FINDINGS AND DATA ANALYSIS – Chapter VI
CHAPTER VI
FINDINGS AND DATA ANALYSIS
CONTENTS
6.1 Introduction
6.2 Data cleaning and screening
6.3 Coding of responses
6.4 Over View of Organizations under study
6.5 Respondent Demographic Profile
6.6 Unions at Workplace
6.7 Degree of Involvement in WPM Schemes
6.8 Awareness Regarding Various Forms of WPM
6.9 Areas that Provide Maximum Opportunity of Participation
6.10 Importance of WPM in Different Areas of Decision Making
6.11 Determinants of Effective WPM
6.12Effectiveness of WPM Schemes
6.13 Importance of Workers Participation in Management
6.14 Importance of Workers Participation in Management in Sugar Industry v/s
Fertilizer Industry
6.15 Results of Open Ended Questions
6.16 Results of Group Interview
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FINDINGS AND DATA ANALYSIS – Chapter VI
CHAPTER VI
FINDINGS AND DATA ANALYSIS
6.1 Introduction
The methodology to collect data for this research was described in the previous chapter. This
chapter then reports the results of analyzing that data. Firstly, a preliminary examination of the data
is done which includes steps such as coding the responses, cleaning, screening the data.
6.2 DataCleaning and Screening
This section presents the screening and cleaning of raw data before they were analyzed. Two broad
categories of problems are discussed: case-related issues such as the accuracy of the data input,
missing observations, and outliers; and distribution issues such as normality (Hair et al.,1998;
Tabachnick & Fidell 2001). Screening of the data sets was conducted through an examination of
basic descriptive statistics and frequency distributions. Values that were found to be out of range or
improperly coded were detected with straightforward checks (Kassim 2001). A frequency test was
run for every variable to detect any illegal and missing responses.
However, 101 cases of the completed questionnaires were found to be unusable because of missing
responses. An inspection of the data set revealed that there were incomplete responses in Section A,
B and C of the questionnaire. Hence, these missing responses were discarded immediately which
resulted in 249 usable responses. This procedure is known as casewise deletion (Malhotra, 1999)
and was preferred to other methods of analyzing missing responses. In case-wise deletion only
cases with complete records are included, i.e., all analyses are conducted with the same cases (Kline
1998), and hence consistency is maintained. Although the deletion of cases resulted in a
substantially smaller than the original sample size, the sample of 249 was adequate for further
analysis.
On the other hand, an alternative approach of pair-wise deletion of cases excludes the missing
responses for variables involved in a particular computation. This method uses all possible cases for
each calculation, but it will result in inconsistency of the effective sample size from analysis to
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FINDINGS AND DATA ANALYSIS – Chapter VI
analysis. That is, results may be derived from different sample sizes. This feature of pair-wise
deletion presents a potential drawback for multivariate analysis with grouped data because of the
out-of-range correlations or co-variances that occur (Kline 1998). Imputation is another method
usedfor analyzing missing responses — this technique involves pattern matching which replaces “a
missing observation with a score from another case with a similar profile of scores across other
variables” (Kline 1998). In this study, due to the nature of sample imputation was not feasible.
6.3 Coding of Responses
This task involved identifying, classifying and assigning a numeric or character symbol to data,
which may be done in two ways: pre-coded and post-coded (Luck & Rubin 1987; Wong, 1999). In
this study, most of the responses were pre-coded except for questions 7-a and 7-b, which required
post-coding. Taken from the list of responses, a number corresponding to a particular selection was
given. This process was applied to every earlier question that needed this treatment. Upon
completion, the data were then entered to a statistical analysis software package, SPSS version 15,
for the next steps.
6.4 Over View of Organizations under study
The study was conducted in the state of Uttar Pradesh with a sample size of six factories across two
industries i.e., Sugar Industry and Fertilizer Industry. Three units of sugar industry and three of
fertilizer industry were taken into consideration for collecting the data. A brief profile of some of
the factories visited along with the scenario of WPM schemes is discussed as follows:
6.4.1 ROSA SUGAR WORKS – Oudh Sugar Mills Limited
The Oudh Sugar Mills Limited (OSML) belongs to the renowned K.K. Birla Group of Companies.
K.K. Birla Group is a major player in key industries like fertilizers, chemicals, heavy engineering,
textiles, shipping, media etc. apart from sugar. From a modest beginning in 1932, OSML has grown
to become the pioneers in Sugar Industry. It is one of the largest and rapidly growing companies in
the Sugar Industry.
