See discussions, stats, and author profiles for this publication at: https://www.researchgate.

net/publication/359055293

Effect of Cost Estimation on Project Performance in Construction Firms in

Abuja

Article · January 2022

CITATIONS READS

0 2,286

3 authors:

Ike Egboga Cross Ogohi Daniel

Nile University of Nigeria Nile University of Nigeria Abuja- Honoris United Universities
5 PUBLICATIONS 1 CITATION 147 PUBLICATIONS 662 CITATIONS

SEE PROFILE SEE PROFILE

Hauwa Lamino Abubakar

Nile University of Nigeria
43 PUBLICATIONS 11 CITATIONS

SEE PROFILE

All content following this page was uploaded by Cross Ogohi Daniel on 07 March 2022.

The user has requested enhancement of the downloaded file.

 effect of cost estimation on project performance in construction firms in abuja

Turkish Online Journal of Qualitative Inquiry (TOJQI)

Volume 13, Issue 1, January 2022: 1052-1063

Effect of Cost Estimation on Project Performance in Construction Firms in

Abuja

Ike Egbogaa, Dr Cross Ogohi Danielb, Dr Hauwa Lamino Abubakarc

a
Department of Business Administration, Nile University of Nigeria, Abuja
b
Department of Business Administration, Nile University of Nigeria, Abuja
c
Department of Business Administration, Nile University of Nigeria, Abuja

Abstract

Adverse profitability in the construction industry is associated with the organizational leaders’
inability to accurately estimate project costs and manage project schedules. This results to high rate
of abandoned project and building, to be able to boost of successful completed projects, effective and
efficient cost estimation and planning is the very key. The study investigated the effect of cost
estimation on project performance in construction firms in Abuja. The objectives of the study are; To
determine the effect of bottom up cost estimating and parametric cost estimating on the realization of
work scope / specifications in construction firms in Abuja, To assess the impact of bottom up cost
estimating and parametric cost estimating on time / schedule performance in construction firms in
Abuja, To ascertain the impact of bottom up cost estimating and parametric cost estimating on cost
performance in construction firms in Abuja. The researcher adopted the descriptive research design
and structured questionnaire was used as instrument for data collection. The purposive sampling
technique was adopted in the study. Data analysis was committed to descriptive statistics of mean
and percentages as well as inferential statistics of correlation and multiple regression analysis. The
results showed that both bottom-up estimation and parametric cost estimating are both positively and
significantly influenced by scope/specifications, time/schedule and cost. The study concludes that
project managers need to be cognizant of this relationship and focus on developing estimates and
schedules using modern project management tools that would project accurate costs and schedules.
It was recommended that for successful completion of projects, construction project managers should
be fully abreast of cost estimating techniques through intensive training awareness and the use of
both bottom up and parametric estimating techniques be adopted for construction projects as
appropriate.

Keywords: Cost estimation, bottom-up estimating, parametric estimating, project performance

1 Introduction

Infrastructural development has been identified as one of the key activities that contribute
significantly to the Gross Domestic Product of Nigeria and other nations (Amadi & Amadi, 2020). It
involves projects which are usually complex and risky but plays a pivotal role in driving economic

1052
 Ike Egboga, Dr Cross Ogohi Daniel, Dr Hauwa Lamino Abubakar

growth and employment generation in both developed and developing nations (Amadi & Amadi,
2020).

The projects usually require the investment of large sums of money. Consequently, projects failure or
abandonment would lead to huge financial losses. The losses are often due to poor cost and time
estimations or non-existent risk management practices associated with the projects (Renuka et al,
2014). These losses associated with project failure makes it important to understand what makes for
a successful project performance (Kishk & Ukaga, 2018) and how effective cost estimation
contributes to such performance.

Project cost estimation is challenging but very effective for use in bidding, negotiations, cost
monitoring and controls (Valtanen, 2020) and is closely related to the success or failure of projects
(Jiang, 2020). When projects fail or are abandoned, the resources already invested becomes a waste
to the organisation or nation in the face of competing needs. If the project succeeds, on the other
hand, a valuable asset is created that will satisfy its intended use and adds value to the organisation,
industry or nation. The value addition may include enhanced profitability, employment creation,
infrastructural development that will enhance economic development, income to government by way
of taxes and multiplier economic effects. It is therefore compelling that proper cost estimation
procedures are developed and adopted prior to and in the course of project execution to enhance
project performance and ensure project objectives are met.

