Next Article in Journal
The Accuracy of Land Use and Cover Mapping across Time in Environmental Disaster Zones: The Case of the B1 Tailings Dam Rupture in Brumadinho, Brazil
Previous Article in Journal
An Evaluation of ASEAN Renewable Energy Path to Carbon Neutrality
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Sustainability
Volume 15
Issue 8
10.3390/su15086963
sustainability-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles thumb_up
...
Endorse textsms
...
Comment
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Expert Opinion on the Key Influencing Factors of Cost Control for Water Engineering Contractors

by
Zhenhan Ding
,
Xun Liu
*,
Zhiyuan Xue
and
Xiaobo Li
School of Civil Engineering, Suzhou University of Science and Technology, No. 1 Kerui Rd, New District, Suzhou 215000, China
*
Author to whom correspondence should be addressed.
Sustainability 2023, 15(8), 6963; https://doi.org/10.3390/su15086963
Submission received: 3 March 2023 / Revised: 7 April 2023 / Accepted: 18 April 2023 / Published: 20 April 2023
(This article belongs to the Section Sustainable Water Management)
Browse Figure
Review Reports Versions Notes

Abstract

:
There are many unpredictable circumstances during the implementation process of a water conservancy project, which often cause financial loss, increased construction costs and schedule delays. This paper investigates the influence factors for water conservancy project cost control. The present study used the factor analysis method to extract the major cost control influence factors, and performed a correlation analysis to clarify the relationship between these cost control influence factors and the sub-factors under each factor. Several water conservancy project practitioners were invited to analyze the sub-factors of the cost control influence factors and to provide some strategic suggestions in terms of minimizing the impact of the influence factors. The results of the study illustrate that in construction, water engineering contractors who want to reduce costs need to focus on the lack of a clear definition for the scope of works, subcontractors’ insufficient ability to perform the work, site construction conditions and the escalation of the construction material cost. The correlation analysis demonstrated that unreasonable requirements from the supervision unit and unfair standpoints of the supervision unit are highly correlated; the insufficient mobilization ability and lack of management capability of the subcontractor are medium correlated; the site construction conditions and the lack of a clear definition for the scope of works are medium correlated; and the escalation of the construction material cost and shortage of construction materials are medium correlated. This facilitates future water works contractors to identify the underlying causes of cost increases.

1. Introduction

There are many unpredictable circumstances during the implementation process of a water conservancy project, which often cause financial loss, increased construction costs and schedule delays [1,2,3]. These unpredictable situations are usually caused by many influencing factors. The unpredictable circumstances may come from a contractor or subcontractor’s attitude to construction projects or design defects [4]. Factors including an unexpected design, geology, weather and third party, the subcontractor and material, etc., usually affect an engineer’s evaluation of the cost and schedule control of a project [5,6,7]. In the existing literature, much research has been conducted to study cost overrun causes in different types of infrastructure projects, e.g., power plant [8]; transport [9]; road [10], rail [11], bridge [12] and tunnel [13]; highway [14,15]; or even oil and gas construction projects [4,16]. There are also many realistic projects that adopt various cost-saving measures. For example, the Santa Elena project takes full advantage of the low price of local labor in Ecuador, and employing local employees has significant advantages in terms of project cost savings. The second phase of the Hanbei River flood control project (Jingmen section) in Hubei Province, China, was further costed by the contractor’s collusion with the superintendent, which led to jerry-building and repeated reworking. However, so far, the research on the cost impact factors of water conservancy projects is not perfect. The construction of a water conservancy project is a series of processes including a feasibility study, project evaluation, as well as planning, design and construction stages [17,18,19,20,21]. Identifying the factors that affect costs and how to effectively control the cost is important for contractors for water conservancy projects, and if the contractors get this wrong, there will be an increased cost for the project [13,22]. Therefore, it is important to identify the key influencing factors and take effective actions for the successful implementation of a project [16,23,24]. The increased or decreased cost attributed to the improper behavior of stakeholders and unpredictable circumstances lies in their liability during the project implementation process of construction engineering [25]. At present, there have been a multitude of studies on the influence factors of engineering cost control, and many evaluation index systems were established [26,27,28]. However, most of them were considered for the owner party, investors and government party, but not for contractors [29,30,31,32]. Therefore, it is essential to consider the cost impact factors from the perspective of a water engineering contractor. At the same time, the practical experience of experts in the field of water resources engineering is particularly important, so it is crucial to refer to their opinions in the analysis of influencing factors. The present study investigates the influencing factors of cost control for water conservancy project contractors, identifies key influencing factors of cost control and understands the important role of cost control in the implementation of water conservancy construction projects.

2. Establish the Influencing Factors Set for the Cost Control of Water Conservancy Projects

The literature review is summarized in this section. Through investigating 84 contractors from the UK, Aouam considered the main influencing factors of a contractor’s cost control to come from the complexity of the project, construction scale and scope, construction market, construction technologies, construction site conditions, the owners’ financial situations, the location of the project, etc. [33]. On the basis of the owner’s demand, project characteristics, the market characteristics, the requirements from contractors, local laws and regulations, the type of owner and the design organization of owner, Le listed 47 influencing factors that affect project cost control [34]. Meanwhile, Sharareh and Apurva summarized 45 key factors affecting a project’s cost and schedule control based on the completed 67 worldwide engineering projects [35]. However, the above-mentioned projects do not show the cost control features of water engineering. Dongzagla analyzed a completed water conservancy construction project in Ghana, and suggested that the five factors, which include the bad financial payment problem of owner, the disordered management of the subcontractor, the unprogressive technologies, the increased price of material and the materials ordering process, were the reasons for the increased cost [36]. Schultz et al. investigated 258 completed construction projects, including railways, bridges, tunnels and highways in Denmark, and summarized that the extended project period, the over-scaled project and the project entities’ ambiguity were the main reasons for the increased cost [37]. Abdul Nabi showed 34 factors affecting a project’s cost and schedule based on the owner, project, design and contractor’s characteristics, laws and regulations, risk management and claim and dispute [38]. The risk allocation of the construction engineering contract, claim and dispute and contract defect was also discussed. Some articles analyzed the impact of the construction capability, mobilization ability and management ability of subcontractors on the cost performance. The impact of labor cost, construction material price, equipment cost, fuel and instrument cost, and the use of new materials and technologies on the projects’ cost was also discussed [39,40]. Some individuals analyzed the impact of engineering alteration on cost control, and listed 73 influencing factors and ranked these factors from the aspects of the owner, the construction side and the consulting party. Other individuals explored the 15 reasons that cause the increased engineering cost from aspects of the participants’ management system, project conditions and management level [41]. However, there is no cost control study that focuses on cost impact factors from the contractor’s perspective. On the basis of the above literature review and summary, and considering the construction projects’ characteristics, the present study conducted face-to-face interviews with an experienced owner and contractor, and finally selected 20 influencing factors representing key factors that affect construction engineering project contractors’ cost control, which are shown in Table 1.

