· 2016-12-30 · store format selection (ebo vs mbo) for sportswear and sports accessories in...

ELK ASIA PACIFIC JOURNAL OF MARKETING AND RETAIL MANAGEMENT

ISSN 2349-2317 (Online); DOI: 10.16962/EAPJMRM/issn. 2349-2317/2015; Volume 7 Issue 4 (2016)

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OFFLINE RETAIL ENTRY STRATEGY: A STUDY ON THE FACTORS EFFECTING

STORE FORMAT SELECTION (EBO VS MBO) FOR SPORTSWEAR AND SPORTS

ACCESSORIES IN TIER-III CITY OF INDIA (SHILLONG)

Arnab Banerjee

Assistant Professor, National Institute of

Fashion Technology, Shillong, India

Corresponding Author email:

[email protected]

Nayanika Barman,

National Institute of Fashion Technology,

Shillong

Email: [email protected]

Abhilekh Aggarwal,

National Institute of Fashion Technology,

Shillong


Deep Sagar Verma

Assistant Professor, National Institute of

Fashion Technology, Jodhpur, India


ABSTRACT

Tier 3 cities of India are home to one of the fastest growing socio-economic powers in the world especially in the

fashion retailing sector. The current study investigates the factors that should be considered critical by new entrants

while entering a new Tier-III Indian market. The study carries out 129 successfully structured mall-intercept interviews

in the town of Shillong, Meghalaya in an attempt to understand the SBO and MBO shoppers. The research primarily

relies on Factor Analysis and Multivariate regression to comment on the similarities among the various types of

customers and critical variables for those customers that needs to be referred to by new entrants. Demographic

variables itself do not show any store format preference although discounts do attract the lower income group more

while clear difference is observed among genders when it comes to importance of ambience, and it is more pronounced

for SBO patrons. SBO patrons are more focused while MBO patrons are more into leisure shopping. Price is the most

important predictor of satisfaction especially for MBO shoppers. The market shows three basic segments i.e

experiential, relationship and value shoppers.

Key words: Store Format; EBO vs MBO; Factor Analysis

1. Introduction

Exclusive Brand Outlets (EBOs) is a retail

outlet format where the store only houses

products from a single brand or maker, on the

contrary Multi-Brand Outlet (MBO) is a

retail set up where various brands are stored.

These are two of the most preferred offline

store format, especially for fashion brands.

The retail industry, world over, accounts for

over USD 15 trillion in global revenues

(Global Retail Report, 2013) which is almost 8

http://dx.doi.org/10.16962/EAPJSS/issn.2394-9392/2014

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times the nominal GDP of India (1.842 trillion

USD, 2012). India housing 17% of world

population only accounts for 3.25% of

Worldwide Retail revenues (Technopak

Analysis, 2013). Indian Fashion retail

(including apparel, jewelry/watches and

footwear) accounts for about 15% (8%, 6%

and 1% respectively) of the total retail

revenues in India i.e. approximately USD 73

billion per year (Technopak Analysis, 2013).

After adjusting for purchasing power an

average Indian just spends $88 on apparels,

which is only half of the world average. All of

it implies a tremendous potential in the

Fashion and Retail Sectors of India. While the

above has been well accepted globally, the

question on the correct entry strategy to the

Indian Market, especially the Tier III city

markets, which houses a majority of the

growing Indian Population, remains under

scrutiny. The current paper intends to throw

some light on the variables that needs to be

considered while entering Tier III cities of

India. Here we also try to understand the basis

on which the clientele can be divided, so that a

retailer may have a more focused approach

while deciding on the store format, that is

which format suits what kind of clients.

2. Objective

The objective of the current study is to

understand “As far as store format goes,

between SBOs and MBOs, which format

gives a greater scope for business in Tier III

cities?”

The comparison was made on the following

major aspects:

What demographic Profile is attracted

by each format? Is there any difference

between the two? Can we predict

based on the demographic profile,

what kind of store are they likely to

visit when they intend to buy a

product (purposive).

What are the most important factors

that customers consider while deciding

on the shop they want to visit? Are

they same or different for both the

retail formats?

What factors predict the customers

overall satisfaction levels greatest with

a retail outlet. Are they same or

different for both the retail formats?

