part 3 - factor analysis for various attributes of...

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CHAPTER IV

RESULTS AND DISCUSSIONS

PART 3 - FACTOR ANALYSIS FOR VARIOUS ATTRIBUTES OF

FRUITS AND VEGETABLES

4.3 INTRODUCTION

Factor Analysis and Principal Components Analysis (PCA) are both statistical techniques

used to reduce a large set of variables to a smaller number of manageable dimensions and

components that explain the important dimensions of variability. These techniques are

commonly used when developing a questionnaire to see the relationship between the

variables in the questionnaire and underlying dimensions. Specifically, factor analysis

aims to find underlying latent factors, whereas principal components analysis aims to

summarise observed variability by a smaller number of components (see Chapter II, 2.6).

The main objective is to investigate the brand elements ( product attributes) that have been

shown to be relevant and decisive in purchasing FFV and WTP extra for the FFV if

consumers favourite attributes are assured in the branded FFV.

Specific objectives:

a) To investigate the favourite attributes of F&V for the consumers belong to LIG, MIG

and HIG (using Factor Analysis technique).

b) To know the WTP extra for the F&V by the consumers of LIG, MIG and HIG if their

favourite attributes are assured.

c) To study the relationship of the ratings given by the consumers of MIG and HIG for

various attributes of F&V (using Chi -square Test).

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d) To find the common favourite attributes of F&V by the consumers of MIG and HIG if

there is strong relationship between the income and attributes ratings (using Factor

Analysis).

4.3. A. APPLE

4.3. A.1 FACTOR ANALYSIS FOR VARIOUS ATTRIBUTES OF APPLE RATED

BY THE CUSTOMER OF LOW INCOME GROUP

Factor analysis was conducted using PCA on 14 attributes with varimax rotation. Kaiser-

Meyer-Olkin (KMO) and Bartlett test were used to measure sampling adequacy and the

presence of correlation among the attributes and confirmed appropriateness of conducting

the PCA.

Table 4.3.1: Rotated Component Matrixa-

APPLE (LIG)

Component

1 2 3 4 5 6

price .758

size .650

colour .800

freshness .809

origin .566

variety

Brand .729

Texture .708

Taste .890

Juiciness .878

shelf life .543

skin thickness .702

direct eating .739

juice making .534

fruit salad

jam preparation

Extraction Method: Principal Component Analysis.

Rotation Method: Varimax with Kaiser Normalization.

a. Rotation converged in 7 iterations.

See Annexure –B1 (p- 225) for KMO, Bartlett's Test and Total Variance Explained,

Source: SPSS Output

Interpretation: From the table 4.3.1 we can see that the attributes under component 1

are; direct eating and skin thickness, have a high loading of 0.739 and 0.702.This suggests

that component 1 is a combination of these two attributes which represent the ultimate use

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of direct eating of the apple (table purpose). Therefore this component 1can be termed as

‘Table fruit’.

Component 2 shows that the attributes; taste and juiciness of apple having higher loading

of 0.890 and 0.878, which represents the quality aspects of apple which are most desired

by the consumers of LIG. They use apple more for direct eating than other purposes like

preparing juice, salads, jams etc. This component is also related to direct eating. Hence the

researcher intends to club both component 1 and 2. It can be termed as ‘Quality of table

fruit’.

In component 3, attribute price has a loading of 0.758. Since LIG consumers are price

conscious they prefer apples with lower price and seek worth in it. Therefore they may not

look at other aspects of use of apple. It is called as ‘Value for money’.

In component 4 attributes; brand and texture have a higher loading of 0.729 and 0.708.

LIG consumers identify the quality apples by their brand name and texture. Brand as seen

today even by the working class people. We understand that the texture of these branded

apples are different and there is strong association of texture with brands. This represents

the identification aspects of apple for table purpose. It is called as ‘Identity of table fruit’.

In component 5 and 6, we see that attributes, colour and freshness have factor loading of

0.800 and 0.809. LIG consumers buying decision would be based on the colour and

freshness of apple for table purpose. These are the good characteristics of apple for direct

eating purpose. Therefore both the attributes can be termed as ‘Good table fruit’.

From the above analysis we can conclude that, LIG consumers use apple primarily for

direct consumption as a table fruit. They looks for the apples having good taste, more juicy

and value for money which are major attributes for choosing the apple for direct eating.

Hence, they may not look for other attributes of apple suitable for processing. We also

understand from the analysis that, the urge to explore preparing new things with fruit as

seen on television, magazines, and other sources is less with LIG consumer. They would

not prefer to spend more money on preparing juice, jams, salads etc.

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4.3. A.2 FACTOR ANALYSIS FOR VARIOUS ATTRIBUTES OF APPLE RATED

BY THE CUSTOMER OF MIDDLE INCOME GROUP

Factor analysis was conducted using PCA on 14 attributes with varimax rotation. KMO

and Bartlett test were used to measure sampling adequacy and the presence of correlation

among the attributes and confirmed appropriateness of conducting the PCA.

Table 4.3.2:Rotated Component Matrix-

APPLE (MIG)

Component

1 2 3 4 5

price .720

size .792

colour .668

freshness .761

origin .795

variety .595

brand .851

texture .725

taste .633

juiciness .754

shelf life .514

skin thickness .713

direct eating .877

juice making .855

fruit salad .813

jam preparation .588




See Annexure –B2 (p-226) for KMO, Bartlett's Test and Total Variance Explained

Source: SPSS Output

Interpretation: From the above table 4.3.2 we can see that the attributes under component

1 are direct eating, juice making, and fruit salad having the high loadings of 0.877, 0.855

and 0.813. This suggests that MIG consumers use apple for multiple purpose like direct

eating, juice making and for making salads. Therefore it can be termed as ‘Multipurpose

fruit’.

Component 2 shows the attributes; juiciness, texture and skin thickness having higher

loading of 0.754, 0.725 and 0.713. These are good quality aspects of apples for direct

eating and to prepare juice, salads, etc. It can be termed as ‘Quality for multipurposeness’.

Component 3 shows the attributes; size, price, and colour have the high loading of 0.792,

0.720 and 0.668. The MIG consumers prefer apples with optimum size and attractive

colour. They feel worth buying if these attributes are assured in the apple. Therefore, the

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willingness to pay premium price may depend on the size and colour of the fruit. These

attributes may be the parameters for paying more price. It can be termed as ‘Value for

money’.

Component 4 shows the attributes; brand and origin have the high loadings of 0.851 and

.795. This suggests that MIG consumer identify their apple with favourite attributes with

Geographic Indications (GIs) or brands and varieties. It can be termed as ‘Identity of

multipurposeness of apples’

Component 5 has the attribute freshness with high loading of 0.761 and can be termed as

‘Fresh multipurpose apple’.

From the above analysis we can conclude that, MIG consumers consumption is more in

the form of direct eating, juice making and as salads. They look for juicy, smooth texture,

thin skinned and graded apples. Value for money will be important for buying apples.

They identify quality apples which are used for different purposes by their place of origin

and brand names.

4.3.A.3 FACTOR ANALYSIS FOR VARIOUS ATTRIBUTES OF APPLE RATED

BY THE CUSTOMER OF HIGH INCOME GROUP

Factor analysis was conducted using principal PCA on 14 attributes with varimax rotation.

KMO and Bartlett test were used to measure sampling adequacy and the presence of

correlation among the attributes and confirmed appropriateness of conducting the PCA.

Table 4.3.3: Rotated Component Matrix –

APPLE (HIG)

Component

1 2 3 4 5

price .701

size .849

colour .814

freshness .810

origin .740

variety .781

Brand .743

Texture .754

Taste .582

Juiciness .538

shelf life .669

skin thickness .640

direct eating .826

juice making .874

fruit salad .836





See Annexure -B3 (p-227) for KMO, Bartlett's Test and Total Variance Explained

6

Source: SPSS Output

Interpretation: From the table 4.3.3, we can see that attributes under component 1are

juice making, fruit salad and direct eating having the loading of 0.874, 0.836 and 0.826.

This suggests that HIG consumers use apple for multiple purposes, mainly for juice and

salad preparation. They consume more by value addition and relatively less as a table fruit.

This can be termed as ‘Multipurpose fruit’.

Component 2 shows the attributes; size, colour and price have high loadings of 0.849,

0.814 and 0.701.And component 3 shows the attribute, texture having the higher loading

of 0.754. These two components can be clubbed as they are quality aspects of apples. HIG

consumers prefer apples with proper size, attractive colour and texture. They feel worth

buying if those attributes are assured in the apple. Therefore the willingness to pay

premium price may depend on the size, colour and texture of the fruit. These attributes

may be the parameters for paying more price. It can be termed as ‘Quality apples’.

Component 4 showing the attributes; variety, brand and origin have the high loading of

0.781, 0.743 and 0.740. It can be termed as ‘Identity of quality apples’ because apples are

identified by Geographic Indications (GIs) or brands and varieties.

Component 5 showing attribute, freshness has the loading of 0.810. It can be termed as

‘Fresh multipurpose apple’.

From the above analysis we can conclude that, HIG consumers use apples for juice

making and salad preparation. Few use it as table fruit and to prepare jam. HIG consumers

are more health conscious and have different lifestyle compared to other income groups.

They like to prepare various dishes using apples. It is mainly because HIG consumers

being affluent are exposed to various aspects of using the apples. Fresh fruits with

standard size, smooth texture and attractive colour are preferred. They consider variety,

brand, and country of origin to identify the quality of apples during purchase.

Researcher intends to find the relation between the income and ratings given for the

attributes. MIG and HIG consumer’s ratings are considered for the comparison.

The following hypothesis is formulated to test whether income plays a major role in

choosing the attributes in apple.

H0: Ratings allotted to various attributes by the consumers are not dependent of income

H1: Ratings allotted to various attributes by the consumers are dependent of income

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The above hypothesis is tested using Chi square test for each attributes of apple. Results

are shown in table no. 4.3.4.

Table 4.3.4: Chi square tests for each attributes of apple

Source: SPSS output

Interpretation: Table 4.3.4 shows that ratings for all the attributes are not dependent on

income. It means that MIG and HIG respondents have given good ratings for all the

aspects and need all the attributes in the apple. Income is not an issue when they rate the

apple on various attributes. It implies that consumers are ready to pay provided all the

above attributes are met.

4.3.A.4 WILLINGNESS TO PAY (WTP) EXTRA ABOVE THE MARKET PRICE

FOR THE BRANDED APPLES BY THE CONSUMERS OF DIFFERENT

INCOME GROUPS

Source: Survey Data

AttributesPearsons Chi

Square Valuedf

Asymp.Sig

(2 sided)Interpretation

price 9.12 4 0.058 P- value is less than 0.10 at 90 % confidence level.The null hypothesis is accepted.

size 4.485 4 0.344 P- value is greater than 0.10 at 90 % confidence level.The null hypothesis is accepted.

colour 4.687 4 0.322 P- value is greater than 0.10 at 90 % confidence level.The null hypothesis is accepted.

freshness 1.426 4 0.84 P- value is greater than 0.10 at 90 % confidence level.The null hypothesis is accepted.

origin 1.489 4 0.829 P- value is greater than 0.10 at 90 % confidence level.The null hypothesis is accepted.

variety 4.131 4 0.389 P- value is greater than 0.10 at 90 % confidence level.The null hypothesis is accepted.

brand 2.027 4 0.731 P- value is greater than 0.10 at 90 % confidence level.The null hypothesis is accepted.

texture 4.612 4 0.33 P- value is greater than 0.10 at 90 % confidence level.The null hypothesis is accepted.

taste 7.095 4 0.131 P- value is greater than 0.10 at 90 % confidence level.The null hypothesis is accepted.

juiciness 3.651 4 0.455 P- value is greater than 0.10 at 90 % confidence level.The null hypothesis is accepted.

shelflife 2.086 4 0.72 P- value is greater than 0.10 at 90 % confidence level.The null hypothesis is accepted.

skin thickness 2.967 4 0.563 P- value is greater than 0.10 at 90 % confidence level.The null hypothesis is accepted.

direct eating 2.314 4 0.678 P- value is greater than 0.10 at 90 % confidence level.The null hypothesis is accepted.

juice making 3.817 4 0.431 P- value is greater than 0.10 at 90 % confidence level.The null hypothesis is accepted.

fruit salad 3.599 4 0.463 P- value is greater than 0.10 at 90 % confidence level.The null hypothesis is accepted.

jam preparation 2.661 4 0.616 P- value is greater than 0.10 at 90 % confidence level.The null hypothesis is accepted.