Project performance is difficult to define because of the complexity and dynamics of the concepts of
the project. Construction project success or performance has been discussed by many researchers and
until now, there are myriad of opinions on the critical factors that should be used to measure project
performance (Bodicha, 2015). The performance or success of a project is measured by the full
actualisation of the project objectives. The objectives include achieving the agreed work scope and
specifications within the constraints of cost, time and quality (Al-Hammadi & Bernard, 2016;
Sylvester & Rani, 2011). Also, Oyedele (2012), define project performance as the ability of a
project to meet the planned cost, time, quality, safety and stakeholder satisfaction.

Estimating project costs and schedules are extremely difficult because large projects contain a
complex web of cost-influencing factors including material cost, possible design and scope changes,
ground conditions, duration, the size of the project, type of client, tendering method, and other
technical requirements (Ali & Chew, 2017). A well-controlled project schedule and good estimates
are critical for project performance and delivery in this highly competitive global market because it
leads to performance improvement. Therefore, this study will examine effect of cost estimation on
project performance in construction firms.

2. Significance of the Study

Cost estimating is an important aspect of a construction project. The performance and overall project
success is often dependent on how closely the actual cost compares to the estimated cost. Thus,
accurate cost estimates for construction projects are extremely important to both the clients and
contractors because it provides the basis for the contractor to submit a tender. It also allows the
parties to understand their respective commitments at the early stages of a project as well as for
monitoring and evaluation during the projects execution phase.

1053
 effect of cost estimation on project performance in construction firms in abuja

Based on the above, it is imperative to evaluate the effect of cost estimates on construction projects
performance which this study aims to achieve and propose appropriate measures that would help to
encourage its use in order to reduce inaccuracies in cost estimating, thereby enhancing the
performance of construction projects. Successful projects completion will have a multiplier effect on
the economy through business continuity, creation of complementary businesses, job creation,
enhanced GDP and additional taxes to both national and state governments.

3. Review of related Literature

3.1The Concept of Cost Estimation

Cost estimation is a vital process required of every project because it is the predecessor for budget
estimates, resource allocation, monitoring and control of the project for successful completion
(Hashemi & Ebadati, 2020). Cost estimation has been defined by The Association for the
Advancement of Cost engineering (AACE, 1990) as “the determination of the quantity and the
predicting, or forecasting, within a defined scope of the costs required to construct and equip a
facility, to manufacture goods, or to furnish a service. Included in these costs are assessments and
evaluation of risks and uncertainties”. Cost estimatio3n accounts for each element required
including both direct and indirect costs required to bring a project to completion. These costs, include
labour cost (direct labour and indirect labour), materials cost, equipment cost, services and facilities,
overhead costs (site overhead and office overhead) and mark up (risk contingencies and profit) adds
up to a total amount that determines a project's budget. Cost estimate is vital to construction contract
tendering and provides the basis for establishing the likely cost of resource elements of the bid price
for construction projects. Also, the approximate total project cost, called the cost estimate, is used to
authorize a project’s budget and manage its costs (Bello & Odusami, 2013).

Project cost estimation applies to everything from building a bridge to developing that new killer
app. It all costs money, so the clearer you are on the amount required, the more likely you’ll achieve
your objective. Cost estimates are typically revised and updated as the project’s scope becomes more
precise and as project risks are realized as noted by the Project Management Body of Knowledge
(PMBOK). Cost estimating is an iterative process that requires constant review and update as
circumstances change and new facts emerge. To usefully serve its purpose, project cost estimate
requires a reasonable level of accuracy, reliability, efficiency and transparency and has to be justified
with underlying assumptions clearly documented. The data forming the cost estimation bases must
be relevant, current, appropriate, and of good quality and value.

