Building Cox's Bazar as a Brand

1.1 Background of the problem
People are now very busy around the world. They have very little time to spend on amusement. So
they want to go in a place where they can find every possible thing for amusement. Traveling is
good refreshment in crowded life. People wants to feel relax. They want to feel fun and want to
enjoy some time from their busy life with their families. Sea beaches around the world providing
one of the best way to amuse the tourists. Tourists feel calm and peaceful in the sea beaches.

Cox‟s Bazar is the product that able to provide every enjoyment, refreshment, calm, and enchanted
feeling to everyone. Around the world there are so many sea beaches from which some are artificial
and some are natural. Bangladesh fortunately gets this by nature. Cox‟s Bazar is the largest
unbroken beach in the world. But Bangladesh tourism hasn‟t able to make Cox‟s Bazar as a brand.
Local those who are affluent and foreign travelers don‟t feel more curiosity to travel Cox‟s Bazar
than other beaches in the world though it‟s the largest one. There locate a problem behind this.
Cox‟s Bazar is rich enough for its scenic beauty but not avail sufficient option for tourist. Branding
helps to create an image to the people‟s mind. Brand helps to place object with some added
attention. Branding helps to get Cox‟s Bazar that additional consideration from people all over the
world. Bangladesh Porjoton Corporation has scope to build Cox‟s Bazar as a brand. Therefore, it is
necessary to uncover the reasons of building that helps to build Cox‟s Bazar a brand.

1.2 Problem statement
Management Decision Problem Can Cox‟s Bazar be branded?

Marketing Research Problem To determine the factors that are essential for
building Cox‟s Bazar a brand.

1.3 Objective
Broad Statement:
 Build Cox‟s Bazar a brand

Specific component:
 Focus on the perception of tourist about Cox‟s Bazar.
 Identify attitude toward Cox‟s Bazar.
 Specify that is needed to build Cox‟s Bazar a brand.

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1.4 Problem Variables
Dependent Variable Build Cox‟s Bazar a Brand
Independent Variables 1. Slogan
2. Logos & Symbols
3. Image
4. International Advertisement
5. Tourist securities
6. Food Facilities in Hotels
7. Entertainment Facilities
8. Importance of beach environment
9. Cost

1.5 Approach to the problem
Branding is a promise to the consumer, an expectation of performance and a mark of integrity and
reputation (Kaplanidou & Vogt, 2003.) It builds up continuously in the mind of the consumer and is
affected by memories, experiences and other visitor‟s comments. Branding also aims to secure
repeat purchases through some degree of brand security.

“Branding is perhaps the most powerful marketing weapon available to contemporary destination
marketers” (Morgan and Pritchard, 2002, p.11)

These elements are very much important as brand components: identity, essence, personality,
image, character and culture (Kaplanidou & Vogt, 2003).

There are some components of branding and those are given bellow:

Brand Identity: “Brand identity should help establishing a relationship between the brand and the
customer by generating a value propositioning involving functional, emotional or self expressive
benefits”. (Aaker 1996:68)

Brand Image: Brand Image is related to how the brand is currently perceived by consumers (Aaker,
1996:71).

Brand Personality: The personality of the brand can be associated with a set of human
characteristics, such as gender, age, warmth, socio-economic class etc.

Brand Essence or Soul: The Soul of the brand includes the emotional elements and values of the
brand. These emotional elements should be part of a long term positioning strategy that does not

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change too often and which strategically positions the brand in the mind of the consumer
(Kaplanidou & Vogt, 2003).

Brand character: The character of the brand is perceived in terms of integrity, trustworthiness and
honesty. This also relates to the promise of the brand to deliver the experience associated with the
brand (Kaplanidou, 2003).

According to the WTO there are various tools which could assist in effectively communicating the
brand image. Those are:
 Slogans
 Themes
 Visual symbols
 A tone of voice and
 Design styles (WTO, 29 January 2007)

Cox‟s Bazar is a tourist destination. It may become a good brand also. If we want to make Cox‟s
Bazar a good brand then the process will known as a destination branding. Destination branding is,
“A strong national brand stands for something and does it with authenticity, distinctiveness, style,
emotion and co-ordination” (Hildreth, 2006).

Destination branding is a process used to develop a unique identity and personality that is different
from all competitive destinations.

Destination branding is “selecting a consistent brand element mix to identify and distinguish a
destination through positive image building” (Cai, 2000)

„„Destination branding‟‟ (DB) has been considered a potent marketing tool (Morgan, Pritchard, Pride
2004). It is defined as „„selecting a consistent element mix to identify and distinguish [a
destination] through positive image-building‟‟ (Cai 2002:722), and has been considered
synonymous with (re)positioning (Gilmore 2002), image-building (Curtis 2001), and image-
reconstruction (Hall 2002) of a destination. DB has been also analogous to corporate or umbrella
branding, whereby a destination functions like a company that produces various product/service
brands. (Gnoth 2002; Papadopoulos and Heslop 2002)

According to the Department of Parks, Recreation and Tourism Resources of Michigan State
University, “a destination brand is all about how visitors perceive the destination in their minds.
Destination branding stretches much further than a destination logo or slogan but rather captures
the distinct elements of the destination in the brand and communicates.”

The branding of destinations has to aim to create an appropriate image, which is appealing and yet
truthful in content and style. (Lumsdon, 1997: 169) Branding of a product does not only
differentiate the product competitively but also serves as means of adding perceived value to the
product. A strong branding image will create a strong identity of the product and/or service. The

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visible manifestation within a competitive market and the desire to create a competitive advantage
to reinforce product and service differentiation is therefore extremely important.

Destination branding is about combining all things associated with the 'place' (i.e., its products and
services from various industries:
 agriculture;
 tourism;
 sports;
 arts;
 investment;
 technology;
 Education, etc.

That collaborates under one brand. Its aim is to capture the essence of the destination, in a unified
manner, and can be consumed simultaneously at a symbolic and experiential level. It is then used
to market those unique added values to consumer needs and sustaining its success in the face of
competition.

A destination brand is:

 A way to communicate a destination‟s unique identity to visitors
 A means of differentiating a destination from its competitors
 A uniform “look” that all destination partners can consistently use
 A symbol, name, term or design, or combination of these elements

A destination brand is not just:

 An advertising slogan (or tag line)
 A logo to stick on a t-shirt
 A distinctive color scheme
 A brochure or Web site

An emotional attachment to the destination must be built with a brand that is:

 Credible
 Deliverable
 Differentiating
 Conveying powerful ideas
 Enthusing for partners and stakeholders
 Resonating with visitors

Five Phase of Destination branding

 Phase1: Market investigation, analysis and strategic recommendations
 Phase 2: Brand identity development
 Phase 3: Brand launch and introduction – communicating the vision
 Phase 4: Brand implementation
 Phase 5: Monitoring, evaluation and review

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Brand drivers are the essence of a place – the destination‟s unique and own-able qualities that
make it different from other places.