Chi-Square Tests

62%

30%

6% 2%

52%

34%

7% 7%

40%

33%

15% 12%

0%

10%

20%

30%

40%

50%

60%

70%

Upto Rs.10 Rs. 11 to 20 Rs.21 to 30 Above Rs.30

Re

spo

nd

en

ts in

(%

)

Price Range

Chart 4.3.1 :Willingness to pay extra over the market price (Rs.160)

for the branded apples by different income groups

LIG

MIG

HIG

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Interpretation: In the chart 4.3.1 we see that, three different income groups have

expressed their WTP extra over market price for the branded apples. LIG consumers are

WTP extra of about Rs.10 (62%), Rs.11 to 20 (30%), Rs.21 to 30 (6%) and above Rs.30

(2%). MIG consumers are WTP extra of about Rs.10 (52%), Rs.11 to 20 (34%), Rs.21 to

30 (7%) and above Rs.30 (7%). HIG consumers are WTP extra of about Rs.10 (40%),

Rs.11 to 20 (33%), Rs.21 to 30 (15%) and above Rs.30 (12%). We see that irrespective of

income level there is a decrease in WTP extra as the price range increases.

4.3.A.5 COMPARISON OF WTP EXTRA OVER THE MARKET PRICE (RS.160)

FOR THE BRANDED APPLES BY LOWER INCOME GROUP AND

COMBINING BOTH MIDDLE INCOME GROUP, HIGH INCOME

GROUP

Source: Survey Data

Interpretation: In the Chart 4.3.1 we see that, MIG and HIG are WTP more premium

than the LIG. By combining MIG and HIG (Chart 4.3.2) we see the average per cent of

respondents WTP extra up to Rs.10 (45%), Rs. 11to 20 (34%), Rs. 21to30 (11%) and

above Rs.30 (10%). More (62%) per cent of population falling in LIG are WTP up to

Rs.10. Very less per cent (8%) of LIG population are WTP more than Rs.21. There are

21% of MIG and HIG population who said WTP above Rs.21. Hence, MIG and HIG

consumers can be clubbed to get their rated common favourite attributes, which are

helpful in deciding the attributes to be stressed in branding apples.

62%

30%

6% 2%

45%

34%

11% 10%

0%

10%

20%

30%

40%

50%

60%

70%

Upto Rs.10 Rs. 11 to 20 Rs.21 to 30 Above Rs.30

Re

spo

nd

en

ts in

%

Price range

Chart .4.3.2 :Comparison of WTP extra over the market price (Rs.160)

for the branded apples by LIG and combined MIG,HIG

LIG

MIG & HIG

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4.3.A.6 Factor Analysis for various Attributes of Apple Rated by the Customer of

both Middle Income Group and High Income Group




Table 4.3.5:Rotated Component Matrix-

APPLE (MIG &HIG)

Component

1 2 3 4 5

price .721

size .808

colour .730

freshness .857

origin .750

variety .692

brand .740

texture .746

taste .646

juiciness

shelf life .536 .518

skin thickness .706

direct eating .826

juice making .873

fruit salad .849





See Annexure -B4 (p-228) for KMO, Bartlett's Test and Total Variance Explained

Source: SPSS Output

Interpretation: From the table.4.3.5 for component 1, we see the attributes; juice making

(.873) fruit salad (.849), direct eating (.826) having the higher factor loadings. This

suggests that both MIG and HIG consumers use apple for multiple purposes, mainly for

juice and salad preparation. They consume more by value to the fruit and less as raw form

(as table fruit). This component 1 can be termed as ‘Multipurpose fruit’.

Component 2 shows the attributes; texture (.746) and skin thickness (.706) have the higher

factor loadings. These attributes helps MIG and HIG customers to recognise the quality

aspects of apple. This can be termed as ‘Quality apples’.

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Component 3 shows the attributes; size (.808), colour (.730) and price (.721) having the

higher loadings. This suggests that MIG and HIG consumers look for apples of standard

size with attractive colour. Value for money is also important to choose the apple. These

are the attributes of graded apples. It can be termed as ‘Graded apple’.

Component 4 showing the attributes; origin (.750), brand (.740) and variety (.692) have

the higher factor loadings. MIG and HIG consumers identify the apples by their

Geographic Indications (GIs) or brands or varieties. Therefore, this can be termed as

‘Identity of apple’.

Finally in component 5, the attribute; freshness has the highest factor loading of .857 and

it can be termed as ‘Fresh apple’.

4.3. B. ORANGE

4.3.B.1 FACTOR ANALYSIS FOR VARIOUS ATTRIBUTES OF ORANGE

RATED BY THE CUSTOMER OF LOW INCOME GROUP




Table 4.3.6:Rotated Component Matrix

ORANGE ( LIG)a

Component

1 2 3

price .809

size .716

colour .674

freshness

origin .778

variety .720

taste .799

juiciness .752

shelf life .564

skin thickness .603

Juice making .654

direct eating .810




See Annexure –B 5 (p- 229) for KMO, Bartlett's Test and Total Variance Explained

Source: SPSS Output

Interpretation: In table 4.3.6 component 1 shows the attributes; direct eating (.810), taste

(.799), juiciness (.752) having the highest component loadings. This suggests that

component 1 is a combination of these three attributes which represent the ultimate use of

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direct eating of the orange (table purpose). Therefore this component 1 is termed as ‘Table

fruit’.

Component 2, shows the attributes; price (.809), size (.716) and colour (.674) have the

highest loadings. This suggests that LIG consumers are price conscious and look for

proper size and colour in oranges. Therefore, it can be termed as ‘Value for money ’.

Component 3 shows the attributes; origin (.778) and variety (.720) with the highest

loadings. This represents the identification aspects of oranges. It is called as ‘Identity of

table fruit’.

From the above analysis we can conclude that, LIG consumers consume oranges more in

the form of direct eating than as juice. Tasty and juicy oranges are preferred to consume in

raw form. LIG consumers are price conscious. They look for oranges having standard size

and which are relatively cheaper. They identify the quality of oranges by the origin (GIs)

and varieties.

4.3.B.2 FACTOR ANALYSIS FOR VARIOUS ATTRIBUTES OF ORANGES

RATED BY THE CUSTOMER OF MIDDLE INCOME GROUP




See Annexure –B 6 (p-230) for KMO, Bartlett's Test and Total Variance Explained

Source: SPSS Output

Table 4.3.7. Rotated Component Matrix

ORANGE (MIG)

Component

1 2 3 4

price .718

size .680

colour .532

freshness .804

origin .740

variety .690

taste .749 .502

juiciness .764

Shelf life .520 .514

skin thickness .557 .517

Juice making .786

direct eating .787




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Interpretation: In table 4.3.7 for component 1 we see the attributes; direct eating (.787)

juice making (.786), juiciness (.764) having the highest loadings. MIG customers use

oranges both for direct eating and making juice. It is called as ‘Multipurpose fruit’.

Component 2 shows the attributes; origin (.740) and variety (.690) have the highest

loadings. This suggests that MIG consumers look for the oranges from the specific place

of origin (GIs) and varieties. It is called as ‘Identity of oranges’.

Component 3 shows the attributes; price (.718), size (.680) have the highest loadings. It is

termed as ‘Value for money’.

Component 4 shows the attribute; freshness has the higher loading of 0.804. Freshness is

the key attribute which influence consumer to buy. Many chances to decline purchase if

oranges are not fresh. This can be termed as ‘Fresh oranges’.

From the above analysis we can conclude that, MIG consumers use oranges equally for

direct eating and juice making. Oranges which are juicy, value for money and of standard

size are preferred. Place of origin (GIs) and varieties of oranges are important aspects to

identify their favourite fruit.

4.3.B.3. FACTOR ANALYSIS FOR VARIOUS ATTRIBUTES OF ORANGE

RATED BY THE CUSTOMER OF HIGH INCOME GROUP




Table 4.3.8.Rotated Component Matrixa

ORANGE (HIG)

Component

1 2 3 4

price .595

size .827

colour .555

freshness .760

origin .870

variety .600

taste .603

juiciness .756

Shelf life .652

skin thickness .547

Juice making .764

direct eating .730




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Source: SPSS Output

Interpretation: In table 4.3.8 for component 1 we see the attributes; juice making (.764),

direct eating (.730) juiciness (.756) having the highest loadings. This suggest that HIG

consumers consume oranges more by making juice and less by direct eating. Therefore,

juicy and tasty oranges are preferred. It can be termed as ‘Juicy oranges’.


loadings. This suggests that HIG consumers look for the oranges from the specific place of

origin (GIs) and varieties. It is called as ‘Identity of juicy oranges’.

Component 3 shows the attribute; size has the higher loading of 0.827. This suggests,

optimum sized oranges for juice making and direct eating are preferred by the HIG

consumers. This can be termed as ‘Desirable orange size’

Component 4 shows the attribute; freshness has the higher loading of 0.760. It can be

termed as ‘Fresh juicy fruit’.

From the above analysis we can conclude that, HIG consumers use oranges more for juice

making hence they prefer juicy fruit. They look for fresh and optimum sized oranges both

for making juice and for direct eating. The origin (GIs) and varieties of oranges are the

important aspects to identify the suitable oranges for making juice and also for direct

eating.


choosing the attributes in oranges.



Above hypothesis is tested using Chi square test for each attributes of oranges. Results are

shown in table 4.3.9.

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Table 4.3.9: Chi Square tests for each attributes of oranges

Source: SPSS output

Interpretation: From the above table 4.3.9 it is clear that attributes; juice making and

shelf life are depending on the income, other attributes are not dependent on income. This

suggests that preference for only these two attributes depends on the income level. Other

attributes are not dependent on income. Therefore, we can conclude that consumers of

MIG and HIG behave similarly in choosing attributes which are not dependant on income.

4.3.B.4 WTP EXTRA ABOVE THE MARKET PRICE FOR THE BRANDED

ORANGES BY THE CONSUMERS OF DIFFERENT INCOME GROUPS

Source: Survey Data


Square Valuedf

Asymp.Sig


price 2.811 4 0.59 P- value is greater than 0.10 at 90 % confidence level.The null hypothesis is accepted.








shelf life 25.907 4 0 P- value is less than 0.01, at 99% confidence level.The null hypotheis is rejected.

skin thickness 6.771 4 0.148 P- value is greater than 0.10 at 90 % confidence level.The null hypothesis is accepted.

juice making 59.901 4 0 P- value is less than 0.01, at 99% confidence level.The null hypotheis is rejected.

direct eating 5.166 4 0.271 P- value is greater than 0.10 at 90 % confidence level.The null hypothesis is accepted.

Chi-Square Tests

57%

30%

13%

0%

43%

32%

20%

5%

25%

32% 29%

14%

0%

10%

20%

30%

40%

50%

60%

Below Rs.10 Rs. 11 to 20 Rs.21 to 30 Above Rs.30

Res

po

nd

ents

in

(%

)

Price range

Chart 4.3.3 Willingness to pay extra over the market price (Rs.110)

for the branded oranges by different income groups

LIG

MIG

HIG

15

Interpretation: Chart 4.3.3 shows that, WTP extra by the different income groups for the

branded oranges.