Construction cost estimate can be used for one of three purposes: design, bid and control. Each
comes at different stages of project development with the required levels of accuracy varying
accordingly. The design estimate which usually emanates from the project owner comes in four
stages: rough order of magnitude estimate made before the project design at the project initiation
phase based on the cost data of similar projects in the past and the accuracy range is -50% to +75%;
preliminary or conceptual estimates based on the project’s conceptual design at the early project
planning phase which has become available and the accuracy range is -30% to +50%; detailed
estimate made when the project work scope has been clearly defined based on a detailed design with
an accuracy of -15% to +30% and the work elements can be broken down into smaller packages and
the engineer’s estimate arising from the final plans and specifications at the time the project owner is

1054
 Ike Egboga, Dr Cross Ogohi Daniel, Dr Hauwa Lamino Abubakar

already to invite bids from construction vendors (accuracy ranges from -5% to +10%). The bid
estimate comes from the construction vendor and is usually a reflection of both the estimating tools
available to the vendor and their desire to win the tender. The owner provides the design and
specifications upon which the vendor extracts the materials take off for the preparation of their bids.
The control estimate is required by both the project owner and the vendors which forms the baseline
for project control during execution. For the owner, this may be the same as the detailed estimate
which must be revised periodically to take account of change orders, unexpected cost overruns or
savings.

3.2 Dimensions of Cost Estimation

The Project Management institute (PMI, 2017) recommends several tools and techniques for
estimating cost (bottom up, parametric, analogous, three point, top down and expert judgment
estimating techniques). However, this study has adopted two of the most commonly used methods or
techniques as follows as the independent variables for this study:

Bottom up estimating

This is the most reliable method for cost estimating when the work scope is properly defined and the
work can be broken down into smaller packages known as Work breakdown Structures (WBS) (Goh,
2015). The cost of each of the smaller packages or deliverables are separately determined more
precisely and aggregated to determine the project cost estimate. However, the development of the
packages or deliverables is usually time consuming, especially for complex projects. This belongs to
the detailed estimating group as it can only be done when detailed information about the project is
available. Bottom-up estimating is a method of estimating a component of work. The cost of
individual work packages or activities is estimated to the greatest level of specified detail. The
detailed cost is then summarized or rolled up to higher levels for subsequent reporting and tracking
purposes. The cost and accuracy of bottom-up cost estimating are typically influenced by the size
and complexity of the individual activity or work package. The major setback of this technique is the
great amount of details required and time required. On the other hand, the process for deriving the
cost estimate is easily understandable and repeatable.

Parametric Estimating

This method uses independent variables from historical data and parameters and applies it to the
current project. It is very popular in construction project estimating (Chan, 2015). This technique is
based on the building of “Cost Estimation Relationships” (CERs) which are simple mathematic
relations between the costs of a work element and some of its parameters called ‘cost drivers’. For
instance, the knowledge of the cost per sq meter of floor space for building or cost per km of a road
of given width can be applied to determine current cost estimate. It is more accurate than analogous
method but requires more initial data and needs correct and realistic unit costs for the independent
variable. This method belongs to the conceptual group since more detailed information are not
available at this time.

1055
 effect of cost estimation on project performance in construction firms in abuja

3.3Measures of Project Performance

When do we say a project has been successfully performed? The project life cycle starts from
initiation. Initiation is preceded by some objectives in mind and plans are then put in place to achieve
these objectives through project execution and control. The extent to which these objectives have
been met upon completion is a measure of the project performance (Takim et al, 2018).

These four variables; Cost (budget), Schedule (time), scope (specifications) and quality (CSSQ) are
the major measures or indicators of project performance and the ultimate objective of the project is to
ensure that these four measures are satisfied. Based on the triple constraints theory, three of these
(scope, time and cost0 has been selected as the dependent variables for this study.

Scope (specifications): Scoping projects accurately is an important skill. The scope must be clearly
defined and understood by the various stakeholders in the project. With this common understanding,
a Works Breakdown structure is created with a scope management plan. This is continually
revalidated, monitored and controlled to ensure scope creep and avoidable changes do not arise to
impact on time and cost. Unfamiliarity with project scope and project complexity has been adduced
as one of the causes of poor project execution leading to project failure. It also causes valuation
disputes which could lead to project execution delays with attendant cost and time overrun. Scope
creep is also a major issue affecting project execution outcome. Scope creep is an increase in scope
without a commensurate increase in resources or an extension to the project schedule.

