Building information-modeling-and-construction-projects-performance-the-effect-project-scheduling

International Journal of Scientific and Technological Research
(IJSTER) ISSN: 2617-6416 1 (1) 80-96, November, 2018
www.oircjournals.org
Natome and Muchelule (2018) www.oircjournals.org
Building Information Modelling and
Construction Projects Performance in
Uasin Gishu County Government. The
Effect of Project Scheduling.
1Natome Christine 2Yusuf Muchelule
Postgraduate Student Jomo Kenyatta University of Agriculture
and Technology
Lecturer Jomo Kenyatta University of Agriculture and Technology
Type of the Paper: Research Paper.
Type of Review: Peer Reviewed.
Indexed in: worldwide web.
Google Scholar Citation: IJSTER
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How to Cite this Paper:
Natome C., and Muchelule Y., (2018). Building Information Modelling and
Construction Projects Performance in Uasin Gishu, County Government.
The Effect Project Scheduling. International Journal of Scientific and Technological
Research (IJSTER), 1 (1) 80-96.

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Building Information Modelling and Construction
Projects Performance in Uasin Gishu County
Government. The Effect of Project Scheduling
1Natome Christine 2Yusuf Muchelule
Postgraduate Student Jomo Kenyatta University of Agriculture and Technology
Lecturer Jomo Kenyatta University of Agriculture and Technology
Abstract
In most of the construction projects, there is
always an element of running into delays in
project completion time, costs overruns from
variations and associated time overruns, lack of
satisfying client requirements, clashes on site
during construction – just to mention a few.
Building Information Modelling (BIM) is being
used to solve most of these challenges that pose
such risks to a project. The study looked the effect
of scheduling on performance of project
constructions in Uasin Gishu County. The study targeted a population of 197 respondents who constitute of
Technical staff and Non - technical staff. The study used census research design. Questionnaires were used to
collect information from respondents. In order to ascertain reliability of the research instruments, the researcher
piloted the instruments by distributing 30 questionnaires to respondents from Uasin Gishu County Government
selected randomly from the various sections, which were not be part of the county to be sampled for this study.
Descriptive statistics was used to analyse the data. Descriptive statistics included frequency, percentages, means,
standard deviation and frequency distribution. Inferential statistics used was correlation and linear regression.
The study found out that there was a significant and positive effect of project scheduling on construction projects
performance Uasin Gishu County Government (β=0.198; p<0.05). The study concluded that proper project
scheduling leads to an increased project performance risk management plays an important role in project
management because without it project managers cannot define their objectives for future and project monitoring
plays a vital role in project manager’s decision making processes since it helps project managers and their teams
to foresee potential risks and obstacles that if left unaddressed could derail the project. The study recommends
that the County Government should continue with good practices of ensuring resources are allocated with good
practices of ensuring resources are allocated to projects from interception until closure.
1.0 Introduction
Whenever you undertake a project, there is always
some element of risk, whether from cost overruns,
project delays or buildings not performing as
expected. While adopting building information
modelling (BIM) cannot eradicate all risks, it
enables us to de-risk many areas across the project
life cycle. This provides greater certainty and
ensures that key project milestones are met, and
assets delivered as expected (Pryke, 2016).
According to Olatunji et al (2012) performance of a
project is measured as its ability to deliver the
building or structure at the right time, cost and
quality as well as achieving a high level of client
satisfaction. It therefore stands to reason that quality
performance is results oriented and seeks evidence
of quality awareness within the operations and
output of a building/construction team. Quality
performance is also defined over the long term for
the effect to be permanent (Idrus, & Sodangi, 2010).
In other words, quality performance improvements
are expected to increase the productivity and
profitability of contractors as well as increasing
client satisfaction.
Globally, between 50% and 80% of projects
implementation efforts fail to perform (Atkinson,
2016). According to Egelhoff (2011) Asian firms
that have fail to complete projects successfully
because of modelling strategies. Zaribaf and
Bayrami (2010) indicated that majority of large
organizations in US had problems with project
ARTICLE INFO
Received 15th October, 2018
Received in Revised Form 30th October, 2018
Accepted on 28th October, 2018
Published online 2nd November, 2018
Keywords: Building information modelling,
Construction, Project Scheduling