Walter Landor, Landor Associates, San Francisco as quoted in: “More than a logo: Building a brand
identity” by Kathleen Cassedy, HSMAI Marketing Review, Summer 2001

According to Fabricius (2006); from the WTO the benefits of effective branding and positioning
include:

Customer loyalty and also industry loyalty
Not only do tourists „buy in‟ to the destination but industry stakeholders also share in a national
umbrella branding strategy. Quality, service and efficiency of the tourism products can thus become
continuing elements carried throughout the hierarchical structure of tourism role players and
stakeholders. The national branding strategy could encourage and foster loyalty from governmental
role players (NTB), to Non Governmental Organizations (NGO‟s), to accommodation suppliers and
also to attraction and activity offerings (Marconi, 2000; Fabricius, 2006).

Commercial value of a destination can also be increased an effective branding strategy has the
ability to increase market share dramatically (Fabricius, 2006).

Enabler for private and public partnerships and Seamless communications
Branding strategies have the power to blur boundaries between public and private partnerships. The
elimination of such boundaries can ensure more effectiveness and efficiency through cooperation
and also communication (Fabricius, 2006).

Promotional tool
An effective branding strategy will also serve as a promotional tool as past, current and potential
visitors‟ deciding and motivating phases of travelling can be affected by the branding strategy. The
deciding and motivating phase to a certain destination can either be convinced or de-motivated to
travel to a destination, depending on how appropriate, attractive and appealing the branding
strategy is perceived. (Fabricius, 2006)

Local support, however, is usually a necessary component for a successful tourism strategy, as
noted by Bourke and Luloff (1995), and echoed by Brass (1996), Burr (1995), and Woods (1992).
That is why tourism strategies must be consistent with local goals and be sensitive to sustaining a
community‟s character and traditions. McDaniel‟s (2001) article of southwestern Virginia, which
highlights the tourism potential of the region‟s scenic and abundant recreational activities, is a
representative exampleThe common measures for Destination Branding effectiveness were target
markets‟ awareness level (destination name recall), perceptions of a destination, touristic behavior
(activities, season, travel frequency, and information sources), and future tourism intentions.

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As Mundt (2002) argued, „„branding‟‟ seems to be used as a replacement for „„image-building.‟‟
Accordingly, the measures to evaluate effectiveness of destination branding are not different from
those for image. Thus, the term, DB might be „„old wine in a new bottle.‟‟ That is, it may be re-
adorned jargon to emphasize the need for unequivocal „„focus‟‟ in marketing a destination to
appeal to tourists. It may also be that branding strategies would work only under certain conditions,
while other destinations do not need it if the existing marketing function does the job. For
example, branding strategies would be effective if a nation is undergoing redefinition of identity,
such as Central and Eastern European countries (Hall 2002).

As destination branding is becoming prevalent for its touted benefits, it seems evident that a
clearer conceptualization needs to be established. To do so, empirically testable models based on
integrative understanding of various cases rather than currently existing normative models („„what
should be‟‟) are called for to evaluate the validity of DB. The current study was intended to initiate
the development of its theoretical foundations and should assist destination marketing
organizations in deciding if it is viable to invest their limited resources in destination branding
(Henderson 2000).

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1.6 Conceptual Framework

Physical Emotional
Benefit Benefit

Experience

Tourist Evaluation

Brand
Awareness
Image

Brand
Imagery

Brand Feeling

Positioning

Brand
Resonance

Cox’s Bazar a Brand

Figure: Conceptual Framework of building Cox’s Bazar as a brand

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1.7 Research Question
A structured questionnaire has been used to collect data. Multiple choice questions and
dichotomous questions have been used to develop structured questionnaire.
a. Q1: Is there any scope for building Cox‟s Bazar brand?
b. H1: There is potential scope for building Cox‟s Bazar a brand

1.8 Hypotheses
a. H0: There is not potential scope for building Cox‟s Bazar a brand.
b. H1: There is potential scope for building Cox‟s Bazar a brand

1.9 Analytical Model
1.9.a Verbal Model

To build Cox‟s Bazar a brand, facilities of hotels, rooms and beaches and other variables help to
draw an experience. That experience helps to build and image and other variables like slogan,
logos, advertisement helps to build an association to make Cox‟s Bazar a brand.

1.9.b Graphical model

Food Facility Slogan Logos &
Symbols

Image
Cost of Travel

International
Entertainment
Advertisement
Facility
Build Cox’s Bazar a Brand Security
Local
transportation

Beach
environment

Figure: Graphical model of building Cox‟s Bazar as a brand

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1.9.c Mathematical Model

y = a0 + Σ ai xi
Where,
Y = building brand
a0, ai = model parameters to be estimated statistically
Xi = factors that helps to build brand

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2.1 Types of Research
Building Cox‟s Bazar as a brand is a descriptive research. It needs some quantitative information to
take a decision. Secondary information about the area of this research is not available. That‟s why
we had to collect primary information from the sample.

2.2 Information Needed
To do the research we used information that focus on
I) Issues that a tourist thinks about Cox‟s Bazar depending on different aspects.
ii) Obstacle of developing Cox‟s Bazar a brand.

2.3 Data Collection Method
As we collected primary information, we choose personal, and mail interviews method as data
collection tool.

2.4 Scaling Technique
The questionnaire consists of some questions of brand related issues on which the respondent
tourists have been asked to document their response on different dimensions of Nine-point rating
scale. These objective questions have attempted to collect objective data like the extent of tourists'
positive and negative attitudes on different tourism arrangements/services in Bangladesh and also
other attributes that related to building brand. The questionnaire for the respondent tourists will
contain a nine-point Likert Scale.

2.5 Nature of question
Questions will focus on the considerations of factors that will help to build Cox‟ Bazar a brand.
1. Tourism facilities that require building brand. For example, some questions state through
open ended, some close ended and questionnaire also includes dichotomous, and 9 point
Likert scale.
2. Questionnaire contains 25 questions asked to the respondents.
3. Average interviewing time is 30 minutes.

2.6 Population of the study
There are some categories of population have been set based on the objectives and scope of the
study. These are (i) Executives of the BPC, (ii) owners of private tour operators (TOAB members), (iii)
Tourists visiting the country, (iv) Owners of travel agencies, (v) Hotel managers, and (vi) Restaurants
managers. The first category consists of currently employed executives of the BPC. An up to date

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TOAB (Tour Operators Association of Bangladesh) members' list consisting of names and addresses
of 32 firms has been used as the base of population for the second category. The size and
composition of population for the tourist category is not precisely known and their proper
categorization could not be made. Since no up to date list of travel agencies, hotels, and
restaurants operating in Bangladesh has been found, the sizes of population for those three sectors
remain unknown.

