This study addressed the complexity involved in integrating the causative risk factors influencing construction profitability. Most of the existing studies cover the individual effects of profitability influencing factors. Very few focus on the systematic impact without incorporating the complexity and associated dynamics, presenting a gap targeted by the current study. The current study aimed to assess causative interrelations and interdependencies between profitability influencing risk factors (PIRF), through systems thinking (ST) and system dynamics (SD) modeling. The SD approach was used to evaluate the integrated impacts on profitability-influencing risk categories (PIRC) in construction projects. The causative influencing factors affecting construction profitability were identified through a comprehensive literature review. These were ranked using content analysis, and categorized into significant issues. Through 250 structured surveys and 15 expert opinion meetings, the path for quantitative and qualitative evaluations was prepared. Following these investigations, a causal loop diagram (CLD) was established using the ST technique, and the integrated effect was quantified using SD modeling. The study finds the rising cost of material, supply chain process, payment issues, planning and scheduling problems, financial difficulties, and effective control of manpower and equipment resources as the most critical PIRFs. The integrated effects of PIRFs on PIRC were quantified using SD modeling. This study helps field professionals with profitability-influencing factors, diagnosing issues, and integrating impacts regarding decision-making and policy formulation. For researchers, it presents a list of factors that can be investigated in detail, and the holistic interrelationships established.

Effective leadership and creative performance are the predominant factors for the success of modern projects in the global construction industry. However, rigorous research has not explored the nexus between such factors and the leader–member exchange (LMX). To address this gap, this study explores the relationship between dimensions of paternalistic leadership and employee creativity achieved through LMX in the context of the construction industry. Based on social exchange theory (SET), six relevant hypotheses were proposed in this study. The data were collected through a structured questionnaire. An online survey form was used for data collection, through which 288 responses were collected from the construction industry employees working in Pakistan. The collected data were analyzed using Smart PLS in two stages, i.e., measurement model evaluation (reliability analysis, convergent and discriminant validity) and structural model evaluation (R², F², and path coefficient). The findings of the current study reveal a positive association of authoritarian, benevolent, and moral leadership with employee creativity. In addition, LMX significantly mediates the relationship between the two dimensions of paternalistic leadership (benevolent and moral leadership) and creativity, except for authoritarian leadership. Based on the results, this study contributes to the body of knowledge related to the appropriate leadership style in the local construction industry that can be extended to other developing countries with similar dynamics. It also helps the managers target and develops relevant skills to acquire positive outcomes from their team members.

Cash is considered the most critical resource in construction projects. However, many contractors fail to obtain adequate liquidity due to a lack of proper cash flow management. Therefore, numerous research studies have been conducted to address cash flow-related issues in the construction industry. However, the literature still lacks a comprehensive review of cash flow management, methods and topics, in the construction industry. This study contributes by providing a holistic, up-to-date, and thorough review of 172 journal articles on construction cash flow. To achieve this primary objective, the study applies a mixed review methodology using scientometric and systematic reviews. The scientometric analysis provides the most contributing scholars, the timeline of cash flow research attention, and keywords clustering. On the other hand, the systematic analysis categorizes the cash flow themes, identifies current literature gaps, and highlights future research areas in the cash flow domain. The results show that cash flow analysis gained more research attention in the last two decades, cash flow-based schedule is the most frequent topic in the literature, and optimization techniques are predominant in the literature. Consequently, the study highlights five potential research frontiers. Further, an automated payment framework for modular construction projects using Blockchain-based smart contracts is developed to address some of the literature limitations. This study provides a guideline for future research efforts and raises researchers’ awareness of the latest trends and methods of construction cash flow analysis.

Construction processes are complex and dynamic. Like its other components, the construction supply chain (CSC) involves multiple stakeholders requiring varying levels of information sharing. In addition, the intensity and diversity of information in CSCs require dexterous management. Studies reveal that information complexity can be reduced using collaborative technologies (CTs). However, the barriers to information management (IM) hinder the CTs’ adoption process and cause complexity in CSCs. This research identifies barriers to IM and factors affecting the adoption of CTs in developing countries. In order to understand and address complexity, the system dynamics (SD) approach is adopted in this study. The aim is to investigate if SD can reduce information complexity using CTs. Causal loop diagrams (CLDs) were developed to understand the relationship between the IM barriers and CT adoption factors. The SD model, when simulated, highlighted three main components, i.e., complexity, top management support, and trust and cooperation, among others, as factors affecting the adoption of CTs. Addressing these factors will reduce information complexity and result in better IM in construction projects.

Resistance value (R-value) is one of the basic subgrade stiffness characterizations that express a material’s resistance to deformation. In this paper, artificial intelligence (AI)-based models—especially M5P, support vector machine (SVM), and Gaussian process regression (GPR) algorithms—are built for R-value evaluation that meets the high precision and rapidity requirements in highway engineering. The dataset of this study comprises seven parameters: hydrated lime-activated rice husk ash, liquid limit, plastic limit, plasticity index, optimum moisture content, and maximum dry density. The available data are divided into three parts: training set (70%), test set (15%), and validation set (15%). The output (i.e., R-value) of the developed models is evaluated using the performance measures coefficient of determination (R²), mean absolute error (MAE), relative squared error (RSE), root mean square error (RMSE), relative root mean square error (RRMSE), performance indicator (ρ), and visual framework (Taylor diagram). GPR is concluded to be the best performing model (R², MAE, RSE, RMSE, RRMSE, and ρ equal to 0.9996, 0.0258, 0.0032, 0.0012, 0.0012, and 0.0006, respectively, in the validation phase), very closely followed by SVM, and M5P. The application used for the aforementioned approaches for predicting the R-value is also compared with the recently developed artificial neural network model in the literature. The analysis of performance measures for the R-value dataset demonstrates that all the AI-based models achieved comparatively better and reliable results and thus should be encouraged in further research. Sensitivity analysis suggests that all the input parameters have a significant influence on the output, with maximum dry density being the highest.

Public construction projects in Kingdom of Saudi Arabia (KSA), particularly in the Makkah region, are important for the infrastructure and economic growth of the country. However, these projects often face challenges from change orders (COs), which lead to notable cost and schedule overruns. This study examines 32 public construction projects completed between 2010 and 2019 to analyze the impacts of COs on project performance. Results indicated that COs are associated with significant schedule overruns, ranging from 22% to 200%, and cost increases between 2.67% and 42.86%. Approximately 80.65% of the projects experienced cost overruns, with only one project adhering to its original budget. The ANOVA results revealed that differences in cost and schedule overruns based on project size, type, and managing department are not statistically significant. This highlighted that challenges posed by COs are consistent across different project categories and departments, showing systemic inefficiencies rather than project-specific issues. Variability in overruns within certain groups, such as departments, emphasizes the need for further exploration into localized practices and conditions. Finally, the study provides actionable recommendations to mitigate the adverse effects of COs, including standardized protocols for CO management, early stakeholder engagement during planning phases, adoption of advanced monitoring tools such as BIM, and the inclusion of contingency funds aligned with project complexity.