CHAPTER 4 DATA ANALYSIS 4.1 INTRODUCTIONshodhganga.inflibnet.ac.in/bitstream/10603/7924/9/09_chapter...

Data Analysis

56 Ph. D. Thesis

CHAPTER 4

DATA ANALYSIS

4.1 INTRODUCTION

This chapter intends to accomplish the objectives of the study by holistically investigating

various dimensions of The chapter is

divided into five sections:

Section I: Analyzing .

Section II: Analyzing various influence strategies children use to persuade their parents.

Section III: Analyz

Section IV: Analyz ence in different buying stages and sub-decisions.

Section V: Profiling based on product categories

Section VI e in family buying process.

4.2 PRIMARY DATA ANALYSIS

The main objective of study is to analyze the family buying

process based on primary data collected from field survey. Keeping in mind the objectives of

the study two dedicated questionnaires were developed and used as an instrument to gauge the

factors impacting s were sent to more than 600

respondents, of which 374 responded. Of these 374, 350 completely filled questionnaires were

verified, checked and matched manually. The questions and responses were coded and entered

in the computer using Microsoft Excel Software. Data analysis in a quantitative research is

essential as the interpretation and coding of responses can be very critical. Therefore, required

analysis was done with the aid of Statistical Package for Social Sciences (SPSS) 17.0 Version

and AMOS 21.0 Version. The variables were coded in SPSS and certain statistical methods

were applied on the data to get the results which are analyzed. This chapter discusses the

findings which highlight children different products and at different

stages of family buying process and also influence strategies used by children.

Analysis of influence level is done in a systematic and methodical manner. The data

analysis aimed at analyzing i) demographic variables of the child and parents; ii)

Chapter 4

Ph. D. Thesis 57

consumer socialization agents; iii) influence strategies used by children; iv) product categories

and level across the three buying stages

and sub-stages.

Firstly, reliability of the instrument was measured with the help of cronbach alpha and Kaiser-

Meyer-Olkin Measure of Adequacy (KMO) .

Secondly, factor analysis was done to extract various constructs. Thirdly, the constructs were

compared across personal, family and socialization factors using t-test and MANOVA.

Finally, correlation and regression analysis was done to study the contribution of each factor

to .

4.2.1 Personal profile of respondents

To begin with, the personal profile of the respondents was calculated. The findings are

discussed in the following two parts: one for the child and another for the parents.

4.2.1.1 Child

al profile which

included age, gender, number of siblings, birth-order and education. These characteristics are

shown in Table 4.1.

Table 4.1: Demographic Profile of Children (N=175)

Characteristics n (frequency) Percentage Age-group

8 10 92 52.57 11 12 83 47.42

Gender Male 116 66.28

Female 59 33.71 No. of siblings

Single child 36 20.57 With siblings 139 79.40

Birth Order Youngest 61 34.86

Eldest 66 37.71 Middle-one 12 06.85

Single Child 36 20.57

Data Analysis

58 Ph. D. Thesis

Grade III 12 06.85 IV 34 19.42 V 39 22.29

VI 35 20.00 VII 29 16.57

VIII 26 14.86

Age: two age-groups, 8-10 years and 11-12 years. Child

respondents were almost equally distributed in two age-groups. The frequency and percentage

of child respondents are shown in Table 4.1. Of 175, 92 child respondents (52.57%) fell in the

younger age-group i.e. between 8 and 10. Rest 83 respondents fell in the older age-group of

11-12 years.

Figure 4.1: n

Gender: Since gender had only two categories, it was taken as dummy variable (boy = 1 and

girl = 2). Out of the 175 child respondents, 116 were boys (66.28%) and 59 were girls

(33.71%).

Chapter 4

Ph. D. Thesis 59

Figure 4.2: er distribution

No. of siblings: Number of siblings a child had, was also coded as dummy variable (single

child = 0 and child with siblings = 1). Out of all the children surveyed, 79.4% of the children

were having one or more siblings while the rest 20.57% were single child of their parents.

Figure 4.3:

Birth-order: Talking about birth order, 79.4% of the children had siblings. Sixty one children

were youngest in their family, 66 were eldest and only 12 were the middle ones in their

family.

Data Analysis

60 Ph. D. Thesis

Figure 4.4: -order in the family

Class: Since the questionnaire was deliberately administered on children within the age group

of 8- 12 years, the respondents were primarily from III to VIII grades. Twelve children

(6.85%) were from grade III, 34 (19.42%) from grade IV, 39 (22.29%) from grade V, 35

(20%) from grade VI, 29 (16.57%) from grade VII and 26 (14.86%) were from grade VIII.

Figure 4.5: G

Chapter 4

Ph. D. Thesis 61

4.2.1.2 Parent

t gathered information about personal profile of parents

which included age, qualification, occupation and type of family structure. Education and

The characteristics are

shown in Table 4.2.

Table 4.2: Profile of Parent and family characteristics (N=175)

Characteristics n (frequency) Percentage

Age-group

30-35 45 25.7

36-40 87 49.7

> 40 43 24.6

Father Qualification

Graduate 110 62.9

Post-Graduate 65 37.1

Mother Qualification

Graduate 113 64.6

Post-Graduate 62 35.6

Father Occupation

Business 52 29.7

Govt. Service 34 19.4

Private Service 89 50.9

Mother Occupation

Working 44 25.1

Not Working 131 74.9

Family Structure

Joint Family 71 40.6

Nuclear Family 104 59.4

Data Analysis

62 Ph. D. Thesis

Age: Parent respondents were distributed in three age-groups. The frequency and percentage

of parent respondents are shown in Table 4.2. Of 175, 45 respondents (25.7%) fell in younger

age-group i.e. between 30 and 35 years. Of 175, 87 respondents (49.7%) fell in middle age-

group i.e. between 36 and 40 years and rest 43 respondents fell in older age-group of more

than 40 years of age. The mean age of sample population was 38.54 years.

Figure 4.6:

Qualification: Amongst fathers, 10.86% of them were undergraduates, 52% were graduates

and 37.14% were post graduates while amongst the surveyed mothers, 25.14% were

undergraduates, 39.43% were graduates and 35.42% were post graduates.

Chapter 4

Ph. D. Thesis 63

Figure 4.7: qualification

Occupation: Fathers occupation was grouped in three; business, government service and

private service. Of 175 parents, 52 (29.7%) were doing their own business, 34 (19.4%) were

in government services and a major chunk 89 (50.9%) were in private service. Mothers

occupation was grouped in two groups; working and non-working. Of 175 mothers, 44

(25.1%) were working and rest 131 (74.9%) were not working.

Figure 4.8:

Data Analysis

64 Ph. D. Thesis

Figure 4.9:

Family Structure: Out of 175 families contacted for study, 71 (4.06%) were joint families

(children living with their parents and grandparents) and 104 (59.4%) were nuclear families.

Figure 4.10:

Chapter 4

Ph. D. Thesis 65

4.3 SECTION I: CONSUMER SOCIALIZATION AGENTS

4.3.1 Research Question 1: Identification

One of the objectives i its

pestering power through various influence strategies. There had been many socialization

agents as identified by many researchers. Through the study of relevant literature, three

primary and most influential agents were identified as family, friends and media. With

extensive literature review and productive focus group discussions, a list of fifteen statements

was prepared to identify socialization agents for our study. The young respondents were asked

to state the extent to which they agree or disagree with different statements on a 3 point Likert

scale [192] ranging from 1 to 3, 1 being never, 2 being sometimes and 3 being always. After

pilot study, few statements were dropped and the final eleven statements presented in the

questionnaire are enlisted in Table 4.3.

Table 4.3: Statements for Consumer socialization agents

The Kaiser-Meyer-Olkin (KMO) Measure of Sampling Adequacy is a statistic that indicates

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

KMO value for the instrument was 0.653, which was acceptable as a middling value [199]

[201]. Similarly, Bartlett's test of sphericity tests the hypothesis that the correlation matrix is

an identity matrix, which would indicate that the variables are unrelated and therefore

Statements

1. You watch lot of television programs in a day.

2. You surf lot of internet in a day.

3. You go for shopping.

4. You want to buy the products advertised on television.

5. You usually buy the same stuff as your friends.

6. You discuss with your friends about the things you want to buy.

7. Your parents discuss with you about the things they want to buy.

8. You use internet to find information about products from internet.

9. You use internet for school assignments.

10. You came to know about the new products from your parents.

11. Your parents ask for your opinion before buying a product.

Data Analysis

66 Ph. D. Thesis

instrument was accepted for further study (Table 4.4).

Table 4.4: Cronbach Alpha and KMO Test Value (Socialization)

Cronbach's Alpha 0.606

No. of Items 11

Kaiser-Meyer-Olkin Measure of Sampling Adequacy 0.653

Bartlett's test of Sphericity: Approx. Chi-Square Degree of freedom Significance

192.093 55.000 0.000

Factor analysis was done to extr

and skill. Principal Component Analysis was the method of extraction. Varimax was the

rotation method. As per the Kaiser criterion, only factors with eigenvalues greater than 1 were

retained [99] [202]. Three factors in the initial solution had eigenvalues greater than 1.

Together, they accounted for almost 55% of the variability in the original variables. The items

falling under each of these factors were then dealt with quite prudently. Table 4.5 shows the

communality and eigenvalues of the factors. Table 4.6 shows the extracted factors along with

their factor loadings. It is followed by a screeplot (Figure 4.13).

Table 4.5: Communality and Eigen values of the factors (Socialization)

Variable Communality Factor Eigenvalue

Percentage of Variance

Cumulative Variance

You watch lot of television programs in a day. 0.374648 1 2.337 21.248 21.248

You surf lot of internet in a day. 0.627596 2 1.560 14.181 35.429

You go for shopping. 0.844578 3 1.129 10.261 45.690

You want to buy the products advertised on television. 0.320077 4 1.006 9.144 54.833

You usually buy the same stuff as your friends. 0.609170

You discuss with your friends about the things you want to buy.

0.472176

Your parents discuss with you about the things they want to buy.

0.507516

You use internet to find information about products from internet.

0.538497

Chapter 4

Ph. D. Thesis 67

You use internet for school assignments. 0.635884

You came to know about the new products from your parents.

0.455140

Your parents ask for your opinion before buying a product.

0.646384

Figure 4.11: Screeplot of the Components Extracted From Factor Analysis

Table 4.6: Factor Loadings for Socialization Agents

Statements FACTOR 1 FACTOR 2 FACTOR 3 FACTOR 4

You watch lot of television programs in a day. .565 .159 .073 .158

You surf lot of internet in a day. -.172 .762 .005 .130

You go for shopping. .134 .045 .060 .906

Data Analysis

68 Ph. D. Thesis

You want to buy the products advertised on television. .438 .128 .334 .023

You usually buy the same stuff as your friends. .777 -.018 .026 -.065

You discuss with your friends about the things you want to buy. .665 -.130 .060 .096

Your parents discuss with you about the things they want to buy. .167 .037 .689 -.056

You use internet to find information about products from internet. .180 .696 .052 .138

You use internet for school assignments. .070 .696 .124 -.362

You came to know about the new products from your parents. .276 .184 .517 .279

Your parents ask for your opinion before buying a product. -.083 -.018 .799 .021

The four factors extracted for further study are shown in Table 4.7. These four factors are

referred to as consumer socialization agents in the analysis. Table 4.7 is followed by the

explanation of socialization agents.

Table 4.7: Factor Analysis of socialization

Factor Item Factor Loading

Factor Name

1

You watch lot of television programs in a day. 0.57

Friends & TV

You want to buy the products advertised on television. 0.44

You usually buy the same stuff as your friends. 0.78

You discuss with your friends about the things you want to buy. 0.67

2

You surf lot of internet in a day. 0.76

Internet You use internet to find information about products from internet. 0.70

You use internet for school assignments. 0.70

3.

Your parents discuss with you about the things they want to buy. 0.69

Parents You came to know about the new products from your parents. 0.52

Your parents ask for your opinion before buying a product. 0.80

4. You go out for shopping. 0.906 Shopping

Chapter 4

Ph. D. Thesis 69

Friends and TV (FTV): FTV was the name given to the first socialization agent identified

through factor analysis. As shown in Table 4.7, this factor contained two prominent items

namely, friends and television. Though friends and television were identified as separate

agents in many related studies but in our study, there was very little distinction to consider

them separate.

and may be similar because kids try to imitate things which they see on TV or on any of their

friends. So both were combined and made into one factor as FTV.

Internet: Internet was the second factor identified through factor analysis. As can be seen

from Table 4.7, this factor included three items about how much internet the child access in a

day, whether child uses internet for school assignments and for finding information about

products and services. The internet has formed a new learning culture, which allows children

to share, discuss, influence and learn interactively from each other [111].

Parents: The third factor identified was parents. The importance of parents as a socializing

agent had been observed by many studies [29] [76] [77]. As can be seen from Table 4.7, this

factor included three items about how the parents were allowing and motivating children to

participate in buying process. Parents discuss with their kids about the things they want to buy

and also provide information about the various new products in the market.

Shopping: Shopping is the fourth socialization agent. This had just one item; how frequently

children go out for shopping. With kids getting more buying and influencing power, they go

out shopping with their parents and friends very frequently. It would be very interesting to see

how this agent impacts t

The factor analysis as explained in previous section resulted in four consumer socialization

agents namely: FTV, Internet, Parents and Shopping. The ranking using the mean scores and

standard deviation are given in Table 4.8. It is clear from Table that the shopping exposure is

the most prominent agent t mean of

2.05, stating that young Indian children get lot of information through shopping trips with

their parents. Kids may not be shopping themselves but they are very much present and they

acquire their consumer skills when they get live shopping experience. Standard deviation for

the same is .589. It is followed by parents as a socialization agent (mean = 2.00, sd = .434),

Data Analysis

70 Ph. D. Thesis

then friends and television (mean = 1.838, sd = .437). The fourth agent is internet with mean

value of 1.737 (sd = .55). The same is being shown graphically in figure 4.14.

Table 4.8: Mean and Standard Deviations of Socialization agents

Figure 4.12: Graphical representation of means and standard deviations of socialization agents

4.3.2 Research Question 2:

various personal characteristics

For comparing the four consumer socialization agents across personal characteristics, t-tests

and MANOVA were conducted. The section below explains this in detail.