2.7 Sample Size
Sample selection
We used stratified sampling technique for selecting respondents considering hotels and different
foreign cultural center as strata. We took 30 respondents as the sample of our study.
i. Tourism Firms:
Five types of tourism firms have been examined in this study. These include BPC, Private Tour
operators, Travel Agencies, Hotels, and Restaurants. Hotels and restaurants have been studied as
they play important roles to serve tourist guests and in adopting tour operators' services, and thus
accelerating the development of tourism activities in Bangladesh.

ii. Tourists: The study has included the foreign tourists & local tourist who have visited different
destinations of the country. It will help to demonstrate perceptions and preference of them.

iii. Cultural Centers: British Council, Russian cultural center and others consider as sampling frame
because number of foreigners are there as tourist. As these foreigners travel throughout the world,
their assessment can make our research reliable and valid.

2.8 Fieldwork
Field operation is the phase of the project during which researchers make contact with the
respondents, administrator the data collection instruments, record the data, and return the data to
a central location for processing.
We have collected data by contacting our respective respondent in different cultural center, tourist
firms and hotels. We have collected data within 7 days, and take other seven days for preparing
analysis and report.

Data Collection Data Input Report Preparation

7 days 2 days 5 days

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3.1 Analysis of Variance & Analysis of Covariance
Analysis of variance is a statistical technique for examining the difference among means for two or
more populations. If two factors are involved, the analysis is termed two-way analysis of variance.
In our research we consider two ways ANOVA for examining difference. Independent variables need
to be categorical and dependent variable need to be metric. In analysis of variance we estimate two
measures of variation: within group and between groups. F Ratio helps to identify rejection or
acceptance of null hypotheses. If F value is larger than the critical value, null hypothesis is rejected.
Analysis of Covariance – An advanced analysis of variance procedure in which the effects of one or
more metric-scaled extraneous variables are removed from the dependent variable before
conducting the ANOVA. In our research we consider weather as covariate or extraneous factor
which effects need to be removed from the dependent variable, building Cox‟s Bazar a brand.
Hypotheses for ANOVA-
Ho: µ1 = µ2 = µ3 = µ4 = µ5
H1: µ1 ≠ µ2 ≠ µ3 ≠µ4 ≠ µ5

For analyzing the data we were instructed to use “Multiple Regression Analysis”, “Discriminant
Analysis”, Logit analysis, Multidimensional scale, Analysis of variance and Analysis of covariance.
We have collected data according to these model need. Following part of the report illustrate overall
analysis of building Cox‟s Bazar a brand.

3.2 Multiple Regression Analysis
“Multiple Regression Analysis” is a statistical technique that simultaneously develops a
mathematical relationship between two or more independent variables and an interval-scaled
dependent variable. For building Cox‟s Bazar a brand we need to consider different variable. These
variables influence the dependent variable building Cox‟s Bazar a brand. Regression analysis states
the associative relationship between metric dependent variable and independent variable. The
coefficients associated with each of the independent variables are denoted by β. This coefficient
denotes how much relationship exists between that particular independent variable and the
dependent variable. From this research, the multiple regression equation would stand:

A/T= C+ β1S+ β2 IA+ β3IE+ β4 CT+ β5BN+ β6 SE+ β7 IF+ β8
For building Cox‟s Bazar a brand
Where,
S = Slogan
IA= International advertisement
IE= Insufficient Entertainment facility
CT= Cost of Travel
BE= Beach Environment

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SE= Security
IF= Inadequate Food
I= Image

Null hypothesis state there is no potential scope for building Cox‟s Bazar a brand and the
alternative hypothesis is that there are potential scope for building Cox‟s Bazar a brand. If the β of
all independent variable is same that it means the independent variables have no effect on the
dependent variable of each package. Which means, the null hypothesis is accepted. But if the β of
all the independent variables are not same for each variable then it can be said that the null
hypothesis is rejected and automatically the alternative hypothesis would be accepted.
We can also use the R2 Value to test hypothesis. If this value is 0 for each variable then it can be
said that there is no relationship among the dependent and independent variables for each
package. If not, then we can say there are some relationships among the variables, which means we
can reject the null hypothesis and accept alternative hypothesis.

Hypotheses test with multiple regression analysis given below-

1st method, using β
H0: β1= β2=β3= β4= β5= β6= β7
H1: β1≠β2≠β3≠β4≠β5≠β6≠β7
2nd method, using R2 Value

H0: R2 =0
H1: R2 ≠0

3.3 Factor Analysis
Mathematically, factor analysis is somewhat similar to multiple regression analysis, in that each
variable expressed as a linear combination of underlying factors. This technique used to determine
the underlying dimensions of a larger set of inter correlated variable.
X I =Ai1 F1+ Ai2 F2 + Ai3 F3+ ViUi
The analytical process is based on a matrix of correlations between the variables. For the factor
analysis to be appropriate, the variables must be correlated. If the correlations between all the
variables are small, factor analysis may not be appropriate. Barlett test of Sphericity can be used to
test null hypothesis that the variables are uncorrelated in the population. Another useful statistic is
the Kaise-Meyer-Olkin (KMO) measure of sampling adequacy. KMO statistic indicates that the
correlations between pairs of variables cannot be explained by other variables and that factor
analysis may not be appropriate. Generally, a value greater than 0.5 is desirable.

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We also analyzed Eigen vale that explained the total variance by each factor. Factor matrix helps to
understand the standardized variables in terms of the factors. These coefficients, the factor
loadings, represent the correlations between the factor and the variables are closely related. The
coefficient can be used to interpret the factors. Interpretation is facilitated by identifying the
variables that have large loadings on the same factor.
Here in this model hypotheses are,

When it is determined that factor analysis is an appropriate for analyzing the data, an appropriate
method must be selected. We select principal component factor that consider the total variance in
the data.

3.4 Discriminant Analysis
Discriminant Analysis is described as a technique for analyzing marketing research data when the
criterion or dependent variable is categorical and the predictor or independent variables are interval
in nature. In our research, of building Cox‟s Bazar a brand, we collect data of those tourists who
went Cox‟s Bazar in last three years frequent or non-frequently. That helps to divide them into two
groups. As discriminant analysis use to classify an object or person into two groups it helps to
identify significant differences exist among the groups, in terms of the predictor variable.

