Investigating the use of uncertainty

Civil Engineering and Urban Planning:An International Journal(CiVEJ) Vol.2,No.1, March 2015
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INVESTIGATING THE USE OF UNCERTAINTY
MANAGEMENT TOOLS AND TECHNIQUES
WITHIN THE CONSTRUCTION SECTOR IN
NIGERIA
Samuel Ekung* & Samuel Onwusonye, PhD
Department of Quantity Surveying, Imo State University, Owerri, Nigeria
ABSTRACT
This study investigated the use of uncertainty management tools within the construction sector in Nigeria.
The objectives were to evaluate the level of use of uncertainty management tools and techniques and to
determine the factors responsible for the low knowledge and misconception of uncertainty within the
construction sector. Views from project managers obtained from two sub-sectors, the real estate and the oil
and gas was evaluated. Finding of the study revealed low level of use of uncertainty management tools and
techniques due to the lack of clear understanding of uncertainty and its management parameters. Further
finding identifies the type of project management practice in use, and over reliance on the use of
deterministic approach as factors responsible for the low knowledge of uncertainties. The challenge
therefore is how to improve the state of misconception among practitioners and to explore refinements to
the traditional project management framework. To develop appropriate knowledge about uncertainty
management, the notion that uncertainty management is about creating previously unknown information,
must be repealed to embrace approaches encouraging information sharing, learning, and competence. The
study provides an insightful perspective into understanding uncertainty in projects;and highlight possible
refinements to extant project management practice in order to enhance uncertainty management within the
construction sector.
Keywords:
construction Sector, Nigeria, risk, uncertainty, and uncertainty management tools
1.INTRODUCTION
One of the distinguishing feature that stand out the manufacturing sector, and set it as a model for
adoption within the construction industry, is an understanding of its system. Every operational
activity consists of three routines: processes; repetitive works; and projects. Although
construction products are mainly one-off projects, there is sometimes an aspect of repetitive
works. However, the introduction of the manufacturing technologies has not shifted the emphatic
characteristic of the construction sector entirely, there are yet residual collocation prompted by
inherent culturalpractice. The drive towards attaining competitive advantage means constant
innovation; better briefing processes and improvement in the use of information technologies;
better understanding and customisation of client needs; and the quest towards internalisation also
require more dexterity. Customer orientation and continuous improvement are also identified in
[20]. In all spheres, constant changes in the procurement and delivery perspectives of
constructions projects create the need to manage change. Evolved procurement and construction
approaches infuse complexity to the routinely traditional management systems notably in
developing countries[7], where advance project management expertise is dearth.

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Although, complexity is not the goal of this research, the research context need be x-rayed using
complexity dynamics. Complexity naturally triggers uncertainty within a system and according to
Meyer, Loch & Pich [13]; early project management practices and tools were developed to
manage complexity. Stewart [25] in [28] identified two level of complexity: algorithmic and
organisational, but mechanics of organisational complexity is explored in this study. According to
Wood, Piroozfar, & Farr[29] organisational complexity refers to the behaviour of a system and its
analysis. Complex systems within the construction industry are evolving e.g. procurement
approaches such as concession and purchase of service. At this level, two major complexities can
be established namely: task and relational complexity [26]. Task complexity is the number of
interacting components within a system[30]. Relational complexity results from the management
of relationship involving multiple stakeholders in the project with differing interest. The
congruence of conflicting objective in a single contract can generate incongruity in respect of
project objectives. At this point, the system dynamics degenerates into uncertainty. Uncertainty
has been related to events that impedes the realisation of expected project outcome [20].
The concept of uncertainty has never been abandoned within the project management body of
knowledge [13], neither is it relatively recent. A study by Sarden and Engstrom[23] on
uncertainty in traditional constructions identified three major sources of uncertainty. According to
Sarden and Engstrom, there are uncertainties originating from the sector a project emerges (sector
uncertainties); organisation uncertainties (corporate uncertainties) and project uncertainties.
Tools such as network analysis, and its probabilistic sub-sets and other qualitative approaches
were designed to facilitate the management of uncertainty in projects in the 1950s and 1960s.