3. Research Methods

In order to explore the investigated phenomenon, this study mainly adopts an empiricist approach. That is, the initial factors were outlined through extensive literature research, and then a questionnaire instrument was prepared with the factors obtained from the literature [42]. A questionnaire instrument was prepared, predicated on the factors obtained from the literature review, thus constituting a virtuous cycle of knowledge [43,44]. After an extensive review of the literature, a questionnaire was planned to gather opinions, and 15 representative enterprises in the Sinohydro Construction Corporation were selected to test their senior managers [9]. An expert survey was conducted following the pilot study. Quantitative data collection was used to conduct a statistical analysis of the data and determine non-numerical results. In the analysis process, the data of participants were transformed into numerical data for analysis, and the collected data were pre-checked to determine their reliability and normality indicators [45]. Valid responses were then tested and the results discussed [46]. It should be noted that the study does not claim that any generalizations are possible, as the study is limited by the selection and number of companies included in the survey. The methodology for this study is illustrated in Figure 1 and discussed in depth in a later section.
Based on a 20-factor questionnaire, the respondents were asked to rate the importance of the occupational ethics risk induced by each factor according to the 5-point Likert scale (1 = significantly unimportant; 5 = significant importance). This study invited a variety of respondents, which can reveal different perspectives on the research topic. The questionnaire was either emailed or delivered by hand to each participant. This study adopts the method of the self-evaluation of project stakeholders, that is, to expand the sample by asking eligible respondents to identify other project stakeholders to participate in the study. With the expansion and accumulation of samples, sufficient research data are obtained.
In the analysis process, the basic data of a descriptive statistical analysis is first carried out to verify the reliability and effectiveness of the data, and the influence degree of various factors on the cost control of the water conservancy project general contracting is analyzed, before the importance of each factor is ranked. Following the evaluation of the barriers, the variables were factorially analyzed to enable related barriers to be clustered under a unique construct. The EFA tool was used to conduct this analysis due to its ability to factorize a large number of variables into significantly fewer constructs. EFA has two key stages: factor rotation and factor extraction. Moreover, the other tests ingrained in the EFA tool to check the appropriateness of the data are Bartlett’s test of sphericity and the Kaiser Meyer-Olkin (KMO) test. To test whether the data are suitable for factor analysis, the Kaiser-Meyer-Olkin (KMO) test and Bartlett’s Test were used. KMO is an index to compare the observed correlation coefficient with the partial correlation coefficient. Given a range of 0 to 1, any KMO value closer to zero connotes a dispersed interrelated pattern of variables, which makes the dataset unsuitable for EFA. Conversely, a KMO value of or closer to 1 represents perfectly compacted interconnection patterns, which ensures data suitability for EFA. However, any KMO value of 0.5 is satisfactory for EFA to proceed. Bartlett’s Test is used to test the homogeneity of variance, a necessary condition for factor analysis. Both the KMO test and Bartlett’s test of sphericity were conducted to examine the appropriateness of employing the EFA technique in this study. A KMO value should be higher than the 0.5 threshold; meanwhile, the significance level of Bartlett’s test for sphericity should also be small (e.g., p-value = 0.000). Factor extraction determines the initial factor grouping between variables. The methods of factor extraction include the principal component method and principal axis factor method. This study used the principal component analysis method to identify the grouping of factors. The purpose of factor rotation is to better understand and explain the practical significance of the factor and determine the final value of the grouping. Factor rotation can be orthogonal or oblique. This study adopted the Promax rotation to analyze the data with the correlation of components.

4. Empirical Study

4.1. Questionnaire Design and Data Collecting

4.1.1. Questionnaire Design

As the investigation is about the techniques and management of the engineering project, respondents should have a clear understanding of its process, including planning, organization, implementation and control [47,48,49,50]. Thus, the respondents are mainly experts in project management, project managers and technicians with senior and intermediate titles, professors and associate professors engaging in the study of project management, senior consultants engaging in project management consulting and project managers who have long engaged in project contracts. Using the method of project stakeholders’ self-assessment, this research introduces factors that should be considered in cost control and examines the importance of these factors. The questionnaire is designed based on the literature review of a project’s cost control. Before sending out the questionnaire, 15 representative water construction enterprises of the Sinohydro Corporation are selected to conduct in-depth interviews with the senior managers. On the one hand, this is done to have a deep understanding of the actual situation of the enterprise; on the other hand, the questionnaire can be further refined to make the wording as clear as possible [51]. The questionnaire contains three parts: background of survey, respondents’ information and influencing factors of project cost control.
(1)
Survey background: The questionnaire’s respondents are mainly experts in project management. To draw their attention and interest to gain cooperation, the survey’s purpose and significance are elaborated in this part.
(2)
Information about respondents: Respondents of the survey are mainly experts of project management, whose titles reflect their level of education and experience, to some extent. While judging the factors affecting the design of the project transaction pattern and the importance of these factors, different respondents have different decisions based on their project experience and the properties of their institutions [52,53]. Thus, information about the respondents is included in this survey to know the reasonability of the respondents, and whether their understanding of the influencing factors vary from group to group, such as the project clients, intermediaries and contractors.
(3)
Influencing factors of project’s cost control: The purpose of this survey is to examine the influencing factors of project cost control and the importance of these factors. The influencing factors are numerous, and most of them are qualitative indicators involving the respondents’ mental activities, such as their attitudes and opinions. In this survey, the influencing factors are divided into four aspects, which are decomposed into several sub-issues.

