Multivariate Analysis of Factors Influencing Construction Costs in Saudi Arabia

Abstract

Cost overruns present a continuing challenge within the construction industry worldwide, carrying substantial financial consequences for project stakeholders, specifically in developing economies such as Saudi Arabia. This study employs a dual-method approach combining Principal Component Analysis (PCA) and Analysis of Variance (ANOVA) to comprehensively analyze the factors influencing construction cost during the construction phase in Saudi Arabia. Utilizing survey data collected from 1076 engineers working in the construction industry of Saudi Arabia, PCA identified three key components: (1) project management and technical deficiencies, (2) external and regulatory influences, and (3) financial and economic risks. Meanwhile, the ANOVA examined differences in the perception of those factors across several demographics and project-specific characteristics. Findings highlight critical areas where focused intervention could improve cost management practices. Combining these results, this study presents an integrated framework for construction industry stakeholders with helpful recommendations. This integrated analysis provides a robust framework for construction professionals and policymakers to prioritize cost management strategies, in accordance with Saudi Arabia’s Vision 2030 goals to encourage a resilient and efficient construction industry.

1. Introduction

Cost overruns represent a significant issue within the construction industry and pose a risk to the financial independence of any nation [1]. This challenge is not only widespread, but also growing, as current projects increase in complexity and scale. The construction industry consistently experiences cost overruns during the construction phase, specifically in large-scale and complex infrastructure projects [2]. These challenges cause significant concerns, as cost inefficiencies reduce resources and delay timely project completion. Inadequate cost performance has been identified as a global concern [3], leading to substantial cost overruns encountered by the industry internationally [4]. Cost overruns have traditionally been common rather than the exception in both developed and developing nations [5], with delays and cost overruns especially evident in developing countries [6]. Limited resources and increased risks enhance the negative impacts of overruns. Generally, any expense exceeding the allocated budget is classified as a cost overrun, an ongoing issue that affects construction projects globally [7]. Overcoming this widespread problem is essential for promoting economic practices and achieving sustainable development in the industry.

Previous research studies mostly explored individual factors that affect construction costs during the construction phase [8]. While these findings offer significant insights into cost drivers, they lack structured clustering of these factors, allowing stakeholders to focus on specific areas more efficiently. Thus, through Principal Component Analysis (PCA), this paper will identify the cluster of factors influencing construction cost. Furthermore, this paper will go deeper in analysis by exploring through Analysis of Variance (ANOVA) tests how perceptions of these factors differ across stakeholder demographics and project-specific characteristics. Combining these results, this study presents an integrated framework for construction industry stakeholders with helpful recommendations and a comprehensive understanding of construction cost-influencing factors during the construction phase in Saudi Arabia. This integrated framework can be implemented through regulatory alignment, industry training, project planning, and contractual adjustments.

The objectives of this study are as follows:

• Use PCA to identify clusters of factors influencing construction costs specific to Saudi Arabia.
• To examine the variability of these factors across demographic and project-specific characteristics using an ANOVA test.
• To establish an integrated framework for construction industry stakeholders with structured recommendations to improve cost management practices and mitigate cost overruns.

Overcoming the issues related to cost overruns may significantly enhance industry efficiency and support broader national objectives, such as ‘Saudi Arabia’s Vision 2030’ [9]. Although the construction cost-influencing factors were identified in a previous research study, a more systematic approach to these factors is necessary for improved cost control strategies. This research study clusters cost-influencing factors as well as how participant demographics and project-specific characteristics affected various factors influencing construction cost, enhancing previous research and presenting a new perspective for targeted intervention. Furthermore, it will provide construction professionals and policymakers with an integrated framework, which will enhance their capacity to prioritize management initiatives. Moreover, it will advocate for a more data-driven strategy to cost management, thereby enhancing the efficiency of Saudi Arabia’s construction industry as it rapidly develops.

2. Literature Review

2.1. Global Perspective on Cost Overruns

Cost overruns are common in the construction industry and frequently result in disputes among project stakeholders, particularly between government owners, project managers, and contractors [10]. These disputes may result in delays and greater expenses, thereby worsening the difficulties caused by the overruns. Cost changes and overruns frequently occur in infrastructure projects [11], typically resulting from inadequate project cost management. This highlights the necessity for robust cost control measures, as weaknesses in this area directly affect project success. Effective project cost control is vital as it significantly influences project decision-making and comprehensive investment management [12]. The absence of clear financial oversight threatens projects with uncontrollable costs. A study concluded that a well-defined project scope in the contract and stringent cost management are significant factors in preventing cost overruns [13]. By defining clear project limits, stakeholders can identify potential challenges and mitigate them immediately. To mitigate the effects of cost overruns, it is necessary to identify and comprehend the underlying causes and contributing factors [14]. The causes may be diverse and complex, requiring a comprehensive understanding of several economic and operational components. The evaluation of construction cost is complicated, including inflation, seasonal variations, and variable construction activity levels [15]. These fluctuations introduce complexities and uncertainty, making precise cost control difficult. Cost overruns in construction projects are frequently linked to insufficient professionalism among stakeholders engaged in the construction process from concept through to completion [16]. The absence of professionalism may be obvious through negligence, inadequate planning, and inadequate communication. A study revealed that project cost is influenced by both project-specific characteristics and the attributes of the project team, suggesting that it is determined by a combination of interrelated variables rather than a singular factor [17]. Therefore, cost control requires a comprehensive approach that considers various variables. Clients and contractors are the most significant factors, followed by issues pertaining to building information modeling, fabrication, and external influences [18]. Thus, internal inefficiencies, external factors, and financial uncertainties collectively cause construction cost overruns. Where internal inefficiencies include factors such as poor planning, external factors include factors such as regularity changes, and financial uncertainties include factors such as currency exchange rate fluctuations. Effective engagement and the behavior of project stakeholders are crucial for addressing these cost overrun factors [7], particularly in distinctive projects where proactive decision-making may prevent unnecessary expenditures [18]. In these cases, transparent communication and early intervention are vital for mitigating financial risk.