Contract formalisation and conflict management are also available for resolving issues linked
with relational complexity now days. These are however management flexibility promoters
requisite for prompting quick alternate activities in managing scenarios uncertainty [13]. The use
of these tools are wide spread across Europe, UK and America [13], but the level of embedding
and use in developing countries remains vastly unexplored. The trend subsists despite the
increasing scope of complexity in the industry resulting from the changes forced on the system by
scarce resources and globalisation [6]. Although uncertainty cannot be eliminated completely, it
is believed, reflective learning and information exchange makes it adaptable by minimising it
meaningfully [20]. The challenge before this study and the aim of this study is to explore
mechanisms for embedding intuitive reflective understanding of uncertainty and its management
tools in the construction industry in Nigeria. According to Perminova, Gustafsson, &
Wikstrom[20], failure to recognise uncertainty is a neglect of evolution, and evolution is a clear
way of improving project performance. Project uncertainty management has also establishedan
appropriate framework for treating risk as uncertainty [4] but extant practice develop towards
traditional practices. The traditional practice lack reflective understanding of uncertaintyand this
has been blamed on the lack of sufficient tool to manage them [20].
To improve reflective understanding of uncertainty in the construction project delivery process,
this study evaluates uncertainty management within the construction sector in Nigeria. The
objective is to evaluate the level of use of uncertainty management tools and techniques and to
determine factors associated with misconception of uncertainty within the construction sector in
Nigeria.
1.1 Theoretical Framework
Two fundamental Economic thoughtsunderpin the basic distinction between risk and uncertainty,
notably the works of Frank Knight, ‘Risk, Uncertainty and Profit’ [10] and Keynes [16].
According to Frank Knight, ‘risks are events subject to known and knowable probability, whereas

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uncertainty refers to events for which it is impossible to specify numerical likelihoods’. These
views may not have escaped unscathed by academic debates,these debates shape the fundamental,
upon which literature that attempt distinguishing both terms are based. Keynes’ view also
explained uncertainty by the inability to assign ‘definite probability but however percept
uncertainty as a system dynamics that cannot be separated. Accordingly, when the project
manager is unable to calculate risk, uncertainty eschews. Perminova, Gustafsson, &
Wikstrom[20]embed the term uncertainty in the theory of psychology when they described it as a
‘state of mind characterised by a conscious lack of knowledge about the outcomes of an event’.
The viewpoints from the various school of thought explored above portrays two imminent
sources of uncertainty in project delivery. The external environment is strongly conveyed in
Frank Knight and Keynes’ assertions, the psychological school bring to bear internal factors that
create uncertainty in a system. Accordingly, there is an uncertainty in the mind of he who doubts
[16].
To define context with realities obtainable in practice, ‘uncertainty as a contextualised risks are
events having a negative impact on the project’s outcomes, or opportunities, as events that have
beneficial impact on project performance’ [20]. This definition does not only identify both
positive and negative perspectives to uncertainty but also reinforced the level of embedding of
risk within uncertainty. Within this established framework, risk management, create previously
unknown data [20]. It does not however, suggests that, uncertainty can be managed in the same
way as risk. The traditional project management inputs are planning, monitoring and control, and
in the traditional risk management, this translates into risk analysis, mitigation and control [15].
The impropriety of risk management tools in curbing uncertainty stems from the extended
ambient that identifies opportunity in uncertainty. Uncertainty management practice should
therefore, pictures the project life cycle. This view is without prejudice to others which sees risk
management as a life cycle process ( [21]; [30]). Perminova, Gustafsson, & Wikstrom[20]
contends that even when risk is regarded as life cycle process, in practice, its forecasting
procedures are not repeated through at the various stages of the project development.
Furthermore, control depend on historical cost data, and the implication is that, the source of
uncertainty had already taken place and not futuristic.
The theoretical perspective explored in the foregoing section established the need for a careful
examination of uncertainty management practices of construction industry practitioners. It
portends that a careful and an effective management of uncertainty can engender positive
improvement or refinements to the realisation of the project objectives than inherent viewpoints
that sees uncertainty as entropy to the project management system.
1.2 Types of Uncertainty and their Management Tools
Various attributes characterise project uncertainty, and these features are used in categorising
uncertainty in management research. Schrader, Riggs & Smith [25] identified uncertainty in the
perspective of technology and marketing scenery, Steward [24]provides generalised attributes and
differentiated between uncertainty impact parameter when the relationship are known, only the
values are unknown, and ambiguity. In ambiguity, both the parameter and purposeful affiliation
are unknown. Building on these classifications, Meyer, Loch & Pich [13] profiled four types of
uncertainty: variation; foreseen uncertainty; unforeseen uncertainty; and chaos and turbulence.
Variation is a common source of uncertainty in project outcome. The implication is that the
characteristics of the project are known from the outset, planned and coordinated, but schedules
and budget exhibit variability around forecasted values. To guide against variability in cost,
schedule and other performance objectives, contingencies and buffers are built into the budget

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estimates and timescale [11]. Statistical process control charts, earned value management (EVM),
and linear responsibility chart are increasingly used in managing variation associated uncertainty.
