Vol. 6( 7), pp. 80-89, September , 2015 [600877]

Vol. 6( 7), pp. 80-89, September , 2015
DOI: 10.5897/JHMT201 5.0158
Article Number: F226DDA55038
ISSN 2141 -6575
Copyright © 2015
Author(s) retain the copyright of this article
http://www.academicjournals.org/JHMT
Journal of Hospitality and Management
Tourism

Full Length Research Paper

An analysis of the image of destination Cross River and
effect on visitors’ future intentions

Bassey Benjamin Esu

Department of Marketing, University of Calabar, Nigeria.

Received 11 June 2015; Accepted 29 July, 20 15

The literature on destination image spanned over four decades. Despite this long period of knowledge
accumulation, there is not yet a generally accepted measurement for destination image. This paper
seeks to determine the underlying structure of touri sm destination image and to investigate the effect
of destination image on visitors’ future intentions. An emerging tourism destination in Nigeria (Cross
River State) was used as the study area. A systematic sample of 367 onsite visitors was recruited for
the study. A well -structured and written questionnaire containing 35 destination image attributes was
used to elicit data for the study. Exploratory factor analysis, t test equality test and regression analysis
were utilized to identify attributes that und erpinned destination image and underlying structure. The
exploratory factor analysis produced six dimensions: destination quality of life, natural attractions and
facilities, quality of public services, destination product quality and education, industry h ospitality and
environmental ambience, communication and security. Tourism destination image index of the
destination was rated somewhat poor. Inferential statistic shows that there is significant difference in
the tourists’ perception of four destination image dimensions (destination quality of life, natural
attractions and facilities, quality of public services, destination product quality and education, industry
hospitality and environmental ambience and communication and security) based on whether they are
domestic or international tourists. Two of the dimensions (quality of public services and
communication and security) did not indicate significant difference based on place of residence. The
study also shows that there is a significant relationship bet ween tourism destination image dimensions
and visitors’ behavioural intentions. Specifically, two destination image dimensions were found to
predict visitors’ future intentions (industry hospitality and environmental ambience and natural
attractions and f acilities). The result of this study is expected to influence the formulation of destination
product development and branding strategy which is necessary to create and grow the number of
visitor arrivals in the destination.

Key words: Tourism destination image, destination attractiveness, future intentions, visitor attraction, social
construction, destination attributes.

INTRODUCTION

The need to understand the nature and impact of
destination image on tourist consumer behaviour has
received much atten tion from marketing researchers and tourism practitioners because of its strategic importance
in tourism planning and development. Destination image
has been suggested as one of the most important factors

*Corresponding author. E -mail: esubenjamin@yahoo .com. Tel: +234034740556

Author(s) agree that this article remain permanently open access under the terms of the Creative Commons
Attribution License 4.0 International License

that influence visitors flow to a destination (Vaughan,
2007). The literature on destination image has span ned
over four decades . Despite this long period of knowledge
accumulation, there is not yet a generally accepted
measurement for destination image. Acco rding to Fakeye
and Crompton (1991) as cited by Vaughan (2007),
“destinations with positive image are thought more likely
to prosper while those with negative image may never
prosper”. It is interesting to note that d estination marketing
managers are still grappling with the problem of
determining which set of tourism destination image ( TDI)
dimensions are most effective in growing tourist arrivals.
Some destinations h ave spent huge sums of money in
destination product development and packaging strate –
gies that have not impacted significantly on visitors‟
arrivals. To provide answer to the above managerial
problem, it is imperative to determine factors or attributes
of tourist destination that would create meaningful
impressions with subsequent influence on visitor arrival s
if implemented . This paper therefore seeks to contribute
to the literature on tourism destination image by
analyzing more comprehensively the underlying structure
of TDI and the effect of destination image on visitors‟
future intentions using „Destination Cross River ‟ as the
study area .
This paper is divided into 5 sections. There is a brief
review of academic literature on tourist destination image
and growth in the conceptual measurement of destina tion
image. This is followed by detail ed research methodology,
results of data analysis and interpretation. The results are
then presented in terms of cognitive evaluation of Cross
River State (destination image index) , critical destination
image factors affecting behavioural intentions (repeat
visit). The discussion section describes the findings and
strategic implication s of findings.

