chapter vi problems of smes in chennai and tiruvallur...
TRANSCRIPT
198
CHAPTER – VI
PROBLEMS OF SMEs IN CHENNAI AND
TIRUVALLUR DISTRICT
An earnest attempt has been made in this chapter to present a vide picture
of the multifarious problems faced by the small-scale industrial units in Chennai
and Trivallur district. These problems are of varied nature, some of them are
purely in an internal nature and some others are caused by some extraneous
forces, conditions and circumstances. In order to have a first hand information
and practical insight into these problems faced by the SME units in Chennai and
Tiruvallur district a questionnaire has been constructed and administered to the
managements of the SME units, inviting their responses to the various questions
posed therein. Addition to this, several unstructured interviews has also been
conducted with various officials and non-officials dealing with the problems of
small-scale industrialists in Chennai and Tiruvallur district. Resources have also
been taken by referring a number of documents and papers both published and
unpublished and available in the offices of the Central and State Governments as
well as the small-scale units chosen for a detailed study of this research work, in
order to have insight into the problems encountered by the small-scale units in
Chennai and Tiruvallur district. The logical outcomes of all these exercises at
analyzing the various problems faced by the small-scale industries in the districts
are presented in this chapter. The sophisticated statistical tools are employed to
produce torrent of results.
This chapter is broadly divided into two sections, section one dealing with
the profile of the sample units chosen for a detailed study of this research work
and section two with the analysis of various problems faced by the small-scale
industries of Chennai and Tiruvallur district. In part one, profile of the sample
units, detailed analysis of the age, pattern of ownership, education qualifications
199
of the SMEs, product lines manufactured, relationship with ancillaries, capacity
utilization, size of investment and extent of borrowing etc., of the sample small-
scale units in the districts are presented. In section two, detailed discussions on
the problems faced by the small-scale industries in the district, for example,
problems of setting up of the units, technological problems, problems arising
accomplying with the various government rules and regulations and procedures,
bureaucratic delays. Problems of choice of the line of business, problems arising
in the course of availing the various incentives offered by the Government to
small-scale units, production problems, problems of procedural usage of raw
materials, problems of supply of power, high cost of production, utilization of
waste materials and manufacturing of by-products, problems arising out of the
utilization of the installed capacity, financial problems, marketing problems and
a number of other miscellaneous general problems of the small-scale units.
Small-scale industries have been playing an important role in the
development of Indian economy. These small-scale industries not only help to
create employment opportunities, but also generate income, investment and
savings in the economy. Further, these industries may also help in developing
regional economy, promotion of export potential, promotion of market facilities,
development of infra-structural facilities etc. Small-scale industries may also
help in the eradicating poverty, unemployment, social-economic inequality etc.
in the economy.
Factors of financial problems
Majority of the sample SMEs of small-scale have raised initial capital
form self source, relatives and friends whereas only 21% of the new and 27% of
the established SMEs have availed financial help from Institutions. The small-
scale industry owners felt that the financial institutions and commercial banks
hesitate to provide initial capital and as they go to private sources, they incur
heavy interest burden. The SMEs also expressed that the quantum of assistance
by the Government institutions is also inadequate and delayed.
200
Procuring Term Loans
It is observed that nearly 95% of the new and 97% of the established
small-scale industries have received Term loans from the Tamilnadu Industrial
Investment Corporation and Commercial banks. While availing the term loans
from these institutions, the sample SMEs has experienced inordinate delay,
which ranges from 30 to 120. They also expressed that the complicated
procedures of institutions cause delay in the disbursement of loan.
Problems relating to working capital
It is evident from the study that nearly 74% of the new and 79% of the
established small-scale industries have raised working capital from the
Government Institutions and Banks. Only 26% of the new and 21% of the
established small-scale industries have raised working capital from private
sources. The small-scale industries complaint hat apart from the complicated
procedures, the banks are now insisting on collateral security for giving working
capital assistance. Their complaint is against insufficient working capital and
the moneylender attitude of the financial institutions. Heavy delay is caused
before effecting disbursement.
201
Factor analysis by principle component method is applied on 9 variables of
financial problems.
Table 6.1
Total Variance Explained for analysis by principal component method is
applied on 9 variables of financial problems.
Component Initial Eigenvalues
Rotation Sums of Squared
Loadings
Total
% of
Variance
Cumulative
% Total
% of
Variance
Cumulative
%
1 3.391 37.675 37.675 3.319 36.879 36.879
2 1.232 13.688 51.363 1.304 14.484 51.363
3 .994 11.045 62.408
4 .814 9.046 71.455
5 .670 7.443 78.897
6 .630 7.004 85.901
7 .494 5.487 91.388
8 .428 4.756 96.144
9 .347 3.856 100.000
Extraction Method: Principal Component Analysis.
202
Table 6.2
Rotated Component Matrix for analysis by principal component method is
applied on 9 variables of financial problems
Component
1 2
SMEProblem38 .825
SMEProblem34 .789
SMEProblem36 .751
SMEProblem35 .726
SMEProblem37 .660
SMEProblem33 .504
SMEProblem31 .739
SMEProblem30 .554
SMEProblem32 -.437
Extraction Method: Principal Component Analysis.
Rotation Method: Varimax with Kaiser Normalization.
a Rotation converged in 3 iterations.
From the tables 6.1 and 6.2, it is ascertained that the variables explain
51.363% of the total variance and two factors are extracted. The first factor is
called “Interest rate and Payment delay” (IRDP and the second factor is named
as “Loan difficulties” (LD) due to following factor loadings.
Factor 1:
33 - Private moneylenders demand high rate of interest
34 - Delay in payment on Government supply 45 to 90 days
35 - Loan sanction depends upon the ability of SMEs
36 - Discretion of authorities of financial institutions also creates financial
problems
37 - Enthusiasm and energy of SMEs are wasted on proving the
eligibility and quantum of assistance sought.
203
38 - SIDCO provide 80% on the bills supplied to SME and remaining 20% at
the time of return
Factor 2:
30 - It is difficult to get loans from authorized financial institutions
31 - Tiresome procedures are followed in all nationalized banks
32 - The credit worthiness of SMEs is weak
The one sample t-test and paired sample test are applied on the factors of
financial problems. The factors IRDP (mean = 4.37) is prevailing more in SMEs
in Chennai and Tiruvallur district followed by LD (mean = 3.78). Between these
two financial problems IRDP has more vigour in affecting the progress of SMEs.
Table 6.3
One-Sample Statistics for principal component method is applied on 9
variables of financial problems.
