The use of competitive intelligence for selection
of municipality’s contractors
Mohammad Jani Darmian
1
and Mehdi Momeni Ragh Abadi
2
*
1
MSc. Civil Engineering, Construction Management Engineering, Faculty of Engineering, Islamic Azad
University, Kerman Branch, Iran
2
PhD of Geotechnical, Assistant Professor of Civil Engineering, Department of Civil Engineering, Islamic
Azad University, Kerman Branch, Iran
ABSTRACT
Nowadays, growth and development as well as more complex industrial projects in line with the progress of science
and technology has led to the problem and contractor selection process as key factor in the success of industrial
designs and researchers and industrialists as more specialized approach to adopt in relation to this issue, selection of
competent and quali ed contractor, a multi-criteria decision problem is the rst step towards the implementation of
a development project in terms of cost, time and quality, and competitive intelligence factor in the selection of con-
tractors is the important development projects. In the present study attempts to in uence of competitive intelligence
in choosing the most quali ed contractors to examine projects Municipal Development in South Khorasan province.
In this regard, after an extensive study of the history of the issue, the proposed criteria and indicators were identi-
ed.Finally, it was found that factors of price, technology plays a big part in choosing a contractor for Municipal
Development in South Khorasan province have played.
KEY WORDS:
CONTRACTOR SELECTION, COMPETITIVE INTELLIGENCE, MUNICIPALITY OF SOUTH KHORASAN PROVINCE
318
ARTICLE INFORMATION:
*Corresponding Author: Omrani.asadieye@Yahoo.com
Received 27
th
Dec, 2016
Accepted after revision 2
nd
March, 2017
BBRC Print ISSN: 0974-6455
Online ISSN: 2321-4007
Thomson Reuters ISI ESC and Crossref Indexed Journal
NAAS Journal Score 2017: 4.31 Cosmos IF : 4.006
© A Society of Science and Nature Publication, 2017. All rights
reserved.
Online Contents Available at: http//www.bbrc.in/
Biosci. Biotech. Res. Comm. Special Issue No 1:318-324 (2017)
INTRODUCTION
Choosing a contractor on construction projects is one
of the crucial and strategic decisions that must be made
regarding how to manage projects. It is necessary to
select a contractor to contractor status of various aspects
based on multiple criteria and factors detailed review.
The absence or lack of information and accurate data,
opaque and incomplete information leads to errors in
the selection of contractors and ultimately will lead to
irreparable losses. On the other hand, sometimes con ict
due to a variety of qualitative and quantitative crite-
ria decision making requires much more complex is the
right choice. In recent years, due to incorrect selection
Jani Darmian and Ragh Abadi
BIOSCIENCE BIOTECHNOLOGY RESEARCH COMMUNICATIONS THE USE OF COMPETITIVE INTELLIGENCE FOR SELECTION OF MUNICIPALITY’S CONTRACTORS 319
of contractors in the construction industry saw a loss
of nancial resources in Iran (including related pro-
jects with municipalities). Selecting the right contractor,
through certi ed contractors can be one of the solutions
available to solve such problems (Marzouk, 2006 and
Chules, 2013).
These decisions are usually complex and in this con-
nection can be used to assess many qualitative and quan-
titative factors. Failure to do so would be to implement
the project in terms of longer, lower quality and higher
costs (due to depreciation expense and capital lodging
or damage arising from the lack of complete and timely
delivery of projects carried out( and thus destroy eco-
nomic feasibility of projects (Thomas, 2001). Contractors
and are important as an integral part of the project are in
process. In fact, major supplier of equipment and services
required for the project. In the context of different pro-
jects, there are potentially number of contractors, quali-
cations and abilities required to perform the treaty, but
the problem here is that any contractor should be selected
Indeed, process of selection of contractors for outsourced
systems, multi-criteria problem that including both quali-
tative and quantitative criteria (Skitmore, 1999). Dixon
is one of the rst people who worked on the selection
of contractors and more than 23 criteria that managers
use them to determine the selection of contractors. Later,
many researchers regarding the selection of contractors
to research methods and techniques in this eld were pre-
sented (Palaneeswaran et al., 2001).