LITERATURE REVIEW

Destination tourism image and importance

The Oxford Advanced Learner‟s Dictionary defines
destination image a s “an impression that a pe rson, an
organization or a product gives to the public and/or a
mental picture that you have of what something is like or
looks like” (Hornby, 2011: 748). Destination is a mental
image formed by expo sure to destination attributes
(Bagoglu, 1999; Baloglu and McCleary, 1999 ; Gallarza et
al., 2002). The importance of imagery cannot be
overemphasized as tourist s make their decision s based
on these images and information before selecting a
destination to visit (Mohan, 2010). An understanding of
TDI is critical a s it influences tourist preferences for
destination, motivation for choice of destination, and by
extension purchase behaviour. It is noted that attitudes
and behaviour are formed on the basis of an individual
tourist‟s derived image which are not easily changed or Esu 81

eroded except by the introduction of a new idea,
information or experience (Cooper et al., 1998) .Cooper et
al. (1998) took a leap from the United Nation World
Tourism Organization (UNWTO ) and define tourism image
as “ideas , conceptions held individually or collectively of
the destination”. Marino ( n.d.) observed that a tourist
destination with a strong and consolidated image in the
market has a better guarantee of prosperity and has
important influence on the behavior of th e tourist. The
role of d estination image is critical in the selection of a
destination and determines which destination remains in
the opportunity set and the realizable set for further
evaluation and consideration into the choice set and
eventually into t he holiday set (Gartner, 1993) . Farias et
al. (2013: 109)) define destination image as a set of
complex mental impression and total feelings that a
potential tourist holds of a product, place or tourism
destination. Globally, destination image can be defin e as
a composite of mental impressions a potential and/or
actual tourist gets from the evaluation of the functional
and psychological attributes of a destination and the
functional and psychological h olistic or imagery of the
environment of a destination.

Destination image formation

Literature search reveal s a plethora of knowledge in this
subject. They include Gunn (1972), Fakeye and Crompton
(1991), Gartner (1993) in his article, „image formation
process , and Baloglu and McCleary (1999) in their artic le,
„a model of destination image formation processes . Gunn
(1972) „‟suggest s two sources or agents of destination
image: induced image and organic image ‟‟. Induced
image is formed from information generated from desti –
nation advertising, while organic ima ge is formed from
the tourist past experiences during visit to a destination.
A third component (complex image) was later added by
Fakeye and Crompton (1991). Complex image is formed
from the evaluation of tourist consumption experience at
the destination. The image at complex image stage
undergoes three outcomes: it is modif ied, correct ed or
remove d depending on whether elements or impressions
already gathered from the two previous stages about the
destination are consistent or inconsistent with the actual
trip experiences. If consistent, the image is reinforced. On
the other hand, if the impression is inconsistent, the
image is modified or removed. Post consumption image
(complex image) was used for this study since the
destination image that the researche r seeks to measure
was formed during visitors‟ stay in the destination visited.
Gartner (1993) presents a three level hierarchical image
formation structure: cognitive image, affective image and
conative image . Cognitive image refers to the image
formed from knowledge or perception a tourist has about
a destination‟s attributes evaluation. Affective image
refers to the image formed from the feelings a place,

82 J. Hosp. Manage. Tourism

people or event arouse in a tourist or the value that
tourist attaches to a destination based on socio –
psychological motivat ion. Conative image refers to a
tourist future behavioural intention . Gartner (1993) is
credited with expanding Gunn‟s (1972) two components
destination image formation agent model (induced and
organic). He divided the induced image component into:
overt induced 1, overt induced 11 , cover t induced 1 and
covert induced 11 . The organic image component was
divided into unsolicited organic (unsolicited information
from friends and relative s) and solicited organic (solicited
information given by friends and relatives ) (Jorgensen
(2004) . It is noted that the cognitive stage is the most
critical because at this stage , the destination marketer
can directly influence the tourist destination choi ce by
creating appropriate information about th e destination
(Echtner and Ritchie, 1991). Baloglu and McCleary
(1999) expanded Gartner (1993) model and further
conceptualize a destination image formation process
made of three components: personal factors, stimuli
factor and desti -nation image. Personal factors consist of
psychological variables such as value, motivation and
personality and social variables such as age, educational
level, marital status, etc. The stimuli factors consist of
information source s and previous experience.
Vaughan (2007) summarizes the image formation
process b y stati ng that the literature on TDI can be
reduced into three perspectives: image as a composite
constru ct (that is the sum of beliefs, ideas, and im –
pressions a person has about a destination), image as an
attitudinal construct (this consist the physical traits,
affects and emotional response to destination attributes)
and image as a societal concept (the social and political
environment of business).

