Introduction

Infrastructure development is crucial to Ethiopia’s economic growth and poverty reduction. Investments in transport, power, and ICT sectors have significantly contributed to the country’s development1,2. The construction industry, accounting for 12–25% of GNP in developing nations, is vital for socioeconomic advancement3. Ethiopia has made substantial progress in infrastructure development, with annual investments exceeding 15% of GDP4. This expansion has positively impacted economic growth, although challenges persist. The transport sector, particularly road infrastructure, is essential for Ethiopia’s development, but poor quality remains a significant obstacle3,5,6. While long-term economic benefits of infrastructure development are evident, short-term adjustments are slow, with only 6% of disequilibrium corrected annually3,5. Despite ongoing challenges, continued prioritization of infrastructure development is crucial for Ethiopia’s economic transformation and sustainable growth7.

According to Ethiopia Road Authority reports road construction projects in Ethiopia face significant challenges, including delays and poor management practices. Critical risks include unforeseen site conditions, improper design, and incomplete contract documents8. Key factors causing delays are difficulties in financing, material price escalation, and ineffective planning9,10. The road construction industry’s performance is unsatisfactory, with low safety, risk, and time management practices. Projects experience 61–80% schedule slippage and 21–40% deviation in costs and other variables9. Critical success factors differ between the public and private sectors. For project management, critical success factors (CSFs) are characteristics, situations, or variables that, when properly maintained, controlled, or sustained, can significantly affect the project’s success. Therefore, crucial project management success factor analysis is a joint procedure to determine the possible causes of the crisis, develop a proactive strategy to address the crisis and resolve the issue in a way that will last11.

Construction project delays are a significant issue globally, impacting economic growth and project outcomes. In Zambia, delays have led to average schedule extensions of 227%12. Common causes of delays include late progress payments, difficulties in project financing, and changes in project scope13,14. These delays often result in time extensions and cost overruns12,14,15. A comparative study of Australia, Malaysia, and Ghana revealed that the most influential delay factors vary by country, with planning deficiencies, payment delays, and improper contractor planning being key issues in the respective countries16,17,18. In Zambia, insufficient initial cost analysis, change orders, and financial difficulties of contractors were identified as major causes of cost escalation and schedule overruns13.

Road construction project delays and cost overruns are significant challenges in the industry, caused by various factors including cash flow issues, inadequate planning, and slow decision-making19,20. These delays lead to excessive costs, extended durations, and strained relationships among stakeholders20,21. Key factors contributing to delays include inaccurate budgeting, weak cash flow, and inefficient resource planning20. To address these issues, the construction industry is increasingly adopting artificial intelligence (AI) technologies for improved project planning, risk management, and overall efficiency. Among the many difficulties facing the construction sector are labor shortages, productivity limitations, and schedule and cost overruns. In every project step, it lags behind other industries in terms of digitization. The construction industry is undergoing a revolution thanks to the revolutionary technologies of artificial intelligence (AI) and machine learning (ML)22.

Effective strategies to minimize delays include adequate project planning, budgeting, and implementing modern project management tools like MS Project and Primavera20,23. Additionally, optimizing material management and resource utilization can help reduce delays and control costs in construction projects.

The Woliso–Ambo Road is a crucial link between the Addis Ababa-Jimma Trunk Road and the Addis Ababa-Nekemte Road. This 63.8 km route connects the towns of Woliso and Ambo, administrative centers of the West Shewa and Southwest Shewa Zones in the Oromia Regional State, respectively. The road traverses a predominantly agricultural region, passing through small towns like Wenchi and Chitu and numerous villages with local markets. A significant tourist attraction, Crater Lake (Wenchi), is accessible via a junction at Wenchi. The development of the Woliso–Ambo Road is expected to further enhance tourism in this region.

The Woliso–Ambo Road project, initiated in June 2010 with a projected completion time of three and a half years, faced significant delays and was ultimately finalized in 2024. The government contracted separate companies for design and construction by employing the Design-Bid-Build delivery method. The project was divided between two contractors: ElSAMEX, responsible for 12 km, and METEK, currently undertaking the remaining 21 km. Despite METEK taking over the project in 2019 with a target completion date of July 2024, the project remains incomplete with 30 km yet to be constructed.