Foreseen uncertainties are recognised, but unpredicted, impact in a project [13]. While variation
may predispose the project manager to prescribe a range of event that may influence a project,
foreseen uncertainty is simply a recognisable event with single effect on the project plan. Unlike
variation, in which a single course of action can proffer solution, foreseen uncertainties may
necessitate ‘contingent paths’ in the project plan. Foreseen uncertainty can be managed using
decision trees diagram. The key improvement in this technique is; it may prompt the project
manager to consider the implication of early decisions on later risks. Similarly, the branches of
the tree can create a different decision taking. The use of decision tree enhances the exploration
of preferences inherent in the branches, however working out these preferences is not easy to
implement.
Unforeseen uncertainty is unexpected during the project planning stage of the project development.
Because these uncertainties are unanticipated, the project managers do not have predetermined
response to the events. This state of indecision is lassie faire and emerges from two fronts: either the
events are entirely unforeseen or the likelihood of occurrence is negligibly low to be prioritised. The
common pragmatic approach is to transform the uncertain events to foreseen. The transformation
process however requires a great deal of effort and investment. As a result, contingency planning is
made very difficult due to inability to predict outcomes and impact of branches in the decision tree. In
the context of unforeseen uncertainty, the decision evolves as the project progresses from one phase to
another. The management of unforeseen uncertainty is therefore iterative. Information, learning and
knowledge sharing is critical at this point. This can be achieved by constantly adding branches as
information emerges, courses of action identified in sufficient to the branches.
Chaos or turbulence refers to uncertainty that threatens the fundamental framework of the project
management plans. Turbulence is imminent in innovative projects where theoretical insight is
lacking. In this scenario, the formulation of organisational policy is itself uncertain and
incomprehensive. The essential management perspective is flexibility and review of feedback.
This is because, a fundamental change cannot be treated as a branch in the decision tree analysis,
and rather, it requires a definite re-conceptualisation of the project. No specific approach might be
suitable but a combination of alternates tool simultaneously may suffice. Similarly, the team
approach advocated in the previous section may not suffice in turbulent environment; rather
autonomy of the project team is essential. Meyer, Loch & Pich [13] advocated the autonomy of
an entrepreneur. However, there is need to observe restraint in order to balance the organisation’s
philosophy in cutting excesses when the probability of success significantly become too small.
Meyer, Loch & Pich [13] therefore recommends collaborative management framework. The
success of this approach to a large extent depends on long term relationship of trust. The state of
trust embedding in projectdelivery is subjective and the extent in which project participants are
willing to collaborate is marred with differing outcome (Wood, 2005).
1.3 Uncertainty Management
The traditional project management inputs are planning, monitoring and control.In traditional risk
management, these inputs translate into risk analysis, mitigation and control (Nikanda &
Eloranta, 1997). While these tools have helped in solving problems encountered in every day
project, they can also curb the management of foreseeable uncertainty (Meyer, Loch & Pich,
2002). There are however, advanced levels of uncertainties which are unforeseeable. The
management of uncertainty at this level therefore, requires another level of management. The

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impropriety of traditional risk management tools in curbing advanced level of uncertainty
emanates from the extended perspective that identifies opportunity in uncertainty.
To provide an understanding into uncertainty management, first, the notion that risk management
is about creating previously unknown information must be repealed to embrace contemporary
practice, which is, as encompassing information sharing, learning, knowledge and competence.
Second, there is the need to start a project by understanding the context and the profile of inherent
uncertainties in the project system. This dimension was identified by McFarlan (Portfolio
Approach to Information Systems) in asserting that the effectiveness of project management tool
dependent significantly on the attributes of the project). Third, it is not in every situation, external
and internal to the project that can influence project outcome, thereby constituting a source of
uncertainty. Continuous reflective learning and information sharing underpins two critical
parameters that can be explored in managing uncertainty [21].
Contributing to the search towards uncertainty management practice, Meyer, Loch & Pich [13]
argued that an understanding of the type of uncertainty determines the management approach.
They identified three elements guiding the selection of an approach to be adopted namely: project
management style, task management, and relationship management. These elements have been
considered under the type of uncertainty literature. Meyer, Loch & Pich [13] therefore proposes
three steps ‘road map’ to uncertain management: project diagnosis; organisational phase; and an
assignment phase. Project diagnosis evaluates the uncertainty profile of the project. Traditionally,
projects began with the identification of task, contemporary practice emphasises uncertainty
determination, and therefore sees task as secondary[11]. The first step towards managing
uncertainty profiling is designing lists of various potential sources, and assessing and screening
them into variation (common cause uncertainty), foreseen uncertainty (transferable source
turbulences), unforeseen uncertainty (unexpected sources of opportunities, and turbulence
(building a project on falsehood), and scoring both their likelihood of occurrence and the degree
of impact.
