COMPARITIVE ASSESSMENT OF TRIVENI SUPERMARKET,...

Comparative Assessment of Triveni Supermarket, Margin – free Markets and Private…..

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7.1 Measures 7.2 Validity and Reliability of Data 7.3 Retail factor variables considered to compare Triveni

Supermarkets with Margin-Free Markets and other Private Supermarkets

7.4 Factor Analysis Using the Selected Retail Variables of Private Supermarkets

7.5 Factor Analysis Using the Selected Retail Variables of Triveni Supermarkets

7.6 Factor Analysis Using the Selected Retail Variables of Margin- Free Supermarkets

7.7 Comparative Assessment of Triveni Supermarkets, Margin- Free Markets and Private Supermarkets Using Selected Retail Factors

7.8 Statistical Tests for Comparing the Outlets

Consumer goods retailing has become a highly competitive form of

business as many players offer the same categories of products in the same

areas. At the same time, because of a number of reasons, the retailing of

consumer goods has been an attractive sector of business. As a result, different

types of players have established ventures in almost all places of the country

with the objective of making maximum returns. Large scale retail outlets like

supermarkets or hypermarkets or value chain stores or margin free consumer

stores offer products to the public in almost all towns of Kerala and in rural

areas. Retailing is a customer -driven marketing strategy comprising the art

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nt

en

ts

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and science of choosing target markets and building profitable relations with

customers. To design a winning marketing strategy, the marketing manager

must answer two important questions: what customers to serve (what is our

target market)? And how can we serve these customers best (what is our value

proposition?)? (Philip Kotler. 2008)1.

Supermarkets offer numerous consumer products under one roof and

provide the convenience of self service. They normally display a wide range of

packed products in racks and the space layout may suit the free movement of

customers from rack to rack to select their preferred brands. They incepted the

concept of self service from full service where the customers enjoy the

advantages of seeing, feeling, touch and assurance (Prathibha & Sharma.

2011)2. Modern consumers seek improved availability of goods and quality,

reasonable price, pleasant shopping experience, services, etc. All these retail

variables are inevitable for the long run success of a large consumer store. The

present study intends to compare the Triveni Supermarkets, Margin- Free

Markets and Private Supermarkets on the basis of the responses of common

customers of these three types of outlets on selected retail variables by the factor

analysis method. Triveni Supermarkets are in the cooperative sector, managed

and controlled by the CONSUMERFED. Margin- Free Supermarkets are retail

outlets supplying consumer goods and work under a charitable society known as

the Consumer Protection and Guidance Society. Both types of retail outlets are

found in all parts of the state of Kerala. Likewise, hundreds of single and chain

supermarkets exist in all parts of Kerala managed by individuals and corporate

entities. All these three types of consumer stores compete with one another in

the supply of food grains and other consumer goods. In the light of stiff

competition between these types of stores success depends upon the consumers’


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response to the stores based on their perception on pricing strategy, quality of

goods, service attractiveness, facilities offered, etc.

7.1 Measures

For measuring the opinion of customers for the purpose of comparing

the outlets such as Triveni Supermarkets, Private Supermarkets and Margin-

Free Markets 18 variables are used. Some of the attributes in the study were

selected taking in to account the Asian Shoppers Pyramid as given in the

research conducted by IBM for Coca Cola Research Council. According to this

pyramid, the attributes selected varied from basic to value attributes. The

attributes comprise price, quality, choice, location, offers and discounts, service

level and store size. Other attributes applicable to supermarkets such as

attractiveness in display, space layout in the store, quality of packing, complaint

redress mechanism, speed in billing, cleanliness in the shop, parking facilities

and the availability of fresh stock are also included in the present study.

In this part of the study, the consumers of Triveni Supermarkets who use

Margin-Free Markets and other Private Supermarkets (outlets offering food

and grocery) were asked to rate their perception on eighteen retail variables

applicable to these three types of outlets separately and these data were used to

compare the stores. The seven point scale (7 means ‘highest’ and 1 means

‘lowest’)-one of the most commonly used scales among marketing researchers

to assess psychographic variables-is used for collecting responses from

selected common customers (Hair Jr.2004)3. Statistical tools ‘Factor Analysis’,

One way ANOVA and ‘Repeated Measure MANOVA’ are used in the study.

Exploratory Factor Analysis Method is used to reduce the large number

of variables in to classes for drawing meaningful conclusions. Repeated

Measure MANOVA is a suitable tool as the data is collected from Triveni

customers who also frequently use other types of retail outlets. The validity

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and reliability of the scales are assessed to identify and preclude any unreliable

and/or invalid measure that could arise from using multiple items

(Field.A.2000)4. Initially, a content validity was assessed to ensure that the

instrument contained the representative sample of the universe of the subject

matter of interest (J.Stevans.1998)5. This was done by examining the questions

for clarity and completeness using feedback from experts.

7.2 Validity and Reliability of Data

Before assessing construct validity, correlation matrix of variables was

drawn to check for multi- colinearity, communalities of all items individually

to understand the common variance within items. Discriminant validity was

assessed through principal component analysis using Verimax rotation, and

factor correlation matrix (Cheng. 2001)6. Only those variables were retained

which had Eigen values greater than unity and only those items were retained

which had a factor loading greater than 0.475. Convergent validity was

assessed through factor loading and item-total correlations (Cooper &

Schindler 2006)7. For factor loading, the commonly accepted value is 0.3 and

above (Hair Jr. 2004)8. This was done to determine the number of factors

needed for the study. Reliability was assessed through Cronbach’s alpha,

alpha if item deleted, and item –correlations are used to assess internal

consistency. All the tests used in this research were done using the statistical

programme ‘Statistical Package for Social Sciences’ (SPSS- 17).

7.3 Retail factor variables considered to compare Triveni Supermarkets with Margin-Free Markets and other Private Supermarkets

Triveni Stores, Margin- Free Markets and Private Supermarkets offer the

same type of consumer goods to the public and their outlets are located in the


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same areas and so they have to compete with one another. All these outlets

claim to sell quality consumer goods at low prices. Essential goods used for

food preparation like food grains, powdered grains and other powders and oil ,

sugar, biscuits, nuts and other packed bakery items, cleaning items, personal

and home care products and other staple items are commonly offered by

these three outlets. The price level and quality of these items as perceived by

the common customers, service attractions, promotions, space layout,

availability of brands, etc offered by the three outlets are considered for the

study. The pilot study has revealed that 18 variables are important in

determining the competency of a consumer retail outlet to satisfy their

customers. The presumed retail variables used to collect data in the study are

18 in number. Common customers of these three types of outlets are asked to

rate each variable applicable to these outlets in the light of their perception.

These eighteen variables are factored in to six factor variables by using Factor

Analysis Method with the help of SPSS. Then the mean scores of the

individual variables as well as factor variables (cumulated mean scores of

variables included in the factor) are computed. The mean scores of factor

variables so computed are used in the comparison of Triveni Stores with

Margin-Free Markets and other Private Supermarkets in Kerala.

Variables used in the study to compare the types of consumer stores are:

1) Price of food and groceries

2) Quality of food and groceries

3) Price of convenience goods

4) Quality of convenience goods

5) Customer personal care

6) Availability of fresh stock

7) Availability of brands

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8) Location advantages

9) Attractiveness in display

10) Space layout in store

11) Quality of Packing

12) Parking facilities.

13) Complaint redress mechanism

14) Speediness in billing

15) Discount available

16) Cleanliness in the shop

17) Facilities such as toilet, water ,seating facilities, etc

18) Working hours

Data about the above mentioned eighteen retail variables are collected at

the seven point scale for comparing Triveni Supermarkets with Margin-Free

Markets and other Private Supermarkets.