4.1.2. Sample Collecting

The total number of distributed questionnaires is 200, recycling questionnaire 157, the recovery rate 78.5%, valid questionnaires 113, invalid questionnaires 44, and questionnaires efficiency 71.97%, which meets the requirements of investigation. The screening of questionnaires is mainly based on the completeness of filling out the questionnaire, whether the score is repeated or excessively regular. While recycling the questionnaire, the respondents are divided into three types (client, consultant or contractor) based on their roles in the project. For the 113 valid questionnaires, 37 experts are contractors, accounting for 32.74% of the total; 45 experts are consultants, accounting for 39.82% of the total; 31 experts are clients, accounting for 27.43% of the total. As for the titles, 43 experts are with senior titles, accounting for 38.05% of the total; 46 have vice senior titles, accounting for 40.71% of the total; and 24 experts have intermediate titles, accounting for 21.24% of the total. All the respondents are experts on construction projects, and 21 have over 20 years of experience, accounting for 18.58% of the total; 17 have 16–20 years of experience, accounting for 15.04% of the total; 31 have 11–15 years of experience, accounting for 27.43% of the total; 29 have 6–10 years of experience, accounting for 25.66% of the total; and 15 have 5 years of experience, accounting for 13.27% of the total. Experienced respondents meet the requirement of this study. The respondents’ views on specific factors are quantified by Likert Scales, in which “1” represents strongly unimportant or disagree; “2” represents not important or agree; “3” represents ordinary or neutral; “4” represents important or agree; and “5” represents strongly important or agree. Before the formal data analysis, Cronbach alpha is used to do an authenticity test on the amount of scale. The reliability of the scale is high, with the coefficient of alpha reliability set as 0.9343.

4.2. Factor Analysis of Cost Control

4.2.1. Data Analysis

Factor analysis is used to analyze sample data with software SPSS. Through the KMO test and Bartlett’s test, the coefficient matrix of the variables is not the unit matrix, and the value of KMO is 0.818, as shown in Table 2, which shows that the factor analysis on these variables is feasible.

4.2.2. Determining the Number of Main Factor

Through the calculation of factor communality with software SPSS, it could be found that there is strong relationship between the index variables and factors, and factors can fully reflect the information of the sample index. So, the effect of factor analysis is significant. Based on the analysis and judgment standard of discrimination validity, a characteristic value greater than 1 is selected as the standard of factor selection, and the factor loadings of different items arrive with principal component analysis. The results show that in the matrix, for the first four factors whose characteristic values are greater than 1, and whose cumulative percentage of explanation for variance is 66.608%, as shown in Table 3 and Table 4, main factors are selected.

4.2.3. Factors Extraction and Nomination

Taking “Variance maximization” as a criterion for factor orthogonal rotation, the factor load matrix arrives. Moreover, after rotation, the total factors’ cumulative contribution rate is constant. This means that the four factors’ cumulative contribution rate is still 66.608%. The four factors, after being assigned with components, have a factor loading of more than 0.5 after rotating. For the degree of influence factor of the rotated component matrix, the greater the value, the greater the degree of influence. Definite factors with economic significance could be found according to the load matrix after rotation, which is shown in Table 4.
Through factor analysis, there are four components whose characteristic values are greater than one. The number of components assigned to the four factors is more than three. The 20 that are indexed are dropped to four dimensions to carry on the study with the results of factor analysis. The condition of major factors is shown in Table 5.
According to the rotated factor matrix in Table 4, the common factor extracted can basically pass all variable information. The square root of the load distribution of the common factor and the common factor variance of each variable and factor contain high load variable meaning, and the meaning of the four common factors are explained and named as follows.
F1 is named as factors of the client, designer and supervision institution. Its main content includes seven factors: supervisor’s unreasonable requirements, lack of fairness for supervisor’s position, client’s unreasonable demands, unclear blueprints, supervisor’s slow decision-making, unclear definition of construction scope and design error. Three parties are related: client, designer and supervision institution. In the construction, the supervision and management of the construction are entrusted to the supervision institute by the clients. The supervision institute stands for the client’s wishes and interests. Similarly, the designer also stands for the client’s wishes and interests, and the design of public projects are usually with supervision. Thus, the components above are named as “factors of client, designer and supervision”.
F2 is named as the factor of the subcontractor. The main content includes four components: subcontractor’s insufficient mobilization ability, insufficient construction ability, insufficient management ability and shortage of fund. These four aspects are caused by the subcontractor, so they are named as “subcontractor factors”.
F3 is named as the factor of the construction safety and environment. The main content includes: safety incident, implementations of project site, land demolition/internal migration, geological condition and poor construction coordination. The factors related are construction safety, environmental, the third party and the coordination of the contractor’s company. For the opposition or resistance to the construction of the residents nearby belongs to the environment of the construction, internal factors of contractor’s company belonging to the environmental of the company, so this part is named as “construction safety and environmental factors”.
F4 is named as the factor of the construction economy and policy. The main content includes: a rise in the price of construction material, a shortage of construction material and the limitation of the date for the construction’s completion. The rise in price of construction materials and the shortage of construction materials are most likely caused by the construction economy or policy. For construction time limits, nearby residents’ daily lives are considered. For a project that is vital to people’s livelihoods, the completion date is generally restricted, such as the South-to-North Water Diversion Project, the Beijing Shanghai high-speed rail project, which is affected by the national policy. So, this part is named as the “factor of construction economy and policy”.

5. Components’ Correlation Analysis

After factor extraction and nomination, the correlation between factors’ components and the influence between them are analyzed through SPSS software.