About 57% of LIG consumers are WTP extra of Rs.10, 30% of Rs.11 to 20, 13% of Rs.21

to 30 and none (0%) above Rs.30.

About 43% MIG consumers are WTP extra of Rs.10, 32% of Rs.11 to 20, 20% of Rs.21 to

30 and 5% above Rs.30.

HIG consumers are WTP extra of about Rs.10 (25%), Rs.11 to 20 (32%), Rs.21 to 30

(29%) and above Rs.30 (14%).

4.3.B.5 COMPARISON OF WTP EXTRA OVER THE MARKET PRICE (RS.110)

FOR THE BRANDED ORANGES BY LOWER INCOME GROUP AND


GROUP

Source: Survey Data

Interpretation: From the chart 4.3.3, it is clear that, MIG and HIG are WTP more

premium than the LIG. By combining MIG and HIG (Chart 4.3.4.) we see the average per

cent of respondents WTP extra up to Rs.10 (34%), Rs. 11to 20 (32%), Rs. 21to30 (25%)

and above Rs.30 (10%). More per cent of population (57%) in LIG are WTP up to Rs.10.

About 13% of LIG population are WTP Rs.21 to 30. There are 17.5% of MIG and HIG

population who said WTP above Rs.21. Hence MIG and HIG population can be clubbed

to get the common favourite attributes for branding oranges to get the significant profit

margin.

57%

30%

13%

0%

34% 32%

25%

10%

0%

10%

20%

30%

40%

50%

60%


Re

spo

nd

en

ts in

%

Price range

Chart 4.3.4: Comparison of WTP extra over the market price

(Rs.110) for the branded oranges by LIG and combined MIG,HIG

LIG

MIG&HIG

16

4.3.B.6 FACTOR ANALYSIS FOR VARIOUS ATTRIBUTES OF ORANGE

RATED BY THE CUSTOMER OF BOTH MIDDLE INCOME GROUP

AND HIGH INCOME GROUP




Table 4.3.10. Rotated Component Matrixa

ORANGE (MIG&HIG)

Component

1 2 3 4

price .743

size .841

colour

freshness .811

origin .690

variety .636

taste .513 .598

juiciness .798

Shelf life .620

skin thickness .685 .510

Juice making .801

direct eating .698


Rotation Method: Varimax with Kaiser

Normalization.



Source: SPSS Output

Interpretation: From the above table we see that component 1 showing the attributes;

juice making (.801), juiciness (.798) have the highest loadings. This suggests that both

MIG and HIG consumers prefer making juice. They choose juicy and tasty oranges. It can

be called as ‘Juicy oranges’.


loadings and it can be termed as ‘Identity of juicy oranges’.

Component 3 shows the attributes; size and price have the higher loadings of 0.841 and

.743. It means consumers look for graded oranges with optimum size and they look for

value for money. It can be termed as ‘Value for money’.

For component 4, attribute; freshness has the higher loading of 0.811. It can be termed as

‘Fresh juicy orange’.

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From the above analysis we can conclude that, MIG and HIG consumers commonly use

oranges more for juice making, and they prefer juicy oranges. They look for fresh, graded

oranges with value for money. They identify the quality oranges more by their origins

(GIs) and varieties. Freshness is an important attribute for deciding the purchase of

oranges.

4.3.C. SWEET LIME

4.3.C.1 FACTOR ANALYSIS FOR VARIOUS ATTRIBUTES OF SWEET LIME






Source: SPSS Output

Interpretation: In table 4.3.11 component 1 shows the attributes; direct eating (.885) and

taste (.856) have the higher component loadings. This suggests that LIG consumers prefer

to consume sweet lime as raw form (direct eating). This suggests that taste is extremely

important attribute for direct consumption. It can be termed as ‘Tasty table fruit’.

Component 2 shows the attribute, skin thickness (.790) has the highest component

loading. This suggests that peeling sweet lime should be easier for direct eating. LIG

consumers desire to have thin skinned sweet lime. It can be termed as ‘Thin skinned fruit’.

Table 4.3.11.Rotated Component Matrix SWEET LIME (LIG)a

Component

1 2 3 4

price .766

size .695

colour

freshness .598

origin .821

variety .671

taste .856

juiciness .763

Shelf life .589

skin thickness .790

direct eating .885

Juice making .524




18

Component 3 shows attributes; origin (.821) and variety (.671) have the highest

component loadings. This suggests that identification of desirable table fruit is by the

place of origin and variety of the fruit. It can be termed as ‘Identity of table fruit’.

Component 4 shows the attributes; price (.766) and size (.695) have the higher component

loadings. It means consumers look for graded sweet limes with optimum size and they

look for value for money. It can be termed as ‘Value for money’.

From the above analysis we can conclude that, LIG consumer prefers to have sweet lime

by direct eating. Attributes like good taste and juiciness, thin skin are important. They

identify the quality oranges by their origin (GIs) and varieties. LIG consumers are price

conscious they look for value for money.

4.3.C.2 FACTOR ANALYSIS FOR VARIOUS ATTRIBUTES OF SWEET LIMES





Table.4.3.12.Rotated Component Matrix SWEET LIME (MIG)

Component

1 2 3 4

price

size .757

colour .694

freshness .670

origin .733

variety .603 .701

taste .812

juiciness .627

shelf life .727

skin thickness .841

direct eating .603

juice making .779





Source: SPSS Output

Interpretation: In table 4.3.12 for component 1 we see the attributes; taste (.812) and

juice making (.779) have the higher component loadings. MIG consumers prefer to buy

tasty fruits suitable for making juice. It can be termed as ‘Tasty and juicy fruit’.

19

Component 2 shows the attributes: size (.757) and colour (.694) have the highest loadings.

These are the quality aspects of sweet lime. This means MIG customer prefer graded fruits

according to their size and colour. It can be termed as ‘Quality fruit’.

Component 3 shows the attributes, skin thickness (.841) and shelf life (.727) have the

highest loadings. These are the desired attributes over size and colour to see more quality

in the fruit by the MIG consumers. It is called as ‘Desirable fruit’

Component 4 shows the attributes, origin (.733) and variety (.701) have the higher

component loadings. This suggests that MIG consumers look for the sweet lime from the

specific place of origin (GIs) and varieties. It can be termed as ‘Identity of quality fruit’.

From the above analysis we can conclude that, MIG consumers prefer to consume sweet

lime by adding value to the fruit than as raw form. Tasty and juicy fruits are preferred by

them. Fruits with standard size and attractive colour are much preferred. MIG consumers

prefer to store the fruits; hence skin thickness and shelf life are important.

4.3.C.3 FACTOR ANALYSIS FOR VARIOUS ATTRIBUTES OF SWEET LIMES





Table.4.3.13.Rotated Component Matrix

SWEET LIME (HIG)

Component

1 2 3 4

price

size .814

colour .605

freshness .731

origin .744

variety .855

taste .742

juiciness .816

shelf life .502 .513

skin thickness .886

direct eating

Juice making .808





Source: SPSS Output

20

Interpretation: In table 4.3.13 for component 1 we see the attributes; juiciness (.816) and

juice making (.808) have the higher component loadings. This suggests that HIG

consumers prefer to use sweet lime by adding value to it i.e. making juice. Hence they

look for only fruits which are suitable for making juice. It can be termed as ‘Juicy fruit’.

Component 2 shows attributes; skin thickness (.886) and freshness (.731) have the higher

component loadings. This suggests that fruits with thin skin are preferred by the HIG

consumer. These are the quality aspects of fruit. It can be termed as ‘Quality fruit’.

Component 3 shows the attributes; size (.814) and colour (.605) have the higher

component loadings. These are also the quality aspects of fruit. Researcher intends to club

this component 3 with the component 2 to call it as ‘Quality fruit’

Component 4 shows the attributes; variety (.855) and origin (.744). These are the identity

aspects of the fruit which are looked by the HIG consumers. It can be termed as ‘Identity

of juicy fruit’.

From the above analysis we can conclude that, HIG consumers prefer sweet lime for

making juice. Hence, quality fruits are chosen. As HIG consumers are affluent, price

would not be a constraint to buy quality fruits.


choosing the attributes in sweet limes.

H0: Ratings allotted to various attributes by the consumers are not dependent on income.

H1: Ratings allotted to various attributes by the consumers are dependent on income.

Above hypothesis is tested using Chi square test for each attributes of sweet lime. Results

are shown in table 4.3.14

Table.4.3.14: Chi Square tests for each attributes of sweet limes

Source: SPSS Output


Square Valuedf

Asymp.Sig







variety 26.945 4 0 P- value is less than 0.01, at 99% confidence level.The null hypotheis is rejected.




skinthickness 4.129 4 0.389 P- value is greater than 0.10 at 90 % confidence level.The null hypothesis is accepted.

directeating 1.413 4 0.842 P- value is greater than 0.10 at 90 % confidence level.The null hypothesis is accepted.

Juicemaking 17.252 4 0.002 P- value is less than 0.01, at 99% confidence level.The null hypotheis is rejected.

Chi-Square Tests

21

Interpretation: From the table 4.3.14 it is clear that, the attributes ‘variety’ and ‘juice

making’ are depending on the income groups, other attributes are not dependent on

income. This suggests that preference for only these two attributes depends on the income

level. Other attributes are not dependent on income. Therefore, we can conclude that

consumers of MIG and HIG behave similarly in choosing attributes which are not

dependant on income.

4.3.C.4 WTP EXTRA OVER THE MARKET PRICE (RS.50) FOR THE BRANDED

SWEET LIMES BY DIFFERENT INCOME GROUPS

Source: Survey Data

Interpretation: In chart 4.3.5, we see that three different income groups have expressed

their WTP extra over market price for the branded sweet limes. LIG consumers are WTP

extra of about Rs.10 (58%), Rs.11 to 20 (24%), Rs.21 to 30 (16%) and above Rs.30 (0%).

MIG consumers are WTP extra of about Rs.10 (42%), Rs.11 to 20 (32%), Rs.21 to 30

(18%) and above Rs.30 (8%). HIG consumers are WTP extra of about Rs.10 (32%),

Rs.11 to 20 (29%), Rs.21 to 30 (27%) and above Rs.30 (12%).

58%

26%

16%

0%

42%

32%

18%

8%

32% 29% 27%

12%

0%

10%

20%

30%

40%

50%

60%

70%


Re

spo

nd

en

ts in

%

Price range

Chart 4.3.5: Willingness to pay extra over the market price (Rs.50)

for the branded sweet lime by different income groups

LIG

MIG

HIG

22

4.3.C.5 COMPARISON OF WTP EXTRA OVER THE MARKET PRICE (RS.50)

FOR THE BRANDED SWEET LIME BY LOWER INCOME GROUP AND


GROUP

Source: Survey Data

Interpretation: In the chart 4.3.5, it is clear that, MIG and HIG are WTP more premium

than the LIG consumers. By combining MIG and HIG (Chart 4.3.6) we see the average

per cent of respondents WTP extra up to Rs.10 (37%), Rs. 11to 20 (31%), Rs. 21to30

(23%) and above Rs.30 (10%). More per cent of population (58%) in LIG are WTP up to

Rs.10. About 23% of LIG population are WTP Rs.21 to 30. There are 16.5% of MIG and

HIG population who said WTP above Rs.21. Hence, MIG and HIG population can be

clubbed to get the common favourite attributes for branding sweet lime to get the

significant profit margin.

