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The health of an economy is heavily dependent on the productivity of the economy's major industries including construction. While most macro-measures of productivity in the USA construction industry indicate a decline, corresponding studies at the individual task level indicate an increase in productivity. Therefore, this paper aims to identify areas where productivity challenges exist and thus provide recommendations for improvement in the construction industry.

A model that relates the way construction projects are executed with the sources of data that inform productivity analyses is developed and presented. This effort/value-flow model informs the data analysis that is performed to determine productivity trends for management and field labor. Further analysis for field labor productivity using field data and management productivity was separately conducted. Management productivity was particularly difficult to gauge, resulting in the use of surrogate measures.

It was observed that while both field labor and management productivities at the industry level have been decreasing, the decrease in management productivity was five times that of field labor productivity. A similar trend was observed for management productivity at the project level.

The primary contribution of this paper to the body of knowledge and industry is the introduction of a holistic analysis of USA construction productivity. Recommendations to improve management productivity include the use of technology, especially project management software.

Sources highlight that lack of systematic labour training components results in low performance and productivity of labour, which leads the construction industry of many countries to face various challenges. This study aims to quantify the variations in the performance and productivity levels of labour in building construction projects through the applications of effective work-based training components.

A comprehensive literature review and a series of experts’ discussions with action-oriented communication approaches were conducted to develop a set of practices related to labour training, performance assessment and productivity measurements within a framework. The developed practices were applied to around 100 labourers working on nine building construction projects through a construction supervisory training programme.

The study presents the detailed patterns of the significant changes in labour performance and productivity levels. The majority of trained labourers have grown to perform the work process with some relevant theoretical and operational knowledge and skills. The overall results spotlight the significant behavioural changes that can be observed in workforce operations by improving labour performance, which resulted in implementing effective labour-rewarding practices within a framework.

Although the study findings were limited to the Sri Lankan context, the proposed practices can be applied to the industry practices of the construction sector of other developing countries and the other developing industries in similar ways/scenarios.

The study outcomes contribute to uplifting the work qualities of labourers with life-long learning opportunities and unlocking the potential barriers for expanding the local labour supply while controlling the excessive inclination of the local firms towards foreign labour. This paper describes further implications and future scopes of the study elaborately.

The study provides generalised mechanisms and practices that transform the labour characteristics and add new attributes for strengthening the values of construction supervision practices to obtain well-improved work outputs. The study outcomes reinforce the chain relationships among the training elements, labour performance and productivity levels, leading to upgrading current planning and operational management practices, especially adding constructive mechanisms in resource levelling and productivity benchmarking practices.

Proper prediction of productivity can enable the enhanced estimation, realistic scheduling, and accurate cost forecasting of construction processes. Due to the existence of different labor sources (unionized and non-unionized), the prediction of productivity is still a significant problem in India. Moreover, the construction procurement processes and on-site performance are the predominant elements that can result in improved project outcomes. Thereby, the consideration of labor constraints and site conditions will play an important role in productivity improvement.

This study investigates the factors affecting construction site productivity. A total of 28 factors are grouped under 7 categories as follows: labor constraints, safety and quality procurements, material and equipment (ME), site management, project working condition, delay controls, construction methods and techniques, and external factors. Furthermore, by involving these factors, the questionnaire survey was conducted among Indian construction practitioners. As a result, 204 responses were received and the data were analyzed using a reliability test, relative importance index (RII), and analysis of variance (ANOVA).

The result of this study highlighted the importance of strategic construction management activities in terms of effective planning of ME, planning and realistic scheduling of construction activities, proper communication, information sharing, etc. Thus, this study provides a clear insight to the Indian construction practitioners in determining the effect of these site factors on the successful execution of their projects.

In this paper, the problem of construction productivity in India and its causes are explained effectively. This study examines the preference of labor contract, labor source, and most importantly, the factors affecting site productivity. Moreover, the other lagging issues regarding the management of construction activities are also described in detail.

The study is aimed to compare the prediction success of basic machine learning and ensemble machine learning models and accordingly create novel prediction models by combining machine learning models to increase the prediction success in construction labor productivity prediction models.

Categorical and numerical data used in prediction models in many studies in the literature for the prediction of construction labor productivity were made ready for analysis by preprocessing. The Python programming language was used to develop machine learning models. As a result of many variation trials, the models were combined and the proposed novel voting and stacking meta-ensemble machine learning models were constituted. Finally, the models were compared to Target and Taylor diagram.

Meta-ensemble models have been developed for labor productivity prediction by combining machine learning models. Voting ensemble by combining et, gbm, xgboost, lightgbm, catboost and mlp models and stacking ensemble by combining et, gbm, xgboost, catboost and mlp models were created and finally the Et model as meta-learner was selected. Considering the prediction success, it has been determined that the voting and stacking meta-ensemble algorithms have higher prediction success than other machine learning algorithms. Model evaluation metrics, namely MAE, MSE, RMSE and R², were selected to measure the prediction success. For the voting meta-ensemble algorithm, the values of the model evaluation metrics MAE, MSE, RMSE and R² are 0.0499, 0.0045, 0.0671 and 0.7886, respectively. For the stacking meta-ensemble algorithm, the values of the model evaluation metrics MAE, MSE, RMSE and R² are 0.0469, 0.0043, 0.0658 and 0.7967, respectively.