N Mean Std. Deviation Std. Error Mean
IRDP 402 4.3673 .69770 .03480
LD 402 3.7819 .83012 .04140
Table 6.3 clearly revealed that IRDP (mean=4.37) is existing more than LD
(mean=3.78). The significance of the mean is checked by the following one
sample t-test
204
Table 6.4
One-Sample Test for principal component method is applied on 9 variables
of financial problems.
Test Value = 3
t df
Sig. (2-
tailed)
Mean
Difference
95% Confidence Interval of
the Difference
Lower Upper
IRDP 39.293 401 .000 1.36733 1.2989 1.4357
LD 18.886 401 .000 .78192 .7005 .8633
Table 6.4 revealed that both the IRDP (t=39.293) and LD (t=18.886) are
significant among the SME units.
Table 6.5
Paired Samples Test
t df Sig. (2-tailed)
Pair 1 IRDP - LD 12.991 401 .000
Table 6.5 indicates that there is a significant difference between the two
financial problems of SME. Between these two factors, the IRDP is dominant
factor affecting the SME than LD. It is ascertained that the proprietors of small-
scale industries in Chennai and Tiruvallur districts are continuously affected by
the heavy interest rates for the loan amounts. They are supplying to their
purchasers in time, but the purchasers procrastinate their payments. This leads to
serious financial crisis for the small-scale industries. The entrepreneurs are not
able to get the loans in time from the government sources and private sources.
There is a popular feeling prevails among the sources of loans of SMEs that their
repaying capacity is very low.
205
Problems relating to raw material
The raw material problems are also emerging in SME units in Chennai
and Tiruvallur district. Nearly 85% of the new and 69% of the established small-
scale industries have purchased raw materials from the private agencies and only
15% of the new and 31% of the established SMEs have purchased SIDCO
depots. The SMEs faced ever-present price fluctuation with the private
agencies, and limited variety and supply of raw materials by SIDCO depots.
Factors of raw material
To identify the major factors are raw material problems, factors analysis
is applied on 9 variables and the following results are obtained.
Table 6.6
Total Variance Explained for Raw material
Component
Initial Eigenvalues
Extraction Sums of Squared
Loadings
Total
% of
Variance
Cumulative
% Total
% of
Variance
Cumulative
%
1 4.931 54.783 54.783 4.931 54.783 54.783
2 .926 10.286 65.070
3 .762 8.470 73.540
4 .545 6.054 79.594
5 .460 5.111 84.705
6 .390 4.334 89.039
7 .365 4.054 93.093
8 .352 3.911 97.004
9 .270 2.996 100.000
Extraction Method: Principal Component Analysis.
206
The table 6.6 clearly revealed that a single factor is extracted with
54.783% of the total variance. So the factor is called raw material problem
(RM). To check its severity among SME units a one-sample t-test is used with
test value 4.
Table 6.7
One-Sample Statistics for raw material
N Mean
Std.
Deviation
Std.
Error
Mean
RM 402 4.3021 .74416 .03712
Table 6.8
One-Sample Test for raw material
Test Value = 4
t df
Sig. (2-
tailed)
Mean
Differen
ce
95% Confidence
Interval of the
Difference
Lower Upper
RM 8.139 401 .000 .30210 .2291 .3751
The table 6.7 and 6.8 indicate that the SME units in Chennai and
Tiruvallur district are very much affected by the problems of raw material (mean
= 4.30, t = 8.139, p = 0.000).
It is found that the SMEs in Chennai and Tiruvallur districts are facing
enormous amount of Raw material problem. They are not able to get the raw
materials in time, both government and private suppliers are delaying in their
supply. This attitude of the suppliers affect the continuous flow of production of
small-scale industries in the two districts.
207
Power problem
The study reveals that the sample small-scale industries have suffered doe
to procedural difficulties and delay caused by the TNEB in giving initial power
connection. Majority of the SMEs have waited for more than 6 months to get
power connection.
The SME units are facing the notorious power problems and stumbled by
its regulations and tariffs. In order to make a microscopic examination over
power problems, the factor analysis has become indispensable in this context.
It is observed from the study that the sample SMEs face the following
problems regarding power supply:
01.High power tariff
02.Power-shedding
03.Fluctuation in voltage etc.
Factor analysis is applied on the seven variables of power problems and
the following results are obtained.
208
Table 6.9
Total Variance Explained for power problem
Component Initial Eigenvalues
Rotation Sums of Squared
Loadings
Total
% of
Variance
Cumulative
% Total
% of
Variance
Cumulative
%
1 2.649 37.838 37.838 2.407 34.389 34.389
2 1.217 17.386 55.224 1.458 20.835 55.224
3 .808 11.547 66.771
4 .706 10.087 76.858
5 .606 8.652 85.510
6 .553 7.900 93.410
7 .461 6.590 100.000
Extraction Method: Principal Component Analysis.
Table 6.10
Rotated Component Matrix for power problem
Component
1 2
PWProblem50 .769
PWProblem52 .739
PWProblem53 .685
PWProblem51 .676
PWProblem48 .547
PWProblem55 .836
PWProblem54 .769
Extraction Method: Principal Component Analysis.
Rotation Method: Varimax with Kaiser Normalization.
Rotation converged in 3 iterations
209
The table 6.9 and 6.10 clearly explains that the two factors emerged with
55.224% of the total variance.
The first factor is called “Limited hours supply” (LPS) with the
variable loadings in it.
48 - Power supply is not properly regulated
50 - Within the limited hours of power supply, it is difficult to complete the
production
51 - Many labour hours are wasted and unutilized during power – cut
52 - SMEs cannot go for installing alternatives like generators and thermal
power units
53 - Time management to maximize the production within the specific hours
is highly difficult
The second factor is known as “High tariff and Power fluctuation”
(HTPF) with the variable loadings
54 - Tariff of power is high for small-scale in SMEs
55 - Captive power in Tamil nadu plant leads to fluctuation of power affects the
small-scale industries.
The one sample t-test and paired sample t-test clearly revealed these two
factors LPS and HTPF are equally prominent. So it is ascertained that the SMEs
in Chennai and Tiruvallur districts are facing constraints of limited hours of
power supply and High Tariff for the current usage. These are affecting their
continuous production and severe financial crisis. It is identified that around
20% of SMEs in these two districts have been closed by the action Tamil nadu
electricity board for non-payment of electricity bills.
210
In marketing the products, the SMEs are facing the formidable
hindrances. By means of factor analysis, the marketing problems are classified
in the following ways.
Table 6.11
Total Variance Explained for marketing problem
Component Initial Eigenvalues
Rotation Sums of Squared
Loadings
Total
% of
Variance
Cumulative
% Total
% of
Variance
Cumulative
%
1 4.244 53.055 53.055 3.011 37.639 37.639
2 1.146 14.323 67.378 2.379 29.739 67.378
3 .591 7.391 74.769
4 .535 6.686 81.456
5 .507 6.338 87.794
6 .386 4.820 92.614
7 .362 4.530 97.143
8 .229 2.857 100.000
Extraction Method: Principal Component Analysis.