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performance. This shows that the current world
belong to those projects which are able to develop
faster than their competitors offering the same goods
and services. The organizational modeling of the
future is a learning organization which is focusing at
creating and gaining knowledge for improved
performance and building a competitive edge (Singh
and Saldanha, 2013). As indicated by Hubbard
(2010), project modeling is a critical factor to project
performance. In Jordan it is accounted for that poor
project performance is because of poor financing by
the legislature. Consequently, impacts of project
modeling on effective completion of construction
projects are the worry of the Jordan government.
The formal structures are moderate, financing takes
edges of endorsement, and gradualness in receiving
present day advancements.
Brazil is accounted for to have adequate support of
HR that can offer help in construction industry.
Modeling is a pivotal component in producing future
development and flourishing projects. Construction
designing in Brazil has contributed colossally to
fruitful completion of thousands of construction
projects. Subsequently, the productive conveyance
of construction projects relies upon how great labor
is keeping up convenient completion of construction
projections. It is essential to prepare, create and keep
up quality workforce in order to have quality project
performance (Loosemore et al., 2003) In China is
taken as the best in construction of project in the
world. The advancement of construction modeling
has driven China to have the best condition of
construction projects in the world. Dominant part of
the construction projects are built and completed
within the required period. The records management
limit is facilitating by having appropriate models
and framework. The advancement is the helpful
segment in usage of another or fundamentally
enhanced thought, great, administration, process or
practice that is planned to be valuable. Dodds (2007)
indicated that strategic modeling is helping China to
have a big role in projects constructions works in a
much-enhanced manner.
In Africa, during the last fifty years the construction
industry has been heavily criticized for its
performance and productivity in relation to other
industries. With the turn of the new millennium, it
appears that the construction industry is going
through an intense period of introspection which is
exacerbated by increased technological and social
change. These changes are altering the tempo of the
environment within which construction operates. In
a related study, Oyegbile et al., (2012) revealed that
over the last 10 years, the incidence of building
collapse in Nigeria has become so alarming and does
not show any sign of abating. Agbenyega (2014)
also states that a section of the Methodist Church
building under construction at Sakaman, a suburb of
Accra had collapsed. Subsequently, a two storey
building was also reported to have collapsed at
“Asene Dzornshie” near Old Accra or Bukom
Square, while another three-storey structure also
collapsed in the Ashanti Regional town of Obuasi.
In Kenya, there are many construction projects that
fail in performance. In addition, performance
measurement systems are not effective or efficient
to overcome this problem. Construction projects
performance problem appears in many aspects in the
Kenya. There are many constructed projects that fail
in time performance, others fail in cost performance
and others fail in other performance factors. In 2009
there were many projects which finished with poor
performance because of many evidential reasons
such as: obstacles by client, non-availability of
materials, road closure, amendment of the design
and drawing, additional works, waiting the
decision, handing over, variation order amendments
in Bill of Quantity (B.O.Q) and delay of receiving
drawings (Ngugi, 2017).
There are other factors for problems of performance
in Kenya such as project management, coordination
between participants, monitoring, and feedback and
leadership skills. In addition, political, economic
and cultural issues are three important indicators
related to failures of projects' performance in the
Kenya. The Performance is related to many topics
and factors such as time, cost, quality, client
satisfaction; productivity and safety. Construction
industry in the Kenya suffers from many problems
and complex issues in performance (Muchungu,
2012). Work on providing construction services in
Uasin Gishu has made considerable progress since
the ministry of transport assumed responsibility for
them, but the construction companies have had to
build from a low base, including a huge backlog of
rehabilitation and development work, few
institutions, and very little funding. So, they have
had to work in every difficult physical, social,
political, economic and institutional circumstance.
For a number of reasons, the performance of
construction projects has not been as impressive,
fundamentally because of the government failure to
establish a coherent institutional and policy
framework (Kagiri, 2015).
Statement of the Problem
A lot of construction projects in Kenya do not get
completed in time or totally not completed or poorly
done. According to Kenya Rural Roads Authority,
(2013) there have been several projects which were
not completed by the end of the required period. In

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2009 Kenyan officials estimated that 65% of
Kenya’s buildings fail to meet code standards.
Between 2006 and 2014, seventeen buildings
spontaneously collapsed in Kenya alone, and caused
eighty-four deaths and more than 290 injuries
(Fernandez, 2014). This is because structural defects
are frequently identified too late, often after
catastrophic collapse. Therefore these indicate poor
performance of construction projects in Kenya.
According to Ministry of Public Works, most of the
buildings, bridges collapse and roads having boodles
are due to poor supervision, poor construction
procedures and poor inspection (MOPW Report,
2006). This has led to either stalled or failed projects.
Similarly, for the few projects that get completed,
they are associated with; scope creep, cost overruns,
poor workmanship or project time delays (Navon,
2005). Consequently, arising from the creation of
“white elephant” projects, huge resources are
wasted, business opportunities lost, customers get
dissatisfied and the overall development is retarded
among others. Several studies have been done on
Kenyan construction projects performance.
Gichunge (2000) did research on risk management
in the building industry in Kenya; an analysis of time
and cost risks. Talukhaba (1999) did an investigation
into factors causing construction project delays in
Kenya. Kimemia (2015) did a study on the
determinants of project delays in the construction
industry in Kenya; the case of selected road projects
implemented by Kenya National Highways
Authority in Kenya’s Coast region. Musyoka (2012)
researched on success of capital projects in Kenya.
However, none of the studies has dealt with the
construction projects performance by utilizing the
BIM technology which presents a knowledge gap.
This study therefore sought to fill the gap by
investigating the influence of building information
modelling on Construction Projects Performance
Uasin Gishu County Government.
Study Objective
To establish the influence of project scheduling on
Construction Projects Performance Uasin Gishu
County Government.
Research Hypothesis
Ho1: There is no significant effect of project
scheduling management and Construction
Projects Performance Uasin Gishu County
Government.
2.0 Literature Review
Technology Acceptance Model
Technology acceptance model (TAM) was
formulated by Richard Bagozzi, Fred Davis and Paul
Warshaw in 1980’s. It explains the factors that
influence the decision about how and when users
will use a new technology – which include: 1)
Perceived Usefulness (PU) which was defined by
Fred Davis as “the degree to which a person believes
that using a particular system would enhance his or
her job performance; 2) Perceived ease-of-use
(PEOU) which was defined by Davis as “the degree
to which a person believes that using a particular
system would be free from effort (Davis, 1989).
Assumption of the theory states that because new
technologies such as personal computers are
complex and an element of uncertainty exists in the
minds of decision makers with respect to the
successful adoption of them, people form attitudes
and intentions toward trying to learn to use the new
technology prior to initiating efforts directed at
using. Attitudes towards usage and intentions to use
may be ill-formed or lacking in conviction or else
may occur only after preliminary strivings to learn
to use the technology evolve. Thus, actual usage
may not be a direct or immediate consequence of
such attitudes and intentions.
Figure 1.1 The Technology Acceptance Model.
Critics show that the Model as a predictor of using
information systems to acquire information literacy
skills asserted that external variables which are
factors outside an individual such as training,
experience and system quality affects the two factors
PU and PEOU which influence the users’ attitude