Shri C.S. Nopany, Chairman cum Managing Director, is in the overall management of the Company
and is the driving force of the Company.
Through organic and inorganic modes of growth, the company has cautiously but consistently
grown from a single unit sugar manufacturing company to a company having four sugar
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FINDINGS AND DATA ANALYSIS – Chapter VI
manufacturing units with an aggregate crushing capacity of about 28,700 tonnes of sugarcane per
day, two Distilleries producing 160 kilo litre per day (KLPD) of industrial alcohol/ethanol, three
Co-generation Power Plants with a total capacity of 60 MW Power, Bio-Compost plant producing
organic fertilizer.
Workers Participation at ROSA, Rosa (Shahjahanpur)
Representation is through Union at ROSA sugar works. Both Bipartite and Tripartite committees
are active at the plant. There are 3 registered unions namely:
ROSA Sugar Works Mazdoor Sangh (INTUC),
ROSA Sugar Works Mazdoor Union (BMS) and
ROSA Sugar Works Karamchari Union (CITU)
The plant has Employee Suggestion Scheme working effectively to add to the participation of
workers.
6.4.2 BALRAMPUR CHINI MILLS LTD, Balrampur
Balrampur Chini Mills Ltd is one of the largest integrated sugar manufacturing companies in India.
Its allied business consists of manufacturing and marketing of Ethyl Alcohol & Ethanol, generation
and selling of power and manufacturing and marketing of organic manure. Company has nine sugar
factories located in Eastern U.P. having an aggregate crushing capacity of 73,500 tons per day.
BCML has taken over the management of Indo Gulf Industries Ltd (IGIL), which has since become
the subsidiary of the company. IGIL has a sugar cane crushing capacity of 3000 TCD and it is
located at Maizapur.
Workers’ Participation at Balrampur Chini Mills Ltd.
There are six registered unions and participation is done through Unions. Both bipartite and
tripartite committees are functional at BCM Ltd. Besides this there are several committees viz.,
Safety Committee, Welfare Committee, Cooperative society.
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FINDINGS AND DATA ANALYSIS – Chapter VI
Co-operative Society helps the members on personal front like education and marriage of children
where members are required to contribute only 30 Rs per month to the society.
Suggestion scheme is also very effective at Balrampur Chini Mills.A suggestion box is installed at
the entrance of the factory gate and on the occasion of ‘Vishwakarma Puja’ the best suggestions of
the year are rewarded.
The Safety Committee has the ambulance facility which is available 24 hrs for workers in case of
emergency within the factory premises.
All the above committees meet frequently at a regular interval at least 3 – 4 times in a month.
There is a DGM for disciplinary procedures and the problems are directed to Chief General
Manager. Though there is no board level participation but employee stock option is effective after
every five years for the employees who are older than five year in the organization.
Training session is conducted twice every year in which workers are provided information about
any technological updation or about new machinery.
6.4.3 KRIBHCO Shyam Fertilizers Limited
KRIBHCO Shyam Fertilizers Limited manufactures nitrogenous fertilizer viz. urea through
integrated urea and ammonia manufacturing facility at Shahjahanpur in the state of Uttar Pradesh in
India. The Fertilizer Plant is the latest Greenfield Urea Plant in India- commissioned in November
1995. The Fertilizer Plant is strategically located in North India, right in the middle of a high urea
consumption belt and based on Natural Gas as feedstock supplied through the Hazira-VijaypurJagdishpur ("HVJ") gas pipeline.
The Fertilizer Plant is located at Village Piprola, on State Highway No. 29 and is about 12 km from
the District Headquarters of Shahjahanpur. It has an installed capacity of 864,600 MTPA of urea
and 501,600 MTPA of ammonia. We have been operating our Fertilizer Plant at more than 100%
capacity utilization since acquisition. The plant is ISO 9001:2000 and ISO 14001:1996 certified by
KPMG Quality Registrar (a business unit of KPMG). The marketing of Urea and surplus ammonia
produced by the Company is undertaken by KRIBHCO, one of the promoters Company. The urea is
sold under the brand
’.
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FINDINGS AND DATA ANALYSIS – Chapter VI
Workers Participation at KRIBHCO, Shahjahanpur
Following committees are functional at KRIBHCO plant:
1.