Destination image att ributes and scale measurement
development

Research on TDI started about f our decades ago
following the work of Hunt in 1971 (Mohan , 2010). Since
then, extensive research has continued on the
phenomenon (Echtner and Ritchie , 1991 ; Fakeye and
Crompton , 1991). Echtner and Ritchie (1991, 1993 , 2003)
conceptualize destination image as consisting of : (i) of
initial two dimensions : those that are attributes based and
those that are holistic. (ii) the component of attribute
based and holistic based are further di vided into
functional based attributes (tangible characteristic) and
psychological based holistic (intangible or abstract
characteristics). These resulted into four dimensions:
functional -attributes dimension (climate, prices, road, and
infrastructure ), functional – holistic dimension (mental
pictures of physical characteristic of landscapes) ,
psychological -attributes dimension (friendly people and
safety) and psychological holistic dimension (general
feeling of atmosphere) . (iii) They later modifi ed the

attribute -holistic dimension and functional -psychological
dimension of destination image into three dimensions,
which ranged from those based on more common
functional and psychological traits to those based on
more unique features, events and feeling or auras .
Matos et al. (2012 :112) conceptualize a destination
image as having three broad components: controllable
forces ( induced image) which is represented by variables
such as external stimuli, promotion activities, access
routes, infrastruc ture and uncontrollable forces (organic
image) represented by pers onal factors such as
motivation past travel experience and external stimuli
such as residents, time and space distance and service
providers . They went further to conclude that destination
affects a tourist destination choice at three points: before
the trip ( a prior ), during the tourist‟s stay in the destination
(in loco) and after the tourist returned home (a posterior ).
The most remarkable and ground breaking attempt to
solve the problem of identifying elements of tourism
destination image was by Beerlie and Martin (2004) who
from extensive literature review gene rated a list of
variables which could potentially be used as
measurement instrument. The elements include: natural
resource; to urist, leisure and recreation, n atural
environment; general infrastructure, cultural, history and
arts; social environment; tourist infrastructure; politics and
economics and atmosphere of place. Beelie and Martin
(2004) list of attributes were assumed to in corporate
every aspect of a destination which could potentially be
used as an instrument of measurement. All factor
influencing image assessment made by individuals were
incorporated and classified into distinct dimensions .
Mohan (2010) observed that Pikes (2002) had
reviewed 142 papers destination image papers published
from 1973 to 2000 and concluded that there had been
recurring criticism of the list of attributes. Mohan (2010)
corroborated Pikes (2002) and assert s that there is no
clearly conc eptual bas e leading destination image
studies, especially the ones investigating image and
other concepts. Fronchot and Kreziak (2008) similarly
observed that there is still some problem with the
conceptual development and measurement of tourism
destination image an d attractiveness. What this means is
that authors are not in agreement on what constitutes a
generally acceptable measurement scales for measuring
TDI. The lack of conceptual framework regarding the
notion of tourism destination image (TDI) is still an are a
of concern to date in view of the fact that TDI is widely
acclaimed to be a critical element in tourist visitation
(Mohan, 2010; Fronchot and Kreziak, 2008). Unlike TDI,
concepts in tourism research such as resident perception
and attitude have standardi zed scale of measurement
(Viviers and Slabbert, 2012; Delamere et al., 2001).
Previous attempts to development a TDI measuring scale
were criticized on the following basis : the scales produced
by researchers lack homogeneity with respect to the
attributes which define an individual‟s perceptual image,

criticism of the attributes list , absence of an accept able
theory to replace the multi -attributes models, di fficulty in
measuring consumers overall perceptions of a
destination , the absence of validity a nd reliability of
scales used in measuring destination and attractiveness
casting doubt on their psychometric properties , etc. This
study therefore seeks to contribute to the development of
TDI measurement scale that will overcome the
weaknesses aforementi oned in previous studies .

CONCEPTUAL FRAMEWORK AND HYPOTHESIS
TESTING

Image is influenced by the characteristics of a destination,
exposure to information received about the destin ation,
personal factors such as motivation and socio -demgraphic
character istics and previous experience (Bagoglu, 1999;
Baloglu and McCleary, 1999 and Gallarza et al ., 2002).
Frochot and Kreziak (2008 ) found the following themes in
the study of TDI : mountain authenticity, services at the
resort, skiing, nonski snow activities, conviviality, and
challenge. Mohan (2010) investigated the impact that
destination image has on sport tourists‟ decision to travel
using linear regression. He found that the significant
image dimensions were weather, safety, cost and
hospitality. Molina et al. (2010) studied the relationship
between tourist destination image and means of
promotion and brochures and found that the use of
information sources as promotion tools has a strong
influence on the formation of tourist destination image.
Buhelis (2010) proposed and conceptualized image as
predictor of quality and perceived valued and found a
significant relationship which in turn affect tourist
satisfaction and behaviour intention s. Navratil et al.
(2012) using an exploratory approach found that the
image of a tourist destination is multilateral. He found that
the cognitive appreciation of water, natural attractions,
and cultura -historic were predictors of tourist behavioural
intentions. Edwards et al. (2009) in an Australian study
conceptualized desti nation image as city environment,
city experience, large attractions, services and food
services. Images are said to have an impac t upon the
formation of service quality evaluation, customer‟s
satisfaction and future recommendations (Bigne et al.,
2001 ; Pike, 2002). Mohan (2010) asserts that the
importance of destination image in consumer decision
making has long received universal acceptance. This is
because it influences tourist perception and consequent
behaviour and destination choice. Vaughan (2007) al so
found that respondents were significantly different in their
perception of the image of Romania as a result of place
of residence of respondents (Munich, Oporto, Leon and
Bournemouth) (Figure 1) . In view of the above we
therefore postulate that:

