1. Introduction
Construction cost control is a critical criterion for project success, yet cost overruns remain a persistent global challenge in the construction industry [1]. Recent studies indicate the prevalence of this issue—over 55% of construction projects in Malaysia experienced cost overruns, while 65% of public projects in Jordan faced similar challenges [2]. This problem transcends economic boundaries, affecting both developing and developed nations, as evidenced by significant budget overruns in major projects like the London Olympics [3].
Given the widespread nature of this issue, effective cost control is crucial to preventing expenses from escalating into larger issues like project delays or economic pressures due to unexpected expenditures [4]. Enshassi et al. (2010) emphasized that cost overruns are a global issue that affects projects worldwide, regardless of socioeconomic status [5].
Researchers have conducted extensive investigations into the factors contributing to cost overruns in construction projects globally. Love et al. (2016) summarized that in Australian construction projects, management errors and design changes were the key factors impacting project costs [6]. Srinivasan et al. (2016) identified that rising raw material prices, delays in planned activities, and lack of coordination between construction parties were critical factors causing cost overruns in India [7]. These studies provide insights into the overarching challenges and specific hurdles countries face in managing cost escalation.
Wang and Yuan (2011) identified risk factors leading to cost overruns, including design changes, rising material prices, client changes, and ambiguities in contract conditions [8]. Xie et al. (2022) also pointed out that poor project management and changes during the design phase were major contributors to these overruns [9]. Shen et al. (2011) emphasized market uncertainties, such as material price fluctuations and supply chain management deficiencies, as significant external factors, along with management challenges like complex government approval processes [10].
The existing literature identified that the influencing factors of cost overrun can be roughly summarized into three levels: project management, resource supply chain, and external environment. Project management is the most direct level of influence, involving the scientific management of project planning, design, construction, change, and other aspects of the project, representing the factors that the project team can actively control (Doloi, 2013 [11]; Johnson & Babu, 2018 [12]). The resource supply chain represents the various types of resource elements required for project implementation, such as materials, equipment, and labor, reflecting the efficiency and stability of project resource allocation (Álvarez-Pozo et al., 2024 [13]). The external environment represents the objective conditions that are difficult to control but must be actively dealt with, such as market conditions, policies and regulations, force majeure, and other factors, reflecting the external risks and uncertainties faced by the project (Annamalaisami & Kuppuswamy, 2021 [14]).
Based on the above understanding, this study proposes a framework for analyzing the factors that influence cost overrun, covering three dimensions: project management, resource supply chain, and external environment. This framework is mainly derived from the systematic categorization idea put forward by Chandra et al. (2023) [15] in their study of the factors affecting the productivity of construction equipment. We believe that this idea is not only applicable to the study of equipment productivity, but also can better guide the systematic sorting of the factors affecting cost overruns. On the one hand, cost management and equipment utilization efficiency are closely related; on the other hand, there are many commonalities in the influencing factors of the two topics, such as management level, resource supply, environmental constraints, etc. Therefore, this study tries to categorize the influencing factors of cost overruns in a systematic way. Therefore, this study proposed a multidimensional framework for analyzing the influencing factors of cost overruns based on Chandra et al. and incorporated more observational variables, so as to realize a more comprehensive and precise analysis of the influencing paths. This study aimed to systematically investigate the relationships between key factors affecting construction cost overruns.
1.1. Project Management
Research has consistently identified poor cost estimation and budgeting practices as critical factors contributing to construction cost overruns. Ahiaga-Dagbui and Smith (2014) found that cognitive biases, particularly optimism bias, often lead project managers to underestimate costs during the planning phase. Their study of 1600 construction projects revealed that initial cost estimates were typically 20–30% below the final project cost, largely due to inadequate risk assessment and contingency planning [16].
Doloi (2013) conducted an extensive analysis demonstrating that ineffective project planning and monitoring could explain up to 23.9% of cost variations in construction projects. The study particularly highlighted how initial budgeting errors tend to compound throughout the project lifecycle, creating cascading effects on overall cost performance [11].
Contract management issues significantly impact project cost performance. Asiedu and Adaku (2019) found that poor contract planning and monitoring, and change order problems are the main causes of cost overruns in public construction projects, and these two factors together explain about 70% of cost overruns. Their research specifically highlights how unclear scope definitions and frequent design changes lead to claims and disputes, which ultimately increase project costs [17].
Plebankiewicz and Wieczorek (2020) found that change orders are one of the main factors contributing to project cost overruns, and analysis using a fuzzy inference model shows that they can explain approximately 8.54% of cost overruns [18].
Project scheduling and time management have emerged as crucial factors affecting cost performance. Heravi and Mohammadian (2019) found that construction delays are highly correlated with cost overruns—88% of projects face both problems—by studying 72 urban construction projects [19].
In the study by Wanjari and Dobariya (2016), the lack of coordination among construction parties was listed as the third leading factor contributing to cost overruns, and 46% of respondents considered it to be a significant problem. This is manifested in frequent design changes due to insufficient communication between the client and the consultant, delays in decision-making between the consultant and the contractor, and untimely payments between the client and the contractor [20].
In examining the causes of cost overruns and delays in construction projects, Adam et al. (2017) identified management factors as the most important cause, with poor site management being one of the key management issues [21].
In construction projects, design changes are one of the major factors leading to cost overruns. A study by Johnson and Babu (2018) showed that client- and consultant-initiated design changes are the top influencing factors leading to cost overruns in construction projects, and that such changes often stem from the uniqueness of the project, tight schedules, and budgetary constraints during the pre-planning phase. Also, the study found that inappropriate project cost estimation is one of the major factors leading to cost overruns, and that such inaccurate cost assessments can directly affect the successful implementation of a project, often stemming from inadequate assessment of market conditions and potential risks during the pre-project phase [12].
The study by Shoar et al. (2023) identified several key factors that contribute to cost overruns. Among these, claims were identified as one of the high-level factors that can directly lead to cost overruns. The study also pointed out contractor incompetency as a fundamental issue that can ultimately lead to increased project costs by affecting the quality of project implementation. These findings highlight the importance of focusing on both direct factors and root causes in the project management process [22].
Álvarez-Pozo et al. (2024) pointed out that EPC projects face many technical challenges during implementation, from quality defects in preliminary engineering design documents to various changes and modifications during the construction phase, which can significantly affect project progress and costs. The study particularly emphasizes that in highly complex industrial projects, if these technical challenges are not fully identified and addressed during the front-end engineering design (FEED) phase, they will have a greater impact on time and cost in subsequent phases [13].
There are multiple key factors influencing construction cost overruns. According to Annamalaisami and Kuppuswamy (2021), additional changes in the quantity of engineering work and on-site overruns reflect the dynamic nature of the project scope; inadequate risk management is reflected in deficiencies in project control; inappropriate construction methods are mainly reflected in poor site management and poor technical performance; construction errors during the construction process are a quality issue, and it is difficult to implement corrective measures; the practice of selecting the lowest bidder as a professional skill deficiency often lays hidden dangers before construction; and incorrect planning and scheduling directly affect the efficiency of project implementation [14].
