Contractors Lack Of Financial Resources

Published Date: 02 Nov 2017

Chapter 5

At the initiation phase, there were 73 risk variables which were observed but many of these variables not considered for the research work because either they had the same meaning or they were irrelevant while studying cost overrun.

Hence to filter out these risk variables I have taken help of the senior professionals from the steel industry. These experienced personals were asked to weight these risk variables. On the basis of the weights, the 31 variables were selected. The table of these weights is provided at the appendix A.

The data analysis and interpretation is done one by one for each of the objectives in the research.

5.2 Objective 1:

To identify various risk factors associated with the project cost performance in establishing steel plant in India.

5.2.1 Data Analysis for this objective

To identify the various risk factors associated with the project cost performance, I have used the "factor analysis" and have found out seven factors that have a significant contribution in the project cost performance.

The paper adopts factor analysis to simplify the performance evaluation of variables. Factor analysis is suitable for variables ranges from 20 to 50. The extraction number of common factors would be not accurate if number of the variable were out of this range. The research uses 31 variables and hence it is suitable to apply factor analysis.

5.2.2 Cronbach’s Alpha Value

Cronbach's alpha is a coefficient of internal consistency. It is commonly used as an estimate of the reliability of a psychometric test for a sample of examinees. The value of Cronbach’s alpha should be more than 0.6 for the model to be significant and hence the Cronbach’s Alpha value of 0.918 is highly significant source

Reliability Statistics

Cronbach's Alpha

N of Items

.918

31

KMO and Bartlett's Test

Kaiser-Meyer-Olkin Measure of Sampling Adequacy.

.868

Bartlett's Test of Sphericity

Approx. Chi-Square

7117.943

Df

496

Sig.

.000

5.2.3 Interpretation:

As shown from Table above, the KMO value is 0.868, which is larger than 0.5. The Sampling Adequacy of Bartlett’s test is rather high. Therefore, it is suitable for the factor analysis. As shown on Table-1, 7 groups of factors are extracted. The variable numbering is equal to the number in the questionnaire.

Hence the Model is highly significant as per the KMO value.

5.3 Screen Plot

The number of factors here were determined based on Eigen values. In this approach, only Performance Indicators (factors) with Eigen values greater than 1.0 were retained. The other indicators (factors) were not included in the model. An eigen value represents the amount of variance associated with factor. Performance Indicators (factors) less than 1.0 are no longer better a single variable, because, due to standardization, each variable has a variance of 1.0.

Table-1: The Risk Factors chosen for the study and the variables associated with them

Factor Name

S.no

Variable Name

Bureaucracy and Management.

(Factor-1)

1

Instability of Economic Condition

2

High Level of Bureaucracy

3

Unavailability of Equipment

4

Poor /Incomplete design

5

Strict Environmental Regulations

6

Vagueness of Contract Clauses

7

Poor Site Supervision

8

Contractors lack of Financial Resources

9

Poor Project Scope Management

10

Poor Project time Management

11

Poor Project Risk management

12

Poor Procurement management

13

Change in Site/ Project Organisation

14

Rebellion/Terrorism/Naxelism

15

Delays/Interruption

Contractor Management.

(Factor-2)

16

Unavailability of Infrastructure

17

Strict Project Management Requirement

18

Contractors lack of Experience in similar Project

19

Poor Human resource management

20

Changes in Currency Rate

Skill Management. (Factor-3)

21

Unavailability of Local skilled labor

22

Unavailability of Local skilled Sub Contractors

23

Lack of Site Facilities

Design Management. (Factor-4)

24

Unavailability of Local Material

25

Design Errors

26

Contractual Errors

Works Management. (Factor-5)

27

Increase in Amount of work

28

Increase in Unit cost of work

Administration Management (Factor-6)

29

Instability of Government

30

Complexity of Construction Method

Materials Management. (Factor-7)

31

Change in Availability of Materials

Table: The Risk Factor Evaluation shift to annexures

SL.No.

Variable Names

Factor1

Factor 2

Factor 3

Factor 4

Factor 5

Factor 6

Factor 7

1.

Instability of Economic Condition

.780

.033

-.014

.203

-.090

.110

.006

2.

High Level of Bureaucracy

.757

.273

-.050

.049

.051

-.101

.065

3.

Unavailability of Equipment

.680

-.071

.132

.417

.130

-.008

.000

4.

