A COMPARATIVE ANALYSIS OF FACTORS AFFECTING CONTRACTORS’
MARK-UP DECISION BASED ON SELECTED PROJECT AND
ORGANISATIONAL CHARACTERISTICS
Jonathan Zishim DANJUMA1*, Ahmed Doko IBRAHIM2 and Peter Gangas CHINDO3
1*
Department of Quantity Surveying, Ahmadu Bello University, Zaria, Nigeria;
zishimdanjuma@gmail.com
2
Department of Quantity Surveying, Ahmadu Bello University, Zaria, Nigeria;
adibrahim2@yahoo.com
3
Department of Quantity Surveying, Ahmadu Bello University, Zaria, Nigeria;
pcgangas@yahoo.com
ABSTRACT
The Nigerian Construction Industry (NCI) is characterised with a highest bidder mentality
through a competitive bidding process. Therefore, any contractor who must remain in
business within the industry must be determined and use optimal bid mark-ups low enough to
win the job, at the same time high enough to provide the minimum expected profit. To help
contractors decide on the optimum mark-up to insert in their bids, factors affecting
contractors’ mark-up decision have been studied across the globe. However, these studies
cannot be extrapolated because these studies are limited to countries/regions and the factors
differ from project to project, and from one organisation to another. Therefore, the aim of this
research was to appraise the factors affecting contractors’ mark-up decision in Nigeria
through a comparative analysis of factors affecting contractors’ mark-up decision based on
selected project and organisational characteristics. A self-administered questionnaire survey
was used to source information on the project and organisational characteristics considered in
the study as well as to assess the extent of influence of the qualitative factors on contractors’
mark-up decision. Independent sample t-test and Analysis of Variance (ANOVA) were used
for data analysis. The comparative analysis carried with respect to some selected project and
organisational characteristics showed that the factors affecting contractors mark-up decision
do vary from one project to another and from organisation to organisation. Competition was
discovered to pose a great challenge to small-sized contractors, which can be mitigated by
contractors keeping and studying the records of their potential competitors. Client-contractor
relationships are also vital in the bidding decision made by contractors as it could influence
the mark-up they insert in their bids.
Keywords: Bidding process, Contractors, Mark-up, Organisational characteristics, Project
characteristics.
1
INTRODUCTION
The Nigerian construction industry, as is the case in most developing countries, is governed
by a competitive business environment driven by a lowest cost mentality (Dulaimi and Shan,
2002; Jarkas, 2013; Oyeyipo et al., 2016) and this has led to a considerable decrease in the
profit margins of contractors competing for jobs in the construction industry (Baloi & Price,
2003). Akintoye (1991) also discovered that there is high level of fluctuation and disparity
between annual rate of tender price and building cost. Akintoye was baffled about these
occurrences that he had to sort for reasons why these disparities exist. However in 1992,
Akintoye discovered that tender prices are the sum of cost estimates and mark-up(s), and that
1
estimating building cost from tender price is complicated because it includes that premium
known as “mark-up”. This premium is considered of great importance knowing that for a
contractor to remain solvent (i.e., in good financial standing) and a viable business entity, he
must take into account the issues of profit, risk, overhead and market conditions by adding a
financial premium (i.e., mark-up) to his unit costs (Younis et al., 2016).
To fully understand this concept of mark-up, a considerable body of literature on estimating
the optimum mark-up and factors affecting mark-up decision have been reviewed. However,
there is no consensus on how mark-up should be defined following current practice within the
construction industry, as was also noted by Hegazy & Moselhi (1995) and Wu et al. (2006).
Researchers have also argued that contractors’ mark-up decisions are based on intuition and
experience and involve emotional responses to the pressures of the moment (Fayek, 1998; Xu
and Tiong, 2001; Dulaimi & Shan, 2002; Oyeyipo et al., 2016). There is therefore a need to
possess a sound knowledge of the factors affecting the contractors’ bid mark-up decision as it
has become vital in identifying the ‘optimum’ bid mark-up.
Several studies relating to factors affecting contractors’ mark-up decision have been carried
out, amongst which project related factors (such as project size, type, and duration), and
contractors related factors (such as overhead incurred in previous jobs and profitability) have
been identified to have high influence on contractors’ mark-up decisions (Ahmad and
Minkarah, 1988; Shash and Abdul-Hadi, 1992 & 1993; Shash, 1993; Clough and Sears, 1994;
Ling and Liu, 2005; Oo et al., 2007 & 2010; Hai, 2009; Enshassi et al., 2010; Jarkas, 2013;
Pobutdee, 2016). However, owning to the knowledge that mark-up is largely affected by the
market situation and reflects contractors’ objective in tendering; thus it varies from project to
project and from company to company (Fayek et al., 1998), studies have not been carried out
to discover how these factors vary from one project characteristic to another and from one
organisational characteristic to another.
This study aims to appraise the factors affecting contractors’ mark-up decision in Nigeria by
carrying out a comparative analysis on some selected project and organisational
characteristics. These comparative analysis were conducted using independent sample t-test
and analysis of variance (ANOVA). Competition was discovered to pose a great challenge to
small-sized contractors, which can be mitigated by contractors keeping and studying the
records of their potential competitors. Client-contractor relationships are also vital in the
bidding decision made by contractors as it could influence the mark-up they insert in their
bids.
This study is relevant to construction industry players such as contractors, consultant quantity
surveyors, other consultants and clients because it will help them to enhance the industry’s
performance. Local and regional researchers who interface with the construction industry, in
addition to international researches, could use the outcome of this study to further their
studies. It could also be helpful in developing a wider and deeper perspective of the main
factors governing contractors’ decisions on the most suitable general overhead cost and profit
margins.
2
2
LITERARTURE REVIEW
2.1
Competitive Bidding
In the construction industry, competitive bidding is traditionally and widely used and major
construction works are obtained through these competitive bidding in the Nigerian
Construction Industry (NCI) (Oyediran and Asuquo, 2011). This bidding method normally
awards contracts to the lowest responsive bidder and it is designed to promote competition in
an attempt to ensure the lowest price for the project. In other words, competitive bidding is
used to encourage efficiency and innovation by the participating contractors, thereby
providing the owner with a constructed project of specified quality at the lowest possible
price (Clough and Sears, 1994).
The competitive bidding practice has been generally criticized by many contractors as the
basic challenges of the construction industry. The competitive bidding is characterized as
highly competitive and lowest profit margin to contractors. If the contractor bids low enough,
he gets the job yet unable to make a fair profit. On the other hand, if he bids high enough to
make a fair profit, he may not be able to get a job (Baloi, 2003). These unpleasant alternatives
place the contractor in an extremely awkward position.