The second task is to develop the project implementation infrastructure. The project
implementation infrastructure is the planning systems, the coordination of and incentives and
tracking [13]. Planning is the heart of project management, even without uncertainty in project,
project still present a level of challenges ([12]). Appropriate determination of buffer and
contingency could suffice for variation, but foreseen uncertainty requires flexibility while
unforeseen uncertainty, dynamic flexibility. This requires the additional capability to work out
responses to abrupt occurrences at every level. The last, turbulence needs prompt turnaround
ability and on the spot decision making.
1.4 Research Methodology
The study is both descriptive and inferential research involving questionnaire survey. The core
construction industry practitioners (architects, builders, engineers and quantity surveyors and
others) who practice as project managers and construction managersin varying organisations were
targeted in the study. Owing to the large size of the population, it is impracticable to investigate
all its members. The study’s sample was first identified in two sectors, real estate and oil and gas.
The real estate practitioners are those in general construction practice, building and civil
engineering. Two sampling techniques stratified random and the snowballingwas used. The
stratified random sampling was used to sample professionals with practice inscription ‘A & B
partnership, quantity surveyors& project managers’. Lists of registered professional practices
were obtained from the respective professional bodies. 105 professionals are listed with the
respective professional bodies of Architects, Builders,Quantity Surveyors and Engineering in the

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study area but 28 do not practice with the inscription and they were not considered for the study.
Also, as a result of the lack of an appropriate database of respective professionals in the oil and
gas sector, currently working in the various oil fields at the time of study, the size of the
population in the sector could not be obtained due to varying degree of platforms. Thus,
snowballing technique was employed in identifying 28 professionals to participate in the study. A
total of 105 sample respondents were randomly sampled.
The study was conducted in Akwa Ibom State, Nigeria. The State is situated in the Niger Delta
region of Nigeria, an oil rich region with very high content of indigenous and foreign nationals in
the real estate and oil and gas sectors. The study was conducted in three locations namely: Uyo
the state capital; Eket; and Oron. The state was selected for the study due to its shifting chaotic
environment that significantly influences the delivery of construction projects.
The questionnaire consists of two parts, A and B. Part A elicited background information of the
study’s participants, and level of understanding of the term uncertainty.This section consists of
seven questions. Questions 1-3 elicit respondents’ on the nature of practice, academic
qualification and years of experience; 4-7 address the level of understanding of the term
uncertainty. Part B collected data on the specific objectives of the study that is;factors associated
with misconception of the term uncertainty and level of use of uncertainty management tools.
Sixteen uncertainty management tools ranging from variation and foreseen uncertainty
management to the advance relationship and experimentation based tools were presented; as well
as seven factors that could be responsible to the misconception of uncertainty generated
individually and collectively form the literature. Piloting involving 4 participants was carried out
and feedbacks obtained deployed in refining the questionnaire. A 5-points Likert scale (where
5=very high to 1=very low) was used to rank the level of use and the factors associated with
uncertainty misconception. Because no previous study had aggregated these tools for a study, it
became pertinent to carry out reliability and validity test. Reliability measures the stability in
instrument while validity measures the extent in which instrument capture the hemisphere of the
subject matter ([2]). Alpha Cronbach reliability test was conducted. Alpha-Cronbach is valid at
0.7 and above and where the number of items in the scale is less than 10, it tends to yield low
value. Correction using inter-item correlation was applied. The applied correction yielded a high
Cronbach’s value of .85.
Data for the study were processed and analysed with the aid of Statistical Packages for Social
Science (SPSS). The mean item score and percentages were used in analysing collected data and
the test of hypothesis involved t-test and Homogeneity test.Rating scores provided by the
respondents were aggregated in data analysis package and their means calculated that is, (1n + 2n
+ 3n + 4n +5n/N); where n is number of respondent rating 1-5, and N is total number of
respondents. The factor or tool with the highest score is assigned the highest rank in no specific
order.
1.5 Results
The focal objectives of the study were to evaluate the level of use of uncertainty management
tools and techniques; and to determine problems associated with the low knowledge and
misconception of uncertainty within the construction sector. The result of the study is presented
in the following sections.
First the study evaluates the respondents’ characteristics. The study focused mainly on
professionals with strong construction project management background. In Table 1, 76% of the
respondents are current industry practitioners in the real estate sector while 24% are practitioners
in the oil and gas sector. The blend of their various perceptions about the subject of uncertainty

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helps in determining the root cause of the misconception surrounding the term uncertainty.
Similarly, 61% are holders of first and second degree in their respective fields and construction
project management, while only 14% are first degree holders. One quarter of the sampled
population are PhD holders in the related fields. 41% also have less than 10 and 20 years working
experience respectively and 18% had been practising for over 20 years.