5.1. Analysis of the Factor of Client, Designer and Supervision

The factor of client, designer and supervision institution includes seven components. Table 6 shows this factor’s correlation analysis. The coefficient shows that Pearson correlation coefficients are between 0.4~0.6, indicating that the correlation between components is moderate. For unclear blueprint, the Pearson correlation coefficients with supervision institution’s lack of fairness and slow decision-making are 0.350 ** and 0.313 **, which is a weak correlation. For supervision institution’s lack of fairness and its unreasonable demands, the Pearson correlation coefficient is 0.690 **, which is highly correlated. This shows that the supervision institute’s unreasonable demands and lack of fairness are the two components that are highly correlated. Unreasonable demands by oversight bodies usually add significant costs for water works contractors, and studies have shown that their unreasonable demands are closely related to their lack of fairness. Future water works contractors can focus on highly relevant risk factors.

5.2. Correlation Analysis of the Factor of Subcontractor

The Pearson correlation coefficient between the subcontractor’s insufficient construction ability, insufficient mobilization ability and insufficient management ability are 0.783 ** and 0.775 **, which are highly correlated. The subcontractor’s shortage of funds is moderately related to their insufficient construction ability, insufficient mobilization ability and insufficient management ability. The Pearson correlation coefficient between the subcontractor’s insufficient mobilization ability and shortage of funds is 0.594 **, close to 0.6, which is highly correlated. The Pearson correlation coefficient between the subcontractor’s insufficient mobilization ability and insufficient management ability is 0.803 **, which is highly correlated. The correlation analysis of the subcontractor is shown in Table 7. It is interesting to note from the description above and from Table 7 that the lack of the mobilization capacity of subcontractors is a very important influencing factor, even more than their lack of management capacity. However, at the same time, the mobilization capacity of subcontractors is highly correlated with their management capacity. Therefore, it is most important to examine the management capability and mobilization capability of the subcontractors for the management of subcontracting costs for water works contractors.

5.3. Correlation Analysis of the Factor of Construction Safety and Environment

The components of the factor of construction safety and environment are moderately correlated. The correlation between land demolition/internal migration and safety accident is weak, as is that between internal corporation and construction coordination. So, safety incident is not correlated with land demolition/internal migration and the condition of the construction site. The Pearson correlation coefficient between land demolition/internal migration and the condition of the construction site is moderate. The geological condition is moderately related with safety incident and the condition of the construction site, and is weakly related with land demolition/internal migration and poor construction scheduling inside the company. The correlation analysis of the safety and environmental factor is shown in Table 8. In the study in Table 8, it can be found that safety incidents do not actually have a significant impact on land demolition/internal migration, contrary to what traditional managers might assume, but the results of the study do not mean that safety incidents are not important in cost management. Water contractors can reduce the valuation risk of safety incidents in land demolition/internal migration, but this does not mean that safety issues can be ignored.

5.4. Correlation Analysis of Construction Economy and Policy

For the factor of construction economy and policy, the Pearson correlation coefficient between the rise in the price of construction material and the shortage of construction materials is 0.598 **, which is a medium correlation. This shows that building material prices may cause a shortage of construction materials, construction materials or shortage caused by rising prices of construction materials; national and local policies and regulations on the construction material price the completion of the project for the period of rise, medium, national and local policies and regulations that have a certain impact on the market price of building materials, construction of water conservancy project completion; in the project completion date, restrictions and building materials costs rise, construction material shortage correlation is low and the mutual influence between each other is not displayed. The construction market and policy factors correlation analysis is shown in Table 9. The analysis in Table 9 shows that there is no strong link between the completion date of the project and the cost of construction materials, which is consistent with the expectation that contractors purchase materials during the construction process. In addition, national and local policies and regulations do not have as much of an impact on construction material costs as material shortages. Contractors should consider the material cost when considering material shortage as the primary consideration.

6. Conclusions and Recommendation

In construction, a contractor’s cost control can be attributed to four influence factors: factor of client, designer and supervision institution, factor of construction safety and environment, and factor of construction economy and policy. Components that have a greater influence on cost control in the four factors are “unclear construction scope”, “subcontractor’s insufficient capacity”, “land demolition/internal migration” and “rise in price of construction materials”.
Correlation analysis shows that, in the factor of client, designer and supervision, supervision institution’s unreasonable demands and its unfairness of position are highly related. In the factor of the subcontractor, the subcontractor’s insufficient mobilization is highly relevant to the subcontractor’s management ability. In the factor of the construction environment and safety accident, there is a moderate correlation between the construction environment and safety accident. In the factor of the construction economy and policy, there is a moderate correlation between construction materials’ rise in price and shortage of construction materials. In the subcontractor factor, the top three components are “subcontractor’s insufficient construction ability”, subcontractor ‘s insufficient mobilization ability” and “subcontractor insufficient management ability”. This shows that the subcontractor is relatively important in the cost control of construction, and the contractor needs to be cautious in the choice of the subcontractor.
The four factors of contractors influence one another. Their factors are especially influential to subcontractors’ management, mobilization and construction, which are the factor of client, designer and supervision, the factor of construction safety and environment and the factor of construction economy and policy. Therefore, in addition to choosing excellent subcontractors, the contractor should carry on their effective management on Party A, the factor of construction safety and environment and the factor of construction economy and policy. This is done to reduce subcontractors’ influence on cost control through the previous three factors. For the constructions involving long periods, large investments, complex technologies and a wide range of professions, to reduce the abnormal construction cost, the contractor should choose excellent subcontractors and control the three factors, which are the factor of client, designer and supervision, the factor of construction safety and environment and the factor of construction economy and policy.
Among the owner, design and supervision factors, most of them are caused by the contractor’s pressure to carry out the works from the technical factors and human factors. The owner is expected to assist the contractor to get the job done smoothly while considering the design, supervision and owner factors. As it is often difficult for contractors to carry out project construction, the contractor and owner should communicate well with each other, report design defects in a timely manner and seek the most proper solution. The owners are also expected to keep relevant files on their communications to coordinate unreasonable requirements from the supervision party. For the unreasonable requirements from the owner part, mutual communications are also necessary in order for potential compensation claim from the owner. The construction ability, mobilization ability and management ability of the subcontractor correspond with each other, which indicates that the subcontractor’s incapability in any could bring about subsequent difficulties. Therefore, the contractor needs to carefully assess the qualification of the subcontractor and choose reputable subcontractors to avoid the uncontrollable costs in the process of project implementation.
The construction safety and environmental factors mentioned in the construction environment and construction safety is closely related. It is necessary to improve the construction environment to avoid construction safety accidents, or during project planning, to try to avoid using the non-construction area and seismic zone to save project costs. In the construction market and policy factors, the price rise of construction materials and the shortage of construction materials are mentioned. Because construction materials account for more than 70% of the construction cost of construction projects, construction materials have a great impact on engineering construction, and therefore, the procurement of materials is mostly the procurement of bulk materials. In terms of the construction completion time limit, the owners usually have demands to complete the project ahead of schedule, while for contractors, they must hurry while building projects to match their pace with the pace of the demand, and crashing often results in increased costs; in this way, the contractors should expect to cover the costs of owners.
This paper innovatively transforms the cost control of water engineering contractors into interrelated influencing factors through expert survey and literature research, which provides some ideas for future water engineering contractors to solve realistic cost problems. At the same time, this paper has some limitations; for example, the expert research method used in this paper will have some subjective influence on the influencing factors and the number of experts searched for will also have some influence. In addition, this paper is biased towards cost control in the literature research and may be inadequate for the summary of the influencing conditions of the project itself. Future research on the cost control of water conservancy contractors can expand the scope of experts, looking for experts in both the field of cost control and water engineering, and can read more extensively in the literature research. Lastly, the sensitivity analysis of each influencing factor can be more detailed.