58%

26%

16%

0%

37%

31%

23%

10%

0%

10%

20%

30%

40%

50%

60%

70%


Re

spo

nd

en

ts in

%

Price range

Chart 4.3.6. Comparison of WTP extra over the market price (Rs.50)

for the branded sweet lime by LIG and combined MIG,HIG

LIG

MIG & HIG

23

4.3.C.6 FACTOR ANALYSIS FOR VARIOUS ATTRIBUTES OF SWEET LIME

RATED BY THE CUSTOMER OF BOTH MIDDLE INCOME GROUP

AND HIGH INCOME GROUP




Table 4.3.15.Rotated Component Matrix SWEET LIME (MIG&HIG)

Component

1 2 3 4

price .672

size .775

colour .538

freshness .620

origin .808

variety .649

taste .810

juiciness .785

shelf life .563

skin thickness .729

direct eating

Juice making .801




See Annexure – B 12 (p-236) for KMO, Bartlett's Test and Total Variance Explained

Source: SPSS Output

Interpretation: In table 4.3.15 for component 1 attributes; juice making (.801) and

juiciness (.785) have the higher component loadings. It suggests that MIG and HIG

consumers use sweet lime more for making juice. It can be termed as ‘Juicy fruit’.

Component 2 shows the attributes; taste (.810) and skin thickness (.729) have the higher

component loadings. This suggests that both MIG and HIG consumers prefer tasty and

thin skinned fruits. They consider these are important quality parameters of fruits. It can

be termed as ‘Quality juicy fruit’.

Component 3 shows the attribute; origin (.808) and variety (.649) have the higher loading.

This suggests that both MIG and HIG consumers look for the sweet lime from the specific

place of origin (GIs) and varieties. It can be termed as ‘Identity juicy fruit’.

Component 4 shows attributes; size (.775) and price (.672) have the higher component

loadings. This suggests both MIG and HIG consumers look for value for money. It can be


24

4.3. D. POMEGRANATE

4.3.D.1 FACTOR ANALYSIS FOR VARIOUS ATTRIBUTES OF

POMEGRANATE RATED BY THE CUSTOMER OF LOW INCOME

GROUP





POMEGRANATE (LIG)

Component

1 2 3 4 5

price .821 -.551

size .709

Seed colour

origin .718

variety .889

taste .794

juiciness .892

Shelf life .626

Juice making .555

direct eating .803

Salad preparation .501

Dish preparation .601





Source: SPSS Output

Interpretation: In table 4.3.16, for component 1 attribute, direct eating (.803) has the

highest component loading. This suggests that LIG consumers, mainly consume directly.

They rarely use pomegranate for salad and dish preparations. It can be termed as ‘Table

fruit’.

Component 2 shows the attribute; juiciness (.892) and taste (.794) have the highest

component loading. This suggests that LIG consumers need juicy and tasty fruit for direct

eating which are quality aspects of fruit. It can be termed as ‘Quality table fruit’.

Component 3 shows the attribute; price (.821) and size (.709) have the higher loadings.

This suggests that LIG consumers are price sensitive and they seek value for money for

graded fruits. It can be termed as ‘Value for money’.

Component 4 shows the attribute origin (.718) and component 5 shows attribute; variety

(.889) have the highest component loadings respectively. This suggests that identification

25

of desirable table fruit is by the place of origin and variety of the fruit. It can be termed as

‘Identity of table fruit’.

From the above analysis we can conclude that, LIG consumers use pomegranate more as

table fruit. Therefore, taste and juiciness will become important deciding factor for

purchase. The size and seed colour of the fruit also important where the quality is being

judged on these attributes. The origin and variety are the main attributes for the

identification of fruit.


POMEGRANATE RATED BY THE CUSTOMER OF MIDDLE INCOME

GROUP





POMEGRANATE (MIG)

Component

1 2 3 4

price .744

size .777

Seed colour .708

origin .744

variety .731

taste .774

juiciness .704

Shelf life

Juice making .686

direct eating .831

salad preparation .822

dish preparation .853





Source: SPSS Output

Interpretation: In table 4.3.17, for component 1 attributes; dish preparation (.853), direct

eating (.831) and salad preparation (.822) have the highest component loadings. This

suggests that MIG consumers use pomegranate more for dish preparation and less for

salad preparation. Moderately more for direct eating. It can be termed as ‘Multi use fruit’.

26

Component 2 shows attributes; size (.777) and price (.744) have the highest component

loadings. This suggests that MIG consumers are also price sensitive like LIG, it is because

the market price of pomegranate is high most of the time. They feel worth buying proper

sized fruits. It can be termed as ‘Value for money’.

Component 3 shows attributes; taste (.774) and juiciness (.704) have the high component

loadings. This suggests that MIG consumers look for tasty and juicy fruits because they

use it for preparation of juice and also for direct eating. These are quality aspects of the

fruits. It can be termed as ‘Quality fruit’.

Component 4 shows attributes; origin (.744) and variety (.731) have the highest

component loadings. This suggests that MIG consumers are specific to the place of origin

and varieties of the fruit. It can be termed as ‘Identity quality fruit’.

From the above analysis we can conclude that, MIG consume more pomegranate fruit by

adding value in it than as direct consumption. Tasty and juicy fruits are preferred and they

look for value for money. The place of origin and variety are important parameters to

judge the quality of fruits.


POMEGRANATE RATED BY THE CUSTOMER OF HIGH INCOME

GROUP





POMEGRANATE (HIG)

Component

1 2 3 4

price .692

size .740

Seed colour .710

origin .814

variety .709

taste .820

juiciness .768

Shelf life .598

Juice making .742

direct eating .780







Source: SPSS Output

27

Interpretation: In table 4.3.18, for component 1 attributes; dish preparation (.862), salad

preparations (.835) have the higher component loadings. This suggests that HIG

consumers use pomegranate more for dish and salad preparation. These are value added

products of pomegranate. It can be termed as ‘Fruit for value addition’.

Component 2 shows attributes; size (.740) and seed colour (.710) have the higher

component loadings. This suggests that good fruit size with attractive seed colour are

preferred by HIG consumers. It can be termed as ‘Quality fruit for value addition’.


component loadings. It can be termed as ‘Identity of quality fruits’.

Component 4 shows the attributes; taste (.820) and juiciness (.768) have the higher

component loadings. This suggests that HIG consumers desire to have fruits with taste and

more juice. It can be termed as ‘Desirable quality fruits’.

From the above analysis we can conclude that, HIG consumers use pomegranate more for

value addition than the direct consumption. They look for colour of the seeds and size of

the fruit. They are less sensitive towards price as compared to other income group people.

They identify the product quality by origin and varieties. The taste and juiciness are

desirable attributes in the fruit.


choosing the attributes in pomegranate.



Above hypothesis is tested using Chi-Square test for each attributes of pomegranate.

Results are shown in table 4.3.19.

28

Table 4.3.19 Chi square tests for each attributes of pomegranate.

Source: SPSS output

Interpretation: From the table 4.3.19, it is clear that, only the attributes shelf life and

salad preparation look important. Other attributes are not dependent on income. This

suggests that preference for only these two attributes depends on the income level.

Therefore, we can conclude that consumers of MIG and HIG behave similarly in choosing

attributes which are not dependant on income.

4.3.D.4 WTP EXTRA OVER THE MARKET PRICE FOR THE BRANDED

POMEGRANATES BY DIFFERENT INCOME GROUP

Source: Survey Data


Square Valuedf

Asymp.Si

g

(2 sided)

Interpretation



seedcolour 5.564 4 0.234 P- value is greater than 0.10 at 90 % confidence level.The null hypothesis is accepted.





shelflife 10.663 4 0.031 P- value is less than 0.05, at 95% confidence level.The null hypotheis is rejected.

juicemaking 0.471 4 0.976 P- value is greater than 0.10 at 90 % confidence level.The null hypothesis is accepted.

directeating 2.749 4 0.601 P- value is greater than 0.10 at 90 % confidence level.The null hypothesis is accepted.

saladprepation 14.954 4 0.005 P- value is less than 0.01, at 99% confidence level.The null hypotheis is rejected.

dishpreparation 6.433 4 0.169 P- value is greater than 0.10 at 90 % confidence level.The null hypothesis is accepted.

Chi-Square Tests

43%

36%

17%

4%

31% 28%

32%

9%

19%

32% 34%

15%

0%

5%

10%

15%

20%

25%

30%

35%

40%

45%

50%


Re

spo

nd

en

ts in

%

Price range

Chart 4.3.7. Willingness to pay extra over the market price (Rs.120)

for the branded pomegranate by different income groups

LIG

MIG

HIG

29

Interpretation: From chart 4.3.7 we see that, three different income groups have

expressed their WTP extra over market price for the branded pomegranates. LIG

consumers are WTP extra of about Rs.10 (43%), Rs.11 to 20 (36%), Rs.21 to 30 (17%)

and above Rs.30 (4%). MIG consumers are WTPP of about Rs.10 (31%), Rs.11 to 20

(28%), Rs.21 to 30 (32%) and above Rs.30 (9%). HIG consumers are WTP extra of about

Rs.10 (19%), Rs.11 to 20 (32%), Rs.21 to 30 (34%) and above Rs.30 (15%).

4.3.D.5 COMPARISON OF WTP EXTRA OVER THE MARKET PRICE (RS.160)

FOR THE BRANDED POMEGRANATE BY LIG AND COMBINED MIG,

HIG

Source: Survey Data

Interpretation: In table no. 4.3.7 we see that, MIG and HIG are WTP more premium than

the LIG. By combining MIG and HIG (Table No 4.3.8) we see the average per cent of

respondents WTP extra up to Rs.10 (25%), Rs. 11to 20 (30%), Rs. 21to30 (33%) and

above Rs.30 (12%). More (43%) per cent of population falling in LIG are WTP up to

Rs.10. Only 10 per cent of LIG population are WTP more than Rs.21. There are 22.5% of

MIG and HIG population who said WTP above Rs.21. Hence MIG and HIG population

can be clubbed to get the common favourite attributes for branding apples to get the

significant profit margin.

43%

36%

17%

4%

25%

30% 33%

12%

0%

10%

20%

30%

40%

50%


Re

spo

nd

en

ts in

%

Price range

Chart 4.3.8. Comparison of WTP extra over the market price

(Rs.160) for the branded pomegranate by LIG and combined

MIG,HIG

LIG

MIG & HIG

30

4.3.D.6 FACTOR ANALYSIS FOR THE ATTRIBUTES OF POMEGRANATE

RATED BY COMBINING BOTH THE MIDDLE INCOME GROUP AND

HIGH INCOME GROUP





Source: SPSS Output

Interpretation: In table 4.3.20, for component 1 attributes; dish preparation (.874) and

salad preparations (.852) have the higher component loadings. This suggests that MIG and

HIG consumers prefer to have pomegranate by adding value. It can be termed as ‘Fruits

for value addition’.

Component 2 shows the attributes; size (.786) and price (.750) have the higher component

loadings. MIG and HIG consumers prefer graded fruits. They are more users so they look

for value for money in the fruit. It can be termed as ‘Value for money’.

Component 3 shows the attributes; taste (.794) and juiciness (.739) have the higher

component loadings. MIG and HIG consumers look for tasty and juicy fruits for salad and

dish preparation. It can be termed as ‘Quality fruits’.

