Effect of Project Control Practices on the Performance of Building Construction Companies in Uganda: A Case Study of the City of Kampala

Abstract

This research paper analytically evaluates the project control practice levels used by the building construction companies within Kampala, Uganda. The research also assesses the impact of project control practices on the productivity of companies. The research was performed to ascertain the current control practices among 160 respondents from various construction companies registered with the Uganda Registration Services Bureau. This research used amalgamation from multiple studies in the literature to obtain the variables. This research adopts 34 standard control practices from 4 vital project control duties: planning, monitoring, analyzing, and reporting. These project control tasks were organized using mean response ratings grounded in their relevance to the construction companies. This process authorized researchers to prioritize tasks based on their perceived importance or relevance to the construction companies involved. Results showed that evaluating performance with the use of S-curves (4.32), timely access to information and encouragement (4.55), report representation using quantitative tools 4.75, and cost value comparison application during analysis (4.76) were rated least among the control practices. On the other hand, the top project control practices included formulation of the project schedule (8.88), project feasibility validation (8.86), budgeting for each activity (8.84), key project route definition (8.81), team awareness of the budget (8.77), setting realistic targets for projects (8.50), and consultation from subcontractors (8.74). From the results obtained by the sample respondents specified, it can be concluded that planning is the most vital project control task practiced in the building construction industry in Uganda. In addition, this research ascertained a substantial relationship between project control practices and the performance of building construction companies. Accordingly, this research recommends that project control practices be effectively observed by both contracting and consulting companies to enhance their overall performance and governance.

1. Introduction

Project control practices are pivotal in successfully executing construction projects, particularly civil engineering. Project control practices involve various processes and tools that aim to ensure the successful completion of projects within the specified scope, budget, and time frame [1]. This is particularly important in civil engineering, where complex projects require detailed planning and coordination. Construction projects are complex and large in scale. To effectively manage these projects, project control practices offer a structured approach to handling resources, schedules, and costs. Refs. [2,3] note that using project control techniques in civil engineering leads to better project outcomes by reducing delays, optimizing the use of resources, and enhancing overall project efficiency. A critical factor in project management for civil engineering is the focus on thorough planning and scheduling. Widely practiced tools like the Critical Path Method (CPM) and Program Evaluation and Review Technique (PERT) create detailed project schedules. Project Evaluation and Review Technique (PERT) and Critical Path Method (CPM) are scheduling methods designed to plan a construction project and to analyze and represent the tasks involved in completing a given project by employing a network of associated and similar activities, coordinating optimum cost and time criteria [4]. These techniques help identify critical activities and dependencies, enabling project managers to allocate resources and minimize potential bottlenecks efficiently [5]. By utilizing these scheduling methodologies, civil engineering projects can maintain a structured timeline, reducing the risk of delays and cost overruns [3].

In civil engineering project management, cost control is as important as scheduling. According to researchers [6], it is crucial to have cost estimation and monitoring mechanisms in place to ensure that projects remain within budgetary constraints. Tools such as Earned Value Management (EVM) provide a systematic approach to tracking project costs against the planned budget, enabling real-time modifications and proactive decision making [7].

Techniques such as CPM, PERT, and EVM provide a systematic approach to planning, resource allocation, and cost management [8]. When we consider the case study of the city of Kampala in Uganda, it is vital to understand how these practices impact the performance of building construction companies in the local context. The construction industry is known for its complex and challenging large-scale projects that require robust project control practices. According to [9], uncertainties relating to weather conditions, resource unavailability, and unforeseen site conditions are expected. Project control practices are essential to alleviate these uncertainties by providing a structured framework for planning, monitoring, and adjusting project activities. Mega construction projects worldwide have high stakes and minimal margin for error, as noted by [2]. Project control practices are adopted to ensure the successful completion of these projects and contribute to overall economic development.

The economic impact of construction projects is substantial, and effective project control practices play a pivotal role in optimizing costs. According to a study by [6], cost overruns are prevalent in the construction industry, leading to financial strain on public and private stakeholders. Project control practices, such as cost estimation, help identify potential cost issues early in the project lifecycle [10]. By integrating these practices, building construction companies can enhance cost efficiency, avoid budget overruns, and contribute to the sustainable development of the construction sector. This economic stability is crucial for the companies involved and the overall growth of economies globally.

Effective collaboration and communication among stakeholders are imperative for the success of construction projects. Project control practices provide a structured platform for communication, ensuring that all stakeholders are well informed and aligned with project objectives. According to [11], the lack of communication and coordination significantly contributes to project delays and disputes. Project control practices, such as Building Information Modelling (BIM) and collaborative project management tools, facilitate real-time communication and information sharing among project participants [12]. Fostering a collaborative environment, reducing conflicts, and enhancing the efficiency and effectiveness of construction projects are achieved through this approach.

Transitioning from the global perspective to a developing country context underscores the critical role of project control practices in overcoming inimitable challenges. In developing countries, including Uganda, construction projects face additional hurdles related to inadequate infrastructure, limited resources, and regulatory constraints [13]. Collaboration with policymakers and regulatory agencies could facilitate a better understanding of regulatory requirements and potential areas for streamlining processes. Effective project control practices are crucial to overcoming challenges in construction projects by providing a structured approach to managing resources, ensuring quality, and mitigating risks. In Uganda’s building sector, it is essential to apply these practices to promote sustainable development, enhance infrastructure resilience, and cater to the unique needs of the local communities [14].

In the context of Uganda, a developing country with a burgeoning construction sector, the need for effective project control practices becomes even more pronounced. Like many other developing nations, Uganda has challenges such as limited financial resources, weak infrastructure, and evolving regulatory frameworks [15]. Project control practices offer a tailored solution to address these challenges by fostering a disciplined approach to project management.

Uganda’s construction industry is an essential economic growth and development driver, contributing significantly to the nation’s Gross Domestic Product (GDP), according to [16]. Infrastructure development is a crucial priority for Uganda’s government, aiming to improve connectivity, create employment opportunities, and enhance living standards. In this context, project control practices are vital in ensuring that infrastructure projects are executed efficiently, on schedule, and within budgetary constraints. The alignment of project objectives with national development goals is crucial for maximizing the positive impact of construction projects on the local economy [14].

Resource constraints are a common challenge in the construction sector of developing countries, including Uganda. Limited skilled labor, materials, and equipment availability can impede project progress and spearhead delays. Project control practices offer mechanisms to optimize resource allocation, mitigate risks associated with resource scarcity, and enhance overall project resilience. For instance, effective workforce management strategies and supply chain optimization, as highlighted by [17], become instrumental in navigating resource challenges specific to Uganda’s construction landscape.