The study shows the comparison between machine learning algorithms and created novel meta-ensemble machine learning algorithms to predict the labor productivity of construction formwork activity. The practitioners and project planners can use this model as reliable and accurate tool for predicting the labor productivity of construction formwork activity prior to construction planning.

The study provides insight into the application of ensemble machine learning algorithms in predicting construction labor productivity. Additionally, novel meta-ensemble algorithms have been used and proposed. Therefore, it is hoped that predicting the labor productivity of construction formwork activity with high accuracy will make a great contribution to construction project management.

The aim of this study is to measure the impact of the factors affecting construction labor productivity by focusing on different types of construction works during and after the COVID-19 pandemic in Turkey, as well as discuss solutions and immediate actions.

This research was conducted in two steps. First, a quantitative survey was carried out to determine the dimension of factors negatively affecting construction labor productivity and the loss rate of different construction works from the employee perspective. The factors were identified through a literature review. The crucial relationships were highlighted as a result of a statistical analysis. Second, a survey was performed to determine the loss rate through a comparison of man-hour values before and after the beginning of the pandemic from the employer perspective. After an analysis and comparison of the results, semi-structured interviews were performed to discuss all findings and discover ways to mitigate the impacts of COVID-19 on construction labor productivity.

The results of the study clearly show that construction labor productivity was deeply affected by the coronavirus disease (COVID-19) pandemic. Legal obligations, such as social distancing, wearing masks, and limitations on the number of workers, have been major drivers for lower labor productivity. Such obligations have a profound impact on interior construction works, especially based on teamwork. Concerning employer and labor-related factors, problems with getting payments on time, loss of income, and financial hardships are the leading factors resulting in decreased worker performance. Excavation, insulation, and plastering works were determined as the most affected construction works under the influence of the COVID-19 pandemic.

The quantitative portion of this study is limited to a sample of respondents in the Turkish construction industry. Further research is necessary to provide an in-depth review into construction labor productivity in other countries with a larger respondent sample. Another limitation is sourced by the dynamics of the COVID-19 pandemic, which may turn out that some findings are outdated. Despite these limitations, the insights from this study may enable employers to understand the major drivers and deep impacts of labor productivity loss by uncovering the main vulnerabilities during the pandemic. Recommended measures may also help policy-makers and stakeholders in the construction industry take necessary and immediate actions to ensure better construction labor productivity.

The study may contribute to a better understanding of a pandemic's impact on labor productivity by focusing on both employee and employer perspectives, especially in developing countries. The paper may help employers decide which priority measures are required for each construction work separately. The study is crucial not only for minimizing the negative effects of the COVID-19 outbreak on labor productivity but also for preparing for the post-pandemic era.

This paper aims to model different causal relations among factors interacting with labour productivity in order to recognize the most important factors influencing and influenced by it.

Top 60 factors affecting labour productivity were determined and grouped into 5 major groups by reviewing previous research and interviewing relevant experts. The interactions of factors were modelled using system dynamics (SD) approach. The resulting causal loop diagrams obtained from SD were then applied for identifying the most crucial factors influencing and influenced by labour productivity through the decision-making trial and evaluation laboratory (DEMATEL) method. The impact of factors on each other was finally determined based on the opinions of 63 experts selected from the Iranian construction industry.

The results indicated that factors such as fatigue, lack of labour motivation and lack of skill are the most influencing, and factors such as schedule delay and inflation in the cost of execution are the most influenced by labour productivity. In the end, a set of recommendations to improve construction labour productivity was also presented.

The main contribution of the study is proposing a novel method which is capable of providing insights into how causes and effects of construction labour productivity are interrelated. Furthermore, the proposed method makes this study distinct from previous research in the light of prioritizing factors and offering recommendations according to the interrelationships among factors.

As the construction industry undertakes complex and innovative projects, improving the labor productivity that helps accomplish the triple bottom line dimensions (time, schedule and performance) assumes greater importance. It is of even greater importance in developing countries like India where most of building construction takes place on a manual basis. In response, the purpose of this paper is to develop an evaluation framework for assessing the labor productivity in the construction industry. The application of the framework is demonstrated for the case of the Indian construction industry and key insights are reported.

The literature review and expert survey has helped to identify 4 main dimensions and 14 different factors affecting labor productivity in the construction industry. An application of Decision-Making Trial and Evaluation Laboratory was deployed to investigate into the causality of labor productivity dimensions.

