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Our main objective in this article is to discuss the problems of construction labour market stabilisation in the context of technological change. To study this topic the article is divided into two parts. In the first part there is an analysis of the existing features of the construction labour market in Canada and the Province of Newfoundland and Labrador, in the light of economic stabilisation and technological change. In the second part an econometric model for the construction labour market in the Province of Newfoundland and Labrador is developed and estimated. The statistical results are analysed in the light of achieving economic stabilisation with an equally capital‐labour augmenting type of technological change.

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 construction industry plays a significant role to the economy of Hong Kong not only in terms of output but also the employment. The sector, however, has been severely hit by the economic downturn in recent years resulting in serious unemployment. Employment planning becomes one of the critical aspects for the recovery of the economy. The main objective of this paper is to establish a labour demand model for the Hong Kong construction industry. The unique characteristics and the current conditions of the construction labour market are reviewed. Regression analysis based on 123 construction projects was used to compute the relationship between expenditure and site workers employed. The best predictor of average labour demand of construction projects in Hong Kong is found to be DL = 463 C 0.934, where DL is the actual labour demand in man‐days, C is the final cost of contract in millions. The labour demand‐cost relationship can be applied as a manpower forecasting model to estimate the total labour required for a given type of project. The developed model enables a more reliable and accurate planning of manpower requirements in the construction industry.

Efficient manpower planning has been recognized as a critical aspect for the development of an economy. In 2001, the Works Bureau of the Hong Kong SAR Government (predecessor of Environment, Transport and Works Bureau) commissioned an HKPolyU consultancy team to develop a computer‐based model to estimate the demand for different categories of construction personnel. This article presents the concept and features of the manpower demand‐forecasting model developed for the construction industry of Hong Kong. The forecasting model is formulated on the basis of the labour multiplier approach by deriving the relationship between the number of workers required and the project expenditure in the given project duration. Multipliers for 61 project types were derived for 38 labour trades using completed project data. The labour demand by occupation for each project can then be estimated by multiplying the corresponding multipliers and the estimated project expenditure. Several unique features of the model have been developed, including “normalization” and “contract cost adjustment factor”. Normalizing the labour multipliers can facilitate the prediction of occupational labour requirements at different stages of a construction project. The adjustment factor is introduced to eliminate the discrepancy between the original estimates and final contract values so as to enhance the estimation accuracy. The model can also be used to predict the number of jobs created for a given level of investment. The government can apply this model to check and compare which project types will generate most jobs before committing public money. This model could be easily adopted and adapted by foreign construction authorities while planning manpower.

Manpower is the most valuable asset in the construction industry. Based on an examination of literature, selected key data sources, and views from 29 key informants, this paper addresses the important labour resource context related to the construction industry in the case of Hong Kong. These include the trends of the critical indicators of the labour market in construction and the implications of the changing markets and technology on the future pattern of skill requirements, and the government policies on construction personnel. The findings are of immense importance to anyone involved in the construction industry, particularly training organizations and policy makers in their mission to maintain a skilled, competitive and adequate workforce able to meet the future demands of the industry. The changing labour market trends and skill requirements pose challenges for construction personnel in terms of upgrading their skills. Further research is recommended to construct robust models predicting the occupational trends in labour resources for effective manpower planning and to establish a labour market information system which could lead to capturing periodic labour market signals with a view to assisting the process of policy making on various human resource development aspects of construction workforce in Hong Kong.

The aim of this article is to analyse how different policies and actors have structured the current migrant labour regime in the Finnish construction sector and to discuss the consequences for migrants. Our study shows that a strong industrial relations system such as in Finland is able to curb the posting of workers regime (the most disadvantageous for migrant workers). The position of labour migrants has become more diverse in the segmented labour market, although it remains inferior compared to that of the natives. Consideration of the policy development revolving around the changing migrant labour regimes constitutes the first part of the analysis and is based on government and trade union officials’ accounts. The more substantial part of the study draws upon biographical interviews with Estonian construction workers and analyses the division of migrant labour according to their employment in four ‘patterns of firm ownership’ that range from the most unfavourable to most favourable position: workers posted by Estonian firms; workers employed by firms registered in Finland but operated by Estonians; self-employed/small business owners and workers employed by Finnish firms. The structuring of the regime according to the pattern of firm ownership can be interpreted as a manifestation of employers’ intentional strategies to adapt to or avoid national regulations and to some extent as also reflecting workers’ individual and collective agency.

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.

Studies highlight that poor labour supervision and inadequate labour training facilities are the primary factors that result in labour skill shortages and productivity-related challenges among construction firms. This study aims to assess the construction supervisors’ abilities in providing work-based training elements and evaluating labour skills in construction.

A construction supervisory training programme was newly designed with a set of labour training exercises using comprehensive approaches. A total of 64 construction supervisors were trained to deliver the labour training components for more than 250 labourers working on 23 construction projects in Sri Lanka. The supervisors’ competencies were assessed using a detailed marking guide developed through expert discussions and literature reviews.

The results show the detailed cross-section of a wide range of competencies of the construction supervisors in providing labour training elements with the levels of standards/descriptions. The generalisability of the study applications and the reliability of the results were ensured using statistical tests and expert reviews. The findings further describe the impacts of the well-improved competencies of construction supervisors on labour working patterns and work outputs.

Though the study findings were limited to the Sri Lankan construction sector, the study applications can have a considerable impact on the current/future practices of the construction sector in developing countries as well as other developing industries.

The study outcomes may contribute to a rapid increase in the number of construction supervisors becoming certified assessors of National Vocational Qualifications up to certain levels. This paper describes the further extensive implications and future scopes of the study elaborately.

The study adds new characteristics and values to construction supervision practices that can be remarkable in achieving higher levels of performance and productivity in labour operations. Importantly, the study contributes to adorning the job role of construction supervisors with the title of “labour training expert”.

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.

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.