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The Influence of Pre-Project Planning on the Budget Absorption
Rate of Public Funded Infrastructure Projects in Kenya a
Comparative Case Study of Narok, Migori, and Kisii County
Government Projects
QS. Geoffry Kimutai Cheruiyot
1
., Sylvester Munguti Masu
2
., Dr. Sarah Gitau
3
1
Ongoing Masters student (TUK), Registered and practicing Quantity Surveyor and Construction
Project Manager, Kenya
2
Professor in Department of Construction & Property Studies, TUK, Nairobi, Kenya
3
Senior Lecturer, Department of Construction and Property Studies (DCPS), TUK, Nairobi, Kenya
DOI: https://doi.org/10.51244/IJRSI.2025.120800129
Received: 08 Aug 2025; Accepted: 16 Aug 2025; Published: 13 September 2025
ABSTRACT
This research investigates the persistent problem of low budget absorption in publicly financed infrastructure
projects within Kenya's devolved governance system. It examines the impact of Pre-Project Planning (PPP) on
this issue, with key objectives of exploring how scope definition, clarity of objectives, stakeholder
involvement, and risk identification lead to better budget absorption. This study is grounded in Construction
Management Theory, Systems Theory, and the Organizational Decision-Making Model, which guided the
analysis of project processes and outcomes.
Adopting a causal-comparative case study design, the research utilized a mixed-methods approach, collecting
data from various project stakeholders through structured questionnaires, interviews, and document analysis.
Quantitative data were subjected to Pearson’s correlation and multiple regression analysis.
The findings establish a significant positive correlation between effective risk identification and budget
absorption, confirming its central role in fiscal success. In contrast, a statistically significant negative
correlation was found between clarity of objectives and budget absorption, an unexpected finding suggesting
that overly rigid plans may hinder financial flexibility. The research concludes that while PPP is central to
improving budget absorption, its success is inextricably linked to addressing concurrent governance challenges
and proactive management of project delays. This study makes a key empirical contribution by providing
actionable insights for policymakers and project managers on how to enhance project delivery within Kenya's
devolved governance environment.
Keywords: Budget Absorption, Public-Funded Infrastructure, Project Success, Kenya, Pre-Project Planning.
INTRODUCTION
In public infrastructure development, pre-project planning (PPP) has emerged as a cornerstone of project
success. It is defined as "the process of developing sufficient strategic information with which owners can
address risk and decide to commit resources to maximize the chance for a successful project" (Wang &
Gibson, 2006). PPP enables government agencies to align project objectives with available resources,
stakeholder expectations, and long-term development goals. Other scholars such as (Koskinen, 2020) and
(Sarde, 2016) highlighted PPP as synonymous with conceptual planning, front-end engineering design
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(FEED), schematic design, and front-end loading, among others. (Terry, Hang, Knut and Edkins, 2019),
emphasize that most project failures stem from poor decisions made during the front-end stage the period
when the project's foundations are laid and strategic value is created. Therefore, this phase is often referred to
as the “make-or-break” point in project management.
Despite the acknowledged importance of PPP, its application in Kenya's devolved governance context
especially in counties remains inconsistent and under-researched. County governments are now responsible for
a significant share of Kenya's infrastructural development, as outlined in Schedule Four of the Constitution of
Kenya (GOK, 2010). Yet, many counties continue to report alarmingly low development budget absorption
rates, which threaten the delivery of critical public services and erode public trust in the devolution framework.
According to the Controller of Budget reported that “in the first half of the FY 2023/2024, Narok County
posted a budget absorption rate of 41%, Migori County 14.3%, and Kisii County a mere 3.9%” (GOK, 2024).
These figures reflect a concerning disconnect between budgetary planning and actual project execution.
Budget absorption is defined as the proportion of actual expenditure compared to the allocated or budgeted
funds Anthony et. al, (2021). (Laiboni, 2021) adds that this metric reflects how efficiently a government or
organization utilizes allocated financial resources within a fiscal year. Ideally, high absorption rates suggest
efficient planning, procurement, and project implementation processes, while low absorption rates indicate
poor planning, bottlenecks in execution, and systemic inefficiencies. (Andriati, 2017), notes that a
government’s performance is often judged by its ability to fully absorb planned budgets, as unspent funds may
signify stalled projects, wasted opportunities, or failed service delivery.
Given the complexity of infrastructure projects, the construction industry is a crucial barometer of planning
and financial efficiency. Studies from across the world confirm the sector's significant contribution to GDP
and its catalytic role in job creation, economic diversification, and poverty reduction (Mobolaji & Wale ,
2012), (Zahir et al, (2011). For instance, in Indonesia, the construction sector contributed 10.6% to the GDP
between 2013 and 2018, the highest globally during that period Musarat et. al, (2020). Nigeria's construction
sector contributed 9.5% in 2021 (Saka, Adegbembo, 2022), while in Kenya, the sector's contribution to GDP
declined sharply from 5.2% in 2021 to just 3.1% in 2023 (Kenya National Bureau of Statistic, 2023), (Statista,
2024).
This declining trend is often linked to poor planning, procurement delays, inflationary pressures, and capacity
gaps challenges that PPP aims to resolve.
Evidence suggests that weak PPP practices contribute to common project failures, such as cost overruns,
delayed timelines, and substandard outputs. Yue and Demisew both document widespread delays in African
construction projects due to poor scoping and scheduling (Yue, 2018), (Demisew, 2020). Similarly, projects
must have clear starting and ending points, budget frameworks, defined scopes, and performance criteria all of
which are shaped during the PPP phase (Joseph, 2012). Wang, Yu-Ren, and Edward, emphasize that poor
scope definition during the front-end planning phase is a critical factor undermining project performance
(Wang & Gibson, 2006). Furthermore, H+M Industrial also contend that schedule risks in capital projects can
largely be mitigated if a proper execution strategy and scope are defined early in the planning process (H+M
Industrial, 2021).