Table 1: Respondents Characteristics
Respondents’ Practices Number Percentage
Quantity Surveyors & Project Managers 6 18.00
Builders & Project Managers 2 6.00
Engineers & Project Managers 2 6.00
Architects & Project Managers 5 15.00
Builders & Construction Managers 4 13.00
Engineers & Construction Managers 6 18.00
Architects & Construction Managers 2 6.00
Members of the Academics 7 24.00
Total 32 100.00
Respondents Academic Qualifications
BSc Building & MSc Construction Project Management 6 18.00
BSc Quantity Surveying & Construction Project Management 6 18.00
BSc Engineering & MSc Construction Project Management 8 25.00
BSc Building, Architecture, Quantity Surveying & Engineering 4 14.00
BSc/MSc/PhD 8 25.00
Total 32 100
Years of Experience
0-5 years 8 25.00
5-10 years 5 16.00
10-15 years 5 16.00
15-20 years 8 25.00
Above 20 years 6 18.00
Total 32 100.00
An industry wide perceptions and understanding of the term is presented in Table 2. The term
uncertainty is widely misconstrued by many to be synonymous with risk in practice. The result of
the survey witnessed three quarter of the study’s sample aligning concordance with this
misconception. However, one quarter differs with this widely held misconception by agreeing
both terms are not synonymous. Similar understanding was sought to know whether in practice,
risk and uncertainty could imply the same thing. Half of the study’s population maintained earlier
position by agreeing they convey same meaning. One quarter of the study still differ in their
opinion,and maintainedan earlier position that, they are actuallydifferent. 32% are however
indifferent. This neutral position could be explainedas the lack of proper understanding about
what the term mean.Again, the understanding of the term uncertainty cannot create or eliminate
it. The opinion of respondents in this question is disperse with 82% agreeing, it create, eliminate
and not create, and not eliminate uncertainty in projects. But 18% are again indifferent, and the
implication portrays a balance perspective to the true state of inherency of uncertainty in every
projects. A proper understanding of the term means ability to successfully manage them and
improve project performance. This is the view of over 90% of the study’s sample; negligible 6%
however still doubt this possibility.

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Table 2: Basic Understanding of the term Uncertainty
Is the term uncertainty synonymous with risk conceptually?
Yes 24 75.00
No 8 25.00
Total 32 100.00
In practice can they mean the same thing?
Yes 16 50.00
No 6 19.00
Indifferent 10 31.00
Total 32 100
Do you think managers idiosyncrasies (attitude and understanding) of uncertainty
can create or eliminate it?
Yes it can eliminate 8 25.00
Yes it can create 5 16.00
No it cannot create 5 16.00
No it cannot eliminate 8 25.00
Indifferent 6 18.00
Total 32 100.00
Would proper understanding of the term facilitate your understanding in sense
making by seeking alternative management tool for uncertainty?
Yes 30 94.00
No 2 6.00
Total 32 100
1.6 Problems Associated with the Low Knowledge and Misconception of
Uncertainty
In this section (Table 3),the rating opinion of the perceived factors associated with misconception
of uncertainty is presented. Seven factors rated by respondents yielded mean score between 2.76
and 3.76 (Table 3).The most significant problem underpinning the misconception of uncertainty
within the construction sector is the project management practice in use. The construction
industry in Nigeria is bedevilled with traditional project management practice. In this practice, the
architect led other project team, the expertise and management know-howof the project leader in
this practice have been widely questioned. This result in risks and uncertainty being enveloped
together and contingency assigned. There is also a significant lack of awareness on uncertainty
management tools in the construction sector. This is the second most significant problem
influencing misconception of uncertainty. The traditional project management simply employs
basic risk management inputs, and the propriety of these tools was earlier argued against in the
foregoing literature review section. There is also a general lack of requisite project management
skills; (3rd
) most significant problem. Others are the use of deterministic approach, (4th
); lackof
perceptual skills to evaluate information emanating from different spheres of the project, (5th
).