Author Contributions

Conceptualization, X.L. (Xun Liu) and Z.D.; Methodology, X.L. (Xun Liu) and Z.D.; software, Z.D. and Z.X.; validation, X.L. (Xun Liu) and X.L. (Xiaobo Li).; formal analysis, X.L. (Xun Liu); investigation, X.L. (Xun Liu); resources, Z.D.; data curation, X.L. (Xun Liu); writing—original draft preparation, X.L. (Xun Liu); writing—review and editing, Z.D.; visualization, Z.X.; supervision X.L. (Xiaobo Li), Z.X.; project administration, X.L. (Xun Liu); funding acquisition, X.L. (Xun Liu). All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by Philosophy and Social Science Research in Colleges and Universities in Jiangsu Province (No. 2020SJA1394), Fundamental Research Funds for the Central Universities (No. 331711105), Jiangsu Provincial Construction System Science and Technology Project of Housing and Urban and Rural Development Department (No. 2017ZD074), Postgraduate Research and Practice Innovation Program of Jiangsu Province (SJCX21_1417), Postgraduate Research and Practice Innovation Program of Jiangsu Province (SJCX22_1568).

Institutional Review Board Statement

Ethical review and approval were waived for this study, due to this study not involving biological human experiment and patient data, which was not within the scope of review by the Institutional Review Board of Suzhou University of Science and Technology.

Informed Consent Statement

This article does not include any studies of human participants performed by the authors.

Data Availability Statement

The data presented in this study are available on request from the corresponding author.