3. Literature Review

Retail shopping has been studied from a

number of different perspectives. Messinger

and Narsimhan (1997) studied store choice

behavior and found that utility-maximization

in terms of the number of interdependent

variables are the core factors influencing the

store choice format. Sinha and Banerjee

(2004) found that the evolution of the markets

have a direct relation to store format choice

behavior, while Waterschoot et.al (2008)




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found that it is the shopping situation that

impacts store selection. Marketing policies of

the retailer’s along with certain demographic

characteristics of the shoppers also affect store

choice (Fox et al., 2004). Among some of the

other variables that influences store selection

is availability of time for shopping, the

pressures perceived by the customer in the

store (Iyer, 1989) comparison of prices

(Kolodinsky, 1990) and number of categories

of the retail outlet vs the convenience in terms

of time saving (Messinger and Narsimhan,

1997). Thill and Thomas (2010) found that for

the customers who are sensitive to time and

money, location of the store heavily impacts

the store choice behaviour. Store choice

selection has a cognitive as well as affective

component to it (Sproles and Kendall, 1986).

This implies that consumers have different

styles of store selection for different products

since different products will have different

characteristics. Which in turn means that, their

attitude towards store format selection can not

be the same for different kinds of products?

Hence the demographics of the consumer play

an extremely significant role in store choices

(Kenhove et al., 1999). Thus, decision style

towards different products is very consumer

specific and reflects his mental orientation.

Gender has an important effect on consumer

choices. While males perceive threat from

intimacy females perceive threat from

separation (Prakash and Flores, 1985). There

is a psychological difference between gender

which may determine the extent of

involvement and attitude towards an

advertisement and subsequently attitude

towards the brand (Prakash and Flores,1985).

In terms of environmental factors, men are

more aware while making purchases

(Mohammed Mostafa,2007). In a separate

study (Basu, Sengupta and Guin, 2012) it was

found that males exhibit greater affinity

towards Multi Brand Outlet while females are

more inclined towards Single Brand outlets

and this phenomena has been explained by the

patriarchal society of India where the

shopping behavior are more leisure based

while that of females are more towards

specific needs based.

Age also has been found as a factor that causes

people not to select/ reject a particular retail

format-both online as well as certain physical

ones, especially among the older population

(Mącik, Mącik and Nalewajek, 2013). Age

wise analysis shows distinct choice pattern

and the distinction becomes more spelt with

older people as far as the choice between

single and multi brand outlet is concerned

(Basu, Sengupta and Guin, 2014). The above

45 years age category shows a higher

preference for the single brand store to the

multi-brand option (Basu, Sengupta and Guin,

2014).




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As far as income is concerned Basu, Sengupta

and Guin (2014) found that the monthly

personal income has a great deal more impact

on store choice behavior. Also they found that

< 20,000 per month are the ones with lesser

affinity towards Multi Brand Formats, while

the 20,000 per month and above classes have

higher affinity towards Multi Brand Formats

than their respective SBPs.

In addition to demographics a number of

factors are to be considered to understand

what attracts and causes purchase from a

particular store format while rejecting the

others. The emotional status of a customer

such as pleasure, arousal or dominance (PAD)

subsequently leading to approach or avoidance

behavior can be explained using

environmental psychology which basically

deals with customer interaction with

environmental cues and stimulus (Mehrabian,

Russell and Russell, 1974; Donovan, 1994).

Factors such as store ambience consisting of

lighting, air quality, interior design, store

fragrance open space among others bring

about cognitive, affective, physiological and

behavioral reactions (Heide and Gronhaug,

2006).

Customer service also is a factor that has some

impact on the store format selection as it

determines the overall shopping experience.

After sales services, exchange return facility,

attending customer complaints,

knowledgeable staff was found to be some

major indicators of customer service (Jain and

Bagdare, 2009).

Interestingly through price discounts and

offers, perception of value and interests in

product also increases, although these

increases are no greater for prestigious stores

than that offered by less prestigious store,

which indicates that today’s savvy consumers

have become so price conscious and

knowledgeable about competitive pricing that

the image of the store and price discounts

from the past don’t have much of an effect on

their reference point and hence no effect on

their store selection(Wu, Petroshius and

Newell, 2004). The above also holds for

frequency of price discounts that is, a store

with seldom price discounts don’t hold an

edge over stores that frequently give price

discounts (Wu, Petroshius and Newell, 2004).