Various factors contribute to the cost control and project duration of construction projects. These include political instabilities, inflation of material prices, poor controls and regulations, unreliable government guidelines, poor time management, and changes in design and estimates. Studies have shown that design changes and material price inflation are the most significant factors that cause cost overruns and delays in project completion. Planning is also crucial, as insufficient cash flow can lead to delays and even abandonment of unfinished structures. Client’s insurance that their demand for design alterations during the construction period has no adverse effects on the critical activities to avoid causing adjournments is vital [18]. Despite global efforts to meet construction objectives, the complexity of modern-day projects is higher than ever.

There is a mismatch in appreciating how project control practices expedite amalgamating social responsibility and dependability into construction projects in Kampala. According to research conducted by [19], it is crucial to integrate social responsibility into construction projects and ensure that they align with the local communities’ cultural and social subtleties. They encourage collaboration between construction companies, NGOs, and community groups to effectively identify and address social concerns within project management frameworks. The amalgamation of project control practices such as social impact assessments, community consultations, and sustainable development initiatives can significantly contribute to the success of the project and the overall well-being of the communities involved. Circumnavigating regulatory frameworks is a significant aspect of construction projects in Uganda. Project control practices assist building construction companies in adhering to local regulations and standards. Thorough risk assessments, compliance monitoring, and the incorporation of flexibility in project plans to accommodate regulatory changes are involved in this process [20]. Regulatory compliance is crucial for project sustainability and avoiding legal impediments, particularly in a developing country milieu where regulatory environments may be dynamic and subject to change. Generally, adopting project control practices in Uganda’s building construction sector is domineering for overcoming local challenges, optimizing resource utilization, and ensuring that construction projects contribute positively to economic development and community well-being. As Uganda strives for sustainable development, integrating effective project control practices remains central to the success and resilience of its construction industry [21].

Effective project control practices are essential for the success of building construction companies. However, the construction industry faces various challenges in achieving effective project control practices. One of the significant challenges is the dynamic nature of construction projects, as highlighted by [22]. The complexity and variability inherent in construction projects, driven by changing weather conditions, site-specific challenges, and evolving client requirements, make implementing rigid project control practices difficult. Therefore, it becomes crucial to maintain adaptability within project control frameworks to address astonishing challenges and ensure project success on a global scale. One of the main hitches developing countries such as Uganda face is the effective management and allocation of resources. These nations often lack skilled labor, materials, and financial resources, which makes it challenging to implement project control practices effectively, as highlighted by [23]. In Uganda’s construction sector, achieving optimal resource allocation requires innovative solutions and strategic planning to overcome the constraints imposed by limited resources. Failure to address these challenges through project control practices may lead to delays, cost overruns, and compromised project outcomes—the prerequisite for exploration of alternative solutions and tactics to overcome limitations in technological infrastructure. Infrastructure deficiencies and technological gaps present formidable challenges in pursuing effective project control practices. In many developing countries, including Uganda, inadequate technological infrastructure hampers the adoption of advanced project control tools and software [24]. The lack of reliable internet connectivity and access to cutting-edge project management technologies can impede real-time communication and collaboration. Project control practices must be adapted to local technological capabilities to overcome this, ensuring they remain effective in environments with limited technological resources. Advocation for investments in improving technological infrastructure in Uganda to support adopting advanced project control tools and software can be targeted.

Navigating regulatory frameworks and governance issues is a critical challenge in the context of developing countries. The prevailing lack of discussion on specific mechanisms within project control practices to ensure compliance with regulatory frameworks seems to be one of the breaches in Kampala. Like many other flexible developing nations, Uganda may face bureaucratic red tape, inconsistent regulatory enforcement, and ambiguous legal frameworks [25]. This poses a significant challenge to implementing standardized project control practices (SPM). Without a clear and stable regulatory environment, construction companies in Uganda may struggle to establish the necessary frameworks for project planning, risk management, and compliance monitoring. Understanding how project control practices are bespoke to suit unique challenges in Uganda’s construction industry is still lacking. Flexible project control practices that account for the local regulatory landscape are essential in overcoming these challenges. The reassurance of an alliance between academia, industry experts, and policymakers to develop guidelines or frameworks for adapting project control practices to the local context can be connected to the shortage of skilled professionals in the construction industry, which poses a challenge to the effective implementation of project control practices in developing countries. Uganda’s narrow focus on how project control practices contribute to training and capacity building in project management and construction hinders the sector’s growth. There is a shortage of trained project managers and personnel proficient in modern project control methodologies, which is a significant challenge [26]. Addressing this challenge requires a concerted effort toward capacity building, partnering with educational institutions and industry associations to develop training programs tailored to the needs of project managers and construction personnel, and skills development within the local workforce. The instituting of training programs and knowledge transfer initiatives precisely targeting project management skills within the construction industry in Uganda’s project control practices must incorporate training programs and knowledge transfer initiatives to build the necessary expertise in project management, ensuring that the workforce is equipped to accomplish projects.

In summary, the challenges in achieving efficacious project control practices extend globally but manifest uniquely in developing countries like Uganda [27]. From resource constraints to infrastructure deficiencies and regulatory complexities [25], these challenges demand adaptive and context-specific approaches within project control frameworks to foster successful construction project outcomes.

In a nutshell, this research paper attempts to investigate the profound impact of project control practices on the performance of building construction companies in Uganda. Unsatisfactory detail on the extent of quantitative analysis conducted regarding the effects of project control practices longs for further research [28]. The study aims to discern the specific impacts of project control practices on crucial performance indicators within the construction industry. Qualitative and quantitative research approaches are employed [29]. These methods allow for a comprehensive analysis of existing scholarly works, enabling the identification and synthesis of key findings related to the effects of project control practices. The subsequent sections of this paper delve into the research methods prevalent in construction studies, offering insights into the unique challenges and opportunities associated with project control in the building construction context. Following this, the paper outlines the research methodologies adopted to attain the objectives outlined in this scrutiny by ensuring transparency regarding the data collection and analysis methodologies. However, there is a necessity for additional quantitative analysis regarding the effects of project control practices [30]. Backup of collaboration with industry partners to access and analyze relevant datasets, such as project performance metrics and financial records, and involvement in gathering data on project completion rates, cost overruns, schedule adherence, and stakeholder satisfaction levels for hearty quantitative analysis.

Analyzing the various positive effects, such as increased cost efficiency, timely project completion, recuperated collaboration with stakeholders, and overall project success, leads to the company’s performance. The research results are poised to provide valuable insights for professionals in the industry, policymakers, and scholars, contributing to a deeper understanding of the intricate relationships between project control methods and the operational success of building construction companies in Uganda.

Research Objectives

The main objectives of this study are as follows:

1—To identify the control practices implemented by building companies in Kampala, in particular, to investigate the regulatory frameworks, internal control mechanisms, and other strategies used to manage and regulate companies’ operations.

2—To assess the performance of building companies by utilizing pertinent metrics, including financial performance, project completion time, quality of construction, and client satisfaction.

3—To examine how control practices influence the performance of building companies by evaluating the effectiveness of control measures in accomplishing predetermined objectives and ensuring adherence to standards within the construction industry.