4.3.2.1

T-test was done to examine whether there was a significant difference in consumer

socialization of child through various agents by those two age-groups of children. From Table

4.9, we can see that the t value is greater than 1.96 for three socialization agents namely, FTV,

Socialization Agents Mean Std. Deviation

FTV 1.838571 .43798

Internet 1.737143 .55431

Parents 2.00381 .43474

Shopping 2.051429 .58984

Chapter 4

Ph. D. Thesis 71

internet and parents. It means that for these three agents the t value is significant (p = 0.008, p

= .002 and p = 0.020 respectively). The results are that young children are more socialized

through friends and TV as compared to their older counterparts. On the other hand older

children in the age group of 11-12 years were more socialized through internet. The reason is

straight, that as child grew he/she can understand and operate the computer and internet more

effectively. Parents also emerge as significant socializing agent for young children (8-10

years) than the older lot. Shopping was the only agent for which the socialization is similar for

both the age groups.

Table 4.9: Comparison of socialization agents between two age-groups

Socialization Agents Mean scores and standard deviation

1 = 8-10 years (n=92) 2 = 11-12 years (n=83)

t-test for equality of means

Age-groups Mean Std. Deviation t Sig. (2-tailed)

FTV 1 2

1.9211 1.7470

.48171

.36521 2.673 .008**

Internet 1 2

1.6123 1.8755

.53057

.55022 -3.220 .002**

Parents 1 2

2.0761 1.9237

.49195

.34659 2.345 .020*

Shopping 1 2

2.1304 1.9639

.55899

.61378 1.879 .062 NS

* Significant at .05 level ** Significant at 0.01 level NS Not Significant

4.3.2.2 Gender

Another t-test was done to find the difference in consumer socialization between boys and

girls (Table 4.10). Significant difference was found in the mean values of only one out of four

socialization agents between boys and girls. Internet socialization had significant difference in

the mean values (p = 0.046). The boys were socialized more through internet than girls

(µ1=2.01 is greater than µ2=1.97).There were no significant differences as far as FTV, parents

and shopping were concerned.

Table 4.10: Comparison of socialization agents between boy and girl child

Socialization Agents

Mean scores and standard deviation 1 = Boy (n=116) 2 = Girl (n=59)


Gender Mean Std. Deviation t Sig. (2-tailed)

FTV 1 2

1.8772 1.7627

.46450

.37262 1.642 .102 NS

Data Analysis

72 Ph. D. Thesis

Internet 1 2

1.7960 1.6215

.58445

.47326 1.985 .049*

Parents 1 2

2.0172 1.9774

.46072

.38089 .572 .568 NS

Shopping 1 2

2.0517 2.0508

.58748

.59953 .009 .993 NS

* Significant at .05 level NS Not Significant

4.3.2.3 Number of siblings

To examine whether there was a significant difference in consumer socialization of child

through various agents between single child and child with siblings, t-test was done. As seen

from Table 4.11, the t value is insignificant for all the four agents. This indicates that no. of

siblings does not make any significant difference in the consumer socialization of kids

through various agents.

Table 4.11: iblings


Mean scores and standard deviation 0 = Single Child (n=36)

1 = With siblings (n=139)


Siblings Mean Std. Deviation T Sig. (2-tailed)

FTV 0 1

1.8819 1.8273

.48729

.42547 .666 .507 NS

Internet 0 1

1.6111 1.7698

.54336

.55437 -1.537 .126 NS

Parents 0 1

1.9630 2.0144

.39663

.44481 -.631 .529 NS

Shopping 0 1

2.0556 2.0504

.47476

.61766 .047 .963 NS

NS Not Significant

4.3.2.4 Birth Order

Multivariate analysis of variance (MANOVA) was applied along with post-hoc tests in order

to compare -orders of the child.

Homogeneity of covariance was tested by calculating Box's Test of Equality of Covariance

Matrices [203] [204]. If the significance value is less than .001 (p < .001) then the assumption

of homogeneity of covariance is violated. However, Table 4.12 shows that the assumption is

satisfied, the covariance were homogeneous (p = .272).

Chapter 4

Ph. D. Thesis 73

Table 4.12: Box's Test of Equality of Covariance Matrices of Sociali

Box's M 25.625

F 1.168

df1 20.000

df2 3535.364

Sig. .272

There were no significant differences the mean values of any consumer socialization agents

of the children. The three birth orders of the child as shown in Table 4.13 are youngest as

Bo1, eldest as Bo2 and middle one as Bo3. There were no significant differences in the

socialization of child in any birth-order category. With respect to FTV, internet, parents and

shopping, findings showed no significant difference at .01 levels in mean and standard

deviation values, with F value of .385, 2.641, .683 and 1.737 respectively. Table 4.13 also

shows the pair wise significant differences among different agents. There were no significant

differences between; Bo1 Vs Bo2, Bo1 Vs Bo3 and Bo2 Vs Bo3.

Table 4.13: socialization agents with respect to birth-order in the family

NS Not Significant

4.3.2.5 Family Structure

Another t-test was done to examine the difference between consumer socialization and family

structure (Table 4.14). Significant difference was found in the mean values of only one out of

four socialization agents. Internet socialization had significant difference in the mean values

(p = 0.020). Children in the joint family were more socialized through internet in the joint

family structure (µ1=1.6571 is less than µ2=1.8545).There were no significant differences as

far as FTV, parents and shopping were concerned.


Youngest Bo1(N=61)

Eldest Bo2(N= 66)

Middle One Bo3(N=12)

Mean Diff.

Bo1 v/s Bo2

Mean Diff.

Bo1 v/s Bo3

Mean Diff.

Bo2 v/s Bo3

F-value Mean SD Mean SD Mean SD

FTV 1.84 .403 1.84 .442 1.71 .462 .003 .132 .129 .385 NS

Internet 1.79 .599 1.78 .537 1.61 .398 .004 .171 .0175 2.641 NS

Parents 2.02 .469 2.02 .445 1.97 .332 .004 .048 .044 .683 NS

Shopping 2.03 .576 2.06 .629 2.08 .793 .027 .023 .050 1.737 NS

Data Analysis

74 Ph. D. Thesis

Table 4.14: Comparison of socialization agents with family structure


Mean scores and standard deviation 1 = Nuclear Family Structure (n=104)

2 = Joint Family Structure (n=71)


Family structure Mean Std. Deviation t Sig. (2-tailed)

FTV 1 2

1.7957 1.9014

.37804

.50969 -1.575 .117 NS

Internet 1 2

1.6571 1.8545

.52645

.57661 -2.343 .020*

Parents 1 2

2.0000 2.0094

.39957

.48459 -.140 .889 NS

Shopping 1 2

2.0096 2.1127

.61526

.54901 -1.136 .258 NS


4.3.2.6 Father and Mother Qualification

Another set of t-tests were conducted to examine whether there were significant differences in

consumer socialization of child through various agents acro able

4.15 and Table 4.16). Significant difference was found in the mean values of only one out of

four socialization agents. Shopping socialization had significant difference in the mean values

(p = 0.046) between the graduate and post grade fathers.

children were more socialized through shopping trips (µ1=2.13 is greater than µ2=1.90).There

were no significant differences as far as FTV, internet and parents as socialization agents were

concerned. On the other hand, there were no differences in the socialization agents between

graduate and post graduate mothers (Table 4.16).

Table 4.15: Comparison of socialization agents with fa


Mean scores and standard deviation 1 = Graduate (n=110)

2 = Post Graduate (n=65)


Qualification Mean Std. Deviation t Sig. (2-tailed)

FTV 1 2

1.8114 1.8846

.43656

.43990 -1.070 .286 NS

Internet 1 2

1.7576 1.7026

.56559

.53724 .633 .527 NS

Parents 1 2

2.0121 1.9897

.40869

.47860 .328 .743 NS

Shopping 1 2

2.1364 1.9077

.59781

.55122 2.516 .013*


Chapter 4

Ph. D. Thesis 75

Table 4.16: Comparison of socialization agents with






FTV 1 2

1.8319 1.8508

.46103

.39583 -.273 .785 NS

Internet 1 2

1.7345 1.7419

.54765

.57075 -.084 .933 NS

Parents 1 2

2.0147 1.9839

.43276

.44118 .448 .654 NS

Shopping 1 2

2.0265 2.0968

.63330

.50277 -.752 .453 NS

NS Not Significant

4.3.2.7 Father and Mother Occupation

More analysis was done to

-hoc test was

applied. Table 4.17 shows that the assumption is satisfied, the covariance were homogeneous

(p = .005). Table 4.17: Comparisons of

** Significant at 0.01 level NS Not Significant

Significant differences were found in the mean values of two consumer socialization agents of

the children namely; FTV and parents. The three occupations of father as shown in Table 4.17

are business as O1, government service as O2 and private service as O3. There were no

significant differences in the socialization of child through internet and shopping. Table 4.17

also shows the pair wise significant differences among different agents. With respect to FTV

and parents, findings showed significant differences at .01 levels in mean and standard

deviation values, with F values of 6.65 and 5.09 respectively. This means that children whose


Business O1 (N=52)

Govt. Service

O2 (N= 34)

Pvt. Service O3 (N=89)

Mean Diff.

O1 v/s O2

Mean Diff.

O1 v/s O3

Mean Diff.

O2 v/s O3

F-value

Mean SD Mean SD Mean SD

FTV 1.96 .57 1.84 .34 1.77 .37 0.118 0.187 0.069 6.65**

Internet 1.74 .54 1.86 .61 1.69 .54 0.126 0.048 0.174 1.59 NS

Parents 2.08 .46 2.01 .29 1.96 .46 0.067 0.118 0.051 5.09**

Shopping 2.10 .57 2.00 .55 2.04 .62 0.096 0.051 0.045 .784 NS

Data Analysis

76 Ph. D. Thesis

fathers who were doing their own business were more socialized through friends and TV.

Similarly these were the children who were more socialized through their parents.

To compare between working and non-working mothers, another t-test was done to examine

whether there was a significant difference in consumer socialization of child through various

agents (Table 4.18). Significant differences were not found in the mean values of any

socialization agents. This means that mothers working or non-working status had no

was concerned.

Table 4.18: Comparison of socialization agents with


Mean scores and standard 1 = Working (n=44)

2 =Non-Working (n=131) t-test for equality of means


FTV 1 2

1.750 1.868

0.337 0.464

-1.557 .121 NS

Internet 1 2

1.803 1.715

0.563 0.552

.911 .364 NS

Parents 1 2

1.992 2.008

0.384 0.452

-.200 .842 NS

Shopping 1 2

2.136 2.023

0.510 0.614

1.105 .271 NS

NS Not Significant

4.3.3 Section I Conclusion

This first section of the chapter identifies and analyzes the consumer socialization of Indian

children. Exploratory factor analysis resulted in four distinct socialization agents; FTV,

Internet, Parents and Shopping. Except internet, all the agents are common among related

studies also [29] [71] [74] [76] [77] [78] [92] [102] [103] [104] [205] [206] [207]. Internet

became more popular and effective in 21st century and since then it became an interesting area

to study. Recent studies have explored th [111] [113]

[116] [208]. Further analysis compared these socialization agents across the personal

characteristics of the child like child age, gender, no. of siblings, birth-order, family

fication and occupation. Various t-test and MANOVA showed that

young children were more socialized through friends and TV and older children in the age

group of 11-12 years were more socialized through internet. Boys were more influenced by

TV. This is sim me area [72]. Boys were the ones who

Chapter 4

Ph. D. Thesis 77

make any significant difference in the consumer socialization agents. The analysis also

showed that children in the joint family were more socialized through internet than in nuclear

d was

impactful; significant difference were found in the mean values of two consumer socialization

agents namely; friends & TV and p

qualification and occupation made no difference in the socialization agents of children. Since

in India, female literacy and workability are still is nascent stage, this result is not in line with

the western studies where mother has been identified as an important socialization agent [80]

[82] [209].

4.4 SECTION II: INFLUENCE STRATEGIES

4.4.1 Research Question 1: Identification of Influence strategies

Next step wa

products and services. A list of sixteen different influence tactics was prepared identified

through extensive literature review and productive focus group discussions with children and

their parents separately. These 16 influence tactics were used for pilot study and were

converted into a questionnaire. The respondents were asked to rate how often the child use

these influence tactics on a 5 point Likert scale ranging from 1 to 5, 1 being child had never

used this tactic, 2 being rarely used by child, 3 being sometimes, 4 being most of the times

and 5 being every time child used this influence tactic. Table 4.19 enlists all the 16 items that

were translated into questions in the questionnaire and were used for factor analysis.

Table 4.19: Various Influence Tactics

Influence Tactics

1. Offer deals (e.g., clean your room in exchange for purchases)

2. Express opinion on product 3. Insisting that this is what he/she want 4. Use begging strategies 5. Tell that all friends have it 6. Tell about the TV ad he/she saw about product 7. Tell that the brand is famous 8. Bringing an external reason 9. Propose fair competition

Data Analysis

78 Ph. D. Thesis

To test the validity of the instrument, cronbach alpha [195] and Kaiser-Meyer-Olkin tests

were done. The cronbach alpha came as 0.790 as shown in Table 4.20, thus the instrument

was reliable for the study. Bartlett's test of sphericity [203] also showed a significant level and

hence the instrument was apt for further study.

Table 4.20: Cronbach Alpha and KMO Test Value (Influence Strategies)


No. of Items 16



586.471 120.000 0.000

Factor analysis was done to identify distinct influence strategies clusters on the basis of

was the

method of extraction. Varimax was the rotation method. Only factors with eigenvalues greater

than 1 are retained [199]. Five factors in the initial solution had eigen values greater than 1.

Together, they accounted for almost 58.24% of the variability in the original variables, which

can be regarded as sufficient. After the number of extracted factors was decided upon, the

factors were interpreted by identifying which factors were associated with the influence

strategy. The five factors we

characteristics. The five factors extracted for further study are shown in Table 4.21 and are

referred as the influence strategies in further analysis. Table 4.21 is followed by the

explanation of these influence strategies.

10. Nagging & Whining 11. Express Anger 12. Be unnaturally nice to parents 13. Pretending illness to make parents sympathize 14. Not Eating 15. Stubbornly acting 16. Hide things in the shopping trolley

Chapter 4

Ph. D. Thesis 79

Table 4.21: Communality and Eigen values of the factors (Influence Strategies)

Variable Communality Factor Eigen value


Cumulative Variance

Offer deals (e.g., clean your room in exchange for purchases) .473 1 4.012 25.073 25.073

Express opinion on product .471 2 1.590 9.938 35.011

Insisting that this is what he/she want .463 3 1.402 8.760 43.771

Use begging strategies .683 4 1.258 7.862 51.632

Tell that all friends have it .602 5 1.058 6.611 58.243

Tell about the TV ad he/she saw about product .618

Tell that the brand is famous .542

Bringing an external reason .633

Propose fair competition .564

Nagging & Whining .561

Express Anger .574

Be unnaturally nice to parents .678

Pretending illness to make parents sympathize .692

Not Eating .648

Stubbornly acting .673

Hide things in the shopping trolley .444

The factors along with their loadings are mentioned in Table 4.22.