Schedule (time). This is very important in assessing the success of a project. The work schedule
must be properly and skillfully developed through a detailed activities listing and sequencing. The
schedule is then monitored and controlled throughout project duration to avoid avoidable delays that
could create variation orders which may impact on cost and quality. It has been established that
delay is a common issue faced in projects execution all over the world especially in developing
countries and consequently, most projects do not end successfully

Cost (budget). With the activities listing loaded with required resources, costs can be estimated and
projects budget determined. The budget is closely monitored and controlled to avoid unnecessary
cost overruns. Next to poor risk management is cost overruns in terms of ranking for project failures
arising from poor project execution. Managing costs within approved budget is acknowledged as a
critical project execution parameter based on studies. This is more so as money is the scarce and
driving force everywhere.

Quality. A quality management plan must be in place before project execution. This is designed to
minimise failures / defects and to meet the expectations of the customer. Quality assurance and
quality controls must be performed all through the execution phase to ensure that the desired quality
of product is produced that will meet customer’s expectations.

3.4 Theoretical Anchor: The Triple Constraints Theory

The recognition of the triple constraints theory as a veritable tool for measuring project performance
as envisaged by Barnes differs amongst different scholars. While many scholars recognize the theory
as appropriate and offering a concise definition of project success, many others do not agree with it.
Scholars like Parker et al (2015) and Sridararan et al (2017) are in agreement with the theory

1056
 Ike Egboga, Dr Cross Ogohi Daniel, Dr Hauwa Lamino Abubakar

positing that the triple constraints are clear and effective indicators that project managers
traditionally use to measure project performance. However, some scholars while adopting the three
constraints of cost, time and scope added some additional measures like profit (Franklin &
Christina, 2015) and customer satisfaction (Joslin & Muller, 2016).

A second group of scholars rejected the triple constraints as a measure of project performance but
rather regarded the theory as simply a project management approach (Rugenyi, 2015) to govern the
tradeoff between the triple constraints. In their opinion, the determination of project success or
performance goes beyond meeting the project scope, time and cost. A third group of scholars
(Turner & Xue, 2018) also rejected the triple constraints as a measure of project performance but
rather as a measure of project efficiency or project management success by delivery of the project
scope on time and within budget. In their opinion, a project may be delivered efficiently, yet the
owners do not realize satisfactory benefits from the project. They believe that a better measure of
success or performance would be delivering desired outcomes / objectives and benefits, positive net
present values and meeting business or public needs. Some other scholars (Scheumer, 2017) are in
agreement with this school of thought but only to the extent of regarding the triple constraints as an
efficiency indicator. The most important measure of performance in their opinion is customer /
stakeholder’s satisfaction.

From the above submissions, the recognition of the triple constraints as the measure of project
performance varies among scholars. While the first group recognized the theory as a measure of
project performance, the second group considered it as just a project management approach to
resolve tradeoff in the constraints. The last group considered it as a measure of efficiency or project
management success. However, despite the objections by the second and third group of scholars,
there is wider acceptance of the views of the first group. It is also generally regarded as easy to use
since the triple constraints are all measurable. This position had been adopted by the Project
Management Institute (PMBOK, 2017) and accordingly used as the theoretical framework for this
study.

Project managers work within three project constraints: budget, scope and schedule. Schedule (or
time) is at the top of the model (shaped like a triangle). Scope is on the left of the triangle and budget
(or cost) is on the right. Depending on the project or who is involved, each of these project
constraints could be the most important to the end-user. Quality resides in the middle of the project
triangle, and effective project managers must balance the ebb and flow of tradeoffs within these three
constraints in order to achieve success. This longstanding model provides a dynamic way to
approach priorities on a project and supports describing items of value in a project team (particularly
since each team member likely values something different)

4. Objectives of the Study

1. To determine the effect of bottom up cost estimating on the realization of work scope/
specifications in construction firms in Abuja.
2. To assess the impact of bottom up cost estimating on schedule performance in construction
firms in Abuja.
3. To ascertain the impact of bottom up cost estimating on budget performance in construction
firms in Abuja.