Regarding the inadequacy of contingency provisions and the issue of consultation materials, a study by Asiedu et al. (2017) points out that the determination of contingency lacks a scientific basis and is not based on a reasonable assessment of risk factors. This unscientific method of determining contingency is particularly worrying in the construction industry in Ghana, where it is associated with actual cost increases and changes. The main reasons for this are the lack of rigorous assessment mechanisms during the design and estimation stages, as well as errors and contradictions in the contract documents [23].
According to a study by Kamaruddeen et al. (2020), errors during construction are one of the most important factors contributing to cost overruns (RII = 0.6169). If the contractor fails to perform the work as required by the contract or installs unapproved materials, it may result in the demolition and reworking of the project. These returns will hinder the progress of the project and ultimately lead to cost overruns. At the same time, the study also pointed out that inadequate project management and cost control (RII = 0.5972) is another important reason. Improper planning of the design and construction stages can affect the completion time, budgeted cost, and required quality of the project [24].
Therefore, the first hypothesis of this study is as follows:
Project management can have a significant impact on cost overruns.
1.2. Resources and Supply Chain
Material management challenges have been extensively documented in construction research. According to a study by Wanjari and Dobariya (2016), rising raw material prices are the primary cause of cost overruns in Indian construction projects, and 67% of respondents agree with this. The study shows that when the tender documents include a price-adjustment clause, the owner needs to bear the cost of price increases, which may lead to cost overruns or even suspension of the project [20].
Material cost management faces new challenges with the increasing adoption of new materials in construction structures. Advancements in construction materials, such as UHTCC, Basalt Micro Fiber, CFRP, etc., are used in construction to strengthen the structure [25,26,27]. Recent experimental research by Zhang et al. has demonstrated that while CFRP strengthening with different configurations can effectively enhance shear wall capacity, the strengthening efficiency varies significantly among different configurations. At the same CFRP ratio, the combined strip method significantly enhanced the shear bearing capacity by 27.8% compared to traditional methods [27]. Additionally, Al-Zu’bi et al. conducted comprehensive mechanical performance and life cycle assessments of BFRP-reinforced AAC slabs, revealing that the integration of new materials can achieve both structural enhancement and environmental sustainability, though careful cost–benefit analysis is needed in practical applications [28].
The study by Shoar et al. (2023) shows through an ISM analysis that price fluctuations are located at the highest level of cost overruns and can directly lead to rising project costs. This effect is particularly pronounced in developing countries, as it is closely linked to the instability of the overall economic environment [22].
Material supply shortages and price fluctuations are significant contributors to cost overruns in construction projects. According to Nguyen et al. (2025), these material-related issues can create a cascading effect from initial price fluctuations to financial difficulties, ultimately leading to project-cost escalation [29].
The availability of materials and equipment is one of the major challenges facing industrial projects. As Álvarez-Pozo et al. (2024) point out, “issues such as unreliable supplier performance, delayed equipment delivery, material shortages, and price fluctuations often result in significant cost overruns. In particular, in the procurement management of long-lead-time equipment (LLIs), improper scheduling can seriously affect the project schedule and have a knock-on effect” [13].
Labor-related factors have shown a significant impact on project costs. The study emphasizes the following: “From the design team to the construction site, the lack of professionals can have a significant impact on the project. This is manifested in high staff turnover rates, insufficient skill levels, and difficulties in recruiting foreign employees. These problems not only directly affect work efficiency, but also further push up the total project cost due to rising labor costs” [13].
According to Gosling et al. (2010), supplier distance and disruption risk in supply chain management are important dimensions. The study proposes a supply chain flexibility framework, which is divided into two key prerequisite elements: vendor flexibility and sourcing flexibility. Vendor flexibility includes transportation flexibility. The study states the following: “Due to the small site, inventory space is limited. Once products are produced, it is difficult to maintain flexible storage for these products in the event of project delays. Most items can only be stored on site”. This illustrates the important impact of supplier distance and inventory capacity on supply chain stability [29].
Therefore, the second hypothesis of this study is as follows:
Resources and supply chain can have a significant impact on cost overruns.
1.3. External and Environmental Influences
The impact of external environmental factors on project costs should not be overlooked. Research shows that changes in market prices and inflation, fluctuations in currency exchange rates, and updates to laws and regulations are all financial and design-related influencing factors. These factors usually become apparent after the procurement stage and during project implementation, and require corresponding response strategies to be formulated early in the project. In particular, fluctuations in prices and exchange rates can have a direct impact on project costs, while changes in regulations and policies may lead to adjustments to project design and implementation plans [14].
According to Ghazal and Hammad (2020), changes in market prices and changes in government regulations are key external influences on cost overruns in construction projects. The study states that things such as inflation, market conditions, and unanticipated changes in regulator regulations can cause project costs to deviate significantly from the original budget [30]. The study states that things such as inflation, market conditions, and unanticipated changes in regulator regulations can cause project costs to deviate significantly from the original budget [31].
Weak institutions and corruption are also important factors that should not be ignored. Asiedu et al. (2017) showed that the construction industry in Ghana has long been plagued by problems such as fragmented industry and a lack of unified regulation. There is no dedicated agency to uniformly manage and coordinate the industry. Such institutional deficiencies have led to confrontational contracting, quality defects, and cost and time overruns during project implementation, seriously hindering the construction industry’s role in promoting economic development. The study also found that political interference and corruption have further exacerbated these problems [17].
In their study, Shoar et al. (2023) included external factors such as bad weather in the analysis framework of cost overruns. They pointed out that although such factors cannot be directly controlled, their negative impact can be reduced by considering them in advance in contract terms and project plans [22]. This view is supported by Pham et al. (2020), who found that various risks, including natural disasters, weather conditions, and adverse geological conditions, significantly contributed to project cost overruns [32]. Major public health events such as the new crown epidemic are typical force majeure factors that will affect project costs in ways such as increasing expenditure on epidemic prevention materials and causing delays in the construction period. Studies have shown that this impact includes both direct epidemic prevention costs and indirect losses such as equipment idleness and personnel waiting to work [9].
In terms of the impact of project location restrictions, the study by Kamaruddeen et al. (2020) pays particular attention to the impact of geographical location on the transportation costs of building materials. Taking Sarawak as an example, as it is located in the northwest of the island of Borneo, across the sea from peninsular Malaysia, most building materials need to be imported by sea, and this logistics characteristic may affect the cost of construction projects. The study shows that geographical location and related transportation factors are important considerations that affect project costs [24].
Cost management of construction projects is undergoing positive changes with the development of new technologies such as BIM. Although cost overruns cannot be completely avoided, the application of new technologies can help to better identify and manage cost risks and improve prediction accuracy [9].