Table 6.12
Rotated Component Matrix for marketing problem
Component
1 2
MktProblem58 .782
MktProblem60 .754
MktProblem59 .753
MktProblem56 .751
MktProblem57 .713
MktProblem63 .867
MktProblem61 .811
MktProblem62 .794
Extraction Method: Principal Component Analysis.
Rotation Method: Varimax with Kaiser Normalization.
Rotation converged in 3 iterations
211
The eight variables of marketing problems are converted into 2 major
factors namely “Advertisement and local market “ (ALM) and competition
(COM) with following factors loading and 67.38% of total variance.
ALM
56- Ancillary SME units are forced to sell their products in a local market
57- The productions of SME have to travel long distance for marketing
58- Lack of procuring the distant markets minimize the operations
59- SME units are not able to advertise in mega manner.
60- Absence of well-defined system creates big problems of marketing
COM
61- Buyer – seller meet is arranged by SIDCO
62- Competition in marketing the productions
63- Difficulty in availing the help from SIDCO in trade fair participation
The one sample t-test and paired sample t-test are applied on these two
factors of marketing problems. It is found that the ALM (mean=4.51) is more
affecting the SMEs in Chennai and Tiruvallur districts than COM (mean=4.23).
This result clearly revealed that the SMEs products have not got proper
advertisement and popularity of the products is also less. In fact they are not able
compete with the productions of large industry in price as well as the popularity.
They are forced to sell their products to a specified buyers with fixed profit.
Their scope for different marketing avenues are totally obscured by the these sort
of buyers. So far government has not taken strenuous efforts to curb the
marketing problems of SMEs.
212
General problems
Besides the above-mentioned prominent problems, the SMEs in Chennai
and Tiruvallur districts are facing some general problems also.
Problems relating to labour
It is found from the study that nearly 35% of the SMEs had complained
about the high labour turnover and nearly 27% of the SMEs complain about
absenteeism. Nearly 16% of the SMEs had encountered strikes. The SMEs
expressed concern over shortage of skilled labour and interference by the Labour
Union.
Problem relating to subsidies and incentives:
Only 31% of the SMEs are eligible to get backward area concessions.
They complain that the officials do not implement the schemes immediately and
they take enormous time to sanction the subsidy. The officials insist on a lot of
documents and certificates for availing the benefit, with the result, for getting a
small amount of incentive, they have to spend large amount of time and energy.
The SMEs complain that every time they had to travel to the city to answer the
queries of the files. They felt that the subsidies and incentives are not easily
available and hence do not help the SMEs in time.
Problems relating to infrastructure facilities:
Most of the sample SMEs face variety of problems relating to sheds,
transports, water, power and other civic amenities, which form the basic
infrastructure for a unit to function smoothly. Due to lack of all these facilities,
the SMEs meet losses and many workers hesitate to take up jobs in the industrial
estates situated in backward areas.
Problems relating to payment from big companies:
It is clear form the study that majority of the SMEs are living at the mercy
of big companies, in the sense, they have to wait for payment from big
213
companies. Unfortunately only 15% of the SMEs get their bills collected within
45 days. Most of the SMEs felt this inordinate delay creates further problems in
the unit, like shortage of working capita, non-payment of wages lack of funds to
meet contingency expenses and to make payment to creditors, particularly the
suppliers of raw materials.
Problems relating to diversification, expansion and modernization:
Most of the established SMEs could not make much headway in the
expansion of the unit due to (a) Lack of finance (b) non-availability of improved
technology (c) non-availability of spare parts and skilled workers and (d)
absence of testing facilities in respect of new products. The SMEs further
expressed the view that the banks and financial institutions insist for more
collateral security for entertaining their applications for developmental activities.
So the factor analysis is applied on these 13 variables of general problems
214
Table 6.13
Total Variance Explained for general problems
Component Initial Eigenvalues
Rotation Sums of Squared
Loadings
Total
% of
Variance
Cumulative
% Total
% of
Variance
Cumulative
%
1 2.833 21.790 21.790 2.041 15.697 15.697
2 1.591 12.235 34.025 1.906 14.665 30.362
3 1.534 11.804 45.829 1.769 13.609 43.971
4 1.202 9.243 55.072 1.355 10.426 54.396
5 1.135 8.734 63.806 1.223 9.410 63.806
6 .883 6.791 70.597
7 .728 5.600 76.197
8 .693 5.330 81.527
9 .599 4.605 86.132
10 .541 4.165 90.297
11 .480 3.693 93.990
12 .413 3.174 97.164
13 .369 2.836 100.000
Extraction Method: Principal Component Analysis.
215
Table 6.14 - Rotated Component Matrix for general problems
Component
1 2 3 4 5
GenProblem67 .702
GenProblem71 .690
GenProblem66 .642
GenProblem72 .820
GenProblem70 .749
GenProblem73 .536
GenProblem69 .502
GenProblem64 .851
GenProblem65 .833
GenProblem74 .850
GenProblem68 .634
GenProblem76 .808
GenProblem75 .604
Extraction Method: Principal Component Analysis. Rotation
Method: Varimax with Kaiser Normalization.
Rotation converged in 9 iterations.
From the above tables 6.13 and 6.14, it is found that 5 problems arise in SME
units of these two districts namely “Rental and mortality” (REMO), “Poor
planning and workers” (PPW), “Competition with large industries” (CLI),
“Modern technology” (MOT) and “Quality development “(QD)
The above-mentioned factors have the following loadings with 63.81% of total
variance.
216
REMO:
66- The financial institutions are not sufficient
67- Rental problems of business establishment.
71- High rate of mortality
PPW:
69 - There are no established channels of negotiation between employers and
employee
70 - The sudden non-cooperation of workers leads to closure of SMEs
72 - Feasibility studies are not followed by SMEs
73 - Lack of technology
CLI:
64 - When SME elevates to higher Order industry, they are not able to get
proper encouragement
65 - Facing open competition with large-scale industries
MOT:
68 - Deteriorating industrial relations
74 - There is no special help from Government and other organization for
modernization
QD:
75- Difficulty in improving quality standards and productivity
76- SME are not considered as skill development centers
The extracted factors are subjected to one-sample t-test to identify and order the
predominant factors of general problems faced by SMEs in Chennai and
Tiruvallur districts.