Canteen Committee:

The main objective of this committee is to review & monitor the performance of the
canteen to maintain the satisfaction level.

2.
Resolving grievances related to the canteen function.
House Allotment committee:

This committee mainly allott the quarters to the employees on the application and
seniority basis.
3.
Transport Committee: (as welfare measure company provides transport facility to all
theEmployees for Shahjahanpur city for marketing, for Railway Station & Bus Stand)

This committee looks after the smooth functioning of the vehicles.

Resolving grievances related to the transport function.
Note: All the above committees are consist of the combination of officers (from management) and
the representative nominated by the Workers Union.
6.4.4 IFFCO, Bareilly (Aonla)
Indian Farmers Fertilizer Cooperative Limited (IFFCO), Aonla operates fertilizer plant for
manufacturing Urea at P.O. IFFCO Township, Aonla, Distt. Bareilly (U.P.). It comprises of two
Ammonia Plant of 1520 MT per day and four streams of Urea plant each of capacity 1310 MT per
day and a captive power plant of 2 X 18.0 MW. Urea silo, bagging plant and other related offsite
facilities like - water treatment plant, effluent treatment plant, inert gas plant, cooling towers,
naphtha. Ammonia storage and supply of utilities like compressed air, water etc. also exists for
smooth operation of plant.
The site is located at a distance of about 28 km south-east of Bareilly, on Bareilly-Aonla highway
(SH-33, 22 KMS upto Bareilly- Bhamora-Badaun road and about 6km on Bhamora-Aonla road).
The entire project site occupies an area of 1273 acres out of which about 673 acres is occupied by
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FINDINGS AND DATA ANALYSIS – Chapter VI
the plant where industrial activity is performed and the balance is occupied by the IFFCO
residential township, drains, open spaces, etc. The site is well far away from the crowded
population and near by vicinity only after 2 kms the thin populated villages are there.
Management of IFFCO is as per the guidelines of a Co-operative society and provision for workers
participation in the management through a representative exists at IFFCO. Employer development
leads to the organizational development IFFCO has grasped thisfundamental truth. A fully
functional Training and Development Section has been created toprovide learning avenues to the
employees. Most of the employees are recruited as traineesand men required to undergo extensive
off the job and on the job training in variousdisciplines. Other managerial and development training
is also arranged in house as well as atthe other Prestigious institutions. The development activities
do not end with the employeesbut extended to the families also.
Workers’ Participation at IFFCO, Bareilly (Aonla)
The organization has set up healthy traditions in encouraging and fostering cordial and,harmonious
industrial relations in dealing with employees.
There are four registered unions and participation is done through Unions. Both bipartite and
tripartite committees are functional. Besides this there are several committees viz., Safety
Committee, Welfare Committee, Cooperative society.
IFFCO-AONLA has identified Health and Safety as a major thrust area since beginning and is
continuously putting efforts for improvement in the Safety, Health and Environment
Management.IFFCO’s continuous best efforts to implement Safety, Health and Environment
Systems in the organization have been appreciated and recognised by several Government and
safety regulating bodies.
Labor welfare work is undertaken by various groups within and outside anorganization to improve
the living conditions of workers. The objective is to make theworker happy, healthy, committed and
loyal. Employers with a progressive outlook havealways invested heavy amounts in enriching the
life of workers.
The participative management is encouraged by formation and working of variouscommittees like
workers committees, safety committee, canteen committee House allotment committee, etc.
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FINDINGS AND DATA ANALYSIS – Chapter VI
6.5 Respondents Demographic Profile
In this section, frequency distributions were calculated for all cases in this research and were
organization in Table 6.1 below:
Table 6.1:Sex distribution of Respondents
Gender
No of Respondents
%
Male
249
100
Female
0
0
Gender is an issue of great concern in both the type of industries. No female workers were found on
the rolls in both the industries.
Maximum respondents were in the age group of 35 – 45, almost 37 percent respondents fall in this
category followed by 25 – 35 which have 33 percent respondents. More than 70 percent of the
sample is from this age bracket. The people in the age bracket of 18 – 25 and 45 – 55 is relatively
very low i.e. 17.67% and 12.05%. (Table 6.2)
Table 6.2: Composition of Age-group for the Sample
Age – Group
No of Respondents
%
18 – 25
44
17.67
25 – 35
83
33.33
35 – 45
92
36.95
45 – 55
30
12.05
249
100
Total
The industry wise distribution of age – group revels that Sugar industry is having more percentage
of employees in higher age – group whereas fertilizer industry is having more young blood. Table
.3, clearly indicates the composition of age group in the both the industry.
