Construction Management and Economics (May 2005) 23, 423–430
Predicting the risk of contractor default in Saudi Arabia
utilizing artificial neural network (ANN) and genetic
algorithm (GA) techniques
OBAID SAAD AL-SOBIEI1, DAVID ARDITI2* and GUL POLAT3
1
Technical Support Section, GDMW/MODA, PO Box 46539, Riyadh, 11542, Kingdom of Saudi Arabia
Illinois Institute of Technology, Department of Civil and Architectural Engineering, Chicago, IL 60616, USA
3
Istanbul Technical University, Faculty of Civil Engineering, Istanbul, Turkey
2
Received 26 January 2003; accepted 2 December 2004
The construction project is subject to several risks, one of the most important of which is contractor default
because contractor default may increase the final project cost considerably. In the US construction industry,
owners commonly shield themselves from the risk of contractor default by transferring this risk to the
contractor, who in turn transfers this risk to a surety company. On the other hand, the General Directorate of
Military Works (GDMW) of the Kingdom of Saudi Arabia retains the risk of contractor default rather than
transferring it to a third party. An artificial neural network (ANN) and a genetic algorithm (GA) are used in this
study to predict the risk of contractor default in construction projects undertaken for the Saudi armed forces.
Based on this prediction, the Saudi GDMW can make a decision to engage or not to engage the services of a
contractor. In case the models are not able to generate reliable predictions (or generate contradictory
outcomes), the GDMW will have to augment its budget with contingency funds to be used in the event of
contractor default. The outcome of this study is of particular relevance to construction owners because it
proposes an approach that can allow them to replace an indiscriminate blanket policy by a policy that is rational,
effective, prudent and economical.
Keywords: Artificial neural networks, contractor default, genetic algorithms, prediction model
Introduction
The construction process involves a large number of
activities that are carried out by different parties. Each
activity has its own risk, which results in accumulative
associated risk for the project. These risks include the
occurrence of unexpected events such as natural
disasters, unforeseen site conditions, material and
equipment delivery delays and equipment breakdowns.
One such risk that may have very severe consequences
on the owner’s plans is the risk of contractor default.
The literature mostly claims that risk is improperly
allocated in many construction projects (Kangari,
1995; Molenaar et al., 2000). This occurs because the
owner tends to contractually pass responsibility for
many project risks to the contractors. This is done via
disclaimer clauses to fend off most risks and via
*Author for correspondence. E-mail: arditi@iit.edu
contract bonds to deal with the risk of contractor
default. Contractors, in turn, must protect themselves
by attaching premiums, hidden or explicitly identified,
to their bid pricing. The contractor therefore shifts the
risk back to the owner by charging an increased price
for the work and by transferring the actual risk to an
insurance and/or surety company. It is claimed that,
usually, this indiscriminate shift of risk from the owner
to the contractor really benefits no party. A more
realistic and economical position is for owners to accept
greater risk. Responsible owners ought to take the
position that it is in their best interest to discriminate
between project risks and to retain some of the risks
while transferring others.
The armed forces in the Kingdom of Saudi Arabia
follow this premise. While they transfer some of the
risks to contractors (who in turn buy insurance to cover
themselves), they retain an important risk, the risk of
contractor default. In the institutionalized practice in
Construction Management and Economics
ISSN 0144-6193 print/ISSN 1466-433X online # 2005 Taylor & Francis Group Ltd
http://www.tandf.co.uk/journals
DOI: 10.1080/01446190500041578
424
the contracts let out by the armed forces, contractors
are not required to submit contract bonds at the time of
the signing of the contract. If no (or very few)
contractors ever default, i.e. if the risk of contractor
default is nil (or extremely low), this approach appears
to be reasonable. It would be wrong to assume,
however, that no contractor ever defaults in Saudi
Arabia. For example, in the 5-year study period, it was
possible to identify 23 of a few hundred projects
commissioned by the Saudi armed forces where
contractors defaulted.
It follows that the Saudi armed forces should have a
clear understanding of the risk of contractor default and
should allocate the necessary contingencies to their
budget to meet the extra cost of probable contractor
default. The objective of the study reported in this
paper was to predict the likelihood of contractor default
in projects commissioned by the General Directorate of
Military Works (GDMW) in Saudi Arabia. The
knowledge of the likelihood of contractor default will
allow the GDMW in Saudi Arabia to contract only with
desirable contractors (i.e. contractors that are not likely
to default). It will also allow the GDMW in Saudi
Arabia to allocate the necessary contingency to their
budget in order to face the crises caused by defaulting
contractors.