1057
 effect of cost estimation on project performance in construction firms in abuja

4. To determine the impact of parametric cost estimating on the achievement of work scope /
specifications in construction firms in Abuja
5. To assess the effect of parametric cost estimating on schedule performance in construction
firms in Abuja
6. To ascertain the effect of parametric cost estimating on budget performance in construction
firms in Abuja

5. Hypotheses of the Study

HO1: Bottom up cost estimating has no significant effect on work scope completion in construction
firms in Abuja.
HO2: Bottom up cost estimating has no positive impact on schedule performance in construction
firms in Abuja.
HO3: Bottom up cost estimating has no significant effect on budget performance in construction
firms in Abuja
HO4: Parametric cost estimating has no significant effect on work scope completion in construction
firms in Abuja
HO5: There is no significant effect of parametric cost estimating on schedule performance in
construction firms in Abuja
HO6: Parametric cost estimating has no significant effect on budget performance in construction
firms in Abuja
6 Population and Sample

The study adopted survey design. The population of this study comprised 286 employees of selected
building construction firms in Abuja, Nigeria. The participating companies were selected from the
list of building companies in Abuja using purposive sampling. This is an acceptable non probabilistic
sampling technique using the researcher’s judgment regarding the experience, competence and
capability of the firms. The respondents were selected from the junior, middle and senior
management of the participating firms as well as some of the project consultants and project owners.
The questionnaire was divided into 2 major sections. Section A sought information on the
demography of respondents. Section B elicited information relevant for answering the single research
questions posed in the study. 274 of the 286 questionnaires administered were duly completed and
returned.

6.1 Statistical Technique Used in the Present Study

While the descriptive statistics was used to analyze the data gotten from the questionnaire generally,
multiple regression analysis was used to test the stated hypotheses. This helped to determine the
effect of the independent variables (Bottom Up cost estimation and Parametric cost estimation) in
conjunction with the moderating variable (project monitoring and evaluation) affect the dependent
variables (Scope, Time and Budget), which is project performance in the selected companies in
Abuja, Nigeria. Specifically, statistical software called Statistical Package for the Social Sciences
(SPSS) version 24.0 and EViews version 8.0 were used to conduct the necessary analysis and
hypotheses tested at 5% level of significance. Finally, the Durbin-Watson statistic was used to rule
out multi-collinearity in the model.

1058
 Ike Egboga, Dr Cross Ogohi Daniel, Dr Hauwa Lamino Abubakar

6.2 Model Specification

The theoretical framework for analyzing project performance was adopted from previous
studies. Based on the conceptual framework, project performance is theorized to interact with cost
estimation. The cost estimation factors or constructs for empirical investigation are bottom up cost
estimating and parametric cost estimating. The project performance factors or constructs are scope
completion, schedule performance and budget performance. The sole moderating variable is project
monitoring and evaluation.

The functional relationship is expressed as;

PERF = 𝛽0 + 𝛽1 𝐵𝐶𝐸𝑖 + 𝛽2 𝑃𝐶𝐸𝑖 + 𝜀𝑖 …………………………………………… (3.1)

Where;

PERF = Project Performance (Scope [PPS], Time [PPT], Budget [PPB])

The regression models relating to the variables of the study are given as:

Model 1

𝑃𝑃𝑆𝑖 = 𝛽0 + 𝛽1 𝐵𝐶𝐸𝑖 + 𝛽2 𝑃𝐶𝐸𝑖 + 𝜀𝑖 …………………………………………… (3.2)

Model 2

𝑃𝑃𝑇𝑖 = 𝛽0 + 𝛽1 𝐵𝐶𝐸𝑖 + 𝛽2 𝑃𝐶𝐸𝑖 + 𝜀𝑖 …………………………………………… (3.3)

Model 3

𝑃𝑃𝐵𝑖 = 𝛽0 + 𝛽1 𝐵𝐶𝐸𝑖 + 𝛽2 𝑃𝐶𝐸 + 𝜀𝑖 …………………………………………… (3.4)

Where:

PPS = Project performance (Scope)

PPT = Project performance (Time)

PPB = Project performance (Budget)

BCE = Bottom up cost estimating

PCE = Parametric cost estimating

𝛽0= Constant

𝜀= Error term

And a priori expectations: 𝛽1 𝑎𝑛𝑑𝛽2 > 0

6.3 Regression Analysis Results and Interpretation

Regression analysis was performed to establish the effect of cost estimation on project performance
in selected construction firms in Abuja. The results of the regression analysis are shown in table 1

1059
 effect of cost estimation on project performance in construction firms in abuja

below. The table shows the relationship between cost estimation dimensions (bottom up cost
estimating and parametric cost estimating) and the three constructs of project performance (scope,
time / schedule and budget).

