6.Claim Cause Relationship.full

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International Journal of Civil, Structural,

Environmental and Infrastructure Engineering

Research and Development (IJCSEIERD)

ISSN 2249-6866

Vol. 3, Issue 2, Jun 2013, 43-54

TJPRC Pvt. Ltd.

CLAIM-CAUSE RELATIONSHIP STUDY OF HIGHWAY CONSTRUCTION PROJECTS IN

INDIA THROUGH FACTOR ANALYSIS

DHAVAL M. PARIKH1

& G. J. JOSHI2

1Executive Director, SAI Consulting Engineers Private limited, India

2Associate Professor, Civil Engineering Department, SV National Institute of Technology, Surat, Gujarat, India

ABSTRACT

Indian highway construction industry has seen a multifold increase in the spending in the development of

highways in India. It has been observed that the majority of the construction contracts, due to variety of unanticipated and

indefinite parameters, run into problems, giving rise to claims and disputes among its stake holders, namely, the employer,the contractor and the engineer. This paper presents the study of the claimcause relationship and its behavior from 573

claim and dispute incidents from 77 highway construction contracts in India during the period of year 2000 to 2010.

Principal Component analyses were performed on the data thus collected to study their inter-relationship.

KEYWORDS: Claims, Causes of Claims, Highway Construction, Contracts, Factor Analysis, Principal Component

Analysis

INTRODUCTION

In todays context Construction is a complex process. Alluri (2004) observed that the construction industry in

India since ages have been in a disorganised state. There was a time when there were simple transactions and construction

contracts were routine jobs, but when engineering works of higher magnitude and of complex nature came for execution, a

number of parties had to be involved increased. Though the ultimate goal of each of them is to complete the project, yet

aims and path chosen to achieve this goal differs among the stake holders. Today, majority of the construction contracts,

due to variety of unanticipated and indefinite parameters, run into problems, giving rise to claims and disputes among its

stake holders, namely, the employer, the contractor and the engineer. Finishing a project on schedule is a difficult task to

accomplish in the uncertain, complex, multiparty, and dynamic environment of construction projects (Kartam, 1999).

Claim-cause behaviour study, therefore, in construction contracts, in recent times, have become very relevant and

quintessential. Projects suffer due to claims and disputes, consequent time overrun and cost over run takes place, and the

society as a whole is deprived of timely benefit from the project. This paper presents the study of the claim cause

relationship and behavior from the various claim and dispute incidents collected by the authors especially for the highway

construction contracts in India during the period of year 2000 to 2010. Principal Component analyses were performed on

the data thus collected to arrive at their inter-relationship and the cause-effect impact.

HIGHWAY CONSTRUCTION SECTOR IN INDIA

Highway sector constitutes a substantial part of infrastructure in India. India has the second largest road network

in the world 3.3 Million kilometers. The primary network, the National Highways (NH), has a length of about 70,500

km, to provide mobility to goods and passenger movements. Government of India (GoI) is primarily responsible for

development and maintenance of NH. The secondary network of State Highways (SH) is about 135,000 km long, while the


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44 Dhaval M. Parikh & G. J. Joshi

tertiary network comprises other district roads and village roads and has a total of about 3.1 million km primarily meant to

provide accessibility. The secondary and tertiary networks are primarily the responsibility of state governments.

National Highways (NHs) constituting only 2% of length, share almost 40% of the total traffic on Indian Roads.

Ever-growing traffic due to accelerated growth of economy has eventually congestion on several sections of present

network adversely affecting the mobility requirements. This has brought about the need to augment highway capacity to

cater for this increasing traffic volume, in addition to maintaining existing facilities. In this regard, development of

highways has been recognized as one of the key factors which shall have a positive impact on Indias economic growth.

