INTERNATIONAL JOURNAL OF LATEST TECHNOLOGY IN ENGINEERING,
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ISSN 2278-2540 | DOI: 10.51583/IJLTEMAS | Volume XV, Issue I, January 2026
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The Role of Project Management Practices in Achieving Quality
Delivery in Public Sector Projects
Afenfia Wenibuoebi Richard
1
, AlexanderN.Okpala
2
1
Director Price Intelligence Due Process Bureau, Bayelsa State, Nigeria.
2
Department of Mechanical Engineering, Faculty of Engineering, Niger Delta University, Wilberforce
Island, Amassoma, Bayelsa State, Nigeria.
DOI:
https://doi.org/10.51583/IJLTEMAS.2026.150100079
Received: 10 January 2026; Accepted: 15 January 2026; Published: 10 February 2026
ABSTRACT
The impact of project management practices in achieving quality delivery in public sector projects is the target
focus of research scholars. This trend of study is subjected to an investigation of the role of project management
practices in achieving quality delivery in public sector projects, particularly within Bayelsa State, Nigeria. Thus,
the presentation of this paper is one of such using SPSS software for the data analysis. This study involves the
collection of empirical data, primarily through a structured Likert-scale questionnaire. The methodological
approach adopted was a descriptive survey design, within a quantitative research framework. To examine the
influence of the independent variables on the dependent variable, Multiple Regression Analysis (MRA) was
employed. The findings from the data analysis reveal a substantial 75% correlation between the independent and
dependent variables, accompanied by a coefficient of determination (R-squared) of 0.563, or 56.3%. In addition,
the analysis yielded a highly statistically significant p-value (p < 0.001). These results collectively depicts that
the predictive model, involving six independent variables such as project planning, resource management, risk
management, monitoring/evaluation, stakeholder engagement, and the utilization of project management
tools/methodologies, is highly significant in predicting the role of project management practices in achieving
quality delivery in public sector projects.
Keywords: Quality Delivery, Project Management, Public Sector, Project Planning, SPSS
INTRODUCTION
Project success or effective implementation is typically evaluated based on three primary factors: time, cost, and
quality of delivery, collectively known as the triple constraint. These elements function as Key Performance
Indicators (KPIs). To ascertain whether a project has been successfully executed, one must refer back to its initial
objectives concerning time, cost, and quality, and measure the degree to which each was achieved (Ocharo and
Kimutai, 2018). Ocharo and Kimutai (2018) further noted that public sector project execution often lacks clear
guiding policies to streamline processes and encourage potential suppliers. For instance, current regulations tend
to discourage supplier development and collaborative efforts, largely due to the short-term orientation of many
public entities. Additionally, the protracted payment procedures for supplied goods and completed services
significantly elevate procurement risks. Ensuring the satisfactory provision of public goods and services to
citizens consistently presents a challenge. It is evident that many governments do not operate optimally in
fulfilling their service delivery mandates, owing to various underlying reasons. Mc Lennan (2009) describes a
state-controlled system for delivering goods and services where political dynamics fundamentally shape the
power relationship between the government, its populace, and the economy. This viewpoint supports Mbecke’s
(2014) assertion regarding the inherent limitations of public services in delivering goods and services, often due
to insufficient expertise and political biases. Mbecke (2014) advocates for the adoption or adaptation of
successful management practices from the private sector into public administration. He argues that traditional
public service management methods proved inadequate and that incorporating business management models
INTERNATIONAL JOURNAL OF LATEST TECHNOLOGY IN ENGINEERING,
MANAGEMENT & APPLIED SCIENCE (IJLTEMAS)
ISSN 2278-2540 | DOI: 10.51583/IJLTEMAS | Volume XV, Issue I, January 2026
www.ijltemas.in Page 909
could offer a viable solution. He emphasized the necessity for public services to operate like private enterprises,
thereby highlighting the potential efficacy of this management transfer approach (Mbecke, 2014). To enhance
the quality of service delivery, public sector projects require effective management. Consequently, this paper
intends to investigate how proficient public sector project management can contribute to improved quality
outcomes in selected government agencies within Bayelsa State and Delta State, Nigeria. To achieve enhanced
quality delivery through project management in public sector initiatives, this study considers six crucial
performance indicators: project planning, resource management, risk management, monitoring and evaluation,
stakeholder engagement, and the utilization of project management tools and methodologies.
MATERIALS
The empirical phase of this investigation centered on data acquisition, for which a structured survey instrument
served as the principal method. This tool was selected due to its inherent versatility, which not only facilitates
respondent comprehension but also enables the corroboration of provided information. The questionnaire's
formulation was meticulously aligned with the research's overarching objectives, specific inquiries, and guiding
propositions, thereby ensuring the elicitation of germane and precise data from participants. To enhance both
lucidity and consistency in responses, the instrument incorporated closed-format questions, specifically designed
to obtain succinct and unambiguous answers. A four-point Likert-type scale was employed for the quantification
of responses, spanning from a high of 4 (Strongly Agree) to a low of 1 (Strongly Disagree). Respondents were
explicitly directed to denote their degree of concurrence with each statement. Regarding the operationalization
of study constructs, the independent variable, 'project management within public sector initiatives,' was
evaluated by examining its constituent elements, as previously delineated.
RESEARCH METHODS
This investigation employed a descriptive survey research design, situated within a quantitative methodological
framework. This approach was deemed appropriate for exploring the amelioration of quality outcomes, as it
facilitates the systematic acquisition of uniform data through structured survey instruments. Such a methodology
enables the procurement of quantifiable data from a substantial sample, thereby supporting the generalizability
of findings to the broader target population. To ensure adequate representation of project management practices
across the designated area, a purposive sampling technique was utilized for the selection of study sites. This non-
probability method was considered apposite given the research objectives, which necessitated the inclusion of
governmental agencies and departments within Bayelsa and Delta States of Nigeria particularly pertinent to
public sector project management. The selection criteria encompassed balanced geographical distribution,
institutional diversity and specialization, documented contributions to relevant academic and practical discourse,
operational accessibility, availability of robust data infrastructure, and pre-existing organizational affiliations
that could expedite data collection. These parameters guaranteed that the chosen entities not only covered the
geographical expanse but also exhibited operational characteristics directly relevant to the inquiry. Consequently,
eighteen governmental bodies and departments were selected from Bayelsa State, alongside one from Delta State
of Nigeria, yielding a combined total of nineteen institutions specializing in technical and engineering-related
domains. These organizations constitute the primary geographical and organizational nexus of the study,
strategically chosen to enhance the salience, profundity, and applicability of the research outcomes. The
integration of a descriptive survey design with purposive site selection thus established a comprehensive
methodological foundation for investigating factors contributing to improved quality delivery within public
sector project management initiatives.
Study Population
The target population for this study comprised all personnel employed within nineteen carefully selected public
sector organizations and departments located across Bayelsa and Delta States of Nigeria. These entities were
deliberately chosen to ensure both geographical representation and institutional relevance to the study's
objectives. The total population amounted to 284 individuals. This figure represents the complete staff
complement within each designated institution and formed the basis for determining the study's sample size. The
INTERNATIONAL JOURNAL OF LATEST TECHNOLOGY IN ENGINEERING,
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ISSN 2278-2540 | DOI: 10.51583/IJLTEMAS | Volume XV, Issue I, January 2026
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comprehensive inclusion of staff from all identified agencies and departments ensures that the investigation
captures a diverse and representative spectrum of organizations engaged in project management activities.
Sample Size Determination
The optimal sample size for the current investigation was computed using Taro Yamane’s equation, a widely
recognized formula (Adam, 2020) acknowledged for its appropriateness in determining sample numbers from a
finite population.
n =
2
)(1 eN
N
1
Where:
n = Sample size
N = Population size (284)
e = Level of precision or allowable error (0.05)
1 = Constant
Given that;
N = 240, e = 0.05
Substituting the corresponding values into (1) gives:
Assuming a 5% or 0.05 level of significance, the sample size can be calculated thus:
166
Hence, the sample size is 166 respondents.
The percentage of the structured questionnaires sampled and received was determined using the equation
presented below:
2
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The percentage retrieval depict that the number of structured questionnaires are reasonable enough to carry on
with the analysis.
Reliability of the Instrument
The internal reliability of the measurement instrument was ascertained through the application of Cronbach's
Alpha coefficient, utilizing the Statistical Package for the Social Sciences (SPSS). This particular statistic was
purposefully chosen due to its recognized efficacy in precisely evaluating the internal homogeneity among the
constituent elements of the survey. A pre-specified threshold of 0.70 was adopted to delineate an acceptable level
for the reliability coefficient. Consequently, components exhibiting a Cronbach's Alpha score below 0.70 were
deemed to possess insufficient internal consistency, whereas those reaching or exceeding this benchmark were
considered to display a satisfactory level of reliability. The findings pertaining to the instrument's internal
consistency are enumerated in Table 1.
Table 1: Cronbach’s Alpha Reliability Test Results
Variable
Number of Items
Cronbach’s Alpha (α)
Project Planning
3
0.815
Resource Management
3
0.798
Risk Management
2
0.811
Monitoring and Evaluation
3
0.791
Stakeholder Engagement
3
0.824
Use of Project Management Tools/Materials
2
0.810
Quality Delivery in Public Sector Projects
4
0.776
Source: SPSS output, 2025 the research instrument is reliable because the Cronbach’s Alpha is 82.7%.
Data Analysis Approach
The present investigation utilized Multiple Regression Analysis (MRA) to ascertain the influence of the predictor
variables on the response variable. All statistical computations were executed employing version 23.0 of the
Statistical Package for the Social Sciences (SPSS). Subsequently, thorough diagnostic assessments were
undertaken to validate the integrity and reliability of the obtained findings. The linear relationship inherent in
the MRA framework, which quantifies the magnitude of the association between the outcome variable and the
explanatory variables, is formally expressed by the equation presented below:
y = f(
) 3
4
Where:
= the response variable
= the predictor variables
= the intercept or constant term
= the regression coefficients for each predictor variable
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= the error term
RESULTS AND DISCUSSION
Multiple Regression Analysis (MRA) was conducted to investigate the hypothesized relationships between the
independent and dependent variables.
Hypothesis
Ho: There is no statistically significant relationship between the independent variables and the dependent
variable.
Table 2: Shows the Variables Entered/Removed
Model
Variables Entered
Removed
Method
Use_of_Project_Management_Tools_Methods,
Stakeholder_Engagement,
Project_Planning,
Risk_Management,
Resouce_Management,
Monitoring_and_Evaluation
Enter
Dependent variable Quality _Delivery_in_Public_Sector_Projects
Source: SPSS output 2025
Table 2 illustrates that the independent variables incorporated into the analysis comprise the utilization of project
management methodologies and instruments, stakeholder involvement, project planning activities, risk
management strategies, resource allocation and control, and performance monitoring and evaluation.
Conversely, the sole dependent variable under investigation is the attainment of quality deliverables within public
sector initiatives, a structural arrangement that affirms the application of Multiple Regression Analysis.
Table 3: Shows the Model Summary
Model
R
R Squared
Adjusted R
Squared
Sts Error of the
Estimate
Durbin
Watson
1
0.750
0.563
0.540
0.34129
1.847
Source: SPSS output 2025
The correlation coefficient (R) indicates a robust positive linear relationship between the explanatory variables
and the outcome variable, registering a value of 0.750. This magnitude signifies a substantial degree of
association between the constructs under investigation. Subsequently, the coefficient of determination (R-
squared) illustrates that 0.563, or 56.3%, of the total variability within the response variable can be elucidated
or explained by the specified predictor variables.
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Table 4: Shows the ANOVA
Model
Sum of
Squares
df
Mean
Square
F
Sig
1. Multiple
Regression
Residual
Total
17.403
13.511
30.915
6
116
122
2.901
0.116
24.902
0.000
Source: SPSS output 2025
The ANOVA table serves to evaluate the overall efficacy of the model by determining its statistical significance.
Specifically, it assesses whether the model, which incorporates six explanatory variables, provides a superior fit
compared to a null model based solely on the grand mean. The obtained p-value of 0.000 is considerably less
than the chosen significance level of 0.005. This outcome signifies that the model, comprising the six predictor
variables, is a statistically significant determinant of the dependent variable, "quality delivery in public sector
projects." Consequently, a demonstrable statistical association exists between the independent variables and the
response variable. Therefore, the null hypothesis (H₀), which postulates no significant relationship between the
independent and dependent variables, is decisively rejected. This finding confirms the overall utility and validity
of the proposed model.
Table 5: Shows the Coefficients
Model
(Constant)
Unstandardized
Coefficient
95% confidence Interval
Min Bound
Max Bound
Project _ Planning
Resource _ Management
Risk _ Management
Monitoring _ and _ Evaluation
Stakeholder _ Engagement
Use _ of _ Project _ Management _ Tools _
Methods
-.122
.165
.080
.224
.141
.156
.227
-.745
.032
-.102
.117
-.077
.029
.108
.502
.297
.263
.370
.358
.283
.347
Source: SPSS Output 2025
This table shows us how the model works. Recall that our model is
4
This implies that
The analytical model indicates that several factors significantly influence the quality of public sector project
delivery. Specifically:
* Each unit increment in “project planning” is associated with a statistically significant enhancement of 0.165
in project quality.
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* A one-unit increase in “resource management” correlates with a notable improvement of 0.080 in the calibre
of project outcomes.
* Furthermore, an additional unit in “risk management”practices leads to a substantial augmentation of 0.244
in delivery quality.
* Similarly, a one-unit advancement in “monitoring and evaluation” corresponds to a significant gain of 0.141
in project quality.
* Heightened “stakeholder engagement, measured by a one-unit rise, significantly elevates project delivery
quality by 0.156.
* Lastly, each unit increase in the application of “project management tools and methods” contributes
significantly to a 0.227 improvement in the overall quality of public sector project outputs.
CONCLUSION
The findings from the data analysis unequivocally demonstrate a statistically significant nexus between the
independent variables (components of project management in public sector projects) and the dependent variable
(enhanced quality delivery). The multiple regression analysis further revealed that each independent variable
significantly contributes to quality delivery. Specifically, a one-unit increase in project planning yielded a
0.165unit augmentation in quality delivery, resource management contributed 0.080, risk management 0.244,
monitoring and evaluation 0.141, stakeholder engagement 0.156, and the utilization of project management
tools/methods 0.227, all demonstrating significant positive impacts on quality delivery in public sector projects.
REFERENCES
1. Adam, A (2020). Sample Size Determinationin Survey Research. Journal of Scientific Research and
Reports. JSRR, 26(5). 90-97.
2. Mbecke, Z. M. P. (2014). Operations and quality management for public service delivery
improvement. Journal of Governance and Regulation/Volume, 3(4).
3. MC Lennan, A. (2009). The Delivery Paradox. In Mc Lennan, A and Munslow, B. (Eds) 2009. The
Politics of Service Delivery. Wits University Press: Johannesburg.
4. Ocharo, R. N., & Kimutai, G. (2018). Project management practices and implementation of power
projects in Kenya. International Academic Journal of Information Sciences and Project
Management, 3(1), 28-46.