Table 1: Estimation of the relationship among independent and dependent variables

Model 1 Model 2 Model 3

Variables Project Project

Project Performance
Performance Performance Time
Budget (PPB)
Scope (PPS) (PPT)
0.2579 -0.6556 0.2018
C (0.7398) (2.0429) (0.5267)
{0.4600} {0.4240} {0.5988}
0.5574 0.4924 0.4811
Bottom up cost
(7.4348) (7.1351) (5.8377)
estimating (BCE)
{0.0000} {0.0000} {0.0000}
0.2829 0.2403 0.3315
Parametric Estimating
(2.9919) (2.7601) (3.1882)
(PCE)
{0.0030} {0.0062} {0.0016}
R-Squared 0.2903 0.2701 0.2289
Adj. R-Squared 0.2851 0.2648 0.2232
F-statistic 55.4248 50.1524 40.2218
Prob (F-statistic) 0.0000 0.0000 0.0000
Durbin Watson 2.0215 1.8918 1.7535
Number of Observations 274 274 274
Note: t-statistic values are in brackets while p-values are presented parentheses

Table 1, model 1 revealed that bottom up cost estimating [β= 0.5574; p<0.05] and parametric
estimating [β= 0.2829; p<0.05] positively and significantly related to project performance scope
(PPS). The result also shows that the coefficient of determination (R2) of the model is 0.2903. The
value of the Adjusted R2 is 0.2851 which indicates that the independent variables explained 28.51%
of the variation in the dependent variable. The F-statistic of 55.4284 is statistically significant at
p<0.05. Finally, the Durbin-Watson statistic of 2.0215 rules out multi-collinearity in the model.

Similarly, Table 1 model 2 revealed that bottom up cost estimating [β= 0.4924; p<0.05] and
parametric estimating [β= 0.2403; p<0.05] positively and significantly related to project
performance time/schedule (PPT). The result also shows that the coefficient of determination (R2) of
the model is 0.2701. The value of the Adjusted R2 is 0.2648 which indicates that the independent
variables explained 26.48% of the variation in the dependent variable. The F-statistic of 50.1524 is
statistically significant at p<0.05. Finally, the Durbin-Watson statistic of 1.8918 rules out multi-
collinearity in the model.

Finally, Table 1, model 3 revealed that bottom up cost estimating [β= 0.4811; p<0.05] and
parametric estimating [β= 0.3315; p<0.05] positively and significantly related to project
performance budget (PPT). The result also shows that the coefficient of determination (R 2) of the

1060
 Ike Egboga, Dr Cross Ogohi Daniel, Dr Hauwa Lamino Abubakar

model is 0.2289. The value of the Adjusted R2 is 0.2232 which indicates that the independent
variables explained 22.32% of the variation in the dependent variable. The F-statistic of 50.1524 is
statistically significant at p<0.05. Finally, the Durbin-Watson statistic of 1.7535 rules out multi-
collinearity in the model.

6.4 Hypotheses Testing

The results in Table 1 were used to test the hypotheses stated for this study.

Hypothesis One: Bottom up cost estimating has no significant effect on work scope completion in
construction firms in Abuja.

Table 1, model 1 shows that there is a positive and significant relationship between bottom up cost
estimating and work scope completion (β= 0.5574; p<0.05). The t-statistic of 7.4348 and p-value of
less than 5% confirmed the result. Based on the result, we reject the null hypothesis. We therefore
conclude Bottom up cost estimating has significant effect on work scope completion in construction
firms in Abuja.

Hypothesis Two: Bottom up cost estimating has no positive impact on time / schedule performance
in construction firms in Abuja.

Table 1, model 2 shows that there is a positive and significant relationship between bottom up cost
estimating and time/schedule performance (β=0.4924 p<0.05). The t-statistic of 7.1351 and p-value
of less than 5% confirmed the result. Based on the result, we reject the null hypothesis. We therefore
conclude Bottom up cost estimating has significant effect on schedule performance in construction
firms in Abuja.

Hypothesis Three: Bottom up cost estimating has no significant effect on budget performance in
construction firms in Abuja.