H1: There is no significant difference in the perceived
tourism destination image of visitors based on place of Esu 83

residence.
H2: There is no direct significant relations hip between
tourist destination image and visitor behavioural intentio ns

RESEARCH METHODOLOGY

Areas of study

Cross River State was used for this study. It is an emerging
destination. The destination is richly endowed with exotic tourism
sites which are currently being enhanced to „visitor read iness state ‟.
There are 85 p otential tourist sites (16 nature -based , 42 historical
and cultural, 16 recreational and 11 others (industrial, educational,
religious based , etc.). Out of these number s, 32 sit es were classified
as visitor ready ( sites designated for tourism business with basic
ancillary tourism facilities in situ), 50 sites were classified as semi
visitor read y (sites designated for tourism , have limited activities
because of absence of basic tourist facilities ), and 3 sites were
classified as not visitor ready ( sites d esignated for tourism business
with little or no tourist activities because of non-enhancement for
touristic use). The destination has 344 accommodation establish –
ments (194 hotels, 105 guest houses, 33 lodges, 8 resorts and 4
motels ). Total numbers of rooms available are 5,015 . Total number
of food and beverage outlets is 3,223 . A total of 386 ,404 visitors
visited tourist s ites in the second half of 2012. A total of 467, 852
visitors attended spe cial events at the same period. Total number of
nonresidents‟ arrivals statewide was 65,000. The destination had a
total of 356,188 guest nights and 18.6% average room occupancy .
See CRSTB Statistical Report (201 2) for detail s.

Sample size and sampling procedure

A sample size of 367 was generated through s tatistical estimation
using Taro Yame ne Formular ( Yamene, 1967 ) at 0.05 error margin
and a population of arrivals of 65,000 non-residents (total number
of nonresident visitors to Calabar in the past one year ). Ten visitor
ready sites in Calabar were used for the study ( National Museum,
Tinap a Business and Leisure Resort, Cultural Center, Botanical
Garden, Pandrillus Wildlife Conservation Center, Cercopan Wildl ife
conservation Center, Millenium Park , Marina Resort , Slave Trade
Museum and Obong ‟s Place ). Systematic sampling design was
used for drawing subjects into the sample. The sample units were
drawn from the population of visitors by counting every five visitor
entering the site : an interval skip of five onsite visitors at attraction
was used. The study was limi ted to Calabar Tourism Cluster
because it is the major entry point into the destination by land, air
and sea and is the hub of tourism business in Cross River State .

Instrumentation

A well-structured written questionnaire was designed and used in
data c ollection. T he content of the instrument dre w heavily from the
works of Mohan (2010), Edwards et al. (2009) and Navratil et al.
(2012). The instrument was partitioned into three parts. Part one
had four items on demographics of respondent (age, gender,
education, place of residence). The second part contained questions
on destination attributes. A total of 35 items represented specific
destination attributes. Respondents were asked to rate their
perception of the destination image forming attributes on a fi ve
point Likert scale (1= very poor and 5= very good). Part three of the
instrument measured visitor‟s tendency to repeat visit (behavioural
intentions). Future intention was treated as one item construct
(tendency to repeat visit to destination). It was m easured on a five

84 J. Hosp. Manage. Tourism

Figure 1. Model showing the conceptual model for TDI and effect on visitor‟s future intentions . Visit:
Dependent Variable: Repeat.

point Likert scale with 1= strongly disagreed and 5= s trongly
agreed. In all, this measurement scale seeks to measure the
perceived image of the study area ( Destination Cross River ) and
element of the destination image that significantly predicts visitors‟
behavioural intentions.

Pre-test study

The questi onnaire was pretested to ensure content validity by
engaging destination managers in the private and public tourism
subsector to carry out face validity of the instrument. Five experts in
the field of destination management were presented with the
instrume nt for perusal and to make their contribution in the wording
and scope of tourism destination attributes (TDI). The experts
include: The Economic Adviser to the State Governor, Marketing
General Manager (Tourism Bureau), Unical Hotel Manager,
Managing Dire ctor (Tourism Bureau). Their inputs were most
significant in framing the words of questionnaire items for better
understanding. Reliability analysis was based on the test of internal
consistency that was done before exploratory factor analysis was
carried out.