Table 4.22: Factor Loadings for Influence Strategies

Variable FACTOR 1 FACTOR 2 FACTOR 3 FACTOR 4 FACTOR 5

Offer deals (e.g., clean your room in exchange for purchases)

.036 .181 .636 .185 .011

Express opinion on product -.017 .428 .414 -.044 .338

Insisting that this is what he/she want .128 .612 .234 .131 -.020

Use begging strategies .202 .779 .049 .176 .031

Tell that all friends have it .305 .147 .325 .332 -.521

Tell about the TV ad he/she saw about product -.056 .312 -.047 .703 -.142

Data Analysis

80 Ph. D. Thesis

Tell that the brand is famous .003 .155 .200 .679 .131

Bringing an external reason .275 .009 .461 .440 .390

Propose fair competition .044 -.015 .749 .013 -.037

Nagging & Whining .468 .533 -.033 .217 .101

Express Anger .699 .089 .184 .022 -.207

Be unnaturally nice to parents .151 .094 .100 .133 .787

Pretending illness to make parents sympathize .382 -.458 .137 .551 .115

Not Eating .794 .093 -.068 -.064 .005

Stubbornly acting .760 .114 .038 .118 .258

Hide things in the shopping trolley .477 .194 .391 .070 .145

These extracted five factors included the items which had loadings of more than 0.5. Table

4.23 is followed by the explanation of all these five influence strategies

Table 4.23: Factor Analysis of Influence strategies

Factor Item Factor Loading Factor Name

1

Express Anger 0.70

Aggressive Strategies Not Eating 0.79

Stubbornly acting 0.76

2

Express opinion on product 0.43

Persuasive Strategies

Insisting that this is what he/she want 0.61

Use begging strategies 0.78

Nagging & Whining 0.53

Pretending illness to make parents sympathize 0.46

3.

Offer Deals 0.64

Rational Strategies Bringing an external reason 0.46

Propose fair competition 0.75

Hide things in the shopping trolley 0.39

4. Tell about the TV ad he/she saw about product 0.70

Knowledge Strategies Tell that the brand is famous 0.68

5. Tell that all friends have it 0.52

Emotional Strategies Be unnaturally nice to parents 0.79

Chapter 4

Ph. D. Thesis 81

Aggressive Influence Strategies: These strategies were those in which the child displayed

some form of verbal or nonverbal aggression to parents. Tactics like not eating, showing

anger and acting stubbornly belonged to Aggressive Strategies.

Persuasion Influence Strategies: These strategies were those in which a child attempt to

move parents by argument or entreaty to a belief, position, or course of action. It incorporated

xpression of opinion on product, insisting by child that this is what he/she

wants, begging by the child, nagging, whining and pretending illness to make parents

sympathize.

Rational Influence Strategies: Under rational strategies, child brings some logical

explanation of his/her demand into conversation like offering deals (example: clean room in

return of a chocolate), bringing some external reason, propose fair competition (example: coin

toss) and mischief like hiding things in the shopping trolley.

Knowledge Influence Strategies: Knowledge strategies included tactics in which child

displays his/her knowledge about the product or brand. Child persuades parents by telling

about the TV ad he/she saw about product or the fact that this particular brand is famous.

Emotional Influence Strategies: Last were the Emotional Strategies in which the child acts

affectionately in verbal expression or behavior. Children are unnaturally nice to parents or

they emotionally blackmail that their all friends have it and so they also want it.

The strategies are then ranked. The ranking using the mean scores and standard deviation are

given in Table 4.24. It is clear from Table that the emotional strategies had the highest mean

of 2.80, stating that according to young Indian children, they use emotional strategies most

often to influence their parents to purchase any product. Standard deviation for the same is

.90. It is followed by knowledge strategies (mean = 2.59, sd = 1.00), then persuasion

strategies (mean = 2.56, sd = .72). The aggressive strategies were not very popular with mean

value of 2.26 (sd = .99). The least used strategy by Indian kids were rational strategies (mean

= 2.12, sd = .83). Figure 4.13 shows the means and standard deviation in histograms.

Data Analysis

82 Ph. D. Thesis

Table 4.24: Mean and Standard Deviations for Influence strategies

Influence Strategies Mean Std. Deviation

Aggressive Strategies 2.2610 .99094

Persuasion strategies 2.5646 .72580

Rational Strategies 2.1200 .82865

Knowledge Strategies 2.5914 1.00154

Emotional Strategies 2.8029 .90162

Figure 4.13: Graphical representation of means and standard deviations of influence strategies


various personal characteristics

The factor analysis as explained in previous section resulted in five dimensions of influence

strategy namely: Aggressive, Persuasive, Rational, Knowledge and Emotional influence

strategies. For comparing the

characteristics, various t-tests were done to see whether demographic factors (gender, age,

class, no. of siblings, birth-order) had an effect on the type of influence strategies used by

children.

Chapter 4

Ph. D. Thesis 83

4.4.2.1

The first t-test was done to find out whether there was a significant difference in the use of

influence tactics by the two age-groups of children. As seen from Table 4.25, the t value is

greater than 1.96 for two influence strategies namely, knowledge and emotional strategies to

influence parents. It means that for these two strategies the t value is significant (p = 0.006

and p = 0.046 respectively). Children between the age group of 11-12 years had more

knowledge about brands, so they used this strategy more often than their other counterparts

(as seen from Table 4.25 mean score of 11-12 years of age group µ2=2.81 is greater than

µ1=2.40). Similarly this age group also used emotional strategies to influence their parents

(µ2=2.95 is greater than µ1=2.67). With age, child can understand the complex human emotion

system and hence they can use the emotional strategy very well than the younger children of

8-10 years.

Table 4.25: Comparison of influence strategies between two age-groups

Influence Strategies Mean scores and standard deviation

1 = 8-10 years (n=92) 2 = 11-12 years (n=83)



Aggressive Strategies 1 2

2.15 2.38

0.87 1.10 -1.53 0.127 NS

Persuasion strategies 1 2

2.51 2.62

0.77 0.67 -0.99 0.324 NS

Rational Strategies 1 2

2.01 2.24

0.75 0.90 -1.80 0.074 NS

Knowledge Strategies 1 2

2.40 2.81

1.00 0.96 -2.76 0.006**

Emotional Strategies 1 2

2.67 2.95

0.96 0.82 -2.01 0.046*

* Significant at .05 level ** Significant at 0.01 level NS Not Significant

4.4.2.2 Gender

Second t-test was done to examine whether there was a significant difference in the use of

influence strategies between boys and girls (Table 4.26). Significant difference was found in

the mean values of only one out of five influence strategies used by boys and girls Knowledge

Strategies had a significant difference in the mean values (p = 0.026). The boys used

knowledge strategies of influencing parents more often than girls (µ1=2.71 is greater than

Data Analysis

84 Ph. D. Thesis

µ2=2.36). There were no significant differences as far as aggressive, persuasive, rational and

emotional strategies were concerned.

Table 4.26: Comparison of influence strategies between boy and girl child


1 = Boy (n=116) 2 = Girl (n=59)




2.16 2.45

0.98 1.00 -1.83 0.069 NS


2.57 2.56

0.75 0.68 0.07 0.946 NS


2.08 2.20

0.79 0.90 -0.95 0.344 NS


2.71 2.36

1.02 0.94 2.24 0.026*


2.87 2.68

0.92 0.85 1.31 0.192 NS



T-test was also done to examine the difference in the use of influence tactics between single

child and child with siblings (Table 4.27). The t value is significant for only one strategy;

emotional strategy with a significant difference between the two groups (p = 0.046). This

strategy was used more often by those children who were single child of their parents

(µ0=3.07 is greater than µ1=2.73). There were no significant differences as far as other

strategies were concerned.

Table 4.27:


0 = Single Child (n=36) 1 = With siblings (n=139)


Siblings Mean Std. Deviation t Sig. (2-tailed)


2.52 2.19

0.98 0.99 1.760 0.80 NS


2.57 2.56

0.74 0.72 0.071 0.944 NS


2.28 2.08

0.86 0.82 1.341 0.182 NS


2.43 2.63

0.85 1.03 -1.082 0.281 NS


3.07 2.73

0.96 0.87 2.008 0.046*


Chapter 4

Ph. D. Thesis 85

4.4.2.4 Birth-order

As in section I, multivariate analysis of variance (MANOVA) was done again with post-hoc

-orders of the child.

Table 4.28 shows that the satisfied assumption of homogeneous covariance with p = .479.

Table 4.28: Box's Test of Equality of Covariance Matrices of Influence Strategies with c

Box's M 29.761

F .881

df1 30.000

df2 3265.990

Sig. .653

In the mean values of influence strategies, no significant differences were found. There were

-order category.

Table 4.29 also shows the pair wise significant differences among different stages. With

respect to aggressive, persuasive, rational and emotional influence strategies, findings

showed no significant differences at .01 levels in mean and standard deviation values, with F

value of .433, .236, 592 and .203 respectively. There were no significant differences

between; Bo1 Vs Bo2, Bo1 Vs Bo3 and Bo2 Vs Bo3. The comparison of means highlighted

that children who were youngest in the family used aggressive strategies more often than

other children. The middle child in the family (mean = 2.62) used persuasion strategies

slightly more often than the youngest child (mean = 2.60), while the eldest child (mean =

2.52) used this strategy the least. The eldest child (2.16) in the family used rational strategy

more often than the youngest (mean = 2.02) and the middle child (mean = 1.96) in the

family. Regarding knowledge influence strategies also, findings showed no significant

differences at .01 levels in mean and standard deviation values, with F value of 2.778. There

were no significant differences between; Bo1 Vs Bo2 and Bo2 Vs Bo3. But significant

difference was found between Bo1 and Bo3. Youngest child (mean = 2.84) in the family used

knowledge strategy significantly more often than the middle child (mean = 2.17) of the

family.

Data Analysis

86 Ph. D. Thesis

Table 4.29: Comparisons of c s birth-order in the family

NS Not Significant

Table 4.29

three birth orders of the child. The mean scores indicate that the use of varied influence

strategies was highest by the youngest child of the family (mean = 2.484). The eldest child

also frequently used influence strategies to influence parents (mean = 2.424). The middle

child does use them as frequently (mean = 2.32). Middle child as compared to those of

youngest and eldest child in the family can be associated with less influence, probably

because in such families, number of children is more.


Another t-test was done to examine the difference in the use of influence tactics between

nuclear family and joint family structures. As seen from Table 4.30, no significant differences

were found in the mean values of any strategy. Both, the children from joint family and

nuclear family used all the strategies to persuade parents. Though the mean differences were

not significant, but children from nuclear families used influence strategies more often than

their counterparts from joint families.

Influence Strategies

Youngest Bo1(N=61)

Eldest Bo2(N= 66)

Middle One Bo3(N=12) Mean

Diff. Bo1 v/s

Bo2

Mean Diff. Bo1 v/s

Bo3

Mean Diff. Bo2 v/s

Bo3


Aggressive Strategies 2.28 1.01 2.14 .97 2.06 .97 .137 .223 .086 .433

NS Persuasion strategies 2.60 .74 2.52 .74 2.62 .60 .082 -.017 -.098 .236

NS

Rational Strategies 2.02 .82 2.16 .80 1.96 .90 -.139 .058 .197 .592

NS Knowledge Strategies 2.84 1.08 2.53 1.01 2.17 .75 .306 .669* .364 2.78

NS Emotional Strategies 2.68 .89 2.77 .90 2.79 .66 -.092 -.111 -.019 .203

NS

Mean Score 2.48 2.42 2.32

Chapter 4

Ph. D. Thesis 87

Table 4.30: Comparison of influence strategies with family structure


1 = Nuclear Family Structure (n=104) 2 = Joint Family Structure (n=71)


Family Structure Mean Std. Deviation t Sig. (2-tailed)


2.30 2.19

.98 1.01 .702 .483 NS


2.60 2.50

.74

.70 .994 .322 NS


2.11 2.13

.83

.87 -.089 .929 NS


2.60 2.60

.97 1.04 -.001 .999 NS


2.86 2.72

.89

.91 .939 .349 NS

NS Not Significant


Another set of t-tests were conducted to examine whether there was a significant difference in

the use of influence strategies across the parents qualification (Table 4.31 and Table 4.32).

Significant difference was found in the mean values of only one out of five influence

had significant difference

in the mean values (p = 0.027) between graduate and post grade fathers. Children used less

strategies with post graduate fathers (µ1=2.92 is greater than µ2=2.61). There was no

significant difference as far as other strategies were concerned. On the other hand, when the

was concerned, there were no differences in the use of influence

between graduate and post graduate mothers (Table 4.32).

Table 4.31: Comparison of influence strategies with


1 = Graduate (n=110) 2 = Post Graduate (n=65)




2.32 2.15

1.03 .91 1.153 .251 NS


2.60 2.51

.75

.69 .710 .479 NS


2.08 2.18

.80

.87 .698 .486 NS


2.63 2.52

.95 1.09 .693 .489 NS


2.92 2.61

.87

.93 2.226 .027*

*Significant at .05 level NS Not Significant

Data Analysis

88 Ph. D. Thesis

Table 4.32: Comparison of influence strategies with s qualification






2.26 2.26

1.04 .89 .025 .980 NS


2.54 2.60

.73

.73 .521 .603 NS


2.10 2.14

.83

.82 .249 .804 NS


2.56 2.65

1.01 .98 .525 .601 NS


2.75 2.90

.89

.92 1.09 .277 NS

NS: Not Significant


Further analysis was done use of influence strategies across

-

hoc test was applied. The covariance were homogeneous (p = .536). Significant difference

was found in the mean values of only one strategy namely; persuasion strategy (F = 3.73).

Children whose fathers were in private service used persuasion strategy more often than other

children. For other strategies, there were no significant differences. Table 4.33 also shows the

pair wise differences among different agents.

Table 4.33: Comparisons of use of influence strategies


Influence Strategies

Business O1(N=52)

Govt. Service O2(N= 34)

Pvt. Service O3(N=89)

Mean Diff.

O1 v/s O2

Mean Diff. O1 v/s O3

Mean Diff. O2 v/s O3


Aggressive Strategies 2.19 .14 2.25 .17 2.30 .10 .0626 .1111 .0485 .598 NS

Persuasion strategies 2.56 .10 2.37 .12 2.63 .07 .1966 .0584 .2550 3.73 NS

Rational Strategies 2.07 .11 2.04 .14 2.17 .08 .0280 .1049 .1328 .895 NS

Knowledge Strategies 2.56 .14 2.70 .17 2.57 .11 .1482 .0097 .1385 .611 NS

Emotional Strategies 2.81 .13 2.72 .16 2.83 .09 .0967 .0085 .1053 1.030 NS

Chapter 4

Ph. D. Thesis 89

Then t-test was conducted to examine whether there was a significant difference in the use of

influence strategies by children between working and non-working mothers (Table 4.34).

