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Labours are one of the key resources of construction projects having a significant impact on economic development. With time, the construction industry is getting a boom due to numerous construction projects in which labours’ contribution is significant. Project betterment is associated with the project productivity which relates to the labours. Wages play a vital role in retaining labours in the construction industry.

This study focuses on investigating the increasing and decreasing behaviour of labour wages in the construction industry and observing the impact of the inflation rate in deviating labour wages. Initially, the percentage deviation was calculated to observe the changing behaviour of the data and then the Spearman correlation test was used to find the relationship between the inflation rate and the labour wages.

It is evident that even with a good economy and a stable construction sector, still, the labour wages faced a decline over time, which is a matter of concern. Also, based on the correlation coefficient, it was revealed that several labour wages categories are moderately and strongly correlated with the inflation rate and can impact project cost if the inflation rate is not considered while finalizing the budget. Besides that, the changing behaviour of labour wages due to the inflation rate impacts significantly on gross domestic product (GDP); therefore, it requires vital attention.

Changes in labour wages are not considered in budget estimation which drag the project towards cost overrun. This study brings the attention of the stakeholders on the issue with experimental justification. Also, how the inflation rate is affecting the labour wages has also been addressed.

The present study adopted a system thinking methodology to investigate the influence of top management commitment (TMC) on sustainable risk management (SRM) influenced by stakeholder pressures.

Three variables and 23 measurement items were derived from existing literature sources. Moreover, the methodology employed for data collection in this study involved a questionnaire. The constructs previously documented in the literature were utilized as the foundation for developing the questionnaire. Of 220 sent questionnaires, 106 valid responses were used for further analysis. To enhance objectivity, a causal loop diagram and a stock and flow diagram were created using a system thinking approach. These visual representations aim to illustrate the impact of top management factors on SRM under stakeholder pressure-related factors.

The results of the descriptive test indicate that the safety and quality issues emerged as the most significant, with the highest mean impact of 4.08 on sustainable risk impact assessments. Employee influence on customer relationships also showed a high mean impact of 4.08, while understanding customer requirements closely followed with a mean of 3.98. Additionally, causal loop analysis uncovered complex interconnections among these factors, emphasizing the intricate nature of SRM. Further, a five-year simulation analysis highlighted that SRM effectiveness is significantly enhanced when top management fosters a robust organizational culture. These findings emphasize the critical roles of safety, quality and customer-focused factors in sustainability, underscoring the importance of leadership in fostering effective, holistic sustainability and risk management strategies.

This research highlights top management’s pivotal role in effective SRM by integrating sustainability into core strategies. It identifies critical factors and emphasizes the solid organizational culture fostered by senior management, which is essential for long-term SRM effectiveness. The study’s focus on safety, quality and customer relations underscores the need for comprehensive sustainability strategies.

Rebar is the prime component of reinforced concrete structures, and rebar monitoring is a time-consuming and technical job. With the emergence of the fourth industrial revolution, the construction industry practices have evolved toward digitalization. Still, hesitation remains among stakeholders toward the adoption of advanced technologies and one of the significant reasons is the unavailability of knowledge frameworks and implementation guidelines. This study aims to investigate technical factors impacting automated monitoring of rebar for the understanding, confidence gain and effective implementation by construction industry stakeholders.

A structured study pipeline has been adopted, which includes a systematic literature collection, semistructured interviews, pilot survey, questionnaire survey and statistical analyses via merging two techniques, i.e. structural equation modeling and relative importance index.

The achieved model highlights “digital images” and “scanning” as two main categories being adopted for automated rebar monitoring. Moreover, “external influence”, “data-capturing”, “image quality”, and “environment” have been identified as the main factors under “digital images”. On the other hand, “object distance”, “rebar shape”, “occlusion” and “rebar spacing” have been highlighted as the main contributing factors under “scanning”.

The study provides a base guideline for the construction industry stakeholders to gain confidence in automated monitoring of rebar via vision-based technologies and effective implementation of the progress-monitoring processes. This study, via structured data collection, performed qualitative and quantitative analyses to investigate technical factors for effective rebar monitoring via vision-based technologies in the form of a mathematical model.

The unique nature of the construction sector makes it fall behind other sectors in terms of productivity. Monitoring construction productivity is crucial for the construction project's success. Current practices for construction productivity monitoring are time-consuming, manned and error prone. Although previous studies have been implemented toward reducing these limitations, a gap still exists in the automated monitoring of construction productivity.

This study aims to investigate and assess the different techniques used for monitoring productivity in building construction projects. Therefore, a mixed review methodology (bibliometric analysis and systematic review) was adopted. All the related publications were collected from different databases, which were further screened to get the most relevant based on the Preferred Reporting Items for Systematic Review and Meta-Analyses (PRISMA) criteria.

A detailed review was performed, and it was found that traditional methods, computer vision-based and photogrammetry are the most adopted data acquisition for productivity monitoring of building projects, respectively. Machine learning algorithms (ANN, SVM) and BIM were integrated with monitoring tools and technologies to enhance the automated monitoring performance in construction productivity. Also, it was observed that current studies did not cover all the complex construction job sites and they were applied based on a small sample of construction workers and machines separately.

This review paper contributes to the literature on construction management by providing insight into different productivity monitoring techniques.

Amid rapid technological progress, the construction industry is embracing Construction 4.0, redefining work practices through emerging technologies. However, the implications of Construction 4.0 technologies to enhancing well-being are still poorly understood. Particularly, the challenge lies in selecting technologies that critically contribute to well-being enhancement. Therefore, this study aims to evaluate the implications of Construction 4.0 technologies to enhancing well-being.

A list of Construction 4.0 technologies was identified from a national strategic plan on Construction 4.0, using Malaysia as a case study. Fourteen construction industry experts were selected to evaluate the implications of Construction 4.0 technologies on well-being using fuzzy Technique for Order Preference by Similarity to Ideal Solution (TOPSIS). The expert judgment was measured using linguistic variables that were transformed into fuzzy values. Then, the collected data was analyzed using the following analyses: fuzzy TOPSIS, Pareto, normalization, sensitivity, ranking performance and correlation.

Six Construction 4.0 technologies are critical to enhancing well-being: cloud & real-time collaboration, big data & predictive analytics, Internet of Things, building information modeling, autonomous construction and augmented reality & virtualization. In addition, artificial intelligence and advanced building materials are recommended to be implemented simultaneously as a very strong correlation exists between them.

The novelty of this study lies in a comprehensive understanding of the implications of Construction 4.0 technologies to enhancing well-being. The findings can assist researchers, industry practitioners and policymakers in making well-informed decisions to select Construction 4.0 technologies when targeting the enhancement of the overall well-being of the local construction industry.

The purpose of this study is to investigate the impact of Corona Virus Disease 2019 (COVID-19) on the outcome of construction projects and explore the moderating effects of emerging technologies on the relationship between COVID-19 and construction project outcomes.

Data for the study was collected through a Web-based, semistructured questionnaire. The responses of 62 construction practitioners were analyzed using a hierarchical linear regression model. The model consists of 16 independent variables, three control variables (organization size, organization type and project size), one moderator (adoption level of emerging technologies) and three dependent variables (project time, project cost and project quality).

The study confirms the negative significant impact of the COVID-19 pandemic on the performance of construction projects. It also identifies the significant moderating effects of emerging technologies in mitigating the impact of COVID-19 on construction projects. Further, it shows a significant increase in the application of emerging technologies in construction projects during the COVID-19 pandemic. Based on the findings related to the moderating impact of the technology, this study provides a clear set of recommendations for construction firms, public sector and research community in combating the unavoidable situation similar to the COVID-19 pandemic in the future.

To the best of the authors’ knowledge, this is the first study to identify the moderating role of technology on the impact of COVID-19 on the performance of the construction sector in Pakistan. The findings can also be used for the construction sectors of other developing countries.

The cash flow from government agencies to contractors, called progress payment, is a critical step in public projects. The delays in progress payments significantly affect the project performance of contractors and lead to conflicts between two parties in the Turkish construction industry. Although some previous studies focused on the issues in internal cash flows (e.g. inflows and outflows) of construction companies, the context of cash flows from public agencies to contractors in public projects is still unclear. Therefore, the primary objective of this study is to develop and test diverse machine learning-based predictive models on the progress payment performance of Turkish public agencies and improve the predictive performance of these models with two different optimization algorithms (e.g. first-order and second-order). In addition, this study explored the attributes that make the most significant contribution to predicting the payment performance of Turkish public agencies.

In total, project information of 2,319 building projects tendered by the Turkish public agencies was collected. Six different machine learning algorithms were developed and two different optimization methods were applied to achieve the best machine learning (ML) model for Turkish public agencies' cash flow performance in this study. The current research tested the effectiveness of each optimization algorithm for each ML model developed. In addition, the effect size achieved in the ML models was evaluated and ranked for each attribute, so that it is possible to observe which attributes make significant contributions to predicting the cash flow performance of Turkish public agencies.

The results show that the attributes “inflation rate” (F5; 11.2%), “consumer price index” (F6; 10.55%) and “total project duration” (T1; 10.9%) are the most significant factors affecting the progress payment performance of government agencies. While decision tree (DT) shows the best performance among ML models before optimization process, the prediction performance of models support vector machine (SVM) and genetic algorithm (GA) has been significantly improved by Broyden–Fletcher–Goldfarb–Shanno (BFGS)-based Quasi-Newton optimization algorithm by 14.3% and 18.65%, respectively, based on accuracy, AUROC (Area Under the Receiver Operating Characteristics) and F1 values.

The most effective ML model can be used and integrated into proactive systems in real Turkish public construction projects, which provides management of cash flow issues from public agencies to contractors and reduces conflicts between two parties.

The development and comparison of various predictive ML models on the progress payment performance of Turkish public owners in construction projects will be the first empirical attempt in the body of knowledge. This study has been carried out by using a high number of project information with diverse 27 attributes, which distinguishes this study in the body of knowledge. For the optimization process, a new hyper parameter tuning strategy, the Bayesian technique, was adopted for two different optimization methods. Thus, it is available to find the best predictive model to be integrated into real proactive systems in forecasting the cash flow performance of Turkish public agencies in public works projects. This study will also make novel contributions to the body of knowledge in understanding the key parameters that have a negative impact on the payment progress of public agencies.

The research analyzes the influence of three environmental triggers, i.e. awareness, concern and knowledge on environmental attachment and green motivation that affect tourists' pro-environmental behavior in the Pakistan’s tourism industry. Furthermore, this study has analyzed the moderating role of moral obligation concerning environmental attachment and green motivation on tourists' pro-environmental behavior.

Data were gathered via a structured questionnaire by 237 local (domestic) tourists of Pakistan. Furthermore, the data were examined by employing SmartPLS.

Findings demonstrate that all three environmental triggers have a positive and significant relationship with environmental attachment and green motivation. Accordingly, environmental attachment and green motivation promote tourists' pro-environmental behavior. Furthermore, the moderating role of moral obligations has also been incorporated in the study. The finding reveals a strong and positive relationship among environmental attachment and tourists' pro-environmental behaviors during high moral obligations. In contrast, moral obligations do not moderate association between green motivation and tourists' pro-environmental behavior. Therefore, competent authorities should facilitate tourists to adopt environmentally friendly practices; which will ultimately promote pro-environmental behavior.

This study provides useful insights regarding the role of tourism in fostering environmental attachment and green motivation that sequentially influence tourist pro-environmental behavior. Secondly, this research has employed moral obligations as a moderator to identify the changes in tourists’ pro-environmental behavior based on individuals' ethical considerations. Hence, the study provides an in-depth insight into tourists' behavior. Lastly, the present research offers effective strategies for the tourism sector and other competent authorities to increase green activities that can embed the importance of the environment among individuals.

The engineering courses offered in Somali universities attract many students, ranging between 300 and 500 every semester, making the management and delivery of the course challenging. The increasing popularity of massive open online courses (MOOCs) has led to rapid growth in enrollment, posing difficulties in effectively managing and delivering content to large volumes of learners. To this end, this study aimed to explore the influence of MOOC implementation factors on learners’ continuance intention and satisfaction to provide insights that can enhance the learning experience and ensure long-term engagement.

The study utilized a survey approach based on an extensive literature review to collect data on the challenges faced by Somali universities in managing and delivering engineering courses. The survey included a series of questions, and 148 responses were collected from students enrolled in different programs. The collected data were analyzed using partial least squares-structural equation modeling and deep neural network approaches.

The result demonstrated that MOOC implementation factors, including course design quality, instructor reputation, self-paced flexibility, information relevance, platform usability and student support services, significantly affect students’ continuance intention and satisfaction. Therefore, the study recommends universities should enhance MOOC implementation factors to improve the quality of teaching and increase students’ continuance intention to study in a MOOC environment.

The study provides empirical evidence on how MOOC implementation factors affect the level of satisfaction and continuance intention of engineering students. It suggests that the findings could be useful for university management and lecturers to increase teaching and learning quality in the course and develop new strategies and approaches that suit modern-day learners. The study also aims to enhance the efficiency and effectiveness of class delivery and improve student engagement in the learning process.