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towards the use of a given technology and its
ultimate use. The main external factors that are
usually manifested are social factors, cultural factors
and political factors. Social factors include
language, skills and facilitating conditions. Political
factors are mainly the impact of using technology in
politics and political crisis. The attitude to use is
concerned with the user’s evaluation of the
desirability of employing a particular information
system application.
Behavioural intention is the measure of the
likelihood of a person employing the application
(Surendran, 2012). Dr. Mohamed Al Haderi noted
that other information quality affects user’s intention
to adopt the technology especially after its
usefulness has been established and also established
a positive effect that government and top
management support has on the intentional behavior
throughout the positive effect on perceived
usefulness and ease of use (Al-Haderi, 2014).
Institutional factors refer to the aspects within the
organization related to work and the instrument to
facilitate in the accomplishment of the work. For
example, organizational support and rewards
influence workers’ beliefs in using technology to
accomplish the work (Lewis, Agarwal, &
Sambamurthy, 2003). The model was used to
explain the first objective which sought to explain
effect of records management on construction
projects coordination.
Theory of Constraints
The theory of constraints (TOC) was introduced by
Eliyahu Goldratt in 1984. The theory of constraints
developed a revolutionary method for production
scheduling which was in stark contrast to accepted
methods available at the time, such as MRP. The
theory of constraints (TOC) adopts the common
idiom "A chain is no stronger than its weakest link"
as a new management paradigm. Assumptions of
The theory of constraints asserts that every complex
systems and processes, are made up of interrelated
activities and one among the activities might pose a
constraint to the entire system, which becomes the
weakest link in the chain.
Critics of the theory of constraints argue that the
processes and organizations are vulnerable because
the weakest person or part can always damage or
break them or at least adversely affect the outcome.
In the third revised edition, Goldratt (2004) further
stated that the analytic approach with TOC comes
from the contention that any manageable system is
limited in achieving more of its goals by a minimal
number of constraints and that there is always at
least one constraint (Goldratt E. M., 2004). Hence
the TOC process seeks to identify the constraint and
restructure the rest of the organization around it.
Goldratt and Fox (1986) state that the secret to
success lies in managing these constraints and the
system as it interacts with these constraints, to get
the best out of the whole system (Goldratt & Fox,
1986).
Further critic show that the theory of constraints
(TOC) is a management philosophy that has been
effectively applied to Manufacturing processes and
procedures to improve Organizational effectiveness.
Klein and De Bruine (1995) in their study noted that
TOC had developed rapidly regarding both
methodology and area of applications. In the field of
project management, most of the work is carried out
in the application of Logistics Paradigm. The
methodology used in the project management is
critical chain project management to find the critical
chain and to find the project buffers and the feeding
buffers. Theory of constraints proposes a
methodology to detect and limit or eliminate the
influence posed by a constraint to a goal. The 5 steps
model include: constraint identification, decision on
constraint exploitation, subordinate everything else,
constraint elevation (constraint elimination) and
back to the first step (constraint identification)
(Wilkinson, 2013). Poor estimation of cost, time and
related resources negatively affects project success.
Thus, an effective project estimation tool to
undertake prediction is required, for instance poor
estimation in infrastructural construction project can
cause delays, emerging from project complexity,
construction and technology methods used and
related resources inefficiency (Elbeltagi, Hosny,
Dawood, & Elhakeem, 2014).

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Figure 1.2 Theory of Constraints
The managers make three decisions when dealing
with constraints: What to change? What to change to
and how to cause the change? The TOC logical
thinking process has evolved to answer these generic
questions. Past studies have shown that it is often
managerial policies that most often the main
constraint and the thinking process also helps in
these situations. However, critiques to the this
theory have highlighted a major challenge, whereby
one might be working to reduce the effect of a
constraint or to eliminate it only to find that it was
caused by another constraining factor or the
constraint is not directly related to the existing
problem. This might lead to resource wastage on
factors that do not contribute towards project
success. It has also been criticized for its focus on
short-term goals as opposed to long term goals, in
that it only evaluates what is happening currently.
The theory supports the second objective which
explains the influence of project scheduling on
construction projects coordination.
Conceptual Framework
Young (2009) defines a conceptual framework as a
diagrammatical representation that shows the
relationship between dependent variable and
independent variables. According to Sekaran (2010)
a conceptual framework is a systematically
organized, described and detailed model that
facilitates the conceptualization of the relationship
between the factors identified in a study (Sekaran,
2010). The dependent variable for this study was
construction projects risk management and the
relationship with the independent variables is shown
on figure 2.1.
Independent Variables Dependent Variable
Figure 2.1 Conceptual Framework
Empirical Review of Relevant Study
This section presents the empirical review on related
studies on Records Management, project
scheduling, risk management and monitoring.
Project Scheduling and Construction Project
Performance
Kerzner (2017) determined project management on
a systems approach to planning, scheduling, and
controlling. The study findings showed that
Construction Project
performance
 Quality
 Time
 Cost
Project Scheduling
 Goals and objectives
 Resource Allocation
 Value engineering
 Task Management