In 1999, national Highways Development Project (NHDP) was launched. This comprises the four or six-laning of

about 13,000 km of national Highways in the country at the estimated cost of US$13 billion. The NHDP is being

implemented by National Highways Authority of India (NHAI), established under the Union Ministry of Shipping, Road

Transport & Highways. GoI has also provided funds for the construction and strengthening of the other roads (state

highways, major district road and village roads) from a separate fund, the Central Road Fund. The government has furtherexpanded the scope of the NHDP with a view to enhancing the socio-economic growth of the country through public

private partnerships model for accelerated development of highway network. It is estimated that the total development will

involve investments of about US$50 billion over the next 8-10 years. This will also attract significant amount of foreign

direct investments in addition to the investment by the government.

DISPUTE DATA ANALYSIS

Exhaustive and comprehensive data collection efforts were put in place to collect recently published Arbitration

Awards related to the claims and disputes occurred in highway construction projects in India. A number of Government

Departments, agencies (Employers) and Contractors were connected requesting them to part with the copies of the various

Arbitration Awards. Besides these, a few of the prominent lawyers and practicing arbitrators were also contacted to supply

copies of such awards.

The information of disputes/claims for 77 contracts totalling to 573 claim cases under the highway projects

implemented over a period of last 10 years (between year 2000 to 2010) in India was collected. These are highway

construction projects implemented in various states of India. The information collected is in form of claims and disputes

and its settlements through arbitration under Indian Arbitration and Reconciliation Act, 1996.

The information of the 77 construction contracts detailing the disputes between the Employer and the contractor

was studied. The construction contracts were categorized based on the construction cost and also based on the length of the

highway project, and the summary is presented in Table-1 below.

Table 1: Project Categorization Based on Construction Cost and Highway Length

Construction Cost No. of Projects Highway Length No. of Projects

Up to USD 20 million 14 Up to 20 km. 15

USD 20 mUSD 40 million 23 2050 km. 27

USD 40 mUSD 60 million 23 5070 km. 22

>USD 60 million 17 >70 km. 13

From the above table it is seen that more than 50% of the construction contracts were in the range of USD 20 to

USD 60 million. As regards to the length of the projects more than 60% were having length between 20 km and 50 km.


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Claim-Cause Relationship Study of Highway Construction Projects in India through Factor Analysis 45

CLAIM-CAUSE BREAKDOWN

Study of literature reveled that attempts have been made by several researchers to identify the reasons/causes of

claims. Merani (1998) comprehensively dealt with the subject matter of modern trends of Dispute Resolution in India. He

categorised the types of disputes in two categories; (1) Claims by the Contractor, and (2) Claims by the Employers. The

commonly pleaded reasons for the disputes by the parties were also identified and listed by him. Zaneldin (2006) collected

the data on claims related to different construction projects in Abu Dhabi and Dubai and the data was analysed to discover

the rankings of variety of claim types, and claim causes and their frequencies. OConnor et. al. (1993) attempted analysis

for Highway projects claims, by grouping them under two categories, Damage Type and Highway Element. The analysis

of the claims also extracted the Fundamental causes of claims and grouped under 8 major categories.

The detailed study of the disputes occurred in the 77 construction contracts revealed that a total of 573 claims

were raised by the contractors. The extracted claim-causes of the types of claims under study were then grouped under

following major categories. These categories were devised based on the explanation provided against each of them. At thebeginning of this classification, it was assumed that each of these claim-cause categories were mutually independent of

each other. This hypothesis was put to test during the data analysis.

Change in Law: The claim-causes attributable to the variety of changes in the laws were clubbed under this broad

category of claim-cause.

Delay in Site Handing Over: It was observed during the study of the types of claims and disputes that numerous

causes attribute to delay in site handing over and resultant claims and disputes. The causes contributing to this claim-cause

were grouped under this category of claim-cause.

Improper Contract Management: Study revealed that many claims and disputes arose due to improper

management of the contracts. This improper management was quite evident during the study of the construction contracts

and arbitration awards. Further, both parties to the dispute, the Employer and the Contractor were found to be engaged in

improper contract management. The claim-causes found to be contributing to improper contract management were grouped

under this category.