Data collection method

The data used for this study was collected as part of a larger study
conducted by the Cross River Sta te Tourism Bureau 2012. The
entire research project was supervised by the author as a team
Leader . The questionnaire was self-administered on on -site visitors
in ten visitor attractions (only same day and over night visitors were
considered ). Staff of the Department of Research and Planning of
the Cross River State and volunteer research staff from Sustainable
Tourism Initiati ve (NGO) were engaged as field st aff and
enumerators. The questionnaire was administered on every five
visitors entering the ten tourist sites. The next v isitor was contacted
where the fifth contact results in a non-response . An average of 36
questionnaire s w as administered in each site . Completed
questionnaires were collected before the visitor leaves the tourist
site. Data collection for this study took place between 24th to 28th December, 2012 .

Data analysis

SPSS Window 16.0 was used for organi zation of data in this study.
The data collected were analyzed using descriptive statistics
(frequency, mean, standard deviation) and multivariate analysis
(factor and regression). F requency distribution was used to capture
the demographics of visitors to the destination . Simple average was
used in calculating the perceived tourism destination image
dimensions. This was helpful in calibrat ing and interpreting the
tourism destination image index. Leven and Pubin (1991) calibrated
destination image in three z ones: Good= 3.6 -5 point, Fair = 2.6 – 3.5
point and Poor = 1 -2.5 points.
Cronbach‟s alpha reliability test was done to measure the scale
reliability (internal consistency of the 35 items ). The acceptable
lower limit could be as low as 0.5 (Field, 2005). At the preliminary
stage, reliability test of the 35 destination image items was done
using inter –item correlation and any items below 0.5 were deleted.
Correlation matrix was proposed as preliminary means of assessing
the presence of multi -collinearity. Val ue of inter -item correlation
must not be very large (r = 0.8 to 0.9) and values below 0.3 were
not accepted. Construct and factorial validity was ascertained
through exploratory factor ana lysis (EFA) using principal axis
factoring (PAF) with varimax rotat ion on the 35 TDI attributes (Field,
2005) .For appropriateness of data for factor analysis, Kaiser -Meyer –
Olkin (KMO) measure of sample adequacy must not be less tha n
0.5. Bartlett‟s test of spheri city test shows that there is some
relationship between the variables (A significant test tells us that the
R- matrix is not an identity matrix and therefore appropriate for
factor analysis). Extraction and retention of factors was based on
factor loading of 0.3 and eigenvalue greater one. The first
hypothesis wa s tested using t test to establish if there is any
significance difference in the perceived tourism destination image
dimensions of domestic and international visitors. Multiple
regression models was used in hypothesis two to test t he statistical
relations hip between perceived destination image and repeat visit in
hypothesis one (Y=α + βi TDI i + E).

RESEARCH RESULTS AND FINDINGS

Profile of respondents

Out of a sample of 367 visitors who completed and
returned the questionnaire, only 235 copies of t he
questionnaire were found fit for data analysis. This
represented 64% question naire response rate. The
sample comprises 16% foreigners and 84 % Nigerian s.
The respondents were aged between 22 to 50 years.
Most of the respondents were professionals and self-
employed people. And m ost of the m visited the
destination in the company of family members or friends .

Factor analysis

Preliminary reliability test with Cronbach‟s alpha test
yielded values of α between 0.856 to 0.873 . The values in
the correlation matrix were not very large (0.3 to 0.6) as
to cause error or unreliable measures as they were
within acceptable limits (critical level= 0.8 to 0.9). This
was to ensure the internal consistency of the items that
were used to measure the destination image dimensions.
Secondly , it was intended to rule out initial problems of
multi -collinearity. Exploratory Factor analysis was done
to determine the underlying structure of d estination image
attributes. KMO value was 0.850 w hich was greater than
the bench mark of 0.5. The value of Bartlett‟s sphericity
test was (χ2=.00337, df = 595 , p = 0.000). On the basis of
this statistics, the data was deemed suitable for factor
analysis. All the 35 items were used for factor analysis
because no sign of multi -collinearit y was detected with
the inter -item correlation. With eigenvalue greater than 1,
six dimensions of destination image were produced. The
six dimensions had a total variance of 55.52% which was
good enoug h. Th e eigenvalue range between 1.07 to
7.22. The enti re factor s loaded at values above 0.3.
A careful examination of the items loaded in each of the
dimensions guided us in renaming the factors/
dimensions. See details in Table 1. Factor one loaded 8
items and was named destination quality of life . This
dimension had a composite reliability test value of 0.876.
Factor two loaded 7 items and was named natural
attractions and facilities . This dimension had a composite
reliability test value of 0.725. Factor three loaded 6 items
and was named quality of service providers . This
dimension had a composite reliability test value of 0.790.
Factor four loaded 6 items and was named destination
product quality and education . This dimension had a
composite reliability test value of 0.724. Factor five
loaded 4 items and w as named industry hospitality and
environment ambience . This dimension had a composite
reliability test value of 0.716. Factor six loaded 4 items
and was named communication and security . This
dimension had a composite reliability test value of 0.690
(Tabl e 1). Esu 85