7.4 Factor Analysis Using the Selected Retail Variables of Private Supermarkets Private Supermarkets are retail outlets offering consumer goods and they

claim that they offer quality goods at lowest prices. In the study, they are

perceived to a type of consumer stores and do not mean a particular private

supermarket. Customers selected (Respondents) for collecting sample data

were common customers of the Private Supermarkets, Margin- Free Markets

and Triveni Supermarkets. The sampled customers claimed that they use the

three types of consumer outlets simultaneously for purchasing essential and

household items. The Exploratory Factor Analysis technique is used in the

study to reduce the number of variables for assessing the customer rating about

the three types of stores. Factor Analysis is a statistical technique designed to


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represent a wide range of variables on a smaller number of dimensions. For

using this technique, Factor Correlation Matrix, Communalities Table, table

provides Eigen Values and Rotated Component Matrix are used. It has been

ensured from correlation matrix that eighteen retail variables are not correlated

each other.

Factor Analysis is a data reduction technique which identifies a small

number of factors that explain most of the variance observed in a large set of

variables. This is generally employed to generate hypothesis regarding causal

mechanism for a problem. Using the correlation structure, factor analysis is

employed to identify underlying hypothecated variables called factors that

explain the correlation pattern explained.

The KMO and Bartlett's Test is used for knowing the suitability of the

collected data for factor analysis.

Table 7.1 KMO and Bartlett's Test

Kaiser-Meyer-Olkin Measure of Sampling Adequacy. .710

Approx. Chi-Square 2920.730

Df 190

Bartlett's Test of Sphericity

Sig. .000 Source: Primary data.

Table 7.1 shows the results of KMO and Bartlett's Test. The Kaiser-

Mayer-Olkin Measure of Sampling Adequacy is a statistic that indicates the

proportion of variance in the variables that might be caused by underlying

factors. High values (close to 1.00) generally indicate that generally factor

analysis is useful with the data set and the values; less than 0.50 indicates that

the results of the factor analysis are not very apt in the context. The result of

KMO and Bartlett's Test indicates that the factor analysis is appropriate for

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the present data base, as the KMO value is.770 (between 0.50 and 1.00) and

statistical results of Bartlett’s test of sphericity are significant (where p=0.000

with df 190) for the factor correlations with in a correlation matrix. Small

values less than 0.05 of the significance level indicate that a factor analysis is

useful to a particular set of data set. From the KMO and Bartlett's Test, we can

observe that the significance level is 0.000 <0.05, indicating the data is

appropriate to conduct a factor analysis for the study.

The correlation matrix of the variables extracted is given below.

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The correlation matrix would help to assess the discriminant validity of

the exploratory tendency of the variables. The correlation matrix shown in

Table 7.2 depicts that no significant correlation exists between any of the

variables. The correlation co-efficient were far below the 0.85 cut off mark,

(Gerberg & Anderson.2008) 9 which was used as rule of thumb. This implies

that there is no conceptual overlapping among the variables, and discriminant

validity of the sub-scales is proved once again.

The output from analysis is viewed after validating the variables using the communalities.

Table 7.3 Communalities extracted for the retail variables of Private Supermarkets

Variables Initial Extraction Price of food and groceries 1.000 .791 Quality of food and groceries 1.000 .769 Price of convenience goods 1.000 .760 Quality of convenience goods 1.000 .768 Customer personal care 1.000 .523 Availability of fresh stock 1.000 .648 Availability of brands 1.000 .552 Location advantages 1.000 .567 Attractiveness in display 1.000 .606 Space layout in store 1.000 .616 Quality of Packing 1.000 .637 Parking facilities. 1.000 .675 Complaint redress mechanism 1.000 .482 Speediness in billing 1.000 .484 Discount available 1.000 .807 Cleanliness in shop 1.000 .636 Facilities such as toilet, water, seating facilities 1.000 .680 Working hours 1.000 .632 Extraction Method: Principal Component Analysis.

Source: Primary data


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Since all extraction communalities are fairly large (>0.482) it is supposed

to be a good set of variables. The principal component extraction followed by

varimax rotation are reported in the following table.

Table 7.4 Total Variance Explained for retail variables of Private Supermarkets

Initial Eigen values Extraction Sums of Squared Loadings

Rotation Sums of Squared Loadings

Component

Total

% o

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Tot

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% o

f V

aria

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C

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e %

Tot

al

% o

f V

aria

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C

umul

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e %

1 5.371 29.842 29.842 5.371 29.842 29.842 2.680 14.888 14.888 2 1.601 8.893 38.735 1.601 8.893 38.735 2.522 14.012 28.900 3 1.395 7.750 46.485 1.395 7.750 46.485 1.992 11.068 39.968 4 1.185 6.586 53.071 1.185 6.586 53.071 1.700 9.446 49.414 5 1.065 5.915 58.986 1.065 5.915 58.986 1.634 9.081 58.494 6 1.015 5.637 64.624 1.015 5.637 64.624 1.103 6.130 64.624 7 .880 4.888 69.512 8 .725 4.027 73.539 9 .698 3.875 77.415

10 .590 3.276 80.691 11 .537 2.985 83.675 12 .523 2.905 86.580 13 .456 2.534 89.114 14 .431 2.394 91.508 15 .413 2.293 93.801 16 .401 2.225 96.026 17 .385 2.140 98.167 18 .330 1.833 100.000

Extraction Method: Principal Component Analysis, Rotation method: Vermax with Kaiser Normalisation; N = 432 Source: Primary data

It is a general practice in factor analysis to consider as many factors as

there are Eigen values greater than1 (Eigen Values are variances to the factors).

Using this procedure, six factors are found dominant which cumulatively

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account for about 64.624 per cent of explanation of total variance. Therefore,

eighteen variables will have to be classified in to six factor variables, which

are enough to explain all these variables. The associated rotated component

matrix is given below:

Table 7.5 Rotated Component Matrix for the retail variables of Private Supermarkets.

Variables Component 1 2 3 4 5 6 Price of food and groceries .100 .094 .075 .124 .867 .004

Quality of food and groceries .135 .130 .152 .821 .188 -.029

Price of convenience goods in .137 .096 .092 .213 .824 -.008

Quality of convenience goods .128 .124 .136 .827 .173 .062

Customer personal care .017 .709 .084 .067 .059 .068

Availability of fresh stock .075 .718 -.049 .340 -.047 -.084

Availability of brands .250 .656 .125 .029 .179 -.106

Location advantages .347 .636 .175 -.027 .096 -.024

Attractiveness in display .503 .541 .231 .055 .030 .063

Space layout in store .642 .379 .211 .086 .049 .072

Quality of Packing .761 .177 .083 .133 .030 -.030

Parking facilities .780 .018 .164 .047 .171 .089

Complaint redress mechanism .485 .153 -.063 .080 .142 -.439

Speediness in billing .475 .209 .296 .232 .071 -.261

Discount available .075 .031 -.084 .057 .047 .888

Cleanliness in shop .257 .032 .723 .209 .047 .000

Facilities such as toilet, water .206 .047 .779 .143 -.004 -.090

Working hours -.008 .283 .722 -.034 .172 .008 Extraction Method: Principal Component Analysis. Verimax Rotation Source: Primary data

The rotated component matrix in table 7.5 shows that there were no

cross- factor loadings of the variables (above 0.475 absolute value of

loading).This proves the discriminant validity of the sub-scales.


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From this matrix using the factor loadings, it is seen that the first factor

is loaded with five variables i.e. Space layout, Quality of packing, Complaint

redressel, Parking facilities and Speediness in billing. Most of the variables in

this group are more or less related with self service and service provided by

the outlets and the factor is named ‘Service’.