4—To identify the obstacles encountered by building companies in implementing control practices, encompassing financial limitations, shortages of skilled personnel, regulatory intricacies, and technological shortcomings affecting the adoption and efficacy of control measures.

5—To provide recommendations to improve control practices and overall performance in Uganda’s building industry.

2. Methods and Materials

2.1. Research Methodology

A mixed research approach, utilizing both quantitative and qualitative research methods, was employed to achieve the aims and objectives of this study [31]. Mixed methods research integrates various methods to gain a qualitative and quantitative perspective. Choosing a methodology based solely on its strengths may lead to blind spots and skewed results. Qualitative research analyses non-numerical data to uncover underlying meanings and patterns, while quantitative research is commonly used for social research [31,32]. Quantitative research is characterized by collecting numerical data using deductive reasoning to link theory and research using experiments and surveys. Quantitative research is characterized by collecting numerical data using inductive reasoning to link theory and research by understanding the social world through the interpretation of social participants and a constructionist ontological position, which argues that social interactions influence social phenomena through a case study [32]. The use of two research techniques so that distinct components of an investigation can be combined: quantitative research to support qualitative research findings or vice versa, and one research methodology to support research using the other research methodology. In this research, the cross-sectional survey policy was employed in which the qualitative and quantitative procedures were used in the process of gathering comprehensive information which was used in the same way as what [33] used in their research where the quantitative and qualitative policies help in quantifying the research results of the study and the compilation of chronicle descriptive evidence, which will eventually support effect analysis on the company operation, respectively. The quantitative approach also helps in finding out and drawing conclusions about the findings from an enormous population, and decisions are made on a massive population.

2.2. Data Collection and Data Analysis

Data for the research were collected through a questionnaire that asked representatives from various construction companies for their insights. The data collection used a dual sampling approach that combined purposive sampling to target specific expertise and stratified simple random sampling for a broader and unbiased industry representation. This strategic approach ensured that the dataset was diverse and comprehensive, facilitating in-depth analysis of project control practices and their impact on construction companies’ performance. The collected responses were checked to ensure they were complete and readable before being processed through Excel 2019 and the Statistical Package for the Social Sciences (SPSS 24.0) for Analysis. Different percentages and ranks were calculated as part of descriptive analysis using various statistical techniques, including descriptive statistics approaches using Excel and SPSS, pattern matrix and reliability test, KMO and Bartlett’s tests to assess the suitability of data for factor analysis, Cronbach’s alpha test to evaluate the internal consistency reliability of the test [34], Total Variance Explained to guide and help in understanding how much of the variability in the data collected is captured by extracted factors, regression analysis for understanding the relationships between variables (dependent and independent), and Analysis of Variance (ANOVA) for standardized and unstandardized coefficients to compare means across the groups and conclude the research, similar to [35].

2.3. Scope of the Research

This research investigates the effect of project control practices on the performance of building construction companies in Uganda, with the city of Kampala as a case study. The study encompasses qualitative and quantitative research approaches to comprehensively analyze the intricate dynamics between project control practices and the performance of registered building construction companies in Kampala. The primary focus of this research is on the city of Kampala, Uganda, and it aims to investigate building construction companies that are fully registered with the Uganda Registration Services Bureau (URSB) and operate within this urban area. This study intends to provide a localized and context-specific understanding of project control practices and their impact on performance. This study concentrates on control practices in Uganda’s building construction industry and how they impact financial performance, project completion time, construction quality, and client satisfaction.

2.4. Study Area

This study was conducted in the city of Kampala, the capital of Uganda, as a case study. Kampala city is bordered by Mukono district to the east, Buikwe district to the south, Luweero district to the north, and Wakiso district to the west. It is one of the fastest-growing cities in Africa. Kampala’s estimated population for 2023 is around 3,846,102, with an annual population growth rate of about 4.03%, according to [36]. Its geographical coordinates are 0.3152° N and 32.5816° E, and it spans an area of 72.97 square miles, comprising 68 square miles of land and 5.0 square miles of water [37]. Kampala is 1200 m (3900 feet) above sea level [38]. It is characterized by hills and valleys filled with sluggish rivers and swamps, as shown in Figure 1. The city has five divisions: Central, Kawempe, Makindye, Nakawa, and Rubaga. Each division is headed by an elected Mayor who is popularly directed. Kampala serves as Uganda’s national and commercial capital and borders Lake Victoria, the largest lake in Africa [39]. Hills covered with red-tiled villas and trees encircle a modern urban center of skyscrapers. This information was obtained from the Kampala Capital City Authority, which is entirely in charge of the city.

2.5. Study Design

Sample Size and Population Targeted

Using different research approaches, research methods can be classified as qualitative or quantitative research, which stresses quantification in data collection and examination [29]. Determining the connection between theory and research takes into account a provable manner, and stress is kept on the confirmation of theories. The quantitative research method integrates the norms and practices of the natural scientific model and positivism. It views the social portent as an outer objective truth [29]. A systematic literature review can be classified as quantitative or qualitative as it identifies, selects, and critically appraises research to answer a formulated question [40]. This research aimed to explore the effect of project control practices on the performance of building companies in Uganda. A mixed research approach using qualitative and quantitative methods [41] was best suited to accomplish the aims and objectives of this research. The research started using a qualitative approach to identify the control practices most construction companies in Kampala commonly use.

After finding out the most commonly used control practices from the literature associated with the construction companies in Kampala, a quantitative approach was adopted to rank the essential and adequately used control practices that play a significant role in the performance of construction companies. A similar approach was also adopted by [42] in their research on the spaces under highways and bridges. A questionnaire was then developed and was sent to the selected respondents. This questionnaire aimed to establish the ranking of the control practices at the planning, monitoring, reporting, and analyzing levels, considering the profession, education level, company type, and the company’s annual turnover. The selected are the building and construction companies, construction maintenance companies, and consultancy companies that are fully registered with the Uganda Registration Services Bureau (URSB), and the study targeted practicing people with adverse knowledge about construction, ranging from Civil Engineers, Valuers, Masons (builders), Surveyors, and Architects. The population choice was open for as long as they were in the construction line, experienced, and ready to provide unbiased information. We used two sampling methods: purposive and simple random sampling [43]. We resorted to purposive sampling because it minimizes variations, aligns with research objectives, and is efficient. Purposive sampling targeted practitioners who have been in the construction field for more than 25 years and have characteristics needed in the study. Confidentiality protection was critical and emphasized throughout. Simple random sampling was used to avoid biased and statistical inferences, results, and information about the different operations of some control practices, as [44] asserted. This study also used the quantitative approach, and the number of respondents was 160 out of the 190 questionnaires supplied to different registered companies.