Tourism destination image index of Cross River State

This study also produced an additive tourism destination
index which will help in the comprehension of the
constructs (Fakeye and Crompton, 1991 ; Bagoglu and
MCcleary, 1999). T he six attrib utes produced by
exploratory factor analysis were used to create a
destination image index for Cross River State (Table 2).
Each of the six image dimensions produced by EFA
represents a TDI dimens ion. The TDI index was
computed by calculatin g the mean of each of the TDI
dimensions (Mohan 2010). Interpretation of the
destination image was done in line with (Leven and
Pubin, 1991) as cited by Mohan (2010).
Overall, the image of the destination is somewhat poor.
The result of the analysis indic ates that the TDI index
portrays the state as having a not very good image. Out
of the six TDI dimensions only one dimension was scored
good (industry hospitality and ambience was rated 3.70
on the TDI index ). Destination quality of life was scored
fair on the TDI index (2.87) and the other destination
image dimensions were scored poor (<2.5) on the TDI
index. Descriptively it was found that there were
differences in the perceived image of the destination
based on place of residence of tourist (domestic and
international tourist). The domestic tourists had higher
perceived TDI on four image dimensions of the
destination: destination quality of life (do mestic =3.037
and international =1.997); natural attractions and facilities
(domestic= 2.201 and internationa l =1.618); destination
product quality and education (domestic =2.085 and
international=1.75); industry hospitality and ambience
(domestic=3.781and internati onal=1.75), while
international tourist had higher perceived TDI on two
image dimensions: quality o f public services (domestic=
2.081 and international=2.171)and communication and
security (domestic=1.935 and international=2.171). The
detail is shown in T able 2 .

Hypothesis testing

Visitor type and perceived image of tourist
destination dimensions : To determine if there is a
significant difference in the perceived image of the
destination by domestic and international tourists, each
TDI image dimension was tested for equality of means
using independent t test. The result shows that the
perceived ima ge of four TDI dimensions
were significantly different (destination quality of life: t=
4.379, p=0.000; natural attractions and facilities: t=2.299,
p <0.05; product quality and education: t=2.317, p <0.05;
and industry hospitality and ambience: t=3.787, p=0.001.
Two TDI dimensions did not show significant difference in
the perceived TDI (quality of public services: t= -0.519, p
>0.05 and communication and security: t = -1-717, p >
0.05).This is shown in Table 3.

86 J. Hosp. Manage. Tourism

Table 1. Exploratory factor analysis using Varimax rotation on destination image attributes.

Item Individual
item mean Factor
loading Commun
alities Composite
Reliability (x) Item reliability
(x) Destination quality of life
X:19: Clean water 3.13 .790 .686 .876 .855
X15:Receational parks 3.22 .748 .645 .851
X13:Cultural experience 3.41 .722 .605 .861
X18: Existing business opportunity 2.35 .687 .563 .866
X11: Weather and pleasant climate 3.12 .667 .536 .860
X9: Cuisine an drinks 2.87 .650 .477 .864
X17: Reasonable pricing 2.56 .612 .529 .862
X16: Ease to use facilities 2.51 .591 .635 .856

Natural attractions and facilities
X8: Beautiful beaches 2.47 .720 .566 .725 .638
X5: Attractive animals and games 2.14 .674 .526 .643
X10 : Deep sea fishing 1.13 .635 .459 .695
X4: Uncrowded and unspoiled parks 2.71 .592 ..573 .679
X14:Modern equipment & facilities 2.65 .589 .699 .856
X12: Well -equipped information centers 2.49 .575 .654 .857
X7:Adequate and safe facilities 3.06 .485 .521 .864

Quality of public service
S 5. Custom and immigration services at airport 1.67 .770 .616 .790 .746
S8: Quality of guides 2.15 .693 .561 .756
S6:Police services 2.27 .644 .518 .748
S9: Knowledge of foreign language 2.00 .638 .554 .759
S 7: Access to local transport 2.40 .630 5.13 .754
S4: Medical and health services 2.12 .466 .467 .784