These delays in road construction projects lead to increased costs, resource wastage, and public inconvenience. Understanding the determinants of delays improves project management, enabling stakeholders to develop strategies to mitigate future issues. Delays hinder economic activities, affect trade, and limit access to essential services24,25. Stakeholder engagement is fostered by highlighting the roles of different stakeholders and their contributions to delays. The Woliso–Ambo Road construction project provides a detailed case study, contributing to academic knowledge and raising public awareness about delays in road construction.

As a result, it is necessary to investigate and analyze the factors that contribute to this delay in road projects and provide a suitable solution for the study area and for other delayed projects. The objective of this study is to evaluate the determinants of road project construction delays in the case of the Woliso–Ambo Road construction project.

Case study description

Location

The Woliso–Ambo Road project is situated in the Oromia region of Ethiopia, spanning the southwestern Shoa zone of Woliso and the western Shoa zone of Ambo. The project area is characterized by an average elevation of 2101 m above sea level and coordinates of 8° 59’ N and 37° 51’ E. The road was designed to connect the administrative centers of West Shewa and Southwest Shewa, Woliso, and Ambo, respectively, to the existing all-weather gravel road network. The route traverses numerous small villages and two towns, Haro-Wenchi and Chitu, between Woliso and Ambo (Fig. 1). A significant portion of the road project, 55%, lies within Wenchi Woreda, followed by Ambo Woreda at 37%, and Woliso Woreda at 8%26.

Fig. 1
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Map of the study area (https://www.google.com.sg/maps/dir/woliso/Ambo+Rd,+Ethiopia/@8.809468,38.0187425,10z/data=!3m1!4b1!4m13!4m12!1m5!1m1!1s0x164d2825da748493:0x5e88448dbd4ce426!2m2!1d37.9720733!2d8.539817!1m5!1m1!1s0x164b884fd4e66b7d:0x59d8ef010048dff1!2m2!1d38.6636156!2d9.0659877?entry=ttu&g_ep=EgoyMDI0MDkxMC4wIKXMDSoASAFQAw%3D%3D).

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Target population of the study area

The target populations for this study include key stakeholders directly involved in road construction projects within the Woliso–Ambo Road project area. These stakeholders encompass clients, contractors, consultants, project managers, office engineers, site engineers, and supervisors, all of whom possess valuable experience in road construction within this region.

Over the years, several studies have examined the causes of delays in road construction projects. Numerous researchers refined and reorganized the multiple groups and reasons for delays into different categories. Road projects execution constantly confronts problems, with delays being one of the most important ones. Delays must be adequately handled before they escalate to the point where they negatively impact the project’s budget, schedule, and quality.

Projects in Ethiopia have experienced delays due to various factors, including client design changes, poor planning and management, late payments, and construction flaws27,28. Consultants face issues such as low productivity, technical issues, and cash flow issues. In Addis Ababa, delays are primarily caused by missed progress payments, poor consultant planning, inadequate site management, labor shortages, and lack of funding28. These delays can significantly impact the overall project timeline and quality and was carried out to determine the reasons behind the excessive delays in road project completion that occurred throughout the construction phase as a result of the contractor, consultant, and employer’s inadequacies in Addis Ababa City Road Authority projects. To rank the three construction parties based on their respective areas of responsibility and importance as judged by the respondents, which factor is causing road project delays, the Relative Importance Index (RII) analysis was utilized to test the agreement between various groups of respondents who took part in the questionnaire survey. The findings indicate that contractors have the largest share of liability for the approximately 40% delay. Second place went to the employer, who made up 26.15%, and third place went to the consultant29,30,31.

Also32 research on constructing road project delays in Ethiopia revealed significant factors such as lack of communication, design flaws, material shortages, slow decision-making, financial problems, cash-flow issues during construction, and an increase in quality. The Garson algorithm (GA) was integrated with the artificial neural network (ANN) inference model to train the database and predict the project schedule delay. The relative weights of challenging factors with rankings were calculated and identified, and the top ten significant factors were: poor site management by the contractor; ineffective planning, scheduling, controlling, and quality monitoring; delays in construction activities due to weather changes; late land acquisition; lack of materials; financial problems of contractors; poor supervision; cash-flow issues (irregular payments); and financial capability of the client28.