Relevant information related to the potential respondents for purposive sampling arrangement that included qualification, expertise, research interest experience, and email was obtained from their organization company websites and web pages to confirm eligibility similar to what [29] used. This criterion was based on educational qualifications, experience, expertise, and research interest to help receive proper information about the effectiveness of control practices on the performance of construction companies, and it was carried out with integrity. For respondents who used simple random sampling, caution was taken that they should lie within the qualification gaps and be able to provide accurate information for the study. The academic respondents’ minimum rank was a Bachelor’s degree, with a minimum of 5 years of working experience and interest in providing thorough information about the study. The respondents were required to have research interests relevant to the leitmotif of the current research, that is, the effect of control practices on the performance of building construction companies in Uganda through a case study of the city of Kampala.

The questionnaire developed based on the data collected during the initial stage of the research was divided into eight segments. The first segment provided information about this study’s purpose, confidentiality policy, and consent to participation. The second segment was about the personal information of the respondents, including gender, academic qualification, age, experience, and research interests. The third segment was based on the specialization and location of the respondents. Segments four to eight of the questionnaire were related to planning control practices, monitoring control practices, recording control practices, and analyzing control practices. Each level was divided into several sub-points, where respondents were asked to rate their effectiveness on a Likert scale of 1 (least effective) to 10 (most effective). Some of the questions asked included but were not limited to the following: How effectively does your company formulate a clear project schedule before commencing construction projects? How periodically is the progress of work inspected against predetermined milestones and benchmarks? How accurately are project data and information recorded, honestly verified, and documented throughout construction projects? How regularly are S-curves used in performance evaluation in your company? Many researchers, including [29], use a specific approach to rank the effects in their research studies. In the final section of the questionnaire, respondents were allowed to provide any additional comments they felt were essential for the study. Descriptive analyses were conducted using the Statistical Package for the Social Sciences SPSS (24.0) to determine the overall ranking of different control practices on companies’ performance, similar to the methods used by [29]. Control practices with a mean score of 6 or higher were considered adequate in this research, while those scoring below 6 were considered least effective. The table below displays this information.

Table 1. Selection technique, targeted, sampled population, and sample size.

Profession	Companies				Frequency/Population	Size Sampled	Percentage	Sampling Technique
	Consultancy Companies	Maintenance Companies	Estate Developer	Building and Construction
Civil Engineer	4	4	6	23	37	45	23.1	Purposive sampling
Quantity Surveyors	6	4	7	25	42	50	26.2	Simple random sampling
Masons	2	2	3	24	31	40	19.4	Purposive sampling
Architects	5	4	13	25	47	50	29.4	Purposive sampling
Valuers	1	-	-	2	03	05	1.9	Simple random sampling
Total	18	14	29	99	160	190	100

presents the selection technique, targeted population, sampled population, and sample size.

2.6. Research Framework

This study looks into the role of operational management techniques in the delivery and performance of construction companies, especially in Uganda’s capital, Kampala. This study aims to investigate how project control practices impact the performance of construction entities in Kampala. Examining this connection seeks to provide insights into enhancing construction project management in Uganda’s capital city. The authors conducted a thorough literature review to determine appropriate indicators for these two areas. They identified several indicators for project control, such as planning, monitoring, reporting, and analyzing. For company performance, they looked at management skills and financial ability indicators. Based on these six leading indicators, the authors derived several components that can be used to categorize them under project control or company performance. An important finding from the interview research is that, in practice, project cost and time control are frequently separate. Consequently, the principal approach to project control in practice typically addresses time and cost control separately, aligning with standard practices. This research also reveals that project control typically embodies multiple tasks, as [45] highlighted.

3. Results

Ref. [46] accentuated the substance of adhering to the research method and process, even in the case of minor research endeavors. This study utilized a quantitative approach to gather relevant data from various building construction companies in the city of Kampala, Uganda. Experts such as Quantity Surveyors, Architects, Masons, Civil Engineers, and relevant departments within each company were targeted due to their construction cost and time control expertise. A questionnaire was designed with a 10-point rating scale, which allowed the respondents to assess project control practices obtained from the literature. One hundred ninety questionnaires were distributed to these firms to gather information on their project control practices and processes. Out of the 190, 160 respondent companies completed and returned the questionnaires, resulting in an 84% response rate deemed satisfactory for the research. The accumulated data were analyzed to evaluate the level of control practices within the building construction companies and to assess the impact of these practices on company performance, utilizing various statistical methods. Firstly, descriptive statistics were presented for respondent demographics such as gender, age, profession, work experience, education level, company type, establishment year, total technical staff, and annual revenue. After conducting a mean item rating to assess the level of project control practices, a factor analysis was performed to determine how these practices impact performance and operation. This research is concluded based on the study’s results, and the findings are presented below.

Table 2. Demographic details regarding the respondents.

Variables	Categorization	Frequency	Percentage
	Quantity Surveyors	42	26.2
Profession	Architects	47	29.4
	Masons	31	19.4
	Civil Engineers	37	23.1
	Valuers	3	1.9
	Total	160	100.0
	National Diploma	72	45.0
Education level	Bachelor’s Degree	61	38.1
	Master’s Degree	24	15.0
	Doctorate	3	1.9
	Total	160	100.0
	Local	89	55.6
Company type	International	26	16.3
	Joint Venture/Combined	45	28.1
	Total	160	100.0
	<5 million	21	13.1
Company yearly revenue	5–50 million	26	16.2
	51–235 million	47	29.4
	236–500 million	40	25.0
	501 million–2 billion	15	9.4
	Above 2 billion	11	6.9
	Total	160	100.0

displays demographic details regarding the respondents and their respective companies, which furnishes essential information for assessing the data’s reliability and dependability. As explained below, these categories outlined comprehend the respondents’ career, education level, company type, and revenue.

Table 2 defines the respondents’ demographic synopsis, indicating that 23.1% were Civil Engineers, 26.2% were Quantity Surveyors, 29.4% were Architects, 19.4% were Masons, and 1.9% were Valuers. Concerning academic qualifications, this study found that 45.0% held National Diplomas, 38.1% possessed a Bachelor’s of construction management and project management, 15.0% held a Master’s of project management and construction management, and 1.9% had obtained a Doctorate. Regarding company categorization, 55.6% were local, 16.3% were international, and the remaining 28.1% were (combined) joint ventures. Examining the company’s annual revenue, Table 2 discloses that 13.1% of companies reported a yearly income of less than 5 million Ugandan shillings, 16.3% had revenue between 5 to 50 million Ugandan shillings, 29.4% fell within the 51 to 235 million Ugandan shillings, 25.0% reported turnovers between 236 to 500 million Ugandan shillings, 9.4% varied from 501 million Ugandan shillings to 2 billion Ugandan shillings, and 6.9% reported yearly revenue exceeding 2 billion Ugandan shillings. Table 2 defines the respondents’ demographic synopsis, indicating that 23.1% were Civil Engineers, 26.2% were Quantity Surveyors, 29.4% were Architects, 19.4% were Masons, and 1.9% were Valuers. Concerning academic qualifications, this study found that 45.0% held National Diplomas, 38.1% possessed a Bachelor’s of construction management and project management, 15.0% held a Master’s of project management and construction management, and 1.9% had obtained a Doctorate. Regarding company categorization, 55.6% were local, 16.3% were international, and the remaining 28.1% were (combined) joint ventures. Examining the company’s annual revenue, Table 2 discloses that 13.1% of companies reported a yearly income of less than 5 million Ugandan shillings, 16.3% had revenue between 5 to 50 million Ugandan shillings, 29.4% fell within the range of 51 to 235 million Ugandan shillings, 25.0% reported turnovers between 236 to 500 million Ugandan shillings, 9.4% varied from 501 million Ugandan shillings to 2 billion Ugandan shillings, and 6.9% reported yearly revenue exceeding 2 billion Ugandan shillings.