Improper Study Prior to Tendering the Contract: The study of the tender document and the existing site

conditions are of prime importance prior to execution of the construction works. Typically, this activity is performed by

both parties to the contract, Employer and Contractor, independent of each other. Whilst the study is initiated by the

Employer prior to the formation of the tender documents, the Contractors undertakes the same after receipt of the tender

documents. The study of the claims and disputes revealed that there are a number of causes which contribute to the

disputes and claims due to improper study performed by the parties. Such claim-causes are considered under this category.

Legal Costs: The legal costs arising out of the dispute and claim settlement are clubbed under this category of

claim-cause. These legal cost invariably formed part of the claims raised by either party.

Beyond the Control of the Parties: The claim-causes which are not controlled by or initiated by either party to

the contract, i.e. Employer and Contractor are collated under this category. They primarily comprises of forces of nature

and third party events/actions which create hindrances in project execution.

In summary, based on the broad study performed on the above methodology, the following groups of claim-causes

have emerged. They were termed as First Level (Level-1) claim-causes. For purposes of referencing throughout the

research it was decided to annotate them with F1, F2..etc. Henceforth, they are referenced as;


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F1: Change in Law F2: Delay in Site handing over F3: Improper Contract Management F4: Improper study prior to tendering the contract F5: Legal Costs F6: Beyond the control of the parties

While segregating and grouping 77 contracts using the Level-1 claim-causes, it was felt that these claim-causes

are of broad nature. Further differentiation and classification of these broad claim-causes was carried out, again by using

the information available from 573 claim and dispute events from 77 contracts.

The First Level claim-causes presented above are henceforth referred as Level-1 claim-causes. The further

classification of the claim-causes, since they are the subsets of the First Level claim-causes (Level-1 claim-causes), was

defined as Second Level (Level-2) claim-causes. The Level-2 claim-causes were given the annotations S1, S2,etc. Level-

1 and Level-2 claim-causes are presented in Table-2.

As explained above, the analysis was structured by creating 2 level claim-cause break down. Level-1 (First Level

designated as F1, F2...etc.) was created to develop macro level claim-cause structure and Level-2 (Second Level

designated as S1, S2...etc.) was created to reflect more drilled down claim-cause structure. Claim-causes of Level-2 are

more detailed breakdown of Level-1 Claim-causes.

Table 2: Causes of Claims

Causes of Claims, Level-1 and Level-2

F1 Change in Law F4 Improper Study prior to Tendering the Contract

S1 Imposition of New Taxes S22 Improper study by the Employer

S2 Revision in Entry Tax S23 Improper study by the contractor

S3 Revision in Excise Duty S24 Change in scope by the Employer

S4 Revision in Royalty Charges on Material S25 Ambiguous Contract Clause

F2 Delay in Site Handing Over F5 Legal Costs

S5 Delay in Land Acquisition S26 Lawyer fees

S6 Delay in Removal of Encroachments S27 Cost of Arbitration

S7Delay in Environmental/ForestClearance

S8 Delay in Compensation Payments (RAP) F6 Beyond the Control of the PartiesS9 Employer Default S28 Natural Calamity

S10 Losses due to EOT S29 Increase in Material / Fuel Cost

S11 Increased guarantee charges S30 Strike, agitation, etc.

S12 Idling of tools, plants, manpower S31 Court intervention

S32 Terrorism risk

F3 Improper Contract Management S33 Statutory Charges

S13 Derived BOQ item rate and Payment

S14 Non-BOQ item rate and Payment

S15 Delayed / Reduced Payment

S16 escalation/price adjustment

S17 Poor quality construction

S18 Poor planning of activities by theContractor

S19 Non granting of Completion

S20 Loss of Interest

S21 Stoppage of Work by Employer


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FACTOR ANALYSIS

Factor analysis is a statistical method used to describe variability among observed, correlated variables in terms of

a potentially lower number of unobserved variables called factors. There are several methods of factor analysis, but they do

not necessarily give same results (Kothari, 2004). As such, factor analysis is not a single unique method but a set of

techniques. Important methods of factor analysis are; centroid methods, maximum likelihood method, and principal

components (PC) method.