Conventionally, marketing places a high level

of importance on nature, type, quality and

variety of the product offered in store for the

success of any store format and in order to

fulfil basic shopping motives the store also

must have wide assortment of products from

preferred brand with latest style and design

(Jain and Bagdare, 2009).

Also the features of a great store include

visual simplicity and transparency including

navigational tools (Burke, 2005). A

framework for considering the various bases




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and methods available for conducting

segmentation studies has been discussed by

Michel and Kamakura (2000).

Based on the above researches and the

mentioned objectives the following hypothesis

statements needs to be tested so as to

understand whether different store formats

attract different types of customers and if so

what are those differentiating factors, for

SBOs vs MBOs

1. Hoa : There is no impact of gender in

store format selection

2. Hob : Age groups do not impact store

format selection

3. Hoc : Employment status does not

impact store format selection

4. Hod: Annual Household income does

not impact store format selection

5. Hoe: Individuals patronizing a

particular format can be discriminated on the

basis of the factors on which they lay

greater importance

6. H0f : Individuals patronizing different

store formats lay greater importance on

differing factors when it comes to their

satisfaction levels

4. Methodology

This is a descriptive research study done in the

city of Shillong, Meghalaya which is

considered to be the fashion capital of India,

across two stores consisting of similar

merchandise that is sport wear with special

weightage to footwear. The stores that have

been selected primarily house similar products

so as to assure there is no major difference in

the clientele on the basis of the nature of the

product. The sampling method basically

followed is non-random sampling. At first a

quota for the various subgroups was pre-fixed

followed by mall intercept method wherein the

various subgroups were interviewed through

structured questionnaires.

There were essentially 3 subgroups formed

based on their patronization of a store and on

whether they were purposive or non

purposive. So the three subgroups that were

formed were Purposive patrons, Purposive non

Patrons, and Browsers. Purposive Patrons are

those who have purchased some product and

do so consistently from one of the two stores

(i.e SBO or MBO). Purposive non-patrons are

those customers who have purchased some

product, but do not patronize any one of the

two stores in particular and hence may

purchase from either one, based on some of

the factors affecting their purchase behavior.

Browsers are the customers who are visiting

the store just to browse through the

merchandise without making any purchase.

The framework of the design can be

understood through the following diagram: (as

suggested by Barman and Aggarwal, 2014)




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The purpose of the classification is to actually

be able to differentiate between the customers

exhibiting varying patronage and understand

what causes their patronage to vary. Also

through the classification we may pin point the

factors which may predict the variation in

patronage and the factors a store should work

on to increase their patronage. (Refer Fig.1 or

2) (Refer Table 1)

Thus the total sample size is 129 across two

store formats and across 3 sub-groups. The

study dealt with understanding the impact of 8

carefully chosen variables on a consumer’s

patronage behavior towards either an SBO

(EBO) or an MBO, so as to accumulate vital

information on what causes format

patronization. Also these 8 variables have

been used to understand, which one of the

variables has a clear impact on the overall

satisfaction level of a consumer while

shopping. The variables have been evaluated

against a 5 point Likert scale, on agreeability

to understand the importance of each variable

on shopping choices (Completely Agree=1

and Completely Disagree=5); and in order to

understand the impact on overall satisfaction,

the variables are evaluated on a 5 point Likert

scale ranging from Extremely Satisfied (1) to

Not at All Satisfied (5)

5. Discussion

Each of the shoppers were asked their degree

of importance of 8 different factors on a Likert

scale indicating how important each of those

factors were in selecting the store for

shopping. The idea was to bottle down a few

types of shoppers and their shopping

preference. Hence a factor analysis was

performed to narrow in on those few

components that bring certain types of

shoppers together. However at first to find the

appropriateness of a factor analysis on the

data, the KMO and Bartlett’s test was

performed: (Refer Table 2)

A value of 0.63 for KMO coupled with sig.

value of 0.000 for Bartlett’s clearly indicates

the appropriateness of Factor Analysis. (Refer

Table 3). The model clearly indicates the

emergence of two dominant components of

importance a consumer prefers. Both these

Components put together depict 45.45% of

variance with the first component accounting

for 29% and second component accounting for

19.32%. (Refer Table 4)

Component 1 depicts underlying variables

such as ambience, brand, assortment,

merchandise and sales staff attention thus all

having an inclination towards leisure

shopping. On the other hand Component 2

has a greater inclination towards utility

shopping with underlying variables such as

price, discount and quality of products.