Poor /Incomplete design

.733

.123

-.115

.222

.152

.184

.097

5.

Strict Environmental Regulations

.848

-.008

.125

-.115

.030

.003

.032

6.

Vagueness of Contract Clauses

.648

.157

.205

.383

.133

-.184

.156

7.

Poor Site Supervision

.648

-.054

.489

.197

-.065

-.085

.017

8.

Contractors lack of Financial Resources

.641

-.057

-.170

-.056

.399

-.094

.091

9.

Poor Project Scope Management

.891

-.166

.022

.099

.058

-.028

-.003

10

Poor Project time Management

.740

.102

.167

.041

-.096

.159

.071

11

Poor Project Risk management

.750

.177

.086

.247

.018

.037

-.011

12

Poor Procurement management

.863

.021

.174

.063

.013

.027

-.033

13

Change in Site/ Project Organisation

.551

.085

-.022

-.293

.090

-.050

.498

14

Rebellion/Terrorism/Naxelism

.754

.159

.321

.198

-.126

.014

-.034

15

Delays/Interruption

.711

.206

.334

-.058

-.082

-.169

-.108

16

Unavailability of Infrastructure

.237

.582

.244

.137

.173

.117

-.095

17

Strict Project Management Requirement

-.419

.564

.033

.070

.153

.139

.142

18

Contractors lack of Experience in similar Project

.313

.564

.001

.217

.274

-.050

-.133

19

Poor Human resource management

.287

.692

.133

.042

.032

-.141

.176

20

Changes in Currency Rate

-.185

.620

.098

.131

-.150

.251

.184

21

Unavailability of Local skilled labor

.027

.053

.648

.045

.312

.386

.066

22

Unavailability of Local skilled Sub Contractors

.221

.435

.617

.058

.128

.253

-.053

23

Lack of Site Facilities

.242

.255

.589

.270

-.048

-.039

-.083

24

Unavailability of Local Material

.269

.259

.086

.557

.353

-.038

.222

25

Design Errors

.356

.224

-.182

.476

.088

.564

-.048

26

Contractual Errors

.189

.187

.298

.690

.014

-.020

.047

27

Increase in Amount of work

-.024

.501

.103

-.101

.618

-.027

-.108

28

Increase in Unit cost of work

-.046

.089

.119

.238

.806

.143

.192

29

Complexity of Construction Method

-.100

-.062

.051

-.218

.204

.701

.026

30

Instability of Government

.041

.127

.187

.081

-.124

.641

.040

31

Change in Availability of Materials

.025

.095

-.042

.167

.090

.076

.892

5.4 Data Analysis for Objective 2

To identify the various risk variables responsible for the project cost overrun for construction of steel plant in India.

In order to identify various risk variables responsible for the project cost overrun for the construction of a steel plant in India, the weighted mean of the 31 variables was calculated and the top ten variables were listed as shown in the table below. To calculate the mean of these variables the following formula was used:

Mean

S.No.

Variable Name

Mean/Average Score

1

Instability of Economic Condition

3.997214

2

Poor Project Scope Management

3.738162

3

Poor Procurement management

3.671309

4

Strict Environmental Regulations

3.554318

5

Delays/Interruption

3.426184

6

Rebellion/Terrorism/Naxelism

3.392758

7

Contractors lack of Financial Resources

3.364903

8

Poor Project time Management

3.284123

9

Unavailability of Equipment

3.270195

10

Poor Project Risk management

3.261838

11

Poor Site Supervision

3.253482

12

Poor & Incomplete Design

3.183844

13

Vagueness of Contract Clauses

3.161560

14

High Level of Bureaucracy

3.150418

15

Increase in Amount of work

3.144847

16

Change in Site/ Project Organisation

3.119777

17

Unavailability of Local skilled Sub Contractors

3.025070

18

Increase in Unit cost of work

3.016713

19

Unavailability of Local skilled labor

3.011142

20

Unavailability of Local Material

2.988858

21

Poor Human resource management

2.938719

22

Unavailability of Infrastructure

2.930362

23

Design Errors

2.930362

24

Lack of Site Facilities

2.924791

25

Complexity of Construction Method

2.916435

26

Change in Availability of Materials

2.902507

27

Contractors lack of Experience in similar Project

2.888579

28

Instability of Government

2.885794

29

Contractual Errors

2.830084

30

Changes in Currency Rate

2.693593

31

Strict Project Management Requirement

2.493036

5.4.1 Interpretation:

The top ten risk variables responsible for the project cost overrun for construction of steel plant in India are found out on the basis of their weighted average score:

Instability of Economic Condition

Poor Project Scope Management

Poor Procurement management

Strict Environmental Regulations

Delays/ Interruption

Rebellion/ Terrorism/ Naxelism

Contractors lack of Financial Resources

Poor Project time Management

Unavailability of Equipment

Poor Project Risk management

5.5 Data Analysis of Objective-3

To determine the relationship between the major risk factors identified and cost overrun for construction of a steel plant in India.

For this purpose I have used "Binary Logistic Regression Technique"

Logistic regression is helpful when you want to predict a categorical variable from a set of predictor variables. Binary logistic regression is similar to linear regression except that it is used when the dependent variable is dichotomous. Multinomial logistic regression is used when the dependent/ outcome variable has more than two categories, but that is complex and less common. Logistic regression also is useful when some or all of the independent variables are dichotomous; others can be continuous.

Binary logistic regression assumes that the dependent or outcome variable is dichotomous and, like most other statistics, that the outcomes are independent and mutually exclusive; that is, a single case can only be represented once and must be in one group or the other. Finally, logistic regression requires large samples to be accurate: Some say there should be a minimum of 20 cases per predictor, with a minimum of 60 total cases.

Results from Logistic Regression

Case Processing Summary

Unweighted Cases

N

Percent

Selected Cases

Included in Analysis

345

100.0

Missing Cases

0

.0

Total

345

100.0

Unselected Cases

0

.0

Total

345

100.0

Interpretation: None of the variables have any missing data

Dependent Variable Encoding

Original Value

Internal Value

Taken

0

Not Taken

1

Interpretation: The Cost Overrun is the dependent outcome variable and is coded 0 and 1 wherein;

0 = Taken

1 = Not Taken

Classification Tablea,b

Observed

Predicted

VAR00003

Percentage Correct

Taken

Not Taken

Step 0

VAR00003

Taken

260

0

100.0

Not taken

85

0

.0

Overall Percentage

75.4

a. Constant is included in the model.

b. The cut value is .500

Interpretation: This implies that cost overrun is reflected by the 75.4% of the factors considered

Variables in the Equation

B

S.E.

Wald

Df

Sig.

Exp(B)

Step 0

Constant

-1.118

.125

80.072

1

.000

.327

Variables not in the Equation

Variables not in the Equation

Score

Df

Sig.

Step 0

Variables

VAR00004

40.574

1

.000

VAR00005

21.032

1

.000

VAR00006

.341

1

.559

VAR00007

8.527

1

.003

VAR00008

.186

1

.666

VAR00009

.624

1

.429

VAR00010

1.002

1

.317

Overall Statistics

72.287

7

.000

Interpretation: Values less than 0.05 are significant

Hence looking at above table the 4 (Four) Variables are coming to be significant.

Omnibus Tests of Model Coefficients

Omnibus Tests of Model Coefficients

Chi-square

df

Sig.

Step 1

Step

66.372

7

.000

Block

66.372

7

.000

Model

66.372

7

.000

Interpretation: The overall model is significant when all 7 (Seven) independent variables are entered.

Model Summary

Step

-2 Log likelihood

Cox & Snell R Square

Nagelkerke R Square

1

318.869

0.175

0.260

Interpretation: These are similar to R2 and give a rough estimate of the variance that can be predicted from the combination of the seven variables.

Classification Table

Observed

Predicted

VAR00003

Percentage Correct

Taken

Not Taken

Step 1

VAR00003

Taken

250

10

96.2

Not Taken

50

35

41.2

Overall Percentage

82.6

Interpretation: The overall model is significant when all 7 (Seven) independent variables are entered.

Variables in the Equation

B

S.E.

Wald

Df

Sig.

Exp(B)

Step 1a

VAR00004

.733

.139

27.718

1

.000

2.081

VAR00005

.582

.154

14.228

1

.000

1.790

VAR00006

.028

.142

.038

1

.846

1.028

VAR00007

.330

.136

5.894

1

.015

1.391

VAR00008

.080

.140

.332

1

.565

1.084

VAR00009

-.106

.142

.558

1

.455

.899

VAR00010

-.121

.138

.774

1

.379

.886

Constant

-1.295

.145

79.189

1

.000

.274

Interpretation: The overall model is significant when all 7 (Seven) independent variables are entered.