Kothari (2004) reported that Centroid method of factor analysis was quite extensively used till about 1950 before

the large capacity high-speed computers were invented. The arithmetic underlying the Maximum Livelihood method (ML

method) is relatively difficult in comparison to that involved in PC method. Further, Mulaik (1972) explained that the

iterative procedures are more difficult than that in PC method. Hence, ML method is generally not also used for factor

analysis in practice. Out of the 3 methods of factor analysis, the Principal Component method was adopted for the data

analysis under this research. Principal Components method (or simply PC method) of factor analysis, developed by H.Hotelling, seeks to maximize the sum of squared loadings of each factor extracted in turn. Accordingly, PC factor explains

more variance than would the loadings obtained from any other method of factoring. PC method seeks a linear combination

of variables such that the maximum variance is extracted from the variables. It then removes this variance and seeks a

second linear combination which explains the maximum proportion of the remaining variance, and so on. This is called the

principal axis method and results in orthogonal (uncorrelated) factors. PC method analyses total (common and unique)

variance.

This method of factor analysis was considered appropriate because of the limited a priori knowledge available

about the number of different cluster relationships that could be expected for the data (Hair et al., 1998). The literature also

indicated that PC method provided a deterministic method to group elements into meaningful subdivisions in order to

overcome multi-colinearity problems in the project data. As well, it was a statistical procedure that could uncover

relationships among many variables. In the context of this research the variables were the claim-causes in highway

construction projects in India. In the factor analysis methods, correlations and interactions among variables are summarised

into a small number of underlying factors. In other words, many variables are grouped under a few factors. The method

aimed at identifying key variables or groups of variables that influenced the claim/dispute scenarios in highway

construction projects.

The suitability of using the PC method for the data set of this research was also established from the literature.

Guidelines for the minimum sample size needed to conduct factor analysis suggested a minimum sample size of 100 to 200

observations (Guadagnoli and Velicer, 1988). Some researchers have suggested the ratio of sample size to number of

variables as a criterion, with recommendations ranging from 2:1 through to 20:1. It was understood from the literature

study that the larger the sample size would give better coverage of the factors extracted from the data. Tabachnick and

Fidell (2001) have advised the following regarding sample size: 50 observations is very poor, 100 is poor, 200 is fair, 300

is good, 500 is very good and 1000 or more is excellent. As a rule of thumb, a bare minimum of 10 observations per

variable is desirable to avoid computational difficulties.

In order to facilitate the interpretation of factors, factor analysis requires the rotation of axes. The rotation

procedure does not affect the goodness-of-fit of the factor solutions but serves to make the output more understandable.

Three rotation techniques are in general use: varimax, equimax and quartermax. Out of these, the most popular is Kaiser's

varimax algorithm, which is known to provide the best parsimonious analytical solution (Harman, 1967). This minimises


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the number of variables with high loadings on factors, thus causing the factor loadings of each variable to be more clearly

differentiated.

An important decision is the determination of the number of factors to be extracted for which several guidelines

are available. One of the most common is the minimum eigenvalue criterion. Essentially this method involved taking the

principal components of all the variables, ranking their eigenvalues from highest to lowest, then the number of eigenvalue

greater than one is selected as the criteria for the number of factors included in the analysis. The scree plot of eigenvalues

against the number of factors is also used as part of this process. This plot is used as a cut-off point to support the adoption

of the desired number of factors (Velicer and Jackson, 1990).