Attitude Pattern

The attitude of the respondents were analysed

to understand on whether there is any




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significant impact of demographic factors on

store format selection (patronage).

Hoa: There is no impact of gender on store

format selection or patronage as the

Pearson’s Chi Square Coefficient value is

0.237 which has a significance (p) of 0.994.

Hob: Age groups do not impact store format

selection: With a Chi square value of 27.425

and corresponding p value of 0.124 even age

fails to effect store patronage

Hoc: Employment status does not impact store

format selection: Chi square value for the test

between qualification and patronage shows p

value of 0.241 and hence here to there is no

statistically valid relation between the two.

Hod: Annual Household income did not

impact store format selection: For the annual

household income vs patronage cross tab

there appears to be a relationship at 10%

significance level as the p value turns out to

0.078. (Refer Table 5)

(1= Extremely Important; 2=Important; 3=

Neutral; 4=Not Important; 5= Not at all

Important)

In the above table, the comparison depicts

that there is marginal difference at 10%

significance level between SBO and MBO

Purposive Patrons for the variable price,

which means that MBO purposive patrons

give lesser importance to price than SBO

purposive patrons. This could be explained

by the assumption that people visiting MBO

are more into leisure shopping and those into

SBO are more into focused shopping and thus

are price sensitive.

Among the 8 variables under study only

ambience seems to be the factor that has

differential importance between the genders,

with the females showing a much

significantly greater importance at 10%

significant towards it with p equal to 0.062 as

depicted in the below table. This could

probably draw from the idea that peripheral

cues are more important for female shoppers.

This can indicate that stores which are mainly

designed for male visitors may focus a little

lesser on ambience than the once that are

predominantly designed for the females.

(Refer Table 6)

The above becomes more pronounced when

only the SBO purposive patrons are studied in

solitude. With a sig. value of 0.045 for

ambience, the females patronizing SBOs give

even greater importance to ambience than the

males patronizing SBOs. It thus is indicative

of the fact that for SBOs catering

predominantly to females, ambience becomes

all the more important than those serving to

males only. In addition to ambience, for SBO

purposive patrons, even quality of product

shows significantly (p= 0.067) different

importance among the genders with the




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females giving significantly greater

importance to quality.

When it comes to discount or assortment,

there seems to be heterogeneity in the

variances among the various groups in terms

of patronage with the Levene statistic of 3.064

and 8.297 corresponding to significance of

0.019 and 0.000. This indicates that ANOVA

on the two parameters or variables of discount

and assortment across the patronage group

will not yield any useful results.

The one factor that reveals a great deal of

importance when it comes to the difference

between the various patronage groups is price

with the F value of 4.470 (p=.002).On

performance of a post hoc analysis by means

of a l.s.d, clear difference is observed by

between the Purposive Patrons of MBO and

Purposive patrons of SBO as well as

Purposive non-patrons of SBO. The means

plot for the same depicts that purposive non-

patrons SBO give least importance to price

while the purposive patrons of SBOs give the

greatest importance to price. This is an

interesting finding, considering the data was

collected from sportswear/ goods shops for

both SBO and MBO. This may indicate that

purposive patrons of SBOs may be more value

sensitive and feel that SBOs give them a

greater value for money. This also backs up

the idea that MBO patrons are more into

leisure shopping than SBO patrons who are

focused shoppers. Comparing the variables for

the SBO patron vs the MBO patron, there is

significant difference (p= 0.055) at 10%

significance level, between the importance

given to price for the two groups with a t value

of 1.957.

If we compare the various variables such as

price, discount etc. across income groups, it

appears that when it comes to price various

households belonging to various income

groups give differential importance to it,

however, if we look closer, Levene Test

indicates that there is far too much difference

among the variances of the various groups and

hence the observed difference across groups

are not reliable when it comes to price.

However, when it comes to discount, there

seems to be a clear difference across groups i.e

not all groups behave in the same way when it

comes to discounts. Means plot graph very

clearly depicts that people in the income

bracket of 1 to 2.5 lacs are a lot more sensitive

to discounts while its importance consistently

comes down among the higher income groups.

The above finding is a clear indicator that

higher prices with discount labels such as

“Sale” attracts the lower income strata a whole

lot more than merely giving low prices.

