Construction Cost-Influencing Factors: Insights from a Survey of Engineers in Saudi Arabia

Abstract

Cost overruns represent a continuous challenge within the construction industry, frequently affecting the success of projects. This study explores the factors influencing cost during the construction phase in Saudi Arabia, utilizing data from a survey of 1076 engineers working in the Saudi construction industry. The results identify a number of cost-related factors, including inadequate project management, poor cost estimation, and design errors. Interestingly, some factors, such as currency exchange rate fluctuations and social and cultural influences, were found to have a limited impact on construction costs. Furthermore, the study highlights the role of experience and education level in shaping engineers’ perceptions of these cost factors. The study employs statistical analysis, including Pearson’s chi-squared test, to demonstrate associations between demographics, project characteristics, and cost-influencing factors. The findings suggest the need for refined project management practices, enhanced technical training, and the implementation of digital technologies such as Construction 4.0 to mitigate cost-related risks. This research provides significant insights for construction professionals and policymakers seeking to enhance cost management within the Saudi construction sector, thereby contributing to the ongoing development initiatives aligned with Saudi Arabia’s Vision 2030.

1. Introduction

Although the construction industry has a crucial role in the continuous improvement of the quality of human life, it has been noted that this industry has resulted in various environmental complications that have impacted our world [1]. This dual impact makes the management of construction projects mostly challenging. Sophisticated and dynamic environments with many interrelated factors cause high uncertainty in construction projects [2]. This uncertainty is further compounded by the inherent complexity of construction projects. Construction projects are fundamentally complex and involve numerous variables that can significantly impact their costs. Cost is the most important factor in any business, including the construction industry [3]. Therefore, effective cost management is not just important but essential. Managing these costs effectively is crucial for the successful completion of projects within budgetary constraints. To achieve this, it is essential to implement cost management from the beginning of the project in order to accomplish it within budget and ensure that it is on time and of high quality [4].

Construction projects play a pivotal role in economic development, yet they frequently encounter significant challenges that lead to cost overruns, impacting the overall success and viability of projects. These challenges are a global phenomenon and not limited to any one region. The construction industry faces several major challenges, including developing accurate estimates in context with recent technological and intelligence advancements, as well as effectively implementing projects within these cost budgets [5]. Despite advancements in technology and project management techniques, the majority of construction projects were completed at a significantly higher cost compared to the original contract value [6]. This has led to a situation where many projects are not completed within the allocated project budget, which eventually leads to cost overruns and causes a negative effect on the client and the contractor–consultant relationship, leading to mistrust, litigation, and arbitration [7]. Nevertheless, the continuous advancement in digital construction technologies, such as building information modeling (BIM), artificial intelligence (AI), and automation, will provide strategies on how to reduce cost overruns. For instance, BIM can be used to provide accurate quantities for procurement [8], which increases efficiencies in cost estimation. Furthermore, AI can analyze historical project data and identify patterns that lead to more accurate cost forecasts [9]. Moreover, automation and robotics can assist in reducing labor costs and increasing efficiencies on construction sites [10].

In Saudi Arabia, the construction sector plays a vital role in the national economy, driven by ambitious development plans and large-scale infrastructure projects. However, this sector also faces challenges related to cost management. As is the case in other parts of the world, cost overruns are common in the construction industry in Saudi Arabia [11]. Many factors are related to this cost overrun, ranging from technical issues, such as design changes and equipment breakdowns, to external influences like economic fluctuations and regulatory requirements. For instance, inflation affects construction project prices, causing cost overruns that require budget adjustments [12]. Another significant factor is rework, which contributed to 52% of a project’s cost growth and 26% of its variance [13]. Given these challenges, understanding the factors that influence costs during the construction phase is essential for project managers, contractors, and stakeholders to mitigate risks and ensure financial sustainability.

Despite the extensive body of research on construction cost management [2,5,7,14,15,16,17,18,19,20], there remains a need for region-specific studies that address the unique conditions and challenges faced by the construction industry in Saudi Arabia. This research aims to fill this gap by investigating the factors influencing construction costs during the construction phase within the context of Saudi Arabia. This is due to the distinct nature of the construction industry of Saudi Arabia, characterized by large-scale infrastructure projects driven by the country’s Vision 2030 development plan. This context introduces unique challenges that are not addressed in global studies. The construction phase was selected for this research because most cost-intensive costs occur during this stage and it involves a high number of uncertainties. This study aims to answer the following research questions: (1) How do engineers’ demographics influence their perception of construction cost-influencing factors? (2) How do project-specific characteristics affect engineers’ perceptions of cost-influencing factors? and (3) What are the implications and policy recommendations for construction industry practices? Furthermore, the study seeks to provide valuable insights and practical recommendations for improving cost management practices in the construction industry.

To achieve this objective, a comprehensive literature review was conducted to identify key factors influencing construction costs. Based on the findings, a detailed questionnaire was developed and distributed to engineers in the construction industry using Google Forms. The collected data were then analyzed using statistical methods to identify significant factors and their impact on construction costs. The results of this study will contribute to the existing body of knowledge and offer practical guidance for stakeholders involved in construction projects in Saudi Arabia. This research addresses a critical aspect of construction project management by focusing on the factors that influence costs during the construction phase. The findings aim to enhance the understanding of cost management in the Saudi Arabian construction industry, providing a foundation for better planning, budgeting, and risk mitigation strategies.