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construction project life-cycle processes must be
managed in a more effective and predictable way to
meet project stakeholders’ needs. However, there is
increasing concern about whether know-how
effectively improves understanding of underlying
theories of project management processes for
construction organizations and their project
managers. Project planning and scheduling are
considered as key and challenging tools in
controlling and monitoring project performance, but
many worldwide construction projects appear to
give insufficient attention to effective management
and definition of project planning, including
preplanning stages. Indeed, some planning issues
have been completely overlooked, resulting in
unsuccessful project performance.
There is a lack of knowledge of, and understanding
about, the significance of applications of project
planning and scheduling theory in construction
projects. Thus, improving such knowledge should be
incorporated with new management strategies or
tools to improve organizational learning and
integration in the context of project planning and
scheduling. This implies a need to assess project
stakeholders’ understanding on the application of
project planning and scheduling theories to practice.
The main aim was to study and describe project
stakeholders’ perspectives regarding a set of
identified criteria comprising aspects assumed to be
significant in successful project planning and
scheduling. The main research question was
developed as follows: What level of understanding
do project stakeholders have about the application of
project planning and scheduling theories in practices
of construction projects? This key question is
divided into a number of specific questions
concerned with various aspects of project planning
and scheduling.
Three different questionnaire surveys were
considered and designed in order to collect and
analyze data relevant to the empirical studies
presented and discussed under the scope of this
thesis. The study context is Oman. The thesis is
based on a summary of five appended papers, of
which four represent empirical survey studies. The
results form the basis of discussions and reflections,
and the four key factors identified are: highlighting
management tools needed to improve organizational
knowledge and understanding of project planning
theories and methods; paying particular
consideration to the significant factors (enablers and
barriers) impacting project planning and scheduling;
identifying project management roles and
organizational behaviour in planning and
scheduling; and increasing project stakeholders’
awareness of front-end planning for a more
successful project execution.
Shamp (2017) examined scheduling Strategies for
Construction Project Managers toward on Time
Delivery. The study found that construction
management projects involve complex, dynamic
environments resulting in uncertainty and risk,
compounded by demanding time constraints.
Research indicated project managers have struggled
to identify best practices for scheduling construction
projects via critical path methodologies while
searching for tools to increase timely job
completions and budget profits. The purpose of this
single case study was to explore the strategies that
construction project managers used to manage
scheduled construction project delivery on time. The
constructivist philosophical worldview was used as
the framework for this study. Data were collected
from semi structured interviews from 7 project
managers from 5 different construction companies
selected via purposive sampling throughout Florida.
All project managers had at least 15 years of
experience and multiple construction projects with
managing scheduled project deliveries. Three
themes emerged through thematic analysis: project,
time delay, and cost. A construction project can have
many variables that project managers cannot control
such as the issue of on-time scheduling.
Project managers identified that a project could be
within the budget or cost set for the project and still
be on time and go over budget or be within budget
and not meet schedule. No broad support was found
for agile project management, and no confirmation
could be made that principles of philosophical
theories were critical for project success.
Implications for a positive social change result in
creating new jobs during and after construction,
bringing new individuals to neighborhoods, schools,
and area businesses. Negendahl (2015) stated that
the schedule is usually not dynamically linked back
to the building design. It relies on those who created
the schedule, through analysis of the building
design, to make any changes or updates to the
schedule, if and when the design changes. This is
perhaps one of the more significant gaps in the
traditional process that is bridged through the use of
BIM. Ultimately, successful organisations were able
to closely manage their use of increasingly scarce
and expensive resources; demonstrating greater
value for money by reducing costs and meeting
environmental objectives. BIM is not essential to
improving a project’s resource efficiency.
Sfrent and Pop (2015) examined asymptotic
scheduling for many task computing in big data
platforms. Traditional scheduling methods rely on a
few people familiar with the tasks to be performed

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to make the schedule. These members of the project
team determine how long each task should take to
complete, and in what order they need to be
completed. They must also include any other
associated logic or precedence between the tasks. As
technology has developed over time, this scheduling
process has been transformed from an all-paper
process to one involving scheduling software, not
unlike other forms of documentation mentioned
previously.
3.0 Research Methodology
Research Design
The research design used in this study was
descriptive survey design. This is because
descriptive research design does not involve
modifying the situation under study or determining
the cause-effect relationship. It also enables the
researcher to obtain the opinions of project
managers involved in roads construction projects in
their natural setting. This research design was also
useful in management decision making. It involved
acquiring information about a certain segment of the
population and getting information on their
characteristics, opinions or attitudes (Orodho, 2003).
Churchill and Brown (2004) also observed that
descriptive research design is appropriate where the
study sought to describe the characteristics of certain
groups, estimated the proportion of people who have
certain characteristics and make predictions.
Target Population
The target population in statistics is the specific
population about which information is desired.
According to Denscombe (2008), a population is a
well-defined or set of people, services, elements, and
events, group of things or households that are being
investigated. The study targeted a population of 197
respondents who constitute of Technical staff and
Non - technical staff in Uasin Gishu County
government projects.
Table 3.1 Target population
Categories Target
Technical staff
Resident engineers 9
Assistant engineers 17
Materials engineers 9
Inspectorate 35
Surveyors 75
Non - technical staff
Project managers 10
Procurement 15
Administrators 12
Accountants 15
Total 197
Source (Uasin Gishu County Government Records, 2018)
Data Collection Procedure
Upon getting the consent of the University, the
researcher proceeded to getting permission from the
county government offices. On the set date,
questionnaires was administered directly to the
respondent using drop and pick method and a follow
up was conducted by the researcher to ensure the
questionnaires are filled in accordance with the
research. The respondents were given enough time
to complete the copies of the questionnaire before
picking them for analysis. The questionnaire
included both closed and open-ended questions.
This allowed the respondents to give their own
views. The researcher explained the purpose of the
visit to the respondents. This assured the
respondents of their confidentiality of any
information they gave.
Pilot Study
In order to ascertain reliability and validity of the
research instruments, the researcher pilot the
instruments by distributing 20 questionnaires to
respondents from Nandi County Government, which
were not be part of the county to be sampled for this
study. The pilot respondents represented 10% of the
sample size. The results of the piloted questionnaires
enabled the researcher to determine the consistency
of responses to be made by respondents and adjust
the items accordingly by revising the document.
Validity
The content validity of the questionnaire was
established by the researcher by seeking the
opinions of experts in the field of study. Validity
relates to the extent to which the research data and
the methods for obtaining the data is accurate,

