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This study aims to investigate two public private partnership (PPP) road projects in Nigeria for exploring factors that can motivate end-user stakeholders for contributing towards sustaining a PPP project in the long-term.

Using a case study methodology approach, this study adopts two-way data collection strategies via in-depth interviews with PPP experts and end-user stakeholders in Nigeria host communities and a questionnaire survey to relevant stakeholders.

The study identifies an eight-factor structure indicating critical success factors for ensuring end-user stakeholders support PPP projects on a long-term basis in their host communities.

Results of the study have huge implications for policymakers and project companies by encouraging the early integration of far-sighted measures that will promote long-term support and sustainability for PPP projects amongst the end-user stakeholders.

Amidst all solutions posited to address sustainable construction practices in Nigeria, the implementation plans are repudiated by sustainable barriers. This study examines and confirms the strategy with the most significant impacts on the identified barrier to sustainable construction practice (SCP).

The study deployed a questionnaire survey to evaluate the perspective of 100 construction actors on the barriers and strategies of sustainable construction practice in Nigeria. Factor Analysis was employed to categorize key barriers and strategies into their underlying clusters for further analysis. Partial least squares-structural equation modelling (PLS-SEM) was used to confirm the construct's significant relationship and magnitude, thereby establishing the strategies with the highest impacts on the barriers to sustainable construction practices.

The findings revealed three clusters of barriers and four groups of strategies to SCP, including technopolitic barrier, perception and awareness barrier and sociocultural barrier. For the significant strategies, education and training, stakeholder regulation, incentive support and government and legislative support strategies were established. Overall, education and training strategy was identified as the most dominant and effective strategy to mitigate the barriers of SCP in Nigeria.

The paper establishes education and training as the key strategy to achieving sustainable quest in the AEC industry. The practical implication is that policymakers, educators and professional bodies can harness sustainable knowledge transfer through education and training to improve sustainable construction practices in Nigeria.

Several factors influence OSC adoption, but extant literature did not articulate the dominant barriers or drivers influencing adoption. Therefore, this research has not only ventured into analyzing the core influencing factors but has also employed one of the best-known predictive means, Machine Learning, to identify the most influencing OSC adoption factors.

The research approach is deductive in nature, focusing on finding out the most critical factors through literature review and reinforcing — the factors through a 5- point Likert scale survey questionnaire. The responses received were tested for reliability before being run through Machine Learning algorithms to determine the most influencing OSC factors within the Nigerian Construction Industry (NCI).

The research outcome identifies seven (7) best-performing algorithms for predicting OSC adoption: Decision Tree, Random Forest, K-Nearest Neighbour, Extra-Trees, AdaBoost, Support Vector Machine and Artificial Neural Network. It also reported finance, awareness, use of Building Information Modeling (BIM) and belief in OSC as the main influencing factors.

Data were primarily collected among the NCI professionals/workers and the whole exercise was Nigeria region-based. The research outcome, however, provides a foundation for OSC adoption potential within Nigeria, Africa and beyond.

The research concluded that with detailed attention paid to the identified factors, OSC usage could find its footing in Nigeria and, consequently, Africa. The models can also serve as a template for other regions where OSC adoption is being considered.

The research establishes the most effective algorithms for the prediction of OSC adoption possibilities as well as critical influencing factors to successfully adopting OSC within the NCI as a means to surmount its housing shortage.

This paper aims to establish the most underlying factors causing construction projects delay from the most applicable.

The paper conducted survey of experts using systematic review of vast body of literature which revealed 23 common factors affecting construction delay. Consequently, this study carried out reliability analysis, ranking using the significance index measurement of delay parameters (SIDP), correlation analysis and factor analysis. From the result of factor analysis, this study grouped a specific underlying factor into three of the six applicable factors that correlated strongly with construction project delay.

The paper finds all factors from the reliability test to be consistent. It suggests project quality control, project schedule/program of work, contractors’ financial difficulties, political influence, site conditions and price fluctuation to be the six most applicable factors for construction project delay, which are in the top 25% according to the SIDP score and at the same time are strongly associated with construction project delay.

This paper is recommending that prospective research should use a qualitative and inductive approach to investigate whether any new, not previously identified, underlying factors that impact construction projects delay can be discovered as it followed an inductive research approach.

The paper includes implications for the policymakers in the construction industry in Nigeria to focus on measuring the key suppliers’ delivery performance as late delivery of materials by supplier can result in rescheduling of work activities and extra time or waiting time for construction workers as well as for the management team at site. Also, construction stakeholders in Nigeria are encouraged to leverage the amount of data produced from backlog of project schedules, as-built drawings and models, computer-aided designs (CAD), costs, invoices and employee details, among many others through the aid of state-of-the-art data driven technologies such as artificial intelligence or machine learning to make key business decisions that will help drive further profitability. Furthermore, this study suggests that these stakeholders use climatological data that can be obtained from weather observations to minimize impact of bad weather during construction.

This paper establishes the three underlying factors (late delivery of materials by supplier, poor decision-making and Inclement or bad weather) causing construction projects delay from the most applicable.

This paper evaluates the benefits of harnessing artificial intelligence (AI) tools for safety compliance on construction projects in Nigeria.

This study employed a specialised approach by combining qualitative and quantitative approach. The study carried out a brief systematic literature review (SLR) to identify the variables of the study. These variables were prepared in a questionnaire which was distributed among professionals within the Nigerian construction sector using purposive sampling. A total of 140 questionnaires were retrieved. The collected data were analysed using Relative Importance Index (RII), Ginni’s Mean (GM) and exploratory factor analysis (EFA).

The analysis revealed that all the identified benefits hold considerable importance, with an average RII of 0.86, with real-time monitoring as the most prominent advantage. However, using the GM which was 0.861, the study identified “mitigation of hazards on worksites” as the stationary benefit of AI in safety compliance.

The study was conducted exclusively within Nigeria’s Federal Capital Territory, using a cross-sectional survey approach.

The results will be valuable for professionals and practitioners in the Nigerian construction sector, as they will acquire insights into the potential advantages of utilising AI tools for monitoring of safety compliance on construction projects.

The study adopted a robust approach by identifying the stationary benefit using the GM in combination with RII and EFA.

Evaluating project success within the construction industry presents challenges due to the unique characteristics of the sector, the complexity of projects, and the involvement of diverse stakeholders. Conducting a bibliometric analysis, this paper aims to unravel the major research themes and methodologies utilised by researchers in studying the critical success criteria for construction projects, as well as extracting these success criteria.

The researchers systematically searched and screened 95 papers from Scopus and Web of Science (WoS) databases. This study conducted research focus parallelship network (RFPN) analysis and keywords co-occurrence network (KCON) analysis using BibExcel and Gephi to cluster the papers, illuminate the relationships among keywords within each cluster, and identify the primary research directions.

Using the RFPN analysis, this study classified the papers into three distinct clusters: infrastructure and public projects success, risk and knowledge management, and contractors and procurement management. Statistical techniques such as structural equation modelling (SEM) and multi-criteria decision-making methods such as analytic hierarchy process (AHP) have been used to analyse project success in the construction industry.

Considering the intensified demand for streamlined digital interactions and the increasing emphasis on sustainability and safety performance, construction companies are recommended to allocate greater investments toward the automation and digitisation of their products and processes. Prioritising modular construction and embracing transformative technologies alongside data science is crucial for enabling well-informed decision-making, and enhancing project success.

This study contributes to the existing body of knowledge by conducting a quantitative and systematic evaluation of the literature on project success criteria in the construction industry and uncovering key research areas. It addresses the pressing need to understand the complexities of construction projects amidst evolving industry dynamics and emerging disruptions. Moreover, by highlighting the implications of digital innovations and modular construction, this study urges deeper exploration into their impact on project performance and stakeholder satisfaction. This research sets a comprehensive framework for investigating the interplay between project complexity, technological advancements, and sustainable practices in the construction sector, paving the way for strategic advancements in the field.