The second factor loading shows another five variables can be grouped

as another factor. They are Customer Personal care, Availability of fresh stock,

Availability of various brands, Location advantages and Attractiveness in

display. This factor is named ‘Availability and Ambience’.

As per the Rotated Component Matrix, another three variables show the

maximum coefficient values are Cleanliness in the shop, Facilities such as

drinking water, seating arrangements, etc. and Working hours of the outlet.

These retail variables determine the facilities in a store and so the factor is

named ‘Facilities’.

The fourth factor variable includes two variables, viz. Quality of food

and grocery and Quality of other convenience goods. The factor is named

‘Quality of Goods’

As per factor loading, the fifth factor is named ‘Price of Goods’ as it

include two variables- 1) Price of food and grocery and 2) Price of other

convenience goods.

Discount is the only variable included in the sixth factor named ‘Discount’.

Hence, Eighteen selected retail variables used for comparing the three

types of consumer outlets are reduced to six retail factors such as: 1) Service,

2) Availability and Ambience, 3) Facilities, 4) Quality of Goods,5) Price of

Goods and 6) Discount. Hereafter, these generated factor variables are used for

comparative analysis of the three types of outlets.

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Table 7.6 List of Retail Variables Loaded in Retail Factors

Name of Retail Factor Variables used for comparing Triveni Supermarkets

with Margin-Free Markets and Private Supermarkets

Retail Variables

1. Service 1. Space layout in store

2. Quality of Packing

3. Parking facilities

4. Complaint redress mechanism

5. Speediness in billing

2. Availability and Ambience 1. Customer personal care

2. Availability of fresh stock

3. Availability of brands

4. Location advantages

5 Attractiveness in display

3. Facilities 1. Cleanliness in shop

2. Facilities such as toilet, water

3. Working hours

4. Quality of Goods 1. Quality of food and grocery

2. Quality of other convenience goods

5. Price of Goods 1. Price of food and groceries

2. Price of other convenience goods.

6. Discount 1. Discount Source: Primary data

The factor variables

The set of variables generated through factor analysis are named 1. Service, 2.

Availability and Ambience, 3. Facilities, 4. Quality of Goods, 5. Price of Goods

and 6. Discount. Hereinafter, they will be referred to as factor variables.

The following table shows the factor variables, number of variables

included in factor variables, mean value of variables and standard deviation of

factor variables of Private Supermarkets.


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Table 7.7 Descriptive of Retail Factor Variables of Private Supermarkets

Sl.No. Factor Variables No. of

variables included

Mean scores of

factor variables

Comparative mean of factors

Standard deviation of factor variable

1 Service 5 25.42 5.08 2.73 2 Availability and

Ambience 5 25.71 5.14 2.68

3 Facilities 3 15.63 5.21 1.93 4 Quality of goods 2 11.04 5.52 1.40 5 Price of goods 2 12.01 6.01 1.12 6 Discount 1 2.65 2.65 0.92


As per the table 7.7, the mean scores of the retail factor variable ‘Price

of Goods’ in private supermarket is 12.01 and the ‘Quality of Goods’ is 11.04.

It means that private supermarkets charge high prices to their products and the

quality is also high. Though the price and quality match, the price is

comparatively high. As per the rating of customers, the discount offered by the

supermarkets is low as its mean score is 2.65.

The parametric test, one way ANOVA (F- test) is attempted to test whether

any significant variations exist in the mean values of retail factors of private

supermarkets among the central, southern and northern regions of Kerala. One way

ANOVA is most appropriate when dealing with categorical independent variables

and continuous (interval) dependent variables (Norman& Streiner. 1994)10.

Hypothesis

H0: There is no difference in the means of retail factor variables of

Private Supermarkets among the regions.

H1: There is difference in the means of retail factor variables of

Private Supermarkets among the regions.

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For validating the hypothesis one way ANOVA is used. Table 7.8 shows

F-Test having Sum of Squares, df, Mean Squares, F values and Significant

levels of all the six variables considered.

Table 7.8 ANOVA table : Region-Wise Variations of Retail Factor Variables of Private Supermarkets

Factors Sum of Squares df Mean

Square F Sig.

Between Groups 120.212 2 60.106 8.309 .000 Within Groups 3060.032 423 7.234

SERVICE

Total 3180.244 425 Between Groups 118.487 2 59.243 8.513 .000 Within Groups 2943.835 423 6.959

AVAILABILITY AND AMBIENCE


FACILITIES


QUALITY OF GOODS


PRICE OF GOODS

Total 528.941 425 Between Groups 5.373 2 2.686 3.146 .044 Within Groups 361.209 423 .854

DISCOUNT

Total 366.582 425 Source: Primary data

As seen from the ANOVA table, in all six factor variables the null

hypothesis is rejected since p < .05. It is therefore, proved that there exists

significant difference in the level of performance perceived by customers

while considering all retail factor variables of Private Supermarkets among the

central, the southern and the northern regions of Kerala.


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7.5 Factor Analysis Using the Selected Retail Variables of Triveni Supermarkets As per the opinion given by common customers of the three types of

enterprises, the selected retail variables of Triveni Supermarket were factored

into six retail factors using factors generated for Private Super Markets using

the same method. Retail factor variables of Triveni Supermarkets so obtained

are considered below and their mean values and standard deviations

ascertained are given below.

Table 7.9 Descriptive of retail factor variables of Triveni Supermarkets

Sl. No.

Retail Factor Variable

No. of variables included

Mean scores of

factor variable




Ambience 5 23.80 4.76 2.97

3 Facilities 3 13.78 4.59 1.84 4 Quality of goods 2 10.51 5.25 1.23 5 Price of goods 2 5.46 2.73 1.37 6 Discount 1 5.71 5.71 .78


The mean scores of retail factor variable ‘Price of Goods (5.46)’ of Triveni

Supermarkets and the mean score of ‘Quality of Goods’ (10.51) reveal that they

charge comparatively low prices and the quality of goods rated by the sample

customers is high. The customer rating for discount is also high (5.71) and it

indicates that they supply goods at a low price. The following hypothesis is

generated to recognize the variation of mean scores of factors among regions.

Hypothesis

H0: There is no difference in the means of retail factor variables of Triveni Supermarkets among the regions.

H1: There is difference in the means of retail factor variables of Triveni Supermarkets among the regions.

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For testing the hypothesis on mean differences of retail factor variables

one way ANOVA is used. Table 7.10 depicts whether region-wise variation

exists in the mean scores of factor retail variables of Triveni supermarkets.

Table 7.10 ANOVA table : Region-wise Variations of Retail Factor Variables of Triveni Supermarkets


Square F Sig.


Service


Availablity And Ambience


Facilities


Quality of Goods

Total 655.852 431 Between Groups 21.060 2 10.530 18.630 .000 Within Groups 242.486 429 .565

Discount


Price of Goods


ANOVA table depicts Mean Square, F- values and the significant

levels (p values) of all the six factor variables considered. The null

hypothesis is rejected as p < .05 and the alternative hypothesis is accepted as

there exists significant difference in the means of all retail factor variables of

Triveni Supermarkets among the three regions. This shows that there is


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significant difference in the performance of Triveni Stores among various

regions based on the responses of the respondents.

7.6 Factor Analysis Using the Selected Retail Variables of Margin- Free Supermarkets As per the opinion given by common customers of the three types of

enterprises, the selected retail variables of Margin- Free Markets are (rated by

customers) factored into six factors using factors generated for Private

Supermarkets as the reference. Retail factor variables of Margin-Free Markets

so obtained are given below and their mean values and standard deviations

ascertained are also given in the table 7.11.

Table 7.11 Descriptives of retail factor variables of Margin-Free Markets

Sl. No.