217
Table 6.15
One-Sample Statistics for general problems
N Mean Std. Deviation Std. Error Mean
REMO 402 3.5398 .99357 .04955
PPW 402 3.5591 .82591 .04119
CLI 402 4.3731 .77609 .03871
MOD 402 3.1455 1.15963 .05784
QD 402 3.0833 1.15285 .05750
Table 6.16
One-Sample Test for general problems
Test Value = 3
t df
Sig. (2-
tailed)
Mean
Difference
95% Confidence Interval of
the Difference
Lower Upper
REMO 10.893 401 .000 .53980 .4424 .6372
PPW 13.572 401 .000 .55908 .4781 .6401
CLI 35.474 401 .000 1.37313 1.2970 1.4492
MOD 2.516 401 .012 .14552 .0318 .2592
QD 1.449 401 .148 .08333 -.0297 .1964
From the above tables 6.15 and 6.16 it is found that the SMEs in Chennai
and Tiruvallur districts are facing severe competition from large industries
followed by poor planning and workers, Rental and mortality, modern
technology. But they are profoundly believed that the products they produce are
known for their good quality.
It is inferred that the SMEs in Chennai and Tiruvallur districts are
helpless in selling the products in the midst of heavy competition from the large
industries. The entrepreneurs of SMEs possess poor planning and insincere
218
workers due to financial and other prominent constraints. The financial
constraints cease their development in the form of rent and other important
expenses. The modern technology is identified as a one of the serious problems
faced by the SMEs in present situation. They are not in the position to modernize
their industry with small capital.
Classification of SME units in Chennai and Tiruvallur district:
The problems of finance, raw material, marketing power and other are
playing crucial role in affecting the progress of SME units. Based on the factors
of problems, the SME units in Chennai and Tiruvallur districts are classified into
3 groups using K-means cluster analysis.
Table 6.17
Final Cluster Centers for SME units in Chennai and Tiruvallur district
Cluster
1 2 3
IRDP 3.60 4.59 4.65
LD 3.46 4.06 3.76
RM 3.37 4.58 4.64
LPS 3.40 4.37 4.51
HTPF 4.08 4.58 4.84
ALM 3.47 4.54 4.58
COM 3.35 4.65 4.84
REMO 3.18 4.02 3.38
PPW 3.12 3.94 3.53
CLI 3.37 4.64 4.76
MOD 2.91 4.16 2.49
QD 2.88 3.89 2.57
219
Table 6.18
Number of Cases in each Cluster for SME units in Chennai and Tiruvallur
district
Cluster 1 100.000
2 133.000
3 169.000
Valid 402.000
The table 6.18 indicates that the three clusters are formed with respect to
the different problems faced by SME. The first cluster consists of 100 SME
units (24.87%), second cluster comprises 122 SME units (33.09%) and the third
cluster has the frequency of 160 SME units (42.04%). The following table
explains the measure of problems faced by SME units in Chennai and Tiruvallur
district based on clusters of SMEs. The mean values of each problem of SMEs in
the respective clusters are mentioned below:
Table 6.19 indicates the mean scores and standard deviations of
multifarious problems of SMEs in cluster one
Table 6.19
One-Sample Statistics for multifarious problems of SMEs
N Mean Std. Deviation Std. Error Mean
IRDP 100 3.5967 .69839 .06984
LD 100 3.4567 .74559 .07456
RM 100 3.3667 .67179 .06718
LPS 100 3.4020 .69077 .06908
HTPF 100 4.0750 .83900 .08390
ALM 100 3.4680 .73538 .07354
COM 100 3.3500 .88335 .08833
REMO 100 3.1750 .93034 .09303
PPW 100 3.1150 .64297 .06430
CLI 100 3.3650 .73805 .07381
MOD 100 2.9100 .83901 .08390
QD 100 2.8800 1.05916 .10592
a Cluster Number of Case = 1
220
From the above table it is found that the mean values of problems of
SMEs are ranging from 2.88 (QD) to 4.07(HTPF). Table 6.22 depicts t-test
values of the mean scores with the test value 3.
Table 6.20
One-Sample Test for multifarious problems of SMEs
Test Value = 3
T df
Sig. (2-
tailed)
Mean
Difference
95% Confidence Interval of
the Difference
Lower Upper
IRDP 8.543 99 .000 .59667 .4581 .7352
LD 6.125 99 .000 .45667 .3087 .6046
RM 5.458 99 .000 .36667 .2334 .5000
LPS 5.820 99 .000 .40200 .2649 .5391
HTPF 12.813 99 .000 1.07500 .9085 1.2415
ALM 6.364 99 .000 .46800 .3221 .6139
COM 3.962 99 .000 .35000 .1747 .5253
REMO 1.881 99 .063 .17500 -.0096 .3596
PPW 1.789 99 .077 .11500 -.0126 .2426
CLI 4.945 99 .000 .36500 .2186 .5114
MOD -1.073 99 .286 -.09000 -.2565 .0765
QD -1.133 99 .260 -.12000 -.3302 .0902
a Cluster Number of Case = 1
From the t-values in the above table it is inferred that in first cluster
SMEs in Chennai and Tiruvallur district are not facing the REMO, PPW, MOD
and QD significantly, 24.87% of SMEs are effectively generating the income for
rent and other expenses They have modern technology in their premises to
produce the high quality products. But they face so many other problems like
loans, raw material and power problems. Table 6.21 presents the means and
standard deviations of problems prevailing in second cluster
221
Table 6.21
One-Sample Statistics for problems prevailing in second cluster
N Mean Std. Deviation Std. Error Mean
IRDP 133 4.5915 .43105 .03738
LD 133 4.0551 .82794 .07179
RM 133 4.5823 .39287 .03407
LPS 133 4.3669 .50223 .04355
HTPF 133 4.5827 .74666 .06474
ALM 133 4.5429 .53005 .04596
COM 133 4.6504 .46866 .04064
REMO 133 4.0226 .86791 .07526
PPW 133 3.9361 .66902 .05801
CLI 133 4.6353 .45697 .03962
MOD 133 4.1579 .66677 .05782
QD 133 3.8910 .76212 .06608
a Cluster Number of Case = 2
From the mean values in the above table it is ascertained that the second
cluster SMEs face more problems of CLI and less problems of QD. Table 6.22
indicates t-test values of means of cluster two
222
Table 6.22
One-Sample Test for problems prevailing in second cluster
Test Value = 4
T df
Sig. (2-
tailed)
Mean
Difference
95% Confidence Interval of
the Difference
Lower Upper
IRDP 15.825 132 .000 .59148 .5175 .6654
LD .768 132 .444 .05514 -.0869 .1971
RM 17.093 132 .000 .58229 .5149 .6497
LPS 8.425 132 .000 .36692 .2808 .4531
HTPF 9.000 132 .000 .58271 .4546 .7108
ALM 11.811 132 .000 .54286 .4519 .6338
COM 16.004 132 .000 .65038 .5700 .7308
REMO .300 132 .765 .02256 -.1263 .1714
PPW -1.102 132 .273 -.06391 -.1787 .0508
CLI 16.034 132 .000 .63534 .5570 .7137
MOD 2.731 132 .007 .15789 .0435 .2723
QD -1.650 132 .101 -.10902 -.2397 .0217
a Cluster Number of Case = 2
The above table clearly presents that the SMEs in second cluster face all
the problems severely. It is found that 33.09% of SMEs in Chennai and
Tiruvallur districts face severe financial problems, raw material problems, power
problems and marketing problems. The SMEs in second cluster are very much
confident about their production.