Component 4 shows the, attributes origin (.795) and variety (.701) has the highest

component loading. It can be termed as ‘Identity of quality fruits’


POMEGRANATE (MIG&HIG)

Component

1 2 3 4

price .750

size .786

Seed colour .669

origin .795

variety .701

taste .794

juiciness .739

Shelf life

Juice making .699

direct eating .742






31

4.3.E. BANANA

4.3.E.1 FACTOR ANALYSIS FOR VARIOUS ATTRIBUTES OF BANANA


Factor analysis was conducted using PCA on 9 attributes with varimax rotation. KMO and

Bartlett test were used to measure sampling adequacy and the presence of correlation


Table 4.3.21. Rotated Component Matrix a –

BANANA (LIG)

Component

1 2 3 4

price .863

variety .898

appearance .807

ripeness .653

origin .632

taste .735

shelf life

direct eating .857





See Annexure –B 17(p-241) for KMO, Bartlett's Test and Total Variance Explained

Source: SPSS Output

Interpretation: In table 4.3.21, for component 1we see the attributes; direct eating and

dish preparation have high loading of 0.857 and 0.814. This suggests that LIG consumers

use banana more for direct eating and less for preparing dishes. Therefore, this component

can be termed as ‘Table fruit’.

In component 2 we see the attributes; appearance and ripeness having the higher loadings

of 0.807 and 0.653.Therefore, this component can be termed as ‘Quality table fruit’.

In component 3 we see the attribute; variety has the highest loading of 0.898. LIG

consumers desire to have specific variety for direct eating. It can be termed as ‘Desirable

variety’.

For component 4 we see that price is having highest loading of 0.863. As LIG customer

are price sensitive, they look for value for money. It can be termed as ‘Value for money’.

From the above analysis we can conclude that, LIG consumers use bananas more for direct

consumption and less or dish preparation. Taste of banana will be an important attribute

32

for eating in raw form. Appearance of bananas with proper ripening will be an important

component to judge the quality. Variety of banana will be an identity component with

value for money.






Table 4.3.22.Rotated Component Matrix-

BANANA (MIG)a

Component

1 2 3

price .752

variety .776

appearance .863

ripeness .787

origin

taste .560

shelf life .597

direct eating .908






Source: SPSS Output

Interpretation: In the table 4.3.22, for component 1 we see the attributes; direct eating

and dish preparation have high loading of 0.908 and 0.822.This suggests that MIG

consumers use banana more for direct eating than for preparing dishes. It is termed as

‘Table fruit’.

Component 2 shows the attributes; appearance and ripeness having the higher loadings of

0.863 and 0.787.These are the quality parameters of good quality bananas. Therefore, this

component can be termed as ‘Quality table fruit’.

Component 3 shows the attributes; variety and price have the highest loadings of 0.776

and .752. These attributes are important for MIG customer to recognise the type of

bananas before they buy. And it should be worth buying. The quantity of purchase will

increase if there is value for money if they find suitable variety. Therefore, it can be


33

From the above analysis we can conclude that, MIG consumers are no different from LIG

as far as bananas usage is concerned. MIG consumers use banana more for direct

consumption and less for dish preparation. The appearance of bananas with proper

ripening will be an important deciding component to buy bananas for direct consumption.






Table 4.3.23.Rotated Component Matrix-

BANANA (HIG)a

Component

1 2 3

price .643

variety .851

appearance .813

ripeness .695

origin .639

taste .575

shelf life .683

direct eating .913






Source: SPSS Output

Interpretation: In table 4.3.23, for component 1 we see the attributes; direct eating and

dish preparation have high loading of 0.913 and 0.907. This suggests that HIG consumers

use banana almost equally for direct eating and for preparing dishes. This component can

be termed as ‘Multi use fruit’.

Component 2 shows the attributes; appearance and ripeness having the higher loadings of

0.813 and 0.695. Same as LIG and MIG consumers HIGs see the appearance and ripeness

of the fruit. These are the quality aspects of bananas. This component can be termed as

‘Quality multi use fruit’.


and 0.643. This suggests that HIG consumers ask for specific variety before purchase.

34

Varieties may differ for direct eating and dish preparation. Purchase also depends on the

price of fruit. They may prefer to buy more if there is value for money. It can be termed as

‘Desirable fruit’.

From the above analysis we can conclude that, HIG use equally same for direct

consumption and dish preparation. The appearance of bananas with proper ripening will be

an important deciding component to buy bananas. They are very specific with banana and

variety give importance to value for money.


choosing the attributes in Banana.



Above hypothesis is tested using Chi square test for each attributes of banana. Results are

shown in below table 4.3.24.

Table.4.3.24: Chi Square tests for each attributes of Banana

Source: SPSS output

Interpretation:

From the above table it is clear that, only the attribute dish preparation depending on the

income other attributes are not dependent on income. This suggests that preference for

only this attribute depends on the income level. Therefore, we can conclude that

consumers of MIG and HIG behave similarly in choosing attributes which are not

dependant of income.


Square Valuedf

Asymp.Sig


price 1.957 4 0.744 p- value is greater than 0.10 at 90 % confidence level.The null hypothesis is accepted.

variety 4.838 4 0.304 p- value is greater than 0.10 at 90 % confidence level.The null hypothesis is accepted.

appearance 7.361 4 0.118 p- value is greater than 0.10 at 90 % confidence level.The null hypothesis is accepted.

ripeness 1.926 4 0.749 p- value is greater than 0.10 at 90 % confidence level.The null hypothesis is accepted.

origin 1.706 4 0.79 p- value is greater than 0.10 at 90 % confidence level.The null hypothesis is accepted.

taste 6.911 4 0.141 p- value is greater than 0.10 at 90 % confidence level.The null hypothesis is accepted.

shelflife 3.497 4 0.478 p- value is greater than 0.10 at 90 % confidence level.The null hypothesis is accepted.

directeating 6.269 4 0.18 p- value is greater than 0.10 at 90 % confidence level.The null hypothesis is accepted.

dishpreparation 12.023 4 0.017 p- value is less than 0.05, at 95% confidence level.The null hypotheis is rejected.

Chi-Square Tests

35

4.3.E.4 WTP EXTRA OVER THE MARKET PRICE FOR THE BRANDED

BANANAS BY DIFFERENT INCOME GROUPS

Source: Survey Data

Interpretation: From the chart 4.3.9 we see that, three different income groups have

expressed their WTP extra over market price for the branded bananas. LIG consumers are

WTP extra of about Rs.10 (53%), Rs.11 to 20 (32%), Rs.21 to 30 (13%) and above Rs.30

(2%). MIG consumers are WTP extra of about Rs.10 (43%), Rs.11 to 20 (36%), Rs.21 to

30 (16%) and above Rs.30 (5%). HIG consumers are WTP extra of about Rs.10 (33%),

Rs.11 to 20 (41%), Rs.21 to 30 (19%) and above Rs.30 (7%).

4.3.E.5 COMPARISON OF WTP EXTRA OVER THE MARKET PRICE FOR THE

BRANDED BANANA BY LOWER INCOME GROUP AND COMBINING

BOTH MIDDLE INCOME GROUP, HIGH INCOME GROUP

Source: Survey Data

53%

32%

13%

2%

43%

36%

16%

5%

33%

41%

19%

7%

0%

10%

20%

30%

40%

50%

60%


Re

spo

nd

en

ts in

%

Price range

Chart 4.3.9. Willingness to pay extra over the market price

(Rs.45) for the branded bananas by different income groups

LIG

MIG

HIG

53%

32%

13%

2%

38% 39%

18%

6%

0%

10%

20%

30%

40%

50%

60%


Re

spo

nd

en

ts in

%

Price range

Chart 4.3.10: Comparison of WTP extra over the market

price (Rs.45) for the branded bananas by LIG and combined

MIG, HIG

LIG

MIG&HIG

36

Interpretation: From the chart 4.3.10, we see that, MIG and HIG are WTP more

premium than the LIG. By combining MIG and HIG (Table No 4.2) we see the average

per cent of respondents WTP extra up to Rs.10 (38%), Rs. 11to 20 (39%), Rs. 21to30

(18%) and above Rs.30 (6%). More (53%) per cent of population falling in LIG are WTP

up to Rs.10. Very less per cent (7.5%) of LIG population are WTP more than Rs.21. There

are 12% of MIG and HIG population who said WTP above Rs.21. Hence MIG and HIG

population can be clubbed to get the common favourite attributes for branding bananas to

get the significant profit margin.

4.3.E.6 FACTOR ANALYSIS FOR THE ATTRIBUTES OF BANANA RATED BY

COMBINING BOTH THE MIDDLE INCOME GROUP AND HIGH

INCOME GROUP





BANANA (MIG&HIG)

Component

1 2 3

price .774

variety .893

appearance .839

ripeness .741

origin .506

taste .575

shelf life .630

direct eating .906






Source: SPSS Output

Interpretation: From the table 4.3.25, for component 1 we see the attributes; direct eating

and dish preparation have high loading of 0.906 and 0.841. This suggests that both MIG

and HIG consumers use more bananas for direct eating than for preparing dishes.

Therefore this component can be termed as ‘Table fruit’.

37

In component 2 we see that attributes; appearance and ripeness having the higher loadings

of 0.839 and 0.741. Both MIG and HIG consumers find the quality bananas by their

appearance level of ripeness. This component can be termed as ‘Quality table fruit’.


and 0.774. It means MIG and HIG consumers are willing to pay more if their desired

variety is available. It can be termed as ‘Desirable fruit’.

4.3. F.ONION

4.3.F.1 FACTOR ANALYSIS FOR VARIOUS ATTRIBUTES OF ONION RATED

BY THE CUSTOMER OF LOW INCOME GROUP





ONION(LIG)

Component

1 2 3

freshness .650

size .785

origin .635

colour .629

variety .768

pungency .674

sprouting .717

cleanliness .710

shelf life

sambar .679





Source: SPSS Output


are, sprouting (.717) and cleanliness (.710) have the higher component loadings. This

suggests that LIG customers look for clean and not sprouted onions. Sprouted onions are

likely to be rejected by the LIG customers. It can be termed as ‘Ideal onions’.

Component 2 shows the attributes; size (.785) and freshness (.650) have the higher

component loadings. This suggests that LIG customers desire to buy fresh onions having

uniform, standard size. It can be termed as ‘Desired onions’.

38

Component 3 shows the attributes; variety (.768) and origin (.635) have the higher

component loadings. Geographic identifications (GIs) will help them to distinguish the

onions. It can be termed as ‘Identity of ideal onions’.

From the above analysis we can conclude that, LIG consumers identify ideal onions by

their good quality judged mainly by their sprouting and cleanliness attributes. They look

for onions having uniform size. GIs (origin) is also the important deciding factor for

desired onions.

4.3.F.2 FACTOR ANALYSIS FOR VARIOUS ATTRIBUTES OF ONION RATED

BY THE CUSTOMER OF MIDDLE INCOME GROUP





ONION(MIG)

Component

1 2 3 4

freshness .682

size .696

origin .561

colour .845

variety .861

pungency .776

sprouting .841

cleanliness .547

shelf life .748

sambar .798





Source: SPSS Output

Interpretation: From the above table we can see that the attributes under component 1

are, variety (.861) and colour (.845) having the higher component loadings. This suggests

that MIG consumers identify the onions by their variety and colour. This also suggests that

different varieties are identified by the colour of the vegetable. It can be termed as

‘Identity of onion’.

Component 2 shows the attributes; sprouting (.841) and pungency (.776) have the higher

component loadings. This suggests that the good quality onions are known by their level

39

of pungency. Sprouted onions are likely to be rejected by the MIG customers. It can be

termed as ‘Good quality onions’.

Component 3 shows that the attributes; sambar (.798) and shelf life (.748) have the higher

component loading. This suggests that MIG customers buy onions which are suitable for

preparing sambar. Shelf life of the vegetable should be more so that they can store for long

duration. It is because of high fluctuation of onion price in the market. It can be termed as

‘Long shelf life onion’.

Component 4 shows the attributes, size (.696) and freshness (.682) have the higher

component loadings. This suggests that MIG customers desire to have onions with

optimum size and should be fresh. It can be termed as ‘Desirable onion’.