2.2. Cost Management and Estimation Challenges

Cost estimation is an essential function for capital projects, valuable to both project owners and engineering/construction firms by offering a fundamental comprehension of project expenses [19]. This comprehension is essential, as accurate estimations establish the foundation for all-succeeding financial planning. Accurate cost estimates fulfill various functions, including facilitating organizational budgeting, aiding in loan applications, calculating financing expenses, and assessing the project’s commercial viability [20]. These functions demonstrate the significant impact of accurate estimates, going beyond project implementation to affect the broad organizational strategy. Preliminary estimates fulfill multiple critical functions, such as providing a foundation for cost–benefit analysis, assisting in the selection of potential delivery partner, informing the decision to construct or not, and acting as a standard for upcoming performance evaluations [21]. Therefore, preliminary estimates perform as both a forecasting and a directional tool for project stakeholders. Accurate cost estimation is essential for project success, as it indicates the effectiveness of pre-contract cost estimation and tender pricing procedures [5]. Nevertheless, when inaccuracies occur, they frequently escalate into more significant problems, affecting project completion and financial objectives. The frequent occurrence of cost overruns in project delivery indicates a substantial deficiency in comprehending the impact of risks on construction cost estimates, emphasizing the importance for enhanced risk awareness among estimators [22]. Addressing this weakness requires a systematic approach to risk identification and inclusion into the estimation process. By resolving known cost-influencing factors, estimators can enhance the accuracy of their estimates and more effectively align project budgets with actual expenditures [23]. Consequently, accurate and practical cost and time assessments are essential for effective project cost management and for achieving financial expectations [24]. These evaluations establish a foundation for project finances, facilitating improved resource distribution and risk management during the project lifecycle.

2.3. The Saudi Construction Industry and Cost Challenges

The construction industry in Saudi Arabia is undergoing substantial expansion driven by the demand for Saudi Arabia’s Vision 2030 initiatives, particularly large-scale projects [25]. This ambitious national strategy has prioritized infrastructure development as a fundamental element for economic diversification. In the last twenty years, Saudi Arabia has experienced a remarkable construction rise, characterized by a rapid growth of new cities, airports, public and private buildings, highways, and other infrastructure projects [25]. These developments are transforming the nation’s urban environment and generating substantial opportunities for industry stakeholders. This expansion has attracted construction specialists worldwide, supported by the country’s robust economy and significant oil revenues [25]. Nevertheless, together with these opportunities, the construction industry encounters several challenges, specifically in the management of project costs. Cost overruns continue to be a significant challenge in the Saudi construction industry, adversely impacting projects concerning time, cost, quality, and safety [25]. These overruns not only increase project budgets, but also generate delays and affect overall project quality. Various factors lead to cost overruns in Saudi construction projects, such as insufficient planning, design changes, site conflicts, financial management issues on-site, and limited experience [26]. This study aimed to identify the clusters of factors influencing construction costs in Saudi Arabia. By classifying these factors, it becomes easier to use more specific strategies for cost management. Thus, by comprehending and addressing these specific causes of cost, the Saudi construction industry can more effectively navigate the complexities introduced by rapid and large-scale development demands.

3. Methodology

This research builds upon the 2024 study by Mosly, utilizing the same dataset collected through a questionnaire survey [8]. The previous study explored the individual factors influencing costs during the construction phase in Saudi Arabia [8], whereas this study implements PCA and ANOVA to identify structured clusters of cost-influencing factors. The number of respondents who participated in this questionnaire survey were 1076 engineers working in the construction industry of Saudi Arabia [8]. The respondents were selected using stratified random sampling to ensure representation across different specialization, experience levels, and project types. The Research Ethics Committee at King Abdulaziz University exempted this study from ethical approval. Prior to beginning the questionnaire, participants were informed of the purpose of this study, and their consent was obtained. Data were anonymized to protect participants’ identities, and all responses were securely archived. The data analysis was conducted utilizing SPSS (Statistical Package for the Social Sciences), version 22. Figure 1 presents the methodology diagram followed in this research.