In Kenya, systemic inefficiencies in managing construction risks, attributing much of this to outdated
contractual practices and poor PPP processes (Gichunge, 2000). While the National Construction Authority
made calls to improve construction management practices through research and capacity development (NCA-
Kenya, 2021), much remains to be done in counties where planning frameworks are weak and under-
resourced. This is compounded by evidence from Kipkirui (Kipkirui, 2020), who documented that
development budget absorption rates across counties have fluctuated between 49% and 66% in recent years
well below the 100% ideal. The Controller of Budget further confirmed a downward trend, reporting a national
absorption rate of 50.9% in FY 2021/2022, down from 60.1% the previous year (GOK, 2022).
This study examines how pre-project planning influences the absorption rate of development budgets in
public-funded infrastructure projects across three counties: Narok, Migori, and Kisii. These counties were
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selected based on their varying demographic profiles, geographical coverage, and performance in budget
absorption. According to the Kenya National Bureau of Statistics, Narok has a population of 1,157,873 and
covers 15,263 km², Migori has 1,161,343 residents over 2,586 km², while Kisii hosts 1,266,860 people within
1,318 km² (Kenya National Bureau of Statistics, 2019). These counties present ideal case studies for
understanding how local-level planning mechanisms affect infrastructure delivery and financial performance.
Furthermore, the study is anchored in Kenya’s constitutional vision of devolution, which aims to promote
social and economic development and ensure equitable access to government services (GOK, 2010).
Infrastructure is the backbone of this development vision, and PPP is the vehicle through which well-scoped,
timely, and cost-effective projects are initiated and implemented. With over 60% of devolved functions under
Schedule Four requiring infrastructure inputs, weak planning equates to stalled devolution (Wikipedia, 2024).
Kwakye affirms that local governments are not only implementers but strategic planners of development,
making their approach to PPP a key determinant of service delivery success (Kwakye, 1997).
Pre-project planning (PPP) plays a crucial role in aligning strategic goals with project execution, especially in
the public sector where accountability and resource optimization are critical. The success or failure of many
infrastructure projects in developing countries has been closely linked to the quality of planning conducted
before project execution. Decisions made during the front-end planning stages greatly influence a project’s
success trajectory (Terry, et al. 2019). In Kenya, where infrastructural development is fundamental to realizing
the goals of devolution, the effectiveness of PPP becomes even more vital. This is particularly true in counties
such as Narok, Migori, and Kisii, where poor planning practices often result in incomplete projects, delays,
and unspent development budgets despite the availability of funds.
Kenya's construction industry, while contributing to national GDP and social development, has struggled with
low project completion rates and inefficient fund absorption, undermining the goals of Vision 2030 and the
Big Four Agenda. The construction sector's GDP contribution has consistently declined, from 5.2% in 2021 to
3.1% in 2023 (Kenya National Bureau of Statistic, 2023), a trend partially due to planning-related
inefficiencies. While most research on project delays focuses on implementation-phase issues like contractor
performance and external disruptions (Msafiri, 2015) and (Yue, 2018), this study emphasizes the overlooked
role of pre-project planning. By investigating how planning practices influence budget absorption, particularly
in county governments, this research seeks to offer actionable insights into optimizing project delivery
frameworks, thus promoting more effective public investment outcomes.
In sum, this study investigates the relationship between pre-project planning and budget absorption in public
infrastructure projects. It seeks to fill a critical gap in both academic literature and policy discourse, offering
practical insights for county governments, policymakers, development partners, and project managers. By
focusing on Narok, Migori, and Kisii counties, the study draws comparative lessons and proposes
recommendations that could shape Kenya’s devolved development trajectory for years to come.
Theoretical Framework And Literature Review
This section provides an analytical overview of the theoretical, legal, and conceptual frameworks, as well as
existing literature, relevant to pre-project planning and budget absorption, laying the groundwork for
addressing the core research problem of persistent low budget absorption in public-funded infrastructure
projects in Kenyan counties.
Theoretical Framework
This study was underpinned by three core theories that provide the analytical lens to understand the
relationship between pre-project planning practices and project outcomes. These theories were instrumental in
framing the research questions and guiding the investigation.
i) Construction Management Theory
Construction Management Theory, rooted in the principles of scientific management, provides the
fundamental framework for this study. The Project Management Institute (PMI) argued that a project's success
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is determined by the effective management of its key variables, namely scope, time, cost, and quality (PMI,
2021). The central research question of this study seeked to determine the effect of pre-project planning on
budget absorption. This was directly addressed by this theory, which highlighted how robust planning in early
project phases including the establishment of realistic budgets and schedules is crucial for managing costs and
ensuring efficient expenditure control. This theory guided the investigation into how PPP practices influenced
project's ability to absorb allocated funds effectively and promotes the use of planning tools to achieve
efficiency.
ii) Systems Theory
Developed by Ludwig von Bertalanffy, Systems Theory views an infrastructure project as a complex,
interconnected system. This theory was particularly relevant to the study's objective of examining the role of
stakeholders and institutional actors. It allowed for an analysis of how various internal and external factors,
such as the project owner, consultants, communities, and government institutions, interact to influence project
outcomes. This theoretical perspective was essential for addressing the research question on the role of
stakeholder engagement. It also provided a robust way to analyze how the alignment of a county’s project
systems with national government objectives and institutional actors, such as the Controller of Budget and
Auditor General, is essential for successful budget absorption.
iii) Organizational Decision-Making Model
This model provides a framework for analyzing the critical decisions made during the pre-project planning
phase. It assumes that clear roles, responsibilities, and effective decision-making processes are essential for
successful project outcomes (Ali, 2002). This model was crucial for investigating the study's objective of
determining the effect of project governance on budget absorption. It allowed for the assessment of how
structured governance frameworks and decision-making processes particularly those related to stakeholder
engagement and risk mitigation contribute to improved budget absorption rates. This model directly informed
the investigation into how the lack of a structured approach to project-related decisions can lead to
inefficiencies and poor budget performance.
LITERATURE REVIEW AND LEGAL FRAMEWORK
A significant body of literature consistently acknowledges the importance of planning in public project
performance. (Andriati, 2017) highlighted in his study on Indonesian government institutions that “research
has often focused on financial administration while overlooking the direct influence of pre-project
preparedness”. Similarly, (Ogano & Pretorius, 2013) emphasized the critical role of front-end planning in
reducing project uncertainties within Sub-Saharan Africa's electricity utility sector, though their research did
not directly connect this planning to budget absorption outcomes. This research filled these conceptual gaps by
focusing specifically on how effective pre-project planning enhances budget utilization in Kenyan counties,
thereby addressing the core research problem.
On an international level, the literatures consistently emphasized the importance of clear project definition and
structured planning tools in mitigating project uncertainties. The argument by (Fageha & Aibinu, 2014) and
(Wang & Gibson, 2006) is that comprehensive and clear scope definition is essential for enhancing project
outcomes and preventing issues like scope creep, which can lead to cost overruns and poor budget utilization.
This aligned with the study’s focus on the alignment between a project’s scope, budget estimates, and
timelines, as a misalignment often signals underutilization of development funds. The conceptual tools for
assessing planning readiness have been a focal point of research. (Rahat et al. (2023) demonstrated that
frameworks like the Project Definition Rating Index (PDRI) can reduce project delays and costs in the USA.
However, their study did not evaluate the PDRI's specific impact on budget absorption, which was a core focus
of this research. Similarly, (Sherif & Price, 1999) introduced the Agreement Matrix as a structured approach to
assess planning readiness and enhance project outcomes through stakeholder alignment and a comprehensive
scope definition.
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In the Sub-Saharan African context, the literatures highlights both the challenges and the importance of front-
end planning. A framework of critical success factors for infrastructure projects in South Africa was developed
by Rasebotsa, et al. (2025), which included stakeholder management and risk mitigation as key elements. This
study built on their findings by focusing on how these factors, when implemented during pre-project planning,
influence budget absorption.
In Kenya, while previous studies have acknowledged the importance of planning, they had not treated pre-
project planning as a distinct phase with a measurable impact on project success. For instance, (Desmond &
Mohinder, 2020) and (Langat, 2015) recognized planning and funding as key factors in public school
infrastructure projects but they did not isolate pre-project planning as a distinct phase. The current study
addresses this void by isolating pre-project planning variables such as project definition, stakeholder
alignment, and risk mitigation, and evaluating their direct influence on budget absorption. Furthermore,
Kimathi et al. (2021) examined budgeting practices in Nyeri County and noted that inefficiencies could
hamper budget absorption, but they did not isolate the specific contributions of pre-project planning activities
like project definition. This study extends their findings by analyzing how the clarity of project objectives and
the quality of project definition, as measured by tools like a comprehensive project charter, influence budget
absorption across multiple counties.
The persistent struggle with low budget absorption in counties, despite a robust legal framework like the
Public Finance Management Act (2012), suggests that legal compliance alone is insufficient without the
effective implementation of pre-project planning practices. (Mohamed, 2018) highlighted the implications of
low budget absorption, linking it to inefficient service delivery and rightly pointing out that a failure to utilize
allocated resources results in poor budget performance. This study built on his work by empirically evaluating
specific planning practices and the utilization of tools like County Integrated Development Plans (CIDPs) and
County Fiscal Strategy Papers (CFSPs) to address this concern. In a similar vein, (Kipkirui, 2020) emphasized
that high budget allocation promotes efficiency but he did not delve into the specific project phases where
planning deficiencies are most prevalent. This study extends his work by examining how the utilization of pre-
project planning tools impacts budget absorption.
Conceptual Framework
The conceptual framework for this study is a synthesis of the reviewed literature and is grounded in the
theoretical models discussed above. It asserts that pre-project planning practices serve as the independent
variables influencing the dependent variable which is budget absorption. Building on the premise that effective
planning is critical for project success, this study's conceptual framework empirically evaluates how specific
planning practices influenced a public project's budget absorption rate. The six planning practices identified for
this study are project definition, objective clarity, scope completeness, estimate accuracy, stakeholder
engagement, and risk identification.
The relationship between these variables was guided by core theories, which suggests that effective
management in the initial phases of a project is crucial for its overall success. This study aimed to empirically
test this relationship within the context of public-funded projects in Kenya using a multiple linear regression
model.
CONCLUSION
In summary, the literature reveals a consistent acknowledgment of the importance of planning in public project
performance. However, it also exposes a critical research gap, particularly in directly linking the specific
elements of pre-project planning to budget absorption rates. This study, guided by the Construction
Management Theory, Systems Theory, and the Organizational Decision-Making Model, contributes to filling
this void by systematically analyzing how various elements of pre-project planning influence budget
absorption in public-funded infrastructure projects in Kenya. The conceptual framework for this study
operationalizes this relationship by using a multiple linear regression model
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(Y= β0 + β1X1 + β2X2 + β3X3 + β4X4 + β5X5 + β6X6 + ϵ) to test the causal influence of six key planning
variables on the budget absorption rate.
METHODOLOGY
Population and Sampling
The study targeted all infrastructure development projects in the selected counties scheduled for
implementation during the 2023/2024 financial year, each with an estimated cost of at least KES 10 million. A
total of 63 projects were identified from the Annual Development Plans (ADPs), comprising 17 from Narok,
22 from Migori, and 24 from Kisii. To ensure sample validity and sufficient statistical power, the sample size
was calculated using Cochran’s formula to ensure sufficient statistical power, yielding minimum sample
requirements of 12 projects for Narok, 14 for Migori, and 15 for Kisii.
Equation 1- Cochran’s Formulae
Source; https://www.socscistatistics.com/tests/samplesize/default.aspx, 2025)
i) n = Sample Size (The number of projects selected for the study).
ii) Z = Z-score (the standard normal deviate corresponding to the desired confidence level of 1.96 for 95%
confidence).
iii) p = Estimated Proportion (the anticipated proportion of the characteristic being studied in the population
set to 0.5 for known maximum sample size).
iv) E = 5% was selected as the desired level of precision (Margin of error).