The most important of these techniques include: TOP-
SIS Fuzzy, AHP, CBR, DEA, SMART, VIKOR, ANP, etc.
One of the new procedures for selecting contractors as
independent model and its results can be used as input
for other models and techniques to be used is model of
competitive intelligence (CI). In fact, CI models need to
check the size and location of project schedule, nan-
cial constraints, philosophy, ownership, management
team dynamics and overall strategy and, ultimately, the
choice of 5 main contractor cost, schedule, operational
services and engineering rank, experience and stabil-
ity consider the contractor’s nancial stability. One of
these steps is selection of contractors for execution of
the project (Russell et al., 1988). Due to the diversity and
multiplicity of potential contractors, quali cations and
abilities required to perform the treaty and the project, it
is important that any contractor should be selected. So
inevitably, you must rst assess contractors, ratings and
afterwards, selected to implement the project can best
be guaranteed. In other words, the main goal contractor
selection process reduces project risk, maximize qual-
ity of work and maintain relationships between different
units of project (Shrestha et al., 2004).
Therefore, it is necessary to evaluate the optimal
models and frameworks of contractors. In general, there
are two main methods of fee-based contractor selec-
tion process and competency based approach, of course,
many employers in the selection of contractors and their
primary concern was to cost it considers the most imme-
diate evaluation index. Cost alone should not be a suit-
able criterion for assessing the contractor. On the other
hand research that has been done in this regard that
multivariate process should be used in the selection of
contractors (Thomas, 2001). This is important in coun-
tries like Iran, whose economy is much more dependent
on the state (Wong, 2004).
Because a detailed set of criteria and scores are
selected and optimized with the addition of some eco-
nomic corruption and contractors to prevent unfair con-
tracts prevents waste and construction budgets as well
as the duration of projects. Therefore, the aim of present
study is that with help of CI model parameters, correct
criteria and municipal construction projects have been
identi ed factors in the selection of contractors and
nally ranking pattern suitable for the evaluation and
selection of optimal and ultimately be presented in such
contractor.
METHODS
This research is applied - descriptive based on its goal.
POPULATION, SAMPLING METHOD
The study population included all experts and authorities
are tender and transfer of development projects of which
there are 23 municipalities in South Khorasan province
that population is not due to limited sampling and all
individuals are selected for the sample. When necessary,
information will be collected through interviews.
PROCEDURE
Since the evaluation indices each system, depending on
the intention is to provide and important tasks expected
of it and the type of system and the factors in uenc-
ing cost of treatment will be different therefore, to iden-
tify indicators of competitive intelligence contractors in
construction projects following step will be removed.
By collecting questionnaires, all indicators affecting the
choice of contractor determined, ultimately, by collect-
ing the opinions of experts quali ed contractor will be
selected on the basis of competitive intelligence theory.
By referring to the background of a number of stud-
ies in this area are identi ed index and to ensure the
effectiveness of the measures identi ed in the process of
selecting a contractor questionnaire was prepared and
distributed among experts. In this research in order to
answer the research questions and one-sample t test was
used to conclude it.
Jani Darmian and Ragh Abadi
320 THE USE OF COMPETITIVE INTELLIGENCE FOR SELECTION OF MUNICIPALITY’S CONTRACTORS BIOSCIENCE BIOTECHNOLOGY RESEARCH COMMUNICATIONS
RESULTS
Variable of “comprehensive system of planning
and control of the project
The mean of the test () was considered more than 2
to examine status of the studied variables so following
hypothesis is tested:
H0: Observance of standards and technical speci cations
on previous projects compared to other contractors as one
of competitive intelligence parameters in the selection of
contractor is not in desirable status;
H1: Observance of standards and technical speci cations
on previous projects compared to other contractors as one
of competitive intelligence parameters in the selection of
the contractor is in desirable status;
As seen in Table 05. The level of signi cance is smaller
than, 0.05 so the null hypothesis is rejected and the
means which studied population in terms of Observance
of standards and technical speci cations on previous
projects compared to other contractors as one of com-
petitive intelligence parameters in the selection of the
contractor is in desirable status.