Table 3: Problems Associated with low Knowledge and Misconception of Uncertainty
Problems Mean Rank
Lack of management skills 3.08 3rd

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Over reliance historical data and use of deterministic approach 3.06 4th
Type of project management practice in use 3.76 1st
Lack of intuitive judgement to evaluate information emerging from various sphere
of project (within and outside project organisations)
3.00 5th
Project manager personality profiles 2.76 7th
Project management leadership style 2.86 6th
Lack of awareness on appropriate management tool and techniques 3.09 2nd
The use of rigid contractual form 3.05 5th
1.7 Uncertainty Management Tools and Techniques
In this section of the result presentation, the level of use of different tools and techniques for
managing uncertainty were tested. The objective sought to evaluate the level of use of uncertainty
management tools and techniques within the construction sector, the results are presented in
Table 4. Among the respondents in the oil and gas sector, the extent of knowledge yielded mean
score between 2.56 to 3.48. The level of knowledge of the sixteen tools among these respondents
is significant and above average. Earned value management is rated first, tree diagram 2nd
,
flexible contracting 3rd
, relationship management 4th
Gantt chart 5th
. Among the first five tools,
only the Gantt chart appears conventional, and others are indeed unheard of. Within this band of
tools, estimating using risk analysis is least known and is rated(16th
); SCERT5
(Synergistic
Contingency Evaluation and ReviewTechniqueVersion 5) (15th
), GERT4
(Graphical valuation
Review Technique Version 4) (14th
); and force field analysis (13th
) in descending order.
Among the practitioners in the real estate sector, contingencies planning is rated 1st
, critical path
2nd
, Gantt chart 3rd
, force field analysis 4th
, conflict management 5th
most familiar tools. Among
the first five tools in this category is again, the Gantt chart now 3rd
against 5th
rated by theoil and
gas practitioners’ category. This result underscore the wide spread use of the tool in project
management. Among the least familiar tools are the tree diagram 16th
, estimating using risk
analysis 15th
, earned value management 14th
, SCERT4
13th
, flexible contracting 12th
, and GERT4
.
These results reflect the persistent use of deterministic approaches to uncertainty management.
To determine whether the extent of knowledgeof project/construction managers determines
applicability of these tools, Table 4 also presents data on the level of use. The Gantt chart is again
very popular and widely used; this tool is rated 1st
with a mean score of 3.16. Critical path is also
popular and the 2nd
most widely used tool. The Gantt chart and the critical path are
complimentary and can be obtained in single application software, Microsoft projects. The
complimentary tool to the Gantt, the Bar chart can also be prepared using Microsoft Excel, and
these packages are relatively cheap and easy to come by. Contingencies’ planning is 3rd
most
deployed uncertainty management tool. This is a form of the deterministic techniques where
pockets of buffers are added to scheduled time and estimated costs to address uncertainty. Force
field analysis is also common among the practitioners in Nigeria. This technique mainly used in
foreseen and variation uncertainty is traditionally, a risk management tool. It is rated 4th
with a
mean score of 3.01. Conflict management although not very apparent as a management tool, its
applicability should be viewed in the context that conflict is a source of uncertainty. This tool is
the 5th
most popular tool used in the construction industry. On the down side of the scale are
relationship management (16th
), this is understandable in the context that, non-relational contracts
proliferates the construction industry in Nigeria. Estimating using risk analysis (15th
) is a
relativistic perspective into uncertainty management. This approach bridges the divide by
enabling quantitative profiling of uncertainty and transforming them into risk for appropriate
mitigation. SCERT5
(14th
) and GERT5
(13th
) are relatively unknown statistical uncertainty

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management tools. Their application in the Nigerian construction industry for uncertainty
management is low.
Table 4: Tools and Techniques for Uncertainty Management
EoK LoU
Oil & Gas Real Estate
Tools & Techniques MS Rank MS Rank MS Rank
Critical Paths 3.01 11th
3.23 2nd
3.10 2nd
Gantt Chart 3.09 5th
3.11 3rd
3.16 1st
Force field analysis 2.86 13th
3.09 4th
3.01 4th
GERT4
2.76 14th
2.32 11th
2.00 13th
SCERT5
2.56 16th
2.23 13th
1.99 14th
Analysis of problems 2.91 12th
2.45 7th
2.09 12th
Contract formalisation 3.07 6th
2.66 6th
2.56 6th
Conflict management 3.06 7th
2.76 5th
2.66 5th
Statistical control process 3.05 8th
2.34 10th
2.32 8th
EVM 3.48 1st
2.22 14th
2.24 9th
Tree diagram 3.36 2nd
2.18 16th
2.19 11th
Flexible contracting 3.29 3rd
2.30 12th
2.23 10th
Relationship management 3.12 4th
2.41 8th
1.97 16th
Transformation 3.04 9th
2.36 9th
2.33 7th
ERA 2.70 15th
2.21 15th
1.98 15th
Contingencies planning 3.02 10th
3.45 1st
3.09 3rd
EoK = extent of knowledge; LoU = level of use; MS = mean score; EVM = earned value management;
estimating using risk analysis
Based on the use of cross sector population, the study sought to establish whether there is a
significant difference in the rating opinion of respondents in the oil and gas, and the real estate
sectors using an inferential statistics. To determine this relationship, a hypothesis was set up. The
hypothesis states that, there is no significant relationship between the level of use in the real
estate, and oil and gas sub-sectors of the construction industry. The test of the hypothesis
involved Student t-test and Homogeneity test, and is valid at critical p-value ≤ 0.005. The result is
presented in Table 5.