Research Design
This study employed a causal-comparative case study design to examine the influence of pre-project planning
(PPP) practices on the budget absorption rate of publicly funded infrastructure projects in Kenya. The selected
counties Narok, Migori, and Kisii represented distinct levels of budget absorption performance based on
reports from the Office of the Auditor General (GOK, 2024) with Narok performing among the highest,
Migori at the median, and Kisii among the lowest. This design enabled a comparative exploration of PPP
practices and their correlation with financial outcomes, aligning with Creswell and Creswell’s (Creswell &
Creswell, 2012) characterization of causal-comparative research as a non-experimental approach that
investigates existing differences across groups to explore potential cause-effect relationships.
Data Collection and Measurement
Data collection incorporated both primary and secondary sources to provide a comprehensive understanding of
the study variables. Primary data were obtained via structured Google Form questionnaires that included both
closed-ended and open-ended questions. These were administered to planning and implementation teams
associated with each project. Additionally, in-depth interviews were conducted with stakeholders, such as
project users and community leaders, to obtain qualitative insights into the effectiveness of PPP processes and
perceived barriers to efficient budget absorption. Secondary data were sourced from county-level financial and
planning documents, including the County Budget Review and Outlook Papers (CBROPs) for Narok, Migori,
and Kisii (2024), the 2023/2024 ADPs for each county, and the Controller of Budget’s (2024) County
Governments Budget Implementation Review Report.
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Data Analysis
Data were analysed using SPSS. Descriptive statistics were computed to summarize variable distributions,
while multiple linear regression analysis was used to assess the relationship between PPP practices and budget
absorption rates. The study applied the following causal formula:
Y = β0 + β1X1 + β2X2 + β3X3 + β4X4 + β5X5 + β6X6 + ϵ
Where, the Budget Absorption Rate (Y) was the dependent variable, while the six planning practices were the
independent variables (X1 to X6).
The beta coefficients (β1 to β6) were calculated by regressing the six planning practices against the dependent
variable, Budget Absorption Rate (Y), to quantify each practice's unique contribution. This rigorous approach
was used to address the study's research questions. The acceptability of a beta value was determined by its
statistical significance (p-value). A p-value less than the 0.05 significance level was the criterion for a beta
coefficient to be considered acceptable, as it indicated a statistically significant relationship that was unlikely
to be due to chance.
Conversely, if a beta value had a p-value greater than 0.05, the relationship was considered not statistically
significant. Such a finding would imply that the observed relationship was likely a result of random chance
and that the specific planning practice did not have a reliable, measurable influence on the budget absorption
rate. Therefore, even if a beta value appeared large, it would not be deemed acceptable for making conclusions
if its corresponding p-value was too high.
This statistical process provided empirical evidence to address the study's research questions by identifying
which planning practices were most influential in improving budget absorption in Kenyan public projects. The
error term (ϵ) captured any unmeasured factors or random variation affecting the projects, ensuring that the
final conclusions were based on the specific relationships identified within the model.
The study's conceptual framework and operationalization of variables are summarized in the table 1.
Table 1- Operationalization of Variables
Variable
How It Was Measured (Indicators)
Variable Code
Project Definition
Feasibility study conducted? How well were objectives
rated?
X1
Clarity of Objectives
Did a project charter and business case exist?
X2
Project Scope
Cost and schedule accuracy, and clarity of objective ratings.
X3
Estimates & Schedules
Cost and scheduling methods used, schedule delays and
update frequency.
X4
Stakeholder
Engagement
Methods and effectiveness of stakeholder involvement.
X5
Risk Identification
A rating of how well risks were considered.
X6
Budget Absorption Rate
The percentage of the budget spent, from project records.
Y
Source; Author’s own construct 2025
A secondary model introduced dummy variables for county location (Narok and Migori), using Kisii as the
reference category, to control for regional effects.
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Qualitative data from interviews and document reviews were subjected to thematic content analysis. This
analysis assessed the quality and completeness of PPP documentation, such as project charters, business cases,
feasibility studies, risk registers, and stakeholder engagement plans, and contextualized them within the
broader administrative and institutional frameworks of each county.
To ensure data quality, a comprehensive validation process was conducted. This involved cleaning the dataset,
converting qualitative responses into numeric codes, and addressing missing values through listwise deletion.
List wise deletion is a method for handling missing data where any case (row) with at least one missing value
is completely removed from the dataset before analysis (Hair, Black, Babin, & Anderson, 2019).
Cronbach's Alpha was used to assess the internal consistency of multi-item scales, with expectation that all
major constructs meet the acceptable reliability threshold of 0.70 to 1.0.
Equation 2- Cronbach's Alpha formula.
Source: https://www.bachelorprint.com/au/
Where “n” refers to the number of scale items, “s² (Xi)” refers to the variance associated with item, and “s²
(Y)” refers to the variance associated with the observed total scores. The interpretation of these variables for
each of the study's constructs is provided in Table 2.
Table 2- Details of Multi-item Variable
Variable
How It Was Measured
(Indicators)
Variable
Codes(n)
Individual Item
Variance (s2(Xi))
Total Score
Variance (s2(Y))
Project
Definition
How well were objectives
rated?
X1_3, X1_4,
X1_5, X1_6,
X1_7, X1_8,
X1_9, X1_10
Variance of each
of the 8 indicators
Variance of the
total scores for the
variable
Clarity of
Objectives
Did a project charter and
business case exist?
X2_1, X2_2,
X2_3, X2_4,
X2_5, X2_6
Variance of each
of the 6 indicators
Variance of the
total scores for the
variable
Project Scope
Existence of Cost and
schedule at PPP phase,
Resource Allocation,
Communication Plan,
Quality Management Plan,
feasibility study conducted
and how feasibility study
informs the project
definition inform project
definition.
X3_1, X3_2 ,
X3_3, X3_4,
X3_5, X3_6 ,
X3_8.