Variable of “implementation of previous projects
in terms of anticipated quality, cost and schedule”
One-Sample Statistics
N Mean Std. Deviation Std. Error Mean
q1 23 2.6522 .83168 .17342
One-Sample Test
Test Value = 2
t df Sig.
(2-tailed)
Mean
Difference
95%
Con dence
Interval of the
Difference
Lower Upper
q1 3.761 22 .001 .65217 .2925 1.0118
The mean of the test () was considered more than 2
to examine status of the studied variables so following
hypothesis is tested:
H0: having comprehensive system of planning and con-
trol of projects compared to other contractors as one of
competitive intelligence parameters in the selection of the
contractor is not in desirable status;
H1: having comprehensive system of planning and con-
trol of projects compared to other contractors as one of
competitive intelligence parameters in the selection of the
contractor is in desirable status;
As seen in Table 05. The level of signi cance is smaller
than, 0.05 so the null hypothesis is rejected and the
means which studied population in terms of having
comprehensive system of planning and control of pro-
jects compared to other contractors as one of competi-
tive intelligence parameters in the selection of the con-
tractor is in desirable status.
Variable of “Observance of standards and technical
speci cations on previous projects”
One-Sample Statistics
N Mean Std. Deviation Std. Error Mean
q2 23 2.9565 1.06508 .22208
One-Sample Test
Test Value = 2
t df Sig.
(2-tailed)
Mean
Difference
95%
Con dence
Interval of the
Difference
Lower Upper
q2 4.307 22 .000 .95652 .4959 1.4171
One-Sample Statistics
N Mean Std.
Deviation
Std. Error
Mean
q3 23 2.7391 .91539 .19087
One-Sample Test
Test Value = 2
t df Sig.
(2-tailed)
Mean
Difference
95%
Con dence
Interval of the
Difference
Lower Upper
q3 3.872 22 .001 .73913 .3433 1.1350
The mean of the test () was considered more than 2
to examine status of the studied variables so following
hypothesis is tested:
H0: implementation of previous projects in terms of antici-
pated quality, cost and schedule compared to other con-
tractors as one of competitive intelligence parameters in
selection of contractor is not in desirable status;
H1: implementation of previous projects in terms of antici-
pated quality, cost and schedule compared to other con-
tractors as one of competitive intelligence parameters in
selection of the contractor is in desirable status;
As seen in Table 05. The level of signi cance is smaller
than, 0.05 so the null hypothesis is rejected and the
Jani Darmian and Ragh Abadi
BIOSCIENCE BIOTECHNOLOGY RESEARCH COMMUNICATIONS THE USE OF COMPETITIVE INTELLIGENCE FOR SELECTION OF MUNICIPALITY’S CONTRACTORS 321
means which studied population in terms of anticipated
quality, cost and schedule as one of competitive intel-
ligence parameters in the selection of the contractor is
in desirable status.
Variable “Creativity and Innovation in previous projects”
The mean of the test () was considered more than 2
to examine status of the studied variables so following
hypothesis is tested:
H0: use of new technologies compared to other contractors
as one of competitive intelligence parameters in the selec-
tion of the contractor is not in desirable status;
H1: use of new technologies compared to other contractors
as one of competitive intelligence parameters in the selec-
tion of the contractor is in desirable status;
As seen in Table 05. The level of signi cance is
smaller than, 0.05 so the null hypothesis is rejected and
the means which studied population in terms of use of
new technologies as one of competitive intelligence
parameters in the selection of the contractor is in desir-
able status.
Variable of “level of education, discipline, and
experience of executive staff and key elements”
One-Sample Statistics
N Mean Std. Deviation Std. Error Mean
q4 23 3.0435 .82453 .17193
One-Sample Test
Test Value = 2
t df Sig.
(2-tailed)
Mean
Difference
95%
Con dence
Interval of the
Difference
Lower Upper
q4 6.069 22 .000 1.04348 .6869 1.4000
The mean of the test () was considered more than 2
to examine status of the studied variables so following
hypothesis is tested:
H0: Creativity and Innovation in previous projects com-
pared to other contractors as one of competitive intelli-
gence parameters in the selection of the contractor is not
in desirable status;
H1: Creativity and Innovation in previous projects com-
pared to other contractors as one of competitive intelli-
gence parameters in the selection of the contractor is in
desirable status;
As seen in Table 05. The level of signi cance is
smaller than, 0.05 so the null hypothesis is rejected and
the means which studied population in terms of creativ-
ity and Innovation in previous projects as one of com-
petitive intelligence parameters in the selection of the
contractor is in desirable status.
Variable “use of new technologies”
One-Sample Statistics
N Mean Std. Deviation Std. Error Mean
q5 23 3.1739 .83406 .17391
One-Sample Test
Test Value = 2
t df Sig.
(2-tailed)
Mean
Difference
95%
Con dence
Interval of the
Difference
Lower Upper
q5 6.750 22 .000 1.17391 .8132 1.5346
One-Sample Statistics
N Mean Std. Deviation Std. Error Mean
q6 23 2.9130 .79275 .16530
One-Sample Test
Test Value = 2
t df Sig.
(2-tailed)
Mean
Difference
95%
Con dence
Interval of the
Difference
Lower Upper
q6 5.524 22 .000 .91304 .5702 1.2559
The mean of the test () was considered more than 2
to examine status of the studied variables so following
hypothesis is tested:
H0: level of education, discipline, and experience of execu-
tive staff and key elements as one of competitive intelli-
gence parameters in the selection of the contractor is not
in desirable status;
H1: level of education, discipline, and experience of execu-
tive staff and key elements as one of competitive intelli-
gence parameters in the selection of the contractor is in
desirable status;
As seen in Table 05. The level of signi cance is
smaller than, 0.05 so the null hypothesis is rejected and
the means which studied population in terms of level
of education, discipline, and experience of executive
322 THE USE OF COMPETITIVE INTELLIGENCE FOR SELECTION OF MUNICIPALITY’S CONTRACTORS BIOSCIENCE BIOTECHNOLOGY RESEARCH COMMUNICATIONS
Jani Darmian and Ragh Abadi
staff and key elements as one of competitive intelligence
parameters in the selection of the contractor is in desir-
able status.
Variable of “status and the ability to use the new
equipment and machines”
The mean of the test () was considered more than 2
to examine status of studied variables so following
hypothesis is tested:
H0: Continued training of employees as one of competitive
intelligence parameters in the selection of the contractor is
not in desirable status;
H1: Continued training of employees as one of competitive
intelligence parameters in the selection of the contractor is
in desirable status;
As seen in Table 05. The level of signi cance is
smaller than, 0.05 so the null hypothesis is rejected and
the means which studied population in terms of Contin-
ued training of employees as one of competitive intel-
ligence parameters in the selection of the contractor is
in desirable status.
As seen in Table signi cance level is greater than 05.
Therefore, the null hypothesis is not rejected and this
means that the study population in terms of status and
use of equipment and machinery to get better compared
with other competitive intelligence contractors as one of
the parameters in the selection of the contractor is not
desirable statue.
Variable of suitable suggested price
One-Sample Statistics
N Mean Std.
Deviation
Std. Error Mean
q7 23 3.3043 .63495 .13240
One-Sample Test
Test Value = 2
t df Sig.
(2-tailed)
Mean
Difference
95%
Con dence
Interval of the
Difference
Lower Upper
q7 9.852 22 .000 1.30435 1.0298 1.5789
The mean of the test () was considered more than 2
to examine status of the studied variables so following
hypothesis is tested:
H0: status and the ability to use the new equipment and
machines as one of competitive intelligence parameters in
the selection of the contractor is not in desirable status;
H1: status and the ability to use the new equipment and
machines as one of competitive intelligence parameters in
selection of contractor is in desirable status;
As seen in Table 05. The level of signi cance is
smaller than, 0.05 so the null hypothesis is rejected and
the means which studied population in terms of status
and the ability to use the new equipment and machines
as one of competitive intelligence parameters in the
selection of the contractor is in desirable status.