2. Literature Review

2.1. Cost Overrun Issues in the Construction Industry Worldwide

The cost overruns in the residential construction sector range from 21% to 55.19% [20]. This statistic highlights the severity of the problem and its impact on the construction industry. A number of studies have been conducted across different countries to identify the main factors contributing to these cost overruns.

A significant observation has been made regarding the challenges faced by the majority of project managers and contractors in Egypt when it comes to managing construction project costs [21]. These challenges arise from several issues such as change orders, design changes, design errors, the deteriorating economic situation, project delays, and the increasing price of materials [21]. These factors are not limited to Egypt; rather, cost overruns are a global challenge in the construction industry.

In Malaysia, a study that involved 79 cost-influencing factors found that client requirements on finishing quality were found to be the most critical influencing factor on construction cost [19]. Additionally, another study in Malaysia that explored the factors influencing cost variance during the construction phase resulted in identifying the highest influencing factors. The top five were, as follows: incomplete design drawings and specifications; changes in client requirements; financial difficulties faced by the contractor; fluctuations in material prices; and poor planning, scheduling, and monitoring [6]. These findings outline the complexity of construction cost management and the various factors that can cause deviations from the initial planned budget.

A study that involved experts from the Chinese construction industry showed that the most critical and common factors influencing construction cost are, as follows: market price changes; national policy changes; currency exchange rate fluctuations; inadequate contract management; inadequate risk management; insufficient design; major infectious diseases; natural disasters; project location limitations; design changes; poor drawing design; and fraud behavior and rebate [7]. Similarly, the impact of external factors, such as market price changes and national policy changes, cannot be overlooked.

Another study that took place in Trinidad and Tobago found that the top factor that contributes to cost overruns in public sector construction projects is the selection of politically aligned contractors [22]. This study highlights the influence of non-technical factors on construction costs in certain countries.

A Saudi Arabian study has identified six types of construction materials and trades that have significant impact on construction project costs, these are, as follows: thermal and moisture protection; site work; concrete work; conveying systems; electrical work; and mechanical work [23]. Material costs have a significant role in determining the total project cost, as any fluctuation in their prices can lead to substantial cost overruns.

In certain types of construction projects, such as road projects, a study found that cost underestimation is more predominant than cost overestimation [24]. This finding suggests that initial project estimation usually fails to account for all possible cost triggers.

An Indonesian study found that the rise in fuel prices is considered the variable with the highest impact on construction costs and may lead to an increase in costs of 20–40% of the original contract value [25]. This reflects the sensitivity of construction project costs to fluctuations in essential elements such as fuel.

In India, the Ministry of Statistics and Program Implementation identified the following factors for construction overruns: time overruns; changes in foreign exchange rate; expensive environmental protection and rehabilitation measures; high land acquisition cost; project scope changes; bidders charging extra in certain areas; underestimation; and increases in prices [16]. These factors emphasize the complicated nature of cost overruns, involving both technical and external elements. Another study in India identified the following as the most significant factors of cost overruns: rework; construction delays; ineffective planning and monitoring; delays in decision-making; disputes; and inflation [26]. All these factors contribute directly to increased costs and are common in the construction industry in other countries as well.

A study that investigated the factors leading to cost overruns in Vietnam found that the top five factors are, as follows: (1) project implementation schedule extension; (2) rapid increase in site worker demand; (3) contract suspension or arbitrary cancellation; (4) contractors’ poor management and supervision of quantities; and (5) construction site handover delays and incompleteness [18]. This highlights the vital role of effective project management and supervision in controlling construction costs.

2.2. Factors Influencing Construction Cost

Understanding the factors that influence construction costs is crucial for effective project management and successful project completion. Construction projects are complex and involve various uncertainties and risks that can significantly impact costs. Identifying and analyzing these factors helps in planning, budgeting, and mitigating potential cost overruns. The objective of this literature review was to study the available research on the factors affecting construction costs during the construction stage and provide a comprehensive understanding of these influences.

Table 1. Factors influencing construction cost during the construction phase.

Factor	Brief Description	References
Currency exchange rate fluctuations	The increase/decrease in value of local currency.	[7,15,16,25,26,27]
Delay in project/owner payment	Delays in payments from project owners.	[5,7,11,15,18,19,21,26,28,29,30,31,32,33]
Design changes	Design changes that occur during the construction stage.	[5,7,11,15,16,18,19,21,25,26,27,29,31,32,33,34,35,36,37]
Design error/weakness	Existence of errors or quality issues in the project designs.	[7,11,21,26,28,30,31]
Economic fluctuation/market price changes	Inflation and changes in prices of materials, fuel, and labor services.	[7,15,16,19,21,25,26,27,28,29,31,34,36,37,38,39,40]
Equipment breakdowns and inefficiencies	Malfunctioning or underperformance of machinery or a tool.	[7,19,26,27,28,31,36]
Force majeure and environmental issues	Natural disasters or extreme weather conditions.	[7,11,15,18,19,21,25,26,28,29,30,36,37,38,41,42]
Governmental regulations	Compliance with strict government regulations or changes that occur to it.	[7,19,25,26,29,30,36]
Inadequate cost estimation	Inaccuracy in estimating the project budget.	[7,15,16,18,21,26,27,28,29,31,32,34,37,38,40,43]
Inadequate management of project, contract, and communication	Insufficient or ineffective ways to manage projects, contracts, or communication.	[7,11,15,18,19,21,26,28,31,33,36]
Inadequate planning and scheduling	Insufficient or ineffective planning and scheduling of a project.	[7,11,15,18,19,21,26,28,29,31,33,36,37,39]
Lack of technical knowledge and experience	Personnel deficiency in required knowledge, skills, and experience.	[5,7,11,18,19,26,27,28,31,33,35,38,40,41,44]
Legal disputes between various parties	Conflict or disagreement between two or more parties.	[7,11,26,30,31,45,46]
Poor/unclear drawing	Unreadable drawings due to printing issues or other damages.	[7,15,31]
Rework	Redoing a job due to errors, design changes, or defects.	[5,7,26,27,29,31,32,47,48]
Safety issues and accidents	Incidents that may lead to harm, injury, or death of a person.	[5,7,18,19,36]
Social and cultural influences	Community opposition or strikes.	[7,11,18,19,25,36]
Staff corruption	Unethical or illegal behavior of an employee.	[7,18,28,30]

illustrates the factors influencing construction cost during construction phase that were identified from the literature review process of this study.

