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Small and medium-sized contractors are critical to micro and macroeconomic performance. These contractors in South Africa have long been confronted with the problem of business failure because of a plethora of factors, including poor productivity. The purpose of this study is to investigate salient issues undermining the productivity of small and medium-sized contractors in South Africa. This study proposes alternative possibilities to engender productivity improvement.

Qualitative data were collected using semi-structured interviews with 15 contractors in Gauteng Province, South Africa. The research data were analysed using content and causal layered analyses.

Challenges to contractors’ productivity were associated with inadequately skilled workers, management competence and political factors. Skills development, construction business and political factors were dominant stakeholders’ perceptions. Metaphors for construction labour productivity are presented and reconstructed as alternative directions for productivity improvement.

Contractors lose a substantial amount of South African Rand to poor productivity. Alternative directions provided in this study can be leveraged to increase profitability in construction organizations, enhance the social well-being of South Africans and ultimately improve the contribution of contractors to the South African economy.

The causal layered analysis (CLA) applied in this study is novel to construction labour productivity research. The four connected layers of CLA, which make a greater depth of inquiry possible, were explored to investigate labour productivity in construction organizations.

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.

The global construction industry is significant to economic development, whereas the sector, particularly its small and medium sized enterprises (SMEs) have continued to suffer from low labour productivity for decades. This has given rise to the concern of relevant construction stakeholders on the need to address the challenges undermining labour productivity growth in construction. Hence, this study aims to conduct a meta-data analysis of factors that hamper productivity growth of construction SMEs in developing countries.

A systematic review of existing studies relative to factors affecting construction labour productivity (CLP) is presented. Thereafter, eight developing countries-based studies that are specific to SMEs were selected for meta-data analysis using relative importance index values from the studies.

The essential productivity influencing factors were identified and quantitative data of the selected studies were synthesised. The effect summaries derived from the meta-data analysis revealed that the most significant factors that negatively affect CLP amongst SMEs include: workers’ skills, inadequate training, rework, management style and incentive to labour.

The study is limited to scientifically analysed secondary data relative to SME contractors in developing countries.

The findings of the study can be adopted by construction stakeholders to evolve productivity growth policies for construction SMEs in developing countries.

Synthesis of quantitative data of different studies has lent deeper insight into a more realistic and scientific precision of factors affecting labour productivity of construction SMEs.

Construction is repeatedly criticised for its low productivity based on statistical data that do not represent the output of construction adequately. The purpose of this paper is to improve the understanding of construction output – being the numerator in construction productivity calculations – by focussing on changes in quantity of the products, product characteristics and composition of the aggregate rather than as changes in price.

The research design of this study applies statistical data from the national accounts along with data from four paradigmatic case studies of social housing projects covering a period of 50 years.

The results indicate that while construction output prices have increased threefold over the past 50 years, improvements in performance can only explain approximately 20 per cent.

The developed four-step method has demonstrated its value as a means to measure changes in the characteristics of the product, but more studies on the actual figures and results over time and regions are required before solid conclusions can be drawn.

This study has added new knowledge of construction output that supports the development of a more accurate construction statistics, which in turn can assist the design of more effective and evidence-based policies for improving construction productivity.

This paper describes and demonstrates a novel performance-based methodology for addressing changes in the characteristics of the products in a longitudinally perspective, which can potentially provide a better understanding of changes in productivity.

The purpose of this paper is to identify and prioritize management practices that have the potential to improve labor productivity in multi-storey building construction projects.

The study adopted two-phase mixed-methods research design and 58 project managers, contract administrators and project coordinators were involved in the survey. During Phase I, qualitative data were collected from 19 experts using interviews and the management practices that could enhance labor productivity in multi-storey building construction projects were identified. In Phase II, quantitative data were collected from 39 contractors involved in the delivery of multi-storey building projects by using questionnaires. The data were analyzed to prioritize the practices identified in Phase I.

Well-defined scope of work, safety and health policy, safety and health plan, hazard analysis, long-lead materials identification, safe work method statement, and toolbox safety meetings are the top seven practices that have the potential to improve labor productivity in multi-storey building projects.