Variable “Continued training of employees”
One-Sample Statistics
N Mean Std. Deviation Std. Error Mean
q8 23 2.4348 1.03687 .21620
One-Sample Test
Test Value = 2
t df Sig.
(2-tailed)
Mean
Difference
95%
Con dence
Interval of the
Difference
Lower Upper
q8 2.011 22 .057 .43478 -.0136 .8832
One-Sample Statistics
N Mean Std. Deviation Std. Error
Mean
q9 15 3.8000 .41404 .10690
One-Sample Test
Test Value = 2
t df Sig.
(2-tailed)
Mean
Difference
95%
Con dence
Interval of the
Difference
Lower Upper
q9 16.837 14 .000 1.80000 1.5707 2.0293
The mean of the test () was considered more than 2
to examine status of the studied variables so following
hypothesis is tested:
H0: Suitable suggested price compared to other competi-
tive intelligence contractors as one of the parameters in the
selection of the contractor is not in good condition;
H1: proposed price of better t compared with other com-
petitive intelligence contractors as one of the parameters in
selection of contractor desirable;
Jani Darmian and Ragh Abadi
BIOSCIENCE BIOTECHNOLOGY RESEARCH COMMUNICATIONS THE USE OF COMPETITIVE INTELLIGENCE FOR SELECTION OF MUNICIPALITY’S CONTRACTORS 323
comprehensive system of planning and project control
How to standards and technical speci cations on previous
projects
Implementation of previous projects in terms of quality,
cost and schedule anticipated
Creativity and innovation in the previous project
The use of new technologies
Education, discipline, and experience of executive staff
and key elements
Machine-to-date status and usability
Continued training of employees
Suitable suggested price
As seen in Table 05. The level of signi cance is
smaller so null hypothesis is rejected and the means
which t studied better in terms of suggested price com-
pared with other competitive intelligence contractors as
one of the parameters in the selection of the contractor
are in desirable status.
DISCUSSION AND CONCLUSION
Several factors are involved in the selection of contrac-
tors in the meantime, according to research topic param-
eters related to competitive intelligence derived from the
questionnaires about them in choosing contractor for
Municipal Development in South Khorasan province is
evaluated.
themselves in areas that are superior in terms of munici-
pal experts; upgrade can obtain better results in munici-
pal tenders.
Table ranking of competitive intelligence factors in
choosing contractor for construction in municipality of
South Khorasan province
Based on the above factors, the proposed price,
innovation and machines have an important role in
the selection of construction contractors in South Kho-
rasan Province while other factors are in the middle
range and the continuous training of employees with
the lowest degree of importance from the point of view
of experts and professionals. So, using the results of
the study are presented in the following suggestions in
elderly:
It is suggested to increase the ef ciency of algorithm
research, software algorithms to be designed for mecha-
nization in speci c time periods, if there was a similar
project could be implemented.
It is suggested in the speci ed time period, contractor
selection criteria be revised in case we need new index
was added or index improved old markers that have
applied to be removed. According to the results of this
study, the following is proposed for future research:
It is suggested that similar research data envelopment
analysis (DEA), VIKOR (VIKOR), LINMAP, etc. are used
for this work. This technique could be used in a fuzzy
environment or logical.
Indicators of this research, according to research ter-
ritory in accordance with the municipality of Birjand
city in South Khorasan Province was established so
it’s recommended by examining other similar organi-
zations and companies in this project is a compre-
hensive model that overtake all indicators of involved
organizations.
The above factors, which are subset of competi-
tive intelligence, signi cant relationship in choosing
contractor for Municipal Development in South Kho-
rasan province on the outcome contractors presented
Jani Darmian and Ragh Abadi
324 THE USE OF COMPETITIVE INTELLIGENCE FOR SELECTION OF MUNICIPALITY’S CONTRACTORS BIOSCIENCE BIOTECHNOLOGY RESEARCH COMMUNICATIONS
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