The analysis was done based on 4 factors- “Wilk‟s λ Analysis” which represents the influencing
ability of the independent variables, “F Ratio” and “Significance” measurement for the significance of
each independent variables and “Standard Canonical Discriminant Function Coefficient (SCDFC)” to
measure the importance of the independent variable.

For “Wilk‟s λ Analysis”, it was assumed that, the higher the value of Wilk‟s λ, the lower the
influencing ability of that independent variable and vice versa. Significance was measured directly.
To prove the significance is true, “F Ratio” was used. The higher the value of F, the higher the
significance of the independent variable. At last, importance of each independent variable was
measured using “Standard Canonical Discriminant Function Coefficient”, which refers to the highest
valued independent variable as the most important one and the lowest valued independent variable
as the least important one.
Hypotheses test was also done with this analysis. For this purpose, group means of both the group
of frequent visitor and non-frequent visitor were selected as the group parameter.

The null hypothesis would be that, these means are equal for each group. The alternative
hypothesis would be these group means are not equal. The hypotheses test has given in a brief in
the following-

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H0: µh=µl
H1: µh ≠µl
µh= Group mean of the frequent visitor
µl= Group mean of the non-frequent visitor

3.5 Logit Analysis
This model considers binary dependent variable and metric independent variable. In logistic
regression, commonly used measures of model fit are based on the likelihood function and are Cox
& Snell R square and Negelkerke R square. In this case the significance of the estimated coefficients
is based on Wald‟s statistic. Thus the size of the change in the log odds of the dependent variable
event when the corresponding independent variable Xi is increased by one unit and the effect of the
other independent variables is held constant.
In our research we use logit model to asses the significance of variable on binary dependent
variable. We consider frequent and non-frequent visitor group of people to assess their attitude on
building Cox‟s Bazar a brand.

3.6 Multidimensional scale
It is used for representing perceptions and preferences of respondents spatially by means of a
visual display. In our research, we use multidimensional scale for identifying respondent‟s
perception about Cox‟s Bazar based on three dimensions than other beaches in the world. In MDS
R Square should be examined. R square correlation index that indicates the proportion of variance
of the optimally scaled data that can be account for MDS procedure. Spatial map perceived
relationship among beaches are represented as geometric relationship among points in a
multidimensional space.

3.7 Conjoint Analysis
It attends to determine the relative importance of different attributes or combination offered to
tourist. It illustrates which combination is more significant for building Cox‟s Bazar a brand. In our
research we select important nine profiles. This nine profile is the combination of hotel rent,
transportation mode and food facility. We classify all these three attributes into 3 categories like

Hotel rent Food
1= 5,000 1= Sea Food
2 = 10,000 2 = Standard Food
3 = 15,000 3 = Foreign Food

Transportation Mode
1= Rickshaw
2 = Jeep
3 = Private Car

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4.1 ANOVA & ANCOVA

Univariate Analysis of Variance

T es ts o f Betw ee n -Su bje cts Effe cts

Dependent Variable: Building c ox 's Bazar a brand
Ty pe III Sum
Sourc e of Squares df Mean Square F Sig.
Correc ted Model 27.767 a 21 1.322 1.090 .478
Intercept 792.688 1 792.688 653.763 .000
security 3.433 2 1.716 1.416 .298
inad_f oo 3.508 2 1.754 1.446 .291
ins_ent 5.357 1 5.357 4.418 .069
inter_ad .367 1 .367 .302 .597
cs t_tra 2.862 2 1.431 1.180 .356
security * inad_f oo .000 0 . . .
security * ins_ent .650 2 .325 .268 .771
inad_f oo * ins_ent .000 0 . . .
security * inad_f oo * ins_
.000 0 . . .
ent
security * inter_ad 1.545 2 .772 .637 .554
inad_f oo * inter_ad 1.786 1 1.786 1.473 .260
security * inad_f oo * inter_
.000 0 . . .
ad
ins_ent * inter_ad .000 0 . . .
security * ins_ent * inter_
.000 0 . . .
ad
inad_f oo * ins_ent * inter_
.000 0 . . .
ad
.000 0 . . .
ent * inter_ad
security * c st_tra 8.703 2 4.352 3.589 .077
inad_f oo * cs t_tra .000 0 . . .
security * inad_f oo * c st_
.000 0 . . .
tra
ins_ent * c st_tra .643 2 .321 .265 .774
security * ins_ent * cst_tra .000 0 . . .
inad_f oo * ins_ent * c st_
.000 0 . . .
tra
.000 0 . . .
ent * cs t_tra
inter_ad * c st_tra .316 1 .316 .261 .623
security * inter_ad * cst_
.058 1 .058 .048 .832
tra
inad_f oo * inter_ad * c st_
.000 0 . . .
tra
security * inad_f oo * inter_
.000 0 . . .
ad * cs t_tra
ins_ent * inter_ad * cst_
.000 0 . . .
tra
security * ins_ent * inter_
.000 0 . . .
ad * cs t_tra
inad_f oo * ins_ent * inter_
.000 0 . . .
ad * cs t_tra
.000 0 . . .
ent * inter_ad * cs t_tra
Error 9.700 8 1.213
Total 1710.000 30
Correc ted Total 37.467 29
a. R Squared = .741 (A djusted R Squared = .061)

From this table we find that insufficient entertainment facility is significant. Sufficient entertainment
facilities can help to build Cox‟s Bazar a brand. F value is larger than critical value that indicates its

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significance. Cox‟s Bazar is the largest sea beach in the world but entertainment faculty is not
appropriate for foreign tourist.
Univariate Analysis of Variance

Tes ts of Betw ee n-Subje cts Effe cts

Dependent Variable: Building c ox's Bazar a brand
Ty pe III Sum
Sourc e of Squares df Mean Square F Sig.
Correc ted Model 2.029a 4 .507 .358 .836
Intercept 38.334 1 38.334 27.043 .000
w eather .889 1 .889 .627 .436
ins_ent .403 1 .403 .284 .599
inter_ad .163 1 .163 .115 .737
ins_ent * inter_ad .044 1 .044 .031 .861
Error 35.438 25 1.418
Total 1710.000 30
Correc ted Total 37.467 29
a. R Squared = .054 (A djusted R Squared = -.097)

From this output of ANCOVA, we find that covariate weather is not significant. Thus it can be said
that weather is insignificant as covariate. For building Cox‟s bazaar brand weather is less significant
factor.