Table 6.23 presents the mean scores and standard deviations of problems
of third cluster
223
Table 6.23
One-Sample Statistics for problems prevailing in third cluster
N Mean Std. Deviation Std. Error Mean
IRDP 169 4.6469 .50651 .03896
LD 169 3.7594 .80996 .06230
RM 169 4.6351 .48993 .03769
LPS 169 4.5065 .47910 .03685
HTPF 169 4.8432 .39787 .03061
ALM 169 4.5775 .61622 .04740
COM 169 4.8373 .35586 .02737
REMO 169 3.3757 .98165 .07551
PPW 169 3.5251 .89544 .06888
CLI 169 4.7633 .37427 .02879
MOD 169 2.4882 1.08006 .08308
QD 169 2.5680 1.11662 .08589
a Cluster Number of Case = 3
The above table clearly revealed that the mean scores are ranging from
2.48(MOD) to 4.84(HTPF) Table 6.24 shows the significance of mean scores
Table 6.24
One-Sample Test for problems prevailing in third cluster
Test Value = 4
T Df
Sig. (2-
tailed)
Mean
Difference
95% Confidence Interval of
the Difference
Lower Upper
IRDP 16.604 168 .000 .64694 .5700 .7239
LD -3.862 168 .000 -.24063 -.3636 -.1176
RM 16.852 168 .000 .63511 .5607 .7095
LPS 13.744 168 .000 .50651 .4338 .5793
HTPF 27.550 168 .000 .84320 .7828 .9036
ALM 12.183 168 .000 .57751 .4839 .6711
COM 30.587 168 .000 .83728 .7832 .8913
REMO -8.267 168 .000 -.62426 -.7733 -.4752
PPW -6.894 168 .000 -.47485 -.6108 -.3389
CLI 26.513 168 .000 .76331 .7065 .8201
MOD -
18.197 168 .000 -1.51183 -1.6759 -1.3478
QD -
16.671 168 .000 -1.43195 -1.6015 -1.2624
a Cluster Number of Case = 3
224
The SMEs in third cluster are not facing MOD and QD where as they are
facing large dimensions of HTPF and COM. It is ascertained that the third
cluster differ from first cluster in financial problems and resemble in technology
and quality. It is identified that 42.04% face serious financial problems and they
are not able to meet all the expenses of their industries. Besides that all the three
clusters face power problems with different dimensions of severity. The third
cluster SMEs also face raw material and marketing problems and hampered by
their severity. Table 6.25 summarizes the above discussions regarding problems
faced by SMEs in Chennai and Tiruvallur district
Table- 6.25
Problems faced by SMEs in Chennai and Tiruvallur district
Cluster1 Cluster 2 Cluster 3
IRDP Moderately affected Affected Fully affected
LD Moderately affected Fully affected Affected
RM Moderately affected Affected Fully affected
LPS Moderately affected Affected Fully affected
HTPF Moderately affected Affected Fully affected
ALM Moderately affected Affected Fully affected
COM Moderately affected Affected Fully affected
REMO No comments Fully affected Affected
PPW No comments Fully affected Affected
CLI Moderately affected Affected Fully affected
MOD No comments Fully affected Not Affected
QD No comments Affected Not Affected
The above table clearly revealed that 24.87% SME units in first cluster
are moderately affected by IRDP, LD, RM, CPS, HTPF, ALM, COM, CLI
where they do not worry about REMO, PPW, MOD and QD. It is also extracted
that 33.09% of SME units in cluster II are fully affected by LD, REMO, PPW
225
and MOD whereas 42.04% in cluster III are fully affected by IRDP, RM, LPS,
HTPF, ALM, COM and CLI and not at all affected by MOD and QD.
So on the whole it is summarized that the SMEs in second and third
clusters(33.09%+44.04%=77.13%) are facing raw material, power, marketing,
and other general problems with severity ranging from moderate to high. The
first cluster SMEs with 23.87% of frequency are not accessible to rent, planning,
workers, modern technology and quality. It is also found that the quality of
products of SMEs in Chennai and Tiruvallur districts are good and welcome by
the purchasers.
The cluster classification of SME units in Chennai and Tiruvallur district
is justified using discriminate analysis. In this analysis the factors of problems
are considered as independent variables and cluster as grouping variables.
Table 6.26
Tests of Equality of Group Means
Wilks' Lambda F Df1 df2 Sig.
IRDP .594 136.461 2 399 .000
LD .925 16.065 2 399 .000
RM .475 220.907 2 399 .000
LPS .589 139.334 2 399 .000
HTPF .821 43.607 2 399 .000
ALM .632 116.330 2 399 .000
COM .458 236.066 2 399 .000
REMO .877 28.086 2 399 .000
PPW .858 32.995 2 399 .000
CLI .435 259.302 2 399 .000
MOD .601 132.171 2 399 .000
QD .745 68.189 2 399 .000
226
Table 6.27
Eigenvalues
Function Eigenvalue % of Variance Cumulative % Canonical Correlation
1 3.417(a) 67.0 67.0 .880
2 1.680(a) 33.0 100.0 .792
a First 2 canonical discriminant functions were used in the analysis.
Table 6.28
Wilks' Lambda
Test of
Function(s)
Wilks'
Lambda
Chi-
square df Sig.
1 through 2 .084 972.478 24 .000
2 .373 387.950 11 .000
The table 6.27 revealed the significant contribution independent variables
in the analysis. Table 6.27 and 6.28 states the justification of cluster
classification by the significant canonical correlation values, Wilk’s lambda
values and chi square values. It is ascertained that the clusters of SMEs
prevailing in Chennai and Tiruvallur districts are justified accurately.
Government encouragements
SMEs where recognized by the government of India after independence
of the country in the Industrial Policy Resolution of 1948 that small industries
particularly suited for better utilizations of local resources to achieve self
sufficiency in respect of certain types of essential consumer goods. With the
inception of the five-year plans a more comprehensive programme of assistance
to small-scale industries was initiated. In the first Five Year Plan it was
emphasized that “small industries derive part of their significance from their
potential value for the employment of trained and educated persons”2 In order to
help these industries, some protection need to be provided by reserving certain
spheres of productive activities only four small-scale industries.