From the above analysis we can conclude that, MIG consumers identify good onions by

their varieties and colour. The quantity purchase will depend on the market price. Local

varieties of onion are preferred than the hybrids. Good quality of onions is judged by the

sprouting and pungency attributes.

4.3.F.3 FACTOR ANALYSIS FOR THE ATTRIBUTES OF ONION RATED BY

THE HIGH INCOME GROUP





ONION(HIG)

Component

1 2 3 4

freshness .818

size .931

origin .969

colour .578

variety .831

pungency .659

sprouting .749

cleanliness .741

shelf life .540

sambar .802





Source: SPSS Output

40


are, sprouting (.749) and cleanliness (.741) have the higher component loadings. This

suggests that the good quality onions are known by their level of pungency. Sprouted

onions are likely to be rejected by the HIG customers. It can be termed as ‘Ideal onions’.

Component 2 shows the attributes; freshness (.818) and sambar (.802) have the higher

component loadings. This suggests that HIG consumer desire onion to be fresh and should

be suitable for sambar preparation. It can be termed as ‘Desirable onions’.

Component 3 shows the attribute size (.931) has the highest component loading. This

suggests that size of the onion is extremely important for the HIG customers. They prefer

graded onions according to size. It can be termed as ‘Graded onions’.


component loadings. Geographic identifications (GIs) will help HIG customers to

distinguish the varieties of onions. It can be termed as ‘Identity of ideal onions’.

From the above analysis we can conclude that, HIG consumers identify ideal onions by

their cleanliness and sprouting. They look for ideal size and fresh for sambar preparation.

The GI and varieties are important attributes for the purchase of onions.


choosing the attributes in onion.



Above hypothesis is tested using Chi square test for each attributes of onion. Results are

shown in table 4.3.29.

Table 4.3.29 Chi square test for each attributes of onions.

Source: SPSS output

Interpretation: From the table 4.3.29 it is clear that ratings given by the consumers for

the attributes, freshness, colour, sprouting are not dependent on income of MIG and HIG

Attributes

Pearsons Chi

Square

Value

dfAsymp.Sig



size 21.858 4 0 P- value is less than 0.01, at 99% confidence level.The null hypotheis is rejected.

origin 8.464 4 0.006 P- value is less than 0.01, at 99% confidence level.The null hypotheis is rejected.


variety 13.435 4 0.009 P- value is less than 0.01, at 99% confidence level.The null hypotheis is rejected.

pungency 16.682 4 0.002 P- value is less than 0.01, at 99% confidence level.The null hypotheis is rejected.

sprouting 7.963 4 0.093 P- value is less than 0.10, at 90% confidence level.The null hypotheis is accepted.

cleanliness 14.216 4 0.007 P- value is less than 0.01, at 99% confidence level.The null hypotheis is rejected.

shelflife 13.908 4 0.008 P- value is less than 0.01, at 99% confidence level.The null hypotheis is rejected.

sambar 37.34 4 0 P- value is less than 0.01, at 99% confidence level.The null hypotheis is rejected.

Chi-Square Tests

41

consumers. Other attributes are dependent on income. Hence, there is close association

between income of the consumers and their ratings for the various attributes of onions.

4.3.F.4 WTP EXTRAABOVE THE MARKET PRICE FOR THE BRANDED

ONIONS BY THE CUSTOMERS OF DIFFERENT INCOME GROUPS

Source: Survey Data

Interpretation: Three different income groups have expressed their WTP extra over

market price for the branded onions. LIG consumers are WTP extra of about Rs.5 (49%),

Rs.6 to 10 (28%), Rs.11 to 15 (15%), Rs.16 to 20 (7%) and above Rs. 20 (1%).

MIG consumers are WTP extra of about Rs.5 (34%), Rs.6 to 10 (30%), Rs.11 to 15 (24%),

Rs.16 to 20 (10%) and above Rs. 20 (2%).

HIG consumers are WTP extra of about Rs.5 (20%), Rs.6 to 10 (34%), Rs.11 to 15 (26%),

Rs.16 to 20 (15%) and above Rs. 20 (5%).

49%

28%

15%

7%

1%

34% 30%

24%

10%

2%

20%

34%

26%

15%

5%

0%

10%

20%

30%

40%

50%

60%

Below Rs.5 Rs.6 to 10 Rs.11 to 15 Rs.16 to 20 Above Rs.20

Re

spo

nd

en

ts in

%

Price range

Chart 4.3.11: Willingness to pay extra over the market price (Rs.34) for

the branded onions by different income groups

LIG

MIG

HIG

42

4.3. G.POTATO

4.3.G.1 FACTOR ANALYSIS FOR VARIOUS ATTRIBUTES OF POTATO






POTATO (LIG) Component

1 2 3 4 5

freshness .729

firmness .731

size & shape .542

origin .741

variety .777

cleanliness .809

no greening .837

taste .617

shelf life .792

skin thickness .637

bhaji .849

bajji .790

chips .696

pealabity before cooking .776

pealability after cooking .674





Source: SPSS Output


are, bhaji (.849) and bajji (.790) having the higher component loadings. This suggests that

component 1 is a combination of these two attributes, which represent the usage aspects of

potatoes by LIG customers. LIG customers prefer to prepare more of bhaji and bajji. The

cost of preparing chips would be higher to them. Therefore this component can be termed

as ‘Potato for bhaji and bajji’.

Component 2 shows that the attributes; firmness (.731) and freshness (.729) have the

higher component loadings. These are the quality aspects of the potatoes for preparing

bhaji and bajji. LIG customers look for firm and fresh potatoes for preparing bhaji and

bajji. This component 2 can be called as ‘Quality potato’


component loadings. This suggests that LIG customers’ look for particular variety and

43

origin of potatoes to prepare bhaji and bajji. They identify potatoes by their origin and

varieties. It can be termed as ‘Identity of potato’.

Component 4 shows the attributes; shelf life (.792) and skin thickness (.637) have the

higher component loadings. This suggests that LIG customers look for keeping quality and

thin skin for potatoes for preparing bhaji and bajji. These are the quality aspects of

potatoes. It can be termed as ‘Quality of potato’.

Component 5 shows the attributes; no greening (.837) and cleanliness (.809) have the

higher component loadings. These are the desired quality aspects by the LIG customers.

Therefore it can be termed as ‘Desirable quality potato’.

From the above analysis we can conclude that, LIG customers prefer to prepare more of

bhaji and bajji than the chips. Quality of potatoes is being judged based on firmness, no

greening, skin thickness, cleanliness etc. Cost of preparing chips at home is more; because

of their low disposable income they use more potatoes for preparing bhaji and bajji but

very less for chips.

4.3.G.2 FACTOR ANALYSIS FOR THE ATTRIBUTES OF POTATO RATED BY

THE MIDDLE INCOME GROUP





POTATO (MIG)

Component

1 2 3 4 5

freshness .807

firmness .710

size & shape .675

origin .710

variety .646

cleanliness

no greening -.545

taste .719

shelf life .752

skin thickness .517

bhaji .830

bajji .800

chips .815


pealabity after cooking .808




See Annexure –B 25( p-249) for KMO, Bartlett's Test and Total Variance Explained

Source: SPSS Output

44


are, bhaji (.830), chips (.815), and bajji (.800). This suggests that MIG customers use

potatoes more for preparing bhaji, chips and bajji. As their disposable income is more than

the MIG customers, preparation of chips would not be difficult. Potatoes are being used

for multipurpose. Therefore it is called as ‘Multipurpose potatoes’

Component 2 shows the attributes; pealability after cooking (.808) and pealability before

coking (.796) have the higher component loadings. This suggests that MIG customers

desire to buy potatoes with these attributes. It can be termed as ‘Desirable potato’.

Component 3 shows the attributes; freshness (.807) and firmness (.710) have the higher

component loadings. This suggests that MIG customers look for fresh and firm potatoes to

prepare bajji, chips and bhaji. These are also quality aspects of potatoes. Component 2 can

be clubbed with component 3 to call it as ‘Good quality potato’.

Component 4 shows the attributes; shelf life (.752) and taste (.719) have the higher

component loadings. MIG customers prefer to buy potatoes with more shelf life and tasty

ones. It can be termed as ‘Tasty potatoes’.

Component 5 shows the attributes; origin (.710) and variety (.646) have the higher

component loadings. This suggests that MIG customers look for the potatoes from the

specific place of origin (GIs) and varieties. It is called as ‘Identity of potatoes’.

From the above analysis we can conclude that, MIG customers prefer to prepare more of

bhaji, chips and bajji. Quality of potatoes is being judged based on pealability before and

after cooking, freshness, shelf life and taste of the potatoes. They prefer to prepare chips at

home because of their high disposable income.

45

4.3.G.3 FACTOR ANALYSIS FOR THE ATTRIBUTES OF POTATO RATED BY






POTATO (HIG)

Component

1 2 3 4 5 6

freshness .508

firmness .817

size & shape .664

origin .799

variety .861

cleanliness .812

no greening .884

taste .560

shelf life .822

skin thickness

bhaji .873

bajji .854

chips .864


pealabity after cooking .894





Source: SPSS Output

Interpretation: From the above table we can see that the attributes under component 1

are, bhaji (.873) chips (.864) and bajji (.854) have the higher component loadings. This

suggests that HIG customers use potatoes more for preparing bhaji, chips and bajji. As

their disposable income is more than the LIG and MIG customers, preparation of chips

would not be difficult. Potatoes are being used for multipurpose. Therefore, it is called as

‘Multipurpose potatoes’

Component 2 shows the attributes; pealability after cooking (.894) and pealability before

coking (.893) have the higher component loadings. This suggests that HIG customers

desire to buy potatoes with these attributes. It can be termed as ‘Desirable potato’.

Component 3 shows the attributes; firmness (.817) and size & shape (.664) have the higher

component loadings. This suggests HIG customers prefer to buy graded potatoes

according to their size and shape. Firm potatoes are fresh ones. It can be termed as

‘Graded potatoes’.

46

Component 4 shows the attributes; shelf life (.822) has the higher component loading.

HIG customer would like to buy in bulk and store for more days. It can be termed as

‘Keeping quality of potato’.


component loading. This suggests that HIG customers look for the potatoes from the

specific place of origin (GIs) and varieties. It is called as ‘Identity of potatoes’.

Component 6 shows the attributes; no greening (.884) and cleanliness (.812) have the

higher component loadings. This suggests that HIG customers look for the quality

potatoes. This can be called as ‘Quality potatoes’.

From the above analysis we can conclude that, HIG customer behaves almost similarly

like MIG in choosing the favourite attributes. HIG have high disposable income than LIG

and MIG, they prefer more quality potatoes.


choosing the attributes in potatoes.



Above hypothesis is tested using Chi square test for each attributes of potatoes. Results are

shown in table no. 4.3.33

Table No. 4.3.33. Chi square Tests for each attributes of potatoes.

Source: SPSS Output

Attributes

Pearsons Chi

Square

Value

dfAsymp.Sig


freshness 30.821 4 0 P- value is less than 0.01, at 99% confidence level.The null hypotheis is rejected.

firmness 11.49 4 0.022 P- value is less than 0.05, at 95% confidence level.The null hypotheis is rejected.

size & shape 21.177 4 0 P- value is less than 0.01, at 99% confidence level.The null hypotheis is rejected.

origin 11.68 4 0.02 P- value is less than 0.05, at 95% confidence level.The null hypotheis is rejected.


cleanliness 25.691 4 0 P- value is less than 0.01, at 99% confidence level.The null hypotheis is rejected.

Nogreening 3.149 4 0.533 P- value is greater than 0.10 at 90 % confidence level.The null hypothesis is accepted.

taste 17.277 4 0.002 P- value is less than 0.01, at 99% confidence level.The null hypotheis is rejected.


skinthickness 21.065 4 0 P- value is less than 0.01, at 99% confidence level.The null hypotheis is rejected.

bhaji 48.812 4 0 P- value is less than 0.01, at 99% confidence level.The null hypotheis is rejected.

bajji 43.239 4 0 P- value is less than 0.01, at 99% confidence level.The null hypotheis is rejected.

chips 63.613 4 0 P- value is less than 0.01, at 99% confidence level.The null hypotheis is rejected.

pealabityBC 20.897 4 0 P- value is less than 0.01, at 99% confidence level.The null hypotheis is rejected.

pealabilityAC 25.703 4 0 P- value is less than 0.01, at 99% confidence level.The null hypotheis is rejected.

Chi-Square Tests

47


the attributes, no greening and shelf life are not dependent on income of MIG and HIG


between income of the consumers and their ratings for the various attributes of potato.

4.3.G.4 WTP EXTRA OVER THE MARKET PRICE FOR THE BRANDED

POTATOES BY DIFFERENT INCOME GROUPS

Source: Survey Data


market price for the branded onions. LIG consumers are WTP extra of about Rs.5 (56%),

Rs.6 to 10 (34%), Rs.11 to 15 (9%), and above Rs.16 to 20 (1%) and above Rs. 20 (0%).


Rs.16 to 20 (3%) and above Rs. 20 (0%).


Rs.16 to 20 (5%) and above Rs. 20 (0%).

56%

34%

9%

1% 0%

45%

38%

14%

3% 0%

39% 40%

16%

5% 0%

0%

10%

20%

30%

40%

50%

60%


Re

spo

nd

en

ts in

%

Price range

Chart 4.3.12: Willingness to pay extra over the market price

(Rs.20) for the branded potatoes by different income groups

LIG

MIG

HIG

48

4.3.H. BRINJAL

4.3.H.1 FACTOR ANALYSIS FOR VARIOUS ATTRIBUTES OF BRINJAL





Table 4.3.34.Rotated Component Matrixa-

BRINJAL (LIG)

Component

1 2 3 4 5

freshness .820

tenderness .793

colour .605

lustre .705

shape .745

size .709

infestation .686

variety

taste

skin thickness .702

shelf life .678

seeds .740

uniformity .729

bharta .857

vangibath .742

bajji .865

masala .867





Source: SPSS Output

Interpretation: In table 4.3.34 we see the attributes; masala, bajji and bharta have higher

loadings of 0.867, 0.865 and 0.857 on component 1. This suggests that component 1 is a

combination of these three attributes which represent the usage aspects of brinjal by the

LIG customers. Therefore, this component can be termed as ‘Utility of brinjal’.

Component 2 shows the attributes; seeds, uniformity and skin thickness, have the higher

component loadings of 0.740, 0.729 and 0.702.These are the quality aspects of brinjal.

LIG customers see quality aspects while buying the brinjal. It can be termed as ‘Quality

brinjal’.

49

Component 3 shows the attributes; shape and lustre have the higher loadings of 0.745 and

0.705. LIG customers desire to buy lustrous and ideal shape brinjal. It can be termed as

‘Desired brinjal’.

Component 4 shows the attributes; freshness and tenderness have the higher component

loadings of 0.820 and 0.793. It can be termed as ‘Fresh brinjal’.

Component 5 shows the attributes; size and infestation have the higher component

loadings of 0.709 and 0.686. LIG customers look for infection free brinjal with proper

size. This can be clubbed with the component 2 and termed same as component 2 i.e.

‘Quality brinjal’.

From the above analysis we can conclude that, LIG consumers use brinjal to prepare three

different dishes, mainly for masala, bajji and bhaji. They prefer quality brinjal with less

seeds, uniform size, and with less skin thickness. Lustre, freshness and less infestation

attributes are important for deciding good quality brinjal.




BRINJAL (MIG)

Component

1 2 3 4 5 6

freshness .844

tenderness .800

colour

lustre .676

shape .698

size .815

infestation .672

variety .625

taste

skin thickness .856

shelf life .565

seeds .833

uniformity .805

bharta .863

vangibath .761

bajji .842

masala .826





Source: SPSS Output

50





are, bharta, masala and bajji have higher loadings of 0.863, 0.842 and 0.826. MIG

consumers use brinjal mainly to prepare these three dishes. It can be termed as

‘Multipurpose brinjal’.

Component 2 shows the attributes; lustre, infestation and variety having higher loadings of

0.676, 0.672 and 0.625. These are identification of quality aspects of brinjal for MIG

customers. This can be termed as ‘Identity of brinjal’.

Component 3 shows the attributes; freshness and tenderness having higher component

loadings of 0.844 and 0.800. This suggests that MIG customers look for fresh and tender

brinjal. The purchase decision mainly depends on these attributes. It can be termed as

‘Fresh brinjal’.

Component 4 shows the attributes; seeds and uniformity having higher loadings of 0.833

and 0.805. These are the quality aspects of the brinjal. MIG customers prefer to have less

seeds in brinjal and should be of uniform size. These are the quality aspects of brinjal. It

can be termed as ‘Quality brinjal’.

Component 5 shows the attribute size has the highest component loading of 0.815. MIG

customers look for graded brinjal. It can be termed as ‘Uniform size brinjal’.

Component 6 shows the attribute skin thickness has highest loading of 0.856. MIG

customers like to buy brinjal with thin skin. This component can be clubbed with

component 4 as it is also a quality aspect of brinjal. It is called as ‘Quality brinjal’.

From the above analysis we can conclude that, MIG population use brinjal more for

preparing bharta, masala and bajji. Good quality brinjal are identified by the attributes

lustre, less infestation, size, skin thickness, less seeds etc.

51







BRINJAL (HIG)

Component

1 2 3 4 5

freshness .873

tenderness .701

colour .561

lustre .641

shape .827

size

infestation .643

variety .718

taste .601

skin thickness .708

shelf life

seeds .575

uniformity .774

bharta .867

vangibath .829

bajji .851

masala .904


Rotation Method: Varimax with Kaiser

Normalization.



Source: SPSS Output


are, masala, bharta, bajji and vangibath have higher loadings of 0.904, 0.867, 0.851 and

0.829. HIG customers have high disposable income compared to others. They prepare

various dishes. It can be interpreted as ‘Multiple uses of brinjal’.

Component 2 shows the attributes; infestation and lustre having higher loadings of 0.643

and 0.641.These are the quality aspects of brinjal. It is called as ‘Quality brinjal’.

Component 3 shows the attributes; variety and skin thickness have higher loadings of

0.718 and 0.708. These are also the quality aspects of brinjal. This component can be

clubbed with the component 2. It can be called ‘Quality of brinjal’ (same as component 2).

52

Component 4 shows the attributes; shape and uniformity have higher loadings of 0.827

and 0.774. HIG customers prefer to buy graded brinjal. It can be interpreted as ‘Graded

brinjal’.

Component 5 shows the attributes; freshness and tenderness have the higher loadings of

0.873 and 0.701. HIG customers prefer to buy fresh and tender brinjal. It can be

interpreted as ‘Fresh brinjal’.

From the above analysis we can conclude that, HIG population use brinjal almost equally

for preparing masala, bharta, bajji and vangibath. They look for tender and less infected

brinjal. Identification of quality brinjal mainly by the varieties, and should have the thin

skin.


choosing the attributes in brinjal.



The above hypothesis is tested using Chi square test for each attributes of onion. Results

are shown in table no. 4.3.37.

Table No. 4.3.37. Chi square Tests for each attributes of brinjal.

Source: SPSS Output

Attributes

Pearsons

Chi

Square

Value

dfAsymp.Sig

(2 sided)

freshness 13.828 4 0.008

tenderness 26.132 4 0

colour 11.206 4 0.024

lustre 38.706 4 0

shape 38.525 4 0

size 28.031 4 0

infestation 4.345 4 0.361

variety 5.469 4 0.242

taste 42.103 4 0

skin thickness 8.309 4 0.081

shelf life 10.293 4 0.036

seeds 28.871 4 0

uniformity 28.102 4 0

bharta 40.829 4 0

vangibath 34.525 4 0

bajji 49.826 4 0

masala 51.772 4 0

P- value is less than 0.01, at 99% confidence level.The null hypotheis is rejected.



P- value is greater than 0.10 at 90 % confidence level.The null hypothesis is accepted.

P- value is greater than 0.10 at 90 % confidence level.The null hypothesis is accepted.


P- value is less than 0.1, at 90% confidence level.The null hypotheis is accepted.








Interpretation

Chi-Square Tests




53


the attributes, infestation and variety are not dependent on income of MIG and HIG


between income of the consumers and their ratings for the various attributes of potato.

4.3.H.4 WTP EXTRA OVER THE MARKET PRICE FOR THE BRANDED

BRINJAL BY DIFFERENT INCOME GROUPS

Source: Survey Data


market price for the branded brinjal. LIG consumers are WTP extra of about Rs.5 (46%),

Rs.6 to 10 (35%), Rs.11 to 15 (17%), Rs.16 to 20 (2%) and above Rs. 20 (0%).


Rs.16 to 20 (6%) and above Rs. 20 (1%).


Rs.16 to 20 (8%) and above Rs. 20 (5%).

46%

35%

17%

2% 0%

38% 36%

19%

6%

1%

23%

35%

29%

8% 5%

0%

5%

10%

15%

20%

25%

30%

35%

40%

45%

50%


Re

spo

nd

en

ts in

%

Price range


for the branded brinjal by different income groups

LIG

MIG

HIG

54

4.3.I. TOMATO

4.3.I.1 FACTOR ANALYSIS FOR VARIOUS ATTRIBUTES OF TOMATO






TOMATO (LIG)

Component

1 2 3 4

freshness .639

colour .683

origin

size & shape .648

variety .604

infestation .667

taste .543

sourness .798

sweetness .799

shelf life .738

salad .604

soup .503

ketchup .501

sambar .901





Source: SPSS Output


are; sambar (.901) and shelf life (.738) have the higher component loadings. This suggests

that, LIG consumers use tomato more for sambar than preparing other dishes. It can be

termed as ‘Sambar tomato’.

Component 2 shows the attributes; colour (.683), size & shape (.648) and freshness (.639)

have the higher component loadings. This suggests that LIG customers see the colour, size

& shape and freshness of tomatoes for sambar purpose. These are the quality aspects of

tomatoes. It can be termed as ‘Quality tomatoes’.

Component 3 shows the attributes; sweetness (.799) and sourness (.798) have the higher

component loadings. This suggests that LIG customers are conscious about the tastiness

before buying the tomatoes for sambar preparation. It can be termed as ‘Tasty tomato’.

55

Component 4 shows the attributes; infestation (.667) and variety (.604) have the higher

component loadings. This suggests that LIG customers desire to buy infestation free and

particular variety of tomato for sambar preparation. It can be termed as ‘Desired

tomatoes’.

From the above analysis we can conclude that, LIG consumers use tomato primary for

sambar preparation. Therefore attribute ‘colour’ is more important deciding factor for

them which indicate maturity of the tomato. To prepare sambar the taste of the tomato is

extremely important. Again there are types of sambar where in consumers look for

different types of tomatoes. So depending upon the requirement they choose the taste they

need. They identify the tomatoes required for sambar preparation mainly by the variety.

Therefore, they look for no or less infected tomatoes.

4.3.I.2 FACTOR ANALYSIS FOR THE ATTRIBUTES OF TOMATO RATED BY

THE MIDDLE INCOME GROUP





TOMATO(MIG)

Component

1 2 3 4

freshness .672

colour .680

origin .594

size & shape .632

variety .539

infestation .564

taste .626

sourness .526

sweetness .782

shelf life .793

salad .522 .578

soup .760

ketchup .761

sambar .891





Source: SPSS Output


are, sambar (.891) ketchup (.761), soup (.760) have the higher loadings. These are the

utility aspects of the tomatoes. This suggests that MIG customers use tomatoes for

multipurpose. It can be termed as ‘Tomatoes for multipurpose’.

56

Component 2 shows the attributes; size &shape (.632) and taste (.626) have the higher

component loadings. These are the quality parameters of tomatoes. MIG customers prefer

tasty and graded tomatoes. These are the desirable attributes for the tomatoes. It can be

termed as ‘Desirable tomatoes’.

Component 3 shows the attributes; shelf life (.793) and sweetness (.782) have the higher

component loadings. This suggests MIG customers look for tomatoes with longer shelf

life and the sweetness also matters for preparation of various dishes. It can be termed as

‘Desirable quality tomatoes’.

Component 4 shows the attributes; colour (.680) and freshness (.672) have the higher

component loadings. This suggests that colour is the most important attribute of tomatoes

which decides the maturity level and freshness. It can be termed as ‘Colour of tomatoes’.

From the above analysis we can conclude that, MIG consumers’ use tomatoes more for

preparing sambar, ketch up and soup preparation. Tomatoes should be classified according

to the suitability for the various dishes. MIG customers prefer tomatoes which are graded

according to their size and shape, colour etc.

4.3.I.3 FACTOR ANALYSIS FOR THE ATTRIBUTES OF TOMATO RATED BY






TOMATO (HIG)

Component

1 2 3 4 5

freshness .832

colour .617

origin .793

size & shape .852

variety .655

infestation .593

taste .514

sourness .740

sweetness .798

shelf life .531

salad .738

soup .812

ketchup .863

sambar .899





Source: SPSS Output

57


are, sambar (.899), ketchup (.863) and soup (.812) having the higher component loadings.

These are the utility aspects of the tomatoes. This suggests that HIG customers use

tomatoes for multipurposeness. It can be termed as ‘Tomatoes for multipurposeness’.

Component 2 shows the attributes; sweetness (.798) and sourness (.740) have the higher

component loadings. This suggests that HIG customers look for taste attributes as

important in tomatoes. They desire to have different taste suitable for the various dishes. It

can be termed as ‘Desirable tasty tomatoes’.

Component 3 shows the attributes; origin (.793) and colour (.617) have the higher

component loadings. This suggests that place of origin of tomatoes and colours are

associated. HIG customers like to use tomatoes which are most suitable for the dishes they

like most. It can be termed as ‘Identity of tomatoes’.

Component 4 and 5 shows the attributes, freshness (.832) and size &shape (.852) higher

component loadings. This suggests that HIG customers give much importantance to

freshness and size &shape of the tomatoes. It can be termed as ‘Fresh graded tomatoes’.

From the above analysis we can conclude that, HIG consumers use tomato primarily for

sambar and for ketch up preparation. They look for tasty tomatoes for preparing sambar

and also for ketch up. There are some varieties in tomatoes which can be used for

preparing different types of sambar and ketch up. So, depending upon the requirement

they choose the tomatoes. They identify the tomatoes required for sambar preparation

mainly by the origin and also by the variety. Freshness of tomatoes is important as they

use tomato for salads also.


choosing the attributes in tomatoes.



Above hypothesis is tested using Chi square test for each attributes of tomatoes. Results

are shown in table 4.3.41

58

Table No. 4.3.41. Chi square Tests for each attributes of tomatoes.

Source: SPSS Output


the attributes, infestation and origin are not dependent on income of MIG and HIG


between income of the consumers and their ratings for the various attributes of tomato.

4.3.I.4 WTP EXTRA OVER THE MARKET PRICE FOR THE BRANDED

TOMATOES BY DIFFERENT INCOME GROUPS

Source: Survey Data


Square Valuedf

Asymp.Si

g

(2 sided)

Interpretation

colour 12.292 4 0.015 P- value is less than 0.05, at 95% confidence level.The null hypotheis is rejected.


size & shape 25.8 4 0 P- value is less than 0.01, at 99% confidence level.The null hypotheis is rejected.

variety 29.91 4 0 P- value is less than 0.01, at 99% confidence level.The null hypotheis is rejected.

infestation 1.018 4 0.907 p- value is greater than 0.10 at 90 % confidence level.The null hypothesis is accepted.

taste 28.558 4 0 P- value is less than 0.01, at 99% confidence level.The null hypotheis is rejected.

sourness 10.903 4 0.028 p- value is less than 0.05, at 95% confidence level.The null hypotheis is rejected.

sweetness 18.652 4 0.001 P- value is less than 0.01, at 99% confidence level.The null hypotheis is rejected.

shelflife 22.684 4 0 P- value is less than 0.01, at 99% confidence level.The null hypotheis is rejected.

salad 42.586 4 0 P- value is less than 0.01, at 99% confidence level.The null hypotheis is rejected.

soup 73.992 4 0 P- value is less than 0.01, at 99% confidence level.The null hypotheis is rejected.

ketchup 60.415 4 0 P- value is less than 0.01, at 99% confidence level.The null hypotheis is rejected.

sambar 53.689 4 0 P- value is less than 0.01, at 99% confidence level.The null hypotheis is rejected.

Chi-Square Tests

55%

26%

17%

2% 0%

32%

40%

19%

9%

0%

20%

45%

21%

11%

3%

0%

10%

20%

30%

40%

50%

60%


Re

spo

nd

en

ts in

%

Price range


for the branded tomatoes by different income groups

LIG

MIG

HIG

59


market price for the branded tomatoes. LIG consumers are WTP extra of about Rs.5

(55%), Rs.6 to 10 (26%), Rs.11 to 15 (17%), Rs.16 to 20 (2%) and above Rs. 20 (0%).


Rs.16 to 20 (9%) and above Rs. 20 (0%).


Rs.16 to 20 (11%) and above Rs. 20 (3%).

4.3. J. LADIES FINGER

4.3.J.1 FACTOR ANALYSIS FOR THE ATTRIBUTES OF LADIES FINGER

RATED BY THE LOW INCOME GROUP





LADIES FINGER (LIG)

Component

1 2 3

freshness .847

tenderness .805

size .765

variety .699

taste .615

shelf life .508

seededness

frying .543

bhaji .836





Source: SPSS Output


are, bhaji (.836) size (.765) have the higher component loadings. This suggests that LIG

customers use ladies finger more for preparing bhaji, therefore size is important. It is

called as ‘Bhaji ladies finger’.

Component 2 shows the attributes; variety (.699) and taste (.615) have the higher

component loadings. This suggests that LIG customers desire to buy varieties suitable for

60

preparing bhaji. From the component loading we understand that taste depends on

varieties of ladies finger. Thus it can be termed as ‘Variety of ladies finger’.

Component 3 shows the attributes; freshness (.847) and tenderness (.805) have the higher

component loadings. This suggests that LIG customers desire to buy fresh and tender

ladies finger. Thus it can be termed as ‘Fresh ladies finger’.

From the above analysis we can conclude that, LIG customers mainly use ladies finger for

preparing bhaji. They may not prefer to fry and consume. Varieties of ladies finger

influence them to buy. Fresh and tender ladies finger are preferred by them.


RATED BY THE MIDDLE INCOME GROUP





LADIES FINGER (MIG)

Component

1 2 3

freshness .864

tenderness .788

size

variety .651

taste .678

shelf life .787

seededness .746

frying .759

bhaji .673





Source: SPSS Output


are, frying (.759) taste (.678) and bhaji (.673) have the higher component loadings. This

suggests that MIG customers use ladies finger more for frying and preparing bhaji,

therefore taste is important. It is called as ‘Ladies finger for multi use’.


component loadings. This suggests that MIG customers desire to buy fresh and tender


61

Component 3 shows the attributes; shelf life (.787) and seededness (.746) have the higher

component loadings. This suggests that MIG customers desire to buy more and store the

vegetable. Less seeded vegetables are preferred by the MIG consumers. These are the

quality aspects of vegetable. Thus it can be termed as ‘Quality ladies finger’.

From the above analysis we can conclude that, MIG customers use ladies finger for frying

and preparing bhaji. Fresh and tender vegetables with quality are preferred.


RATED BY THE HIGH INCOME GROUP





LADIES FINGER (HIG)

Component

1 2 3

freshness .874

tenderness .710

size .524

variety .783

taste .644

shelf life

seededness .753

frying .901

bhaji .894





Source: SPSS Output

Interpretation: From the table we can see that the attributes under component 1 are,

frying (.901) and bhaji (.894) have the higher component loadings. This suggests that HIG

customers use ladies finger more for frying and preparing bhaji. It is called as ‘Ladies

finger for multi use’.


component loadings. This suggests that HIG customers desire to buy fresh and tender


Component 3 shows the attributes; variety (.783) and seededness (.753) have the higher

component loadings. This suggests that HIG customers desire to buy different varieties to

62

prepare various dishes with less seeds in it. Thus it can be termed as ‘Variety of ladies

finger’.

From the above analysis we can conclude that, HIG consumers use both for frying and

bhaji preparation. They prefer particular variety of ladies finger for preparing the bhaji and

frying. Fresh and tender are the important attributes of the variety with less seeds in it.


choosing the attributes in ladies finger.



Above hypothesis is tested using Chi square test for each attributes of ladies finger.

Results are shown in table 4.3.45.

Table.4.3.45: Chi Square tests for each attributes of Ladies finger

Source: SPSS Output

Interpretation: From the table 4.3.45 it is clear the ratings given by the consumers for the

attribute shelf life is not dependent on income of MIG and HIG consumers. Other

attributes are dependent on income. Hence, there is close association between income of

the consumers and their ratings for the various attributes of ladies finger.


Square Valuedf

Asymp.Sig

(2 sided)

freshness 13.04 4 0.011 P- value is less than 0.05, at 95% confidence level.The null hypotheis is rejected.

tenderness 25.831 4 0 P- value is less than 0.01, at 99% confidence level.The null hypotheis is rejected.

size 11.544 4 0.021 P- value is less than 0.05, at 95% confidence level.The null hypotheis is rejected.


taste 53.607 4 0 P- value is less than 0.01, at 99% confidence level.The null hypotheis is rejected.


seededness 4.882 4 0.007 P- value is less than 0.01, at 99% confidence level.The null hypotheis is rejected.

frying 47.198 4 0 P- value is less than 0.01, at 99% confidence level.The null hypotheis is rejected.

bhaji 45.325 4 0 P- value is less than 0.01, at 99% confidence level.The null hypotheis is rejected.

Chi-Square Tests

Interpretation

63

4.3.J.3 WTP EXTRA ABOVE THE MARKET PRICE FOR THE BRANDED

LADIES FINGER BY THE CUSTOMERS OF DIFFERENT INCOME

GROUPS

Source: Survey Data


market price for the branded ladies finger. LIG consumers are WTP extra of about Rs.5

(49%), Rs.6 to 10 (39%), Rs.11 to 15 (10%), Rs.16 to 20 (2%) and above Rs. 20 (0%).

MIG consumers are WTP extra of about Rs.5 (34%), Rs.6 to 10 (36%), Rs.11 to 21(10%),

Rs.16 to 20 (7%) and above Rs. 20 (2%).


Rs.16 to 20 (9%) and above Rs. 20 (3%).

49%

39%

10%

2% 0%

34% 36%

21%

7% 2%

22%

37%

29%

9% 3%

0%

10%

20%

30%

40%

50%

60%


Re

spo

nd

en

ts in

%

Price range

Chart .4.3. 15: Willingness to pay extra over the market price (Rs.40)

for the branded ladies finger by different income groups

LIG

MIG

HIG

part 3 - factor analysis for various attributes of...

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