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FINDINGS AND DATA ANALYSIS – Chapter VI
Table6.3:Factory wise Age distribution of Respondents
Sugar Mills
Industry
Age Group
Total
No.
%
No.
%
16
13.45
28
21.54
44
18 – 25
36
30.25
47
36.15
83
25 – 35
51
42.86
41
31.54
92
35 – 45
16
13.45
14
10.77
30
45 – 55 & above
Total 119
100.00 130
100.00
249
Majority of respondents were educated upto the level of Senior or Intermediate. Table6.4 depicts
the educational qualifications of respondents in total.
Table6.4:Respondents educational qualifications
Educational Qualifications No of Respondents
%
Primary or below
23
9.23
SeniorSecondary
81
32.53
Intermediate
82
32.93
Graduate
63
25.30
Total
249
100
Table6.5 depicts the educational profile of respondents Industry wise cum Factory wise. All the
workers in Fertilizer Industries were above senior secondary level, whereas Sugar Industry also had
few workers who were educated upto primary or even below.
Table 6.5: Industry wise Respondents Educational Qualification
Qualifications
Sugar Industry
Fertilizer Industry
Rosa
Balrampur Sarsawa
IFFCO
IFFCO
Sugar
Chini
Sugar KRIBHCO Bariley Phoolpur
Primary or Below
6
7
10
0
0
0
Senior Secondary
17
12
16
9
15
12
Intermediate
9
11
9
22
18
13
Graduate
9
7
6
15
16
10
41
37
41
46
49
35
Total
105
FINDINGS AND DATA ANALYSIS – Chapter VI
Table 6.6 depicts the job position of respondents in both the industries. In case of sugar industry 46
workers out of 119 (i.e. 38.65%) workers surveyed from sugar industry were semi-skilled, 42 out of
119 (i.e. 35.29%) were skilled and 31 out of 119 (i.e. 26.06%) were above foreman level. Whereas,
in case of fertilizer industry 31 workers out of 130 (i.e. 23.84%) workers surveyed from sugar
industry were semi-skilled, 45 out of 130 (i.e. 36.62%) were skilled and 54 out of 130 (i.e. 41.54%)
were above foreman level.
Table6.6:Job Positions of Respondents
Job Position
Industry
Sugar
Fertilizer
Total
Semi Skilled
46
31
77
Skilled
42
45
87
Above
foreman
31
54
85
Total
119
130
249
6.6 Unions at Work Place
Study of Unions at work place is important to understand the functioning and effectiveness of
WPM. The Unions act as a platform for WPM. Therefore, the study investigated the presence of
Unions at all the sampling units.
Table 6.7Members of Union – Industry wise
Industry
Rosa Sugar Mill
Balrampur Chini
Sarsawa Sugar Mill
KRIBHCO
IFFCO Barilley
IFFCO Phoolpur
Total
Sample Size
41
37
41
46
49
35
249
Members
30
30
32
36
40
29
197
Non-Membes
11
7
9
10
9
6
52
It was found that all the factories selected for study were having at-least two to three Unions, and
majority of workers was members of one or the other Union. Out of 249 workers surveyed 197
were members and remaining 52 had not joined any union so far. The main reason for not joining
106
FINDINGS AND DATA ANALYSIS – Chapter VI
was not shared openly by the workers but simply said that they don’t felt the need to join any such
union. Majority of member said that they might join some union in the future if they are asked to
join.
Table 6.8Willingness to join Union if asked to do so
Yes
No
39
13
Non Members
The results indicate that 75% of non-members would join a union if they had the opportunity to do
so. As to the reasons for not joining a union, the survey found little evidence of actual resistance to
trade unions. Only 14 % of respondents said that “they were not sure what unions could do for
them”, while 70 % said that they “had never been approached”. The results indicate that there is
difference in people’s views towards unions across different social classes: 75 % of those in the
skilled manual, partly-skilled and unskilled occupations said that they would join a union if they
had the opportunity.