Several pre-tests are available to measure the sample characteristics necessary for successful factor analysis. One

is the Kaiser Meyer Olkins test (KMO) for sampling adequacy. KMO values vary from 0 to 1.0 and values closer to 1 are

better. An overall KMO should be 0.60 or higher to develop successful factor analysis (Hutcheson and Sofroniou, 1999).

KMO test was performed during the analysis. Another test is the Bartlett Test of Sphericity, which checks if the samplewas randomly drawn from a population in which the correlation matrix was an identity matrix. This uses the determinant of

the correlation matrix to tests the null hypothesis that the correlation matrix is an identity matrix using a chi-square

approximation and is particularly relevant when dealing with a relatively small sample of data (10). The Bartlett test sets up a chi-square approximation to determine whether the developed

correlation matrix is an identical matrix in the analysis.

In summary, the suitability of the PC method was established as explained herein above, and the analysis of data

on disputes/claims events using Principal Component (PC) analysis was carried out to determine possible reduction in the

dimension of data. This application was made on the claim-causes for both Level-1 and Level-2 to arrive at correlated

components. The Principle Component Analysis was carried out using SPSS software. The analysis was carried out to

determine the possible reduction in the dimension of data. This application was made on the claim-causes for both Level-1

and Level-2. Further, at the time of categorization of the claim-causes in two level structure the assumption was that these

claim-causes are independent of each other. This assumption was put to test during this stage of the research.

PRINCIPLE COMPONENT ANALYSIS FOR LEVEL-1

This analysis was performed to explore the possibility of developing the correlation among the claim-causes, in

this case F1 to F6 (level-1 claim-causes). It was derived from the analysis that six Level-1 claim-causes can be converted

into 3 components covering 77.6% of the data. From the plot of eigen value (scree plot) it was observed that threecomponents have eigen value greater than one. Further, Kaiser-Meyer-Olkin (KMO) measure of sampling adequacy test

and Bartlett's Test of sphericity tests were also performed on the data and it was observed that the significant value (p-

value) is 0.000< 0.05. This shows that Principal Component analysis is valid for the data.

Rotated Component Matrix extracted from the analysis establishes the correlation between the claim-causes. It

can be derived that Component 1 consists of the claim-causes F4 and F3; similarly Component 2 consists of claim-causes

F5 and F2; and Component 3 consists of claim-causes F1 and F6. From the correlation matrix derived through the

analysis, it was observed that the cause F4 and F3; F5 and F2; and F1 and F6 are positively correlated causes. The

following Table-3 shows the components and also demonstrates the correlation as derived from this analysis. That is,

increase in one cause implies increase in the other correlated cause. Correlated claim-causes for Level-1 are named as First

Level Component (FLC). In the present case three components are defined as FLC1, FLC2 and FLC3.


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Table 3: Components and Correlation (Level-1)

Component CorrelationCorrelation

Coefficient

FLC1 F4: Improper study prior to tendering the contract 0.875

F3: Improper Contract Management 0.855FLC2 F5: Legal Costs 0.916

F2: Delay in Site handing over 0.803

FLC3 F1: Change in Law 0.866

F6: Beyond the control of the parties 0.846

The Component Score matrix as derived through the analysis is presented in Table-4 below. The component

score coefficients are presented in the table. This is used to generate the score of the component from the entry of the

causes in the component.

Table 4: Component Score Coefficient Matrix

First Level Component (FLC)

1 2 3

F1 -0.106 0.024 0.594

F2 0.093 0.508 -0.035

F3 0.527 -0.066 0.034

F4 0.562 -0.102 -0.085

F5 -0.203 0.664 0.032

F6 0.046 -0.019 0.563

The score equation for each component is arrived as follows:

FLC1 = -0.106F1 + 0.093F2 + 0.527F3 + 0.562F4 - 0.203F5 + 0.046F6

FLC2 = 0.024F1 + 0.508F20.066F30.102F4 + 0.664F50.019F6

FLC3 = 0.594F1 -0.035F2 + 0.034F3 - 0.085F4 + 0. 032F5 + 0.563F6

In general, the score equation for a component can be expressed as under:

nComponent Score, FLCk = i Fi

i=1

Where,

FLCk = Score of k component, k = 1 to n

i = component score coefficient for ith factor

Fi = Level-1 claim-causes

While arriving at the claim cause breakdown, the underlying assumption was that the claim causes at Level-1 are

independent of each other. This was derived based on the very nature of each of the Level 1 claim-causes. However, the

PC analysis has demonstrated here that a correlation does exist among them. The correlation as was generated using this

statistical analysis among six Level-1 claim-causes is depicted in Table 3. This essentially means that if one desires, these

six claim-causes can be grouped into 3 components. The PC analysis further explained that these 3 components can explain

77.6% variance in the dataset.


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PRINCIPLE COMPONENT ANALYSIS FOR LEVEL-2

This analysis is performed to explore the possibility of developing the correlation among the claim-causes, in this

case S1 to S33 (Level-2 claim-causes). From the correlation matrix derived through the analysis, it was observed that the

33 nos. of Level-2 claim-causes can be converted into 13 nos of components. These components for Level-2 are named as

Second Level Components (SLC). In the present case components are defined as SLC1 to SLC13.

It was also derived from that analysis that 78.9% of total variance is explained by these thirteen components. 33

Level-2 claim-causes can therefore be converted into 13 components covering 78.9% of the data spread. It was also seen

from the plot of eigen value (scree plot), in which 13 components have eigen value greater than one. Kaiser-Meyer-Olkin

(KMO) Measure of Sampling Adequacy Test and Bartlett's Test of Sphericity tests were also performed on the data and it

was observed that the significant value (P-value) is 0.000< 0.05. This shows that Principal Component analysis is valid for

the data. Rotated Component Matrix establishes the correlation between the Level-2 claim-causes. Components consist of

one to several claim-causes of Level-2. It was observed that the variables which are loading high on Second LevelComponent-1 have low loadings on Second Level Components 2 to 13. Likewise, those loading high on a Component have

low loadings on the remaining Components. This is close to being an ideal situation. In case a variable is found to be

loading high on more than one components, it would just be best to drop that variable from the analysis, and carry out

revision in the analysis. PC components of Level-2 claim-causes along with the correlation as derived from this analysis

are presented in Table-5 below:

Table 5: Components and Correlation (Level-2)

Component Correlation Correlation Coefficient

SLC1

S9: Employer Default 0.781S14: Non-BOQ item rate and Payment 0.67

S17: Poor quality construction 0.631

S18: Poor planning of activities by the Contractor 0.586

S20: Loss of Interest 0.563

SLC2

S12: Idling of tools, plants, manpower 0.873

S11: Increased guarantee charges 0.846

S27: Cost of Arbitration 0.654

S10: Losses due to EOT 0.642

SLC3

S32: Terrorism risk 0.839

S2: Revision in Entry Tax 0.8

S4: Revision in Royalty Charges on Material 0.67

SLC4

S33: Statutory Charges 0.934

S24: Change in scope by the Employer 0.836S15: Delayed / Reduced Payment 0.492

SLC5S23: Improper study by the contractor 0.919

S25: Ambiguous Contract Clause 0.767

SLC6S19: Non granting of Completion 0.779

S1: Imposition of New Taxes 0.779

SLC7

S21: Stoppage of Work by Employer 0.91

S31: Court intervention 0.732

S6: Delay in Removal of Encroachments 0.454

SLC8S30: Strike, agitation, etc. 0.877

S5: Delay in Land Acquisition 0.777

SLC9 S16: escalation/price adjustment 0.799

SLC10 S29: Increase in Material / Fuel Cost 0.892SLC11

S28: Natural Calamity 0.823

S13: Derived BOQ item rate and Payment 0.65

SLC12 S22: Improper study by the Employer 0.816

SLC13 S3: Revision in Excise Duty 0.921


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The Component Score matrix was derived from the PC method analysis. The component score coefficients for

each of the SLC were used to generate the score equation for each of SLC. The score equation is generically presented as

follows:

pComponent Score (SLCp) = j Sj

j=1

Where, SLCp = Score of p component, j = 1 to p; j = component score coefficient for jth factor; Sj = Level-2

claim-causes

Using the values of j from the following Table-6 and value of the causes in the above equation, the score of each

component can be worked out and score equations for each SLC can be obtained.