3. Methodology

The methodology of this research involved a multi-step process to thoroughly understand the factors influencing construction costs during the construction phase. Initially, a comprehensive literature review was conducted using the Saudi Digital Library to gather scholarly research on the relevant factors that exist in a global context. The focus was given to peer-reviewed journal articles and conference papers that directly relate to the study topic. Keywords such as ‘construction industry’, ‘cost overruns’, ‘factors’, and ‘Saudi Arabia’ were used to search for relevant studies in the literature. Insights from this review informed the development of a detailed questionnaire, which was designed to include demographics and project-specific characteristics affecting construction costs. Google Forms was selected for its ease of use, accessibility, and the need to collect data from a large and geographically diverse sample across Saudi Arabia. The questionnaire was actively collecting data from 28 November 2023 to 3 April 2024. However, data collection efforts exceeded expectations, resulting in 1211 responses. Incomplete responses were then excluded from the study, and 1076 responses were deemed valid for analysis. To ensure a diverse and representative sample, the questionnaire was distributed by visiting several random locations where engineers can be found working, including, but not limited to, consulting engineering offices, contracting companies, real estate development companies, and ongoing construction projects. The selection of engineers was driven by their critical influence on cost management, whether they were site engineers, project managers, construction managers, designers, or hold any other role in their institution. They are directly involved in decisions that impact project costs, making them the most relevant stakeholders for assessing the factors influencing construction costs. Formal ethical approval was exempted for this study by the Research Ethics Committee at King Abdulaziz University. Participants were informed of the study purpose, and participation consent was obtained before they started the questionnaire. Data were treated anonymously to protect participants’ identities, and all responses were securely stored.

The collected data were then analyzed using SPSS 22 (Statistical Package for the Social Sciences). It was chosen due to its vigorous capabilities in handling large datasets and a wide range of statistical analyses. The analysis included frequency distributions and percentages, as well as descriptive statistics to understand the central tendencies and dispersion of the data by calculating the mean of each factor. To measure the consistency of the questionnaire used for data collection in this study, a reliability analysis was used. This was conducted using Cronbach’s alpha, which measures the scale of reliability and is considered the most commonly used analysis method [49]. Cronbach’s alpha was calculated for each of the multi-item scales representing the factors influencing construction costs. Furthermore, Pearson’s chi-squared (χ²) test was used to compare the collected data to a hypothetical model [50]. This was conducted to explore the relationship between categorical variables [49], specifically exploring the association between participant demographics, project-specific characteristics, and construction cost-influencing factors. For this reason, the Pearson’s chi-squared (χ²) test represents an appropriate choice for analyzing the relationships between variables in this study. The Pearson’s chi-squared (χ²) test was conducted using Equations (1) and (2) [49], as follows:

$${\chi }^2=\sum {\displaystyle \frac{{(O_{ij}-M_{ij})}^2}{M_{ij}}}$$

(1)

where

• χ² represents the Pearson’s chi-squared statistic;
• i represents the row in the contingency table;
• j represents the columns in the contingency table;
• O_ij represents the observed frequency;
• M_ij represents the model frequency, as follows:

$$df=(r-1)(c-1)$$

(2)

where

• df represents the degree of freedom;
• r represents the number of rows;
• c represents the number of columns.

The chi-squared (χ²) statistic was compared against a critical value from the chi-squared distribution table at a significant level of 0.05 for the purpose of hypotheses testing. A p-value less than 0.05 indicated a statistically significant association between the variables, leading to the rejection of the null hypothesis (H₀) in favor of the alternative hypotheses (H₁).

This research aims to answer the following questions:

• How do engineers’ demographics influence their perception of construction cost influencing factors?
• How do project-specific characteristics affect engineers’ perceptions of cost-influencing factors?
• What are the implications and policy recommendations for construction industry practices?

The main hypothesis for this research is as follows:

• Engineers’ demographics vs. factors influencing construction cost.

H₀: There is no significant association between the engineers’ demographics and their perception of factors influencing construction costs.

H₁: There is a significant association between the engineers’ demographics and their perception of factors influencing construction costs;

2.

Project-specific characteristics vs. factors influencing construction costs.

H₀: There is no significant association between the project-specific characteristic and engineers’ perceptions of factors influencing construction costs.

H₁: There is a significant association between the project-specific characteristic and engineers’ perceptions of factors influencing construction costs.

This structured methodology ensured a thorough investigation of the factors influencing construction costs, providing reliable and valid results to inform future practices and policies in the construction industry. Figure 1 represents the steps conducted to perform this study.