6.7 Degree of Involvement in WPM Schemes
Degree of Involvement in WPM Schemes
Figure6.1: Degree of Involvement in WPM Schemes
Very Low
Low
Fertilizer
Moderate
Sugar
High
Very High
0
10
20
107
30
40
50
FINDINGS AND DATA ANALYSIS – Chapter VI
Findings on respondent’s frequency of involvement in the WPM schemes of their organization as
reflected in table 6.9 shows that respondents in two industries demonstrated moderate involvement.
However, it was observed that workers in Fertilizer Industry have a higher frequency of
involvement than those in Sugar Industry. (Annexure iii)
Table 6.9: Degree of Involvement in WPM Schemes
Degree of Involvement
Org. type
Very High
Sugar Mills
0.1764
Fertilizer Factories
0.2769
Sugar Mills
0.2436
Fertilizer Factories
0.2615
Sugar Mills
0.3277
Fertilizer Factories
0.2384
Sugar Mills
0.1680
Fertilizer Factories
0.1384
Sugar Mills
0.0840
Fertilizer Factories
0.0846
High
Moderate
Low
Very Low
Mean
Std. Error
0.053303
T
-1.88457
Sig.
0.0490
0.01306
-1.3705
0.04719
0.057034
1.565194
0.0487
0.045624
0.648907
0.57687
0.035256
-0.0165
0.69725
In order to check the statistical significance of difference between the degree of involvement among
Sugar and Fertilizer workers hypothesis test for proportions is carried out as under. Cumulative
Scores for degree of participation for Sugar and Fertilizer Industry are calculated using following
formula
Cumulative proportion = [R1 x n1 + R2 x n2 …………. + Rn x nn] /(N x R1)
where, R= Rank (R1is for the highest rank R5 is the lowest rank, and n= no.of respondents
Sugar (p1)
= 0.6521
Mean Score =3.26
Fertilizer (p2)
= 0.7015
Mean Score = 3.51
Total Mean Score
= 0.6779
or
3.39
Formula used for T-Test for proportions is as follows:
T
=
(p1 – p2)/SE
SE
=
SE = sqrt{ p * ( 1 - p ) * [ (1/n1) + (1/n2) ] }
p
=
(p1*n1 + p2*n2)/(n1 + n2)
Where: n1 = 119, n2 = 130
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FINDINGS AND DATA ANALYSIS – Chapter VI
However, the results in the table below rejects the null hypothesis (H1 : p1 = p2) and the alternative
hypothesis (H2 : p1 ≠ p2) is accepted. Since, the Sig is 0.0310 which is lower than 0.05 (table 6.9-a),
it can be concluded that workers in Fertilizer Industry have a higher frequency of involvement than
those in Sugar Industry.
Table 6.9-a: Degree of Participation – Sugar Mills v/s Fertilizer Industry
Industry
Sugar Mills
Fertilizer
H1:
Mean
0.6521
0.7015
Std. Error
T
Sig.
0.024892
-1.98457
0.0310
Involvement in WPM is dependent upon Demographics (Age, Education, Job Position and
Work Experience)
This variation could be explained by the relatively higher educational status of workers of Fertilizer
Industry.Table6.10 and 6.10(a) shows that there is a statistically significant relation between
respondents’ educational qualifications and degree of involvement in WPM schemes.
Table6.10:Educational Qualification * Degree of Involvement Cross-tabulation
Degree of Involvement
Very
Low
Educational
Qualification
Primary or
below
Count
Expected Count
Senior
Count
secondary
Expected Count
Intermediate Count
Expected Count
Graduate
Total
Count
Expected Count
Count
Expected Count
3
1.9
8
6.8
7
6.9
3
5.3
21
21.0
109
Low Moderate High
7
12
1
3.5
6.5
5.8
12
26
20
12.4
22.8 20.5
11
18
24
12.5
23.1 20.7
8
9.6
38
38.0
14
17.7
70
70.0
18
15.9
63
63.0
Very
High
0
5.3
15
18.5
22
18.8
Total
23
23.0
81
81.0
82
82.0
20
14.4
57
57.0
63
63.0
249
249.0
FINDINGS AND DATA ANALYSIS – Chapter VI
Table6.10(a)Chi-Square:Educational Qualification * Degree of Involvement
Asymp. Sig. (2sided)
Df
Value
Pearson Chi-Square
26.236a
12
.010
N of Valid Cases
249
a. 2 cells (10.0%) have expected count less than 5. The minimum expected count is 1.94.