The result of the hypothesis reveals no significant relationship in the level of use of the identified
uncertainty tools in the real estate and the oil and gas sector (p-values are less than critical P ≤
0.005). This result applies despite the significant level of knowledge exhibited by the project
managers in the oil and gas sector (Table 4). This assertion is demonstrated in the significant
homogeneoustest values of 11.420 and 4.131. But both result are significantly high, the
implication signifies similarity in the level of use of uncertainty management by practitioner in
both sectors. This is also, an indication of the need for improved awareness and usage in order to
improve the management of uncertainty in construction project delivery.

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Table 5: Test Statistics Summary
T-test Homogeneity Test
p-value Sig(2-
tailed)
Statistics Sig
.002 .652 11.420 .500
.003 .618 4.131 .740
2. Discussion
There is the lack of clear understanding of the term uncertainty and low level of application of
uncertainty management tool by construction professionals in the real estate and oil and gas
sector in Nigeria. This result is obtained despite the findings presented in Zuofa & Ocheng [31].
These authors studied risk perception in offshore oil and gas projects, and respondents in their
study had established a significant level of cognizance of the presence of uncertainties in oil and
gas projects. The imperativesignifies there is the lack of preventive management protocol
evolution,and the use of established management techniques in that sector is also low.
Conceptually, the term is misconstrued to mean the same thing as risk. This view is the same in
practice, and more than half of the study’ssample strongly agrees to this position. However, there
is a segment of the study’s sample, who maintains anonymous position. This could mean the lack
of proper understanding about what the terms actually mean (misconception). The lack of
understanding cannot create nor eliminate uncertainty because uncertainty is inherent in every
construction projects. This misconception is not new, Beurtey, Inga-Abere & Kumi [1] had
acknowledged that the term uncertainty is most misunderstood within the construction sector. The
perception is also responsible for the deterministic perspectives touncertainty management using
contingencies planning. The use of contingencies in Nigeria, although widespread, only account
for cost within the region of foreseen uncertainty (Jimoh & Adama and Otali & Odesola). The
finding of this study affirm this wide spread use and identify the use of contingencies planning as
the 3rd
most used uncertainty management tool. The use of this approach has been criticised for
the failure to address unforeseen and chaotic uncertainty. The use of Gantt chart and critical path
is also wide spread.Although, no singular tool may be a one cap fit all for any given
circumstance, it is imperative to always switch instinctively between tools or a combination of
tools for effective management of construction project uncertainties.
Many events in uncertainty may be expected and manageable using risk management tools, the
level of use of risk management tool in the construction sector in Nigeria is reportedly low [32].
Obtaining a related result in this study is therefore not surprising. This state of affairs in the
global construction industry perspective is blamed on the ‘off-the-record’ events that are not
documented [37]. The risk management tools tested in Adedokun’s et al[32] study include
sensitivity analysis, system dynamic, probabilistic influence diagrams, event and fault tree, fuzzy
logic and Monte Carlo Simulation. The implication therefore suggests that uncertainty and its
management tools are integral with risk management and control. Stare [35]; Heldman and
Heldman [32] and Oni [34] sees this view point similarly but however, embeds uncertainty within
the change management theory. While this appears useful in other sector, its application in the
construction sector is in the area of uncertainty assessment only since it provides mechanism for
prevention, early detection and effective realisation. The propriety of this viewpoint in
uncertainty management is not certain because, change are ordered, directed and tailored within

24
organisational and scope objectives. Uncertainty on the other is weird and non-directional, in this
view therefore, change management application is considered insufficient. This is not to say that,
change management protocol can’t be applied in uncertainty management. Flexibility as
uncertainty management tool for instance explores uncertainty requirements, evaluates approval
and realised improvement [33]. According to Gosling & Naim [36], there is the lack of literature
on the subject within construction context. Although, extant literature associates flexibility to
environmental uncertainty; and is describe as a system’s tendency to adapt to variation in with
little or no impact on cost, time and effort.