In the context of real-word bidding problems, bid/no bid and mark-up decisions are made
based on multiple conflicting and inadequate criteria or characteristics. Several researchers
such as Bageis and Fortune (2006) have proposed multi-criteria decision making, with
discrete alternatives such as multiple-attribute utility theory, the analytic hierarchy process
and other methods, to solve decision-making problems in public sector areas such as
healthcare, planning and macroeconomics. It was only in the late 1990s that multi-criteria
decision making with discrete alternatives has started to be used in order to solve competitive
bidding decision problems (Shi and Zeleny, 2000). However, large number of these
competitive bidding strategy models are never used in practice because they do not suit the
actual practices of the construction industry (Fayek, 1998; Shapton, 2017).
2.2
Mark-up
2.2.1 Background
Consensus on how mark-up should be defined has not been reached, but mark-up could be
explained to a layman as an amount added to the cost price to determine the selling price.
Also, it is the amount added by a seller to the cost of a commodity to cover expenses and
profit to arrive at a selling price. According to Clough and Sears (1994), the mark-up is
customarily determined as a percentage of the cost and it may vary from 5 to more than 20
percent of the project cost. The mark-up usually contains three elements; an allowance for
company overheads, an allowance for contingencies and an allowance for profit (McCaffer
and Baldwin, 1984). Also, Hegazy & Moselhi and Hegazy (1995) reported that based on
construction contractors’ survey of 400 contractors in Canada and the U.S., mark-up may
include profit only, profit plus contingency, profit plus general overhead, or profit plus
general overhead plus contingency. They also noted that mark-up has been computed as a
percentage of direct costs, project overhead, and general overhead; a percentage of direct cost
plus project overhead; or a percentage of direct cost only.
Moreover, according to Shapton (2017), Younis et al. (2016) and Rehan et al. (2016), markup is comprised of three components, including contingency on costs of work, contingency
on cost of risk, and price of profit. With regards to these considerations about mark-up, this
3
study has adopted the concept of mark-up to be the contractor’s general overhead (otherwise
known as contractor’s overhead) cost and profit (Peterson, 2009; Shim & Kim, 2016).
2.2.2 The Difficulty in Determining a Mark-up
According to Park (1979) and Nourah (2013), some contractors are of the opinion that their
overhead costs and can be reduced by simply reducing their prices on jobs. This thought is a
dangerous illusion because as these costs are reduced, they consequently reduce the profit,
not overhead costs. A contractor can increase his mark-up, but by minimizing his chances of
being the lowest bidder. On the extreme, the contractor can minimize his mark-up so that his
chances to win are maximized; however, this situation may and will cause loss which is not
the objective of the contractor (Shash and Abdul-Hadi, 1990). This means that if the
contractor includes too large profit, the bid may not qualify to win the project. On the other
hand, if he includes too low profit in his bid to ensure winning the contract, he might find the
job to be unprofitable and the actual costs of the job may exceed the contractor’s estimated
costs to the extent that they exceed his profit.
2.2.3
“Right” Mark-up
According to Egemen and Mohamed (2007), a contractor who intends to survive in this
competitive construction business is competent to determining a “right” mark-up in his/her
bidding. Without “right” mark-ups, selecting “right” projects will be meaningless (Egemen
and Mohamed, 2007). Therefore, “right” mark-up is very important in bidding where
profitability and high competitiveness are unavoidable (Clough and Sears, 1994). There are
many explanation and definition to the “right” mark-up. Egemen and Mohamed (2007)
explained “right” mark-up as the optimum balance between a bid prices that is as ‘practically
low’ as possible to win the tender and as ‘practically high’ as possible to maximize profit.
Park (1979) determined the “right” mark-up as the result in the highest possible profit
obtainable under an existing competitive situation. According to Clough and Sears (1994),
“right” mark-up is the potential to maximum possible profit, at the same time keeping the bid
at a competitive level. Shash and Abdul-Hadi (1993) said that “right” mark-up is that which
will assure them of winning sufficient projects with reasonable profits.
As briefly discussed in above, all of the “right” mark-up considerations have similar
explanations and definitions. It can be in summary that “right” mark-up is the highest
profitability in bidding, at the same time within the highest competitive level. However,
determining the “right” mark-up size is not an easy task. According to Shash and Abdul-Hadi
(1992), if a contractor can determine and identify all the factors that affect his mark-up, then
the chances of applying the right amount of mark-up to the right project will be better
improved.
2.2.4 Factors Affecting Contractors’ Mark-up Decisions
Several research works have been carried out with respect to factors affecting contractors’
mark-up decisions in the past. Ahmad and Minkarah (1988) were one of the earliest to be in
this area of research. They used a questionnaire survey to get information about firms and
evaluate the level of importance of 31 factors that can affect their bidding decision. Their
research came up with some important findings, which can be summarised as follows:
1. Competition and profitability are not the only factors of importance in bidding
process decisions.
4
2. Experience, judgment and subjective assessments are used by contractors in the
bidding process. However, statistical and mathematics tools are not utilised.
3. The level of importance of certain factors differs when comparing the bid/no bid
decision and the bid mark-up decision.
The findings of this research provided the researchers with new information for identifying
the factors that affect the bidding decision process. This new information allowed them to
develop a system that could help contractors to make bid decisions. The system is based on a
multi-attribute utility model, where a bidder inputs a judgment into the system in order to get
help with the bidding process (Ahmad and Minkarah, 1988).
In 1993, a study to identify factors affecting bid/no bid and bid mark-up decisions was
conducted in the UK by Shash. The research used a questionnaire method in order to collect
data. The questionnaire was designed in a similar way to the questionnaire used by Ahmad
and Minkarah in 1988. However, 55 factors were presented in the questionnaire. The findings
of this research can be summarised as follows:
1. Top contractors rely on a mental model when making bidding decisions, using
judgment and perception.
2. The use of statistical or mathematical models is not common among top
contractors.
3. Top contractors are comfortable with how they make their bidding decisions.
Most of these findings agree with the findings of Ahmad and Minkarah (1988). Nevertheless,
the findings of Shash’s research provided a foundation for further research into the
development of a realistic bidding model (Shash 1993).