3.1. Principal Component Analysis (PCA)

The dataset used was assessed for suitability by the Kaiser–Meyer–Olkin (KMO) measure of sampling adequacy and Bartlett’s Test of Sphericity [27]. The KMO was 0.937, indicating excellent adequacy of the sample. Moreover, the Bartlett’s Test of Sphericity was highly significant at p < 0.001, confirming that the dataset was adequate for factor analysis.

PCA is a statistical technique that categorizes variables into different components [28]. This method is especially effective in identifying hidden patterns within huge datasets, simplifying complex relationships into understandable themes. Each component consists of a collection of homogenous, well-aligned factors that describe a theme [28]. In exploratory factor analysis, PCA with varimax rotation is employed to categorize the variables [9], making it easier to interpret the identified components. Furthermore, many studies have used PCA in the construction industry [9,28,29,30]. Each of these studies illustrates PCA’s effectiveness in providing actionable insights, enabling stakeholders to address specific cost factors within their respective construction sectors.

PCA was used for factor extraction with an aim to reduce the dimensionality of the dataset and explain the maximum amount of variance. In accordance with Kaiser’s criterion, factors were extracted based on eigenvalues greater than 1. Varimax was implemented to enhance the interpretability of the factor solution. The rotation converged in 7 iterations. The factor analysis resulted in three extracted components that explained a cumulative total of 55.45% of the variance. The rotated component matrix was used for the interpretation of the factors. Variables with loadings greater than 0.4 were considered significant for each factor. The factor analysis resulted in reducing the original dataset into three key components that identify the main factors influencing construction cost during the construction phase in Saudi Arabia.

3.2. Analysis of Variance (ANOVA)

Examining how a number of participant demographics and project-specific characteristics affected various factors influencing construction cost during the construction phase in Saudi Arabia was the aim of the ANOVA. Several studies have used ANOVA to analyze their datasets and come up with interesting findings that improve the construction industry [31,32,33,34,35].

In order to uncover differences in cost-related perceptions among several participant demographics and project-specific characteristics, ANOVA was selected since it enables the comparison of mean differences across many groups [27]. The 18 factors identified by [8] were adopted and considered as the dependent variables in this study. Participant demographics and project-specific characteristics data collected from the same study were considered here as the independent variables, including age, specialization, academic qualification, experience, project type, project size, and project location.

The assumptions of ANOVA were verified prior to the analysis. This included normality, homogeneity of variance, and independence of observations. The dataset met these assumptions and analyses were carried out.

In order to evaluate variations in mean responses among participant demographics and project-specific characteristics, each component influencing construction cost was examined independently. To ascertain the differences, the F-statistics and associated p-values were analyzed. Participant demographics and project-specific characteristics differences were deemed significant for factors whose p-value were less than the significance level of 0.05. To identify particular changes between participant demographics and project-specific characteristics groups, post hoc tests were performed for factors that displayed significant ANOVA findings. This allows for pairwise comparisons and can account for type 1 errors in multiple comparisons [27].

3.3. Integrated Framework

The combination of PCA and ANOVA findings resulted in the development of targeted strategies. These were incorporated in an integrated framework that offers industry stakeholders helpful recommendations to address critical challenges. This framework is based on multivariate analysis and addresses the practical needs of industry stakeholders, offering a comprehensive perspective on managing construction costs.

4. Results and Discussion

The results and discussion are structured into four subsections: Section 4.1 KMO findings to assess data suitability, Section 4.2 Bartlett’s Test of Sphericity findings to confirm relationships among variables, Section 4.3 PCA findings to identify key cost factor clusters, and Section 4.4 ANOVA findings to examine how factors vary across demographics and project-specific characteristics.

4.1. Kaiser–Meyer–Olkin (KMO)

The KMO test determines if the data is suitable for factor analysis [27]. The KMO value for this study data was found to be 0.937. A KMO value that is above 0.9 is considered superb [36], indicating highly suitable data for factor analysis.

4.2. Bartlett’s Test of Sphericity

The Bartlett’s Test of Sphericity assesses the existence of a relationship among the study variables [27]. A Bartlett’s test that is p < 0.001 is considered highly significant and indicates that factor analysis is appropriate to implement [36]. The results of this study show that the Bartlett’s test is highly significant at a value of p < 0.001, and that the variables are related enough to proceed with factor analysis.

4.3. PCA with Varimax Rotation

The rotated component matrix illustrates the final factor loadings after rotation (see

Table 1. Summary of exploratory factor analysis results.

Component Number	Eigenvalue	% of Variance	Component	Factor	Factor Loading
1	5.253	29.186	Project management and Technical deficiencies	Inadequate management of project, contract, communication	0.797
				Inadequate planning and scheduling	0.786
				Lack of technical knowledge and experience	0.743
				Inadequate cost estimation	0.741
				Design error/weakness	0.680
				Poor/unclear drawing	0.657
				Rework	0.638
				Design changes	0.634
				Staff corruption	0.511
				Equipment breakdowns and inefficiencies	0.401
2	2.752	15.287	External and Regulatory Influences	Social and cultural influences	0.795
				Safety issues and accidents	0.652
				Governmental regulations	0.560
				Legal disputes between various parties	0.512
				Force majeure and environmental issues	0.485
3	1.977	10.982	Financial and Economic Risks	Currency exchange rate fluctuations	0.784
				Economic fluctuation/market price changes	0.740
				Delay in project/owner payment	0.414

Extraction Method: Principal Component Analysis. Rotation Method: Varimax with Kaiser Normalization.