Destination product quality& education
S14: Availability of shopping facilities 2.05 .719 .547 .724 .666
S15:Accessibility of attractions 2.04 .712 .616 .658
S16: Visitor education at attractions 2.13 .709 .626 .650
S11:Quality of lodges 2.06 .664 .581 .665
S13:Provision of children facilities 1.82 .557 .459 .694
S12:Quality of restaurants 2.16 .510 .374 .802

Industry hospitality and environmental ambience
X3:Attractive and appealing environment 3.93 .728 .583. .716 .630
X2:Hospitality of service providers 4.03 .686 .550 .645
X1:Enjoyed the whole experience 4.04 .605 .479 .656
X6:Responsive staff 2.88 .565 .558 .693

Communication and security
S2:Personal safety 1.99 .718 .603 .690 .571
S3:Telecom services 2.16 .677 .467 .620
S1: Convenient airport 1.67 .520 .566 .640
S10:Friendliness of locals 2.07 .458 .504 .670

Effect of tourism destination image and visitors’
future intentions

Regression analysis was used to test the effect of destination image on visitor s‟ future intentions and to
specifically determine the image dimensions which
predict visitors‟ future intention s. The overall model
shows that there is a positive and significant relationship

Esu 87

Table 2. Tourism d estination image index of destination Cross River.

TDI Dimensions
N Range TDI of
Nigerians TDI of
foreigners Image
rating
Destinati on quality of life 235 4.00 3.037 1.997 Fair
Natural attractions & facilities 235 4.00 2.201 1.618 Poor
Quality of public services 235 4.00 2.081 2.171 Poor
Destination product quality and education 235 4.00 2.085 1.75 Poor
Community hospitality and a mbience 235 4.00 3.781 3.223 Good
Communication and Security 235 4.00 1.935 2.171 Poor

Table 3. Influence of tourist type on perceived image of destination.

T-test for Equality of Means
T Df Sig. (2 –
tailed) Mean
Difference Std. Error
Difference 95% Confidence Interval
of the Difference
Lower Upper
Destination quality of life 4.379 233 .000 1.04073 .23769 .57244 1.50901
Natural attractions and facilities 2.299 233 .022 .58209 .25315 .08332 1.08085
Quality of public services -.519 233 .604 -.09017 .17376 -.43251 .25216
Destination product quality and education 2.137 233 .034 .33460 .15660 .02607 .64313
Community hospitality and ambience 3.287 233 .001 .55804 .16979 .22353 .89255
Communication and security -1.717 233 .087 -.23577 .13728 -.50624 .03469

Table 4. Relationship between destination image and future intention.

Model Sum of Squares Df Mean Square F Sig.
Regression 123.650 6 20.608 43.771 .000a
1 Residual 107.346 228 .471
Total 230.996 234

Model 1: R2=53.5%, DW=2.12 7.

between destination image and repeat visit (R2= 53.5%,
F=43.771, p =0.000). This means that over fifty percent
of the change in the dependent variable is accounted for
by variation in the destination image attributes. This
indicates that the null h ypothesis should be rejected. The
value of R2 shows that the model has a reasonable good
fit to predict the criterion variable. The value of Durbin
Watson ( 2.127 ) was within normal range and so any
autocorrelation problem was rule d out in the model fittest .
See T able 4 for details. The effect of each of the tourism
destination image dimensions on behavioural intentions
was measured using multiple regression analysis. The
analysis shows that natural attraction and facilities (p <
0.05, t= -2.52, b = -0.096) and industry hospitality and
ambience (p = 0.000, t= 13.076, b = 0.777) predicted
repeat visit. The other four dimensions did not predict
repeat visit (p > 0.05). This is shown in Table 5. DISCUSSION OF FINDINGS AND MANAGERIAL
IMPLICATIONS

The result of e xploratory factor analysis produced six
tourism destination image dimensions or elements:
destination quality of life, natural attractions and facilities,
quality of public services, destination product quality and
education, industry hospitality and ambie nce and
communication and security. The principle of validity and
reliability were taken into consideration in generating
dimensions that subsequently formed what in this study
represents the underlying structure of the construct
known as tourism destinati on image. These dimensions
were empirically generated which made it unique formed
unlike the methodology used in some previous works on
this subject (Pike, 2002,), Mohan (2010) and Frochot
(2008).

88 J. Hosp. Manage. Tourism

Table 5. Effect of Tourism Destination Image on Visitors Future Intentions.

