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1
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).
2
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
3
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.
4
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
Extraction Method: Principal Component Analysis.
Rotation Method: Varimax with Kaiser Normalization.
a. Rotation converged in 6 iterations.
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
5
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
jam preparation .615
Extraction Method: Principal Component Analysis.
Rotation Method: Varimax with Kaiser Normalization.
a. Rotation converged in 7 iterations.
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
7
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
8
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
9
4.3.A.6 Factor Analysis for various Attributes of Apple Rated by the Customer of
both Middle Income Group and 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 4.3.5: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
jam preparation .572
Extraction Method: Principal Component Analysis.
Rotation Method: Varimax with Kaiser Normalization.
a. Rotation converged in 6 iterations.
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’.
10
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
Factor analysis was conducted using principal PCA on 12 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 4.3.6: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
Extraction Method: Principal Component Analysis.
Rotation Method: Varimax with Kaiser Normalization.
a. Rotation converged in 7 iterations.
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
11
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
Factor analysis was conducted using principal PCA on 12 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.
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
Extraction Method: Principal Component Analysis.
Rotation Method: Varimax with Kaiser Normalization.
a. Rotation converged in 8 iterations.
12
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
Factor analysis was conducted using principal PCA on 12 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 4.3.8.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
Extraction Method: Principal Component Analysis.
Rotation Method: Varimax with Kaiser Normalization.
a. Rotation converged in 8 iterations.
13
See Annexure –B 7 (p-231) for KMO, Bartlett's Test and Total Variance Explained
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’.
Component 2 shows the attributes; origin (.870) and variety (.600) have the highest
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.
The following hypothesis is formulated to test whether income plays a major role in
choosing the attributes in oranges.
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
Above hypothesis is tested using Chi square test for each attributes of oranges. Results are
shown in table 4.3.9.
14
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
AttributesPearsons Chi
Square Valuedf
Asymp.Sig
(2 sided)Interpretation
price 2.811 4 0.59 P- value is greater than 0.10 at 90 % confidence level.The null hypothesis is accepted.
size 3.43 4 0.489 P- value is greater than 0.10 at 90 % confidence level.The null hypothesis is accepted.
colour 4.174 4 0.383 P- value is greater than 0.10 at 90 % confidence level.The null hypothesis is accepted.
freshness 13.526 4 0.209 P- value is greater than 0.10 at 90 % confidence level.The null hypothesis is accepted.
origin 5.128 4 0.274 P- value is greater than 0.10 at 90 % confidence level.The null hypothesis is accepted.
variety 2.895 4 0.576 P- value is greater than 0.10 at 90 % confidence level.The null hypothesis is accepted.
taste 11.543 4 0.121 P- value is greater than 0.10 at 90 % confidence level.The null hypothesis is accepted.
juiciness 1.693 4 0.792 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
COMBINING BOTH MIDDLE INCOME GROUP, HIGH INCOME
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%
Below Rs.10 Rs. 11 to 20 Rs.21 to 30 Above Rs.30
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
Factor analysis was conducted using principal PCA on 12 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 4.3.10. 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
Extraction Method: Principal Component Analysis.
Rotation Method: Varimax with Kaiser
Normalization.
a. Rotation converged in 15 iterations.
See Annexure –B 8 (p-232) for KMO, Bartlett's Test and Total Variance Explained
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’.
Component 2 shows the attributes; origin (.690) and variety (.636) have the highest
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’.
17
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
RATED BY THE CUSTOMER OF LOW INCOME GROUP
Factor analysis was conducted using principal PCA on 12 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.
See Annexure –B 9 (p-233) for KMO, Bartlett's Test and Total Variance Explained
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
Extraction Method: Principal Component Analysis.
Rotation Method: Varimax with Kaiser Normalization.
a. Rotation converged in 6 iterations.
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
RATED BY THE CUSTOMER OF MIDDLE INCOME GROUP
Factor analysis was conducted using principal PCA on 12 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.4.3.12.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
Extraction Method: Principal Component Analysis.
Rotation Method: Varimax with Kaiser Normalization.
a. Rotation converged in 6 iterations.
See Annexure –B 10 (p-234) for KMO, Bartlett's Test and Total Variance Explained
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
RATED BY THE CUSTOMER OF HIGH INCOME GROUP
Factor analysis was conducted using principal PCA on 12 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.4.3.13.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
Extraction Method: Principal Component Analysis.
Rotation Method: Varimax with Kaiser Normalization.
a. Rotation converged in 6 iterations.
See Annexure –B 11 (p-235) for KMO, Bartlett's Test and Total Variance Explained
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.
The following hypothesis is formulated to test whether income plays a major role in
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
AttributesPearsons Chi
Square Valuedf
Asymp.Sig
(2 sided)Interpretation
price 7.548 4 0.11 P- value is greater than 0.10 at 90 % confidence level.The null hypothesis is accepted.
size 6.152 4 0.188 P- value is greater than 0.10 at 90 % confidence level.The null hypothesis is accepted.
colour 6.955 4 0.138 P- value is greater than 0.10 at 90 % confidence level.The null hypothesis is accepted.
freshness 4.818 4 0.306 P- value is greater than 0.10 at 90 % confidence level.The null hypothesis is accepted.
origin 2.909 4 0.573 P- value is greater than 0.10 at 90 % confidence level.The null hypothesis is accepted.
variety 26.945 4 0 P- value is less than 0.01, at 99% confidence level.The null hypotheis is rejected.
taste 2.412 4 0.66 P- value is greater than 0.10 at 90 % confidence level.The null hypothesis is accepted.
juiciness 5.617 4 0.23 P- value is greater than 0.10 at 90 % confidence level.The null hypothesis is accepted.
shelflife 4.876 4 0.3 P- value is greater than 0.10 at 90 % confidence level.The null hypothesis is accepted.
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%
Below Rs.10 Rs. 11 to 20 Rs.21 to 30 Above Rs.30
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
COMBINING BOTH MIDDLE INCOME GROUP, HIGH INCOME
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%
Below Rs.10 Rs. 11 to 20 Rs.21 to 30 Above Rs.30
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
Factor analysis was conducted using PCA on 12 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 4.3.15.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
Extraction Method: Principal Component Analysis.
Rotation Method: Varimax with Kaiser Normalization.
a. Rotation converged in 6 iterations.
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
termed as ‘Value for money’.
24
4.3. D. POMEGRANATE
4.3.D.1 FACTOR ANALYSIS FOR VARIOUS ATTRIBUTES OF
POMEGRANATE RATED BY THE CUSTOMER OF LOW INCOME
GROUP
Factor analysis was conducted using principal PCA on 12 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 4.3.16. Rotated Component Matrix
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
Extraction Method: Principal Component Analysis.
Rotation Method: Varimax with Kaiser Normalization.
a. Rotation converged in 7 iterations.
See Annexure –B 13 (p-237) for KMO, Bartlett's Test and Total Variance Explained
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.
4.3.D.2 FACTOR ANALYSIS FOR VARIOUS ATTRIBUTES OF
POMEGRANATE RATED BY THE CUSTOMER OF MIDDLE INCOME
GROUP
Factor analysis was conducted using PCA on 12 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 4.3.17.Rotated Component Matrixa
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
Extraction Method: Principal Component Analysis.
Rotation Method: Varimax with Kaiser Normalization.
a. Rotation converged in 6 iterations.
See Annexure –B 14 (p-238) for KMO, Bartlett's Test and Total Variance Explained
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.
4.3.D.3 FACTOR ANALYSIS FOR VARIOUS ATTRIBUTES OF
POMEGRANATE RATED BY THE CUSTOMER OF HIGH INCOME
GROUP
Factor analysis was conducted using PCA on 12 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 4.3.18. Rotated Component Matrix
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
salad preparation .835
dish preparation .862
Extraction Method: Principal Component Analysis.
Rotation Method: Varimax with Kaiser Normalization.
a. Rotation converged in 5 iterations.
See Annexure –B 15 (p-239) for KMO, Bartlett's Test and Total Variance Explained
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 3 shows the attributes; origin (.814) and variety (.709) have the highest
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.
The following hypothesis is formulated to test whether income plays a major role in
choosing the attributes in pomegranate.
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 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
AttributesPearsons Chi
Square Valuedf
Asymp.Si
g
(2 sided)
Interpretation
price 1.918 4 0.751 P- value is greater than 0.10 at 90 % confidence level.The null hypothesis is accepted.
size 4.323 4 0.364 P- value is greater than 0.10 at 90 % confidence level.The null hypothesis is accepted.
seedcolour 5.564 4 0.234 P- value is greater than 0.10 at 90 % confidence level.The null hypothesis is accepted.
origin 6.126 4 0.19 P- value is greater than 0.10 at 90 % confidence level.The null hypothesis is accepted.
variety 3.241 4 0.518 P- value is greater than 0.10 at 90 % confidence level.The null hypothesis is accepted.
taste 7.12 4 0.13 P- value is greater than 0.10 at 90 % confidence level.The null hypothesis is accepted.
juiciness 3.89 4 0.421 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%
Below Rs.10 Rs. 11 to 20 Rs.21 to 30 Above Rs.30
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%
Below Rs.10 Rs. 11 to 20 Rs.21 to 30 Above Rs.30
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
Factor analysis was conducted using PCA on 12 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.
See Annexure –B 16 (p-240) for KMO, Bartlett's Test and Total Variance Explained
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’
Table 4.3.20. Rotated Component Matrixa
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
salad preparation .852
dish preparation .874
Extraction Method: Principal Component Analysis.
Rotation Method: Varimax with Kaiser Normalization.
a. Rotation converged in 5 iterations.
31
4.3.E. BANANA
4.3.E.1 FACTOR ANALYSIS FOR VARIOUS ATTRIBUTES OF BANANA
RATED BY THE CUSTOMER OF LOW INCOME GROUP
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
among the attributes and confirmed appropriateness of conducting the PCA.
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
dish preparation .814
Extraction Method: Principal Component Analysis.
Rotation Method: Varimax with Kaiser Normalization.
a. Rotation converged in 6 iterations.
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.
4.3.E.2 FACTOR ANALYSIS FOR VARIOUS ATTRIBUTES OF BANANA
RATED BY THE CUSTOMER OF MIDDLE INCOME GROUP
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
among the attributes and confirmed appropriateness of conducting the PCA.
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
dish preparation .822
Extraction Method: Principal Component Analysis.
Rotation Method: Varimax with Kaiser Normalization.
a. Rotation converged in 4 iterations.
See Annexure –B 18 (p-242) for KMO, Bartlett's Test and Total Variance Explained
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
termed as ‘Value for money’.
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.
4.3.E.3 FACTOR ANALYSIS FOR VARIOUS ATTRIBUTES OF BANANA
RATED BY THE CUSTOMER OF HIGH INCOME GROUP
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
among the attributes and confirmed appropriateness of conducting the PCA.
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
dish preparation .907
Extraction Method: Principal Component Analysis.
Rotation Method: Varimax with Kaiser Normalization.
a. Rotation converged in 4 iterations.
See Annexure –B 19 (p-243) for KMO, Bartlett's Test and Total Variance Explained
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’.
Component 3 shows the attributes; variety and price have the highest loadings of 0.851
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.
The following hypothesis is formulated to test whether income plays a major role in
choosing the attributes in Banana.
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 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.
AttributesPearsons Chi
Square Valuedf
Asymp.Sig
(2 sided)Interpretation
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%
Below Rs.10 Rs. 11 to 20 Rs.21 to 30 Above Rs.30
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%
Below Rs.10 Rs. 11 to 20 Rs.21 to 30 Above Rs.30
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
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
among the attributes and confirmed appropriateness of conducting the PCA.
Table 4.3.25.Rotated Component Matrixa
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
dish preparation .841
Extraction Method: Principal Component Analysis.
Rotation Method: Varimax with Kaiser Normalization.
a. Rotation converged in 5 iterations.
See Annexure –B 20 (p-244) for KMO, Bartlett's Test and Total Variance Explained
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’.
Component 3 shows the attributes; variety and price have the highest loadings of 0.893
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
Factor analysis was conducted using PCA on 10 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 4.3.26. Rotated Component Matrixa
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
Extraction Method: Principal Component Analysis.
Rotation Method: Varimax with Kaiser Normalization.
a. Rotation converged in 4 iterations.
See Annexure –B 21(p-245) for KMO, Bartlett's Test and Total Variance Explained
Source: SPSS Output
Interpretation: From the table 4.3.26 we can see that the attributes under component 1
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
Factor analysis was conducted using PCA on 10 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 4.3.27. Rotated Component Matrixa
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
Extraction Method: Principal Component Analysis.
Rotation Method: Varimax with Kaiser Normalization.
a. Rotation converged in 6 iterations.
See Annexure –B 22 (p-246) for KMO, Bartlett's Test and Total Variance Explained
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
Factor analysis was conducted using PCA on 10 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 4.3.28. Rotated Component Matrixa
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
Extraction Method: Principal Component Analysis.
Rotation Method: Varimax with Kaiser Normalization.
a. Rotation converged in 5 iterations.
See Annexure –B 23 (p-247) for KMO, Bartlett's Test and Total Variance Explained
Source: SPSS Output
40
Interpretation: From the table 4.3.28 we can see that the attributes under component 1
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 4 shows the attributes; origin (.969) and variety (.831) have the highest
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.
The following hypothesis is formulated to test whether income plays a major role in
choosing the attributes in onion.
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 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
(2 sided)Interpretation
freshness 3.682 4 0.451 P- value is greater than 0.10 at 90 % confidence level.The null hypothesis is accepted.
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.
colour 3.251 4 0.517 P- value is greater than 0.10 at 90 % confidence level.The null hypothesis is accepted.
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
RATED BY THE CUSTOMER OF LOW INCOME GROUP
Factor analysis was conducted using PCA on 15 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 4.3.30. Rotated Component Matrixa
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
Extraction Method: Principal Component Analysis.
Rotation Method: Varimax with Kaiser Normalization.
a. Rotation converged in 8 iterations.
See Annexure –B 24 (p-248) for KMO, Bartlett's Test and Total Variance Explained
Source: SPSS Output
Interpretation: From the table 4.3.30 we can see that the attributes under component 1
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 3 shows the attributes; variety (.777) and origin (.741) have the higher
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
Factor analysis was conducted using PCA on 15 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 4.3.31. Rotated Component Matrixa
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 before cooking .796
pealabity after cooking .808
Extraction Method: Principal Component Analysis.
Rotation Method: Varimax with Kaiser Normalization.
a. Rotation converged in 6 iterations.
See Annexure –B 25( p-249) for KMO, Bartlett's Test and Total Variance Explained
Source: SPSS Output
44
Interpretation: From the table 4.3.31 we can see that the attributes under component 1
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
THE HIGH INCOME GROUP
Factor analysis was conducted using PCA on 15 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 4.3.32.Rotated Component Matrixa
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 before cooking .893
pealabity after cooking .894
Extraction Method: Principal Component Analysis.
Rotation Method: Varimax with Kaiser Normalization.
a. Rotation converged in 7 iterations.
See Annexure –B 26(p-250) for KMO, Bartlett's Test and Total Variance Explained
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 5 shows the attributes; variety (.861) and origin (.799) have the higher
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.
The following hypothesis is formulated to test whether income plays a major role in
choosing the attributes in potatoes.
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
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
(2 sided)Interpretation
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.
variety 15.649 4 0.004 P- value is less than 0.01, at 99% 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.
shelflife 2.64 4 0.62 P- value is greater than 0.10 at 90 % confidence level.The null hypothesis is accepted.
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
Interpretation: From the table 4.3.33 it is clear that ratings given by the consumers for
the attributes, no greening and shelf life are 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 potato.
4.3.G.4 WTP EXTRA OVER THE MARKET PRICE FOR THE BRANDED
POTATOES BY 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 (56%),
Rs.6 to 10 (34%), Rs.11 to 15 (9%), and above Rs.16 to 20 (1%) and above Rs. 20 (0%).
MIG consumers are WTP extra of about Rs.5 (45%), Rs.6 to 10 (38%), Rs.11 to 15 (14%),
Rs.16 to 20 (3%) and above Rs. 20 (0%).
HIG consumers are WTP extra of about Rs.5 (39%), Rs.6 to 10 (40%), Rs.11 to 15 (16%),
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%
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 4.3.12: 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
RATED BY THE CUSTOMER OF LOW INCOME GROUP
Factor analysis was conducted using PCA on 17 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 4.3.34.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
Extraction Method: Principal Component Analysis.
Rotation Method: Varimax with Kaiser Normalization.
a. Rotation converged in 6 iterations.
See Annexure –B 27(p-251) for KMO, Bartlett's Test and Total Variance Explained
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.
4.3.H.2 FACTOR ANALYSIS FOR VARIOUS ATTRIBUTES OF BRINJAL
RATED BY THE CUSTOMER OF MIDDLE INCOME GROUP
Table 4.3.35.Rotated Component Matrixa
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
Extraction Method: Principal Component Analysis.
Rotation Method: Varimax with Kaiser Normalization.
a. Rotation converged in 7 iterations.
See Annexure –B 28 (p-252) for KMO, Bartlett's Test and Total Variance Explained
Source: SPSS Output
50
Factor analysis was conducted using PCA on 17 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.
Interpretation: From the table 4.3.35 we can see that the attributes under component 1
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
4.3.H.3 FACTOR ANALYSIS FOR VARIOUS ATTRIBUTES OF BRINJAL
RATED BY THE CUSTOMER OF HIGH INCOME GROUP
Factor analysis was conducted using PCA on 17 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 4.3.36. Rotated Component Matrixa
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
Extraction Method: Principal Component Analysis.
Rotation Method: Varimax with Kaiser
Normalization.
a. Rotation converged in 25 iterations.
See Annexure –B 29 (p-253) for KMO, Bartlett's Test and Total Variance Explained
Source: SPSS Output
Interpretation: From the table 4.3.36 we can see that the attributes under component 1
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.
The following hypothesis is formulated to test whether income plays a major role in
choosing the attributes in brinjal.
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.
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 less than 0.01, at 99% confidence level.The null hypotheis is rejected.
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 0.01, at 99% confidence level.The null hypotheis is rejected.
P- value is less than 0.1, at 90% confidence level.The null hypotheis is accepted.
P- value is less than 0.05, at 95% confidence level.The null hypotheis is rejected.
P- value is less than 0.01, at 99% confidence level.The null hypotheis is rejected.
P- value is less than 0.01, at 99% confidence level.The null hypotheis is rejected.
P- value is less than 0.01, at 99% confidence level.The null hypotheis is rejected.
P- value is less than 0.01, at 99% confidence level.The null hypotheis is rejected.
P- value is less than 0.01, at 99% confidence level.The null hypotheis is rejected.
P- value is less than 0.01, at 99% confidence level.The null hypotheis is rejected.
Interpretation
Chi-Square Tests
P- value is less than 0.01, at 99% confidence level.The null hypotheis is rejected.
P- value is less than 0.01, at 99% confidence level.The null hypotheis is rejected.
P- value is less than 0.05, at 95% confidence level.The null hypotheis is rejected.
53
Interpretation: From the table 4.3.37 it is clear that ratings given by the consumers for
the attributes, infestation and variety are 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 potato.
4.3.H.4 WTP EXTRA OVER THE MARKET PRICE FOR THE BRANDED
BRINJAL BY DIFFERENT INCOME GROUPS
Source: Survey Data
Interpretation: Three different income groups have expressed their WTP extra over
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%).
MIG consumers are WTP extra of about Rs.5 (38%), Rs.6 to 10 (36%), Rs.11 to 15 (19%),
Rs.16 to 20 (6%) and above Rs. 20 (1%).
HIG consumers are WTP extra of about Rs.5 (23%), Rs.6 to 10 (35%), Rs.11 to 15 (29%),
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%
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 4.3.13: Willingness to pay extra over the market price (Rs.30)
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
RATED BY THE CUSTOMER OF LOW 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 4.3.38.Rotated Component Matrixa
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
Extraction Method: Principal Component Analysis.
Rotation Method: Varimax with Kaiser Normalization.
a. Rotation converged in 6 iterations.
See Annexure –B 30 (p-254) for KMO, Bartlett's Test and Total Variance Explained
Source: SPSS Output
Interpretation: From the table 4.3.38 we can see that the attributes under component 1
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
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 4.3.39.Rotated Component Matrixa
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
Extraction Method: Principal Component Analysis.
Rotation Method: Varimax with Kaiser Normalization.
a. Rotation converged in 6 iterations.
See Annexure –B 31 (p-255) for KMO, Bartlett's Test and Total Variance Explained
Source: SPSS Output
Interpretation: From the table 4.3.39 we can see that the attributes under component 1
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
THE HIGH 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 4.3.40.Rotated Component Matrixa
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
Extraction Method: Principal Component Analysis.
Rotation Method: Varimax with Kaiser Normalization.
a. Rotation converged in 7 iterations.
See Annexure –B 32 (p-256) for KMO, Bartlett's Test and Total Variance Explained
Source: SPSS Output
57
Interpretation: From the table 4.3.40 we can see that the attributes under component 1
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.
The following hypothesis is formulated to test whether income plays a major role in
choosing the attributes in tomatoes.
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 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
Interpretation: From the table 4.3.41 it is clear that ratings given by the consumers for
the attributes, infestation and origin are 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 tomato.
4.3.I.4 WTP EXTRA OVER THE MARKET PRICE FOR THE BRANDED
TOMATOES BY DIFFERENT INCOME GROUPS
Source: Survey Data
AttributesPearsons Chi
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.
origin 5.612 4 0.23 P- value is greater than 0.10 at 90 % confidence level.The null hypothesis is accepted.
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%
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 4.3.14: Willingness to pay extra over the market price (Rs.35)
for the branded tomatoes by different income groups
LIG
MIG
HIG
59
Interpretation: Three different income groups have expressed their WTP extra over
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%).
MIG consumers are WTP extra of about Rs.5 (32%), Rs.6 to 10 (40%), Rs.11 to 15 (19%),
Rs.16 to 20 (9%) and above Rs. 20 (0%).
HIG consumers are WTP extra of about Rs.5 (20%), Rs.6 to 10 (45%), Rs.11 to 15 (21%),
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
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
among the attributes and confirmed appropriateness of conducting the PCA.
Table 4.3.42.Rotated Component Matrixa
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
Extraction Method: Principal Component Analysis.
Rotation Method: Varimax with Kaiser Normalization.
a. Rotation converged in 5 iterations.
See Annexure –B 33 (p-257) for KMO, Bartlett's Test and Total Variance Explained
Source: SPSS Output
Interpretation: From the table 4.3.42 we can see that the attributes under component 1
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.
4.3.J.2 FACTOR ANALYSIS FOR THE ATTRIBUTES OF LADIES FINGER
RATED BY THE MIDDLE INCOME GROUP
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
among the attributes and confirmed appropriateness of conducting the PCA.
Table 4.3.43.Rotated Component Matrixa
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
Extraction Method: Principal Component Analysis.
Rotation Method: Varimax with Kaiser Normalization.
a. Rotation converged in 5 iterations.
See Annexure –B 34 (p-258) for KMO, Bartlett's Test and Total Variance Explained
Source: SPSS Output
Interpretation: From the table 4.3.43 we can see that the attributes under component 1
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 2 shows the attributes; freshness (.864) and tenderness (.788) have the higher
component loadings. This suggests that MIG customers desire to buy fresh and tender
ladies finger. Thus it can be termed as ‘Fresh ladies finger’.
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.
4.3.J.3 FACTOR ANALYSIS FOR THE ATTRIBUTES OF LADIES FINGER
RATED BY THE HIGH INCOME GROUP
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
among the attributes and confirmed appropriateness of conducting the PCA.
Table 4.3.44.Rotated Component Matrixa
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
Extraction Method: Principal Component Analysis.
Rotation Method: Varimax with Kaiser Normalization.
a. Rotation converged in 5 iterations.
See Annexure –B 35(p-259) for KMO, Bartlett's Test and Total Variance Explained
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 2 shows the attributes; freshness (.874) and tenderness (.710) have the higher
component loadings. This suggests that HIG customers desire to buy fresh and tender
ladies finger. Thus it can be termed as ‘Fresh ladies finger’.
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.
The following hypothesis is formulated to test whether income plays a major role in
choosing the attributes in ladies finger.
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 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.
AttributesPearsons Chi
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.
variety 15.788 4 0.003 P- value is less than 0.01, at 99% 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.
shelflife 14.229 4 0.3 P- value is greater than 0.10 at 90 % confidence level.The null hypothesis is accepted.
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
Interpretation: Three different income groups have expressed their WTP extra over
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%).
HIG consumers are WTP extra of about Rs.5 (22%), Rs.6 to 10 (37%), Rs.11 to 15 (29%),
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%
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. 15: Willingness to pay extra over the market price (Rs.40)
for the branded ladies finger by different income groups
LIG
MIG
HIG