Al-Sobiei et al.
project, contract type and variation between the
contractor’s bid price and the next lowest bidder’s
price, etc.) (Russell, 1990, 1991, 1992; Kangari and
Bakheet, 2001).
The factors that affect the risk of contractor default are
presented on the left side of Figure 1. It is postulated that
the risk of contractor default is dependent upon the
overall health of the contractor, the particular characteristics of the contract, and the nature of the project. The 23
factors listed in the three boxes on the left of Figure 1
include information that provides sufficient and indicative data to represent the decision pattern appropriately.
The characteristics of the contractor, the particulars of
the contract, and the nature of the project are defined in
Tables 1, 2 and 3. respectively.
The data were obtained from the files of the GDMW
in Saudi Arabia. The function of this directorate is to
supervise the construction projects on behalf of the
Ministry of Defence in Saudi Arabia. The information
retrieved was recorded in hard copy forms, and then
input into the computer using Microsoft Excel.
However, NeuroShell Predictor reads only text files
(sometimes called ASCII files). Therefore, the file type
was changed into a CSV (comma separated) format by
using Excel’s capabilities.
Contractor default prediction model
Several studies have been carried out to understand the
phenomenon of contractor default and to predict the
failure of contractors by using discrete choice modeling
(see Russell and Jaselskis, 1992; Severson et al., 1994)
and stochastic modeling (Russell and Zhai, 1996). It is
important to use the past history of similar situations to
predict contractor default in construction projects. A
prediction model that combines the abilities of artificial
neural networks and genetic algorithms is proposed
for this purpose. Once the likelihood of default is
predicted, an action plan is recommended to the
construction owner. The flowchart in Figure 1 shows
the ‘Contractor Default Prediction Model’.
Factors that affect contractor default
The fundamental factors that are considered by surety
bonding companies in the US during the contractor
evaluation process are the contractor’s qualifications
(i.e. financial strength, past experience, business plan,
work capacity, quality and experience of the technical
personnel, etc.), and project characteristics (i.e. work
schedule, type, value, duration, complexity, location of
Figure 1 Prediction model for contractor default
Contractor default in Saudi Arabia
Table 1
425
Contractor’s input attributes
Contractor attributes
Definition
Type of business
This attribute is described by one of three linguistic variables. Corporation, partnership, and
individual proprietorship
Contractors are classified into general, heavy/highway, residential, and others
The dollar value is used as an input
Type of contractor
Value of largest project
completed
Value of largest
uncompleted work
Number of projects in
progress
Value of contracts in
progress
Number of past similar
projects
Construction equipment
Number of years in
construction business
Number of workers
Financial status
The contract value of the job where the contractor defaulted is used as an input value. If this
attribute is not applicable such as in the case of successful contractors a zero value is entered as
input for this variable
This attribute is an integer value. This attribute is related to the capability of the contractor of
handling several jobs at the same time
This dollar value represents all in-progress works. This variable is related to the capacity of the
contractor
This attribute describes how much a contractor is familiar with the type of project in question
One of three linguistic terms (owned, leased, and rented) are selected to describe the status of
this input attribute
This attribute describes how much a contractor is familiar with the type of project in question
The number of workers includes the entire job site and the office workers. This attribute is
related to the capability of the contractor to execute the work
The financial information was extracted from the bank report attached to the bid documents.
This report shows the credit line of the contractor
The data collected represent 21 projects, where the
contractor defaulted and 33 projects where the contractor
completed the project satisfactorily, of the projects
commissioned by the GDMW in Saudi Arabia in a
period of 5 years. Five additional random projects (in two
of which the contractor defaulted and the remaining three
were completed satisfactorily) were retained for testing.
Prediction model
NeuroShellH Predictor was the prediction software
used in the study because it has two different training
Table 2
strategies, namely the neural and genetic training
strategies. The Contractor Default Prediction Model
makes use of both training strategies. A brief description of these two strategies is presented below
(NeuroShell Predictor, 2000).