Table 1, model 3 shows that there is a positive and significant relationship between bottom up cost
estimating and budget performance (β=0.4811; p<0.05). The t-statistic of 5.8377 and p-value of less
than 5% confirmed the result. Based on the result, we reject the null hypothesis. We therefore
conclude Bottom up cost estimating has significant effect on budget performance in construction
firms in Abuja.

Hypothesis Four: Parametric cost estimating has no significant effect on work scope completion in
construction firms in Abuja.

Table 1, model 1 shows that there is a positive and significant relationship between bottom up cost
estimating and work scope completion (β= 0.2829; p<0.05). The t-statistic of 2.9919 and p-value of
less than 5% confirmed the result. Based on the result, we reject the null hypothesis. We therefore
conclude Parametric cost estimating has significant effect on work scope completion in construction
firms in Abuja.

Hypothesis Five: Parametric cost estimating has no positive impact on time / schedule performance
in construction firms in Abuja.

1061
 effect of cost estimation on project performance in construction firms in abuja

Table 1, model 2 shows that there is a positive and significant relationship between bottom up cost
estimating and time/schedule performance (β=0.2403; p<0.05). The t-statistic of 2.7601 and p-value
of less than 5% confirmed the result. Based on the result, we reject the null hypothesis. We therefore
conclude Parametric cost estimating has significant effect on schedule performance in construction
firms in Abuja.

Hypothesis Six: Bottom up cost estimating has no significant effect on budget performance in
construction firms in Abuja.

Table 1, model 3 shows that there is a positive and significant relationship between bottom up cost
estimating and budget performance (β=0.3315; p<0.05). The t-statistic of 3.1882 and p-value of less
than 5% confirmed the result. Based on the result, we reject the null hypothesis. We therefore
conclude Parametric cost estimating has significant effect on budget performance in construction
firms in Abuja.

7 Recommendations

i. For the timely completion of projects, construction project managers should be fully abreast
of estimating techniques that include the use of cost estimation tools for estimation of work
elements through adequate and related intensive project training and an awareness campaign.

ii. Due to the positive effect of parametric cost estimating approach on projects performance,
it’s use should be encouraged when available data is not sufficient for a bottom up estimate.
This approach requires the use of previous estimates with modifications where necessary.

iii. When sufficient data is available at the time of cost estimation, bottom-up estimation method
is most recommended. In this approach, the cost of individual work programs or activities is
estimated at the highest level of detail available. This may include cost estimates on
contingency reserves to address cost uncertainty and ensure that updates project documents
depending on the risk record

iv. More focus should be placed on the major factors affecting construction cost in order to
reduce the cost of construction, enhance construction performance and generate confidence
within the construction industry.

8 Conclusion

The purpose of this research was to examine Effect of Cost Estimation on Project Performance in
Selected Construction Firms in Abuja. The results of the statistical test using multiple regression
analysis showed that there exists a statistically significant relationship between the independent
variables (bottom up cost estimation and parametric cost estimation) and the dependent variables
(scope/specifications, time / scheduling, and Budget). Based on this result, project managers need to
be cognizant of this relationship and focus on developing estimates and schedules using modern
project management tools that would project accurate costs and schedules.

References (APA)
[1]. AACE International (2017).Cost engineering terminology, recommended practice. Online Available at:
www.aacei.org Accessed 5 May (2012).