The Discriminating Variable

In order to understand which variables are

more important in discriminating the SBO and




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the MBO patrons, a Discriminant analysis has

been performed. But before reading into the

Discriminant analysis we may look at the

variables such as Box’s M of 40.33 with p=

0.534 which tells us that the assumption of

covariance across groups is not true and hence

there is no problem with going ahead with the

Discriminant. The Discriminant analysis thus

performed gives a Discriminant function with

an Eigen value of 0.150 (p= 0.361) which

corresponds to a Canonical Correlation value

of 0.361, which means the that a meager 13%

of the variance of the dependent variable i.e

store patronage is explained by the given

model. Hence it won’t make much sense to

delve any deeper in the analysis. This is

supported by an insignificant Wilk’s Lambda

of 0.870 corresponding to significance of

0.398. Although the standardized canonical

Discriminant function coefficient as well as

the structure matrix point heavily towards

price as being the one determining variable for

patronage, along with ambience, we should

not rely on it as suggested by the previous

statistic of Eigen value, Wilk’s Lambda and

Cannonical Coefficient of the Discriminant

function. This could be an outcome of a

relatively small sample size.

The above finding about the data is further

supported by the classification matrix where

the model completely fails at predicting the

cross validated cases and in fact ends up

predicted with great deal of inaccuracy as the

proportion of off diagonal items exceed the

diagonal items, in spite of taking prior

probability measures that corresponds to

60.6% and 39.4% for MBO and SBO

purposive patrons respectively.

The Satisfaction factors

After a pilot survey, 8 variables were short

listed to gauge into the satisfaction levels of

the clients. Factor analysis was performed on

the eight variables to understand what is the

underlying factor binding the variables

together and also which of the variables play a

more important role in determining the

satisfaction levels of the customer. (Refer

Table 7)

KMO test gave a result of 0.705 and the

Bartlett’s test of sphericity gave a result of

180.128 with a significance of 0.000. Both the

results assure that running the factor analysis

will give good results. On extraction only 2

components of the eight gave an Eigen value

of greater than 1 and the corresponding

proportion of variance explained by these two

factors are 31.6% and 19.33% respectively. In

order to perform the Factor analysis to find out

the minimum number variables (dimension

reduction), Principal Component analysis was

used with Varimax rotation. (Refer Table 8)

Price, Quality, discount and assortment give

the highest factor loadings of 0.519, 0.660,

0.742 and 0.750 respectively. Hence they may

be assumed to be the more important factors.




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On running a regression with all the eight

factors we get an adjusted R2 value of 12.5%;

that is all the eight variables put together just

explains 12.5% of the variance in the

independent variable. While if we consider

just the four above mentioned variables we are

still able to predict 11.6% of the variance in

the dependent variable. Although the model

does not give a great result, there is not much

predictability loss when we use just the four

variables instead of all eight. (Refer Table 9)

Out the four mentioned variables price has the

greatest Beta coefficient indicating that it has

the greatest impact on overall satisfaction

levels. However price also reflects a tolerance

towards collinerity of 87.9% which means that

about 12% of variance of price is impacted by

other variables, which is indicative of the fact

that price is not a completely independent

factor and hence retailers need to be careful

when it comes to deciding about the pricing as

it may lead to perceptions about other factors

such as quality (colinearity of -0.146).

However when regression for satisfaction is

run for the various patronage separately, the

model gives much diverse results. (Refer

Table 10) For MBOs the regression model

gives much better result than for SBO or even

all the variables put together. The model

explains 27.3% variance in the overall

satisfaction of the MBO patrons while the

variance explained by the same models for the

SBO patrons is insignificant. On running a

factor analysis in order to reduce the number

of predictors it is found that Assortment, price

and ambience itself accounts for 26.7% of the

variance in the overall satisfaction of the MBO

clientele and this is a significant result. Within

this model price with a Beta coefficient of

0.573 is the most significant predictor. No

such results are eminent for the SBO clientele.

This can mean that for the MBO clientele,

pricing is of great importance as they use

multiple reference point to get a good deal,

and often feel satisfied when they feel that

they have got the product at a reasonable

price. Ambience and assortment are the two

other factors that define the overall

satisfaction of the MBO patrons thereby

reconfirming the leisure shopping nature of

the patrons. Nothing concrete can be predicted

about the SBO patrons from the above model.

The above finding may appear to counter one

of the previous findings where the mean

importance of price for purposive patrons

MBO is much lower (1.85) than that of

purposive patrons SBO (1.54). However there

is a major difference between the two, as the

first question is on direct importance given to

each factor while store selection, while the

second question finds out which factor causes

greatest satisfaction and implies that getting a

good deal in terms of value and hence price in

an MBO is much more satisfying. (Refer

Table 11)




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Factor analysis using Principal Axis Factoring

to find out the underlying parameters that are

common to each of the factor, indicates that

the people can be divided into three clear

categories that is, the people for whom

shopping is an experience and hence variables

like ambience, latest merchandise, brands and

assortment become extremely important. The

second category pays great importance to sales

staff attention and hence can be categorized as

relationship shoppers, while the third group

gives great importance to the value

proposition via price, discounts and quality of

product. This implies that retailers should

clearly be able to position its store for any one

or more of the above categories.

Conclusion

The study tries to focus on the differences

between the shoppers that patronize SBOs and

those who patronize MBOs in a Tier 3 Indian

town that is considered to be fashionably

aware. Demographic variables such as age,

gender, household income and employment

status does not impact the overall store format

selection however females tend to give greater

weightage to ambience and especially so in

SBOs. Discount impacts the store selection for

the lower income group, while price is not

considered that important when it comes to

store selection. But, when it comes to

satisfaction from purchases, price is the single

most important factor, and for MBO purposive

patrons it is the most important predictor.

Thus customer satisfaction which is important

for customer repeat purchase is immensely

impacted by correct pricing so one does not

feel cheated, more so for MBO purposive

patrons, since they have more choices and

more reference points. SBO patrons are more

focused shoppers while MBO patrons are

more into leisure shopping. There is no one

variable that clearly discriminates between the

SBO and MBO patrons when it comes to store

selection. Also, the clientele could be

segmented into 3 primary segments that is,

experiential, relationship and value shoppers.

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…………………………………………………………………………………………………………

LIST OF FIGURS:

Fig 1: The theoretical framework based on which the classification of the three groups is made is

explained in the diagram below

Step -1

Step - 1

CATEGORIZE the sample population into three

groups.

BROWSERS

PURPOSIVE NON-PATRON

PURPOSIVE PATRON

Step -2

ANALYSIS of these three groups on the basis

of various different factors that might

influence the Customer towards the store

Step – 3

Comparison of the two different store

formats from the results so obtained




…………………………………………………………………………………………………………

Fig 2: Classification of Types of Consumers that were interviewed

:

PATRONAGE PURPOSE

PATRON NON- PATRON PURPOSIVE NON-PURPOSIVE

PURPOSIVE

PATRON

(Revenue

Generating

Customers)

PURPOSIVE

NON-

PATRON

BROWSERS BROWSERS

For Both SINGLE/ MULTI BRAND OUTLETS




…………………………………………………………………………………………………………

LIST OF TABLES:

The sample Profile is explained in the following table

Table 1: Profiling of the Interviewees

:Profiling Base Category No.s Percentage (%)