The research identifies the management practices that can be implemented to enhance labor productivity in multi-storey building construction projects in the context of Australia. Being the first study in the Australian context, the findings can be used as benchmark for international comparison.

Despite recognition of its importance to Singapore’s economy, the construction industry is plagued by poor safety and productivity performance. Improvement efforts by the government and industry have yielded little results. The purpose of this paper is to propose a framework for developing a productivity and safety monitoring system using Building Information Modelling (BIM).

The framework, Intelligent Productivity and Safety System (IPASS), takes advantage of mandatory requirements for building plans to be submitted for approval in Singapore in BIM format. IPASS is based on a study comprising interviews and a questionnaire-based survey. It uses BIM to integrate buildable design, prevention and control of hazards, and safety assessment.

The authors illustrate a development of IPASS capable of generating productivity and safety scores for construction projects by analysing BIM model information.

The paper demonstrates that BIM can be used to monitor productivity and safety as a project progresses, and help to enhance performance under the two parameters.

IPASS enables collaboration among project stakeholders as they can base their work on analysis of productivity and safety performance before projects start, and as they progress. It is suggested that the BIM model submitted to the authorities should be used for the IPASS application.

IPASS has rule-checking, hazards identification and quality checking capabilities. It is able to identify hazards and risks with the rule-checking capabilities. IPASS enables practitioners to check mistakes and the rationality of a design. It helps to mitigate risks as there are built-in safety measures/controls rules to overcome the problems caused by design deficiency, wrong-material-choice, and more.

The purpose of the research discussed in this paper is to ascertain the perception, from the project manager's viewpoint, of factors affecting construction productivity in the State of Queensland, Australia.

The research was conducted by a structured questionnaire that was sent to 89 randomly selected construction project managers in Queensland, Australia. This questionnaire requested background information about the respondents and then sought a score, using a 0-4 Likert scale, from each of them with respect to the importance of 47 factors identified from the literature that were considered likely to affect construction productivity. The factors were stratified into primary factors and secondary factors contributing to three of the primary factors. There were 36 responses. These factors were rated by the respondents and then ranked using a relative importance index approach.

The research evaluated the relative importance of the primary factors with respect to their effect on construction productivity. The 15 highest ranking factors are discussed. Three factors – rework, poor supervisor competency, and incomplete drawings – were ranked as having a strong effect on construction productivity. There was also an analysis of the secondary factors in relation to three of the primary factors.

The research focused on the State of Queensland in Australia. It had a response rate of 40 per cent. It provides insight into the factors affecting productivity on construction projects in Australia. Further research to investigate the identified factors in depth, using targeted interviews of expert project management professionals, is currently being undertaken.

The construction industry can use the findings in this paper as a basis for improving the productivity of construction projects.

This research is original research, which has highlighted a number of key areas of which construction productivity can be improved.

The purpose of this paper is to investigate how on-site blockwork craft gangs’ learning impacts productivity within the production environment on-site to optimise their productivity.

The research is adopting a quantitative method with the observation of seven craft gangs’ blockwork with an average of five members in each gang, using the learning curve model application in a 17-storey tri-tower construction project in Nigeria. The linear regression method was employed in the analysis stage of this study using labour-recorded productivity time input as the dependent variables.

The paper provides empirical insights about the significance of on-site craft gangs’ learning. The overall blockwork craft gangs learning observed at the site level shows an average learning rate of 94.21 per cent resulting in 5.79 per cent improvement gains.

Due to the nature of the study and the research question, the observations in this research study were limited to FCDA construction project in Nigeria. The limitation of this scenario is that the research results may lack generalisability. Therefore, there is the need for further study on the learning rate.

This research study includes the implications for the development of on-site blockwork craft gangs learning; the significant impact of learning rate of 94.21 per cent resulting in 5.79 per cent improvement gain can be used in the planning and to fast track the productivity of craft gangs’ construction.

This paper identified the need to improve construction productivity through craft gangs’ on-site learning with the application of the learning curve theory.