Acknowledgments

The authors would like to appreciate the reviewers for all their helpful comments, and would like to thank the foundation of Philosophy and Social Science Research in Colleges and Universities in Jiangsu Province (No. 2020SJA1394), Fundamental Research Funds for the Central Universities (No. 331711105), Jiangsu Provincial Construction System Science and Technology Project of Housing and Urban and Rural Development Department (No. 2017ZD074), Jiangsu Province Joint Education Program High-Standard Example Project, Postgraduate Research and Practice Innovation Program of Jiangsu Province (SJCX21_1417), Postgraduate Research and Practice Innovation Program of Jiangsu Province (SJCX22_1568) for their support.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. 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, 1029–1036. [Google Scholar] [CrossRef]
  2. Wen, Q.-J.; Ren, Z.-J.; Lu, H.; Wu, J.-F. The progress and trend of BIM research: A bibliometrics-based visualization analysis. Autom. Constr. 2021, 124, 103558. [Google Scholar] [CrossRef]
  3. Li, S. Cost Management and Forecasting Method for the Whole Life Cycle of Water Conservancy Projects Based on Multiple Regression Analysis. Sci. Program. 2022, 2022, 2183093. [Google Scholar] [CrossRef]
  4. Bin Seddeeq, A.; Assaf, S.; Abdallah, A.; Hassanain, M.A. Time and Cost Overrun in the Saudi Arabian Oil and Gas Construction Industry. Buildings 2019, 9, 41. [Google Scholar] [CrossRef]
  5. Bhargava, A.; Labi, S.; Chen, S.K.; Saeed, T.U.; Sinha, K.C. Predicting Cost Escalation Pathways and Deviation Severities of Infrastructure Projects Using Risk-Based Econometric Models and Monte Carlo Simulation. Comput. Aided Civ. Infrastruct. Eng. 2017, 32, 620–640. [Google Scholar] [CrossRef]
  6. Habibi, M.; Kermanshachi, S. Phase-based analysis of key cost and schedule performance causes and preventive strategies: Research trends and implications. Eng. Constr. Archit. Manag. 2018, 25, 1009–1033. [Google Scholar] [CrossRef]
  7. Hoseini, E.; Bosch-Rekveldt, M.; Hertogh, M. Cost Contingency and Cost Evolvement of Construction Projects in the Preconstruction Phase. J. Constr. Eng. Manag. 2020, 146, 05020006. [Google Scholar] [CrossRef]
  8. Islam, M.S.; Nepal, M.P.; Skitmore, M.; Kabir, G. A knowledge-based expert system to assess power plant project cost overrun risks. Expert Syst. Appl. 2019, 136, 12–32. [Google Scholar] [CrossRef]
  9. Cavalieri, M.; Cristaudo, R.; Guccio, C. On the magnitude of cost overruns throughout the project life-cycle: An assessment for the Italian transport infrastructure projects. Transp. Policy 2019, 79, 21–36. [Google Scholar] [CrossRef]
  10. Wang, P.; Liu, P.; Wang, C.; Wang, A.; Guan, H.; Li, S.; Xia, X. Optimal dynamic investment allocation on construction of intelligent transportation infrastructure and road maintenance with environmental costs. J. Clean. Prod. 2021, 284, 124786. [Google Scholar] [CrossRef]
  11. Dong, C.; Transchel, S. A dual sourcing inventory model for modal split transport: Structural properties and optimal solution. Eur. J. Oper. Res. 2020, 283, 883–900. [Google Scholar] [CrossRef]
  12. Shi, X.; Wang, Y.; Wang, W. Development of Life Cycle Cost Model for Urban Rail Transit Equipment. In Proceedings of the 5th International Conference on Electrical Engineering and Information Technologies for Rail Transportation, EITRT 2021, Virtual, 21–23 October 2021; Springer Science and Business Media Deutschland GmbH: Berlin/Heidelberg, Germany, 2021; pp. 309–315. [Google Scholar]
  13. Huo, T.; Ren, H.; Cai, W.; Shen, G.Q.; Liu, B.; Zhu, M.; Wu, H. Measurement and Dependence Analysis of Cost Overruns in Megatransport Infrastructure Projects: Case Study in Hong Kong. J. Constr. Eng. Manag. 2018, 144, 05018001. [Google Scholar] [CrossRef]
  14. Abu El-Maaty, A.E.; El-Kholy, A.M.; Akal, A.Y. Modeling schedule overrun and cost escalation percentages of highway projects using fuzzy approach. Eng. Constr. Archit. Manag. 2017, 24, 809–827. [Google Scholar] [CrossRef]
  15. Mathew, S.A.; Tran, D.Q.; Nguyen, P.H.D. Evaluation of Cost Growth Factors in Design-Build Highway Projects Using Structural Equation Modeling. J. Constr. Eng. Manag. 2021, 147, 04021070. [Google Scholar] [CrossRef]
  16. Derakhshanalavijeh, R.; Cardoso Teixeira, J.M. Cost overrun in construction projects in developing countries, gas-oil industry of iran as a case study. J. Civ. Eng. Manag. 2017, 23, 125–136. [Google Scholar] [CrossRef]
  17. Liu, J.; Zang, C.; Tian, S.; Liu, J.; Yang, H.; Jia, S.; You, L.; Liu, B.; Zhang, M. Water conservancy projects in China: Achievements, challenges and way forward. Glob. Environ. Change-Hum. Policy Dimens. 2013, 23, 633–643. [Google Scholar] [CrossRef]
  18. Asiedu, R.O.; Adaku, E. Cost overruns of public sector construction projects: A developing country perspective. Int. J. Manag. Proj. Bus. 2019, 13, 66–84. [Google Scholar] [CrossRef]
  19. Wang, G.; Huang, J. Study on the Environmental Assessment of the Surrounding Water Conservancy Project. Basic Clin. Pharmacol. Toxicol. 2021, 128, 31–32. [Google Scholar]
  20. Nie, X.; Wang, Y.; Wang, B. Quality Control of Water Conservancy Construction Projects Considering Contractor’s Credibility. J. Coast. Res. 2020, 104, 410–414. [Google Scholar] [CrossRef]
  21. Abdel-Basset, M.; Ali, M.; Atef, A. Uncertainty assessments of linear time-cost tradeoffs using neutrosophic set. Comput. Ind. Eng. 2020, 141, 106286. [Google Scholar] [CrossRef]
  22. Hoseini, E.; van Veen, P.; Bosch-Rekveldt, M.; Hertogh, M. Cost Performance and Cost Contingency during Project Execution: Comparing Client and Contractor Perspectives. J. Manag. Eng. 2020, 36, 05020006. [Google Scholar] [CrossRef]
  23. Gunduz, M.; Maki, O.L. Assessing the risk perception of cost overrun through importance rating. Technol. Econ. Dev. Econ. 2018, 24, 1829–1844. [Google Scholar] [CrossRef]
  24. Flyvbjerg, B.; Ansar, A.; Budzier, A.; Buhl, S.; Cantarelli, C.; Garbuio, M.; Glenting, C.; Holm, M.S.; Lovallo, D.; Lunn, D.; et al. Five things you should know about cost overrun. Transp. Res. Part A Policy Pract. 2018, 118, 174–190. [Google Scholar] [CrossRef]
  25. Durdyev, S. Review of construction journals on causes of project cost overruns. Eng. Constr. Archit. Manag. 2021, 28, 1241–1260. [Google Scholar] [CrossRef]