1062
 Ike Egboga, Dr Cross Ogohi Daniel, Dr Hauwa Lamino Abubakar

[2[. Al-Hammadi, N., & Bernard, J. (2016).Key performance indicators for assessing project performance in the oil and
gas industry of UAE. 6thInternational Engaged Management Scholarship Conference.
[3]. Ali, J., Chew, T.G., & Tang, T. C. (2017).Knowledge management in agile organizations. Sunway Academic
Journal, 1, 13–20.
[4]. Amadi, K.C., & Amadi, A.I. (2020). Government expenditure on infrastructure as a driver for economic growth
in Nigeria. Journal of International Business and Marketing, 5(2), 20-26
[5]. Barnes, M. (2007).Some origins of modern project management: a personal history .Project Management World
Journal, 2(XI), 1-2. Retrieved from https://www.pmworldjournal.net
[6]. Bello, W., & Odusami, K. (2013).Weak Management of the Predictability of Contingency Allowance in
Construction Projects in Nigeria.In: Smith, S.D and Ahiaga-Dagbui, D.D (Eds) Proceedings of the 29th Annual
ARCOM Conference, 2-4 September 2013, Reading, UK, Association of Researchers in Construction
Management.
[7]. Bodicha, H. (2015). How to measure the effect of project risk management process on the success of construction
projects: A critical literature review. International Journal of Business and Management, 3(12), 99-110.
[8]. Chan, M., & Park, M. (2015). A comparative study of causes of time overruns in Hong Kong construction projects.
International Journal of Project Management, 15(1), 55-63.
[9]. Franklin, J. M., & Cristina, D. P. (2015). Project management success: A bibliometric analysis. Revista De Gestão e
Projetos, 6(1), 28-44. doi:10.5585/gep.v6i1.310
[10]. Goh, J. & Hall, N.G. (2015). Total cost control in project management via satisficing. Working paper, revised for
publication, Fisher College of Business, The Ohio State University, Columbus, Ohio
[11]. Hashemi, S., Ebadati, O. & Kaur, H. (2020). Cost estimation and prediction in construction projects: a
systematic review on machine learning techniques. SN Applied Sciences, 2, 1703 / https://doi.org/10.1007/s42452-
020-03497-1
[12]. Jiang, Q. (2020). Estimation of construction project building cost by back-propagation neural network. Journal of
Engineering Design and Technology, 18(3), 601-609
[13]. Joslin, R., & Müller, R. (2016).The relationship between project governance and project success. International
Journal of Project Management, 34, 613-626. doi:10.1016/j.ijproman.2016.01.008
[14]. Kishk, M. & Ukaga, C. (2008).The impact of effective risk management on project success. In: Dainty A. (Ed):
Process of 24th Annual ARCOM Conference, 1-3 September, 2008, 799-808.
[15]. Nguyen, T. A., & Chovichien, V. (2014).A practical list of criteria for evaluating construction project success in
developing countries. ASEAN Engineering Journal, 3(2), 21-41.
[16]. Oyedele, O. A. (2012). The roles of project management in Bridging the IT gap in developing countries. Being
Paper presented at the Africa6IT Conference on March 22, 2012 at Lagos, Nigeria.
[17]. Parker, D. W., Parsons, N., & Isharyanto, F. (2015).Inclusion of strategic management theories to project
management. International Journal of Managing Projects in Business, 8, 552-573.doi:10.1108/ijmpb-11-2014-0079
[18]. Project Management Institute (2017), Project Management Body of Knowledge (5th Edition) PMI Publications,
New York
[19]. Renuka, S., Umarani, C., & Kemal, S. (2014). A review on critical risk factors in the life cycle of construction
projects. Journal of Civil Engineering Research,4(2A), 31-36.
[20]. Rugenyi, F. (2015). Assessment of the triple constraints in projects in Nairobi: The project managers’ perspective.
International Journal of Academic Research in Business and Social Sciences, 5(11), 1-16. doi:10.6007/ijarbss/v5-
i11/1889.
[21]. Scheuchner, G. A. (2017). Strategies to promote IT project success (Doctoral dissertations). Retrieved from
ProQuest Digital Dissertations and Theses database (UMI No. 1986002893)
[22]. Sridarran, P., Keraminiyage, K., & Herszon, L. (2017).Improving the cost estimates of complex projects in the
project-based industries. Built Environment Project and Asset Management, 7, 173-184. doi:10.1108/bepam-10-
2016-0050
[23]. Sylvester, D., & Rani, N. (2011). Theoretical framework: Factors for project success in oil and gas companies in
Mira, Sawarak, Malaysia. 2010International Conference on Business and Economics Research, Kuala Lumpur,
Malaysia, 1,100-103.
[24]. Takim, R., & Adnan, H. (2008). Analysis of effectiveness measures of construction project success in
Malaysia. Asian Social Science, 4(7), 74-91.
[25]. Turner, J. R., & Xue, Y. (2018).On the success of megaprojects. International Journal of Managing Projects in
Business, 11, 783.doi:10.1108/IJMPB-06-2017-0062
[26]. Valtanen, A. (2020). Project Cost estimation: Promoting quality of cost estimation to reduce project variance. MSc
Thesis, Faculty of Management and Business, Tampere University.

1063

View publication stats