No. of variables included

Mean scores of

factor variable




Ambience 5 21.92 4.38 2.66

3 Facilities 3 12.76 4.25 2.01 4 Quality of goods 2 8.86 4.43 1.16 5 Price of goods 2 10.14 5.07 1.62 6 Discount 1 2.78 2.78 .92


As per the table 7.11, it is seen that the mean value of the retail factor variable

‘Price of Goods’ (10.14) was higher than that of the factor variable ‘Quality of

Goods’ (8.86) and the average rating for discount was also low. This led to the

conclusion that Margin-Free Markets charge comparatively higher prices for the

goods compared to the quality of goods that they offer. The comparative ratings

given by the selected customers for Margin-Free Markets led us to conclude that

the quality of goods and the price of goods offered by this type of stores do not

match. It was also found that these stores offer low discount as well as premium

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products to the customers. To identify the variation among the regions are

significant or not, the following hypothesis is framed

Hypothesis

H0: There is no difference in the means of retail factor variables of

Margin-Free Markets among the regions.

H1: There is difference in the means of retail factor variables of

Margin- Free Markets among the regions.

For validating the hypothesis, one way ANOVA is used.

Table 7.12 ANOVA table for testing region-wise variations of factor retail variables of Margin- Free Markets.


Square F Sig.


Service

Total 3154.656 426 Between Groups 20.052 6 3.342 .467 .833 Within Groups 2699.604 420 7.161

Availability and Ambience


Facilities


Quality of Goods


Price of Goods

Total 1009.122 426 Between Groups 5.672 6 .945 1.100 .362 Within Groups 323.953 420 .859

Discount



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As seen from the ANOVA table 7.12, by evaluating the, Mean Squares,

F values and sig. level of factor variables, all factor variables except

‘Price of Goods’, the null hypothesis is accepted as p > .05. It is inferred

that there exists no difference among the means of retail factor variables

‘Service’, ‘Availability and Ambience’, ‘Facilities’, ‘Quality of Goods’ and

‘Discount’ of Margin-free markets among the regions. This shows there is no

significant difference in the responses of customers relating to the five factors

mentioned above among the three regions. In the case of factor variable

‘Price of goods’, the null hypothesis is rejected as P<.05. Here the

alternative hypothesis is accepted as there is significant difference among the

means of this factor variable of Margin-Free Markets among the regions.

7.7 Comparative Assessment of Triveni Supermarkets, Margin- Free Markets and Private Supermarkets Using Selected Retail Factors For comparison, the mean scores and standard deviations of factor

variables of the three types of stores are considered. Mean scores obtained for

factor variables are used for drawing bar diagrams, which will lend clarity to

the comparison of the retail outlets. Repeated measure MANOVA will be

useful in this respect to test whether statistically significant differences

exist among the three types of retail outlets. Here two types of tests are

resorted to: 1) Test for knowing whether significant differences exist among

the means of retail factor variables for the three different types of retail outlets

and 2) Test for knowing whether significant differences exist among the

means of retail factors for the outlets on regions, income levels of customers

and the areas of residence of customers.

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Table 7.13 Group Statistics of Retail Factor Variables for the comparison of three types of supermarkets

Mean scores Std. Deviation

Sl.No

Retail Factors T

rive

ni

Supe

rma

rket

s

Mar

gin-

Free

M

arke

ts

Priv

ate

Supe

rma

rket

s

Tri

veni

Su

perm

ark

ets

Mar

gin-

Free

M

arke

ts

Priv

ate

Supe

rma

rket

s

1 Service 24.15 20.68 25.42 3.04 2.86 2.73 2

Availability and Ambience

23.80 21.92 25.71 2.97 2.66 2.68

3 Facilities 13.78 12.76 15.63 1.84 2.01 1.93 4 Quality of

Goods 10.51 8.86 11.04 1.23 1.16 1.40

5 Price of Goods 5.46 10.14 12.01 1.37 1.62 1.12 6 Discount 5.71 2.78 2.65 0.78 0.92 0.92

Source: Primary data.

7.7.1 Retail factor ‘Service’

Table 7.13 gives the mean values and standard deviations of retail factor

variables of all the three types of retail outlets studied. Comparing the mean

value of factor variable ‘Service’ of the three types of retail outlets, Triveni

Supermarkets (24.15) stay behind the Private Supermarkets (25.42) but occupy

a better position than the Margin- Free Markets (20.68). . However, Triveni

stores are not at all far behind Private supermarkets as their mean values do

not have much difference (25.42 and 24.15). But, Margin- Free Markets are far

behind Private Supermarkets and Triveni Supermarkets in this regard.

Considering the customer rating on satisfaction, variables included in the

factor, namely space layout, quality of packing, parking facilities, complaint

redress mechanism and speediness in billing are found to be comparatively

better in the Private Supermarkets than in the other two types of retail outlets.

This suggests that Triveni Supermarkets and Margin- Free Markets need to


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improve their performance in the variables mentioned in the factor ‘Service’ to

make them more competitive enterprises in the retail business. Their quality of

packing is better than that of Private Supermarkets and they redress the

complaints of customers satisfactorily.


Figure 7.1 Comparative analysis of mean score of three retail outlets on Retail factor ‘Service’

7.7.2 Retail factor: ‘Availability and Ambience’

In the case of Retail factor ‘Availability and Ambience’, Triveni

Supermarkets are in the second position based on the mean value of the

customer rating (23.8), Private Supermarkets have secured the highest mean

score in the factor ‘Availability and Ambience’ variables (25.71) and Margin-

free Markets having 21.92 mean value, occupies third position in this respect.

Therefore, customer personal care, availability of fresh stock and brands,

location advantage and attractiveness in display are perceived to be better in

Private Supermarkets than in the other two. From this one can safely infer that

without equipping Triveni staff to offer better care for customers and making

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available popular brands and fresh stock through Triveni outlets, the enterprise

cannot hope to compete with Private Supermarkets. This is applicable to

Margin- free markets as well. Again, it is desirable for Triveni Supermarkets

and Margin-Free Supermarkets to focus on better attractiveness in the display

of products.


Figure 7.2 Comparative analysis of mean score of retail outlets on Retail factor ‘Availability and Ambience’

7.7.3 Retail Factor variable: ‘Facilities’

The mean value of retail factor ‘Facilities’ is found highest in Private

Supermarkets (15.63), compared to Triveni Stores (13.78) and Margin- Free

Markets (12.76) as per the ratings provided by the selected respondents. This

shows that the latter two types of retail outlets should certainly improve their

facilities such as cleanliness in the store, seating arrangements to customers,

drinking water facilities, etc. to engage in a healthy competition with modern

private retail outlets.


385


Figure 7.3 Comparative analysis of mean scores of retail outlets on Retail factor ‘Facilities’.

7.7.4 Retail factor: ‘Quality of Goods’

The fourth retail factor variable considered and used for comparison

between the three type of consumer outlets is ‘Quality of Goods’. The mean

scores of the factor for Triveni, Margin- Free and Private Supermarkets are

10.51, 8.86 and 11.04 respectively. It shows that the quality of goods is better

in Triveni Supermarkets compared to Margin-Free Markets but inferior

compared to Private supermarkets. This is considered a state of dereliction as

Triveni Stores and Margin- Free Markets are poor in supplying quality goods.

Sooner, they will reach a derogatory stage unless and otherwise they supply

quality goods to the customers in the near future.

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Figure 7.4 Comparative analysis of mean score of retail outlets on Retail factor ‘Quality of Goods’

7.7.5 Retail factor: Price of Goods

Price, the fifth retail factor variable depicts that as per customers’ rating

Triveni Supermarkets charge very low price for goods (5.46) compared to

Private Supermarkets (12.01) and Margin-Free Supermarkets (10.14). The

mean difference among the three types of outlets shows the existing price

variations among the supermarkets. The price level of Triveni Supermarkets is

far below that of the other two types of enterprises. Private Supermarkets and

Margin-Free Markets are private entities, they charge highest price for

commodities which depicts their undue attitude for making more profit

through exploiting customers.


