chapter 4 data analysis and interpretation 4.1...
TRANSCRIPT
CHAPTER 4
DATA ANALYSIS AND INTERPRETATION
4.1 INTRODUCTION
There are many research models that have been used in research work, but the
appropriateness of a model for the specific place and population has to be determined. Working
with the models, the investigator found that the Chi-Square Test proved to be more effective in
establishing the significance of demographic variables. Shanthi Nachiappan and Shanti N.,
(2007), Naresh Singh and Ashish Mitra (2007) and Tawari, C.C. and Davies, O.A. (2009) have
used the Chi-Square Test to determine whether there is a significant difference between the
expected frequencies and the observed frequencies in one or more categories. This test will also
single out the non significant variables and establish the variables that require for further studies.
In addition, coefficient of contingency was added-in the analysis to define the strength of the
relationship among variables. Further statistical tests on the independent variables have been
carried out using ANOVA, Cross Tabulation Analysis and K-Mean Cluster Analysis.
Laura G et al (2005), Robert A. Opoku (2006) and Lloyd W Fernald Jr (2003) have used
the Cross Tabulation Analysis to evaluate their data leading to a remarkable results. On the other
hand, Lavvanya Latha K and B.E.V.V.N. Murthy (2009) have used the ANOVA to analyse its.
Thomas A. Brunner and Michael Siegrist (2011); Henri Hakala and Marko K (2011), Luis J.
Callarisa Fiol et al (2011) and Hart O Awa, et al (2011) have used the K-Mean Cluster Analysis
in arriving at their result. We found that the statistical tools used by the earlier researchers have
given excellent results. As such, we have decided to adopt similar statistical tools in our research
to examine the entrepreneurial activities within the PURA Scheme clusters in Thanjavur District.
4.2 VARIABLES
Aspects of entrepreneurship development in PURA Scheme Villages are dependent on
the independent variables which were the deciding factors for villagers to chose
entrepreneurship. The independent variables were AGE, GENDER, MARITAL STATUS,
EDUCATION, EXPERIENCE, INCOME, SKILL BASED TRAINING and
ENTREPRENEURSHIP DEVELOPMENT PROGRAMME. As for the Independent variables
“Skill Based Training” and “Entrepreneurship Development Programme” number of hours and
number of times (Frequency) attended are taken respectively. The variables were selected based
on the findings of earlier researchers.
4.3 SAMPLING OF POPULATION
Sample size of nine hundred was planned for the research study but only six hundred
villagers responded to our request. Nevertheless, the sample from six clusters which comprises
of 600 respondents, aged between 18 and 65 who are either employed or unemployed, self -
employed or business owners who reside in the study area of Thanjavur District was selected.
Table 4-1 shows that employed (wage workers)1 and unemployed people are the dominant
category in the sample (74 %) followed by business owners (26 %)
1 Wage workers is not daily rated workers; their income depended on the availability of job as per day.
4.3.1 Breakdown of Sampling
Table 4-1
Breakdown of the Sampling
Categories Number (%)
Employed(wage workers) and unemployed
316 53
Self –employed
125
21
SSI (Business Owners)
159
26
Total 600 100
For the purpose of the study, the researcher has divided overall sample into two sub-samples.
This consists of all individuals of working age population of non-entrepreneurs and
entrepreneurs. As argued by Verheul et al.(2001,2006) several problems are associated with
measurement of entrepreneurship and in particular, due to difficulty in comparisons over
countries and time. Self-employed and business owners can be considered as equivalent to
entrepreneurs. Non-entrepreneurs’ group includes both employed individuals regardless of the
type of employment and others who are in the labour force but are not economically active. The
entrepreneurs’ group includes both individuals who declared themselves as self-employed and
those who declared that they own a private firm and employ others (i.e. Small Scale
Industries/Business Owners). The share of non-entrepreneurs and entrepreneurs taken for the
sampling according to regions has been illustrated in Table 4-2.
4.3.2 Breakdown of Non-Entrepreneurs and Entrepreneurs
Table 4-2
Breakdowns of Non-entrepreneurs and Entrepreneurs
Clusters Non-entrepreneurs
Entrepreneurs Total % in the sample
Vallam 52 42 94 15.66
Achampatti 23 32 55 9.16
Budhalur 99 86 185 30.83
Veeramarasanpatti 32 42 74 12.33
Rayamundanpatti 38 29 67 11.16
Palayapatti 72 53 125 20.83
Total 316 (52.7) 284(47.3) 600 100 Note: The figures in the parentheses are the percentage of the population
4.4 DATA ANALYSES
The Chi-Square Test was used in testing the selected variables to determine their
significance in this research. However to test the strength of the variables in relation, the
Coefficient of Contingency (COC) was used. In addition, F-Test and ANOVA were used in
examining the correlation of the tested independent variables to identify the factor that
encourages the villagers to be entrepreneurs. Cross Tabulation Technique has been used in
differentiating each clusters’ status on the PURA Scheme’s implementation of Knowledge
Connectivity, Economic Connectivity, Electronic Connectivity and Physical Connectivity.
4.4.1 CHI SQUARE TEST ON ENTREPRENUERSHIP AND AGE
H0 Age and Entrepreneurship Development in PURA Scheme Village are independent
H1 Age and Entrepreneurship Development in PURA Scheme Village are not
independent
Table 4-3 Relationship between Entrepreneurship and Age
Age Group
(Years) Category of Respondents Total
χ
2 value
Non-Entrepreneur
Entrepreneur
18 – 29
30 – 39
40 – 49
50 – 59
60 - 65
51
130
115
16
4
75
77
78
32
22
126(21)
207(34.5)
193(32.2)
48(8)
26(4.3)
41.44
Total 316 284 600 Notes: Figures in parentheses represent percentages
Analysis of Table 4-3 revealed that the relationship between entrepreneurship and age of
respondents in the Clusters was high. This implied that there is significant relationship between
entrepreneurship and age of respondents. The calculated value of χ2 (41.44) is more than the
table value of χ2
(9.488) at P ≤ 0.05 level, df = 4. The null hypothesis (H0) “Age and
Entrepreneurship Development in PURA Scheme Village are independent”, was rejected while
the alternative hypothesis (H1) “Age and Entrepreneurship Development in PURA Scheme
Village are not independent” was accepted. Since chi-square usually indicates statistical
significance but does not express the magnitude of relationship, the coefficient of contingency
was used in determining the strength of relationship.
The coefficient of calculated contingency was C = 0.25. It was found that age as a
parameter had significant role on the respondents’ perception of the entrepreneurial activities.
The evidence from the illustrations of Table 4-3 shows that highest percentage (34.5%) of
respondents came from the average age group of 30 to 39 years. However, the strength of
relationship is rather weak but it has influenced Entrepreneurship Development in the PURA
Scheme Village. According to the findings of Storey (1994), an individual’s age is an important
factor influencing the decision to start-up a business and normally the business owners will fall
between 25-45 years, which was close to the investigator’s findings.
4.4.2 CHI SQUARE TEST ON ENTREPRENUERSHIP AND GENDER
H0 Gender and Entrepreneurship Development in PURA Scheme Village are
independent
H1 Gender and Entrepreneurship Development in PURA Scheme Village are not
independent
Table 4-4
Relationship between entrepreneurship and Gender
Gender Group
Category of Respondents Total
χ2 value
Non-Entrepreneur
Entrepreneur
Male Female
162
154
110
174
272(45.3)
328(54.6)
9.481
Total
316
284
600
Notes: Figures in parentheses represent percentages Analysis of Table 4-4, revealed that the relationship between entrepreneurship and gender of
respondents in the Clusters was high. This implied that there is significant relationship between
entrepreneurship and the gender of respondents. The calculated value of χ2 (9.481) is more than
the table value of χ2
(3.841) at P ≤ 0.05 level, df = 1. The null hypothesis (H0) “Gender and
Entrepreneurship Development in PURA Scheme Village are independent”, thus, is rejected
while the alternative hypothesis (H1) “Gender and Entrepreneurship Development in PURA
Scheme Village are not independent” was accepted. The coefficient of calculated contingency
was C = 0.12. It was found that gender as a parameter had significant role in the respondents’
attitude towards entrepreneurial activities. This is evident from the illustrations of Table 4-4
where the highest percentage (54.6%) of respondents came from the Female Gender Group.