Therefore, the third hypothesis of this study is as follows:
External and environmental influences can have a significant impact on cost overruns.
2. Research Methodology
This study reviews the research on construction cost overruns; thus, it is limited to academic articles published in construction-related journals. Due to its comprehensive coverage and accuracy features, Scopus was selected as the primary database for article retrieval. A systematic search was conducted using the keywords “construction”, “cost overrun”, “cost escalation”, and “cost variation”. The search was performed including all categories offered by the database.
Based on the article titles and publishing journals, 245 articles were initially identified. The search was limited to articles published between 2010 and 2025, and it only included published, in press, and review articles from construction management journals. For quality assurance, the search was further limited to journals ranked in the top 20 construction management outlets. After reviewing the abstracts and full texts to ensure their relevance to construction cost overruns, 30 articles were selected for detailed analysis.
Through the analysis of these 30 articles, 37 common cost overrun factors were identified. To verify these factors and ensure their applicability in the Chinese construction context, the research team conducted in-depth interviews with 4 industry experts (2 senior project managers, 1 contractor, and 1 supply chain management expert). Based on the expert evaluation and factor analysis, 24 key factors were finally selected, as shown in Table 1. The main factors to be eliminated include the following:
Variables that highly overlap with other factors (e.g., poor contract management, poor contract planning and supervision, etc.);
Factors that experts assess as having little impact or low relevance in the context of the Chinese construction industry (e.g. optimistic bias and strategic misdirection, etc.).
The cost overrun’s influencing factors selected in this study are mainly based on the accumulation of previous studies. In the project-management dimension, the variables PM2, PM4, PM5, PM6, and PM9 were derived from Ahiaga-Dagbui and Smith (2014) [16], Doloi (2013) [11], Heravi and Mohammadian (2019) [19], and Johnson and Babu (2018) [12], respectively, and other literature works. These scholars reveal the critical impact path of project management from the perspectives of project planning, schedule control, design management, and change claims. The variables RS1, RS3, RS4, and RS5 of the resource supply chain dimension mainly refer to the studies of Álvarez-Pozo et al. (2024) [13] and Shoar et al. (2023) [22], highlighting the important impacts of the resource elements, such as material supply, labor allocation, and supply chain stability. The selection of variables in the external environment dimension, on the one hand, absorbed the idea of Shoar et al. (2023) [22] to include force majeure factors for analysis; on the other hand, referring to the studies of Annamalaisami and Kuppuswamy (2021) [14] and Asiedu et al. (2017) [17], it included macro factors, such as changes in the market economic environment, policies, and regulations.
It is worth mentioning that this study incorporates the variables selection of the lowest bidder (PM7) and errors during construction (PM8) in the project management dimension. Annamalaisami and Kuppuswamy (2021) [14] pointed out that the blind pursuit of low bidding can lead to hidden problems before project implementation. The study of Kamaruddeen et al. (2020) [24] also found that construction errors are an important causative factor for rework and cost overruns. Therefore, this study considers it necessary to include these two factors in the analytical framework. In addition, in the external-environment dimension, two relatively innovative variables, project location constraints (EI5) and impact of technological advances (EI8), were also included in this study. On the one hand, Kamaruddeen et al. (2020) [24] focused on the resource-availability challenges faced by location factors, especially in remote areas of construction; on the other hand, Xie et al. (2022) [9] mentioned that information technology applications are increasingly affecting project cost management. Therefore, this study argues that examining external environmental factors from the perspectives of location constraints and technological advances can help expand the analytical perspectives of previous studies.
This study uses the Likert-quantified questionnaire to express the specific opinions of the participants clearly. Most of the statements in the survey are single-choice questions. As a result, a 5-point Likert scale was used for exploratory factor analysis (EFA) and structural equation model (SEM) and analyzed data. After analyzing the information collected from the interviewees, the factors affecting the construction cost of the building project were determined and analyzed.
According to the possible connection between concepts, first-hand data show that project management, resources and supply chain, and external and environmental impacts have a certain direct relationship with the cost overrun of the construction project. These factors are different from the cost of the project. In the proposed model, the arrow represents these factors’ positive or negative impact on the cost overrun. Project management, resources and supply chain, and external and environmental impacts are the three main impact categories. All subfactors (E1–E24) are associated with these three types of key factors to measure the oversupply of each factor to the cost of building project costs. As shown in Figure 1, the arrows in the model reveal how these factors affect the cost of building projects in different ways, thereby helping to determine the key driving factor that causes cost overruns.
3. Study Respondents
The questionnaire is distributed via the Internet. In order to make the questionnaire response effective and reliable, several factors need to be considered, including the nature of the respondent’s work unit, the respondent’s position, and the respondent’s experience in the construction industry. The 400 construction industry employees were selected through a multi-stage sampling method. First, a comprehensive list of construction-related companies across different regions was compiled. Then, a random sampling was carried out among these companies to select a representative subset. From each selected company, employees were further randomly selected according to their job types and levels to ensure a diverse representation.
In factor analysis, the adequacy of the sample amount is essential for the reliability and stability of the analysis results. The sample amount is usually required to reach at least 5 to 10 times the number of variables; each variable requires at least 5 to 10 samples to ensure the stability of the factor structure. Before entering SEM analysis, each measurement variable must be equipped with 5–10 samples. The questionnaire design of this study covers 24 key variables. Based on this standard, the sample volume should reach at least 120 to 240 to ensure the accuracy of factor analysis and SEM applicability and results. Therefore, considering that there may be invalid questionnaires or non-responses, this study decided to issue questionnaires to the 400 construction industry employees. The study finally recovered and analyzed 212 valid questionnaires, with a recovery rate of 53%. This recovery rate is within the reasonable scope of a questionnaire survey, which can ensure the sample’s representativeness and provide reliable data support for subsequent statistical analysis.
Regarding the potential influence of the organizational type and job distribution of the selected construction industry employees on the results, we have the following:
−. Organizational type: Different organizational types in the construction industry may have distinct perspectives and operational models. For example, contractors are directly involved in the construction process, while design units focus on the planning and design stage. The high proportion of contractors (31%, 66 respondents) and construction units (25%, 53 respondents) in our sample may lead to a stronger emphasis on on-site construction-related issues in the results. If the proportion of design or consulting units was higher, the results might have been more influenced by design-oriented or management-consulting-related viewpoints.
−. Job distribution: Technical personnel accounted for the highest proportion (25%, 52 respondents), followed by project managers (21%, 45 respondents). Technical personnel are more likely to provide insights into construction techniques and quality-control on-site, while project managers focus more on overall project management, such as schedule and resource allocation. The relatively high proportion of technical personnel may make the results more inclined toward technical-based suggestions and findings. If the proportion of project managers was higher, the results might have placed more emphasis on project management-related strategies and challenges.