387

Source: Primary data Figure 7.5 Comparative analysis of mean score of retail outlets on Retail factor

‘Price of Goods’

7.7.6 Retail factor: ‘Discount’

Discount is the amount of reduction offered from the printed price given on

the package of articles or it is the amount of difference perceived by the customer

compared to the general price prevailing in the neighborhood stores. The

following chart shows the comparative mean scores of the factor variable

‘Discount’ of Triveni, Margin-Free and Private Supermarkets.

Source: Primary data Figure 7.6 Comparative analysis of mean score of retail outlets on Retail factor

‘Discounts’

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388

It is clear from the above diagram that, Triveni Stores are far ahead in the

mean scores of the retail factor ‘Discount’ of other two types of private consumer

outlets as the Triveni Stores has a mean value of 5.71 (Margin- free markets -

2.78 and Private Supermarkets-2.65). This means that the rate of discount offered

by Triveni consumer outlets is considerably higher than the other two types of

outlets compared based on customer opinion and comparative assessment.

7.8 Statistical Tests for Comparing the Outlets

To compare the set of retail factor variables of the three types of outlets,

repeated measure MANOVA is used. These six factor variables are

considered under MANOVA to assess the variations in their behavior among

Triveni Supermarkets, Margin-Free Markets and Private Supermarkets.

A repeated measure MANOVA is used when all the members of a

random sample are measured under a number of different conditions. As the

sample is exposed to each condition in turn, the measurement of the dependent

variable is repeated. Using a standard MANOVA in this case is not

appropriate because the repeated measures tend to be correlated: the data

violate the MANOVA assumption of independence. For example, one can use

a repeated measure MANOVA to compare the factor variables obtained from

among the sample respondents repeatedly at the three retail outlets considered

in this exercise. A sample member is called a subject.

When a set of dependent variables is measured repeatedly for all sample

members across a set of conditions, this set of conditions is called a within-

subjects factor. The conditions that constitute this type of factor are called

trials. The selection of the 3 retail outlets might be on certain conditions which

constitute within-subjects factor for the problem.


389

When a dependent variable is measured on independent groups of

sample members, where each group is exposed to a different condition, the set

of conditions is called a between-subjects factor. The conditions that constitute

this factor type are called groups. In this case the regional groups will be

between-subjects factor.

When an analysis has both within-subjects factors and between-subjects

factors, it is called a repeated measure MANOVA with between-subjects factors.

In this problem, the researcher aims at examining the impact of the type of

retail outlets on factor one to factor six. To investigate the issue, data collected

from a sample of individuals are grouped according to their characteristics. In

addition to these between-subjects factors, a single within-subjects factor also is

included in the analysis. Each subject's measurement on factor variables is at

three levels i.e., of retail outlets, for convenience, and calling it as type.

Within-Subjects Main Effect

Does type influence factor variables? (Do means of factor variables

change across the trials by type?) This is the test for a within-subjects main

effect of time.

Between-Subjects Main Effects

Do regional, income levels or area -wise difference influence factor

variables? (Do the three regions, five income levels and three areas have

different means of factor variables?) This is the test for a between-subjects

main effect of treatment difference.

Within-Subjects by Between-Subjects Interaction Effects

Do the regional, income level and area-wise influence on factor variables

depend upon type? (Does the pattern of differences among means of factor

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390

variables for the regions, income levels or areas change at each type of retail

outlet?) This is the test for a between-subject by within-subjects interaction of

treatment preference by time.

Within-Subject Tests: The Univariate versus the Multivariate Approach

The first set of tests is for the within-subjects effects. When there are

more than two levels of a within-subjects factor (here we have 3 levels), two

different sets of within-subjects hypothesis tests: one using the multivariate

approach, the other using the univariate approach is reported. Generally, both

sets of tests yield similar results.

Repeated measure MANOVA carries the standard set of assumptions

associated with an ordinary analysis of variance, extended to the matrix case:

multivariate normality, homogeneity of covariance matrices, and

independence. Repeated measures MANOVA is robust to violations of the

first two assumptions. Violations of independence produce a non-normal

distribution of the residuals, which results in invalid F ratios.

In addition to these assumptions, the univariate approach to tests of the

within-subject effects requires the assumption of sphericity, which is described

in more detail below. When sample sizes are small, the univariate approach

can be more powerful, but this is true only when the assumption of a common

spherical covariance matrix has been met.

Mauchly's Sphericity Test and Epsilon Adjustment Values

The test of sphericity, when requested, immediately precedes both sets

of within-subjects tests. The test for the comparison of Triveni, Private

Supermarkets and Margin- Mree markets in research in retailing, dataset

appears below.


391

Table 7.14 Mauchly's Test of Sphericityb

Epsilona Within Subjects

Effect

Retail factors

Mauchly's W

Approx. Chi-

Square df Sig. Greenhouse-

Geisser Huynh-

Feldt Lower-bound

Services .984 6.115 2 .047 .984 .994 .500 Availability and Ambience

.956 16.750 2 .000 .958 .968 .500

Facilities .987 4.708 2 .095 .988 .998 .500 Quality of goods

.965 13.486 2 .001 .966 .976 .500

Price of goods

.994 2.384 2 .304 .994 1.000 .500

Discount .945 21.155 2 .000 .948 .958 .500 Tests the null hypothesis that the error covariance matrix of the orthonormalized transformed dependent variables is proportional to an identity matrix. a. May be used to adjust the degrees of freedom for the averaged tests of significance. Corrected tests are displayed in the Tests of Within-Subjects Effects table. Source: Primary data

Mauchly's sphericity test examines the form of the common covariance

matrix. A spherical matrix has equal variances and covariances equal to zero.

The common covariance matrix of the transformed within-subject variables

must be spherical, or the F tests and associated p values for the univariate

approach to testing within-subjects hypotheses are invalid. If the Chi-square

approximation has an associated p value less than the alpha level, the

sphericity assumption has been violated. The chi-square approximation for

this test is significant at 5 per cent level for four out of six variables

considered. Since this is less than the alpha level of 0.05, we can be

confident that the data do not meet the sphericity assumption. Hence,

multivariate tests are used.

For practical purposes, these issues are important only in helping to

decide which output to use, and if the output should be adjusted. If we use

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392

the univariate output, we may have more power to reject the null hypothesis

in favor of the alternative hypothesis. However, the univariate approach is

appropriate only when the sphericity assumption is not violated. If the

sphericity assumption is violated, then in most situations it is better off

staying with the multivariate output. (It amounts to using Multivariate

output often).

7.8.1 Test for knowing whether significant difference exists among the means of retail factor variables for three different types of retail outlets Multivariate Approach to Within-Subjects Tests:

As noted above, the multivariate output is still valid even if the

sphericity assumption is not met. SPSS prints the multivariate approach to

testing the within-subjects factors after Mauchly's test of sphericity. The first

multivariate test of a within-subjects effect is the within-subjects main effect

test. It examines changes in factor variables as a function of type of retail

outlets considered. The null hypothesis is that the means of factor variables

do not change across different types of retail outlets considered.

Hypothesis:

H0: There is no difference in the means of factor variables for

different types of retail outlets.

H1: There is difference in the means of factor variables for

different types of retail outlets.