4.2 Multiple Regression Analysis
From the results shown in the SPSS analysis for regression analysis, the following relationship was
found for the independent and dependent variables

Model Sum m ary

Adjusted Std. Error of
Model R R Square R Square the Estimate
1 .671 a .450 .241 .990
a. Predictors: (Constant), image ref lec ts pleasure, w ell
secured, slogan helps to build brand, inadequate f ood
f ac ility, cos t of trav el low , international ad helps to build
a brand , ins suf f icient entertainment f acility , nois y
beac h env ironment

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Coefficientsa

Unstandardized Standardized
Coefficients Coefficients
Model B Std. Error Beta t Sig.
1 (Constant) 15.474 3.585 4.316 .000
slogan helps to build
.192 .152 .221 1.262 .221
brand
international ad helps to
-.644 .349 -.379 -1.847 .079
build a brand
insufficient
-.450 .175 -.536 -2.580 .017
entertainment facility
cost of travel low -.314 .147 -.431 -2.133 .045
noisy beach environment -.136 .110 -.280 -1.235 .230
well secured .224 .128 .322 1.742 .096
inadequate food facility -.473 .193 -.444 -2.453 .023
image reflects pleasure -.041 .125 -.060 -.328 .746
a. Dependent Variable: Building Cox’s Bazar a brand

We consider 8 variables for explaining relation of these variables with building Cox‟s Bazar a brand.
From the table coefficient, we find that significance level of insufficient entertainment facilities is
least than any other variable. It represent that sufficient entertainment facility helps to build Cox‟s
Bazar a brand. Inadequate food facility also has significant relationship that illustrate that if food
facilities is standard, it is able to make an effect on tourist mind that helps to build brand.

Here R = .450
R square = .241
It indicates that R ≠ 0 which lead to the rejection of null hypotheses. It means variables have
correlation among them.

A/T= C+ .221S - .379 IA-.536IE-.431CT- .280BN+ .322SE-.444 IF-.060I

Table 1: Independent variable that influence dependent variable

Variable name Significance value

Entertainment facility 0.17
Food facility 0.23
Cost of travel 0.45
International advertisement 0.79

Page : 18
18

4.3 Factor Analysis

KMO and Bartle tt's Te s t
Kais er-Mey er-Olkin Meas ure of Sampling
A dequacy. .531

Bartlett's Test of A pprox. Chi-Square 46.880
Sphericity df 28
Sig. .014

KMO value indicates that the correlation between pair of variables can be explained by other
variables and the factor analysis is appropriate.

Total Variance Explained

Initial Eigenvalues Extraction Sums of Squared Loadings Rotation Sums of Squared Loadings
Component Total % of Variance Cumulative % Total % of Variance Cumulative % Total % of Variance Cumulative %
1 2.243 28.035 28.035 2.243 28.035 28.035 2.142 26.772 26.772
2 1.647 20.593 48.627 1.647 20.593 48.627 1.590 19.872 46.645
3 1.407 17.592 66.219 1.407 17.592 66.219 1.566 19.574 66.219
4 .827 10.335 76.555
5 .723 9.031 85.586
6 .532 6.647 92.233
7 .326 4.073 96.306
8 .296 3.694 100.000
Extraction Method: Principal Component Analysis.

Interpreting this result we can see Eigen value of variables. The Eigen value for a factor indicates the
total variance attributed to that factor. Factor 1 account for variance of 28% and other two factor
accounts 20% and 17% respectively. In rotated component matrix factor 1 has high coefficient with
variables insufficient entertainment facility, noisy beach environment, cost of travel. Therefore this
factor may be labeled as Primary facility. Second factor relates with security and transportation. This
factor can be labeled as secondary facility. Third factor relates with logos & symbol, international
advertisement, pleasurable image. This factor can be labeled as branding factor.

Page : 19
19

a
Rotate d Co m p o ne nt M atrix

Component
1 2 3
logos & sy mbol helps to
.018 .329 .690
build brand
local transportation is not
.461 .701 .141
appropriate
-.398 -.053 .636
build a brand
w ell s ecured -.151 .904 -.022
inss uf f ic ient
-.776 .269 -.126
entertainment f acility
nois y beach environment .852 .092 -.004
image ref lec ts pleasure -.140 .121 -.770
cost of trav el low .631 .275 -.236
Ex traction Method: Princ ipal Component A naly sis .
Rotation Method: V arimax w ith Kaiser Normaliz ation.
a. Rotation converged in 6 iterations.

Factor Name Variable Name

Factor 1- Primary Entertainment facility
Facility Beach environment
cost of travel
Factor 2- Secondary Well security
Facility Appropriate transportation
Factor 3- Branding International Advertisement
Facility Logos & Symbol
Pleasurable Image

4.4 Discriminant Analysis

The interpretation of the discriminant coefficients is similar to that in multiple regression analysis.
The value of the coefficient for a particular predictor depends on the other predictors included in
the discriminant function. The signs of the coefficients are arbitrary, but they indicate which
variable values result in large and small function values and associate them with particular groups.
Generally, predictors with relatively large coefficients contribute more to the discriminating power
of the function, as compared with predictors with smaller coefficients are therefore, more
important.

Page : 20
20

Tes ts of Equality of Group M eans

Wilks '
Lambda F df 1 df 2 Sig.
.989 .303 1 28 .587
brand
.932 2.059 1 28 .162
build brand
.988 .340 1 28 .564
appropriate
.936 1.923 1 28 .176
build a brand
w ell s ecured .861 4.509 1 28 .043
inadequate f ood f acility .991 .257 1 28 .616
inss uf f ic ient
.894 3.332 1 28 .079
nois y beach environment .771 8.331 1 28 .007
image ref lec ts pleasure .949 1.496 1 28 .231
cost of trav el low .879 3.837 1 28 .060

From this table we find that noisy environment is discriminate mostly. F ratio indicates that when
the predictors are considered individually, only security, and logos & symbol differentiate between
those who visited Cox‟s Bazar frequently and non-frequently. The Eigenvalue associated with
function is 1.66 and it accounts for 100 percent of the explained variance.
The canonical correlation associated with that is (0.790)2 indicates that 62 percent of variance.

Eigenvalues

Canonical
Function Eigenvalue % of V arianc e Cumulativ e % Correlation
1 1.666a 100.0 100.0 .790
a. First 1 canonical discriminant f unctions w ere us ed in the
analysis.

Wilks ' Lam bda

Wilks '
Test of Function(s) Lambda Chi-s quare df Sig.
1 .375 22.550 10 .013

Page : 21
21

Structure Matrix

Function
1
noisy beach .433
environment
well secured -.318
cost of travel low .294
insufficient -.274
entertainment facility
logos & symbol helps .215
to build brand
local transportation is -.183
not appropriate
international ad helps -.096
to build a brand
image reflects .087
pleasure
slogan helps to build .082
brand
inadequate food .034
facility

Pooled within-groups correlations between discriminating variables and standardized canonical
discriminant functions Variables ordered by absolute size of correlation within function.