Table6.11:Educational Qualification of Sugar Workers * Degree of Involvement of Sugar Workers
Cross-tabulation
Educational
Qualification of
Sugar Workers
Primary or
below
Count
Expected Count
Senior
Count
secondary
Expected Count
Intermediate Count
Degree of Involvement of Sugar Workers
Very
Very
Low
Low Moderate High
High
3
7
12
1
0
1.9
3.9
7.5
5.6
4.1
5
7
17
10
6
3.8
7.6
14.7
11.0
7.9
1
4
5
11
8
Expected Count
Graduate
Total
2.4
4.9
9.5
7.1
5.1
29.0
1
2
5
7
7
22
1.8
10
10.0
3.7
20
20.0
7.2
39
39.0
5.4
29
29.0
3.9
21
21.0
22.0
119
119.0
Count
Expected Count
Count
Expected Count
Table6.11(a):Chi-Square Tests: Educational Qualification of Sugar Workers *
Degree of Involvement of Sugar Workers
Pearson Chi-Square
Total
23
23.0
45
45.0
29
Value
26.754a
N of Valid Cases
119
110
Asymp. Sig. (2sided)
df
12
.008
FINDINGS AND DATA ANALYSIS – Chapter VI
Table6.11(a):Chi-Square Tests: Educational Qualification of Sugar Workers *
Degree of Involvement of Sugar Workers
Value
26.754a
Pearson Chi-Square
N of Valid Cases
Asymp. Sig. (2sided)
df
12
119
.008
a. 9 cells (45.0%) have expected count less than 5. The minimum expected count
is 1.85.
Table6.11 and 6.11(a) shows that there is a statistically significant relationship between respondents
educational qualifications and degree of involvement in WPM schemes in Sugar Industry whereas,
such relationship is not present in Fertilizer Industry [Table6.12 and Table6.12 (a)].
Table6.12:Educational Qualification of FertilizerWorkers * Degree of Involvement
Degree of Involvement of Fertilizer Workers
Very
Very
Low
Low Moderate High
High
Educational Senior
Count
3
5
9
10
9
Qualification secondary
Expected Count
3.0
5.0
8.6
9.4
10.0
of Fertilizer Intermediate Count
6
7
13
13
14
Workers
Expected Count
4.5
7.3
12.6
13.9
14.7
Graduate
Total
Count
Expected Count
Count
Expected Count
2
3.5
11
11.0
6
5.7
18
18.0
9
9.8
31
31.0
11
10.7
34
34.0
13
11.4
36
36.0
[Table6.12 (a)] Chi-Square Tests
Asymp. Sig. (2sided)
Value
Df
a
Pearson Chi-Square
1.722
8
.988
N of Valid Cases
130
a. 4 cells (26.7%) have expected count less than 5. The minimum expected count
is 3.05.
111
Total
36
36.0
53
53.0
41
41.0
130
130.0
FINDINGS AND DATA ANALYSIS – Chapter VI
Table 6.13 depicts the contingency matrix for relationship between Job Position and Degree of
participation and 6.13(a) suggests that there is a significant relationship between job position held
and degree of participation. Workers at higher job position were found to be more participative as
compared to workers holding junior job positions.
Job
Position
Held
Total
Table6.13: Job Position Held * Degree of InvolvementCross tabulation
Degree of Participation
Very
Very
Low
Low Moderate High
High
Semi-skilled or Count
12
17
27
12
9
below
Expected Count
6.5
11.8
21.6
19.5
17.6
Skilled
Count
7
11
32
22
15
Expected Count
7.3
13.3
24.5
22.0
19.9
Foreman Level Count
2
10
11
29
33
Expected Count
7.2
13.0
23.9
21.5
19.5
Count
21
38
70
63
57
Expected Count
21.0
38.0
70.0
63.0
57.0
Total
77
77.0
87
87.0
85
85.0
249
249.0
Table6.13 (a):Chi-Square Tests: Job Position Held * Degree of Involvement
Pearson Chi-Square
N of Valid Cases
Value
42.781a
Df
Asymp. Sig. (2sided)
8
.000
249
a. 0 cells (.0%) have expected count less than 5. The minimum expected count is 6.49.
Table 6.14depicts the contingency matrix for relationship between Job Position and Degree of
participation for sugar industry and 6.14 (a) suggested that in sugar industry there is a significant
relationship between job position held and degree of participation.
