CHAPTER IV PRE-PURCHASE RURAL CONSUMER BEHAVIOUR AN …shodhganga.inflibnet.ac.in/bitstream/10603/7676/8/07_chapter 4.pdf · PRE-PURCHASE RURAL CONSUMER BEHAVIOUR – AN ANALYSIS

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CHAPTER IV

PRE-PURCHASE RURAL CONSUMER BEHAVIOUR –

AN ANALYSIS

4.1 INTRODUCTION

Theories on consumer behaviour, features of rural consumers and

the profile of rural consumers of Salem District have been discussed in

the earlier chapters. With these background it is to be enquired how

actually rural consumers behave in practice. This requires empirical

investigation. For this purpose, what the authorities in the field opine

about the consumer behaviour requires to be discussed first in nut shell.

According to them, whole process of consumer behaviour can be

divided into five major stages: (i) Need Recognition, (ii) Information

Search, (iii) Evaluation of Alternatives (iv) Purchase, and (v) Post-

purchase Behaviour. Empirical studies on these activities have also

been made in developed economies. But in developing economy like

India, it is very difficult to investigate empirically all the stages a rural

consumer in India follows. That is why, for this study the first three

above stages have been clubbed together under the broad head “Pre-

Purchase Consumer Behaviour” and the next two stages under

“Purchase and Post-Purchase Behaviour”.

Rural consumers may differ from their urban counterparts. But

stages of behaviour of both the groups are not basically different. That

is why literature on the processes of need recognition, information

search and evaluation of alternatives and purchase decision has been

surveyed first. Then an attempt has been made to make a list of

questions necessary for enquiring how far these concepts hold good to

the area under study. Lastly, answers to these questions put to the

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selected rural consumers have been tabulated and interpreted under the

heading Analysis and Interpretation.

4.2 BRAND WISE DISTRIBUTION OF SAMPLE UNITS TO

EACH TYPE OF FAST MOVING CONSUMER GOODS

Fast moving consumer goods are those which have a quick

turnover, relatively low cost and will be replaced within a year. I have

selected five fast moving consumer goods for my study, i.e. shampoo,

biscuits, bathing soap, toothpaste and mosquito repellent.

In the behaviour of rural consumers‟ brand of the fast moving

consumer goods is more important than any other attribute because of

extraordinary impact created by multimedia on rural consumers. The

rural consumers are easily influenced by attractive advertisements,

celebrity endorsements and attributes of the Fast moving consumer

goods they use. Brand association, product knowledge and brand recall

make them comfortable to materialize their purchase decision

dynamically. It leads to the indispensability of brand wise distribution

of Fast moving consumer goods with respect to the research domain

Salem District.

Table – 4.1

Brand Wise Distribution of Sample Units to each type of Fast Moving Consumer Goods

Variable Brand Frequency Percentage

Shampoo

Clinic plus Sunsilk Pantene Head & Shoulder Vatika Meera Chik Any other

205 55 53 88 29 67 59 44

34.2 9.2 8.8 14.7 4.8 11.2 9.8 7.3

87

Variable Brand Frequency Percentage

Biscuits

Britania Parle Sunfeast Anyother

239 227

91 43

39.8 37.8 15.2 7.2

Bathing Soap

Hamam Cinthol Medimix Lifebuoy Margo Lux Mysoresandal Anyother

228 83 53 69 39 73 35 20

38.0 13.8 8.8 11.5 6.5 12.2 5.8

3.3

Toothpaste

Colgate Pepsodent Close-up Neem Promise Vicco Anchor Anyother

314 114

52 33 26 24 24 13

52.3 19.0 8.7 5.5 4.3 4.0 4.0 2.2

Mosquito Repellent

Goodnight Allout Mortein Power on Jumbo coil Tortoise Anyother

225 171 17 49 52 85

1

37.5 28.5 2.8 8.2 8.7 14.2 0.2

(Source: primary Data) 4.2.1 SHAMPOO

The shampoo market has a total penetration of 13 percent only.

Rural Indians use various natural products like reetha seeds and amla

for hair cleansing. That explains the under penetration of shampoos in

India. Besides most of the shampoos are beyond the reach of rural

Indians. To encourage usage of shampoos, they are now being

distributed in sachets form that accounts for nearly 40 percent of the

total shampoo sales in India. Shampoos in India are available as a

general hair cleanser and in anti-dandruff versions. Shampoos are

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generally viewed as expensive products and the lower consumer group

uses the ordinary toilet soaps for hair wash. Hence, it does not come as

a surprise that urban markets account for nearly 80 percent of the total

shampoo market. From the table 4.1 it is found that clinic plus occupies

34.2 percent, Head and shoulder 14.7 percent, Chik 9.8 percent, Meera

11.2 percent and Sunsilk 9.2 percent. The other brands are not

popularly used by the rural consumers. The above table exhibits that

clinic plus, Meera and Head & shoulder are the three brands considered

largely by the respondents followed by Chik.

4.2.2 BISCUITS

It is evident from the table 4.1 that Britannia biscuits occupies

first place in priority list 39.8 percent followed by Parle 37.8 percent.

These two brands are predominantly used by the rural consumers. Of

all the brands of Biscuits, Britannia and Sun feast are the two brands

mostly preferred by the sample unit of the study.

4.2.3 BATHING SOAP

Oil or animal fat constitute as much as 80 percent of the raw

material that to into making of soaps. However, usage of animal fats for

production of cosmetics is banned in India. Hence, only vegetable oils

are used by the soap and cosmetic manufacturers in India, even though

the animal fat is technically a far superior ingredient. Therefore, Indian

soaps by and large are of poorer configuration as compared to the ones

available globally. Indian manufacturers use various kinds of oils such

as rice bran, palm, soyabean, neem, karanji, olive, kopra, etc., depending

on desired quality of the final product. As an established trend, rice

bran is the most popular raw material followed by palm with additions

from various other ingredients for lather and smell. All these additives

except for base oil do no form a substantial component of input. That

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leaves the product with the cost of packaging, which higher for

premium soaps as compared to mass products. Therefore, the cost of

packaging is a major component for pricing of premium products.

Soaps are perhaps one of the most widely available FMCG

products in the country with availability in outlets in excess of 5 mn in

number. Nearly three quarters of such outlets are situated in rural

areas. Considering 75 percent on Indian consumers reside in retail

areas, it may be considered that nearly 50 percent of the total

consumption emanates from rural areas. As disposable income in rural

households increase, consumers graduate to higher product range; that

drives up the price of products. The spiraling of price prompts some

consumers to migrate to cheaper substitutes. From the table 4.1 it is

inferred that 38 percent of the respondents use Hamam, 13.8 percent use

cinthol, 12.2 percent use lux and 11.5 percent use lifebuoy soap. The

other brands are in similar place with meager differences in their

distribution. Medimix 8.8 percent, Margo 6.5 percent and Mysore

sandal 5.8 percent. The above figures clearly show that Hamam is the

most popular brand in rural areas of Salem District.

4.2.4 TOOTHPASTE

Markets for oral care products are divisible into toothpastes,

toothpowder and toothbrushes. Total penetration of oral care products

is three times higher in urban areas than in rural areas. The usage of

toothpaste or toothpowders in India has been 1.5 times per individual

per day as against 2 times in developed markets. Similarly, per capita

production of toothpaste in India is 70 gms as compared to 300 gms in

Europe. Hence, the oral care products market in India is constrained

both in terms of penetration and rate of consumption. It is observed

from the table 4.1 that most of the respondents prefer Colgate 52.8

90

percent followed by Pepsodent 19 percent and close-up 8.7 percent.

More than 50 percent of the respondents use Colgate brand that shows

its familiarity in rural areas.

4.2.5 MOSQUITO REPELLENT

Mosquito Repellent is a highly well distributed product as

evidenced from a high penetration level of 87 percent, which has a

leveled distribution both in rural and urban areas. From the table 4.1, it

is clear that 37.5 percent of the respondents use Goodnight, 28.5 percent

use All out followed by Mortein 14.2 percent. The proportion of other

brands in the sample units is very less. Goodnight and All out is neck to

neck popular in the rural areas of Salem District.

91

CHART 4.1

BRAND WISE DISTRIBUTION OF SAMPLE UNITS TO EACH TYPE OF FAST MOVING CONSUMER GOODS

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4.3 ATTRIBUTES AND BENEFITS OF FAST MOVING CONSUMER

GOODS

The attributes and benefits of a product make a consumer get

satisfied and extent the continued patronage for such products.

Therefore, it tends to comprehend awareness and the performance of

the product. More over, it provides complete information to acquire

effective knowledge of the brand or product. Also, it creates loyalty by

maximizing satisfaction, and finally restores equity. The perception of

rural consumers of Salem District about the essential Fast moving

consumer goods they use namely shampoo, Biscuits, bathing soap,

toothpaste and Mosquito repellent are ascertained through the

parametric values mean and standard deviation. Besides this the

statistically significant„t‟ values and standard errors clearly foretells the

prevailing marketing scenario in rural areas of Salem District. The

statistical significance of attributes and benefits are brought out by

using the t test.

Table – 4.2

One-Sample t-test for Attributes and Benefits of Shampoo

Attributes and benefits

N

Mea

n

Std

. D

ev

iati

on

Std

. E

rro

r M

ean

t v

alu

e

Sig

(2

-ta

ile

d)

Prevents dandruff 600 4.1933 .98873 .04036 29.564 .000

Softens the hair 600 3.9150 .96574 .03943 23.208 .000 Keeps the hair root healthy

600 3.4867 1.17848 .04811 10.115 .000

Prevents grey hair 600 3.2367 1.26754 .05175 4.574 .000 Removes the stickiness of the hair

600 3.5033 1.38438 .05652 8.906 .000

From the table 4.2 it is found that the shampoo consumers are

well acquainted with the shampoo attributes like prevents

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dandruff(Mean= 4.19), hair softening(Mean= 3.91), Keeps the hair root

healthy(Mean= 3.48), Prevents grey hair(Mean = 3.23) and removes the

stickiness of the hair(Mean= 3.50). This shows the consumers

agreeableness towards the shampoo attributes. The t values of the

respective five attributes 29.564, 23.208, 10.115, 4.574 and 8.906 are

statistically significant at 5 percent level. Therefore it is concluded that

the rural consumers of Salem District are highly aware of shampoos

significance in preventing the dandruff. But hair softening, keeping hair

root healthy, prevents grey hair are just agreed by the rural consumers

as the specific attributes and benefits of the shampoo.

Table – 4.3

One-Sample t-test for Attributes and Benefits of Biscuits

Attributes and Benefits N

Mea

n

Std

. D

ev

iati

on

Std

. E

rro

r M

ean

t v

alu

e

Sig

(2

-ta

ile

d)

It reduces hunger 600 4.1233 1.03436 .04223 26.602 .000 It is very healthy 600 3.6950 1.09574 .04473 15.537 .000

It is very tasty 600 3.5450 1.08317 .04422 12.325 .000 It provides Refreshment

600 3.5150 1.11138 .04537 11.351 .000

It gives energy 600 3.4933 1.29850 .05301 9.306 .000

Table 4.3 clearly shows that rural consumers of Salem District are

well aware of the attributes and benefits of Biscuits like reduces the

hunger (Mean= 4.12), It is very healthy (Mean= 3.69), It is provides good

taste (Mean= 3.54), It is useful to refresh (Mean= 3.51) and gives more

energy from Fibres (Mean= 3.49). The t values of the respective five

attributes 26.60, 15.53, 12.32, 11.35, 9.30 are statistically significant at 5

percent level. By comparing the mean values of attributes and benefits

of Biscuits, it is inferred that the first attribute reduction of hunger is

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strongly agreed by the rural consumers of Salem District. But the other

attributes healthy, tasty, provides refreshment and gives more energy

just agreed by the rural consumers of Salem District as the specific

attributes and benefits of the Biscuits.

Table – 4.4

One-Sample t-test for Attributes and Benefits of Bathing Soap

Attributes and Benefits

N

Mea

n

Std

. D

ev

iati

on

Std

. E

rro

r M

ean

t va

lue

Sig

(2

-ta

ile

d)

Helps to acquire smoothness of the skin

600 3.9867 1.11889 .04568 21.600 .000

Gives fragrance 600 4.0583 .91997 .03756 28.179 .000

Kills germs 600 3.7267 1.07574 .04392 16.546 .000

Provides glowing and clean skin

600 3.8050 1.02081 .04167 19.316 .000

Stops the bad odour 600 4.1883 .98208 .04009 29.639 .000

Gives refreshing feel 600 4.0317 .97583 .03984 25.896 .000 Prevents diseases and unhealthy conditions

600 3.7133 1.16193 .04744 15.038 .000

Table 4.4 clearly shows the mean values of attributes and benefits

of Bathing soap like helps to acquire smoothness of the skin ( Mean =

3.98), Gives fragrance(Mean = 4.05), kills germs ( Mean = 3.72) provides

glowing and clean skin(Mean = 3.80), stops the bad odour ( Mean =

4.18), gives refreshing feel ( Mean = 4.03) and prevents diseases and

unhealthy conditions (Mean = 3.71). The t values of the respective seven

attributes 21.6, 28.17, 16.54, 19.31, 29.63, 25.89 and 15.03 are statistically

significant at 5 percent level. Hence the researcher has concluded that

the attributes gives fragrance, gives refreshing feel and stops the bad

odour are strongly agreed by the rural consumer of Salem District. But

the other attributes helps to acquire smoothness of the skin, kills germs,

provides glowing and clean skin, prevents diseases and unhealthy

95

conditions are just agreed by the rural consumers. By comparing the

mean values of attributes of Soap, the attributes gives fragrance, gives

refreshing feel and stops the bad odour is considered as predominant

attributes and benefits since the mean value of these three variables are

greater than four and also greater than other attributes and benefits of

bathing soap.

Table – 4.5 One-Sample t-test for Attributes and Benefits of Toothpaste


Mea

n

Std

. D

ev

iati

on

Std

. E

rro

r M

ean

t v

alu

e

Sig

(2

-ta

ile

d)

Whitens teeth 600 4.3083 .88952 .03631 36.028 .000 Increases the beauty of teeth

600 3.7433 1.09672 .04477 16.602 .000

Gives taste and foaminess

600 3.8483 1.08810 .04442 19.097 .000

Kills Germs 600 3.9300 .96781 .03951 23.538 .000 Strengthens tooth 600 3.9197 1.05791 .04326 21.260 .000

Removes the food particles between teeth

600 3.9050 1.07295 .04380 20.661 .000

Fights tooth decay 600 3.8117 1.07306 .04381 18.528 .000 Gives fresh breath 600 3.9267 1.08470 .04428 20.926 .000

Prevents tooth ache 600 3.6517 1.21633 .04966 13.123 .000

The mean values as found in Table 4.5 indicates that the

toothpaste customers emphasize on attributes and benefits like whitens

teeth (Mean = 4.30), increases the beauty of teeth (Mean = 3.74), gives

taste and foaminess (Mean = 3.84), Kills germs (Mean = 3.93),

Strengthens tooth (Mean = 3.91), Removes the food particle between

teeth (Mean = 3.90), Fights tooth decay (Mean = 3.81), gives fresh breath

(Mean = 3.92) and prevents tooth ache (Mean = 3.65). From the table 4.5,

the t values of the respective nine attributes 36.028, 16.602, 19.097,

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23.538, 21.260, 20.661, 18.528, 20.926, and 13.123 which are statistically

significant at 5 percent level. The mean values of the attribute whitens

teeth is significantly greater than four which clearly shows that rural

consumers of Salem District strongly agree for the first attribute of

toothpaste. The mean values of the other attributes are also greater than

the test value 3 which clearly indicates that the rural consumers just

agree for the other attributes and benefits of Toothpaste.

Table – 4.6

One-Sample t-test for Attributes and Benefits of Mosquito Repellent


Mea

n

Std

. D

ev

iati

on

Std

. E

rro

r M

ean

t v

alu

e

Sig

(2

-ta

ile

d)

It safe guards from mosquitoes

600 4.2033 .94877 .03873 31.067 .000

It gives good fragrance

600 4.0083 .99158 .04048 24.909 .000

It is a slow poison 600 3.7600 1.05083 .04290 17.716 .000

It affects the health 600 3.6417 1.14110 .04659 13.774 .000 It kills other insects also

600 3.7067 1.18306 .04830 14.631 .000

From the table 4.6 the mean values 4.20, 4, 3.76, 3.64 and 3.70 are

obtained for the Mosquito repellent attributes and benefits. The mean

values of the attributes safeguards from mosquitoes and gives good

fragrance are significantly greater than the test value 3 which means the

rural consumer strongly agree for these two attributes and benefits of

Mosquito repellent. The other three attributes slow poison, affecting the

health and kills other insects are just agreed by the rural consumers of

Mosquito repellent. The t values of the attributes 31.067, 24.909, 17.716,

13.774 and 14.631 are statistically significant at 5 percent level. Hence

the researcher has concluded that the attributes keeps the mosquitoes

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away and gives good fragrance, poisoning the atmosphere and affecting

the health and promotes hygiene atmosphere are considered as an

important attribute and benefit of Mosquito repellent by the rural

consumers of Salem District.

4.4 LOGISTIC REGRESSION ANALYSIS FOR AWARENESS

AND ATTRIBUTES OF FMCG

So far the perceptions of rural consumers in Salem District are

ascertained through the parametric approaches. As a further

development in analysis it is more important to correlate the awareness

level and perception of product attributes. In this juncture, the Logistic

Regression Analysis is found suitable to unite awareness level and the

product attributes.

Logistic Regression Analysis is a dichotomous approach in

statistics and a powerful regression analysis to identify the variables

influencing the dichotomous variable or the bi-polar responses. It

actually computes two different domains namely variables in the

equation and variables not in the equation. The Logistic Regression

equation is a composition of independent and dependent variable and

also as the algebral configuration Y= ax x b. The coefficients a and b are

computed through Cox and Snell and Hosmer and Lemeshow

demonstrations.

In this study the dichotomous response awareness of Fast moving

consumer goods is considered as dependent variable and the

independent attributes of the five fast moving consumer goods

shampoo, Biscuits, bathing soap, toothpaste and Mosquito repellent

have the qualities of independent variable. The results of Logistic

Regression Analysis are presented below.

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The performance of Logistic Regression Analysis for the

dichotomous variable awareness leads to the following results.

Table – 4.7 Classification Table (a, b)

Observed Predicted

Q21 Percentage

Correct

Step 0

1.00 2.00 1.00

Awareness of Fast moving consumer goods

Yes 463 0 100.0 No 137 0 .0

Overall Percentage 77.2

a Constant is included in the model. b. The cut value is .500

From the table in step 0, 77.17 percent of rural consumers of

Salem District are aware of Fast moving consumer goods and 22.83

percent are not fully aware of Fast moving consumer goods.

Table – 4.8 Variables in the equation

B S.E. Wald df Sig. Exp(B)

Step 0 Constant -1.218 .097 156.770 1 .000 .296

In this logistics of step 0 the constant -1.218 is statistically

significant to open the step 0 to conclude the suitability of Logistic

Regression. The following blocks of table reveal the goodness of fit of

Logistic Regression using omnibus test of model coefficient and Hosmer

Lemeshow Test.

4.4.1 Logistic Regression Analysis for Awareness and Attributes of Shampoo

Table – 4.9 Omnibus Tests of Model Coefficients

Chi-square df Sig.

Step 1

Step 12.981 5 .024

Block 12.981 5 .024 Model 12.981 5 .024

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Table – 4.10 Model Summary

Step -2 Log likelihood

Cox & Snell R Square

Nagelkerke R Square

1 631.724(a) .021 .033

a Estimation terminated at iteration number 4 because Parameter estimates changed by less than .001.

Table – 4.11

Hosmer and Lemeshow Test

Step Chi-square df Sig.

1 13.245 8 .104

From the above table, it is found that the Chi-square value for

step, block and model is 12.981 is statistically significant at 5 percent

level. The log likelihood, Cox Snell R2 values 631.724 and 0.021 are also

statistically significant with variance ranging from 2.1 percent to 3.3

percent. This shows that the Logistic Regression fit is suitable to explain

the impact of independent variables on dependent variables. Further

the Hosmer and Lemeshow test clearly revealed the Chi-square value

13.245 is insignificant at 5 percent level. This shows that the variance

exhibited by the independent variable is exactly vary from 2.1 percent to

3.3 percent perfectly. The following table shows the statues of

independent variable on the dependent dichotomous variable

awareness.

Table – 4.12 Variables in the Equation


Step 1(a)

Prevents dandruff .185 .115 2.592 1 .107 .831 Softens the hair .143 .120 1.415 1 .234 .867

Keeps the hair roots healthy

.043 .100 .186 1 .667 .958

Prevents grey hair .315 .096 10.685 1 .001 1.371 Removes the stickiness of the hair

.018 .083 .049 1 .826 1.019

Constant .854 .487 3.071 1 .080 .426

100

From the above table it is found that the variable prevents grey

hair (wald coefficient=10.685) is statistically significant at 5 percent

level. Therefore it is concluded that the awareness of customers of Fast

moving consumer goods in Salem District on shampoo depends upon

their awareness on attribute grey hair protection. In rural marketing

areas the primary aim of the products play the major role in

determining their awareness rather than the secondary effects and

characteristic features of the product.

4.4.2 Logistic Regression Analysis for Awareness and Attributes of Biscuits

Table – 4.13

Omnibus Tests of Model Coefficients

Chi-square df Sig.

Step 1 Step 2.045 5 .843 Block 2.045 5 .843

Model 2.045 5 .843

Table – 4.14

Model Summary

Step -2 Log likelihood Cox & Snell R Square

Nagelkerke R Square

1 642.660(a) .003 .005

a Estimation terminated at iteration number 4 because parameter estimates changed by less than .001.

Table – 4.15


Step Chi-square Df Sig.

1 24.165 8 .002

From the above table the statistical significance as well as

insignificance is broadly classified in terms of non-parametric chi-square

value and Hosmer and Lemeshow Test. It is found that the Chi-square

value for step, block and model is 2.045 is insignificant at 5 percent level.

The log likelihood, Cox Snell R2 values 631.724 and 0.021 are also

101

insignificant with variance ranging from 0.3 percent to 0.5 percent.

Therefore further tests are performed to analyze the impact of independent

variables on dependent dichotomous variables. Further the Hosmer and

Lemeshow test clearly revealed the Chi-square value 24.165 is statistically

significant at 5 percent level. This shows that the variance exhibited by the

independent variable can be considered to vary from 0.3 percent to 0.5

percent. The following table shows the statues of independent variable on

the dependent dichotomous variable awareness.



Step 1(a)

It reduces hunger .127 .117 1.168 1 .280 1.135 It is very healthy -.043 .112 .146 1 .702 .958

It is very tasty -.064 .114 .314 1 .575 .938 It provides Refreshment

.050 .099 .252 1 .616 1.051

It gives energy -.053 .084 .398 1 .528 .949

Constant -1.350 .485 7.757 1 .005 .259

From the above table it is found that all the variables of attributes

and benefits of Biscuit are insignificant at 5 percent level. Therefore it is

concluded that the awareness of consumer of fast moving consumer

goods in Salem District on Biscuits does not depend on the awareness

on attributes and benefits of Biscuits. It shows that the rural consumers

in Salem District mechanically use the Biscuits rather than analyzing its

effect on body.

4.4.3 Logistics Regression Analysis for Awareness and Attributes of Bathing Soap

Table – 4.17 Omnibus Tests of Model Coefficients

Chi-square df Sig.

Step 1

Step 22.149 7 .002

Block 22.149 7 .002 Model 22.149 7 .002

102

Table – 4.18 Model Summary

Step -2 Log

likelihood Cox & Snell

R Square Nagelkerke

R Square

1 622.557(a) .036 .055


Table – 4.19 Hosmer and Lemeshow Test


1 10.031 8 .263

The significance of chi-square value for step, block and model is

supported by the significant value 22.149 at 5 percent level. The

statistical significance contributed in the regression process in the form

of Cox Snell R2 Value 0.036 is also statistically significant with variance

ranging from 3.6 percent to 5.5 percent. This shows that the Logistic

Regression fit is suitable to explain the impact of independent variables

on dependent variables. Further the Hosmer and Lemeshow test clearly

revealed the Chi-square value 10.031 is insignificant at 5 percent level.

This shows that the variance exhibited by the independent variable is

exactly vary from 3.6 percent to 5.5 percent perfectly. The following

table shows the statues of independent variable on the dependent

dichotomous variable awareness.



Step

1(a)

Helps to acquire smoothness of the skin

.018 .098 .034 1 .853 1.018

Gives Fragrance -.093 .118 .626 1 .429 .911

Kills germs -.261 .104 6.349 1 .012 .770

Provides glowing and clean skin

-.240 .104 5.360 1 .021 .786

Stops the bad odour .205 .123 2.805 1 .094 1.228

Gives refreshing feel .182 .115 2.496 1 .114 1.200

Prevents diseases and unhealthy conditions

.216 .100 4.632 1 .031 1.241

Constant -1.478 .662 4.985 1 .026 .228

103

From the above table it is found that the variables kills germs

(wald coefficient = 6.349), provides glowing and clean skin (wald

coefficient = 5.360) and prevents diseases and unhealthy conditions

(wald coefficient = 4.632) is statistically significant at 5 percent level. It

is inferred from the above table that the awareness of customers of Fast

moving consumer goods in Salem District on bathing soap depends

upon their awareness on attributes kills‟ germs, provides glowing and

clean skin and prevents diseases and unhealthy conditions.

4.4.4 Logistics Regression Analysis for Awareness and Attributes of Toothpaste

Table – 4.21


Chi-square df Sig.

Step 1

Step 5.381 9 .800

Block 5.381 9 .800 Model 5.381 9 .800

Table – 4.22

Model Summary

Step -2 Log

likelihood


Nagelkerke R

Square

1 638.286(a) .009 .014


Table – 4.23



1 8.683 8 .370

The awareness level and product attributes of toothpaste and

their association is proved through the chi-square value 5.381 is

insignificant at 5 percent level. The log likelihood, Cox Snell R2 values

638.286 and 0.009 are also insignificant with variance ranging from 0.9

104

percent to 1.4 percent. Therefore further tests are performed to analyze

the impact of independent variables on dependent dichotomous

variables. Further the Hosmer and Lemeshow test clearly revealed the

Chi-square value 8.683 is also insignificant at 5 percent level. But the

range of the variance is 0.5 (1.4-0.9) which is meager and does not

express the individual significance of the independent variables. The

following table shows the statues of independent variable on the

dependent dichotomous variable awareness.



Step 1(a)

Whitens teeth -.184 .117 2.452 1 .117 .832

Increases the beauty of teeth

.050 .109 .212 1 .646 1.051

Gives taste and foaminess

.125 .108 1.336 1 .248 1.133

Kills germs -.045 .124 .130 1 .718 .956

Strengthens tooth .088 .110 .647 1 .421 1.093 Removes the food particles between teeth

-.088 .107 .686 1 .408 .915

Fights tooth decay -.035 .111 .099 1 .753 .966

Gives fresh breath -.029 .110 .070 1 .791 .971 Prevents tooth ache .035 .092 .148 1 .701 1.036

Constant -.814 .647 1.585 1 .208 .443

From the above table it is found that all the variables of attributes

and benefits of toothpaste are insignificant at 5 percent level. It is

further concluded that the attributes of toothpaste do not create more

impact on rural consumers in Salem District. They are not able to

distinguish their awareness technically in terms of fresh breath, tooth

decay and killing the germs.

105

4.4.5 Logistics Regression Analysis for Awareness and Attributes of Mosquito repellent

Table – 4.25


Chi-square df Sig.

Step 1 Step 9.697 5 .084 Block 9.697 5 .084

Model 9.697 5 .084

Table – 4.26

Model Summary

Step -2 Log likelihood


Nagelkerke R Square

1 635.008(a) .016 .024


Table – 4.27



1 16.401 8 .037

In the case of Mosquito repellent insignificant non-parametric

Chi-square value 16.401 and the variance exhibited through Cox and

Snell coefficient 635.008 and 0.016 are essential to build the fundamental

blocks of relationship between product awareness and attributes. This

shows that the variance exhibited by the independent variable can be

considered to vary from 1.6 percent to 2.4 percent. The following table

shows the statues of independent variable on the dependent

dichotomous variable awareness.

106



Step 1(a)

It safeguards from mosquitoes

.034 .116 .084 1 .772 1.034

It gives good fragrance -.320 .113 7.948 1 .005 .726 It is slow poison .172 .115 2.221 1 .136 1.188

It affects health -.071 .100 .499 1 .480 .932 It kills other insects also .018 .092 .039 1 .843 1.018

Constant -.559 .527 1.128 1 .288 .572

From the above table it is found that the variable promotes hair

growth (Wald coefficient=7.948) is statistically significant at 5 percent

level. The rural consumers of Fast moving consumer goods in Salem

District are highly aware that the continuous usage of Mosquito

repellent safeguards from Mosquitos‟ bytes and malaria fever makes

them to grow healthily. This attribute has direct impact on the

awareness of the usage of Mosquito repellent. Infact the rural

consumers possess the awareness of healthy growth which is directly

correlated to the maximum usage of Mosquito repellent regularly.

During the purchase decision process the rural consumers recall the

product quality and brand awareness. The awareness always come

from suitable information search and buying process at one point of

time.

4.5 FACTOR ANALYSIS OF NEED RECOGNITION

The data reduction process is indispensable to establish a concise

research consistently comprising all the characteristic features of

variables involved in this study. The data reduction process is an

ingenious method to represent the variable in the form of predominant

factors with proper mathematical support. In social science research the

research gap generates numerous variables to be examined in the

research and they emerge in the form of well framed interview

107

schedule. In particular, the perceptional studies depend upon the

responses of the respondents in Likert five point scales. The assignment

of numerical values in Likert five point scales for each variable creates

co-variances and the variables in the same domain. These co-variances

and co-efficient of correlation are useful statistical parameters to group

likely variables to form an innovative factor. This mechanism is

achieved through a statistical tool called Factor Analysis by Principal

Component Method. It reduces the numerous variables into major

factors; each factor comprises likely variables with nearest co-variance

and correlation value. The generated factor always represents the

composite variables within them. The researcher coins a new name

which would reveal the characteristic features of the composite variable

in the study. The present study‟s research tool interview schedule

comprises enormous number of variables for product attributes, need

recognition, evaluation of alternatives and post purchase behaviour.

The data reduction tool is required at this juncture and the Principal

Component Analysis is brought into action on those variables.

The need recognition of rural consumers consists of fifteen

variables. The reduction of these variables is achieved through the

application of Factor Analysis by Principal Component Method. The

following results are the upshots of factor analysis application.

Table – 4.29

KMO and Bartlett's test for Need Recognition

Kaiser-Meyer-Olkin Measure of Sampling Adequacy. .836

Bartlett's Test of Sphericity Approx. Chi-Square 1966.033 df 105

Sig. .000

108

From the above table 4.29 it is found that KMO measure of

sampling adequacy is 0.836, Bartlett‟s test of sphericity is 1966.033 are

statistically significant at 5 percent level. Therefore it is concluded that

the fifteen variables of need recognition perfectly involve themselves in

representing the factors. The sample size is also adequate and expresses

its suitability for the application of factor analysis. The communality

values for all the fifteen variables are represented in the table below.

Table – 4.30

Communalities for Need Recognition

Variables Initial Extraction

Change in environmental circumstances 1.000 .421

Changing financial position 1.000 .463 Emerging beauty consciousness 1.000 .325

Interest on Fast moving consumer goods 1.000 .531 To protect health 1.000 .534

To be fashionable 1.000 .529

To live healthy and hygienic life 1.000 .481 To withstand pollution 1.000 .396

To change along with changes in civilization 1.000 .441 Sales and promotional activities of firms 1.000 .518

Low-unit price of Fast moving consumer goods 1.000 .428 Impressed by advertisements 1.000 .575

To attain prestige and social status 1.000 .468 Impress others 1.000 .511

Inherent features and attributes of Fast moving consumer goods

1.000 .383

Extraction Method: Principal Component Analysis

From the above table 4.30 it is found that the twelfth variable

impressed by advertisements (0.575) possess high communality value

whereas the third variable emerging beauty consciousness (0.325)

acquired least value. This implies the individual variances of fifteen

variables range from 32.5 percent to 57.5 percent respectively. Around

25 percent oscillation (57.5 – 32.5) is well established among fifteen

variables.This implies individually all the variables adequately

109

represent their contribution in the formation of factors. The number of

factors emerged is presented in the table below.

Table – 4.31

Number of Factors of Need Recognition

Co

mp

on

en

t

Initial Eigen values Rotation Sums of Squared

Loadings

Total % of

Variance Cumulative

% Total

% of Variance

Cumulative %

1 4.323 28.817 28.817 2.421 16.142 16.142

2 1.563 10.418 39.235 2.375 15.830 31.972 3 1.119 7.458 46.693 2.208 14.721 46.693

4 .966 6.439 53.132

5 .952 6.348 59.480 6 .836 5.573 65.053

7 .811 5.404 70.457 8 .752 5.011 75.468

9 .690 4.602 80.070 10 .605 4.036 84.106

11 .553 3.688 87.794 12 .527 3.514 91.308

13 .454 3.023 94.331 14 .434 2.890 97.222

15 .417 2.778 100.000

Extraction Method: Principal Component Analysis. From the table 4.31 it is found that the fifteen variables on the

whole explained 46.693 percent of total variance and three factors are

emerged out of the variables. The individual eigen values 2.421, 2.375

and 2.208 along with individual variances 16.142, 15.830 and 14.721

respectively.The three factors evolved are represented by the

component variables as presented in the table below.

110

Table – 4.32

Variables and Variables Loadings for the Factors of Need Recognition

Item no

Variables / factors Variable Loadings

Factor I - Consumer awareness

4 Interest on Fast moving consumer goods 0.711

9 To change along with changes in civilization 0.640

15 Inherent features and attributes of Fast moving consumer goods

0.575

10 Sales and promotional activities of firm 0.564

3 Emerging beauty consciousness 0.486 8 To withstand pollution 0.438

Factor II - Consumer Value

12 Impressed by advertisements 0.749

11 Low unit price of Fast moving consumer goods

0.645

13 To attain prestige and social status 0.595

14 Impress others 0.581 Factor III - Health and affordability

5 To protect health 0.728

7 To live healthy and hygienic life 0.670

6 To be fashionable 0.623 2 Changing financial position 0.510

1 Change in environmental circumstances 0.508

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

a Rotation converged in 6 iterations. From the above 4.32, it is observed that first factor consists of six

variables and named as „Consumer awareness‟. The second factor

consists of four variables and is named as „Consumer Value‟. The third

factor consists of five variables which are named as „Health and

affordability‟. Interest on Fast moving consumer goods, impressed by

advertisements and the protect health are considered as the major and

important reasons which necessitated and created the need for

purchasing the Fast moving consumer goods in the rural areas of Salem

District. Low unit price, changes along with changes in civilization and

111

living healthy life are also considered to be some of the reasons for

purchasing Fast moving consumer goods.

The need recognition of product is a process to be explored by the

manufacturers and other researchers. The utility of the product and

factors influencing the rural consumers to maximize their utility

through the suitable behaviour.

4.6 CLUSTER ANALYSIS OF NEED RECOGNITION

Cluster analysis is a multi-variant tool exploited in this context to

classify the perception of respondents into various heterogeneous

groups. Each heterogeneous group is homogeneous with in them based

on the independent variables. The basic idea of cluster analysis is to

group similar objects together. It is a method for classifying variables

into clusters. A cluster consists of variables that have high correlation

with one another and comparatively low correlation with variables in

other clusters.

4.7 CLASSIFYING THE RURAL CONSUMERS BASED ON THEIR

PERCEPTION OF FACTORS OF NEED RECOGNITION

The rural consumers of Salem District identify the need

recognition for purchasing the Fast moving consumer goods. The factor

analysis by Principal Component Method ascertained three

predominant factors namely consumer awareness, consumer value and

health and affordability. The Fast moving consumer goods and its

purchase are simultaneously materialized due to consumer awareness

and their estimated value of the product based on the characteristic

features. Since the products are health and hygienic oriented the

consumers also think about the health and affordability to materialize

their purchase. The opinion of all the respondents emerged in the form

112

of above mentioned factors. Therefore at this juncture it is essential to

classify the perception of rural consumers with respect to meaningful

factors. This paves the way to categories the rural consumers and also

useful to suggest the marketers to prepare their strategies. In this

juncture K-means cluster analysis is applied to classify the consumers‟

perception. Hierarchical cluster method with Agglomeration schedule

decides the number of clusters. It is found that three large co-efficient in

the Agglomeration schedule are significantly large in their differences,

therefore, it is concluded that three cluster exists. The following table

presents the existence of three clusters.

Table – 4.33 Final Cluster Centers for the Factors of Need Recognition

Factors Cluster

Dynamic consumers

Casual consumers

Mechanical consumers

Consumer Awareness 4.31 3.25 3.18

Consumer Value 4.17 2.46 3.68 Health and Affordability 4.46 3.86 3.65

Table – 4.34

Number of Cases in each Cluster of Need Recognition

Cluster Dynamic

consumers Casual

consumers Mechanical consumers

Valid

279.000 157.000 164.000 600.000

From the above table 4.33 and 4.34, it is found that the first cluster

is strong in consumer awareness, value and health and affordability. It

is also found that the first cluster comprises 46.5 percent of consumers

with strong qualities of need recognition. Hence the first cluster is

known as Dynamic Consumers. The second cluster consists of 26.16

percent of consumers with weak qualities of consumer value. They do

not give more value for the need recognition of Fast moving consumer

goods. So they are called Casual Consumers. The third cluster

113

possesses the frequency 27.33 percent of consumers with moderate

quality of need recognition. Therefore this group is identified as

Mechanical Consumers.

4.8 DEMOGRAPHIC DETAILS OF THE SAMPLE RESPONDENTS

Demographic characteristics are vital in any marketing research

for the segmentation purpose. Demographics describe a population in

terms of its size, distribution, and structure. Demographics influence

consumption behaviours both directly and by affecting other attributes

of individuals, such as their personal values and decision styles (Mc

Donald 1993). This segmentation is useful for the marketers and the

manufacturers to produce the required products and also to alter its

appearance, colour and characteristic features. It is the useful concept to

underpin the nature of consumers and their behavioural aspects

towards the products. The durability and non durability of the

products have deep correlation with the nature and characteristic

features of the consumers. In particular gender age, educational

qualification, marital status, occupation, family income, number of

family members and nature of family are essential to ascertain the need

for the purchase and behavioural aspects towards the product. A brief

description of demographic and economic aspects of the area under

study is necessary for studying how the inhabitants of the area behave

as consumers. That is why the present research concentrates on the

demographic variables and they are considered as independent in

nature throughout the thesis. The following table 4.35 presents

demographic segmentation of the sample unit with frequency 600.

114

Table –4.35 Demographic Details of the Sample Respondents

Variable Classification Frequency Percentage

Gender a. Male b. Female

290 310

48.3 51.7

Age

a. Upto 25 years b. 26-35 Years c. 36-45 Years d. 46-55 Years e. 55 Years above

237 168

91 57 47

39.5 28.0 15.2 9.5 7.8

Education

a. School level b. Graduate c. Diploma level c. PG level d. Professionals

209 201 85 67 38

34.8 33.5 14.2 11.2 6.3

Marital Status a. Single b. Married

254 346

42.3 57.7

Occupation

a. Agriculture b. Self-employed c. Businessman d. Housewife e. Govt. employee f. Private employee g. Student h. Others

118 61 48 62 72 74 132

33

19.7 10.2 8.0 10.3 12.0 12.3 22.0 5.5

Family Monthly Income

a. Upto Rs. 5,000 b. Rs. 5,001 - Rs.10,000 c. Rs. 10,001 – Rs.15,000 d. Rs. 15,001 – Rs.20,000 e. Above Rs. 20,000

211 211 86 45 47

35.2 35.2 14.3 7.5 7.8

Size of the Family a. 2 members only b. 3-4 members c. 5 and above

69 369 160

11.5 61.5 26.7

Nature of Family a. Joint b. Nuclear

241 359

40.2 59.8

(Source: Primary Data) 4.8.1 GENDER WISE CLASSIFICATION OF THE RESPONDENT

Gender has always been a distinguishing segmentation variable.

In all societies, males and females are generally assigned certain

115

characteristics and roles. Males are typically thought to be independent,

aggressive, dominating and self-confident in almost all societies. They

are viewed as the bread earners. Females, on the other hand, are viewed

as gentle, submissive, tender, compassionate, tactful and talkative.

Gender roles are ascribed roles, which mean that a role is based on an

attribute over which the individual has little or no control (Sathish

2004).As the sample of the study has both male and female respondents

an attempt was made to find if the consumer behaviour differed among

the males and females. The social setup in India has undergone a rapid

change. Indian women have been taking a lot of buying decisions as

literacy levels are on the rise. Further influence of Cable Television has

created product awareness for them. Not only are women increasingly

influencing buying decisions, but also turning out to be one of the

fastest growing consumer categories. Fast Moving Consumer Goods

(FMCG) companies are competing with one another to cater to the

needs of women (Nitin Mehrotra 2005).

The respondents were asked to indicate their gender in the

interview schedule. From the table 4.35 it can be seen that out of 600

respondents, 290 (48.3 percent) are male and 310 (51.7 percent) are females.

It implies that the sample is almost equally distributed gender wise.

4.8.2 AGE WISE CLASSIFICATION OF THE RESPONDENT

A products need and interest often varies with the consumers‟

age. Marketers have found age as a useful demographic variable for

distinguishing segments. It is clear from the table 4.35 that nearly 39.55

percent of the respondents fall in the category of Upto 25 years while 28

percent of the sample respondents fall between the 26-35 years age

group. 15.2 percent are in the age group of 36-45 years, 9.5 percent are

in the age group of 46-55 years and only 7.8 percent fall above 55 years

116

age group. The predominance of youth population has a great influence

on the overall consumer behaviour. Such implication need research

investigation which helps in understanding the consumer behaviour

better (Arunabha Sinha 2005).

4.8.3 EDUCATIONAL LEVEL WISE CLASSIFICATION OF THE

RESPONDENT

Education influences what one can purchase by partially

determining one‟s income and occupation. It also influences how one

thinks, makes decisions, and relates to others (Smith 1996). Those with a

limited education are generally at a disadvantage not only in earning

money but in spending it wisely (Mathios 1996). The educational level

of respondents is classified into four categories. As shown in Table 4.35,

those who hold Higher Secondary School Education formed the

majority of the sample respondents with 209 (34.8 percent). They are

followed by graduates 201 (33.5 percent) and Diploma holders 85 (14.2

percent). The Post-graduates constitute 67 (11.2 percent) and only 38

(6.3 percent) are professionals. There are no illiterates in the sample

respondents. Today, the young and the educated in the villages are

already large in number and this number is increasing. Already, 40

percent of all those graduating from colleges are rural youth. They are

the decision-makers and are not very different in education, exposure,

attitudes and aspirations from their counterparts at least in smaller cities

and towns (Kannan 2001). It is the youngsters who decide what to buy

(Krishnamoorthy 2000).

4.8.4 MARITAL STATUS WISE CLASSIFICATION OF THE

RESPONDENT

The Marital status plays a vital role in the selection of Fast

moving consumer goods. The married people usually consult their

117

spouses before purchasing Fast moving consumer goods. From the

table 4.35 it is evident that out of 600 respondents 346 (57.7 percent) are

married and 254 (42.3 percent) are unmarried. It is inferred from the

above analysis that the majority of the respondents are married.

4.8.5 OCCUPATION WISE CLASSIFICATION OF THE

RESPONDENT

Occupation is probably the most widely applied singly cue we

use to initially evaluate and define individuals we meet. One‟s

occupation provides status and income. In addition, the type of work

one does and the types of individuals‟ one works with over time also

directly influence one‟s values, lifestyle, and all aspects of the

consumption process. Change in rural occupational pattern has been

slow, as the household industry is not that widespread and rural

economy is still primarily Agri-produce oriented and not agri-

processing oriented or mechanized in nature (Del I Hawkind 2007). The

primary occupations of respondents are shown in Table 4.35. Most of

the respondents reported that they depend on more than one source of

income. However, all the sources do no contribute equally. They

reported their major profession. It is revealed from the study that

agriculture is the major source of 19.7 percent of respondents, private

employees to 12.3 percent, Govt. employees to 12 percent and

Businessmen 8 percent. 22 percent of the respondents are students force

and 10.3 percent of the respondents are housewife.

4.8.6 FAMILY MONTHLY INCOME WISE CLASSIFICATION OF

THE RESPONDENT

The economic development of rural areas has increased the

disposable income of rural people. Rural people are now purchasing

soaps, toothpowder, paste, tobacco products, radio, TV, bicycles,

118

motorcycles, cooking utensils, wrist watch, razor blades, detergents and

so on. Today rural incomes generate not only from agricultural section

but also from other sections. There is a sizeable salaried class in rural

areas. The government implemented various schemes, such as, JRY,

IRDP, NREP, etc., to provide work and self employment opportunities

in rural areas (Dey N B 1998). Occupation and education directly

influence preferences for products, media, and activities; income

provides the means to acquire them (Mulhern F J 1998). Thus, income

is generally more effective as a segmentation variable when used in

conjunction with other demographic variables. The disposable income is

indispensable in deciding over the issues relating to number of units to

be purchased, quality of the goods considered and frequency of

purchase of particular product. Table 4.35 reveals that there are 211

(35.2 percent) respondents in the group of earning Upto Rs. 5,000 and

earnings between Rs. 5,001 Rs.-10,000. Those who are earning Rs.

10,001- Rs. 15,000 are placed in the second with 86 (14.3 percent). The

other two groups earning between Rs. 15,001- Rs. 20,000 and above Rs.

20,000 are 45 (7.5 percent) and 47 (7.8 percent).

4.8.7 SIZE OF THE FAMILY WISE CLASSIFICATION OF THE

RESPONDENT

Family as a unit has always been given recognition in

demographics and economics. A simple definition of family will be that

it is two or more persons living together usually related to by blood,

marriage or adoption. For the purpose of our consumer behaviour

study, the emphasis is on the aspect of living together rather than being

related. Therefore, all households where persons live together are

treated as families (Raju M.S 2004). Persons in the family tend to have

primary responsibility for products associated with their role (Rbert W

1985). Each family member is more involved in some product decision

119

than in others. Role within the family has a bearing on this involvement

and information sharing (Mark E 1985). The size of the family plays a

vital role in determining the quantity of purchase and compromise on

the quality of the product bought. Family members constitute the most

influential primary reference group. Marketers are interested in the

roles and relative influence of the husband, wife and children in the

purchase of large variety of products and services. For the purpose of

this study, family size of the respondents is divided into three groups‟

viz., two member‟ family, three to four members‟ family and five and

above members‟ family. Table 4.35 brings to the light that 11.5 percent

of families had upto two members in the family, 61.5 percent of families

fall in the size 3-4 members and 26.7 percent of families belong to the

size of 5 and above.

4.8.8 NATURE OF FAMILY WISE CLASSIFICATION OF THE

RESPONDENT

In rural areas, in addition to demographic characteristics like age

and sex, family size and structure is also an important feature. The joint

family system and large families still predominate in rural India. As

family size increases, so does the consumption of products.

Simultaneously, the splitting up of joint families is also taking place in

rural India, leading to the creation of an increasing number of small

families. This result in greater demand (Pradeep Kashyap 2006). From

the table 4.35 it is clear that 59.8 percent of respondents are in nuclear

family and 40.2 percent only are in joint family. The above figures show

clearly that the concept of joint family system is decreasing in rural

areas.

120

CHART 4.2

DEMOGRAPHIC DETAILS OF THE SAMPLE RESPONDENTS

121

4.9 CORRESPONDENCE ANALYSIS OF NEED RECOGNITION

K-means cluster analysis and the principal component analysis

method classified the rural consumers based on the factors of need

recognition. These clusters are perfectly characterized by the

demographic variables of rural consumers. The powerful demographic

segmentation and their intensified association with three types of

consumers‟ namely dynamic, casual and mechanical consumers. It

throws the light to identify which demographic segmentation is well

associated with the consumer classification. The correspondence

analysis for the two segmentations like gender, marital status and type

of family will not be performed.

4.9.1 Correspondence between Age and Classifications based on

Need Recognition

Different age groups of consumers‟ Upto 25 years to above 55

years in rural environment are classified into dynamic consumers,

casual and mechanical consumers based on the factors of need

recognition. The application of correspondence analysis in two

dimensional cases with age in one axis and clusters in another axis

revealed the following diagram.

122

Diagram 4.1

The results of the correspondence analysis indicate that good

correspondence is established between dynamic consumers and the

consumers in the age group of 46-55 years. This clearly shows that in

rural market the consumers in the age group of 46-55 years are dynamic

in identifying the need of the Fast moving consumer goods as well as

they dynamically recognize the importance of Fast moving consumer

goods for their healthy appearance. The cross tabulation between age

and the clusters of need recognition is presented in the table below.

123

Table – 4.36 Age Cluster Number of Case Cross Tabulation

Cluster Number of Case Total Dynamic

consumers Casual consumers


Age

Upto 25 years 82 81 74 237

26-35 years 85 32 51 168 36-45 years 57 18 16 91

46-55 years 34 12 11 57 55 years above 21 14 12 47

Total 279 157 164 600

Table – 4.37

Chi-Square Tests

Value Df

Asymp. Sig. (2-sided)

Pearson Chi-Square 32.805(a) 8 .000

Likelihood Ratio 33.349 8 .000 Linear-by-Linear Association 10.745 1 .001

No of Valid Cases 600

a 0 cells (.0 percent) have expected count less than 5. The minimum expected count is 12.30.

Hypothesis- There is no Correspondence between Age and

Classifications based on Need Recognition.

From the above table 4.36 and 4.37, it is found that the maximum

frequency 14.16 percent is dumped at the cell (2, 1) whereas the

minimum frequency 1.83 percent is established at the cell (4, 3). It is

found that the dynamic consumers are also found maximum in the age

group of 26-35 years. On the whole it is concluded that in the rural

market the dynamic consumers are thickly distributed in the age groups

of 26-35 years and 46-55 years respectively. From the chi-square

analysis table, it is found that Pearsons Chi-Square value is 32.805,

likelihood ratio 33.349, linear by linear association 10.745 are statistically

significant at 5 percent level. Hence, it is concluded that age of the rural

consumers has the good correspondence with need recognition factors.

The classifications dynamic consumers, casual consumers and

124

mechanical consumers are found rationally in all the age group of rural

consumers of Fast moving consumer goods.

4.9.2 Correspondence between Educational Qualification and

Classifications based on Need Recognition

The educational qualification of rural consumers in Salem District

ranging from school level education to professional education are

classified into dynamic consumers, casual consumers and mechanical

consumers based on the factors of need recognition. The application of

correspondence analysis in two dimensional cases with education in one

axis and clusters in another axis are depicted in the following diagram.

Diagram 4.2

From the diagram 4.2, it is observed that the dynamic consumers

(cluster 1), casual consumers (cluster 2) and mechanical consumers

(cluster 3) are positioned far away from one another. The results of the

correspondence analysis clearly show that good correspondence is

established between casual consumers and the consumers with PG level

125

education. This result clearly indicates that the consumers with PG

level education are very casual in recognizing the need and importance

of Fast moving consumer goods. The cross tabulation between

education and the clusters of need recognition is presented in the table

below:

Table – 4.38 Education Cluster Number of Case Cross Tabulation

Cluster Number of Case Total

Dynamic consumers

Casual Consumers


Ed

uca

tio

n School level 104 55 50 209

Graduate 81 60 60 201

Diploma 50 12 23 85 PG level 28 23 16 67

Professionals 16 7 15 38

Total 279 157 164 600

Table – 4.39

Chi-Square Tests

Value Df



Likelihood Ratio 17.946 8 .022 Linear-by-Linear Association .915 1 .339



Hypothesis - There is no Correspondence between Educational Qualification and Classifications based on Need Recognition.

From the above table 4.38 and 4.39, it is ascertained that the

maximum frequency 17.33 percent is arrived at the cell (1, 1) whereas

the minimum frequency 1.17 percent is established at the cell (5, 2). It is

now very clear that the dynamic consumers are found maximum at the

school level education. From the chi-square analysis table it is found

that Pearson‟s chi-square value is 17.430, likelihood ratio 17.946 and

126

linear by linear association 0.915 are statistically significant at 5 percent

level. So it is concluded that the education level of the rural consumers

has good correspondence with need recognition factors.

4.9.3 Correspondence between Occupation and Classifications Based

on Need Recognition

The rural consumers of Salem District involved in different

occupations are classified into dynamic consumers, casual consumers

and mechanical consumers based on the factors of need recognition.

The application of correspondence analysis in two dimensional cases

with occupation in one axis and clusters in another axis revealed the

following diagram:

Diagram 4.3

Dimension 1

0.750.500.250.00-0.25-0.50

Dim

en

sio

n 2

0.4

0.2

0.0

-0.2

-0.4

-0.6

8.00

7.006.00

5.00

4.00

3

2

1

Row and Column Points

Occupation

Cluster Number of Case

Symmetrical Normalization

The results of the correspondence analysis explain that the good

correspondence is built between dynamic consumers and the consumers

occupying the position of government employees. The above diagram

127

depicts that the government employees in rural areas of Salem District

are dynamic in identifying and recognizing the need for Fast moving

consumer goods. The cross tabulation between occupation and need

recognition is presented in the table below:

Table –4.40 Occupation Cluster Number of Case Cross tabulation

Cluster Number of Case Total Dynamic consumers

Casual consumers


Occ

up

ati

on

Agriculture 55 29 34 118 Self-employed 30 16 15 61

Businessman 27 9 12 48 Housewife 30 18 14 62

Govt. employee 42 13 17 72 Private employee 40 14 20 74

Student 37 52 43 132 Others 18 6 9 33

Total 279 157 164 600

Table - 4.41

Chi-Square Tests

Value Df Asymp. Sig. (2-sided)





Hypothesis - There is no Correspondence between Occupation and Classifications based on Need Recognition.

The above table well explains that the maximum frequency 9.17

percent is accumulated at the cell (1, 1) whereas the minimum frequency

1 percent is established at the cell (8, 2). These figures well explains that

the dynamic consumers are found maximum at the agriculture

occupation level. On the whole it is concluded that dynamic consumers

are thickly distributed at the agricultural occupation level. From the chi-

128

square analysis table 4.41, it is found that Pearsons Chi-square value is

31.152, likelihood ratio 31.844 and the linear by linear association 1.958

are statistically significant at 5 percent level. The correspondence

analysis revealed different occupations like Agriculture, Self-employed,

businessman, housewife, govt. employee, private employee, student

and others are rationally found in the groups‟ dynamic, casual and

mechanical consumers in rural markets of Salem District.

4.9.4 Correspondence between Family Monthly Income and

Classifications based on Need Recognition

The economic development of rural areas has increased the

disposable income of rural people. Today rural income generate not

only from agricultural section but also from other sections. The rural

consumers earning less than Rs. 5,000 p.m. to above Rs. 20 ,000 p.m.

are classified into dynamic, casual and mechanical consumers taking

into account the factors of need recognition. The application of

correspondence analysis in two dimensional cases with family

monthly income in one axis and clusters in another axis result the

following diagram.

Diagram 4.4

129

It is observed from the diagram 4.4 that fair and reasonable

correspondence exists between mechanical consumers and the

consumers earning monthly income of Rs.15,001 – Rs.20,000.The

consumers in the income category of Rs.15,001 to Rs.20,000 well

recognize consumer value, health and affordability and consumers

awareness. The need and importance of Fast moving consumer goods

are mechanically recognized by the consumers in the income level of Rs.

15,001 to Rs. 20,000. The cross tabulation between income and the

clusters of need recognition is presented in the table below.

Table – 4.42

Family Monthly Income Cluster Number of Case Cross Tabulation


To

tal

Dynamic consumers

Casual consumers


Fam

ily

Mo

nth

ly

inco

me

Upto Rs. 5,000 84 63 64 211

Rs 5,001 – Rs10,000 120 42 49 211

Rs10,001 – Rs15,000 32 33 21 86

Rs15,001 –Rs20,000 17 11 17 45

Above Rs 20,000 26 8 13 47

Total 279 157 164 600

Table – 4.43

Chi-Square Tests

Value df


Pearson Chi-Square 24.804(a) 8 .002 Likelihood Ratio 24.402 8 .002

Linear-by-Linear Association .151 1 .697 No of Valid Cases 600


Hypothesis - There is no Correspondence between Family Monthly

Income and Classifications based on Need Recognition.

130

From the table, it is evident that the maximum frequency 20

percent is found at the cell (2, 1) whereas the minimum frequency 1.33

percent is established at the cell (4, 2). 20 percent of the dynamic

consumers are concentrated at the income level of Rs. 5,001 to Rs.

10,000. So, it is concluded that the mechanical consumers are closely

associated with the income level of Rs. 15,001 to Rs. 20,000 and dynamic

consumers are thickly concentrated at the income level of Rs. 5,001 to

Rs. 10,000. From the Chi-square analysis table 4.43, it is clear that

Pearson‟s Chi-square value is 24.804, likelihood ration 24.402 and linear

by linear association 0.151 are statistically significant at 5 percent level.

The rural consumers in Salem District with different occupations set to

influence the perceptional differences in terms of dynamic, casual and

mechanical consumers. The income of the rural consumers made them

to seek different categories in the consumer classifications.

4.9.5 Correspondence between Family Size and Classifications based

on Need Recognition

The size of the family in the rural areas of Salem District is

classified into three types with 2 member family, 3-4 member family,

and five and above member family and respondents in these families is

classified into dynamic, casual and mechanical consumers based on the

factors of need recognition. The application of correspondence analysis

in two dimensional cases with size of the family in one axis and clusters

in another axis.

131

Diagram 4.5

It is evident from the Diagram 4.5 that correspondence does not

exist between the size of the family and factors of Post-purchase

behaviour. It is further concluded that the family size criterion is replete

with all the three types of clusters dynamic consumers, casual

consumers and mechanical consumers. The cross tabulation between

family size and the clusters of post-purchase behaviour is presented in

the table below.

Table – 4.44

Family Size Cluster Number of Case Cross Tabulation


Total Dynamic consumers

Casual consumers


Family size

2 members only

31 18 20 69

3 – 4 members

176 105 88 369

5 and above 72 34 56 162 Total 279 157 164 600

132

Table – 4.45

Chi-Square Tests

Value df



Linear-by-Linear Association 1.081 1 .299 No of Valid Cases 600


Hypothesis - There is no Correspondence between Family Size and Classifications based on Need Recognition.

From the table, it is evident that the maximum frequency 29.33

percent is found at the cell (2, 1) whereas the minimum frequency 3

percent is established at the cell (1, 2). 29.33 percent of the dynamic

consumers are concentrated in the family size having 3 – 4 members in

the family. From the Chi-square analysis table 4.45, it is clear that

Pearson‟s Chi-square value is 7.508, likelihood ratio 7.441 and linear by

linear association 1.081 are not statistically significant at 5 percent level.

Hence it is concluded that family size of the rural consumers does not

have good correspondence with the factors of post-purchase behaviour.

The family sizes of the rural consumers are not dynamic in ascertaining

the product attributes and knowledge. In fact, the irrationality in family

size among rural consumers creates many series of problem in

materializing the purchase. The correspondence analysis profoundly

concludes the relationship between demographic variables and need

recognition and evaluation of alternatives. But a pre test is required

non-parametrically to find frequency distributions of the cells created by

the rural consumer classification.

133

4.10 ASSOCIATION ANALYSIS OF NEED RECOGNITION

4.10.1 Association between Gender and Clusters of Need Recognition

The chi-square test is applied to find out the association between

gender and clusters of need recognition. The cross tabulation between

gender and the clusters of need recognition is presented in the table

below:

Table – 4.46 Gender and Cluster of Need Recognition


Dynamic consumers

Casual consumers


Gender Male 141 70 79 290

Female 138 87 85 310 Total 279 157 164 600

Table – 4.47

Chi-Square Tests

Value df Asymp. Sig.

(2-sided)




Hypothesis - There is no Association between Gender and clusters of

Need Recognition.

From the above table, it is ascertained that the maximum

frequency 23.5 percent and 23 percent is dumped at the cell (1, 1) and (2,

1) respectively. This clearly indicates that the dynamic consumers are

almost equally distributed in the gender. The dynamic consumers are

concentrated equally between male and female consumers. From the

chi-square analysis it is found that Pearson‟s chi-square value is 1.427,

134

likelihood ratio 1.429 and linear by linear association 0.391 is not

significant at 5 percent level. Hence, it is concluded that the gender of

consumers and the classification based on need recognition are not at all

associated. This implies that gender does not determine the

classification of consumers based on need recognition of consumers of

Fast moving consumer goods.

4.10.2 Association between Marital Status and Clusters of Need

Recognition


marital status and clusters of need recognition. The cross tabulation

between marital status and the clusters of need recognition is presented

in the table below:

Table – 4.48 Marital Status and Clusters of Need Recognition


Dynamic consumers

Casual consumers


Marital status Single 89 85 80 254

Married 190 72 84 346 Total 279 157 164 600

Table – 4.49 Chi-Square Tests

Value df



Linear-by-Linear Association 15.298 1 .000



Hypothesis - There is no Association between Marital Status and

clusters of Need Recognition.

135


frequency 31.67 percent is established at the cell (2, 1). This clearly

indicates that the dynamic consumers are thickly found in the married

rural consumer category. From the chi-square analysis it is found that

Pearson‟s chi-square value is 24.199, likelihood ratio 24.419 and linear

by linear association 15.298 is significant at 5 percent level. Hence it is

concluded that the marital status of consumers and the classification

based on need recognition are well associated. The married as well as

unmarried rural consumers are able to recognize the factors inducing

the consumers to materialize the purchase.

4.10.3 Association between Nature of Family and Need Recognition


nature of family and clusters of need recognition. The cross tabulation

between marital status and the clusters of post-purchase behaviour is

presented in the table below:

Table – 4.50 Nature of family and Cluster of need recognition


Dynamic consumers

Casual consumers


Nature of family

Joint 123 54 64 241

Nuclear 156 103 100 359 Total 279 157 164 600


Value df Asymp. Sig.

(2-sided)


Likelihood Ratio 4.072 2 .131

Linear-by-Linear Association 1.619 1 .203 No of Valid Cases 600

a 0 cells (.0 percent) have expected count less than 5. The minimum expected count is 63.06.s

136

Hypothesis - There is no Association between Nature of Family and

clusters of Need Recognition.

The above table clearly indicates that maximum frequency 26

percent is dumped at the cell (2, 1) and the minimum frequency 9

percent is established at the cell (1, 2). This indicates that the dynamic

consumers are thickly found in the nuclear family. From the chi-square

analysis it is found that Pearson‟s chi-square value is 4.049, likelihood

ratio 4.072 and linear by linear association 1.619 is not significant at 5

percent level. So it is concluded that the nature of family and the

classification based on need recognition are not at all associated. The

joint and nuclear families of rural consumers perceived the need

recognition and evaluation of alternatives emphatically and also throw

the light that the joint families and nuclear families are not able to

identify the different groups of rural consumers.

4.11 CONSUMER INFORMATION SEARCH

Once a problem is recognized, then the consumer must decide

what to do. The initial step is an internal search within memory to

determine whether or not enough is known about alternatives to permit

some kind of choice. Often, one brand of the product what he thought

of purchasing will be strongly preferred to others based on past

experience and a decision will then be taken on the spot to make

purchase as early as possible. If the consumer is not able to finalize his

choice within the internal search, then it is more probable that he will

look into external search making use of variety of information sources

available to him.

The information sources chosen will vary from individual to

individual and from one situation to another. Search refers to the

137

process whereby a consumer seeks information to learn about the

advantages and disadvantages of the various alternatives, to satisfy a

problem that has become recognized. Whether search will occur or not

and the extent to which it occurs depends on the consumer‟s

perceptions of the benefits and the costs involved. Costs included are

time, money, psychological discomfort and the dangers of information

overload. Consumers vary in types and number of sources of

information they consult, when they need information for reaching a

purchase decision.

A consumer may gather information from one source and that

source need or need not be helpful to him in the final stage of his taking

the decision. A consumer might have received first information from

one source. But it is quite possible that this source may or may not be

that important when he actually takes the decision to buy. As a

consequence, there can be significant difference between exposure and

effectiveness (i.e. actual use of the information).

In all marketing research information search is one of the

marketing aspects to study the minds of consumers and their nature of

relationship with marketers. In India, the sources of information are

abundantly obtained from family members, relatives and friends. At

the same time they also grasp much useful information from

commercial sources and public sources. There are a great number and

variety of sources of consumers information available to consumers

with which he could sort out those clues that are relevant to particular

purchase decision to permit him to take a „rational‟ and „optimal‟ choice.

The consumer is human and he is faced with limited capacities of time,

money and effort. Often he must be satisfied with less than perfect

information and less than optimal choice. Variation in information

138

search also depends on consumer‟s abilities to seek and use information

and their perceptions of the need for pre-purchase information.

4.12 PERSONAL INFORMATION SOURCES

Personal information source includes the consumer‟s own

experience and activities and the experience of others. He gains access

to the latter through his non-commercial, personal relationship. The

consumer‟s own experience can be viewed as the result of learning over

time (John A 1963). It is valuable guide in deciding on an often-

purchased and used product. The following are the findings of some

studies relating to personal sources of information.

i. Almost fifty percent of male and female students discussed

clothing brands, styles, retail outlets and prices with their friends

(John R 1970).

ii. Another study found that the source of information most

frequently consulted by durable goods buyers was friends and

relatives (George Katona 1955).

iii. A study of consumer attitude towards health are found that

largest segment of consumers state the most important reason for

choosing their doctor was a recommendation by a friend or

relative (Roger. D 1977).

Other studies have also found personal communication source to

be very important in the purchase of food items and cleaning agents, in

motion picture selections, hairdo styles, makeup techniques, general

fashion, dental product and service, farming practices and physicians.

By gaining social approval before making a final decision, information

search from personal sources can be viewed as risk reducing process

(John Arndt 1968).

139

4.13 COMMERCIAL INFORMATION SOURCES

Salesman at the retail establishment plays an important role in

high involvement decision situations. People are used to seek advice

from druggists on many aspects of health and drugs usage. It may be

interesting to note that a highly dependent consumer tends to be

susceptible and hence prefers assistance by a sales person in decision-

making, whereas an independent person prefers a minimum of

suggestion and assistance (Zokerman M 1950). The independent person,

in fact, seems to respond more positively in aggressive selling (James E

1965). Advertising and promotion constitute one of the most important

and pervasive sources of information in the market place. From the

manufacturer‟s and retailer‟s view the primary objective of advertising

and promotion is the selling function. From the consumer‟s view,

advertising and promotion serves the very useful function of

acquainting him with the market offer.

4.14 PUBLIC SOURCES

With the emergence and widespread acceptance of major

newspapers, magazines, radio and television, the mass media have

become one of the most ubiquitous and inescapable fact of society. The

mass media represent a powerful force influencing and shaping the

values, tastes and quality of life. Television is the fastest growing, most

powerful and most popular medium both in rural and urban India.

With increasing ownership of TV and influx of cable and satellite in

addition to terrestrial TV, this medium is gaining new ground in rural

areas. The ownership of TV has increased tremendously in the last two

years because of the introduction of free TV sets to the people of Tamil

Nadu by the Government. Irrespective of literacy levels, topography,

geographical location, or area of residence, radio reaches people easily.

It continues to be the cheapest mass medium with the widest reach. The

140

fact that there is a very high ownership of transistorized radios in rural

India makes radio a popular and very cost-effective medium to reach

rural people. For the majority of the unprivileged in rural areas, radio

continues to be the main source of news and entertainment.

The following t-test application identifies the depth of

information sources for the rural marketers.

Table – 4.52

One-sample t-test for Personal Sources of Information

Personal sources

N Mean Std.

Deviation

Std. Error Mean

t value

Sig (2-

tailed)

Family 600 4.5100 .87245 .03562 42.395 .000

Relatives 600 3.9333 .96632 .03945 23.659 .000 Friends 600 4.0583 .93079 .03800 27.851 .000

Doctor 600 3.9883 1.02397 .04191 23.582 .000 Past experience 600 3.9750 1.00800 .04115 23.693 .000

Word-of-mouth 600 3.5200 1.18544 .04840 10.745 .000 Colleagues 600 3.5600 1.20367 .04914 11.396 .000

Table 4.52 clearly indicates the mean values of personal sources of

information like Family (mean = 4.51), relatives (mean = 3.93), friends

(mean = 4.05), Doctor (mean = 3.98), Past experience (mean = 3.97),

Word-of-mouth (mean = 3.52) and Colleagues (mean = 3.56). Since the

larger mean value establishes its numerical concern value it is

concluded that family sources play a vital role as a source of

information for the rural consumers of Salem District in purchasing the

Fast moving consumer goods. Similarly friends of the rural consumers

are considered as an abundant source in getting information about Fast

moving consumer goods. The consumers feel family members as well as

friends are the reliable sources to give authentic information about the

Fast moving consumer goods. The other personal sources relatives,

doctor, past experience, word-of-mouth and colleagues are also

141

considered as an important source in getting information about

Fast moving consumer goods. The t values of the respective seven

personal sources 42.395, 23.659, 27.851, 23.582, 23.693, 10.745, 11.396

are statistically significant at 5 percent level. So from the above

analysis it is concluded that family and friends is the most important

personal source for rural consumers of Salem District for making the

final choice

Table – 4.53

One-Sample t-test for Commercial Sources of Information

Commercial sources

N Mean Std.

Deviation

Std. Error Mean

t value

Sig (2-tailed)

Sales persons or representatives

600 4.1383 1.06195 .04335 26.257 .000

Traders 600 3.8217 .99071 .04045 20.315 .000

Promotional offers

600 3.7191 1.02466 .04190 17.161 .000

Table 4.53 clearly explains that rural consumers of Salem District

are very keen in collecting information about Fast moving consumer

goods. The mean value of commercial sources of information is 4.13,

3.82 and 3.71 for sales persons or representatives, traders and

promotional offers respectively. The information from sales persons or

representatives are considered as very important commercial source of

information about Fast moving consumer goods since it has larger mean

value(mean=4.13). The other sources traders and promotional offers are

just important for the rural consumers of Salem District.

142

Table – 4.54 One-Sample t-test for Public Sources of Information

Public sources

N Mean Std.

Deviation

Std. Error Mean

t value

Sig (2-

tailed)

Radio 600 4.0336 1.03658 .04246 24.342 .000 TV 600 4.1583 .99410 .04058 28.542 .000

Magazine 600 3.8395 .97420 .03984 21.072 .000 Newspapers 600 4.0251 1.00803 .04122 24.868 .000

Table 4.54 clearly shows that the public sources like Radio,

Television and Newspapers carry more important information of Fast

moving consumer goods to their consumers (Mean= 4.05, 4.15 and 4.02).

The„t‟ values of various public sources of information are 24.342, 28.542,

21.072, and 24.868 are statistically significant at 5 percent level.

Television is considered as a very important public source of

information to the rural consumers of Salem District since it has larger

mean value. The mean values of the information sources radio and

newspapers are also greater than the test value 3 which indicates that

these sources of information is also considered as an important source

of information for collecting information about the Fast moving

consumer goods.

4.15 PERSONS IN THE FAMILY PROVIDING PRODUCT

INFORMATION

A person in the family who first provides the product

information plays an important role in pre-purchase behaviour.

Questions on the issue were asked and the responses on the same are

tabulated below.

143

Table – 4.55 Persons in the Family Providing Product Information

Frequency

Valid Percent

Cumulative Percent

Product information

Father 141 23.5 23.5

Mother 207 34.5 58.0 Husband 88 14.7 72.7

Wife 104 17.3 90.0 Children 60 10.0 100.0

Total 600 100.0

It is evident from the above table 4.55 that in 52 percent cases

mother and wife are the initial provider of information about the Fast

moving consumer goods. Since the women are care taker of the family

they are collecting the information about Fast moving consumer goods

from the TV media. Now the status of rural women is changing. They

are more educated and more aware about the health and education

needs of the family. The growing presence of media, too, exerts a strong

influence on their role and behaviour. They are no longer confined

within their homes. They step out for several purposes- education,

health services, social services, functions and festivals. In short, women

are more empowered today.

CHART 4.3

PERSONS IN THE FAMILY PROVIDING PRODUCT INFORMATION

144

4.16 IMPORTANCE ASSIGNED TO THE PRODUCT

INFORMATION

Before purchasing a consumer collects information about the

desired product. Respondents of the selected rural consumers as to the

importance they attach to the product information are tabulated below.

Table – 4.56

Importance of Product Information

Frequency

Valid Percent

Cumulative Percent

Product information

As much information as possible about the product

234 39.0 39.0

Just enough to make the choice

249 41.5 80.5

I decide just like that

117 19.5 100.0

Total 600 100.0

Thus it appears that 39 percent of the respondents try to get as

much information as possible about the product and 41.5 percent of the

respondents want to get information just enough to make the choice.

4.17 PRE-PURCHASE VISIT TO SHOPS FOR INFORMATION

SEARCH

It is often stated that rural people are cautious and they visit

several shops for information search before they actually purchase

goods. Responses on the issue are tabulated below.

Table – 4.57 Distribution of Pre-purchase Visit to Shops for Information Search

Frequency Percent

Valid Percent

Cumulative Percent

Pre-purchase visit to shops

Yes 400 66.4 66.7 66.7 No 200 33.2 33.3 100.0

Total 600 99.7 100.0

145

Thus it appears that two-third (66.7 percent) of the respondents

visited several shops for information search. Other one-third

respondents have reported that since they purchase branded goods,

they rely on the honesty of the store-owners, that they do no like to

enquire much about the product on the floor of the shop.

4.18 FREQUENCY DISTRIBUTION OF YEAR WISE

CLASSIFICATION OF USING OF A PARTICULAR BRAND OF FMCG

Table – 4.58 Number of years using the brand

No. of years/

Percentage

<3 Years

% 3-6

years %

6-9 years

% >9

years %

Shampoo 309 51.5 135 22.5 48 8 108 18

Biscuits 125 20.8 216 36 87 14.5 172 28.7 Bathing Soap

105 17.5 125 20.8 145 24.2 225 37.5

Tooth paste 127 21.2 93 15.5 131 21.8 249 41.5 Mosquito repellent

123 20.5 77 12.8 65 10.8 335 55.9

(Source: Primary Data) The Fast moving consumer goods mentioned in Table 4.58 are

consumed both in urban and rural areas of Salem District. But

consumption of the same penetrates in rural areas of Salem District later.

Enquiry on this issue provides us an idea as to how long ago these

products have penetrated in rural areas of Salem District. Thus it is

evident from Table 4.58 that more than 50 percent of respondents using the

particular brand of shampoo for less than three years; 22.5 percent

respondents using the particular brand of shampoo for 3-6 years; 8 percent

respondents use the particular brand for 6-9 years and 18 percent

respondents use the particular brand for more than 9 years. This figure

clearly indicates that penetration of shampoo in rural areas of Salem

District has gained momentum in the last five years only. Regarding the

146

Biscuits, 20.8 percent respondents use the particular brand for less than 3

years; 36 percent respondents use it for 3-6 years; 14.5 percent respondents

use it for 6-9 years and 28.7 percent respondents use for more than 9 years.

This clearly shows that penetration of Biscuits in rural areas of Salem

District has gained momentum in nineties itself. With regard to bathing

soap, 37.5 percent respondents use it for more than nine years; 24.2 percent

respondents use it for 6-9 years and only 17.5 percent respondents use it

for less than three years. This clearly shows that rural consumers in Salem

District are not ready to switch over from their brand and they are loyal to

their brand. With regard to toothpaste 41.5 percent respondents use it for

more than nine years; 21.8 percent respondents use it for 6-9 years and

only 21.2 percent respondents use it for less than three years. As compared

to shampoo, Biscuits, bathing soap and toothpaste the customers of

Mosquito repellent are very much loyal to their brand since 55.9 percent

respondents using it for more than nine years. It clearly shows that the

consumers of Mosquito repellent are not ready to change their brand.

CHART 4.4

NUMBER OF YEARS USING THE BRAND

147

4.19 FACTOR ANALYSIS FOR EVALUATION OF ALTERNATIVES

The evaluation of alternatives consists of nine variables. The

reduction of these variables is achieved through the application of factor

analysis by Principal Component Method. The following results are the

upshots of factor analysis application.

Table – 4.59 KMO and Bartlett's Test for Evaluation of Alternatives

Kaiser-Meyer-Olkin Measure of Sampling Adequacy. .764

Bartlett's Test of Sphericity Approx. Chi-Square 714.564 Df 36

Sig. .000

From the above table it is found that KMO measure of sampling

adequacy is 0.764, Bartlett‟s test of sphericity is 714.564 are statistically

significant at 5 percent level. Therefore it is concluded that the nine

variables of evaluation of alternatives perfectly involve themselves in

representing the factors. The sample size is also adequate and expresses

its suitability for the application of factor analysis. The communality

values for all the nine variables are represented in the table below.

Table – 4.60 Communalities for Evaluation of Alternatives

Variables Initial Extraction

The quality of the product is important to me before the decision of purchase

1.000 .124

Price is an important criterion 1.000 .637

Quantity must be reasonably good 1.000 .269 Availability is very important 1.000 .530

Attractive packaging is necessary 1.000 .308 The ingredients are important to buy the selected brands

1.000 .384

For their medicinal value 1.000 .665

Herbal nature of the product is an important criterion

1.000 .614

Size and weight of products are important 1.000 .277

Extraction Method: Principal Component Analysis

148

From the above table, it is found that the seventh variable „for

their medicinal value (0.665)‟ possess high communality value whereas

the first variable „the quality of the product is important to me before

the decision of purchase (0.124)‟ acquired least value. This implies the

individual variances of nine variables range from 66.5 percent to 12.4

percent respectively. Around 54.10 percent oscillation (66.5 – 12.4) is

well established among nine variables. This implies individually all the

variables adequately represent their contribution in the formation of

factors. The number of factors emerged is presented in the table below.

Table – 4.61

Number of Factors for Evaluation of Alternatives

Co

mp

on

en

t

Initial Eigen values Rotation Sums of Squared

Loadings

Total % of

Variance Cumulative

% Total

% of Variance

Cumulative %

1 2.715 30.170 30.170 1.972 21.910 21.910

2 1.094 12.152 42.322 1.837 20.413 42.322 3 .990 11.002 53.324

4 .908 10.088 63.413 5 .850 9.447 72.860

6 .709 7.882 80.742 7 .682 7.583 88.325

8 .572 6.350 94.675 9 .479 5.325 100.000

Extraction Method: Principal Component Analysis. From the table 4.61 it is found that the nine variables on the

whole explained 42.322 percent of total variance and two factors are

emerged out of the variables. The individual eigen values 1.972 and

1.837 along with individual variances 21.910 and 20.413 respectively.

The two factors evolved are represented by the component variables as

presented in the table below.

149

Table – 4.62 Variables and Variables Loadings for the Factors of Evaluation of

Alternatives

Item no

Variables / factors Variable Loadings

Factor I – Consumer purchase decision

2 Price is an important criterion 0.779

4 Availability is very important 0.700

6 The ingredients are important to buy the selected brands

0.544

5 Attractive packaging is necessary 0.432 Factor II – Product properties

7 For their medicinal value 0.811

8 Herbal nature of product is an important criterion

0.772

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

a Rotation converged in 6 iterations.

From the above table 4.62, it is very clear that the first factor

consists of four variables and named as „Consumer purchase decision‟.

The second factor consists of two variables which are named as „Product

properties‟. Medicinal value of Fast moving consumer goods are

considered to be the main and important criterion while evaluating the

various brand of Fast moving consumer goods by the rural consumers

of Salem District to take the final decision. Price, availability and herbal

nature of Fast moving consumer goods are also taken into account while

evaluating the various brands by the rural consumers.

The predominant factors identified through factor analysis forms

the basis and also create a research question what are the different

blocks of consumers exists among rural consumers in Salem District.

This classification actually enumerates the factors involved in

perceptional differences among the consumers.

150

4.20 CLASSIFYING THE RURAL CONSUMERS BASED ON THEIR PERCEPTION OF FACTORS OF EVALUATION OF

ALTERNATIVES

The factor analysis revealed that evaluation of alternatives is

aimed at consumer purchase decision and product properties. Now an

attempt is made to classify the consumers‟ perception about the above

mentioned factors of evaluation of alternatives. The K-means cluster

analysis is applied on these factors by identifying the co-efficient of

hierarchical clusters and frequency of each cluster. The results are

displayed in the following tables.

Table – 4.63

Final Cluster Centers for the Factors of Evaluation of Alternatives

Factors Cluster

Knowledgeable Consumers

Need based consumers

Consumer purchase Decision 4.21 3.46

Product properties 4.49 3.06

Table – 4.64

Final Cluster Centers for the Factors of Evaluation of Alternatives

Cluster Knowledgeable consumers


Valid

402.000 198.000 600.000

From the table 4.63 and 4.64, it is clear that 67 percent of rural

consumers in Salem District are found in first cluster with strong

qualities and more knowledge about the factors consumer purchase

decisions and product properties and, therefore, the first cluster of

respondents is known as Knowledgeable Consumers. The second

cluster consists of 33 percent of rural consumers with weak qualities

and knowledge about the factors consumer purchase decisions and

product properties and, therefore, it is called as Need Based Consumers.

151

The above table clearly shows that most of the rural consumers widely

use the various evaluative criteria before purchasing the Fast moving

consumer goods. Only 33 percent respondents are less aware about the

evaluative criteria. It is finally concluded that the knowledgeable

consumers posses a clear wisdom about the factors consumer purchase

decisions and product properties.

4.21 ASSOCIATION ANALYSIS OF EVALUATION OF

ALTERNATIVES

4.21.1 Association between Gender and Clusters of Evaluation of

Alternatives


gender and clusters of evaluation of alternatives. The cross tabulation

between gender and the clusters of evaluation of alternative is presented

in the table below:

Table – 4.65 Gender and Clusters of Evaluation of Alternatives

Cluster Number of Case Total Knowledgeable

Consumers Need based consumers

Gender Male 188 102 290

Female 214 96 310 Total 402 198 600


Value df

Asymp. Sig.

(2-sided)

Exact Sig. (2-sided)


Pearson Chi-Square 1.198(b) 1 .274

Continuity Correction(a)

1.015 1 .314

Likelihood Ratio 1.198 1 .274

Fisher's Exact Test .297 .157

152

Value df

Asymp. Sig.

(2-sided)



Linear-by-Linear Association

1.196 1 .274


a Computed only for a 2x2 table b 0 cells (.0 percent) have expected count less than 5. The minimum

expected count is 95.70. Hypothesis - There is no Association between Gender and clusters of

Evaluation of Alternatives.


frequency 35.66 percent is concentrated at the cell (2, 1). This clearly

indicates that the knowledgeable consumers are thickly found in the

female consumer category. From the chi-square analysis it is found that

Pearson‟s chi-square value is 1.198, likelihood ratio 1.198 and linear by

linear association 1.196 is not significant at 5 percent level. Hence it is

concluded that the gender of consumers and the classification based on

evaluation of alternatives are not at all associated. This implies that

gender does not determine the classification of consumers based on

evaluation of alternatives.

4.21.2 Association between Marital Status and Clusters of Evaluation

of Alternatives


marital status and clusters of evaluation of alternatives. The cross

tabulation between marital status and the clusters of evaluation of

alternatives is presented in the table below:

153

Table - 4.67 Marital Status and Clusters of Evaluation of Alternatives


Knowledgeable consumers


Marital status Single 167 87 254

Married 235 111 346 Total 402 198 600

Table - 4.68 Chi-Square Tests

Value df Asymp.

Sig. (2-sided)

Exact Sig.

(2-sided)

Exact Sig.

(1-sided) Pearson Chi-Square

.312(b) 1 .576

Continuity Correction(a)

.222 1 .638

Likelihood Ratio .312 1 .577

Fisher's Exact Test .599 .318

Linear-by-Linear Association

.312 1 .577


a Computed only for a 2x2 table b 0 cells (.0 percent) have expected count less than 5.

The minimum expected count is 83.82.

Hypothesis - There is no Association between Marital Status and

clusters of Evaluation of Alternatives.

From the table, it is evident that the maximum frequency 39.16

percent is established at the cell (2,1) . This clearly indicates that the

knowledgeable consumers are thickly found in the married rural

consumer category. From the chi-square analysis it is found that


linear association 0.312 is not significant at 5 percent level. Hence it is

concluded that the marital status of consumers and the classification

based on evaluation of alternatives are not at all associated.

154

4.21.3 Association between Nature of Family and Clusters of

Evaluation of Alternatives


nature of family and clusters of evaluation of alternatives. The cross

tabulation between nature of family and the clusters of evaluation of


Table – 4.69

Nature of Family and Clusters of Evaluation of Alternatives


Total



Nature of family Joint 164 77 241 Nuclear 238 121 359

Total 402 198 600

Table – 4.70

Chi-Square Test

Value df Asymp. Sig. (2-sided)



Pearson Chi-Square .201(b) 1 .654

Continuity Correction(a) .129 1 .719 Likelihood Ratio .201 1 .654

Fisher's Exact Test .723 .360 Linear-by-Linear Association

.200 1 .654


a Computed only for a 2x2 table b 0 cells (.0 percent) have expected count less than 5.

The minimum expected count is 79.53.

Hypothesis - There is no Association between Nature of Family and


155

The above table clearly indicates that maximum frequency 39.66

percent is dumped at the cell (2, 1) and the minimum frequency 12.83

percent is established at the cell (1, 2). This indicates that the dynamic

consumers are thickly found in the nuclear family. From the chi-square

analysis, it is found that Pearson‟s Chi-square value is 0.201, likelihood

ratio 0.201 and linear by linear association 0.200 is not significant at 5

percent level. So, it is concluded that the nature of family and the

classification based on evaluation of alternatives are not at all associated.

4.21.4 Association between Age and clusters of Evaluation of

Alternatives


age and clusters of evaluation of alternatives. The cross tabulation

between age and the clusters of evaluation of alternatives is presented in

the table below:

Table – 4.71 Age and Clusters of Evaluation of Alternatives




Age

Upto 25 years 159 78 237

26 - 35 Years 109 59 168 36 - 45 Years 68 23 91

46 - 55 Years 39 18 57

55 Years above 27 20 47 Total 402 198 600


Value df



Likelihood Ratio 4.827 4 .306 Linear-by-Linear Association .119 1 .730



156

Hypothesis - There is no Association between Age and clusters of

Evaluation of Alternatives.

From the above table, it is evident that maximum frequency 26.5

percent is concentrated at the cell (1, 1) and the minimum frequency 3

percent is established at the cell (4, 2). From the chi-square analysis it is

found that Pearson‟s chi-square value is 4.790, likelihood ratio 4.827 and

linear by linear association 0.119 is not significant at 5 percent level. So,

it is concluded that the age and the classification based on evaluation of

alternatives are not at all associated. This also proves age of the rural

consumers is independent and does not categories different groups of

rural consumers based on evaluation of alternatives.

4.21.5 Association between Educational Qualification and Clusters of



education and clusters of evaluation of alternatives. The cross tabulation

between age and the clusters of evaluation of alternatives is presented in

the table below:

Table – 4.73 Educational Qualification and Clusters of Evaluation of Alternatives




Education

School level 150 59 209

Graduate 132 69 201 Diploma level 56 29 85

PG level 44 23 67 Professionals 20 18 38

Total 402 198 600

157


Value df Asymp. Sig. (2-sided)


Likelihood Ratio 5.827 4 .212 Linear-by-Linear Association

4.347 1 .037



Hypothesis - There is no Association between Educational

Qualification and clusters of Evaluation of Alternatives.


percent is concentrated at the cell (1, 2) and the minimum frequency 3

percent is established at the cell (5, 2). This indicates that the

knowledgeable consumers are thickly found in the school level

education consumers. From the chi-square analysis it is found that


linear association 4.347 is not significant at 5 percent level. So, it is

concluded that the education and the classification based on evaluation

of alternatives are not at all associated.

4.21.6 Association between Occupation and Clusters of Evaluation of

Alternatives


occupation and clusters of evaluation of alternatives. The cross

tabulation between occupation and the clusters of evaluation of


158

Table – 4.75 Occupation and Clusters of Evaluation of Alternatives




Occupation

Agriculture 70 48 118

Self-employed 35 26 61 Businessman 33 15 48

Housewife 53 9 62 Govt. employee 48 24 72

Private employee 45 29 74 Student 97 35 132

Others 21 12 33

Total 402 198 600

Table – 4.76

Chi-Square Tests

Value df






Hypothesis - There is no Association between Occupation and


From the above table it is very clear that the maximum frequency

16.17 percent is concentrated at the cell (7, 1) and the minimum

frequency 1.5 percent is established at the cell (4, 2). This indicates that

the knowledgeable consumers are thickly found in the students‟ level.

From the chi-square analysis it is found that Pearson‟s chi-square value

is 19.313, likelihood ratio 20.628 and linear by linear association 3.354 is

significant at 5 percent level. So it is concluded that the occupations and

the classification based on evaluation of alternatives are well associated.

159

CHART 4.5

OCCUPATION AND CLUSTERS OF EVALUATION OF

ALTERNATIVES

4.21.7 Association between Family Monthly Income and Clusters of



monthly income and clusters of evaluation of alternatives. The cross

tabulation between monthly income and the clusters of evaluation of


Table – 4.77

Family Monthly Income and Clusters of Evaluation of Alternatives


Total



Family Monthly income

Upto Rs. 5,000 138 73 211

Rs. 5,001 – Rs. 10,000 146 65 211 Rs. 10,001 – Rs.15,000 55 31 86

Rs. 15,001 – Rs. 20,000 29 16 45 Above Rs. 20,000 34 13 47

Total 402 198 600

160

Table – 4.78

Chi-Square Tests

Value df





Hypothesis - There is no Association between Family Monthly

Income and clusters of Evaluation of Alternatives.

The above table clearly indicates that maximum frequency 24.33

percent is concentrated at the cell (2, 1) and the minimum frequency 2.16


knowledgeable consumers are thickly found in the income level of Rs.

5001- Rs.10,000. From the chi-square analysis it is found that Pearson‟s

chi-square value is 1.803, likelihood ratio 1.818 and linear by linear

association 0.183 is not significant at 5 percent level. So it is concluded

that the monthly income of the family and the classification based on

evaluation of alternatives are not at all associated.

4.21.8 Association between Size of the Family and Clusters of



size of the family and clusters of evaluation of alternatives. The cross

tabulation between size of the family and the clusters of evaluation of


161

Table – 4.79

Family Size and Clusters of Evaluation of Alternatives


Total



Family size 2 members only 42 27 69 3 – 4 members 252 117 369

5 and above 108 54 162 Total 402 198 600

Table – 4.80

Chi-Square Tests

Value df



Linear-by-Linear Association .284 1 .594



Hypothesis - There is no Association between Size of the Family and



percent is concentrated at the cell (2, 1) and the minimum frequency 4.5


knowledgeable consumers are thickly found in the families having 3 – 4

members in the family. From the chi-square analysis it is found that

Pearson‟s Chi-square value is 1.460, likelihood ratio 1.430 and linear by

linear association 0.284 is not significant at 5 percent level. So it is

concluded that the size of the family does not determine the

classification of consumers based on evaluation of alternatives and both

are not at all associated. Family size variations lead to different notions

about evaluating suitable alternatives.

162

4.22 AWARENESS ON THE SIGNIFICANCE OF FAST MOVING

CONSUMER GOODS

The rural consumers and their awareness is an important

behavioural aspect for the success of marketers. Full awareness and its

significance on health and hygiene help the marketers to augment their

business domain. The following frequency distribution clearly presents

the awareness percentage of rural consumers in Salem District.

Table – 4.81

Awareness of Fast moving consumer goods

Frequency

Valid Percent

Cumulative Percent

Awareness

Yes 463 77.2 77.2

No 137 22.8 100.0 Total 600 100.0

From the table 4.81, it is evident that 77.2 percent of the

respondents have knowledge about the Fast moving consumer goods

and 22.8 percent of the respondents are not much aware about the Fast

moving consumer goods.

CHART 4.6

AWARENESS OF FAST MOVING CONSUMER GOODS

163

4.23 RURAL CONSUMERS VIEW ABOUT BRAND AWARENESS

Brand awareness refers to the strength of the brand presence in

the consumer‟s mind. Brand awareness consists of brand recall and

brand recognition. Brand recognition relates consumer ability to

confirm prior exposure to the brand when given the brand as a cue.

Brand recall relates to consumer ability to retrieve the brand when given

the product category, the needs fulfilled by the category or some other

types of probe as a cue. Brand awareness makes it easier form

consumers to identify products with the well-known brand names. One

sample t-test is applied to find out the brand awareness and the benefits

of the brand used by the rural consumers of Salem District and the

results are tabulated below.

Table – 4.82 One Sample t-test for Brand Awareness

Variables N Mean Std.

Deviation

Std. Error Mean t-

va

lue

Sig

(2

-tai

led

)

The brand of Fast moving consumer goods stays fixed in my mind

600 4.2767 .87640 .03578 35.682 .000

Able to distinguish one brand from the other by its performance

600 3.9267 .90332 .03688 25.128 .000

Able to discriminated between brands, as I have prior knowledge about them

600 3.9717 .99793 .04074 23.850 .000

Awareness of the other brands produced by the same manufacturer

600 3.6817 1.07031 .04370 15.600 .000

Good exposure to the brand plays an Important role for remembrance

600 4.1250 .93318 .03810 29.530 .000

Brand recognition of the Fast moving consumer goods is due to its performance

600 3.9565 .99317 .04061 23.552 .000

164

From the above table, it is evident that by comparing the mean

values of variables of Brand Awareness the first variable brand stays

fixed in mind and the fifth variable good exposure to the brand plays an

important role for remembrance are strongly agreed by the rural

consumers (Mean = 4.27 and 4.12). The other variables of brand

awareness like awareness of other brands, brand recognition of the fast

moving consumer goods and knowledge about the discrimination of

brands are agreed by the rural consumers of Salem District since the

mean values are less than four. The t-values of the respective seven

variables are statistically significant at 5 percent level. Therefore it is

concluded that the rural consumers of Salem District are aware of the

various brands of Fast moving consumer goods.

4.24 MAJOR INFLUENCING MEDIA CHANNEL

A medium is called a mass medium when it reaches millions of

people. But poor exposure to the mass media and the ineffectiveness of

universalized communication aimed at heterogeneous rural audiences,

make it difficult for the mass media to address the communication need

in rural markets in an effective manner. The role and relevance of the

mass media is explored in the following table.

Table – 4.83 Major influencing media channel

Frequency

Valid Percent

Cumulative Percent

Medias

TV 352 58.7 58.7 Radio 78 13.0 71.7

Newspapers 57 9.5 81.2

Magazines 25 4.2 85.3 Friends 47 7.8 93.2

Neighbours 22 3.7 96.8 Colleagues 12 2.0 98.8

Trade associations 7 1.2 100.0 Total 600 100.0

(Source: Primary Data)

165

From the table 4.83 it is evident that TV occupies predominant

role (58.7 percent) with regard to the media for Fast moving consumer

goods and reaching the messages and advertisement of Fast moving

consumer goods to the consumers of Salem District. The viewing of TV

in rural areas of Salem District has increased because of the distribution

of free TV sets by the Govt. of Tamilnadu. Television is the fastest

growing, most powerful and most popular medium both in rural and

urban areas. 13 percent of the respondents came to know about the Fast

moving consumer goods through Radio. Irrespective of literacy levels,

topography, geographical location, or area of residence, radio reaches

people easily. It continues to be the cheapest mass medium with the

widest reach. The other mediums are not popular in the rural areas of

Salem District.

CHART 4.7

MAJOR INFLUENCING MEDIA CHANNEL

166

4.25 TYPE OF SHOP MOST FAVOURED

With the spread of consumers across various population

categories, marketers face the problem of accessing these markets.

Apart from ensuring the reach of their product to retail outlets,

marketers also need to motivate retailers to stock their product or brand.

It is an easy proposition to distribute a truckload of soaps in the urban

market covering a small geographical area. But to sell the same

truckload in the rural market one has to cover hundreds of small and

large villages, which increases the distribution and promotion costs

significantly. The preference for retail outlet by the respondents in the

rural areas of Salem District is represented in the following table.

Table – 4.84

Type of Shop Most Favoured

Frequency Valid Percent

Cumulative Percent

Retail outlet

Shop in rural area 182 30.33 30.33

Shop in town 266 44.33 74.66 Departmental stores 60 10 85.1

From anywhere it is available

92 15.34 100.0

Total 600 100.0

(Source: Primary Data) From the table 4.84 it is clear that 44.6 percent of the respondents

prefer to buy the Fast moving consumer goods from the retail out let in

urban area and 30.5 percent of the respondents prefer to buy the Fast

moving consumer goods from the shop in rural area and 14.9 percent of

the respondents buy the Fast moving consumer goods from anywhere it

is available. The above results clearly indicates that though retail outlets

are situated in the rural areas nearly 50 percent of the respondents in

the rural areas prefer to buy the Fast moving consumer goods from the

urban retail outlet only. The location of the shop is not much important

167

to most of the rural consumers, since most of the respondents in rural

area purchase Fast Moving Consumers Goods from the retail outlet in

town only.

4.26 PRICE PERCEPTION FOR FAST MOVING CONSUMER

GOODS

In the rural market, the level of consumer involvement with the

product and the nature of competition in the particular industry play

significant roles in the selection of the pricing objective. Rural

consumers have limited and fluctuating purchasing power. Therefore,

the marketers need to build the pricing strategy of the company keeping

in mind not only the customers‟ limited ability to pay but also the

modes of payments and their timings, that rural consumers usually

adopt. The perception of the rural consumers about the price is depicted

in the following table:

Table – 4.85 Price Perception of Fast moving consumer goods

Frequency Valid Percent

Cumulative Percent

Price

Very high 139 23.2 23.2

Some what high 225 37.5 60.7 High 139 23.2 83.8

Not high 65 10.8 94.7 Not very high 32 5.3 100.0

Total 600 100.0

(Source: Primary Data) Table 4.85 clearly shows that 37.4 percent of the respondents feel

that the price of Fast moving consumer goods are some what high and

23.1 percent of the respondents feel the price of Fast moving consumer

goods are very high. Only 10.8 percent of the respondents feel the price

is not high. So it is concluded that the rural consumers in Salem District

feel the price of Fast moving consumer goods are high. As almost one

168

third of the rural employed are daily wage earners, they never have

sufficient money on any given day. Therefore, companies need to

introduce low-point price packs so that their product is included in the

daily basket of purchases of the wage earner.

CHART 4.8

PRICE PERCEPTION OF FAST MOVING CONSUMER GOODS

4.27 QUALITY PREFERENCE OF FAST MOVING CONSUMER

GOODS

The perceptions of the rural consumers of Salem District with

regard to the quality of Fast moving consumer goods are depicted in the

following table.

Table – 4.86

Quality Preference of Fast moving consumer goods

Frequency

Valid Percent

Cumulative Percent

Quality Highest quality 171 28.5 28.5

Higher quality 140 23.3 51.8

169

Frequency

Valid Percent

Cumulative Percent

Reasonable quality 265 44.2 96.0

Low quality 24 4.0 100.0 Total 600 100.0

(Source: Primary Data) It appears from the above table that 44.2 percent of the

respondents prefer to buy Fast moving consumer goods with reasonable

quality only. They are also not ready to buy Fast moving consumer

goods with low quality as only 4 percent respondents prefer low quality

Fast moving consumer goods. This clearly shows that rural consumers

are ready to pay for the quality product. They are not ready to sacrifice

the quality for the sake of any reason.

CHART 4.9

QUALITY PREFERENCE OF FAST MOVING CONSUMER GOODS

4.28 RURAL CONSUMERS VIEW ABOUT BRAND KNOWLEDGE

Brand knowledge is an unequivocal corner stone of brand equity

concept. It is referred to the breadth and depth of brand awareness,

170

strength, favourability and uniqueness of brand association and brand

responses held in consumer memory and the nature of consumer brand

relationships. The brand knowledge also reveals what the customers

have learned, felt, seen and heard about the brand as a result of their

experiences over time. Thus brand knowledge can be expressed as a

sum of brand awareness and brand image. It facilitates the consumers

to have familiarity with a brand, have a better encoding ability and

better developed procedural knowledge. The brand knowledge effects

through brand awareness and brand association, the benefits of brand

are underlined as outcomes. Questions were framed to measure the

brand knowledge of rural consumers of Salem District and the results

are tabulated below:

Table – 4.87

One Sample t-test for Brand Knowledge

Variables N Mean Std.

Deviation

Std

.Err

or

Me

an

t-v

alu

e

Sig

.

(2-t

ail

ed

)

Sure decision about the

brand before buying 600 4.3950 .82671 .03375 41.333 .000

Helps to recall the

attributes of the product 600 4.1017 .84014 .03430 32.120 .000

Brand knowledge helps to identify the Ingredients of the

product

600 3.9017 .92921 .03793 23.769 .000

Stimulates the use of the same brand

600 4.1650 .92873 .03792 30.726 .000

Enables one to understand the image of

the brand

600 3.9967 1.00250 .04100 24.311 .000

Able to distinguish between original brand and spurious brand

600 3.9417 1.10920 .04528 20.795 .000

171

It is evident from the above table 4.87 that the rural consumers of

Salem District strongly agree the variables; Sure decision about the

brand before buying, recalling the attributes and benefits of the brand

and stimulates the use of the same brand (Mean = 4.39, 4.10, 4.16).

Before buying the Fast moving consumer goods, the rural consumers

are very clear in their view of brand selection. The mean values of the

other variables like brand knowledge, brand image and knowledge

about original and spurious brand are also maintained at three which

shows that the rural consumers of Salem District have good knowledge

about the various brands of Fast moving consumer goods.

172

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