Consumer Type

Purposive Patron 66 51.2

Purposive Non-Patron 42 32.6

Browser 21 16.3

Store Format SBO 40 31.0

MBO 89 69.0

Gender Male 70 54.3

Femlale 59 45.7

Age Group

Below 18 7 5.4

18-25 39 30.2

26-30 40 31.0

31-35 30 23.3

36-40 12 9.3

41-45 1 0.8

Annual Household Income

Less than 1 lac 1 0.8

1 to less than 2.5 lacs 20 15.5

2.5 lacs to less than 4 lac 59 45.7

4 lacs to less than 5.5

lacs

44 34.1

5.5 lacs and above 5 3.9




…………………………………………………………………………………………………………

Table 2: KMO Bartlett’s Table

Kaiser-Meyer-Olkin Measure of Sampling

Adequacy. .643

Bartlett's Test of

Sphericity

Approx. Chi-Square 123.364

df 28

Sig. .000

Table 3: Factor Analysis Model

Componen

t

Extraction Sums of Squared Loadings Rotation Sums of Squared Loadings

Total

% of

Variance

Cumulative

% Total

% of

Variance

Cumulative

%

1 2.333 29.161 29.161 2.090 26.129 26.129

2 1.303 16.292 45.453 1.546 19.325 45.453




…………………………………………………………………………………………………………

Table 4: The Factor Model

Rotated Component Matrixa

Component

1 2

Chronbach’s Alpha 64.7% 44.6%

While selecting a store, I place huge importance on price - .677

Ambience is a deciding factor while I select a store .707 -

While selecting a store, I place huge importance on brand .630 -

Sales staff attention is of utmost significance when i select a store .410 -

Discount is a major factor, when i am selecting a store. - .759

During store selection, I place much importance on the quality of

products. - .582

Assortment of the product in the store is the key factor when select a

store. .676 -

While selecting a store, I place huge importance on latest merchandise. .688 -

Extraction Method: Principal Component Analysis. Rotation Method: Varimax with Kaiser

Normalization.

Rotation converged in 3 iterations




…………………………………………………………………………………………………………

Table 5: T-test for individual variables to compare the means for MBO purposive patrons vs SBO

purposive patrons

Factors Avg. Score of MBO

Purposive Patrons

(40)

Avg. Score of SBO

Purposive Patrons(26)

Sig. (2-tailed) of T

Price 1.85 1.54 .055

Ambience 2.40 2.08 .165

Brand 1.70 1.65 .818

Sales Staff

Attention

2.28 2.35 .745

Discount 1.63 1.58 .822

Quality 1.53 1.50 .878

Assortment 1.90 2.08 .453

Merchandise 2.05 1.88 .480

Table 6: Importance of Ambience in store format selection for males vs females

q1) gender N Mean

Std.

Deviation Std. Error Mean

Q7)b) Ambience is a

deciding factor while I

select a store

Male 70 2.40 .907 .108

Female 59 2.10 .885 .115

Table 7: KMO and Bartlett’s test

KMO and Bartlett's Test

Kaiser-Meyer-Olkin Measure of Sampling

Adequacy. .705

Bartlett's Test of

Sphericity

Approx. Chi-Square 180.128

df 28

Sig. .000




…………………………………………………………………………………………………………

Table 8: Rotated Factor Matrix to reduce the number of variables

Component

1 2

PRICE .450 .519

DISCOUNT .065 .742

AMBIENCE .437 -.631

QUALITY .660 .308

BRAND .630 .012

SALES STAFF ATTENTION .535 -.059

ASSORTMENT .750 -.118

LATEST MERCHANDISE .629 -.523

Extraction Method: Principal Component Analysis.

Rotation Method: Varimax with Kaiser Normalization.a

a. Rotation converged in 3 iterations.

Table 9: Regression Model for overall satisfaction with individual satisfaction factors

Model Summary

Model R R Square Adjusted R Square

Std. Error of the

Estimate

1 .379a .144 .116 .551

a. Predictors: (Constant), Q9)D) QUALITY, Q9) b) DISCOUNT, Q9)a) PRICE, Q9)G) ASSORTMENT

Table 10: Regression models for SBO and MBO patrons separately

Model Summarya

Q6)

Patronage R R Square Adjusted R Square

Std. Error of the

Estimate

1 .650b .422 .273 .480

2 .530c .281 -.057 .543

Predictors: (Constant), Q9)H) LATEST MERCHANDISE, Q9)a) PRICE, Q9)E) BRAND, Q9) b) DISCOUNT,

Q9)F)SALES STAFF ATTENTION, Q9)D) QUALITY, Q9)C) AMBIENCE, Q9)G) ASSORTMENT




…………………………………………………………………………………………………………

Table 11: Rotated factor matrix to find the underlying parameters

The Underlying parameters

Rotated Factor Matrixa

Factor

Experiential

Shoppers

Relationship

Shoppers

Value

Shoppers

While selecting a store, I place huge importance on

price .203 -.054 .707

Ambience is a deciding factor while I select a

store .611 .055 -.024


brand .547 .035 .135

Sales staff attention is of utmost significance

when i select a store .196 .890 .106

Discount is a major factor, when i am selecting a

store. -.049 .107 .432

During store selection, I place much importance on

the quality of products. .175 .242 .288

Assortment of the product in the store is the key

factor when select a store. .481 .171 .044


latest merchandise. .562 .129 .115

Extraction Method: Principal Axis Factoring.

Rotation Method: Varimax with Kaiser Normalization.

Rotation converged in 5 iterations.


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