Variance of each
of the 7 indicators
Variance of the
total scores for the
variable
Estimates and
Schedules
Accuracy of Cost estimate
and scheduling methods
used, schedule delays and
X4_1, X4_2,
X4_3, X4_4,
X4_5, X4_6,
Variance of each
of the 7 indicators
Variance of the
total scores for the
variable
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update frequency.
X4_7
Stakeholder
Engagement
Methods and effectiveness
of stakeholder involvement.
X5_1, X5_2,
X5_3
Variance of each
of the 3 indicators
Variance of the
total scores for the
variable
Source; Author’s own construct 2025
To account for projects with more than one respondent, the study employed data aggregation. For each project,
responses from multiple individuals were averaged to create a single, representative score for each variable,
thus maintaining the project as the primary unit of analysis for quantitative regression. This approach ensured
that the project-level analysis was based on a consolidated view rather than on individual perspectives,
providing a more robust and reliable dataset.
RESULTS
This section presents the analysis and interpretation of data collected from Narok, Migori, and Kisii counties
on the relationship between pre-project planning (PPP) practices and budget absorption in publicly funded
infrastructure projects. The findings are organized thematically based on the research objectives and statistical
tests, including descriptive statistics, correlation analysis, and ANOVA, as appropriate.
Procedure and Data Preparation
The data for this analysis was collected from respondents using questionnaires, interviews, and document
verification, covering project-specific information, budget absorption rates, and various planning indicators.
Data preparation involved:
i) Relevant numerical and categorical data points were manually extracted from the questionnaire forms.
ii) Standardization was performed to ensure consistent naming conventions and data types across different
datasets.
iii) Qualitative responses such as "Yes/No" or specific methods were converted into binary numerical values 1
indicating presence/affirmative and 0 indicating absence/negative while "Not sure" or blank responses
were treated as missing data (NaN).
iv) Rows with missing values (NaNs) in the variables being analyzed were excluded from each specific
correlation calculation to ensure valid paired observations.
v) A zero-variance check was conducted prior to correlation analysis to confirm that both variables had
variation; variables with no variance were excluded and documented accordingly.
vi) These preprocessing steps ensured that only clean, standardized, and statistically valid data were used in
the correlation analysis.
Validation of Data Collected
The actual sample sizes collected in form of projects (unit of analysis), determined by counting project IDs
from the aggregated data, were analyzed for sufficiency against the calculated required sample sizes. The
actual sample sizes were: Narok = 13 projects, Migori = 16 projects, and Kisii = 14 projects.
Table 4.2 summarizes the sample size analysis, which confirmed that the data collected from all the counties
were sufficient to achieve the desired precision and confidence for estimating population means.
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Table 3- Sample Size Analysis for sufficiency
County
Population
(N)
Required (Means,
σ=0.15)
Actual Sample
received (n)
Sufficiency
% for Unit of
Analysis Reached.
Narok
17
12
13
Yes (13 ≥ 12)
76.47%
Migori
22
14
16
Yes (16 ≥ 14)
72.73%
Kisii
24
15
15
Yes (15 = 15)
62.50%
Source: Author’s Field Survey
Data Reliability Analysis
The internal consistency of the multi-item scales was assessed using Cronbach's alpha. The Pilot Study
instrument's reliability was also confirmed using a test-retest method, demonstrating its stability over time.
The Cronbach's alpha analysis revealed that all the items exhibited acceptable to excellent levels of internal
consistency as shown in table 4.
Table 4- Cronbach's Alpha results
Items
Cronbach's Alpha results
Remarks
Pilot study (All Items)
0.89
Good
X1-Project Definition
0.75
Acceptable
X2-Clarity of Objectives
0.97
Excellent
X3-Project Scope
0.79
Acceptable
X4-Estimates and Schedules
0.71
Acceptable
X5- Stakeholder Engagement
N/A
Not Applicable
Source: Author’s Field Survey
The X5-Stakeholder Engagement measure, consisting of a single qualitative item, was excluded from this
analysis as it was not amenable to Cronbach's alpha testing.
These findings collectively indicated that the survey instrument was largely reliable and consistent in its
measurement of the key constructs
Data analysis
Response Rates and Sample Validity
The data collection for this study involved two key response rates: participant and project level. A total of 108
out of 150 questionnaires were completed, yielding a participant response rate of 72%. This rate is considered
"excellent" and surpasses the 50% threshold for data analysis (Mugenda and Mugenda, 2003). Concurrently,
data was successfully collected for 44 of the 63 targeted projects, resulting in a project-level response rate of
69.84%.
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Both robust response rates ensure the analysed data is highly representative of the target project population.
This high level of participation lends considerable credibility to the empirical evidence, significantly
enhancing the external validity and generalizability of the study's findings while mitigating potential non-
response bias.
Demographic Profile of Respondents Analysis
The demographic analysis of participants is represented in table 4.
The participants' varied expertise and prior experience in budgeting significantly bolster the data's credibility.
The high representation of professionals from finance-centric roles, like Quantity Surveyors (81.25% with
experience) and Procurement Officers and Internal Auditors (100% experience), ensures that the findings on
project budgeting and financial oversight are grounded in real-world expertise.
The contributions from technical professionals like engineers and project managers provide a crucial
perspective. Their insights into how financial planning impacts project implementation, combined with the
specialized knowledge of budgeting experts, validate the survey's conclusions from multiple angles,
strengthening data reliability.
Table 5- Demographic Analysis of Participants.