The research delivers a quantitative labor productivity assessment framework for evaluating causal relationships among the factors influencing labor productivity in the construction industry. The result obtained for a typical case of the Indian construction industry showed that the safety at construction site has a profound effect on labor-related factors.

The paper makes two contributions in the domain of labor productivity assessment in the construction industry. First, it proposes an evaluation framework to investigate into the causal relationships among the labor productivity factors. This proposed framework is robust and can be used to compare the performance of different construction projects using an integrated approach proposed by this research. Such exploratory analysis would assist benchmarking studies. Second, it undertakes an empirical investigation for the Indian construction industry to develop key managerial insights and extend policy-related recommendations.

The purpose of this paper is to develop a motivation framework that will enhance labour productivity for Iskandar Malaysia (IM) construction projects. The vision of IM development corridor is to become Southern Peninsular Malaysia’s most developed region by the year 2025. IM cannot realise this foresight without effective labour productivity. Previous studies have reported that the labour productivity of IM construction projects was six times lower than the labour productivity of Singapore construction projects, due to lack of motivation among IM labourers, and a shortage of local skilled labour. Therefore, there is a need to study how to motivate IM construction labourers, so as to increase their productivity.

A quantitative research method was used to collect data from IM construction skilled labourers and construction professionals, using two sets of questionnaire. The respondents were selected using a purposive sampling technique. In total, 40 skilled labourers and 50 construction professionals responded to the questionnaire survey, and the data were analysed using Statistical Package for Social Science software (version 22).

The analysis revealed the major factors that motivate labourers participating in IM construction projects. The factors were ranked hierarchically using Relative Importance Index (RII) and the outcome of the ranking indicated that effective management, viable construction practices, financial incentives, continuous training and development, and safe working environment were the most significant motivation strategies that positively influence IM construction labourers.

The study developed and validated a framework that can be used to boost the morale of IM construction labourers, so that their productivity can be increased. Implementation of the established motivation framework will also lead to career progression of IM construction labourers, based on the training elements in the framework. This career prospect will attract local skilled labourers to participate in IM construction projects.

Building upon the results of a critical review of previous studies, the purpose of this paper is to present a framework to enhance the reliability of the theoretical model for international benchmarking of labour productivity (TMIBLP) method for construction project activities. The next part of the paper presents the results of implementing the proposed framework for construction activities in the Iranian construction industry.

This paper draws upon a critical review of the literature to highlight the drawbacks of the previous studies concerning implementing TMIBLP within the construction industry. Identifying these shortcomings, the authors propose a framework for construction projects, which presents the procedure for deploying TMIBLP for construction activities in a reliable manner. The final section of the paper demonstrates the empirical implementation of the proposed framework within the Iranian construction industry.

The primary contributions of the paper include: identifying the drawbacks of previous studies within the construction context, the framework that would lead construction managers towards more reliable implementation of benchmarking for construction projects, and determining the baseline of erecting steel structures in a developing country.

A practical implementation of the TMIBLP method was presented to address the lack of research in Iran and to show the feasibility of using the framework developed. The authors investigated the daily labour productivity for the activity of structural steel erection for six projects in Tehran in order to determine the values of the baseline productivity for each project. Accordingly, the disruption index, performance ratio and project management index as the project benchmarks were calculated for all six projects.

Underlining the necessity of implementing methods such as TMIBLP, this study outlines the outcomes of the first study on the benchmarking of construction activities deploying the proposed framework and using the data on erecting steel structures from six construction projects in Iran. The discussions provide guidelines for construction project managers regarding benchmarking labour productivity. The paper concludes with suggestions for future research opportunities.

Understanding and simulating construction activities is a vital issue from a macro-perspective, since construction is an important contributor in economic development. Although the construction labor productivity frontier has attracted much research effort, the temporal and regional characteristics have not yet been explored. The purpose of this paper is to investigate the long-run equilibrium and dynamics within construction development under a conditional frontier context.

Analogous to the simplified production function, this research adopts the conditional frontier theory to investigate the convergence of construction labor productivity across regions and over time. Error correction models are implemented to identify the long-run equilibrium and dynamics of construction labor productivity against three types of convergence hypotheses, while a panel regression method is used to capture the regional heterogeneity. The developed models are applied to investigate and simulate the construction labor productivity in the Australian states and territories.

The results suggest that construction labor productivity in Australia should converge to stable frontiers in a long-run perspective. The dynamics of the productivity are mainly caused by the technology utilization efficiency levels of the local construction industry, while the influences of changes in technology level and capital depending appear limited. Five regional clusters of the Australian construction labor productivity are suggested by the simulation results, including New South Wales; Australian Capital Territory; Northern Territory, Queensland, and Western Australia; South Australia; and Tasmania and Victoria.

Three types of frontier of construction labor productivity is proposed. An econometric approach is developed to identify the convergence frontier of construction labor productivity across regions over time. The specified model can provides accurate predictions of the construction labor productivity.