393

Table 7.15 Multivariate Test for Within-Subjects Main Effect

Within Subjects Effect Value F Hypothesis df Error df Sig. Pillai's Trace 1.575 460.684 12.000 1492.000 .000 Wilks' Lambda .027 637.329a 12.000 1490.000 .000 Hotelling's Trace 13.987 867.209 12.000 1488.000 .000

factor1

Roy's Largest Root 12.121 1506.997b 6.000 746.000 .000 a. Exact statistic b. The statistic is an upper bound on F that yields a lower bound on the significance level. c. Design: Intercept + Region Within Subjects Design: factor1 d. Tests are based on averaged variables. Source: Primary data

There are four tests, each reporting a separate multivariate test statistic

(Pillai’s, Hotelling's, Wilks', and Roy's); among them the Wilks’ test is

commonly used. These multivariate statistics are converted to ‘ F’ values. In

some cases, the converted F and its degrees of freedom are approximations.

When this is not the case, a note at the bottom of the output states that the

statistics are exact.

Since, the F ratio for this hypothesis is very large [F(12,1490) =

637.329, P < .0001], we confidently reject the null hypothesis and conclude

that the means of factor variables change with type of retail outlets in the

population from which the sample was drawn. This is concluded by

stating that there exist significant differences in the means of all six retail

factor variables for the three different types of retail outlets.

7.8.2 Test for the hypothesis that regional differences interact with type of retail outlets considered.

Table 7.16 gives the region-wise mean scores and standard deviations of

six factor variables of Triveni Supermarkets, Margin-Free Markets and Private

Supermarkets.

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394

Table 7.16 Region-wise means and standard deviations of retail factor variables of the retail outlets.

Region

Triveni Supermarkets

Margin-free Supermarkets

Private

Supermarkets Retail factors

Mean

Std. Deviation Mean

Std. Deviation Mean

Std. Deviation

Central 24.0000 1.97183 21.3169 2.69059 25.9504 2.01893

Southern 23.2569 3.38187 19.4397 2.95289 24.6950 3.35292

Northern 25.2014 3.28076 21.4851 2.39422 25.6319 2.53023

Services

Total 24.1528 3.04944 20.6719 2.86997 25.4272 2.73549

Central 23.6042 2.34809 22.8944 1.90820 26.0496 2.18543

Southern 22.7431 3.39219 20.4539 2.91909 24.9574 3.33782

Northern 25.0556 2.61191 22.6040 2.29817 26.1042 2.23675

Availability and ambience

Total 23.8009 2.97042 21.9219 2.66476 25.7066 2.68430

Central 13.5903 1.37619 13.1268 1.50111 16.1986 1.48430

Southern 13.1458 2.29615 11.6667 2.21574 14.8652 2.34588

Northern 14.6042 1.39539 13.7822 1.59752 15.8403 1.61576

Facilities

Total 13.7801 1.84310 12.7630 2.01394 15.6362 1.93214

Central 10.7778 1.03400 9.1690 1.01738 11.3759 1.38120

Southern 9.8819 1.26544 8.4681 1.21628 10.8369 1.49104

Northern 10.8958 1.13876 9.0099 1.12690 10.9167 1.28193

Quality of goods

Total 10.5185 1.23357 8.8698 1.16239 11.0423 1.40356

Central 5.7708 1.34174 11.1056 1.52384 12.3546 1.04290

Southern 5.8194 1.27182 9.1631 1.36080 11.4468 1.13657

Northern 4.8056 1.27525 10.1584 1.24686 12.2292 .94402

Price of goods

Total 5.4653 1.37552 10.1432 1.62320 12.0117 1.11560

Central 5.8264 .74167 2.3169 .64544 2.4965 .88337

Southern 5.4097 .79696 3.2766 .93432 2.7589 .88560

Northern 5.9167 .71449 2.7426 .91273 2.7014 .99706

Discount

Total 5.7176 .78197 2.7813 .92771 2.6526 .92873 Source: Primary data Red: Above average and black: below average.


395

As wide variations exist among the means of factor variables in different

regions for different types of retail outlets, following line charts are drawn to

express these variations clearly and Multivariate test is used to identify the

variations are statistically significant or not.


Figure 7.7. Means of ‘SERVICE’


Figure7.8. Means of Availability and Ambience

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396


Figure 7.9. Means of Facilities


Figure 7.10. Means of Quality of goods


397


Figure 7.11. Means of Quality of goods


Figure 7. 12: Means of Discount

Under Repeated measure MANOVA format, Multivariate test is

used for testing hypothesis of those regional differences interacting with

type of retail outlets.

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398

Hypothesis:

H0: There is no difference in the means of factor variables among

regions for different types of retail outlets.

H1: There is difference in the means of factor variables among

regions for different types of retail outlets.

Between- Subjects Effects Test

To test whether the regional means of factor variables of the three types

of outlets are significantly different between each other, between subjects main

effect test is used and reported below.

Table 7.17 Multivariate Test for Between-Subjects Main Effects for Region

Between Subjects Effect Value F Hypothesis df Error df Sig.

Pillai's Trace .354 12.087 24.000 2992.000 .000

Wilks' Lambda .671 13.114 24.000 2600.205 .000

Hotelling's Trace .453 14.047 24.000 2974.000 .000

factor1 * Region

Roy's Largest Root .359 44.741b 6.000 748.000 .000 Source: Primary data

In this instance, as per Wilks’ test (most commonly used test), the F

value associated with these multivariate tests of the interaction is 13.114;

therefore, the associated p value is low [F(24,2600) = 13.114, p < .0001].

Hence the null hypothesis is rejected and it is concluded that variations in

the means of factors across type of retail outlets depend upon regions.

Within-Subjects by Between-Subjects Interaction Effects for Region

Following is the multivariate test of significance for within-subjects by

between interaction effects for region.


399

Table 7.18 Tests of Hypotheses for Between Subjects Effects for Region

Source Retail factors Type I Sum of Squares df Mean

Square F Sig.

Service 513.849 2 256.925 29.069 .000 Availability and Ambiance

702.471 2 351.236 37.247 .000

Facilities 459.482 2 229.741 51.196 .000 Quality of goods 113.604 2 56.802 29.765 .000 Price of goods 18.351 2 9.175 13.509 .000

Region

Discount 193.399 2 96.700 61.198 .000 Source: Primary data

This is the sum of squares, degrees of freedom, and mean square for

‘region’. This test also shows the F value and associated p value for the test of

the group hypothesis. In this case, a p value less than .0001, has a statistically

significant effect (using the alpha criterion of .05 to define "statistical

significance"). It is therefore, concluded that a statistically significant

difference exists among regions with respect to the means of factor

variables. This clearly shows that regional mean values of all the three

types of outlets are different. In other words; there is a main effect for

treatment difference. This shows that the perception of selected customers is

varied with the performance of outlets considering different regions of Kerala.

7.8.3. Test for the hypothesis to know whether income level difference interacts with type of retail outlets.

It has been understood that statistically significant difference exists among

regions with respect to the means of factor variables of three types of retail

outlets. Yet another test is resorted to here to understand whether significant

difference exists among income levels of customers with respect to the means of


Supermarkets. Table 7.19 provides the mean scores and standard deviations on

the basis of income levels of respondents of three types of retail outlets.