4. Results

4.1. Reliability Analysis

Cronbach’s alpha reliability analysis was carried out on the 18 factors influencing construction costs in Saudi Arabia that form the basis of this study. The value of α = 0.911 for the 18 factors indicates a high level of consistency among these factors. A Cronbach’s alpha value that is above 0.90 indicates excellent reliability [50]. Thus, confidence can be given to the data and findings of this study due to the high reliability score.

4.2. Demographic Profile and Project-Specific Characteristics

Most of the participants, representing 40.6% of the sample, were among the 26–35 age group, an indication that young engineers have a good presence in this study. Furthermore, the participants’ age groups of 46–55 combined with the older than 55 age group make up, together, about 28.8% of the study participants, indicating a representative number of senior professionals. This shows that the study has a balanced number of participants from young and older professionals in the field of construction.

Table 2. Age profiles of questionnaire participants.

Age	Frequency	Percent
20–25	54	5.1
26–35	432	40.6
36–45	271	25.5
46–55	143	13.4
More than 55	164	15.4
Total	1064	100.0

illustrates the age profiles of questionnaire participants.

The majority of participants in this study specialized in civil engineering (89.5%). Since this study context is in the area of construction costs, a high participation of civil engineers will ensure direct benefits and robust data collected in the same field. Engineers from other specializations have also participated in this study in limited numbers, suggesting some interdisciplinary input in probably very specific aspects of construction projects.

Table 3. Specialization profiles of questionnaire participants.

Specialization	Frequency	Percent
Civil Engineering	952	89.5
Architecture	41	3.9
Mechanical Engineering	25	2.3
Electrical Engineering	28	2.6
Industrial Engineering	5	0.5
Health and Safety Engineering	4	0.4
Chemical Engineering	6	0.6
Computer Engineering	2	0.2
Mining engineering	1	0.1
Total	1064	100.0

illustrates the specialization profiles of questionnaire participants.

Participants with bachelor’s degrees, at 81.5%, represent most of the sample size of this study. Postgraduate studies holders of both master’s degrees and PhDs represent 18.6% of the participants, indicating the involvement of professionals with managerial expertise along with research backgrounds.

Table 4. Academic qualifications profiles of questionnaire participants.

Academic Qualification	Frequency	Percent
Bachelor	867	81.5
Master	156	14.7
Ph.D.	41	3.9
Total	1064	100.0

illustrates the academic qualifications profiles of questionnaire participants.

A balanced distribution of participants with various experience levels participated in this study, ranging from participants with less than 5 years of experience, at 16.3%, to more than 20 years of experience, at 30.6%. The involvement of highly experienced professionals suggests a considerable amount of input from these experts, who likely have encountered several challenges related to construction costs in their projects. This wide range of input from different levels of experience provides comprehensive insights into the factors influencing construction costs at different carrier phases.

Table 5. Experience profiles of questionnaire participants.

Experience	Frequency	Percent
Less than 5	173	16.3
5–10	257	24.2
11–15	183	17.2
16–20	125	11.7
More than 20	326	30.6
Total	1064	100.0

illustrates the experience profiles of questionnaire participants.

Most participants’ involvement was found to be in residential construction projects, at 41.1%, while the least amount of involvement was in industrial construction projects, at 10.3%. These figures reflect the nature of the construction industry of Saudi Arabia, where the majority of construction projects are residential projects, followed by infrastructure and commercial construction projects. This is influenced by the objectives of the Ministry of Housing as well as the mega projects that are part of the Saudi Vision 2030.

Table 6. Types of projects questionnaire participants were involved in.

Type of Project	Frequency	Percent
Residential Construction	437	41.1
Commercial Construction	254	23.9
Industrial Construction	110	10.3
Infrastructure Construction	263	24.7
Total	1064	100.0

illustrates the types of projects that questionnaire participants were involved in.

At 64.4%, the majority of participants were involved in large-scale construction projects valued at more than 20 million Saudi riyals, reflecting the considerable amount of investment in construction activities in Saudi Arabia. The participants’ involvement in other project size categories was fairly equal, except for projects that fell in the range of 1–5 million Saudi riyals, which showed a greater amount of involvement.

Table 7. Project sizes that questionnaire participants were involved in.

Project Size	Frequency	Percent
Less than 1 million	64	6.0
1–5 million	153	14.4
6–10 million	82	7.7
11–20 million	80	7.5
More than 20 million	685	64.4
Total	1064	100.0

illustrates the project sizes that questionnaire participants were involved in.

The findings show that 38.9% of participants work in the Saudi Arabian capital, Riyadh, which is considered a major economic hub. The city of Jeddah follows, with 18.8% of participants working there. Riyadh and Jeddah are the largest cities in Saudi Arabia in terms of size and population. Participants spread across various cities in Saudi Arabia are represented in this study. This guarantees widespread geographical insights into construction cost factors in Saudi Arabia.

Table 8. Project locations that questionnaire participants were involved in.

Project Location	Frequency	Percent
Rabigh	17	1.6
Riyadh	414	38.9
Taif	7	0.7
Jeddah	200	18.8
Al Khobar	20	1.9
Makkah	67	6.3
Al Jubail	17	1.6
Neom and Red Sea Project	52	4.9
Jazan	32	3.0
Madinah	38	3.6
Sakakah	6	0.6
Hail	23	2.2
Al Qassim	29	2.7
Abha	16	1.5
Yanbu	9	0.8
AlUla	7	0.7
Tabuk	18	1.7
Al Hasa	15	1.4
Najran	7	0.7
Buraydah	6	0.6
Dammam	58	5.5
Al Bahah	6	0.6
Total	1064	100.0

illustrates the project locations that questionnaire participants were involved in.