Among the 212 valid questionnaires, in terms of organizational type, contractors accounted for the highest proportion, at 31% (66 respondents), followed by construction units, at 25% (53 respondents); design units, at 16% (34 respondents); consulting units, at 15% (32 respondents); and supervision units accounted for the lowest proportion, at 13% (27 respondents). In terms of job distribution, technical personnel accounted for the highest proportion, at 25% (52 respondents), followed by project managers, at 21% (45 respondents); site supervisors, at 20% (42 respondents); cost engineers, at 18% (38 respondents); and other positions, at 16% (35 respondents). This indicates that a large proportion of the respondents are technical personnel. In terms of work experience, 42% (89 respondents) have 5–10 years of work experience, 35% (75 respondents) have more than 10 years of experience, and 23% (48 respondents) have less than 5 years of work experience. This indicates that most respondents have extensive industry experience. In terms of project type, residential projects accounted for 29% (62 respondents), commercial projects accounted for 27% (58 respondents), industrial projects accounted for 22% (47 respondents), and infrastructure projects accounted for 21% (45 respondents). In terms of project scale, 40% (85 respondents) participated in projects with an investment of more than 200 million yuan, 39% (82 respondents) participated in projects with an investment of 50 million to 200 million yuan, and 21% (45 respondents) participated in projects with an investment of less than 50 million yuan.
In order to preliminarily understand the impact of various factors on cost overruns, this study conducted a descriptive statistical analysis of all measurement indicators (see Table 2). The measurement uses a Likert 5-point scale, with 1 representing “no impact” and 5 representing “great impact”. The sample size is 212, and the score range of all indicators is 1–5 points.
To ensure data quality, outlier detection was conducted using the Mean ± 3 Standard Deviation method. This approach identifies extreme values that deviate significantly from the data. As shown in Table 2, our mean values range between 3.4 and 3.8, with SD values ranging from 1.0 to 1.365, and the calculated upper and lower thresholds remained within the predefined 1–5 Likert scale. Since no responses exceeded these limits, no extreme outliers were detected or removed. For the project management factors, errors in the construction process (M = 3.936), inappropriate contract management (M = 3.922), and inappropriate resource allocation (M = 3.915) had the highest degree of impact, with standard deviations of 2.0 or more, indicating that these factors were the most important issues requiring attention in project management. In the group of resource and supply chain factors, the mean values of all indicators are between 3.481 and 3.558, and the standard deviations range between 1.145 and 1.365, indicating that there is a large difference in the evaluation of these factors by the respondents, which may be related to the different project types and scales. Among the external environmental factors, Currency exchange rate fluctuation (M = 3.588), weak project systems and economic environment (M = 3.581) and corruption and fraud (M = 3.575) had the most significant impacts, and the standard deviations of these factors were relatively small (1.205–1.333), suggesting that there was a relatively consistent evaluation of their degree of impact by the respondents.
Overall, among the three types of factors, project management and external environment factors are generally rated higher than resource and supply chain factors, and the standard deviations are relatively large, suggesting that these two types of factors may be the main causes of cost overruns, but the degree of their influence may vary widely among different projects. This preliminary finding provides a basis for subsequent factor analysis and structural equation modeling. At the same time, the standard deviations of all variables are large, indicating that the degree of influence of each factor varies significantly across projects, thus also suggesting that it is necessary to further analyze the structure of the relationship between the influencing factors.
4. Data Analysis
In this study, SPSS and AMOS software were used for exploratory factor analysis (EFA) and structural equation model (SEM) analysis, and the hypothetical model of the productivity factors of building equipment was used to conduct an experimental assessment of the hypothetical model of building equipment. SPSS is a statistical data analysis application for social science, and many scholars use it for research. Most well-known researchers use SPSS to analyze the survey data and extract text data to maximize the role of their research and investigation activities.
4.1. Exploratory Factor Analysis (EFA)
In this study, the selection of exploration factor analysis (EFA) to identify the potential structure that affects the cost of building projects. This method helps to extract several less representative factors from a large variable, and then simplify the data structure and reveal the potential reasons behind the cost overrun. To facilitate the explanation of the extracted variables, we rotate the extracted factor. In SPSS, we chose the maximum variance (Varimax) instead of other available rotation techniques because it is often widely used in important rotation components. Therefore, this process needs to rotate factors to increase the diversity of the square factor load, which is easier to understand the load of load according to its importance. Factor loadings less than 0.40 were considered insufficient, and these items were excluded from further analysis [33]. This criterion represents the lowest acceptable threshold in factor analysis, as loadings below 0.40 explain less than 16% of the variance (as 0.402 = 0.16), indicating a weak relationship between the variable and the factor. Based on this criterion, two items were deleted from the data list: improper contract management (PM1) and improper resource allocation (PM3).
Table 3 lists 22 basic elements after screening, as well as factor loads determined by SPSS software and Kaiser–Meyer–Olkin (KMO) value. The Kaiser–Meyer–Olkin (KMO) value measures the sampling adequacy of data to determine whether the data are suitable for factor analysis. The three basic ingredients are project management, resource and supply chain, and external and environmental influences.
After classifying and naming factors, we used AMOS software to determine the Cronbach’s Alpha to ensure that the component of each factor has internal consistency. When Cronbach’s Alpha is greater than 0.7, the classification level is considered high, indicating that it has internal consistency. Table 4 Shows the values of Cronbach’s Alpha, along with the composite reliability and Average Variance Extracted (AVE), which determines the reliability and value of the data.
4.2. Structural Equation Modeling (SEM)
SEM consists of two parts: an estimation model and a primary model. As mentioned in the preceding section, model optimization can be accomplished through the elimination of weakly connected linkages or by eliminating attributes with weak relationships to their latent parameters. Improper contract management (PM1) and improper resource allocation (PM3) were two attributes that had weak correlations with their latent components. These two attributes had factor loadings below 0.40, as mentioned in the previous section. Therefore, they were removed as part of the model modification. Figure 2 shows how the factors that affect cost overruns were modified.
The path coefficients in Figure 2 reveal important relationships between the three major factors. The positive correlation (1.36) between project management, and resource and supply chain factors indicates that when project management issues increase by one unit, resource-related problems tend to increase by 1.36 units. This suggests that poor project management significantly amplifies resource management challenges. Conversely, the negative correlation (−2.39) between resource management and the external environment implies that strong resource management practices can effectively reduce the impact of external challenges—specifically, a one-unit improvement in resource management can decrease the impact of external factors by 2.39 units. Similarly, the negative correlation (−1.11) between project management and external environment suggests that effective project management can help buffer against external challenges, with a one-unit improvement in project management reducing external impact by 1.11 units.
Figure 2 also validates the factor framework related to cost overruns and shows the interactions between the three main factors, including project management, resource and supply chain management, and external and environmental factors. The correlation between these latent variables is represented by bidirectional arrows, and the positive or negative correlation between them is shown by the path coefficients (e.g., 1.36, −2.39, and −1.11). These results indicate that there are significant interactions between different factors, which may further indirectly affect cost overruns. As shown in Table 5, the standardized path coefficients of each variable are statistically significant (p ≤ 0.001), supporting their importance in the overall factor framework.