This study identified gaps in the literature by concentrating on characteristics associated with road construction projects delays in the study area. Variables were selected according to their influence on project schedules and ranked according to their applicability to the particular difficulties faced by the research area, such as material availability and peace and security concerns. The list of possible variables was narrowed during the data gathering phase by incorporating stakeholder insights and expert opinions. To make sure that only elements with a strong predictive link were included in the regression model, correlation analysis was used to find factors that were strongly associated to delays. Additionally, the review assisted in identifying gaps in the body of knowledge regarding Ethiopia’s infrastructure context.

Escalation of material prices The problem of rising costs in construction materials is a significant issue in Ethiopia, particularly in developing nations like the country. Inflationary pressure from local and global market forces, coupled with Ethiopia’s increasing reliance on imports for specific building materials, contributes to delays in infrastructure projects33,34.

Delays in subcontractors’ work Subcontractor delays have a well-established influence in Ethiopia. Delays in their performance have a major effect on project timetables because road construction projects sometimes depend on several subcontractors for specialized work. This is a prevalent problem in the case study area of road construction projects because of things like insufficient capacity, a lack of labor, and uneven quality control, according to expert interviews done for this study.

Material and equipment influence Infrastructure project delays have frequently been attributed to the timely and adequate delivery of equipment and materials. Delays in acquiring supplies and equipment particularly noticeable in Ethiopia, where problems with the supply chain, inadequate infrastructure, or logistical difficulties (particularly in the study area)35,36.

Inadequate management and supervision by consultants For construction projects to remain on schedule, consultants must manage and supervise them effectively. In the Ethiopian setting, local consulting businesses’ capacity deficiencies and lack of technical ability have frequently been connected to poor management. Furthermore, professionalizing project management is becoming more and more important, and numerous research on infrastructure projects in Ethiopia have shown that consultant management plays a significant role in project delays37.

Peace and security situation It is impossible to overestimate the significance of security in building projects, especially in developing nations. Construction operations in Ethiopia have occasionally been hampered by elements such regional security concerns, civil upheaval, and ethnic conflicts. The planning and implementation of road construction projects have been directly impacted by these security issues, particularly in the study area with persistent instability, as mentioned in local reports and expert interviews. Because of its contextual significance, these findings were taken into account while choosing this component for the study.

Methods

Data collection

Data collection is a systematic method for acquiring and analyzing information to answer research questions and analyze findings. The quality of the collected data is a major determinant of the suitability of the information for decision-making and inference. To complete this study, primary and secondary data were collected via various methodologies to obtain distinct datasets.

Primary data were collected by visiting the project area. Interviews were conducted with different stakeholders in the road project to gain knowledge of current project practices, standards used to assess current project delays, mitigation practices for project delays and the satisfaction of road users.

The secondary data were derived from current and forthcoming schedule management documents from project stakeholders; the documents included the cause and mitigation practices of this construction delay from project stakeholders; risk management documents from project stakeholders; and satellite images and an open-street map of the project area obtained from Google.

Sampling technique and sample size

To gather primary data, a questionnaire was administered to a representative sample of the project area’s population. A simple random sampling technique was employed, ensuring equal probability of selection for all individuals. Additionally, purpose sampling, a non-probability method, was utilized to target specific stakeholders based on their expertise. Although purpose sampling offers efficiency benefits, it has limitations such as susceptibility to researcher bias and the potential for participant manipulation of data. The research encompassed all project participants, with questions derived from the insights of various stakeholders. The total professional workforce in the project area, including project managers, engineers, team leaders, consultants, directors, and technical support staff, as well as permanent labour workers, amounts to 194 individuals. Equation 1 outlines the methodology used to calculate the sample size for this known population.

The sample size should be determined via Yamane’s formula via the following formula:

Equation 1: Sample size determined by38:

$$\text{Samplesize}=\left(\frac{\text{Number of population}}{{ 1+\text{Number of population}*(\text{Margin of error}}^{2})}\right)$$
(1)

A margin of error (confidence interval) of + /- 5% was assumed. Then,

$$\begin{aligned} {\text{Sample size}} = & \left( {\left( {{194}} \right) \times {1} + \left( {{194 }\left( {0.0{5}} \right)^{{2}} } \right)} \right) \\ = & \, \left( {\left( {{194}} \right) \times \left( {{1} + \, 0.{485}} \right)} \right) \\ = & \, \left( {{194}/{1}.{485}} \right) \\ = & {13}0.0{2} \\ \end{aligned}$$

Therefore, 130 respondents were needed for the analysis.

Study variables

Dependent and Independent variables

Road construction delays, which in this study refer to a time overrun past the time negotiated by the construction parties for the project or the contract date to be finished. It is also the period spent on the completion date or time specified for the completion and submission of the construction project, as determined by both parties, which is the dependent variable in this study.

After the process of validation and reliability test on the variables, the following are selected due to their acceptance on the case study area. Escalation of the material price influence, delays in subcontractor work influence, material and equipment influence, inadequate management and supervision by consultants, and peace and security situations are independent variables that were covered in this study, which impact the project’s delay.

Materials used for this study

The Statistical Package for the Social Sciences (SPPS) was used for descriptive statistics, inferential statistics, factor analyses, regression analyses, and other statistical techniques. This spreadsheet application is used for statistical, mathematical, and graphical data analysis. It has capabilities, including calculations, graphing tools, pivot tables, and functions. Large datasets were arranged, analyzed, and analyzed via Microsoft Excel.

Method of data analysis

This study utilized descriptive analysis, a type of empirical analysis, to understand people’s preferences, actions, and viewpoints regarding project delays. Data were collected through surveys and interviews, shedding light on variables such as material price influence, subcontractor delays, material and equipment influence, inadequate management by consultants, and methods of peace and security situations. Higo39 highlighted the relationship between questionnaire analysis and data verification, as it aids in calibration and validation. The data were analyzed via the IBM SPSS statistics V-27 program, which includes data preprocessing and cleaning, data entry, descriptive and inferential statistical calculations, and data visualization40. The Index of Relative Importance (RII) was used to rank the causes and consequences of delays from various angles, allowing for a comprehensive understanding of project schedules and enabling more precise and realistic simulations via Eq. 2.

Equation 2: Relative importance index determined by Tholibon et al.41

$$\text{RII}\hspace{0.17em}=\hspace{0.17em}\frac{\sum W}{A*N}$$
(2)

where: Relative Important Index (RII), W is the weighting assigned to each factor by the respondents (ranging from one to five), A is the highest weight (i.e., 5 in this case), and N is the total number of participants.

Modeling technique

Inappropriate management of road construction delays is a significant problem, as evidenced by the regression model used in this study. The model predicts the degree of delays based on various independent variables, such as material price increases, subcontractor work delays, material and equipment influences, inadequate management and supervision by consultants, and situations of peace and security. By analyzing these variables, the regression model helps to understand the degree and direction of these correlations, enabling the development of more accurate forecasts. The model was developed via the MS Excel add-in data analysis tools Pak and IBM SPSS, allowing a comprehensive understanding of the factors influencing delays in the construction process.

Validity and reliability tests

Content validity refers to how well the questionnaire addresses the idea being studied. To ensure this, accessible literature was reviewed, and the advisor and other experts were consulted to confirm that the questions cover the key elements of evaluating delays in road construction projects. Cronbach’s alpha test coefficient was calculated using SPSS software to assess the reliability of the questionnaires. This coefficient ranges between 0 and 1, with the following standards by J. Nunally (1978): 0.8 > Cα > 0.7: Good, 0.7 > Cα > 0.5: Satisfactory, Cα < 0.5: Poor, and Cα > 0.8: Excellent. The table below shows the reliability of the data analysis.

Table 1 shows the following influences: material price 0.79, delay in subcontracting 0.67, material and equipment 0.82, inadequate management and supervision by consultants 0.77, and peace and security situation 0.63. The average Cronbach’s alpha value for these variables was 0.736. Since all values were above the acceptable threshold of 0.7, it is reasonable to conclude that the questionnaire was effective as a data-gathering tool.

Table 1 Summary of the Cronbach’s alpha reliability coefficient.
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Results

Descriptive analysis

This study aimed to identify factors causing delays in the construction of the Woliso–Ambo Road using Microsoft Excel and SPSS software. Respondents rated their agreement on a 5-point Likert scale from strongly disagree to strongly agree. The analysis results are presented in tables focusing on construction delays. Mean scores are classified as low, moderate, or high, based on Zaidation and Bagheri’s framework. Scores below 3.39 are considered low, between 3.4 and 3.79 moderate, and above 3.8 are high. The study found a skew towards agreement on five questions about road construction delays, with most mean scores exceeding 3.6.

Table 2 shows that the Woliso–Ambo Road project has faced delays due to space constraints, a shortage of building supplies, and rising material prices. Most respondents agreed with this, with a mean score of 4.24. The increase in material prices significantly impacted the project timeline. Mean scores above 3.6 indicate a strong agreement among respondents, and standard deviations below one show a normal distribution of responses. This study underscores the need to address material price escalation for efficient road construction.

Table 2 Descriptive statistics of material and equipment influence.
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The escalation of construction material costs is a significant issue in road construction projects, and factors such as market competition, government and legal regulations, and uncompensated increases in costs are discussed in Table 3. Clients often resist escalation clauses, causing delays. Descriptive statistics were used to assess the impact of subcontractor work on the construction of the Woliso Ambo road project. The results showed a bias toward agreement in the respondents’ responses, with standard deviations of less than one. An increase in construction material costs is a major problem that contributes to project delays.

Table 3 Descriptive Statistics of Escalation of Material Price Influence.
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Table 4 shows that subcontractors play a significant role in the planning and scheduling of projects, with a minimum mean score of 3.56 and a maximum mean score of 4.00. Their performance in these areas has a significant influence on the project. However, the survey study also revealed a bias toward agreement in the respondents’ responses, with a mean value of more than 3.6. The standard deviations were smaller than one, indicating the closeness of the responses of the respondents. This study also examined the impact of inadequate management and supervision by consultants on the performance of road construction on the Woliso Ambo Road.

Table 4 Descriptive statistics of the influence of subcontractor work.
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Table 5 shows that inadequate management and supervision by consultants negatively impacted the Woliso Ambo Road project, with mean scores ranging from 4.20 to 3.24. This indicates significant issues in managing time, money, and resources. Respondents agreed that the consultant’s management and monitoring were inadequate, as shown by mean values above 3.6. The study evaluated operational performance based on five criteria: peace and security, site security restrictions, changes in government regulations, delays in moving utilities due to peace, and reduced accessibility for site personnel. Respondents rated the peace and security condition on a 5-point scale, with 1 indicating strong disagreement and 5 indicating strong agreement.

Table 5 Descriptive statistics of inadequate management and supervision by consultants.
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Table 6 shows that the Woliso–Ambo Road Project has faced delays due to peace and security measures, with a mean respondent score of 3.64. The range of mean values for peace and security issues, from changes in government regulation laws to the highest score of 3.64, indicates agreement on operational performance concerns.

Table 6 Descriptive Statistics of the Peace and Security Situation.
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Causes of delay ranking

Road construction projects face delays due to various reasons. Twenty-five delay-causing factors were identified and classified into five categories: material and equipment issues, material price increases, subcontractor delays, and insufficient management, and peace and security conditions. Respondents rated these factors on a five-point Likert scale based on their frequency.

The factors were categorized into five broad groups, and the relative importance index (RII) was used to rank them. Based on the survey results, the contribution of each factor to the delays in the Woliso–Ambo Road project was rated according to its relevance. The relative importance index was calculated using Eq. 241.

$$\text{RII}\hspace{0.17em}=\hspace{0.17em}\frac{\sum W}{A*N}$$
(2)

Where RII is the relative importance index of each factor in each group of respondents, and W is the weighting assigned to each factor by the respondents (ranging from one to five). A is the highest weight (5 in this case), and N is the total number of participants.

The escalation of material prices, with a relative relevance index value of 3.98, is the most significant contributor to project delays, according to the ranking in Table 7. These delays stem mainly from internal factors such as faulty estimation, poor planning, and confusing contract clauses. Additionally, external factors like changes in the country’s currency exchange rate and inflation rate also play a role.

Table 7 Ranking of group causes.
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Top delay cause

Table 8 shows that among the 25 causes of delays in the Woliso Ambo Road Construction Project, the uncompensated increase in the cost of construction materials had the most significant negative impact, with the highest RII value of 0.90. The second biggest factor was a shortage of construction materials, with an RII of 0.87. The lack of risk analysis and management by consultants, along with construction price indices that overestimate or underestimate market conditions, was the third influencing factor, with an RII of 0.82. The study concludes that the increase in costs and lack of available materials have greatly contributed to delays in the project.

Table 8 The eight main delays in the Woliso Ambo road project.
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Importance of the ranking cause of delay

Delays in road construction can have various consequences, and 12 significant effects have been reported in the literature. The following Fig. 2 shows the ranking of these consequences based on data from clients, consultants, and contractors in the Woliso Ambo road construction projects. An analysis of the questionnaire data revealed seven main effects of delays: time overrun 97.4%, cost overrun 85.6%, financial loss 76.7%, damage to company reputation 70.4%, poor quality of work 69.01%, contract relocation 65.7%, and overall poor quality 60.3%. Other possible effects include poor quality 59%, disputes 57%, wealth loss 49%, court cases 45%, and arbitration 31%.

Fig. 2
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Woliso–Ambo Road construction delays result.

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Multiple regression analysis

The adjusted R-square in multiple regressions measures the strength of the link between independent and dependent variables. It indicates the portion of the dependent variable’s variation that can be explained by the independent variables. In this study, the adjusted R-square measures the associations between five delay-causing factors and the dependent variable (Y). A higher R-square value indicates a stronger association.

The MR model for predicting road construction delays was developed using SPSS software. The variation in road construction delay (the dependent variable) can be predicted from independent variables like material price increases, subcontractor work delays, material and equipment influences, inadequate management and supervision by consultants, and peace and security situations. We evaluated how well these factors explained differences in operational performance.

The MR model performed exceptionally well, with a strong R-value of 0.893 and an adjusted R-square of 0.797, indicating that the selected factors explained 79% of the variation in road construction delays Table 9.

Table 9 Multiple regression model values.
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Analysis of variance (ANOVA)

Table 10 shows that ANOVA was performed to determine if there is a statistically significant difference between the independent and dependent variables. The F value is 68.082, which is significant at p < 0.05. This suggests that the link between the independent and dependent variables is statistically explained by our multiple regression model.

Table 10 ANOVA (F test).
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Given that the regression model’s F value of 68.082 is highly significant, it is used as a prediction model to explain road construction delays and their independent factors. Therefore, it is important to forecast future road construction performance based on these delay patterns.

Coefficients of regression analysis

Equation 3: MR model for predicting road construction delays via primary data.

$$\text{Y}=\text{ b }+\text{ b}1\text{X}1+\text{ b}2\text{X}2 +\text{ bX}3 +\text{ b}4\text{X}4 +\text{ b}5\text{X}5$$
(3)

Where Y is the dependent variable (road construction delay) and X1, X2, X3, X4 and X5 are independent variables. The term b is the intercept coefficient, whereas b1, b2, b3, b4, and b5 are the coefficients of the independent variables. The independent variables were the parameters described in Table 11, and the following results were obtained and summarized via the PAK SPPS data analysis tool.

Table 11 Coefficients of Independent Variables. Source: own survey 2024.
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The coefficients in Table 11 are substituted into Eq. 3, and the following equation from the MLR model is developed.

RCD = 0.542 + 0.276EMP + 0.208DSW + 0.356MEI + 2.61IMS + 0.303PSS.

RCD road construction delays EMP scaling of the material price influence, DSW delays subcontractor work influence, MEI material and equipment influence, inadequate IMS management and supervision by consultants, and poor PSS peace and security situations.

Assumption of linearity

This describes the correlation between changes in the independent variables and the dependent variable. Using SPSS software, plots of the regression residuals were created to determine if there was a linear relationship between the dependent variable (road construction delay) and the independent variables (escalation of material prices, subcontractor work delays, material and equipment influence, inadequate management and supervision by consultants, and peace and security).

The purpose of the linearity test, shown in Fig. 3, was to determine if there was a linear relationship between the independent variables and the dependent variable. The scatter plot of residuals is nearly a straight line, suggesting that the linearity assumption holds true and that the relationship in the regression result is linear.

Fig. 3
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Predicted RCD with Observed RCD.

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Analysis of interviews

Unstructured interviews with selected groups provided suggestions to minimize delays in road construction. Respondents highlighted key actions such as resolving location and right-of-way issues before starting the project, modifying the bidding process to ensure qualified construction businesses get the projects, and implementing measures to prevent corruption without affecting bidding procedures. Additionally, spending extra time in the preconstruction stage to organize, schedule, and assess the design and overall plan is crucial. Regularly monitoring the project’s progress, from contract administration to procurement, choosing contractors and consultants with strong work ethics and backgrounds, and establishing a framework to manage project scope and design modification issues are also essential. Enhancing the employer’s capacity to oversee all city road projects from design to construction, bringing in superior personnel and well-developed structures, implementing modern project management practices, adhering to schedules, and fostering positive relationships and effective communication between construction partners are fundamental for minimizing delays in road construction projects.

Discussion

Escalation of material prices and material and equipment influence

The primary objective of this study was to identify factors influencing the construction of the Woliso Ambo Road. The study revealed a positive correlation between the dependent variable and the increase in material prices. Specifically, a 1% increase in material prices led to a 27.6% delay in the project, as indicated in Table 11.

There is a significant relationship between material price escalation and delays in construction projects within the study area33,42,43. The major impact of increased construction material costs includes significant financial difficulties and budget overruns, resulting in delays and extensions of the project completion date44. Additionally, material shortages significantly affect construction projects by increasing costs for contractors and compromising the quality of the project30.

The RII value of 0.87 in Table 8 underscores the necessity of regularly evaluating material availability and implementing strategic planning to mitigate delays. This study highlights the importance of addressing cost increases and material shortages to improve the timeliness and efficiency of construction projects.

Delays in subcontractor work

In the studies of Fashina et al.45, Samsudin et al.46, and Kshaf et al.47, delays in subcontractor work negatively influenced project delays by 34%. However, as shown in Table 11 of this study, subcontractor work delays impacted the project by 20.8% when the variable increased by 1%. The variable of material and equipment shows a positive correlation with subcontractor work delays36.

BIM enhances lean management in construction projects through 5D scheduling features. However, direct 5D simulation is not feasible due to BIM measurement scale differences. BIM-based techniques save time and are quicker than traditional management strategies48.

Quality problems and delays arise from insufficient planning, environmental considerations, and miscommunication, leading to cost overruns and increased expenses. Poor design guidelines, material choices, and building techniques result in expensive infrastructure maintenance or repair. Peaceful conditions are crucial for successful stakeholder engagement.

Insufficient management and supervision

The dependent variable, which is peace, is linked to poor management and supervision by the consultant, as shown in Table 11. Poor management causes project delays and poor supervision increases the dependent variable. When the situation gets worse by 1%, peace and security change by 30.3%, indicating a positive link between peace and security conditions49.

Project delays come from communication issues, unexpected hazards, and lack of materials, which negatively impact Lake Wenchi, Ethiopia’s largest ecotourism site50. Inaccurate estimates and shortcuts also delay projects, raising maintenance costs. Contractors face problems with construction schedules due to labor shortages, bad weather, and supply chain interruptions51. These issues lower the quality of road infrastructure and create unsafe projects. To maintain trust and reputation, construction workers and stakeholders must ensure safety in road construction projects.

An RII of 0.73 shows that the long-term sustainability of road infrastructure depends on peace and security, as indicated in Table 8. Stable conditions ensure road maintenance, economic stability, and job creation, all of which positively affect road projects. Public, government, and stakeholder perceptions influence these conditions, leading to increased trust and support for infrastructure projects.

The direction and strength of the relationships between each independent variable and the dependent variable in the regression model are revealed by beta values. The data analysis of project delays for the Woliso–Ambo Road shows that the main cause of the delay is the increase in construction material costs, as indicated in Table 11, with an RII of 0.90, as shown in Table 8.

The data, approach, methodology, and outcome value for numerous cities across nations are summarized in Table 12.

Table 12 Comparing similar research carried out in different countries.
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Study validation

The study evaluates factors contributing to project delays in the Woliso–Ambo route construction using primary data and questionnaires. Researchers used survey protocols and questionnaires, analyzing data using IBM SPSS and Epi Info. Secondary data from city agencies and authorities was analyzed using Microsoft Office, identifying areas of agreement and disagreement for general applicability. The study investigates delays in the construction of the Woliso–Ambo route using surveys and questionnaires. Researchers used IBM SPSS and Epi Info for analysis, and Microsoft Office for multiple regression analysis on secondary data from city agencies and authorities.

As in Fig. 4 the study’s conclusions are validated and supported by the strong correlation between primary and secondary data, indicating their importance as more dependable sources.

Fig. 4
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validation of the study.

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The study’s agreement analysis reveals moderate to good agreement among evaluators regarding construction delays, supported by Cohen’s Kappa values ranging from 0.60 to 0.85. This uniformity suggests industry experts consistently view delay reasons. The Cronbach’s Alpha value of 0.82 supports the claim that equipment availability and managerial inefficiencies are reliable indicators of delays, demonstrating the study’s internal consistency shown in Table 1. Additionally, there was a significant level of agreement compared to secondary data from government publications, especially regarding the connection between delays and rising material prices. The findings’ accuracy is confirmed by this outside validation, which also gives assurance that the study’s conclusions are consistent with actual construction situations in Ethiopia. Additionally, there was a significant level of agreement compared to secondary data from government publications, especially regarding the connection between delays and rising material prices. The findings’ accuracy is confirmed by this outside validation, which also gives assurance that the study’s conclusions are consistent with actual construction situations in Ethiopia. Agreement analysis enhances the dependability of the study’s findings by ensuring that the delay factors found in the study are legitimate and consistent across many evaluators and data sources.

By ensuring that the delay factors found in the study are legitimate and consistent across many evaluators and data sources, agreement analysis enhances the overall dependability of the study’s conclusions.

Conclusions

This study examined the factors causing delays in the Woliso–Ambo Road construction project using regression analysis. Based on the findings, the following conclusions can be drawn:

  • The impact of materials and equipment was a major cause of project delays, ranking third among all factors. Problems like lack of construction materials, late delivery of materials, poor quality materials, not enough equipment, and limited space at the site for temporary and permanent equipment all contributed to the delays.

  • The rising cost of materials is the top factor causing delays in the Woliso Ambo road construction project. This happens because price indices may not accurately reflect market conditions, competition, government and legal rules, and the true rise in material costs. Also, clients resisting price increase agreements add to the problem.

  • Delays due to subcontractors work and poor management by consultants are the second and fourth biggest issues affecting the project. Problems with subcontractors include poor planning and scheduling, slow mobilization, slow document preparation, lack of experience, and poor communication with the environment. For consultants, issues include poor site management and supervision, lack of decision-making experience, poor risk analysis and management, communication barriers, and inadequate handling of project progress.

  • The unstable peace and security situation of the site area and the country are causing project delays. Factors include site security restrictions, changes in government regulations and laws, delays in moving utilities, and limited access for site workers.

  • To sum up, this study offers insightful information that helps practitioners and policymakers in the construction sector. While practitioners implement more efficient project management techniques, enhance risk management, and develop local capability, policymakers can address material cost volatility, strengthen regulatory frameworks, and improve planning and coordination. The Ethiopian road construction sector can decrease delays, increase project efficiency, and improve the general standard of infrastructure development by taking care of these issues.

Limitations and recommendations

This research is limited to the Woliso–Ambo Road project area and only includes consultants and contractors from this area. It does not consider the effects of delays in other projects. The current study looks at how factors not included affect road projects. Future research should focus on the causes of delays in the Woliso–Ambo Road project. Researchers should also look at national road construction projects to find common causes of delays. Future studies should identify the factors that lead to cost overruns in asphalt road construction projects.