Shash and Abdul-Hadi in 1990 conducted a research in order to determine the factors that
affect bidding mark-up in the bidding process and to test whether the levels of importance of
these factors differs depending on the sizes of contractors in Saudi Arabia. The research
identified 37 factors, which were classified into five groups. These groups were project
characteristics, project documents, company characteristics, the bidding situation, and the
economic situation.
Another study conducted in Saudi Arabia was that of Abdulrahman Bageis and Chris Fortune
in 2008. The aim of their study was to identify factors that affect the bid/no bid decision in
order to develop a bid decision tool to help contractors make bid/no bid decisions. The main
finding of this research was that the level of importance of the various factors is affected by
the characteristics of the contractor and their main clients. Due to this, the model proposed by
their research to help contractors make bid/no bid decisions considered the contract type and
the main client in order to determine the levels of importance of the factors that affect the
bid/no bid decision (Bageis, 2008).
Dulaimi and Shan (2002) studied the construction industry in Singapore and included a
literature review of Ahmad and Minkarah (1988), Shash and Abdul-Hadi (1992) and Shash
(1993). Based on this literature review they identified 40 common factors that influence
bidding mark-up decision. They found that these factors differ between medium- and largesized contractors. Additionally, they found that large-sized contractors are concerned about
5
the type of work, whereas medium-sized contractors are more concerned about their
company’s finances. Their study was a starting point for the development of a bidding
strategy model (Dulaimi and Shan, 2002).
Fayek (1998) conducted a study that identified 90 factors that influence bidding decisions in
terms of setting margin size. The study used the fuzzy set theory to develop a competitive
bidding strategy model which improved the quality of the decision making process used
when setting a margin (Fayek, 1998).
Liu and Ling (2005) identified the factors affecting the mark-up decisions of a profitable
contractor in Singapore. They investigated 52 factors and found that there were 21 significant
factors which influenced bidding mark-up decisions (Liu and Ling, 2005).
Egemena and Mohamed (2007) identified the key factors which help a contracting
organisation reach the correct bid/no bid decision, as well as the correct mark-up decision.
The study confirmed that factors relating to strategic consideration have a significant role in
both bidding process decisions. This study helped to complete a framework for a knowledgebased system model (Egemena and Mohamed, 2007).
Nourah (2013), while developing a bidding model, identified and analysed 58 factors and
conducted interviews in order to explore the current practical practice in setting mark-up in
Saudi Arabia and to identify factors that may influence bid mark-up decision in the Saudi
Arabian construction industry. He made an important discovery on the level of importance
and rank of factors that influence bid mark-up as they differ based on contractors'
characteristics (contractor size) and main client. As a result, a bidding model to determine
mark-up based on contractors' size and main clients was developed (Nourah, 2013).
Several other research works have been conducted across the globe on factors affecting
contractors’ mark-up decisions. Some of them include Oo et al. (2007) and (2010), Hai
(2009), Enshassi et al. (2010), Oyediran and Asuquo (2011) and Jarkas (2013), amongst
others.
3
RESEARCH METHODOLOGY
To achieve the aim of this paper and satisfy its objectives, a quantitative research technique
involving the use of a questionnaire survey was adopted. The researcher’s attention was
drawn to this technique due to the fact that it deals with measurable and quantifiable aspects
of occurrences. It focuses on questions such as; “to what extent?” “How much?” “What
relationship that exists between factors?” and “What causes particular processes or
situations?”
To achieve the objectives of this paper, a review of literature was used to provide theoretical
background to the research, identify the research variables and articulate such variables. A
field survey using self-administered questionnaires was conducted, and an analysis of the
data collected was carried out to enable the researcher analyse the factors affecting
contractors' mark-up decision.
Considering the information required to meet the objectives, construction firms within Abuja
(FCT), Nigeria were selected for the field survey. These construction firms were those listed
on the Federal Inland Revenue Service (FIRS) tax complaint list as at May, 2017.
6
These construction firms were considered best suited for this study because they have more
accurate knowledge of the factors affecting contractors’ mark-up decision. This is because
they are responsible for pricing construction bids and are legally qualified to bid for jobs
within the Nigerian construction industry (BPP, 2007). Abuja (FCT) was considered for this
study because it is amongst the major cities in Nigeria, having most construction firms
situated in it and due its central location, geographically.
A total study population of 965 (nine hundred and sixty-five) construction firms were
considered. This population was drawn from the complete number of contractors found on
the FIRS tax compliant list. Several formulas have been used by several researchers to
determine the most appropriate sample size adequate enough to represent a population.
However, to obtain a statistically representative sample that represents a significant
proportion (e.g. over 5%) of the population, a formula which takes note of the finite
population correction factor was used. This research has a finite population, therefore the
formula shown below was adopted to calculate the sample size for this research.

Hogg and Tannis (2009) formula:
𝑛=
𝑚
(𝑚−1)……………………………………………..………………. (1)
1+
𝑁
Where: n; m; and N, represent the sample size of the limited, unlimited and available
population, respectively. On the other hand, m is estimated by equation below:
𝑚=
𝑧 2 ∗𝑝∗((1−𝑝))
𝜀2
……………………………………………………. (2)
Where: z is the statistic value for the confidence level used, i.e. 2.575, 1.96, and 1.645, for
99, 95, and 90 per cent confidence levels, respectively; p is the value of the population
proportion which is being estimated; and ε is the sampling error of the point estimate
assumed to be 0.1 (10% sampling error). Since the value of p is unknown, Sincich et al.
(2002) suggested that a conservative value of 0.50 can be used so that a sample size that is at
least as large as required can be obtained.
The sample size that was calculated from the formula above was 89 construction firms (out of
965 construction firms). To reduce the chances of non-responses and inadequate responses to
the questions asked in the questionnaire, a convenience sampling was adopted to select
construction firms who responded to the questionnaire. The data collection phase spanned
approximately 2 months and a total of 91 out of 125 self-administered questionnaires, were
completed and returned.
The questionnaire comprise of two sections (Section A & B). Section A asked for the
background data of the respondents. The background data include their designation in the
company, their years of working experience, their profession, etc., through the use of closeended questions. Section B of the questionnaire started with a portion containing classes of
project scope related questions followed by ordinal-scaled qualitative factors. Respondents
were requested to use their experience from a single construction project they have bided for
in the past to select the best fit options to such projects and rank the set of factors in
accordance to how they affect their mark-up decisions on such projects. This was done to
enable the researcher gather relevant information with regards to organisational and project
7
characteristics, within the scope of the study and to achieve the second and third objectives of
this study.