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honest and on target (Denscombe 2003). Before
using a research instrument it is important to ensure
that it has some validity.
Reliability
According to Orodho, (2009), defined reliability as
a measure of the degree to which a research
instrument yields consistent results or data after
repeated trials. According to Pallant (2011) when
using the Cronbach‘s Alpha value to test reliability,
a value above 0.7 is considered acceptable; however,
a value above 0.8 is preferable. This method requires
neither the splitting of items into halves nor the
multiple administrations of instruments. The internal
consistency method provides a unique estimate of
reliability for the given test administration. Before
the instruments are used for collecting data, a pilot
study was conducted in Nandi County Government.
The respondents to be used for piloting will not take
part in the study. Once the questionnaires are
constructed, they were tried out in the field.
Data Processing and Analysis
Data collected was prepared for analysis by editing,
coding, classification and tabulation of data before
analysis. Data analysis is a systematic process of
transcribing, collating, editing, coding and reporting
the data in a manner that makes it sensible and
accessible to the reader and researcher for the
purposes of interpretation and discussion (Jwan &
Ong'ondo, 2011). The data collected was analysed
by both descriptive and inferential statistics.
Descriptive statistics refers to the use of percentages,
frequencies, mean, standard deviations and variance
whereas the inferential statistics involves the use of
Pearson product moment, correlation coefficient and
multiple regression analysis (Cooper & Schindler,
2011).
Multiple regression analysis is a measure of the
ability of independent variable(s) to predict an
outcome of a dependent variable where there is a
linear relationship between them. In this study
regression analysis was done to establish whether
independent variables predict the dependent
variable. The R square, t-tests and F-tests and
Analysis of Variances (ANOVA) tests were all
generated by SPSS to test the significance of the
relationship between the variables under the study
and establish the extent to which the predictor
variables explain the variation in dependent variable
(Brace, Kemp & Snelgar, 2012). The research
hypotheses was tested using the p value approach at
95% confidence level based on linear regression
analysis output produced by SPSS. The statistical
overall model used for analysis was in the form of
Ordinary Least Square (OLS) model as shown:
𝒀 = 𝜷 𝟎 + 𝜷 𝟏 𝑿 𝟏 + 𝜷 𝟐 𝑿 𝟐 + 𝜷 𝟑 𝑿 𝟑 +
𝜷 𝟒 𝑿 𝟒 + 𝜺……………...……Equation 3.1
Where
Y represents the dependent variable
(project performance)
β0 represents the constant
β1 represents the coefficient of the
independent variables
X2 represents project scheduling
ε represents the error term
The data was presented using frequency tables, pie
charts and graphs. The researcher preferred this
mode of presentation to enhance visualization of
statistical information and thus make the data easier
to understand.
4.0 Research Findings and Discussion
Response Rate
Response rate is the number of people who answered
the survey divided by the number of people in the
sample (Nulty, 2008). A total of 197 questionnaires
were issued out and only 175 were returned. This
represented a response rate of 88.8%. This response
rate was adequate for data analysis and conforms to
Mugenda and Mugenda (2003) stipulation that a
response rate of 70% and over was adequate. The 22
questionnaires were accounted by those respondents
who never returned the distributed questionnaires as
they were busy on their duties. Some questionnaires
were never filled completely hence were not used for
this study. The results were presented in Table 4.1.
Table 4.1 Response Rate
Category Frequency Percentage
Administered 197 100.0
Returned 175 88.8
Reliability Test Results
Reliability tested the internal consistency of the
research questionnaire. The results of analysis are
shown in Table 4.2. The results indicated that
records management had Cronbach’s alpha
coefficient value of 0.986. Project scheduling had
Cronbach’s alpha coefficient 0. 988. Risk
management had Cronbach’s alpha coefficient value
of 0.981. Monitoring had Cronbach’s alpha
coefficient value 0.987. Projects Performance had
Cronbach’s alpha coefficient value of 0.987. This
implies that the research questionnaire was reliable
as all the 5 constructs had Cronbach’s alpha

89 | P a g e
coefficients greater than 0.7. The study results
concur with Pallant (2011) that when using the
Cronbach‘s Alpha value to test reliability, a value
above 0.7 is considered acceptable; however, a value
above 0.8 is preferable. The internal consistency
method provides a unique estimate of reliability for
the given test administration.
Table 4.2 Reliability of the Research Questionnaire
Cronbach's Alpha N of Items
Project Scheduling .988 4
Projects Performance .987 4
Demographic Information of
Respondents
The demographic information considered in this
study included the respondents’ gender, level of
education, respondents’ age bracket and work
experience. The study results were presented under
the following subtopics;
Gender of the Respondent
The respondents were asked to indicate their gender
in order to ensure that the results obtained captures
the views of both gender. The results were presented
in Table 4.3. The results indicate that 116(66.3%) of
the respondents were male while 59(33.7%) of the
respondents were female. The respondents were
slightly made up of more male than female. The
findings indicate that the male and female difference
was not significant and therefore this implies that the
study was not influenced by gender imbalance.
Table 4.3 Gender of the Respondents
Frequency Percent
Male 116 66.3
Female 59 33.7
Total 175 100.0
Level of Education of the Respondents
The level of education was important as it enabled
the respondents to answer the questions
appropriately. The results are presented in Table 4.4.
The results indicate that 38(21.7%) of the
respondents had attained certificate level, 63(36%)
of the respondents indicated that they have attained
diploma while 50(28.6%) of the respondents said
that degree was their highest level of education and
24(13.7%) said that their highest level of education
was masters. The results indicate that majority of the
respondents have attained diploma level education.
The results indicate that the respondents were aware
of the topic under the study.
Table 4.4 Level of Education of the Respondents
Frequency Percent
Certificate 38 21.7
Diploma 63 36.0
Graduate 50 28.6
Masters 24 13.7
Total 175 100.0
Age of the Respondents
The respondents were asked to indicate their age
bracket since it was important for the study. The
results were presented in Table 4.5. The results on
the respondents’ age bracket indicate that 16(9.1%)
of the respondents’ age bracket to be between 18 and
29 years; 63(36%) of the respondents indicated their
age bracket to be between 30 and 39 years;
79(45.1%) of the respondents said that their age
bracket was between 40 and 49 years; another
17(9.7%) of the respondents indicated their age
bracket was above 50 years. The results indicate that
majority of the respondents were between 40 and 49
years and therefore they were old enough to provide
reliable information.
Table 4.5 Age of the Respondents
Frequency Percent

