outstanding models underpin all of the studies in the ?eld of
consumer behavior analysis from a processional perspective.
IJCTHR
2,4
334
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
:
0
6
2
4
J
a
n
u
a
r
y
2
0
1
6
(
P
T
)
The Nicosia (1966) model focuses on the communication that takes place between
the consumer and the company, in which the latter, through tactics, seeks to persuade
the consumer into purchasing speci?c product(s). The work carried out by Engel et al.
(1978) is one of the most extensive literature review works available on consumer
behavior. Howard and Sheth’s (1969) model, which employs the input concept in
consumer behavior models, suggest forms of sequencing these inputs in the
decision-making process. This model continues to be one of the most important
contributions on consumer behavior theories.
Studies on tourist consumer behavior from the perspective of decision-making
processes began to appear in the 1970s. Most models explain the tourist decision
process in terms of sequenced, interrelated stages, varying in number between three
and ?ve. They look to assess different constructs of decision-making and their
interplay from the pre-purchase stage to the post-purchase stage (Crompton, 1979;
Middleton, 1994; Moutinho, 1982, 1987; Nicolau and Ma´s, 2005; Ryan, 1994; Um and
Crompton, 1990; Woodside and Lysonski, 1989; Woodside and King, 2001). Regardless
of the number of stages proposed in the literature, the models vary essentially in terms
of the focus placed on perception shaping and on post-purchase stage evaluation. They
rely on inferential and observational analysis. Similar to other behavioral models, these
models only allow cognitive factors to be considered. Foxall and Goldsmith (1994)
suggest that the models, although conveying very little meaning, help to understand
consumer actions. According to these authors, consumer decisions follow a series of
stages. Though decisions are nonlinear, the models serve to clarify which variables
predominate in consumer decision-making. In considering different choice
frameworks, cognitive elements will impact differently when making a decision and
in?uence attitude in distinct ways. These structural models serve as the theoretical
basis for the model proposed in the paper.
The ?rst structural models, developed by Rosenberg (1956) and Fishbein (1967)
apply the principle that decision-making is a function of objective perceptions and
destination attributes. Several researchers, however, concluded that cognitive elements
could differ in qualitative terms and, thus, be organized into different frameworks
and categories. Researchers involved in attitudinal studies believe that consumers need
to compare purchasing attitudes or intentions within different conceptual frameworks
of their behavior. Before ?nal decision making is possible, individuals will need to
settle cognitive, affective, and connotative factors and benchmark these against each
other. After weighing all elements, the ?nal choice occurs (Cohen et al., 1972; Fishbein,
1967; Fishbein and Ajzen, 1980; Rosenberg, 1956).
Fishbein and Ajzen (1980) propose the behavioral intention model, which
constitutes an extension of Fishbein’s (1967) original model. While maintaining that
behavioral theory represents the model’s basis, they consider behavioral intentions as
expectation functions, together with social and individual factors (Fishbein and Ajzen,
1980). The model allows the assumption that objects can be evaluated based on
multiple attributes that generate costs and bene?ts at different levels. The attitude
index does not increase inde?nitely when acquiring new expectations because attitude
is explained based on limited number of visible attributes.
Multi-attribute models consider that products possess several self-compensating or
compensatory attributes, taking the value-expectancy theory (Edwards, 1954) as the
underlying basis. This theory de?nes expectation as the probability that a certain
Decision-making
processes
335
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
:
0
6
2
4
J
a
n
u
a
r
y
2
0
1
6
(
P
T
)
attitude will lead to positive or negative bene?ts, isolating behavioral factors and
establishing in what way expectation and value combine to achieve a settled decision.
The value-expectancy theory measures subjective utility, and builds from Edwards’
(1954) behavioral decision theory. This theory views expectation as the consequence of
adopting a certain behavior, explained in terms of prospects and values resulting from
the choice the individuals makes. The advantage of the value-expectancy theory is that
it allows for the integration of other components such as related emotions and reasons
for traveling, discussed in studies on tourist motivation. Furthermore, it can take into
account push and pull factors, and personality assessments. Finally, the theory allows
a more realistic and re?ned view of tourist motivation.
The literature assesses tourism behavior from an exploratory analysis of
motivations, expectations, perceptions, and satisfaction. Gallarza et al. (2002) apply
statistical multivariate techniques that rely on a principal component analysis,
correlation tests, cluster analysis, multiple discriminant analysis, and homogeneity
analysis to tourism. Discrete-choice models, in particular qualitative choice models, can
be used to assess tourism behavior. These rely on binomial logit (Barros and Proenc¸a,
2005; Fleischer and Pizam, 2002; Kockelman and Krishnamurthy, 2004; Perales, 2002;
Stynes and Peterson, 1984) or multinomial logit (Hong et al., 2006; Kockelman and
Krishnamurthy, 2004; Luce, 1959; McFadden, 1981; Morley, 1994; Nicolau and Ma´s,
2005; Seddighi and Theocharous, 2002; Taplin and Qiu, 1997). More recently, tourist
behavior has been assessed through structural equation modeling. Baker and
Crompton (1998) test the effect of perceived quality performance on behavioral
intentions, Yoon and Uysal (2005) test causal relationships among push and pull
motivations, satisfaction and destination loyalty, Vogt and Andereck (2003) explain
how emotion and cognition can in?uence perceptions, Silvestre and Correia (2005),
from a second-order factor analysis, explain the image of Algarve as a tourist
destination, Correia et al. (2007b) assess motivations and perceptions about exotic
destinations, and Kim and Yoon (2003) observe perceptions from a conceptual point
of view.
The decision process
The tourist decision process assumes three essential stages, namely, the pre-decision,
decision and post-purchase evaluation stages (Bentler and Speckart, 1979; Correia,
2002; Crompton, 1992; Crompton and Ankomah, 1993; Middleton, 1994; Moutinho,
1982; Ryan, 1994; Um and Crompton, 1990).
The pre-decision stage can often occur on products, such as tourist destinations,
that are intangible or invisible before or at the time of purchase, oftentimes involving
decision-making from a range of competing alternatives. Destination choice and
associated factors that form part and parcel of any holiday planning, involve a group of
complex decisions that take up time and energy. However, most tourists take pleasure
in this process (Crouch and Jordan, 2004). The pre-decision stage serves to build up
motivations, in?uenced by available destination information sources that contribute to
perception development.
The decision stage includes the evaluation of perceptions through which consumers
base their decisions in terms of time and budget constraints, conditioning factors that
restrict choice. Given the time interval between purchase and use, the former
represents a transitory process.
IJCTHR
2,4
336
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
:
0
6
2
4
J
a
n
u
a
r
y
2
0
1
6
(
P
T
)
Post-purchase evaluation results from other stimuli that in?uence the choice
process and evaluates satisfaction gained from the product (destination). This stage is
also important in estimating the probability of repeating the purchase of a speci?c
destination and/or the intention to recommend visiting the destination. The paper
discusses the theoretical underpinning of this decision-making process in the following
sub-section.
Theoretical constructs
Motivational constructs
Motivation refers to an individual’s need to adopt a certain behavior in order to satisfy
this condition. Fodness (1994) argues that motivation theories describe a dynamic
process of internal psychological factors (needs, desires and goals) which generate a
level of tension in an individual and in?uence him or her towards purchase. Baloglu
(1997), Dann (1996) and Gartner (1993) suggest that motivations have a direct in?uence
on the affective component of an image such as a destination that generates certain
feelings. Individuals with different motivations may similarly evaluate a tourist
destination if the destination is able to succeed in the desired bene?ts.
Crompton’s (1979) widely accepted push-pull model represent two forces in tourism
research. Push motivations correspond to forces whereby individuals are pushed by
motivational factors into making travel decisions and seen as the desire for personal
achievement, satisfaction, rest and relaxation, adventure, knowledge, getting away, and
social interaction. Pull motivations, on the other hand, re?ect internal or emotional factors
prompted by the attributes of a destination (Uysal et al., 1996). The characteristics or
attributes of a destination allow the tourist to create expectations in terms of satisfying
motivational needs. Several studies have explored motivational determinants in the
tourism context (Beerli and Mart? ´n, 2004; Correia and Crouch, 2004; Correia et al., 2007b;
Crompton, 1979; Dann, 1981; Fodness, 1994; Gnoth, 1997; Iso-Ahola and Mannel, 1987;
Lundberg, 1990; Mohsin and Ryan, 2003; Pearce, 1982; Shoemaker, 1989; Uysal and
Hagan, 1993; Uysal et al., 1996; Yoon and Uysal, 2005).
After examining the context of needs, the tourist enters the learning stage and
searches for the destination capable of yielding satisfaction and ful?llment.
Learning process
Learning is the process whereby the consumer acquires knowledge about a product
and subsequent consumption experience when considering future behaviors. Bettman
and Park (1980) examine learning processes and develop an information-processing
model in which the consumer, who possesses limited memory, performs decisions
through simpli?cation processes. The consumer is only capable of retaining a
maximum of seven destinations and a minimum of two according to Miller (1956).
The simpli?cation process is a mechanism that consumers use to ?lter extraneous
information from an array of products (destinations) put before him or her. Reducing
the list of options to a limited set is referred to in the literature as the evoked set,
whereby information is more manageable in terms of consumer processing and
cognitive retention (Moutinho, 1987; Um and Crompton, 1990; Woodside and Lysonski,
1989).
Howard and Sheth (1969) discuss the different activation levels of destinations in
the consumer’s memory. In limiting his or her set of destinations, the consumer is able
Decision-making
processes
337
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
:
0
6
2
4
J
a
n
u
a
r
y
2
0
1
6
(
P
T
)
to evaluate and display interest. Inert set destinations represent those destinations
causing indecisiveness, with inept set destinations being those destinations that
represent no interest to the consumer at all (Woodside and Sherrell, 1977). When
consumers process these elements, the learning stage begins.
Analyzing the way in which the consumers learn by considering behavioral or
cognitive issues is possible. The behavioral perspective allows learning to occur based
on three factors: information gathering, choice, and experience. The bene?ts obtained
result in repeat behavior. The cognitive perspective assumes that learning derives from
an unresolved problem.
Information about a destination that is processed and stored can be separated into
cognitive (evaluation of the product’s attributes) and affective components
(motivations that affect the underlying desires about the destination). The learning
process associated to product knowledge refers to the behavioral variations that can
occur as a result of internal and external stimuli. This can develop from previous
experience, recommendations from family and friends, publicity and promotion, word
of mouth and so on. These stimuli serve to ?rst activate the individual’s needs
and motivations, which shape decision making and help the consumer tourist
(in?uenced by personality and psycho-sociological characteristics) build images for
each alternative destination.
Studies on tourist learning processes and the impact of information sources in
portraying the destination’s image have addressed some of the stimuli that in?uence
the cognitive process. Beesley (2005) proposes a model that relates communication,
individual cognition, social contingencies, affection, and values in order to understand
the dynamic process of learning. Fisher (2004) argues that the learning process can
identify four types of tourist behavior: exact imitation, accidental inexact imitation,
and social learning. Walmsley and Jenkins (1992) present a methodology for
understanding the learning process based on cognitive mapping. Here, tourists rapidly
develop cognitive images of destinations in?uencing cognitive maps, both in the
immediate sense (time spent in destination) and in the more general sense (lifestyle to
which the tourist is accustomed). Guy et al. (1990) point out that previous experience
and the tourist’s direct and indirect sources of information are antecedent factors for
?rst time visitors. Money and Crotts (2003) show that consumers from national
cultures, characterized by higher levels of uncertainty, prefer information sources such
as travel agencies rather than personal, destination marketing related, or mass media
sources. Fodness and Murray (1997) demonstrate that the information research is the
result of a number of situational, tourist and marketplace contingencies. Jamrozy et al.
(1996) prove that highly involved travelers tend to be more receptive to information
concerning the travel product or destination, and disseminate information willingly.
Perception building towards destinations is part of the learning process.
Perception construct
Previous researchers have de?ned perceptions as the perceived value of a product
(Correia and Crouch, 2004; Correia et al., 2007c; Holbrook, 1996; Oh, 2000; Sheth et al.,
1991; Zeithaml, 1988). This concept develops from cognitive and behavioral
perspectives, resulting from the learning and motivational processes rendered by the
tourist. Past research on tourist motivation has shown that affective factors play a
critical role in the tourist travel selection and evaluation (Fodness, 1994).
IJCTHR
2,4
338
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
:
0
6
2
4
J
a
n
u
a
r
y
2
0
1
6
(
P
T
)
Internal and external motivations to travel lead to different perceptions about the
destination. This assumption taken from Baloglu (1997), Baloglu and McCleary (1999),
Correia et al. (2007c), Dann (1996) and Gartner (1993) shows that perceptions are a
function of internal motivations (push motivations) and external motivations (pull
motivations). This occurs because perception is a process whereby consumers select,
organize, and interpret stimuli into meaningful and coherent information, varying
according to the attributes of the product. Consumer perception depends on how the
tourist perceives the characteristics of a product on an individual basis and not
necessarily on their true attributes (Dann, 1981; Pearce, 1982). Selective perception
includes selective exposure, selective attention, perceptual defense, and perceptual
blockage. Consumers aware of their needs and desires, block out unnecessary,
displeasing or painful stimuli.
Perception encompasses two concepts that form part of the learning process:
cognitive and emotional concepts (Gnoth, 1997). Cognitive perception ensues from the
evaluation of destination attributes while emotional perception represents how the
individual actually perceives the destination.
This paper suggests that resulting perceptions follow from cognitive and emotional
evaluation (Otto, 1997; Otto and Ritchie, 1995). Both cognitive and emotional measures
are necessary for perception modeling when evaluating destinations. The study
throughout de?nes perceptions in terms of the overall performance of the product
(destination) from the consumer perspective. These perceptions represent preferential
levels that lead the tourist to purchase a particular destination. Kimand Yoon (2003) and
Vogt and Andereck (2003) discuss perception building, froma conceptual viewpoint and
use structural models (SEM) to analyze how emotions and cognitions can in?uence
perceptions on tourist destinations. Seddighi and Theocharous (2002) use a conditional
logit model to measure the perceptions/feelings about the characteristics of tourist
destinations. This methodology predicts the probability of revisiting the destination.
Murphy et al. (2000) de?ne a structural model that relates return intentions (as proxy of
satisfaction/quality) to destination perceptions. Driscoll et al. (1994) test the consistency
of two semantic differential scales to measure perceptions and discover that resulting
perceptions differ depending on the use of different data collection formats.
Satisfaction construct
Since each individual measures satisfaction differently, de?nitions vary, though most
consider the comparison between expectations and experience (Woodside et al., 1989).
Bultena and Klessig (1969, p. 349) state that a satisfactory experience “is a function of the
degree of congruency between aspirations and the perceived reality of experiences.”
From a purely cognitive perspective, Hunt (1977, p. 459) states that “satisfaction is not
the pleasurableness of the experience, it is the evaluation rendered that the experience
was at least as good as it was supposed to be.” Others, such as LaTour and Peat (1979),
argue that satisfaction is nothing more than the brand’s attitude.
Recent studies examine the in?uence of affective reactions to consumption
experiences on post-purchase satisfaction responses (Barsky, 1992; Madrigal, 1995;
Oliver, 1993; Spreng et al., 1996). The assumption is that tourist satisfaction is a
function of the product’s performance, perception and motivations. Satisfaction
increases as the performance/perception ratio increases (Moutinho, 1982), the basis
being the quality of the outcome experience in relation to those anticipated and desired.
Decision-making
processes
339
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
:
0
6
2
4
J
a
n
u
a
r
y
2
0
1
6
(
P
T
)
Dissatisfaction is measured in terms of to the degree of disparity between expectation
and the product’s performance. This concept of satisfaction, based on expectations has
been widely criticized. Arnould and Price (1993) suggest that satisfaction often appears
to be associated with surprise, while Miller (1977) proposes that satisfaction can take
on different forms: desirable, ideal, or tolerable. Research on the tourist perceptions and
motivations shows correlation between the level of holiday satisfaction and
motivation. Truong (2005) states that the attractiveness of a destination is
associated with its capacity to satisfy tourist needs and motivations. The product
(or destination) consists of a group of factors and tangible attractions that the
individual deems appropriate to satisfy his or her explicit and implicit desires.
Post-purchase behavior measuring attributes and destination’s capacity to satisfy the
individual’s intrinsic motivation is natural. From this assumption the push and pull
concepts in terms of satisfaction are adaptable to include push and pull motivations,
allowing us to measure the tangible and intangible components of post-purchase
purchase. To measure such components, developing and administering two
questionnaires was necessary.
The ?rst questionnaire distributed and collected prior to contact with destination,
gathers data such as destination information sources, destination perceptions, and
intrinsic (push) and extrinsic (pull) motivations. The second questionnaire distributed
and collected after contact with destination set out to measure the disparity between
tourist motivations and satisfactions after the holiday period, as well as future
behavioral intentions.
Traditionally, measuring the concept of satisfaction is by product logic rather than
by consumer logic. Therefore, tools used to measure displacement between the
anticipated and the actual product obtained at the various levels of tourist destination,
form part of the approach (Parasuraman et al., 1985; Moutinho, 1987; Kozak and
Rimmington, 2000).
According to Barsky and Labagh (1992), the consumer satisfaction analysis was an
important challenge in the 1990s. To identify how the components of a product or
service affect consumers represents the possibility of maximizing consumer
satisfaction (Petrick et al., 1999). While there are no guarantees that a satis?ed
consumer will repeat his or her visit, an unsatis?ed consumer will generally not return
(Dube et al., 1994). These studies suggest that if an experience has a positive effect on
an individual, then he or she is more likely to repeat the experience. This means that
satisfaction in?uences behavioral intentions (Oppermann, 2000).
Behavioral intention construct
The intention to purchase is a function of the attitude towards behavioral and social
norms. Expectations affect attitude; expectations include, the possibility of adopting
certain behavior and the evaluation of how the consumer feels about engaging in the
behavior (Fishbein and Ajzen, 1980).
Lam and Hsu (2006), whose work develops from reasoned action theory (Fishbein
and Ajzen, 1980), show that attitude, perceived behavior and past behavior are related
to the behavioral intention of choosing a destination. Similarly, Bigne´ et al. (2001) treat
return intentions as a proxy of satisfaction/quality based on perceptions and
destination image through a structural model. The results of this study show that
tourism image is a direct antecedent of perceived quality, satisfaction, intention to
IJCTHR
2,4
340
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
:
0
6
2
4
J
a
n
u
a
r
y
2
0
1
6
(
P
T
)
return and willingness to recommend the destination. With reference to the other
relationships, ?ndings show that quality has a positive in?uence on satisfaction
and return intentions, and that satisfaction in?uences the willingness to recommend
the destination.
Kozak (2001) shows that overall satisfaction and the number of previous visits
considerably in?uences return intention rates, especially in mature destinations. Baker
and Crompton (2000) test a structural model to show that perceived performance
quality has a stronger total effect on behavioral intentions than that of satisfaction.
Mazursky (1989) uses latent variables to explain the construction of future behavioral
intentions in terms of norms and measures from experience. Correia et al. (2007a)
examine a random parameter logit model to analyze which characteristics (e.g.
individual characteristics, motivations, tripographic variables and common attributes
of golf destinations) are associated with the probability of golf tourists returning to
Algarve, Portugal. According to the literature, the variables that in?uence future
behavior are motivations, information sources, perceptions, and satisfaction.
Theoretical model and hypotheses
Figure 1 shows the hypothetical causal model. The adopted constructs of the model,
taken from the literature, looks to more thoroughly de?ne consumer behavior, that is, a
sequential dynamic and organized process whereby diverse factors compete along side
each other towards a decision.
Previous studies show that behavioral intentions are the result of the post-purchase
evaluation stage (Dick and Basu, 1994; Oliver, 1999; Yoon and Uysal, 2005). Other
authors showthat post-purchase evaluationdepends ontourist motivation (Correia et al.,
2007a, c; Mannell and Iso-Ahola, 1987; Ross and Iso-Ahola, 1991; Silvestre and Correia,
2005). Woodside and Lysonski (1989) discuss that personal and material information
tends to activate consumer needs and, therefore, serve as a motivational element.
The consumer in the perception building phase (in which the consumer seeks
information about the destination) also uses these sources. This model breaks motivations
down into two constructs: push motivations (internal forces) and pull motivation(external
forces), (Baloglu and McCleary, 1999; Correia and Crouch, 2004; Correia et al., 2007c; Dann,
1977; Driscoll et al., 1994; Goosens, 2000; McCabe, 2000; Yoon and Uysal, 2005).
In order to categorize motivations into pull and push, consider that motivations are
satis?ed by the end of their vacation; tourists are able to evaluate each motivation as a
component of their overall satisfaction. Therefore, this model also separates
satisfaction into two constructs: push satisfaction and pull satisfaction.
Subsequently, the model examines the structural causal relationships among the
information sources, push and pull motivations, perceptions, push and pull satisfactions,
and behavioral intentions. The model contributes in identifying relationships between the
above-mentioned constructs, and the new concept of satisfaction.
The model assumes the tourist has already decided to go on holiday abroad, and
decided on the duration of the stay and budget spending. Given that there is no choice
in this framework, the tourist consumption behavior can be analyzed in two stages: the
pre-decision stage and post-purchase stage. These stages are crucial in explaining
tourist consumption behavior, since during these periods the most relevant constructs
within purchase processing develop: sources of information, motivations, perceptions,
satisfactions, and behavioral intentions.
Decision-making
processes
341
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
:
0
6
2
4
J
a
n
u
a
r
y
2
0
1
6
(
P
T
)
Figure 1.
Theoretical model of
consumer behavior
I
n
f
o
r
m
a
t
i
o
n
S
o
u
r
c
e
s
S
o
u
r
c
e
:
O
w
n
e
l
a
b
o
r
a
t
i
o
n
P
u
s
h
M
o
t
i
v
e
s
P
u
s
h
M
2
P
u
s
h
M
1
P
u
s
h
M
n
H
1
H
6
H
7
H
5
H
8
H
4
H
9
P
r
o
m
o
t
i
o
n
s
B
r
o
c
h
u
r
e
s
N
e
w
s
M
o
v
i
e
s
M
a
i
l
T
r
a
v
e
l
a
g
e
n
c
y
P
e
r
c
e
p
t
i
o
n
s
…
H
3
H
2
H
1
7
P
u
l
l
M
2
P
u
l
l
M
1
P
u
l
l
M
n
…
P
u
s
h
M
o
t
i
v
e
s
P
u
l
l
S
a
t
i
s
f
a
c
t
i
o
n
P
u
l
l
S
2
P
u
l
l
S
1
P
u
l
l
S
n
H
1
2
H
1
5
H
1
6
H
1
3
H
1
4
…
H
1
1
H
1
0
P
u
s
h
S
2
P
u
s
h
S
1
P
u
s
h
S
n
B
e
h
a
v
i
o
u
r
I
n
t
e
n
t
i
o
n
P
u
l
l
S
a
t
i
s
f
a
c
t
i
o
n
R
e
c
o
m
e
n
d
a
t
i
o
n
R
e
t
u
r
n
IJCTHR
2,4
342
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
:
0
6
2
4
J
a
n
u
a
r
y
2
0
1
6
(
P
T
)
H
1
. A tourist uses a set of information sources. Baloglu and McCleary
(1999) establish that tourists use various sources of information to
gain a complete picture of understanding of the destination. Fakeye
and Crompton (1991), Gunn (1972) and Um and Crompton (1990)
identify the importance of information sources (such as promotional
material and media, friends and relatives and word of mouth) in the
decision-making process. The search for information makes use of
four basic types of sources: neutral (tourism of?ces); commercial
(travel agents); social (friends and relatives, family); and promotional
(newspapers, magazines, radio, television, internet), as stated by
Bargeman and Poel (2006), Crotts (1999) and Moutinho (1982).
H
2
. The information sources activate push motivations in the tourist’s
mind. Um and Crompton (1990) show that information sources help
to form either a cognitive image or an affective image. The
understanding of tourism as a product requires higher decision
processing whereby various sources of information serve towards
reaching a decision (Woodside and Lysonski, 1989). Money and
Crotts (2003) show that consumers from national cultures are
characterized by higher levels of uncertainty avoidance and use
information sources to channel preference (e.g. travel agent).
Crompton (1979) and Kotler et al. (1993) suggest that motivation is
a result of the need for social acceptance and stimulated from
publicity and promotion. Publicity and promotion are one of the
most important sources for tourists.
H
3
. Information sources activate pull motivations on the tourist’s mind.
Gartner (1993), Holbrook (1978) and Woodside and Lysonski (1989)
state that information sources are considered as forces in?uencing
pull motivations.
H
4
. A set of internal forces in?uence push motivations. Dann (1977)
classi?es push motivations that in?uence the vacation as internal
factors: loneliness, getting away from it all, and social recognition.
On the other hand, Crompton (1990) refers to motivations that lead to
the desire to travel, an escape from the daily routine, relaxation,
prestige, regression, and social interaction.
H
5
. A set of external forces in?uence pull motivations. Uysal and Hagan
(1993) state that, generally, pull factors relate to the attributes of the
tourist destination. On the other hand, Crompton (1990) states that
pull motivations are those that in?uence the choice of the location.
Factors such as landscape, hospitality, lodging, price, heritage
interests, gastronomy, sports, nightlife, shopping, and accessibility
are examples of pull motivations.
H
6
. The expectation is that push motivations in?uence pull motivations
positively. Correia et al. (2007c) show that when the tourist has
internal motivations (push), they are more likely to perceive pull
motivations. Although most authors accept that cognitive and
Decision-making
processes
343
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
:
0
6
2
4
J
a
n
u
a
r
y
2
0
1
6
(
P
T
)
affective images are related (Baloglu and McCleary, 1999; Gunn, 1972;
Um and Crompton, 1990; Yoon and Uysal, 2005), no empirical
evidence support this relationship. Crompton (1979) argues that, in
practice, all human behaviors is motivated; however, the choices made
in order to satisfy these motivations may depend on other
psychological variables. When individuals decide to travel for
recreational purposes, they do so for several motivations and reasons,
all of which are interdependent.
H
7
. Perceptions of the destination are in?uenced by push motivations.
Perceptions are a cognitive and behavioral measure of the value of the
tourism destination (Baloglu and McCleary, 1999; Morrison, 1989),
since they are formed in a consciously, unconscious manner.
Perceptions as a behavioral cognitive measure are expected to develop
according to the emotional state of the tourist (motivations) (Correia
et al., 2007c; Crompton, 1979; Dann, 1996; Gartner, 1993; Murphy et al.,
2000; Woodside and Lysonski, 1989). Beerli and Mart? ´n (2004) added
the concept of affective perception of the destination.
H
8
. Perceptions of the destination are in?uenced by pull motivations.
According to Gnoth (1997), measuring perceptions as a cognitive
component suggests that the tourist evaluates and perceives the
destination’s attributes. On the other hand, measurement of perceptions
by a personal component means that the perceptionof a destination is as
the tourist intends it to be. In fact, perceptions can be different from the
true attributes of the product depending on how the individual receives
and processes the information, as explained by tourist motivations
about the destination’s attributes (Dann, 1981; Pearce, 1982).
H
9
. Information sources interact to form perceptions. According to
Mazursky (1989), perception building derives from the information
obtainedbeforehandused to help the consumer inhis or her assessment
of alternative destinations (UmandCrompton, 1990).UmandCrompton
(1990) argue that beliefs and expectations about destination attributes
are constructed by individuals according to the information received.
Burgess (1978) states that information sources in?uence the image of
the destination. Baloglu and McCleary (1999), Gartner (1993) and Gunn
(1972) stress the importance of perceptions building.
H
10
and H
11
. Different perceptions lead to different levels of push(H
10
) andpull (H
11
)
satisfactions. According to the motivation theory already mentioned, it
could be argued that the tourist on holiday looks for rewards:
psychological rewards (relaxation, rest, and refreshment), social
rewards (recognition and prestige) (Gnoth, 1997); and economic
rewards (measured by the perceived value of the destination). Baloglu
and McCleary (1999), Gartner (1993), Gnoth (1997) and Petrick (2002)
amongothers, have statedthat the tourist has anaffective andcognitive
image of the destination since perception are expectedto in?uence push
and pull satisfactions.
IJCTHR
2,4
344
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
:
0
6
2
4
J
a
n
u
a
r
y
2
0
1
6
(
P
T
)
H
12
. A set of internal rewards affect push satisfaction. Push satisfaction
occurs when the tourist has a feeling of psychological and social
ful?llment.
H
13
. The perceived value of the destination affects pull satisfaction.
Perceived value is the global evaluation of the use of a product or
service based on the perceptions of what you get versus what you give
(Zeithaml, 1988). Furthermore, higher levels of perceived value likely
results in purchase and in higher levels of consumer satisfaction
(Bojanic, 1996).
H
14
. Pull satisfaction in?uences push satisfaction. Through a perceived
performance model, Tse andWilton (1988) holdthat tourist satisfaction
depends on motivations, expectations, and performance.
H
15
and H
16
. The level of push (H
15
) and pull (H
16
) satisfactions affects future
behavioral intentions. LaTour and Peat (1979), Levitt (1981) and
Whipple and Thatch (1988) state that the evaluation of the product’s
attributes could be crucial in determining behavioral intentions.
Madrigal (1995) and Oliver (1993) argue that if an experience has a
positive effect on the tourist, he or she is more likely to return. Festinger
(1954) argues that destination satisfaction in?uences future choices.
Beerli and Mart? ´n (2004) show that sun and beach destinations
achieving positive image feedback also achieve high levels of repeat
visits.
H
17
. Behavioral intentions are explainable throughprobabilityof returnand
intentions to recommend. Behavioral intentions that represent the
willingness to return and the intention to recommend are the two likely
outcomes of future behavior (Yoon and Uysal, 2005).
Method
Survey
The questionnaires employed both open and closed questions in order to evaluate the
sources of information, motivations, perceptions, the level of post-purchase satisfaction
and future behavioral intentions using seven-point Likert scales, as suggested by Maio
and Olson (1994). Passengers were invited to participate in the survey by completing
questionnaires during the ?ight, and just before arriving at the airport. Data collection
took place between July and September 2004, as this time period is when the majority
of Portuguese citizens go on holiday. A pre-test of the questionnaires used a sample of
150 passengers on departure and on return arrival. This pre-test enhanced the validity
and reliability of the questionnaires. After this pre-test, minor amendments were made
to avoid eventual logic and/or question perception errors.
The ?rst questionnaire began with the tourist’s personal characteristics (age, sex,
marital status, profession, education, and nationality). The logistics and travel experience
section looked to analyse vacation budgets, frequency of travel, time restrictions, average
length of stay, number of people traveling together, type of vacation lodging, booking
means and previous experience. Next, the study analyzes learning processes, motivations
andperceptions. Onlymeasuringthe level of importance for eachof the alternative sources
Decision-making
processes
345
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
:
0
6
2
4
J
a
n
u
a
r
y
2
0
1
6
(
P
T
)
of information in the purchase decision was possible. The information used came from
sevensources: travel agencies, brochures and guidebooks, friends andfamily, advertising,
books and ?lms, articles and news, movies, and direct mail. Rojek (1990) discusses the role
of advertising and television in explaining consumer behavior. Scho?eld (1996) argues
that the consumer actually buys signals and images rather than products. Goosens (2000)
de?nes involvement as the state that de?nes the interests andmotivations of the consumer
about the product, and explains the way in which he or she looks for more information in
order to learn about the product. According to Goosens, the tourist perceives the value of a
destination according to the marketing stimuli supplied in magazines, brochures and
publicity, among others. His model looks to explain how tourists perceive visual and
external information. Woodside and Dubelaar (2002) de?ne the theory of tourism
consumption systems as the inter-relationship between different sets of variables inwhich
the role of web advertising, information guides, marketing and advertisements play a
central role in explaining consumer decision. The model focuses on the role of information
sources to explain motivations, perceptions and the post-purchase evaluation. Fodness
and Murray (1999) conclude that tourists use different sources of information to plan their
vacations. The main information sources used by tourists were brochures, guidebooks,
friends and relatives, magazines, newspapers, previous experience, travel agents sources
as well as the internet and direct mail.
The second question centered on 21 issues that re?ect the main motivational factors
identi?ed in the literature (Correia et al., 2007b, c; Fodness, 1994; Iso-Ahola and Mannel,
1987; Lundberg, 1990; Mohsin and Ryan, 2003; Silvestre and Correia, 2005; Shoemaker,
1989; Uysal et al., 1996) The motivations considered are shown in Table I.
Under the same heading, the model established the destination perceptions as
shown in Table I. This construct follows Baloglu and McCleary (1999) and Correia et al.
(2007c) who assume that perceptions are a function of external and internal
motivations.
The distribution of the second questionnaire occurred after the holiday experience,
on the return ?ight. The questionnaire consists of a ?rst set of questions about the
tourist, a second set of questions relating to behavioral intentions and a third set on
satisfaction.
The behavioral intention is evident fromthe tourist’s level of satisfaction and assessed
in terms of the probability of returning and recommending the destination to friends
and family (Moutinho, 1987; Baker and Crompton, 1998). With these questions, return and
recommendation probability can be veri?ed. Behavioral intentions represent concepts
related reasoned action theories (Fishbein and Ajzen, 1980), which assume that intentions
can explain consumer behavior. Similar application and testing has only been carried out
by Baker and Crompton (1998) and Cronin et al. (1992). Backman and Crompton (1991)
argue that de?nitionof loyaltyis relatednot onlyto behavioral intentions but withattitude
as well. Learning a tourist’s attitude towards the destination is necessary ?rst before
learning his or her loyalty. In achieving such learning, explaining associating behavioral
intentions is possible. The 21 attributes measure satisfaction, important in determining
the decision process, and earlier de?ned as pull satisfaction.
According to Spreng et al. (1996), satisfaction is an emotional state directed towards
a product or service. This de?nition goes beyond the traditional concept of
con?rmation/discon?rmation, widely de?ned as quality or performance of a product
(Oliver, 1980; Parasuraman, 2000). It follows psychological theories (Oliver, 1980)
IJCTHR
2,4
346
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
:
0
6
2
4
J
a
n
u
a
r
y
2
0
1
6
(
P
T
)
P
e
r
c
e
i
v
e
d
v
a
r
i
a
b
l
e
s
Q
u
e
s
t
i
o
n
s
R
e
s
p
o
n
s
e
c
a
t
e
g
o
r
i
e
s
I
n
f
o
r
m
a
t
i
o
n
s
o
u
r
c
e
s
c
o
n
s
t
r
u
c
t
s
T
r
a
v
e
l
a
g
e
n
c
y
;
b
r
o
c
h
u
r
e
s
;
f
a
m
i
l
y
;
p
r
o
m
o
t
i
o
n
;
m
o
v
i
e
s
;
n
e
w
s
;
m
a
i
l
H
o
w
i
m
p
o
r
t
a
n
t
i
s
e
a
c
h
s
o
u
r
c
e
o
f
i
n
f
o
r
m
a
t
i
o
n
w
h
e
n
c
h
o
o
s
i
n
g
a
d
e
s
t
i
n
a
t
i
o
n
?
1
–
n
o
t
i
m
p
o
r
t
a
n
t
;
2
–
n
o
t
v
e
r
y
i
m
p
o
r
t
a
n
t
;
3
–
o
f
v
e
r
y
l
i
t
t
l
e
i
m
p
o
r
t
a
n
c
e
;
4
–
i
m
p
o
r
t
a
n
t
;
5
–
m
o
r
e
t
h
a
n
i
m
p
o
r
t
a
n
t
;
6
–
v
e
r
y
i
m
p
o
r
t
a
n
t
;
7
–
e
x
t
r
e
m
e
l
y
i
m
p
o
r
t
a
n
t
P
u
l
l
m
o
t
i
v
e
s
c
o
n
s
t
r
u
c
t
s
G
a
s
t
r
o
n
o
m
y
;
s
o
c
i
a
l
e
n
v
i
r
o
n
m
e
n
t
;
a
c
c
e
s
s
i
b
i
l
i
t
i
e
s
;
r
e
l
a
x
i
n
g
a
t
m
o
s
p
h
e
r
e
;
s
e
c
u
r
i
t
y
;
w
e
a
t
h
e
r
;
i
n
f
o
r
m
a
t
i
o
n
;
l
a
n
d
s
c
a
p
e
;
n
a
t
u
r
a
l
e
n
v
i
r
o
n
m
e
n
t
;
c
u
l
t
u
r
a
l
a
t
t
r
a
c
t
i
o
n
s
;
s
h
o
p
p
i
n
g
f
a
c
i
l
i
t
i
e
s
;
n
i
g
h
t
-
l
i
f
e
;
s
p
o
r
t
s
e
q
u
i
p
m
e
n
t
;
t
r
a
n
s
p
o
r
t
s
;
a
c
c
o
m
m
o
d
a
t
i
o
n
s
;
b
e
a
c
h
;
h
o
s
p
i
t
a
l
i
t
y
;
e
x
o
t
i
c
n
e
s
s
;
e
t
h
n
i
c
i
t
i
e
s
;
l
i
f
e
s
t
y
l
e
s
;
d
i
s
t
a
n
c
e
H
o
w
i
m
p
o
r
t
a
n
t
i
s
e
a
c
h
o
f
t
h
e
f
a
c
t
o
r
s
w
h
e
n
c
h
o
o
s
i
n
g
a
d
e
s
t
i
n
a
t
i
o
n
?
1
–
n
o
t
i
m
p
o
r
t
a
n
t
;
2
–
n
o
t
v
e
r
y
i
m
p
o
r
t
a
n
t
;
3
–
o
f
v
e
r
y
l
i
t
t
l
e
i
m
p
o
r
t
a
n
c
e
;
4
–
i
m
p
o
r
t
a
n
t
;
5
–
m
o
r
e
t
h
a
n
i
m
p
o
r
t
a
n
t
;
6
–
v
e
r
y
i
m
p
o
r
t
a
n
t
;
7
–
e
x
t
r
e
m
e
l
y
i
m
p
o
r
t
a
n
t
P
u
s
h
m
o
t
i
v
e
s
c
o
n
s
t
r
u
c
t
s
E
x
p
e
r
i
e
n
c
i
n
g
d
i
f
f
e
r
e
n
t
c
u
l
t
u
r
e
s
a
n
d
l
i
f
e
s
t
y
l
e
s
;
i
n
c
r
e
a
s
i
n
g
k
n
o
w
l
e
d
g
e
;
e
n
r
i
c
h
i
n
g
m
y
s
e
l
f
i
n
t
e
l
l
e
c
t
u
a
l
l
y
;
v
i
s
i
t
i
n
g
n
e
w
p
l
a
c
e
s
;
a
m
u
s
e
m
e
n
t
;
g
o
i
n
g
p
l
a
c
e
s
m
y
f
r
i
e
n
d
s
h
a
v
e
n
o
t
b
e
e
n
;
t
e
l
l
i
n
g
m
y
f
r
i
e
n
d
s
a
b
o
u
t
t
h
e
v
a
c
a
t
i
o
n
;
d
e
v
e
l
o
p
i
n
g
c
l
o
s
e
f
r
i
e
n
d
s
h
i
p
s
;
r
e
l
i
e
v
i
n
g
s
t
r
e
s
s
;
e
s
c
a
p
i
n
g
f
r
o
m
r
o
u
t
i
n
e
;
p
h
y
s
i
c
a
l
r
e
l
a
x
a
t
i
o
n
;
g
e
t
t
i
n
g
a
w
a
y
f
r
o
m
c
r
o
w
d
s
;
m
e
e
t
i
n
g
i
n
t
e
r
e
s
t
i
n
g
p
e
o
p
l
e
;
d
o
i
n
g
d
i
f
f
e
r
e
n
t
t
h
i
n
g
s
;
s
t
i
m
u
l
a
t
i
n
g
e
m
o
t
i
o
n
s
a
n
d
s
e
n
s
a
t
i
o
n
s
;
b
e
i
n
g
a
n
a
d
v
e
n
t
u
r
e
r
H
o
w
i
m
p
o
r
t
a
n
t
i
s
e
a
c
h
o
f
t
h
e
f
a
c
t
o
r
s
w
h
e
n
c
h
o
o
s
i
n
g
a
d
e
s
t
i
n
a
t
i
o
n
?
1
–
n
o
t
i
m
p
o
r
t
a
n
t
;
2
–
n
o
t
v
e
r
y
i
m
p
o
r
t
a
n
t
;
3
–
o
f
v
e
r
y
l
i
t
t
l
e
i
m
p
o
r
t
a
n
c
e
;
4
–
i
m
p
o
r
t
a
n
t
;
5
–
m
o
r
e
t
h
a
n
i
m
p
o
r
t
a
n
t
;
6
–
v
e
r
y
i
m
p
o
r
t
a
n
t
;
7
–
e
x
t
r
e
m
e
l
y
i
m
p
o
r
t
a
n
t
(
c
o
n
t
i
n
u
e
d
)
Table I.
Questionnaire’s structure
and the formation of the
constructs
Decision-making
processes
347
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
:
0
6
2
4
J
a
n
u
a
r
y
2
0
1
6
(
P
T
)
P
e
r
c
e
i
v
e
d
v
a
r
i
a
b
l
e
s
Q
u
e
s
t
i
o
n
s
R
e
s
p
o
n
s
e
c
a
t
e
g
o
r
i
e
s
P
e
r
c
e
p
t
i
o
n
s
P
e
r
c
e
p
t
i
o
n
s
W
h
a
t
a
r
e
y
o
u
r
p
e
r
c
e
p
t
i
o
n
s
r
e
g
a
r
d
i
n
g
t
h
e
d
e
s
t
i
n
a
t
i
o
n
?
1
–
v
e
r
y
l
o
w
;
2
–
l
o
w
;
3
–
q
u
i
t
e
l
o
w
;
4
–
a
v
e
r
a
g
e
;
5
–
q
u
i
t
e
h
i
g
h
;
6
–
h
i
g
h
;
7
–
v
e
r
y
h
i
g
h
P
u
l
l
s
a
t
i
s
f
a
c
t
i
o
n
c
o
n
s
t
r
u
c
t
s
G
a
s
t
r
o
n
o
m
y
;
s
o
c
i
a
l
e
n
v
i
r
o
n
m
e
n
t
;
a
c
c
e
s
s
i
b
i
l
i
t
i
e
s
;
r
e
l
a
x
i
n
g
a
t
m
o
s
p
h
e
r
e
;
s
e
c
u
r
i
t
y
;
w
e
a
t
h
e
r
;
i
n
f
o
r
m
a
t
i
o
n
;
l
a
n
d
s
c
a
p
e
;
n
a
t
u
r
a
l
e
n
v
i
r
o
n
m
e
n
t
;
c
u
l
t
u
r
a
l
a
t
t
r
a
c
t
i
o
n
s
;
s
h
o
p
p
i
n
g
f
a
c
i
l
i
t
i
e
s
;
n
i
g
h
t
-
l
i
f
e
;
s
p
o
r
t
s
e
q
u
i
p
m
e
n
t
;
t
r
a
n
s
p
o
r
t
s
;
a
c
c
o
m
m
o
d
a
t
i
o
n
s
;
b
e
a
c
h
;
h
o
s
p
i
t
a
l
i
t
y
;
e
x
o
t
i
c
n
e
s
s
;
e
t
h
n
i
c
i
t
i
e
s
;
l
i
f
e
s
t
y
l
e
s
;
d
i
s
t
a
n
c
e
H
o
w
w
o
u
l
d
y
o
u
c
l
a
s
s
i
f
y
y
o
u
r
l
e
v
e
l
o
f
s
a
t
i
s
f
a
c
t
i
o
n
r
e
g
a
r
d
i
n
g
t
h
e
f
o
l
l
o
w
i
n
g
f
a
c
t
o
r
s
?
1
–
w
o
r
s
e
t
h
a
n
I
e
x
p
e
c
t
e
d
;
2
–
l
o
w
e
r
t
h
a
n
I
e
x
p
e
c
t
e
d
;
3
–
b
e
l
o
w
a
v
e
r
a
g
e
t
h
a
n
I
e
x
p
e
c
t
e
d
;
4
–
a
s
e
x
p
e
c
t
e
d
;
5
–
a
b
o
v
e
w
h
a
t
I
e
x
p
e
c
t
e
d
;
6
–
b
e
t
t
e
r
t
h
a
n
I
e
x
p
e
c
t
e
d
;
7
–
s
u
r
p
a
s
s
e
d
m
y
e
x
p
e
c
t
a
t
i
o
n
s
P
u
s
h
s
a
t
i
s
f
a
c
t
i
o
n
c
o
n
s
t
r
u
c
t
s
E
x
p
e
r
i
e
n
c
i
n
g
d
i
f
f
e
r
e
n
t
c
u
l
t
u
r
e
s
a
n
d
l
i
f
e
s
t
y
l
e
s
;
i
n
c
r
e
a
s
i
n
g
k
n
o
w
l
e
d
g
e
;
e
n
r
i
c
h
i
n
g
m
y
s
e
l
f
i
n
t
e
l
l
e
c
t
u
a
l
l
y
;
v
i
s
i
t
i
n
g
n
e
w
p
l
a
c
e
s
;
a
m
u
s
e
m
e
n
t
;
g
o
i
n
g
p
l
a
c
e
s
m
y
f
r
i
e
n
d
s
h
a
v
e
n
o
t
b
e
e
n
;
t
e
l
l
i
n
g
m
y
f
r
i
e
n
d
s
a
b
o
u
t
t
h
e
v
a
c
a
t
i
o
n
;
d
e
v
e
l
o
p
i
n
g
c
l
o
s
e
f
r
i
e
n
d
s
h
i
p
s
;
r
e
l
i
e
v
i
n
g
s
t
r
e
s
s
;
e
s
c
a
p
i
n
g
f
r
o
m
r
o
u
t
i
n
e
;
p
h
y
s
i
c
a
l
r
e
l
a
x
a
t
i
o
n
;
g
e
t
t
i
n
g
a
w
a
y
f
r
o
m
c
r
o
w
d
s
;
m
e
e
t
i
n
g
i
n
t
e
r
e
s
t
i
n
g
p
e
o
p
l
e
;
d
o
i
n
g
d
i
f
f
e
r
e
n
t
t
h
i
n
g
s
;
s
t
i
m
u
l
a
t
i
n
g
e
m
o
t
i
o
n
s
a
n
d
s
e
n
s
a
t
i
o
n
s
;
b
e
i
n
g
a
n
a
d
v
e
n
t
u
r
e
r
H
o
w
w
o
u
l
d
y
o
u
c
l
a
s
s
i
f
y
y
o
u
r
l
e
v
e
l
o
f
s
a
t
i
s
f
a
c
t
i
o
n
r
e
g
a
r
d
i
n
g
t
h
e
f
o
l
l
o
w
i
n
g
f
a
c
t
o
r
s
?
1
–
w
o
r
s
e
t
h
a
n
I
e
x
p
e
c
t
e
d
;
2
–
l
o
w
e
r
t
h
a
n
I
e
x
p
e
c
t
e
d
;
3
–
b
e
l
o
w
a
v
e
r
a
g
e
t
h
a
n
I
e
x
p
e
c
t
e
d
;
4
–
a
s
e
x
p
e
c
t
e
d
;
5
–
a
b
o
v
e
w
h
a
t
I
e
x
p
e
c
t
e
d
;
6
–
b
e
t
t
e
r
t
h
a
n
I
e
x
p
e
c
t
e
d
;
7
–
s
u
r
p
a
s
s
e
d
m
y
e
x
p
e
c
t
a
t
i
o
n
s
B
e
h
a
v
i
o
r
a
l
i
n
t
e
n
t
i
o
n
c
o
n
s
t
r
u
c
t
s
R
e
t
u
r
n
D
o
y
o
u
i
n
t
e
n
d
t
o
v
i
s
i
t
t
h
i
s
d
e
s
t
i
n
a
t
i
o
n
a
g
a
i
n
?
1
–
n
e
v
e
r
a
g
a
i
n
;
2
–
a
b
s
o
l
u
t
e
l
y
n
o
t
;
3
–
n
o
;
4
–
p
r
o
b
a
b
l
y
;
5
–
v
e
r
y
p
r
o
b
a
b
l
y
;
6
–
a
l
m
o
s
t
c
e
r
t
a
i
n
l
y
;
7
–
c
e
r
t
a
i
n
l
y
R
e
c
o
m
m
e
n
d
D
o
y
o
u
i
n
t
e
n
d
t
o
r
e
c
o
m
m
e
n
d
t
h
i
s
d
e
s
t
i
n
a
t
i
o
n
t
o
f
r
i
e
n
d
s
a
n
d
f
a
m
i
l
y
?
1
–
a
b
s
o
l
u
t
e
l
y
n
o
t
;
2
–
a
s
a
p
o
s
s
i
b
l
e
d
e
s
t
i
n
a
t
i
o
n
;
3
–
a
s
a
g
o
o
d
d
e
s
t
i
n
a
t
i
o
n
;
4
–
p
r
o
b
a
b
l
y
;
5
–
v
e
r
y
p
r
o
b
a
b
l
y
;
6
–
a
l
m
o
s
t
c
e
r
t
a
i
n
l
y
;
7
–
c
e
r
t
a
i
n
l
y
Table I.
IJCTHR
2,4
348
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
:
0
6
2
4
J
a
n
u
a
r
y
2
0
1
6
(
P
T
)
that consider overall satisfaction to be also a socio-psychological state of being that
leads the consumer to evaluate the product differently, according to his or her
emotional state. The model takes a different of view by establishing two separate levels
of satisfaction: pull satisfaction (in which level of achievement of internal motivations
is measured though the sense of emotional ful?llment), and cognitive satisfaction
(measured by the perceived quality of the destination attributes). Within this level,
internal satisfaction can be con?rmed or not using a seven-point Likert scale. This
evaluation takes into account the sixteen motivations that drive or push the tourist
towards the destination, referred to as push satisfaction. In terms of quality
performance, the 21 attributes of the destination are evaluated using the same seven
point Likert scale (Table I). This form of evaluating satisfaction is referred to as pull
satisfaction.
The questionnaires were individually checked and numbered. The descriptive and
multivariable statistical analyses were performedusingthe software SPSS14.0(SPSSInc.,
2005). The structural model was estimated using AMOS 6 (AMOS Inc., 2006). This
approach permits veri?cation of each of the questions and validate the importance of
variables when explaining tourist behavior of the tourist as a consumer. The study
explores the cause-effect relationships inthe decision process thoughperceptual mapping.
Data
In order to test the proposed hypotheses, the study includes using a strati?ed, random
sample of Air Luxor incoming and outgoing ?ight passengers. The ?rst of two
questionnaires looked to analyze the information sources used to learn about the
destination, the motivations that led to the choice of that destination, and perceptions
held.
The second questionnaire was issued on return ?ights in order to evaluate the level
of satisfaction from the experience obtained and behavioral intentions. Strati?cation of
the sample was done according to destination, using the airline’s database. Budgetary
restrictions and the limited time available allowed for only 1,097 questionnaires to be
collected. Questionnaires were distributed during ?ights to destinations such as Brazil,
Morocco, Egypt, the Dominican Republic, and Sao Tome and Principe, and assumed
two separate surveying periods. The 1,097 questionnaires distributed on departure
addressed perceptions and motivations, and the 1,091 questionnaires on return arrival
addressed quality and satisfaction levels of destination. Out of the 1,091 questionnaires
completed, 453 represented responses from the same outbound and inbound tourists.
Given the speci?city of the analysis, a response rate of 42 percent was obtained. Also,
questionnaires were not always distributed to passengers on the same planes or going
to the same destination both on departure and arrival. Nonetheless, these 453 Air
Luxor passengers, represented Portuguese tourists traveling to South America and
Africa destinations, since such people mostly travel on Air Luxor charter ?ights.
First, to the study includes performing a univariate descriptive analysis of the valid
cases by calculating summary measures (measures of central tendency, dispersion and
absolute and relative frequencies). The main goal of this preliminary analysis was to
characterize those surveyed socio-demographically and in terms of information
sources that conditioned their destination choice. The social classes were de?ned
according to Dubois (1993): high class (corporate executives, liberal professions,
high-salaried professionals), high medium (lower-paid professionals), and
Decision-making
processes
349
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
:
0
6
2
4
J
a
n
u
a
r
y
2
0
1
6
(
P
T
)
medium-lower (blue-collar workers and manual laborers). The characterization of the
sample is shown in Table II.
In terms of gender, 49 percent were male while 51 percent were female. The relative
balance between the two may suggest the predominance of family holidays. The
average age of tourists was 36 years, although the mode is 27 years of age. With a
below average purchasing power, the majority of the individuals surveyed held a
higher level of education, were married, and had no young children. Travel experience
and logistics are shown in Table III.
The majority of individuals questioned traveled once a year, with a budget of less
than e1,500 (73 percent). Without prior travel experience (84 percent), but having
obtained affordable prices at travel agencies (95 percent), a signi?cant number made
bookings less than one month in advance (76 percent). On average, the number of
family members who traveled together was 1.8, with an average length of stay of 9 days
in destinations such as Brazil (25 percent), Egypt (25 percent), and the Dominican
Republic (41 percent). The majority of people who answered the questionnaires stayed
at beach hotels (51 percent) or at resorts (31 percent) with full board (53 percent).
Data analysis
The main objective of this study is to test a structural model that allows for a
representation of a more encompassing decision-making process of the tourist. SEM
evaluates how well a conceptual model that contains observed and latent variables
explains and ?ts the data (Yoon and Uysal, 2005). Adopted in the study, this technique
also allows us to measure causal relationships among latent constructs, estimating the
amount of unexplained variance (Yoon and Uysal, 2005). This SEM analysis was
performed in two stages. Firstly, an EFA used as a preliminary technique to ?nd the
underlying dimensions or constructs in the data. This procedure, available on AMOS 6,
reduces data and identi?es the latent constructs that explain most of the variance of the
observed variables. The extraction method applied was the maximum-likelihood, an
interactive algorithm that produces parameter estimates based on an observed
Frequency (percent) Average SD
Gender
Masculine 48.6
Feminine 51.4
Average age 35.6 11.8
Social status
High 21.6
High-Medium 25.2
Medium-low 53.2
Marital status
Single 22.6
Married 58.9
With young children 18.5
Education
Primary school 27.4
Secondary school 24.7
Higher education 47.9
Table II.
Socio-demographic
characterization of the
sample
IJCTHR
2,4
350
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
:
0
6
2
4
J
a
n
u
a
r
y
2
0
1
6
(
P
T
)
correlation matrix. Correlation weighting employed the inverse of the uniqueness of the
variables. The method of factor rotation used was varimax, an orthogonal rotation
method that minimizes the number of observed variables with high loadings on each
latent construct, allowing for easier interpretation of the factors. The analysis includes
considering a latent root criterion of 1.0 for factor inclusion. To extract factors, the
cut-off of 0.5 was the criterion adopted. A subsequent con?rmatory factor analysis
(CFA) allowed for evaluation of the resulting scales. This analysis speci?es the
relationships of the observed variables to the latent constructs, and suggests that all
the constructs can be inter-correlated freely. Alpha (Cronbach, 1951) coef?cient
measures the reliability of the obtained factors, with independent analysis carried out
to con?rm the goodness-of-?t for each construct. Validation of the scales allowed the
estimation of the structural model.
Tourist experience Percent Logistics of the vacation Percent
Travel restrictions Destination
School holidays 14.3 Morroco 6.8
Family restrictions 5.5 Brazil 25.4
Imposed by job 21.4 Egypt 25.4
Enticing prices 20.6 The Dominican Republic 40.6
Weather conditions of
the destination
14.6 Sao Tome and Principe 1.8
Others 23.6 Budget
Travel frequency Less than e1,000 41.3
Never 10.2 From e1,000 to 1,499 31.8
Once a year 53.6 From e1,500 to 1,999 14.8
Twice a year 23.2 From e2,000 to 2,499 7.3
More than three times
a year
13.0 e2,500 or more 4.9
Previous experience Holiday booking
No 83.7 Travel agency 95.1
Yes 16.3 Directly with the operator 3.8
Booking in advance Operator’s call centre 0.2
Less than 15 days 52.1 Internet 0.9
15 days or more and
less than a month
23.6
Booking regime
1 month or more and
less than 3 months
15.9 Half pension 29.4
3 months or more 8.4 Everything included 53.2
Bed and breakfast 17.4
Average length of stay (SD) 9.1 (17.0)
Average number of family
elements (SD)
1.8 (2.2)
Type of accommodation
booked
Hotel in the city 13.7
Beach hotel 51.4
Aparthotel in the city 0.2
Beach Aparthotel 1.3
Resorts 31.1
Others 2.2
Table III.
Characterization of the
tourist experience and
logistics of the vacation
Decision-making
processes
351
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
:
0
6
2
4
J
a
n
u
a
r
y
2
0
1
6
(
P
T
)
Having identi?ed the factors that contribute the most to the formation of each
construct, SEM estimation allowed for research hypotheses assessment. The factors
obtained by means of EFA serve as indicators of the latent constructs: information
sources, push and pull motivations, push and pull satisfactions, and behavioral
intentions. AMOS 6 (Arbuckle and Wothke, 1999) estimates the model evaluating the
model ?t by following the approach suggested by Hair et al. (1998), that is, assessment
of the overall model ?t, followed by individual measurement and structural modeling
(Correia et al., 2007c).
The types of overall model ?t measures often used are absolute, incremental and
parsimoniously ?t measures. Absolute ?t measures evaluate how the theoretical model
?ts the sample data. The incremental ?t measures compare a target model with a more
restricted model, while parsimonious ?t measures diagnose the degree to which the
model ?t has improved by excess of variables.
Results from a x
2
goodness-of-?t test give an indication of absolute ?t measures,
which however are sensitive to sample size. Therefore, the analysis employs the
goodness-of-?t index (GFI) (Joreskog and Sorbom, 1986), the root mean square residual
(RMSR) and the root mean square residual of approximation (RMSEA) (Steiger, 1990) to
evaluate the model’s overall absolute ?t. The incremental ?t measures used to evaluate
the proposed model’s ?t include the: adjusted goodness-of-?t index (AGFI)
( Joreskog and Sorbom, 1986); normed ?t index (NFI) (Bentler and Bonnet, 1980),
Tucker and Lewis index (TLI) (Tucker and Lewis, 1973), incremental ?t index (IFI)
(Bollen, 1988) relative ?t index (Bollen, 1986) and the comparative ?t index (CFI)
(Bentler, 1990).
In general, the measurement model is acceptable if the indices were closer to one
(perfect ?t), as values closer to zero indicate no ?t. In the particular case of RMSR and
RMSEA, smaller values are better (zero indicates a perfect ?t).The evaluation of the
measurement model depended on assessing each latent variable separately by
examining the standardized loading, the construct reliability, and the variance
extracted. Furthermore, parameter estimate testing through the analysis of sign and
statistical signi?cance served to analyze the ?t of the structural model. Standardized
estimates are useful in comparing the parameters’ effect throughout the model, since
they remove scaling information. Proposed hypotheses were tested by observing the
statistical signi?cance of the corresponding paths in the structural model.
The methodology concludes with the representation of the relationship between
information sources, push factors, pull factors, perceptions, push and pull satisfactions
and behavioral intentions, on perceptual maps. Performance of perceptual maps uses
CATPCA. The analysis required the recoding of the main components of information
sources, motivations, satisfaction, and behavioral intentions for proper conversion into
categorical variables. This approach allows us to simultaneously correlate a group
of categorical variables and present the results geometrically in a bidimensional space,
called a perceptual map, in which the visualisation of the connections made among the
categories allows for an easier interpretation of the results. On the map the categories
represented by relatively close points held similar distributions and show strong
association among variables. On the other hand, categorical variables with very
different distributions suggest non-correlation among variables. In this study, the
perceptual maps illustrate a connection between information sources used, motivational
factors, elements of satisfaction and behavioral intentions. This explanatory approach
IJCTHR
2,4
352
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
:
0
6
2
4
J
a
n
u
a
r
y
2
0
1
6
(
P
T
)
increases understanding of howdifferent or relating factors can explain tourist behavior
in decision-making.
Results
The constructs of the model
The results of the EFA established signi?cant correlated factors, including four
information sources, three push motivations, three pull motivations, three pull
satisfactions, three push satisfactions, and two behavioral intentions. These factors are
relevant because they have signi?cant loadings. The following subsections present the
latent constructs of the model as well as the indicators for each latent variable.
Information sources
The study considers information sources supplied by the tourists using a Likert scale
to an EFA. The EFA allowed us to extract one factor that represents 53 percent of the
total variation (Table IV). Information source variables such as mail, travel agencies,
and family and friends were removed because the EFA ?t well without them. As such,
information sources possessed high loadings for the following observed variables:
movies, news, promotion and brochures.
Table IV shows the relative importance of each attribute (average) as an information
source, using a Likert scale from one (not important), to seven (extremely important).
The results obtained follow similarly to Fodness and Murray (1999) and Woodside and
Dubelaar (2002), who assume that brochures are one of the main sources of information
in which tourists ?nd most of the information they need, followed by newspapers.
Movies and promotion, understood to be complementary sources, activate tourist
motivations and fuel the learning process about the destination. The study’s
expectations included travel agencies, and family and friends to appear as privileged
information sources; however, this did not occur. Limited out of country vacations in the
past among the Portuguese would seemto explain why recommendations by family and
friends were low. Only 16 percent of the sample had previous holiday traveling
experience abroad. Interestingly, travel agents do represent important sources, as
95 percent of sample respondents booked through a travel agency; see Table III.
Motivation
In order to ascertain push motivations, the study considered sixteen original variables
to an EFA, which resulted in three motivational factors that served to explain
61 percent of the total variables after varimax rotation, as shown in Table V.
Destination characteristics showed dominating signi?cance in the choice phase.
Hence, the study considers the level of importance of all factors as decisive elements in
the decision process. These factors, which appear in the literature as pull motivations,
through EFA, resulted in three factors that explained 53 percent of the total variance.
Factors Loadings Percentage of variance explained Mean SD
Movies 0.83 53.42 4.0 1.50
News 0.82 4.3 1.57
Promotion 0.71 4.0 1.51
Brochures 0.53 4.6 1.48
Table IV.
The results of EFA for
information sources
construct
Decision-making
processes
353
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
:
0
6
2
4
J
a
n
u
a
r
y
2
0
1
6
(
P
T
)
Motivations with high-loadings results in the following push factors: knowledge
motivation, social motivation and recreational motivation. The pull factors were:
facilities, core attractions and landscape.
The ?rst push factor, motivation knowledge, refers more speci?cally to the need to
do and learn new things as well as to explore different cultures and places. This
includes increasing knowledge, amusement, getting to know different cultures and
lifestyles, intellectual growth, and visiting new places. The second push factor, social
motivation, put forward the need to visit places that friends had not been to, telling
friends about the vacation, and developing close friendships. These factors relate to the
Factors Loadings
Percentage of
variance explained Mean SD
Push motivations
Knowledge motivation 28.79
Doing different things 0.60 5.9 1.39
Stimulating emotions and sensations 0.57 5.6 1.61
Being an adventurer 0.52 5.3 1.75
Amusement 0.64 6.1 1.31
Increasing knowledge 0.81 5.9 1.44
Experiencing different cultures and life styles 0.83 5.9 1.39
Enriching myself intellectually 0.75 5.1 1.42
Visiting new places 0.68 6.0 1.34
Meeting interesting people 0.56 5.6 1.60
Social motivation 17.24
Going places my friends have not been 0.90 4.1 2.26
Telling my friends about the vacation 0.88 4.4 2.10
Developing close friendships 0.54 5.3 1.68
Recreational motivation 14.46
Relieving stress 0.84 6.2 1.30
Physical relaxation 0.73 6.0 1.42
Escaping from routine 0.69 6.2 1.42
Pull motivations
Facilities motivations 26.43
Weather 0.64 6.1 1.33
Accessibilities 0.68 5.6 1.53
Beach 0.60 6.1 1.35
Gastronomy 0.74 5.9 1.43
Security 0.66
Distance 0.54
Relaxing atmosphere 0.67 5.8 1.47
Social environment 0.73 5.5 1.60
Hospitality 0.71
Exoticness 0.60
Core attractions 10.46
Shopping facilities 0.79 4.6 1.54
Night-life 0.70 4.8 1.54
Sports equipment 0.75 4.3 1.62
Transports 0.65 4.8 1.54
Landscape motivations 16.10
Landscape 0.84 6.00 1.38
Natural environment 0.83 5.8 1.32
Table V.
The results of EFA
for motivations
IJCTHR
2,4
354
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
:
0
6
2
4
J
a
n
u
a
r
y
2
0
1
6
(
P
T
)
social rewards also discussed by Gnoth (1997). The push factor recreational motivation
include motivations related to personal well-being, such as stress relief, escape from
routine and physical relaxation, presented as physical rewards. These results follow
similarly to previous studies, in particular Gnoth (1997), who discusses three different
push motivations: self-actualization, sense of self-esteem and social status.
The ?rst pull motivation, referred to as facilities, relates to weather, accessibility,
gastronomy, security, relaxing atmosphere and social environment. The second, core
attractions, relates to shopping facilities, nightlife, and sports, while landscape
motivations include natural environment and landscape.
The mean scores obtained and presented in the fourth column of the Table V show
the main leading push motivations that lead tourists to visit South America and Africa
destinations. In terms of knowledge, motivations include amusement, getting to know
new places and cultures, and doing different things. Regarding social motivations,
tourists value new friendships and talking about the holidays with friends. The leading
recreational motivations include stress relief and escape from routine. With pull
motivations, social factors contribute less to destination choice, with only the social
atmosphere variable showing distinction. The natural environment and tourism
facilities in?uence the formation of the destination’s image, since the majority of
Portuguese tourists classi?ed these as being very important. These include attributes
such as landscape and nature, security, weather and facilities – indicative that a
destination’s natural resources constitute competitive components.
Satisfaction
Satisfaction is a state of well-being resulting from the feeling that holidays
compensate the intrinsic motivations and the services used during the vacation. To
assess emotional and cognitive satisfaction, the analysis includes using the sixteen
factors to measure push motivations and the replicated 21 factors to measure pull
motivations. Two EFA analyses treated these factors based on maximum likelihood
estimation. The ?rst, being push satisfaction, (on a scale of 1 – worse than expected; to
7 – surpassed my expectations) and resulted in three emotional satisfaction factors
that explained 72 percent of the total variance after varimax rotation, as shown in
Table VI. Pull motivations comprehended 21 destination attributes. As can be
observed in Table VI, three factors explained 49 percent of the total variance in terms
of pull satisfaction.
The ?rst push satisfaction factor, recreational satisfaction, mainly concerns the
evaluation of emotional states related to personal well-being, such as stress relief,
escape from routine, physical relaxation, and getting away from crowds. The second
factor, knowledge satisfaction, is related more speci?cally to performing and learning
new things, exploring different cultures and places, further knowledge development
discovering different cultures and lifestyles, visiting new places and, going to places
not yet visited by friends. The third push satisfaction factor, adventure satisfaction,
represents experience associated to challenging emotions and adventure.
Pull satisfaction is related to the cognitive evaluation of the quality level of the
services tourists experience during the vacation. The ?rst pull satisfaction factor,
referred to as facilities, brings together the tourist’s level of satisfaction with the social
environment, hospitality, relaxing atmosphere, information, gastronomy, and exoticness.
The second, referred to as core attractions, understands cultural attractions, shopping
Decision-making
processes
355
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
:
0
6
2
4
J
a
n
u
a
r
y
2
0
1
6
(
P
T
)
facilities, nightlife, sports, and transport. Sun and sand satisfaction combine weather with
landscape and beach.
The mean scores obtained and presented in the fourth column of Table VI show that
the most positive aspects of these destinations are weather, beaches, landscape, and
hospitality.
Behavioral intentions
Behavioral intentions are measured by means of an EFA that combine two factors, the
intention to return to the destination and the willingness to recommend it to family and
friends (Oppermann, 2000). From this analysis, behavioral intentions represented
79 percent of the total variance (Table VII).
Loadings
Percentage of
variance explained Mean SD
Push satisfaction
Recreational satisfaction 33.64
Relieving stress 0.78 4.8 1.33
Physical Relaxation 0.83 4.6 1.36
Getting away from crowds 0.74 4.5 1.39
Escaping from the routine 0.73 5.0 1.26
Knowledge satisfaction 24.33
Increasing knowledge 0.83 4.8 1.27
Experiencing different cultures and lifestyles 0.89 4.9 1.26
Enriching myself intellectually 0.80 4.7 1.30
Visiting new places 0.78 5.0 1.26
Meeting interesting people 0.72 4.8 1.34
Going places where my friends have not been 0.592 4.6 1.22
Adventure satisfaction 14.26
Doing different things 0.62 4.9 1.31
Stimulating emotions and sensations 0.72 4.8 1.27
Being an adventurer 0.62 4.7 1.30
Pull satisfaction
Facilities satisfaction 18.72
Social environment 0.75 5.1 1.46
Hospitality 0.72 5.6 1.31
Relaxing atmosphere 0.65 5.4 1.31
Information 0.60 4.6 1.34
Gastronomy 0.51 4.7 1.51
Exoticness 0.56 5.3 1.26
Core attractions satisfaction 17.74
Night-life 0.70 4.6 1.37
Shopping facilities 0.69 4.6 1.40
Cultural attractions 0.69 4.9 1.47
Sports equipment 0.61 4.4 1.27
Transports 0.54 4.2 1.45
Sun and sand satisfaction 12.71
Weather 0.79 5.9 1.07
Beach 0.66 5.9 1.25
Landscape 0.47 5.7 1.20
Table VI.
The results of EFA
for satisfaction
IJCTHR
2,4
356
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
:
0
6
2
4
J
a
n
u
a
r
y
2
0
1
6
(
P
T
)
Considering a Likert scale of seven points, behavioral intentions show
high-signi?cance levels, although tourists are more likely to revisit than to
recommend. This results from the standard deviation being higher for willingness
to recommend than for probability of returning.
The measurement model
The CFA of the measurement model speci?es the relationships of each observed
variable with the latent construct. Assuming that all the constructs are freely
inter-correlated, an analysis was performed on each construct separately before testing
the measurement and structural model. This a priori analysis allows us to understand
which constructs and observed variables need to be respeci?ed to improve the
structural model.
The hypothetical model is a structural equation system with observable variables
and latent constructs. By imposing the constraints on the loadings resulting from the
EFA, the CFA is able to assess and validate the measurement model, with constructs
being freely inter-correlated. This model ?ts the data well. The regression coef?cients
and the covariance factor are all signi?cant at the 1 and 5 percent level. Coef?cient
alphas for the latent variables appear in Table VIII. All the factors show good
reliability because all values are greater than 0.70.
The measurement model demonstrates an adequate reliability and good ?t indices;
hence. The following subsection looks at structural modelling estimation.
The structural model
Estimation of the complete model included using the asymptotically distribution-free
method using the AMOS 6. This method was adopted because of the unusual distribution
of the data. The standardized coef?cients estimated are in Figure 2. All the coef?cients are
signi?cant at 1 percent signi?cance level, withonlythe pathbetweenpull motivations and
perceptions not seen as signi?cant. As the x
2
is an adjustment measure strongly
in?uenced by sample size, the analysis includes applying other adjustment measures
to evaluate the model. The selected overall ?t indices can be observed in Table IX.
Loadings Percentage of variance explained Mean SD
Return 0.89 79.25 4.7 1.42
Recommend 0.89 4.7 1.97
Table VII.
The results of EFA for
behavioral intentions
Reliability (Cronbach’s a)
Information sources 0.75
Push motivations 0.95
Pull motivations 0.92
Push satisfaction 0.95
Pull satisfaction 0.93
Behavioral intentions 0.72
Table VIII.
Results of the
measurement model
Decision-making
processes
357
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
:
0
6
2
4
J
a
n
u
a
r
y
2
0
1
6
(
P
T
)
Figure 2.
Structural model of tourist
decision-making processes
P
e
r
c
e
p
t
i
o
n
s
S
u
n
a
n
d
S
a
n
d
S
a
t
i
s
f
a
c
t
i
o
n
S
C
o
r
e
A
t
t
r
a
c
t
i
o
n
s
S
F
a
c
i
l
i
t
i
e
s
B
e
h
a
v
i
o
r
I
n
t
e
n
t
i
o
n
R
e
c
o
m
e
n
d
a
t
i
o
n
R
e
t
u
r
n
P
u
l
l
S
a
t
i
s
f
a
c
t
i
o
n
P
u
s
h
S
a
t
i
s
f
a
c
t
i
o
n
H
7
H
1
1
H
1
0
H
1
3
H
1
6
H
1
5
H
1
4
.
4
0
2
H
1
2
H
1
7
.
1
5
3
M
R
e
c
r
e
a
t
i
o
n
a
l
M
K
n
o
w
l
e
d
g
e
M
S
o
c
i
a
l
S
R
e
c
r
e
a
t
i
o
n
a
l
S
A
d
v
e
n
t
u
r
e
S
K
n
o
w
l
e
d
g
e
.
1
2
5
.
2
8
1
.
4
8
9
.
3
1
6
.
1
2
9
.
7
1
8
.
5
7
5
.
8
1
1
.
6
5
9
.
8
5
4
.
8
7
8
.
6
6
6
.
2
0
1
.
3
2
6
.
3
2
4
.
4
3
6
.
3
0
9
.
4
3
3
.
3
5
1
.
3
5
9
.
3
6
2
.
4
7
1
.
0
5
5
.
0
8
0
.
4
0
8
.
1
5
1
.
0
7
6
.
4
1
5
.
3
6
7
.
5
1
9
.
4
2
8
.
5
9
3
.
3
8
2
H
4
H
1
H
2
H
3
H
6
H
5
H
8
H
9
–
0
.
3
4
8
.
1
4
1
.
2
6
8
.
0
3
8
.
4
7
0
.
2
4
4
.
2
6
8
–
0
.
2
0
1
.
6
4
5
.
3
3
0
.
5
8
1
.
1
3
9
.
4
7
4
.
4
3
9
.
0
9
9
.
0
8
0
.
1
8
8
.
2
4
2
M
L
a
n
d
s
c
a
p
e
M
C
o
r
e
A
t
t
r
a
c
t
i
o
n
s
P
r
o
m
o
t
i
o
n
N
e
w
s
M
o
v
i
e
s
B
r
o
c
h
u
r
e
s
M
F
a
c
i
l
i
t
i
e
s
N
o
t
e
s
:
A
l
l
t
h
e
c
o
e
f
f
i
c
i
e
n
t
s
h
a
v
e
a
t
-
v
a
l
u
e
s
i
g
n
i
f
i
c
a
n
t
a
t
1
p
e
r
c
e
n
t
s
i
g
n
i
f
i
c
a
n
c
e
l
e
v
e
l
(
p
<
0
.
0
0
1
)
;
H
y
p
o
t
h
e
s
e
s
r
e
j
e
c
t
e
d
P
u
l
l
M
o
t
i
v
e
s
P
u
s
h
M
o
t
i
v
e
s
I
n
f
o
r
m
a
t
i
o
n
S
o
u
r
c
e
s
.
2
6
4
IJCTHR
2,4
358
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
:
0
6
2
4
J
a
n
u
a
r
y
2
0
1
6
(
P
T
)
The x
2
statistic indicates that the model ?ts the data well (x
2
¼ 139.339; df ¼ 125;
p ¼ 0.180 . 0.05). The other goodness-of-?t measures also indicate a good overall model
?t (GFI ¼ 0.99 exceeds the level of 0.9; the RMSR ¼ 0.02 and RMSEA ¼ 0.02 are closer
to zero, as desired). The other indicators closer to 1 indicate a good incremental and
parsimonious ?t.
The empirical model ?ts the data well and allows us to accept the hypotheses, with
the exception of H
6
, H
8
and H
9
.
The study anticipated tourists using different sources of information in order to
learn more about destination (H
1
). According to the results, news reports and travel
movies positively in?uence the latent variable information sources, as well as
promotion and brochures; however, these present lower standardized coef?cients,
respectively, 0.67 and 0.44. This suggests that brochures and promotion are not as
important to the tourist as are news reports and travel movies.
Information sources may positively in?uence the shaping of push motivations (H
2
);
when the standardized coef?cient estimated is 0.15. Contrary to expectations,
information sources negatively in?uenced pull motivations (H
3
), with a standardized
coef?cient of 20.20. This result shows that information sources tend to portray idyllic
and idealistic destinations, rather than realistic attributes and facilities.
According to Dann (1977) and Crompton (1990), the motivations that lead tourists to
travel include social engagement, rest and cultural (knowledge) forces. As shown by
Correia et al. (2007b), the internal motivations that positively in?uence the desire to
travel (H
4
) are knowledge, recreation, and social motivations.
According to Uysal and Hagan (1993) and Crompton (1990), pull motivations relate
to the attributes of the destination and its attractions. The empirical model allows us to
identify three pull motivations (facilities, core attractions and landscape), all of which
positively in?uence the latent variable pull motivation (H
5
).
Since pull motivations are unaffected by push motivations (H
6
), the hypothesis is
rejected. Push motivations in?uence perceptions of the destination (H
7
), as
demonstrated by the standardized coef?cient of 0.13 and a t-value of 2.85. Pull
motivations, as well as information sources do not in?uence tourist perceptions;
therefore, hypotheses H
8
and H
9
were rejected. Emotional satisfaction (push) negatively
in?uenced by perceptions (H
10
), with a standardized coef?cient of 20.35. In?uence on
satisfaction with the destination’s attributes occurred through perceptions (H
11
) as
demonstrated by a standardized coef?cient of 0.28. Emotional satisfactions (H
12
) result
from knowledge, recreation and adventure; satisfaction factors that present positive
standardized coef?cients, 0.43, 0.59 and 0.382, respectively. Destination perception takes
place in the presence of the sun and sand, core attractions, and facilities (H
13
). These
factors present positive standardized coef?cients, 0.42, 0.37 and 0.52, respectively.
Absolute ?t measures Incremental and parsimonious ?t measures
GFI ¼ 0.99
AGFI ¼ 0.99
x
2
¼ 139.34 (df ¼ 13; p ¼ 0.18) NFI ¼ 0.92
RMSR ¼ 0.016 TLI ¼ 0.99
RMSEA ¼ 0.021 IFI ¼ 0.99
CFI ¼ 0.99
Table IX.
Goodness-of-?t measures
for the structural
equation model
Decision-making
processes
359
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
:
0
6
2
4
J
a
n
u
a
r
y
2
0
1
6
(
P
T
)
Pull satisfaction positively in?uences push satisfaction (H
14
), with standardized
coef?cients of 0.40, thus suggesting that cognitive satisfaction in?uences emotional
satisfaction.
Push (H
15
) and pull (H
16
) satisfaction positively explain behavioral intentions, with
standardizedcoef?cients of 0.49 and0.32, anda statistical signi?cance at the 1 percent level.
Intention to recommend and the probability of returning (H
17
) explain behavioral
intentions, presenting positively positive standardized coef?cients, statistically
signi?cant at the 1 percent level, 0.66 and 0.81, respectively.
Categorical principal component analysis
In order to explore the relationships between each factor in the constructs of the model
more deeply (information sources, push motivations, pull motivations, push satisfaction,
pull satisfaction, and behavioral intentions), the study turned to a CATPCA on the
categorized factors. The structural model demonstrates that information sources are a
signi?cant in?uence in the shaping of push and pull motivations.
In Figure 3, those who found sources of information to be very important were those
showing greater intrinsic motivations about the vacation. Diametrical lines
representing push motivations and information sources, show that the tourists
explored information indiscriminately.
Indiscriminate information search about the destination relates only to push
motivations. Although the tourist may use other information sources randomly to learn
about the destination’s core attractions, brochures provide the majority of the
information. This can be observed given the proximity of the orange and light blue
lines Figure 4 shows.
Figure 4 shows that the individuals who ?nd sources of information to be more
important are also those who show more highly developed perceptions about the
destination.
The perceptual map shown in Figure 5 allows us to come to two conclusions. The
more highly motivated the tourist is, the better developed his or her perceptions are.
Figure 3.
Information sources and
push motivations
1.5 1.0 0.5 0.0 – 0.5 –1.0 –1.5
Dimension 1
1.5
1.0
0.5
0.0
– 0.5
–1.0
D
i
m
e
n
s
i
o
n
2
VI
I
WI
VI
I
WI
VI
I
WI
VI
I
WI
VI
I
WI
VI I
WI
VI
I
WI
Promotion
News
Msocial
Movies
MRecreational
MKnowledge
Brochures
WI – Without Important
I – Important
VI –Very Important
IJCTHR
2,4
360
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
:
0
6
2
4
J
a
n
u
a
r
y
2
0
1
6
(
P
T
)
On the other hand, social motivations lead to perceptions about the destination, as the
narrowed cream and purple lines show.
The perceptual map that relates perceptions to push satisfaction (Figure 6) shows
that more highly developed perceptions are associated to greater levels of emotional
satisfaction. This connection is more obvious with recreational satisfaction, as can be
seen by the proximity of the blue line to the purple line.
The perceptual map that relates perceptions with pull satisfaction (Figure 7) con?rms
that more highly developed perceptions relate to greater levels of cognitive satisfaction.
Figure 4.
Information sources and
pull motivations
Dimension 1
D
i
m
e
n
s
i
o
n
2
1.5 1.0 0.5 0.0 – 0.5 –1.0 –1.5
0.5
0.0
– 0.5
–1.0
VI
VI
I
WI
VI
I
WI
VI
I
VI
I
VI
I
WI
VI
I
WI
Promotion
News
Movies
MLandscape
MFacilities
MCore Attractions
Brochures
WI – Without Important
I – Important
VI – Very Important
WI
WI
WI
I
Figure 5.
Perceptions and push
motivations
1.5 1.0 0.5 0.0 – 0.5 –1.0
Dimension 1
1.5
1.0
0.5
0.0
– 0.5
D
i
m
e
n
s
i
o
n
2
WI
VI
I
WI
VI I WI
VI
I
WI
VI
WI
M social
M Recreational
M Landscape
M knowledge
M Facilities
MCore Attractions
WI – Without Important
I – Important
VI – Very Important
WI
I
VI I
VI
I
Decision-making
processes
361
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
:
0
6
2
4
J
a
n
u
a
r
y
2
0
1
6
(
P
T
)
This connection is more obvious with core attraction satisfaction while is the attribute
that they perceive better.
The perceptual map that relates push and pull satisfactions (Figure 8) shows that
intellectual reward (knowledge) is associated to the satisfaction of core attractions. On
the other hand, satisfaction with recreation shows association to satisfaction with
facilities. Satisfaction with sun and sand are separate from the others.
The perceptual map that relates push satisfaction to behavioral intentions (Figure 9)
shows that tourists recommend these destinations and intend to return resulting from
satisfaction from recreational facilities. This result suggests that this type of
Figure 6.
Perceptions and push
satisfaction
1.5 1.0 0.5 0.0 – 0.5 –1.0
Dimension 1
0.5
0.0
– 0.5
–1.0
D
i
m
e
n
s
i
o
n
2
H
A L
VI
I
WI
VI
I
WI
Perceptions
Msocial
MRecreational
Mknowledge
WI – Without Important
I – Important
VI – Very Important
L – Lower
A – Average
H – High
VI
WI
I
Figure 7.
Perceptions and pull
satisfaction
1.5 1.0 0.5 0.0 – 0.5 –1.0
Dimension 1
1.0
0.5
0.0
– 0.5
–1.0
–1.5
D
i
m
e
n
s
i
o
n
2
B
Av
W
B
Av
W
B
Av
W
H
A
L
SFacilities
SCore Attractions
Perceptions
W – Worst
Av – Average
B – Better
L – Lower
A – Average
H – High
SSun Sand
IJCTHR
2,4
362
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
:
0
6
2
4
J
a
n
u
a
r
y
2
0
1
6
(
P
T
)
destination is very good for rest and relaxation, even if wanting to engage in sports or
nightlife activities, as was the case.
In terms of satisfaction with destination attributes, facilities explain positive
behavioral intentions, suggesting that this type of destination is good enough in terms
of tourism resources, even if tourists are not aware of them through the information
source (Figure 10).
Discussion and managerial implications
This study develops a conceptual model of tourist decision-making, and provides
evidence of the importance of six different constructs to achieve the ?nal decision and
to predict post-purchase behavior. The tourist’s decision process involves two stages:
Figure 8.
Push and pull satisfaction
1.5 1.0 0.5 0.0 – 0.5 –1.0
Dimension 1
1.0
0.5
0.0
– 0.5
D
i
m
e
n
s
i
o
n
2
B
Av
W
B
Av
W
B
Av
W
B
Av
W
B
E
B
Av
W
SSun Sand
SRecreational
SKnowledge
SFacilities
SCore Attractions
SAdventure
W – Worst
Av – Average
B – Better
W
Figure 9.
Push satisfaction and
behavioral intentions
Dimension 1
D
i
m
e
n
s
i
o
n
2
1.0 0.5 0.0 – 0.5 –1.0
0.8
0.6
0.4
0.2
0.0
– 0.2
– 0.4
– 0.6
B
Av
W
B
W
B
Av
W
D
N
D
N
SRecreational
SKnowledge
SAdventure
Return
Recommend
W – Worst
Av – Average
B – Better
N – No
P – Probably
D – Definitely
P
P
I
Decision-making
processes
363
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
:
0
6
2
4
J
a
n
u
a
r
y
2
0
1
6
(
P
T
)
the pre-decision stages ending with choice and the post-decision stage, in which
emotional satisfaction and cognitive satisfaction in?uence future behavioral intentions.
In the pre-decision stage, external stimuli and motivations in?uence ?nal perceptions
about the destination, leading to the ?nal decision being reached.
This model, which combines the two different stages of tourist behavior, shows
tangible empirical results, representing a step forward in the tourist behavior literature.
Although much discussion and testing of these constructs has been widely studied in the
literature, examination of the causal relationships had not yet been addressed.
The major ?ndings of this study have some signi?cant managerial implications,
especially for the marketers who promote such destinations as those addressed in this
paper. This study provides evidence of the relevancy of different information sources
in the tourist decision-making processes. The main information sources activating the
need to travel in the tourist’s mind were movies, news reports, promotion, and
brochures, as these positively in?uence push motivations. For the purposes of
gathering of information on destination attributes (pull motivations), these sources,
and particularly brochures, appear to be insuf?cient as the structural model shows a
negative path between information sources and pull motivations (Figure 2). This result
suggests that the information available attempts to “sell dreams”; in other words, to
activate the need for the holiday, rather than to provide information about the tourism
destination resources. Managers should include more information in brochures about
the core product to allow the tourist to better plan his or her vacation. Since selling
dreams likely is as important as selling products, information sources should separate
these issues through two types of promotional brochures; the ?rst appealing to the need
for travel, depicting beautiful scenery and other capturing features, while the second
should provide information about the core attractions and facilities provided.
The mean scores for the importance of each information source allow marketers to
prioritize the means they have at their disposal in approaching the tourist. Since the
most important sources of information are brochures and travel movies, these should
be organized and designed according to the expectations of tourists.
Figure 10.
Pull satisfaction and
behavioral intentions
1.5 1.0 0.5 0.0 – 0.5 –1.0 –1.5
Dimension 1
2.0
1.5
1.0
0.5
0.0
– 0.5
–1.0
D
i
m
e
n
s
i
o
n
2
B
Av
W
B
Av
W
B
Av
D
N
N
SSun Sand
SFacilities
SCore Attractions
Return
Recommend
W – Worst
Av – Average
B – Better
N – No
P – Probably
D – Definitely
P
W
P
D
IJCTHR
2,4
364
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
:
0
6
2
4
J
a
n
u
a
r
y
2
0
1
6
(
P
T
)
The EFAperformed on motivations shows that tourists perceive three different push
motivations and three pull motivations, although only push motivations contribute to
the tourist’s perception of the selected destination, consequently in?uencing satisfaction
and future behavior. Thus, managers should consider these variables as determining
factors in improving satisfaction levels. Knowledge motivations fundamentally relate to
amusement, visiting new places, encountering different cultures and doing different
things. Social motivations relate to developing friendships and social acceptance, while
recreational motivations relate to stress relief and escape from daily routine. These
results constitute important issues in bettering marketing strategies.
The mean scores of these motivations show that the most important factors that
drive Portuguese tourists to travel relate to social status and knowledge. Core
attractions are not relevant, but landscape and facilities are important.
Tourists develop perceptions about the destinations they are traveling to, although
these perceptions are based only on push motivations as the structural model shows.
Furthermore, the perceptual map (Figure 5) shows that the more motivated the tourist
is, the more favorably he or she perceives the destination. These perceptions are
especially high due to the social connotations that these destinations re?ect. These
results suggest that managers should encourage tourists to perceive these destinations
as a core product in which facilities and quality are also competitive advantages that
can go together to achieve the “social dream” of traveling to a far-away destination.
The EFA performed on satisfactions demonstrates highlighted variables that tourists
evaluated: emotional (push) satisfaction and cognitive (pull) satisfaction. The emotional
dimension relies on three evaluation factors: recreation, knowledge, and adventure.
Cognitive assessment suggests signi?cant connection between facilities, core attractions,
and sun and sand attributes. The mean scores obtained for each factor suggest that the
competitive advantages perceived by tourists in South America and Africa destinations
are weather, beaches, landscape and hospitality. The signi?cant path between
perceptions, push satisfaction, and associated perceptual map (Figure 6) con?rms that
this type of destination is seen as the ideal place in which to ful?ll a dream as well as to
pursue the sun and sand facilities (Figure 7). The structural model also shows a signi?cant
path between emotional satisfaction and cognitive satisfaction, suggesting that the
greater the personal satisfaction, the better the service evaluation. The perceptual map
(Figure 8) reinforces this result, showing which push satisfaction factors are more
correlated with which pull satisfaction factors. Knowledge satisfaction correlates with
core attraction satisfaction. On the other hand, recreational satisfaction relates with
facilities. Sun and sand satisfaction are uncorrelated with the other factors. These issues
are fundamental for tourism managers to consider when positioning tourist destinations.
As the measurement model demonstrates, estimating the structural model that
empirically tests the conceptual model with an adequate degree of reliability is possible.
The ?ndings support fourteen of the seventeen hypotheses. Those hypotheses not
supported were paths between information sources and perceptions, pull motivations and
perceptions, and between push and pull motivations. Hence, tourists may not understand
the destination’s attributes, but simply decide and evaluate based on intuition.
Finally, satisfaction explains future behavioral intentions, since analysis
established a signi?cant path between push satisfaction and behavioral intentions,
as well as between pull satisfaction and future behavior. The perceptual maps
(Figures 9 and 10) go deeper into these relationships, showing that the main leading
Decision-making
processes
365
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
:
0
6
2
4
J
a
n
u
a
r
y
2
0
1
6
(
P
T
)
satisfaction factors that explain future behavior are facilities and recreational. This
suggests that exotic destinations and relaxing environments encourage tourists to
perceive them as predominantly recreational. Good tourism facilities were observed
during the vacation despite the lack of information about these.
Conclusions
Tourist behavioral intentions have causal relationships with information sources,
motivations, perceptions and satisfaction. The model divides these motivations into push
and pull concepts, and shows evidence for each push motivations, positively in?uencing
perceptions about the destination and providing different levels of emotional and
cognitive satisfaction at the end of the vacation. These factors in?uence future behavior.
In the literature, although the importance of these constructs has been widely
discussed, little testing on the reliability and structural relationships taking into
consideration all the constructs discussed. Future behavior re?ects emotional and
cognitive satisfaction, in?uenced, in turn, by perceptions that are in?uenced by
emotional motivations stimulated by existing sources of information.
Although the model tested only Portuguese tourists, the study is extendable to cover
larger spectrums of tourists. Still, further research is necessary to establish such issues
as the degree to which information sources in?uence post-purchase satisfaction, the risk
inherent in decision-making, and destination attitude in measuring tourist loyalty.
References
Abelson, R. and Levi, A. (1985), “Decision making and decision theory”, in Lindzey, G. and
Aronson, E. (Eds), The Handbook of Social Psychology, 3rd ed., Vol. 1, Random House,
New York, NY.
AMOS Inc. (2006), AMOS 6, AMOS Inc., Murphy, NC.
Arbuckle, J. andWothke, W. (1999), AMOS4.0User’s Guide, Small Waters Corporation, Chicago, IL.
Archer, B. (1976), “Demand forecasting in tourism”, in Revell, J. (Ed.), Bangor Occasional Papers
in Economics, University of Wales Press, Bangor.
Arnould, E. and Price, L. (1993), “River magic: extraordinary experience and the extended service
encounter”, Journal of Consumer Research, Vol. 20, pp. 24-45.
Artus, J. (1972), “An econometric analysis of international travel”, Annals of Tourism Research,
Vol. 18, pp. 663-5.
Backman, S. and Crompton, J. (1991), “The usefulness of selected variables for predicting activity
loyalty”, Leisure Sciences, Vol. 13, pp. 205-20.
Baker, D. and Crompton, J. (1998), “Exploring the relationship between quality, satisfaction, and
behavioral intentions in the context of a festival”, Annals of Tourism Research, Vol. 27
No. 3, pp. 785-804.
Baker, D. and Crompton, J. (2000), “Quality, satisfaction and behavioral intentions”, Annals of
Tourism Research, Vol. 27 No. 3, pp. 785-804.
Baloglu, S. (1997), “The relationship between destination images and socio-demographic and trip
characteristics of international travelers”, Journal of Vacation Marketing, Vol. 3, pp. 221-33.
Baloglu, S. and McCleary, K. (1999), “A model of destination image formation”, Annals of
Tourism Research, Vol. 26 No. 4, pp. 868-97.
Bargeman, B. and Poel, H. (2006), “The role of routines in the vacation decision-making process
of Dutch vacationers”, Tourism Management, Vol. 27 No. 4, pp. 707-20.
IJCTHR
2,4
366
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
:
0
6
2
4
J
a
n
u
a
r
y
2
0
1
6
(
P
T
)
Barros, C. and Proenc¸a, I. (2005), “Mixed logit estimation of radical Islamic terrorism in Europe
and North America”, The Journal of Con?ict Resolution, Vol. 49 No. 2, pp. 298-314.
Barsky, J. (1992), “Costumer satisfaction in the hotel industry: meaning and measurement”,
Hospitality Research Journal, Vol. 16, pp. 51-73.
Barsky, J. and Labagh, R. (1992), “A strategy for customer satisfaction”, The Cornell Hotel &
Restaurant Administration Quarterly, Vol. 33, pp. 32-7.
Beerli, A. and Mart? ´n, J. (2004), “Factors in?uencing destination image”, Annals of Tourism
Research, Vol. 31, pp. 657-81.
Beesley (2005), “The management of emotion in collaborative tourism research settings”,
Tourism Management, Vol. 26 No. 2, pp. 261-75.
Bentler, P. (1990), “Comparative ?t indexes in structural models”, Psychological Bulletin, Vol. 107,
pp. 238-46.
Bentler, P. and Bonnet, D. (1980), “Signi?cance tests and goodness-of-?t in the analysis of
covariances structures”, Psychological Bulletin, Vol. 88, pp. 588-606.
Bentler, P. and Speckart, G. (1979), “Models of attitude behavior relations”, Psychological Review,
Vol. 86 No. 5, pp. 452-64.
Bettman, J. and Park, C. (1980), “Effects of prior knowledge and experience and phase of the
choice process on consumer decision, a protocol analysis”, Journal of Consumer Research,
Vol. 7, pp. 234-48.
Bigne´, J., Sa´nchez, M. and Sa´nchez, J. (2001), “Tourism image, evaluation variables and after
purchase behavior: inter-relationship”, Tourism Management, Vol. 22, pp. 607-16.
Bojanic, D. (1996), “Consumer perceptions of price, value and satisfaction in the hotel industry:
an exploratory study”, Journal of Hospitality and Leisure Marketing, Vol. 14 No. 1, pp. 5-22.
Bollen, K. (1986), “Sample size and Bentler and Bonett’s nonnormed ?t index”, Psychometrika,
Vol. 51, pp. 375-7.
Bollen, K. (1988), “A new incremental ?t index for general structural equation models”, paper
presented at 1988 Southern Sociological Society Meetings, Nashville, TN.
Bultena, C. and Klessig, L. (1969), “Satisfaction in camping: a conceptualization and guide to
social research”, Journal of Leisure Research, Vol. 1 No. 1, pp. 348-64.
Burgess, J. (1978), “Image and identity”, Occasional Papers in Geography, No. 23, University of
Hull Publications, Hull.
Cohen, J., Fishbein, M. and Ahtola, O. (1972), “The nature and uses of expectancy – value models
in consumer attitude research”, Journal of Marketing Research, Vol. 9, pp. 456-60.
Correia, A. (2000), “A procura tur? ´stica no Algarve”, unpublished PhD thesis in Economics,
Unidade de Cieˆncias Econo´micas e Empresariais, Universidade do Algarve, Faro.
Correia, A. (2002), “How do tourist choose – a conceptual framework”, Tourism an International
Interdisciplinary Journal, Vol. 50 No. 1, pp. 21-9.
Correia, A. and Crouch, G. (2004), “A study of tourist decision processes: Algarve, Portugal”,
in Crouch, G., Perdue, R., Timmermans, H. and Uysal, M. (Eds), Consumer Psychology of
Tourism, Hospitality and Leisure, Vol. 3, CABI Publishing, Wallingford.
Correia, A., Barros, C. and Silvestre, A. (2007a), “Tourism golf repeat choice behavior in the
Algarve, a mixed logit approach”, Tourism Economics, Vol. 13 No. 1, pp. 111-27.
Correia, A., Valle, P. and Moc¸o, C. (2007b), “Modelling motivations and perceptions of Portuguese
tourists”, Journal of Business Research, Vol. 60 No. 1, pp. 76-80.
Correia, A., Valle, P. and Moc¸o, C. (2007c), “Why people travel to exotic places?”, International
Journal of Culture, Tourism and Hospitality Research, Vol. 1 No. 1, pp. 45-61.
Decision-making
processes
367
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
:
0
6
2
4
J
a
n
u
a
r
y
2
0
1
6
(
P
T
)
Crompton, J. (1979), “Motivations for pleasure vacations”, Annals of Tourism Research, Vol. 6
No. 4, pp. 408-24.
Crompton, J. (1990), “Claiming our share of the tourism dollar”, Parks and Recreation, pp. 42-88,
March.
Crompton, J. (1992), “Structure of vacation destination choice sets”, Annals of Tourism Research,
Vol. 19, pp. 420-34.
Crompton, J. and Ankomah, P. (1993), “Choice set propositions in destination decisions”, Annals
of Tourism Research, Vol. 20, pp. 461-76.
Cronbach, L. (1951), “Coef?cient alpha and the internal structure of tests”, Psychometrika, Vol. 16
No. 3, pp. 297-334.
Cronin, J., Joseph, J. and Taylot, S. (1992), “A measuring service quality: a reexamination and
extension”, Journal of Marketing, Vol. 56 No. 3, pp. 55-68.
Crotts, J. (1999), “Consumer decision making and prepurchase information search”, in Pizam, A.
and Yoel, M. (Eds), Consumer Behavior in Travel and Tourism, The Haworth Hospitality
Press, Binghamton, NY.
Crouch, G. (1994), “The study of international tourism demand: a review of ?ndings”, Journal of
Travel Research, No. 1, pp. 13-23.
Crouch, G. and Jordan, L. (2004), “The determinants of convention site selection: a logistic choice
model from experimental data”, Journal of Travel Research, Vol. 43 No. 2, pp. 118-30.
Dann, G. (1977), “Anomie, ego-enhancement and tourism”, Annals of Tourism Research, Vol. 4
No. 4, pp. 184-94.
Dann, G. (1981), “Tourist motivation – an appraisal”, Annals of Tourism Research, Vol. 8 No. 2,
pp. 187-219.
Dann, G. (1996), “Tourists’ images of a destination – an alternative analysis”, Recent Advances
and Tourism Marketing Research, Vol. 5 Nos 1/2, pp. 41-55.
Decrop, A. (1999), “Tourists’ decision-making and behavior processes”, in Pizam, A. and
Mansfeld, Y. (Eds), Consumer Behavior in Travel and Tourism, The Haworth Hospitality
Press, Binghamton, NY.
DGT (2006), S? ´ntese das Fe´rias dos Portugueses 2005, Direcc¸a˜o-Geral do Turismo, Lisboa.
Dick, A. and Basu, K. (1994), “Customer loyalty: toward an integrated conceptual framework”,
Journal of the Academy of Marketing Science, Vol. 22 No. 2, pp. 99-113.
Driscoll, A., Lawson, R. and Niven, B. (1994), “Measuring tourist destination’s perceptions”,
Annals of Tourism Research, Vol. 21 No. 3, pp. 499-511.
Dube, L., Renaghan, L. and Miller, J. (1994), “Measuring customer satisfaction for strategic
management”, Cornell Hotel and Restaurant Administration Quarterly, Vol. 35, pp. 39-47.
Dubois, B. (1993), Compreender o Consumidor, Publicac¸o˜es D. Quixote, Lisboa.
Edwards, W. (1954), “The theory of decision making”, Psychology. Bulletin, Vol. 51, pp. 380-417.
Engel, J., Kollat, D. and Blackwell, R. (1978), Consumer Behavior, The Dryden Press, Hinsdale, IL.
Fakeye, P. and Crompton, J. (1991), “Image differences between prospectives, ?rst-time, and repeat
visitors to the lower Rio Grande valley”, Journal of Travel Research, Vol. 32, pp. 10-16.
Festinger, L. (1954), “A theory of social comparison processes”, Human Relations, Vol. 7 No. 2,
pp. 117-40.
Fishbein, M. (1967), Readings in Attitude Theory and Measurement, Wiley, New York, NY.
Fishbein, M. and Ajzen, I. (1980), Predicting and Understanding Consumer Behavior: Attitude
Behavior Correspondence, Prentice-Hall, Englewood Cliffs, NJ.
IJCTHR
2,4
368
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
:
0
6
2
4
J
a
n
u
a
r
y
2
0
1
6
(
P
T
)
Fisher, D. (2004), “The demonstration effect revisited”, Annals of TourismResearch, Vol. 31 No. 2,
pp. 428-46.
Fleischer, A. and Pizam, A. (2002), “Tourism constraints among Israelis seniors”, Annals of
Tourism Research, Vol. 29 No. 1, pp. 106-23.
Fodness, D. (1994), “Measuring tourist motivation”, Annals of Tourism Research, Vol. 21 No. 3,
pp. 555-81.
Fodness, D. and Murray, B. (1997), “Tourist information search”, Annals of Tourism Research,
Vol. 24 No. 3, pp. 503-23.
Fodness, D. and Murray, B. (1999), “A model of tourist information search behavior”, Journal of
Travel Research, Vol. 37 No. 1, pp. 220-30.
Foxall, G. and Goldsmith, R. (1994), Consumer Psychology for Marketing, Routledge, London.
Gallarza, M., Saura, I. and Garcia, H. (2002), “Destination image: towards a conceptual
framework”, Annals of Tourism Research, Vol. 29 No. 1, pp. 56-78.
Gartner, W. (1993), “Image formation process”, in Uysal, M. and Fesenmaier, D. (Eds),
Communication and Channel Systems in Tourism Marketing, Haworth Press, New York,
NY, pp. 191-215.
Gnoth, J. (1997), “Tourism motivation and expectation formation”, Annals of Tourism Research,
Vol. 24 No. 2, pp. 283-304.
Goosens, C. (2000), “Tourist information and pleasure motivation”, Annals of Tourism Research,
Vol. 27 No. 3, pp. 301-21.
Gunn, C. (1972), Vacationscape: Designing Tourist Regions, Taylor and Francis/University of
Texas, Washington, DC.
Guy, B., Curtis, W. and Crotts, J. (1990), “Environmental learning of ?rst-time travellers”, Annals
of Tourism Research, Vol. 17 No. 3, pp. 419-31.
Hair, J., Anderson, R., Tatham, R. and Black, W. (1998), Multivariate Data Analysis, Prentice-Hall,
Upper Saddle River, NJ.
Holbrook, M. (1978), “Beyond attitude structure: toward the informational determinants of
attitude”, Journal of Marketing Research, Vol. 15, pp. 545-56.
Holbrook, M. (1996), “Customer value – a framework for analysis and research”, Advances in
Consumer Research, Vol. 23, pp. 138-42.
Hong, S., Kim, J., Jang, H. and Lee, S. (2006), “The roles of categorization, affective image and
constraints on destination choice: an application of the NMNL model”, Tourism
Management, Vol. 27.
Howard, J. and Sheth, J. (1969), The Theory of Buyer Behavior, Wiley, New York, NY.
Hunt, H. (1977), “CS/D – overview and future directions”, in Hunt, H. (Ed.), Conceptualization and
Measurement of Consumer Satisfaction and Dissatisfaction, Marketing Science Institute,
Cambridge, MA.
Iso-Ahola, S. and Mannel, R. (1987), “Psychological nature of leisure and tourism experience”,
Annals of Tourism Research, Vol. 14 No. 3, pp. 314-31.
Jamrozy, U., Backman, S. and Backman, K. (1996), “Involvement and opinion leadership in
tourism”, Annals of Tourism Research, Vol. 23 No. 4, pp. 908-24.
Joreskog, K. and Sorbom, D. (1986), LISREL VII: Analysis of Linear Structural Relationship by
Maximum Likelihood and Least Square Method, Scienti?c Software, Mooresville, NC.
Kim, S. and Yoon, Y. (2003), “The hierarchical effects of effective and cognitive components on
the tourism destination image”, Journal of Travel & Tourism Marketing, Vol. 14 No. 20,
pp. 1-22.
Decision-making
processes
369
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
:
0
6
2
4
J
a
n
u
a
r
y
2
0
1
6
(
P
T
)
Kockelman, K. and Krishnamurthy, S. (2004), “A new approach for travel demand modelling:
linking Roy’s identity to discrete choice”, Transportation Research Part B, Vol. 38,
pp. 459-75.
Kotler, P., Haider, D. and Rein, I. (1993), Marketing Places, Free Press, New York, NY.
Kozak, M. (2001), “Repeaters’ behavior at two distinct destinations”, Annals of Tourism
Research, Vol. 28 No. 3, pp. 784-807.
Kozak, M. and Rimmington, M. (2000), “Tourist satisfaction with Mallorca, Spain, as an
off-season holiday destination”, Journal of Travel Research, Vol. 38 No. 3, pp. 260-9.
Lam, T. and Hsu, C. (2006), “Predicting behavioral intention of choosing a travel destination”,
Tourism Management, Vol. 27 No. 4, pp. 589-99.
Lancaster, K. (1966), “A new approach to consumer theory”, Journal of Political Economy, Vol. 74
No. 2, pp. 132-57.
LaTour, S. and Peat, N. (1979), “Conceptual and methodological issues in consumer satisfaction
research”, Advances in Consumer Research, Vol. 6, pp. 431-7.
Levitt, T. (1981), “Marketing intangible products and product intangibles”, Harvard Business
Review, pp. 94-102, May/June.
Lim, C. (1997), “An econometric classi?cation and review of international tourism demand
models”, Tourism Economics, Vol. 3 No. 1, pp. 69-81.
Luce, D. (1959), Individual Choice Behavior, Wiley, New York, NY.
Lundberg (1990), The Tourist Business, 6th ed., van Nostrand Reinhold, New York, NY.
McCabe, A. (2000), “Tourism motivation process”, Annals of Tourism Research, Vol. 27 No. 4,
pp. 1049-52.
McFadden, D. (1981), “Conditional logit analysis of qualitative choice behavior”, in Zarembka, P.
(Ed.), Frontiers in Econometrics, Academic Press, New York, NY.
Madrigal, R. (1995), “Cognitive and effective determinants of fan satisfaction with sporting event
attendance”, Journal of Leisure Research, Vol. 27 No. 3, pp. 205-27.
Maio, G. and Olson, J. (1994), “Value-attitude-behavior relations: the moderating role of attitude
functions”, British Journal of Social Psychology, Vol. 33, pp. 301-12.
Mannell, R. and Iso-Ahola, S. (1987), “Psychological nature of leisure and tourism experience”,
Annals of Tourism Research, Vol. 14 No. 3, pp. 314-31.
Mansfeld, Y. (1992), “From motivation to actual travel”, Annals of Tourism Research, Vol. 19
No. 3, pp. 399-419.
Marshal, A. (1920), Principles of Economics, 8th ed., Macmillan and Co., London.
Mazursky, D. (1989), “Past experience and future tourism decisions”, Annals of Tourism
Research, Vol. 16 No. 3, pp. 333-44.
Middleton, V. (1994), Marketing in Travel and Tourism, Butterworth-Heinemann, London.
Miller, G. (1956), “The magic number seven, plus or minus two: some limits on our capacity for
processing information”, The Psychological Review, Vol. 63, pp. 81-9.
Miller, J. (1977), “Studying satisfaction, modifying models, eliciting expectations, posing
problems, and making meaningful measurements”, in Hunt, J. (Ed.), Conceptualization and
Measurement of Consumer Satisfaction and Dissatisfaction, Marketing Science Institute,
Cambridge, MA.
Mohsin, A. and Ryan, C. (2003), “Backpackers in the northern territory of Australia”, The
International Journal of Tourism Research, Vol. 5 No. 2, pp. 113-21.
IJCTHR
2,4
370
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
:
0
6
2
4
J
a
n
u
a
r
y
2
0
1
6
(
P
T
)
Money, R. and Crotts, J. (2003), “The effect of uncertainty avoidance on information search,
planning and purchases of international travel vacations”, Tourism Management, Vol. 24,
pp. 191-202.
Morley, C. (1992), “A microeconomic theory of international tourism demand”, Annals of
Tourism Research, Vol. 19, pp. 250-67.
Morley, C. (1994), “Discrete choice analysis of the impact of tourism prices”, Journal of Travel
Research, Fall, pp. 8-14.
Morrison, A. (1989), Hospitality and Tourism Marketing, Delmar, Albany, NY.
Moutinho, L. (1982), “An investigation of tourist behavior in Portugal – a comparative analysis of
pre-decision buying and post-purchasing attitudes of British, American and West German
Tourists”, PhD, University of Shef?eld, Shef?eld.
Moutinho, L. (1987), “Consumer behavior in tourism”, European Journal of Marketing, Vol. 21
No. 10, pp. 1-44.
Murphy, P., Pritchard, M. and Smith, B. (2000), “The destination product and its impact on
traveler perceptions”, Tourism Management, Vol. 21 No. 1, pp. 43-52.
Nicolau, J. and Ma´s, F. (2005), “Stochastic modelling: a three-stage tourist choice process”, Annals
of Tourism Research, Vol. 32 No. 1, pp. 49-69.
Nicosia, F. (1966), Consumer Decision Process, Prentice-Hall, Englewood Cliffs, NJ.
Oh, H. (2000), “The effect of brand class, brand awareness, and price on customer value and
behavioral intentions”, Journal of Hospitality and Tourism Research, Vol. 24 No. 2,
pp. 136-62.
O’Hagan, J. and Harrison, M. (1984), “Market shares of US tourist expenditures in Europe: an
econometric analysis”, Applied Economics, Vol. 16 No. 6, pp. 919-31.
Oliver, R. (1980), “A cognitive model of antecedents and consequences of satisfaction decisions”,
Journal of Marketing Research, Vol. 17, pp. 460-9.
Oliver, R. (1993), “Cognitive, affective, and attributes base of the satisfaction response”, Journal
of Consumer Research, Vol. 20, pp. 418-30.
Oliver, R. (1999), “Whence consumer loyalty?”, Journal of Marketing, Vol. 63, pp. 33-44.
Oppermann, M. (2000), “Tourism destination loyalty”, Journal of Travel Research, Vol. 39,
pp. 78-84.
Otto, J. (1997), “The role of the affective experience in the service experience chain”, unpublished
PhD, The University of Calgary, Calgary.
Otto, J. and Ritchie, J. (1995), “Exploring the quality of the service experience: a theoretical and
empirical analysis”, Advances in Services Marketing and Management, Vol. 4, pp. 37-61.
Paraskevopoulos, G. (1977), “An econometric analysis of international tourism”, Paper 31, Centre
of Planning & Economic Research, Athens.
Parasuraman, A., Zeithaml, V. and Berry, L. (1985), “A conceptual model of service quality and
its implications for future research”, Journal of Marketing, Vol. 49, pp. 41-50.
Parasuraman, M. (2000), “Tourism destination loyalty”, Journal of Travel Research, Vol. 39,
pp. 78-84.
Pearce, P. (1982), “Perceived changes in holiday destinations”, Annals of TourismResearch, Vol. 9
No. 2, pp. 145-64.
Perales, R. (2002), “Rural tourism in Spain”, Annals of Tourism Research, Vol. 29 No. 4,
pp. 1101-10.
Decision-making
processes
371
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
:
0
6
2
4
J
a
n
u
a
r
y
2
0
1
6
(
P
T
)
Petrick, J. (2002), “Experience use history as a segmentation tool to examine golf travellers’
satisfaction perceived value and repurchase intentions”, Journal of Vacation Marketing,
Vol. 8 No. 4, pp. 332-42.
Petrick, J., Backman, S. and Bixler, R. (1999), “An investigation of selected factors effect on golfer
satisfaction and perceived value”, Journal of Park and Recreation Management, Vol. 17
No. 1, pp. 40-59.
Rojek, C. (1990), “Baudrillard and leisure”, Leisure Studies, Vol. 9 No. 1, pp. 7-20.
Rosenberg, M. (1956), “Cognitive structure and attitudinal affect”, Journal of Abnormal and
Social Psychology, Vol. 53, pp. 376-82.
Ross, E. and Iso-Ahola, S. (1991), “Sightseeing tourists motivation and satisfaction”, Annals of
Tourism Research, Vol. 18, pp. 226-37.
Ryan, C. (1994), “Leisure and tourism – the application of leisure concepts to tourist behavior – a
proposed model”, in Seaton, A. (Ed.), Tourism: The State of the Art, Wiley, New York, NY.
Ryan, C. and Glendon, I. (1998), “Application of leisure motivation scale to tourism”, Annals of
Tourism Research, Vol. 25 No. 1, pp. 169-84.
Samuelson, P. (1981), Economia, 11th ed., Fundac¸a˜o Calouste Gulbenkian, Lisboa.
Scho?eld, P. (1996), “Cinematographic images of a city: alternative heritage tourism in
Manchester”, Tourism Management, Vol. 17 No. 5, pp. 333-40.
Seddighi, H. and Theocharous, A. (2002), “A model of tourism destination choice: a theoretical
and empirical analysis”, Tourism Management, Vol. 23 No. 5, pp. 475-87.
Sheldon, P. (1990), “A review of tourism expenditure research”, in Cooper, C.P. (Ed.), Progress in
Tourism, Recreation and Hospitality Management, Belhaven Press, New York, NY.
Sheth, J., Newman, B. and Gross, B. (1991), “Why we buy what we buy: a theory of consumption
values”, Journal of Business Research, Vol. 22, pp. 159-70.
Shoemaker, S. (1989), “Segmentation of the senior pleasure travel market”, Journal of Travel
Research, Vol. 27 No. 3, pp. 14-21.
Silvestre, A. and Correia, A. (2005), “Asecond- order factor analysis model for measuring tourist’s
overall image of Algarve (Portugal)”, Tourism Economics, Vol. 11 No. 4, pp. 539-54.
Song, H. and Witt, S. (2000), Tourism Demand Modelling and Forecasting: Modern Econometric
Approaches, Pergamon, Oxford.
Spreng, R., Mackenzie, S. and Olshavsky, B. (1996), “A re-examination of the determinants of
consumer satisfaction”, Journal of Marketing, Vol. 60 No. 3, pp. 15-22.
SPSS Inc. (2005), SPSS 14.0, SPSS Inc., Chicago, IL.
Steiger, J. (1990), “Structural model evaluation and modi?cation: an interval estimation
approach”, Multivariate Behavior Research, Vol. 25, pp. 173-80.
Stynes, D. and Peterson, G. (1984), “A review of logit models with implications for modelling
recreational choices”, Journal of Leisure Research, Vol. 16, pp. 295-310.
Taplin, J. and Qiu, M. (1997), “Car trip attraction and route choice in Australia”, Annals of
Tourism Research, Vol. 24 No. 3, pp. 624-37.
Truong, T. (2005), “Assessing holiday satisfaction of Australian travellers in Vietnam: an
application of the HOLSATmodel”, Asia Paci?c Journal of TourismResearch, Vol. 10 No. 3,
pp. 227-46.
Tse, D. and Wilton, P. (1988), “Models of consumer satisfaction formation: an examination”,
Journal of Marketing Research, Vol. 25, pp. 204-12.
IJCTHR
2,4
372
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
:
0
6
2
4
J
a
n
u
a
r
y
2
0
1
6
(
P
T
)
Tucker, L. and Lewis, C. (1973), “A reliability coef?cient for maximum likelihood factor
analysis”, Psychometrika, Vol. 38, pp. 1-10.
Um, S. and Crompton, J. (1990), “Attitude determinants in tourism destination choice”, Annals of
Tourism Research, Vol. 17, pp. 432-48.
Uysal, M. and Hagan, L. (1993), “Motivation of pleasure to travel and tourism”, in Khan, M.,
Olsen, M. and Var, T. (Eds), VNR’S Encyclopedia of Hospitality and Tourism, Van
Nostrand Reinhold, New York, NY.
Uysal, M., Mclellan, R. and Syrakaya, E. (1996), “Modelling vacation destination decisions: a
behavioral approach”, Recent Advances in Tourism Marketing Research, Vol. 5 Nos 1/2,
pp. 57-75.
Vogt, C. and Andereck, K. (2003), “Destination perceptions across a vacation”, Journal of Travel
Research, Vol. 41 No. 4, pp. 348-54.
Walmsley, D. and Jenkins, J. (1992), “Tourism cognitive mapping of unfamiliar environments”,
Annals of Tourism Research, Vol. 19, pp. 268-86.
Whipple, T. and Thatch, S. (1988), “Group tour management: does good service produce satis?ed
customers?”, Journal of Travel Research, Vol. 27 No. 2, pp. 16-21.
Witt, S. (1992), “Tourism forecasting: how well do private and public sector organizations
perform?”, Tourism Management, Vol. 13 No. 1, pp. 79-84.
Witt, S. and Martin, C. (1987), “International tourism demand models – inclusion of marketing
variables”, Tourism Management, Vol. 8 No. 1, pp. 33-44.
Woodside, A. (2005), “Advancing from subjective to con?rmatory personal introspection in
consumer research”, Psychology & Marketing, Vol. 21 No. 12, pp. 987-1010.
Woodside, A. and Dubelaar, C. (2002), “A general theory of tourism consumption systems: a
conceptual framework and an empirical exploration”, Journal of Travel Research, Vol. 41
No. 2, pp. 120-32.
Woodside, A. and King, R. (2001), “Tourism consumption systems: theory and empirical
research”, Journal of Travel and Tourism Research, Vol. 10 No. 1, pp. 3-27.
Woodside, A. and Lysonski, S. (1989), “A general model of travel destination choice”, Journal of
Travel Research, Vol. 27 No. 4, pp. 8-14.
Woodside, A. and Sherrell, D. (1977), “Travel evoked, inept, and inert sets of vacation
destinations”, Journal of Travel Research, Vol. 16 No. 3, pp. 2-6.
Woodside, A., Frey, L. and Daly, R. (1989), “Linking service quality, customer satisfaction and
behavioral intention”, Journal oh Health Care Marketing, Vol. 9, pp. 5-17.
WTO (2005), Tourism Highlights, 2005 Edition, World Tourism Organization, Madrid.
Yoon, Y. and Uysal, M. (2005), “An examination of the effects of motivation and satisfaction on
destination loyalty: a structural model”, Tourism Management, Vol. 26 No. 1, pp. 45-56.
Zeithaml, V. (1988), “Consumer perceptions of price, quality and value: a means-end model and
synthesis of evidence”, Journal of Marketing, Vol. 52, pp. 2-22.
Corresponding author
Anto´nia Correia can be contacted at: [email protected]
Decision-making
processes
373
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
:
0
6
2
4
J
a
n
u
a
r
y
2
0
1
6
(
P
T
)
This article has been cited by:
1. Ana Isabel Rodrigues, Antónia Correia, Metin Kozak, Anja TuohinoLake-Destination Image Attributes:
Content Analysis of Text and Pictures 293-314. [Abstract] [Full Text] [PDF] [PDF]
2. Leila Etaati, David Sundaram. 2015. Adaptive tourist recommendation system: conceptual frameworks
and implementations. Vietnam Journal of Computer Science 2, 95-107. [CrossRef]
3. Kuan-Huei Lee, Jan Packer, Noel Scott. 2015. Travel lifestyle preferences and destination activity choices
of Slow Food members and non-members. Tourism Management 46, 1-10. [CrossRef]
4. Sirvan Sen Demir, Metin Kozak, Antonia Correia. 2014. Modelling Consumer Behavior: An Essay with
Domestic Tourists in Turkey. Journal of Travel & Tourism Marketing 31, 303-312. [CrossRef]
5. Antónia Correia, Metin Kozak, João Ferradeira. 2013. From tourist motivations to tourist satisfaction.
International Journal of Culture, Tourism and Hospitality Research 7:4, 411-424. [Abstract] [Full Text]
[PDF]
6. Helena Reis, Antonia Correia. 2013. Gender Asymmetries in Golf Participation. Journal of Hospitality
Marketing & Management 22, 67-91. [CrossRef]
7. Antónia Correia, Metin Kozak. 2012. Exploring prestige and status on domestic destinations: The case
of algarve. Annals of Tourism Research 39, 1951-1967. [CrossRef]
8. Asad Mohsin, Abdulaziz Mohammed Alsawafi. 2011. Exploring attitudes of Omani students towards
vacations. Anatolia 22, 35-46. [CrossRef]
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
:
0
6
2
4
J
a
n
u
a
r
y
2
0
1
6
(
P
T
)
doc_219921037.pdf
This paper aims to study the decision-making processes of Portuguese tourists traveling
to South America and Africa destinations by developing a conceptual framework that focuses on
information sources, motivations, perceptions, satisfactions, and behavioral intentions
International Journal of Culture, Tourism and Hospitality Research
Decision-making processes of Portuguese tourist travelling to South America and Africa
Antónia Correia Adriano Pimpão
Article information:
To cite this document:
Antónia Correia Adriano Pimpão, (2008),"Decision-making processes of Portuguese tourist travelling to
South America and Africa", International J ournal of Culture, Tourism and Hospitality Research, Vol. 2 Iss 4
pp. 330 - 373
Permanent link to this document:
http://dx.doi.org/10.1108/17506180810908989
Downloaded on: 24 January 2016, At: 22:06 (PT)
References: this document contains references to 165 other documents.
To copy this document: [email protected]
The fulltext of this document has been downloaded 2287 times since 2008*
Users who downloaded this article also downloaded:
Antónia Correia, Metin Kozak, J oão Ferradeira, (2013),"From tourist motivations to tourist satisfaction",
International J ournal of Culture, Tourism and Hospitality Research, Vol. 7 Iss 4 pp. 411-424 http://
dx.doi.org/10.1108/IJ CTHR-05-2012-0022
Lan-Lan Chang, Kenneth F. Backman, Yu Chih Huang, (2014),"Creative tourism: a preliminary examination
of creative tourists’ motivation, experience, perceived value and revisit intention", International J ournal
of Culture, Tourism and Hospitality Research, Vol. 8 Iss 4 pp. 401-419 http://dx.doi.org/10.1108/
IJ CTHR-04-2014-0032
Songshan (Sam) Huang, Cathy H.C. Hsu, (2009),"Travel motivation: linking theory to practice",
International J ournal of Culture, Tourism and Hospitality Research, Vol. 3 Iss 4 pp. 287-295 http://
dx.doi.org/10.1108/17506180910994505
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
:
0
6
2
4
J
a
n
u
a
r
y
2
0
1
6
(
P
T
)
Decision-making processes
of Portuguese tourist travelling
to South America and Africa
Anto´nia Correia and Adriano Pimpa˜o
Faculty of Economics, University of Algarve, Faro, Portugal
Abstract
Purpose – This paper aims to study the decision-making processes of Portuguese tourists traveling
to South America and Africa destinations by developing a conceptual framework that focuses on
information sources, motivations, perceptions, satisfactions, and behavioral intentions.
Design/methodology/approach – The study applies a structural model that looks to explain the
factors behind decision making and the relationships present. The relationships are observed in detail
through the application of a categorical principal component analysis.
Findings – The results of the empirical study show that behavioral intentions precede emotional and
cognitive satisfaction, which in turn, are explained through perceptions and motivations. Tourists
perceive tourism destinations as places of leisure although little information is available on existing
facilities and core attractions.
Research limitations/implications – The study has the restriction of being limited to the
Portuguese tourists. However, these ?ndings open paths for further investigation, namely extending to
other destinations and to tourists with different motivations.
Originality/value – This study contributes to the overall understanding of the decision-making
processes of tourists. Speci?cally, the decision processes is assess by considering two stages: the
pre-purchase stage and the post-purchase stage. These two phases were analyzed in order to
understand how people decide to travel to a certain destination.
Keywords Motivation (psychology), Decision making, Tourism, Portugal, South America, Africa
Paper type Research paper
Introduction
Tourism is one of the major growth sectors worldwide particularly in Portugal. As
tourism develops globally, where tourists and tourism play an important role, the need
to understand the reasons behind tourist behavior is of fundamental importance.
Strategic management of tourist destinations stems from the development of consumer
behavior theories from which understanding and prediction of tourist choice is a
challenge towards excellence.
Tourism demand is a topic receiving growing attention from a large number of
scholars. Tourism demand is of increasing concern for destination policy makers. This
topic, having ?rst appeared in the tourism literature in the 1950s, depended
fundamentally on variables that were tourism demand related. Backed by econometric
modeling, the ?rst studies predicted the demand for tourism through a more
The current issue and full text archive of this journal is available at
www.emeraldinsight.com/1750-6182.htm
The authors acknowledge the ?nancial support of AIR LUXOR, SA, the comments of Arch
Woodside and Carlos Barros, the editing help of Cla´udia Moc¸o and ?nal proofreading assistance
of Susy Rodrigues.
IJCTHR
2,4
330
Received February 2008
Revised April 2008
Accepted May 2008
International Journal of Culture,
Tourism and Hospitality Research
Vol. 2 No. 4, 2008
pp. 330-373
qEmerald Group Publishing Limited
1750-6182
DOI 10.1108/17506180810908989
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
:
0
6
2
4
J
a
n
u
a
r
y
2
0
1
6
(
P
T
)
macro-economic approach (Crouch, 1994). In the econometric or time-series ?elds,
studies that forecast tourism demand based on aggregated data have steadily
increased (Witt, 1992).
The micro-economic principles, which base their conclusions on Marshall’s (1920)
and Lancaster’s (1966) theories of classical economics, focus on consumer
heterogeneity and the need to ?nd each individual’s demand curve. This approach
towards tourism demand acknowledges that man is a rational being who behaves in
terms of maximum satisfaction with decisions based on in-depth knowledge of all
possible alternatives. This form of trying to understand human behavior is however
limited, namely, in man’s incapacity to perceive and evaluate all existing alternatives
(Decrop, 1999; Mansfeld, 1992).
This results, ?rstly, from the existence of an in?nite number of possibilities of
maximum consumer utility. Within the context of tourism, the situation grows even
more complex, where destination diversity, accommodation, recreation, means of
transport and motivation all compete for equal use. Secondly, the human condition
itself lacks the capacity to apprehend. Man’s decision-making process, despite its
limitations, has been widely used in modeling demand (Crouch, 1994; Lim, 1997). Most
of these demand function studies observe tourist behavior through time-series,
single-equation or simultaneous equation modeling, where lodging, guest numbers or
arrivals represent explained variables and budget and cost factors associated to the
destination (such as price, exchange rates and travel expenses) represent explanatory
variables (Archer, 1976; Artus, 1972; Sheldon, 1990; Witt and Martin, 1987).
These demand functions through econometric modeling allow estimation of price
elasticity and income demand in order to develop destination strategies, through
neglecting consumer heterogeneity and consumer cognitive capacity.
The consumer, as a rational human being, possesses dynamic behavior with
increasingly sophisticated needs and complex motivations (Correia, 2000). Within the
context of dynamics and complex cognitive interactions, the cognitive tourist thinker
decides according to a destination’s attributes, his or her intrinsic motivations, and
destination knowledge learned (Howard and Sheth, 1969). Consumer behavioral
models can thus be transversal, allowing different processes to be analyzed before ?nal
decisions are reached.
Such factors as motivations and external stimuli in determining preferences
(conducive to choice) and satisfaction (conducive to future behavior) in?uence the
decision-making process. Although this dynamic system of decision-making builds
from other ?elds of study and is adapted to explain tourism behavior, its
interrelationships remain an issue that deserves more research (Woodside, 2005).
This paper presents a conceptual decision-making framework based on
microeconomic theory and behavioral models that allow us to examine variables
that in?uence travel behavior. The chapter provides an empirical basis for
understanding attitude-behavior interrelationships.
The proposed framework is useful for examining Portuguese tourists who travel to
South America and Africa destinations such as Brazil, Morocco, Egypt, the Dominican
Republic and Sao Tome and Principe through the application of a structural equation
model (henceforth, SEM) (Joreskog and Sorbom, 1986) and a categorical principal
component analysis (CATPCA). The SEM adopted by Rosenberg (1956) and Fishbein
(1967) assumes that choice is a function of the perceptions and attributes towards
Decision-making
processes
331
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
:
0
6
2
4
J
a
n
u
a
r
y
2
0
1
6
(
P
T
)
a particular destination. Later, Fishbein and Ajzen (1980) added a behavioral intention
component to Fishbein’s original model as a function of expectations together with
social and individual factors.
However, the majority of studies about holiday decision-making processes do not
follow this approach. Research focuses on exploratory statistics, with SEM being
rarely adopted. Following Yoon and Uysal’s (2005) framework, this model introduces
new constructs such as the role of information sources (brochures, advertising, travel
movies and news) in shaping motivations, perceptions, satisfactions and behavioral
intentions.
Internal forces push and external forces pull individuals who travel (Correia and
Crouch, 2004; Correia et al., 2007b; Crompton, 1979; Dann, 1977; Kozak, 2001; Uysal and
Hagan, 1993). However, the interplay between push and pull motivations needs to be
considered in terms of satisfaction. To achieve this, the model assumes two additional
constructs: push satisfaction (emotional) and pull satisfaction (cognitive). Studies on
satisfactions have received signi?cant attention (Baker and Crompton, 2000; Moutinho,
1987; Ryan and Glendon, 1998). Researchers often refer to satisfaction as an emotional
state of ful?llment after concluding an experience. Since ful?llment does not only rely
on destination attributes, it makes sense to use the traditional breakdown of
motivations into push and pull motives in the satisfaction assessment.
This paper describes push and pull satisfactions as two individual factors that
contribute to the overall evaluation. Push satisfaction is an individual’s internal state of
well-being towards his or her holiday, and in harmony to his or her main push
motivations. A tourist goes on holiday because there is need to achieve intellectual,
physical and social rewards, and the concept of push satisfaction measures the level of
internal achievement perceived by the tourist. Pull satisfaction con?rms tourist
expectations in terms of destination attributes, a concept traditionally explored in
tourism studies (Bigne´ et al., 2001; Correia et al., 2007a; Murphy et al., 2000; Yoon and
Uysal, 2005; Ryan and Glendon, 1998).
This paper develops and empirically validates a SEM capable of measuring the
decision-making process of Portuguese tourists who visit South America and Africa
destinations. Considering not only the pre-purchase phase wherein motivations and
perceptions are analysed (Correia et al., 2007c) but also the post-purchase phase
wherein satisfaction and behavioral intentions are also considered. This study is
therefore an extended version of the paper presented by Correia et al. (2007c) who
combine motivations and perceptions in order to understand why people travel to
exotic places. Thus, the paper’s conceptual framework considers all measurable
constructs related to holiday decision-making processes found in the literature with the
development of new interrelationships of alternative variables, through SEM.
This study adopts CATPCA to explore the cause-effect relationships among each of
the observed variables in the model. This statistical method represents a set of
categorical variables on perceptual maps and allows us to explore the simultaneous
relations among the observed variables used to measure the latent constructs in the
structural model. This model, which lets us identify signi?cant statistical variables
that explain how and why tourists behave as they do in such places, has clear
implications in the de?nition of marketing strategies and tourist choice factors that
strongly contribute to the overall image of South America and Africa destinations.
IJCTHR
2,4
332
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
:
0
6
2
4
J
a
n
u
a
r
y
2
0
1
6
(
P
T
)
This chapter has the following structure. The next section presents the contextual
setting while the following section presents a literature review on seminal models of
consumer behavior theory, decision-making processes and their constructs. The next
section addresses the conceptual model adopted and introduces the hypotheses
associated constructs and adopted methodology. The following section presents the
empirical model. This section is organized into four sub-sections:
(1) the shaping of each construct through exploratory factor analysis (EFA);
(2) the measurement model;
(3) the structural model testing; and
(4) the relationship of each set of variables that measured the latent constructs of
the model.
The penultimate section discusses the results and underline implications at the
management level. Finally, the last section presents the conclusions, limitations, and
extensions of the research.
Contextual setting: Portuguese tourists in South America and Africa
destinations
Although Portugal is one of the foremost holiday destinations in Europe, the
twenty-?rst century witnesses the beginning of Portuguese citizens traveling outside
their home country. Tourism has seen some retraction in countries such as Asia and
the Paci?c who have now awaken as tourism markets on a worldwide scale, digesting
20 percent of international arrivals which represent a 28 percent increase compared to
2003 (WTO, 2005). Faster and more economical air transportation, as well as shortened
?ight durations explains the increase in new routes and greater investment on more
long-haul destinations.
Between 2000 and 2004, results show a reduction from 71 to 53 percent in
Portuguese individuals over the age of 15 who traveled on holiday, with a signi?cant
increase to 56 percent in 2005. The number of Portuguese tourists traveling abroad
showed little increase, from 19 percent in 2004 to 21 percent in 2005, although these
numbers only represent one ?fth of the population who normally travel. In 2005,
Europe continued to lead the trend with about 50 percent of tourists traveling abroad,
followed by South America (18 percent), Asia (3 percent), and Africa (1 percent).
Increased tourism to destinations such as Asia is backed by the growing trend of
Portuguese tourists seeking different holiday destinations. In 2005, more than
65 percent of tourists preferred a sun and sea product while 20 percent opted for the
countryside (DGT, 2006).
The Portuguese generally go on holiday between the months of August and
September (81 percent) often because these months coincide with school summer break
or form part of employment contract agreements. However, 16 percent of tourists
depend on travel agencies for assistance, while the majority prefers to make their own
travel arrangements (42 percent) or via the internet (8 percent). In fact, preferential use
of the internet as a means for holiday planning has doubled since 2004. The majority of
Portuguese holidaymakers are under the age of 45 (64 percent) and on socio-economical
terms, the majority belong to higher social classes, as would be expected.
In addition, the average daily expense while on holiday abroad increased signi?cantly
from 2004 to 2005, that is, e76 in 2005 compared to e61 in 2004 per person day.
Decision-making
processes
333
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
:
0
6
2
4
J
a
n
u
a
r
y
2
0
1
6
(
P
T
)
When traveling abroad, Portuguese tourists prefer to travel on chartered ?ights to
destinations such as Brazil, Mexico, Tunisia, Morocco, Sao Tome and Principe and Egypt.
Air Luxor, SA is a privately owned Portuguese airline company, which is among the top
three national tour operators. Air Luxor operates charter ?ights from Portugal to South
American tourist resorts, namely Brazil and Mexico; to Africa, namely, Morocco, Tunisia,
and Egypt; to the tropical island of Sal in Cape Verde; and, to the islands of Sao Tome and
Principe. Air Luxor also offers scheduled ?ights within Europe as well as Africa, namely,
Sao Tome and Guinea-Bissau in Africa.
The study analyses the in?uence of various sources of information on decision
motivations, destination perceptions, satisfaction levels, and future behavioral intentions.
The ?ndings were obtained from conducted surveys on two separate occasions: upon
departure and upon return. Data collection took place aboard Air Luxor planes.
Literature review
Seminal models of consumer behavior
Consumer behavior is a dynamic and complex process. When applied to tourism, this
process becomes even more complex by the intangibility of the product and by the
discontinuity and accumulation of purchasing power (Correia, 2002). The
interdisciplinary nature of the subject under study has given rise to three distinct
groups of models: microeconomic models, structural models, and processional models.
Microeconomic models assume that the tourist looks to maximize his or her utility
based on a set of attributes and three constraints: time, money, and technology (Morley,
1992). The structural models examine the relationship between an input (stimulus) and
an output (response), while the processional models examine individuals’ decisions,
concentrating on the cognitive processes (transformation process between the input and
the output) generated prior to the ?nal decision being taken (Abelson and Levi, 1985).
The microeconomic models of consumer behavior follow classic economic theory
(Marshal, 1920). However, this theory is only able to handle simple product demand,
and as such, presents some limitations in terms of tourism analysis due to the elements
that characterize it. According to Samuelson (1981), the concept of individual
maximization towards product tradeoffs can account for composed product analysis,
such as tourism. Considering that tourist destinations appear, not as an object of direct
use, but as products whose characteristics permit us to endow them with utility
(Lancaster, 1966), maximizing their utility is possible, subject to a certain number of
restrictions. Morley (1992) presents microeconomic theory applications to tourism
(Lim, 1997; McFadden, 1981; O’Hagan and Harrison, 1984; Paraskevopoulos, 1977;
Song and Witt, 2000; Witt and Martin, 1987).
Processional models examine the complete decision-making process by
concentrating on the cognitive processes generated prior to making his or her ?nal
decision. They provide information on consumer behavior during the decision process
that goes unnoticed by the individual himself. The variable in processional models
is the decision process itself, in addition to other factors that in?uence this process.
Any tourism product boasts a multiplicity of attributes that de?ne and distinguish it
from competing alternatives. The majority of consumers are unable to process a large
number of variables simultaneously, and hence apply little criteria when reaching a
?nal decision. Three outstanding models underpin all of the studies in the ?eld of
consumer behavior analysis from a processional perspective.
IJCTHR
2,4
334
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
:
0
6
2
4
J
a
n
u
a
r
y
2
0
1
6
(
P
T
)
The Nicosia (1966) model focuses on the communication that takes place between
the consumer and the company, in which the latter, through tactics, seeks to persuade
the consumer into purchasing speci?c product(s). The work carried out by Engel et al.
(1978) is one of the most extensive literature review works available on consumer
behavior. Howard and Sheth’s (1969) model, which employs the input concept in
consumer behavior models, suggest forms of sequencing these inputs in the
decision-making process. This model continues to be one of the most important
contributions on consumer behavior theories.
Studies on tourist consumer behavior from the perspective of decision-making
processes began to appear in the 1970s. Most models explain the tourist decision
process in terms of sequenced, interrelated stages, varying in number between three
and ?ve. They look to assess different constructs of decision-making and their
interplay from the pre-purchase stage to the post-purchase stage (Crompton, 1979;
Middleton, 1994; Moutinho, 1982, 1987; Nicolau and Ma´s, 2005; Ryan, 1994; Um and
Crompton, 1990; Woodside and Lysonski, 1989; Woodside and King, 2001). Regardless
of the number of stages proposed in the literature, the models vary essentially in terms
of the focus placed on perception shaping and on post-purchase stage evaluation. They
rely on inferential and observational analysis. Similar to other behavioral models, these
models only allow cognitive factors to be considered. Foxall and Goldsmith (1994)
suggest that the models, although conveying very little meaning, help to understand
consumer actions. According to these authors, consumer decisions follow a series of
stages. Though decisions are nonlinear, the models serve to clarify which variables
predominate in consumer decision-making. In considering different choice
frameworks, cognitive elements will impact differently when making a decision and
in?uence attitude in distinct ways. These structural models serve as the theoretical
basis for the model proposed in the paper.
The ?rst structural models, developed by Rosenberg (1956) and Fishbein (1967)
apply the principle that decision-making is a function of objective perceptions and
destination attributes. Several researchers, however, concluded that cognitive elements
could differ in qualitative terms and, thus, be organized into different frameworks
and categories. Researchers involved in attitudinal studies believe that consumers need
to compare purchasing attitudes or intentions within different conceptual frameworks
of their behavior. Before ?nal decision making is possible, individuals will need to
settle cognitive, affective, and connotative factors and benchmark these against each
other. After weighing all elements, the ?nal choice occurs (Cohen et al., 1972; Fishbein,
1967; Fishbein and Ajzen, 1980; Rosenberg, 1956).
Fishbein and Ajzen (1980) propose the behavioral intention model, which
constitutes an extension of Fishbein’s (1967) original model. While maintaining that
behavioral theory represents the model’s basis, they consider behavioral intentions as
expectation functions, together with social and individual factors (Fishbein and Ajzen,
1980). The model allows the assumption that objects can be evaluated based on
multiple attributes that generate costs and bene?ts at different levels. The attitude
index does not increase inde?nitely when acquiring new expectations because attitude
is explained based on limited number of visible attributes.
Multi-attribute models consider that products possess several self-compensating or
compensatory attributes, taking the value-expectancy theory (Edwards, 1954) as the
underlying basis. This theory de?nes expectation as the probability that a certain
Decision-making
processes
335
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
:
0
6
2
4
J
a
n
u
a
r
y
2
0
1
6
(
P
T
)
attitude will lead to positive or negative bene?ts, isolating behavioral factors and
establishing in what way expectation and value combine to achieve a settled decision.
The value-expectancy theory measures subjective utility, and builds from Edwards’
(1954) behavioral decision theory. This theory views expectation as the consequence of
adopting a certain behavior, explained in terms of prospects and values resulting from
the choice the individuals makes. The advantage of the value-expectancy theory is that
it allows for the integration of other components such as related emotions and reasons
for traveling, discussed in studies on tourist motivation. Furthermore, it can take into
account push and pull factors, and personality assessments. Finally, the theory allows
a more realistic and re?ned view of tourist motivation.
The literature assesses tourism behavior from an exploratory analysis of
motivations, expectations, perceptions, and satisfaction. Gallarza et al. (2002) apply
statistical multivariate techniques that rely on a principal component analysis,
correlation tests, cluster analysis, multiple discriminant analysis, and homogeneity
analysis to tourism. Discrete-choice models, in particular qualitative choice models, can
be used to assess tourism behavior. These rely on binomial logit (Barros and Proenc¸a,
2005; Fleischer and Pizam, 2002; Kockelman and Krishnamurthy, 2004; Perales, 2002;
Stynes and Peterson, 1984) or multinomial logit (Hong et al., 2006; Kockelman and
Krishnamurthy, 2004; Luce, 1959; McFadden, 1981; Morley, 1994; Nicolau and Ma´s,
2005; Seddighi and Theocharous, 2002; Taplin and Qiu, 1997). More recently, tourist
behavior has been assessed through structural equation modeling. Baker and
Crompton (1998) test the effect of perceived quality performance on behavioral
intentions, Yoon and Uysal (2005) test causal relationships among push and pull
motivations, satisfaction and destination loyalty, Vogt and Andereck (2003) explain
how emotion and cognition can in?uence perceptions, Silvestre and Correia (2005),
from a second-order factor analysis, explain the image of Algarve as a tourist
destination, Correia et al. (2007b) assess motivations and perceptions about exotic
destinations, and Kim and Yoon (2003) observe perceptions from a conceptual point
of view.
The decision process
The tourist decision process assumes three essential stages, namely, the pre-decision,
decision and post-purchase evaluation stages (Bentler and Speckart, 1979; Correia,
2002; Crompton, 1992; Crompton and Ankomah, 1993; Middleton, 1994; Moutinho,
1982; Ryan, 1994; Um and Crompton, 1990).
The pre-decision stage can often occur on products, such as tourist destinations,
that are intangible or invisible before or at the time of purchase, oftentimes involving
decision-making from a range of competing alternatives. Destination choice and
associated factors that form part and parcel of any holiday planning, involve a group of
complex decisions that take up time and energy. However, most tourists take pleasure
in this process (Crouch and Jordan, 2004). The pre-decision stage serves to build up
motivations, in?uenced by available destination information sources that contribute to
perception development.
The decision stage includes the evaluation of perceptions through which consumers
base their decisions in terms of time and budget constraints, conditioning factors that
restrict choice. Given the time interval between purchase and use, the former
represents a transitory process.
IJCTHR
2,4
336
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
:
0
6
2
4
J
a
n
u
a
r
y
2
0
1
6
(
P
T
)
Post-purchase evaluation results from other stimuli that in?uence the choice
process and evaluates satisfaction gained from the product (destination). This stage is
also important in estimating the probability of repeating the purchase of a speci?c
destination and/or the intention to recommend visiting the destination. The paper
discusses the theoretical underpinning of this decision-making process in the following
sub-section.
Theoretical constructs
Motivational constructs
Motivation refers to an individual’s need to adopt a certain behavior in order to satisfy
this condition. Fodness (1994) argues that motivation theories describe a dynamic
process of internal psychological factors (needs, desires and goals) which generate a
level of tension in an individual and in?uence him or her towards purchase. Baloglu
(1997), Dann (1996) and Gartner (1993) suggest that motivations have a direct in?uence
on the affective component of an image such as a destination that generates certain
feelings. Individuals with different motivations may similarly evaluate a tourist
destination if the destination is able to succeed in the desired bene?ts.
Crompton’s (1979) widely accepted push-pull model represent two forces in tourism
research. Push motivations correspond to forces whereby individuals are pushed by
motivational factors into making travel decisions and seen as the desire for personal
achievement, satisfaction, rest and relaxation, adventure, knowledge, getting away, and
social interaction. Pull motivations, on the other hand, re?ect internal or emotional factors
prompted by the attributes of a destination (Uysal et al., 1996). The characteristics or
attributes of a destination allow the tourist to create expectations in terms of satisfying
motivational needs. Several studies have explored motivational determinants in the
tourism context (Beerli and Mart? ´n, 2004; Correia and Crouch, 2004; Correia et al., 2007b;
Crompton, 1979; Dann, 1981; Fodness, 1994; Gnoth, 1997; Iso-Ahola and Mannel, 1987;
Lundberg, 1990; Mohsin and Ryan, 2003; Pearce, 1982; Shoemaker, 1989; Uysal and
Hagan, 1993; Uysal et al., 1996; Yoon and Uysal, 2005).
After examining the context of needs, the tourist enters the learning stage and
searches for the destination capable of yielding satisfaction and ful?llment.
Learning process
Learning is the process whereby the consumer acquires knowledge about a product
and subsequent consumption experience when considering future behaviors. Bettman
and Park (1980) examine learning processes and develop an information-processing
model in which the consumer, who possesses limited memory, performs decisions
through simpli?cation processes. The consumer is only capable of retaining a
maximum of seven destinations and a minimum of two according to Miller (1956).
The simpli?cation process is a mechanism that consumers use to ?lter extraneous
information from an array of products (destinations) put before him or her. Reducing
the list of options to a limited set is referred to in the literature as the evoked set,
whereby information is more manageable in terms of consumer processing and
cognitive retention (Moutinho, 1987; Um and Crompton, 1990; Woodside and Lysonski,
1989).
Howard and Sheth (1969) discuss the different activation levels of destinations in
the consumer’s memory. In limiting his or her set of destinations, the consumer is able
Decision-making
processes
337
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
:
0
6
2
4
J
a
n
u
a
r
y
2
0
1
6
(
P
T
)
to evaluate and display interest. Inert set destinations represent those destinations
causing indecisiveness, with inept set destinations being those destinations that
represent no interest to the consumer at all (Woodside and Sherrell, 1977). When
consumers process these elements, the learning stage begins.
Analyzing the way in which the consumers learn by considering behavioral or
cognitive issues is possible. The behavioral perspective allows learning to occur based
on three factors: information gathering, choice, and experience. The bene?ts obtained
result in repeat behavior. The cognitive perspective assumes that learning derives from
an unresolved problem.
Information about a destination that is processed and stored can be separated into
cognitive (evaluation of the product’s attributes) and affective components
(motivations that affect the underlying desires about the destination). The learning
process associated to product knowledge refers to the behavioral variations that can
occur as a result of internal and external stimuli. This can develop from previous
experience, recommendations from family and friends, publicity and promotion, word
of mouth and so on. These stimuli serve to ?rst activate the individual’s needs
and motivations, which shape decision making and help the consumer tourist
(in?uenced by personality and psycho-sociological characteristics) build images for
each alternative destination.
Studies on tourist learning processes and the impact of information sources in
portraying the destination’s image have addressed some of the stimuli that in?uence
the cognitive process. Beesley (2005) proposes a model that relates communication,
individual cognition, social contingencies, affection, and values in order to understand
the dynamic process of learning. Fisher (2004) argues that the learning process can
identify four types of tourist behavior: exact imitation, accidental inexact imitation,
and social learning. Walmsley and Jenkins (1992) present a methodology for
understanding the learning process based on cognitive mapping. Here, tourists rapidly
develop cognitive images of destinations in?uencing cognitive maps, both in the
immediate sense (time spent in destination) and in the more general sense (lifestyle to
which the tourist is accustomed). Guy et al. (1990) point out that previous experience
and the tourist’s direct and indirect sources of information are antecedent factors for
?rst time visitors. Money and Crotts (2003) show that consumers from national
cultures, characterized by higher levels of uncertainty, prefer information sources such
as travel agencies rather than personal, destination marketing related, or mass media
sources. Fodness and Murray (1997) demonstrate that the information research is the
result of a number of situational, tourist and marketplace contingencies. Jamrozy et al.
(1996) prove that highly involved travelers tend to be more receptive to information
concerning the travel product or destination, and disseminate information willingly.
Perception building towards destinations is part of the learning process.
Perception construct
Previous researchers have de?ned perceptions as the perceived value of a product
(Correia and Crouch, 2004; Correia et al., 2007c; Holbrook, 1996; Oh, 2000; Sheth et al.,
1991; Zeithaml, 1988). This concept develops from cognitive and behavioral
perspectives, resulting from the learning and motivational processes rendered by the
tourist. Past research on tourist motivation has shown that affective factors play a
critical role in the tourist travel selection and evaluation (Fodness, 1994).
IJCTHR
2,4
338
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
:
0
6
2
4
J
a
n
u
a
r
y
2
0
1
6
(
P
T
)
Internal and external motivations to travel lead to different perceptions about the
destination. This assumption taken from Baloglu (1997), Baloglu and McCleary (1999),
Correia et al. (2007c), Dann (1996) and Gartner (1993) shows that perceptions are a
function of internal motivations (push motivations) and external motivations (pull
motivations). This occurs because perception is a process whereby consumers select,
organize, and interpret stimuli into meaningful and coherent information, varying
according to the attributes of the product. Consumer perception depends on how the
tourist perceives the characteristics of a product on an individual basis and not
necessarily on their true attributes (Dann, 1981; Pearce, 1982). Selective perception
includes selective exposure, selective attention, perceptual defense, and perceptual
blockage. Consumers aware of their needs and desires, block out unnecessary,
displeasing or painful stimuli.
Perception encompasses two concepts that form part of the learning process:
cognitive and emotional concepts (Gnoth, 1997). Cognitive perception ensues from the
evaluation of destination attributes while emotional perception represents how the
individual actually perceives the destination.
This paper suggests that resulting perceptions follow from cognitive and emotional
evaluation (Otto, 1997; Otto and Ritchie, 1995). Both cognitive and emotional measures
are necessary for perception modeling when evaluating destinations. The study
throughout de?nes perceptions in terms of the overall performance of the product
(destination) from the consumer perspective. These perceptions represent preferential
levels that lead the tourist to purchase a particular destination. Kimand Yoon (2003) and
Vogt and Andereck (2003) discuss perception building, froma conceptual viewpoint and
use structural models (SEM) to analyze how emotions and cognitions can in?uence
perceptions on tourist destinations. Seddighi and Theocharous (2002) use a conditional
logit model to measure the perceptions/feelings about the characteristics of tourist
destinations. This methodology predicts the probability of revisiting the destination.
Murphy et al. (2000) de?ne a structural model that relates return intentions (as proxy of
satisfaction/quality) to destination perceptions. Driscoll et al. (1994) test the consistency
of two semantic differential scales to measure perceptions and discover that resulting
perceptions differ depending on the use of different data collection formats.
Satisfaction construct
Since each individual measures satisfaction differently, de?nitions vary, though most
consider the comparison between expectations and experience (Woodside et al., 1989).
Bultena and Klessig (1969, p. 349) state that a satisfactory experience “is a function of the
degree of congruency between aspirations and the perceived reality of experiences.”
From a purely cognitive perspective, Hunt (1977, p. 459) states that “satisfaction is not
the pleasurableness of the experience, it is the evaluation rendered that the experience
was at least as good as it was supposed to be.” Others, such as LaTour and Peat (1979),
argue that satisfaction is nothing more than the brand’s attitude.
Recent studies examine the in?uence of affective reactions to consumption
experiences on post-purchase satisfaction responses (Barsky, 1992; Madrigal, 1995;
Oliver, 1993; Spreng et al., 1996). The assumption is that tourist satisfaction is a
function of the product’s performance, perception and motivations. Satisfaction
increases as the performance/perception ratio increases (Moutinho, 1982), the basis
being the quality of the outcome experience in relation to those anticipated and desired.
Decision-making
processes
339
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
:
0
6
2
4
J
a
n
u
a
r
y
2
0
1
6
(
P
T
)
Dissatisfaction is measured in terms of to the degree of disparity between expectation
and the product’s performance. This concept of satisfaction, based on expectations has
been widely criticized. Arnould and Price (1993) suggest that satisfaction often appears
to be associated with surprise, while Miller (1977) proposes that satisfaction can take
on different forms: desirable, ideal, or tolerable. Research on the tourist perceptions and
motivations shows correlation between the level of holiday satisfaction and
motivation. Truong (2005) states that the attractiveness of a destination is
associated with its capacity to satisfy tourist needs and motivations. The product
(or destination) consists of a group of factors and tangible attractions that the
individual deems appropriate to satisfy his or her explicit and implicit desires.
Post-purchase behavior measuring attributes and destination’s capacity to satisfy the
individual’s intrinsic motivation is natural. From this assumption the push and pull
concepts in terms of satisfaction are adaptable to include push and pull motivations,
allowing us to measure the tangible and intangible components of post-purchase
purchase. To measure such components, developing and administering two
questionnaires was necessary.
The ?rst questionnaire distributed and collected prior to contact with destination,
gathers data such as destination information sources, destination perceptions, and
intrinsic (push) and extrinsic (pull) motivations. The second questionnaire distributed
and collected after contact with destination set out to measure the disparity between
tourist motivations and satisfactions after the holiday period, as well as future
behavioral intentions.
Traditionally, measuring the concept of satisfaction is by product logic rather than
by consumer logic. Therefore, tools used to measure displacement between the
anticipated and the actual product obtained at the various levels of tourist destination,
form part of the approach (Parasuraman et al., 1985; Moutinho, 1987; Kozak and
Rimmington, 2000).
According to Barsky and Labagh (1992), the consumer satisfaction analysis was an
important challenge in the 1990s. To identify how the components of a product or
service affect consumers represents the possibility of maximizing consumer
satisfaction (Petrick et al., 1999). While there are no guarantees that a satis?ed
consumer will repeat his or her visit, an unsatis?ed consumer will generally not return
(Dube et al., 1994). These studies suggest that if an experience has a positive effect on
an individual, then he or she is more likely to repeat the experience. This means that
satisfaction in?uences behavioral intentions (Oppermann, 2000).
Behavioral intention construct
The intention to purchase is a function of the attitude towards behavioral and social
norms. Expectations affect attitude; expectations include, the possibility of adopting
certain behavior and the evaluation of how the consumer feels about engaging in the
behavior (Fishbein and Ajzen, 1980).
Lam and Hsu (2006), whose work develops from reasoned action theory (Fishbein
and Ajzen, 1980), show that attitude, perceived behavior and past behavior are related
to the behavioral intention of choosing a destination. Similarly, Bigne´ et al. (2001) treat
return intentions as a proxy of satisfaction/quality based on perceptions and
destination image through a structural model. The results of this study show that
tourism image is a direct antecedent of perceived quality, satisfaction, intention to
IJCTHR
2,4
340
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
:
0
6
2
4
J
a
n
u
a
r
y
2
0
1
6
(
P
T
)
return and willingness to recommend the destination. With reference to the other
relationships, ?ndings show that quality has a positive in?uence on satisfaction
and return intentions, and that satisfaction in?uences the willingness to recommend
the destination.
Kozak (2001) shows that overall satisfaction and the number of previous visits
considerably in?uences return intention rates, especially in mature destinations. Baker
and Crompton (2000) test a structural model to show that perceived performance
quality has a stronger total effect on behavioral intentions than that of satisfaction.
Mazursky (1989) uses latent variables to explain the construction of future behavioral
intentions in terms of norms and measures from experience. Correia et al. (2007a)
examine a random parameter logit model to analyze which characteristics (e.g.
individual characteristics, motivations, tripographic variables and common attributes
of golf destinations) are associated with the probability of golf tourists returning to
Algarve, Portugal. According to the literature, the variables that in?uence future
behavior are motivations, information sources, perceptions, and satisfaction.
Theoretical model and hypotheses
Figure 1 shows the hypothetical causal model. The adopted constructs of the model,
taken from the literature, looks to more thoroughly de?ne consumer behavior, that is, a
sequential dynamic and organized process whereby diverse factors compete along side
each other towards a decision.
Previous studies show that behavioral intentions are the result of the post-purchase
evaluation stage (Dick and Basu, 1994; Oliver, 1999; Yoon and Uysal, 2005). Other
authors showthat post-purchase evaluationdepends ontourist motivation (Correia et al.,
2007a, c; Mannell and Iso-Ahola, 1987; Ross and Iso-Ahola, 1991; Silvestre and Correia,
2005). Woodside and Lysonski (1989) discuss that personal and material information
tends to activate consumer needs and, therefore, serve as a motivational element.
The consumer in the perception building phase (in which the consumer seeks
information about the destination) also uses these sources. This model breaks motivations
down into two constructs: push motivations (internal forces) and pull motivation(external
forces), (Baloglu and McCleary, 1999; Correia and Crouch, 2004; Correia et al., 2007c; Dann,
1977; Driscoll et al., 1994; Goosens, 2000; McCabe, 2000; Yoon and Uysal, 2005).
In order to categorize motivations into pull and push, consider that motivations are
satis?ed by the end of their vacation; tourists are able to evaluate each motivation as a
component of their overall satisfaction. Therefore, this model also separates
satisfaction into two constructs: push satisfaction and pull satisfaction.
Subsequently, the model examines the structural causal relationships among the
information sources, push and pull motivations, perceptions, push and pull satisfactions,
and behavioral intentions. The model contributes in identifying relationships between the
above-mentioned constructs, and the new concept of satisfaction.
The model assumes the tourist has already decided to go on holiday abroad, and
decided on the duration of the stay and budget spending. Given that there is no choice
in this framework, the tourist consumption behavior can be analyzed in two stages: the
pre-decision stage and post-purchase stage. These stages are crucial in explaining
tourist consumption behavior, since during these periods the most relevant constructs
within purchase processing develop: sources of information, motivations, perceptions,
satisfactions, and behavioral intentions.
Decision-making
processes
341
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
:
0
6
2
4
J
a
n
u
a
r
y
2
0
1
6
(
P
T
)
Figure 1.
Theoretical model of
consumer behavior
I
n
f
o
r
m
a
t
i
o
n
S
o
u
r
c
e
s
S
o
u
r
c
e
:
O
w
n
e
l
a
b
o
r
a
t
i
o
n
P
u
s
h
M
o
t
i
v
e
s
P
u
s
h
M
2
P
u
s
h
M
1
P
u
s
h
M
n
H
1
H
6
H
7
H
5
H
8
H
4
H
9
P
r
o
m
o
t
i
o
n
s
B
r
o
c
h
u
r
e
s
N
e
w
s
M
o
v
i
e
s
M
a
i
l
T
r
a
v
e
l
a
g
e
n
c
y
P
e
r
c
e
p
t
i
o
n
s
…
H
3
H
2
H
1
7
P
u
l
l
M
2
P
u
l
l
M
1
P
u
l
l
M
n
…
P
u
s
h
M
o
t
i
v
e
s
P
u
l
l
S
a
t
i
s
f
a
c
t
i
o
n
P
u
l
l
S
2
P
u
l
l
S
1
P
u
l
l
S
n
H
1
2
H
1
5
H
1
6
H
1
3
H
1
4
…
H
1
1
H
1
0
P
u
s
h
S
2
P
u
s
h
S
1
P
u
s
h
S
n
B
e
h
a
v
i
o
u
r
I
n
t
e
n
t
i
o
n
P
u
l
l
S
a
t
i
s
f
a
c
t
i
o
n
R
e
c
o
m
e
n
d
a
t
i
o
n
R
e
t
u
r
n
IJCTHR
2,4
342
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
:
0
6
2
4
J
a
n
u
a
r
y
2
0
1
6
(
P
T
)
H
1
. A tourist uses a set of information sources. Baloglu and McCleary
(1999) establish that tourists use various sources of information to
gain a complete picture of understanding of the destination. Fakeye
and Crompton (1991), Gunn (1972) and Um and Crompton (1990)
identify the importance of information sources (such as promotional
material and media, friends and relatives and word of mouth) in the
decision-making process. The search for information makes use of
four basic types of sources: neutral (tourism of?ces); commercial
(travel agents); social (friends and relatives, family); and promotional
(newspapers, magazines, radio, television, internet), as stated by
Bargeman and Poel (2006), Crotts (1999) and Moutinho (1982).
H
2
. The information sources activate push motivations in the tourist’s
mind. Um and Crompton (1990) show that information sources help
to form either a cognitive image or an affective image. The
understanding of tourism as a product requires higher decision
processing whereby various sources of information serve towards
reaching a decision (Woodside and Lysonski, 1989). Money and
Crotts (2003) show that consumers from national cultures are
characterized by higher levels of uncertainty avoidance and use
information sources to channel preference (e.g. travel agent).
Crompton (1979) and Kotler et al. (1993) suggest that motivation is
a result of the need for social acceptance and stimulated from
publicity and promotion. Publicity and promotion are one of the
most important sources for tourists.
H
3
. Information sources activate pull motivations on the tourist’s mind.
Gartner (1993), Holbrook (1978) and Woodside and Lysonski (1989)
state that information sources are considered as forces in?uencing
pull motivations.
H
4
. A set of internal forces in?uence push motivations. Dann (1977)
classi?es push motivations that in?uence the vacation as internal
factors: loneliness, getting away from it all, and social recognition.
On the other hand, Crompton (1990) refers to motivations that lead to
the desire to travel, an escape from the daily routine, relaxation,
prestige, regression, and social interaction.
H
5
. A set of external forces in?uence pull motivations. Uysal and Hagan
(1993) state that, generally, pull factors relate to the attributes of the
tourist destination. On the other hand, Crompton (1990) states that
pull motivations are those that in?uence the choice of the location.
Factors such as landscape, hospitality, lodging, price, heritage
interests, gastronomy, sports, nightlife, shopping, and accessibility
are examples of pull motivations.
H
6
. The expectation is that push motivations in?uence pull motivations
positively. Correia et al. (2007c) show that when the tourist has
internal motivations (push), they are more likely to perceive pull
motivations. Although most authors accept that cognitive and
Decision-making
processes
343
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
:
0
6
2
4
J
a
n
u
a
r
y
2
0
1
6
(
P
T
)
affective images are related (Baloglu and McCleary, 1999; Gunn, 1972;
Um and Crompton, 1990; Yoon and Uysal, 2005), no empirical
evidence support this relationship. Crompton (1979) argues that, in
practice, all human behaviors is motivated; however, the choices made
in order to satisfy these motivations may depend on other
psychological variables. When individuals decide to travel for
recreational purposes, they do so for several motivations and reasons,
all of which are interdependent.
H
7
. Perceptions of the destination are in?uenced by push motivations.
Perceptions are a cognitive and behavioral measure of the value of the
tourism destination (Baloglu and McCleary, 1999; Morrison, 1989),
since they are formed in a consciously, unconscious manner.
Perceptions as a behavioral cognitive measure are expected to develop
according to the emotional state of the tourist (motivations) (Correia
et al., 2007c; Crompton, 1979; Dann, 1996; Gartner, 1993; Murphy et al.,
2000; Woodside and Lysonski, 1989). Beerli and Mart? ´n (2004) added
the concept of affective perception of the destination.
H
8
. Perceptions of the destination are in?uenced by pull motivations.
According to Gnoth (1997), measuring perceptions as a cognitive
component suggests that the tourist evaluates and perceives the
destination’s attributes. On the other hand, measurement of perceptions
by a personal component means that the perceptionof a destination is as
the tourist intends it to be. In fact, perceptions can be different from the
true attributes of the product depending on how the individual receives
and processes the information, as explained by tourist motivations
about the destination’s attributes (Dann, 1981; Pearce, 1982).
H
9
. Information sources interact to form perceptions. According to
Mazursky (1989), perception building derives from the information
obtainedbeforehandused to help the consumer inhis or her assessment
of alternative destinations (UmandCrompton, 1990).UmandCrompton
(1990) argue that beliefs and expectations about destination attributes
are constructed by individuals according to the information received.
Burgess (1978) states that information sources in?uence the image of
the destination. Baloglu and McCleary (1999), Gartner (1993) and Gunn
(1972) stress the importance of perceptions building.
H
10
and H
11
. Different perceptions lead to different levels of push(H
10
) andpull (H
11
)
satisfactions. According to the motivation theory already mentioned, it
could be argued that the tourist on holiday looks for rewards:
psychological rewards (relaxation, rest, and refreshment), social
rewards (recognition and prestige) (Gnoth, 1997); and economic
rewards (measured by the perceived value of the destination). Baloglu
and McCleary (1999), Gartner (1993), Gnoth (1997) and Petrick (2002)
amongothers, have statedthat the tourist has anaffective andcognitive
image of the destination since perception are expectedto in?uence push
and pull satisfactions.
IJCTHR
2,4
344
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
:
0
6
2
4
J
a
n
u
a
r
y
2
0
1
6
(
P
T
)
H
12
. A set of internal rewards affect push satisfaction. Push satisfaction
occurs when the tourist has a feeling of psychological and social
ful?llment.
H
13
. The perceived value of the destination affects pull satisfaction.
Perceived value is the global evaluation of the use of a product or
service based on the perceptions of what you get versus what you give
(Zeithaml, 1988). Furthermore, higher levels of perceived value likely
results in purchase and in higher levels of consumer satisfaction
(Bojanic, 1996).
H
14
. Pull satisfaction in?uences push satisfaction. Through a perceived
performance model, Tse andWilton (1988) holdthat tourist satisfaction
depends on motivations, expectations, and performance.
H
15
and H
16
. The level of push (H
15
) and pull (H
16
) satisfactions affects future
behavioral intentions. LaTour and Peat (1979), Levitt (1981) and
Whipple and Thatch (1988) state that the evaluation of the product’s
attributes could be crucial in determining behavioral intentions.
Madrigal (1995) and Oliver (1993) argue that if an experience has a
positive effect on the tourist, he or she is more likely to return. Festinger
(1954) argues that destination satisfaction in?uences future choices.
Beerli and Mart? ´n (2004) show that sun and beach destinations
achieving positive image feedback also achieve high levels of repeat
visits.
H
17
. Behavioral intentions are explainable throughprobabilityof returnand
intentions to recommend. Behavioral intentions that represent the
willingness to return and the intention to recommend are the two likely
outcomes of future behavior (Yoon and Uysal, 2005).
Method
Survey
The questionnaires employed both open and closed questions in order to evaluate the
sources of information, motivations, perceptions, the level of post-purchase satisfaction
and future behavioral intentions using seven-point Likert scales, as suggested by Maio
and Olson (1994). Passengers were invited to participate in the survey by completing
questionnaires during the ?ight, and just before arriving at the airport. Data collection
took place between July and September 2004, as this time period is when the majority
of Portuguese citizens go on holiday. A pre-test of the questionnaires used a sample of
150 passengers on departure and on return arrival. This pre-test enhanced the validity
and reliability of the questionnaires. After this pre-test, minor amendments were made
to avoid eventual logic and/or question perception errors.
The ?rst questionnaire began with the tourist’s personal characteristics (age, sex,
marital status, profession, education, and nationality). The logistics and travel experience
section looked to analyse vacation budgets, frequency of travel, time restrictions, average
length of stay, number of people traveling together, type of vacation lodging, booking
means and previous experience. Next, the study analyzes learning processes, motivations
andperceptions. Onlymeasuringthe level of importance for eachof the alternative sources
Decision-making
processes
345
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
:
0
6
2
4
J
a
n
u
a
r
y
2
0
1
6
(
P
T
)
of information in the purchase decision was possible. The information used came from
sevensources: travel agencies, brochures and guidebooks, friends andfamily, advertising,
books and ?lms, articles and news, movies, and direct mail. Rojek (1990) discusses the role
of advertising and television in explaining consumer behavior. Scho?eld (1996) argues
that the consumer actually buys signals and images rather than products. Goosens (2000)
de?nes involvement as the state that de?nes the interests andmotivations of the consumer
about the product, and explains the way in which he or she looks for more information in
order to learn about the product. According to Goosens, the tourist perceives the value of a
destination according to the marketing stimuli supplied in magazines, brochures and
publicity, among others. His model looks to explain how tourists perceive visual and
external information. Woodside and Dubelaar (2002) de?ne the theory of tourism
consumption systems as the inter-relationship between different sets of variables inwhich
the role of web advertising, information guides, marketing and advertisements play a
central role in explaining consumer decision. The model focuses on the role of information
sources to explain motivations, perceptions and the post-purchase evaluation. Fodness
and Murray (1999) conclude that tourists use different sources of information to plan their
vacations. The main information sources used by tourists were brochures, guidebooks,
friends and relatives, magazines, newspapers, previous experience, travel agents sources
as well as the internet and direct mail.
The second question centered on 21 issues that re?ect the main motivational factors
identi?ed in the literature (Correia et al., 2007b, c; Fodness, 1994; Iso-Ahola and Mannel,
1987; Lundberg, 1990; Mohsin and Ryan, 2003; Silvestre and Correia, 2005; Shoemaker,
1989; Uysal et al., 1996) The motivations considered are shown in Table I.
Under the same heading, the model established the destination perceptions as
shown in Table I. This construct follows Baloglu and McCleary (1999) and Correia et al.
(2007c) who assume that perceptions are a function of external and internal
motivations.
The distribution of the second questionnaire occurred after the holiday experience,
on the return ?ight. The questionnaire consists of a ?rst set of questions about the
tourist, a second set of questions relating to behavioral intentions and a third set on
satisfaction.
The behavioral intention is evident fromthe tourist’s level of satisfaction and assessed
in terms of the probability of returning and recommending the destination to friends
and family (Moutinho, 1987; Baker and Crompton, 1998). With these questions, return and
recommendation probability can be veri?ed. Behavioral intentions represent concepts
related reasoned action theories (Fishbein and Ajzen, 1980), which assume that intentions
can explain consumer behavior. Similar application and testing has only been carried out
by Baker and Crompton (1998) and Cronin et al. (1992). Backman and Crompton (1991)
argue that de?nitionof loyaltyis relatednot onlyto behavioral intentions but withattitude
as well. Learning a tourist’s attitude towards the destination is necessary ?rst before
learning his or her loyalty. In achieving such learning, explaining associating behavioral
intentions is possible. The 21 attributes measure satisfaction, important in determining
the decision process, and earlier de?ned as pull satisfaction.
According to Spreng et al. (1996), satisfaction is an emotional state directed towards
a product or service. This de?nition goes beyond the traditional concept of
con?rmation/discon?rmation, widely de?ned as quality or performance of a product
(Oliver, 1980; Parasuraman, 2000). It follows psychological theories (Oliver, 1980)
IJCTHR
2,4
346
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
:
0
6
2
4
J
a
n
u
a
r
y
2
0
1
6
(
P
T
)
P
e
r
c
e
i
v
e
d
v
a
r
i
a
b
l
e
s
Q
u
e
s
t
i
o
n
s
R
e
s
p
o
n
s
e
c
a
t
e
g
o
r
i
e
s
I
n
f
o
r
m
a
t
i
o
n
s
o
u
r
c
e
s
c
o
n
s
t
r
u
c
t
s
T
r
a
v
e
l
a
g
e
n
c
y
;
b
r
o
c
h
u
r
e
s
;
f
a
m
i
l
y
;
p
r
o
m
o
t
i
o
n
;
m
o
v
i
e
s
;
n
e
w
s
;
m
a
i
l
H
o
w
i
m
p
o
r
t
a
n
t
i
s
e
a
c
h
s
o
u
r
c
e
o
f
i
n
f
o
r
m
a
t
i
o
n
w
h
e
n
c
h
o
o
s
i
n
g
a
d
e
s
t
i
n
a
t
i
o
n
?
1
–
n
o
t
i
m
p
o
r
t
a
n
t
;
2
–
n
o
t
v
e
r
y
i
m
p
o
r
t
a
n
t
;
3
–
o
f
v
e
r
y
l
i
t
t
l
e
i
m
p
o
r
t
a
n
c
e
;
4
–
i
m
p
o
r
t
a
n
t
;
5
–
m
o
r
e
t
h
a
n
i
m
p
o
r
t
a
n
t
;
6
–
v
e
r
y
i
m
p
o
r
t
a
n
t
;
7
–
e
x
t
r
e
m
e
l
y
i
m
p
o
r
t
a
n
t
P
u
l
l
m
o
t
i
v
e
s
c
o
n
s
t
r
u
c
t
s
G
a
s
t
r
o
n
o
m
y
;
s
o
c
i
a
l
e
n
v
i
r
o
n
m
e
n
t
;
a
c
c
e
s
s
i
b
i
l
i
t
i
e
s
;
r
e
l
a
x
i
n
g
a
t
m
o
s
p
h
e
r
e
;
s
e
c
u
r
i
t
y
;
w
e
a
t
h
e
r
;
i
n
f
o
r
m
a
t
i
o
n
;
l
a
n
d
s
c
a
p
e
;
n
a
t
u
r
a
l
e
n
v
i
r
o
n
m
e
n
t
;
c
u
l
t
u
r
a
l
a
t
t
r
a
c
t
i
o
n
s
;
s
h
o
p
p
i
n
g
f
a
c
i
l
i
t
i
e
s
;
n
i
g
h
t
-
l
i
f
e
;
s
p
o
r
t
s
e
q
u
i
p
m
e
n
t
;
t
r
a
n
s
p
o
r
t
s
;
a
c
c
o
m
m
o
d
a
t
i
o
n
s
;
b
e
a
c
h
;
h
o
s
p
i
t
a
l
i
t
y
;
e
x
o
t
i
c
n
e
s
s
;
e
t
h
n
i
c
i
t
i
e
s
;
l
i
f
e
s
t
y
l
e
s
;
d
i
s
t
a
n
c
e
H
o
w
i
m
p
o
r
t
a
n
t
i
s
e
a
c
h
o
f
t
h
e
f
a
c
t
o
r
s
w
h
e
n
c
h
o
o
s
i
n
g
a
d
e
s
t
i
n
a
t
i
o
n
?
1
–
n
o
t
i
m
p
o
r
t
a
n
t
;
2
–
n
o
t
v
e
r
y
i
m
p
o
r
t
a
n
t
;
3
–
o
f
v
e
r
y
l
i
t
t
l
e
i
m
p
o
r
t
a
n
c
e
;
4
–
i
m
p
o
r
t
a
n
t
;
5
–
m
o
r
e
t
h
a
n
i
m
p
o
r
t
a
n
t
;
6
–
v
e
r
y
i
m
p
o
r
t
a
n
t
;
7
–
e
x
t
r
e
m
e
l
y
i
m
p
o
r
t
a
n
t
P
u
s
h
m
o
t
i
v
e
s
c
o
n
s
t
r
u
c
t
s
E
x
p
e
r
i
e
n
c
i
n
g
d
i
f
f
e
r
e
n
t
c
u
l
t
u
r
e
s
a
n
d
l
i
f
e
s
t
y
l
e
s
;
i
n
c
r
e
a
s
i
n
g
k
n
o
w
l
e
d
g
e
;
e
n
r
i
c
h
i
n
g
m
y
s
e
l
f
i
n
t
e
l
l
e
c
t
u
a
l
l
y
;
v
i
s
i
t
i
n
g
n
e
w
p
l
a
c
e
s
;
a
m
u
s
e
m
e
n
t
;
g
o
i
n
g
p
l
a
c
e
s
m
y
f
r
i
e
n
d
s
h
a
v
e
n
o
t
b
e
e
n
;
t
e
l
l
i
n
g
m
y
f
r
i
e
n
d
s
a
b
o
u
t
t
h
e
v
a
c
a
t
i
o
n
;
d
e
v
e
l
o
p
i
n
g
c
l
o
s
e
f
r
i
e
n
d
s
h
i
p
s
;
r
e
l
i
e
v
i
n
g
s
t
r
e
s
s
;
e
s
c
a
p
i
n
g
f
r
o
m
r
o
u
t
i
n
e
;
p
h
y
s
i
c
a
l
r
e
l
a
x
a
t
i
o
n
;
g
e
t
t
i
n
g
a
w
a
y
f
r
o
m
c
r
o
w
d
s
;
m
e
e
t
i
n
g
i
n
t
e
r
e
s
t
i
n
g
p
e
o
p
l
e
;
d
o
i
n
g
d
i
f
f
e
r
e
n
t
t
h
i
n
g
s
;
s
t
i
m
u
l
a
t
i
n
g
e
m
o
t
i
o
n
s
a
n
d
s
e
n
s
a
t
i
o
n
s
;
b
e
i
n
g
a
n
a
d
v
e
n
t
u
r
e
r
H
o
w
i
m
p
o
r
t
a
n
t
i
s
e
a
c
h
o
f
t
h
e
f
a
c
t
o
r
s
w
h
e
n
c
h
o
o
s
i
n
g
a
d
e
s
t
i
n
a
t
i
o
n
?
1
–
n
o
t
i
m
p
o
r
t
a
n
t
;
2
–
n
o
t
v
e
r
y
i
m
p
o
r
t
a
n
t
;
3
–
o
f
v
e
r
y
l
i
t
t
l
e
i
m
p
o
r
t
a
n
c
e
;
4
–
i
m
p
o
r
t
a
n
t
;
5
–
m
o
r
e
t
h
a
n
i
m
p
o
r
t
a
n
t
;
6
–
v
e
r
y
i
m
p
o
r
t
a
n
t
;
7
–
e
x
t
r
e
m
e
l
y
i
m
p
o
r
t
a
n
t
(
c
o
n
t
i
n
u
e
d
)
Table I.
Questionnaire’s structure
and the formation of the
constructs
Decision-making
processes
347
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
:
0
6
2
4
J
a
n
u
a
r
y
2
0
1
6
(
P
T
)
P
e
r
c
e
i
v
e
d
v
a
r
i
a
b
l
e
s
Q
u
e
s
t
i
o
n
s
R
e
s
p
o
n
s
e
c
a
t
e
g
o
r
i
e
s
P
e
r
c
e
p
t
i
o
n
s
P
e
r
c
e
p
t
i
o
n
s
W
h
a
t
a
r
e
y
o
u
r
p
e
r
c
e
p
t
i
o
n
s
r
e
g
a
r
d
i
n
g
t
h
e
d
e
s
t
i
n
a
t
i
o
n
?
1
–
v
e
r
y
l
o
w
;
2
–
l
o
w
;
3
–
q
u
i
t
e
l
o
w
;
4
–
a
v
e
r
a
g
e
;
5
–
q
u
i
t
e
h
i
g
h
;
6
–
h
i
g
h
;
7
–
v
e
r
y
h
i
g
h
P
u
l
l
s
a
t
i
s
f
a
c
t
i
o
n
c
o
n
s
t
r
u
c
t
s
G
a
s
t
r
o
n
o
m
y
;
s
o
c
i
a
l
e
n
v
i
r
o
n
m
e
n
t
;
a
c
c
e
s
s
i
b
i
l
i
t
i
e
s
;
r
e
l
a
x
i
n
g
a
t
m
o
s
p
h
e
r
e
;
s
e
c
u
r
i
t
y
;
w
e
a
t
h
e
r
;
i
n
f
o
r
m
a
t
i
o
n
;
l
a
n
d
s
c
a
p
e
;
n
a
t
u
r
a
l
e
n
v
i
r
o
n
m
e
n
t
;
c
u
l
t
u
r
a
l
a
t
t
r
a
c
t
i
o
n
s
;
s
h
o
p
p
i
n
g
f
a
c
i
l
i
t
i
e
s
;
n
i
g
h
t
-
l
i
f
e
;
s
p
o
r
t
s
e
q
u
i
p
m
e
n
t
;
t
r
a
n
s
p
o
r
t
s
;
a
c
c
o
m
m
o
d
a
t
i
o
n
s
;
b
e
a
c
h
;
h
o
s
p
i
t
a
l
i
t
y
;
e
x
o
t
i
c
n
e
s
s
;
e
t
h
n
i
c
i
t
i
e
s
;
l
i
f
e
s
t
y
l
e
s
;
d
i
s
t
a
n
c
e
H
o
w
w
o
u
l
d
y
o
u
c
l
a
s
s
i
f
y
y
o
u
r
l
e
v
e
l
o
f
s
a
t
i
s
f
a
c
t
i
o
n
r
e
g
a
r
d
i
n
g
t
h
e
f
o
l
l
o
w
i
n
g
f
a
c
t
o
r
s
?
1
–
w
o
r
s
e
t
h
a
n
I
e
x
p
e
c
t
e
d
;
2
–
l
o
w
e
r
t
h
a
n
I
e
x
p
e
c
t
e
d
;
3
–
b
e
l
o
w
a
v
e
r
a
g
e
t
h
a
n
I
e
x
p
e
c
t
e
d
;
4
–
a
s
e
x
p
e
c
t
e
d
;
5
–
a
b
o
v
e
w
h
a
t
I
e
x
p
e
c
t
e
d
;
6
–
b
e
t
t
e
r
t
h
a
n
I
e
x
p
e
c
t
e
d
;
7
–
s
u
r
p
a
s
s
e
d
m
y
e
x
p
e
c
t
a
t
i
o
n
s
P
u
s
h
s
a
t
i
s
f
a
c
t
i
o
n
c
o
n
s
t
r
u
c
t
s
E
x
p
e
r
i
e
n
c
i
n
g
d
i
f
f
e
r
e
n
t
c
u
l
t
u
r
e
s
a
n
d
l
i
f
e
s
t
y
l
e
s
;
i
n
c
r
e
a
s
i
n
g
k
n
o
w
l
e
d
g
e
;
e
n
r
i
c
h
i
n
g
m
y
s
e
l
f
i
n
t
e
l
l
e
c
t
u
a
l
l
y
;
v
i
s
i
t
i
n
g
n
e
w
p
l
a
c
e
s
;
a
m
u
s
e
m
e
n
t
;
g
o
i
n
g
p
l
a
c
e
s
m
y
f
r
i
e
n
d
s
h
a
v
e
n
o
t
b
e
e
n
;
t
e
l
l
i
n
g
m
y
f
r
i
e
n
d
s
a
b
o
u
t
t
h
e
v
a
c
a
t
i
o
n
;
d
e
v
e
l
o
p
i
n
g
c
l
o
s
e
f
r
i
e
n
d
s
h
i
p
s
;
r
e
l
i
e
v
i
n
g
s
t
r
e
s
s
;
e
s
c
a
p
i
n
g
f
r
o
m
r
o
u
t
i
n
e
;
p
h
y
s
i
c
a
l
r
e
l
a
x
a
t
i
o
n
;
g
e
t
t
i
n
g
a
w
a
y
f
r
o
m
c
r
o
w
d
s
;
m
e
e
t
i
n
g
i
n
t
e
r
e
s
t
i
n
g
p
e
o
p
l
e
;
d
o
i
n
g
d
i
f
f
e
r
e
n
t
t
h
i
n
g
s
;
s
t
i
m
u
l
a
t
i
n
g
e
m
o
t
i
o
n
s
a
n
d
s
e
n
s
a
t
i
o
n
s
;
b
e
i
n
g
a
n
a
d
v
e
n
t
u
r
e
r
H
o
w
w
o
u
l
d
y
o
u
c
l
a
s
s
i
f
y
y
o
u
r
l
e
v
e
l
o
f
s
a
t
i
s
f
a
c
t
i
o
n
r
e
g
a
r
d
i
n
g
t
h
e
f
o
l
l
o
w
i
n
g
f
a
c
t
o
r
s
?
1
–
w
o
r
s
e
t
h
a
n
I
e
x
p
e
c
t
e
d
;
2
–
l
o
w
e
r
t
h
a
n
I
e
x
p
e
c
t
e
d
;
3
–
b
e
l
o
w
a
v
e
r
a
g
e
t
h
a
n
I
e
x
p
e
c
t
e
d
;
4
–
a
s
e
x
p
e
c
t
e
d
;
5
–
a
b
o
v
e
w
h
a
t
I
e
x
p
e
c
t
e
d
;
6
–
b
e
t
t
e
r
t
h
a
n
I
e
x
p
e
c
t
e
d
;
7
–
s
u
r
p
a
s
s
e
d
m
y
e
x
p
e
c
t
a
t
i
o
n
s
B
e
h
a
v
i
o
r
a
l
i
n
t
e
n
t
i
o
n
c
o
n
s
t
r
u
c
t
s
R
e
t
u
r
n
D
o
y
o
u
i
n
t
e
n
d
t
o
v
i
s
i
t
t
h
i
s
d
e
s
t
i
n
a
t
i
o
n
a
g
a
i
n
?
1
–
n
e
v
e
r
a
g
a
i
n
;
2
–
a
b
s
o
l
u
t
e
l
y
n
o
t
;
3
–
n
o
;
4
–
p
r
o
b
a
b
l
y
;
5
–
v
e
r
y
p
r
o
b
a
b
l
y
;
6
–
a
l
m
o
s
t
c
e
r
t
a
i
n
l
y
;
7
–
c
e
r
t
a
i
n
l
y
R
e
c
o
m
m
e
n
d
D
o
y
o
u
i
n
t
e
n
d
t
o
r
e
c
o
m
m
e
n
d
t
h
i
s
d
e
s
t
i
n
a
t
i
o
n
t
o
f
r
i
e
n
d
s
a
n
d
f
a
m
i
l
y
?
1
–
a
b
s
o
l
u
t
e
l
y
n
o
t
;
2
–
a
s
a
p
o
s
s
i
b
l
e
d
e
s
t
i
n
a
t
i
o
n
;
3
–
a
s
a
g
o
o
d
d
e
s
t
i
n
a
t
i
o
n
;
4
–
p
r
o
b
a
b
l
y
;
5
–
v
e
r
y
p
r
o
b
a
b
l
y
;
6
–
a
l
m
o
s
t
c
e
r
t
a
i
n
l
y
;
7
–
c
e
r
t
a
i
n
l
y
Table I.
IJCTHR
2,4
348
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
:
0
6
2
4
J
a
n
u
a
r
y
2
0
1
6
(
P
T
)
that consider overall satisfaction to be also a socio-psychological state of being that
leads the consumer to evaluate the product differently, according to his or her
emotional state. The model takes a different of view by establishing two separate levels
of satisfaction: pull satisfaction (in which level of achievement of internal motivations
is measured though the sense of emotional ful?llment), and cognitive satisfaction
(measured by the perceived quality of the destination attributes). Within this level,
internal satisfaction can be con?rmed or not using a seven-point Likert scale. This
evaluation takes into account the sixteen motivations that drive or push the tourist
towards the destination, referred to as push satisfaction. In terms of quality
performance, the 21 attributes of the destination are evaluated using the same seven
point Likert scale (Table I). This form of evaluating satisfaction is referred to as pull
satisfaction.
The questionnaires were individually checked and numbered. The descriptive and
multivariable statistical analyses were performedusingthe software SPSS14.0(SPSSInc.,
2005). The structural model was estimated using AMOS 6 (AMOS Inc., 2006). This
approach permits veri?cation of each of the questions and validate the importance of
variables when explaining tourist behavior of the tourist as a consumer. The study
explores the cause-effect relationships inthe decision process thoughperceptual mapping.
Data
In order to test the proposed hypotheses, the study includes using a strati?ed, random
sample of Air Luxor incoming and outgoing ?ight passengers. The ?rst of two
questionnaires looked to analyze the information sources used to learn about the
destination, the motivations that led to the choice of that destination, and perceptions
held.
The second questionnaire was issued on return ?ights in order to evaluate the level
of satisfaction from the experience obtained and behavioral intentions. Strati?cation of
the sample was done according to destination, using the airline’s database. Budgetary
restrictions and the limited time available allowed for only 1,097 questionnaires to be
collected. Questionnaires were distributed during ?ights to destinations such as Brazil,
Morocco, Egypt, the Dominican Republic, and Sao Tome and Principe, and assumed
two separate surveying periods. The 1,097 questionnaires distributed on departure
addressed perceptions and motivations, and the 1,091 questionnaires on return arrival
addressed quality and satisfaction levels of destination. Out of the 1,091 questionnaires
completed, 453 represented responses from the same outbound and inbound tourists.
Given the speci?city of the analysis, a response rate of 42 percent was obtained. Also,
questionnaires were not always distributed to passengers on the same planes or going
to the same destination both on departure and arrival. Nonetheless, these 453 Air
Luxor passengers, represented Portuguese tourists traveling to South America and
Africa destinations, since such people mostly travel on Air Luxor charter ?ights.
First, to the study includes performing a univariate descriptive analysis of the valid
cases by calculating summary measures (measures of central tendency, dispersion and
absolute and relative frequencies). The main goal of this preliminary analysis was to
characterize those surveyed socio-demographically and in terms of information
sources that conditioned their destination choice. The social classes were de?ned
according to Dubois (1993): high class (corporate executives, liberal professions,
high-salaried professionals), high medium (lower-paid professionals), and
Decision-making
processes
349
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
:
0
6
2
4
J
a
n
u
a
r
y
2
0
1
6
(
P
T
)
medium-lower (blue-collar workers and manual laborers). The characterization of the
sample is shown in Table II.
In terms of gender, 49 percent were male while 51 percent were female. The relative
balance between the two may suggest the predominance of family holidays. The
average age of tourists was 36 years, although the mode is 27 years of age. With a
below average purchasing power, the majority of the individuals surveyed held a
higher level of education, were married, and had no young children. Travel experience
and logistics are shown in Table III.
The majority of individuals questioned traveled once a year, with a budget of less
than e1,500 (73 percent). Without prior travel experience (84 percent), but having
obtained affordable prices at travel agencies (95 percent), a signi?cant number made
bookings less than one month in advance (76 percent). On average, the number of
family members who traveled together was 1.8, with an average length of stay of 9 days
in destinations such as Brazil (25 percent), Egypt (25 percent), and the Dominican
Republic (41 percent). The majority of people who answered the questionnaires stayed
at beach hotels (51 percent) or at resorts (31 percent) with full board (53 percent).
Data analysis
The main objective of this study is to test a structural model that allows for a
representation of a more encompassing decision-making process of the tourist. SEM
evaluates how well a conceptual model that contains observed and latent variables
explains and ?ts the data (Yoon and Uysal, 2005). Adopted in the study, this technique
also allows us to measure causal relationships among latent constructs, estimating the
amount of unexplained variance (Yoon and Uysal, 2005). This SEM analysis was
performed in two stages. Firstly, an EFA used as a preliminary technique to ?nd the
underlying dimensions or constructs in the data. This procedure, available on AMOS 6,
reduces data and identi?es the latent constructs that explain most of the variance of the
observed variables. The extraction method applied was the maximum-likelihood, an
interactive algorithm that produces parameter estimates based on an observed
Frequency (percent) Average SD
Gender
Masculine 48.6
Feminine 51.4
Average age 35.6 11.8
Social status
High 21.6
High-Medium 25.2
Medium-low 53.2
Marital status
Single 22.6
Married 58.9
With young children 18.5
Education
Primary school 27.4
Secondary school 24.7
Higher education 47.9
Table II.
Socio-demographic
characterization of the
sample
IJCTHR
2,4
350
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
:
0
6
2
4
J
a
n
u
a
r
y
2
0
1
6
(
P
T
)
correlation matrix. Correlation weighting employed the inverse of the uniqueness of the
variables. The method of factor rotation used was varimax, an orthogonal rotation
method that minimizes the number of observed variables with high loadings on each
latent construct, allowing for easier interpretation of the factors. The analysis includes
considering a latent root criterion of 1.0 for factor inclusion. To extract factors, the
cut-off of 0.5 was the criterion adopted. A subsequent con?rmatory factor analysis
(CFA) allowed for evaluation of the resulting scales. This analysis speci?es the
relationships of the observed variables to the latent constructs, and suggests that all
the constructs can be inter-correlated freely. Alpha (Cronbach, 1951) coef?cient
measures the reliability of the obtained factors, with independent analysis carried out
to con?rm the goodness-of-?t for each construct. Validation of the scales allowed the
estimation of the structural model.
Tourist experience Percent Logistics of the vacation Percent
Travel restrictions Destination
School holidays 14.3 Morroco 6.8
Family restrictions 5.5 Brazil 25.4
Imposed by job 21.4 Egypt 25.4
Enticing prices 20.6 The Dominican Republic 40.6
Weather conditions of
the destination
14.6 Sao Tome and Principe 1.8
Others 23.6 Budget
Travel frequency Less than e1,000 41.3
Never 10.2 From e1,000 to 1,499 31.8
Once a year 53.6 From e1,500 to 1,999 14.8
Twice a year 23.2 From e2,000 to 2,499 7.3
More than three times
a year
13.0 e2,500 or more 4.9
Previous experience Holiday booking
No 83.7 Travel agency 95.1
Yes 16.3 Directly with the operator 3.8
Booking in advance Operator’s call centre 0.2
Less than 15 days 52.1 Internet 0.9
15 days or more and
less than a month
23.6
Booking regime
1 month or more and
less than 3 months
15.9 Half pension 29.4
3 months or more 8.4 Everything included 53.2
Bed and breakfast 17.4
Average length of stay (SD) 9.1 (17.0)
Average number of family
elements (SD)
1.8 (2.2)
Type of accommodation
booked
Hotel in the city 13.7
Beach hotel 51.4
Aparthotel in the city 0.2
Beach Aparthotel 1.3
Resorts 31.1
Others 2.2
Table III.
Characterization of the
tourist experience and
logistics of the vacation
Decision-making
processes
351
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
:
0
6
2
4
J
a
n
u
a
r
y
2
0
1
6
(
P
T
)
Having identi?ed the factors that contribute the most to the formation of each
construct, SEM estimation allowed for research hypotheses assessment. The factors
obtained by means of EFA serve as indicators of the latent constructs: information
sources, push and pull motivations, push and pull satisfactions, and behavioral
intentions. AMOS 6 (Arbuckle and Wothke, 1999) estimates the model evaluating the
model ?t by following the approach suggested by Hair et al. (1998), that is, assessment
of the overall model ?t, followed by individual measurement and structural modeling
(Correia et al., 2007c).
The types of overall model ?t measures often used are absolute, incremental and
parsimoniously ?t measures. Absolute ?t measures evaluate how the theoretical model
?ts the sample data. The incremental ?t measures compare a target model with a more
restricted model, while parsimonious ?t measures diagnose the degree to which the
model ?t has improved by excess of variables.
Results from a x
2
goodness-of-?t test give an indication of absolute ?t measures,
which however are sensitive to sample size. Therefore, the analysis employs the
goodness-of-?t index (GFI) (Joreskog and Sorbom, 1986), the root mean square residual
(RMSR) and the root mean square residual of approximation (RMSEA) (Steiger, 1990) to
evaluate the model’s overall absolute ?t. The incremental ?t measures used to evaluate
the proposed model’s ?t include the: adjusted goodness-of-?t index (AGFI)
( Joreskog and Sorbom, 1986); normed ?t index (NFI) (Bentler and Bonnet, 1980),
Tucker and Lewis index (TLI) (Tucker and Lewis, 1973), incremental ?t index (IFI)
(Bollen, 1988) relative ?t index (Bollen, 1986) and the comparative ?t index (CFI)
(Bentler, 1990).
In general, the measurement model is acceptable if the indices were closer to one
(perfect ?t), as values closer to zero indicate no ?t. In the particular case of RMSR and
RMSEA, smaller values are better (zero indicates a perfect ?t).The evaluation of the
measurement model depended on assessing each latent variable separately by
examining the standardized loading, the construct reliability, and the variance
extracted. Furthermore, parameter estimate testing through the analysis of sign and
statistical signi?cance served to analyze the ?t of the structural model. Standardized
estimates are useful in comparing the parameters’ effect throughout the model, since
they remove scaling information. Proposed hypotheses were tested by observing the
statistical signi?cance of the corresponding paths in the structural model.
The methodology concludes with the representation of the relationship between
information sources, push factors, pull factors, perceptions, push and pull satisfactions
and behavioral intentions, on perceptual maps. Performance of perceptual maps uses
CATPCA. The analysis required the recoding of the main components of information
sources, motivations, satisfaction, and behavioral intentions for proper conversion into
categorical variables. This approach allows us to simultaneously correlate a group
of categorical variables and present the results geometrically in a bidimensional space,
called a perceptual map, in which the visualisation of the connections made among the
categories allows for an easier interpretation of the results. On the map the categories
represented by relatively close points held similar distributions and show strong
association among variables. On the other hand, categorical variables with very
different distributions suggest non-correlation among variables. In this study, the
perceptual maps illustrate a connection between information sources used, motivational
factors, elements of satisfaction and behavioral intentions. This explanatory approach
IJCTHR
2,4
352
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
:
0
6
2
4
J
a
n
u
a
r
y
2
0
1
6
(
P
T
)
increases understanding of howdifferent or relating factors can explain tourist behavior
in decision-making.
Results
The constructs of the model
The results of the EFA established signi?cant correlated factors, including four
information sources, three push motivations, three pull motivations, three pull
satisfactions, three push satisfactions, and two behavioral intentions. These factors are
relevant because they have signi?cant loadings. The following subsections present the
latent constructs of the model as well as the indicators for each latent variable.
Information sources
The study considers information sources supplied by the tourists using a Likert scale
to an EFA. The EFA allowed us to extract one factor that represents 53 percent of the
total variation (Table IV). Information source variables such as mail, travel agencies,
and family and friends were removed because the EFA ?t well without them. As such,
information sources possessed high loadings for the following observed variables:
movies, news, promotion and brochures.
Table IV shows the relative importance of each attribute (average) as an information
source, using a Likert scale from one (not important), to seven (extremely important).
The results obtained follow similarly to Fodness and Murray (1999) and Woodside and
Dubelaar (2002), who assume that brochures are one of the main sources of information
in which tourists ?nd most of the information they need, followed by newspapers.
Movies and promotion, understood to be complementary sources, activate tourist
motivations and fuel the learning process about the destination. The study’s
expectations included travel agencies, and family and friends to appear as privileged
information sources; however, this did not occur. Limited out of country vacations in the
past among the Portuguese would seemto explain why recommendations by family and
friends were low. Only 16 percent of the sample had previous holiday traveling
experience abroad. Interestingly, travel agents do represent important sources, as
95 percent of sample respondents booked through a travel agency; see Table III.
Motivation
In order to ascertain push motivations, the study considered sixteen original variables
to an EFA, which resulted in three motivational factors that served to explain
61 percent of the total variables after varimax rotation, as shown in Table V.
Destination characteristics showed dominating signi?cance in the choice phase.
Hence, the study considers the level of importance of all factors as decisive elements in
the decision process. These factors, which appear in the literature as pull motivations,
through EFA, resulted in three factors that explained 53 percent of the total variance.
Factors Loadings Percentage of variance explained Mean SD
Movies 0.83 53.42 4.0 1.50
News 0.82 4.3 1.57
Promotion 0.71 4.0 1.51
Brochures 0.53 4.6 1.48
Table IV.
The results of EFA for
information sources
construct
Decision-making
processes
353
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
:
0
6
2
4
J
a
n
u
a
r
y
2
0
1
6
(
P
T
)
Motivations with high-loadings results in the following push factors: knowledge
motivation, social motivation and recreational motivation. The pull factors were:
facilities, core attractions and landscape.
The ?rst push factor, motivation knowledge, refers more speci?cally to the need to
do and learn new things as well as to explore different cultures and places. This
includes increasing knowledge, amusement, getting to know different cultures and
lifestyles, intellectual growth, and visiting new places. The second push factor, social
motivation, put forward the need to visit places that friends had not been to, telling
friends about the vacation, and developing close friendships. These factors relate to the
Factors Loadings
Percentage of
variance explained Mean SD
Push motivations
Knowledge motivation 28.79
Doing different things 0.60 5.9 1.39
Stimulating emotions and sensations 0.57 5.6 1.61
Being an adventurer 0.52 5.3 1.75
Amusement 0.64 6.1 1.31
Increasing knowledge 0.81 5.9 1.44
Experiencing different cultures and life styles 0.83 5.9 1.39
Enriching myself intellectually 0.75 5.1 1.42
Visiting new places 0.68 6.0 1.34
Meeting interesting people 0.56 5.6 1.60
Social motivation 17.24
Going places my friends have not been 0.90 4.1 2.26
Telling my friends about the vacation 0.88 4.4 2.10
Developing close friendships 0.54 5.3 1.68
Recreational motivation 14.46
Relieving stress 0.84 6.2 1.30
Physical relaxation 0.73 6.0 1.42
Escaping from routine 0.69 6.2 1.42
Pull motivations
Facilities motivations 26.43
Weather 0.64 6.1 1.33
Accessibilities 0.68 5.6 1.53
Beach 0.60 6.1 1.35
Gastronomy 0.74 5.9 1.43
Security 0.66
Distance 0.54
Relaxing atmosphere 0.67 5.8 1.47
Social environment 0.73 5.5 1.60
Hospitality 0.71
Exoticness 0.60
Core attractions 10.46
Shopping facilities 0.79 4.6 1.54
Night-life 0.70 4.8 1.54
Sports equipment 0.75 4.3 1.62
Transports 0.65 4.8 1.54
Landscape motivations 16.10
Landscape 0.84 6.00 1.38
Natural environment 0.83 5.8 1.32
Table V.
The results of EFA
for motivations
IJCTHR
2,4
354
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
:
0
6
2
4
J
a
n
u
a
r
y
2
0
1
6
(
P
T
)
social rewards also discussed by Gnoth (1997). The push factor recreational motivation
include motivations related to personal well-being, such as stress relief, escape from
routine and physical relaxation, presented as physical rewards. These results follow
similarly to previous studies, in particular Gnoth (1997), who discusses three different
push motivations: self-actualization, sense of self-esteem and social status.
The ?rst pull motivation, referred to as facilities, relates to weather, accessibility,
gastronomy, security, relaxing atmosphere and social environment. The second, core
attractions, relates to shopping facilities, nightlife, and sports, while landscape
motivations include natural environment and landscape.
The mean scores obtained and presented in the fourth column of the Table V show
the main leading push motivations that lead tourists to visit South America and Africa
destinations. In terms of knowledge, motivations include amusement, getting to know
new places and cultures, and doing different things. Regarding social motivations,
tourists value new friendships and talking about the holidays with friends. The leading
recreational motivations include stress relief and escape from routine. With pull
motivations, social factors contribute less to destination choice, with only the social
atmosphere variable showing distinction. The natural environment and tourism
facilities in?uence the formation of the destination’s image, since the majority of
Portuguese tourists classi?ed these as being very important. These include attributes
such as landscape and nature, security, weather and facilities – indicative that a
destination’s natural resources constitute competitive components.
Satisfaction
Satisfaction is a state of well-being resulting from the feeling that holidays
compensate the intrinsic motivations and the services used during the vacation. To
assess emotional and cognitive satisfaction, the analysis includes using the sixteen
factors to measure push motivations and the replicated 21 factors to measure pull
motivations. Two EFA analyses treated these factors based on maximum likelihood
estimation. The ?rst, being push satisfaction, (on a scale of 1 – worse than expected; to
7 – surpassed my expectations) and resulted in three emotional satisfaction factors
that explained 72 percent of the total variance after varimax rotation, as shown in
Table VI. Pull motivations comprehended 21 destination attributes. As can be
observed in Table VI, three factors explained 49 percent of the total variance in terms
of pull satisfaction.
The ?rst push satisfaction factor, recreational satisfaction, mainly concerns the
evaluation of emotional states related to personal well-being, such as stress relief,
escape from routine, physical relaxation, and getting away from crowds. The second
factor, knowledge satisfaction, is related more speci?cally to performing and learning
new things, exploring different cultures and places, further knowledge development
discovering different cultures and lifestyles, visiting new places and, going to places
not yet visited by friends. The third push satisfaction factor, adventure satisfaction,
represents experience associated to challenging emotions and adventure.
Pull satisfaction is related to the cognitive evaluation of the quality level of the
services tourists experience during the vacation. The ?rst pull satisfaction factor,
referred to as facilities, brings together the tourist’s level of satisfaction with the social
environment, hospitality, relaxing atmosphere, information, gastronomy, and exoticness.
The second, referred to as core attractions, understands cultural attractions, shopping
Decision-making
processes
355
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
:
0
6
2
4
J
a
n
u
a
r
y
2
0
1
6
(
P
T
)
facilities, nightlife, sports, and transport. Sun and sand satisfaction combine weather with
landscape and beach.
The mean scores obtained and presented in the fourth column of Table VI show that
the most positive aspects of these destinations are weather, beaches, landscape, and
hospitality.
Behavioral intentions
Behavioral intentions are measured by means of an EFA that combine two factors, the
intention to return to the destination and the willingness to recommend it to family and
friends (Oppermann, 2000). From this analysis, behavioral intentions represented
79 percent of the total variance (Table VII).
Loadings
Percentage of
variance explained Mean SD
Push satisfaction
Recreational satisfaction 33.64
Relieving stress 0.78 4.8 1.33
Physical Relaxation 0.83 4.6 1.36
Getting away from crowds 0.74 4.5 1.39
Escaping from the routine 0.73 5.0 1.26
Knowledge satisfaction 24.33
Increasing knowledge 0.83 4.8 1.27
Experiencing different cultures and lifestyles 0.89 4.9 1.26
Enriching myself intellectually 0.80 4.7 1.30
Visiting new places 0.78 5.0 1.26
Meeting interesting people 0.72 4.8 1.34
Going places where my friends have not been 0.592 4.6 1.22
Adventure satisfaction 14.26
Doing different things 0.62 4.9 1.31
Stimulating emotions and sensations 0.72 4.8 1.27
Being an adventurer 0.62 4.7 1.30
Pull satisfaction
Facilities satisfaction 18.72
Social environment 0.75 5.1 1.46
Hospitality 0.72 5.6 1.31
Relaxing atmosphere 0.65 5.4 1.31
Information 0.60 4.6 1.34
Gastronomy 0.51 4.7 1.51
Exoticness 0.56 5.3 1.26
Core attractions satisfaction 17.74
Night-life 0.70 4.6 1.37
Shopping facilities 0.69 4.6 1.40
Cultural attractions 0.69 4.9 1.47
Sports equipment 0.61 4.4 1.27
Transports 0.54 4.2 1.45
Sun and sand satisfaction 12.71
Weather 0.79 5.9 1.07
Beach 0.66 5.9 1.25
Landscape 0.47 5.7 1.20
Table VI.
The results of EFA
for satisfaction
IJCTHR
2,4
356
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
:
0
6
2
4
J
a
n
u
a
r
y
2
0
1
6
(
P
T
)
Considering a Likert scale of seven points, behavioral intentions show
high-signi?cance levels, although tourists are more likely to revisit than to
recommend. This results from the standard deviation being higher for willingness
to recommend than for probability of returning.
The measurement model
The CFA of the measurement model speci?es the relationships of each observed
variable with the latent construct. Assuming that all the constructs are freely
inter-correlated, an analysis was performed on each construct separately before testing
the measurement and structural model. This a priori analysis allows us to understand
which constructs and observed variables need to be respeci?ed to improve the
structural model.
The hypothetical model is a structural equation system with observable variables
and latent constructs. By imposing the constraints on the loadings resulting from the
EFA, the CFA is able to assess and validate the measurement model, with constructs
being freely inter-correlated. This model ?ts the data well. The regression coef?cients
and the covariance factor are all signi?cant at the 1 and 5 percent level. Coef?cient
alphas for the latent variables appear in Table VIII. All the factors show good
reliability because all values are greater than 0.70.
The measurement model demonstrates an adequate reliability and good ?t indices;
hence. The following subsection looks at structural modelling estimation.
The structural model
Estimation of the complete model included using the asymptotically distribution-free
method using the AMOS 6. This method was adopted because of the unusual distribution
of the data. The standardized coef?cients estimated are in Figure 2. All the coef?cients are
signi?cant at 1 percent signi?cance level, withonlythe pathbetweenpull motivations and
perceptions not seen as signi?cant. As the x
2
is an adjustment measure strongly
in?uenced by sample size, the analysis includes applying other adjustment measures
to evaluate the model. The selected overall ?t indices can be observed in Table IX.
Loadings Percentage of variance explained Mean SD
Return 0.89 79.25 4.7 1.42
Recommend 0.89 4.7 1.97
Table VII.
The results of EFA for
behavioral intentions
Reliability (Cronbach’s a)
Information sources 0.75
Push motivations 0.95
Pull motivations 0.92
Push satisfaction 0.95
Pull satisfaction 0.93
Behavioral intentions 0.72
Table VIII.
Results of the
measurement model
Decision-making
processes
357
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
:
0
6
2
4
J
a
n
u
a
r
y
2
0
1
6
(
P
T
)
Figure 2.
Structural model of tourist
decision-making processes
P
e
r
c
e
p
t
i
o
n
s
S
u
n
a
n
d
S
a
n
d
S
a
t
i
s
f
a
c
t
i
o
n
S
C
o
r
e
A
t
t
r
a
c
t
i
o
n
s
S
F
a
c
i
l
i
t
i
e
s
B
e
h
a
v
i
o
r
I
n
t
e
n
t
i
o
n
R
e
c
o
m
e
n
d
a
t
i
o
n
R
e
t
u
r
n
P
u
l
l
S
a
t
i
s
f
a
c
t
i
o
n
P
u
s
h
S
a
t
i
s
f
a
c
t
i
o
n
H
7
H
1
1
H
1
0
H
1
3
H
1
6
H
1
5
H
1
4
.
4
0
2
H
1
2
H
1
7
.
1
5
3
M
R
e
c
r
e
a
t
i
o
n
a
l
M
K
n
o
w
l
e
d
g
e
M
S
o
c
i
a
l
S
R
e
c
r
e
a
t
i
o
n
a
l
S
A
d
v
e
n
t
u
r
e
S
K
n
o
w
l
e
d
g
e
.
1
2
5
.
2
8
1
.
4
8
9
.
3
1
6
.
1
2
9
.
7
1
8
.
5
7
5
.
8
1
1
.
6
5
9
.
8
5
4
.
8
7
8
.
6
6
6
.
2
0
1
.
3
2
6
.
3
2
4
.
4
3
6
.
3
0
9
.
4
3
3
.
3
5
1
.
3
5
9
.
3
6
2
.
4
7
1
.
0
5
5
.
0
8
0
.
4
0
8
.
1
5
1
.
0
7
6
.
4
1
5
.
3
6
7
.
5
1
9
.
4
2
8
.
5
9
3
.
3
8
2
H
4
H
1
H
2
H
3
H
6
H
5
H
8
H
9
–
0
.
3
4
8
.
1
4
1
.
2
6
8
.
0
3
8
.
4
7
0
.
2
4
4
.
2
6
8
–
0
.
2
0
1
.
6
4
5
.
3
3
0
.
5
8
1
.
1
3
9
.
4
7
4
.
4
3
9
.
0
9
9
.
0
8
0
.
1
8
8
.
2
4
2
M
L
a
n
d
s
c
a
p
e
M
C
o
r
e
A
t
t
r
a
c
t
i
o
n
s
P
r
o
m
o
t
i
o
n
N
e
w
s
M
o
v
i
e
s
B
r
o
c
h
u
r
e
s
M
F
a
c
i
l
i
t
i
e
s
N
o
t
e
s
:
A
l
l
t
h
e
c
o
e
f
f
i
c
i
e
n
t
s
h
a
v
e
a
t
-
v
a
l
u
e
s
i
g
n
i
f
i
c
a
n
t
a
t
1
p
e
r
c
e
n
t
s
i
g
n
i
f
i
c
a
n
c
e
l
e
v
e
l
(
p
<
0
.
0
0
1
)
;
H
y
p
o
t
h
e
s
e
s
r
e
j
e
c
t
e
d
P
u
l
l
M
o
t
i
v
e
s
P
u
s
h
M
o
t
i
v
e
s
I
n
f
o
r
m
a
t
i
o
n
S
o
u
r
c
e
s
.
2
6
4
IJCTHR
2,4
358
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
:
0
6
2
4
J
a
n
u
a
r
y
2
0
1
6
(
P
T
)
The x
2
statistic indicates that the model ?ts the data well (x
2
¼ 139.339; df ¼ 125;
p ¼ 0.180 . 0.05). The other goodness-of-?t measures also indicate a good overall model
?t (GFI ¼ 0.99 exceeds the level of 0.9; the RMSR ¼ 0.02 and RMSEA ¼ 0.02 are closer
to zero, as desired). The other indicators closer to 1 indicate a good incremental and
parsimonious ?t.
The empirical model ?ts the data well and allows us to accept the hypotheses, with
the exception of H
6
, H
8
and H
9
.
The study anticipated tourists using different sources of information in order to
learn more about destination (H
1
). According to the results, news reports and travel
movies positively in?uence the latent variable information sources, as well as
promotion and brochures; however, these present lower standardized coef?cients,
respectively, 0.67 and 0.44. This suggests that brochures and promotion are not as
important to the tourist as are news reports and travel movies.
Information sources may positively in?uence the shaping of push motivations (H
2
);
when the standardized coef?cient estimated is 0.15. Contrary to expectations,
information sources negatively in?uenced pull motivations (H
3
), with a standardized
coef?cient of 20.20. This result shows that information sources tend to portray idyllic
and idealistic destinations, rather than realistic attributes and facilities.
According to Dann (1977) and Crompton (1990), the motivations that lead tourists to
travel include social engagement, rest and cultural (knowledge) forces. As shown by
Correia et al. (2007b), the internal motivations that positively in?uence the desire to
travel (H
4
) are knowledge, recreation, and social motivations.
According to Uysal and Hagan (1993) and Crompton (1990), pull motivations relate
to the attributes of the destination and its attractions. The empirical model allows us to
identify three pull motivations (facilities, core attractions and landscape), all of which
positively in?uence the latent variable pull motivation (H
5
).
Since pull motivations are unaffected by push motivations (H
6
), the hypothesis is
rejected. Push motivations in?uence perceptions of the destination (H
7
), as
demonstrated by the standardized coef?cient of 0.13 and a t-value of 2.85. Pull
motivations, as well as information sources do not in?uence tourist perceptions;
therefore, hypotheses H
8
and H
9
were rejected. Emotional satisfaction (push) negatively
in?uenced by perceptions (H
10
), with a standardized coef?cient of 20.35. In?uence on
satisfaction with the destination’s attributes occurred through perceptions (H
11
) as
demonstrated by a standardized coef?cient of 0.28. Emotional satisfactions (H
12
) result
from knowledge, recreation and adventure; satisfaction factors that present positive
standardized coef?cients, 0.43, 0.59 and 0.382, respectively. Destination perception takes
place in the presence of the sun and sand, core attractions, and facilities (H
13
). These
factors present positive standardized coef?cients, 0.42, 0.37 and 0.52, respectively.
Absolute ?t measures Incremental and parsimonious ?t measures
GFI ¼ 0.99
AGFI ¼ 0.99
x
2
¼ 139.34 (df ¼ 13; p ¼ 0.18) NFI ¼ 0.92
RMSR ¼ 0.016 TLI ¼ 0.99
RMSEA ¼ 0.021 IFI ¼ 0.99
CFI ¼ 0.99
Table IX.
Goodness-of-?t measures
for the structural
equation model
Decision-making
processes
359
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
:
0
6
2
4
J
a
n
u
a
r
y
2
0
1
6
(
P
T
)
Pull satisfaction positively in?uences push satisfaction (H
14
), with standardized
coef?cients of 0.40, thus suggesting that cognitive satisfaction in?uences emotional
satisfaction.
Push (H
15
) and pull (H
16
) satisfaction positively explain behavioral intentions, with
standardizedcoef?cients of 0.49 and0.32, anda statistical signi?cance at the 1 percent level.
Intention to recommend and the probability of returning (H
17
) explain behavioral
intentions, presenting positively positive standardized coef?cients, statistically
signi?cant at the 1 percent level, 0.66 and 0.81, respectively.
Categorical principal component analysis
In order to explore the relationships between each factor in the constructs of the model
more deeply (information sources, push motivations, pull motivations, push satisfaction,
pull satisfaction, and behavioral intentions), the study turned to a CATPCA on the
categorized factors. The structural model demonstrates that information sources are a
signi?cant in?uence in the shaping of push and pull motivations.
In Figure 3, those who found sources of information to be very important were those
showing greater intrinsic motivations about the vacation. Diametrical lines
representing push motivations and information sources, show that the tourists
explored information indiscriminately.
Indiscriminate information search about the destination relates only to push
motivations. Although the tourist may use other information sources randomly to learn
about the destination’s core attractions, brochures provide the majority of the
information. This can be observed given the proximity of the orange and light blue
lines Figure 4 shows.
Figure 4 shows that the individuals who ?nd sources of information to be more
important are also those who show more highly developed perceptions about the
destination.
The perceptual map shown in Figure 5 allows us to come to two conclusions. The
more highly motivated the tourist is, the better developed his or her perceptions are.
Figure 3.
Information sources and
push motivations
1.5 1.0 0.5 0.0 – 0.5 –1.0 –1.5
Dimension 1
1.5
1.0
0.5
0.0
– 0.5
–1.0
D
i
m
e
n
s
i
o
n
2
VI
I
WI
VI
I
WI
VI
I
WI
VI
I
WI
VI
I
WI
VI I
WI
VI
I
WI
Promotion
News
Msocial
Movies
MRecreational
MKnowledge
Brochures
WI – Without Important
I – Important
VI –Very Important
IJCTHR
2,4
360
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
:
0
6
2
4
J
a
n
u
a
r
y
2
0
1
6
(
P
T
)
On the other hand, social motivations lead to perceptions about the destination, as the
narrowed cream and purple lines show.
The perceptual map that relates perceptions to push satisfaction (Figure 6) shows
that more highly developed perceptions are associated to greater levels of emotional
satisfaction. This connection is more obvious with recreational satisfaction, as can be
seen by the proximity of the blue line to the purple line.
The perceptual map that relates perceptions with pull satisfaction (Figure 7) con?rms
that more highly developed perceptions relate to greater levels of cognitive satisfaction.
Figure 4.
Information sources and
pull motivations
Dimension 1
D
i
m
e
n
s
i
o
n
2
1.5 1.0 0.5 0.0 – 0.5 –1.0 –1.5
0.5
0.0
– 0.5
–1.0
VI
VI
I
WI
VI
I
WI
VI
I
VI
I
VI
I
WI
VI
I
WI
Promotion
News
Movies
MLandscape
MFacilities
MCore Attractions
Brochures
WI – Without Important
I – Important
VI – Very Important
WI
WI
WI
I
Figure 5.
Perceptions and push
motivations
1.5 1.0 0.5 0.0 – 0.5 –1.0
Dimension 1
1.5
1.0
0.5
0.0
– 0.5
D
i
m
e
n
s
i
o
n
2
WI
VI
I
WI
VI I WI
VI
I
WI
VI
WI
M social
M Recreational
M Landscape
M knowledge
M Facilities
MCore Attractions
WI – Without Important
I – Important
VI – Very Important
WI
I
VI I
VI
I
Decision-making
processes
361
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
:
0
6
2
4
J
a
n
u
a
r
y
2
0
1
6
(
P
T
)
This connection is more obvious with core attraction satisfaction while is the attribute
that they perceive better.
The perceptual map that relates push and pull satisfactions (Figure 8) shows that
intellectual reward (knowledge) is associated to the satisfaction of core attractions. On
the other hand, satisfaction with recreation shows association to satisfaction with
facilities. Satisfaction with sun and sand are separate from the others.
The perceptual map that relates push satisfaction to behavioral intentions (Figure 9)
shows that tourists recommend these destinations and intend to return resulting from
satisfaction from recreational facilities. This result suggests that this type of
Figure 6.
Perceptions and push
satisfaction
1.5 1.0 0.5 0.0 – 0.5 –1.0
Dimension 1
0.5
0.0
– 0.5
–1.0
D
i
m
e
n
s
i
o
n
2
H
A L
VI
I
WI
VI
I
WI
Perceptions
Msocial
MRecreational
Mknowledge
WI – Without Important
I – Important
VI – Very Important
L – Lower
A – Average
H – High
VI
WI
I
Figure 7.
Perceptions and pull
satisfaction
1.5 1.0 0.5 0.0 – 0.5 –1.0
Dimension 1
1.0
0.5
0.0
– 0.5
–1.0
–1.5
D
i
m
e
n
s
i
o
n
2
B
Av
W
B
Av
W
B
Av
W
H
A
L
SFacilities
SCore Attractions
Perceptions
W – Worst
Av – Average
B – Better
L – Lower
A – Average
H – High
SSun Sand
IJCTHR
2,4
362
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
:
0
6
2
4
J
a
n
u
a
r
y
2
0
1
6
(
P
T
)
destination is very good for rest and relaxation, even if wanting to engage in sports or
nightlife activities, as was the case.
In terms of satisfaction with destination attributes, facilities explain positive
behavioral intentions, suggesting that this type of destination is good enough in terms
of tourism resources, even if tourists are not aware of them through the information
source (Figure 10).
Discussion and managerial implications
This study develops a conceptual model of tourist decision-making, and provides
evidence of the importance of six different constructs to achieve the ?nal decision and
to predict post-purchase behavior. The tourist’s decision process involves two stages:
Figure 8.
Push and pull satisfaction
1.5 1.0 0.5 0.0 – 0.5 –1.0
Dimension 1
1.0
0.5
0.0
– 0.5
D
i
m
e
n
s
i
o
n
2
B
Av
W
B
Av
W
B
Av
W
B
Av
W
B
E
B
Av
W
SSun Sand
SRecreational
SKnowledge
SFacilities
SCore Attractions
SAdventure
W – Worst
Av – Average
B – Better
W
Figure 9.
Push satisfaction and
behavioral intentions
Dimension 1
D
i
m
e
n
s
i
o
n
2
1.0 0.5 0.0 – 0.5 –1.0
0.8
0.6
0.4
0.2
0.0
– 0.2
– 0.4
– 0.6
B
Av
W
B
W
B
Av
W
D
N
D
N
SRecreational
SKnowledge
SAdventure
Return
Recommend
W – Worst
Av – Average
B – Better
N – No
P – Probably
D – Definitely
P
P
I
Decision-making
processes
363
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
:
0
6
2
4
J
a
n
u
a
r
y
2
0
1
6
(
P
T
)
the pre-decision stages ending with choice and the post-decision stage, in which
emotional satisfaction and cognitive satisfaction in?uence future behavioral intentions.
In the pre-decision stage, external stimuli and motivations in?uence ?nal perceptions
about the destination, leading to the ?nal decision being reached.
This model, which combines the two different stages of tourist behavior, shows
tangible empirical results, representing a step forward in the tourist behavior literature.
Although much discussion and testing of these constructs has been widely studied in the
literature, examination of the causal relationships had not yet been addressed.
The major ?ndings of this study have some signi?cant managerial implications,
especially for the marketers who promote such destinations as those addressed in this
paper. This study provides evidence of the relevancy of different information sources
in the tourist decision-making processes. The main information sources activating the
need to travel in the tourist’s mind were movies, news reports, promotion, and
brochures, as these positively in?uence push motivations. For the purposes of
gathering of information on destination attributes (pull motivations), these sources,
and particularly brochures, appear to be insuf?cient as the structural model shows a
negative path between information sources and pull motivations (Figure 2). This result
suggests that the information available attempts to “sell dreams”; in other words, to
activate the need for the holiday, rather than to provide information about the tourism
destination resources. Managers should include more information in brochures about
the core product to allow the tourist to better plan his or her vacation. Since selling
dreams likely is as important as selling products, information sources should separate
these issues through two types of promotional brochures; the ?rst appealing to the need
for travel, depicting beautiful scenery and other capturing features, while the second
should provide information about the core attractions and facilities provided.
The mean scores for the importance of each information source allow marketers to
prioritize the means they have at their disposal in approaching the tourist. Since the
most important sources of information are brochures and travel movies, these should
be organized and designed according to the expectations of tourists.
Figure 10.
Pull satisfaction and
behavioral intentions
1.5 1.0 0.5 0.0 – 0.5 –1.0 –1.5
Dimension 1
2.0
1.5
1.0
0.5
0.0
– 0.5
–1.0
D
i
m
e
n
s
i
o
n
2
B
Av
W
B
Av
W
B
Av
D
N
N
SSun Sand
SFacilities
SCore Attractions
Return
Recommend
W – Worst
Av – Average
B – Better
N – No
P – Probably
D – Definitely
P
W
P
D
IJCTHR
2,4
364
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
:
0
6
2
4
J
a
n
u
a
r
y
2
0
1
6
(
P
T
)
The EFAperformed on motivations shows that tourists perceive three different push
motivations and three pull motivations, although only push motivations contribute to
the tourist’s perception of the selected destination, consequently in?uencing satisfaction
and future behavior. Thus, managers should consider these variables as determining
factors in improving satisfaction levels. Knowledge motivations fundamentally relate to
amusement, visiting new places, encountering different cultures and doing different
things. Social motivations relate to developing friendships and social acceptance, while
recreational motivations relate to stress relief and escape from daily routine. These
results constitute important issues in bettering marketing strategies.
The mean scores of these motivations show that the most important factors that
drive Portuguese tourists to travel relate to social status and knowledge. Core
attractions are not relevant, but landscape and facilities are important.
Tourists develop perceptions about the destinations they are traveling to, although
these perceptions are based only on push motivations as the structural model shows.
Furthermore, the perceptual map (Figure 5) shows that the more motivated the tourist
is, the more favorably he or she perceives the destination. These perceptions are
especially high due to the social connotations that these destinations re?ect. These
results suggest that managers should encourage tourists to perceive these destinations
as a core product in which facilities and quality are also competitive advantages that
can go together to achieve the “social dream” of traveling to a far-away destination.
The EFA performed on satisfactions demonstrates highlighted variables that tourists
evaluated: emotional (push) satisfaction and cognitive (pull) satisfaction. The emotional
dimension relies on three evaluation factors: recreation, knowledge, and adventure.
Cognitive assessment suggests signi?cant connection between facilities, core attractions,
and sun and sand attributes. The mean scores obtained for each factor suggest that the
competitive advantages perceived by tourists in South America and Africa destinations
are weather, beaches, landscape and hospitality. The signi?cant path between
perceptions, push satisfaction, and associated perceptual map (Figure 6) con?rms that
this type of destination is seen as the ideal place in which to ful?ll a dream as well as to
pursue the sun and sand facilities (Figure 7). The structural model also shows a signi?cant
path between emotional satisfaction and cognitive satisfaction, suggesting that the
greater the personal satisfaction, the better the service evaluation. The perceptual map
(Figure 8) reinforces this result, showing which push satisfaction factors are more
correlated with which pull satisfaction factors. Knowledge satisfaction correlates with
core attraction satisfaction. On the other hand, recreational satisfaction relates with
facilities. Sun and sand satisfaction are uncorrelated with the other factors. These issues
are fundamental for tourism managers to consider when positioning tourist destinations.
As the measurement model demonstrates, estimating the structural model that
empirically tests the conceptual model with an adequate degree of reliability is possible.
The ?ndings support fourteen of the seventeen hypotheses. Those hypotheses not
supported were paths between information sources and perceptions, pull motivations and
perceptions, and between push and pull motivations. Hence, tourists may not understand
the destination’s attributes, but simply decide and evaluate based on intuition.
Finally, satisfaction explains future behavioral intentions, since analysis
established a signi?cant path between push satisfaction and behavioral intentions,
as well as between pull satisfaction and future behavior. The perceptual maps
(Figures 9 and 10) go deeper into these relationships, showing that the main leading
Decision-making
processes
365
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
:
0
6
2
4
J
a
n
u
a
r
y
2
0
1
6
(
P
T
)
satisfaction factors that explain future behavior are facilities and recreational. This
suggests that exotic destinations and relaxing environments encourage tourists to
perceive them as predominantly recreational. Good tourism facilities were observed
during the vacation despite the lack of information about these.
Conclusions
Tourist behavioral intentions have causal relationships with information sources,
motivations, perceptions and satisfaction. The model divides these motivations into push
and pull concepts, and shows evidence for each push motivations, positively in?uencing
perceptions about the destination and providing different levels of emotional and
cognitive satisfaction at the end of the vacation. These factors in?uence future behavior.
In the literature, although the importance of these constructs has been widely
discussed, little testing on the reliability and structural relationships taking into
consideration all the constructs discussed. Future behavior re?ects emotional and
cognitive satisfaction, in?uenced, in turn, by perceptions that are in?uenced by
emotional motivations stimulated by existing sources of information.
Although the model tested only Portuguese tourists, the study is extendable to cover
larger spectrums of tourists. Still, further research is necessary to establish such issues
as the degree to which information sources in?uence post-purchase satisfaction, the risk
inherent in decision-making, and destination attitude in measuring tourist loyalty.
References
Abelson, R. and Levi, A. (1985), “Decision making and decision theory”, in Lindzey, G. and
Aronson, E. (Eds), The Handbook of Social Psychology, 3rd ed., Vol. 1, Random House,
New York, NY.
AMOS Inc. (2006), AMOS 6, AMOS Inc., Murphy, NC.
Arbuckle, J. andWothke, W. (1999), AMOS4.0User’s Guide, Small Waters Corporation, Chicago, IL.
Archer, B. (1976), “Demand forecasting in tourism”, in Revell, J. (Ed.), Bangor Occasional Papers
in Economics, University of Wales Press, Bangor.
Arnould, E. and Price, L. (1993), “River magic: extraordinary experience and the extended service
encounter”, Journal of Consumer Research, Vol. 20, pp. 24-45.
Artus, J. (1972), “An econometric analysis of international travel”, Annals of Tourism Research,
Vol. 18, pp. 663-5.
Backman, S. and Crompton, J. (1991), “The usefulness of selected variables for predicting activity
loyalty”, Leisure Sciences, Vol. 13, pp. 205-20.
Baker, D. and Crompton, J. (1998), “Exploring the relationship between quality, satisfaction, and
behavioral intentions in the context of a festival”, Annals of Tourism Research, Vol. 27
No. 3, pp. 785-804.
Baker, D. and Crompton, J. (2000), “Quality, satisfaction and behavioral intentions”, Annals of
Tourism Research, Vol. 27 No. 3, pp. 785-804.
Baloglu, S. (1997), “The relationship between destination images and socio-demographic and trip
characteristics of international travelers”, Journal of Vacation Marketing, Vol. 3, pp. 221-33.
Baloglu, S. and McCleary, K. (1999), “A model of destination image formation”, Annals of
Tourism Research, Vol. 26 No. 4, pp. 868-97.
Bargeman, B. and Poel, H. (2006), “The role of routines in the vacation decision-making process
of Dutch vacationers”, Tourism Management, Vol. 27 No. 4, pp. 707-20.
IJCTHR
2,4
366
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
:
0
6
2
4
J
a
n
u
a
r
y
2
0
1
6
(
P
T
)
Barros, C. and Proenc¸a, I. (2005), “Mixed logit estimation of radical Islamic terrorism in Europe
and North America”, The Journal of Con?ict Resolution, Vol. 49 No. 2, pp. 298-314.
Barsky, J. (1992), “Costumer satisfaction in the hotel industry: meaning and measurement”,
Hospitality Research Journal, Vol. 16, pp. 51-73.
Barsky, J. and Labagh, R. (1992), “A strategy for customer satisfaction”, The Cornell Hotel &
Restaurant Administration Quarterly, Vol. 33, pp. 32-7.
Beerli, A. and Mart? ´n, J. (2004), “Factors in?uencing destination image”, Annals of Tourism
Research, Vol. 31, pp. 657-81.
Beesley (2005), “The management of emotion in collaborative tourism research settings”,
Tourism Management, Vol. 26 No. 2, pp. 261-75.
Bentler, P. (1990), “Comparative ?t indexes in structural models”, Psychological Bulletin, Vol. 107,
pp. 238-46.
Bentler, P. and Bonnet, D. (1980), “Signi?cance tests and goodness-of-?t in the analysis of
covariances structures”, Psychological Bulletin, Vol. 88, pp. 588-606.
Bentler, P. and Speckart, G. (1979), “Models of attitude behavior relations”, Psychological Review,
Vol. 86 No. 5, pp. 452-64.
Bettman, J. and Park, C. (1980), “Effects of prior knowledge and experience and phase of the
choice process on consumer decision, a protocol analysis”, Journal of Consumer Research,
Vol. 7, pp. 234-48.
Bigne´, J., Sa´nchez, M. and Sa´nchez, J. (2001), “Tourism image, evaluation variables and after
purchase behavior: inter-relationship”, Tourism Management, Vol. 22, pp. 607-16.
Bojanic, D. (1996), “Consumer perceptions of price, value and satisfaction in the hotel industry:
an exploratory study”, Journal of Hospitality and Leisure Marketing, Vol. 14 No. 1, pp. 5-22.
Bollen, K. (1986), “Sample size and Bentler and Bonett’s nonnormed ?t index”, Psychometrika,
Vol. 51, pp. 375-7.
Bollen, K. (1988), “A new incremental ?t index for general structural equation models”, paper
presented at 1988 Southern Sociological Society Meetings, Nashville, TN.
Bultena, C. and Klessig, L. (1969), “Satisfaction in camping: a conceptualization and guide to
social research”, Journal of Leisure Research, Vol. 1 No. 1, pp. 348-64.
Burgess, J. (1978), “Image and identity”, Occasional Papers in Geography, No. 23, University of
Hull Publications, Hull.
Cohen, J., Fishbein, M. and Ahtola, O. (1972), “The nature and uses of expectancy – value models
in consumer attitude research”, Journal of Marketing Research, Vol. 9, pp. 456-60.
Correia, A. (2000), “A procura tur? ´stica no Algarve”, unpublished PhD thesis in Economics,
Unidade de Cieˆncias Econo´micas e Empresariais, Universidade do Algarve, Faro.
Correia, A. (2002), “How do tourist choose – a conceptual framework”, Tourism an International
Interdisciplinary Journal, Vol. 50 No. 1, pp. 21-9.
Correia, A. and Crouch, G. (2004), “A study of tourist decision processes: Algarve, Portugal”,
in Crouch, G., Perdue, R., Timmermans, H. and Uysal, M. (Eds), Consumer Psychology of
Tourism, Hospitality and Leisure, Vol. 3, CABI Publishing, Wallingford.
Correia, A., Barros, C. and Silvestre, A. (2007a), “Tourism golf repeat choice behavior in the
Algarve, a mixed logit approach”, Tourism Economics, Vol. 13 No. 1, pp. 111-27.
Correia, A., Valle, P. and Moc¸o, C. (2007b), “Modelling motivations and perceptions of Portuguese
tourists”, Journal of Business Research, Vol. 60 No. 1, pp. 76-80.
Correia, A., Valle, P. and Moc¸o, C. (2007c), “Why people travel to exotic places?”, International
Journal of Culture, Tourism and Hospitality Research, Vol. 1 No. 1, pp. 45-61.
Decision-making
processes
367
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
:
0
6
2
4
J
a
n
u
a
r
y
2
0
1
6
(
P
T
)
Crompton, J. (1979), “Motivations for pleasure vacations”, Annals of Tourism Research, Vol. 6
No. 4, pp. 408-24.
Crompton, J. (1990), “Claiming our share of the tourism dollar”, Parks and Recreation, pp. 42-88,
March.
Crompton, J. (1992), “Structure of vacation destination choice sets”, Annals of Tourism Research,
Vol. 19, pp. 420-34.
Crompton, J. and Ankomah, P. (1993), “Choice set propositions in destination decisions”, Annals
of Tourism Research, Vol. 20, pp. 461-76.
Cronbach, L. (1951), “Coef?cient alpha and the internal structure of tests”, Psychometrika, Vol. 16
No. 3, pp. 297-334.
Cronin, J., Joseph, J. and Taylot, S. (1992), “A measuring service quality: a reexamination and
extension”, Journal of Marketing, Vol. 56 No. 3, pp. 55-68.
Crotts, J. (1999), “Consumer decision making and prepurchase information search”, in Pizam, A.
and Yoel, M. (Eds), Consumer Behavior in Travel and Tourism, The Haworth Hospitality
Press, Binghamton, NY.
Crouch, G. (1994), “The study of international tourism demand: a review of ?ndings”, Journal of
Travel Research, No. 1, pp. 13-23.
Crouch, G. and Jordan, L. (2004), “The determinants of convention site selection: a logistic choice
model from experimental data”, Journal of Travel Research, Vol. 43 No. 2, pp. 118-30.
Dann, G. (1977), “Anomie, ego-enhancement and tourism”, Annals of Tourism Research, Vol. 4
No. 4, pp. 184-94.
Dann, G. (1981), “Tourist motivation – an appraisal”, Annals of Tourism Research, Vol. 8 No. 2,
pp. 187-219.
Dann, G. (1996), “Tourists’ images of a destination – an alternative analysis”, Recent Advances
and Tourism Marketing Research, Vol. 5 Nos 1/2, pp. 41-55.
Decrop, A. (1999), “Tourists’ decision-making and behavior processes”, in Pizam, A. and
Mansfeld, Y. (Eds), Consumer Behavior in Travel and Tourism, The Haworth Hospitality
Press, Binghamton, NY.
DGT (2006), S? ´ntese das Fe´rias dos Portugueses 2005, Direcc¸a˜o-Geral do Turismo, Lisboa.
Dick, A. and Basu, K. (1994), “Customer loyalty: toward an integrated conceptual framework”,
Journal of the Academy of Marketing Science, Vol. 22 No. 2, pp. 99-113.
Driscoll, A., Lawson, R. and Niven, B. (1994), “Measuring tourist destination’s perceptions”,
Annals of Tourism Research, Vol. 21 No. 3, pp. 499-511.
Dube, L., Renaghan, L. and Miller, J. (1994), “Measuring customer satisfaction for strategic
management”, Cornell Hotel and Restaurant Administration Quarterly, Vol. 35, pp. 39-47.
Dubois, B. (1993), Compreender o Consumidor, Publicac¸o˜es D. Quixote, Lisboa.
Edwards, W. (1954), “The theory of decision making”, Psychology. Bulletin, Vol. 51, pp. 380-417.
Engel, J., Kollat, D. and Blackwell, R. (1978), Consumer Behavior, The Dryden Press, Hinsdale, IL.
Fakeye, P. and Crompton, J. (1991), “Image differences between prospectives, ?rst-time, and repeat
visitors to the lower Rio Grande valley”, Journal of Travel Research, Vol. 32, pp. 10-16.
Festinger, L. (1954), “A theory of social comparison processes”, Human Relations, Vol. 7 No. 2,
pp. 117-40.
Fishbein, M. (1967), Readings in Attitude Theory and Measurement, Wiley, New York, NY.
Fishbein, M. and Ajzen, I. (1980), Predicting and Understanding Consumer Behavior: Attitude
Behavior Correspondence, Prentice-Hall, Englewood Cliffs, NJ.
IJCTHR
2,4
368
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
:
0
6
2
4
J
a
n
u
a
r
y
2
0
1
6
(
P
T
)
Fisher, D. (2004), “The demonstration effect revisited”, Annals of TourismResearch, Vol. 31 No. 2,
pp. 428-46.
Fleischer, A. and Pizam, A. (2002), “Tourism constraints among Israelis seniors”, Annals of
Tourism Research, Vol. 29 No. 1, pp. 106-23.
Fodness, D. (1994), “Measuring tourist motivation”, Annals of Tourism Research, Vol. 21 No. 3,
pp. 555-81.
Fodness, D. and Murray, B. (1997), “Tourist information search”, Annals of Tourism Research,
Vol. 24 No. 3, pp. 503-23.
Fodness, D. and Murray, B. (1999), “A model of tourist information search behavior”, Journal of
Travel Research, Vol. 37 No. 1, pp. 220-30.
Foxall, G. and Goldsmith, R. (1994), Consumer Psychology for Marketing, Routledge, London.
Gallarza, M., Saura, I. and Garcia, H. (2002), “Destination image: towards a conceptual
framework”, Annals of Tourism Research, Vol. 29 No. 1, pp. 56-78.
Gartner, W. (1993), “Image formation process”, in Uysal, M. and Fesenmaier, D. (Eds),
Communication and Channel Systems in Tourism Marketing, Haworth Press, New York,
NY, pp. 191-215.
Gnoth, J. (1997), “Tourism motivation and expectation formation”, Annals of Tourism Research,
Vol. 24 No. 2, pp. 283-304.
Goosens, C. (2000), “Tourist information and pleasure motivation”, Annals of Tourism Research,
Vol. 27 No. 3, pp. 301-21.
Gunn, C. (1972), Vacationscape: Designing Tourist Regions, Taylor and Francis/University of
Texas, Washington, DC.
Guy, B., Curtis, W. and Crotts, J. (1990), “Environmental learning of ?rst-time travellers”, Annals
of Tourism Research, Vol. 17 No. 3, pp. 419-31.
Hair, J., Anderson, R., Tatham, R. and Black, W. (1998), Multivariate Data Analysis, Prentice-Hall,
Upper Saddle River, NJ.
Holbrook, M. (1978), “Beyond attitude structure: toward the informational determinants of
attitude”, Journal of Marketing Research, Vol. 15, pp. 545-56.
Holbrook, M. (1996), “Customer value – a framework for analysis and research”, Advances in
Consumer Research, Vol. 23, pp. 138-42.
Hong, S., Kim, J., Jang, H. and Lee, S. (2006), “The roles of categorization, affective image and
constraints on destination choice: an application of the NMNL model”, Tourism
Management, Vol. 27.
Howard, J. and Sheth, J. (1969), The Theory of Buyer Behavior, Wiley, New York, NY.
Hunt, H. (1977), “CS/D – overview and future directions”, in Hunt, H. (Ed.), Conceptualization and
Measurement of Consumer Satisfaction and Dissatisfaction, Marketing Science Institute,
Cambridge, MA.
Iso-Ahola, S. and Mannel, R. (1987), “Psychological nature of leisure and tourism experience”,
Annals of Tourism Research, Vol. 14 No. 3, pp. 314-31.
Jamrozy, U., Backman, S. and Backman, K. (1996), “Involvement and opinion leadership in
tourism”, Annals of Tourism Research, Vol. 23 No. 4, pp. 908-24.
Joreskog, K. and Sorbom, D. (1986), LISREL VII: Analysis of Linear Structural Relationship by
Maximum Likelihood and Least Square Method, Scienti?c Software, Mooresville, NC.
Kim, S. and Yoon, Y. (2003), “The hierarchical effects of effective and cognitive components on
the tourism destination image”, Journal of Travel & Tourism Marketing, Vol. 14 No. 20,
pp. 1-22.
Decision-making
processes
369
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
:
0
6
2
4
J
a
n
u
a
r
y
2
0
1
6
(
P
T
)
Kockelman, K. and Krishnamurthy, S. (2004), “A new approach for travel demand modelling:
linking Roy’s identity to discrete choice”, Transportation Research Part B, Vol. 38,
pp. 459-75.
Kotler, P., Haider, D. and Rein, I. (1993), Marketing Places, Free Press, New York, NY.
Kozak, M. (2001), “Repeaters’ behavior at two distinct destinations”, Annals of Tourism
Research, Vol. 28 No. 3, pp. 784-807.
Kozak, M. and Rimmington, M. (2000), “Tourist satisfaction with Mallorca, Spain, as an
off-season holiday destination”, Journal of Travel Research, Vol. 38 No. 3, pp. 260-9.
Lam, T. and Hsu, C. (2006), “Predicting behavioral intention of choosing a travel destination”,
Tourism Management, Vol. 27 No. 4, pp. 589-99.
Lancaster, K. (1966), “A new approach to consumer theory”, Journal of Political Economy, Vol. 74
No. 2, pp. 132-57.
LaTour, S. and Peat, N. (1979), “Conceptual and methodological issues in consumer satisfaction
research”, Advances in Consumer Research, Vol. 6, pp. 431-7.
Levitt, T. (1981), “Marketing intangible products and product intangibles”, Harvard Business
Review, pp. 94-102, May/June.
Lim, C. (1997), “An econometric classi?cation and review of international tourism demand
models”, Tourism Economics, Vol. 3 No. 1, pp. 69-81.
Luce, D. (1959), Individual Choice Behavior, Wiley, New York, NY.
Lundberg (1990), The Tourist Business, 6th ed., van Nostrand Reinhold, New York, NY.
McCabe, A. (2000), “Tourism motivation process”, Annals of Tourism Research, Vol. 27 No. 4,
pp. 1049-52.
McFadden, D. (1981), “Conditional logit analysis of qualitative choice behavior”, in Zarembka, P.
(Ed.), Frontiers in Econometrics, Academic Press, New York, NY.
Madrigal, R. (1995), “Cognitive and effective determinants of fan satisfaction with sporting event
attendance”, Journal of Leisure Research, Vol. 27 No. 3, pp. 205-27.
Maio, G. and Olson, J. (1994), “Value-attitude-behavior relations: the moderating role of attitude
functions”, British Journal of Social Psychology, Vol. 33, pp. 301-12.
Mannell, R. and Iso-Ahola, S. (1987), “Psychological nature of leisure and tourism experience”,
Annals of Tourism Research, Vol. 14 No. 3, pp. 314-31.
Mansfeld, Y. (1992), “From motivation to actual travel”, Annals of Tourism Research, Vol. 19
No. 3, pp. 399-419.
Marshal, A. (1920), Principles of Economics, 8th ed., Macmillan and Co., London.
Mazursky, D. (1989), “Past experience and future tourism decisions”, Annals of Tourism
Research, Vol. 16 No. 3, pp. 333-44.
Middleton, V. (1994), Marketing in Travel and Tourism, Butterworth-Heinemann, London.
Miller, G. (1956), “The magic number seven, plus or minus two: some limits on our capacity for
processing information”, The Psychological Review, Vol. 63, pp. 81-9.
Miller, J. (1977), “Studying satisfaction, modifying models, eliciting expectations, posing
problems, and making meaningful measurements”, in Hunt, J. (Ed.), Conceptualization and
Measurement of Consumer Satisfaction and Dissatisfaction, Marketing Science Institute,
Cambridge, MA.
Mohsin, A. and Ryan, C. (2003), “Backpackers in the northern territory of Australia”, The
International Journal of Tourism Research, Vol. 5 No. 2, pp. 113-21.
IJCTHR
2,4
370
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
:
0
6
2
4
J
a
n
u
a
r
y
2
0
1
6
(
P
T
)
Money, R. and Crotts, J. (2003), “The effect of uncertainty avoidance on information search,
planning and purchases of international travel vacations”, Tourism Management, Vol. 24,
pp. 191-202.
Morley, C. (1992), “A microeconomic theory of international tourism demand”, Annals of
Tourism Research, Vol. 19, pp. 250-67.
Morley, C. (1994), “Discrete choice analysis of the impact of tourism prices”, Journal of Travel
Research, Fall, pp. 8-14.
Morrison, A. (1989), Hospitality and Tourism Marketing, Delmar, Albany, NY.
Moutinho, L. (1982), “An investigation of tourist behavior in Portugal – a comparative analysis of
pre-decision buying and post-purchasing attitudes of British, American and West German
Tourists”, PhD, University of Shef?eld, Shef?eld.
Moutinho, L. (1987), “Consumer behavior in tourism”, European Journal of Marketing, Vol. 21
No. 10, pp. 1-44.
Murphy, P., Pritchard, M. and Smith, B. (2000), “The destination product and its impact on
traveler perceptions”, Tourism Management, Vol. 21 No. 1, pp. 43-52.
Nicolau, J. and Ma´s, F. (2005), “Stochastic modelling: a three-stage tourist choice process”, Annals
of Tourism Research, Vol. 32 No. 1, pp. 49-69.
Nicosia, F. (1966), Consumer Decision Process, Prentice-Hall, Englewood Cliffs, NJ.
Oh, H. (2000), “The effect of brand class, brand awareness, and price on customer value and
behavioral intentions”, Journal of Hospitality and Tourism Research, Vol. 24 No. 2,
pp. 136-62.
O’Hagan, J. and Harrison, M. (1984), “Market shares of US tourist expenditures in Europe: an
econometric analysis”, Applied Economics, Vol. 16 No. 6, pp. 919-31.
Oliver, R. (1980), “A cognitive model of antecedents and consequences of satisfaction decisions”,
Journal of Marketing Research, Vol. 17, pp. 460-9.
Oliver, R. (1993), “Cognitive, affective, and attributes base of the satisfaction response”, Journal
of Consumer Research, Vol. 20, pp. 418-30.
Oliver, R. (1999), “Whence consumer loyalty?”, Journal of Marketing, Vol. 63, pp. 33-44.
Oppermann, M. (2000), “Tourism destination loyalty”, Journal of Travel Research, Vol. 39,
pp. 78-84.
Otto, J. (1997), “The role of the affective experience in the service experience chain”, unpublished
PhD, The University of Calgary, Calgary.
Otto, J. and Ritchie, J. (1995), “Exploring the quality of the service experience: a theoretical and
empirical analysis”, Advances in Services Marketing and Management, Vol. 4, pp. 37-61.
Paraskevopoulos, G. (1977), “An econometric analysis of international tourism”, Paper 31, Centre
of Planning & Economic Research, Athens.
Parasuraman, A., Zeithaml, V. and Berry, L. (1985), “A conceptual model of service quality and
its implications for future research”, Journal of Marketing, Vol. 49, pp. 41-50.
Parasuraman, M. (2000), “Tourism destination loyalty”, Journal of Travel Research, Vol. 39,
pp. 78-84.
Pearce, P. (1982), “Perceived changes in holiday destinations”, Annals of TourismResearch, Vol. 9
No. 2, pp. 145-64.
Perales, R. (2002), “Rural tourism in Spain”, Annals of Tourism Research, Vol. 29 No. 4,
pp. 1101-10.
Decision-making
processes
371
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
:
0
6
2
4
J
a
n
u
a
r
y
2
0
1
6
(
P
T
)
Petrick, J. (2002), “Experience use history as a segmentation tool to examine golf travellers’
satisfaction perceived value and repurchase intentions”, Journal of Vacation Marketing,
Vol. 8 No. 4, pp. 332-42.
Petrick, J., Backman, S. and Bixler, R. (1999), “An investigation of selected factors effect on golfer
satisfaction and perceived value”, Journal of Park and Recreation Management, Vol. 17
No. 1, pp. 40-59.
Rojek, C. (1990), “Baudrillard and leisure”, Leisure Studies, Vol. 9 No. 1, pp. 7-20.
Rosenberg, M. (1956), “Cognitive structure and attitudinal affect”, Journal of Abnormal and
Social Psychology, Vol. 53, pp. 376-82.
Ross, E. and Iso-Ahola, S. (1991), “Sightseeing tourists motivation and satisfaction”, Annals of
Tourism Research, Vol. 18, pp. 226-37.
Ryan, C. (1994), “Leisure and tourism – the application of leisure concepts to tourist behavior – a
proposed model”, in Seaton, A. (Ed.), Tourism: The State of the Art, Wiley, New York, NY.
Ryan, C. and Glendon, I. (1998), “Application of leisure motivation scale to tourism”, Annals of
Tourism Research, Vol. 25 No. 1, pp. 169-84.
Samuelson, P. (1981), Economia, 11th ed., Fundac¸a˜o Calouste Gulbenkian, Lisboa.
Scho?eld, P. (1996), “Cinematographic images of a city: alternative heritage tourism in
Manchester”, Tourism Management, Vol. 17 No. 5, pp. 333-40.
Seddighi, H. and Theocharous, A. (2002), “A model of tourism destination choice: a theoretical
and empirical analysis”, Tourism Management, Vol. 23 No. 5, pp. 475-87.
Sheldon, P. (1990), “A review of tourism expenditure research”, in Cooper, C.P. (Ed.), Progress in
Tourism, Recreation and Hospitality Management, Belhaven Press, New York, NY.
Sheth, J., Newman, B. and Gross, B. (1991), “Why we buy what we buy: a theory of consumption
values”, Journal of Business Research, Vol. 22, pp. 159-70.
Shoemaker, S. (1989), “Segmentation of the senior pleasure travel market”, Journal of Travel
Research, Vol. 27 No. 3, pp. 14-21.
Silvestre, A. and Correia, A. (2005), “Asecond- order factor analysis model for measuring tourist’s
overall image of Algarve (Portugal)”, Tourism Economics, Vol. 11 No. 4, pp. 539-54.
Song, H. and Witt, S. (2000), Tourism Demand Modelling and Forecasting: Modern Econometric
Approaches, Pergamon, Oxford.
Spreng, R., Mackenzie, S. and Olshavsky, B. (1996), “A re-examination of the determinants of
consumer satisfaction”, Journal of Marketing, Vol. 60 No. 3, pp. 15-22.
SPSS Inc. (2005), SPSS 14.0, SPSS Inc., Chicago, IL.
Steiger, J. (1990), “Structural model evaluation and modi?cation: an interval estimation
approach”, Multivariate Behavior Research, Vol. 25, pp. 173-80.
Stynes, D. and Peterson, G. (1984), “A review of logit models with implications for modelling
recreational choices”, Journal of Leisure Research, Vol. 16, pp. 295-310.
Taplin, J. and Qiu, M. (1997), “Car trip attraction and route choice in Australia”, Annals of
Tourism Research, Vol. 24 No. 3, pp. 624-37.
Truong, T. (2005), “Assessing holiday satisfaction of Australian travellers in Vietnam: an
application of the HOLSATmodel”, Asia Paci?c Journal of TourismResearch, Vol. 10 No. 3,
pp. 227-46.
Tse, D. and Wilton, P. (1988), “Models of consumer satisfaction formation: an examination”,
Journal of Marketing Research, Vol. 25, pp. 204-12.
IJCTHR
2,4
372
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
:
0
6
2
4
J
a
n
u
a
r
y
2
0
1
6
(
P
T
)
Tucker, L. and Lewis, C. (1973), “A reliability coef?cient for maximum likelihood factor
analysis”, Psychometrika, Vol. 38, pp. 1-10.
Um, S. and Crompton, J. (1990), “Attitude determinants in tourism destination choice”, Annals of
Tourism Research, Vol. 17, pp. 432-48.
Uysal, M. and Hagan, L. (1993), “Motivation of pleasure to travel and tourism”, in Khan, M.,
Olsen, M. and Var, T. (Eds), VNR’S Encyclopedia of Hospitality and Tourism, Van
Nostrand Reinhold, New York, NY.
Uysal, M., Mclellan, R. and Syrakaya, E. (1996), “Modelling vacation destination decisions: a
behavioral approach”, Recent Advances in Tourism Marketing Research, Vol. 5 Nos 1/2,
pp. 57-75.
Vogt, C. and Andereck, K. (2003), “Destination perceptions across a vacation”, Journal of Travel
Research, Vol. 41 No. 4, pp. 348-54.
Walmsley, D. and Jenkins, J. (1992), “Tourism cognitive mapping of unfamiliar environments”,
Annals of Tourism Research, Vol. 19, pp. 268-86.
Whipple, T. and Thatch, S. (1988), “Group tour management: does good service produce satis?ed
customers?”, Journal of Travel Research, Vol. 27 No. 2, pp. 16-21.
Witt, S. (1992), “Tourism forecasting: how well do private and public sector organizations
perform?”, Tourism Management, Vol. 13 No. 1, pp. 79-84.
Witt, S. and Martin, C. (1987), “International tourism demand models – inclusion of marketing
variables”, Tourism Management, Vol. 8 No. 1, pp. 33-44.
Woodside, A. (2005), “Advancing from subjective to con?rmatory personal introspection in
consumer research”, Psychology & Marketing, Vol. 21 No. 12, pp. 987-1010.
Woodside, A. and Dubelaar, C. (2002), “A general theory of tourism consumption systems: a
conceptual framework and an empirical exploration”, Journal of Travel Research, Vol. 41
No. 2, pp. 120-32.
Woodside, A. and King, R. (2001), “Tourism consumption systems: theory and empirical
research”, Journal of Travel and Tourism Research, Vol. 10 No. 1, pp. 3-27.
Woodside, A. and Lysonski, S. (1989), “A general model of travel destination choice”, Journal of
Travel Research, Vol. 27 No. 4, pp. 8-14.
Woodside, A. and Sherrell, D. (1977), “Travel evoked, inept, and inert sets of vacation
destinations”, Journal of Travel Research, Vol. 16 No. 3, pp. 2-6.
Woodside, A., Frey, L. and Daly, R. (1989), “Linking service quality, customer satisfaction and
behavioral intention”, Journal oh Health Care Marketing, Vol. 9, pp. 5-17.
WTO (2005), Tourism Highlights, 2005 Edition, World Tourism Organization, Madrid.
Yoon, Y. and Uysal, M. (2005), “An examination of the effects of motivation and satisfaction on
destination loyalty: a structural model”, Tourism Management, Vol. 26 No. 1, pp. 45-56.
Zeithaml, V. (1988), “Consumer perceptions of price, quality and value: a means-end model and
synthesis of evidence”, Journal of Marketing, Vol. 52, pp. 2-22.
Corresponding author
Anto´nia Correia can be contacted at: [email protected]
Decision-making
processes
373
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
:
0
6
2
4
J
a
n
u
a
r
y
2
0
1
6
(
P
T
)
This article has been cited by:
1. Ana Isabel Rodrigues, Antónia Correia, Metin Kozak, Anja TuohinoLake-Destination Image Attributes:
Content Analysis of Text and Pictures 293-314. [Abstract] [Full Text] [PDF] [PDF]
2. Leila Etaati, David Sundaram. 2015. Adaptive tourist recommendation system: conceptual frameworks
and implementations. Vietnam Journal of Computer Science 2, 95-107. [CrossRef]
3. Kuan-Huei Lee, Jan Packer, Noel Scott. 2015. Travel lifestyle preferences and destination activity choices
of Slow Food members and non-members. Tourism Management 46, 1-10. [CrossRef]
4. Sirvan Sen Demir, Metin Kozak, Antonia Correia. 2014. Modelling Consumer Behavior: An Essay with
Domestic Tourists in Turkey. Journal of Travel & Tourism Marketing 31, 303-312. [CrossRef]
5. Antónia Correia, Metin Kozak, João Ferradeira. 2013. From tourist motivations to tourist satisfaction.
International Journal of Culture, Tourism and Hospitality Research 7:4, 411-424. [Abstract] [Full Text]
[PDF]
6. Helena Reis, Antonia Correia. 2013. Gender Asymmetries in Golf Participation. Journal of Hospitality
Marketing & Management 22, 67-91. [CrossRef]
7. Antónia Correia, Metin Kozak. 2012. Exploring prestige and status on domestic destinations: The case
of algarve. Annals of Tourism Research 39, 1951-1967. [CrossRef]
8. Asad Mohsin, Abdulaziz Mohammed Alsawafi. 2011. Exploring attitudes of Omani students towards
vacations. Anatolia 22, 35-46. [CrossRef]
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
:
0
6
2
4
J
a
n
u
a
r
y
2
0
1
6
(
P
T
)
doc_219921037.pdf