These simple correlations between the predictors and the discriminant function are listed in order
of magnitude. It represents that noisy beach environment, security, and cost of travel discriminate
between groups of frequent visitor and non-frequent visitor.

Page : 22
22

4.5 Logit Analysis

Cas e Proces s ing Sum m ary
a
Unw eighted Cases N Percent
Selected Cas es Included in A naly sis 30 100.0
Mis sing Cases 0 .0
Total 30 100.0
Unselected Cases 0 .0
Total 30 100.0
a. If w eight is in ef f ect, s ee c las sif ication table f or the total
number of cases .

Dependent Variable Encoding

Original Value Internal Value
non-f requent 0
f requent v isitor 1

Mode l Sum m ary

-2 Log Cox & Snell Nagelkerke
Step likelihood R Square R Square
1 28.628 .351 .468

V ariables in the Equation

B S.E. Wald df Sig. Ex p(B)
Step
a
INTRA D -.783 .725 1.167 1 .280 .457
1 SECURITY -.870 .413 4.435 1 .035 .419
ENTERTA N -.054 .416 .017 1 .897 .948
COSTTRA .884 .467 3.573 1 .059 2.420
Cons tant 4.171 5.352 .608 1 .436 64.801
a. V ariable(s) entered on step 1: INTRA D, SECURITY , ENTERTA N, COSTTRA.

Interpretation
According to the Wald statistic, it can be assessed that security is prime issue for frequently visitor
and cost of travel also significant issue for frequent visitor. To make Cox‟s Bazar a brand security
need to consider very efficiently.

Page : 23
23

4.6 Multidimensional Scale

The data of the table were treated as rank ordered and scaled using a non-metric multidimensional
scaling procedure. Because one respondent provided these data, an individual level analysis was
conducted.
Figure

Derived Stimulus Configuration

Euclidean distance model

2 paradise

Brand factor

1

phuket cape_town
pataya bondi

0
Primary facility
Dimen sion 2
cox_bazar
Secondary facility

miami
-1

hawai

-1 0 1 2

In this figure, horizontal axis is labeled as primary facility versus secondary facility of beaches.
Beaches with high positive value on this axis are Bondi and Cox‟s Bazar (Primary facility) and
negative value (secondary facility) are Pataya and Phuket. The vertical axis labeled as strong
branding factor verses weak branding factor.

Cox‟s Bazar‟s position on spatial map indicates that Cox‟s Bazar is in higher in primary facility but
poor in brand factor. Brands labeled near each other conflict more intensely. In the spatial map,
Cox‟s Bazar is in a separate position having no close competition. So, according to the multi-
dimension analysis, Cox‟s Bazar has a distinctive position on the respondent‟s mind.

Page : 24
24

4.7 Conjoint Analysis

a
Coe fficients

Unstandardiz ed Standardized
Coef f icients Coef f icients
1 (Cons tant) 1.772 1.119 1.584 .129
Low price_High c ar_
.067 .164 .113 .410 .686
medium f ood
low price_low
.096 .149 .159 .643 .528
trans port_high f ood
Low price_Medium
-.005 .110 -.008 -.041 .967
trans port_low f ood
Medium Price_High
.131 .116 .238 1.125 .274
Transport_High Food
Medium Price_Medium
-.092 .124 -.166 -.744 .465
Transport_high Food
High pric e_high
.266 .147 .447 1.808 .086
tranport_high f ood
High pric e_low
.061 .107 .119 .571 .575
trans port_medium food
high price_medium
-.055 .110 -.132 -.504 .620
trans port_high f ood
Medium price_high
.309 .107 .465 2.885 .009
trans port_low f ood
a. Dependent Variable: Pref erence rating of going Cox's Bazar

In conjoint analysis, we find that by offering different combination to tourist, they prefer most the
combination of high price, high food and high transportation. Here, high price means expensive
hotels; basically foreign tourist mostly emphasis on security. For that they want to spend money to
be secure. High food means foreign food and high transportation means private car. Tourist wants
secure transportation. Foreign tourist likes to use private car as transportation medium for
convenience. Food is a prime factor. Local tourists like good quality Bengali food. But foreigners like
to taste most of the time foreign food. An Italian tourist prefers to taste most of the time their own
food than local sea food. Foreigners like to taste local for one or two times. That‟s why they prefer
to take foreign food.

Page : 25
25

5.1 Conclusion & Recommendations
To build Cox‟s Bazar a brand we consider many variables but the most obvious
thing that is needed is attitude of our people toward Cox‟s Bazar. It is a valuable
asset for us. We need to be rethinking about this treasure. Throughout the
research we find that, tourist wants security at first, then entertainment and an
environment where they can relax completely. But in Cox‟s Bazar security is not
up to that mark which assure tourist not to feel insecure. After that, Bangladeshi
culture is not permitting those activities that foreign tourist used to do in other
foreign beaches. In foreign beaches male and female tourist can easily take sun
bath but in Cox‟s Bazar it is even impossible to think. Entertainment facilities are
also insufficient for foreign tourist. Local tourist go Cox‟s Bazar just to see it as
it is largest in the world but foreign tourist want to go any beaches to make fun
and enjoyment. Though Cox‟s Bazar is the largest sea beach, it is able to fail to
attract because of its low advertisement and also because of no logos and
symbol. Through this research we also uncover that brand building factor such
as image, slogan, international advertisement, logos & symbol facilitate of
branding Cox‟s Bazar. Bangladeshi people know the name of Cox‟s Bazar but lots
of foreign people are not aware about this largest sea beach. Through
advertisement people will know about it which helps to build an image. As we
conducted different model to get quantitative information, so we can say that
respondents wants to be well secured, wants more entertainment facility, and
wants to travel at low cost. Those

Page : 26
26

5.2 Appendix
Good Morning/Good Afternoon everyone, we are conducting a survey for our academic purpose. We
are collecting information on how can Cox‟s Bazar be branded.

General Information
1. What age group are you in?
Under 18 18-25 26-30 31-35 36- 40
41-45 46-50 51-55 Above 56

2. Gender
Male Female

3. Your marital status
Unmarried Married Others

4. What is the highest level of education you have completed?
School Some College Bachelors
Masters Doctors Others

5. Your profession
Student Unemployed Service-holder Businessman
Housewife/husband retired others: be specify______________

6. Slogans help to build Cox‟s Bazar as a Brand:
Extremely Strongly Somewhat Agree Neither Disagree Somewhat Strongly Extremely
agree agree agree agree disagree disagree disagree
nor
disagree
9 8 7 6 5 4 3 2 1

7. Tourists are well secured in Cox‟s Bazar:

nor
disagree
9 8 7 6 5 4 3 2 1

Page : 27
27

8. Food facilities in hotels are not adequate:

nor
disagree
1 2 3 4 5 6 7 8 9

9. Logos& Symbols helps to locate easily recognize than competitive destination

nor
disagree
9 8 7 6 5 4 3 2 1

10. Cost of travel in Cox‟s Bazar is affordable:

nor
disagree
9 8 7 6 5 4 3 2 1

11. Beach environment is not comfortable in Cox‟s Bazar:

Extremely Strongly Agree Somewhat Neither Somewhat Disagree Strongly Extremely
nor
disagree
1 2 3 4 5 6 7 8 9

12. Local transportation are not appropriate for travelling in Cox‟s Bazar:

nor
disagree
1 2 3 4 5 6 7 8 9

Page : 28
28

Image about Cox‟s Bazar refers pleasure

nor
disagree
9 8 7 6 5 4 3 2 1

13. Entertainment facilities for tourists are not sufficient in Cox‟s Bazar:

nor
disagree
1 2 3 4 5 6 7 8 9
14. International Advertisement help to build Cox‟s Bazar as a Brand:

nor
disagree
9 8 7 6 5 4 3 2 1

15. Do you think that Cox‟s Bazar can be built a strong brand?
Yes No

How Many Times you go Cox‟s Bazar in last three years?

a) 1-3 b) 3- more than 3

Page : 29
29

Conjoint Methodology by considering the problem “How Cox-Bazar can be Branded”

1. A combination of rent of Tk 5000 for hotels, private car as transport and a standard food
family is desirable.

Extremely Strongly Disagree Some Neutral Some Agree Strongly Extremely
Disagree Disagree What What Agree Agree
Disagree Agree
1 2 3 4 5 6 7 8 9

2. Blending the options of rent of Tk 5000 for hotels, rickshaw as transport and foreign food
menu availability is expected.

Disagree Agree
1 2 3 4 5 6 7 8 9

3. Arranging a package of rent of Tk 5000 for hotels, jeep as transport and local food menu is
preferable.

Disagree Agreed
1 2 3 4 5 6 7 8 9

4. A mixture of Tk, 10,000 as hotel rent, private car as transport and a foreign food menu is
appreciable.

Disagree Agreed
1 2 3 4 5 6 7 8 9

5. An arrangement of Tk, 10,000 as hotels rent, private car as transport and a foreign food
menu is desirable.

Disagree Agreed
1 2 3 4 5 6 7 8 9

Page : 30
30

6. Combining a rent of Tk, 10000 for hotels, jeep as transportation and a standard food menu
is satisfying.

Disagree Agreed
1 2 3 4 5 6 7 8 9

7. A blend of rent of Tk, 15,000 for hotel, private car as transport and local food menu as food
choice is sufficient

Disagree Agreed
1 2 3 4 5 6 7 8 9

8. A mixture of rent of Tk, 15000 for hotels, rickshaw as transport and standard food menu is
an attractive package.

Disagree Agreed
1 2 3 4 5 6 7 8 9

9. A combination of rent of Tk, 15000 for hotel, jeep as a transport and foreign food is
preferable.

Disagree Agreed
1 2 3 4 5 6 7 8 9

Page : 31
31

Regression

V ariables Enter ed/Re m ovebd

V ariables V ariables
Model Entered Remov ed Method
1
cost of
travel low ,
internation
al ad helps
to build a
brand ,
local
trans portat
ion is not
appropriat
e, slogan
helps to
build
brand, w ell
secured,
inadequat
e f ood
. Enter
f ac ility,
image
ref lec ts
pleasure,
logos &
sy mbol
helps to
build
brand,
nois y
beac h
environme
nt,
inss uf f ic ie
nt
entertainm a
ent f acility

a. A ll requested variables entered.
b. Dependent V ariable: building cox baz ar

Model Sum m ary

Adjusted Std. Error of
Model R R Square R Square the Estimate
1 .608 a .370 .038 1.115
a. Predictors: (Constant), cost of trav el low , international
ad helps to build a brand , loc al transportation is not
appropriate, slogan helps to build brand, w ell sec ured,
inadequate f ood f acility, image ref lects pleas ure, logos
& s ymbol helps to build brand, nois y beach
environment, inss uf f ic ient entertainment f ac ility

Page : 32
32

ANOVAb

Sum of
Model Squares df Mean Square F Sig.
1 Regression 13.862 10 1.386 1.116 .400 a
Residual 23.605 19 1.242
Total 37.467 29
a. Predictors: (Constant), cost of trav el low , international ad helps to build a brand ,
local trans portation is not appropriate, slogan helps to build brand, w ell secured,
inadequate f ood f acility , image ref lec ts pleasure, logos & symbol helps to build
brand, noisy beach environment, inss uf f ic ient entertainment f ac ility
b. Dependent Variable: building c ox bazar

a
Coe fficients

Unstandardiz ed Standardized
Coef f icients Coef f icients
1 (Cons tant) 7.960 2.436 3.267 .004
.249 .185 .287 1.346 .194
brand
-.245 .209 -.279 -1.173 .255
build brand
image ref lec ts pleasure .287 .179 .391 1.606 .125
.065 .199 .080 .325 .749
build a brand
w ell s ecured .054 .180 .078 .301 .767
inadequate f ood f acility .006 .180 .008 .033 .974
inss uf f ic ient
-.344 .228 -.409 -1.510 .148
nois y beach environment -.166 .127 -.341 -1.310 .206
local trans portation is not
.008 .135 .012 .062 .952
appropriate
cost of travel low -.272 .170 -.373 -1.595 .127
a. Dependent V ariable: building c ox bazar

Discriminant

Analysis Case Proce ss ing Sum m ary

Unw eighted Cases N Percent
Valid 30 100.0
Ex cluded Mis sing or out-of -range
0 .0
group codes
At leas t one mis sing
0 .0
disc riminating variable
Both miss ing or
out-of -range group codes
0 .0
and at least one missing
Total 0 .0
Total 30 100.0