The questionnaire was designed to enabled respondents rate the level of effect of factors on
their mark-up decision on a 5-ordinal measurement scale (1 = No effect, 2 = less effect, 3 =
Moderate effect, 4 = High effect and 5 = Very high effect), in section B of the questionnaire.
63 qualitative factors were scaled based on project characteristics such as; types of
construction project and type of client; and contractor characteristics such as contractors’
sizes and types of company ownership. Researches such as Ahmad and Minkarah (1988),
Shash and Abdul-Hadi (1992 and 1993), Shash (1993) and Dulaimi & Shan (2002) have
identified these project characteristics among the top ranked factors affecting mark-up
decision and a deeper understanding of their influence on other factors is deemed necessary.
Data analysis was carried out by applying computer software Statistical Package of the Social
Sciences (SPSS). The method of analysis was determined by the suitability of the variables
available. To ensure suitability of the instrument for data collection used for this study, three
preliminary tests were conducted; (1) One Sample t-Test; (2) accuracy of the Data (ChiSquare Test); and (3) Reliability Analysis (Reliability Test using Cronbach’s α).
Subsequently, to achieve the objectives of this study, independent sample t-test and analysis
of variance (ANOVA) were used.
4
FINDINGS AND DISCUSSIONS
4.1
Data presentation
In Table 1, a distribution of respondents who were involved in the data collection process is
presented.
Table 1: Respondents’ characteristics
Respondents distribution
Type of construction Project:
Building construction works
Civil engineering construction works
Type of client:
Private client
Public client
Company ownership and management:
Indigenous
Expatriate
Company's size:
Small (Less than N300 million)
Medium (Between N300 million and N1 billion)
Large (Above N1 billion)
Respondent's Designation:
Chief estimator
Managing Director
Project manager
Chief executive officer
Respondent's Profession:
Architect
8
cases
Results
Percentage
48
43
52.7
47.3
50
41
54.9
45.1
55
36
60.4
39.6
19
36
36
20.9
39.55
39.55
33
3
50
5
36.3
3.3
54.9
5.5
25
27.5
Quantity Surveyor
Civil engineer
Builder
Others (Land Surveyor)
Company Existence:
1-10 years
11-20 years
21-30 year
Above 30 years
36
20
8
2
39.6
22.0
8.8
2.1
19
25
35
12
20.9
27.5
38.5
13.2
Source: Field survey (2018).
4.2
Comparative Analysis of Factors Affecting Contractors’ Mark-up Decision Based
on Selected Project Characteristics
There are two sets of analysis carried out in this sub-section of the study. Firstly, the study
determined whether there is any significant difference in the way contractors responded to the
questionnaire with respect to different construction project types. Secondly, it determined
whether the responses of the respondents differed significantly when they considered
different types of clients. Both results of the analysis are shown in Table 2 below.
“Risk involved investment” was considered significantly different by the separate groups of
respondents, with respect project characteristics. Civil engineering construction projects are
associated to high level of uncertainty. Therefore, civil engineering contractors are keener to
issues of “force majeure” than building contractors. Thus, to engage in such high risk
construction job, civil engineering contractors would place higher mark-up to compensate
themselves for the impending uncertainties (Hai, 2009).
“Risk involved in investment” was also considered significantly different contractors who
rated factors with respect to private client projects they have undertaken in the past from
those who considered public client projects they have undertaken in the past. This indicates
that contractors engaged by public considered it more risky venturing into such public
investments. This is because public clients have been reported viable to possible shortage or
delayed payment and bad payment habit (Shash and Abdul-Hadi, 1992). Private client
contractors on the other hand do not considered risk involved in investment a critical factor
when deciding on mark-up and would rather rely on their relationships with their clients. The
fact that most private client contractors are either small-scale or medium-scale contractors,
they are more concerned in building a good relationship with their clients for the purpose of
organisational growth.
Public clients are also governed by the Public Procurement Act (2007), which stipulated that
all procurement of construction works, goods and services must be open to the competitive
bidding system, within the Nigerian construction industry. Shash and Abdul-Hadi (1992) and
Adrian (1982) noted that the competitive nature of the construction industry, especially in the
competitive bidding situation should be considered by contractors when deciding their markup. According these studies, the lowest bidder price decreases as the number of competitors
on a project increases. The fact that the winning tender is usually the lowest responsive tender
in competitive tendering process, as unanimously attested to by several researchers and
authors such as Shash and Abdul-Hadi (1992), Fayek et al. (1999) and Dulaimi and Shan
(2002), public client contractors must minimize their mark-up so that their chances of being
the lowest bidders are maximized.
9
Dulaimi and Shan (2002) in his study of medium and large size contractors in Singapore also
identified risk involved in investment as one of the forty factors considered significantly
different by the two groups of contractors. Jarkas (2013) also stated that “First grade”
contractors considered “risk involved in investment” significantly different from the way
“Second grade” and “Third grade” contractors considered the same factor in Kuwait.
10
Table 2: Results of comparative analysis of factors affecting contractors’ mark-up decision based on selected project characteristics
Project Type
S/N
Mean
Factors
(1A) Ra1 (1B) Rb1
1
1.01
1.02
1.03
1.04
1.05
1.06
1.07
1.08
1.09
1.1
1.11
1.12
1.13
1.14
1.15
1.16
1.17
1.18
1.19
2
2.01
2.02
2.03
PROJECT CHARACTERISTICS
Location of the project
Duration of the project
Size of the project (contract sum)
Job start time
The client financial capacity
Project cash flow
Methods of construction (manually, mechanically)
Type of equipment required
Type of labour required
Site accessibility
The project stakeholders’ identity
Design team
Character of consultants (e.g. Strictness)
Safety hazards
Degree of difficulties
Degree of possible alternative design to reduce cost
Possibility of Public objection
The client reputation among other contractors
Prompt payment habit of the client
CONTRACTORS CHARACTERISTICS
Availability of required cash (capital)
Uncertainty in cost estimate
Need for work
Client Type
tvalue
Assmp.
Sig. (2tailed)
Mean
(2A) Ra2 (2B) Rb2
tvalue
Assmp.