The neural training strategy solves problems whose
solutions are difficult to define and do not follow linear
patterns. Boussabaine (1996) conducted a review of
artificial neural network (ANN) methods and their
application in construction management. Risk analysis
was pointed out as one of the fields in construction that
could benefit from using ANN models. Hua (1996)
compared ANN and multiple regression to determine
Contract’s input attributes
Contract attributes
Definition
Method of bidding
This attribute is divided into three input variables namely (i) closed competitive bidding, (ii) open
competitive bidding and (iii) negotiated bidding. Each one of the variables has a binary input (0
and 1). These variables are mutually exclusive
This attribute has one of four linguistic values (i) traditional method, (ii) multiple prime
contractors, (iii) force account method, (iv) design–build and (v) construction management. Each
one of the variables has a binary input (0 and 1). These variables are mutually exclusive
This attribute has one of four linguistic values (fixed price, cost plus, unit price, and lump sum).
Since the rating of this attribute is discrete in nature, this attribute is divided into four input
variables. Each one of the variables has a binary input (0 and 1). These variables are mutually
exclusive
This variable is expressed as a percentage and is calculated as follows:
X5(next lowest bidder bid – contract price)/contract price
Procurement method
Type of contract
Difference between
contract price and
the next lowest bidder
426
Table 3
Al-Sobiei et al.
Project’s input attributes
Project attributes
Definition
Project type
Projects are classified into general, heavy/highway, residential, and other. Because it is not a
continuous variable, this attribute is treated as four variables. Each one of the variables has a binary
input (0 or 1)
The contract price ($) was used as an input. This attribute shows how big the project is
The value of this attribute is numeric in nature, so the number of months is used as input. This
attribute is related to the size of the work
This attribute was divided into two variables: head office in the same city as the project location, and
head office not in the same city as the project location). Each one of the variables has a binary input
(0 or 1)
Project value
Project duration
Distance between
project location and
the contractor’s
head office
Project complexity
Inflation
Material availability
Labor availability
This attribute was obtained from the project supervisor and hence is subjective in nature. This
attribute has one of three linguistic values (simple51, moderately complex52, and complex53)
The inflation rates were extracted from the published statistical economic data for Saudi Arabia in the
last 5 years
This attribute is related to the availability of construction materials in the vicinity of the project’s
geographic location. This attribute is expressed by two linguistic variables (materials available
locally) and (materials imported). Each one of the variables has a binary input (0 or 1)
This attribute deals with the issue related to the availability of construction labor in the vicinity of the
project’s geographic location. This attribute is measured by project supervisors’ experience
regarding the labor environment at the time their project was underway. These linguistic values
include (limited labor availability51, low labor availability52, high labor availability53)
whether ANN can produce better predictions than
multiple regression. The forecasting error of Hua’s
(1996) ANN model was found to be about one-fifth of
that derived from multiple regression models. The
ANN begins by finding linear relationships between the
inputs and the output. Weight values are assigned to
the links between the input and output neurons. After
those relationships are found, neurons are added to the
hidden layer so that non-linear relationships can be
found. Input values in the first layer are multiplied by
the weights and passed to the second (hidden) layer.
Neurons in the hidden layer ‘fire’ or produce outputs
that are based upon the sum of weighted values passed
to them. The hidden layer passes values to the output
layer in the same fashion, and the output layer produces
the desired results (predictions). The network ‘learns’
by adjusting the interconnection weights between
layers. The answers the network produces are repeatedly compared with the actual answers, and each time
the connecting weights are adjusted slightly in the
direction of the correct answers. Additional hidden
neurons are added as necessary to capture features in
the data set.
The genetic training strategy trains more slowly than
the neural strategy (NeuroShell Predictor, 2000). The
genetic training strategy obtains better results if the
data being tested are similar to the training data
(NeuroShell Predictor, 2000). It also works better
when the training data are sparse. A genetic algorithm
(GA) solves optimization problems by creating a
population or group of possible solutions to the
problem. The individuals in this population will carry
chromosomes, which are the values of variables of the
problem. The GA actually solves the problem by
allowing the less fit individuals in the population to
die and selectively breeding the fittest individuals (the
ones that solve the problem best). This process is called
selection, as in selection of the fittest. The GA will take
two fit individuals and mate them (a process called
crossover). The offspring of the mated pair will receive
some of the characteristics of the mother, and some of
the father. In nature offspring often have some slight
abnormalities, called mutations. Usually these mutations are disabling and inhibit the ability of the children
to survive, but occasionally they improve the fitness of
the individual. The GA similarly occasionally causes
mutations in its populations by randomly changing the
value of a variable. After the GA mates fit individuals
and mutates some, the population undergoes a generation change. The population will then consist of
offspring plus a few of the older individuals that the
GA allows to survive to the next generation because
they are the most fit in the population, and one will want
to keep them breeding. These most fit individuals are
called elite individuals. After dozens or even hundreds of
‘generations’, a population eventually emerges wherein
the individuals will solve the problem very well. In fact,
the fittest (elite) individual will be an optimum or close to
optimum solution. The processes of selection, crossover
and mutation are called genetic operators.