The structural equation modeling (SEM) results reveal complex relationships between the three major factors affecting construction project cost overruns.
Project management exhibits a strong direct impact through multiple pathways, with standardized coefficients ranging from 0.918 to 0.951. The highest impacts come from change orders (PM6, β = 0.951) and construction errors (PM8, β = 0.950), explaining 90.4% and 90.2% of the variance, respectively. Resources and Supply Chain show the strongest direct relationships, with coefficients from 0.960 to 0.990. Material supply shortages (RS1, β = 0.990) and supply chain disruption (RS5, β = 0.977) demonstrate particularly high explanatory power, accounting for 98.0% and 95.4% of the variance. External and Environmental factors show relatively lower but significant direct effects (β = 0.886 to 0.941), with economic environment (EI1) having the strongest impact (R2 = 0.885).
The model also reveals a cascading effect among the influencing factors. Improvements in project management can not only directly reduce the negative impact of the external environment but also generate indirect benefits through optimized resource management. For example, when the efficiency of site management (PM10, β = 0.930) is improved, the efficiency of resource utilization (RS3, β = 0.964; RS4, β = 0.960) will be improved, forming a virtuous circle. The statistical significance of these relationships reached the level of p ≤ 0.001, indicating that the model captured a stable influence pattern.
4.3. Model Fit Analysis
The overall model demonstrates excellent fit across multiple indices (Table 6), indicating strong validity for further analysis.
The overall goodness-of-fit indicators of the model (see Table 5) show good performance. χ2/df = 1.416 is less than the threshold of 3.0, RMSEA = 0.044 is less than the criterion of 0.08, and CFI = 0.988 and TLI = 0.987 are both above the requirement of 0.90, suggesting that the model is able to explain the structure of the data well. Together, these indicators support the validity of the model.
4.4. Path Analysis
The path analysis reveals significant relationships between the three major dimensions and their respective observed variables. For project-management factors, the standardized path coefficients range from 0.918 to 0.951, with change orders (PM6, β = 0.951) and construction errors (PM8, β = 0.950) showing the strongest effects. This indicates that project-management issues, particularly those related to change management and quality control, are primary drivers of cost overruns.
Resources and supply chain factors demonstrate consistently high path coefficients (0.960 to 0.990), with material supply shortages (RS1, β = 0.990) and supply chain disruption (RS5, β = 0.977) having the most substantial impact. These results emphasize that resource management is equally critical as project management in controlling cost overruns.
External and environmental factors show relatively lower but still significant coefficients (0.886 to 0.941). The economic environment (EI1, β = 0.941) emerges as the most influential external factor, while project location constraints (EI5, β = 0.886) show comparatively lower impact. This suggests that while external factors are important, they have less direct influence on cost overruns compared to internal factors.
As seen in Table 5, resources and supply chain (RS) is the most important paper because RS holds the three largest values of path coefficient in RS1 (0.990), RS5 (0.977), and RS2 (0.969). With the same reason, project management (PM) is the second most important factor and external and environmental influences (EIs) are the last.
4.5. Factors’ Interactions
Through SEM analysis, the interaction effects extend beyond direct correlations to reveal important patterns regarding how these factors collectively influence cost overruns. The cascade effects in the model demonstrate that issues in project management can trigger a chain reaction through resource management to cost performance. When project planning is inadequate (PM2, β = 0.918), it affects the effectiveness of resource allocation and utilization, which in turn compounds the impact on cost control.
The high path coefficients in both project management (up to 0.951) and resource management (up to 0.990) indicate that problems in these areas tend to reinforce each other. Poor site management (PM10, β = 0.930) often leads to inefficient use of materials and labor (RS3, β = 0.964; RS4, β = 0.960), creating a cycle of increasing inefficiencies in project execution. This reinforcing mechanism helps explain why initial management issues can escalate into significant cost overruns if not addressed promptly.
The consistently high R2 values across resource management factors (ranging from 0.922 to 0.980) suggest that strong resource management systems can provide significant buffering capacity against various challenges. This is particularly evident in material supply management (RS1, R2 = 0.980) and supply chain resilience (RS5, R2 = 0.954). These interaction patterns suggest that effective cost control requires a holistic approach that considers not only individual factor impacts but also their combined and cumulative effects on project outcomes.
4.6. Total Effects and Implications
When considering both direct and indirect effects, project management emerges as the most influential factor (Total Effect = 0.892), followed by resources and supply chain management (Total Effect = 0.847), and external and environmental influences (Total Effect = 0.623). These findings have several important implications for practice.
First, the strong interaction between project management and resource factors suggests the need for an integrated management approach. Project managers should consider both aspects simultaneously in their planning and execution strategies, rather than treating them as separate concerns.
Second, the high impact of resource-related factors highlights the critical importance of supply chain resilience. Organizations should invest in developing robust supply chain systems and maintaining flexible resource allocation strategies to better handle market uncertainties and supply disruptions.
Finally, while external factors show lower direct effects, their influence through resource availability and project execution suggests the need for comprehensive risk management frameworks. Organizations should develop effective environmental scanning mechanisms and adaptive management strategies to better respond to external challenges.
5. Conclusions and Recommendations
This study developed a structural equation model to analyze the factors contributing to cost overruns in construction projects in China. Through a literature review and expert interviews, 24 factors were initially identified and later refined to 22 significant factors through exploratory factor analysis, categorized into three dimensions: project management, resources and supply chain, and external and environmental influences.
Unlike previous research that mainly focused on ranking individual factors contributing to cost overruns, this study makes several unique contributions to the field. First, it synthesizes the numerous factors into three critical dimensions (project management, resources and supply chain, and external and environmental influences) through exploratory factor analysis, providing a more structured understanding of cost overrun mechanisms. Second, it provides a comprehensive quantitative analysis of both direct and indirect relationships between these dimensional factors through structural equation modeling, moving beyond the traditional single-factor analyses. Third, it reveals previously unidentified interaction effects between different factor categories, particularly the amplifying effect of management deficiencies on resource-related problems and the mitigating potential of effective internal management on external challenges.
The SEM analysis revealed several key findings that establish clear pathways for cost overrun management. First, project management emerged as the most influential factor (Total Effect = 0.892), particularly in areas of change orders and construction error management. Second, resources and supply chain management showed substantial impact (Total Effect = 0.847), with material supply shortages and supply chain disruptions being critical concerns. Third, external and environmental factors demonstrated relatively lower direct effects (Total Effect = 0.623), but they significantly influenced project outcomes by interacting with other factors.
The study also identified important factor interactions. The strong positive correlation between project management and resource factors (1.36) suggests that management deficiencies amplify resource-related problems. Conversely, the negative correlations between external factors and both project management (−1.11) and resource management (−2.39) indicate that effective internal management can help mitigate external challenges.
5.1. Practical Recommendations
Based on the identified impact pathways, we propose targeted recommendations for cost control in construction projects. Given project management’s dominant effect (0.892), primary attention should focus on enhancing management systems through severe change management procedures, improved quality control systems, and extensive project-planning frameworks. The strong correlation between management and resource factors (1.36) further emphasizes the need for enhanced coordination among stakeholders and effective communication mechanisms.
For the resource management pathway (0.847), organizations should develop dependable material supply networks with contingency sources, directly addressing the identified supply chain vulnerabilities. The implementation of adaptable resource allocation methods and advanced supply chain management systems can enhance efficiency. The establishment of buffer systems and robust supplier connections is particularly important given the strong interaction between resource and management factors.
To address the external factor pathway (0.623) and leverage the identified negative correlations with internal factors (−1.11, −2.39), organizations should focus on building environmental resilience. This includes establishing systematic risk assessment procedures and proactive alert systems for external changes. The development of adaptable project frameworks and contingency plans should specifically target the interaction between external and internal factors, utilizing the potential for internal management to mitigate external challenges.
5.2. Research Limitations
This study has multiple limitations that must be acknowledged when evaluating its results. A key limitation is the geographical scope, as the study concentrated solely on China’s construction sector. Furthermore, regional disparities within China were not explicitly examined, indicating that the results may overlook substantial variances among provinces or cities. Consequently, the findings may not be immediately relevant to other nations with distinct construction methodologies and regulatory frameworks.
A further constraint refers to the sample characteristics. Despite the adequacy of the 212 valid replies for factor analysis, this sample constitutes merely a fraction of the construction sector. Moreover, the study failed to distinguish across different types of construction projects, thus affecting the applicability of the findings across varied project contexts. The self-reporting aspect of the surveys may introduce answer bias, potentially affecting data reliability.
This study also encountered methodological limitations. The cross-sectional form restricted the examination of temporal evolution. This study concentrated on detecting associations rather than showing causality, emphasizing correlations without demonstrating direct cause-and-effect linkages. Furthermore, the intrinsic complexity of building projects may encompass other components not accounted for in the existing research model.
Despite these limitations, the reliability of our findings is supported by the rigorous statistical analysis and adequate sample size for SEM analysis. The findings are most applicable to medium-to-large-scale construction projects in China’s developed urban areas. While the fundamental relationships identified between project management, resource factors, and external influences may be relevant to other contexts, the specific effect sizes and management recommendations should be carefully evaluated before being applied to different geographical locations or project types.
Conceptualization, S.D. and V.C.; Software, S.D.; Validation, M.A.; Formal analysis, S.D.; Investigation, M.A.; Resources, V.C.; Writing—original draft, S.D.; Writing—review & editing, M.A. and V.C.; Visualization, S.D. and M.A.; Supervision, V.C.; Project administration, V.C. All authors have read and agreed to the published version of the manuscript.
Ethical review and approval were waived for this study because the data was collected only anonymous questionnaires with informed consent regarding the purpose of the study.
Informed consent was obtained from all subjects involved in the study.
The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding author.
The authors declare no conflicts of interest.
The following abbreviations are used in this manuscript:
| PM | project management |
| RS | resources and supply chain |
| EIs | external and environmental influences |
Footnotes
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.
Cost overruns factor list.
| Latent Factors | Observable Variables | References | |
|---|---|---|---|
| Project management (PM) | PM1 | Improper contract management | [ |
| PM2 | Unreasonable project plan | [ | |
| PM3 | Inappropriate resource allocation | [ | |
| PM4 | Ineffective progress control | [ | |
| PM5 | Inadequate design | [ | |
| PM6 | Change orders | [ | |
| PM7 | Selection of the lowest bidder | [ | |
| PM8 | Errors during construction | [ | |
| PM9 | Premature bidding documents | [ | |
| PM10 | Site management effectiveness | [ | |
| PM11 | Contractor efficiency | [ | |
| Resources and supply chain (RS) | RS1 | Material supply shortages/price fluctuations | [ |
| RS2 | Shortage of equipment and spare parts | [ | |
| RS3 | Labor shortage | [ | |
| RS4 | Shortage of skilled labor | [ | |
| RS5 | Supply chain disruption/supplier distance | [ | |
| External and environmental influences (EIs) | EI1 | Weak system and economic environment of the project | [ |
| EI2 | Market price changes | [ | |
| EI3 | Currency exchange rate fluctuation | [ | |
| EI4 | Force majeure events | [ | |
| EI5 | Project location constraints | [ | |
| EI6 | Corruption and fraud | [ | |
| EI7 | Changes in government regulations | [ | |
| EI8 | Impact of technological advances | [ | |
Results of descriptive statistical analysis of measured indicators.
| Factors and Measurements | Sample Size | Mean | Standard Deviation | |
|---|---|---|---|---|
| PM1 | Improper contract management | 212 | 3.922 | 1.036 |
| PM2 | Unreasonable project plan | 212 | 3.844 | 1.129 |
| PM3 | Inappropriate resource allocation | 212 | 3.915 | 1.105 |
| PM4 | Ineffective progress control | 212 | 3.645 | 1.249 |
| PM5 | Inadequate design | 212 | 3.865 | 1.110 |
| PM6 | Change orders | 212 | 3.858 | 1.093 |
| PM7 | Selection of the lowest bidder | 212 | 3.745 | 1.155 |
| PM8 | Errors during construction | 212 | 3.936 | 1.135 |
| PM9 | Premature bidding documents | 212 | 3.688 | 1.160 |
| PM10 | Site management effectiveness | 212 | 3.887 | 1.103 |
| PM11 | Contractor efficiency | 212 | 3.674 | 1.204 |
| RS1 | Material supply shortages/price fluctuations | 212 | 3.500 | 1.196 |
| RS2 | Shortage of equipment and spare parts | 212 | 3.538 | 1.228 |
| RS3 | Labor shortage | 212 | 3.558 | 1.145 |
| RS4 | Shortage of skilled labor | 212 | 3.481 | 1.365 |
| RS5 | Supply chain disruption/supplier distance | 212 | 3.481 | 1.291 |
| EI1 | Weak system and economic environment of the project | 212 | 3.581 | 1.205 |
| EI2 | Market price changes | 212 | 3.538 | 1.273 |
| EI3 | Currency exchange rate fluctuation | 212 | 3.588 | 1.210 |
| EI4 | Force majeure events | 212 | 3.544 | 1.248 |
| EI5 | Project location constraints | 212 | 3.400 | 1.333 |
| EI6 | Corruption and fraud | 212 | 3.575 | 1.262 |
| EI7 | Changes in government regulations | 212 | 3.500 | 1.249 |
| EI8 | Impact of technological advances | 212 | 3.494 | 1.208 |
Factor loadings and KMO values extracted from the EFA.
| Factors | Observable Variables | Factor Loadings | KMO Values | |
|---|---|---|---|---|
| Project management (PM) | PM2 | Unreasonable project plan | 0.912 | 0.975 |
| PM4 | Ineffective progress control | 0.908 | ||
| PM5 | Inadequate design | 0.928 | ||
| PM6 | Change orders | 0.934 | ||
| PM7 | Selection of the lowest bidder | 0.910 | ||
| PM8 | Errors during construction | 0.941 | ||
| PM9 | Premature bidding documents | 0.934 | ||
| PM10 | Site management effectiveness | 0.926 | ||
| PM11 | Contractor efficiency | 0.933 | ||
| Resources and supply chain (RS) | RS1 | Material supply shortages/price fluctuations | 0.612 | 0.892 |
| RS2 | Shortage of equipment and spare parts | 0.584 | ||
| RS3 | Labor shortage | 0.571 | ||
| RS4 | Shortage of skilled labor | 0.598 | ||
| RS5 | Supply chain disruption/supplier distance | 0.610 | ||
| External and environmental influences (EIs) | EI1 | Weak system and economic environment of the project | 0.933 | 0.962 |
| EI2 | Market price changes | 0.898 | ||
| EI3 | Currency exchange rate fluctuation | 0.902 | ||
| EI4 | Force majeure events | 0.904 | ||
| EI5 | Project location constraints | 0.900 | ||
| EI6 | Corruption and fraud | 0.891 | ||
| EI7 | Changes in government regulations | 0.892 | ||
| EI8 | Impact of technological advances | 0.898 | ||
Reliability and validity test results of measurement model.
| Factors | Cronbach’s Alpha | Composite Reliability | AVE |
|---|---|---|---|
| Project management | 0.987 | 0.938 | 0.625 |
| Resources and supply chain | 0.988 | 0.970 | 0.865 |
| External and environmental influences | 0.973 | 0.913 | 0.567 |
Coefficients of standardization and R2 values of the variables.
| Paths | Standardized Path Coefficients (β) | R² |
|---|---|---|
| PM2 ← PM | 0.918 | 0.843 |
| PM4 ← PM | 0.922 | 0.849 |
| PM5 ← PM | 0.930 | 0.865 |
| PM6 ← PM | 0.951 | 0.904 |
| PM7 ← PM | 0.919 | 0.845 |
| PM8 ← PM | 0.950 | 0.902 |
| PM9 ← PM | 0.941 | 0.885 |
| PM10 ← PM | 0.930 | 0.866 |
| PM11 ← PM | 0.941 | 0.886 |
| RS1 ← RS | 0.990 | 0.980 |
| RS2 ← RS | 0.969 | 0.938 |
| RS3 ← RS | 0.964 | 0.929 |
| RS4 ← RS | 0.960 | 0.922 |
| RS5 ← RS | 0.977 | 0.954 |
| EI1 ← EI | 0.941 | 0.885 |
| EI2 ← EI | 0.901 | 0.812 |
| EI3 ← EI | 0.904 | 0.818 |
| EI4 ← EI | 0.907 | 0.822 |
| EI5 ← EI | 0.886 | 0.785 |
| EI6 ← EI | 0.898 | 0.807 |
| EI7 ← EI | 0.897 | 0.804 |
| EI8 ← EI | 0.908 | 0.824 |
The significance level was set at p ≤ 0.001.
Goodness-of-fit statistics for the structural model.
| Model Fit Index | Value | Recommended Threshold | Result |
|---|---|---|---|
| χ2/df | 1.416 | >3.0 | Good fit |
| RMSEA | 0.044 | <0.08 | Good fit |
| CFI | 0.988 | >0.90 | Excellent fit |
| TLI | 0.987 | >0.90 | Excellent fit |
| NFI | 0.961 | >0.90 | Excellent fit |
| SRMR | 0.080 | <0.08 | Acceptable fit |
| GFI | 0.888 | >0.90 | Acceptable fit |
References
1. Herrera, R.F.; Sánchez, O.; Castañeda, K.; Porras, H. Cost overrun causative factors in road infrastructure projects: A frequency and importance analysis. Appl. Sci.; 2020; 10, 5506. [DOI: https://dx.doi.org/10.3390/app10165506]
2. Shah, F.H.; Bhatti, O.S.; Ahmed, S. A review of the effects of project management practices on cost overrun in construction projects. Eng. Proc.; 2023; 44, 1. [DOI: https://dx.doi.org/10.3390/engproc2023044001]
3. Preuss, H. Re-analysis, measurement and misperceptions of cost overruns at Olympic Games. Int. J. Sport Policy Politics; 2022; 14, pp. 381-400. [DOI: https://dx.doi.org/10.1080/19406940.2022.2037685]
4. Memon, A.H.; Rahman, I.A.; Azis, A.A.A. Time and cost performance in construction projects in southern and central regions of Peninsular Malaysia. Int. J. Adv. Appl. Sci.; 2012; 1, pp. 45-52. [DOI: https://dx.doi.org/10.11591/ijaas.v1i1.537]
5. Enshassi, A.; Al-Najjar, J.; Kumaraswamy, M. Significant factors causing time and cost overruns in construction projects in the Gaza strip: Contractors’ perspective. Int. J. Constr. Manag.; 2010; 10, pp. 35-60. [DOI: https://dx.doi.org/10.1080/15623599.2010.10773137]
6. Love, P.E.D.; Ahiaga-Dagbui, D.D.; Irani, Z. Cost overruns in transportation infrastructure projects: Sowing the seeds for a probabilistic theory of causation. Transp. Res. Part A Policy Pract.; 2016; 92, pp. 184-194. [DOI: https://dx.doi.org/10.1016/j.tra.2016.08.007]
7. Srinivasan, S.M.; Shanmugam, N.; Muthuraman, S. Effect of multiple inclusions on natural frequencies of functionally graded plates. Sadhana; 2016; 41, pp. 673-686.
8. Wang, J.Y.; Yuan, H.P. Major cost-overrun risks in construction projects in China. Int. J. Proj. Organ. Manag.; 2011; 3, pp. 227-242. [DOI: https://dx.doi.org/10.1504/IJPOM.2011.042030]
9. Xie, W.; Deng, B.; Yin, Y.; Lv, X.; Deng, Z. Critical factors influencing cost overrun in construction projects: A fuzzy synthetic evaluation. Buildings; 2022; 12, 2028. [DOI: https://dx.doi.org/10.3390/buildings12112028]
10. Shen, L.; Wu, Y.; Zhang, X. Key assessment indicators for the sustainability of infrastructure projects. J. Constr. Eng. Manag.; 2011; 137, pp. 441-451. [DOI: https://dx.doi.org/10.1061/(ASCE)CO.1943-7862.0000315]
11. Doloi, H. Cost Overruns and Failure in Project Management: Understanding the Roles of Key Stakeholders in Construction Projects. J. Constr. Eng. Manag.; 2013; 139, pp. 267-279. [DOI: https://dx.doi.org/10.1061/(ASCE)CO.1943-7862.0000621]
12. Johnson, R.M.; Babu, R.I.I. Time and cost overruns in the UAE construction industry: A critical analysis. Int. J. Constr. Manag.; 2018; 20, pp. 402-411. [DOI: https://dx.doi.org/10.1080/15623599.2018.1484864]
13. Álvarez-Pozo, A.H.; Parma-García, M.I.; Ortiz-Marcos, I.; Bautista, L.F.; Atanes-Sánchez, E. Analysis of causes of delays and cost overruns as well as mitigation measures to improve profitability and sustainability in turnkey industrial projects. Sustainability; 2024; 16, 1449. [DOI: https://dx.doi.org/10.3390/su16041449]
14. Annamalaisami, C.D.; Kuppuswamy, A. Managing cost risks: Toward a taxonomy of cost overrun factors in building construction projects. ASCE-ASME J. Risk Uncertain. Eng. Syst. Part A Civ. Eng.; 2021; 7, 04021021. [DOI: https://dx.doi.org/10.1061/AJRUA6.0001132]
15. Chandra, S.; Sepasgozar, S.; Kumar, V.; Singh, A.; Krishnaraj, L.; Awuzie, B.O. Assessing Factors Affecting Construction Equipment Productivity Using Structural Equation Modeling. Buildings; 2023; 13, 502. [DOI: https://dx.doi.org/10.3390/buildings13020502]
16. Ahiaga-Dagbui, D.; Smith, S. Dealing with construction cost overruns using data mining. Constr. Manag. Econ.; 2014; 32, pp. 682-694. [DOI: https://dx.doi.org/10.1080/01446193.2014.933854]
17. Asiedu, R.O.; Adaku, E. Cost overruns of public sector construction projects: A developing country perspective. Int. J. Manag. Proj. Bus.; 2019; 13, pp. 66-84. [DOI: https://dx.doi.org/10.1108/IJMPB-09-2018-0177]
18. Plebankiewicz, E.; Wieczorek, D. Prediction of Cost Overrun Risk in Construction Projects. Sustainability; 2020; 12, 9341. [DOI: https://dx.doi.org/10.3390/su12229341]
19. Heravi, G.; Mohammadian, M. Investigating cost overruns and delay in urban construction projects in Iran. Int. J. Constr. Manag.; 2019; 21, pp. 958-968. [DOI: https://dx.doi.org/10.1080/15623599.2019.1601394]
20. Wanjari, S.P.; Dobariya, G. Identifying factors causing cost overrun of the construction projects in India. Sadhana; 2016; 41, pp. 679-693. [DOI: https://dx.doi.org/10.1007/s12046-016-0498-3]
21. Adam, A.; Josephson, P.E.; Lindahl, G. Aggregation of factors causing cost overruns and time delays in large public construction projects: Trends and implications. Eng. Constr. Archit. Manag.; 2017; 24, pp. 393-406. [DOI: https://dx.doi.org/10.1108/ECAM-09-2015-0135]
22. Shoar, S.; Yiu, K.T.W.; Payan, S.; Parchamijalal, M. Modeling cost overrun in building construction projects using the interpretive structural modeling approach: A developing country perspective. Eng. Constr. Archit. Manag.; 2023; 30, pp. 365-392. [DOI: https://dx.doi.org/10.1108/ECAM-08-2021-0732]
23. Asiedu, R.O.; Adaku, E.; Owusu-Manu, D.-G. Beyond the causes: Rethinking mitigating measures to avert cost and time overruns in construction projects. Constr. Innov. Inf. Process Manag.; 2017; 17, pp. 363-380. [DOI: https://dx.doi.org/10.1108/CI-01-2016-0003]
24. Kamaruddeen, A.M.; Sung, C.; Wahi, W. A Study on Factors Causing Cost Overrun of Construction Projects in Sarawak, Malaysia. Civ. Eng. Archit.; 2020; 8, pp. 191-199. [DOI: https://dx.doi.org/10.13189/cea.2020.080301]
25. Tong, J.Z.; Wang, L.Q.; Wu, R.M.; Hou, J.; Li, Q.H.; Xu, S.L. Cyclic test and analysis of UHTCC-enhanced buckling-restrained steel plate shear walls. Earthq. Eng. Struct. Dyn.; 2024; 53, pp. 4006-4031. [DOI: https://dx.doi.org/10.1002/eqe.4212]
26. Zhang, B.; Jin, L.; Zhao, O.; Chen, F.; Du, X. Experimental evaluation of CFRP-strengthened shear walls with various CFRP configurations. Thin-Walled Struct.; 2025; 208, 112862. [DOI: https://dx.doi.org/10.1016/j.tws.2024.112862]
27. Al-Zu’bi, M.; Shamass, R.; Ferreira, F.P.V. Mechanical performance and life cycle assessment of BFRP-reinforced AAC slabs strengthened with basalt macro-fibers. Constr. Build. Mater.; 2025; 461, 139917. [DOI: https://dx.doi.org/10.1016/j.conbuildmat.2025.139917]
28. Nguyen, V.T.; Do, S.T.; Tran, C.N.; Pham, L.V. Analysis of interrelationships among constructs of cost overrun causes in consortium projects between construction contractors. Int. J. Constr. Manag.; 2025; pp. 1-13. [DOI: https://dx.doi.org/10.1080/15623599.2025.2454366]
29. Gosling, J.; Purvis, L.; Naim, M.M. Supply chain flexibility as a determinant of supplier selection. Int. J. Prod. Econ.; 2010; 128, pp. 11-21. [DOI: https://dx.doi.org/10.1016/j.ijpe.2009.08.029]
30. Ghazal, M.M.; Hammad, A. Application of knowledge discovery in database (KDD) techniques in cost overrun of construction projects. Int. J. Constr. Manag.; 2020; 22, pp. 1632-1646. [DOI: https://dx.doi.org/10.1080/15623599.2020.1738205]
31. Al-Nahhas, Y.S.; Hadidi, L.A.; Islam, M.S.; Skitmore, M.; Abunada, Z. Modified Mamdani-Fuzzy Influence System for Predicting the Cost Overrun of Construction Projects. Appl. Soft Comput.; 2023; 151, 111152. [DOI: https://dx.doi.org/10.1016/j.asoc.2023.111152]
32. Pham, H.; Luu, T.V.; Kim, S.Y.; Vien, D.T. Assessing the impact of cost overrun causes in transmission lines construction projects. KSCE J. Civ. Eng.; 2020; 24, pp. 1029-1036. [DOI: https://dx.doi.org/10.1007/s12205-020-1391-5]
33. Matsunaga, M. How to Factor-Analyze Your Data Right: Do’s Don’ts, and How-To’s. Int. J. Psychol. Res.; 2010; 3, pp. 97-110. [DOI: https://dx.doi.org/10.21500/20112084.854]
© 2025 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.
Details
; Mukhtar, Ahmed
; Chatpattananan, Vuttichai