Table 6: Component Score Coefficient Matrix

Second Level Component (SLC)1 2 3 4 5 6 7 8 9 10 11 12 13

S1 -0.088 0.06 0.144 -0.046 0.023 0.485 0.035 0.03 0.006 0.073 0.026 -0.053 -0.23

S2 -0.014 0.013 0.356 0.049 -0.047 -0.118 0.039 0.023 0.103 -0.064 0.027 0.024 0.14

S3 -0.019 0.011 0.006 0.03 0.017 -0.003 -0.06 0.013 -0.007 -0.044 0.044 0.029 0.805

S4 0.003 0.007 0.291 -0.031 0.081 0.093 -0.045 -0.055 -0.206 0.16 -0.019 -0.096 -0.055

S5 -0.074 -0.007 -0.018 0.107 -0.015 0.026 -0.002 0.442 -0.065 0.175 -0.002 0.044 -0.046

S6 0.108 -0.038 0.071 -0.077 0.174 -0.062 0.197 -0.011 -0.223 -0.046 0.04 0.239 -0.145

S9 0.268 -0.037 -0.018 -0.051 0.081 0.082 0.02 -0.022 -0.169 0.064 0.121 0.009 0.037

S10 -0.037 0.233 0.071 0.038 -0.025 -0.038 0.095 -0.013 0.033 -0.147 -0.028 0.308 0.051

S11 -0.153 0.409 -0.01 -0.06 -0.026 0.124 0.013 0.035 0.008 0.092 0.005 -0.054 0.003

S12 0.02 0.381 -0.034 -0.026 -0.029 0.036 -0.032 0.057 0.05 0.024 -0.125 -0.075 0.047

S13 -0.209 -0.013 -0.043 0.029 -0.03 0.142 -0.003 0.079 0.091 0.065 0.486 0.211 0.048

S14 0.213 -0.018 0.001 -0.012 -0.049 0.028 0.047 0.047 0.155 -0.083 0.115 -0.031 -0.003

S15 0.054 -0.003 0.097 0.193 -0.093 0.064 0.022 -0.02 0.135 0.194 -0.014 0.009 -0.088

S16 -0.002 -0.015 0.068 -0.035 0.104 -0.048 0.056 -0.014 0.546 -0.023 0.048 0.02 -0.039S17 0.312 -0.043 0.072 -0.132 0.035 -0.171 -0.106 0.236 -0.08 -0.096 -0.14 0.036 0.009