Significant differences were not found in the mean values of different strategies. This means

that mothers working or non-working status had use

of influence strategies were concerned.

Table 4.34: Comparison of influence strategies with occupation

Influence Tactics Mean scores and standard deviation

1 = Working (n=44) 2 =Non-Working (n=131)


Occupation Mean Std. Deviation t Sig. (2-tailed)


2.27 2.26

.95 1.00 .032 .974 NS


2.53 2.58

.80

.70 .393 .695 NS


2.09 2.13

.80

.84 .268 .789 NS


2.43 2.65

1.02 .99 1.224 .223 NS


2.84 2.79

.90

.90 .323 .747 NS

NS Not Significant

4.4.3 Research Question 3: Comparison of Various Influence Strategies as perceived

by parents and child

To further analyze -tests were applied to

compare the perception of child and his / her parents regarding different dimensions of family

buying process. The t-test was conducted to investigate the

influence strategies as perceived by child and his/her parents. Table 4.35 shows the results of

t-test. Table 4.35: Use of influence strategies as perceived by child and parent

Influence Strategies Mean scores and standard deviation of Child (C) and parent (P) (N=175) t-test for equality of means

Respondents Mean Std. Deviation t Sig. (2-tailed)

Aggressive Strategies P C

2.2571 2.2610

.95788

.99094 -.037 .971 NS

Persuasion strategies P C

2.5520 2.5646

.69678

.72580 -.165 .869 NS

Rational Strategies P C

2.2071 2.1200

.76818

.82865 1.020 .308 NS

Knowledge Strategies P C

2.8143 2.5914

1.01204 1.00154 2.071 .039*

Emotional Strategies P C

2.5686 2.8029

.94740

.90162 -2.370 .018*

* Significant at .05 level. ** Significant at 0.01 level NS Not Significant

Data Analysis

90 Ph. D. Thesis

As shown in Table 4.35, the t value is greater than 1.96 for two influence strategy,

wo strategies the t value is

significant as p is less than 0.05 (p = 0.039 and p = 0.018 respectively). Based on the t-test

scores, there was a significant difference in the use of knowledge influence strategy as

perceived by parents and children. Parents perceived their children used this strategy more

often than what their children perceived it (µc = 2.59 is less than µp = 2.81). On the other hand,

in case of emotional influence strategy, children perceived that they used this strategy very

often, but parents perceived it as less used by the children (µc = 2.80 is greater than µp = 2.56).

But for rest of the strategies, the mean differences were not significant between parent and

child responses. Figure 4.14 graphically shows the difference in perception of parents and

their children.

Figure 4.14: Graphical representation of difference in means & standard deviations of child & parent responses

with respect to influence strategies.

4.4.4 Research Question 4: ce

Strategies

factor analysis, the next step was to find out the correlation between them. Relationships were

found out between the various socialization agent

strategies. All the correlations had positive coefficients and some were significant. Thus

Chapter 4

Ph. D. Thesis 91

strategies used by Indian children. It is quite clear from correlation Table 4.36 that FTV,

internet and shopping were significant for three influence strategies. Though important,

parents were not significant for any of the influence strategies. Aggressive and rational

strategies were not

gender, family environment and not consumer socialization. Similarly rational strategies were

used more often by children of higher age-group or with children of highly educated parents.

For the rest three strategies i.e. persuasion, knowledge and emotional strategies could be

explained by the consumer socialization of a child. Table 4.36 shows all the values of

correlation coefficients of socialization agents with various influence strategies.

Table 4.36: Relationships (Correlation coefficients) between socialization agents and influence strategies

(N=175)

Influence Strategies FTV Internet Parents Shopping

Aggressive Strategies .006 NS .143 NS .073 NS .069 NS

Persuasion strategies .227** .066 NS .037 NS .211**

Rational Strategies .133 NS .140 NS .044 NS .046 NS

Knowledge Strategies .122 NS .283** .041 NS .070 NS

Emotional Strategies .174* .108 NS .083 NS .224**

**Significant at .01 level *Significant at .05 level NS Not Significant

In order to compute the model for determining the use of influence strategies, multiple

regressions were done. The independent variables for this part of the study were the consumer

socialization agents of the child namely; F TV, internet, parents and shopping. The dependent

variables were the different influence strategies used by Indian children to influence their

parents. All these five influence strategies were put in the regression process as dependent

variables and socialization agents were put as the dependent variable. To test the relation of

on the three consumer

product categories following hypothesis were formulated:

H1a:

strategies.

H1b: nce

strategies.

Data Analysis

92 Ph. D. Thesis

H1c:

strategies.

H1d:

strategies.

H1e:

strategies.

4.4.4.1 Aggressive Influence Strategies

A step-wise regression analysis was conducted to comprehend the impact of four socialization

agents on the aggressive strategies of children to influence parents. The four agents were put

was

put as the dependent variable. As expected from the result of correlations, there were no

significant determinants of aggressive startegies. Thus, the alternate hypothesis H1a is

rejected.

4.4.4.2 Persuasive Influence Strategies

To gauge the impact of consumer socialization agents on the persuasive influence strategies,

another stepwise regression analysis was done. The four agents were put in the model as

indep rsuasive influence strategies was put as the

dependent variable. The equation which emerged after the process is as follows. Table 4.37

summarizes the determinants of the equation.

Y2= 1.547 + 0.192X1 + 0.172X4

Where,

Y2 = Persuasive Influence Strategies

X1 = FTV

X4 = Shopping

Table 4.37:

Independent Variables

Persuasive Influence Strategies Beta Simple r t-value

FTV .192* * .227** 2.565 Shopping .172* * .211** 2.299

Multiple R = 0.282 R Square = 0.080

**Significant at .01 level

Chapter 4

Ph. D. Thesis 93

The value of multiple R was 0.282 and the value of R square was 0.080 in the equation. It

states t

two significant factors. Eight percent

strategies. The rest can be attributed to so many other factors which were scattered and

individually contribute only little to persuasive strategies. It should be noted here, that the two

independent variable are FTV and shopping. A direct positive relation of these two

socialization agents with persuasive strategy indicates that children acquire consumer

knowledge though friends, television and live shopping which they use through persuasive

strategies to persuade their parents. Other socialization agents namely parents and internet

were not significant for this strategy. Thus, the alternate hypothesis H1b is accepted. Figure

4.15 explains the relationship of p

through FTV and shopping.

Figure 4.15: Relationship of Friends & TV and Shopping Socialization with the use of

Persuasive Influence strategies

Persuasive Influence Strategies R2= 0.080

Shopping You go out for shopping.

FTV You watch lot of television programs in a day. You want to buy the products advertised on

television. You usually buy the same stuff as your friends. You discuss with your friends about the things

you want to buy.

= .192**

.172**

Data Analysis

94 Ph. D. Thesis

4.4.4.3 Rational Influence Strategies

A regression analysis was also done to comprehend the impact of four socialization agents on

the rational strategies to influence parents. The four agents were put as independent variables

aggressive strategies there were no significant determinants of persuasive startegies. Thus,

the alternate hypothesis H1c is rejected.

4.4.4.4 Knowledge Influence Strategies

Stepwise regression analysis was then done to determine the impact of four socialization

agents on the use of knowledge strategies by kids.The four agents were put as independent

strategies as the dependent variable.The

equation which emerged after this process is as follows. Table 4.38 summarizes the

determinants of the equation.

Y4 = 1.702 + 0.283X2

Where,

Y4 = Knowledge Influence Strategies

X2= Internet

Table 4.38: Determina


Knowledge Influence Strategies

Beta Simple r t-value

Internet .283* * .283** 3.886

Multiple R = 0.283

R Square = 0.080 **Significant at .01 level

The value of multiple R was 0.283 and the value of R square was 0.080 in the equation. 8%

knowledge influence strategies. It should be noted that the

dependent variable in the equation

one independent variable namely internet was positively correlated with it. A direct positive

relation of this influence strategy with the internet as socialization agent indicated that modern

Chapter 4

Ph. D. Thesis 95

children acquire consumer knowledge though internet which they use through knowledge

strategies to persuade their parents. Other socialization agents namely FTV, parents and

shopping were not significant for this strategy. Hence, the alternate hypothesis H1d is

accepted. Figure 4.16

socialization through internet.

Figure 4.16: Relationship of Internet Socialization with Knowledge Influence Strategies

4.4.4.5 Emotional Influence Strategies

For determining the impact of four socialization agents on the use of emotional strategies by

kids, stepwise regression analysis was done. The four agents were put in the model as

motional influence strategies was put as the

dependent variable. The equation which emerged after the process is as follows. Table 4.39


Y5= 2.099 + 0.224X4

Where,

Y5 = Emotional Influence Strategies

X4= Shopping

Table 4.39:


Emotional Influence Strategies

Beta Simple r t-value Shopping .224* * .224* * 3.030



Knowledge Influence Strategies R2= 0.080

Internet You surf lot of internet in a day. You use internet to find information

about products from internet. You use internet for school

= .283**

Data Analysis

96 Ph. D. Thesis

The value of multiple R was 0.224 and the value of R square was 0.050 in the equation. It

significant factor; shopping. T

emotional influence strategies and only one independent variable namely shopping was

positively correlated with it. A significant relation of this influence strategy with the shopping

as socialization agent indicated that the children acquire consumer knowledge though

observing and learning from live shopping environment and they use this emotionally to

persuade their parents. Other socialization agents namely FTV, parents and internet were not

significant for this strategy. Hence, the alternate hypothesis H1e is accepted. Figure 4.17

shopping.

Figure 4.13: Relationship of Socialization agents with Emotional Influence Strategies

Figure 4.17: Relationship of Shopping Socialization with Emotional Influence Strategies

4.4.5 Section II Conclusion

Second section of the chapter is devoted to analyzing the influence strategies used by children

to influence their parents. Factor analysis resulted in five influence strategies; Aggressive,

Persuasive, Rational, Knowledge and Emotional strategies. Past researches [125] [148] [149]

[150] [151] [152] also studied similar strategies with different names and approach.

Further analysis compared these strategies across the personal characteristics of the child.

Analysis showed that older children can understand the complex human emotion system and

Emotional Influence Strategies R2= 0.050

Shopping You go out for shopping.

= .224**

Chapter 4

Ph. D. Thesis 97

had more knowledge about brands; hence they used emotional and knowledge strategies more

often than their younger counterparts. Boys used knowledge strategies of influencing parents

more often than girls. Emotional strategies were used more often by those children who were

single child of their parents. No significant difference was found in the mean values of any

ed

the difference in the perception of child and his/her parent when asked about the use of

arents perceived their children use

these strategies more often than what their children thought about it. While for emotional

strategy, children perceived that they use this strategy very often, but parents perceived it as

less used by the children. Lastly, regression analysis was done in order to find out the

contribution of four socialization agents (FTV, Internet, Parents and Shopping) on the various

influence strategies and hence the pester power of the child in influencing parents.

Overall the socialization agents impacted considerably well to the pester power of a child

through different influence strategies. The same was being calculated through regression,

where the socialization agents; friends & TV, internet, parents and shopping were together put

as inde pester power (influence strategies) as the one

dependent variable. Figure 4.18 shows the relationship of socialization agents with pester

power of a child. Almost 10% of the use of influence strategies wa

consumer socialization agents.

Figure 4.18: Relationship of Socialization agents with Influence Strategies

Data Analysis

98 Ph. D. Thesis

4.5 SECTION III: PRODUCT CATEGORIES

4.5.1 Research Question 1: Identification of product categories

Another objective of the study wa

process while purchasing variety of goods and services. Most of the past studies had classified

products into three categories- products for which children are the primary consumers,

products for family consumpti [15] [16] [171] [210]. Though there

had been many categorizations of consumer products but segregation focusing primarily on

the e level in the family buying is needed.

So a list of fifteen diverse products and services was prepared through extensive literature

review and focus group discussions. These 15 products and services were used for pilot study

and were converted into a questionnaire and used for data analysis. The respondents were

Likert scale ranging

from 1 to 5, 1 being no influence of child and 5 being very high influence in the family buying

process. To test the validity of the instrument, cronbach alpha and KMO tests were conducted.

The cronbach alpha came as 0.863 as shown in Table 4.41, thus the instrument was

considered reliable for the study. and hence the

instrument was accepted for further study. Table 4.40 enlists all the 15 items that were

translated into questions in the questionnaire and were used for factor analysis.

Table 4.40: List of products and services Products and services

1. Stationary/Books 2. Food & Beverages 3. Clothes/ Shoes

4. Shampoo

5. Toothpaste

6. Grocery

7. Movie tickets

8. Vacation

9. Dining out (restaurant)

10. Computer

11. Video game

12. Mobile Phone

Chapter 4

Ph. D. Thesis 99

Table 4.41: Cronbach Alpha and KMO Test Value (Product Categories)


No. of Items 15



881.157 105.000 0.000

Since the products listed were large in number and were inter-related, factor analysis was

done to extract the distinct product categories. Principal Component Analysis was the method

of extraction and varimax was the rotation method. Only factors with eigen values greater

than 1 were retained. Three factors in the initial solution had eigenvalues greater than 1.

Together, they accounted for almost 70% of the variability in the original variables. The items

falling under each of these factors were then dealt with quite prudently. Table 4.42 shows the

communality and eigenvalues of the factors. It is followed by a screeplot (Figure 4.19).

Table 4.42: Communality and Eigen values of the factors (Product Categories)

Variable Communality * Factor Eigenvalue


Cumulative Variance

Stationary/Books 0.447846 1 5.265 35.097 35.097

Food & Beverages 0.487137 2 1.585 25.164 60.261

Clothes/ Shoes 0.369775 3 1.255 10.170 70.431

Shampoo 0.695505

Toothpaste 0.644347

Grocery 0.452857

Movie tickets 0.434539

Vacation 0.510876

13. Car

14. Television

15. Washing Machine

Data Analysis

100 Ph. D. Thesis

Dining out (restaurant) 0.618942 *

Computer 0.424667 *

Video game 0.453275 *

Mobile Phone 0.666416 *

Car 0.63346 *

Television 0.681535 *

Washing Machine 0.583426 *

Figure 4.19: Screeplot of the Components Extracted From Factor Analysis

The factors along with their loadings are mentioned in Table 4.43.

Chapter 4

Ph. D. Thesis 101

Table 4.43: Factor Loadings for product categories

Variable FACTOR 1 FACTOR 2 FACTOR 3

Stationary/Books 0.009 0.666 0.069

Food & Beverages 0.079 0.632 0.285

Clothes/ Shoes 0.257 0.486 0.260

Shampoo 0.238 0.072 0.796

Toothpaste -0.035 0.313 0.739

Grocery 0.124 0.037 0.660

Movie tickets 0.445 0.486 -0.008

Vacation 0.554 0.447 0.065

Dining out (restaurant) 0.416 0.667 -0.031

Computer 0.543 0.295 0.208

Video game 0.395 0.545 0.003

Mobile Phone 0.793 0.173 0.084

Car 0.744 0.262 0.103

Television 0.797 0.132 0.172

Washing Machine 0.587 -0.093 0.479

The three factors extracted for further study are shown in Table 4.44. These three factors that

were extracted included the items which have loadings of more than 0.5 (approx) and are

referred as the product categories in further analysis. Table 4.44 is followed by the

explanation of all these three product categories.

Table 4.44: Factor Analysis of product categories

Factor Item Factor Loading Factor Name

1

Vacation 0.55

Loud Goods

Computer 0.54 Mobile Phone 0.79 Car 0.74 Television 0.80 Washing Machine 0.59

2

Stationary Books 0.67

Noisy Goods Food & Beverages 0.63 Clothes & Shoes 0.48 Movie Ticket 0.49

Data Analysis

102 Ph. D. Thesis

Dining out 0.67 Video game 0.55

3. Shampoo 0.80

Quiet Goods Toothpaste 0.74 Grocery 0.66

Loud Goods: Loud Goods was the name given to the first product category identified through

factor analysis. As shown in Table 4.44, this factor contains six products namely: vacation,

computer, mobile phone, car, television and washing machine. All the products in this

category have three commonalities: one they are all expensive products, second the decision

of buying them requires more time and effort of the family members and lastly, these are the

products for which the buying frequency is very less (may be once in 5-10 years). Since these

products are expensive and high risk is associated in their purchase, all the family members

are involved in purchase decision and hence joint family decisions are more likely, this factor

Noisy Goods: Noisy Goods was the name given to the second factor identified through factor

analysis. As can be seen from Table 4.44, this factor includes six products: stationary, books,

food & beverages, clothes, movie tickets, dining out and video games. All these products have

similarities. Firstly, these products are not very expensive, secondly, their buying frequency is

moderate and lastly, these products are directly used by children and hence they have high

involvement in these products. For these products, children make the maximum effort and

Quiet Goods: This was the name given to the third factor identified through factor analysis.

This factor includes three products: shampoo, toothpaste and grocery items. The items falling

under this category are regular household products in which child have least interest. They are

necessities and hence not very expensive. Their buying frequency is also very high (weekly or

After identifying three product categories, next step was to find the correlates and the

determinants of such an influence. For this, first we found the means and standard deviations

of the factors. The children were asked to rate their influence on different products on a scale

of 5, where 5 was very high influence and 1 was no influence at all. After the factor analysis,

Chapter 4

Ph. D. Thesis 103

when three factors emerged, the score of each of the factors was computed by taking out the

mean of the items falling under each factor. For e.g. in order to calculate the mean of first

product category; loud goods, the score of all the products and services falling under loud

goods i.e. vacation, computers, mobile phone, car, television and washing machine were

added and then mean was calculated. Similarly, means and standard deviations were

calculated for the other two factors. These means and standard deviations were used to rank

them. The ranking of the factors are shown in Table 4.45. Figure 4.20 gives the graphical

representation of the same.

Table 4.45: Means and Standard deviation of the product categories

Factor Name Mean Standard Deviation

Loud Goods 3.09 .63

Noisy Goods 3.09 .98

Quiet Goods 2.99 .68

Figure 4.20:

categories

So, in the purchase of noisy goods child was expected to have the strongest influence on

decisions as these are the products for which children are directly involved in consuming. The

results were in line with previous studies [26] [171]. With regard to loud goods, there were

Data Analysis

104 Ph. D. Thesis

mixed results in the literature. Quite a number of studies highlighted that children have least

influence on durable and expensive products [14] [7] [114] [171] [211] [212]. But our study

here contradicts the earlier studies and shows that children not only influence the purchase of

products that are directly consumed only by them, but a much wider range of products for use

by the entire family. The studies that supported our finding were the more recent ones [6] [14]

[213] [214].


categories across various personal characteristics of respondents

The factor analysis as explained in previous section resulted in three product categories

namely: loud goods, noisy goods and quiet goods. For comparing the in

three product categories across various personal characteristics, various t-tests were done to

see whether demographic factors (gender, age, class, no. of siblings, birth-order) have an

effect.

4.5.2.1 ge

From Table 4.46, we can see that the t value is greater than 1.96 for only loud goods. It means

that for these goods the t value is significant as p was equal to 0.05. This analysis indicates

that older children in the age group of 11-12 years were more influential than their younger

counterparts in the buying of loud goods. With age children achieve maturity and parents

begin to give more decision taking power to them. Hence they are more involved and their

influence is high. For the noisy and quite goods, no significant differences were found. Most

related studies had influence grew with their age [10]

[15] [135] [136] [137] [165] [172] [211] [215] [216].

Table 4.46: Comparison of product categories between two age-groups

Product Categories Mean scores and standard deviation

1 = 8-10 years (n=92) 2 = 11-12 years (n=83)



Loud Goods 1 2

2.76 3.06

1.10 0.91 1.97 .050*

Noisy Goods 1 2

3.48 3.63

0.86 0.82 1.21 .227 NS

Quiet Goods 1 2

2.61 2.39

1.14 1.00 1.34 .181 NS


Chapter 4

Ph. D. Thesis 105

4.5.2.2 Gender

As shown in Table 4.47, t value for all the three product categories is less than 1.96 (p > 0.05).

There were no significant differences in the mean values of boys and girls as far as influence

in different product categories was concerned. The findings were congruent with other

previous study [141]. But there are few researchers who had observed boys have greater

influence than girls in the purchase of food for the family [142]. On the contrary, one of the

researchers found girls had a large influence on family purchase [217]. Though there were no

significant differences in the mean values of any product category between boys and girls but

mean values of girl child is more than that of boys for all the product categories. This is in

congruence with other similar studies [117] [145] [215].

Table 4.47: Comparison of product categories between boy and girl child


1 = Boy (n=116) 2 = Girl (n=59)



Loud Goods 1 2

2.86 2.98

1.00 1.08 -.720 .473 NS

Noisy Goods 1 2

3.54 3.57

0.87 0.79 -.173 .863 NS

Quiet Goods 1 2

2.39 2.72

1.09 1.04 -1.921 .056 NS

NS Not Significant


T-test was conducted to examine the

between single child and child with siblings (Table 4.48). From Table 4.48, we can see that

the t value is greater than 1.96 for only loud goods (p = 0.029). This means children with

siblings were more influential in the purchase of loud goods. This may be explained with the

fact that siblings often form a coalition and jointly persuade parents to buy expensive

products. This finding has highlighted the need to probe more into the process of family

buying in India. For noisy and quiet goods, no. of siblings does not make any significant

difference.

Data Analysis

106 Ph. D. Thesis

Table 4.48: Comparison of product categories


0 = Single Child (n=36) 1 = With siblings (n=139)


Siblings Mean Std. Deviation t Sig. (2-tailed)

Loud Goods 0 1

2.57 2.99

1.11 0.98 -2.195 .029*

Noisy Goods 0 1

3.41 3.58

0.76 0.85 -1.103 .272 NS

Quiet Goods 0 1

2.29 2.56

0.98 1.10 -1.301 .195 NS


4.5.2.4 Birth Order

Multivariate analysis of variance was applied along with post-hoc tests in order to compare

-orders of the child. Homogeneity of

covariance was tested by calculating Box's Test of Equality of Covariance Matrices. Table

4.49 shows that the assumption was satisfied, the covariance were homogeneous (P = .869).

Table 4.49: Box's Test of Equality of Covariance Matrices of product categories with c -order

Box's M 7.296

F .568

df1 12.000

df2 4179.895

Sig. .869

There were no significant differences in the mean values of any of the product categories.

There were -order

category. Table 4.50 also shows the pair wise significant differences among different product

categories. With respect to loud goods, noisy goods and quiet goods, findings showed no

significant difference at .05 levels in mean and standard deviation values, with F value of

.387, .721 and .404 respectively. There were no significant differences between; Bo1 Vs

Bo2, Bo1 Vs Bo3 and Bo2 Vs Bo3.

Chapter 4

Ph. D. Thesis 107

Table 4.50: Comparisons of product categories with birth-order in the family

NS Not Significant


T-test results in Table 4.51 show significant differences in the mean values of two out of three

product categories. Loud goods and quiet goods had significant difference in the mean values

(p = 0.021 and p = 0.005 respectively). Children in the joint family were more influential than

those in the nuclear family in the purchase of loud and quiet goods. This could be understood

from the fact that Indian families have strong emotional bonding; kids with grandparents are

more influential and have more say in the buying process. No significant difference was found

in the case of noisy goods. This may be because of the nature of noisy goods. Products like

stationary and beverages are more attractive for kids everywhere in joint as well as nuclear

families.

Table 4.51: Comparison of product categories with family structure


0 = Nuclear Family Structure (n=104) 1 = Joint Family Structure (n=71)


Family structure Mean Std. Deviation t Sig. (2-tailed)

Loud Goods 0 1

2.76 3.12

1.08 .90 -2.321 .021*

Noisy Goods 0 1

3.49 3.63

.83

.85 -1.097 .274 NS

Quiet Goods 0 1

2.32 2.78

1.07 1.04 -2.832 .005**


Product Categories

Youngest Bo1(N=61)

Eldest Bo2(N= 66)


Mean Diff. Bo1 v/s

Bo2

Mean Diff. Bo1 v/s

Bo3

Mean Diff. Bo2 v/s

Bo3


Loud Goods 3.02 .95 2.98 1.00 2.89 1.13 0.037 0.130 0.093 .387 NS

Noisy Goods 3.51 .88 3.72 .83 3.18 .77 0.211 0.333 0.544 .721 NS

Quiet Goods 2.67 1.17 2.46 1.04 2.56 1.11 0.207 0.111 0.096 .404 NS

Data Analysis

108 Ph. D. Thesis


Another set of t-tests were done to examine whether there is

Table 4.52 and Table 4.53).

Significant difference was found in the mean values of only one product category; quiet

goods. Quiet goods had

qualification (p = 0.005 and p = 0.007 respectively). Children of post graduate fathers had

more influence in quiet goods. On the other hand, children of post graduate mothers had less

influence in the quiet goods. This is complex to understand. One explanation may be the

important role of educated mothers. More qualified mothers may not just buy-in

request; instead she may look into the nutritional value and apply economics before buying.

Table 4.52: Comparison of product categories with





Loud Goods 1 2

2.82 3.03

1.04 0.98 -1.278 .203 NS

Noisy Goods 1 2

3.55 3.54

0.83 0.85 .072 .943 NS

Quiet Goods 1 2

2.33 2.80

1.07 1.03 -2.833 .005*


Table 4.53: Comparison of product categories with





Loud Goods 1 2

3.01 2.70

1.01 1.02 1.905 .058 NS

Noisy Goods 1 2

3.55 3.53

0.84 0.84 .170 .866 NS

Quiet Goods 1 2

2.66 2.20

1.11 0.95 2.724 .007**

**Significant at .01 level NS Not Significant

Chapter 4

Ph. D. Thesis 109


More analysis was done

-hoc test was

applied. Homogeneity of covariance was tested by calculating Box's Test of Equality of

Covariance Matrices. The assumption of homogeneous covariance was satisfied with p =

.697. Table 4.54:

NS Not Significant

Table 4.54 shows the pair wise significant differences among different categories. With

respect to loud, noisy and quite goods, findings showed no significant differences at .01

levels in mean and standard deviation values, with F value of 6.65 and 5.09 respectively. To

examine whether there is a significant difference in consumer socialization of child through

various agents between working and non-working mothers, t-test was done (Table 4.55).

Significant differences were not found in the mean values of any socialization agents. This

means that mothers working or non-working status had no significant difference as far as

influence for different products was concerned.

Table 4.55: Comparisons of product categories with

Product Categories

Mean scores and standard deviation of

1 = Working (n=44) 2 =Non-Working (n=131)


Occupation Mean Std. Deviation t Sig. (2-tailed)

Loud Goods 1 2

2.83 2.93

.93 1.06 -.561 .575 NS

Noisy Goods 1 2

3.51 3.56

.79

.86 -.347 .729 NS

Quiet Goods 1 2

2.29 2.56

1.04 1.09 -1.544 .124 NS

NS Not Significant

Product Categories

Business O1(N=52)

Govt. Service

O2(N= 34)


Mean Diff.

O1 v/s O2

Mean Diff.

O1 v/s O3

Mean Diff.

O2 v/s O3

F-value

Mean SD Mean SD Mean SD Loud Goods 3.06 0.98 2.91 0.96 2.81 1.07 0.157 0.253 0.096 .640

NS

Noisy Goods 3.70 0.81 3.36 0.87 3.54 0.84 0.338 0.158 0.180 .633

NS

Quiet Goods 2.61 1.13 2.49 0.85 2.45 1.14 0.119 0.160 0.041 1.342

NS

Data Analysis

110 Ph. D. Thesis

4.5.3 Research Question 3: Comparison of in the purchase of three

product categories as perceived by child and his/her parents.

Parents have always perceived their children as nagging influencers, on a wide variety of

products and services [7] [76] [215]. But, to find out whether child and parents think alike or

different needs more investigation. So t-tests were conducted to investigate whether there is a

significant difference in the perception of parent and child in case of different product

categories.

Table 4.56: t categories as perceived by child and parent

Product Categories

Mean scores and standard deviation of Child (C) and parent (P) (N=175) t-test for equality of means


Loud Goods P C

2.5517 2.9048

.97868 1.02514

-3.290 .001**

Noisy Goods P C

3.4019 3.5495

.72587

.83987 -1.759

.079 NS

Quiet Goods P C

2.5695 2.5048

1.06498 1.08093 .565 .573 NS


As shown in Table 4.56, t value is greater than 1.96 for only loud goods. For these goods, the t

value is significant as p is equal to 0.001. Based on the t-test scores, we can say that there is

influence in the initiation stage of family buying process. Children perceived that they have

more influence in the purchase of loud goods than what their parents perceived (µc = 2.90 is

more than µp = 2.55). For the noisy and quiet goods, parents and their children were on the

same lines. They more or less perceived

what Foxman and Tansuhaj [171] also deduced in their study that adolescents overall rated

their decision influence as greater relative to parents than did mothers in the purchase of

products for their own use. This difference in perception is also shown graphically in terms of

product categories (Figure 4.21) and Figure 4.22 shows the specific products for which there

was dissimilarity in the opinion of parent and child.

Chapter 4

Ph. D. Thesis 111

Figure 4.21: Graphical representation of difference in the perception of Child and Parent for three product

categories

Figure 4.22: Graphical representation of difference in the perception of Child and Parent for specific products

and services


product categories

After identifying the distinct product classification, next step was to find the correlation

between different variables. Relationships were found between the various influence strategies

and product categories. Most of the correlations were significant. Thus stating the fact that the

use of varied influence strategies somewhat determined the

different product categories. It is quite clear from Table 4.57 that persuasive, rational,

knowledge and emotional strategies were significant for loud and noisy goods. For quiet

Data Analysis

112 Ph. D. Thesis

goods, none of the strategies we was least in the case of

quiet goods and hence they would not be using any influence tactics for buying quiet goods.

Table 4.57 shows all the values of correlation coefficients influence strategies with the three

product categories.

Table 4.57: Relationships (Correlation coefficients) between influence st

noisy and quiet goods (N=175)

Product Categories Aggressive Strategies


Rational Strategies

Knowledge Strategies

Emotional Strategies

Loud Goods .081 NS .243** .317** .212** .208**

Noisy Goods .137 NS .329** .292** .304** .380**

Quiet Goods -.010 NS .038 NS .059 NS .077 NS .081 NS


In order to compute the model for determining the use of influence strategies on different

products, multiple regressions were done. The independent variables for this part of the study

were the five influence strategies used by Indian children namely; aggressive, persuasive,

rational, knowledge and emotional strategy. The dependent variables were the

influence levels for the three product categories namely; loud goods, noisy goods and quiet

goods. All the influence strategies were put in the regression process as independent variables

and product categories were put as the dependent variable. To test the relatio

categories following hypothesis were formulated:

H2a: loud goods.

H2b: The chil noisy goods.

H2c: quiet goods.

Chapter 4

Ph. D. Thesis 113

4.5.4.1 Loud Goods

Step-wise regression analysis was conducted to comprehend the impact of influence strategies

variable. The equation which emerged after the process is as follows. Table 4.58 summarizes

the determinants of the equation.

Y1= 2.037 +.317 X3

Where, Y1

X3 = Rational Strategies

Table 4.58: Determinants of influence strategies affectin (N=175)


on Loud Goods


Rational Strategies .317** .317** 4.394

Multiple R = 0.317


The value of multiple R is 0.317 and the value of R square is 0.100 in the equation. It states

The rest can be attributed to so many other factors which are scattered and individually

contribute only little to the loud goods. It should be noted here, that the dependent variable in

independent variable namely Rational Influence Strategies is positively correlated with it. A

direct positive relation of rational influence strategies with the influence on loud goods

indicates that the children use lot of logical and rational reasoning in influencing parents to

buy loud goods. Since loud goods are relatively expensive products, rational strategy works

best for children to persuade their parents. Other strategies like aggressive, persuasive,

knowledge and emotional strategies are not significant for the child to influence in purchasing

loud goods. Thus, the alternate hypothesis H2a is accepted. Figure 4.23 explains the

relationship of r

Data Analysis

114 Ph. D. Thesis

Figure 4.23: Relationship of Influence Strategies with the influence on Loud goods

4.5.4.2 Noisy Goods

Another step-wise regression comprehends the impact of five influence strategies on the

are put as independent

e on noisy goods as the dependent variable. The equation which

emerged after the process is as follows. Table 4.59 summarizes the determinants of the

equation.

Y2 = 2.267 + .209 X4 + .318 X5

Where, Y2

X4 = Knowledge Strategies

X5 = Emotional Strategies

Table 4.59: (N=175)


Noisy Goods


Knowledge Strategies .209* .304** 2.887

Emotional Strategies .318** .380** 4.396

Multiple R = 0.429

R Square = 0.184 **Significant at 0.01 level *Significant at 0.05 level

LOUD GOODS

R2= 0.100

Rational Strategies Offer Deals Bringing an external reason Propose fair competition Hide things in the shopping trolley

competition

.317**

Chapter 4

Ph. D. Thesis 115

Multiple R came out to be 0.429 and R square as 0.184 in the equation. It states that 18.4% of

the use of knowledge

and emotional influence strategies. The rest can be attributed to so many other factors which

are scattered and individually contribute very little to the final decision stage. A direct positive

relation of these influence strategies with the influence on noisy goods indicates that the

children creates lot of noise using varied strategies influencing parents to buy noisy goods.

Constant emotional pressure and also the knowledge which children showcase about the

Other strategies like aggressive, persuasive and rational strategies are not significant for the

child to influence in purchasing noisy goods. Thus, the alternate hypothesis H2b is accepted.

Figure 4.24 explains the relationship of Knowledge and Emotional Influence Strategies with

Figure 4.24: Relationship of Influence Strategies with the influence on Noisy goods

4.5.4.3 Quiet Goods

To examine the impact of various influence strategies on the quiet goods, another regression

model is created. So, the influence strategies are put as ind

influence on quiet goods was put as the dependent variable. As expected none of the strategies

came out as significant contributor for quiet goods. Children are least interested in influencing

NOISY GOODS

R2= 0.184 Emotional Strategies Tell that all friends have it Be unnaturally nice to parents

Knowledge Strategies Tell about the TV ad he/she saw

about the product Tell that the brand is famous

.209*

.318**

Data Analysis

116 Ph. D. Thesis

parents to buy any of the quiet

Thus, the alternate hypothesis H2c is rejected.

4.5.5 Section III Conclusion

were

identified through secondary data and focus group. The exploratory factor analysis was done

on 15 products and services and resulted in three distinct product clusters; loud goods, noisy

influence. Further analysis revealed that children of different groups of age, gender, birth

order, etc influence the family buying. T-tests were conducted to compare across all the

personal characteristics; few t values were significant like age, no. of siblings, family structure

and parents qualification, whereas few are not, like gender, birth order and parents occupation.

Further t-tests also revealed the significant difference in the opinion of child and parent for

loud goods. This all-inclusive analysis gives us a clear coherent picture of the influence

pattern of child.

Regression analysis was also done in order to find out the contribution of various influence

strategies in the buying of three product categories. 10% in loud goods and 18% in noisy

goods was explained by the strategies viz., aggressive, persuasive, rational, knowledge and

emotional.

for loud and noisy goods. The same was being calculated through regression, where the

influence strategies; aggressive, persuasive, rational, knowledge and emotional were put as

dependent variable. The Figure 4.28 shows graphically the relationship between influence

strategies and product categories.

Chapter 4

Ph. D. Thesis 117

Figure 4.25: Relationship of Influence Strategies with different product categories

4.6 SECTION IV: BUYING PROCESS STAGES

ing process stages and sub-decisions. Previous

findings suggest that children tend to have the strongest influence at the problem recognition

stage of the decision process [114] [172] [211] and that the influence declines significantly

with the choice stage [52] [54] [77] [114] [136] [211] [218].

is lowest in the subdecisions of where to purchase [14] [15] 114], where to gather information

[135], and how much to spend [15] [77] [114] [135] [136]. On the other hand, parents allow

children to have increasing influence on the more expressive subdecisions, e.g., product

attributes such as color, model, and brand choices [15] [77] [114] [135] [219].

4.6.1 Research Question 1: Comparis family buying stages

and sub-decisions across various personal characteristics

The means and standard deviations we

process stages and sub-decisions. As seen in Table 4.60

Table 4.60: Mean and Standard Deviations for buying process stages

Buying Process Stages Mean Std. Deviation

Initiation Stage 1.886 .376 Search & Evaluation Stage 1.753 .401 Final decision Stage 1.833 .416

Aggressive Strategy

Persuasive Strategy

Rational Strategy

Knowledge Strategy

Emotional Strategy

Loud & Noisy Goods

R2 = 0.178

=-.046

=.086

=.208*

=.133

=.156

Data Analysis

118 Ph. D. Thesis

Figure 4.26: Graphical representation of means and standard deviations of buying process stages

Table 4.61: Mean and Standard Deviations for sub-decisions

Buying Process Stages Mean Std. Deviation

Where to buy? 1.664 .419

When to buy? 1.658 .391

Which to buy? 1.930 .385

How much to buy? 1.598 .401

Figure 4.27: Graphical representation of means and standard deviations of sub-decisions

Chapter 4

Ph. D. Thesis 119

For comparing the differences in children influence level in buying stages and sub-decisions

strategies across various personal characteristics, various t-tests were done.

4.6.1.1

From Table 4.62, we can see the results of t-tests. The t value is less than 1.96 for all the three

buying stages. It means that for the buying stages: start, search & evaluation and final decision

stage, t value is not significant (p = 0.515, p = .081 and p = 0.876 respectively). Children may

not be able to differentiate among three stages and hence there is no significant difference as

far as their age group is concerned. On the other hand, age-group was quite significant when

evaluating the influence on the various sub-decisions regarding the purchase of any product or

service. Table 4.62 shows that t value is more than 1.96 for the three out of four sub-decisions.

Children between the age group of 11-12 years had more influence when the family decides

about where to buy, when to buy and how much to buy? These three have p value of more

than 0.05 (p = 0.005, p = .002 and p = 0.026 respectively)

Table 4.62: Comparison of buying stages & sub-decisions between two age-groups

Buying Stages and Sub-decisions

Mean scores and standard deviation 1 8-10 years (n=92) 2 11-12 years (n=83)


Age-groups Mean Std. Deviation T Sig. (2-

tailed)

Start Stage 1 2

1.8688 1.9060

.40370

.34438 -.652 .515 NS

Search & Evaluation Stage 1 2

1.7029 1.8088

.43615

.35396 -1.752 .081 NS

Final Decision 1 2

1.8278 1.8378

.41800

.42012 -.156 .876 NS

Where to buy? 1 2

1.5804 1.7574

.42757

.39256 -2.842 .005**

When to buy? 1 2

1.5739 1.7526

.38759

.37547 -3.091 .002**

Which to buy? 1 2

1.8826 1.9839

.39526

.37054 -1.744 .083 NS

How much to buy? 1 2

1.5348 1.6699

.40845

.38381 -2.248 .026*


Data Analysis

120 Ph. D. Thesis

4.6.1.2 Gender

T-test was done to examine the -

decisions between boys and girls (Table 4.63) shows no significant difference was found in

the mean values of the three buying stages between boys and girls. The same was the result

for purchase sub-decision also. Though the differences were not significant, girls were more

influential than boys in the first and second stage, while boys dominated the final decision

stage. In sub-decisions, boys had more influence for decisions like where to buy, when to buy

and which to buy. Girls influenced the decision to decide how much to buy more than the

boys.

Table 4.63: Comparison of buying stages & sub-decisions between boy and girl child


Mean scores and standard deviation 1= Boy (n=116) 2 = Girl (n=59)


Groups Mean Std. Deviation T Sig. (2-

tailed)

Start Stage 1 2

1.8833 1.8927

.38048

.37057 -.155 .877 NS


1.7437 1.7718

.40400

.39985 -.436 .663 NS

Final Decision 1 2

1.8458 1.8068

.44269

.36676 .582 .561 NS

Where to buy? 1 2

1.6724 1.6486

.44104

.37709 .354 .724 NS

When to buy? 1 2

1.6603 1.6554

.40260

.37101 .079 .937 NS

Which to buy? 1 2

1.9494 1.8938

.39764

.36244 .901 .369 NS


1.5948 1.6068

.42073

.36424 -.186 .853 NS

NS Not Significant


To examine the difference in -decisions between

single child and child with siblings, t-test was done (Table 4.64). As against the believed

notion that single child is more pampered and may have high influence in the buying process,

the children with one or more siblings have more influence than those children who are the

single child of their parents. The t value is significant for second stage of search and

evaluation (p = 0.048) and for one sub-decision; which to buy (p = 0.026). This may be

because of the coalition pacts and association forms among siblings to pester very strongly.

Chapter 4

Ph. D. Thesis 121

Table 4.64: Comparison of buying stages & sub-decisions


Mean scores and standard deviation 0 = Single Child (n=36)

1 = With siblings (n=139)


Groups Mean Std. Deviation T Sig. (2-

tailed)

Start Stage 0 1

1.8204 1.9036

.40373

.36824 -1.185 .238 NS


1.6352 1.7837

.40739

.39594 -1.994 .048*

Final Decision 0 1

1.8000 1.8411

.47543

.40294 -.524 .601 NS

Where to buy? 0 1

1.6352 1.6719

.44834

.41323 -.467 .641 NS

When to buy? 0 1

1.6037 1.6729

.43642

.37899 -.946 .346 NS

Which to buy? 0 1

1.8037 1.9635

.45438

.36082 -2.240 .026*


1.5315 1.6163

.42058

.39618 -1.130 .260 NS


4.6.1.4 Birth Order

Multivariate analysis of variance was applied along with post-hoc tests in order to compare

-decisions. The

condition for homogeneity of covariance was satisfied, the covariance were homogeneous

(p = .027).

No significant difference was found in the mean values of all the buying stages and sub-

decisions. Table 4.65 shows the pair wise differences among different product categories.

With respect to start stages, search & evaluation and final buying stage, findings showed no

significant difference at .05 levels in mean and standard deviation values, with F value of

.328, .162 and 2.778 respectively. With respect to sub-decisions also, findings for where to

buy, when to buy, which to buy and how much to buy showed no significant difference at .05

levels in mean and standard deviation values, with F value of .312, .026, 1.648 and .449

respectively.

Data Analysis

122 Ph. D. Thesis

Table 4.65: Comparisons of buying stages & sub- -order in the family

NS Not Significant


T-test results as seen in Table 4.66 showed significant differences in the mean values of two

out of three buying stages. For stage 1 (initiation stage) and stage 3 (final buying stage), there

were significant differences in the mean values (p = 0.016 and p = 0.002 respectively).

Children in the joint family were more influential than those in the nuclear family in the

initiation and the final stage of buying. Indian joint families have strong

participation in the buying process. With grandparents, child is able to influence more as

compared to those children who are in nuclear family set-up. The same is true for sub-

decisions also. Three out of four sub-decisions showed significant differences in the mean

values. Children in joint family had more say in the decisions of where to buy, when to buy

and how much to buy with the p values of .027, .026 and .019 respectively.