Model Unstandardized
Coefficients Standardized
Coefficients
t Sig. Collinearity Statistics
B Std. Error Beta Tolerance VIF
1 (Constant) 1.568 .244 6.417 .000
Destination quality of life .006 .040 .009 .152 .879 .645 1.551
Natural attractions & facilities -.096 .037 -.139 -2.552 .011 .690 1.448
Quality of public services .007 .059 .007 .120 .905 .608 1.645
Destination product quality and education -.053 .058 -.047 -.915 .361 .759 1.318
Community hospitality and ambience .777 .059 .765 13.076 .000 .595 1.681
Communication and security -.080 .075 -.063 -1.062 .289 .589 1.698

The major contribution of this study to literature is the
development of TDI index and the subsequent de ter-
mination of the underlying structure and components of
TDI that predicts visitor s‟ future behavioural intentions in
an emerging tourism destination in a developing country .
The six TDI dimensions produced cover ed most of the
elements that were earlier suggest ed by Beerlie and
Martin (2004) and Mohan (2010), but differ because of
the processes that the authors deployed in gene rating the
underlying structure . Some of the TDI attributes in the
studies listed here were subjectively generated. Further
to tha t, this study is more comprehensive because of the
large array of items which eventually were dimensioned
into six. The methodology used suggests a scale or
framework for measuring TDI which is the main crux of
this paper . The TDI index revealed that visit ors‟ perceived
image of the destination (Cross River State) is somewhat
poor. Perceived tourist image with respect to these TDI
dimensions differ between the two categories of tourist.
The d omestic tourists were found to have a slightly higher
perceived TD I than international tourists.
The result of the analysis further shows that tourism
destina tion image significantly influence visitor s‟ repeat
visit. Out of six TDI dimensions generated through EFA
only two dimensions were found to predict repeat visit:
community hospitality and ambience and natural
attractions and facilities. The other dimensions should be
omitted from the model since those dimensions were not
significant. They do not make significant contribution in
the explanation or prediction of repe at visit to tourist
destinations. The significant dimensions in this study
contained some of the attributes that were found
significant in some previous studies (Bigne et al., 2001;
Edward et al. 2009; Navratil et al., 2012) . Worthy of note
is the fact tha t industry hospitality and environmental
ambience appear to have more impact on visitors‟
behaviour by reason of its regression coefficient value
and even the TDI index. The negative regression
coefficient obtained in natural attractions and facilities
may be associated with the lack of effective product
position ing and brand association of the destination
nature based products and fac ilities (low product enhancement and packaging ). The TDI dimensions are
precursors of the destination brand identity. They a re the
activities created by stakeholders in the tourism industry.
Tourist perception of the TDI dimension destination lends
itself to the formation of a destination brand image . The
two predictors of tourist future intentions should be used
as the basis f or product strategy formulation and
marketing . Effort should be intensi fied to upgrade the
tourism components of the destination that are
responsible for industry hospitality and environmental
ambience and enhancement of nat ural attractions and
facilities . The destination nature based products; environ –
mental attractiveness and hospitality of industry operators
should be improved and used as the destination unique
selling proposition.

Conclusion

The dimensions produce d in this study h ave highlighted
the elements that should be used to represent and
measure destination image. This answers the „what it is
made up of and what it is not ‟ question of TDI . The
results suggest that TDI is structured into six dimensions ,
and that not all the social constructions and operation of
tourism destination managers significantly influence
visitor‟s behavioural intention s. The dimensions that
predict future intentions are critical in the p lanning and
development of destination products and marketing.
Destination competiti veness is based on the tourist flow
and number of repeat visits the destination enjoys .
Repeat visit is important in marketing because of the
belie f that it is cheaper and more profitable to serve a
repeat visitor than a first timer. Notably , the destinati on
image of the study area (Destination Cross River) is poor
as inferred from the very low scores of items rating. To
positively improve the destination image and to maximize
the benefit of increasing tourist flow and repeat visitation,
the destination mana gers need to formulate a new
tourism product development strategy that will make
industry hospitality and environmental ambience ,
enhanc ement of natural attractions and facilities the

major supporting components of the destination product.
The study support s the view that, tourism destination
image is an individual‟s subjective and objective
evaluation of designated features of a location which is
stored in memory and used in taking decision concerning
the future consumption of the location by potential and
prospective tourists .
This study is not without limitation. The use of only one
destination with just data collected from one cluste r of the
destination constitutes a limitation in this study. It is
suggested that further validation of the instrument should
include data collected from several points from a country
or even much more from a region. Secondly, respondents
for the student should include other forms of onsite
visitors such as festivals.

Conflict of Interests

The author have n ot declared any conflict of interests.

Acknowledgement

This paper was presented at th e Global International
Business –Economic Advancement Conference Organized
by the College Hospitality & Technology Leadership,
University of South Florida Held at Clearw ater Beach,
Florida, USA, 15 -18th May 2014 .

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