90 | P a g e
18-29 years 16 9.1
30- 39 years 63 36.0
40 -49 years 79 45.1
50 and above years 17 9.7
Total 175 100.0
Work Experience of the Respondents
The results show that 4(2.3%) of the respondents
have worked for a period of less than 1 year;
67(38.3%) of the respondents indicated that they
have worked for between 1 and 3 years while
82(46.9%) of the respondents said that they have
worked for a period between 3 and 5 years and
22(12.6%) of the respondents had worked for a
period more than 5 years. The results indicate that
majority of the respondents have been working for a
period between 3 and 5 years and therefore they
understand the influence of building information
modeling. The study results were presented in table
4.6.
Table 4.6 Work experience of the respondents
Frequency Percent
Less than 1 year 4 2.3
1-3 years 67 38.3
3-5 years 82 46.9
over 5 years 22 12.6
Total 175 100.0
Descriptive Findings and Discussions
This section presents descriptive analysis of the
study objectives. The statistical analyses concerning
the influence of building information modeling was
collected and the data were presented in five-point
Likert scale as follows; 5=strongly Agree 4= Agree
3= Undecided 2=Disagree 1=Strongly Disagree.
Therefore the results of the study are as shown.
Project Scheduling and Construction Projects
Performance
The study sought to determine the influence of
project scheduling on project performance in Uasin
Gishu County. Table 4.8 presents views of the
respondents on the descriptive statistics for project
scheduling. The findings as presented shows that
the respondents were in agreement that BIM project
scheduling helps the contractors attain the goals and
objectives of project completion (m= 4.03,
SD=1.404); Project scheduling function enhance the
resource allocation and simulation (M=3.82,
SD=1.542); Creation of project schedules using
BIM has proved value engineering in project
performance (m=4.12, SD=1.331) and that BIM
scheduling helps in task management of resources
and better tracking of project performance (m=4.03,
SD=1.266). The study shows that project scheduling
has a positive influence on project performance in
Uasin Gishu County. This means that a project
cannot work without a project plan because it is the
project plan which establishes the timelines,
delivery and availability of project resources,
whether they be personnel, inventory or capital.
Therefore, proper project scheduling leads to an
increased project performance.
The study concurs with Negendahl (2015) who
stated that the schedule is usually not dynamically
linked back to the building design. It relies on those
who created the schedule, through analysis of the
building design, to make any changes or updates to
the schedule, if and when the design changes. This
is perhaps one of the more significant gaps in the
traditional process that is bridged through the use of
BIM. Ultimately, successful organisations were able
to closely manage their use of increasingly scarce
and expensive resources; demonstrating greater
value for money by reducing costs and meeting
environmental objectives. BIM is not essential to
improving a project’s resource efficiency.
The study findings also conceded with Sfrent and
Pop (2015) who examined asymptotic scheduling
for many task computing in big data platforms.
Traditional scheduling methods rely on a few people
familiar with the tasks to be performed to make the
schedule. These members of the project team
determine how long each task should take to
complete, and in what order they need to be
completed. They must also include any other
associated logic or precedence between the tasks. As
technology has developed over time, this scheduling
process has been transformed from an all-paper
process to one involving scheduling software, not
unlike other forms of documentation mentioned
previously.

91 | P a g e
Table 4.8 Descriptive Statistics for Project Scheduling
SD D N A SA Tot
al
Mea
n
Std.
Dev
Min Ma
x
BIM project
scheduling helps the
contractors attain the
goals and objectives
of project completion
F 22 11 4 41 97 175 4.03 1.40 1 5
% 12.6 6.3 2.3 23.4 55.4 100
Project scheduling
function enhance the
resource allocation
and simulation
F 32 7 12 33 91 175 3.82 1.54 1 5
% 18.3 4.0 6.9 18.9 52.0 100
Creation of project
schedules using BIM
has proved value
engineering in project
performance
F 18 10 7 38 102 175 4.12 1.33 1 5
% 10.3 5.7 4.0 21.7 58.3 100
BIM scheduling helps
in task management
of resources and
better tracking of
project performance
F 11 20 11 44 89 175 4.03 1.26 1 5
% 6.3 11.4 6.3 25.1 50.9 100
Project Performance
The study lastly sought to know the level of
subject’s agreement concerning the project
performance. The results of the study are as shown
in table 4.11. The study found out that the most
agreed statement was needed quality affects project
completion indicated by (M=3.98, SD=1.273.)
followed by Cost influence project performance that
evidenced by (M=3.93, SD=1.397). The
respondents also agreed with the statement that
Scope of project influence project performance as
shown by (M=3.84, SD=1.449) and lastly on the
least agreed statement concerning time influence
project performance (M=3.77, SD=1.387).
The study clearly shows that building information
has a strong positive influence on construction
project performance in Uasin Gishu county
Government. The study concurs with Olatunji et al
(2012) who asserted that performance of a project is
measured as its ability to deliver the building or
structure at the right time, cost and quality as well as
achieving a high level of client satisfaction. It
therefore stands to reason that quality performance
is results oriented and seeks evidence of quality
awareness within the operations and output of a
building/construction team.
Table 4.9 Descriptive Statistics for Project Performance
Statements SD D N A SA Total Mea
n
Std.
Dev
Mi
n
M
ax
Cost influence project
performance
F 14 15 19 49 78 175 3.93 1.27 1 5
% 8.0 8.6 10.9 28.0 44.6 100
Time influence project
performance
F 24 12 15 54 70 175 3.77 1.39 1 5
% 13.7 6.9 8.6 30.9 40.0 100
Scope of project
influence project
performance
F 19 27 4 38 87 175 3.84 1.44 1 5
% 10.9 15.4 2.3 21.7 49.7 100
Needed quality affects
project completion
F 22 11 4 49 89 175 3.98 1.38 1 5
% 12.6 6.3 2.3 28.0 50.9 100

92 | P a g e
Correlation Analysis Results
The research used Karl Pearson’s coefficient of
correlation to calculate the degree and direction of
the relationship between linear related variables.
The value of the coefficient of correlation (r) always
lies between -1 and +1 such as; r=+1, perfect
positive correlation. r=-1, perfect negative
correlation. r=0, no correlation. The correlation
showed in the table shows bivariate correlations of
all the variables (records management, project
scheduling, risk management, project monitoring
and project performance). Correlation analysis and
multiple regression analysis was used to compute
the mean of the items since the research instrument
was measured by multiple variables (Wang and
Benbasat, 2007)
From the correlation Table 4.13 it is clear that all the
independent variables (records management, project
scheduling, risk management and monitoring) are
strongly and positively correlated to project
performance since all the correlation coefficients are
greater than 5.0 and p values for all the four variables
are 0.000 implying that all the variables are
statistically significant. The study results further
indicated that there positive, significant and strong
relationship between project scheduling and records
management (r=0.768, p<0.01), there was
significant and strong relationship between risk
management, records management and project
scheduling (r=0.779, p<0.01; r=0.807, p<0.01)
respectively. Lately the study results revealed that
there was significant, strong and positive
relationship between Monitoring, Records
Management, Project Scheduling and Risk
Management (r=0.843, p<0.01; r=0.759, p<0.01;
r=0.833, p<0.01) respectively. Karl Pearson’s
coefficient considers a range of 0.10-0.29 to be
weak, 0.30-0.49 to be medium and 0.5-1.0 to be
strong, Wong and Hiew (2005).
Table 4.10 Correlations Analysis Results
Project Performance Project Scheduling
Project Performance Pearson Correlation 1
Sig. (2-tailed)
Project Scheduling Pearson Correlation .755**
1
Sig. (2-tailed) .000
Regression Analysis Results
The research used multiple regression analysis to
determine the linear statistical relationship between
records management, project scheduling, and risk
management monitoring and construction projects
performance.
Linear Regression Model of Project Scheduling
and Construction Projects Performance
Table 4.11 show correlation coefficient (R) and
determination (R2
) which explains the degree of
association between independent and dependent
variables. A correlation coefficient of 1 means that
for every positive increase in one variable, there is a
positive increase of a fixed proportion in the other.
A correlation coefficient of -1 means that for every
positive increase in one variable, there is a negative
decrease of affixed proportion in the other. Zero
means for every increase, there is no positive or
negative increase. The two aren’t related. On the
other hand R2
is interpreted as the proportion of
variance in the dependent variable that is predictable
from independent variable. The coefficient of
determination is the square of correlation (r)
between predicted y scores and actual y scores; thus,
it ranges from 0 and 1.With linear regression, the
coefficient of determination is also equal to the
square of the correlation between the independent
and dependent variables. An R2
of 0 means the
dependent variables cannot be predicted from the
independent variable. An R2
of 1 means the
dependent variable can be predicted without an error
from independent variable. An R2
between 0 and 1
indicates the extent to which the dependent variable
is predictable.
The coefficient of determination (R2
) and correlation
coefficient (R) shows the extent of relationship
between project scheduling and project
performance. The results of the linear regression in
table shows that R2
=0.570 and R = 0.755. R value
shows a strong linear relationship between the
project scheduling and project performance. The R2
indicates that explanatory power of the independent
variables is 0.570. This means that 57% of the
variation in project performance is explained by the
regression model while 43% remains unexplained
by the model. Adjusted R2
which is 0.568 is slightly
lower than R2
value is a sure indicator that there is a
strong relationship between project scheduling and
project performance, this is because it is so sensitive
to the addition of irrelevant variables. The adjusted
R2
indicates that 56.8% of the changes in project
performance are explained by the model while

93 | P a g e
43.2% is not explained by the model. An indication
that project scheduling affects project performance.
Table 4.11 Model Summary of project scheduling
Model R R Square Adjusted R Square Std. Error of the
Estimate
1 .755a
0.57 0.568 0.74352
Table 4.12 of ANOVA test of project scheduling
shows that the model was fit and there was a
statistical significant relationship between project
scheduling and project performance in Uasin Gishu
County Government. This is evidenced by F of
229.672 and p=0.000<0.05. F test provides an
overall test of significance of fitted regression
model. It indicates that all the variables in the
equation are important hence the overall regression
is significant.
Table 4.12 ANOVA of Project Scheduling
Model Sum of
Squares
Df Mean Square F Sig.
1 Regression 126.968 1 126.968 229.672 .000b
Residual 95.639 173 0.553
Total 222.607 174
Table 4.13 shows that there was positive linear
relationship between project scheduling and project
performance which means that an increase in a unit
of project scheduling increases performance by
0.712 units and price scheduling was significant
(p=0.000) in project performance. A clear show of a
positive influence of project scheduling on project
performance. The study therefore rejects the second
null hypothesis that there is no significant effect of
project scheduling on project performance.
Table 4.13 Regression Analysis of Project Scheduling
Unstandardized Coefficients
Standardized
Coefficients t Sig.
B Std. Error Beta
(Constant) 1.212 0.204 5.955 0.000
Project scheduling 0.712 0.047 0.755 15.155 0.000
Overall Multiple Regression Analysis Results
Table 4.14 show coefficient of correlation
coefficient (R) and determination (R2
) which
explains the degree of association between
independent and dependent variables. A correlation
coefficient of 1 means that for every positive
increase in one variable, there is a positive increase
of a fixed proportion in the other. A correlation
coefficient of -1 means that for every positive
increase in one variable, there is a negative decrease
of affixed proportion in the other. Zero means for
every increase, there is no positive or negative
increase. The two aren’t related. On the other hand
R2
is interpreted as the proportion of variance in the
dependent variable that is predictable from
independent variable. The coefficient of
determination is the square of correlation (r)
between predicted y scores and actual y scores; thus,
it ranges from 0 and 1.With linear regression, the
coefficient of determination is also equal to the
square of the correlation between the independent
and dependent variables. An R2
of 0 means the
dependent variables cannot be predicted from the
independent variable. An R2
of 1 means the
dependent variable can be predicted without an error
from independent variable. An R2
between 0 and 1
indicates the extent to which the dependent variable
is predictable.
In this case the R= 0.842. This means there was a
strong positive relationship between the variables.
This value of R square indicates that the independent
variables can explain 70.8% of the variation in the
dependent variable. This implies that there is a
positive relationship between the dependent and the
independent variables and the data that had been
employed in the regression model were accurate.