227
The aim of the state policy, as enunciated in the Industrial Policy
Resolutions of 1948 and 1956, was to ensure that the small industries sector
would require sufficient vitality to be self-supporting and that its development
was integrated with that of the large scale. It was, therefore, felt that the
government should concentrate on measures design to remains the basic
handicaps of small-scale industries such as lack of technical and financial
assistance, suitable working accommodation inadequacy of tooling, repairs and
maintenance facilities etc. It was also laid down that the technique of production
of the small-scale industries should be constantly improved and modernized, the
pace of transformation being regulated so as to avoid, as far as possible,
technological unemployment.
During the First Five Year Plan, the following two important steps were
taken by the central government of substantial finance for the development of
village and small-scale industries the building up of a network of all India
boards to deal with the problems of the handloom industry, Khadi, and village
industries, handicrafts, small-scale industries, sericulture and coir industry.
Greater attention on the part of the central and state government and the
expending activities of the all India Board have increased production and
employment in a number of industries. The setting up of four regional small
industries services institutes with a number of branch units to provide technical
services, advices and assistance was a step from which may be expected in the
future.
The Government is playing the crucial role for the development of SME
units. The close association of encouragement of Government can be identified
by the important factors using factors analysis, which is applied on 24 variables
of Government encouragement
228
Table 6.29
Total Variance Explained for Government encouragements
Component Initial Eigenvalues
Rotation Sums of Squared
Loadings
Total
% of
Variance
Cumulative
% Total
% of
Variance
Cumulative
%
1 5.425 22.602 22.602 3.544 14.765 14.765
2 2.192 9.131 31.734 2.973 12.388 27.154
3 1.995 8.313 40.046 1.993 8.303 35.457
4 1.404 5.852 45.898 1.572 6.549 42.006
5 1.341 5.590 51.488 1.526 6.359 48.366
6 1.178 4.908 56.395 1.387 5.781 54.147
7 1.084 4.517 60.913 1.360 5.668 59.814
8 1.042 4.340 65.253 1.305 5.439 65.253
9 .966 4.024 69.277
10 .888 3.699 72.977
11 .677 2.821 75.797
12 .657 2.736 78.533
13 .622 2.593 81.126
14 .604 2.516 83.643
15 .545 2.272 85.914
16 .517 2.155 88.069
17 .487 2.029 90.098
18 .433 1.803 91.901
19 .422 1.759 93.660
20 .357 1.489 95.149
21 .347 1.447 96.596
22 .309 1.287 97.883
23 .269 1.119 99.002
24 .240 .998 100.000
Extraction Method: Principal Component Analysis.
229
Table 6.30
Rotated Component Matrix for Government encouragements
Component
1 2 3 4 5 6 7 8
GovProblem85 .777
GovProblem84 .772
GovProblem86 .750
GovProblem87 .672
GovProblem82 .618
GovProblem77 .597
GovProblem96 .774
GovProblem99 .685
GovProblem98 .680
GovProblem100 .661
GovProblem97 .639
GovProblem95 .540 .408
GovProblem89 .831
GovProblem90 .722
GovProblem92 .567
GovProblem79 .763
GovProblem78 .642
GovProblem80 .543
GovProblem94 .786
GovProblem93 .707
GovProblem81 .802
GovProblem91 .769
GovProblem83 .576
GovProblem88 .711
Extraction Method: Principal Component Analysis.
Rotation Method: Varimax with Kaiser Normalization.
Rotation converged in 15 iterations.
230
The table 6.30 indicates that the 24 variables explain 65.253% of total
variance and gives out 8 factors of encouragement of Government. Table 6.30
revealed that
Factor 1 has the following variables in its loading.
77 - The installation of SIDO, SISI, Kadhi developments are helping SMEs to
revive.
82 - Technical assistance is given to SMEs by the Government to increase
production.
84 - Industrial estate programme helps to build good organizational setup
and infrastructure
85- SMEs are treated as priority sector by the government to help them
financially
86- The provision of long term and medium term loans are useful for their
development
87- RBI provides finance for traditional industries through co-operative
banking system.
and it is called as “Loan and development programs’ (LDP).
Factor 2 is called “Subsidy and Tax exemption” (STE) because of its
factor loadings
95- Tax holiday for new industrial undertakings encourages SMEs
96- Investment allowances are given to encourage SME units
97- Capital subsidies to industries in backward areas encourage rural
SMEs
98- Total exemption from excise duty
99- Area development schemes directly promotes SMEs
100- Government encourages developing ancillary units connected to public
Sector enterprises.
231
The third factor is due to the variables
89- State SME corporations rationally distribute the raw materials during
scarcity.
90- Government’s decision to build up a buffer stock prevents raw material
scarcity.
92- Price preference is given by public sector purchase.
Hence it is known as “Smooth raw material supply” (SRM).
“Special policies”(SP) is the fourth factor with factor loadings of the
variables
78- The region and district officers of SME directorate often interacting with
SMEs
79- Policy formation, coordination and continuous monitoring are taken by
the Government.
80- Reservation of certain products for SMEs avoids competition from large-
scale industries.
The fifth factor is named as “Quality and sales outlets”(QSO). It
consists of variables
93- Provision of quality control and testing facility increase the
competitiveness of the product.
94- Arranging market outlets like sales emporium, state cooperative
societies, and trade fairs
The sixth factor is known as “Government Purchase”(POG) obtained
due to the variables: - 81- Government’s decision to purchase reserved products
from SME reduces the marketing burden.
232
The seventh is derived from the two variables
83- The Government special directions for profit operations
91- Government makes direct purchase from SMEs reduces the marketing
burden hence, it is called as “Profit operations”.
The eighth factor is a “less interest rate” (LIR) due to the variables
88- Nationalized banks are given directions to disburse loans for SME units
for less interest.
The eight factors derived through factor analysis are considered to identify and
order based on their predominance. The one sample t-test is applied and the
following results are obtained
Table 6.31
One-Sample Statistics for Government encouragements
N Mean Std. Deviation Std. Error Mean
LDP 402 4.2380 .77785 .03880
STE 402 3.8794 .74099 .03696
SRM 402 3.8972 1.02230 .05099
SP 402 3.9071 .93851 .04681
QSO 402 3.8296 1.10042 .05488
POG 402 3.2761 1.60761 .08018
POP 402 3.2251 1.06682 .05321
LIR 402 3.9080 1.36175 .06792
233
Table 6.32
One-Sample Test for Government encouragements
Test Value = 3
T df
Sig. (2-
tailed)
Mean
Difference
95% Confidence Interval of
the Difference
Lower Upper
LDP 31.910 401 .000 1.23798 1.1617 1.3142
STE 23.794 401 .000 .87935 .8067 .9520
SRM 17.596 401 .000 .89718 .7969 .9974
SP 19.380 401 .000 .90713 .8151 .9992
QSO 15.116 401 .000 .82960 .7217 .9375
POG 3.444 401 .001 .27612 .1185 .4337
POP 4.231 401 .000 .22512 .1205 .3297
LIR 13.369 401 .000 .90796 .7744 1.0415
From the above tables it inferred that the SMEs are accepting that the
government has taken only the moderate efforts for issuing the quick loans for
less interest, subsidies, raw materials, sales out lets and policies for over all
developments. In the domain of priority sector SMEs are considered as most
important for development. In fact the central government has directed the
public sector banks to issue loans for SMEs for entrepreneurial development and
to solve unemployment problems.