  26. Golpira, H. Optimal integration of Line facility location problem into the Multi-project multi-supplier multi-resource Construction Supply Chain network design under the vendor managed inventory strategy. Expert Syst. Appl. 2020, 139, 112841. [Google Scholar] [CrossRef]
  27. Mendomo Meye, S.; Li, G.; Shen, Z.; Zhang, J. Fuzzy Multi-Mode Time-Cost-Quality Trade-Off Optimization in Construction Management of Hydraulic Structure Projects. Appl. Sci. 2022, 12, 6270. [Google Scholar] [CrossRef]
  28. Togan, V.; Eirgash, M.A. Time-Cost Trade-off Optimization of Construction Projects using Teaching Learning Based Optimization. Ksce J. Civ. Eng. 2019, 23, 10–20. [Google Scholar] [CrossRef]
  29. Gharaibeh, H.M. Cost Control in Mega Projects Using the Delphi Method. J. Manag. Eng. 2014, 30, 04014024. [Google Scholar] [CrossRef]
  30. Isaksson, A.; Linderoth, H. Environmental considerations in the Swedish building and construction industry: The role of costs, institutional setting, and information. J. Hous. Built Environ. 2018, 33, 615–632. [Google Scholar] [CrossRef]
  31. Lou, N.; Guo, J. Study on Key Cost Drivers of Prefabricated Buildings Based on System Dynamics. Adv. Civ. Eng. 2020, 2020, 8896435. [Google Scholar] [CrossRef]
  32. Lu, W.; Hua, Y.; Zhang, S. Logistic regression analysis for factors influencing cost performance of design-bid-build and design-build projects. Eng. Constr. Archit. Manag. 2017, 24, 118–132. [Google Scholar] [CrossRef]
  33. Aouam, T.; Vanhoucke, M. An agency perspective for multi-mode project scheduling with time/cost trade-offs. Comput. Oper. Res. 2019, 105, 167–186. [Google Scholar] [CrossRef]
  34. Le, A.T.H.; Sutrisna, M. Project cost control system and enabling-factors model: PLS-SEM approach and importance-performance map analysis. Eng. Constr. Archit. Manag. 2023. Ahead-of-Print. [Google Scholar] [CrossRef]
  35. Kermanshachi, S.; Pamidimukkala, A. Robustness Analysis of Total Project Cost and Schedule Delay and Overrun Indicators of Heavy Industrial Projects. J. Leg. Aff. Disput. Resolut. Eng. Constr. 2023, 15, 04523005. [Google Scholar] [CrossRef]
  36. Dongzagla, A. Socioeconomic and demographic factors affecting urban household access to improved water and sanitation in Ghana. GeoJournal 2022, 87, 4763–4773. [Google Scholar] [CrossRef]
  37. Schultz, C.S.; Jorgensen, K.; Bonke, S.; Rasmussen, G.M.G. Building defects in Danish construction: Project characteristics influencing the occurrence of defects at handover. Archit. Eng. Des. Manag. 2015, 11, 423–439. [Google Scholar] [CrossRef]
  38. Abdul Nabi, M.; El-Adaway, I.H. Understanding the Key Risks Affecting Cost and Schedule Performance of Modular Construction Projects. J. Manag. Eng. 2021, 37, 04021023. [Google Scholar] [CrossRef]
  39. Abdul Rahman, I.; Memon, A.H.; Abd Karim, A.T. Relationship between Factors of Construction Resources Affecting Project Cost. Mod. Appl. Sci. 2012, 7, 67–75. [Google Scholar] [CrossRef]
  40. Olawale, Y.; Sun, M. PCIM: Project Control and Inhibiting-Factors Management Model. J. Manag. Eng. 2013, 29, 60–70. [Google Scholar] [CrossRef]
  41. Rosenfeld, Y. Root-Cause Analysis of Construction-Cost Overruns. J. Constr. Eng. Manag. 2014, 140, 04013039. [Google Scholar] [CrossRef]
  42. Andalib Touchaei, S.; Hazarina Hashim, N. The Antecedents of Mobile Banking Adoption among Senior Citizens in Malaysia. Int. J. Hum. Comput. Interact. 2023, 1–8. [Google Scholar] [CrossRef]
  43. Lyu, S.; Hon, C.K.; Chan, A.P.; Jiang, X.; Skitmore, M. Critical Factors Affecting the Safety Communication of Ethnic Minority Construction Workers. J. Constr. Eng. Manag. 2023, 149, 04022173. [Google Scholar] [CrossRef]
  44. Ameyaw, E.E.; Edwards, D.J.; Kumar, B.; Thurairajah, N.; Owusu-Manu, D.G.; Oppong, G.D. Critical Factors Influencing Adoption of Blockchain-Enabled Smart Contracts in Construction Projects. J. Constr. Eng. Manag. 2023, 149, 04023003. [Google Scholar] [CrossRef]
  45. Gunduz, M.; Abdi, E.A. Motivational Factors and Challenges of Cooperative Partnerships between Contractors in the Construction Industry. J. Manag. Eng. 2020, 36, 04020018. [Google Scholar] [CrossRef]
  46. Olawale, Y.; Sun, M. Construction project control in the UK: Current practice, existing problems and recommendations for future improvement. Int. J. Proj. Manag. 2015, 33, 623–637. [Google Scholar] [CrossRef]
  47. Pehlivan, S.; Oztemir, A.E. Integrated Risk of Progress-Based Costs and Schedule Delays in Construction Projects. Eng. Manag. J. 2018, 30, 108–116. [Google Scholar] [CrossRef]
  48. Sadeh, H.; Mirarchi, C.; Pavan, A. Integrated Approach to Construction Risk Management: Cost Implications. J. Constr. Eng. Manag. 2021, 147, 04021113. [Google Scholar] [CrossRef]
  49. Qiao, Y.; Fricker, J.D.; Labi, S. Influence of Project Bundling on Maintenance of Traffic Costs across Highway Project Types. J. Constr. Eng. Manag. 2019, 145, 05019010. [Google Scholar] [CrossRef]
  50. Wang, D.; Li, H.; Lu, Y. Factors affecting transaction costs in megaprojects: A qualitative comparative analysis. Int. J. Manag. Proj. Bus. 2021, 14, 1245–1269. [Google Scholar] [CrossRef]
  51. Iqbal, Q.; Ahmad, N.H.; Li, Z. Frugal-based innovation model for sustainable development: Technological and market turbulence. Leadersh. Organ. Dev. J. 2021, 42, 396–407. [Google Scholar] [CrossRef]
  52. Iqbal, Q.; Ahmad, N.H.; Li, Z.; Li, Y. To walk in beauty: Sustainable leadership, frugal innovation and environmental performance. Manag. Decis. Econ. 2022, 43, 738–750. [Google Scholar] [CrossRef]
  53. Piwowar-Sulej, K.; Iqbal, Q. Leadership styles and sustainable performance: A systematic literature review. J. Clean. Prod. 2023, 382, 134600. [Google Scholar] [CrossRef]
Sustainability 15 06963 g001 550
Figure 1. Flowchart of the methodological approach summary.
Figure 1. Flowchart of the methodological approach summary.
Sustainability 15 06963 g001
Table
Table 1. Factors for bid division of hydraulic project.
Table 1. Factors for bid division of hydraulic project.
NumberIndexNumberIndex
1Unreasonable demand from the supervision engineer (X1)11Land requisition and demolishing/immigrant (X11)
2Unfair standpoint from the supervision engineer (X2)12Inadequate of the subcontractor’s mobilization capability (X12)
3Increased price of construction material (X3)13Geology condition (X13)
4The owner’s unreasonable demand (X4)14Security incidents (X14)
5Unclear of the design drawings (X5)15Inadequate of the subcontractor’s construction capability (X15)
6Lack of construction material (X6)16National and local’s laws and regulations (X16)
7Delayed decision making from supervision engineer (X7)17Inadequate of the subcontractor’s management capability (X17)
8Field condition of engineering construction (X8)18Project completion date limits (X18)
9Bad construction dispatching (X9)19Design defect (X19)
10Unclear construction scale definition (X10)20Insufficient funds from sub-contractors (X20)
Table
Table 2. KMO and Bartlett’s test.
Table 2. KMO and Bartlett’s test.
Standard of ConditionSampleResult
Proportion of valid questionnaire and items5:15.6:1consistent
Recycled valid questionnaires100113consistent
Kaiser-Meyer-Olkin>0.70.818consistent
Bartlett’s test of Sphericity<0.050.000consistent
Table
Table 3. Accounting for the total variance.
Table 3. Accounting for the total variance.
NumberInitial Characteristic ValueSquare and Extraction of LoadAxis and Load
TotalPercentage of VariancePercentage of AccumulationTotalPercentage of VariancePercentage of AccumulationTotalPercentage of VariancePercentage of Accumulation
16.10430.52030.5206.10430.52030.5204.04720.23720.237
22.92214.61045.1302.92214.61045.1303.19215.96036.198
31.6488.23953.3691.6488.23953.3692.29011.44947.647
41.5137.56360.9321.5137.56360.9322.15310.76458.410
51.1355.67666.6081.1355.67666.6081.6408.19866.608
60.8734.36570.973
70.8074.03575.008
80.7403.70178.709
90.6123.06181.770
100.5712.85584.624
110.5242.62187.245
120.4212.10489.349
130.3991.99591.244
140.3631.81493.158
150.3271.63394.791
160.3131.56796.358
170.2271.13597.493
180.1970.98598.478
190.1670.83699.314
200.1370.686100.000
Table
Table 4. Rotated component matrix.
Table 4. Rotated component matrix.
IndexMain Components
F1F2F3F4
Unclear Definition of Construction Scope0.750−0.0380.2570.116
Unreasonable Demands from Supervising Engineer0.7420.1930.2180.094
Lack of Fairness for Supervising Engineer’s Position0.7250.2190.2120.010
Unclear Blueprints0.701−0.1190.1690.045
Design Error0.6950.0890.1110.186
Clients’ Unreasonable Demands0.6690.1190.2020.117
Supervising Engineer’s Slow Decision-making0.6530.1350.1060.386
Subcontractor’s Insufficiency Mobilization Ability−0.0020.8990.0830.051
Subcontractor’s Insufficiency Construction Ability0.0970.8970.1020.090
Subcontractor’s Insufficiency Management Ability0.0360.8940.0790.152
Subcontractor’s Insufficiency Fund0.2310.740−0.107−0.008
Conditions of Construction Site0.2080.0160.7990.168
Land demolition/internal migration0.312−0.0500.7330.130
Safety Incident0.3180.2010.605−0.122
Poor Construction Scheduling inside the Company0.2630.0940.5150.500
Geological Condition0.198−0.0650.0510.278
Rise in Price of Construction Material−0.0150.1400.0400.841
Shortage of Construction Material0.2890.098−0.0460.771
Limitation of Date for Construction’s Completion0.198−0.0470.3530.587
National and Local Regulations0.005−0.0780.0190.390
Table
Table 5. Distribution of major factors.
Table 5. Distribution of major factors.
Major FactorsF1F2F3F4
Characteristic values5.9612.8241.6351.912
Total number of factors7454
Table
Table 6. Correlation analysis of client, designer and supervision institution factor.
Table 6. Correlation analysis of client, designer and supervision institution factor.
Owner Factor1234567
Client’s unreasonable demands1
Unclear construction blueprint0.427 **1
Unclear construction scope0.473 **0.610 **1
Design error0.453 **0.441 **0.453 **1
Supervision institution’s unreasonable demands0.498 **0.468 **0.478 **0.514 **1
Unfairness of supervision institution’s position0.445 **0.350 **0.535 **0.471 **0.690 **1
Supervising engineer’s slow decision-making0.452 **0.313 **0.543 **0.482 **0.526 **0.582 **1
Note: ** The correlation was significant at 0.01 level (two-tailed test).
Table
Table 7. Correlation analysis of subcontractor’s factors.
Table 7. Correlation analysis of subcontractor’s factors.
Components of Subcontractor1234
Subcontractor’s inefficient construction ability1
Subcontractor’s inefficient mobilization ability0.783 **1
Subcontractor’s inefficient management ability0.775 **0.803 **1
Subcontractor’s shortage of funds0.583 **0.594 **0.553 **1
Note: ** The correlation was significant at 0.01 level (two-tailed test).
Table
Table 8. Correlation analysis of safety and environmental factors.
Table 8. Correlation analysis of safety and environmental factors.
Construction Safety and Environmental Factors12345
Safety accident1
Condition of construction site0.468 **1
Land demolition/internal0.376 **0.542 **1
poor construction scheduling inside the company0.1890.480 **0.457 **1
Geological conditions0.539 **0.528 **0.375 **0.318 **1
Note: ** The correlation was significant at 0.01 level (two-tailed test).
Table
Table 9. Correlation analysis of financial and economic factors.
Table 9. Correlation analysis of financial and economic factors.
Components of Construction Economy and Policy1234
Rise in price of construction materials1
Shortage of construction materials0.598 **1
National and local policy0.585 **0.352 **1
Limitation of construction’s completion0.347 **0.364 **0.579 **1
Note: ** The correlation was significant at 0.01 level (two-tailed test).
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.

Share and Cite

MDPI and ACS Style

Ding, Z.; Liu, X.; Xue, Z.; Li, X. Expert Opinion on the Key Influencing Factors of Cost Control for Water Engineering Contractors. Sustainability 2023, 15, 6963. https://doi.org/10.3390/su15086963

AMA Style

Ding Z, Liu X, Xue Z, Li X. Expert Opinion on the Key Influencing Factors of Cost Control for Water Engineering Contractors. Sustainability. 2023; 15(8):6963. https://doi.org/10.3390/su15086963

Chicago/Turabian Style

Ding, Zhenhan, Xun Liu, Zhiyuan Xue, and Xiaobo Li. 2023. "Expert Opinion on the Key Influencing Factors of Cost Control for Water Engineering Contractors" Sustainability 15, no. 8: 6963. https://doi.org/10.3390/su15086963

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop