Identification and classification of construction-risk factors for ghanaian construction projects: An integrated study with structural equation modelling

Abstract

The construction industry in Ghana is of paramount importance to the socio-economic progress of the country but recurringly faces noteworthy risk factors. The identification of crucial elements within the construction-risk category has not received considerable attention in the Ghanaian construction sector. This research work seeks to identify and classify the most critical construction-risk factors that impact construction projects in Ghana and also attempts to create a confirmatory factor model that could assist in understanding the potential of these factors. The study utilizes a deductive methodology and positivist philosophy. The authors observation of construction projects and literature search led to the identification of 43 factors categorized as construction-risk variables. A questionnaire survey was used to obtain responses from 462 professionals, including 166 quantity surveyors, 158 engineers, 89 contractors, and 49 architects working on various construction projects in Ghana. Information collected from these responses was examined and interpreted using descriptive statistics and multivariate analysis techniques to derive meaningful insights. From the analysis, three factors – “Effective resource planning and control”, “Timely conflict/dispute resolution” and “Knowledge of new technology requirements” – were identified as the most critical construction-risk factors impacting construction projects in Ghana. This study developed a model encompassing all items in three primary components that represent the critical construction-risk factors influencing construction projects in Ghana. A major recommendation of this study is that every project should have a mandatory conflict resolution board to ensure the timely resolution of conflicts to guarantee the success of these projects. The practical significance of this study lies in its identification of crucial construction-risk factors that might affect the outcome of construction projects in Ghana. The initial technique in management is to identify all significant risk factors that might possibly affect the success of construction projects. To reduce the negative effects of these risk variables, project managers must first identify the most important ones.

1. Introduction

The socioeconomic progress of every country is significantly influenced by the construction sector, which makes a huge contribution that is impossible to ignore [1]. proposed a strong relationship between the macroeconomic development rate in developing nations and the construction industry [2]. Claimed that the construction sector's contribution to Ghana's GDP increased from 5 % in 1975 to 15 % in 2007. A report by the Ghana Statistical Service (GSS) stated that between 2009 and 2013, the real estate and construction sectors contributed an average of 14.34 % to the GDP of the nation [3]. In addition, Ref. [4] survey showed that over 600,000 people are employed in the construction sector, making up roughly 7 % of Ghana's working population. The construction industry faces challenges despite its significant contribution to the country's socio-economic progress [[5], [6], [7]]. Efforts have been made since 2014 to establish Construction Industry Development Authority (CIDA) that would solely regulate activities of the construction industry as South Africa is having Construction Industry Development Board (CIDB). The CIDA Bill in 2020 is still before the Parliament of Ghana for consideration to be passed into law. Notwithstanding the absence of this much awaited authority, the various subsectors within the industry have established individual governing institutions such as the Engineering Council, Ghana Institution of Architects (GIA), Ghana Institute of Construction (GIOC), Ghana Institution of Engineers (GhIE), Ghana Institution of Surveyors (GhIS) and Institution of Engineering and Technology, Ghana (IET, Gh). The Government of Ghana has established two ministries, Ministry of Works and Housing (MWH) and the Ministry of Roads and Highways (MRH), which are responsible for all building and accompanied minor civil engineering works, and other major civil engineering works in the country [8]. However, the industry is flooded with more than 90 % small and medium-scale construction enterprises (SMSCEs) [9,11]. Studies show that these SMSCEs, which form the bulk of construction firms in Ghana, encounter a lot of risk challenges ranging from construction to financial [[11], [12], [13], [14]].

Various efforts have been made by researchers to mitigate risk issues in Ghana's construction industry, but a construction-risk management model that incorporates the perspectives of all stakeholders (architects, contractors, engineers, and quantity surveyors) from the various classes of construction firms is yet to be established. The study concentrated on all category of construction professionals or players (architects, contractors, engineers, and quantity surveyors) in the construction industry in Ghana without classification. The focus was on construction related risks from all construction firms in Ghana irrespective of their classification. The study sought to develop a construction-risk management model for only construction risks for all construction projects in Ghana. The following objectives were formulated to help achieve the aim of the study:

• (1)To identify and classify the critical construction risk variables that affect construction projects in Ghana and
• (2)To develop a critical construction-risk management model using structural equation modelling.

2. Risk and risk management

Risks are inevitable in construction projects and could occur at various stages of life cycle. Many stakeholders from diverse cultures, knowledge, and skills are involved in construction projects, and this situation can present risks [10]. The situation mentioned here is the coming together of these individuals (teamwork) to drive construction projects toward their successful end. The effectiveness of a team ensures the success of construction projects [11,12]. Ref. [13] defined risk as the impact of uncertainty on the goals of a project [14]. explained risk as the potential to impact business performance positively or negatively. Risk can have either a positive or negative impact on project performance. Unforeseen events can arise during projects, which can either threaten the success of the project or create opportunities to thrive. Positive risks are sometimes referred to as opportunities and negative ones are occasionally known as threats [15]. Every construction project is unique, and therefore, the management of risks in construction projects is complex [16,17].

The construction sector has shown great interest in project risk management, one of the expert areas within project management [18]. argued that despite the abundant literature and knowledge on risk management practices in projects, the construction industry still suffers from a bad reputation. Poor cost performance [19], late project completion [20], and quality issues [21] are persistent problems that affect reputation [22]. contended that risk management practices are hardly incorporated into the everyday operations of even large and complicated projects. To this end [23], reasoned that risk management is often viewed as a mere formality, rather than a means of adding value. Effective risk management is crucial for construction project success due to the uncertainties that could arise throughout the project lifecycle [17]. Risks in construction projects must be well managed to prevent cost overruns, time overruns, or total failures [24]. Numerous risks exist in construction projects, and some include financial risks, safety risks, external risks, contractual risks, management risks, design risks, environmental risks, construction risks, and post-exploitation risks [25] and this study focuses on construction risks that affect construction projects in Ghana.

3. Construction risks

Construction risk has been explained in different forms, and [26] defined construction risk as the potential for loss or damage arising from unforeseen events or circumstances in construction projects. This includes site accidents, labor disputes, plants/equipment failure, construction defects, material supply chain disruptions, design or technology errors, and weather-related delays. This risk type focusses on project-related risks, and therefore affects project timelines, quality, and safety [27]. Construction risk is described by Ref. [28] as the unfavourable impact of unforeseen circumstances on an organization's goals as it operates in the construction sector. Construction-risk factors refer to the risk factors associated with the execution methods, processes, and operational technologies of a project. Some of the common factors contributing to construction risks are procurement problems related to contracts, quality control problems, low productivity, safety and health concerns, labor disputes, strikes, and difficulties in negotiating change orders, technology shortage, excess inspection, substandard construction techniques, and managerial issues [25,29,30]. A list of the identified variables was prepared and synthesized in this work in order to acknowledge the contributions made by other studies regarding construction risks. The factors in Table 1 summarize some of the construction-risk factors identified through a literature search from previous studies. Table 2 also presents construction-risk variables deduced from other studies and observed about construction projects in the country. These factors were designed to seek the opinions of participants to determine their impacts on construction projects in the country.

Table 1.

Summary of construction-risks factors identified in literature.

S/N	Ref.	Methodology	Type of Risk/Risk Factors Identified
1	[31]	Using a focus group to assist, content analysis based on literature review and questionnaire survey	Quality of the machinery and equipment, the possibility of unanticipated site conditions, poor performance of equipment, disorganised soil or local ground conditions, mistakes in implementation, flawed design, and numerous adjustments.
2	[32]	Questionnaire survey	Wastage of materials, inexperienced project team, misunderstanding of drawings and specifications, lack of project control, delay in schedule, ineffectual subcontractors, poor site management, unorganised material storage, budget overruns, frequent failure of the equipment, time overrun and poor material management
3	[33]	Literature review and questionnaire survey.	Differences in actual quantities, use of faulty material, problems in quality control and quality assurance, undocumented orders to change, differing site conditions, on-site material damage, loss of equipment productivity, surveying work errors, and lack of skilled workers.
4	[34]	Questionnaire survey	Construction delays, defective work and quality issues, and low labour productivity.
5	[35]	Questionnaire survey	Changing scale of the project, the absence of skilled labour, ineffective coordination of the consultants, subpar building methods, rise in costs, lack of proper planning, and the lack of safety awareness.
6	[36]	Questionnaire survey	Machinery, delays due to rain and other factors, unclear market circumstances, contractor productivity issues, and time.
7	[37]	literature review and questionnaire survey	Low productivity, inadequate quality, health and safety concerns, labour disputes and strikes, and change order negotiations.
8	[38]	Literature review and structured interview	Difference in actual quantities and use of faulty material.
9	[39]	Literature review, interview and questionnaire survey	labour productivity, labour disputes, equipment failures, site circumstances, design modifications, excessively strict quality standards, and new technologies.
10	[40]	Questionnaire survey	Special ways of engineering, new technology implementation, high-quality standards, new materials, experimental difficulty, faulty job field survey, inadequate construction planning, and inadequate coordination/related entity.

Table 2.

Referenced and deduced construction-risk factors used for the study.

S/No	Risk variables used for the study	Ref.
1	Availability of construction materials, Availability of skilled labour	[41,42]
2	Timely dispute resolution, High productivity of plant and equipment	[37,39]
3	Continuation of projects by previous government, Availability of plants, tools, and equipment, High quality of workmanship, Low rate of theft and vandalism, Accurate and precise geotechnical information, Expected or known soil conditions, High labour productivity	[43,44]
4	Proper construction methods, Efficient transmittal process/flow of information or communication between stakeholders, Solvency of subcontractors and suppliers Availability of anticorruption policies and structures, Efficient public procurement methods, Known physical site conditions, Project location and accessibility, Complete project designs/drawings, Well-defined scope of work, Prompt deliveries of material, Known new resource requirements, Knowledge of new technology requirements.	[36,37,44,45]
8	Geographical inclusion and knowledge transfer, Health and safety policy compliance, Efficient utilization of materials, Effective resource planning and control, Effective site security systems, Workforce effciency and level of experience, Moderate change orders, Import/export freedom on construction materials, Proper site layout, Effective site health and safety management systems Moderate public/national holidays and observations, Satisfaction of workers on site Good teamwork among employees, Flexible environmental regulations, Involvement of local people/community in projects, Healthy working conditions Safe working environment, Proper coordination between project stakeholders, Clarity and consistency of specifications, Effective contractors'/subcontractors'/suppliers' coordination	[[43], [44], [45]]

4. Construction projects in Ghana

In simple terms, a construction project involves creating physical structures, such as bridges, commercial and industrial buildings, highways, residential buildings, and utilities. Like any other project, a construction project is a short-term endeavor carried out to produce a unique product, service, or outcome within a specified period [46]. A construction project can also be regarded as a short-term endeavor with a well-defined beginning and end. The fact that it is not a repeating process and that the finished product differs from other comparable projects or services is another reason it is deemed unique. Construction projects in Ghana involve the creation of infrastructure, such as bridges, commercial and industrial buildings, roads and highways, residential buildings, and utilities, all of which have specific timelines. The ability of a nation to improve living conditions for its people is measured by the Global Competitiveness Index (GCI), which is based on that nation's infrastructure [47,48]. Infrastructure such as bridges, buildings, power, transport systems, water supply, etc., which are some of the construction projects in Ghana, contributes to better living conditions.

Construction projects have increased dramatically in Ghana; yet, some of these projects do not seem to be doing as well as expected. Notwithstanding the advances in technology and managerial strategies for the construction sector, construction project failures in Ghana remain persistent [49]. Construction projects in Ghana relatively have performance issues in terms of schedule (time), cost and quality. For instance, Ref. [50] revealed that 70 % of road construction in Ghana experience an average 17-month delay. An analysis of construction projects for educational purposes conducted by Ref. [49] confirmed the prevalence of construction projects abandonment in Ghana.

5. Methodology

5.1. Research approach and strategy

This research follows a positivist approach based on deductive, objective, and quantitative methods of questionnaire survey. This philosophy was described in Refs. [51,52]. The reason for adopting this approach was to enable broad generalizations and also replicability of this study. The deductive approach was used because it relies on objectivity and reduces researcher bias [[53], [54]]. This approach can therefore supply policymakers with reliable and quantitative data [55]. argued that the quantitative method provides objective, and a huge amount of statistical data which are reliable, and can be generalized and replicated. Again, this method helps validates theoretical frameworks and develop predictive models [71]. Therefore, this method was adopted in this study. A literature review was conducted to identify the risk factors associated with construction.

5.2. Survey design and administration

A questionnaire was developed to collect data for further analysis, based on the identified factors. The purpose was to create a confirmatory factor analysis (CFA) model and to assess the unidimensionality and reliability of the questionnaire. A total of 43 elements were found and categorized into three groups according to how they affected the construction process. The questionnaire developed was an instrument used for data collection [55,56]. It was adopted to ensure uniform responses and to help code and analyze the data [57].

To ensure reliability, the questionnaire was designed using a five-point rating system, where 1 denotes a very low impact and 5 represents a very high impact. This is due to the fact that reliability is enhanced with larger scale points. Cross-sectional reliability has been demonstrated to improve from 2- to 3- to 5-point scales. The reasons for using questionnaire survey as a tool for data collection was that it is time-efficient, cost-effective, consistent for all respondents, and data from questionnaire are easy to analyze. The questionnaires were then disseminated to participants to assist with data analysis, as outlined in Ref. [58]. The survey study utilized different methods to administer the questionnaires [59]. These included email, Google Forms, WhatsApp, and hard copies personally delivered to respondents. The first section of the questionnaire included an introductory letter describing the goals of the study. The respondents' fundamental profiles including their profession/position, educational qualification, and experience in the construction industry were collected. The second part was designed to include construction related risk factors found in the literature, and deduced from construction projects. Respondents were made to indicate the impact they had on construction projects performance in Ghana. Respondents were asked to rank their perceived impact of construction risk factors that affect project success on a five-point Likert scale in this section. (1 = very low, 2 = low, 3 = neutral, 4 = high, and 5 = very high). The survey questionnaire with a five-point Likert scale (1–5) was chosen because it allows for quantitative data collection and the application of various statistical approaches for data analysis [66].

The population used in this study of 6770 professionals included architects, contractors, professional engineers, and quantity surveyors, who were duly registered with their respective professional associations, and were all in good standing at the time of this study. A probability-based selection strategy was employed to select the sample in Ghana to guarantee the generalizability of the study findings. Since the goal of the study was to gather a single professional opinion on the topic rather than to compare replies from various experts, the sample size was not determined by the percentage of certain subpopulations. Some researchers prefer a larger sample size for generalization and others insist that there is no specific sample size [60,61]. [62] maintained that sample sizes greater than 200 should always be used for robust and complicated models. The researchers used a simple random sampling approach to select subjects and ensure a robust analysis with an appropriate sample size. Samples were randomly selected from a total population of 6770 individuals without replacement from the formula [64]:

$$n=\frac{\left(Z^2\times {\sigma }^2\times N\right)}{\left(E^2\times \left(N-1\right)+Z^2\times {\sigma }^2\right)}$$

Where n = Sample size.

Z = Z-score corresponding to the desired confidence level

σ = population standard deviation (or estimate).

N = population size.

E = desired margin of error.

From the simple random sampling without replacement formula, a sample size of 565 was determined. This is in agreement with Ref. [63] that a sample size of at least 400 is enough for a population of over 5000. The researchers distributed 565 questionnaires and received 478 responses with 16 outliers. Of these, 462 were valid and were used for the analysis. The researchers opted for a larger sample size of 565 participants to ensure a comprehensive and reliable analysis and to account for potential non-responses and other issues that are common in survey research. This study involved 462 professionals, including 166 surveyors, 158 engineers, 89 contractors, and 49 architects. The contractors who responded to the questionnaires were from all categories of construction firms from D1K1/A1B1 to D4K4/A4B4. The construction firms in Ghana are categorized based on their annual turnover, plants and equipment holdings, qualification of personnel etc. [65]. The categories D1K1/A1B1 and D2K2/A2B2 firms are referred to as high-class and medium-class firms respectively with D3K3/A3/B3 and D4K4/A4B4 as low-class firms. The reason for using simple random sampling without replacement was to provide every participant with an equal chance to participate in the survey. The professionals selected for this study were from the construction industry. The researchers believe that their practice, and knowledge in risk management as practitioners could provide valuable insights into ways in which construction-risk issues could be improved in the Ghanaian construction industry.

5.3. Data analysis procedures

The basic profiles of the participants were analyzed using descriptive statistics. The standard deviation (SD) formula recommended by Ref. [66] was used to evaluate the level of agreement among respondents with the listed parameters and spread of the dataset from the mean.

$$\text{SD}=\sqrt{\frac{{\sum }_{i=1}^n{\left(x_i-x\right)}^2}{n-1}}$$

Where:

• $x_i$ = value of the $i^{th}$ point in the data set

• $x$ = the mean value of the data set

• $n$ = the number of data points in the data set

Structural Equation Modeling (SEM) is a multivariate analysis that combines principles from factor analysis and multiple regression analysis to identify underlying factors and assess how one set of variables predicts another [67]. The various types of SEM include path analysis, confirmatory factor analysis, confirmatory composite analysis, partial least square path modelling and latent growth modelling (68). In this study, Confirmatory Factor Analysis (CFA) was chosen because it enables the selection of only relevant constructs for the model [68]. Additionally, quantitative methods require a large amount of statistical data [69]. Robust and complex models should always have sample sizes greater than 200 [62]. Therefore, CFA, which employs Exploratory Factor Analysis (EFA), was considered to be appropriate for this study. EFA was conducted using SPSS version 26, while CFA was performed using AMOS version 22. CFA was used to evaluate the measurement model to determine the structural relationship between latent variables. The reliability of the latent variable, also known as its scale consistency, was evaluated using Cronbach's alpha coefficient. The scale used to measure the latent variable had high reliability with a minimum Cronbach's coefficient of 0.70.

6. Data analyses

6.1. Background details about the participants

A total of 565 questionnaires were distributed, of which 478 were collected. Of the collected questionnaires, 16 were outliers. This resulted in a response rate of 84.6 %, which is considered sufficient for this study. Therefore, there were 462 valid responses for the analysis. According to Ref. [57], a survey result with a response rate lower than 20–30 % is biased, inadequate, and of little significance. However, since the response rate in this study was 84.6 %, the results were considered reliable. Valid responses were provided by 166 quantity surveyors, 158 engineers, 89 architects, and 49 contractors. The data used for the analyses were obtained from the responses provided by stakeholders in the construction industry. A total of 251 respondents had a BSc. or BTech. degree as their highest educational qualification, while 159 had either MTech./MSc./MPhil, and 36 had a diploma. Only 12 had a PhD, and 4 had the least educational qualification of Senior Secondary or Senior High School (SSCE/SHS).

In terms of experience, 205 respondents reported having worked for 6–10 years, 154 for 11–15 years, and 45 for 16–20 years. In addition, 23 respondents had less than five years of experience in the construction sector, 21 had between 21 and 25 years, eight had between 26 and 30 years, and six had more than 30 years. All stakeholders provided sufficient and reliable information for analysis.

6.2. Mean and, standard deviation of construction risk factors

This section of the analyses examines the construction-risk factors that influence projects at various stages of construction. The items under this section were analyzed using mean and standard deviation (SD). The results are presented in Table 3. The results showed that most respondents indicated that “Availability of construction materials” had a high impact on projects at various stages (mean = 4.70, SD = 0.59). Again, respondents agreed that “Availability of skilled labor” had a high impact on projects. This resulted in a mean level of agreement of 4.76 (SD = 0.50). Moreover, respondents indicated that “Availability of plants, tools and equipment” had a high impact on projects, with a mean level of 4.79 (SD = 0.46). In addition, respondents showed a mean level of 4.59 (SD = 0.62) that “Prompt deliveries of material” influenced projects at various stages. The result further shows that “High quality of workmanship” impacts projects at various stages (mean = 4.75, SD = 0.56). Again, respondents expressed that “Efficient public procurement methods” influenced projects at various stages, which was reflected in a mean level of 4.49 (SD = 0.57).

Table 3.

Construction risk (CsR) factors - mean and standard deviation.

Factors	Mean ± SD	Med(1Q,3Q)
Availability of construction materials (CsR1)	4.70 ± 0.59	5.0(5,5)
Availability of skilled labor (CsR2)	4.76 ± 0.50	5.0(5,5)
Availability of plants, tools, and equipment (CsR3)	4.79 ± 0.46	5.0(5,5)
Prompt deliveries of material (CsR4)	4.59 ± 0.62	5.0(4,5)
High quality of workmanship (CsR5)	4.75 ± 0.56	5.0(5,5)
Efficient public procurement methods (CsR6)	4.49 ± 0.57	5.0(4,5)
Low rate of theft and vandalism (CsR7)	3.94 ± 0.70	4.0(4,4)
Availability of anticorruption policies and structures (CsR8)	4.41 ± 0.72	5.0(4,5)
Known physical site conditions (CsR9)	3.98 ± 0.72	4.0(4,4)
Project location and accessibility (CsR10)	4.70 ± 0.53	5.0(4,5)
Complete project designs/drawings (CsR11)	4.54 ± 0.63	5.0(4,5)
Well-defined scope of work (CsR12)	4.54 ± 0.66	5.0(4,5)
Known new resource requirements (CsR13)	4.59 ± 0.64	5.0(4,5)
Knowledge of new technology requirements (CsR14)	4.65 ± 0.60	2.0(2,3)
Geographical inclusion and knowledge transfer (CsR15)	2.56 ± 1.11	5.0(4,5)
Efficient transmittal process/flow of information or communication between stakeholders (CsR16)	4.61 ± 0.61	5.0(4,5)
Health and safety policy compliance (CsR17)	4.61 ± 0.64	4.0(4,5)
Efficient utilization of materials (CsR18)	4.28 ± 0.63	4.0(3,5)
Accurate and precise geotechnical information (CsR19)	3.98 ± 0.92	4.0(3,4)
Effective resources planning and control (CsR20)	3.61 ± 0.85	4.0(3,4)
High labor productivity (CsR21)	4.70 ± 0.57	5.0(4,5)
Effective site security systems (CsR22)	4.06 ± 0.63	4.0(4,4)
Workforce efficiency and level of experience (CsR23)	4.67 ± 0.59	5.0(4,5)
Moderate change orders (CsR24)	3.42 ± 0.86	3.0(3,4)
Continuation of projects by previous government (CsR25)	4.74 ± 0.55	5.0(5,5)
Import/export freedom on construction materials (CsR26)	3.80 ± 0.78	4.0(3,4)
Proper site layout (CsR27)	3.84 ± 0.75	4.0(4,4)
Effective site health and safety management systems (CsR28)	4.09 ± 0.702	4.0(4,5)
Moderate public/national holidays and observations (CsR29)	3.50 ± 0.84	3.5(3,4)
Solvency of subcontractors and suppliers (CsR30)	4.38 ± 0.64	4.0(4,5)
Satisfaction of workers on site (CsR31)	4.41 ± 0.66	5.0(4,5)
Good teamwork among employees (CsR32)	4.52 ± 0.66	5.0(4,5)
Flexible environmental regulations (CsR33)	3.76 ± 0.87	4.0(3,4)
Involvement of local people/community in projects (CsR34)	3.63 ± 0.87	4.0(3,4)
Expected or known soil conditions (CsR35)	4.17 ± 0.75	4.0(4,5)
Healthy working conditions (CsR36)	4.39 ± 0.69	4.0(4,5)
Safe working environment (CsR37)	4.35 ± 0.65	4.0(4,5)
High productivity of plant and equipment (CsR38)	4.67 ± 0.61	5.0(4,5)
Proper coordination between project stakeholders (CsR39)	4.66 ± 0.59	5.0(5,5)
Timely dispute resolution (CsR40)	4.72 ± 0.59	5.0(5,5)
Proper construction methods (CsR41)	4.61 ± 0.62	5.0(4,5)
Clarity and consistency of specifications (CsR42)	4.52 ± 0.70	5.0(4,5)
Effective contractors'/subcontractors'/suppliers' coordination (CsR43)	4.26 ± 0.66	4.0(4,5)

Note: Total Number of respondents = 462; (Mean/Median = low 1 to high 5); M = Mean; SD=Standard Deviation; Med = Median; 1Q = first Quarter; 3Q = Third Quarter.

Respondents also indicated that “Low rates of theft and vandalism” influenced projects. This resulted in a mean level of 3.94 (SD = 0.70). On whether “Availability of anticorruption policies and structures” impacts projects, a mean level of 4.41 (SD = 0.72) was recorded, indicating that it has a high impact on projects at various stages. With regards to “Known physical site conditions”, a mean level of 3.98 (SD = 0.72) was obtained, showing that there was an impact. Respondents further accented that “Project location and accessibility” influenced projects; a mean agreement level of 4.70 (SD = 0.53) proved that. To ascertain whether “Complete project designs/drawings” impact projects, a mean level of 4.54 (SD = 0.63) was recorded, which shows its influence on the project. Respondents further revealed that “Geographical inclusion and knowledge transfer” influenced projects, as shown by a mean level of 2.56 (SD = 1.11).

On whether “Efficient transmittal process/flow of information or communication between stakeholders” influences projects at various stages, a mean level of 4.61 (SD = 0.61) clearly shows that it influences projects at various stages of the project. With regard to “Health and safety policy compliance”, respondents indicated that this had an impact on projects, with a mean level of 4.61 (SD = 0.64). It was revealed that “Efficient utilization of materials”, “Accurate and precise geotechnical information,” and “Effective resources planning and control” affect projects as indicated by their mean levels of 4.28 (SD = 0.63), 3.98 (SD = 0.92) and 3.61 (0.85) respectively. In relation to “High labor productivity, a mean level of 4.70 (SD = 0.57) was obtained showing that it has a high impact on projects. According to the respondents, "Effective site security systems", "Workforce efficiency and level of experience", and "Moderate change orders" have an impact on projects. The mean levels of impact were recorded as 4.06 (SD = 0.63), 4.67 (SD = 0.59), and 3.42 (SD = 0.86), respectively. It was indicated that the “Continuation of projects started by the previous government”, along with the “Freedom to import and export construction materials”, and “Proper site layouts” had an impact on projects at various stages. The mean levels for these factors were 4.74 (SD = 0.55), 3.80 (SD = 0.78), and 3.84 (SD = 0.75), respectively.

According to the study, "Effective management systems for site health and safety," "Moderate public and national holidays and observances," and "Solvency of subcontractors and suppliers" had an impact on projects at various stages, with a mean level of 4.09 (SD = 0.75), 3.50 (SD = 0.84), and 4.38 (SD = 0.64), respectively. The respondents further indicated that “Satisfaction of workers on site”, “Good teamwork among employees” and “Flexible environmental regulations” influenced projects at various stages as depicted in a mean level of 4.41 (SD = 0.66), 4.52 (SD = 0.66) and 3.76 (SD = 0.87). Again, the respondents indicated that “Involvement of local people/community in projects” influenced projects with a mean level of 3.63 (SD = 0.87). Additionally, it was observed that the factors of "Expected or known soil conditions,” "Healthy working conditions" and "Safe working environment" had a significant impact on projects, with mean levels of 4.17 (SD = 0.75), 4.39 (SD = 0.69) and 4.35 (SD = 0.65), respectively. In addition, it was discovered that “High productivity of plant and equipment”, “Proper coordination between project stakeholders” and “Timely dispute resolution” influenced construction projects. This was evidenced by their mean levels of 4.67 (SD = 0.61), 4.66 (SD = 0.59), and 4.72 (SD = 0.59), respectively. Furthermore, the respondents revealed that “Proper construction methods,” “Clarity and consistency of specifications” and “Effective contractors'/subcontractors'/suppliers’ coordination” had a high impact on projects at various stages of the projects with mean levels of 4.61 (SD = 0.62), 4.52 (SD = 0.70) and 4.26 (SD = 0.66) respectively.

To ascertain the appropriate latent components under the construction risk factors, 43 elements classified as construction risk factors were subjected to principal component (PC) rotation using Varimax, an orthogonal rotation method. Items with factor loadings greater than 0.50 were used to confirm the latent components since a higher loading indicates a higher status of the variable as a pure measure of the components.

6.3. Dimensionality of the construction risk factors (CsR) construct through exploratory factor analysis

Exploratory Factor Analysis (EFA) was used to examine the factor structure and internal consistency of the manifest variables associated with the construction-risk constructs (CsR). The primary focus was on evaluating the degree of correlation between the variables or items inside the construction-risk factors to evaluate whether the data were appropriate for factor analysis. This study evaluated the sphericity assumption and sample adequacy using Bartlett's Test of Sphericity and the Kaiser-Meyer-Olkin (KMO) Measure of Sampling Adequacy, respectively. Bartlett's Test of Sphericity showed statistical significance at p < 0.05, and the KMO value of 0.915 is higher than the recommended value of 0.70. These results imply that factor analysis is a suitable method for determining the latent components that correspond to construction risk factors of construction projects.

After evaluating the factorability of the data for construction-risk factors, a decision was made about the number of components to be extracted based on the indicators. In factor analysis, Kaiser's criterion, also called the Eigenvalue rule, is used to determine the number of components to be extracted. Three components were extracted to represent 45.749 % of the variation in construction-risk factors based on eigenvalues of more than one (>1) [70]. recommended that the eigenvalue for extracting components for factor analysis could be greater than one or range between zero and one. This implies that there were three main components under the construction-risk factors, as indicated in Table 4. A threshold of 0.5 was used to evaluate the factor loadings, which is better than the advised 0.4 [71]. The threshold of 0.50 (good) was selected based on a recommendation by Ref. [72], who suggested more stringent cut-offs ranging from 0.32 (poor) to 0.71 (excellent).

Table 4.

Rotated factor matrix for construction risks (CsR).

Factors	Factor			Extraction
	1	2	3
Timely conflict/dispute resolution (CsR40)	0.755			0.544
High productivity of plant and equipment (CsR38)	0.724			0.575
Workforce efficiency and level of experience (CsR23)	0.703			0.542
Project location and accessibility (CsR10)	0.700			0.508
Availability of plants, tools, and equipment (CsR3)	0.682			0.448
Health and safety policy compliance (CsR17)	0.657			0.419
Geographical inclusion and knowledge transfer (CsR15)	0.655			0.421
Availability of skilled labor (CsR2)	0.655			0.544
Proper coordination between project stakeholders (CsR39)	0.643			0.561
Availability to construction materials (CsR1)	0.628			0.622
Continuation of projects by previous government (CsR25)	0.627			0.528
High labor productivity (CsR21)	0.592			0.554
Efficient transmittal process/flow of information or communication between stakeholders (CsR16)	0.591			0.444
Proper construction methods (CsR41)	0.584			0.515
Known new resource requirements (CsR13)	0.545			0.462
High quality of workmanship (CsR5)	0.533			0.532
Solvency of subcontractors and suppliers (CsR30)		0.675		0.582
Good teamwork among employees (CsR32)		0.608		0.308
Well-defined scope of work (CsR12)		0.563		0.465
Complete project designs/drawings (CsR11)		0.562		0.602
Healthy working conditions (CsR36)		0.544		0.621
Satisfaction of workers on site (CsR31)		0.544		0.424
Clarity and consistency of specifications (CsR42)		0.539		0.603
Flexible import/export restrictions on construction materials (CsR26)		0.518		0.566
Prompt deliveries of materials (CsR4)		0.505		0.453
Involvement of local people/community in projects (CsR34)			0.725	0.470
Effective resources planning and control (CsR20)			0.711	0.591
Accurate and precise geotechnical information (CsR19)			0.591	0.503
Moderate change orders (CsR24)			0.588	0.652
Proper site layout (CsR27)			0.58	0.397
Flexible environmental regulations (CsR33)			0.522	0.698
Knowledge of new technology requirements (CsR14)			0.517	0.557
Expected or known soil conditions (CsR35)				0.654
Availability of anticorruption policies and structures (CsR8)				0.747
Efficient public procurement methods (CsR6)				0.807
Effective contractors'/subcontractors'/suppliers' coordination (CsR43)				0.364
Known physical site conditions (CsR9)				0.452
Low rate of theft and vandalism (CsR7)				0.418
Safe working environment (CsR37)				0.409
Effective site health and safety management systems (CsR28)				0.564
Moderate public/national holidays and observations (CsR29)				0.483
Effective site security systems (CsR22)				0.618
Efficient utilization of materials (CsR18)				0.425

The first component comprised sixteen (16) items with a threshold above 0.5, disregarding those below the threshold. They are “Timely conflict/dispute resolution,” “High productivity of plant and equipment”, “Workforce efficiency and level of experience,” “Project location and accessibility,” “Availability of plants, tools, and equipment,” “Health and safety policy compliance,” “Geographical inclusion and knowledge transfer,” “Availability of skilled labor,” “Proper coordination between project stakeholders, “Availability to construction materials,” “Continuation of projects by the previous government,” “High labor productivity,” “Efficient transmittal process/flow of information or communication between stakeholders,” “Proper construction methods,” “Known new resource requirements” and “High quality of workmanship”. These items measure the Construction Project Sustenance (CPS).

Nine (9) items in the second component had thresholds that exceeded 0.5. They are “Solvency of subcontractors and suppliers”, “Good teamwork among employees”, “Well-defined scope of work”, “Complete project designs/drawings”, “Healthy working conditions”, “Satisfaction of workers on site”, “Clarity and consistency of specifications”, “Import/export freedom on construction materials”, and “Prompt deliveries of material”. These items measure Project Information and Execution (PIE). Thus, they represent the Project Information and Execution (PIE).

For the third component, seven (7) items had their thresholds that exceeded 0.5. They are “Involvement of local people/community in projects”, “Effective resources planning and control”, “Accurate and precise geotechnical information”, “Moderate change orders”, “Proper site layout”, “Flexible environmental regulations” and “Knowledge of new technology requirements”. These items measure Project Implementation and Environmental Information Factors (PIEIF).

Following the multivariate EFA to extract the component, the adjusted item-total correlation for each of the three components' items was extracted using the recommended cut-off value of 0.30. The component (Construction Project Sustenance) had a Cronbach's alpha of more than 0.800 at 0.922, indicating that the items were good measurements of this component. The items' good measurements of the component (Project Information and Execution) were further demonstrated by the fact that their Cronbach's alpha was greater than 0.800, at 0.857. The component's (Project Implementation and Environmental Information Factors) items were determined to be good measures of the component because their Cronbach's alphas were both larger than 0.800, at 0.806. This implies that all variables in the three components demonstrated good and acceptable internal reliability [73]. The results of the factor analysis demonstrated that the construction-risk (CsR) factors, as presented in Table 5a, b, and c, are reliable and one-dimensional. These results provide important new information regarding the reliability and validity of the metrics used to evaluate the CsR constructs.

Table 5.

The grouped construction-risk constructs’ unidimensionality and reliability

a) The construction project sustenance

b) Project information and execution

c) Project implementation and environmental information factors.

Factors	Squared Multiple Correlation	Corrected Item-Total Correlation	Cronbach's Alpha if Item Deleted	Cronbach's Alpha
Timely dispute resolution	0.623	0.756	0.913	0.922
High productivity of plant and equipment	0.540	0.680	0.916
Workforce efficiency and level of experience	0.581	0.710	0.915
Project location and accessibility	0.526	0.687	0.916
Availability of plants, tools and equipment	0.488	0.643	0.918
Health and safety policy compliance	0.501	0.694	0.915
Geographical inclusion and knowledge transfer	0.506	0.651	0.917
Availability of skilled labor	0.444	0.611	0.918
Proper coordination between the design and construction teams	0.504	0.637	0.917
Continuation of projects by previous government	0.459	0.640	0.917
High labor productivity	0.501	0.621	0.918
Efficient transmittal process/flow of information or communication between stakeholders	0.433	0.610	0.918
Proper construction methods	0.462	0.588	0.919
Known new resource requirements	0.408	0.560	0.920
High quality of workmanship	0.327	0.501	0.921
Availability to construction materials	0.412	0.500	0.911

Factors	Squared Multiple Correlation	Corrected Item-Total Correlation	Cronbach's Alpha if Item Deleted	Cronbach's Alpha
Solvency of subcontractors and suppliers	0.457	0.648	0.835	0.857
Good teamwork among employees	0.512	0.656	0.834
Well defined scope of work	0.466	0.645	0.835
Complete project designs/drawings	0.400	0.595	0.840
Healthy working conditions	0.458	0.599	0.840
Satisfaction of workers on site	0.375	0.596	0.840
Clarity and consistency of specifications	0.524	0.656	0.834
Flexible import/export restrictions on construction materials	0.157	0.299	0.873
Prompt deliveries of material	0.378	0.597	0.840

Factors	Squared Multiple Correlation	Corrected Item-Total Correlation	Cronbach's Alpha if Item Deleted	Cronbach's Alpha
Involvement of local people/community in projects	0.465	0.633	0.764	0.806
Effective resources planning and control	0.468	0.653	0.761
Accurate and precise geotechnical information	0.289	0.438	0.798
Moderate change orders	0.362	0.575	0.774
Proper site layout	0.340	0.566	0.778
Flexible environmental regulations	0.335	0.539	0.780
Knowledge of new technology requirements	0.249	0.441	0.807

6.4. Confirmatory factor analysis (CFA) for construction risk (CsR) constructs

Confirmatory factor analysis (CFA) was administered after ensuring that the constructs demonstrated unidimensionality and reliability using EFA. Following Ref. [74], a three-statistics method of fit indices was used for the Construction Risk (CsR) analysis strategy for goodness of fit. The Construction-Risk (CsR) model yielded a probability of p = 0.0000 and an S – Bχ2 of 4.116 with 27 degrees of freedom (df). According to Ref. [75], the values of the Root Mean Square Error of Approximation (RMSEA) should fall between 0.05 and 0.08. An acceptable result was obtained, with an RMSEA value of 0.072. Given that the Comparative Fit Index (CFI) value of 0.965 is higher than the suggested cutoff limit of 0.90, as indicated in Ref. [76], the model was deemed appropriate. The value of the CFI estimator should not exceed 1, as it is highly accurate. Furthermore, the derived Parsimony Comparative Fit Index (PCFI) value of 0.776 is deemed acceptable, as PCFI values of 0.6 or higher signify a suitable fit [76]. [75] proposed that Incremental Fit Index (IFI) values near one indicate a very excellent fit while one denotes a perfect fit, hence the derived IFI value of 0.996 is regarded as a very good fit.

As seen in Table 6, the Normed Fit Index (NFI) value of 0.930 falls within the specified range; however, the NFI cutoff value must be greater than 0.90. Based on the results provided in Refs. [[76], [77], [78]], the model was deemed acceptable. A good fit is indicated by the derived Parsimony Normed Fit Index (PNFI) value of 0.745, which is less than the cut-off value of 0.80 [78]. A good fit is indicated by a Root Mean Square Residual (RMR) of 0.025, which is less than 0.05 [78]. A good fit was indicated by the study's Goodness-of-Fit (GFI) value of 0.939, which is higher than the suggested value of 0.90 [[76], [77], [78]]. The Construction Risk (CsR) model's fit indices show that the model can be used in the complete latent variable model analysis because it fits the sample data.

Table 6.

Robust fit index for construction risk (CsR).

Robust fit index	Estimated Value	Permissible limit	Comment
S – Bχ2	4.116	–	–
Df	227	0≥ − Acceptable	Acceptable
RMSEA	0.072	Less than 0.08- Acceptable	Acceptable
CFI	0.965	0.95≥ Good fit 0.90≥ Acceptable	Good fit
PCFI	0.776	Less than 0.80 - Good fit	Good fit
IFI	0.966	Greater than 0.90 -Good fit	Good fit
NFI	0.930	Greater than 0.90 - Good fit	Good fit
PNFI	0.745	Less than 0.80- Good fit	Good fit
RMR	0.025	Less than 0.05 - Good fit	Good fit
GFI	0.939	Greater than 0.90 - Good fit	Good fit

The features are represented by a unidimensional model in Fig. 1 and Table 7. The final CFA analysis used 23 out of the 43 indicator variables obtained [76,79]. Out of the 462 cases analyzed for this particular construct, there were a total of twenty-three (23) indicator variables. These variables were divided into three (3) components namely, CPS (CPS1, CPS2, CPS3, CPS4, CPS5, CPS6, CPS7, CPS8, CPS9, CPS10, CPS12, and CPS14), PIE (PIE1, PIE2, PIE3, PIE4, PIE5, PIE6, PIE7 and PIE9), and PIEIF (PIEIF1, PIEIF2, and PIEIF5).

Fig. 1.

CFA model for construction risk (CsR).

Table 7.

Construction-risk (CsR) indicator variables in the final conceptual model.

Component	Indicator Variable	Label	Measurement Variable
Construction Project Sustenance (CPS)	(CsR40)	CPS1	Timely conflict/dispute resolution
	(CsR38)	CPS2	High productivity of plant and equipment
	(CsR23)	CPS3	Workforce efficiency and level of experience
	(CsR10)	CPS4	Project location and accessibility
	(CsR3)	CPS5	Availability of plants, tools and equipment
	(CsR17)	CPS6	Health and safety policy compliance
	(CsR15)	CPS7	Geographical inclusion and knowledge transfer
	(CsR2)	CPS8	Availability of skilled labor
	(CsR39)	CPS9	Proper coordination between project stakeholders
	(CsR5)	CPS10	High quality of workmanship
	(CsR21)	CPS12	High labor productivity
	(CsR41)	CPS14	Proper construction methods
Project Information and Execution (PIE)	(CsR30)	PIE1	Solvency of subcontractors and suppliers
	(CsR32)	PIE2	Good teamwork among employees
	(CsR12)	PIE3	Well defined scope of work
	(CsR11)	PIE4	Complete project designs/drawings
	(CsR36)	PIE5	Healthy working conditions
	(CsR31)	PIE6	Satisfaction of workers on site
	(CsR14)	PIE7	Knowledge of new technology requirements
	(CsR4)	PIE9	Prompt deliveries of material
Project Implementation and Environmental Information Factors (PIEIF)	(CsR34)	PIEIF1	Involvement of local people/community in projects
	(CsR20)	PIEIF2	Effective resources planning and control
	(CsR27)	PIEIF5	Proper site layout

The correlation coefficients, standard errors, and statistical test results of the final 23-indicator model are presented in Table 8. Each correlation value was below 1.00, and every p-value was below the significance level of 0.05, indicating proper signals. The estimations were deemed to be statistically significant and reasonable. The indicator variable "Effective resources planning and control" (PIEIF2) had the highest standardized coefficient at 0.810. This indicates that it can significantly increase the risks associated with construction projects in Ghana.

Table 8.

Factor loading and P-value of construction risks (CsR).

Hypothesized relations (Path)	Unstandardized factor Coefficient (λ)	Standardized factor Coefficient (λ)	P-Value	R- Square	Significant at 5 % Level
CPS1 ← CPS	1.000	0.799	0.00	0.639	Yes
CPS2 ← CPS	0.936	0.731	0.00	0.535	Yes
CPS3 ← CPS	0.924	0.736	0.00	0.542	Yes
CPS4 ← CPS	0.792	0.704	0.00	0.496	Yes
CPS5 ← CPS	0.681	0.707	0.00	0.500	Yes
CPS6 ← CPS	0.964	0.719	0.00	0.516	Yes
CPS7 ← CPS	0.829	0.667	0.00	0.445	Yes-
CPS8 ← CPS	0.685	0.653	0.00	0.427	Yes
CPS9 ← CPS	0.837	0.671	0.00	0.450	Yes
CPS10 ← CPS	0.849	0.682	0.00	0.465	Yes
CPS12 ← CPS	0.789	0.662	0.00	0.439	Yes
CPS14 ← CPS	0.792	0.606	0.00	0.367	Yes
PIE1 ← PIE	1.000	0.649	0.00	0.421	Yes
PIE2 ← PIE	1.096	0.691	0.00	0.478	Yes
PIE3 ← PIE	1.151	0.716	0.00	0.513	Yes
PIE4 ← PIE	0.990	0.645	0.00	0.416	Yes
PIE5 ← PIE	1.000	0.647	0.00	0.419	Yes
PIE6 ← PIE	1.028	0.641	0.00	0.411	Yes
PIE7 ← PIE	1.277	0.767	0.00	0.589	Yes
PIE9 ← PIE	1.004	0.675	0.00	0.455	Yes
PIEIF1 ← PIEIF	1.000	0.676	0.00	0.457	Yes
PIEIF2 ← PIEIF	1.161	0.810	0.00	0.657	Yes
PIEIF5 ← PIEIF	0.826	0.649	0.00	0.422	Yes

A high correlation value of nearly 1.00 was observed in the majority of the parameter estimates. With regard to the indicator variables and unobserved variables, high correlation values indicate a strong degree of linear relationship (CPS, PIE, and PIEIF). Furthermore, the R-Square values were in proximity to the intended 1.00 value, suggesting that the factors accounted for a greater proportion of the variance observed in the indicator variables. According to the findings, under Construction-Risk (CsR) factors, all measurable variables strongly predict unobserved components (CPS, PIE, and PIEIF).

7. Discussions of results

The first objective, to identify and classify the critical construction-risk variables that affect construction projects in Ghana. This was achieved by identifying the critical construction-risk factors that impact construction projects in Ghana through an extensive literature search and observation of construction projects. The second objective was to develop a critical construction-risk management model which is achieved using three approaches. The first approach involved determining the variability of each construction-risk factors, which was performed using the mean and standard deviation. The standard deviations for all variables were less than one (1), indicating that the responses were not widely dispersed from the sample mean, which agrees with the literature [80]. In order to ascertain model fitness, the second approach involved evaluating the factors in the CsR for unidimensionality and reliability, subsequently confirming the factor structure. The EFA multivariate analysis was used to reject all those factors whose factor loadings fell below a threshold of 0.5 after the evaluation. The accepted risk factors were grouped into three main components under construction-risk factors as indicated in Table 4. The three main components were named “Construction Project Sustenance Factors”, “Project Information and Execution Factors” and “Project Implementation and Environmental Information Factors” as shown in Table 5. The analysis revealed two best factors under each component to include “Timely dispute resolution” [81], “High productivity of plant and equipment” [82], “Solvency of subcontractors and suppliers” [83], “Good teamwork among employees” [12], “Involvement of local people/community in projects” [85], and “Effective resources planning and control” [85]. This is in agreement with other studies that identify them as critical factors affecting construction projects [[80], [81], [82], [83], [84]]. The CsR variables’ Cronbach alphas (0.922, 0.857, and 0.806) revealed good internal reliability and unidimensionality.

In the third approach, the CFA model was developed to provide valuable information on the fitness of the dataset [86]. The model (Fig. 1) was a good fit because it sufficiently described the sample data. This study showed that the CsR variables developed a three-factor model. The correlation and p-values were statistically significant. CFA revealed that "Effective resources planning and control" (PIEIF2) with a standardized coefficient of 0.810 is the CsR factor that can significantly improve construction projects. Validation has shown that “effective resource planning and control” is a crucial tool that impacts the success of construction projects [[87], [88], [89]]. This was followed by "Timely conflict/dispute resolution" and "Knowledge of new technology requirements". This is in agreement with other studies found in literature [81]. Conflicts have caused the failure of several construction projects, and Ref. [90] asserted that the Ghana National Housing Project failed as a result of conflict. Table 7 shows the performance of each relevant construct for the model based on its standardized coefficient. This study contributes to the existing body of knowledge by identifying the critical construction-risk factors and revealing a three-factor CFA model that could impact construction projects in Ghana.

The results from the EFA and CFA reveal that construction project success is contingent on project managers prioritization of timely dispute resolution, effective resource planning and control, and a thorough understanding of new technology requirements as critical factors. Conflicts typically arise among clients and project managers, contractors and project consultants, contractors and clients, contractors and suppliers, or contractors and local communities. The emphasis on timely conflict resolution underscores the significance of efficient communication and collaboration among these stakeholders, resulting in improved relationships and project outcomes. Effective resource planning and control, along with timely conflict resolution, can significantly reduce costs in all sections of construction projects and cost associated with delays, disputes, and inefficiencies. Project managers understanding of new technologies should be a requirement because of the fast-evolving technological era the world presently finds itself in. This calls for huge investment in training, research, and development by contractors and project consultants to improve productivity and innovation in the industry.

8. Conclusion

This study aimed to develop a single construction-risk management model for construction projects based on the practise/knowledge and different views of Ghanaian construction stakeholders in order to help Ghanaian construction projects succeed. This was accomplished by identifying 43 construction-risk factors from the literature, and deductions through observation of construction projects. The stakeholders regarded those factors relevant to their potential impact on construction projects. At first, the dataset's variability was assessed using the mean and standard deviation. After analyzing the mean and standard deviation of each variable, it was determined that the responses were not substantially different from the sample mean, since all standard deviations were less than one. This implies that each of the 43 factors identified in this study could affect construction projects in Ghana. Multivariate statistical analysis was then used to determine the underlying structure of the factors and verify their alignment with the hidden variables.

The CFA that the study sought to develop a critical construction-risk management model ensured that only relevant factors were selected for the model. According to the calculated Cronbach's alpha, the sample data were found to be reliable and unidimensional. The p-values and related values confirmed the significance of the predicted values. This study revealed a three-factor CFA model that critically contributes to construction risk. Effective resource planning and control (PIEIF2), timely conflict/dispute resolution (CPS1), and knowledge of new technology requirements (PIE7) have standardised coefficients that significantly affect the success of construction projects. In this study, we identified construction-risk factors that could affect the success of construction projects and contribute to the body of knowledge on risk management. Moreover, the study offers a strong theoretical basis for future research exploring techniques that could be utilized to mitigate the influences of crucial construction risks that may impact the success of construction projects.

The practical application of this study is to identify important construction-risk factors that may have an impact on the outcome of construction projects in Ghana. Identifying the important risk elements that could affect the success of a particular construction project is one of the first steps in every management approach. To develop methods to minimize the negative implications of these risks, project managers must first identify relevant risk factors. This study can serve as an initial step in fostering motivation among industry leaders and construction stakeholders to tackle the challenge of construction risk in the industry. The stakeholders that could benefit from this study are clients, contractors, insurers, local communities, project managers and suppliers. The study recommends that every project should have a mandatory conflict resolution board to ensure the timely resolution of conflicts. Standards for effective resource planning and control, including training and certification programmes, should be developed and enforced. Incentives such as tax reliefs or priority for government projects should be offered to contractors and project managers to invest in new technologies and training, thereby improving innovation, reducing waste generation, minimising pollution, and lowering energy consumption. This study also recommends further research using alternative research designs to identify more construction risk factors that impact the success of construction projects in Ghana. By identifying and classifying the most critical construction risk elements that potentially affect the success of construction projects, this study contributes to the wider body of knowledge on risk management. The carefully modelled factors in this study are specific to Ghana's construction industry and should not be generalized. The professionals selected for this study were Ghanaian practitioners. Therefore, the findings presented in this study are reflective of their experiences and opinions.

CRediT authorship contribution statement

E.N. Jackson: Writing – original draft, Formal analysis, Data curation. T. Shanmuga Priya: Writing – review & editing, Supervision, Conceptualization.

Declaration of Competing Interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Appendix A. Supplementary data

The following is the Supplementary data to this article: