Proceedings of 21st International Business Research Conference
10 - 11 June, 2013, Ryerson University, Toronto, Canada, ISBN: 978-1-922069-25-2
Developing and Testing a Model Predicting Behavioral
Intentions of Travelers
Robin Nunkoo* and Yashill Raj Ittoo***
The purpose of this research is to investigate the relationship between
information search behavior, destination image and future behavioral
intentions of tourists. A questionnaire was designed, pretested and
administered to a sample of 150 tourists visiting Mauritius. Results
indicated that online information search significantly impacted on offline
information search which was also found to be a significant predictor of
destination image. Destination image attributes and offline information
search significantly predicted future behavioral intentions of tourists.
The research retained four hypotheses out of six. The findings can
support the development and formulation of tourism and travel
strategies to enhance and improve communication and marketing
efforts with the aim to diversify the tourism industry of Mauritius.
Field: Marketing
1. Introduction
In an increasingly competitive marketplace, the success of marketing destinations is
guided by a thorough analysis of the information search behavior of tourists and its
interplay with destination image and future behavioral intentions. A review of tourism
literature reveals an abundance of studies on information search, destination image and
behavioral intentions, but very few researches focus on Small Developing Island States
(SIDS) like Mauritius. Moreover, most tourism studies to date have addressed and
examined information search behavior, destination image and future behavioral
intentions independently without integrating them together in a single model. A
thorough understanding of how potential travelers look for and acquire information, the
images they hold of the destination and their future behavioral intentions is therefore
required. This paper achieves this by empirically testing the following model.
* Dr. Robin Nunkoo, Department of Management, University of Mauritius, E-mail: r.nunkoo@uom.ac.mu
** Mr. Yashill Raj Ittoo, University of Mauritius, E-mail: yittoo@gmail.com
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Proceedings of 21st International Business Research Conference
10 - 11 June, 2013, Ryerson University, Toronto, Canada, ISBN: 978-1-922069-25-2
Online information
search
Destination image
Behavioral intention
Offline information
search
Figure 1: The Proposed Model of the Study
2. Literature Review
2.1 Information Search
In today’s dynamic global environment, understanding how tourists acquire knowledge
is important for marketing management decisions, effective communication campaigns
and service delivery. With the advent of Information and Communication Technology
(ICT), a whole new dimension of information sources and channels has aroused,
complicating the already complex information search process (Lehto, Kim & Morrison,
2006). Information search is the starting point in the holiday decision-making process
(Hyde, 2008; Gursoy & Umbreit, 2004). Fodness and Murray (1999) defined tourist
information search behavior as a process where the travelers make use of various
amount and types of information sources to facilitate trip planning.
2.2 Online and Offline Information Search
Tourism has historically been an early adopter of new technology. As in other service
sectors, technological developments are altering the nature of processes in the tourism
sector. The key to tourist’s decision is the existence of relevant information. The internet
makes such information available and in a better way (Buhalis, 1998) and helps tourists
to plan complex holiday activities (Bieger et al., 2000). One of the most popular
activities undertaken by Internet users is searching for travel information. Research
indicates that the use of the Internet to search for travel information has already
surpassed traditional media sources, and will continue to increase into the foreseeable
future. Other tourism researchers have also posited that traditional distribution systems
could be threatened or even replaced by electronic distribution systems (Buhalis, 1998)
and that the Internet could be the primary force for disintermediation (Inkpen, 1998).
Based on the above discussion, the following hypothesis is developed:
Hypothesis 1 (H1): There is a direct relationship between online and offline information
search.
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Proceedings of 21st International Business Research Conference
10 - 11 June, 2013, Ryerson University, Toronto, Canada, ISBN: 978-1-922069-25-2
2.3 Destination Image
Crompton (1979) defined destination image as an attitudinal concept consisting of the
sum of beliefs, ideas and impressions that a tourist holds of a destination. Blain, Levy,
and Ritchie (2005) supported this view by stating that destination image is intended to
convey the overall idea/experience that the visitor can expect at the destination. Image
formation has been described as the development of a mental construct based upon a
few impressions chosen from a flood of information. Gunn's (1972) theory involves a
constant building and modification of images, which are conceived as being made up of
organic/naive non-tourist information, induced/promoted information and modified
induced images, which are the result of personal experience of the destination. The
organic image is formed through an accumulation of non-tourism sources obtained from
schools, word-of-mouth or non-commercial newspaper reports, magazine articles and
television programs. It exists prior to access to commercial sources promoted by the
destination and can be obtained without having been to the destination. In other words,
it represents the totality of what the person already knows or perceives about a
destination. Information deriving from promotional and commercial materials and/or
through actual visitation will alter the organic image into the induced image. The key
difference between these two types of images is that marketers have little or no control
on organic image, unlike induced image. Gartner (1993) further developed Gunn’s
model by adding a third element, which is autonomous image formation agents (reports,
documentaries, movies or news articles).
Before choosing a destination, tourists always search for information to shape an image
(Fodness and Murray 1998; Baloglu & McCleary, 1999). That image is formed through
information processed from different information sources over time, which is then
mentally organized into the destination image. Research has demonstrated that tourist
information is a valuable concept in understanding destination image and choice,
especially for tourists having limited information about the destination. Previous studies
(Baloglu & McCleary, 1999) confirmed that the quantity, types and number of
information sources affect image formation. Crompton (1990) also argued that the
destination image was affected by both symbolic stimuli (promotional efforts through
media) and social stimuli (word of mouth and recommendations). Based on the above
discussion, the following hypotheses are developed:
Hypothesis 2 (H2): There is a direct relationship between offline information search
and destination image.
Hypothesis 3 (H3): There is a direct relationship between online information search
and destination image.
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Proceedings of 21st International Business Research Conference
10 - 11 June, 2013, Ryerson University, Toronto, Canada, ISBN: 978-1-922069-25-2
2.4 Image Formation and Behavioral Intentions
Behavioral intention is an individual’s planned future behavior. Liu and Jang (2009)
conceptualized tourist behavioral intentions as word-of-mouth, recommendation and
repeat purchase. Thus, behavioral intention is an important concept in understanding
tourist choice of tourism products, future motives and behavior. From the perspective of
tourist consumption process, tourist behavior is an aggregate term including pre-visit
decision-making, onsite experience and evaluations and post-visit behavioral intentions.
It has been generally accepted that destination image has a direct influence on tourist
behaviors (Bigne et al., 2001).
Bigne et al., (2001) further emphasized that destination imagery is not limited only to
destination choice but affects the behavioral intentions of tourists in general. Destination
image perceived post-visit influences the tourist satisfaction, the intention to repeat the
visit in the future and their communication with friends and family (word of mouth). Thus,
knowing what visitors think of a destination is important in product development and
marketing. Based on the above discussion, the following hypothesis has been
proposed:
Hypothesis 4 (H4): There is a direct relationship between destination image and future
behavioral intentions of travelers.
2.5 Information Search and Behavioral Intention
Literature reveals that there are significant inter-relationships among variables such as
information search, image, satisfaction, quality and value and tourists’ behavioral
intentions and loyalty (Baloglu and McCleary, 1999). Kotler et al. (2002) argued that the
post-purchase behavior of consumers is based on their satisfaction/dissatisfaction of the
product, which refers back to the customers’ expectations on messages they initially
received from information sources. Kotler et al. (2002) also added that if the products’
performance has been exaggerated through information sources, it leads to
dissatisfaction which will not yield repeat purchase. These results imply that information
search behavior also influences travelers’ behavioral intentions.
Based on the above review, the following hypotheses were proposed:
Hypothesis 5 (H5): There is a direct relationship between offline information search
and future behavioral intention.
Hypothesis 6 (H6): There is a direct relationship between online information search
and future behavioral intention.
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Proceedings of 21st International Business Research Conference
10 - 11 June, 2013, Ryerson University, Toronto, Canada, ISBN: 978-1-922069-25-2
3. Methodology
A structured questionnaire was designed for data collection. The first part relates to the
information search behavior and information sources used by the respondents. The
second part deals with destination image attributes while the third part relates to the
future behavioral intentions of the respondents. The last part consists of standard
demographic questions. The questions and measurements items were adopted from
previous studies and slightly modified for the purpose of this study.
Proper piloting of the questionnaire was undertaken to improve question wordings and
to ensure that all questions meant the same to all respondents. The questionnaire was
pre-tested to a sample of 30 respondents, selected at the convenience of the
researcher. An Exploratory Factor Analysis was then carried out on the pre-test data
whereby attributes having factor loadings of lower than 0.40 and attributes loading on
more than one factor were eliminated (Chen & Hsu, 2001). Each time a factor was
eliminated, the analysis was run again. A reliability test further narrowed the selection of
only those items with a Cronbach’s Alpha higher than 0.7, which is deemed acceptable
and reliable.
Based on the conclusion of Gorsuch (1983), suggesting an absolute minimum ratio of
five participants to one variable whereby the sample should not be less than 100
participants, a sample size of 150 tourists was adopted for this study. A quota sampling
based on gender was preferred for this study and interviews were carried out on a nextto-pass basis at different tourist locations. In case of refusal from one tourist to
participate, the next tourist was requested to participate. The schedule was designed for
self-completion by the tourists and the researcher taking them through the questions if
necessary.
The data collected from the questionnaires were coded and fed to SPSS 19.0. Prior to
any analysis, Alpha reliability coefficients were calculated to test for internal consistency
and reliability, while the normality of the data was confirmed with skewness values
falling within the acceptable -3 and +3 range (Hair et al., 1998). Standard Multiple
Regression Analysis was used to evaluate the relationships between the variables while
descriptive statistics reflected the profile of the sample population. A total of 150
questionnaires were completed (100% response rate) and retained for data analysis.
4. Findings
4.1 Sample Description
The sample population was balanced in terms of gender (50.7% male against 49.3%
females). The interviews polled on average younger respondents with 28.7% of the
respondents aged between 18 and 27, followed closely by the 58 years and above
segment (22.7%). 50% of the respondents were married while 26.7% were single. The
majority of the respondents were from France (41.3%). Interviewees of British
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Proceedings of 21st International Business Research Conference
10 - 11 June, 2013, Ryerson University, Toronto, Canada, ISBN: 978-1-922069-25-2
nationality accounted for 14.7% followed by respondents from India (8.7%) and China
(6.7%). Other nationalities comprised of small percentages of the sample population.
The majority of the interviewees had a Bachelor degree (49.3%) and was working in
middle management positions (41.3%).
4.2 Multiple Regression Analysis
Multiple regression analysis is a general statistical technique used to analyze the
relationship between a single dependent variable and several independent variables
(Hair et al., 2005). Assumptions of normality and multicollinearity were tested to
ascertain that the data was suitable for the multiple regression analysis. Multicollinearity
was verified by analyzing the tolerance (t) and Variance Inflation Factor (VIF) for each
predictor variable in the model. The collinearity statistics revealed that all the predictor
variables fell within acceptable boundaries of tolerance (> 0.3) and the VIF coefficient (<
10), thereby eliminating any multi-collinearity problems (Field, 2000). Results from a
multiple regression analysis indicate the explanatory power of all predictor variables
with measures of R2 and adjusted R2 as well as the relative importance of each
individual predictor variable after calculating the β coefficients (Musil, Jones, & Warner,
1998).
Table 1 Results of Multiple Regression Analysis
Regression Models
b
t
p
Collinearity
diagnostic
VIF
Tolerance
Model 1: Predicting Offline Information Search
Online IS
0.321
Model Summary
2
R = 0.321; R = 0.103;
2
adjusted R = 0.097;
F = 16.963; p < 0.001
4.119
< 0.001
1.000
1.000
Model 2: Predicting Destination Image
Online IS
0.319 3.906
Offline IS
0.062 0.758
< 0.001
Not sig.
1.115
1.115
0.897
0.897
R = 0.344; R = 0.119;
2
adjusted R = 0.107;
F = 9.88; p < 0.001
Model 3: Predicting Future Behavioral Intentions
Online IS
0.055 0.679 Not sig.
Offline IS
0.147 1.892 < 0.100
Destination Image
0.380 4.834 < 0.001
1.230
1.119
1.134
0.813
0894
0881
R = 0.455; R = 0.207;
2
adjusted R = 0.190;
F = 12.688; p < 0.001
2
2
5. Results
The results of the multiple regression analysis are shown in Table 1. Results from table
1 showed that Model 1 significantly predicted Offline Information Search (F = 16.96; p <
0.001) and explained 10% of variance in the dependent variable (R2 = 0.10). Findings
indicate that Online Information Search was a significant determinant of Offline
Information Search (β = 0.32; t = 4.12; p < 0.001).
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Proceedings of 21st International Business Research Conference
10 - 11 June, 2013, Ryerson University, Toronto, Canada, ISBN: 978-1-922069-25-2
Model 2 was a significant predictor of Destination Image (F = 9.88; p < 0.001) and
explained 12% (R2 = 0.12) of variance in the dependent variable. However, Offline
Information Search did not significantly predict Destination Image (β = 0.06; t = 0.76; p >
0.05). On the other hand, final beta values showed significant independent predictive
effects for Online Information Search (β = 0.32; t = 3.91; p < 0.001).
Model 3 was a significant predictor of Future Behavioral Intentions (F = 12.688; p <
0.001) and explained 21% of variance in the dependent variable (R2 = 0.21). Results
showed that Online Information Search Behaviour did not significantly predict Future
Behavioral Intentions (β = 0.055; t = 0.679; p > 0. 05). However, both Offline Information
Search (β = 0.147; t = 1.892; p < 0.100) and Destination Image (β = 0.380; t = 4.834; p
< 0.001) were significant predictors of Future Behavioral Intentions.
6. Discussion
The findings supported four of the six originally proposed hypotheses. The low
percentages of explained variance are not surprising in tourism research given that
constructs such as information search behavior, destination image and future behavioral
intentions are affected by a multiplicity of factors, not all accounted in this model.
However, the entire model presents a rather good fit, which provides support for the
hypothesized research model.
Hypothesis H1 predicting a direct relationship between online and offline information
search was supported, which meant that tourists still engage in offline information
search alongside their online search. Contradictorily, Morrison et al. (2001) supported
the proliferation of the use of the internet by potential customers to search for
information. Several researchers, including Gursoy and McCleary (2004), argued that
given the low cost and ease of information retrieval on the Internet, searchers will
expend more search effort on the Internet rather than traditional information sources.
However, findings from the analysis is in line with the conclusions of Jun et al. (2007),
stating that some online searchers still switched to offline purchase methods after they
had found what they were looking for online.
Hypothesis H2 which proposed a direct relationship between offline information search
and destination image was not supported. On the other hand, hypothesis H3 which
predicted a direct relationship between online information search and destination image
was supported. Fodness and Murray (1997) also found that tourist always engage in
information search and use various amounts and types of information sources to shape
their destination image. The use of different information sources by travelers has also
been confirmed by Court and Lupton (1997), who further explained that the information
is then organized into the destination image (Leisen, 2001). However, the findings of
Patkose, Stokes & Cook (2004) revealed that the use of the Internet to search for travel
information has already surpassed traditional offline media sources, and will continue to
increase into the foreseeable future. Parasuraman and Zinkhan (2002) also found that
consumers now have increased access to electronic marketplaces and use this
information in a sophisticated way to select their services. Thus it can be said that the
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Proceedings of 21st International Business Research Conference
10 - 11 June, 2013, Ryerson University, Toronto, Canada, ISBN: 978-1-922069-25-2
online media entailed the destruction of the traditional offline travel distribution channel
structure (O’Connor, 2008). Horrigan (2008) also found that one of the most popular
activities undertaken by Internet users was searching for travel information while it was
noted that that tourist-related services are highly promoted and distributed through the
internet.
Table 2: Results of Hypotheses Testing
Hypothesized
Relationship
H1: ONIS
OFFIS
H2: OFFIS
DI
H3: ONIS
H4: DI
DI
BI
Standardized Coefficients
Results
0.321 (+ve)
Supported
0.062 (+ve)
Not Supported
0.319 (+ve)
Supported
0.380 (+ve)
Supported
H5: OFFIS
BI
0.147 (+ve)
Supported
H6: ONIS
BI
0.055 (+ve)
Not Supported
Hypothesis H4 which predicted a direct relationship between destination image and
behavioral intentions was supported by the analysis. The results confirmed the findings
of Bigne, Sanchez and Sanchez (2001) wherein destination image has a direct
influence on tourist behaviors. Bigne et al. (2001) and Echtner and Ritchie (1991) also
revealed that destination image is an integral and influential part of the traveler’s
decision process and consequently their travel behaviors. Individuals having a favorable
destination image would perceive their on-site experiences positively, leading to greater
satisfaction levels and positive behavioral intentions.
Hypothesis H5 which proposed a direct relationship between offline information search
and behavioral intentions was supported. However, hypothesis H6 suggesting a direct
relationship between online information search and behavioral intentions was not
supported. Results contradicted the findings of Kaplanidou and Vogt (2006) highlighting
the significance of websites as an important determinant of travelers’ behavioral
intentions. Respondents related online media to easy access to information, while
offline sources were more related to trust and professionalism, backed by Bailey and
Bakos (1997) suggesting that the need for offline intermediaries is not likely to be
eliminated in the near future, although their roles may become less important as a result
of advances in IT. Gretzel (2006) posited that one major issue with online media is trust
as a number of studies also reported that online information is perceived as being lower
in credibility and likeability due to the absence of source cues in online environments
(Smith, Menon & Sivakumar, 2005).
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Proceedings of 21st International Business Research Conference
10 - 11 June, 2013, Ryerson University, Toronto, Canada, ISBN: 978-1-922069-25-2
Baloglu and McCleary (1999), Beerli and Martin (2004) and Chi and Qu (2008) have
stressed on the significance of the inter-relationships among variables such as
information, image and satisfaction on tourists’ behavioral intentions and loyalty.
6. Conclusion
This study was based on the construction of a theoretical model to explain the
influences of information search behavior, destination image and future behavioral
intentions of travelers. The model was tested on a sample of international tourists
visiting Mauritius to identify causal relationships. The results from the data analysis
revealed that four of the six proposed hypotheses were retained. The research can
contribute to knowledge on both academic and practical levels. The findings from this
study can contribute to the existing literature by highlighting the different influences of
information search behavior, destination image attributes and future behavioral
intentions of travelers.
This study has some conceptual and methodological limitations. One main limitation is
that the study could not incorporate all determinants of the constructs under study in the
model. Moreover, only sources of information were considered and not the actual
content in terms of topics and quality. Another limitation was the relatively small sample
size. A larger sample may affect the magnitude, and even direction, of the relationships
between the constructs. Furthermore, since data was collected mostly from beaches
around hotels and other tourist sites, this might result in an over-representation of lowinvolvement (sunlusts) tourists. The data collection also took place in the months of
June and July, which is the off-peak season.
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