The study’s descriptive statistics identify the rankings of the factors influencing construction costs in Saudi Arabia according to their means (see

Table 9. Mean value for factors influencing construction costs during the construction phase.

Factor	Mean
Inadequate management of project, contract, and communication	3.77
Inadequate cost estimation	3.68
Inadequate planning and scheduling	3.65
Design error/weakness	3.63
Design changes	3.61
Lack of technical knowledge and experience	3.57
Delay in project/owner payment	3.47
Rework	3.44
Economic fluctuation/market price changes	3.43
Staff corruption	3.23
Poor/unclear drawing	3.15
Legal disputes between various parties	3.12
Equipment breakdowns and inefficiencies	3.00
Force majeure and environmental issues	2.86
Safety issues and accidents	2.86
Governmental regulations	2.74
Currency exchange rate fluctuations	2.55
Social and cultural influences	2.39

). The findings show that inadequate management of project, contract, and communication is the highest influencing factor, with a mean score of 3.77. This indicates that inadequate contract management and poor communication among project stakeholders can greatly increase the construction cost. Engineers could have perceived this factor as the most influential for several reasons, including centralized decision-making, inconsistencies in project management practices, and limited training in this factor aspect. This can be addressed by promoting effective project management practices and providing extensive training programs to improve management skills and communication.

The second highly influencing factor is inadequate cost estimation, with a mean score of 3.68. Inaccurate estimations of costs can lead to budget overruns, schedule delays, and the failure of projects. It is necessary to implement a robust estimation methodology that utilizes historical data and incorporate contingency plans to improve cost estimates and prevent unexpected expenses. Engineers could have perceived this factor as the second most influential factor due to the limited availability of historical data or the complexity of large-scale projects with numerous challenges.

Close to inadequate cost estimation is the factor inadequate planning and scheduling, which has a mean score of 3.65, making it another major factor that greatly influences construction costs in Saudi Arabia. Construction projects with poor planning and scheduling also face delays and extra expenses. To mitigate this issue, it is essential to conduct proper planning and scheduling via advanced software that assists in creating detailed project timelines and tracking of activities with a continuous update option.

Factors that relate to design were also found to significantly influence construction costs, including design error/weakness (mean score of 3.63) and design changes (mean score of 3.61). Errors in design will lead to rework and variations, which in turn lead to additional costs and time delays. This needs to be addressed by ensuring high-quality designs. Furthermore, design changes during the construction phase should not be made or at least be kept to a minimum to avoid extra costs.

Lack of technical knowledge and experience also appears among highly influencing factors, with a score mean of 3.57. This emphasizes the importance of having competent professionals on the construction team in order to reduce mistakes and increase cost efficiencies. Project outcomes can be significantly improved by hiring skilled personnel and providing adequate training to employees.

Interestingly, the findings show that factors, such as currency exchange rate fluctuations (mean score of 2.55) and social and cultural influences (mean score of 2.39), are perceived as less influential on construction costs by engineers working in Saudi Arabia. Although several construction materials are imported and should be influenced by currency exchange rate fluctuations, the pegging of the Saudi riyal to the U.S. dollar may have had a role in reducing this influence and achieving these results. Moreover, Saudi culture is very keen on the development of the country, which may have contributed to the social and cultural influences factor ranking as the least influential among factors on the list.

4.3. Chi-Squared Analysis

Pearson’s chi-squared test was used to explore the presence of statistically significant associations between the categorical variables. The null hypothesis (H0) states that there is no significant association between the variables, while the alternative hypothesis (H1) states that a significant association does exist. A p-value of less than 0.05 indicates that the null hypothesis is rejected in favor of the alternative hypothesis and that a significant relationship does exist between the variables. A total of 126 Pearson’s chi-squared tests were carried out for 18 factors against 7 demographics and project-specific characteristics.

Table 10. Chi-squared test results for variables where the null hypothesis was rejected in favor of the alternative hypothesis.

Variables Tested	Test	X2	p-Value
Age vs. Currency exchange rate fluctuations	Pearson Chi-Squared	26.638	0.046
	Linear-by-Linear Association	8.205	0.004
Age vs. Safety issues and accidents	Pearson Chi-Squared	30.446	0.016
	Linear-by-Linear Association	6.101	0.014
Specialization vs. Governmental regulations	Pearson Chi-Squared	67.185	0.000
	Linear-by-Linear Association	1.018	0.313
Academic qualification vs. Design changes	Pearson Chi-Squared	21.597	0.006
	Linear-by-Linear Association	11.834	0.001
Academic qualification vs. Staff corruption	Pearson Chi-Squared	20.357	0.009
	Linear-by-Linear Association	0.240	0.624
Academic qualification vs. Governmental regulations	Pearson Chi-Squared	19.948	0.011
	Linear-by-Linear Association	13.507	0.000
Experience vs. Currency exchange rate fluctuations	Pearson Chi-Squared	40.239	0.001
	Linear-by-Linear Association	12.814	0.000
Experience vs. Design error/weakness	Pearson Chi-Squared	34.961	0.004
	Linear-by-Linear Association	1.562	0.211
Experience vs. Staff Corruption	Pearson Chi-Squared	27.836	0.033
	Linear-by-Linear Association	7.233	0.007
Experience vs. Safety issues and accidents	Pearson Chi-Squared	28.234	0.030
	Linear-by-Linear Association	6.597	0.010
Type of project vs. Design changes	Pearson Chi-Squared	25.360	0.013
	Linear-by-Linear Association	3.340	0.068
Type of project vs. Equipment breakdowns and inefficiencies	Pearson Chi-Squared	47.881	0.000
	Linear-by-Linear Association	14.555	0.000
Type of project vs. Force majeure and environmental issues	Pearson Chi-Squared	30.771	0.002
	Linear-by-Linear Association	7.080	0.008
Type of project vs. Governmental regulations	Pearson Chi-Squared	23.646	0.023
	Linear-by-Linear Association	7.273	0.007
Project size vs. Design changes	Pearson Chi-Squared	29.742	0.019
	Linear-by-Linear Association	7.763	0.005
Project size vs. Design error/weakness	Pearson Chi-Squared	37.480	0.002
	Linear-by-Linear Association	5.675	0.017
Project size vs. Economic fluctuation/market price changes	Pearson Chi-Squared	28.452	0.028
	Linear-by-Linear Association	7.303	0.007
Project size vs. Social and cultural influences	Pearson Chi-Squared	29.424	0.021
	Linear-by-Linear Association	5.329	0.021
Project location vs. Design changes	Pearson Chi-Squared	107.287	0.044
	Linear-by-Linear Association	7.410	0.006

summarizes the chi-squared test results performed to assess the association between several engineers’ demographics and project-specific characteristics and the perception of construction cost-influencing factors, where the null hypothesis was rejected in favor of the alternative hypothesis.

4.3.1. How Do Engineers’ Demographics Influence Their Perception of Construction Cost-Influencing Factors?

Age vs. Currency exchange rate fluctuations: The p-value (0.046) indicates a significant relationship between the age of the participants and their perception of currency exchange rate fluctuations. This could be due to different levels of experience and familiarity with economic factors. Furthermore, the significance was also found in the linear-by-linear association (p-value = 0.004), suggesting a trend related to the perception of currency exchange rate fluctuations and the age of the participant. Younger engineers with less experience are less concerned about currency exchange rate fluctuations due to limited experience compared to older engineers with more experience. Understanding these age-related differences could help in reducing financial risks and developing training programs.

Age vs. Safety issues and accidents: The p-value (0.016) indicates a significant relationship between the age of the participants and their perception of safety issues and accidents. This could be due to different levels of experience, awareness, and training on safety practices and regulations. The linear-by-linear association (p-value = 0.014) suggests a trend related to the perception of safety issues and accidents and the age of the participant. Younger engineers with less experience might perceive safety issues and accidents differently compared to older engineers with more experience and exposure to safety protocols. Understanding these age-related differences could help in designing better safety training programs for younger engineers and utilizing the experience of older engineers and sharing their best practices.

Specialization vs. Governmental regulations: The p-value (0.000) indicates a significant relationship between the specialization of the participants and their perception of governmental regulations. This could be due to different degrees of interaction with government regulations depending on the specialization of the engineers, as some engineers are more involved with daily government regulation in their work activity compared to others. Thus, the finding indicates a need for targeted communication and support from regulatory bodies for those engineers with specializations that deal with these regulatory bodies frequently.

Academic qualification vs. Design changes: The p-value (0.006) indicates a significant relationship between the academic qualifications of the participants and their perception of the impact of design changes on construction costs. This could be due to different levels of theoretical knowledge, practical experience, and exposure to design changes. The linear-by-linear association (p-value = 0.001) suggests a trend related to the perception of the impact of design changes on construction costs and the academic qualification of participants. Bachelor’s degree holders perceive the impact of design changes as moderate to very high. This is followed by master’s degree holders who have a higher overall perception, followed by PhD holders who have the highest perception compared to the others. Thus, a greater awareness of the impact of design changes on cost is needed for engineers with bachelor’s degrees.

Academic qualification vs. Staff corruption: The p-value (0.009) indicates a significant relationship between the academic qualifications of the participants and their perception of the impact of staff corruption on construction costs. This could be due to different levels of exposure to corruption-related issues. For instance, engineers with a PhD perceived a very low to moderate impact of staff corruption, most likely due to their involvement in larger, more complex projects and office-based work. On the other hand, engineers holding bachelor’s and master’s degrees perceived a substantially higher impact of staff corruption on construction costs, probably due to their direct involvement with this issue on construction sites as part of their daily work activities.

Academic qualification vs. Governmental regulations: The p-value (0.011) indicates a significant relationship between the academic qualification of the participants and their perception of the impact of governmental regulations on construction costs. This could be due to different levels of understanding of regulatory frameworks and their implications. The linear-by-linear association (p-value = 0.000) suggests a trend related to the perception of the impact of governmental regulations on construction costs and the academic qualifications of participants. The majority of bachelor’s degree holders perceive the impact of governmental regulations as moderate. This is followed by master’s degree holders who have a higher overall perception, followed by PhD holders, who have the highest perception compared to the others. Thus, we can conclude that engineers with a bachelor’s degree might see governmental regulations as substantial but manageable, that engineers with a master’s degree have greater involvement in navigating regulatory requirements, and that engineers with a PhD are involved in projects that are significantly influenced by regulations.

Experience vs. Currency exchange rate fluctuations: Similar to age vs. currency exchange rate fluctuations, the p-value (0.001) indicates a significant relationship between the experience of the participants and their perception of the impact of currency exchange rate fluctuations on construction costs. This could be due to different levels of exposure to international markets and economies in their professional careers. The linear-by-linear association (p-value = 0.000) suggests a trend related to the perception of the impact of currency exchange rate fluctuations on construction costs and the experience of participants. The majority of engineers with less than 5 years of experience perceive the impact of currency exchange rate fluctuations on construction costs as very low; this is followed by engineers with 5–10 years of experience who have a higher perception of the impact of currency exchange rate fluctuations on construction costs. On the other hand, the majority of engineers with more than 20 years of experience perceived the impact of currency exchange rate fluctuations on construction costs to be the highest. Thus, we can conclude that as engineers gain experience, they recognize the significance of currency exchange rate fluctuations on construction costs due to their extensive experience with multi-projects with economical changes.

Experience vs. Design error/weakness: The p-value (0.004) indicates a significant relationship between the experience of the participants and their perception of the impact of design error/weakness on construction costs. This could be due to different levels of exposure to design challenges and ways to manage them in their professional experience. Engineers with less experience might benefit from extensive design training, while more experienced engineers could focus on advanced design reviews that can lead to better cost management.

Experience vs. Staff Corruption: The p-value (0.033) indicates a significant relationship between the experience of the participants and their perception of the impact of staff corruption on construction costs. This could be due to different levels of exposure to corruption-related issues during their professional career. The linear-by-linear association (p-value = 0.007) suggests a trend related to the perception of the impact of staff corruption on construction costs and the experience of participants. The results show that the more experienced the engineer, the more he recognizes the significant influence of staff corruption on construction costs.

Experience vs. Safety issues and accidents: Similar to age vs. safety issues and accidents, the p-value (0.030) indicates a significant relationship between the experience of the participants and their perception of the impact of safety issues and accidents on construction costs. This could be due to different levels of exposure to safety protocols and incidents involving risks and hazards during their professional careers. The linear-by-linear association (p-value = 0.010) suggests a trend related to the perception of the impact of safety issues and accidents on construction costs and the experience of participants. The results show that highly experienced engineers recognize the significance of safety-related problems but at the same time have developed the skills to manage and mitigate safety concerns, while less experienced engineers, on the other hand, struggle more with safety management.

4.3.2. How Do Project-Specific Characteristics Affect Engineers’ Perceptions of Cost-Influencing Factors?

Type of project vs. Design changes: The p-value (0.013) indicates a significant relationship between the type of project and the perception of the impact of design changes on construction costs. This could be due to the different nature and technical complexity of these projects as well as their vulnerability to design changes. Results show that industrial and infrastructure projects are sensitive to design changes in terms of cost consequences compared to residential and commercial projects.

Type of project vs. Equipment breakdowns and inefficiencies: The p-value (0.000) indicates a significant relationship between the type of project and the perception of the impact of equipment breakdowns and inefficiencies on construction costs. This could be due to the different environments and equipment required for use in these projects. The linear-by-linear association (p-value = 0.000) suggests a trend related to the perception of the impact of equipment breakdowns and inefficiencies on construction costs and the type of project. The results show that equipment breakdowns and inefficiencies in residential and commercial construction projects, while important, are less critical compared to the other cost-related factors. On the other hand, in industrial and infrastructure construction projects, equipment breakdowns and inefficiencies are considered more disruptive and costly.

Type of project vs. Force majeure and environmental issues: The p-value (0.002) indicates a significant relationship between the type of project and the perception of the impact of force majeure and environmental issues on construction costs. This could be due to the different topographic environments and surroundings of these projects. The linear-by-linear association (p-value = 0.008) suggests a trend related to the perception of the impact of force majeure and environmental issues on construction costs and the type of project. The results show that the impact of force majeure and environmental issues in residential and commercial construction is significant but less critical compared to other cost-related factors. On the other hand, in industrial and infrastructure construction projects, the impact of force majeure and environmental issues is considered more expensive to manage.

Type of project vs. Governmental regulations: The p-value (0.023) indicates a significant relationship between the type of project and the perception of the impact of governmental regulations on construction costs. This could be due to the different regulatory requirements for each of these projects. The linear-by-linear association (p-value = 0.007) suggests a trend related to the perception of the impact of governmental regulations on construction costs and the type of project. The results show that the impact of governmental regulations in residential and commercial construction is substantial but less critical compared to other cost-related factors. On the other hand, in industrial and infrastructure construction projects, the impact of governmental regulations is considered more rigorous and involving.

Project size vs. Design changes: The p-value (0.019) indicates a significant relationship between project size and the perception of the impact of design changes on construction costs. This could be due to the various scales and complexities of these projects. The linear-by-linear association (p-value = 0.005) suggests a trend related to the perception of the impact of design changes on construction costs and project size. The results show that, as projects increase in size, engineers perceive design changes to have a larger impact on the construction cost.

Project size vs. Design error/weakness: The p-value (0.002) indicates a significant relationship between project size and the perception of the impact of design error/weakness on construction costs. This could be due to the various scales and complexities of these projects and their vulnerability to design quality issues. The linear-by-linear association (p-value = 0.017) suggests a trend related to the perception of the impact of design error/weakness on construction costs and project size. The results show that small-sized projects are impacted by design error/weakness but that their overall influence is less critical compared to larger projects.

Project size vs. Economic fluctuation/market price changes: The p-value (0.028) indicates a significant relationship between project size and the perception of the impact of economic fluctuation/market price changes on construction costs. This could be due to the different sensitivities of projects to international economic conditions based on their scale. The linear-by-linear association (p-value = 0.007) suggests a trend related to the perception of the impact of economic fluctuation/market price changes on construction costs and project size. The results show that small-sized projects are impacted by economic fluctuation/market price changes, but that their overall influence is less critical compared to larger projects involving major investment, which are very sensitive to fluctuations in prices.

Project size vs. Social and cultural influences: The p-value (0.021) indicates a significant relationship between project size and the perception of the impact of social and cultural influences on construction costs. This could be due to the different sensitivities of projects to social and cultural factors based on their scale and scope. The linear-by-linear association (p-value = 0.021) suggests a trend related to the perception of the impact of social and cultural influences on construction costs and project size. The results show that small-sized projects are impacted by social and cultural influences but that their overall influence is less critical compared to larger projects involving significant interactions with several social and cultural contexts, which require robust assessment and community engagement to minimize risks.

Project location vs. Design changes: The p-value (0.044) indicates a significant relationship between project location and the perception of the impact of design changes on construction costs. This could be due to the different design requirements and tasks for these projects in different cities. The linear-by-linear association (p-value = 0.006) suggests a trend related to the perception of the impact of design changes on construction costs and project location. The results show that engineers perceive design changes to projects in Riyadh city as impactful, likely because of the city size and project complexity. Furthermore, engineers perceive design changes to projects in Neom and Red Sea projects as also impactful, likely due to the large scale of these projects and their high profiles. Other smaller cities, such as Rabigh, Taief, and Skaka have fewer respondents and show varying perceptions with regard to the impact of design changes. Thus, even small cities can differ significantly in terms of design change impact.

4.3.3. What Are the Implications and Policy Recommendations for Construction Industry Practices?

The following are a number of policy recommendations that were derived from the findings of this study and can help to mitigate the risks associated with cost overruns and contribute to more efficient and sustainable project outcomes:

• It is important to promote best practices in project management to address the inadequate management of project, contract, and communication, which is the highest cost-influencing factor;
• To ensure clear communications among stakeholders, it is important to establish standardized communication protocols;
• Robust cost estimation methodologies that integrate historical data and contingency plans to enhance accuracy should be implemented;
• The use of cutting-edge planning and scheduling software to generate comprehensive project timelines and continuously monitor its activities should be promoted to mitigate delays and control costs;
• High-quality designs should be ensured by conducting frequent reviews and implementing strict quality assurance protocols, which in turn will assist in reducing design errors and weaknesses’
• Guidelines should be set to minimize design changes during the construction phase. To manage additional costs, it is important to perform a comprehensive impact assessment and obtain approvals for required changes;
• Construction professionals should be encouraged to enroll in formal education and comprehensive training programs to improve their technical knowledge and expertise;
• The establishment of a culture that prioritizes knowledge-sharing and mentorship within organizations should be encouraged in order to utilize the expertise of senior engineers in favor of younger engineers. Sharing project knowledge can enhance organizational cost-efficiency and performance [51];
• Strategies to minimize financial risks caused by fluctuations in currency exchange rates should be implemented, specifically for large-scale projects that heavily rely on imported materials;
• Support should be offered to engineers who deal with regulatory frameworks to ensure that they possess the necessary skills to successfully overcome any requirements;
• Anti-corruption training and policies should be implemented, with an emphasis on real-life cases in the construction context;
• Artificial intelligence should be adopted to enhance construction cost control. It was shown that the artificial intelligence approaches used in construction management can lead to minimizing cost overruns and improvements in project efficiency [52];
• Construction 4.0 should be adopted by using digital solutions to achieve cost efficiencies. Digitalizing construction improves efficiency, reduces errors, reduces delays, and prevents exceeding project budgets; it promotes collaboration and information-sharing throughout the construction supply chain [53];
• Incentives for fair labor practices and work safety in construction projects should be introduced to enhance ethical practices in the industry;
• Ethical cost management practices should be promoted to increase financial efficiency and minimize environmental impacts;
• Policymakers should establish holistic national standards for cost estimation in construction projects;
• Policymakers should stimulate incentives for companies that adopt digital construction technologies;
• Policymakers should expand on adopting public–private partnerships (PPPs), as it will promote the use of digital construction technologies and best practices in project management;
• Policymakers can mandate a number of certificate programs in advanced project management techniques to ensure consistent standards across the industry.

5. Conclusions

The findings in this study reveal that inadequate management of projects, contracts, and communication is the most significant factor contributing to cost overruns. Additional significant factors included inadequate cost estimation, poor planning and scheduling, design errors, and a lack of technical knowledge among professionals. In contrast, factors such as currency exchange rate fluctuations and social and cultural influences were regarded as having a limited impact, probably owing to the distinct economic stability and cultural context of Saudi Arabia.

The findings highlight the importance of enhancing project management practices, implementing advanced cost estimation techniques, and using cutting-edge tools for planning and scheduling. Moreover, the results indicate that encouraging a culture of knowledge-sharing and mentorship within the construction industry could significantly enhance cost efficiency. Furthermore, the extensive experience of senior engineers can significantly contribute to minimizing errors and improving project outcomes.

This study has resulted in several findings that require further study to provide a comprehensive overview of construction cost-influencing factors in Saudi Arabia. These future studies may include understanding the limited impact on construction costs of both currency exchange rate fluctuations and social and cultural influences. Furthermore, due to the regional variations within Saudi Arabia, it would be beneficial to examine the differences across regions or project types in terms of construction costs. This comparison could also be made with other countries that have similar economic and cultural contexts. Moreover, there is a need to investigate how digital technology adoption may affect the cost of construction in Saudi Arabia and which of these technologies have the highest potential for cost reduction. In addition, longitudinal studies to track the evolution of cost factors over time with the adoption of these technologies would provide valuable insights. Sustainable development is a necessity these days; therefore, research on the role of sustainability-related factors and their influence on construction costs is another interesting topic for future studies.