However, the strength of relationship is rather weak. Nevertheless gender does influence
entrepreneurship development. The findings coincide with the research work done by Sharmina
A.,(2010), Madhavi S., (2010), Shiralashetti A.s., (2010) that women were better gender in
engaging in entrepreneurship activities in the rural area.
4.4.3 CHI SQUARE TEST ON ENTREPRENUERSHIP AND MARITAL STATUS
H0 Marital Status and Entrepreneurship Development in PURA Scheme Village are
independent
H1Marital Status and Entrepreneurship Development in PURA Scheme Village are not
independent
Table 4-5
Relationship between entrepreneurship and Marital Status
Marital Status Category of Respondents Total
χ2 value
Non-Entrepreneur
Entrepreneur
Single Married Widow
112
185
19
127
134
23
239(39.8)
319(53.1)
42(7)
7.791
Total 316 284 600 Notes: Figures in parentheses represent percentages
Table 4-5, revealed that the relationship between the entrepreneurship and marital status of
respondents in the Cluster was high. The calculated value of χ2 (7.791) is more than the table
value of χ2
(5.991) at P ≤ 0.05 level, df = 2. The null hypothesis (H0) “Marital Status and
Entrepreneurship Development in PURA Scheme Village are independent”, thus, was rejected
while the alternative hypothesis(H1) “Marital Status and Entrepreneurship Development in
PURA Scheme Village are not independent” was accepted. The coefficient of calculated
contingency was C = 0.11. It was found that Marital Status as a parameter had significant role on
the respondents’ attitude towards the entrepreneurial activities. This is evident from the
illustrations of Table 4-5 where the highest percentage (53.1%) of respondents came from the
married group. However, the strength of relationship is rather weak; nevertheless marital status
does influence entrepreneurship development.
4.4.4 CHI SQUARETEST ON ENTREPRENUERSHIP AND EDUCATION
H0 Education and Entrepreneurship Development in PURA Scheme Village are
independent
H1 Education and Entrepreneurship Development in PURA Scheme Village are not
independent
Table 4-6
Relationship between entrepreneurship with respect to Education
Education
Category of Respondents Total
χ2 value Non-
Entrepreneur Entrepreneur
Less than SSLC SSLC HSC UG PG
22
104 105 55 30
48
67 60 77 32
68(11.3)
171(28.5) 165(27.5) 132(22) 64(10.6)
32.05
Total 316 284 600 Notes: Figures in parentheses represent percentages
A quick look at Table 4-6, revealed that the relationship between entrepreneurship and
education of respondents in the Cluster was high. The calculated value of χ2 (32.05) is more than
the table value of χ2
(9.488) at P ≤ 0.05 level, df = 4. The null hypothesis (H0) “Education and
Entrepreneurship Development in PURA Scheme Village are independent” , thus, was rejected
while the alternative hypothesis (H1 ) “Education and Entrepreneurship Development in PURA
Scheme Village are not independent” was accepted. The coefficient of calculated contingency
was C = 0.27. It was found that Education status as a parameter had significant role on the
respondents’ attitude towards the entrepreneurial activities. This is evident from the illustrations
of Table 4-6 where the highest percentage 67.6 per cent of respondents came from the SSLC
group 28.5 per cent and UG/PG group percentage of 32.3 per cent. Even though, the strength of
relationship is rather weak, Education does influence entrepreneurship development.
4.4.5 CHI-TEST ON ENTREPRENUERSHIP AND EXPERIENCE
H0 Experiences and Entrepreneurship Development in PURA Scheme Village are
independent
H1 Experiences and Entrepreneurship Development in PURA Scheme Village are not
independent
Table 4-7
Relationship between entrepreneurship and experience
Experience
(Years) Category of Respondents Total
χ2 value
Non-Entrepreneur
Entrepreneur
Less than 42 months More than 42 months
112
204
120
164
232(38.6)
368(61.3)
2.925
316 284 600 Notes: Figures in parentheses represent percentages
A glance at Table 4-7, revealed that the relationship between entrepreneurship and
experience of respondents in the Cluster was low. The calculated value of χ2 (2.925) is less than
the table value of χ2
(3.841) at P ≤ 0.05 level, df = 1. The null hypothesis (H0) “Experiences
and Entrepreneurship Development in PURA Scheme Village are independent”, thus, is accepted
while the alternative hypothesis (H1 ) “Experiences and Entrepreneurship Development in PURA
Scheme Village are not independent” was rejected. The coefficient of calculated contingency
was C = 0.06. It was found that Experience status as a parameter had no significant role on the
respondents’ attitude towards the entrepreneurial activities. The strength of relationship is rather
weak but experience does influence entrepreneurship development. According to Truls Erikson
(2003) experience is not required for entrepreneurship as it is based on the individual attitude
towards business venture. Boyd and Vozikies (1994) mentioned that people with strong beliefs
about their capabilities will be more persistent in their efforts in entrepreneurial activities.
Likewise, villagers in PURA Villages have strong determination and have taken up
entrepreneurship without any formal education or training.
4.4.6 CHI-SQUARED TEST ON ENTREPRENUERSHIP AND INCOME
H0 Income and Entrepreneurship Development in PURA Scheme Village are independent
H1 Income and Entrepreneurship Development in PURA Scheme Village and not
independent
Table 4-8
Relationship between entrepreneurship and Income
Income
( ) Category of Respondents Total
χ
2 value
Non-Entrepreneur
Entrepreneur
less than 2500 2500 to 5000 5000 to 10000 more than 10000
108 90
108 10
23 46
132 83
131(21.8) 136(22.6) 240(40) 93(15.5)
127.7
Total 316 284 600 Notes: Figures in parentheses represent percentages
Table 4-8, revealed that the relationship between entrepreneurship and Income of respondents in
the Cluster was high. The calculated value of χ2 (127.7) is more than the table value of χ
2
(7.815) at P ≤ 0.05 level, df = 3. The null hypothesis (H0) “Income and Entrepreneurship
Development in PURA Scheme Village are independent”, thus is rejected while the alternative
hypothesis (H1 ) “Income and Entrepreneurship Development in PURA Scheme Village are not
independent” was accepted. The coefficient of calculated contingency was C = 0.5. It was found
that Income as a parameter had significant role on the respondents’ in the entrepreneurial
activities. This is evident from the illustrations of Table 4-8 where the highest percentage (40%)
of respondents came from the Income group of `5000 to `10000.
4.4.7 CHI-SQUARE TEST ON ENTREPRENUERSHIP AND SBT(SKILL BASED TRANING) H0 Skill Based Training and Entrepreneurship Development in PURA Scheme Villages
are independent
H1 Skill Based Training and Entrepreneurship Development in PURA Scheme Villagse
are not independent
Table 4-9
Relationship between entrepreneurship and SBT
Attended the
Skill Based Training
Category of Respondents Total
χ2 value Non-
Entrepreneur Entrepreneur
Attended Not Attended
112
204
206
78
318(53)
282(47)
82.61
Total
316
284
600
Notes: Figures in parentheses represent percentages
Table 4-9, revealed that the relationship between the entrepreneurship and Skill Based Training
of respondents in the Cluster was high. The calculated value of χ2 (82.61) is more than the table
value of χ2
(3.841) at P ≤ 0.05 level, df = 1. The null hypothesis (H0) “Skill Based Training and
Entrepreneurship Development in PURA Scheme Village are independent”, thus, was rejected
while the alternative hypothesis (H1 ) “Skill Based Training and Entrepreneurship Development
in PURA Scheme Village are not independent” was accepted. The coefficient of calculated
contingency was C = 0.35. It was found that Skill Based Training status as a parameter had
significant role on the respondents’ approach towards the entrepreneurial activities. This is
evident from the illustrations of Table 4-9 where the highest percentage (53%) of respondents
came from the group who have attended the training. Thus, skill based training does influence
entrepreneurship development. Majority of the young villagers have taken up skill-based training
to have a trade in hand for future needs.
4.4.8 CHI-SQUARE TEST ON ENTREPRENUERSHIP AND EDP
(ENTREPRENEURSHIP DEVELOPMENT PROGRAMME)
H0 Entrepreneurship Development Programme and Entrepreneurship Development in
PURA Scheme Village are independent
H1 Entrepreneurship Development Programme and Entrepreneurship Development in
PURA Scheme Village are not independent
Table 4-10
Relationship between entrepreneurship and EDP
Attended the Entrepreneurship
Development Programme
Category of Respondents Total
χ2 value Non-
Entrepreneur Entrepreneur
Attended Not Attended
178 138
258 26
436(72.6) 164(27.3)
89.72
Total
316
284
600
Notes: Figures in parentheses represent percentages
Table 4-10, revealed that the relationship between the entrepreneurship and EDP of respondents
in the Cluster was high. The calculated value of χ2 (89.72) is more than the table value of χ
2
(3.841) at P ≤ 0.05 level, df = 1. The null hypothesis (H0) “Entrepreneurship Development
Programme and Entrepreneurship Development in PURA Scheme Village is independent” , thus,
was rejected while the alternative hypothesis (H1 ) “Entrepreneurship Development Programme
and Entrepreneurship Development in PURA Scheme Village is not independent” was accepted.
The coefficient of calculated contingency was C = 0.5. It was found that Entrepreneurship
Development Programme status as a parameter had significant role on the respondents’ advance
towards the entrepreneurial activities. This is evident from the illustrations of Table 4-10 where
the highest percentage (72.6%) of respondents came from the group that attended the
Entrepreneurship Development Programme. The strength of relationship is rather strong. As such
Entrepreneurship Development Programme does influence the entrepreneurship development in
the clusters.
4.4.9 CONSOLIDATION OF CHI-SQUARE TEST RESULTS
Table 4-11
Consolidation of Hypothesis Test results on the variables.
Variables Calculated Value of χ2
Table Value of χ2
Coefficient of contingency
Hypothesis test Result
AGE 41.44 9.488 0.25 H1 accepted
GENDER 9.481 3.841 0.12 H1 accepted
MARITAL STATUS 7.791 5.991 0.11 H1 accepted
EDUCATION 32.05 9.488 0.22 H1 accepted
EXPERIENCE 2.925 3.841 0.06 H0 accepted
SBT 82.61 3.841 0.35 H1 accepted
EDP 89.72 3.841 0.36 H1 accepted
INCOME 127.7 7.815 0.50 H1accepted
The consolidated result shown in Table 4-11, illustrates that independent variable “INCOME”
has the highest value of coefficient of contingency among all other independent variables. This
result revealed that INCOME has been the influencing factor for the villagers to become
entrepreneurs. Nevertheless, this has urged the researcher to investigate further on the
correlations of INCOME with other independent variables to know the significance of INCOME
against other independent variables namely experience, Skill Based Training and
Entrepreneurship Development Programme. So, in order to ascertain the relationship of
INCOME and the stated Independent variables the ANOVA was adopted..(Ref. Table 4-12)
4.4.10 MULTIPLE REGRESSION ANALYSIS ON INCOME, EXPERIENCE, SBT and EDP Using the SPSS, the multiple regression was calculated. It is shown in Table 4-12 this reveals
that 80 per cent of variance is explained by INCOME [r2=0.804].
Furthermore, the result also reveals that independent variable ENTREPRENEURISHIP
DEVELOPMENT PROGRAMME (EDP) has the highest coefficient value of β=0.663 followed
by EXPERIENCE with the second highest coefficient value of β=0.181 and SKILL BASED
TRAINING (SBT) that has the least coefficient value β=0.161. As such the results show that the
ENTREPRENEURISHIP DEVELOPMENT PROGRAMME (EDP) has elevated the INCOME
level of the villagers.
Table 4-12
Variables Entered/Removedb
Model Variables Entered
Variables
Removed Method
1 EDP, Experience,
SBTa
. Enter
a. All requested variables entered.
b. Dependent Variable: Income
Model Summary
Model R R Square
Adjusted R
Square
Std. Error of the
Estimate
1 .897a .804 .803 1170.45906
a. Predictors: (Constant), EDP, Experience, SBT
ANOVAb
Model Sum of Squares df Mean Square F Sig.
1 Regression 3.357E9 3 1.119E9 816.798 .000a
Residual 8.165E8 596 1369974.407
Total 4.173E9 599
a. Predictors: (Constant), EDP, Experience, SBT
b. Dependent Variable: Income
Coefficientsa
Model
Unstandardized Coefficients Standardized Coefficients
t Sig. B Std. Error Beta
1 (Constant) -653.363 131.593
-4.965 .000
Experience 6.140 .740 .181 8.299 .000
SBT 72.813 12.311 .161 5.915 .000
EDP 1739.772 70.783 .663 24.579 .000
. Dependent Variable: Income
4.11 CROSSTABULATION ANALYSIS ON KNOWLEDGE CONNECTIVITY
4.4.11.1 Cross Tabulation of Clusters on Entrepreneurship Development
Programme (EDP)
Table 4-13
CLUSTERS K1- Entrepreneurship Development Programme (EDP) has improved my business
Strongly Disagree
Disagree No Opinion
Agree Strongly Agree Total
Vallam Count % within cluster
2 2.1%
26 27.7%
5 5.3%
60 63.8%
1 1.1%
94 100
%
Achampatti Count
% within Cluster
6
10.9%
19
34.5%
2
3.6%
21
38.2%
7
12.7%
55
100%
Budhalur Count
% within Cluster
10
5.5%
60
32.4%
53
26.6%
53
28.6%
9
4.9%
185
100%
Veeramarasanpatti Count
% within Cluster
10
13.5%
18
24.3%
24
32.4%
16
21.6%
6
8.1%
74
100%
Rayamundanpatti Count
% within Cluster
9
13.4%
13
19.4%
21
31.3%
17
25.4%
7
10.4%
67
100%
Palayapatti Count
% within Cluster
0
.0%
7
5.6%
48
38.4%
56
44.8%
14
11.2%
125
100%
Total Count
% within Cluster
37
6.2%
143
23.8%
153
25.5%
223
37.2%
44
7.3%
600
100%
The analysis of cross tabulation on variable (K1) revealed that highest count of 223 out
of 600 (about 37.2 per cent) villagers agree that EDP has assisted them in business
management and 7.3 per cent of villagers were recorded as strongly agreeing that EDP
has improved their business. Among the clusters, Vallam was listed as having the
highest response on this independent variable. About 63.8 percent of the villagers in
Vallam had indicated that they agree on Entrepreneurship Development Programme’s
effectiveness and 1 % of the villagers in Vallam indicated that they strongly agreed that
Entrepreneurship Development Programme had enhanced their business. Comparing
the results, Palayapatti villagers were better off than Vallam as 11.2 percent of villagers
have indicated that that they strongly agree to the EDP which has the impact in their
business as an entrepreneurs. In relation to the findings, 22.9 per cent of villagers
shown their disagreement but 77.1 per cent of the villagers shown agreement on EDP.
have indicated that they strongly agree that EDP had an impact in their business as
entrepreneurs. In relation to the findings, 22.9 per cent of villagers have shown their
disagreement but 77.1 per cent of the villagers have shown agreement on the effectiveness of
EDP.
4.4.11.2 Cross Tabulation of Clusters on Skill Based Training (SBT)
Table 4-14
CLUSTERS K2- Skill Based Training (SBT) has improved my technical skills
Strongly Disagree
Disagree No Opinion
Agree Strongly Agree
Total
Vallam Count % within Cluster
2 2.1%
31 33.0%
16 17.0%
38 40.4%
7 7.4%
94 100 %
Achampatti Count
% within Cluster
1
1.8%
22
40.0%
8
3.6%
24
43.6%
0
.0%
55
100%
Budhalur Count
% within Cluster
5
2.7%
67
36.2%
34
18.4%
71
38.4%
8
4.3%
185
100%
Veeramarasanpat
ti
Count
% within cluster
0
.0%
23
31.1%
19
25.7%
31
41.9%
1
1.4%
74
100%
Rayamundanpatti Count
% within Cluster
0
.0%
14
20.9%
30
44.8%
16
23.9%
7
10.4%
67
100%
Palayapatti Count
% within Cluster
0
.0%
7
13.6%
38
30.4%
38
30.4%
32
25.6%
125
100%
Total Count
% within Cluster
8
1.3%
174
29.0%
145
24.2%
218
36.3%
55
9.2%
600
100%
The analysis of cross tabulation on variable (K2) revealed that the highest count of 218 out of
600 (about 36.3 percent) of the villagers agree and 9.2 per cent of the villagers strongly agree
that skill based training has improved their technical skills. Among the clusters, 41.9 per cent of
villagers in Achampatti indicated that they agree that the Skill Based Training improved their
technical skills. 25.6 per cent of villagers in Palayapatti strongly agree that Skill Based Training
has improved their technical skills. At a glance, 70 per cent of the population have agreed that
Skill Based Training has enhanced their technical skills and also provided an opportunity to set-
up their entrepreneurial activities. With reference to the above, about 30.3 per cent of villagers
have shown their disagreement but 69.7 per cent of the villagers have shown their agreement that
the Skill Based Training has enhanced the villagers’ technical skills and also provided an
opportunity to them to set-up their own business.
4.4.11.3 Cross Tabulation of Cluster on Rural Marketing and Management
Table 4-15
CLUSTERS K3- Rural Marketing has assisted in managing the product
Strongly Disagree
Disagree No Opinion
Agree Strongly Agree
Total
Vallam Count % within Cluster
6 6.4%
29 30.9%
23 24.5%
26 27.7%
10 10.6%
94 100 %
Achampatti Count
% within Cluster
2
3.6%
17
30.9%
15
27.3%
21
38.2%
0
.0%
55
100%
Budhalur Count
% within Cluster
1
.5%
41
22.2%
52
28.1%
65
35.1%
26
14.1%
185
100%
Veeramarasanpat
ti
Count % within
Cluster
0
.0%
15
20.3%
20
27.0%
18
24.3%
21
28.4%
74
100%
Rayamundanpatt
i
Count
% within Cluster
0
.0%
23
34.3%
29
43.3%
15
22.4%
0
.0%
67
100%
Palayapatti Count
% within Cluster
1
.0%
41
32.8%
49
39.2%
34
27.2%
0
.0%
125
100%
Total Count
% within Cluster
10
1.7%
166
27.7%
188
31.3%
179
29.8%
57
9.5%
600
100%
The analysis of cross tabulation on variable (K3) revealed that the highest count of 188 out of
600 (about 31.3 percent) villagers have no opinion with regard to rural marketing. Among the
clusters, 43.3 per cent of villagers in Rayamundanpatti indicated that they have no opinion with
regard to rural marketing. However, 38.2 per cent of villagers in Achampatti and 35.1 per cent of
villagers in Budhalur have indicated that they agree that rural marketing has assisted them in
managing their rural products. Likewise, 28.4 per cent of villagers in Veeramarasanpatti
indicated that they strongly agree that rural marketing has assisted them in managing their rural
products. As regards to the findings, 29.4 per cent of villagers have shown their disagreement but
70.6 per cent have indicated their agreement that Rural Marketing has assisted them in engaging
in rural business.
4.4.11.4 Cross Tabulation of Clusters on Government Policy and Schemes
Table 4-16
CLUSTERS K4- Government Policy and Schemes has assisted in the business
Strongly Disagree
Disagree No Opinion
Agree Strongly Agree
Total
Vallam Count % within Cluster
9 9.6%
26 27.7%
36 38.3%
22 23.4%
1 1.1%
94 100 %
Achampatti Count
% within Cluster
2
3.6%
16
29.1%
8
14.5%
23
41.8%
6
10.9%
55
100%
Budhalur Count
% within Cluster
10
5.4%
49
26.5%
72
38.9%
41
22.2%
13
7.0%
185
100%
Veeramarasanpat
ti
Count
% within Cluster
3
4.1%
27
36.5%
13
17.6%
19
25.7%
12
16.2%
74
100%
Rayamundanpatti Count
% within Cluster
3
4.5%
18
26.9%
20
29.9%
21
31.3%
5
7.5%
67
100%
Palayapatti Count
% within Cluster
5
4.0%
32
25.6%
22
17.6%
59
47.2%
7
5.6%
125
100%
Total Count
% within Cluster
32
1.7%
168
27.7%
171
31.3%
185
29.8%
44
9.5%
600
100%
The analysis of cross tabulation on variable (K4) revealed that highest count of 185 out of 600
(about 29.8 per cent) of villagers indicated they agree that Government Policies and Schemes
assisted in their business; about 9.5 per cent of the villagers strongly agree that the Government
Policies and Scheme has assisted them in their business. Among the clusters, Palayapatti was
listed as having the highest response on this independent variable. 47.2 percent of the villagers in
the Palayapatti have indicated that they agree to the Government Policies and Schemes that
assisted in their business, and 16.2 % of the villagers in Veeramarasanpatti indicated that they
strongly agree that Government Policies and Schemes have assist in their business. On the
whole, 29.4 per cent of villagers have shown their disagreement but 70.6 per cent of the villagers
have indicated their goodwill towards Government Policies and Schemes.
4.4.11.5 SUMMARY OF CROSS TABULATION ANALYSIS ON KNOWLEDGE CONNECTIVITY Table 4-17
The response from the villagers on the effects of knowledge connectivity for all six
clusters was relatively high. Entrepreneurship Development Programme has scored the highest,
223 out of 600 villagers have agreed to the outcome of the Programme; 218 out of 600 villagers
have agreed on the Skill Based Training, 57 out of 600 villagers have strongly agreed on Rural
Marketing and Management. However, 188 out of 600 villagers have no opinion on Rural
Marketing and Management and 37 out of 600 villagers strongly disagreed on the impact of
Government Policies and Schemes.
Nevertheless, most of them have agreed that the Entrepreneurship Development
Programmes, Skill Based Training, Rural Marketing and Management and Government Policies
37
143 153
223
44
8
174
145
218
55
10
166
188 179
57
32
168 171
185
44
0
50
100
150
200
250
Strongly Disagree
Disagree No opinion Agree Strongly Agree
Entrepreneurship Development Programme
Skill Based Training
Rural Marketting and Management
Government Policies and Scheme
and Schemes have enhanced their entrepreneurial activities. Thus, the result revealed that PURA
Scheme has improved the villagers’ mindset through knowledge connectivity.
4.4.11.6 K-Mean Cluster Analysis on Knowledge Connectivity.
We wanted to know the decision of the entire sample of the villagers chosen for analysis.
We have divided the population into two groups so as to determine the popularity of the
knowledge connectivity based on the scores. Using the K-Mean Cluster Analysis, the number of
cases was determined based on the mean value.
Table 4-18
No of Clusters (Mean Value)
No. of Cases Total
1 2 1 2
Entrepreneurship Development Programme
(EDP) has improved my
business
3.42 2.59
412 188 600
Skill Based Training (SBT)
has improved my technical skill
3.63 2.35
Rural Market has assisted in
managing the product
3.56 2.35
Government Policies and schemes has
assisted in the business
3.47 2.31
The result revealed that 412 out of 600 villagers were found with high mean value that was
consolidated from their scores and 188 villagers were with low mean value. This indicates that
68.7 per cent of the population agree to the contribution of knowledge connectivity to the
PURA Scheme Villages. The analysis returned K1- “Entrepreneurship Development Programme
has improved my business” with mean value of X = 3.42, K2-Skill Based Training has improved
my technical skill” with mean value of X = 3.63, K3 – “Rural Marketing has assisted in
managing the product” with mean value of X = 3.56 and K4 – “ Government Policy and Schemes
has assisted in the business” with mean value of X = 3.47. Among the scores, K2- “Skill Based
Training has improved my technical skill” has scored the highest mean value. This indicates that
villagers were interested in learning new skills that will be used in operating a business on their
own.
4.4.12 CROSSTABULATION ANALYSIS ON ECONOMIC CONNECTIVITY
4.4.12.1 Cross Tabulation of Clusters on Cost Effectiveness for Agriculture Products
Table 4-19
CLUSTERS E5- Cost Effectiveness for Agriculture Products assist in product pricing
Strongly Disagree
Disagree No Opinion
Agree Strongly Agree
Total
Vallam Count % within Cluster
2 2.1%
14 14.9%
26 27.7%
50 53.2%
2 2.1%
94 100 %
Achampatti Count
% within Cluster
0
.0%
13
23.6%
13
23.6%
25
45.5%
4
7.3%
55
100%
Budhalur Count
% within Cluster
1
.5%
36
19.5%
62
33.5%
64
34.6%
22
11.9%
185
100%
Veeramarasanpatt
i
Count
% within Cluster
1
1.4%
13
17.6%
33
44.6%
20
27.0%
7
9.5%
74
100%
Rayamundanpatti Count
% within Cluster
0
.0%
22
32.8%
21
31.3%
24
35.8%
0
.0%
67
100%
Palayapatti Count
% within Cluster
0
.0%
35
28.0%
43
34.4%
43
34.4%
4
3.2%
125
100%
Total Count
% within Cluster
4
.7%
168
22.2%
171
33.0%
185
37.7%
44
6.5%
600
100%
The analysis of cross tabulation on variable (E5) revealed that the highest count (185 out of 600,
about 37.7 per cent of villagers) agree that Cost Effectiveness for Agricultural Products has
assisted them in planning their products. About 6.5 per cent of villagers reflected that they
strongly agree that the Cost Effectiveness of Agricultural Products does help them in the product
pricing. Among the clusters, Achampatti returned as having the highest response on this
independent variable. About 45.5 per cent of the villagers in Achampatti agree on the Cost
Effectiveness of Agricultural Products, and 11.9 % of the villagers in Budhalur indicated that
they strongly agree on Cost Effectiveness of Agricultural Products. On the whole, 22.9 per cent
of villagers in total have shown their disagreement but 77.1 per cent of the villagers shown their
acceptance of Cost Effectiveness of Agricultural Product pricing as the most important factor for
marketing.
4.4.12.2 Cross Tabulation of Clusters on Demand & Supply Management
Table 4-20
CLUSTERS E6- Demand & Supply Management assist in business transactions
Strongly Disagree
Disagree No Opinion
Agree Strongly Agree
Total
Vallam Count % within Cluster
1 1.1%
20 21.3%
18 19.1%
41 43.6%
14 14.9%
94 100 %
Achampatti Count
% within Cluster
0
.0%
14
25.5%
11
20.0%
30
54.5%
0
.0%
55
100%
Budhalur Count
% within Cluster
2
1.1%
44
23.8%
54
29.2%
68
36.8%
17
9.2%
185
100%
Veeramarasanpat
ti
Count
% within Cluster
2
2.7%
20
27.0%
22
29.7%
27
36.5%
3
4.1%
74
100%
Rayamundanpatti Count
% within Cluster
0
.0%
27
40.3%
32
47.8%
6
9.0%
2
3.0%
67
100%
Palayapatti Count
% within Cluster
1
.8%
35
28.0%
41
34.4%
43
34.4%
5
3.2%
125
100%
Total Count
% within Cluster
6
1.0%
160
26.7%
178
29.7%
215
35.8%
41
6.8%
600
100%
The analysis of cross tabulation on variable (E6) revealed that the highest count of 215 out of
600 (about 35.8 per cent) villagers agree that the demand and supply management supported
their business transaction and 6.8 per cent of the villagers strongly agree on the subject. Among
the clusters, Achampatti was listed as the one with the highest response on this independent
variable. About 54.4 per cent of the villagers in Achampatti have indicated that they agree that
the Demand & Supply Management helped them in the business transactions, and 9.2 % of the
villagers in Budhalur indicated that they strongly agree on the concept of Demand & Supply
Management. On the subject as a whole, 27.7 per cent of villagers have shown their
disagreement but 72.3 per cent of the villagers have accepted that Demand and Supply
Management helped them in the business operations.
4.4.12.3 Cross Tabulation of Clusters on Harvest and Storage Management
Table 4-21
CLUSTERS E7- Harvest and storage assist in crops management Strongly Disagree
Disagree No Opinion
Agree Strongly Agree
Total
Vallam Count % within Cluster
1 1.1%
22 23.4%
11 11.7%
46 48.9%
14 14.9%
94 100 %
Achampatti Count
% within Cluster
0
.0%
22
40.0%
11
20.0%
15
27.3%
7
12.7%
55
100%
Budhalur Count
% within Cluster
0
.0%
71
38.4%
45
24.3%
47
25.4%
22
11.9%
185
100%
Veeramarasanpat
ti
Count
% within Cluster
0
.0%
27
36.5%
24
32.4%
15
20.3%
8
10.8%
74
100%
Rayamundanpatti Count
% within Cluster
0
.0%
25
37.3%
26
38.8%
12
17.9%
4
6.0%
67
100%
Palayapatti Count
% within Cluster
0
.0%
44
35.2%
45
36.0%
25
20.0%
11
8.8%
125
100%
Total Count
% within Cluster
1
1.0%
211
26.7%
162
29.7%
160
35.8%
66
6.8%
600
100%
The analysis of cross tabulation on variable (E7) revealed that the highest count of 160 out of
600 ( about 35.8 per cent) villagers have agreed that management of harvest and storage of crop
assisted them in projecting the demand and supply, and 6.8 per cent of the villagers strongly
agree on the subject. Among the clusters, Vallam was listed as the one with the highest response
on this independent variable. About 48.9 per cent of the villagers in Vallam have indicated that
they agree on the importance of Harvest and Storage of crops, and 14.9 per cent of the villagers
in Vallam indicated that they strongly agree on the concept of Harvest and Storage of crop
management. On whole, 27.7 per cent of villagers in total have shown their disagreement and
72.3 per cent of the villagers in total have shown their agreement towards management of
Harvest and Storage of Crops.
4.4.12.4 Cross Tabulation of Clusters on Manpower Utilisation
Table 4-22
CLUSTERS E8- Manpower Utilisation assist in productivity Strongly Disagree
Disagree No Opinion
Agree Strongly Agree
Total
Vallam Count % within Cluster
7 7.4%
21 22.3%
20 21.3%
31 33.0%
15 16.0%
94 100 %
Achampatti Count
% within Cluster
1
1.8%
17
30.9%
12
21.8%
24
43.6%
1
1.8%
55
100%
Budhalur Count
% within Cluster
25
13.5%
51
27.6%
48
25.9%
55
29.7%
6
3.2%
185
100%
Veeramarasanpat
ti
Count
% within Cluster
1
1.4%
22
29.7%
30
40.5%
21
28.4%
0
.0%
74
100%
Rayamundanpatti Count
% within Cluster
0
1.5%
24
35.8%
24
35.8%
15
22.4%
3
4.5%
67
100%
Palayapatti Count
% within Cluster
2
1.6%
39
31.2%
36
28.8%
48
38.4%
0
.0%
125
100%
Total Count
% within Cluster
37
6.2%
174
29.0%
170
28.3%
194
32.3%
25
4.2%
600
100%
The analysis of cross tabulation on variable (E8) revealed that the highest count of 194 out of
600 (about 32.3 per cent) villagers have agreed that Manpower Utilisation promotes productivity
and 4.2 per cent of the villagers responded that they strongly agree on the role of Manpower
Utilisation. Among the clusters, Achampatti was listed as the one with the highest response on
this independent variable. About 43.6 per cent of the villagers in Achampatti have indicated that
they agree on the role of Manpower Utilisation, and 16.0 per cent of the villagers in Vallam
indicated that they strongly agree on Manpower Utilisation. On the whole, 35.2 per cent of
villagers in total have shown their disagreement and 64.8 per cent of the villagers in total have
shown their agreement towards the role of Manpower Utilisation.
4.4.12.5 SUMMARY OF CROSS TABULATION ANALYSIS ON ECONOMIC CONNECTIVITY
Table 4-23
Based on the response from the villagers it is found that the effects of Economic connectivity for
all six clusters were relatively high. As for the highest score , 226 out of 600 villagers have
agreed that the Cost effectiveness has helped them in managing the product pricing, 66 out of
600 villagers have indicated that they strongly agreed on the role of Cost Effectiveness for
Agricultural Products. However, 198 out of 600 villagers have no opinion on Cost Effectiveness
and 37 out of 600 strongly disagree on the role of Manpower Utilisation. Nevertheless, most of
4
133
198
226
39
6
160 178
215
41
1
211
162 160
66
37
174 170
194
25
0
50
100
150
200
250
Strongly Disagree
Disagree No Opinion Agree Strongly Agree
Cost Effectiveness for Agricultural Products
Demand & Supply Management
Harvest & Storage Management
Manpower Utilisation
them have indicated that they know the role of Cost Effectiveness for Agricultural Products,
Demand and supply Management, Harvest & Storage Management and Manpower Utilisation.
At a glance, the bar chart reveals the response of the villagers towards Economic Connectivity.
Response towards economics connectivity was relatively high except for the Harvest and
Storage.
4.4.12.6 K-Mean Cluster Analysis on Economic Connectivity.
The entire sample was divided into two groups based on the mean value score from the
response to determine the popularity of the Economic connectivity. Using the K-Mean Cluster
Analysis, the mean value of each group was determined.
Table 4-24
No of Clusters (Mean Value)
No. of Cases Total
1 2 1 2
Cost effective for
agricultural product assisted product pricing
3.15 3.44
347 253 600
Demand & supply
management assisted in business
transactions
2.78 3.79
Harvest and storage
assisted in crops
management
2.44 4.09
Manpower Utilisation assisted in
productivity
2.71 3.38
The result revealed that 253 out of 600 villagers were found with high mean value that
was consolidated from their scores and 347 villagers were with low mean value. This indicates
that 42.2 per cent of the population agreed on Economic connectivity’s contribution to the
PURA Scheme Villages. K5- “Cost Effectiveness for Agriculture Product assisted in product
pricing” had a mean value of X = 3.15 with K6- “Demand and Supply Management assisted in
business transaction” with mean value of X = 3.79, K7 – “Harvest and Storage assisted in crops
management” with mean value of X = 4.09 and K8 – “ Manpower Utilisation assisted in
productivity” with mean value of X = 3.38. Among the scores, K7- “Harvest and Storage
assisted in crops management” has scored the highest mean value. This indicates that villagers
were interested in learning the techniques that will be used in harvesting and storage of their
products.
4.4.13 CROSSTABULATION ANALYSIS ON ELECTRONIC CONNECTIVITY
4.4.13.1 Cross Tabulation of Clusters on Wi-max Services
Table 4-25
CLUSTERS E9- Wi-Max Services assisted in training Strongly Disagree
Disagree No Opinion
Agree Strongly Agree
Total
Vallam Count % within Cluster
11 11.7%
42 44.7%
30 31.9%
11 11.7%
0 .0%
94 100 %
Achampatti Count
% within Cluster
13
23.6%
8
14.5%
19
34.5%
12
21.8%
3
5.5%
55
100%
Budhalur Count
% within Cluster
4
13.5%
29
27.6%
84
25.9%
64
29.7%
4
3.2%
185
100%
Veeramarasanpat
ti
Count
% within Cluster
0
.0%
10
13.5%
36
48.6%
28
37.8%
0
.0%
74
100%
Rayamundanpatti Count
% within Cluster
4
6.0%
10
14.9%
35
52.2%
18
26.9%
0
.0%
67
100%
Palayapatti Count
% within Cluster
3
2.4%
20
16.0%
64
51.2%
37
29.6%
1
.8%
125
100%
Total Count
% within Cluster
35
5.8%
119
19.8%
288
44.7%
170
28.3%
8
1.3%
600
100%
The analysis of cross tabulation on variable (E9) revealed that highest count 288 out of 600
(about 44.7 per cent) was recorded as villagers having no opinion on the Wi-Max services.
Among the clusters, Veeramarasanpatti with 37.8 per cent of the villagers agreed that Wi-Max
has assisted them in training on farming. About 5.5 per cent of the villagers in Achampatti
strongly agree that Wi-Max has assisted them in training. On the whole, 26.6 per cent of villagers
in total have shown their disagreement and 73.4 per cent of the villagers in total have shown their
agreement towards the role of Wi-Max services.
4.4.13.2 Cross Tabulation of Clusters on Usage of Computer
Table 4-26
CLUSTERS E10-Usgae of Computer assist in business Strongly Disagree
Disagree No Opinion
Agree Strongly Agree
Total
Vallam Count % within Cluster
0 .0%
20 21.3%
52 55.3%
20 21.3%
2 2.1%
94 100 %
Achampatti Count %
within Cluster
0
.0%
27
49.1%
17
30.9%
11
20.0%
0
.0%
55
100%
Budhalur Count %
within Cluster
2
1.1%
61
33.0%
79
42.7%
37
20.0%
6
3.2%
185
100%
Veeramarasanpatt
i
Count %
within Cluster
1
1.4%
22
29.7%
43
58.1%
8
10.8%
0
.0%
74
100%
Rayamundanpatti Count %
within Cluster
4
6.0%
28
41.8%
29
43.3%
6
9.0%
0
.0%
67
100%
Palayapatti Count %
within Cluster
4
3.2%
49
39.2%
48
38.4%
20
16.0%
4
3.2%
125
100%
Total Count %
within Cluster
11
1.8%
207
34.5%
268
44.7%
102
17.0%
12
2.0%
600
100%
The analysis of cross tabulation on variable (E10) revealed that highest count 268 out of 600
(about 44.7 per cent) was recorded as villagers having no opinion on the usage of computer and
102 out of 600 (17.0 per cent) registered as villagers agreeing that they the use of computer.
Among the clusters, Budhalur was listed as one with the highest response on this independent
variable. About 42.7 per cent of the villagers in Budhalur have indicated that they have no
opinion on the usage of computer, and 20.0 per cent of the villagers in Budhalur indicated that
they agree that they know how to use a computer. On the whole, 36.3 per cent of villagers in
total have shown their disagreement and 63.7 per cent of the villagers in total have shown their
agreement towards usage of computer
4.4.13.3 Cross Tabulation of Clusters on Telephone Services
Table 4-27
CLUSTERS E11- Telephone Services assisted in communication Strongly Disagree
Disagree No Opinion
Agree Strongly Agree
Total
Vallam Count % within Cluster
2 2.1%
18 19.1%
29 30.9%
44 46.8%
1 1.1%
94 100 %
Achampatti Count %
within Cluster
0
.0%
16
29.1%
20
34.5%
19
21.8%
0
.0%
55
100%
Budhalur Count %
within Cluster
1
.5%
34
18.4%
68
36.8%
77
41.6%
5
2.7%
185
100%
Veeramarasanpatt
i
Count %
within Cluster
0
.0%
20
27.0%
23
31.1%
16
23.9%
3
4.5%
74
100%
Rayamundanpatti Count %
within Cluster
0
6.0%
15
22.4%
33
49.3%
16
23.9%
3
4.5%
67
100%
Palayapatti Count %
within Cluster
1
.8%
29
23.2%
57
45.6%
31
24.8%
7
5.6%
125
100%
Total Count %
within Cluster
4
.7%
132
22.0%
230
38.3%
217
36.2%
17
2.8%
600
100%
The analysis of cross tabulation on variable (E11) revealed that highest count of 230 out of 600
(about 38.3 per cent villagers) were recorded as having no opinion on the telephone services and
217 out of 600 (36.2 per cent of the villagers) agree that they know of Telephone services.
Among the clusters, Rayamundanpatti was listed as having the highest percentage of response on
this independent variable. About 49.3 per cent of the villagers in the Rayamundanpatti and 45.6
per cent of the villagers in Palayapatti have indicated that they have no opinion on Telephone
Services. On the whole, 22.7 per cent of villagers in total have shown their disagreement and
77.3 per cent of the villagers in total have shown their agreement that telephone services have
helped them in communicating to their counterparts during emergency and keeping in touch with
the business partners.
4.4.13.4 Cross Tabulation of Clusters on Internet Services
Table 4-28
CLUSTERS E12- Internet Services assist in sharing the knowledge Strongly Disagree
Disagree No Opinion
Agree Strongly Agree
Total
Vallam Count % within Cluster
3 3.2%
35 37.2%
21 22.3%
34 36.2%
1 1.1%
94 100 %
Achampatti Count
% within Cluster
5
9.1%
22
40.0%
22
40.0%
6
10.9%
0
.0%
55
100%
Budhalur Count
% within Cluster
1
.5%
56
30.3%
67
36.2%
55
29.7%
6
3.2%
185
100%
Veeramarasanpat
ti
Count
% within Cluster
0
.0%
27
36.5%
43
58.1%
4
5.4%
0
.0%
74
100%
Rayamundanpatti Count
% within Cluster
2
3.0%
31
46.3%
26
38.8%
8
11.9%
0
.0%
67
100%
Palayapatti Count
% within Cluster
1
.8%
57
45.6%
47
37.6%
17
13.6%
3
2.4%
125
100%
Total Count
% within Cluster
12
2.0%
228
38.0%
226
37.7%
124
20.7%
10
1.7%
600
100%
The analysis of cross tabulation on variable (E12) revealed that highest count of 228 out of 600
(about 38.0 per cent) was recorded as villagers disagreeing on the awareness of Internet services
and 226 out of 600 (37.7 per cent) villagers having no opinion on Internet services. Among the
clusters, Veeramarasanpatti was listed as the one with highest percentage of response on this
independent variable. About 58.1 per cent of the villagers in the Veeramarasanpatti have
indicated that they have no opinion on Internet Services, and 46.3 per cent of the villagers in
Rayamundanpatti indicated that they disagree on the awareness of Internet Services. However,
36.2 per cent of villagers in Vallam and 29.7 per cent of villagers in Budhalur have indicated that
they agree on the awareness of the internet services. On the whole, 40.0 per cent of villagers in
total have shown their disagreement and 60.0 per cent of the villagers in total have shown their
agreement towards the awareness of Internet services
4.4.13.5 SUMMARY OF CROSSTABULATION ANALYSIS ON ELECTRONIC CONNECTIVITY
Table 4-29
Based on the response from the villagers it is observed that the effects of Electronic connectivity
on all six clusters were relatively high. As for the highest score, 217 out 600 villages agreed that
telephone service assisted in their business contacts and 17 out of 600 villagers strongly agree
that the telephone services have assisted in their business contact. However, 268 out of 600
villagers have indicated that they have no opinion on usage of computer and 35 out of 600
villagers strongly disagreed on the role of Wi-Max Services. Despite the mixed feeling of the
villagers, most of them have indicated that they are aware of Wi-Max Services, Usage of
35
119
268
170
8 11
207
268
102
12 4
132
230 217
17 12
228 226
124
10
0
50
100
150
200
250
300
Strongly Disagree
Disagree No opinion Agree Strongly Agree
Wi-Max Services
Usage of Computer
Telephone Services
Internet Services
Computer, Telephone Services and Internet Services which shows that the Electronic
Connectivity was effective in that area.
4.4.13.6 K-Mean Cluster Analysis on Electronic Connectivity. The entire sampling of the villagers was divided into two groups to determine the popularity of
Electronic Connectivity. Using the K-Mean Cluster Analysis the mean value of each group was
determined.
Table 4-30
No of Clusters (Mean Value)
No. of Cases Total
1 2 1 2
W-Max service assisted in
Training
3.35 3.04
280 320 600
Usage of Computer assisted in Business
3.36 2.35
Telephone services
assisted in communication
3.52 2.53
Internet Service
assisted in Sharing the Knowledge
3.16 2.54
The result revealed that 280 out of 600 villagers were found with high mean value that was
consolidated from their scores and 320 villagers were with low mean value. This indicates that
46.7 per cent of the sampled population agree to the contribution of Electronic Connectivity to
the PURA Scheme Villages. K9- “Wi-Max Service have assisted in the training” has a mean
value of X = 3.35; K10- “Usage of computer assisted in business” mean value of X = 3.36; K11
– “Telephone Services assisted in communication” mean value of X = 3.52 and K12 – “ Internet
Services assisted in sharing the knowledge” mean value of X = 3.16. Among the scores, K11-
“Telephone Services assisted in communication” has scored the highest mean value. This
indicates that villagers were interested in using the electronic connectivity which gives them
access to fast communication channel and also helps in keeping in contact with their clients.
4.4.14 CROSSTABULATION ANALYSIS ON ELECTRONIC CONNECTIVITY
4.4.14.1 Cross Tabulation of Clusters on Road connecting the Village
Table 4-31
CLUSTERS P13-Improvement in road condition Strongly Disagree
Disagree No Opinion
Agree Strongly Agree
Total
Vallam Count % within Cluster
1 1.1%
14 14.9%
33 35.1%
12 12.8%
34 36.2%
94 100 %
Achampatti Count
% within Cluster
0
.0%
6
10.9%
21
38.2%
6
10.9%
22
40.0%
55
100%
Budhalur Count
% within Cluster
2
1.1%
14
7.6%
35
18.9%
127
68.6%
7
3.8%
185
100%
Veeramarasanpat
ti
Count
% within Cluster
1
1.4%
10
13.5%
9
12.2%
47
63.5%
7
9.5%
74
100%
Rayamundanpatti Count
% within Cluster
3
4.5%
6
9.0%
15
22.4%
8
11.9%
35
52.2%
67
100%
Palayapatti Count
% within Cluster
5
4.0%
12
9.6%
23
18.4%
14
11.2%
71
56.8%
125
100%
Total Count
% within Cluster
12
2.0%
62
10.3%
136
22.7%
214
35.7%
176
29.3%
600
100%
The analysis of cross tabulation on variable (P13) revealed that highest count 214 out of 600
(about 35.7 per cent) was recorded as agreeing that the road condition assists them in
transportation and 176 out of 600 (29.3 per cent registered) as strongly agreeing to the role of the
road condition. Among the clusters, Budhalur with 68.6 per cent and Veeramarasanpatti with
63.5 per cent were listed as the ones having the highest percentage respectively for this
independent variable. Similarly, the villagers in Rayamundanpatti (52.2 per cent) and Palayapatti
(56.8 per cent) strongly agree that the road condition has assisted villagers to travel. On the
whole, 12.3 per cent of villagers in total have shown their disagreement and 87.7 per cent of the
villagers in total have shown their agreement towards road condition.
4.4.14.2 Cross Tabulation of Clusters on Sanitary Services
Table 4-32
CLUSTERS P14-Sanitary build-in prevent disease Strongly Disagree
Disagree No Opinion
Agree Strongly Agree
Total
Vallam Count % within Cluster
3 3.2%
5 5.3%
38 40.4%
42 44.7%
6 6.4%
94 100 %
Achampatti Count
% within Cluster
2
3.6%
1
1.8%
13
23.6%
33
60.0%
6
10.9%
55
100%
Budhalur Count
% within Cluster
5
2.7%
13
7.0%
55
29.7%
78
42.2%
34
18.4%
185
100%
Veeramarasanpat
ti
Count
% within Cluster
0
.0%
3
4.1%
1
1.4%
12
16.2%
58
78.4%
74
100%
Rayamundanpatti Count
% within Cluster
6
9.0%
3
4.5%
23
34.3%
32
47.8%
3
4.5%
67
100%
Palayapatti Count
% within Cluster
10
8.0%
5
4.0%
38
30.4%
64
51.2%
8
6.4%
125
100%
Total Count
% within Cluster
26
4.3%
30
5.0%
168
28.0%
261
43.5%
115
19.2%
600
100%
The analysis of cross tabulation on variable (P14) revealed that highest count 261 out of 600
(about 43.5 per cent )was recorded as agreeing on sanitary services and 115 out of 600 (19.2 per
cent ) are registered as villagers strongly agreeing on sanitary services. Among the clusters,
Achampatti with 60.0 per cent and Palayapatti 51.2 per cent were listed as having the highest
percentage respectively on this independent variable. However, villagers in Veeramarasanpatti
with 78.4 per cent strongly agree to the role of sanitary facilities in preventing disease. The result
revealed that PURA Scheme has improved the condition of the sanitary service which in turn
prevents the outbreak of diseases. On the whole, 9.3 per cent of villagers in total have shown
their disagreement and 90.7 per cent of the villagers in total have shown their agreement towards
sanitary upgrades preventing diseases.
4.4.14.3 Cross Tabulation of Clusters on Water Supply
Table 4-33
CLUSTERS P15-Improvement in Water Supply Strongly Disagree
Disagree No Opinion
Agree Strongly Agree
Total
Vallam Count % within Cluster
6 6.4%
15 16.0%
18 19.1%
42 44.7%
13 13.8%
94 100 %
Achampatti Count
% within Cluster
8
14.5%
22
40.0%
13
23.6%
12
21.8%
0
.0%
55
100%
Budhalur Count
% within Cluster
33
17.8%
37
20.0%
46
24.9%
19
10.3%
50
27.0%
185
100%
Veeramarasanpat
ti
Count
% within Cluster
0
.0%
12
16.2%
11
14.9%
10
13.5%
41
55.4%
74
100%
Rayamundanpatti Count
% within Cluster
0
.0%
7
10.4%
12
17.9%
37
55.2%
11
16.4%
67
100%
Palayapatti Count
% within Cluster
12
9.6%
27
21.6%
29
23.2%
34
27.2%
23
18.4%
125
100%
Total Count
% within Cluster
59
9.8%
120
20.0%
129
21.5%
154
25.7%
138
23.0%
600
100%
The analysis of cross tabulation on variable (P15) revealed that highest count of 154 out of 600
(about 25.7 per cent ) was recorded as villagers agreeing on improvement of water supply and
138 out of 600, 23.0 per cent registered as villagers strongly agreeing on improvement of water
supply. Among the clusters, Veeramarasanpatti with 55.4 per cent was listed as the one with the
highest percentage for this independent variable with respect to the improvement of water
supply. However, villagers in Rayamundanpatti with 55.2 per cent agree on the improvement of
water supply. The result revealed that PURA Scheme has improved the water supply to the
villages. Concerning disagreement, 29.8 per cent of villagers in total have shown their
disagreement whereas 70.2 per cent of the villagers in total have shown their agreement towards
improvement of water supply.