94 | P a g e
Adjusted R2
is a modified version of R2
that has been
adjusted for the number of predictors in the model
by less than chance. The adjusted R2
of 0.702 which
is slightly lower than the R2
value is an exact
indicator of the relationship between the
independent and the dependent variables because it
is sensitive to the addition of irrelevant variables.
The adjusted R2
indicates that 70.2% of the changes
in small enterprise performance are explained by the
model while 29.8% is not explained by the model.
This implies that level of records management,
project scheduling, risk management and project
monitoring has a positive relationship on project
performance.
Table 4.14 Overall Model Summary
Model R R Square Adjusted R
Square
Std. Error of the
Estimate
Durbin-Watson
1 .842a
0.708 0.702 0.61783 0.383
Table 4.15 presents the results of regression
ANOVA to test the model fitness at 95%
confidence level. The study results indicated that
there was a significant value (p=0.000<0.05) and F-
value of 103.295. This shows that the regression
model has a probability of less than 0.05 of giving
a correct prediction. Hence, the regression model
used above is a suitable prediction model for
explaining the relationship between independent
and dependent variables.
Table 4.15 ANOVA Results
Model Sum of
Squares
df Mean Square F Sig.
1 Regression 157.716 4 39.429 103.295 .000b
Residual 64.891 170 0.382
Total 222.607 174
Table 4.16 presents the results of regression
coefficients. The study findings showed that all the
variables (records management, project scheduling,
risk management and project monitoring) were
extremely significant since they registered a p-value
of 0.000. The results show that the regression
coefficients of the independent variables are
statistically significant in explaining project
performance. Thus the regression equation becomes;
The regression equation is outlined as follows;
Y represents 0.726+ 0.187X2 + …….Equation 4.1
Where:
Y represents project performance, dependent
variable
β0 represent constant
X1 represent project scheduling
Table 4.16 Multiple Regression Coefficients Results.
Unstandardized
Coefficients
Standardized
Coefficients
t Sig.
B Std. Error Beta
(Constant) 0.726 0.178 4.074 0
Project scheduling 0.187 0.072 0.198 2.615 0.01
5.0 Summary, Conclusion and
Recommendations
Summary of Study Findings
The aim of this study was to establish the influence
of building information modelling on Uasin Gishu
county Government, Kenya. The major findings of
this research together with their corresponding
objectives were summarized.
The objective of project scheduling on Construction
Projects Performance Uasin Gishu County
Government was found out to be having a positive
influence on project performance having. The study
shows that project scheduling has a positive
influence on project performance in Uasin Gishu
County. This means that a project cannot work
without a project plan because it is the project plan
which establishes the timelines, delivery and
availability of project resources, whether they be
personnel, inventory or capital. Therefore, proper

95 | P a g e
project scheduling leads to an increased project
performance.
Moreover the study shows that the respondents were
in agreement that BIM project scheduling helps the
contractors attain the goals and objectives of project
completion; Project scheduling function enhance the
resource allocation and simulation; Creation of
project schedules using BIM has proved value
engineering in project performance and that BIM
scheduling helps in task management of resources
and better tracking of project performance. The
study shows that project scheduling has a positive
influence on project performance in Uasin Gishu
County. This means that a project cannot work
without a project plan because it is the project plan
which establishes the timelines, delivery and
availability of project resources, whether they be
personnel, inventory or capital. Therefore, proper
project scheduling leads to an increased project
performance.
Conclusions of the Study
The conclusion of this study was based of the
objective that there was a statistical significant and
positive effect of project scheduling and
Construction Projects Performance Uasin Gishu
County Government. This implies that a project
cannot work without a project plan because it is the
project plan which establishes the timelines,
delivery and availability of project resources,
whether they be personnel, inventory or capital.
Therefore, proper project scheduling leads to an
increased project performance.
Recommendations for Practice and Policy
The study recommends that the policy makers to
come up with policies which enhance automation of
records management functions and implement
records management awareness programs for non-
records management staff. They should provide
policies for training programs on records
management personnel.
Recommendations for Theories
The study recommends the use of Theory of
Constraints because the theories have highlighted
how an organization might be working to reduce the
effect of a constraint or risk. This might lead to
reduce resource wastage on factors that do not
contribute towards project success. It has also shows
that the organization should not focus on short-term
goals as opposed to long term goals, in that it only
evaluates what is happening currently. The theory
also shows the importance of project scheduling on
construction projects coordination.
Suggestions for Further Studies
A research should further be carried on influence of
project scheduling on construction projects
performance Uasin Gishu county government in
order to get a deeper understanding of scheduling of
projects in order to achieve quality project within the
scheduled time and budget. Further researchers
should focus on influence of monitoring on
Construction Projects Performance with control
variable which are policies.
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