Sig. (2tailed)
3.42
3.06
3.54
3.06
3.33
3.60
3.40
3.48
3.10
3.31
3.04
3.40
3.04
2.58
2.92
3.31
3.06
3.46
4.08
18
41
10
41
28
7
23
14
40
30
44
24
44
54
52
33
41
15
1
3.44
3.28
3.42
3.40
3.47
3.86
3.49
3.58
3.56
3.09
3.09
3.19
3.09
2.86
3.28
3.28
3.12
3.65
4.23
17
28
19
22
16
4
15
9
11
38
39
33
39
52
26
27
36
6
1
-0.103
-0.922
0.536
-1.419
-0.582
-1.351
-0.415
-0.441
-1.869
1.007
-0.229
1.016
-0.234
-1.402
-1.472
0.147
-0.227
-1.086
-0.805
0.918
0.359
0.593
0.159
0.562
0.180
0.679
0.660
0.065
0.317
0.819
0.312
0.816
0.164
0.144
0.884
0.821
0.281
0.423
3.42
3.28
3.70
3.34
3.50
3.74
3.56
3.62
3.34
3.30
3.06
3.30
3.04
2.76
2.98
3.48
3.30
3.58
4.20
23
41
5
34
18
4
13
11
35
38
47
38
48
54
51
21
40
12
1
3.44
3.02
3.22
3.07
3.27
3.71
3.29
3.41
3.29
3.10
3.07
3.29
3.10
2.66
3.22
3.07
2.83
3.51
4.10
11
42
24
34
22
5
21
12
19
31
35
20
31
54
23
35
49
10
1
-0.078
1.086
1.135
1.129
1.025
0.170
1.204
0.886
0.190
0.925
-0.059
0.035
-0.261
0.507
-0.963
1.810
1.525
0.378
0.550
0.938
0.280
0.261
0.262
0.308
0.865
0.232
0.378
0.849
0.358
0.953
0.972
0.795
0.613
0.338
0.074
0.123
0.706
0.584
3.56
3.46
3.52
9
15
11
3.49
3.40
3.40
14
20
20
0.329
0.312
0.622
0.743
0.756
0.536
3.68
3.44
3.54
6
22
15
3.34
3.41
3.37
18
13
14
1.515
0.125
0.861
0.133
0.901
0.391
(continued)
11
2.04
2.05
2.06
2.07
2.08
2.09
2.1
2.11
2.12
2.13
3
3.01
3.02
3.03
3.04
3.05
3.06
3.07
3.08
3.09
3.1
4
4.01
4.02
4.03
4.04
4.05
4.06
4.07
4.08
4.09
General (office) overhead incurred in similar jobs
Current work load
Strength within industry
Specific features that provide competitive advantage
Availability of qualified sub-contractors
Familiarity with site condition
Financial goals of the company
Degree of difficulties in obtaining bank loan
Previous relationship with the client
Past profit in similar job
PROJECT DOCUMENTATION
Type of contract
Completeness of drawings and specification
Clearness of the work and specifications
The ability of modifying the contract
Value of liquidated damages
Consultants’ interpretation of the specification
Contract conditions
Design Quality level
Possibility in shortage or delayed payment
Insurance premium
TENDERING SITUATION
Required bond capacity
Time allowed for submitting bids
Time of bidding (season)
Bidding document price
Prequalification requirements
Tendering duration
Bidding methods
Number of bidders
Identity of bidders
3.42
2.94
3.23
3.40
3.44
3.31
3.21
3.42
3.73
3.50
20
50
35
22
17
32
36
20
4
13
3.30
2.81
3.07
3.19
3.28
3.42
3.37
3.33
3.58
3.26
25
54
43
34
30
18
23
24
8
31
0.552
0.521
0.685
0.962
0.784
-0.441
-0.729
0.444
0.678
1.155
0.583
0.603
0.495
0.339
0.435
0.66
0.468
0.658
0.500
0.251
3.54
3.02
3.34
3.40
3.48
3.36
3.40
3.40
3.68
3.52
16
50
33
27
19
29
25
25
7
17
3.15
2.71
2.93
3.17
3.22
3.37
3.15
3.34
3.63
3.22
29
52
44
28
24
15
29
17
6
24
1.928
1.326
1.797
1.049
1.292
-0.024
1.129
0.284
0.209
1.422
0.057
0.188
0.076
0.297
0.200
0.981
0.262
0.777
0.835
0.158
2.85
3.63
3.27
3.00
3.17
2.94
2.48
3.35
3.90
2.56
53
6
34
47
38
50
56
26
2
55
2.67
3.56
3.16
2.98
2.91
2.91
2.35
3.12
4.00
3.02
55
11
35
48
49
50
56
37
2
45
0.874
0.336
0.532
0.122
1.228
0.127
1.256
1.436
-0.550
-1.838
0.385
0.738
0.596
0.903
0.223
0.899
0.213
0.155
0.584
0.069
2.94
3.64
3.34
3.06
3.20
3.02
2.46
3.36
3.90
2.56
53
10
32
46
42
49
56
31
2
55
2.56
3.54
3.07
2.90
2.85
2.80
2.37
3.10
4.00
3.05
55
9
37
45
48
51
56
33
3
39
1.864
0.519
1.319
0.829
1.643
0.897
0.795
1.583
-0.526
-1.948
0.066
0.605
0.190
0.409
0.104
0.372
0.429
0.117
0.600
0.055
3.33
3.13
3.35
2.15
2.96
2.00
2.96
3.19
3.04
29
39
27
57
48
58
48
37
44
3.00
3.05
3.07
2.12
3.07
1.77
2.88
3.00
2.81
46
44
42
57
41
58
51
46
53
1.493
0.326
1.139
0.191
-0.397
1.400
0.294
0.753
0.956
0.139
0.745
0.258
0.849
0.692
0.165
0.77
0.454
0.341
3.30
3.12
3.36
2.20
3.12
1.88
2.96
3.32
3.14
37
44
30
57
45
58
52
36
43
3.02
3.05
3.05
2.05
2.88
1.90
2.88
2.83
2.68
43
40
40
57
46
58
47
49
53
1.225
0.295
1.244
0.886
0.863
1.200
0.501
1.150
0.321
0.224
0.769
0.217
0.378
0.391
0.233
0.617
0.253
0.749
(continued)
12
5
5.01
5.02
5.03
5.04
5.05
5.06
5.07
ECONOMIC SITUATION
Risk involved in investment
Availability of equipment and materials
Overall economy (availability of work)
Availability of labour
Statutory regulations and requirement
Risks of fluctuation in labour
Risks of fluctuation in material
3.50
3.60
3.67
3.42
3.31
3.35
3.75
12
7
5
18
30
25
3
3.93
3.63
3.72
3.53
3.28
3.23
3.58
3
7
5
13
28
32
9
-2.007
-0.108
-0.289
-0.600
0.173
0.574
0.922
1A. Mean ratings of respondents who carried out Building construction works. N = 48; Ra1. Ranking of 1A.
1B. Mean ratings of respondents who carried out Civil Engineering construction works. N = 43; R b1. Ranking of 1B.
2A. Mean ratings of respondents who responded with respect to private clients. N = 50; R a2. Mean ranking of 2A
2B. Mean ratings of respondents who responded with respect to public clients. N = 41; R b2. Mean ranking of 2B
Statistically significant values are shown in bold.
Source: Field survey (2018).
13
0.048
0.914
0.773
0.550
0.863
0.568
0.359
3.42
3.66
3.66
3.56
3.48
3.38
3.74
24
9
8
14
20
28
3
4.05
3.56
3.73
3.37
3.07
3.20
3.59
2
8
4
15
38
27
7
-3.000
0.449
0.380
0.985
0.958
0.872
0.842
0.004
0.654
0.705
0.327
0.342
0.386
0.402
4.3
Comparative Analysis of Factors Affecting Contractors’ Mark-up Decision Based
on Selected organisational Characteristics
This section also consist of two test which are both shown on Table 3; the first test was
carried out to analyse the responses of respondents based on company sizes using ANOVA
and the second test was on the different company ownerships using independent sample ttest.
“Insurance premium” is the amount of money required to be paid for a stipulated insurance
policy. The higher the insurance premium, the greater the extent of risk (mostly
constructional risk) expected for a given project. Small-sized and medium-sized contractors
(having average turnover below 1 billion Naira) ranked this factor significantly lower than
the large-sized contractors (having average turnover above 1 billion Naira). This implies that
the large-sized contractors must win larger sizes of projects to enable them attain their desired
average turnover. Similarly, they are more concerned with the level of constructional and
contractual risk related to larger sizes of projects (Abdul-Hadi, 1992).
Number of bidders and identity of bidders were both extremely ranked higher by small-sized
contractors with mean rankings of 1st and 3rd, respectively; large-sized contractors ranked
these factors 55th and 56th, respectively. The F-value which shows the difference in the
variance between groups and within group was calculated at 23.769 and 17.051, respectively
with a 0.000 probability of large-sized contractors and small-sized contractors considering
these factors the same way. This implies that the small-sized contractors are more concerned
with the level of competition expected from a proposed bidding process. Small-sized
contractors are insecure, knowing that lowest bidder price decreases as the number of
competitors on a project increases (Adrian, 1982) and are intimidated by a focused
competition when the bidders’ identities are known (Ahmed and Minkarah, 1988).
Dulaimi and Shan (2002) also reported that difference in level emphasis was placed on
number of bidders and the identity of bidders by medium-scale and large-scale contractors in
Singapore during their bid mark-up decisions. They also stated that the above ‘intelligence’
(knowing the number of bidders and their identities) can be argued to provide contractors
with a level of confidence about how much they can mark-up their bid and still have a good
chance of winning a tender.
Number and identity of bidders further corroborating the findings of Dulaimi and Shan
(2002), Egeman and Mohamed (2007), Enshassi et al. (2007), Oo et al. (2007) and Banki et
al. (2008), whose investigations distinguished the influence of this factors on contractors’
mark-up decisions, in Singapore, Turkey and North Cyprus, Gaza Strip, Hong Kong and
Singapore, and Iran, respectively. However, Oyeyipo et al. (2016) in a similar study stated
that their findings revealed that competition (number and identity of competitors) does not
have significant influence on contractors' bidding decisions.
Banki et al. (2008) found an inversely proportional relationship between the number of
bidders and the bid size amongst Iranian contractors. Such a trend is mainly attributed to the
competitive nature of the bidding process, where the probability of each bidder winning the
tender reduces as the number of competitors increases and thus contractors are normally more
competitive with mark-up to maximize their chances of winning the competition. Therefore,
contractors, especially “good ones”, usually keep track records of their competitors, including
previous relationships and experience of their rivals with employers, which can enable them
14
to both predict their chances in submitting the lowest bids in future tenders, and understand
the characteristics of employers.
15
Table 3: Results of comparative analysis of factors affecting contractors’ mark-up decision based on selected organisational
characteristics
Company Size
S/N
Mean
Factors
(3A) Ra3 (3B) Rb3 (3C) Rc3
1
1.01
1.02
1.03
1.04
1.05
1.06
1.07
1.08
1.09
1.1
1.11
1.12
1.13
1.14
1.15
1.16
1.17
1.18
1.19
PROJECT CHARACTERISTICS
Location of the project
Duration of the project
Size of the project (contract sum)
Job start time
The client financial capacity
Project cash flow
Methods of construction (manually,
mechanically)
Type of equipment required
Type of labour required
Site accessibility
The project stakeholders’ identity
Design team
Character of consultants (e.g. Strictness)
Safety hazards
Degree of difficulties
Degree of possible alternative design to
reduce cost
Possibility of Public objection
The client reputation among other
contractors
Prompt payment habit of the client
FValue
Assmp.
Sig. (2tailed)
company ownership
Mean
tAssmp. Sig.