427
Contractor default in Saudi Arabia
The 23 factors described in Tables 1 (contractor
characteristics), 2 (particulars of the contract) and 3
(nature of the project) were used as input variables. The
output variable is the likelihood of contractor default.
Action plan
The costs associated with contractor default may
include: (1) loss of profits due to the project not being
completed on schedule; (2) administrative expenses to
analyze the situation, assess the amount and quality of
work completed, and determine the next course of
action; (3) cost to complete the project beyond the
money yet unpaid to the contractor; (4) fees associated
with the resolution of conflicts involving the contractor
and the owner; and (5) negative publicity and loss of
goodwill within the community. A contingency allowance to avoid project overruns arising from unexpected
events can be calculated in various ways such as by
using a probabilistic model (Touran, 2003) and risk
analysis (Mak and Picken, 2000), the most common
way in the US being to consider around 10% of the
estimated cost (Burger, 2003).
An owner should know how to assess risks and to take
action in order to minimize costs, avoid delays, and
eliminate claims and disputes. Risk management techniques include risk avoidance, risk financing or risk control.
Risk avoidance presumes the knowledge of a reasonably accurate prediction of the outcome of the risk
event; a party may try to avoid an activity altogether
based on the knowledge that a risk event will probably
disturb that activity. For example, an owner may decide
not to be involved with contractors whose likelihood for
default is high, assuming that such predictive knowledge is available to the owner.
Risk financing refers to methods of funding losses.
The primary sources of risk financing are:
N
N
Risk transfer: the contractor most often transfers
the risk of contractor default to a surety company
through the submittal of contract bonds.
Performance and payment bonds ensure that
the contractor will complete the project in
accordance with plans and specifications and
will pay all the parties that provide services and/
or materials. All public construction in the US
with very few and minor exceptions have to be
bonded according to the Miller Act. Transferring
risk does not reduce the criticality of the source
of the risk; it simply transfers it to another party,
i.e. the surety company.
Risk retaining: not all risk can be transferred, but
even if a risk could be transferred it may not
prove to be economical to do so. The risk will
then have to be retained. The Saudi Armed
Forces appear to have chosen this route when
dealing with the risk of contractor default, as no
guarantees are required from contractors at the
time of the signing of the contract.
Risk control involves taking measures to minimize the
possibility of the risk occurring and/or to minimize the
effects of the risk event in case such an event actually
occurs. Risk control can be achieved by:
N
N
Risk prevention: in the context of this research,
risk prevention means taking steps to prevent a
contractor from defaulting. This may take the
form of temporary financial or logistics support
to allow a contractor to overcome a bottleneck.
Risk reduction: reduction involves minimizing
the consequences of contractor default once it
has occurred. For example, speedy contracting
procedures and the use of cost + contracts may
allow an owner to replace a defaulting contractor
in minimum time and at minimum extra cost.
Findings and discussion
When NeuroShell Predictor was run using the neural
network training strategy, experiments had to be
conducted to determine the optimum number of
hidden neurons for obtaining the best results. The
correlation (r) between the actual and predicted
outputs was calculated against an increasing number
of hidden neurons as they are added to the network and
graphed in Figure 2. This graph performs a statistical
measure of the ‘goodness of fit’ between the actual and
predicted outputs and shows that all correlations are
very close to 1 when using up to 14 hidden neurons.
Similarly, the mean squared error (i.e. a statistical
measure of the differences between the actual values
and the predicted values) was also calculated and
plotted against the number of hidden neurons as they
are added to the model (Figure 3). In this measure, the
errors are squared to penalize the larger errors and to
cancel the effect of the positive and negative values of
the differences. Based on the mean squared error
criterion, it was apparent in Figure 3 that the best
results were again obtained with 14 hidden neurons in
one hidden layer. Finally, the performance of the
network was also assessed by measuring R-squared,
also known as the coefficient of multiple determination.
R-squared is a statistical indicator that compares the
accuracy of the model to the accuracy of a trivial
benchmark model wherein the prediction is just the
average of all of the example output values. A perfect fit
would result in an R-squared value of 1, a very good fit
428
Al-Sobiei et al.
Table 4
Figure 2
neurons
Correlation coefficients vs. number of hidden
Figure 3
neurons
Mean squared error vs. number of hidden
near 1, and a poor fit near 0. If the neural model
predictions are worse than one could predict by just
using the average of the output values in the training
data, the R-squared value will be 0. The maximum Rsquared value was reached when 14 hidden neurons
were used in one hidden layer.
When NeuroShell Predictor was run using the
genetic training strategy, the number of generations,
Table 5
Project #
1
2
3
4
5
Training statistics
Training
strategy
Rsquared
Average
error
Correlation
coefficient r
Mean
squared
error
Neural
Genetic
0.98
0.96
0.05
2.09
0.99
0.98
0.01
62.33
and the parameters that need to be optimized (RSquared, average absolute error, correlation coefficient,
mean squared error) had to be specified. The number
of generations allowed by Neuroshell Predictor ranges
between 10 and 1000. The default value of 100
generations was used in the study. The program was
also directed to maximize R-squared and the coefficient
of correlation and to minimize the average error and the
mean squared error.
The statistical reports generated by NeuroShell
Predictor are presented in Table 4. The values related
to the neural network training strategy were obtained
by using 14 hidden neurons. All statistical indicators
(i.e. the correlation coefficient r, the R-squared value,
the average error and the mean squared error) show
that training has been conducted successfully when
using both the neural network and genetic training
strategies. The correlation coefficients are very high
(the lowest being 0.98) and errors are quite low. The
statistical indicators appear to be comparable for the
neural network and the genetic training strategies.
To find the effect of the order in which the input data
is entered into the system, fifty different orders were
selected at random. All these trials gave similar results,
which mean that the outcome of the training does not
change when the order of the input data is changed.
Once the neural network and genetic training
strategies were used to predict contractor default, the
model was then tested by means of data collected from
five projects that had been selected at random and put
aside for this purpose. In two of the five projects the
contractor had defaulted whereas the remaining three
projects were completed satisfactorily.
According to the results presented in Table 5, the
two training strategies display comparable performance
by predicting correctly the outcome of three of five
Prediction of contractor default
Actual occurrence of default
Default (1)
Default (1)
No default (0)
No default (0)
No default (0)
Predicted occurrence of default
using neural strategy
Predicted occurrence of default
using genetic strategy
Default (1)
Default (1)
N/A
N/A
No default (0)
Default (1)
N/A
N/A
No default (0)
No default (0)
429
Contractor default in Saudi Arabia
projects. In this evaluation, any result within the range
¡0.5 is considered to have a value of 0 (no default),
while any result beyond the range ¡0.5 is considered to
have a value of 1 (default). ANN and GA were not able
to predict the outcome of two of the five projects. The
genetic method has built-in protection against making
predictions for which it has no basis to make them.
That is, it will not try to make a prediction for a pattern
if the training set did not include other patterns similar
to that pattern. The pattern will be marked as N/A,
meaning ‘unpredictable’ (NeuroShell Predictor, 2000).
Similarly, given the information in the training set,
ANN may not be able to make a prediction, in which
case we marked the results as N/A. N/A simply means
that the system is not able to make a prediction, i.e. the
result is inconclusive.
The two training strategies produced the following
results in the context of the five projects tested in this
study:
N
N
N
The predictions generated by the two strategies
concerning Projects 1 and 5 agree with each
other and with the actual occurrence. There is of
course no guarantee that the two training
strategies will always produce consistent predictions.
It was not possible to predict the outcome in
Project 3 using either strategy.
While the outcome in Project 2 could not be
predicted using the genetic training strategy it
was correctly predicted by the neural network
strategy, but while the outcome of Project 4 was
correctly predicted by the genetic training strategy, it could not be predicted using the neural
network training strategy.
The last stage in the risk management process is to
evaluate the alternative decisions in the light of the
output of risk analysis. In the context of the risk of
contractor default, this process starts with deciding
whether the owner prefers to avoid, transfer or retain
the risk. Given the established practices in the GDMW
in Saudi Arabia that does not require the use of
contract bonds, it is out of the question for the GDMW
to transfer the risk to the contractor and, by implication, to a surety company. The only option this agency
has is to avoid or retain the risk of contractor default. If
both of the training strategies indicate that the
contractor is likely to default (or if one strategy predicts
default while the other strategy is inconclusive), then it
is prudent for the owner to avoid contracting with this
company. On the other hand, if both training strategies
concur that the contractor will not default (or if one
strategy predicts no default while the other is inconclusive), then the owner should be able to sign a
contract with the said contractor assuming that the risk
of contractor default is quite low. However, some
minimum contingency should be allocated to cover for
the inaccuracies in the predictions. The information
collected from the 23 contractors who defaulted
indicates that the losses associated with contractor
default range between 9% and 24% of the contract
value, with an average of 15% weighted by contract
size. It should therefore be possible for the GDMW in
Saudi Arabia to budget a minimum contingency of 9%
to deal with losses in case of contractor default even if
the ANN and GA methodologies predict otherwise.
If neither training strategy is able to generate a
prediction, or if the predictions generated by the two
strategies are contradictory, the next step involves risk
control, i.e. the prevention and reduction of the
consequences of probable contractor default. The
GDMW in Saudi Arabia should therefore budget an
appropriate contingency and to be prepared to support
the contractor with financial and logistic resources in case
the contractor defaults. If the General Directorate’s
decision-making process is governed by a ‘risk-seeking’
behavior, it is recommended that it budgets 9–15% of the
contract value as a contingency against probable contractor default. For a ‘risk averse’ posture, the budgeted
contingency should be 15–24% of the contract value
whereas for a ‘risk neutral’ posture, the contingency may
be taken as around 15% of the contract value.
Conclusion and recommendations
Contractor default is one of the most costly risk events
for an owner. Construction owners in the US typically
transfer this risk to the contractor who in turn transfers
it to a surety company. In the US practice, the contractor has to submit contract bonds to the public
owner (and often to the private owner) at the time the
contract is signed. However, according to the established practice in the projects let out by the GDMW in
Saudi Arabia, contractors are not expected to submit
contract bonds. In other words, the GDMW always
retains the risk of contractor default; but Saudi
contractors do default at times and the GDMW has
to deal with the consequences, i.e. the losses and delays
associated with such defaults. It is claimed in this
study that automatically retaining the risk of contractor
default is not the only option available to the GDMW,
and that the option of avoiding the risk altogether is
also available if the likelihood of contractor default can
be predicted prior to award. Furthermore, the GDMW
could be prepared for the risk of contractor default in
case such prediction is not possible.
ANN and GA were used in this study to predict the
likelihood of contractor default. The analysis makes use
430
of raw data typically collected by the GDMW in its
projects undertaken in Saudi Arabia. The ANN and GA
models were tested using independent test cases to
demonstrate their validity. If there is agreement between
the training strategies concerning the prediction of the
outcome, the GDMW can make the decision of hiring
(but allocating minimum contingency funds) or not
hiring the said contractor. If, on the other hand, the
predictions do not match or if no prediction is possible
using either strategy, then the GDMW may hire the said
contractor but must allow for some contingency fund in
its budget to accommodate the probable losses to be
incurred in case of contractor default. The amount of the
contingency would be dependent on the risk policy (riskseeking, risk neutral, or risk averse) of the GDMW at the
time the contract is awarded.
The study is limited by the small number of training
and testing cases. No generalizations are possible, given
the small number of cases compared to the large
number of projects handled by the GDMW over the
years. However, the study shows that a strategy exists
that could allow big construction owners to minimize
the overall cost of contractor default.
Established practices in some countries require that
all contractors be automatically bonded regardless of
risk of contractor default (e.g. public projects in the
US). Established practices in some other countries
require that no contractor be bonded regardless of risk
of contractor default (e.g. armed forces projects in
Saudi Arabia). The premise of this study is that such
blanket policies do not discriminate between the
various alternatives available to owners and are not
conducive to economical solutions. The alternatives to
bonding all contractors regardless of the risk of contractor default are investigated elsewhere (Al-Sobiei
et al., 2004). The alternatives to not bonding any
contractor are investigated in the study presented in
this paper. The outcome of this study is of particular
relevance to construction owners because it proposes
an approach that can allow them to follow a policy that
is rational, effective, prudent, and economical. The
policy is ‘rational’ because it avoids blanket policies that
do not discriminate between different situations; it is
‘effective’ because owners are well prepared in the
event of contractor default as they have allocated
enough contingency funds for possible default cases;
it is ‘prudent’ because it allows owners to avoid doing
business with contractors that exhibit high risk of
default; and it is ‘economical’ because owners face a
lower overall cost of contractor default.
Al-Sobiei et al.
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