S18 0.247 -0.045 -0.101 0.008 -0.048 0.112 -0.002 -0.084 -0.039 -0.12 -0.197 -0.076 -0.036

S19 -0.022 0.008 -0.121 -0.009 0.048 0.478 -0.022 0.01 -0.103 -0.078 0.03 0.047 0.225

S20 0.248 0.039 -0.056 -0.018 -0.091 -0.238 -0.046 -0.137 0.216 0.163 -0.06 -0.131 0.008

S21 -0.057 -0.016 -0.035 0.033 -0.071 0.004 0.586 0.037 0.123 -0.118 0 0.043 -0.134

S22 -0.063 -0.047 -0.045 -0.038 -0.007 0.018 -0.071 -0.047 0.004 0.094 0.007 0.618 0.029

S23 -0.011 -0.009 0 0.065 0.477 0.002 -0.043 0.006 -0.022 -0.014 -0.001 0.011 0

S24 0.039 -0.032 -0.04 0.4 0.003 0.009 -0.002 -0.049 -0.003 -0.059 -0.049 0.037 0.024

S25 -0.101 -0.012 -0.094 -0.092 0.466 0.096 -0.016 0.035 0.302 0.096 -0.078 -0.078 0.092

S27 0.045 0.262 0.049 0.028 0.118 -0.074 -0.053 -0.061 -0.169 0.064 0.2 -0.117 -0.079

S28 0.103 -0.033 0.038 -0.017 -0.014 -0.085 0.01 -0.03 -0.023 -0.127 0.562 -0.13 0.025

S29 -0.044 0.021 0.001 -0.059 0.034 -0.016 -0.033 -0.017 -0.011 0.626 -0.038 0.079 -0.04

S30 -0.006 0.046 0.016 -0.062 0.03 0.036 0.064 0.554 0.037 -0.129 0.05 -0.116 0.049

S31 0.001 0.038 -0.046 -0.006 0 0.063 0.438 0.008 0.017 0.219 -0.027 -0.284 0.197

S32 -0.028 0.003 0.4 -0.031 -0.063 0.018 -0.072 0.036 0.099 -0.045 0.011 0.003 -0.08

S33 -0.124 -0.021 -0.009 0.518 -0.016 -0.051 0.014 0.038 -0.035 -0.064 0.045 -0.091 0.065

While arriving at the claim cause breakdown, the underlying assumption was that the claim causes at Level-2 are

independent of each other, since they are derived from the supposedly independent Level-1 claim causes. However, the PC

analysis has demonstrated here that a correlation does exist among Level-2 claim-causes. The correlation as was generated

using this statistical analysis among thirty three Level-2 claim-causes is depicted in Table 5 above. This essentially means

that if one desires, these thirty three claim-causes can be grouped into thirteen components. The PC analysis further

explained that these thirteen components can explain 78.9% variance in the dataset.

CONCLUSIONS

A review of 3 components of PC Analysis for Level-1 claim-causes as presented in Table-3 above and also the

review of the 13 components groupings of PC Analysis for Level-2 claim-causes as presented in Table-5 above, lead to the


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conclusion that no common factor component names were developed in the principal component analysis that could be

allocated to the individual component groups so as to allow consistency in purpose of the group of factors.

The study of the Level-1 claim-causes included in each of First Level Component in the PC analysis of Level-1

claim-causes demonstrates that these claim-causes have no correlation among them and hence, they are perhaps not the

best examples of component portraying commonality with each other. Similarly, the study of the Level-2 claim-causes

included in each of Second Level Component in the analysis of Level-2 claim-causes demonstrates that these factors have

no correlation among them either. There appeared to be no evidence of strong correlations within any of the 13 SLC, even

after rotation. This was particularly evidenced by the diversity associated with each of the principal components.

The assumption at the beginning of the PC method analysis was that the claim-causes identified under Level-1

and Level-2 are independent of each other. Even though the statistical analysis showed the formation of components, the

varied nature of claim-causes were grouped under principal components as demonstrated. The hypothesis of the claim-

causes being independent of each other, thus, holds.

It can also be interpreted that the claim-causes grouped under each of the Components (FLC and SLC) would

perhaps yield similar impact on the claim/dispute occurrences. In other words, even though the claim-causes grouped under

each Component are not correlated to each other, each will have similar impact on the claim/dispute occurrences.

Similarly, each Component will have its own unique impact on the claim/dispute occurrences. The study of cause-effect

can be carried out using the Components (FLC and SLC) derived from the PC analysis instead of considering each claim-

cause separately. The study using Components would perhaps yield the same results as if it was carried out using

individual claim-causes.

ACKNOWLEDGEMENTS

The authors would like to convey a special thank you to Prof. M. N. Patel, Statistical Department of Gujarat

University for facilitating and helping the statistical analysis using SPSS.

REFERENCES

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