Table 4.66: Comparison of buying stages & sub-decisions with family structure


Mean scores and standard deviation 1 = Nuclear Family Structure (n=104)

2 = Joint Family Structure (n=71)


Family structure Mean Std. Deviation T Sig. (2-

tailed)

Start Stage 1 2

1.83 1.97

.34

.41 -2.432 .016*


1.72 1.81

.35

.46 -1.511 .133 NS


Youngest Bo1(N=61)

Eldest Bo2(N= 66)


Mean Diff.

Bo1 v/s Bo2

Mean Diff.

Bo1 v/s Bo3

Mean Diff.

Bo2 v/s Bo3

F-value


Start Stage 1.91 .05 1.92 .05 1.80 .11 .0121 .1071 .1192 .328 NS

Search & Evaluation Stage

1.79 .05 1.79 .05 1.70 .11 .0070 .0880 .0949 .162 NS

Final Decision 1.84 .05 1.86 .05 1.76 .12 .0203 .0838 .1040 2.778

NS

Where to buy? 1.67 .05 1.67 .05 1.64 .12 .0004 .0299 .0303 .312

NS

When to buy? 1.69 .05 1.66 .05 1.64 .11 .0239 .0496 .0258 .026

NS

Which to buy? 1.99 .05 1.97 .04 1.81 .10 .0256 .1801 .1545 1.648

NS

How much to buy? 1.63 .05 1.61 .05 1.57 .12 .0153 .0562 .0409 .449

NS

Chapter 4

Ph. D. Thesis 123

Final Decision 1 2

1.75 1.95

.39

.44 -3.122 .002**

Where to buy? 1 2

1.61 1.75

.38

.47 -2.237 .027*

When to buy? 1 2

1.60 1.74

.34

.44 -2.243 .026*

Which to buy? 1 2

1.89 2.00

.36

.41 -1.870 .063 NS


1.54 1.68

.33

.47 -2.362 .019*



Table 4.67 and 4.68 show the results of t-tests to examine whether there was a significant

qualification. No

significant difference was found in the mean values of any stage or sub-decision. Fathers and

de

buying. Table 4.67: Comparison of buying stages & sub-decisions with





Qualification Mean Std. Deviation T Sig. (2-

tailed)

Start Stage 1 2

1.85 1.94

.37

.39 -1.494 .137 NS


1.74 1.77

.38

.44 -.354 .723 NS

Final Decision 1 2

1.80 1.88

.42

.41 -1.220 .224 NS

Where to buy? 1 2

1.65 1.69

.41

.43 -.576 .565 NS

When to buy? 1 2

1.65 1.67

.39

.39 -.367 .714 NS

Which to buy? 1 2

1.93 1.93

.39

.38 .146 .884 NS


1.58 1.63

.39

.42 -.755 .451 NS

NS Not Significant

Table 4.68: Comparison of buying stages & sub-decisions with






tailed)

Start Stage 1 2

1.90 1.85

.37

.38 .908 .365 NS

Data Analysis

124 Ph. D. Thesis


1.77 1.73

.40

.40 .613 .540 NS

Final Decision 1 2

1.85 1.79

.41

.42 .931 .353 NS

Where to buy? 1 2

1.67 1.66

.41

.44 .122 .903 NS

When to buy? 1 2

1.67 1.64

.38

.40 .499 .619 NS

Which to buy? 1 2

1.95 1.85

.38

.40 .915 .362 NS


1.60 1.59

.40

.41 .155 .877 NS

NS Not Significant


To com -hoc test was applied. No

significant differences were found in the mean values of any stage or sub-decision. Table 4.68

shows the pair wise differences among different stages. The only significant difference was

found between the fathers in government service and fathers in business. The children whose

father was in business were more influential as compared to those children whose fathers were

in government service. This could be attributed to the fact that, usually business families have

comparatively high disposable income as compared to government service family. Children

may get more pester power in such families. T-test was conducted to examine the difference

es between working and non-working mothers (Table

4.70). Significant difference was not found in the mean values of any stage. This means that

mothers working or non-working status had

influence was concerned.

Table 4.69: Comparisons of buying stages & sub-


Business O1(N=52)

Govt. Service

O2(N= 34)


Mean Diff.

O1 v/s O2

Mean Diff.

O1 v/s O3

Mean Diff.

O2 v/s O3

F-value


Start Stage 2.00 .051 1.78 .064 1.86 .039 .1995* .1344 .0651 .620 NS

Search & Evaluation Stage 1.81 .05 1.73 .06 1.73 .04 .0789 .0822 .0033 .708

NS

Final Decision 1.90 .06 1.82 .07 1.80 .04 .0791 .0987 .0196 .612 NS

Where to buy? 1.67 .06 1.65 .07 1.66 .04 .0201 .0094 .0107 2.539 NS

Chapter 4

Ph. D. Thesis 125


Table 4.70: Comparisons of buying stages & sub-decisions with


Mean scores and standard deviation 1 = Working (n=44)

2 =Non-Working (n=131)



tailed)

Start Stage 1 2

1.89 1.88

.39

.37 .029 .977 NS


1.77 1.75

.44

.39 .373 .710 NS

Final Decision 1 2

1.91 1.80

.43

.40 1.486 .139 NS

Where to buy? 1 2

1.70 1.65

.49

.39 .511 .610 NS

When to buy? 1 2

1.71 1.64

.45

.37 1.108 .269 NS

Which to buy? 1 2

1.96 1.92

.43

.37 .654 .514 NS


1.61 1.59

.46

.38 .253 .801 NS

NS Not Significant

4.6.2 Research Question 2: Comparison of nce in the family buying

process stages as perceived by child and his/her parents.

T-test was conducted for the three stages of family buying process. As shown in Table 4.71,

the t value is not significant for any of the stages. Based on the t-test scores, we can say that

there wa

perceived by parents as well as their children. Though not significant, parents perceived their

children had more influence in the search & evaluati

mean score is greater than that of parents in case of first stage of buying process. It can be

deduced that the Indian parents are well aware of the psychology of their children and

understand them very well. There were no significant differences

the purchase of three product categories as perceived by child and his/her parents.

When to buy? 1.69 .05 1.67 .07 1.64 .04 .0186 .0490 .0304 1.496 NS

Which to buy? 1.95 .05 1.96 .06 1.90 .04 .0147 .0436 .0583 .448 NS

How much to buy? 1.64 .05 1.56 .07 1.59 .04 .0842 .0508 .0334 1.133

NS

Data Analysis

126 Ph. D. Thesis

Table 4.71: buying stages as perceived by child and parent

Family Buying Process Stages

Mean scores and standard deviation of Child (C) and parent (P) (N=175) t-test for equality of means


Initiation Stage P C

1.8617 1.8865

.34091

.37612 -.645 .519 NS

Search & Evaluation Stage

P C

1.7610 1.7531

.37838

.40167 .237 .812 NS

Final Buying Decision Stage

P C

1.8590 1.8331

.37887

.41669 .608 .543 NS

NS Not Significant

Figure 4.28: Graphical representation of difference in the perception of Child and Parent for

three buying process stages

4.6.3 Research Question 3: Correlates and Determin in the

family buying process

Now is the turn to find out the correlation between influence strategies and buying process

stages. Relationships were found between the various influence strategies and influence at

different buying stages. Most of the correlations were significant. Thus stating the fact that the

does levels in

different buying stages. It is quite clear from Table 4.72 that for all the three buying stages; at

least four out of five strategies were significant. Table 4.72 shows all the values of correlation

coefficients influence strategies with the three buying stages.

Chapter 4

Ph. D. Thesis 127

Table 4.72: Relationships (Correlat

different buying stages (N=175)

Buying Stages Aggressive Strategies


Rational Strategies

Knowledge Strategies

Emotional Strategies

Initiation Stage .176* .210** .209** .227** .222** Search & Evaluation Stage .142 NS .198** .149* .191* .236**

Final Buying Stage .170* .190* .158* .209** .122 NS **Significant at .01 level *Significant at .05 level NS Not Significant

This section works out the regression model o

ed

the regression equation in the model and examined the strength of the independent variables

in predicting the dependent variable. It was assumed that there is a linear relationship between

regression analysis was conducted with the dependent variable as the five influence strategies

namely: Initiation stage, Search and evaluation stage and Final buying decision stage.

In order to compute the model for determining the impact of influence strategies on three

stages of family buying process, multiple regressions were done. The independent variables

for this part of the study were the five influence strategies used by Indian children namely;

aggressive, persuasive, rational, knowledge and emotional strategy. The dependent variables

we in the three buying stages namely; initiation stage, search &

evaluation stage and final buying stage. The following hypotheses are tested:

H3a:

Stage of Family Buying Process.

H3b:

Evaluation Stage of Family Buying Process.

H3c:

Stage of Family Buying Process.

Data Analysis

128 Ph. D. Thesis

4.6.3.1 Initiation Stage

A step-wise regression analysis was done to comprehend the impact of five influence

strategies on the initiation stage of family buying process. The five influence strategies were

the was the dependent

variable. The equation which emerged after the process is as follows. Table 4.73 summarizes


Y1= 1.517 + 0.176X4 + 0.169X5

Where,

Y1 = Initiation Stage



Table 4.73: Determinants of influence strategies affecting Initiation Stage (N=175)


nitiation Stage



Emotional Strategies .169* .222** 2.203

Multiple R = 0.279

R Square = 0.078 **Significant at .01 level *Significant at .05 level

The value of multiple R is 0.279 and the value of R square is 0.078 in the equation. It states

rest can be attributed to so many other small factors which are scattered. It should be noted

here, that the dependent variable in the equation wa

initiation stage and two independent variables namely Knowledge Strategies and Emotional

Strategies were positively correlated with it. A direct positive relation of these influence

strategies with the initiation stage indicates that the children use emotions and their

knowledge about the product in question to persuade parents to initiate the buying process.

Other strategies like aggressive, persuasive and rational were not significant for the child to

influence in the first stage. Thus, the alternate hypothesis H3a is accepted. Figure 4.29

Chapter 4

Ph. D. Thesis 129

influence level in the initiation stage.

Figure 4.29: Relationship of Initiation Stage with Influence Strategies

4.6.3.2 Search and Evaluation Stage

To gauge the impact of influence strategies used by children to persuade their parents on the

second stage i.e. search and evaluation stage of family buying process, a stepwise regression

analysis was done. The equation which emerged after the process is as follows. Table 4.74


Y2= 1.458 + 0.236X5

Where,

Y2 = Initiation Stage


Table 4.74: Determinants of influence strategies affecting Search & Evaluation Stage (N=175)


Beta Simple r t-value Emotional Strategies .236* .236** 3.197



INITIATION STAGE R2= 0.08

Emotional Strategies Tell that all friends have it Be unnaturally nice to parents


about Tell that the brand is famous

product

.176*

.169*

Data Analysis

130 Ph. D. Thesis

Multiple R is 0.236 and the value of R square is 0.056 in the equation. It states that 5.6% of

search & evaluation stage can be attributed to just one factor. It

should be noted here, that the dependent variable in the equation wa

influence in the search and evaluation stage and only one independent variable namely

emotional strategies were positively correlated with it. A direct positive relation of this

influence strategy with the search and evaluation stage indicates that the children use

emotions to influence parents in the search and evaluation stage of the buying process. Other

strategies like aggressive, persuasive, rational and knowledge strategies were not significant

for the child to influence in the second stage. Thus, the alternate hypothesis H3b is accepted.

The figure 4.30

influence level in the search and evaluation stage.

Figure 4.30: Relationship of Search & Evaluation Stage with Influence Strategies

4.6.3.3 Final Buying Decision Stage

The step-wise regression analysis resulted in the following equation. Table 4.75 summarizes


Y3= 1.486 + 0.148X1 + 0.192X4

Where,

Y3 = Final Buying Decision Stage

X1 = Aggressive Strategies


SEARCH & EVALUATION

STAGE R2= 0.056

Emotional Strategies Tell that all friends have it Be unnaturally nice to parents

.236*

Chapter 4

Ph. D. Thesis 131

Table 4.75: Determinants of influence strategies affecting Final Buying Decision Stage (N=175)


Final Buying Decision Stage

Beta Simple r t-value Aggressive Strategies .148* .170* 1.988


Multiple R = 0.255

R Square = 0.065

**Significant at .01 level *Significant at .05 level

The value of multiple R is 0.255 and the value of R square is 0.065 in the equation. 6.5% is a

process. The dependent variable in the equation wa

decision stage and two independent variables namely aggressive strategies and knowledge

strategies were positively correlated with it. A direct positive relation of these influence

strategies with the final stage indicates that the children use lot of aggression and their

product or service. Other strategies like persuasive, rational and emotional strategies are not

significant for the child to influence in the final stage of family buying process. Thus, the

alternate hypothesis H3c is accepted. Figure 4.31 explains the relationship of Aggressive and

ence level in the final decision stage.

Data Analysis

132 Ph. D. Thesis

Figure 4.31: Relationship of Final Buying decision stage with Influence Strategies

Overall the influence strategies impacted

and noisy goods. Same was being calculated through regression, where the influence

strategies; aggressive, persuasive, rational, knowledge and emotional were put as independent

t

variable. Figure 4.32 shows graphically the impact of various influence strategies with the

buying process stages.

Figure 4.32: Relationship of Family Buying Process Stages with Influence Strategies

Aggressive Strategy

Persuasive Strategy

Rational Strategy

Knowledge Strategy

Emotional Strategy

Family Buying Process Stages

R2 = 0.10

=.109

=.021

=.053

=.165*

=.103

FINAL BUYING DECISION

STAGE R2= 0.065


about the product Tell that the brand is famous

Aggressive Strategies Express Anger Not Eating Stubbornly acting

.148*

.192*

Chapter 4

Ph. D. Thesis 133

4.6.4 Research Question 4: Finding the relation between product categories and buying

process stages

One of the reasearch question is to analyze the realtionship between different product

categories and buying stages. So firstly, we needed to find the correlation between buying

process stages and product categories. Relationships were found out between the three buying

stages and the three product categories, all the correlations are significant. Thus, stating the

influence at different stages significantly determi

level for different products. It is quite clear from Table 4.76 that for the three product

categories and influences at different buying stages are significant. Table 4.76 shows all the

values of correlation coefficients.

Table 4.76: and

product categories (N=175)

Product Categories Initiation Stage Search & Evaluation Stage Final Buying Stage

Loud Goods .605** .591** .606**

Noisy Goods .658** .601** .504**

Quiet Goods .402** .282** .439**


In order to compute the model for determining the

different products, multiple regressions were done. It considered the regression equation in the

model and examines the strength of the independent variables in predicting the dependent

variable. It was assumed that there is a linear relationship between the influence of child at

different satges and for different product categories. A stepwise regression analysis was

conducted with the dependent variable as the three buying process stages namely initiation

stage, search & evaluation stage and final buying decision and the independent variables as

uct categories: loud, noisy and quite goods. The

following hypothesis were formed:

H4a: in the buying stages in Loud Goods.