4.4.14.4 Cross Tabulation of Clusters on Electrical Supply
Table 4-34
CLUSTERS P16-Improvement in Electrical Supply Strongly Disagree
Disagree No Opinion
Agree Strongly Agree
Total
Vallam Count % within Cluster
3 3.2%
19 20.2%
34 36.2%
29 30.9%
9 9.6%
94 100 %
Achampatti Count
% within Cluster
2
3.6%
13
23.6%
19
34.5%
14
25.5%
7
12.7%
55
100%
Budhalur Count
% within Cluster
5
2.7%
13
7.0%
55
29.7%
78
42.2%
34
18.4%
185
100%
Veeramarasanpat
ti
Count
% within Cluster
0
.0%
17
23.0%
19
25.7%
34
45.9%
4
5.4%
74
100%
Rayamundanpatti Count
% within Cluster
3
4.5%
20
29.9%
23
34.4%
18
26.9%
3
4.5%
67
100%
Palayapatti Count
% within Cluster
7
5.6%
37
29.6%
43
34.4%
33
26.4%
5
4.0%
125
100%
Total Count
% within Cluster
20
3.3%
151
25.2%
198
33.0%
193
32.2%
38
6.3%
600
100%
The analysis of cross tabulation on variable (P16) revealed that highest count of 298 out of 600
(about 33.0 per cent) was recorded as having no opinion on improvement of electrical supply to
their villages and 193 out of 600 (32.2 per cent) registered agreeing on the improvement of
electrical supply. Among the clusters, Veeramarasanpatti with 45.9 per cent was listed as having
the highest percentage on this independent variable. However, villagers in Achampatti with 12.7
per cent strongly agree to the improvement of electrical supply. The result revealed that the
PURA Scheme has improved the electrical supply by coordinating with necessary local bodies.
In relation to disagreement, 28.5 per cent of villagers in total have shown their disagreement and
71.5 per cent of the villagers in total have shown their agreement towards improvement of
electrical supply.
4.4.14.5 SUMMARY OF CROSSTABULATION ANALYSIS ON PHYSICAL CONNECTIVITY
Table 4-35
Based on the response from the villagers it is observed that the effects of Physical connectivity
on all six clusters were relatively high. Most of them have indicated that they agree on the
improvement of road condition and sanitary, water and electrical facilities. We could see the
highest among all responses was shown related to the sanitation in the villagers. This has
upgraded the hygiene factor in the village that is most welcome. Next on the list was the road
condition that has been upgraded to have better transportation
12
62
136
214
176
26 30
168
261
115
59
120 129 154
138
20
151
198 193
38
0
50
100
150
200
250
300
Strongly Disagree
Disagree No opinion Agree Strongly Agree
Road Condition
Sanitation
Water Supply
Electrical Supply
4.4.14.6 K-Mean Cluster Analysis on Physical Connectivity.
The entire sample of the villagers was divided into two groups to determine the popularity of
Physical Connectivity. Using the K-Mean Cluster Analysis the mean value of each group was
determined.
Table 4-36
No of Clusters (Mean Value)
No. of Cases Total
1 2 1 2
Improvement in road
condition
3.82 3.78
308 292 600
Improvement in sanitary
3.58 3.78
Improvement in Water Supply
2.32 4.47
Improvement in Electrical
Supply
3.14 3.12
The result revealed that 292 out of 600 villagers were found with high mean value that was
consolidated from their scores and 308 villagers were with low mean value. This indicates that
48.7 per cent of the sampled population agree to the contribution Physical Connectivity to the
PURA Scheme Villages. P13- “Improvement in road condition” has a mean value X = 3.78;
P14- “Sanitary building prevent diseases” mean value X = 3.78; P14 – “Improvement in Water
Supply” mean value = X 4.47; and P15 – “Improvement in Water Supply” mean value= X =
3.16. Among the scores, P14- “Improvement in Electrical Supply” has scored the highest mean
value of X =4.47. This indicates that villagers were well taken care of through the Physical
Connectivity which gives them better facilities to dwell in villages.
4.4.15 Comparison of the Respondents from the clusters on Knowledge Connectivity, Economic Connectivity, Electronic Connectivity and Physical Connectivity.
Table 4-37 Comparison on Response of Villagers in the Cluster
Response Knowledge Connectivity
Economic Connectivity
Electronic Connectivity
Physical connectivity
Strongly Disagree
21.75(4) 12(2) 15.5(3) 29.25(5)
Disagree 162.75(27) 169.5(28) 171.5(29) 90.75(15)
No Opinion 164.75(28) 177(30) 253(42) 157.75(26)
Agree 201.25(33) 198.75(33) 153.25(26) 205.5(34)
Strongly Agree 50(8) 42.75(7) 11.75(2) 116.75(20) Note: Figures in parentheses are percentage
Looking at the overall response from the villagers, the results reveal the strength of the
PURA Scheme in developing the villages into modernised villages that have the essentials of
urban dwelling. The villagers responded that they strongly agree on Knowledge Connectivity
(8%), Economic Connectivity (7%), Electrical Connectivity (2%) and Physical Connectivity
(20%) which has highest score for this response. On an average, 9.25 per cent of the villagers
strongly agree to the innovativeness of the PURA Scheme which has transformed their land to be
active in entrepreneurial activities. The villagers agreed on the role of Knowledge Connectivity
(33%), Economic Connectivity (33%), Electrical Connectivity (26%) and Physical Connectivity
(34%) which was highest score for this response. On the average, 31.5 per cent of the villagers
were agreeing that the improvement has been made to their villages through these connectivities
which has converted their living area into a busy village.
The villagers responded that they have no opinion for Knowledge Connectivity (28%),
Economic Connectivity (30%), Electrical Connectivity (42%) and Physical Connectivity (26%).
The highest score was registered as 42 per cent for Electrical Connectivity as the villagers were
not sure of the supply rendered by Electrical Board of Thanjavur District. This also reflects on
the villagers’ complacency towards the improvement provided by the supporting institutions.
The villages responded as “disagreed and strongly disagreed” in respect of Knowledge
Connectivity (27%, 4%), Economic Connectivity (28%,2%), Electrical Connectivity (29%,3%)
and Physical Connectivity (15%, 5%) respectively. On an average, 28.25 per cent of the
villagers disagreed on the improvements brought by PURA Scheme.
4.5 SUMMARY
The data analysis on the collected data through questionnaires has been carried out by
using the Chi-Square Test, F-Test, Multiple Regression Analysis, Cross Tabulation Analysis and
K-Mean Cluster Analysis. The result shows strong evidence of achievement of PURA Scheme
during the past seven years. Income has become eminent source for villagers to become
entrepreneurs and a deciding factor for the villagers to start a business of their own. It has strong
relationship with experience of the villagers, entrepreneurship development programme and Skill
Based Training. The independent variable ‘experience’ was not found to be significnt during the
Chi-Square Test. This shows that experience was not a factor for becoming entrepreneurs as the
villagers’ experience can be nurtured from the family background, or family business
environment. Since starting a small venture does not need much capital, many have begun with
small business within their village which has given them adequate experience.
Entrepreneurship Development programmes organized by the supporting institutions was
an added value for the villagers to gain the appropriate business skills and also in sustaining their
business in this fast competitive world. Likewise, Skill Based Training has transformed many
youth in the village to become business owners by starting a small scale industry in their village.
This has been popular among younger entrepreneurs, examples are mobile repair shop, two-
wheeler repair shop, welding workshop, carpentry workshop, electrical repair shops etc. Women
in the villages have also learnt technical skills on repairing cooking utensils, sewing machines,
and woodworks. Many women have taken up sewing which can be operated within their home.
In conclusion, the result has shown that PURA Scheme has effectively contributed to villages’
improvement and the economic growth within the villages.