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400

Table 7.19 Customers’ income level descriptive of retail factor variables of retail outlets

Triveni Stores Margin F Markets Private SM Retail

factors Income levels of customers Mean

Std Deviation

Mean Std

Deviation Mean

Std. Deviation

less than 5000 25.0000 1.91485 20.8571 3.71612 25.5714 2.82000 5000-10000 24.6429 2.86945 20.4510 3.13250 25.4630 2.60415 10000-20000 24.0301 2.69101 20.4130 2.75450 25.1656 2.54651 20000-30000 24.3179 3.55222 20.8394 2.88819 25.6800 3.02633 Above 30000 23.4231 2.77486 21.1176 2.76150 25.4615 2.57028

Service

Total 24.1528 3.04944 20.6719 2.86997 25.4272 2.73549 less than 5000 24.4286 3.20713 22.5714 2.63674 26.8571 1.46385 5000-10000 24.3393 2.86215 22.1765 3.14137 25.4630 2.59690 10000-20000 23.4940 2.57905 21.8188 2.52935 25.4540 2.63453 20000-30000 24.0066 3.31561 21.9343 2.58447 25.9600 2.81148 Above 30000 23.5192 3.12793 21.8235 2.79790 25.8654 2.64226

Availability and Ambiance

Total 23.8009 2.97042 21.9219 2.66476 25.7066 2.68430 less than 5000 14.0000 2.00000 13.8571 1.86445 15.4286 2.43975 5000-10000 14.0714 1.98042 12.5490 1.97295 14.7778 2.11613 10000-20000 13.7651 1.66212 12.7826 2.06019 15.6442 2.00210 20000-30000 13.7152 1.92311 12.6861 1.99172 15.8333 1.77006 Above 30000 13.6731 2.01680 12.9804 2.01485 15.9615 1.69152

Facilities

Total 13.7801 1.84310 12.7630 2.01394 15.6362 1.93214 less than 5000 10.7143 1.11270 9.2857 .48795 11.2857 1.38013 5000-10000 10.5536 1.18965 8.9216 .97659 10.9815 1.40740 10000-20000 10.4157 1.18147 8.7826 1.14458 10.9448 1.39776 20000-30000 10.6291 1.33475 8.9270 1.12220 11.0933 1.39677 Above 30000 10.4615 1.16251 8.8431 1.51489 11.2308 1.46348

Quality of Goods

Total 10.5185 1.23357 8.8698 1.16239 11.0423 1.40356 less than 5000 6.1429 1.21499 10.7143 2.36039 11.8571 1.06904 5000-10000 5.6607 1.35213 10.0196 1.51645 11.8889 .98415 10000-20000 5.4819 1.39576 10.1014 1.62683 11.8834 1.23924 20000-30000 5.3642 1.40230 10.1022 1.60094 12.1133 1.03973 Above 30000 5.4038 1.27202 10.4118 1.68732 12.2692 1.01199

Price of Goods

Total 5.4653 1.37552 10.1432 1.62320 12.0117 1.11560 less than 5000 5.5714 .53452 2.7143 .75593 2.7143 .48795 5000-10000 5.7321 .84188 2.9216 .84482 2.7963 1.07070 10000-20000 5.6627 .79814 2.8478 .84493 2.6380 .97387 20000-30000 5.8013 .79179 2.7445 .96276 2.6800 .89981 Above 30000 5.6538 .65334 2.5686 1.11812 2.4615 .72657

Discount

Total 5.7176 .78197 2.7813 .92771 2.6526 .92873 Source: Primary Data * Red: Above average and black: below average.


401

Since wide variations exist among the means of factor variables in

different income groups for different types of retail outlets, line charts are

drawn below, which will help to identify the variations more clearly and

Multivariate test is used to recognize whether these variations are statistically

significant or not.


Figure 7.13 : Retail Factor ' Service'


Figure 7.14 : Retail Factor 'Availablity and Ambience'

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402


Figure 7.15 : Retail factor'Quality of goods'


Figure 7.16 : Retail Factor'Facility'


403


Figure 7.17 : Retail factor 'Price of goods'


Figure 7.18 : Retail factor 'Discount'

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404

Under Repeated MANOVA format,

Multivariate test is used for testing hypothesis that income level

differences interacts with type of retail outlets. The associated hypothesis

is given under:

Hypothesis:


income groups for different types of retail outlets.


income groups for different types of retail outlets.

Between Subjects Tests

To test whether the income level means of factor variables of the three

types of outlets are significantly different to one another, between subjects

main effect test is used and reported below.

Table 7.20 Multivariate Test for Between-Subjects Main Effects (income level)

Between- Subjects Main Effect Value F Hypothesis df Error df Sig.

Pillai's Trace .098 .764 48.000 1460.000 .000

Wilks' Lambda .904 .769 48.000 1396.501 .005

Hotelling's Trace .103 .774 48.000 1442.000 .009

factor * monthly income

Roy's Largest Root .070 2.119b 12.000 365.000 .015

a. Exact statistic Source: Primary data

In this case, as per Wilks’ Lambda test (most commonly used test), the F

value associated with these multivariate tests of the interaction is .904;

therefore, the associated p value is low [F(48,1396.501) = .904, p< .05].

Hence the null hypothesis is rejected and it is concluded that variations in


405

the means of factors across type of retail outlets depend upon the income

level of the respondents.

Within-Subjects by Between-Subjects Interaction Effects for Income

The multivariate tests of significance for within-subjects by between-

subjects interaction effects are reported below (groups). This helps to

understand whether the income level means of six factor variables of the three

outlets vary significantly or not. Only income approach is resorted to in testing

the effects, as mentioned earlier.

Table 7.21 Tests of Hypotheses for Between Subjects Interaction Effects (Monthly Income)

Source Retail factors Type I Sum of Squares df Mean

Square F Sig.

Service 29.503 4 7.376 .724 .000


41.317 4 10.329 .918 .000

Facilities 14.794 4 3.699 .648 .008

Quality of goods 4.099 4 1.025 .463 .000

Price of goods 5.946 4 1.486 2.076 .000

Monthly income

Discount 8.877 4 2.219 1.065 .000 Source: Primary data

Table 7.21 gives out the sum of squares, degrees of freedom, and mean

square for different ‘income levels’. The table also reports the F value and

associated p value for the test of the group hypothesis. In this case, a p value

less than .05, has a statistically significant effect (using the alpha criterion of

.05 to define "statistical significance"). It is therefore concluded that a statistically

significant difference exists in the means of income levels of factor variables

among the three outlets, viz. Triveni, Private Supermarkets and Margin-

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406

Free Markets. In other words; there is a main effect for treatment difference.

May be the mean difference in income interacts or causes in the difference

between the retail outlets.

7.8.4 Test for Hypothesis that Residential Area-wise Differences of Customers Interact with the Type of Retail Outlets Considered.

Table 7.22, gives the area- wise mean scores and standard deviations of


Supermarkets. It may be noted that factor means of all six factors considered

have variations among the three retail outlets area wise, which is among urban,

semi-urban and rural areas. The test used here- test for between subjects

effects (Repeated measure MANOVA) will be helpful to draw a conclusion

that significant variations exist among the three areas by considering the group

means of the three types of outlets.


407

Table 7.22 Area -wise means and std.deviations of retail factor variables of retail outlets