Job
Table6.14:Job Position * Degree of Involvement - Sugar Workers Cross tabulation
Degree of Participation - Sugar Workers
Very
Very
Low Low Moderate High High Total
Semi-skilled Count
6
11
15
6
5
43
112
FINDINGS AND DATA ANALYSIS – Chapter VI
Position
Held Sugar
Workers
or below
Skilled
Expected Count
Count
Expected Count
Count
Expected Count
Count
Expected Count
Foreman
Level
Total
4.5
4
3.8
2
3.8
12
12.0
8.2
6
6.9
5
6.9
22
22.0
11.6
12
9.7
4
9.7
31
31.0
9.7
8
8.1
12
8.1
26
26.0
9.0 43.0
6
36
7.5 36.0
13
36
7.5 36.0
24
115
24.0 115.0
Table6.14 (a):Chi-Square Tests
Value
17.143a
115
Pearson Chi-Square
N of Valid Cases
Df
8
Asymp. Sig. (2sided)
.029
Table6.15:Job Position * Degree of Involvement - Fertilizer Workers Cross tabulation
Degree of Participation - Fertilizer Workers
Very
Very
Low
Low Moderate High
High
Total
Job
Semi-skilled Count
6
6
12
6
4
34
Position
or below
Expected Count
2.3
4.1
9.9
9.4
8.4
34.0
Held Skilled
Count
3
5
20
14
9
51
Fertilizer
Expected Count
3.4
6.1
14.8
14.1
12.6
51.0
Workers
Foreman
Count
0
5
7
17
20
49
Level
Expected Count
3.3
5.9
14.3
13.5
12.1
49.0
Total
Count
9
16
39
37
33
134
Expected Count
9.0
16.0
39.0
37.0
33.0
134.0
Table6.15(a) Chi-Square Tests
Value
df
Asymp. Sig. (2-sided)
Pearson Chi-Square
N of Valid Cases
27.195a
134
8
.001
Table 6.15depicts the contingency matrix for relationship between Job Position and Degree of
participation for fertilizer industry and 6.15(a) suggested that in fertilizer industry there is a
significant relationship between job position held and degree of participation.
113
FINDINGS AND DATA ANALYSIS – Chapter VI
However, no such relationship was found between respondents’ age group and degree of
involvement. The chi-square statistics results (Table6.16(a) indicates that there is no statistical
relationship between the degree of involvement and respondents age group.
Table6.16:Age Group * Degree of Involvement Cross-tabulation
Degree of Involvement
Very
Low
Age
18 - 25 Count
Group
Expected
Count
25 - 35 Count
Expected
Count
35 - 45 Count
Expected
Count
45 - 55 Count
Expected
Count
Total
Count
Expected
Count
Low
5
Moderate
6
10
High
12
Very
High
11
Total
44
3.7
6.7
12.4
11.1
10.1
44.0
6
14
25
19
19
83
7.0
12.7
23.3
21.0
19.0
83.0
4
9
29
26
24
92
7.8
14.0
25.9
23.3
21.1
92.0
6
9
6
6
3
30
2.5
4.6
8.4
7.6
6.9
30.0
21
38
70
63
57
249
21.0
38.0
70.0
63.0
57.0
249.0
Similarly there is no significant relationship between the duration of service and degree of
involvement in WPM schemes. Table 6.17 shows the comparison between duration of service and
degree of involvement. Similarly table 6.17(a) clearly indicates that there is no significant statistical
relationship between the two variables.
Table6.16(a):Chi-Square Tests
Pearson Chi-Square
N of Valid Cases
Value
18.706(a)
df
12
Asymp. Sig. (2-sided)
.096
249
a 3 cells (15.0%) have expected count less than 5. The minimum expected count is 2.53.
114
FINDINGS AND DATA ANALYSIS – Chapter VI
Table6.17: Duration of Service * Degree of InvolvementCross tabulation
Very
Low
Duration Less than 5 years
of
Service
5 - 10 years
10 - 15 years
more than 15
years
Total
Count
Expected
Count
Count
Expected
Count
Count
Expected
Count
Count
Expected
Count
Count
Expected
Count
6
Degree of Involvement
Moderat
Low
e
High
9
6
10
Very
High
3
Total
34
2.9
5.2
9.6
8.6
7.8
34.0
4
9
25
22
18
78
6.6
11.9
21.9
19.7
17.9
78.0
5
12
27
19
25
88
7.4
13.4
24.7
22.3
20.1
88.0
6
8
12
12
11
49
4.1
7.5
13.8
12.4
11.2
49.0
21
38
70
63
57
249
21.0
38.0
70.0
63.0
57.0
249.0
Table6.17(a)Chi-Square Tests
Pearson Chi-Square
N of Valid Cases
Value
17.047(a)
df
12
Asymp. Sig. (2-sided)
.148
249
a 2 cells (10.0%) have expected count less than 5. The minimum expected count is 2.87.
6.8 Awareness Regarding Various Forms of WPM
Table 6.18 depicts the level of awareness among workers regarding various participative schemes.
The percentage distribution suggests that fertilizer industry workers are more aware as compared to
sugar industry workers. According to table 6.18 highest level of awareness is about employee stock
option followed by employee suggestion schemes. Similarly awareness level is high for quality
circles and shop floor level participation. Awareness level was found to be low in for joint
management councils and participation at board level. (Annexure iii)
115
FINDINGS AND DATA ANALYSIS – Chapter VI
Fig 6.2Awareness among workers regarding Forms of WPM
Council Participation at Board Level
Joint Management
IFFCO Phoolpur (35)
Work Councils
IFFCO Bariely (49)
KRIBHCO (46)
Quality Circles
Sarsawa Sugar(41)
Balrampur Chini (37)
Employee Suggestion Scheme
Roza Sugar (41)
Employee Stock Option
Shop Floor Level Participation
0
20
40
60
80
100
Table 6.18: Awareness about various WPM Schemes among Workers (Factory-wise)
Participative Schemes
Shop Floor Level
Participation
Employee Stock Option
Employee Suggestion
Scheme
Roza
Sugar
(41)
Balrampur
Chini (37)
Sarsawa KRIBHCO
Sugar(41)
(46)
IFFCO
Bariely
(49)
IFFCO
Phoolpur
(35)
85.37%
75.68%
48.78%
76.09%
77.55%
94.29%
60.20%
97.30%
100.00%
89.13%
95.92%
100.00%
90.24%
86.49%
65.85%
76.09%
81.63%
94.29%
51.44%
97.30%
83.37%
89.13%
95.92%
90.00%
43.23%
97.30%
68.29%
89.13%
91.84%
98.57%
65.85%
59.46%
29.27%
71.74%
77.55%
88.57%
12.20%
8.11%
0.00%
15.22%
8.16%
11.43%
Quality Circles
Work Councils
Joint Management
Council Participation at
Board Level
116
FINDINGS AND DATA ANALYSIS – Chapter VI
H2:
There is equal awareness about WPM schemes among workers
The research results reveal that there is a significant difference between the level of awareness
about WPM schemes in different factories. In order to the statistical significance of difference
between Sugar and Fertilizer industry t-test for proportions is used. Table 6.18(a) discusses the
results of t-test.
Formula used for T-Test for proportions is as follows:
T
=
(p1 – p2)/SE
SE
=
SE = sqrt{ p * ( 1 - p ) * [ (1/n1) + (1/n2) ] }
p
=
(p1*n1 + p2*n2)/(n1 + n2)
Where: n1 = 119, n2 = 130
Table 6.18(a): Degree of Involvement in WPM Schemes (Industry-wise)
Degree of Involvement
Industry
Mean
Shop Floor Level Participation Sugar Mills
0.6994
Fertilizer Factories
0.8264
Sugar Mills
0.8584
Fertilizer Factories
0.9501
Sugar Mills
0.8086
Fertilizer Factories
0.8400
Sugar Mills
0.7737
Fertilizer Factories
0.9168
Sugar Mills
0.6960
Fertilizer Factories
0.9318
Sugar Mills
0.5152
Fertilizer Factories
0.7928
Council Participation at Board Sugar Mills
Level
Fertilizer Factories
0.0677
Employee Stock Option
Employee Suggestion Scheme
Quality Circles
Work Councils
Joint Management
117
0.1160
Std. Error
T
Sig.
0.05392
-2.44781
0.014
-2.4821
0.013
-0.65135
0.514
.045498
-3.14516
0.001
0.048835
-4.82841
0.000
0.060093
-4.61944
0.000
0.036832
-1.3113
0.036984
0.048207
0.094