To address chaotic uncertainties in projects, collaborative management utilising relationship
management have been canvassed and widely adopted in global construction industry practice
[22]. Although these techniques are not new, the level of awareness (knowledge) and level of
application within the construction real estate sector development is low. Other techniques,
earned value management, event tree, contract formalization, and estimating using risk analysis
are widely deployed in other sector’s project (oil and gas). The use of transformation seems
apparent and may gain possible industry wide usage. But there is a barrier of asymmetric
information disclosure arising from the contractual practice in use. The traditional contractual is
prominent among public and private sector clients in Nigeria. The lack of information disclosure
is one the mechanism used by parties in traditional practice to explore the adversarial context of
its relationship. This assertion is reflected by the respondents rating of factors responsible for the
misconception of uncertainty within the construction sector in Nigeria. In Table 3, the type of
project management practice in use was the highest ranking factor identified by the respondents
as the root cause of low knowledge and misconception of uncertainty. Since transformation
requires sufficient information supply and disclosure, several studies’ recommendation had been
directed towards refining elements of traditional practice. A study by Ekung & Ingirige [3]
advocates the use of flexible contracting and restrain of opportunism. For effectiveness, the
implication is that, parties must agree to collaborate to devised joined management of eschewing
uncertainties in a project.
To enhance industry wide awareness about the context of these advance techniques, a brief
description of some tools is given in the following section.
Earned Value Management:Earned value management is a tool used in generating an
unbiased periodic cost and schedule performance information during the lifecycle of a project.
This is an advance application of traditional monitoring and control practice of comparing
planned against actual expenditure and schedules. This tool highlights variations early and
identifies their sources. The challenge to the use of this tool is not just in mechanics of application
but cultural change in the system.
Event Tree Analysis: Event tree analysis represents a logical combination of numerous events
that may eschew from an originating event. In an event tree, every event in a project is identify
and described graphically. This is effective since it enhance the identification of an event in
respect to time because the tree is related to the sequence of occurrences[5]. The tool can be used
to verify the parameter for improving project performance, criteria for evaluating performance
and management of performance at the various stages of the project. In this way, the event tree is
therefore used to identify parameters for preventing the project system from failure.
Estimating using Risk Analysis (ERA): ERA presents an improved method to the intuition
based ‘traditional’ contingency estimation techniques. ERA involves quantitative uncertainty

25
transformation into a manageable risk event. The steps in the process include risk identification,
classification and costing of uncertainties associated with a project.
Flexible Contracting:Flexibility contracting enhances modification of contract terms to
address unforeseen and chaotic uncertainty arising from the project delivery environment.
Flexibility describe as a system’s tendency to adapt to variation in with little or no impact on cost,
time and effort.
3. Conclusion
Due to the failure of traditional risk management practice to curb extreme uncertainty events, the
management of uncertainty is marred by misconception.To determine the colossus underpinning
effective project management requirements in this area, the literature has evolved various tools
and techniques over the years. However, an in-depth empirical research, seeking to determine the
level of application of these tools and techniques is dearth. This study therefore explored this
research gap and investigated the use of uncertainty management tools and techniques within the
construction sector in Nigeria.The objectives were to evaluate the level of use of uncertainty
management tools and techniques and to determine the factors responsible for the misconception
of uncertainty within the construction sector. Views from project managers in the Nigerian
construction industry,obtained from two sub-sectors, the real estate and the oil and gas was
evaluated. Findings of the study reveals there is the lack of clear understanding of the term
uncertainty. The level of use of uncertainty management tools and techniques by project
managers in the real estate and oil and gas sector in Nigeria is also low. The implication is the
lack of preventive management protocol, and non-application of established management tools
and techniques. Further finding identifies the type of project management practice in use, lack of
awareness on uncertainty management tools and over reliance on the use of deterministic
approach as factors associated with the misconception of uncertainty.
The challenge therefore is how to improve the state of misconception held by practitioners and to
implement refinements to the traditional project management in use. To develop appropriate
knowledge about uncertainty management, the notion that uncertainty management is about
creating previously unknown information, applied to management risk must be repealed to
embrace contemporary practice, which is, as encouraging information sharing, learning,
knowledge and competence. Second, there is the need to start a project by understanding the
context and profile of inherent uncertainties in the project system. This practice strictly requires
collaborative management approach, flexible contracting is the recommended refinements to the
traditional contractual practice to entrench relationship management, contract formalisation and
the use of other advanced tools and techniques such as experimentation.
This study implements survey research design which adopts and tested uncertainty management
tools and techniques from the literature. The tendency subsists that there may be extant bespoke
tools and techniques in used by practitioners in the various sectors not captured by the designed
survey instrument. Qualitative interviewing is another method that can generate these bespoke
approaches for improving the survey instrument. Another recommended perspective is the use of
mixed approach by future study. Focus group discussion can also be used to improve participants’
knowledge of the enlisted tools and techniques in the questionnaire to enhance their rating
opinion. Despite the observed refinements, the data on respondents’ background shows a
population, well educated in the respective professions with appropriate post graduate degrees;
their opinion therefore, cannot be biased on the ground of novice.

26
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