H4b: oods.

H4c:

Data Analysis

134 Ph. D. Thesis

4.6.4.1 Loud Goods and Buying Stages

For loud goods, regression analysis was done

three buying stages on overall influence in loud goods. The influences on three

stages were then put in the model as independent variables and ch in loud goods

was put as the dependent variable. The equation which emerged after the process is as

follows. Table 4.77 summarizes the determinants of the equation.

Y1= .683 +.218 X1 +.246 X2 + .302 X3

Where, Y1

X1 Initiation Stage

X2 Search & Evaluation Stage

X3 = Chi Final Decision Stage

Table 4.77: Child influence in buying stages affecting (N=175)



Initiation Stage .218** .605** 2.123

Search & Evaluation Stage .246** .591** 2.836

Final Decision Stage .302* .606** 3.644

Multiple R = 0.682

R Square = 0.465 **Significant at .01 level *Significant at .05 level

Multiple R is 0.682 and R square is 0.465 in the regression model. It means that 46.5% of the

stages. A direct positive relation of these stages with the loud goods indicates that the children

influences parents at every stage for the products in question and hence very highly impacts

the family buying process. Thus, the alternate hypothesis H4a is accepted. Figure 4.33

explains the relationship of buying stages with the loud goods.

Chapter 4

Ph. D. Thesis 135

Figure 4.33: Relationship of Buying process stages with Loud Goods

4.6.4.2 Noisy Goods and Buying Stages

For noisy goods, regression analysis wa

stages were then put in the model as independent variables and ch

goods was put as the dependent variable. The equation which emerged after the process is as


Y1= .566 +.528 X1 +.200 X2

Where, Y1

X1 Initiation Stage

X2 Search & Evaluation Stage

Table 4.78: Child influence in buying stages a e on noisy goods (N=175)



Initiation Stage .528** .658** 6.204

Search & Evaluation Stage .200** .601** 2.356

Multiple R = 0.693


Data Analysis

136 Ph. D. Thesis

Multiple R is 0.693 and R square is 0.480 in the regression model. It means that 48% of the

of buying process. The direct positive relation of these stages with the noisy goods indicates

that the children influences parents at initiation and search & evaluation stage for the noisy

products. Hence, the alternate hypothesis H4b is accepted. Figure 4.34 explains the

relationship of buying stages with the noisy goods.

Figure 4.34: Relationship of Buying process stages with Noisy Goods

4.6.4.3 Quiet Goods and Buying Stages

For quiet goods, regression analysis wa

at three buying stages on the child

stages were then put in the model as independent variables and ch

goods was put as the dependent variable. The equation which emerged after the process is as


Y1= .419 + .439 X3

Where, Y1

X3 Final Decision Stage

Chapter 4

Ph. D. Thesis 137

Table 4.79: Child influence in buying stages a fluence on quiet goods (N=175)



Final Decision Stage .439** .439** 6.401

Multiple R = 0.439

R Square = 0.192 ** Significant at 0.01 level

Multiple R is 0.439 and R square is 0.192 in the regression model. It means that 19.2% of the

buying process. The direct positive relation of this stage with the quiet goods indicates that the

children influences parents only at the final decision stage for the quiet products. Thus, the

alternate hypothesis H4c is accepted. Figure 4.35 explains the relationship of buying stages

with the quiet goods.

Figure 4.35: Relationship of Buying process stages with Quiet Goods

4.6.5 Section IV Conclusion

Further probe in the family buying process revealed that t in

different stages of buying and related sub-decisions. T-tests were again conducted to compare

across all the personal characteristics -decisions.

Children between the age group of 11-12 years had more influence in decisions about where

to buy, when to buy and how much to buy. The findings also highlighted that the children

Data Analysis

138 Ph. D. Thesis

with one or more siblings had more influence than those children who were the single child of

their parents. This may be because of the pacts and association forms among siblings to pester

very strongly. Family structure was another factor which showed significant differences in

mean values. Children in the joint family were more influential than those in the nuclear

family in the initiation and the final stage of buying and sub-decisions. Indian joint families

have strong influence over chi

child is able to influence more as compared to those children who are in nuclear family set-up.

gender,

regression analysis was done in order to find out the contribution of various influence

strategies in the 8% in initiation stage, 6% in search and

evaluation stage and 6.5% in the final stage was explained by the strategies viz., aggressive,

persuasive, rational, knowledge and emotional. Regression analysis was also done in order to

stages. With the dependent variable as the three buying process stages and independent

ite goods

step-wise regression was conducetd. 46% for loud goods, 48% for noisy goods and 19% for

quiet goods was explained by the influences at different buying stages.

loud, noisy and quiet goods. Same was being calculated through regression, where the buying

stages; initiation, search & evaluation and final buying stages were put as independent

dependent variable. Figure 4.36 shows graphically the relationship between product categories

and buying process stages.

Chapter 4

Ph. D. Thesis 139

Figure 4.36: Relationship of Buying process stages with Loud, Noisy and Quiet Goods

4.7 SECTION V: PROFILING BASED ON PRODUCT CATEGORIES

It would be very insightful for the practitioners to understand children profiling on the basis of

loud, noisy and quiet goods. With all the above analysis, the findings can be summarized as

three distinct profiles. Children who had highest influence on the loud goods have some

distinct characteristics; similarly children who had highest influence on the noisy and quiet

goods also have some specific characteristics. Figure 1.8 captures these children profiles for

these three product categories.

4.7.1 Loud goods

Figure 4.37 shows that for loud goods, children were most socialized from their parents.

These children were of higher age group between 11and 12 years. Most of the times they used

knowledge strategy to influence parents for their choice of products i.e. knowledge from

advertisements and brands. As far as the buying stage is concerned, children were most

influential in the final buying stage during the purchase of loud goods.

4.7.2 Noisy goods

Figure 4.37 also shows that for noisy goods, children were most socialized from shopping &

not parents as in the case of loud goods. Mostly, children used emotional strategy to influence

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140 Ph. D. Thesis

parents for their choice of products. As far as the buying stage is concerned during the

purchase of noisy goods, children were most influential in the initiation stage.

4.7.3 Quiet goods

Figure 4.37 also shows that for quiet goods, children were most socialized from parents,

friends & TV. Mostly emotional strategy was used to influence the parents. These are the

children who were most influential in the final buying stage during the purchase of quiet

goods.

Figure 4.37: Profiling based on product categories

4.8 SECTION VI: STRUCTURAL EQUATION MODELING

Consumer behavior is getting increasingly complex. In order to deal with the new market

environment, companies are no longer aim solely to maximize profits. Instead, they are

managing their relationships with their customers to generate benefits for both customer and

company. This chapter proposes an effective framework to carry out a structural analysis on

fluence in the family purchase for

various products. Structural equation modeling (SEM) is a statistical technique for testing and

estimating causal relations using a combination of statistical data and qualitative causal

assumptions [220] [221] [222]. The following questions were being addressed: Is

use of influence strategies impacted by his/her consumer socialization? How do

Chapter 4

Ph. D. Thesis 141

influence strategies impact his/her role in the various stages of family buying process for

different product categories?

To accomplish the last objective of the study, a structure equation model was employed to

in

the buying of three selected product categories. The linear model was tested and adjusted for

an adequate data-

influence in the family buying process is explained.

4.8.1 Confirmatory Factor Analysis

The analysis was carried out as follows: parameter estimation, testing for fit and model

reconfirmation. There were four latent variables in the model, with 10 estimated parameters

(estimated by maximum likelihood estimation). Confirmatory factor analysis (CFA) is a

special form of factor analysis, most commonly used in social research [223]. Both

exploratory as well as confirmatory factor analysis are engaged to understand shared variance

of variables. The overall fit of the model was assessed by chi-square (x2), goodness of fit

index (GFI), adjusted goodness of fit index (AGFI), Comparative Fit Index (CFI) and Root

Mean Square Error of Approximation (RMSEA). GFI values over 0.9 and AGFI values over

0.8 indicate good data-fitting [224]. Brown and Cudeck [225] suggest that an RMSEA of 0.05

or less is good, 0.05-0.08 is acceptable, and 0.10 or over is bad. Fornell and Larcker [226]

present a measure of composite reliability (CR), which measures the consistency of content

construct indicators. High CR indicates that potential variables are internally consistent; the

recommended value is 0.5 or greater.

4.8.1.1 Consumer Socialization

The exploratory factor analysis resulted in four socialization agents for children namely; FTV,

internet, parents and shopping. A second order CFA model was then constructed, reflecting

Table 4.80.

The value of chi-square is 47.646, p=0.221, GFI=0.954, AGFI= 0.926 and CFI = 0.953.

RMSEA = 0.031. The model's RMSEA is 0.031, which is acceptable.

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142 Ph. D. Thesis

In the adjusted model, the value of CR was 0.5 for parents, 0.6 for internet and 0.55 for

friends and TV. These three variables are internally consistent. Testing therefore suggests that

this three variable model is a good fit for the data.

Table 4.80: Goodness of fit indices for consumer socialization

P GFI AGFI CFI RMSEA CR

47.646 0.221 0.954 0.926 0.953 0.031 Friends and TV = 0.6 Parents = 0.5 Internet = 0.6

Figure 4.38: CFA of second order for consumer socialization

0.38

0.81

0.73

0.38

0.52

0.46

0.46

0.38

0.56

0.53

0.55

0.46

0.62

0.38

Chapter 4

Ph. D. Thesis 143

4.8.1.2 Pester Power

Children used varied influence strategies to pester their parents. Five such strategies were

identified and CFA was constructed on them. For model fitting adjustment one of the

strategies had to be dropped. The strategies were defined as: aggressive, persuasive, rational

and emotional (Figure 4.40). After adjustment chi square=126.884, p=0.000, GFI=0.909,

AGFI=0.865, CFI = 0.884 and RMSEA=0.063 (Table 4.80). Based on the composite

reliability indices were: aggressive; persuasive, 0.6; rational, 0.63 and knowledge, 0.5. These

values were all over 0.5, so the variables were internally consistent. This model was a good fit

for the data.

Table 4.81: Goodness of fit indices for pester power

2(df) P GFI AGFI CFI RMSEA CR

126.884 0.000 0.909 0.865 0.884 0.063

Aggressive = 0.6 Persuasive = 0.6 Rational = 0.63 Knowledge = 0.5

. Figure 4.39: CFA of second order for Pester power (influence strategies)

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144 Ph. D. Thesis

4.8.1.3 Product Categories

were: loud goods, noisy goods

and quiet goods. The cfa resulted in following statistics. After adjustment chi square=165.571,

p=0.000, GFI=0.895, AGFI=0.848, CFI = 0.898 and RMSEA=0.076 (Table 4.82). Based on

the composite reliability indices were: loud goods, 0.83; noisy goods, 0.73 and quiet goods,

0.7. These values were all over 0.5, so the variables were internally consistent. This model

was a good fit for the data.

Table 4.82: Goodness of fit indices for product categories

2(df) P GFI AGFI CFI RMSEA CR

165.571 0.000 0.895 0.848 0.898 0.076 Loud goods = 0.83 Noisy goods = 0.73 Quiet goods = 0.70

Figure 4.40: CFA of second order for product categories

0.8

0.7

0.3

0.7

0.6

0.5

0.3

0.5

0.50.6

0.60.5

0.6

0.50.7

0.7

0.70.5

Chapter 4

Ph. D. Thesis 145

4.8.2 SEM Analysis

Path analysis was then used to test for links between the latent variables as identified. The

structural and measurement model using a correlation matrix with the maximum-likelihood

were estimated simultaneously via AMOS 18. The measurement model assessed how the

latent variables (i.e. Consumer socialization, pester power, family buying stages and product

categories) wer -item reliability between items.

The structural model applied the causal relationships among these latent variables. The overall

fit of the model was assessed by chi-square (x2), goodness of fit index (GFI), adjusted

goodness of fit index (AGFI), Comparative Fit Index (CFI) and Root Mean Square Error of

Approximation (RMSEA).

4.8.2.1 Structural Model

A simultaneous estimation of structural and measurement models was performed using

AMOS 18. The proposed model tested causative relationships among the four latent variables.

In the structural model, there was one exogenous variable

and three endogenous variables pester power, buying stages and product categories. The

model consisted of four observed exogenous indicators for ch

four observed exogenous indicators for pester power, three observed exogenous indicators for

buying process stages and three observed exogenous indicators for consumer product

categories.

Using standardized path coefficients, the contribution of various factors on product categories

are found . The contribution of socialization on pester power is 0.42 (p < 0.05); pester power

on buying stages is 0.35 (p < 0.05), pester power on product categories is 0.18 (p < 0.05) and

buying stages on product categories is 0.83 (p < 0.05). The other important statictics as seen

in Table 4.83 are as follows. The value of chi-square is 1223.580, p=0.000, GFI=0.8, AGFI=

0.75, CFI = 0.85 and RMSEA = 0.052. The figure 4.42 and 4.43 shows in detail the total

impact on purchase of loud goods is 0.83, on noisy goods, 0.91 and on quiet goods 0.56. The

in the initiation stage is 0.92, on search &

evaluation stage is 0.80 and on final decision stage is 0.78. Table 4.83 presents the main

indices for SEM. Except GFI, all other indices x2, CFI and RMSEA are with in the

recommended range. But as Zimund [239] argued that values of GFI lower than 0.9, do not

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146 Ph. D. Thesis

necessarily mean that the model has a poor fit. It is also suggested that for data sets with a

large number of indicators (more than 24) and smaller sample sizes, it beomes necessary to

use more liberal cutoff values [240]. So here with 42 indicators, 11 constructs and sample size

of 175, a lower GFI value = 0.80 could be acceptable.

Table 4.83: Goodness of fit indices for SEM

2(df) P GFI AGFI CFI RMSEA

1223.580 0.000 0.8 0.75 0.85 0.052

Figure 4.41: Detailed path analysis of SEM for the study

Chapter 4

Ph. D. Thesis 147

Figure 4.42:

4.8.3 Section VI Conclusion

This study tes

categories. The empirical results suggest that there exist a significant relationship among

various constructs. The socialization of child did frame his/her use of different types of pester

strategies which in turn affects the role of child in the family buying of different products and

services. The model also validated the earlier part of the study which also individually

concludes that there is significant relationship between socialization and influence strategies,

buying stages and the type of products.

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