Retail factors Area Triveni

supermarkets Margin

F supermarkets

Private

supermarkets

Mean Std. Deviation Mean

Std. Deviation Mean

Std. Deviation

Urban 24.1597 2.33748 19.8951 2.28194 25.2083 2.78074

Semi-urban 25.1181 2.81230 21.1935 2.89288 25.6111 2.40951

Rural 23.1806 3.57273 21.0684 3.27931 25.4638 2.99917

Services

Total 24.1528 3.04944 20.6719 2.86997 25.4272 2.73549

Urban 23.6667 2.61785 21.2448 2.37124 25.2500 2.47928

Semi-urban 24.8958 2.80289 22.4919 2.69446 26.0625 2.41562

Rural 22.8403 3.11922 22.1453 2.81089 25.8116 3.07937


Total 23.8009 2.97042 21.9219 2.66476 25.7066 2.68430

Urban 13.6319 1.69634 12.7483 1.98398 15.6389 1.54019

Semi-urban 14.1944 1.60540 12.7258 1.78676 15.6181 1.91058

Rural 13.5139 2.12539 12.8205 2.27663 15.6522 2.30091

Facilities

Total 13.7801 1.84310 12.7630 2.01394 15.6362 1.93214

Urban 10.3333 1.03775 8.7413 1.07934 10.9236 1.27964

Semi-urban 10.9653 1.05389 8.9113 1.07455 11.0694 1.38247

Rural 10.2569 1.44719 8.9829 1.33250 11.1377 1.54349

Quality of goods

Total 10.5185 1.23357 8.8698 1.16239 11.0423 1.40356

Urban 5.3889 1.15335 9.9860 1.56541 11.8542 1.07722

Semi-urban 5.1458 1.15249 10.0968 1.46181 12.1111 1.00426

Rural 5.8611 1.66667 10.3846 1.82816 12.0725 1.24770

Price of Goods

Total 5.4653 1.37552 10.1432 1.62320 12.0117 1.11560

Urban 5.8194 .74167 2.7622 .93416 2.4931 .75722

Semi-urban 5.8194 .79696 2.7177 .87010 2.6042 .96960

Rural 5.5139 .71449 2.8718 .97855 2.8696 1.00963

Discount

Total 5.7176 .78197 2.7813 .92771 2.6526 .92873 Source: Primary Data. * Red: Above average and black: below average.

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408

As considerable variations exist in the means of factor variables in different

areas for different types of outlets, the following line charts are drawn to express

these variations as clearly as possible. Repeated measure MANOVA is used to

identify where the variations are statistically significant or not. The following

Charts clearly depict the differences in the responses of the customers with regard

to the different factor variables considering the area of residence of customers.


Figure 7.19 : Means of Factor 'Service'


Figure 7. 20: Means of Factor'Availability and Ambiance'


409


Figure 7.21 : Means of factor ' Facilities'


Figure 7.22: Means of factor 'Quality of Goods'

Chapter -7

410


Figure 7.23: Means of Factor 'Price of goods'


Figure 7.24: Means of Factor 'Discount'

Under Repeated MANOVA format, Multivariate test is used for testing

hypothesis that differences in areas interact with type of retail outlets.


411

Hypothesis:


areas for different types of retail outlets.


areas for different types of retail outlets.

Between- Subjects Test for Residential Area of Customers.

To test whether the area-wise means of factor variables of the three types

of outlets are significantly different to one another, between subjects main

effect test is used and reported below.

Table7.23 Multivariate test for between- subjects main effects for Residential area of Customers

Tests Value F Hypothesis df Error df Sig. Pillai's Trace .112 3.592 24.000 2992.000 .000 Wilks' Lambda .890 3.666 24.000 2600.205 .000 Hotelling's Trace .120 3.729 24.000 2974.000 .000

factor1 * area

Roy's Largest Root .094 11.696b 6.000 748.000 .000 a. Exact statistic b. Tests are based on averaged variables. Source: Primary data

The above table shows the value of Wilks’ Lambda test (most commonly

used test), the F value associated with these multivariate tests of the interaction

is 3.666 with the associated p value found to be low [F(24,2600.205) = 3.666 ,

p < .0001]. Hence the null hypothesis is rejected and it is concluded that

variations in the means of factors across types of retail outlets depend

upon the areas of customer residence. This shows that the means of

responses of customers to Private Supermarkets, Triveni Supermarkets and

Margin-Free Markets vary among urban, semi-urban and rural areas of Kerala.

Thus the performance of the three types of outlets while considering the six

Chapter -7

412

retail factors is found to be varied among the three areas of Kerala as per the

ratings of customers.

Within-Subjects by Between-Subjects Interaction Effects for Area

Following the multivariate tests of significance for within-subjects by

between-subjects interaction effects are reported (groups). The multivariate

approach is used to test whether the effects of variations in the means of

different areas influence the difference in factor variables of the outlets.

Table 7.24 Tests of Hypotheses for Between –Subjects Interaction Effects for Area

Transformed Variable: Average

Source Retail factors Type I Sum of Squares df Mean Square F Sig.

Service 128.636 2 64.318 6.519 .002 Availability and Ambiance

234.767 2 117.384 10.994 .000

Facilities 12.250 2 6.125 1.078 .341 Quality of goods 19.170 2 9.585 4.437 .012 Price of goods 23.772 2 11.886 5.848 .003

Area

Discount 1.440 2 .720 4.994 .013 Source: Primary data

Table7.24 depicts the sum of squares, degrees of freedom, and mean

square for the area of customer residence. It also reports the F value and

associated p values for the test of the group hypothesis. In this case, a p value

less than .05 in case of factors- Service, Availability and Ambiance, Quality of

goods, Price of goods and ‘Discount’ has a statistically significant effect

(using the alpha criterion of .05 to define "statistical significance"). It is

therefore, concluded that a statistically significant difference exists among

the areas of residence of customers with respect to the means of the five

factor variables which may cause the variation between the types of retail

outlets. But factor-‘Facilities’ has no significant difference among areas as


413

the p values are higher than .05. In other words; there is a main effect for

treatment difference caused by difference in areas of residence of

customers.

Summary

Triveni Supermarkets, Margin-Free Markets and Private Supermarkets

are supposed to be competed at all the areas where they are exist together. It

was inferred from means of eighteen retail related variables categorized in to

six factors that the three types of consumer outlets dealing mainly in food and

grocery are different in all respects when considering all these factor variables

as a group. The Private Supermarkets are the best in the first four factor

indicators, namely; Services, Availability and Ambience, Facilities, and

Quality of Goods. Triveni Stores are rated second in this respect. In the case of

the Price of goods and Discount factors, Triveni is found to be the best with

Margin-Free Markets following them.

The multivariate statistical test Repeated measure MANOVA,

considered the group behaviour of the variables and helped to draw a concrete

conclusion that region wise, income level and area wise difference exists

clearly among Triveni Supermarkets, Margin-Free Markets and Private

Supermarkets. As per the responses of customers, it was found that the

performance of all the three types of retail outlets is different. The

Multivariate test for Between- Subjects Main Effects for region and income

levels substantiated the difference exists between the three types of outlets in

the perception level of customers considering all the six factors. However, on

area level, the factor ‘Facilities’ has no significant difference exists among

the outlets.

Chapter -7

414

Conclusion

By concluding, Triveni outlets of CONSUMERFED offer goods at low

prices than other private competitors, they have to improve in the major areas

of retailing to attract and satisfy the customers, otherwise they cannot succeed

and remain to be a competitive enterprise in the long run. Private supermarkets,

considering them a separate group attract customers through offering high

quality goods with wide assortment, ambience, facilities and customer

services. Margin-Free Markets are supermarkets, but not been at par with

Private Supermarkets and Triveni Supermarkets in different aspects of

retailing such as facilities, availability, quality and price of goods. They offer

medium quality goods with comparatively high prices as they are independent

stores without enjoying the economies of scale.


415

References

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[3] Hair Jr , (2004). Maltivariate Data Analysis. PHI Learnings Pvt. Ltd, p.321.

[4] Field , A. (2000), Discovering Statistics using SPSS for Windows, New

Delhi: Sage Publications.

[5] Stevans, J, (1998). Applied Multivariate Statistics for the Social

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[6] Cheng, E W L, (2001), ‘SEM being More Effective than Multiple

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[7] Cooper, R D, and Schindler ,P S, (2006), Business Research Methods,

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[8] Hair, Jr. op.cit p.342.

[9] Gerberg and Anderson, (May 2008)‘An Updated Paradigm for Scale

Development Incorporating Unidimensionality and its Assessment’,

Journal of Marketing Research , Vol. 15, pp.186-192.

[10] Norman, G R, and Streiner, D.I, (1994), Biostatistics: The Bare

Essentials, St.Louis, MO: Mosby. New Jersey. 2nd edition. p. 234.

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