Products and clusters of SME
In this analysis, how the SME units are facing the problems based on the
products produced by them.
234
Table 6.33
Products and clusters of SME
Cluster1
N=100
Cluster2
N=133
Cluster3
N=169
Food products = 30
(7.46%)
Cotton textiles = 26
(6.47%)
Jute products = 17(4.23%)
Beverages = 20
(4.98%)
Silk Products = 28
(6.97%)
Paper & paper
Products = 29 7.21%)
Wooden products =
38 (9.45%)
Textile Products = 22
(5.47%)
Rubber,
plastic =32 (7.96%)
Leather products = 10
(2.49%)
Chemicals = 20 (4.98%) Non-metalic and
Mineral products=49
(12.19%)
Others = 2 (0.05%) Metal products = 14
(3.48%)
Basic metal
products=18(4.48%)
Machinery parts =10
(2.49%)
Transport equipment and
Products = 24 (5.97%)
Electrical
machines=13(3.23%)
And appliances
The above table clearly revealed that 23.87% of SMEs in Chennai and
Tiruvallur districts are producing food products, beverages, wooden products,
leather products and others and they do not face the financial, technology and
quality problems. It is also found that 33.09% of SMEs are producing Cotton
textiles, silk products, textile products, chemicals, metal products, machinery
parts, and electrical machines and facing the problems of finance, raw material,
power and marketing problems. It is revealed that 44.04% of SMEs are
producing jute products, paper products, rubber and plastic products, non-
235
metallic and mineral products, base metal products and transport equipments and
they do not face the problems of technology and quality.,
Association between cluster of SME units and their profile
The cluster classification of SME units based on the problems faced by
them and their profile like registration, ownership, investment proportion, loan
source, business establishment, annual turnover and nature of competition. To
find the association a non-parametric chi-square test is applied.
Association between cluster and registration of SME units
In Chennai and Tiruvallur districts both registered and unregistered SME
units are emerging. These two types of SME units are facing many problems.
The association between the registered and unregistered SMEs with different
clusters are established in table 6.34.
Table 6.34
Association between cluster and registration of SME units
Cluster Number of Case
Total 1 2 3
registe
red
1.00 65 94 112 271
2.00 35 39 57 131
Total 100 133 169 402
236
Table 6.35
Chi-Square Tests for Association between cluster and registration of SME
units
Value df
Asymp.
Sig. (2-
sided)
Pearson Chi-
Square 1.010(a) 2 .603
Likelihood Ratio 1.018 2 .601
Linear-by-Linear
Association .004 1 .949
N of Valid Cases 402
a 0 cells (.0%) have expected count less than 5. The minimum
expected count is 32.59.
From the table 6.35, it is found that chi-square = 1.010, p = 0.603 for 3
degrees of freedom and there is no association between clusters and registration
of SME units. So it is inferred that both the registered and un registered of SMEs
in Chennai and Tiruvallur districts are distributed over all the three types of
clusters and facing problems of finance, raw material, marketing and power
problems. The registered SMEs are able to get the aid from the government
easily than the unregistered units.
(B) Association between cluster and ownership of SME units:
In Chennai and Tiruvallur districts the ownership is categorized as sole,
partnership and private limited and these three types of SME ownership are
facing so many problems. The association is established in Table 6.36
237
Table 6.36
Association between cluster and ownership of SME units
Cluster Number of Case
Total 1 2 3
Owner
ship
1.00 33 40 50 123
2.00 29 41 41 111
3.00 38 52 78 168
Total 100 133 169 402
Table 6.37
Chi-Square Tests for Association between cluster and ownership of SME
units
Value df
Asymp.
Sig. (2-
sided)
Pearson Chi-
Square 2.857(a) 4 .582
Likelihood Ratio 2.853 4 .583
Linear-by-Linear
Association 1.273 1 .259
N of Valid Cases 402
0 cells (.0%) have expected count less than 5. The minimum
expected count is 27.61
From the table 6.37, it is found that chi-square = 2.857, p = 0.582 for 4
degrees of freedom and there is no association between clusters and ownership
of SME units. So it is revealed that the owner ship is independent of problems
faced by the SMEs. The SMEs under sole proprietorship, partnership, private
limited are distributed over all the three clusters of SMEs and facing numerous
problems.
238
(C) Association between cluster and loan source of SME units:
In Chennai and Tiruvallur districts the SMEs obtain loan from public
sector banks, private sector, private sources and foreign banks respectively. It is
found that they get maximum help from public sector banks and private
moneylenders.
Table 6.38
Association between cluster and loan source of SME units
Cluster Number of Case
Total 1 2 3
Loan
obtained
1.00 46 67 88 201
2.00 15 14 14 43
4.00 39 52 67 158
Total 100 133 169 402
Table 6.39
Chi-Square Tests for Association between cluster and loan
source of SME units
Value df
Asymp.
Sig. (2-
sided)
Pearson Chi-
Square 3.132(a) 4 .536
Likelihood Ratio 3.023 4 .554
Linear-by-Linear
Association .064 1 .800
N of Valid Cases 402
0 cells (.0%) have expected count less than 5. The minimum
expected count is 10.70.
From the table 6.39, it is found that chi-square = 3.132, p = 0.536 for 4
degrees of freedom and there is no association between clusters and loan source
of SME units. It is inferred that the SMEs in Chennai and Tiruvallur districts are
obtaining loans from all the sources. They get their loans from public sector
239
banks, private sector banks, private finance and foreign banks according their
conveniences like quick loan system and less interest loans with subsidies.
(D) Association between cluster and Business establishment of SME units:
In Chennai and Tiruvallur districts the SMEs are running in proprietors
own places, leased lands and rental premises. The table revealed that the
maximum number of industries are producing the product from rental premises.
Table 6.40
Association between cluster and Business establishment of SME units
Cluster Number of Case
Total 1 2 3
Buss
establishm
ent
1.00 37 55 81 173
2.00 6 6 11 23
3.00 57 72 77 206
Total 100 133 169 402
Table 6.41
Chi-Square Tests for Association between cluster and Business
establishment of SME units
Value df Asymp. Sig. (2-sided)
Pearson Chi-Square 4.340(a) 4 .362
Likelihood Ratio 4.383 4 .357
Linear-by-Linear Association 3.647 1 .056
N of Valid Cases 402
0 cells (.0%) have expected count less than 5. The minimum
expected count is 5.72.
From the table 6.41, it is found that chi-square = 4.340, p = 0.362 for 4
degrees of freedom and there is no association between clusters and business
establishment of SME units. It is inferred that the Business establishment place
is independent of problems faced by the SMEs in Chennai and Tiruvallur
districts. They have own, leased, and rent establishments and present rationally
in all the three clusters.
240
(E) Association between cluster and Annual turnover of SME units:
In Chennai and Tiruvallur districts the annual turn over ranges from 1
lakh to 5 lakhs respectively. It is clear from the table that the maximum number
of SMEs in these two districts are creating a turn over above 5 Lakhs
Table 6.42
Association between cluster and Annual turnover of SME units
Cluster Number of Case
Total 1 2 3
Turno
ver
1.00 37 55 81 173
2.00 6 6 11 23
3.00 57 72 77 206
Total 100 133 169 402
Table 6.43
Chi-Square Tests for Association between cluster and Annual turnover of
SME units
Value df Asymp. Sig. (2-sided)
Pearson Chi-Square 4.340(a) 4 .362
Likelihood Ratio 4.383 4 .357
Linear-by-Linear Association 3.647 1 .056
N of Valid Cases 402
0 cells (.0%) have expected count less than 5. The minimum
expected count is 5.72
From the table 6.43, it is found that chi-square = 4.340, p = 0.362 for 4
degrees of freedom and there is no association between clusters and annual
turnover of SME units. It is found that annual turn over does not distinguish the
SMEs in Chennai and Tiruvallur districts based on their problems. There is
general opinion of SMEs with small and high turn over that they face financial,
raw material, power and marketing problems regularly.
241
(F) Association between cluster and competition of SME units:
In Chennai and Tiruvallur districts the SMEs are facing formidable
competition from large industries. The dimensions of competition are
categorized as small, medium and large. The SMEs agree that they face
maximum number of small competition from various industries.
Table 6.44
Association between cluster and competition of SME units
Cluster Number of Case
Total 1 2 3
competition 1.00 67 102 106 275
2.00 24 27 38 89
3.00 9 4 25 38
Total 100 133 169 402
Table 6.45
Chi-Square Tests for Association between cluster and competition of SME
units
Value df Asymp. Sig. (2-sided)
Pearson Chi-Square 13.495(a) 4 .009
Likelihood Ratio 14.852 4 .005
Linear-by-Linear Association 2.813 1 .094
N of Valid Cases 402
0 cells (.0%) have expected count less than 5. The minimum
expected count is 9.45.
From the table 6.45 it is found that chi-square = 13.495, p = 0.009 for 4
degrees of freedom and there is a association between clusters and competition
of SME units. It is inferred that the first and third clusters of SMEs do not face
severe competition problems from the various industries. The clusters are mainly
classified under the problems of competition.
242
Analysis of variance for characteristics of SME and encouragement of
Government
Factor analysis clearly brought out five factors of characteristics of SME
and eight factors of encouragement of government as stated previously. The
analysis of variance (ANOVA) is useful to identify the significant difference
among the means of the variables of different clusters of the study.
Table 6.46
ANOVA for characteristics of SME and encouragement of Government
Sum of
Squares Df
Mean
Square F Sig.
BAP Between
Groups 74.331 2 37.165 96.657 .000
Within
Groups 153.418 399 .385
Total 227.749 401
LC Between
Groups 32.571 2 16.285 15.631 .000
Within
Groups 415.713 399 1.042
Total 448.283 401
DI Between
Groups 39.994 2 19.997 19.654 .000
Within
Groups 405.959 399 1.017
Total 445.953 401
ED Between
Groups 7.858 2 3.929 1.790 .168
Within
Groups 875.933 399 2.195
Total 883.791 401
EMC Between
Groups 4.060 2 2.030 1.614 .200
Within
Groups 502.042 399 1.258
Total 506.102 401
The above table clearly indicates that the acquaintance of characteristics
of SME differ significantly in all the three clusters of problems of SME units the
243
factors BAP (F=96.657, p = 0.000), LC (F = 15.631, p = 0.000), DICF = 19.654,
p = 0.000 differ in their means, whereas ED and EMC are viewed by all the three
clusters equally. So it is concluded that SMEs expect good employment
opportunities with minimum capital through SMEs. The SMEs in the three
clusters expect various out puts in their SME units.
Analysis of variance of government’s encouragement with respect to
clusters.
The eight factors of encouragement of government in increasing loan
distribution, subsidies, sales out lets and responsible for marketing the products
are tested for group means with respect to three clusters.
Table 6.47
ANOVA for of government’s encouragement with respect to clusters
Sum of
Squares Df
Mean
Square F Sig.
LDP Between
Groups 73.153 2 36.576 86.115 .000
Within
Groups 169.470 399 .425
Total 242.622 401
STE Between
Groups 45.215 2 22.607 51.556 .000
Within
Groups 174.962 399 .439
Total 220.176 401
SRM Between
Groups 29.741 2 14.870 15.239 .000
Within
Groups 389.343 399 .976
Total 419.083 401
SP Between
Groups 20.377 2 10.189 12.214 .000
Within
Groups 332.822 399 .834
Total 353.200 401
244
QSO Between
Groups 52.988 2 26.494 24.437 .000
Within
Groups 432.590 399 1.084
Total 485.578 401
POG Between
Groups 7.794 2 3.897 1.512 .222
Within
Groups
1028.55
7 399 2.578
Total 1036.35
1 401
POP Between
Groups 13.811 2 6.906 6.226 .002
Within
Groups 442.565 399 1.109
Total 456.376 401
LIR Between
Groups 73.643 2 36.821 21.930 .000
Within
Groups 669.952 399 1.679
Total 743.595 401
Table 6.47 clearly indicates that the variables LDP (F=86.115, p = 0.000),
STE (F=51.556, p = 0.000), SRM (F=15.239, p = 0.000) SP (F = 12.214, p =
0.000), QSO (F = 24.437, p = 0.000), POP (F=6.226, p = 0.002) and LIR (F =
21.930, p = 0.000) differ significantly in their means with respect to cluster of
SME. The factor POG is viewed by all the clusters equally. It is concluded that
SMEs expect Government to purchase all their products and they face different
dimensions of problems in acquiring the loans and subsidies based on the
products they produce.