). The results show that three extracted components explained a cumulative total of 55.45% of the variance. Peterson (2000) states that the average variance explained in social science factor analysis is approximately 56%, with a commonly accepted threshold of 50% [37,38]. Although a higher variance explanation is preferable, surpassing 50% is typically adequate in behavioral and social science research. The remaining 44.55% likely signifies project-specific nuances, unexpected conditions, and other latent factors not included in the dataset. Component 1 accounted for 29.186% of the variance, component 2 accounted for 15.287% of the variance, and component 3 accounted for 10.982% of the variance (see Figure 2).

4.3.1. Component 1: Project Management and Technical Deficiencies

This component represents project management and technical deficiencies, with high loadings from 10 cost-influencing factors during the construction stage, including inadequate, management of project, contract, communication, inadequate planning and scheduling, lack of technical knowledge and experience, inadequate cost estimation, design error/weakness, poor/unclear drawing, rework, design changes, staff corruption, and equipment breakdowns and inefficiencies. This component pinpoints the internal issues in managing projects effectively. Internal deficiencies can lead to inefficiencies, delays, and higher costs in construction projects. To address this component, it is essential that the project management process is improved. This could be done by adopting the most recent project management software that allows tracking of the schedule, resources, and costs in real time. Furthermore, more focus should be given to training and development by investing in skilled personnel with sound technical knowledge and supporting them with continuous professional development. Moreover, it is vital that better communication channels are promoted and a collaborative environment is fostered that prioritizes clear communication.

4.3.2. Component 2: External and Regulatory Influences

This component reflects external and regulatory influences, with high loadings from five cost-influencing factors during the construction stage, including social and cultural influences, safety issues and accidents, governmental regulations, legal disputes between various parties, and force majeure and environmental issues. These factors represent external pressures and risks that can affect project outcomes. Issues that can arise outside the project team’s control were clustered under this component.

To address this component, a dedicated team with up-to-date knowledge on local laws and regulations must be established to ensure that all project aspects comply with these mandatory regulations. Furthermore, ongoing safety training and audits must be carried out to guarantee the implementation of safety standards and protocols. Force majeure events can be addressed through risk management plans that include strategies to prepare for these events through insurance coverage, flexible timelines, and contingencies. For legal disputes, it is important to engage legal advisors in the early stages to resolve disputes quickly and avoid lengthy litigation that can lead to project delays.

4.3.3. Component 3: Financial and Economic Risks

This component represents financial and economic risks, with high loadings from three cost-influencing factors during the construction stage, including currency exchange rate fluctuations, economic fluctuations/market price changes, and delays in project/owner payment. These factors indicate the financial uncertainties that can affect a construction project. Furthermore, project stability and profitability can also be influenced by these factors.

A number of strategies can help with such component risks, including currency hedging, which can lock in favorable rates and reduce volatility impact. Furthermore, the use of contracts that provide for price adjustments based on market fluctuation helps to ensure that contractors are not unfairly troubled by an unexpected increase in material cost. Moreover, the inclusion of penalties for late owner payments in contracts as well as an enforceable payment schedule will ensure that owners meet their obligations.

4.4. ANOVA

4.4.1. Age Effect on Factors Influencing Construction Cost in Saudi Arabia

ANOVA was conducted to explore the effect of age on the factors influencing the construction cost in Saudi Arabia. The age groups included, 1. 20–25 years, 2. 26–35 years, 3. 36–45 years, 4. 46–55 years, and 5. more than 55 years. The results show significant differences in perception for several factors across age groups (p < 0.05).

Table 2. ANOVA Significant Results for the Effect of Age on Factors Influencing Construction Costs.

Factor	F (df)	p-Value
Currency Exchange Rate Fluctuations	2.495	0.041
Delays in Owner Payments	2.981	0.018
Legal Disputes Among Various Parties	2.590	0.035
Poor/Unclarified Drawings	3.431	0.008

illustrates the results with significance for age vs. factors.

Significant differences were found for currency exchange rate fluctuations, delays in owner payments, legal disputes among various parties, and poor/unclarified drawings. These factors with significant ANOVA results were further analyzed via post hoc pairwise comparisons of age groups for significant factors.

Table 3. Post Hoc Pairwise Comparisons of Age Groups for Significant Factors.

Factor	Age Groups Compared	p-Value
Legal Disputes Among Various Parties	46–55 vs. More than 55	0.021
Poor/Unclarified Drawings	20–25 vs. More than 55	0.041
	26–35 vs. More than 55	0.012

presents the post hoc results with significant differences (p < 0.05).