(2-tailed)
(4A) Ra4 (4B) Rb4 value
3.32
2.95
3.74
3.37
3.53
3.42
26
46
5
21
14
19
3.25
3.17
3.39
3.19
3.33
3.78
34
39
23
37
27
4
3.67
3.28
3.44
3.17
3.39
3.83
6
32
15
39
20
4
1.292
0.537
0.668
0.212
0.198
1.395
0.280
0.586
0.515
0.809
0.820
0.253
3.42
3.29
3.47
3.18
3.53
3.73
26
33
21
41
18
5
3.44
2.97
3.5
3.28
3.19
3.72
10
40
9
23
27
4
-0.105
1.335
-0.116
-0.397
1.455
0.026
0.916
0.185
0.908
0.693
0.149
0.979
3.37
24
3.53
13
3.39
18
0.206
0.814
3.55
16
3.28
21
1.185
0.239
3.53
3.16
3.05
3
3.26
3
2.53
2.84
14
33
41
44
30
44
54
50
3.44
3.17
3.11
2.83
3.25
3.03
2.75
2.89
20
38
42
53
32
47
54
52
3.61
3.56
3.39
3.33
3.36
3.14
2.78
3.42
10
12
20
27
23
40
50
16
0.203
1.222
0.914
2.089
0.126
0.148
0.476
2.394
0.816
0.300
0.405
0.130
0.881
0.863
0.623
0.097
3.6
3.35
3.05
3
3.22
3.11
2.71
3.13
12
31
50
52
38
47
55
44
3.42
3.28
3.44
3.17
3.42
3
2.72
3.03
13
20
11
29
13
38
48
37
0.776
0.268
-1.772
-0.730
-0.941
0.487
-0.064
0.391
0.440
0.789
0.080
0.467
0.349
0.628
0.949
0.696
3.37
21
3.47
18
3.08
42
1.226
0.298
3.45
22
3.06
35
1.743
0.085
3.37
23
3.19
35
2.83
48
1.709
0.187
3.16
42
2.97
40
0.794
0.429
3.63
8
3.58
8
3.47
14
0.264
0.769
3.67
10
3.36
16
1.736
0.086
4.26
1
4.33
1
3.92
2
2.260
0.110
4.13
1
4.19
1
-0.354
0.724
(continued)
16
2
2.01
2.02
2.03
2.04
2.05
2.06
2.07
2.08
2.09
2.1
2.11
2.12
2.13
3
3.01
3.02
3.03
3.04
3.05
3.06
3.07
3.08
3.09
3.1
4
4.01
4.02
CONTRACTORS CHARACTERISTICS
Availability of required cash (capital)
Uncertainty in cost estimate
Need for work
General (office) overhead incurred in
similar jobs
Current work load
Strength within industry
Specific features that provide competitive
advantage
Availability of qualified sub-contractors
Familiarity with site condition
Financial goals of the company
Degree of difficulties in obtaining bank loan
Previous relationship with the client
Past profit in similar job
PROJECT DOCUMENTATION
Type of contract
Completeness of drawings and specification
Clearness of the work and specifications
The ability of modifying the contract
Value of liquidated damages
Consultants’ interpretation of the
specification
Contract conditions
Design Quality level
Possibility in shortage or delayed payment
Insurance premium
TENDERING SITUATION
Required bond capacity
Time allowed for submitting bids
3.42
3.53
3.58
19
16
12
3.47
3.44
3.53
15
19
9
3.64
3.36
3.33
8
23
26
0.333
0.190
0.545
0.717
0.827
0.582
3.45
3.53
3.6
24
18
11
3.64
3.28
3.25
6
22
24
-0.803
1.220
1.723
0.424
0.226
0.088
3.53
17
3.31
29
3.33
28
0.335
0.716
3.53
17
3.11
31
2.007
0.048
2.58
3.11
53
38
3.17
3.33
41
25
2.75
3
51
45
2.147
0.840
0.123
0.435
3
3.27
51
34
2.69
2.97
50
40
1.273
1.273
0.206
0.206
3.05
43
3.53
9
3.19
38
1.613
0.205
3.44
25
3.08
34
1.600
0.113
3.11
3.26
3.16
3.32
3.63
3.37
39
31
34
28
10
24
3.5
3.36
3.42
3.42
3.78
3.47
14
24
21
22
4
17
3.36
3.42
3.22
3.36
3.56
3.31
22
17
35
25
13
29
1.051
0.111
0.465
0.070
0.418
0.245
0.354
0.896
0.630
0.932
0.660
0.783
3.55
3.56
3.36
3.42
3.73
3.47
15
13
28
26
5
20
3.08
3.06
3.17
3.31
3.56
3.25
33
36
29
19
7
24
2.297
1.518
0.859
0.538
0.772
1.031
0.024
0.137
0.392
0.592
0.442
0.306
2.63
3.53
3.16
2.95
3.11
52
17
35
48
39
2.97
3.58
3.25
3
3.17
51
6
33
50
39
2.64
3.64
3.22
3
2.89
53
7
35
45
47
1.291
0.090
0.056
0.025
0.720
0.280
0.914
0.946
0.975
0.489
2.8
3.73
3.45
3.22
3.13
54
7
22
36
46
2.72
3.39
2.86
2.64
2.92
48
15
46
54
44
0.369
1.692
2.997
3.145
0.972
0.713
0.094
0.004
0.002
0.334
2.95
46
3.03
47
2.81
49
0.344
0.710
3.11
48
2.64
54
1.958
0.053
2.37
3.32
3.58
2.26
55
28
13
56
2.44
3.31
3.97
2.75
56
30
2
54
2.42
3.14
4.11
3.08
54
41
1
42
0.143
0.496
2.269
3.008
0.867
0.611
0.109
0.050
2.42
3.33
4.04
2.85
56
32
2
53
2.42
3.11
3.81
2.67
56
32
2
51
0.014
1.275
1.201
0.723
0.989
0.206
0.233
0.472
3.11
2.89
37
49
3.08
3.03
44
47
3.31
3.25
30
33
0.434
0.680
0.649
0.509
3.15
3.2
43
39
3.22
2.92
26
43
-0.333
1.160
0.740
0.249
(continued)
17
4.03
4.04
4.05
4.06
4.07
4.08
4.09
5
5.01
5.02
5.03
5.04
5.05
5.06
5.07
Time of bidding (season)
Bidding document price
Prequalification requirements
Tendering duration
Bidding methods
Number of bidders
Identity of bidders
ECONOMIC SITUATION
Risk involved in investment
Availability of equipment and materials
Overall economy (availability of work)
Availability of labour
Statutory regulations and requirement
Risks of fluctuation in labour
Risks of fluctuation in material
3.16
2.11
2.79
2
3.05
4.26
3.89
35
57
51
58
41
1
3
3.28
2.14
3.11
1.92
3.06
3.19
3.06
31
57
42
58
45
35
45
3.19
2.14
3.03
1.81
2.72
2.39
2.31
37
57
44
58
52
55
56
0.075
0.015
0.364
0.400
0.826
23.769
17.051
0.928
0.985
0.696
0.672
0.441
0.000
0.000
3.36
2.07
3.2
1.87
3.09
3.22
3.13
30
57
39
58
49
36
45
3
2.22
2.72
1.92
2.67
2.92
2.64
39
57
47
58
51
45
53
1.433
-0.950
1.694
-0.256
1.226
1.421
1.660
0.155
0.345
0.094
0.798
0.223
0.159
0.101
3.74
3.84
3.63
3.63
3.32
3.26
3.63
6
4
10
8
27
31
7
3.53
3.53
3.81
3.47
3.33
3.33
3.58
9
9
3
15
27
26
6
3.86
3.58
3.61
3.39
3.25
3.28
3.78
3
11
9
18
34
31
5
0.939
0.590
0.479
0.413
0.079
0.040
0.468
0.395
0.557
0.621
0.663
0.924
0.961
0.628
3.69
3.73
3.73
3.55
3.36
3.27
3.75
9
7
4
14
28
35
3
3.72
3.44
3.64
3.36
3.19
3.33
3.56
3
12
5
16
28
18
8
-0.140
1.272
0.461
0.919
0.863
-0.280
1.018
0.889
0.207
0.646
0.361
0.390
0.780
0.311
*3A. Mean ratings of small-size contractors. N = 19; Ra. Mean ranking of 3A
*3B. Mean ratings of medium-size contractors. N = 36; Rb. Mean ranking of 3B
*3C. Mean ratings of large-size contractors. N = 36. Rc. Mean ranking of 3C
*4A. Mean ratings of respondents within Indigenous companies. N = 55; Ra4. Mean ranking of 4A
*4B. Mean ratings of respondents within Expatriate companies. N = 36; Rb4. Mean ranking of 4B
*Statistically significant values are shown in bold.