3.1. Level of Control Practices for Planning

This section evaluates the effectiveness of control practices implemented by building construction companies.

Table 3. Descriptive statistics on the level of planning control practices.

Control Practices for Planning	Mean	Std. Deviation	Rank
Formulation of project schedule	8.88	3.0909	1
Project feasibility validation	8.86	1.9235	2
Key project route definition	8.81	1.7676	4
Setting realistic targets for projects	8.50	1.7029	8
Consultation from subcontractors	8.74	1.9677	6
Smooth tender transition facilitation	8.65	2.1243	7
Team awareness of the budget	8.77	1.8676	5
Budgeting for each activity	8.84	1.6239	3
Merging time budget considerations	7.57	2.0971	9

below depicts the implications of control practices in planning, monitoring, reporting, and analysis. Table 3 presents a ranking of variables based on their significance and relevance levels. The outcomes show that the most exploited control practice for planning is the formulation of project schedules, with a mean score of 8.88. The table highlights that companies consider the formulation of project schedules a vital practice, accenting its critical responsibility in the project control process. This finding aligns with ref’s [47] assertion that breaking a project into manageable work allocations through the Work Breakdown Structure augments project control efficiency. Project feasibility validation is the second most employed control practice, receiving a mean score of 8.86. Budgeting for each activity is ranked third with a mean score of 8.84, while the key project route definition claims the fourth position with a score of 8.81. Team awareness of the budget is ranked fifth, earning a score of 8.77. Consultation from subcontractors holds the sixth position with a score of 8.74. Smooth tender transition facilitation is seventh, with a score of 8.65, while setting realistic targets for projects and merging time budget considerations are ranked eighth and ninth, with scores of 8.50 and 5.57, respectively. Companies identify the formulation of project schedules and project feasibility validation as the most prevalent practices, avowing the significance highlighted by [48].

As illustrated in

Table 4. Descriptive statistics on the level of control practices for monitoring.

Monitoring Control Practices	Mean	Std. Deviation	Rank
Periodic monitoring protocol/routine inspection	8.70	2.0493	1
Time wondering along the critical path	7.75	1.9365	4
Real-time monitoring for design adjustments/changes	8.29	2.0241	3
Project cost and time output definition	5.12	1.4467	9
Ongoing tracking of key checkpoints	6.75	5.2440	6
Subcontractors’ cost validation system implementation	5.13	1.5101	8
Monitoring tender allocation for procurement compliance	7.49	2.2359	5
Office-based regular monitoring	8.38	2.1652	2
Educating the site team about control	5.81	1.7456	7

, periodic monitoring protocol/routine inspection emerges as the most effective control practice for monitoring, achieving a score of 8.70. The results indicate that a preponderance of companies in the city of Kampala absorbs this control practice, aligning with the emphasis by [1], who considers periodic monitoring protocol/routine inspection a highly critical and significant approval for operational project monitoring. Office-based regular monitoring is the second most effective control practice, garnering a mean score of 8.38. This supports ref’s [1] statement that project progress monitoring through site visitation is valuable but more fundamental than dependable routine monitoring. Real-time monitoring for design adjustments is the third most effective control practice, with a score of 8.29, while time wandering along the critical path is ranked fourth, with a score of 7.75. Monitoring tender allocation for procurement compliance holds the fifth position with a score of 7.49. Ongoing tracking of key checkpoints is ranked sixth with a score of 6.75, and educating the site team about control is seventh with a score of 5.81. Subcontractors’ cost validation system implementation and project cost and time output definition are ranked eighth and ninth, receiving scores of 5.13 and 5.12, respectively [47].

Table 5. Descriptive statistics on the level of control practice for reporting.

Reporting Control Practices	Mean	Std. Deviation	Rank
Reporting cost and time data verification	8.06	1.1573	4
Verifying report accuracy and honesty	8.48	2.5397	2
Periodic reporting on cost and time	6.25	2.0702	5
Exact data capture	8.57	2.3678	1
Building an honest and open report with management	8.31	2.2283	3
Report representation using quantitative tools	4.75	2.0702	8
Choosing simplicity in reporting technology	5.04	1.0084	6
Integrating qualitative findings into quantitative	4.97	1.6451	7

indicates that exact data capture is the most effective control practice for reporting, receiving a score of 8.57. Following closely, verifying report accuracy and honesty is the second most effective control practice, earning a score of 8.48. Building an honest and open report with management is ranked third with a score of 8.31 while reporting cost and time data verification claims the fourth position with a score of 8.06. Periodic reporting on cost and time is the fifth most effective practice, scoring 6.25. Consequently, choosing simplicity in reporting technology is the sixth most effective practice, with a score of 5.04, and integrating qualitative findings into quantitative is ranked seventh, with a score of 4.97. Report representation using quantitative tools holds the eighth position with a score of 4.75. The results underline the significance of information gathered during the monitoring stage in control practices for reporting. This aligns with the assessment of [48], who emphasized that exact data capture in reporting is a crucial element of a cost control system. Furthermore, the importance of verifying report accuracy and honesty and building an honest and open report with management is highlighted in control practices for reporting [1].

Table 6. Descriptive statistics on the level of control practice for analysis.

Analyzing Control Practices	Mean	Std. Deviation	Rank
Staff employment for resource evaluation	6.57	2.3719	1
Time and cost analysis prediction at project completion	5.07	1.2595	5
Timely access to information and encouragement	4.55	2.3725	7
Cost and time integration in the analytical process	5.50	2.3905	4
Evaluating performance with the use of S-curves	4.32	2.2163	8
Cost value comparison application during analysis	4.76	2.0707	6
Workforce efficiency emphasis on project time and cost	6.06	1.1573	2
Establishing costs for the period and earned value	5.25	2.0702	3

figures out the prominence of control practices for analysis. The findings indicate that staff employment for resource evaluation is the most effective and commonly applied control practice for analysis, scoring 6.57. Following closely, workforce efficiency emphasis on project time and cost is the second most effective control practice with a score of 6.06. Establishing prices for the period and earned value ranks as the third most effective control practice, achieving a score of 5.25. In contrast, cost and time integration in the analytical process is the fourth most significant practice, scoring 5.50. Time and cost analysis prediction at project completion is the fifth considerable practice, scoring 5.07. Additionally, cost value comparison application during analysis is the sixth most significant practice with a score of 4.76, and timely access to information and encouragement ranks seventh with a score of 4.55. Evaluating performance with the use of the S-curves, the eighth scored 4.32. In brief, the research underscores that staff employment for resource evaluation is deemed the most weighty and critical control practice for analysis in the city of Kampala. This is associated with ref’s [48] assertion that staff employment for resource evaluation is vital for ensuring reports are healthy, authentic, and honorable. Thus, the presence of independent higher management-level personnel to verify the believability of reports is regarded as an exceedingly treasured control practice [48].

3.2. Level of Control Practices in the Building Construction Companies

The intensity of control measures instigated within building construction companies relates to the scope and efficiency of the contrivances and processes established by these companies to oversee and govern different facets of their undertakings. As restricted by [48], control practices encompass supervisory actions and systems crafted to guarantee the efficient and effective realization of organizational goals and objectives.

The mean rankings for the thirty-four recommended good control practices from the existing literature are presented in

Table 7. The descriptive statistics level of relevance of control practices.

Rafts	Program	Control Practice	Mean	Std. Deviation	Rank
	C211	Formulation of project schedule	8.88	3.0909	1
CPP	C212	Project feasibility validation	8.86	1.9235	2
	C213	Key project route definition	8.81	1.7676	4
	C214	Setting realistic targets for projects	8.50	1.7029	10
	C215	Consultation from subcontractors	8.74	1.9677	6
	C216	Smooth tender transition facilitation	8.65	2.1243	8
	C217	Team awareness of the budget	8.77	1.8676	5
	C218	Budgeting for each activity	8.84	1.6239	3
	C219	Merging time budget considerations	7.57	2.0971	17
	C311	Periodic monitoring protocol/routine inspection	8.70	2.0493	7
CPM	C312	Time wondering along the critical path	7.75	1.9365	16
	C313	Real-time monitoring for design adjustments/changes	8.29	2.0241	14
	C314	Project cost and time output definition	5.12	1.4467	27
	C316	Ongoing tracking of key checkpoints	6.75	5.2440	19
	C318	Subcontractors’ cost validation system implementation	5.13	1.5101	26
	C3110	Monitoring tender allocation for procurement compliance	7.49	2.2359	18
	C3112	Office-based regular monitoring	8.38	2.1652	12
	C3113	Educating the site team about control	5.81	1.7456	23
CPR	C411	Reporting cost and time data verification	8.06	1.1573	15
	C412	Verifying report accuracy and honesty	8.48	2.5397	11
	C413	Periodic reporting on cost and time	6.25	2.0702	21
	C414	Exact data capture	8.57	2.3678	9
	C415	Building an honest and open report with management	8.31	2.2283	13
	C417	Report representation using quantitative tools	4.75	2.0702	32
	C418	Choosing simplicity in reporting technology	5.04	1.0084	29
	C419	Integrating qualitative findings into quantitative	4.97	1.6451	30
	C511	Staff employment for resource evaluation	6.57	2.3719	20
CPA	C512	Time and cost analysis prediction at project completion	5.07	1.2595	28
	C513	Timely access to information and encouragement	4.55	2.3725	33
	C514	Cost and time integration in the analytical process	5.50	2.3905	24
	C515	Evaluating performance with the use of S-curves	4.32	2.2163	34
	C516	Cost value comparison application during analysis	4.76	2.0707	31
	C517	Workforce efficiency emphasis on project time and cost	6.06	1.1573	22
	C518	Establishing costs for the period and earned value	5.25	2.0702	25

CPP—control practice for planning → C211–C219; CPM—control practice for monitoring C311–C3113; CPR—control practice for recording → C411–C419; CPA—control practice for analyzing → C511–C518.

. Control practices with mean response ratings between 8.00 and 6.00 are categorized as “essential”. Table 7 illustrates the level of relevance of control practices related to (CPP) for planning (CPM) for monitoring (CPR) for reporting and (CPA) for analysis.

Table 7 indicates that the most critical and effective control practice among the recommended thirty-four is the formulation of the project schedule (C211), with a mean score of 8.88. Following closely behind, project feasibility validation (C212) is the second most influential and vital practice, with a score of 8.86, while budgeting for each activity (C218) is the third, with a score of 8.84. Fundamental project route definition (C213) secures the fourth position with a score of 8.81. Other essential practices include team awareness of the budget (C217), scoring 8.77; consultation from subcontractors (C215), with a score of 8.74; and periodic monitoring protocol/routine inspection (C311), with a score of 8.70. Smooth tender transition facilitation (C216) is also considered influential and vital, scoring 8.65. Exact data capture (C414) and setting realistic targets for projects (C214) follow closely with scores of 8.57 and 8.50, respectively. Verifying report accuracy and honesty (C415) is deemed essential, with a mean score of 8.48, while real-time monitoring for design adjustments/changes (C313) is considered necessary, with a mean score of 8.29. Building an honest and open report with management (C415) shows a score of 8.31, office-based regular monitoring (C3112) shows a score of 8.38, and time wandering along the critical path (C312) shows a score of 7.75. Merging time budget considerations (C219) shows a score of 7.57, monitoring tender allocation for procurement compliance (C3110) shows a score of 7.49, ongoing tracking of key checkpoints (C316) shows a score of 6.75, and staff employment for resource evaluation (C511) shows a score of 6.57. Workforce efficiency emphasis on project time and cost (C517) scores 6.06, all being considered among the most crucial control practices in the construction process. Control practices with mean scores below 6.000 are categorized as “helpful”. Remarkably, evaluating performance using S-curves (C515) is the “most helpful” practice, with a score of 4.32. At the same time, timely access to information and encouragement (C513) follows with a mean score of 4.55.

In conclusion, the results indicate that construction companies’ predominant control practices in time and cost in the city of Kampala, Uganda, are related to planning. Specifically, the practices of formulation of project schedule, project feasibility validation, budgeting for each activity, key project route definition, consultation from subcontractors, periodic monitoring protocol/routine inspection, smooth tender transition facilitation, and exact data capture are highly emphasized. These windups align with [49], supporting the idea that project managers principally carry out planning practices in various construction companies.

3.3. Examining the Impact of Control Practices on the Efficiency of Building Construction Companies

Factor analysis was used to calculate factor scores for both control practices and the performance of construction companies. The purpose was to determine the impact of project control practices on the performance of building construction companies. Then, ordinary least square regression was applied to reveal the correlation between project control practices and the performance of building construction companies.

Before initiating factor analysis to ascertain variable factor scores, the outcomes in Table 7 were dissected to ensure the appositeness of the collected data for factor analysis. The purpose of factor analysis and regression is to examine the effect of project control practices, which denotes (independent variables) on the output of building construction enterprise designates (dependent variable) [50]. Specifically, it is recommended that a minimum of five subjects per variable is necessary for factor analysis appropriateness, as suggested by [50,51], which also indicated that a sample size within the range of 150–300 would fit for analysis. With over thirty-four variables, a sample size of sixty, warranting at least three representatives from each company, provided 160 respondents, outstripping the minimum prerequisite for factor analysis. Consequently, data fittingness was assessed before conducting principal component analysis. To determine if the data were suitable for factor analysis, we used the Kaiser–Meyer–Olkin (KMO) Test and Bartlett’s tests of sphericity to ensure the adequacy of the collected data and to measure the partial correlations between the variables (dependent and independent variables) [34].

3.3.1. The KMO and Bartlett’s Test

Table 8. KMO and Bartlett’s test for the effect of control practice on performance.

KMO and Bartlett’s Test
Kaiser–Meyer–Olkin Measure of Sampling Adequacy		0.778
Bartlett’s Test of Sphericity	Approx. Chi-Square	1135.181
	Df	36
	Sig.	0.000

reveals to appraise the appropriateness and adequacy of the data distribution for factor analysis, the Kaiser–Meyer–Olkin (KMO) Test was used and run by SPSS (24.0). Refs. [34,51] alluded to a minimum KMO index value of 0.60 for factor analysis correctness. Ref. [51] projected that the data agreed that achieving a KMO index of 0.50 and Bartlett’s test of sphericity with p < 0.05 is fit for factor analysis. The table below indicates a KMO index of 0.778 (surpassing 0.5) and Bartlett’s test of sphericity with chi-square = 1135.181 at a significance level of p-value = 0.00 (should be <0.05), which was acceptable and implying that the questionnaire is valid as asserted [52,53]. These discoveries ensure that the collected data on control practices, company operations, and performances were suitable for factor analysis.

3.3.2. Total Variance Explained (TVE)

According to

Table 9. Total Variance Explained (TVE) for project control.

Component	Initial Eigenvalues			Extraction Sums of Squared Loadings			Rotation Sums of Squared Loadings
	Total	% of Variance	Cumulative %	Total	% of Variance	Cumulative %	Total
1	5.113	63.908	63.908	5.113	63.908	63.908	4.786
2	1.540	19.256	83.164	1.540	19.256	83.164	2.910
3	0.814	10.176	93.340
4	0.226	2.827	96.166
5	0.137	1.706	97.873
6	0.092	1.146	99.019
7	0.079	0.981	100.000

, the principal component analysis was used to identify linear combinations of the original variables (components or factors) that captured the maximum variance in the data. In this case, two components were extracted. The variance explained by each component is represented by Eigenvalues, where two components with Eigenvalues greater than 1.0 were retained. We use factor loadings to represent the extracted components and determine which variables significantly contribute to each component based on correlations with a cut-off of 0.3 for the original variables [54]. The variables with a factor loading above this cut-off point are considered to be associated with the respective component.

Total Variance Explained indicates the percentage of the total variance in the data explained by each component, where Factor 1 explains 63.9% of the total variance while Factor 2 explains 19.2%. Together, these two components account for approximately 83.1% of the total Cumulative Variance. Once the components have been identified, as in this case, they can be analyzed based on the variables with high factor loadings on each element. These analyses can offer valuable insights into the underlying structure of the data and help comprehend the relationships between the variables.

3.3.3. Pattern Matrix of Variables

Table 10. Pattern matrix of variables.

Factor Loading Component	1	2
Planning control practices	0.812
Monitoring control practices	0.791
Reporting control practices	0.861
Analyzing control practices	0.866
Financial ability		0.623
Management skills		0.873
Cronbach’s Alpha (reliability test)	0.896	0.702

below shows the pattern matrix evidences the affiliation and the bond between project control practices and company operation and performance. The first factor integrates four variables related to control practices for planning, monitoring, reporting, and analyzing. In comparison, the second factor includes two variables, namely financial ability and management skills. Following [30,46], reliability is judged to be acceptable when Cronbach’s Alpha tumbles and drops within the range of 0.6 to 0.7, and it is pondered and considered satisfactory when ranging from 0.8 to 0.95. Values outside these ranges may suggest termination and redundancy; notably, Cronbach’s Alpha values in Table 10 fall within the acceptable thresholds, strengthening the validity of this study’s findings. An ordinary least square regression analysis was conducted to appraise the correlation between project control practices and the performance of the building construction company.

3.3.4. The Regression Model Summary

Table 11. The regression model summary.

Model	R	R Square	Adjusted R Square	Sign’
Control practices	0.435 a	0.186	0.184	0.001

“a” shows that the correlation coefficient (R) of 0.435 is statistically significant.

outlines the strength of the association between project control practices and the performance of building construction companies.

Two variables were used in the regression model that is to say the independent variable (project control practices) comprising project scheduling, project feasibility validation, budgeting for each activity, fundamental project route definition, team awareness of the budget, consultation from subcontractors, and routine inspection and the dependent variable (performance of building construction companies) comprises financial ability and management skills. The independent variable is used to predict and explain the dependent variable, as highlighted by [55]. These variables are analyzed to determine the association between the control practices and the performance of the companies.

The positive regression coefficient, R, is 0.435, which indicates a connection between independent and dependent variables (performance of building companies and control practices). The determination coefficient (R2) of 0.184 suggests that project control practices can explain 18.4 percent of the variability in the performance of building construction companies. This proposes that 18.4 percent of the performance variation can be attributed to the project team and stakeholders’ planning, monitoring, reporting, and analyzing control practices. The 0.0010.07 significance level shows how control practices are critical to building performance. Therefore, effective and efficient control practices are considered severe components that contribute to positive building performance, as asserted by [34]. This suggests that implementing adequate and efficient control practices in the construction industry could positively impact the performance of buildings in Kampala.

3.3.5. Regression Model Coefficients

The Regression model coefficients indicate the magnitude and direction of each independent variable’s effect on the dependent variable by representing the relationship between the independent and dependent variables.

Table 12. Regression model coefficients.

Model		Unstandardized Coefficients		Standardized Coefficients	T	Sig.
		B	Std. Error	Beta
1	(Constant)	1.780 × 10−16	0.621		0.000	1.00
	Control practices	0.406	0.095	0.435	7.62	0.000

below, according to the findings presented, shows a β1Value as the regression coefficient, which shows the change in the dependent variable (the performance indicator) for one unit change in the independent variable (control practices), keeping other factors constant, meaning that the β1 value of 0.435 suggests that for every one unit increase in control practices, there is a 43.5% increase in the performance indicators variable. The T value 7.62 is statistically significant at 0.000, indicating a low probability of obtaining such results by chance. Based on these results, the null hypothesis is rejected in favor of the alternate hypothesis, demonstrating that project control practices, the substantial β1 coefficient, and the T value support this conclusion, signifying that positive modifications in control practices are linked with positive changes in building construction companies’ performance indicators. This outcome aligns with the observations of [56], supporting the argument that critical project management practices are significantly associated with project performance and operation [55].

3.3.6. The Analysis of Variance (ANOVA)

Table 13. The Analysis of Variance (ANOVA).

Model		Sum of Squares	df	Mean Square	F	Sig.
1	Regression	31.678	4	31.678	49.598	0.00 b
	Residual	159.238	103	0.420
	Total	160.917	107

“b” indicates that the p-value is extremely small, likely less than the standard significance level of 0.05.

below presents the ANOVA, which assesses the statistical significance of the regression model and compares the networks between independent and dependent variables. The table below designates that these relationships collectively exhibit significance (F = 49.598; p = 0.000). This implies that the specified regression model is statistically significant and well incorporated [31]. This validates the understanding that the selected study variables (control practices) are joint predators of the dependent variables (performances of building companies), and the data parameters are ideal for determining the research variables. This outcome aligns with the petition by [52], which states a positive association between project control (premeditated planning) and company operation and performance.

4. Discussion

These research paper findings accentuate a significant relationship between project control practices and the performance of building construction companies in Uganda’s capital, Kampala. This research reveals compelling evidence through rigorous statistical analysis, which demonstrates that effective control practices contribute to positive results in building construction projects, and identifying specific control practices, such as planning, monitoring, and reporting, are crucial factors influencing company performance and provide valuable perceptions for stakeholders in the construction industry. The research employed well-established criteria, such as Cronbach’s Alpha test, to ensure the reliability and validity of the findings, enhancing the credibility of the research carried out.

A detailed analysis of the management practices in the context of project control measures and the company performance in building construction, as the results prove. This is carried out by squaring off different variables and practicing advanced statistical techniques. This study comprehensively explains how various control practices influence company purposes and performance. Similarly, the relevant and practical nature of this research is characterized by the use of empirical evidence collected from real-world data (actual data drawn from practical sites of construction), which plays a massive role in giving a solid base for the professionals in the construction industry and the decision makers to utilize. Such a holistic approach makes this research unique, pioneering, and distinct from the existing developments in construction management.

Even though this research has significantly advanced our perspective, a significant research gap still deserves further attention. While the analysis demonstrates a significant association between control practices and company performance, the specific instruments through which these practices influence outcomes may require deeper exploration. Factors such as project size, complexity, geographic location, and stakeholder dynamics may interact with control practices differently, modeling their impact on performance outcomes. Factors that may restrict the adoption and use of control practice implementation within construction firms include reluctance towards change, resource limits, poor technology, and organizational culture. Exploring these barriers and identifying strategies to overcome them represents a critical area for future research. Shedding light on the underlying mechanics and contextual factors determining the linkage between control policies and performance measures is one of the domains of future research work.

In a nutshell, considering the dynamic nature of the construction industry and evolving project management methodologies, there is a need for ongoing research to stay up to date on emerging trends and best practices. Future studies could explore the potential integration of emerging technologies, such as Building Information Modelling and Artificial Intelligence, into project control practices within the construction industry. Addressing these gaps would not only augment the scholarly contribution of this research but also provide actionable guidance for improving project management effectiveness in the construction sector, thus consolidating its position as an innovative endeavor in the construction discipline.

5. Conclusions

This paper inspected the level of control practices in the building construction companies in the city of Kampala, Uganda. Through a comprehensive analysis involving regression modeling and Analysis of Variance, it becomes evident that effective control practices, encompassing planning, monitoring, reporting, and analysis, play a critical role in shaping the performance indicators of construction projects in Kampala city. This research reveals a positive correlation between these control practices and key performance metrics such as financial ability, management skills, profitability, and completion time. Notably, the regression coefficient highlights that for every unit increase value of 0.435 in control practices, there is a substantial increase of 43.5 percent in the performance indicator. Furthermore, the results underscore the statistical significance of the regression model of 0.184, which suggests that project control practices can explain 18.4 percent of the variability in the performance of building construction companies, confirming the joint predictive power of control practices on building company performance. These findings underline the importance of strategic project management practices in enhancing overall operational efficiency and performance outcomes within the construction industry.

This research demonstrates the imperative role of effective project control practices in driving positive outcomes in building construction projects. The verdicts affirm that accurate planning, rigorous monitoring, and insightful analysis significantly enhance financial viability, management efficacy, and project success rates. With control practices explaining a lower percentage of 18.4 variability in performance, this study accentuates the need for stakeholders to prioritize adopting robust project management strategies. By embracing and implementing these practices, construction companies in Kampala can effectively navigate complexities, mitigate risks, and optimize performance, ultimately fostering sustainable growth and success in the ever-dynamic construction environment.

6. Recommendations

Based on the results and conclusions, contractors must carry out indispensable phases to improve project results by increasing monitoring and control efforts. This can be achieved by engaging critical stakeholders during the planning phase and proactively addressing financial and management concerns. To address the challenge of inadequate monitoring in the building construction industry, it is recommended that a standardized procedure be implemented. Comporting variance and trend analyses before each activity and action is indispensable for appropriate and effective corrective actions during the analysis phase, ensuring accurate and realistic information for cost control reporting. Furthermore, further research is essential to understand project control practices better, given the current limitations of existing studies. In conclusion, time and cost control are of dominant importance in the building construction industry and should be a priority for all stakeholders involved.

7. Limitations

Several potential challenges and limitations need to be considered in this research. One of the significant challenges is the skilled worker shortage, which means that the construction industry is facing substantial challenges. The fact that urbanization is the most pressing reason why projects are constantly growing makes it challenging to work effectively, as some tasks like planning and resource allocation require quality human capacity, which is missing. In addition, a prolonged take up of such recent technologies causes additional hurdles, with the young producers being disabled due to finance and awareness issues. This reinforces understanding of the particular problem and actions that may include interventions for upskilling and adopting innovative technologies locally.

Before performance evaluation, the primary task within the process is to combine an objective and subjective method of measuring those metrics. Success, client satisfaction, and quality indexes might behave in contradictory ways and deflect. Multiple stakeholder interests come on the stage and serve as a background for evaluation distortion. Thus, there may be inaccuracy in the evaluation of outcomes. The application of rules for criteria standardization is the main factor that supports trust and honesty in the industry.

Research in Kampala’s construction sector is facing challenges due to factors such as the restricted availability of researchers, economic status, and political situation. The shortage of expertise in the field also affects the credibility of the study results and hinders evidence-based decision making. Furthermore, researchers must navigate complex legal procedures, and inadequate infrastructure, such as an unstable communication system, makes obtaining reliable data difficult.

Despite these obstacles, solutions can be found by addressing regulatory and infrastructure barriers, improving access to skilled labor, encouraging technology adoption, and simplifying regulations. Collaboration among stakeholders is crucial for ensuring sustainable growth in Kampala’s construction sector. The industry can establish quality and efficiency benchmarks throughout Uganda by investing in strategic initiatives.