Although there were significant differences in currency exchange rate fluctuations and delays in owner payments, their post hoc analysis did not reach statistical significance. On the other hand, for legal disputes among various parties, participants aged 46–55 reported higher concerns compared to those aged more than 55 (p = 0.021). Furthermore, for poor/unclarified drawings, the age group 20–25 reported lower concerns compared to those aged more than 55 (p = 0.41), as well as those aged 26–35 (p = 0.012).

Older age groups generally perceive greater significance for certain factors compared to younger age groups. This could be due to their longer experience in the industry and probably to encountering these events during their career. Specifically, concerns such as currency exchange rate fluctuations, delays in owner payments, legal disputes among various parties, and poor/unclarified drawings tend to be greater among older age groups.

Mitigating these concerns might require specific communication and education for different age groups. For instance, implement specialized training for junior professionals regarding the significance of financial stability, accurate documentation, and legal frameworks to enhance their knowledge and competencies. Furthermore, facilitate communication channels among different age groups to exchange experience and effective strategies for addressing payment delays, managing fluctuations in currencies, and resolving legal conflicts. Moreover, implement policy modifications to guarantee prompt payments, enhances financial planning, and improved quality control for project documentation.

4.4.2. Specialization Effect on Factors Influencing Construction Cost in Saudi Arabia

ANOVA was conducted to explore the effect of specialization on the factors influencing construction costs in Saudi Arabia. The specialization groups included, 1. civil engineering, 2. architecture, 3. mechanical engineering, 4. electrical engineering, 5. industrial engineering, 6. health and safety engineering, 7. chemical engineering, 8. computer engineering, and 9. mining engineering. The results show significant differences in perception for several factors across specialization groups (p < 0.05).

Table 4. ANOVA Significant Results for the Effect of Specialization on Factors Influencing Construction Costs.

Factor	F (df)	p-Value
Government Regulations	2.023	0.041

illustrates the results with significance for specialization vs. factors.

Significant differences across specialization groups were found for government regulations. This factor with significant ANOVA results was further analyzed via post hoc pairwise comparisons of specialization groups for significant factors.

Table 5. Post Hoc Pairwise Comparisons of Specialization Groups for Significant Factors.

Factor	Specialization Groups Compared	p-Value
Government Regulations	Civil Engineering vs. Mechanical Engineering	0.041

presents the post hoc results with significant differences (p < 0.05).

For government regulations, a significant difference was found between civil engineers and mechanical engineers (p = 0.021). This could be due to civil engineering projects frequently involving substantial structures (e.g., bridges, roads, dams) and public works, which are generally governed by strict government regulations and oversight, including environmental compliance, zoning, and safety standards. On the other hand, mechanical engineering projects typically focus on systems that are within structures (e.g., HVAC, piping) and might not be subject to the same degree of direct oversight associated with civil engineering projects.

In projects that involve both civil engineers and mechanical engineers, it is preferable to consider regulatory compliance requirements at the start of the planning phase. Identifying the differences may enhance interdisciplinary collaboration, as teams gain awareness of one another’s regulatory challenges. Also, interdisciplinary workshops can improve collaboration where engineers can share insights on regulatory and cost management issues. Furthermore, regulation-intensive projects, such as public infrastructures, may dedicate additional resources to civil engineering positions to guarantee compliance to regulatory standards.

4.4.3. Academic Qualification Effect on Factors Influencing Construction Cost in Saudi Arabia

ANOVA was conducted to explore the effect of academic qualification on the factors influencing the construction cost in Saudi Arabia. The academic qualification groups included, 1. bachelor degree, 2. master’s degree, and 3. Ph.D. The results show significant differences in perception for several factors across academic qualification groups (p < 0.05).

Table 6. ANOVA Significant Results for the Effect of Academic Qualification on Factors Influencing Construction Costs.

Factor	F (df)	p-Value
Design Changes	6.654	0.001
Government Regulations	7.000	0.001

illustrates the results with significance for academic qualification vs. factors.

Significant differences were found for design changes and government regulations. These factors with significant ANOVA results were further analyzed via post hoc pairwise comparisons of academic qualification groups for significant factors.

Table 7. Post Hoc Pairwise Comparisons of Academic Qualification Groups for Significant Factors.

Factor	Academic Qualification Groups Compared	p-Value
Design Changes	Bachelor vs. Master’s	0.003
Government Regulations	Bachelor vs. Master’s	0.007
	Bachelor vs. Ph.D	0.038

presents the post hoc results with significant differences (p < 0.05).

For design changes, a significant difference was found between bachelor and master’s degree holders (p = 0.003). For government regulations, a significant difference was found between bachelor and master’s degree holders (p = 0.007). Additionally, a significant difference was found between bachelor and Ph.D. degree holders (p = 0.038).

Master’s degree holders usually hold managerial positions over bachelor degree holders or more work experience, suggesting why they perceive design changes more significantly than those with a bachelor degree. With respect to government regulations, higher academic qualifications may be associated with greater awareness regarding government regulations compared to bachelor degree holders.

Thus, it is important to design and implement customized training programs that correspond to the different requirements and perspectives of different academic qualification groups. Furthermore, mentorship programs should be implemented so that individuals holding higher academic qualifications can share their experiences on design changes and government regulations to those with bachelor degrees.

4.4.4. Experience Effect on Factors Influencing Construction Cost in Saudi Arabia

ANOVA was conducted to explore the effect of experience on the factors influencing construction costs in Saudi Arabia. The experience groups included, 1. less than 5 years, 2. 5–10 years, 3. 11–15 years, 4. 16–20 years, and 5. more than 20 years. The results show significant differences in perception for several factors across experience groups (p < 0.05).

Table 8. ANOVA Significant Results for the Effect of Experience on Factors Influencing Construction Costs.

Factor	F (df)	p-Value
Currency Exchange Rate Fluctuations	3.527	0.007
Delays in Owner Payments	3.017	0.017
Economic Fluctuations Market Price Changes	2.975	0.019
Equipment Breakdowns and Inefficiencies	2.574	0.036
Staff Corruption	4.155	0.002
Poor Unclarified Drawings	2.591	0.035

illustrates the results with significance for experience vs. factors.

Significant differences were found for currency exchange rate fluctuations, delays in owner payments, economic fluctuations market price changes, equipment breakdowns and inefficiencies, staff corruption, and poor unclarified drawings. These factors with significant ANOVA results were further analyzed via post hoc pairwise comparisons of experience groups for significant factors.

Table 9. Post Hoc Pairwise Comparisons of Experience for Significant Factors.

Factor	Experience Groups Compared	p-Value
Currency Exchange Rate Fluctuations	Less than 5 vs. more than 20	0.008
Delays in Owner Payments	No pairwise comparisons significant	-
Economic Fluctuations Market Price Changes	5–10 vs. more than 20	0.014
Equipment Breakdowns and Inefficiencies	Less than 5 vs. 16–20	0.022
Staff Corruption	Less than 5 vs. 11–15	0.002
	Less than 5 vs. 16–20	0.039
	Less than 5 vs. more than 20	0.036
Poor Unclarified Drawings	Less than 5 vs. more than 20	0.027

presents the post hoc results with significant differences (p < 0.05).

For currency exchange rate fluctuations, a significant difference was found between participants with less than 5 years of experience and participants with more than 20 years of experience (p = 0.008). For delays in owner payments, no pairwise comparison significance was found between the different experience groups. For economic fluctuations and market price changes, significant differences were found between those with 5–10 years of experience and participants with more than 20 years of experience (p = 0.014). Furthermore, for equipment breakdowns and inefficiencies, participants with less than 5 years of experience perceived this factor differently from those with 16–20 years of experience (p = 0.022). For staff corruption, several significant differences were found, specifically between participants with less than 5 years of experience and those with 11–15 years of experience (p = 0.002), 16–20 years of experience (p = 0.039), and more than 20 years of experience (p = 0.036). For poor unclarified drawings, significant differences was found between participants with less than 5 years and those with more than 20 years of experience (p = 0.027).

To enhance the working environment with different expertise groups, a number of implications can be considered. For example, collaboration should be encouraged among teams with different levels of expertise. Experienced professionals can offer guidance and mentor younger workers on sophisticated matters such as financial risks, equipment reliability, and ethical standards. Also, customized training workshops could be offered according to experience levels, emphasizing advanced risk management and technical supervision for experienced personnel, while providing fundamental knowledge to less experienced employees. Furthermore, it is suggested that a continuous feedback systems is implemented that enables experienced professionals to mentor and guide newer employees, encouraging a culture of knowledge sharing and continuous professional development.

Ultimately, as professionals gain more industry experience, their perception of these influencing factors evolve due to increased exposure to contractual complexities, financial management challenges, and stakeholder negotiations.

4.4.5. Project Type Effect on Factors Influencing Construction Cost in Saudi Arabia

ANOVA was conducted to explore the effect of project type on the factors influencing the construction cost in Saudi Arabia. The project type groups included, 1. residential construction, 2. commercial construction, 3. industrial construction, and 4. infrastructure construction. The results show significant differences in perception for several factors across project type groups (p < 0.05).

Table 10. ANOVA Significant Results for the Effect of Project Type on Factors Influencing Construction Costs.

Factor	F (df)	p-Value
Equipment Breakdowns and Inefficiencies	7.363	0.000
Force Majeure and Environmental Issues	4.934	0.002
Safety Issues and Accidents	3.044	0.028

illustrates the results with significance for project type vs. factors.

Significant differences were found for equipment breakdowns and inefficiencies, force majeure and environmental issues, and safety issues and accidents. These factors with significant ANOVA results were further analyzed via post hoc pairwise comparisons of project type groups for significant factors.

Table 11. Post Hoc Pairwise Comparisons of Project Type for Significant Factors.

Factor	Project Type Groups Compared	p-Value
Equipment Breakdowns and Inefficiencies	Residential vs. Infrastructure	0.001
	Commercial vs. Infrastructure	0.000
Force Majeure and Environmental Issues	Residential vs. Infrastructure	0.009
	Commercial vs. Infrastructure	0.047
	Industrial vs. Infrastructure	0.006
Safety Issues and Accidents	Residential vs. Infrastructure	0.024

presents the post hoc results with significant differences (p < 0.05).

For equipment breakdowns and inefficiencies, a significant difference was found between residential and infrastructure projects (p = 0.001), as well as commercial and infrastructure projects (p = 0.000). For force majeure and environmental issues, several significant differences were found, with infrastructure project engineers perceiving these issues as more significant compared to residential (p = 0.009), commercial (p = 0.047), and industrial (p = 0.006) engineers. Furthermore, for safety issues and accidents, a significant difference was found between residential and infrastructure project engineers (p = 0.024).

Thus, is it important to implement strong equipment maintenance and efficiency measures, particularly for infrastructure projects, as a result of equipment breakdowns and inefficiencies. Furthermore, it is important to improve risk management and backup plans for environmental and force majeure problems in infrastructure projects. Moreover, to address concerns about poor planning, especially for infrastructure projects, improve planning and scheduling procedures. For safety issues and accidents, mitigate increased perceived risks by reinforcing safety procedures and training for infrastructure projects.

4.4.6. Project Size Effect on Factors Influencing Construction Cost in Saudi Arabia

ANOVA was conducted to explore the effect of project size on the factors influencing construction costs in Saudi Arabia. The project size groups included, 1. less than SAR 1 million, 2. SAR 1–5 million, 3. SAR 6–10 million, 4. SAR 11–20 million, and 5. more than SAR 20 million. The results show significant differences in perception for several factors across project size groups (p < 0.05).

Table 12. ANOVA Significant Results for the Effect of Project Size on Factors Influencing Construction Costs.

Factor	F (df)	p-Value
Currency Exchange Rate Fluctuations	3.475	0.008
Design Errors Weaknesses	2.433	0.048
Economic Fluctuations Market Price Changes	2.643	0.032
Force Majeure and Environmental Issues	2.690	0.030

illustrates the results with significance for project size vs. factors.

Significant differences were found for currency exchange rate fluctuations, design errors weaknesses, economic fluctuations market price changes, and force majeure and environmental issues. These factors with significant ANOVA results were further analyzed via post hoc pairwise comparisons of project size groups for significant factors.

Table 13. Post Hoc Pairwise Comparisons of Project Size for Significant Factors.

Factor	Project Size Groups Compared	p-Value
Currency Exchange Rate Fluctuations	1–5 million vs. More than 20 million	0.031
Design Errors Weaknesses	1–5 million vs. More than 20 million	0.022
Economic Fluctuations Market Price Changes	1–5 million vs. More than 20 million	0.036
Force Majeure and Environmental Issues	Less than 1 million vs. 1–5 million	0.070

presents the post hoc results with significant differences (p < 0.05).

For currency exchange rate fluctuations, a significant difference was found between projects of SAR 1–5 million and more than SAR 20 million (p = 0.031). Also, for design errors weaknesses, a significant difference was found between projects of SAR 1–5 million and more than SAR 20 million (p = 0.022). For economic fluctuations market price changes, a significant difference was found between projects of SAR 1–5 million and more than SAR 20 million (p = 0.036). Furthermore, for force majeure and environmental issues, a significant difference was found between projects of less than SAR 1 million and SAR 1–5 million (p = 0.070).

There are several implication that can assist with the effect of project size effect on factors influencing construction costs, including reducing the effects of currency exchange rate fluctuation for larger projects by setting strong finance procedures in place. In addition, making sure that quality control procedures and design reviews are comprehensive, particularly for larger projects. Also, create backup plans and adaptable spending plans to account for changes in market price and the economy, especially for larger projects. For smaller projects, risk management and contingency strategies for environmental and force majeure should be improved.

4.4.7. Project Location Effect on Factors Influencing Construction Cost in Saudi Arabia

No factors demonstrated statistically significant differences in the ANOVA across project location in Saudi Arabia. This may indicate that the influence of these factors on construction cost is considered universal across different regions, which can be encouraging for setting a standardized cost management strategy that is considered applicable for all Saudi regions. Post hoc analysis was not performed because no factors showed significant differences across project locations. The project location groups included, 1. Rabigh, 2. Riyadh, 3. Taif, 4. Jeddah, 5. Al Khobar, 6. Makkah, 7. Al Jubail, 8. Neom and Red Sea Project, 9. Jazan, 10. Madinah, 11. Sakakah, 12. Hail, 13. Al Qassim, 14. Abha, 15. Yanbu, 16. AlUla, 17. Tabuk, 18. Al Hasa, 19. Najran, 20. Buraydah, 21. Dammam, and 22. Al Bahah.

5. Integrated Outcome: A Stakeholder-Centric Framework for Cost Management

5.1. Project Management and Technical Deficiencies

PCA findings: Inadequate management of projects, contracts, communication, inadequate planning and scheduling, lack of technical knowledge and experience, inadequate cost estimation, design error/weakness, poor/unclear drawing, rework, design changes, staff corruption, and equipment breakdowns and inefficiencies are critical cost drivers.

ANOVA findings: Due to limited experience, young engineers perceive the issues under this category as less critical compared to older engineers. Furthermore, bachelor degree holders showed less awareness of design-related issues compared to those with postgraduate qualifications.

Stakeholders recommendations:

• Employ advanced project management tools for the real-time monitoring of timelines, financial allocations, and resources.
• Establish structured mentorship programs in which experienced professionals mentor junior staff in planning and technical competencies.
• Implement periodic training, hands-on workshops, and certification for construction professionals highlighting design precision, strategic planning, and effective communication, in addition to providing access to digital knowledge-sharing platforms to document best practices in cost management.
• Encourage the utilization of Building Information Modeling (BIM) to reduce errors and improve coordination.

5.2. External and Regulatory Influences

PCA findings: Social and cultural influences, safety issues and accidents, governmental regulations, legal disputes between various parties, and force majeure and environmental issues significantly influence costs.

ANOVA findings: Civil engineers are more affected by regulatory complexity compared to other engineering specializations. Additionally, infrastructure projects face greater regulatory and environmental challenges than other types of projects.

Stakeholders recommendations:

• Establish specialized compliance teams to oversee and cope with regulatory modifications and safety standards.
• Formulate contingency plans for force majeure events, including insurance protection and adjustable scheduling.
• Optimize regulatory procedures and establish clear guidelines to mitigate compliance-related delays.
• Enhance environmental risk-management policies specifically for infrastructure projects.

5.3. Financial and Economic Risks

PCA findings: Currency exchange rate fluctuations, economic fluctuation/market price changes, and delays in project/owner payment are primary financial risks.

ANOVA findings: Highly experienced professionals view financial risks as more critical than those with less experience. Moreover, it was found that larger projects are more vulnerable to economic risks compared to smaller ones.

Stakeholders recommendations:

• Employ financial hedging strategies to minimize currency risks and incorporate price adjustment provisions in contracts.
• Implement strict payment schedules and incorporate penalties for delayed payments to guarantee cash flow stability.
• Encourage financial transparency and accountability in project financing to increase trust among stakeholders.
• Provide financial awareness workshops for construction professionals to enhance their capacity to predict and mitigate economic risks.

5.4. Targeted Recommendations for Key Stakeholder

• Contractors: Adoption of project management tools, BIM, training, and implementing financial safeguards.
• Project managers: Improved planning and scheduling protocols, communication strategies, and risk assessment procedures.
• Policymakers: Regulatory reforms and enforcing safety and quality standards.
• Developers/owners: Ensuring timely payments, integrating financial risk management measures, and contract standardization.

5.5. Interaction Between Identified Key Components

• Project Management and Technical Deficiencies interact with Financial and Economic Risks: Inadequate planning can lead to delays and financial losses.
• External and regulatory Influences interact with Financial and Economic Risks: Government regulation changes can affect project costs.
• The integrated framework promotes a holistic approach to cost management. Thus, improving one component can mitigate risks in another.

6. Conclusions

This study presents an integrated framework that offers helpful recommendations for stakeholders of the construction industry in Saudi Arabia. This was done by utilizing a multivariate analysis of factors influencing construction costs in Saudi Arabia, including PCA and ANOVA. The PCA findings identified three key components: project management and technical deficiencies, external and regulatory influence, and financial and economic risks. ANOVA revealed significant differences in the perception of these factors according to demographics and project-specific characteristics, providing a detailed understanding of the influence of stakeholder diversity and project context on cost drivers. For example, experienced professionals viewed financial risks as more critical, civil engineers were more affected by regulatory complexities, and infrastructure projects faced higher financial and safety issues than other types of projects. This contributes to the literature by offering a structured clustering of cost factors rather than analyzing them individually. Thus, providing a data-driven and stakeholder-centered framework design for the Saudi Arabian construction industry.

These findings will enhance previous research as well as provide construction stakeholders, such as project managers, contractors, developers/owners and policymakers, with a practical tool to prioritize initiatives, optimize resource allocation, and establish effective cost control measures. Ultimately, this study promotes a transition toward more analytical, evidence-based cost management practices to enhance efficiency within the rapidly growing construction industry of Saudi Arabia.

This study encountered several limitations, specifically in data collection, including self-selection bias. For example, engineers with a stronger interest in cost management may have been more likely to participate. Additionally, regional variation may have affected results, as more data was collected from major cities than rural areas. Also, experience-based bias may have existed in the form of senior engineers perceiving cost drivers differently than younger engineers. ANOVA helped capture these perception differences, but future studies should aim for broader geographic and experience-level representation. Furthermore, future research should consider predictive modeling and machine learning. They can improve cost overrun anticipation by analyzing financial and economic risks, such as currency fluctuations, material price trends, and payment delays. Moreover, there is a lack of up-to-date national statistics on cost overruns in Saudi Arabia’s construction industry. Although a number of studies provided case-specific findings, a centralized database that systematically tracks cost overruns across different sectors is absent.