Source: Field survey (2018).
18
This study has revealed that contractors’ sizes influence the way factors affecting
contractors’ mark-up decision are considered and further substantiates the
findings of Shash and Abdul-Hadi (1993), who examined the mark-up decisions
of small, medium, and large contractors and found that the importance of the
factors considered in mark-up vary as the contractor’s size changes. In other
words, the significant factors that influence mark-up are not the same for small,
medium and large size contracting firms. Again, Dulaimi and Shan (2002)
concluded that there were significant differences in the attitudes of medium and
large size contractors when evaluating the importance of factors influencing bid
mark-up decisions. However, the study by Fayek et al. (1998) did not discover
any obvious drifts in contractors’ mark-up decision between large and small
contractors.
The findings of this study on the influence of project characteristics on factors
affecting mark-up decision agrees with the conclusions of Fayek et al. (1998),
who stated that amount varies from project to project and from company to
company.
The results in Table 4 also showed that indigenous contractors rated “General
(office) overhead incurred in similar jobs” significantly higher than expatriate
contractors at a variance difference (F-value) of 2.007 and a corresponding pvalue of 0.048. Indigenous contractors were more engaged by private clients and
were therefore keener on issues of overhead they will incur in a given project.
This factor is closely related to past experience in similar projects and this further
supports the finding of Jarkas (2013) and Hai (2009) who also found out that this
factor is considered significantly different by different types of contractors in
Kuwait and Malaysia, respectively.
“Availability of qualified sub-contractors” was considered important by studies
such as Oyeyipo et al. (2016), Oyediran and Asuquo (2011), Hai (2009), Egeman
and Mohamed (2007), Oo et al. (2007), Dulaimi and Shan (2002), and AbdulHadi (1992). However, the results of this study indicated that expatiate contractors
considered this factor significantly lower than indigenous contractors. This can
easily be attributed to the fact that most indigenous contractors are small-sized to
medium-sized contractors, therefore finding it difficult to compete with large-size
contractors (Expatriate contractors) in getting qualified sub-contractors.
According to Shash and Abdul-Hadi (1993), large-sized contractors attract
qualified sub-contractors to their organizations for the better pay, benefits and
recognition they offer. Consequently, small-sized contractors end up hiring less
qualified sub-contractors and compensate for this in the determination of their
mark-up.
“Clearness of the work and specifications” and “The ability of modifying the
contract” are related in the way one factor affects the other. Clear description of
work items and specifications directly relates to the issues of constructability,
construction conflicts, change orders, etc. and could further translate to contract
modification. Contractual and construction claims have over the time been
attributed to poor description of work items and specifications (Ashworth, 2006;
Murdoch and Hughes, 2001). These factors were both considered significantly
higher by indigenous contractors and could be attributed to the level of
involvement of indigenous contractors with private clients. Most private clients
19
are known to have difficulty in clearly stating their project objectives and thus are
limited in the scope of the project they intend to achieve (Murdoch and Hughes,
2001).
“Clearness of the work and specifications” and “The ability of modifying the
contract” further have effects on design complexity and design quality level.
These outcomes further substantiate the findings of Ahmad and Minkarah (1988),
Shash (1993), Dulaimi and Shan (2002), Enshassi et al. (2007), Jarkas and Bitar
(2012) and Jarkas (2013), whose research works recognized these factors among
the most significant to construction costs in the USA, the UK, Singapore, Gaza
Strip and Kuwait, respectively.
Similar studies such as Oyeyipo et al. (2016) advocated that no agreement exists
between expatriate and indigenous contractors on the important factors that
determine bid decisions in Nigeria. Hassanein (1996) also reinforced the nonagreement of factors affecting indigenous contractors and foreign contractors in
the results of study in Egypt.
5
CONCLUSION AND RECOMMENDATION
In Nigeria, the construction industry accounts for a sizeable proportion of
Government budgets. While few construction works are executed through direct
labour by independent construction firms, the bulk is executed through a
competitive bidding process. Any improvement in tendering practice in Nigeria
has the potential to enhance the industry’s performance and save the nation
billions of naira in avoidable waste and can also contribute to the survival and
growth of the NCI. Therefore, possessing a sound knowledge of the factors
affecting the contractors’ bid mark-up decision has become vital as it will better
the tendering situation within the NCI.
Prior to this study, a number of factors affecting contractors’ mark-up decision in
construction projects within the NCI were not considered with respect to project
and organisational characteristics. To bridge the gap that existed, the researcher
carried out a comparative analysis of selected project and organisational
characteristics. Finding showed that care must be taken by contractors when
deciding on factors such insurance premium required, competition, availability of
qualified labour and risk involved in investment, as their decision on these factors
could differ depending on the type of construction project, the type of client, the
size of company and the type of ownership of company.
The effect of organisational characteristics on the factors that affect contractor’s
mark-up decision revealed that small-sized contractors are challenged by the level
of competition that exist in a bidding process. This has therefore suggested that,
for contractors to increase their chances of submitting the lowest bid, they must
keep and study the records of their competitors
This study is relevant to construction industry players such as contractors,
consultant quantity surveyors, other consultants and clients because it will help
them to enhance the industry’s performance. This study has provided knowledge
on how project and organisational characteristics affect factors affecting
20
contractors’ mark-up decision, therefore creating a better ground for
improvements in tendering practices. This improvement in the tendering practice
in Nigeria has the potential to enhance the construction industry’s performance.
Similar studies could be conducted in various geographical regions across the
country to enable the use of tools such as Artificial Neural Network (ANN) to
develop an “adaptive” Decision Support System (DSS) for contractors’ mark-up
decision.
6
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