Professional Role
Percentage of Total
Respondents
(%) of "No" Budgeting
Experience
(%) "Yes" Experience in
Budgeting
Quantity Surveyor
18.60%
18.75%
81.25%
Electrical Engineer
15.12%
92.31%
7.69%
Project Manager
9.30%
50.00%
50.00%
Engineer
9.30%
50.00%
50.00%
Community
Representative
6.98%
83.33%
16.67%
Contractor
6.98%
50.00%
50.00%
Project target user
4.65%
100.00%
0.00%
Accounting Officer
4.65%
25.00%
75.00%
Project Architect
4.65%
50.00%
50.00%
Structural Engineer
3.49%
66.67%
33.33%
Planning Officer
3.49%
0.00%
100.00%
Project Coordinator
3.49%
33.33%
66.67%
Internal Auditor
2.33%
0.00%
100.00%
Clerk of Works
2.33%
50.00%
50.00%
Procurement Officer
2.33%
0.00%
100.00%
Mechanical Engineer
2.33%
100.00%
0.00%
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Source: Author’s Field Survey
Respondent Experience and County Budget Absorption Performance
An analysis of the respondents' prior experience in project planning and budgeting was crucial in establishing
the link between human capital and historical county budget absorption rates. Table 6 presents a summary
finding of how respondents' experience correlation with average absorption rates for FY 2023/2024.
Table 6- Aggregated Respondent Experience and Historical Budget Absorption by County
County
Projects
Surveyed
Projects with Respondents
with Prior Experience
% projects with
Experience
Avg. Budget Absorption
(2023/2024)
Kisii
15
8
53.33%
3.9%
Migori
50
15
30.00%
14.3%
Narok
11
11
100.00%
41.0%
Source: Author’s Field Survey
The data reveals a strong correlation between respondent experience and budget absorption. Narok County,
with the highest percentage of experienced respondents (100%) achieved the highest absorption rate. Migori
County, with respondent experience of 30%, reported a mid-range budget absorption rate. Although Kisii
showed a slightly higher experience rate than Migori (53.33%), its absorption rate remained the lowest,
suggesting that experience alone is not sufficient contextual and structural factors may override.
Figure 1- Respondent Experience and Historical Budget Absorption by County
Source: Author’s Field Survey
Analysis of PPP Conceptual Factors and Budget Absorption
The relationship between project variables and budget absorption was investigated through a multi-method
analysis.
A Pearson correlation analysis was first conducted to examine the linear relationships between specific
planning elements and financial performance. This revealed a strong positive correlation between Risk
Identification and budget absorption, while Stakeholder Engagement and Clarity of Objectives showed weak
0.00%
10.00%
20.00%
30.00%
40.00%
50.00%
0
10
20
30
40
50
60
Kisii Migori Narok
Projects Surveyed
Respondents with Prior Experience
% with Experience
Avg. Budget Absorption (2023/2024)
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negative correlations. Following this, a causal-comparative analysis (ANOVA) was performed to investigate
whether the project's county had a statistically significant impact on budget absorption rates.
The combined findings from these analyses suggest that both individual planning practices and a project's
location have a significant influence on budget outcomes.
Finally, a multiple linear regression model was created to assess the collective influence and individual
predictive power of these planning variables on budget absorption. This model confirmed that these variables
collectively explain a substantial portion of the variance in budget absorption.
a) Correlation and Causal-Comparative Analysis
Using Pearson correlation, the analysis of project data across Narok, Migori, and Kisii counties revealed that
specific planning elements and a project's location significantly influence budget outcomes.
Pearson Correlation Matrix
As shown in Table 7, the analysis revealed a strong positive relationship between Risk Identification (X_6)
and Budget Absorption (Y−Actual) (r = 0.50), suggesting that more effective risk identification practices are
strongly associated with higher budget absorption rates. In contrast, a weak negative relationship was found
between Stakeholder Engagement (X_5) and Y-Actual (r = -0.30), and between Clarity of Objectives (X_2)
and Y-Actual (r = -0.28).
Table 7- Pearson Correlation Matrix
Y-Actual
X1
X2
X3
X4
X5
X6
Y-Actual
1.00
0.15
-0.28
0.15
0.06
-0.30
0.50
X1
0.15
1.00
0.06
0.03
-0.00
0.05
0.07
X2
-0.28
0.06
1.00
0.51
0.38
0.24
-0.13
X3
0.15
0.03
0.51
1.00
0.55
0.19
0.11
X4
0.06
-0.00
0.38
0.55
1.00
0.34
-0.02
X5
-0.30
0.05
0.24
0.19
0.34
1.00
-0.15
X6
0.50
0.07
-0.13
0.11
-0.02
-0.15
1.00
Source: Author’s Field Survey
The findings, for instance, showed a strong positive relationship between Risk Identification (X6) and budget
absorption, while a negative relationship was observed with Clarity of Objectives (X2)
b)Causal-Comparative Analysis (ANOVA)
A one-way ANOVA was performed to examine if there were statistically significant differences in mean
budget absorption across the three counties. The analysis showed a statistically significant difference (p-value
= 0.000956) in mean budget absorption, confirming that a project's county has a meaningful impact on its
financial outcome. The descriptive statistics in Table 8 show the average budget absorption for each group,
while the ANOVA table in Table 9 provides the full statistical results.
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Table 8- Descriptive Statistics and Frequency of Observations by County
Project Group (County)
Count
Mean Budget Absorption
Standard Deviation
A (Narok)
13
0.79
0.14
B (Migori)
16
0.41
0.20
C (Kisii)
15
0.59
0.34
Source: Author’s Field Survey
Table 9- ANOVA Table
Sum of Squares
Degrees of Freedom
F-statistic
P-value
County
0.996582
2
8.27741
0.000956
Residual
2.46815
41
nan
nan
Source: Author’s Field Survey
c) Predictive Modeling (Multiple Linear Regression)
A multiple regression model was fitted to predict budget absorption based on the six independent variables.
The model was found to be statistically significant (F-statistic = 4.925, p-value = 0.000860). The model's R-
squared value of 0.444 indicates that these six variables collectively explain 44.4% of the variation in budget
absorption. The full regression summary is shown in Table 10 below.
Table 10- OLS Regression Results
Dep. Variable:
Q("Y-Actual”) R-squared:
0.444
Model:
OLS Adj. R-squared:
0.354
Method:
Least Squares F-statistic:
4.925
Date: Wed, 20 Aug 2025
Prob (F-statistic):
0.000860
Time: 11:40:51
Log-Likelihood:
6.3956
No. Observations: 43
AIC:
1.209
Df Residuals: 37
BIC:
13.70
Df Model: 6
Covariance Type:
Non-robust
Source: Author’s Field Survey
Table 11-OLS Regression Results-B
coef
std err
t
P>|t|
[0.025
0.975]
Intercept
0.0266
0.317
0.084
0.934
-0.617
0.67
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Q("X1")
0.0963
0.081
1.189
0.242
-0.068
0.26
Q("X2")
-0.3681
0.149
-2.468
0.018
-0.67
-0.066
Q("X3")
0.2221
0.14
1.582
0.122
-0.062
0.507
Q("X4")
0.1265
0.126
1.006
0.321
-0.128
0.381
Q("X5")
-0.089
0.044
-2.016
0.051
-0.178
0
Q("X6")
0.1493
0.05
2.976
0.005
0.048
0.251
Source: Author’s Field Survey
Omnibus:
1.917 Durbin-Watson:
2.088
Prob(Omnibus):
0.384 Jarque-Bera (JB):
1.831
Skew:
-0.456 Prob(JB):
0.400
Kurtosis:
2.590 Cond. No.
52.5
Notes:
[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.
d) Analysis of How Involvement of Stakeholders Influence Budget Absorption.
An analysis was conducted using both closed and open-ended questionnaires to assess how stakeholder
involvement during the PPP stage contributed to budget absorption. The findings are summarized in Table 13.
Table 12- Distribution of Budget Absorption by Stakeholder Group and County
Stakeholder Group
County
<50% Absorption
50–79% Absorption
≥80% Absorption
Community
Narok
0
0
3
Kisii
2
1
3
Contractor/Consultant
Narok
0
2
2
County Officials
Narok
0
0
4
Migori
13
36
1
Kisii
4
0
1
Technical Experts
Kisii
3
1
1
Source: Author’s Field Survey
Table 13 and Figure 2 shows how mean budget absorption rates was influence by the involvement of various
stakeholder group at PPP phase.
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Figure 2- Distribution of Budget Absorption by Stakeholder Group and County.
Source: Author’s Field Survey
Table 13-Mean Budget Absorption by Stakeholder Type (Overall)
Stakeholder Group
Mean Budget Absorption (%)
Contractor/Consultant
80.00
Community (Public Input)
76.11
County Officials
56.55
Technical Experts
39.00
Source: Author’s Field Survey
Figure 2: Mean Budget Absorption by Stakeholder Type
Source: Author’s Field Survey
The key finding on stakeholder involvement is that projects with robust engagement from private consultants,
contractors, and the community at the pre-project planning (PPP) stage demonstrated significantly higher
success in budget absorption. This finding is deeply aligned with the legal and theoretical frameworks
governing public finance and project management in Kenya.
This empirical evidence resonates directly with the principles of public participation enshrined in the
Constitution of Kenya, 2010, and is operationalized by the County Governments Act, 2012. Specifically,
0 1 2 3 4 5 6
Community
Contractor/Consultant
County Officials
Technical Experts
(blank)
Sum of ≥80% Absorption by Stakeholder Group
32%
30%
22%
16%
Mean Budget Absorption (%)
Contractor/Consultan
t
Community (Public
Input)
County Officials
Technical Experts
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Section 100 of the Act mandates public participation in county governance, and these findings provide a
quantitative basis for the fiscal benefits of such engagement. The success observed in projects with strong
community and private sector input underscores that participatory development is not merely a legal
requirement but a critical mechanism for achieving fiscal efficiency and project success. This also reinforces
the concept of "front-end loading", where investments in early-stage project planning, particularly those that
ensure inclusivity and draw on specialized expertise, have the greatest influence on downstream project
outcomes, including improved budget utilization.
Table 14 summarizes the correspondence between findings and research objectives.
Table 14- Correspondence of Findings to Research Objectives
Key Finding
Linked Objective(s)
Project Objectives Rating positively influences absorption
Objective 2, 3
Stakeholder Engagement is a key predictor
Objective 2, 3
Structured planning tools (PDRI) enhance absorption
Objective 2, 3, 4
Weak absorption despite formal processes in some counties
Objective 1, 3
Variations by stakeholder type and county
Objective 2, 4
Source: Author’s Field Survey
This results section highlights strong empirical evidence of the role that pre-project planning plays in
enhancing budget absorption. Counties with stronger planning practices, higher stakeholder engagement, and
greater respondent experience (notably Narok) consistently reported superior financial performance.
Conversely, Kisii and Migori counties revealed critical gaps in implementation and stakeholder diversity,
explaining their lower absorption rates. These results lay the foundation for practical policy recommendations
in the discussion section.
DISCUSSION
a) Key Findings and Insights on Pre-Project Planning and Budget Absorption
This study aimed to explore how pre-project planning (PPP) practices influence budget absorption in public-
funded infrastructure projects within Kenya’s devolved governance system, focusing on Kisii, Migori, and
Narok counties. The analysis, grounded in empirical data and statistical correlations, reveals significant
patterns linking specific planning practices to the financial performance of development projects. These
insights build upon previous studies and national audit findings, expanding the understanding of how
structured project planning influences fiscal outcomes at the county level.
A key insight emerging from the findings is the critical role that formalized project planning processes,
particularly those focused on risk, play in enhancing budget absorption. The correlation analysis showed a
strong positive relationship between Risk Identification (X6) and Budget Absorption (Y-Actual) with a
correlation coefficient of 0.50. This was further confirmed in the multiple regression model, where it emerged
as the strongest positive and statistically significant predictor (coefficient = 0.1493, p-value = 0.005). This
suggests that systematic identification and management of project risks are foundational mechanisms that
mitigate uncertainties and contribute to more efficient and effective budget spending.
Interestingly, the study found a counter-intuitive and statistically significant negative relationship between
Clarity of Objectives (X2) and budget absorption, with a correlation coefficient of -0.28. This was reinforced
in the multiple regression model, where it emerged as a significant negative predictor (coefficient = -0.3681, p-
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value = 0.018). This suggests that, within this specific context, an increase in the clarity of project objectives
may be associated with a decrease in budget absorption. This unexpected finding warrants further qualitative
research to determine if overly rigid objectives constrain project execution, thereby hindering flexibility in
financial management.
The study also revealed that other planning elements, such as Project Definition (X1), Project Scope (X3), and
Cost and Scheduling (X4), had weak to negligible correlations and were not statistically significant predictors
in the regression model. This indicates that while these elements are valuable, they do not independently drive
financial performance to the same degree as the overall structure and formalization of planning efforts,
particularly risk management.
b) The Influence of County-Level Context
A significant dimension of the analysis involves the comparison of budget absorption patterns across the three
counties. The ANOVA results showed a statistically significant difference in mean budget absorption,
confirming that a project's county has a meaningful impact on its financial outcome.
Narok County exhibited the highest average budget absorption at 78.6%, with a relatively low standard
deviation, suggesting consistent and effective financial management.
Migori County had a mid-range average of 59%, with the highest standard deviation of the three groups,
indicating a wide range of budget performance. This suggests that while some projects perform well, others
face significant systemic execution barriers.
Kisii County as the lowest performer, demonstrated the weakest average budget absorption at 41.4%, and
showed weak correlations across most planning variables.
These findings underscore that while formal planning is a key factor, it must be accompanied by effective
execution systems. Variations in governance capacity and institutional efficiency across counties shape the
ultimate impact of those planning efforts.
Furthermore, the analysis of stakeholder involvement provides a critical layer of understanding. While the
overall effect of Stakeholder Engagement (X5) was on the cusp of significance, your data shows that the type
of stakeholder is critical. Projects that involved external stakeholders—particularly contractors, consultants,
and community members—reported significantly higher absorption rates compared to those driven solely by
county officials or technical experts. This indicates that inclusive and participatory planning not only fosters
more realistic plans but also enhances community ownership and reduces resistance, directly contributing to
fiscal success.
In sum, the study reinforces the notion that pre-project planning plays a central role in determining project
success within devolved governments. However, it also highlights that planning must be accompanied by
effective execution systems and that variations in governance capacity, stakeholder involvement, and
institutional efficiency across counties shape the ultimate impact of those planning efforts.
CONCLUSION
This study has provided a comprehensive investigation into the influence of Pre-Project Planning (PPP)
practices on budget absorption rates in public-funded infrastructure projects across three Kenyan counties
Narok, Migori, and Kisii. Drawing from both quantitative and qualitative analyses, the findings affirm that
structured and formalized planning practices significantly shape the financial performance of development
projects.
Key pre-project planning components including the use of project charters, risk assessments, budget estimates,
and formal scheduling demonstrated consistent moderate to strong positive correlations with budget absorption
rates. This relationship was especially pronounced in Narok County, where a robust planning framework
aligned with the highest budget absorption performance. Conversely, Kisii County, despite exhibiting the
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potential for positive outcomes through planning, suffered from the adverse effects of severe project delays
and systemic inefficiencies, resulting in extremely low absorption. Migori’s case, marked by weak and
inconsistent correlations, highlighted the influence of broader governance and operational factors not directly
captured by planning variables.
A pivotal conclusion from this research is that the presence of structured PPP documentation and processes is
more impactful than the sophistication of individual components or techniques used within them.
Formalization of planning practices alone appears to significantly contribute to improved budget utilization.
Furthermore, inclusive stakeholder engagement particularly involving contractors and communities emerged
as a powerful enabler of budget absorption, reinforcing the need for participatory planning approaches.
Ultimately, this study concludes that effective pre-project planning is not merely a bureaucratic requirement
but a strategic tool that can transform development outcomes. However, the success of such planning is
contingent on its integration with responsive governance, streamlined execution mechanisms, and consistent
stakeholder collaboration. By institutionalizing robust PPP processes, devolved governments in Kenya can
bridge the gap between allocated funds and actualized development, thereby advancing equity and service
delivery across counties.
RECOMMENDATIONS FOR FURTHER RESEARCH
a) Investigate Non-Linear Relationships and Moderating Factors:
Future research should explore non-linear relationships between PPP practices and budget absorption,
potentially employing advanced statistical techniques (e.g., regression analysis, structural equation modelling)
to uncover more complex interactions.
The weak linear correlations observed in Migori County suggest that other, potentially unmeasured, or non-
linearly interacting factors are at play. This could involve examining how contextual factors such as political
stability, administrative capacity, leadership effectiveness, and levels of corruption (as hinted at in) moderate
the relationship between PPP and budget absorption. Understanding these moderating effects would provide a
more nuanced and complete picture of budget absorption dynamics in diverse county contexts.
b) Qualitative Exploration of "Why" Behind Correlations
Conduct qualitative follow-up studies (e.g., in-depth interviews, focus groups with county officials and project
managers) to understand the underlying reasons why certain correlations are strong or weak, particularly the
stark differences between counties. While quantitative correlations show what relationships exist, qualitative
research can illuminate the how and why. For instance, understanding why Migori's correlations are weak
could involve exploring specific bureaucratic hurdles, political interference, or unique local challenges.
Conversely, understanding the mechanisms behind Kisii and Narok's strong correlations (both positive and
negative) could reveal best practices or critical vulnerabilities in their operational environments. This would
move beyond statistical association to uncover the practical realities and human factors that drive or impede
budget absorption, providing richer information for policy formulation. For example, why is "Confidence in
Accuracy of Budget Allocations" so strongly correlated in Kisii and Narok? Is it a reflection of robust internal
controls, or a consequence of a highly centralized decision-making process.
c) Longitudinal Studies on PPP Implementation and Budget Absorption
Implement longitudinal studies to track the evolution of PPP practices and their impact on budget absorption
over multiple fiscal years. This cross-sectional study provides a snapshot. A longitudinal approach would
allow for the observation of trends, the impact of policy changes, and the long-term effects of sustained PPP
improvements.
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