Page : 33
33

Group Statis tics

V alid N (lis tw is e)
V is itor of cox's Baz ar Mean Std. Deviation Unw eighted Weighted
Non- f requent visitor slogan helps to build
6.9333 1.38701 15 15.000
brand
6.3333 1.11270 15 15.000
build brand
image ref lec ts pleasure 6.4000 1.54919 15 15.000
7.4667 1.12546 15 15.000
build a brand
w ell s ecured 5.3333 1.54303 15 15.000
inadequate f ood f acility 5.4667 1.55226 15 15.000
inss uf f ic ient
3.4000 1.50238 15 15.000
nois y beach environment 3.2000 1.61245 15 15.000
5.8667 1.55226 15 15.000
appropriate
cost of travel low 5.2667 1.70992 15 15.000
Frequent vis itor slogan helps to build
7.2000 1.26491 15 15.000
brand
7.0000 1.41421 15 15.000
build brand
7.1333 1.68466 15 15.000
build a brand
w ell s ecured 4.1333 1.55226 15 15.000
inss uf f ic ient
2.5333 1.06010 15 15.000
5.1333 1.72654 15 15.000
appropriate
Total slogan helps to build
7.0667 1.31131 30 30.000
brand
6.6667 1.29544 30 30.000
build brand
7.3000 1.41787 30 30.000
build a brand
w ell s ecured 4.7333 1.63861 30 30.000
inss uf f ic ient
2.9667 1.35146 30 30.000
5.5000 1.65571 30 30.000
appropriate

Page : 34
34

Tes ts of Equality of Group M eans

Wilks '
Lambda F df 1 df 2 Sig.
.989 .303 1 28 .587
brand
.932 2.059 1 28 .162
build brand
image ref lec ts pleasure .988 .340 1 28 .564
.986 .406 1 28 .529
build a brand
w ell s ecured .861 4.509 1 28 .043
inadequate f ood f acility .998 .051 1 28 .823
inss uf f ic ient
.894 3.332 1 28 .079
nois y beach environment .771 8.331 1 28 .007
.949 1.496 1 28 .231
appropriate
cost of trav el low .879 3.837 1 28 .060

Pooled Within-Groups Matrices

lo cal
lo gos & in ternational in ssuffic ie nt transportation
slogan help s symbol helps image reflects ad helps to in adequate entertain ment nois y beach is not cost of
to build brand to build brand pleasure build a brand w ell secured food facility facility environment appropria te travel lo w
Correlatio n slogan help s to build
1.000 -.014 -.048 .208 -.053 .144 .203 .026 .090 .041
brand
lo gos & symbol helps to
-.014 1.000 .233 .542 .296 -.214 .086 -.081 -.228 -.232
build brand
image reflects ple asure -.048 .233 1.000 .171 .524 .220 -.215 .376 -.009 .271
in ternational ad helps to
.208 .542 .171 1.000 .071 -.346 .003 -.182 -.005 .041
build a brand
w ell secured -.053 .296 .524 .071 1.000 .121 .194 .179 -.037 .356
in adequate food facility .144 -.214 .220 -.346 .121 1.000 -.384 .486 .064 .180
in ssuffic ie nt
.203 .086 -.215 .003 .194 -.384 1.000 -.361 -.004 -.263
entertain ment facility
nois y beach environment .026 -.081 .376 -.182 .179 .486 -.361 1.000 -.046 .335
lo cal transportatio n is not
.090 -.228 -.009 -.005 -.037 .064 -.004 -.046 1.000 .173
appropria te
cost of travel low .041 -.232 .271 .041 .356 .180 -.263 .335 .173 1.000

Page : 35
35

Analysis 1

Summary of Canonical Discriminant Functions

Eigenvalues

Canonical
Function Eigenvalue % of Varianc e Cumulativ e % Correlation
1 1.589a 100.0 100.0 .783
a. First 1 canonical discriminant f unctions w ere us ed in the
analysis.

Wilks ' Lam bda

Wilks '
Test of Function(s) Lambda Chi-s quare df Sig.
1 .386 21.880 10 .016

Standardize d Canonical Discrim inant Function Coefficie nts

Func tion
1
.162
brand
.954
build brand
image ref lec ts pleasure .245
-.656
build a brand
w ell s ecured -1.030
inadequate f ood f acility -.198
inss uf f ic ient
.126
nois y beach environment .342
-.131
appropriate

Page : 36
36

Structure Matrix

Func tion
1
nois y beach environment .433
w ell s ecured -.318
inss uf f ic ient
-.274
.215
build brand
-.183
appropriate
-.096
build a brand
image ref lec ts pleasure .087
.082
brand
inadequate f ood f acility .034
Pooled w ithin-groups c orrelations betw een discriminating
variables and standardized canonic al disc riminant f unctions
V ariables ordered by absolute s iz e of correlation w ithin func tion.

Functions at Group Ce ntroids

Func tion
Vis itor of cox's Bazar 1
Non- f requent v is itor -1.218
Frequent visitor 1.218
Unstandardized c anonical disc riminant
f unctions evaluated at group means

Classification Statistics

Clas sification Pr oces s ing Sum m ary
Proces sed 30
Ex cluded Mis sing or out-of -range
0
group c odes
At leas t one mis sing
0
Used in Output 30

Prior Probabilities for Groups

Cases Used in Analys is
Vis itor of cox's Bazar Prior Unw eighted Weighted
Non- f requent v isitor .500 15 15.000
Frequent visitor .500 15 15.000
Total 1.000 30 30.000

Page : 37
37

b,c
Clas sification Re s ults

Predicted Group
Membership
Non- f requent Frequent
V is itor of cox's Baz ar visitor visitor Total
Original Count Non- f requent visitor 13 2 15
Frequent vis itor 1 14 15
% Non- f requent visitor 86.7 13.3 100.0
Frequent vis itor 6.7 93.3 100.0
Cross -validateda Count Non- f requent visitor 10 5 15
Frequent vis itor 4 11 15
% Non- f requent visitor 66.7 33.3 100.0
Frequent vis itor 26.7 73.3 100.0
a. Cross v alidation is done only f or those c ases in the analy sis . In cros s validation, eac h
case is class if ied by the f unc tions derived f rom all c as es other than that case.
b. 90.0% of original grouped c ases correctly c lassif ied.
c. 70.0% of cros s-v alidated grouped cas es c orrec tly class if ied.

Page : 38
38

Regression

V ariables Enter ed/Re m ovebd

V ariables V ariables
Model Entered Remov ed Method
1
Medium
price_high
trans port_
low f ood ,
Low price_
Medium
trans port_
low f ood ,
High
price_high
tranport_
high f ood ,
low price_
low
trans port_
high f ood,
High
price_low
trans port_
. Enter
medium
f ood,
Medium
Price_High
Trans port_
High Food,
Medium
Price_
Medium
Trans port_
high Food,
high pric e_
medium
trans port_
high f ood ,
Low price_
High car_
medium
a
f ood

a. A ll requested variables entered.
b. Dependent V ariable: Pref erenc e rating
of going Cox's Bazar

Page : 39
39

Building Cox's Bazar as a Brand

Building Cox's Bazar as a Brand

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