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Measuring the Construction Industry’s Productivity Performance: Critique of International Productivity Comparisons at Industry Level
Abstract
The authors review work on international comparisons of construction productivity performance by providing an in-depth critique of the existing literature and highlighting the existing methodological challenges. Using studies and data on the United Kingdom's relative construction productivity performance as an exemplar, it is suggested that any investigation of international productivity differences in construction at the industry level is highly problematic because these productivity estimates do not compare like for like. Data definitions and coverage differ substantially across countries. In addition, deflators and exchange rates used to convert output into a common currency are unreliable. While the new standard industrial classifications 2007 could provide a better basis of cross-country productivity comparisons and further research on deflators, conversion rates, labor inputs, and capital stock estimates could improve the robustness of international comparisons, there are conceptual limitations to an industry-level approach. This paper's contribution is to discuss these methodological challenges in detail and propose a research agenda for enhancing cross-country productivity comparisons for informing government policy intervention on productivity improvement. The authors argue that cross-country productivity at the project level can enable a more detailed analysis of the tangible and intangible inputs to the construction process while accounting for the heterogeneous nature of the industry. However, the existing project-based productivity measures fall short of providing a common framework for systematically gathering comparable cross-country productivity data that could enable robust benchmarking. The authors conclude by calling for the need of facilitating the collection and analysis of robust project-level productivity data, e.g., through an international benchmarking club, in order to support effective policy intervention for enhancing construction productivity performance. (C) 2014 American Society of Civil Engineers.
... Historically, macrolevel productivity data have indicated a stagnant or declining trend in construction productivity, in stark contrast to the rising productivity observed in other sectors (e.g., US Bureau of Labor Statistics 2023). However, several studies highlight that macrolevel investigations have unique challenges such as data definitions, heterogeneity of outputs and inputs, conversion rates, and unreliable output deflators (Abdel-Wahab and Vogl 2011;Rojas and Aramvareekul 2003;Vogl and Abdel-Wahab 2015). This results in inconsistencies in findings, with some studies revealing that macrolevel productivity has declined (Teicholz 2001), while others have measured productivity improvement in some construction subsectors (Sveikauskas et al. 2016(Sveikauskas et al. , 2018. ...
... Advancements in productivity can be achieved by analyzing the actual performance of specific projects, work packages, and activities at the microlevel. The need for microlevel productivity data to suit the project-based nature of the construction industry has been widely acknowledged in previous research (Abdel-Wahab and Vogl 2011;Vogl and Abdel-Wahab 2015). Neve et al. (2020) stressed that a focus on activity-and project-level efficiency is crucial for changing the trends of macrolevel construction labor productivity. ...
... Thomas (2015) called for action in construction productivity research, as opposed to merely ideas, concepts, and theories. Any microlevel productivity framework without the active participation of industry is likely to be unsuccessful (Vogl and Abdel-Wahab 2015). The focus of future research entails both creating new data sets and refining existing ones, with a preference for microlevel data based on company or project accounts (Crawford and Vogl 2006). ...
This research presents a novel micro-level (i.e., project-level) construction productivity framework aimed at measuring and benchmarking productivity consistently within and between projects, work packages, and activities. Using a design science research approach, the framework was rigorously developed with data collected from UK-based construction projects and refined through several cycles of implementation and industry engagement. The framework is useful in providing new insights into project performance, helping understand process variability, setting benchmarks, and conducting data analytics for improved decision-making. This framework can be used for industry-wide measurement both within and across sub-sectors of construction in the UK and internationally.
... The inputs are efforts in form of capital and workforce and the output is what you get out from it for example it could be a product such as facility, car or it could be a service. The construction industry is not homogeneous, you can't measure the output as physical stuff, it´s heterogeneous and harder to measure it as units the solution is the market price being used instead of weights (Vogl & Abdel-Wahab, 2015). ...
... High productivity is lucrative and affects almost everyone, it gives the labors higher salaries, more profit gains and creates a higher tax revenue for the government. Another remarkable advantage with high productive in the construction industry is that consumers get often better and cheaper products this creates a so called zero-sum game in the society (Vogl & Abdel-Wahab, 2015) However, according to (Arditi & Mochtar, 2000), construction productivity shows a trend of declination since the 1980s resulting in increased costs of construction. A comparison between non-agricultural industries and construction industries. ...
... In the United Kingdom the construction industry was accounted between 6-8% of the gross domestic product (GDP) in Sweden that number was 9% (Vogl & Abdel-Wahab, 2015). By changing and improving the construction industry through productivity performance would create a huge cost savings both for the clients and the contractors. ...
Building information modeling (BIM) or Building information management is a technological and organizational approach with potential benefits to the construction industry. BIM can be divided into several aspects, including scope, visualization, integration and automation. By using BIM, the client, consultants and contractors gains many advantages in aspects of communication, time, cost and the quality of the outcome. In this master thesis, an examination of the productivity gains a project can attain by implementing BIM in the design phase is conducted. The thesis uses a case study approach for this examination, where the "West Link" Project was used as the case. Research was further narrowed down to the "Central Station" Bid-pack. This specific Bid-Pack was chosen as it uses a new procurement strategy that enhances the integration process. The case study has been supported by interviews with the client (Trafikverket) and the contractors (NCC), collecting data from Trafikverket's database, using the results of surveys conducted by Trafikverket on employees and from direct observations. The master thesis has also developed several KPIs that has been seen to be repeated consistently within the research and are of importance to Trafikverket. These KPIs has been used as an indicator of the current performance of BIM. The Macleamy Time/Effort curve has also been used as a way in assessing the collaboration between stakeholders. The study is of high importance to Trafikverket as it is the first time they implement BIM in such a big scale. It can also be considered important to other clients willing to implement BIM in their organization without prior knowledge of assessing its advantages. The study finds that by using BIM in Trafikerket, paper documents can be reduced, collaboration, information sharing and trust issues can be enhanced. BIM has created closer collaborations between the client and the contractor. The study also shows that there has been an increase in costs and delay in schedule which can be related to the lack of maturity and psychological barriers within the employees of Trafikverket.
... For example, Shehata and El-Gohary [6] used capital productivity, output per worker, and output per work hour as indicators to assess construction labor productivity. Vogl and Abdel-Wahab [7] suggested using total factor productivity, output per worker, and output per work hour to measure the construction productivity performance at the industry level. Ayele and Fayek [8] developed a framework to measure the total productivity of construction projects, which consists of metrics at three levels: activity, project, and industry, and proposed a metric for measuring the total productivity of construction projects, which considers all resources used, including labor, material, capital, energy, construction project indirect cost, and owner cost. ...
... Generally, there are two types of productivity, namely single factor productivity, and multi-factor or total-factor productivity. Single factor productivity, which includes labor productivity and capital productivity, relates to just one input factor, while multi-factor or total-factor productivity takes into account all the inputs [7]. Labor productivity is a singlefactor productivity often expressed as output per worker or output per hour worked [6,7,10]. ...
... Single factor productivity, which includes labor productivity and capital productivity, relates to just one input factor, while multi-factor or total-factor productivity takes into account all the inputs [7]. Labor productivity is a singlefactor productivity often expressed as output per worker or output per hour worked [6,7,10]. The Construction Industry Institute (CII) [24] attempted to propose definitions of productivity for consistency, where productivity refers to "work-hours performed per units of work completed", and "the ratio of planned productivity to actual productivity" refers to productivity index. ...
Although some studies have used or developed different types of metrics to assess construction productivity in the existing literature, few of them investigated those metrics systematically and the differences between assessment results. This study examined the various types of metrics used in the assessment of the productivity of construction projects. First, a literature review was conducted first to identify prevailing productivity metrics at four levels, namely trade, project, company, and industry. Then, the questionnaire was developed and disseminated to 53 Singapore-based construction companies for data collection. Subsequently, non-parametric statistical tests were conducted to analyze the data collected by the questionnaire. Results showed that the top five metrics in terms of usage frequency and relative importance were “constructability score”, “buildable design score”, “square meter of built-up floor area per man-day”, “square meter per dollar”, and “output per worker.” In addition, results showed that differences existed in the assessment results when productivity metrics at different levels were used to conduct the same measurement. This is the first study to explore the most widely used metrics in productivity assessments of construction projects and investigate possible differences in assessment results. This study could help the authorities to review, evaluate, and modify the productivity metrics used in practice. Thus, this study is beneficial to the practice as well.
... Tangen (2005); ). Concerning construction productivity, the mayor part of the research has focused on developing measures of productivity of separate tasks, projects or the industry (e.g. Park (2002); Liao et al (2012); Vogl and Abdel-Wahab (2015); Ayele and Fayek (2019)); attempts of assessing the effect of different production strategies and other strategic factors on productivity (e.g. Alarcón et al (2008); Goodrum et al (2009); Ghodrati et al (2018); Zhan and Pan (2020)); or identifying, ranking and explaining factors that affect construction productivity ). ...
... Productivity is equal to efficiency times effectiveness, in turn equal to value adding time divided by total time (Jackson and Petersson, 1999) Productivity is the ability to satisfy the market's need for goods and services with a minimum of total resource consumption (Moseng and Rolstadås, 2001) Productivity = Actual work hours/installed quantity (Park, 2002) Productivity is a measure of the efficiency with which the economy turns inputs, such as labor and capital, into outputs. It is measured either as single-factor productivity (capital or labor with respect to an output measure) or total-or multi-factor productivity (the combined impact of all inputs on output) (Vogl and Abdel-Wahab, 2015) Productivity = tangible output (physical units)/tangible inputs ($) (Ayele and Fayek, 2019) As Table 1 indicates, how one defines productivity and what the term accounts for vary. To begin with, the definition applied depends firstly on the purpose for addressing productivity, but also on the context, level of analysis (e.g. ...
... Single factor productivity is the most common method between the two, normally used for measuring labor productivity or economic intensity ). The measure, which follows equation 1, accounts for only one input, normally capital or labor, which is put in relation to an output (Vogl and Abdel-Wahab, 2015). At industry level, state that economics tend to use single factor productivity as a value added labor productivity metric. ...
Global reports over the years indicate that productivity development in construction is poor if compared to other industries, even negative in some countries, calling for a change to increase productivity. However, the construction industry has a problem of defining, measuring and using productivity. Previous research suggest that productivity is a multifaceted term, which meaning depends on the purpose of addressing productivity, the context where one uses the term, the level of analysis (e.g. task, project or industry) or even the background of who addresses the term. The multifaceted meaning seems to have encouraged the development of multiple methods for measuring productivity in construction. The variety of understandings indicate that different aspects of productivity are accounted for by different people. Moreover, the construction productivity measures at different levels account for different things, and there is a lack of a clear connection between the levels of analysis. Subsequently, the multiple meanings and measures hamper how to understand construction productivity, what or how to measure and how to use the measures to improve overall productivity.
This thesis furthers how housebuilding productivity can be understood by drawing on how the literature and on-site housebuilding contractors address productivity concerning how it is defined, measured and used. An understanding of productivity that has a base in how productivity can be defined and measured, including how different performance measures can be applied to represent measures of productivity and then be used to improve productivity. The research questions of how productivity is understood, measured and used by Swedish housebuilding contractors are answered and synthesized to contribute with an enhanced understanding of construction productivity.
Empirical data was collected through 17 semi-structured interviews and four workshops. The interviews were held with practitioners ranging from site-, to project-, to business area managers from one small and four of Sweden’s largest contractors. The workshops were held with senior managers in charge of production development at the four large companies. Data was cross-analyzed to identify commonalities and contrasting findings connected to how productivity is defined or understood to represent and include, how productivity is measured, and how productivity is used.
The findings suggest that productivity in on-site housebuilding production is about how efficiently the production system reaches its goal. That is, how efficiently the building is produced in conformance with the requirements to meet client values. Hence, productivity in housebuilding production integrates efficiency, effectiveness and many other factors that make the production system function better towards reaching its goal. Yet, what is included in, and thus what productivity represents, differs, caused by the choice of level of analysis (e.g. task, project or industry) and the length of the considered system or value chain (e.g. only production, or design, planning and production). The results also indicate that housebuilding productivity includes planning, measurement, control and reporting results. While separate direct productivity measures can be used in planning and for reporting results, many different indirect productivity measures are applied to enable for and control productivity during operations. These different measures represent different factors understood to influence productivity at different levels. However, the choice of measures vary, their use is unstructured and the measures usually stay undocumented.
The results suggest that one measure of productivity is not enough to understand productivity of building production systems, it is rather through the combination of direct and indirect measures of productivity. Yet, there is a need to adopt a systems perspective to understand how to structure and connect the different measures from sub-processes to processes and productivity, which this thesis suggests as a line of future research. Moreover, it is not enough to measure productivity to develop productivity. Systematic routines for measurements, review and action based on the measures must be developed and implemented.
... Industry P1. Total Factor Productivity [30,31] P2. Multi-Factor Productivity [32,33] P3. ...
... Multi-Factor Productivity [32,33] P3. Output per worker [30][31][32][34][35][36][37] P4. Output per work hour [31,32,[34][35][36] P5. ...
... Output per worker [30][31][32][34][35][36][37] P4. Output per work hour [31,32,[34][35][36] P5. Value added per worker [32,33] P6. ...
Although various types of management strategies have been proposed to improve construction productivity, they are seldom prioritized based on contribution. This study aims to bridge the knowledge gap to prioritize the critical management strategies that can help improve productivity in the construction industry. To achieve this goal, a comprehensive literature review was conducted first, to develop a conceptual framework of construction management strategies that are assumed to have positive relationships with productivity performance. A questionnaire was then developed and disseminated to 53 Singapore-based companies for data collection. The collected data were analyzed using partial least squares structural equation modelling. Results showed that “planning and communication” is the most critical management strategy category for productivity improvement, followed by “logistics,” “project strategic management”, “human resources”, and “on-site management.” This study contributes to the current body of knowledge by prioritizing critical management strategies that can help the construction industry improve productivity. Findings are likewise beneficial for the industry practitioners as well, as they can help deepen their understanding of the role played by these management strategies on increasing productivity and thereby formulate the best combinations for their own companies.
... II. Internal project factors such as the size of the project, complexity of the project, type of project and nature of the project (Lim and Alum 1995;Abdul Kadir et al. 2005;Frödell et al. 2008;Mirahadi and Zayed 2015;Vogl and Abdel-Wahab 2015;Molavi and Barral 2016;Tezel and Aziz 2017). III. ...
... III. External factors such as political and social factors, economic factors and technological innovations (Lim and Alum 1995;Abdul Kadir et al. 2005;Frödell et al. 2008;Alkaf et al. 2012;Arashpour et al. 2014;Kazaz et al. 2015;Mirahadi and Zayed 2015;Vogl and Abdel-Wahab 2015;Anvari et al. 2016;Molavi and Barral 2016;Tezel and Aziz 2017;. VI. ...
The elimination of non-conformance is one of the goals of quality management, which can be accomplished by effectively managing and supervising the project. The excellent quality results in increased production and reduced costs, contributing to a rise in the competitive edge. The present body of research has examined the effect of quality management on the performance of building projects in the Indian industry. Different researchers have strived to identify the factors that affect the performance of construction projects. A structured questionnaire was floated to different professionals in the industry, that is, architects, engineers, consultants, developers and researchers, and >152 valid responses were received. The questionnaire asked respondents to respond on a Likert scale of 1–5. The questionnaire asked about the impact of quality on different aspects of the construction project’s performance. Relative importance index (RII) are obtained to rank the elements in order of importance. According to the factor analysis results, three primary components account for 62% of the variance. The results show that the significant aspects of the project affected by quality are rate of rework, project performance, cost, safety, labour productivity and profitability with RII scores of 0.85, 0.82, 0.78, 0.76, 0.75 and 0.74, respectively.
... Afghanistan's construction sector has risen in value from 8.7 billion Afghanis (AFN) in 2002-03 to 124.28 billion AFN in 2015-16, accounting for more than 10% of Afghanistan's economy. The contribution to GDP in 2013-14, 2014-15, and 2015-16 was 7.84, 8.89, and 10.03 respectively [3]. ...
... Therefore, organizational management must focus on labor productivity analysis and its improvement strategies. Productivity cannot be managed or improved without being measured, so it is necessary to first analyze and identify low productivity points and correct them [8]. There are many different methods of measuring construction productivity, each with its own strengths and weaknesses. ...
In the construction industry, productivity is one of the most important key performance indicators and fundamental to project success. Productivity isn't everything, but it is pretty much everything in the long run. Improving construction productivity is therefore one of the priorities of the construction industry in many countries around the world. Productivity cannot be managed or improved without being measured, so it must first be measured and analyzed, and then productivity weaknesses identified and corrected. This paper aims to measure labor productivity in the construction industry and assess the percentage distribution of productive, semi-productive, and non-productive activities by observing construction work. The work sampling method was used to find out how labors spend their time during an eight-hours working day, and to identify percentage of the productive, semi-productive and nonproductive tasks on a working day. The required number of the observations was calculated, the procedure for making observations was determined, and data were collected through observations made on a construction project as a case study. based on this study, it is found that generally about 32.02% of laborers' working time is spent on productive (direct) work, 36.70% on semi-productive (supportive) work, and 31.28% was spent on nonproductive (delayed) work.
... Afghanistan's construction sector has risen in value from 8.7 billion Afghanis (AFN) in 2002-03 to 124.28 billion AFN in 2015-16, accounting for more than 10% of Afghanistan's economy. The contribution to GDP in 2013-14, 2014-15, and 2015-16 was 7.84, 8.89, and 10.03 respectively [3]. ...
... Therefore, organizational management must focus on labor productivity analysis and its improvement strategies. Productivity cannot be managed or improved without being measured, so it is necessary to first analyze and identify low productivity points and correct them [8]. There are many different methods of measuring construction productivity, each with its own strengths and weaknesses. ...
In the construction industry, productivity is one of the most important key performance indicators and fundamental to project success. Productivity isn't everything, but it is pretty much everything in the long run. Improving construction productivity is therefore one of the priorities of the construction industry in many countries around the world. Productivity cannot be managed or improved without being measured, so it must first be measured and analyzed, and then productivity weaknesses identified and corrected. This paper aims to measure labor productivity in the construction industry and assess the percentage distribution of productive, semi-productive, and non-productive activities by observing construction work. The work sampling method was used to find out how labors spend their time during an eight-hours working day, and to identify percentage of the productive, semi-productive and nonproductive tasks on a working day. The required number of the observations was calculated, the procedure for making observations was determined, and data were collected through observations made on a construction project as a case study. based on this study, it is found that generally about 32.02% of laborers' working time is spent on productive (direct) work, 36.70% on semi-productive (supportive) work, and 31.28% was spent on nonproductive (delayed) work.
... Afghanistan's construction sector has risen in value from 8.7 billion Afghanis (AFN) in 2002-03 to 124.28 billion AFN in 2015-16, accounting for more than 10% of Afghanistan's economy. The contribution to GDP in 2013-14, 2014-15, and 2015-16 was 7.84, 8.89, and 10.03 respectively [3]. ...
... Therefore, organizational management must focus on labor productivity analysis and its improvement strategies. Productivity cannot be managed or improved without being measured, so it is necessary to first analyze and identify low productivity points and correct them [8]. There are many different methods of measuring construction productivity, each with its own strengths and weaknesses. ...
In the construction industry, productivity is one of the most important key performance indicators and fundamental to project success. Productivity isn't everything, but it is pretty much everything in the long run. Improving construction productivity is therefore one of the priorities of the construction industry in many countries around the world. Productivity cannot be managed or improved without being measured, so it must first be measured and analyzed, and then productivity weaknesses must be identified and corrected. This paper aims to measure labor productivity in the construction industry and assess the percentage distribution of productive, semi-productive, and non-productive activities by observing construction work. The work sampling method was used to find out how laborers spend their time during an eight-hour working day and to identify the percentage of productive, semi-productive, and nonproductive tasks on a working day. The required number of observations was calculated, the procedure for making observations was determined, and data were collected through observations made on a construction project as a case study. based on this study, it is found that generally about 32.02% of laborers' working time is spent on productive (direct) work, 36.70% on semi-productive (supportive) work, and 31.28% spent on nonproductive (delayed) work.
... In the construction sector, productivity is understood as the units of work produced per man-hour (Ouga, Alinaitwe and Mwesige, 2020). The concept is usually expressed at the activity, project and industry levels (Yi and Chan, 2014), which are respectively concerned with productivity on construction tasks, construction projects and the industry's long-term productivity trends (Shan et al., 2016;Zhao and Dungan, 2014;Vogl and Abdel-Wahab, 2015;Borg and Song, 2015). Due to the growing knowledge that the construction sector considerably contributes to a booming economy, more than ever, productivity growth is becoming more important to the industry's stakeholders and policymakers (Fadejeva and Melihovs, 2010). ...
... Due to the growing knowledge that the construction sector considerably contributes to a booming economy, more than ever, productivity growth is becoming more important to the industry's stakeholders and policymakers (Fadejeva and Melihovs, 2010). This engenders several interventions from the construction stakeholders towards ensuring labour productivity in construction continues to grow (Vogl and Abdel-Wahab, 2015). ...
Despite the importance of the construction sector to economic development, labour productivity in construction is lower than productivity in many sectors. Construction productivity has declined continuously for decades, particularly in developing countries. The challenges of low productivity in construction is considered a chronic problem. This has engendered the concern of construction stakeholders to address the myriad of challenges undermining labour productivity growth. This study conducted a meta-data analysis of factors affecting construction labour productivity growth in the Middle East. A systematic review of the existing construction labour productivity studies was presented. Ten Middle East studies were selected for a meta-data analysis. The key factors affecting construction labour productivity in the region were identified, and quantitative data of the selected studies were synthesized. Effect summaries derived from the analysis revealed delay in responding to requests for information, inadequate workers supervision, a shortage of skilled labour, extent of change orders, and clarity of technical specifications as the major factors affecting productivity. The study is limited to journal articles published from 2000-2020 in the Scopus database. Contractors in the Middle East can adopt the interventions of the study to evolve productivity growth policies for their organisations.
... Productivity in the construction industry is usually expressed at the activity, project and industry levels (Yi and Chan, 2014). These are, respectively, concerned with productivity on construction tasks, construction projects and the industry long-term productivity trends (Shan et al., 2016;Zhao and Dungan, 2014;Vogl and Abdel-Wahab, 2015;Borg and Song, 2015). Resulting from the growing knowledge of the significant contribution of the construction industry to a thriving economy, productivity growth is becoming more vital to the industry's stakeholders and policymakers than ever (Fadejeva and Melihovs, 2010). ...
... Productivity represents an essential parameter for measuring the economic performance of any country, industry and organisation. The significant contribution of the construction industry to economic growth contributes to several interventions of the industry's stakeholders in ensuring that labour productivity in construction continues to grow (Vogl and Abdel-Wahab, 2015). Several research projects have been conducted to address the prevalent low labour productivity in construction (Olomolaiye et al., 1987;Kaming et al., 1997;Alinaitwe et al., 2007;Adebowale and Smallwood, 2020). ...
Purpose
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.
Design/methodology/approach
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.
Findings
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.
Research limitations/implications
The study is limited to scientifically analysed secondary data relative to SME contractors in developing countries.
Practical implications
The findings of the study can be adopted by construction stakeholders to evolve productivity growth policies for construction SMEs in developing countries.
Originality/value
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.
... The construction industry has a significant role in the development of an economy; there are mutual and causal relationships between the industry and gross domestic product (GDP) [1,2]. Despite a slight slowdown, the construction industry continues to grow at a slow pace. ...
... According to the Bureau of Labour Statistics (2013) of U.S. Department of Labour, the definition of productivity is "a measure of economic efficiency which shows how effectively economic inputs are converted into output" [10], whereas Arditi and Mochtar (2000) stated that productivity is "the ratio between total outputs expressed in Dollars and total inputs expressed in Dollars as well" [11]. Vogl and Abdel-Wahab (2015) described productivity as a measure of effectiveness with which the economy runs inputs such as workforce and money into output [2]. Moreover, Horner et al. (1989) defined productivity as the ratio of earned to actual hours [12]. ...
The construction industry has great importance for the economy, especially in developing countries. While having such great importance at macro level, there are many uncertainties for the industry to overcome. Productivity is one of the most important uncertainties in the industry. Most of the companies in the industry do not have any performance measure or efficient criteria for the technical personnel, and the work outcomes of the technical personnel are not quantifiable. This study aims to identify and rank the multi-causal factors affecting the productivity of technical personnel in the construction industry. For this purpose, a detailed questionnaire survey was taken among 400 professionals in the construction industry. Kaiser–Meyer–Olkin measure of sampling adequacy, Barlett’s test of sphericity which makes Cronbach’s alpha, and reliability tests were conducted on the results of the questionnaire survey. The relative importance index was used for ranking the importance level of the factors. The top nine factors affecting technical personnel’s productivity are listed as Management, External and Owner Related, Motivational, Financial, Working Condition, Welfare and Comfort, Lacking Resources, Personal and Workload factors in the study, and 47% of these factors affecting technical personnel’s’ productivity are expressed by management factors. The most important factor groups after the management factors are working conditions, human factors and external factors, respectively. It is thought that the results of this research will shed light on for further research that focus on maximizing the productivity of technical personnel in the construction industry.
... Construction productivity has a great influence on countries' productivity because the gross domestic product (GDP) makes up 13% of the global GDP of the construction industry (Banaitienė et al., 2015;Bughin et al., 2017). Productivity is a measure of the efficiency of outputs in terms of inputs, such as labor and capital (Vogl & Abdel-Wahab, 2015). For improving the productivity, a change in productivity could be analyzed from the past to the present because the reason for the change could be identified and an improved method could be proposed. ...
... Labor productivity is difficult to compare internationally because each country uses data based on different criteria. For international comparison, Vogl and Abdel-Wahab (2015) proposed the project level as the comparison criterion for each country's productivity. Chia et al. (2018) proposed a method of comparing the productivity of international construction labor using purchasing power parities (PPPs). ...
This study analyzes construction productivity based on the construction duration per floor and per gross area over 20 years (1996–2015) and compares the results among the United States, United Kingdom, South Korea, and Japan, which have similar sizes of total construction investment and market risk. Although construction labor productivity is widely used to analyze and compare construction productivity among countries, it does not consider the changed construction duration caused by levels of investment and technology. Therefore, construction duration per floor and gross area was selected analyze and compare construction productivity in this paper. Regular and non-modular buildings with a total of five or more floors and a basement are collected during the analysis period (1996–2015). The total number of collected buildings is 800 and it includes buildings in the United States (194), the United Kingdom (186), South Korea (322) and Japan (98). Construction duration, increase rate and standard deviation are then compared between each country. Finally, factors that influence construction duration are derived and additionally considered to explain and adjust the trends and changes of construction productivity related to construction duration in the four countries. The productivity of the United States is the highest, but the difference between it and other countries decreases steadily because the increase rate of the construction duration in the United stated is larger than those of other countries. Then, the factors influencing the construction duration are derived as a learning effect by the number of ground floors and gross area, as well as the rate of constructed buildings with a first basement floor for efficient productivity management. The rate of the first basement floor influences both the construction duration per floor and per gross area. This study contributes to the field by explaining the productivity change based on the construction duration and proposing the key management point of the productivity by deriving the influence factors
... Năng suất lao động trong ngành xây dựng được tập trung nghiên cứu từ rất sớm. Qua quá trình thống kê, các tác giả nhận thấy các nghiên cứu này có thể được xếp vào các nhóm: (a) so sánh năng suất lao động giữa các ngành nghề, quốc gia, và theo thời gian [6][7][8][9], (b) phân tích các yếu tố liên quan đến năng suất lao động [9][10][11][12], (c) đo lường năng suất lao động [13][14][15][16], (d) các giải pháp tăng năng suất lao động [4,8,17,18]. ...
... Năng suất lao động trong ngành xây dựng được tập trung nghiên cứu từ rất sớm. Qua quá trình thống kê, các tác giả nhận thấy các nghiên cứu này có thể được xếp vào các nhóm: (a) so sánh năng suất lao động giữa các ngành nghề, quốc gia, và theo thời gian [6][7][8][9], (b) phân tích các yếu tố liên quan đến năng suất lao động [9][10][11][12], (c) đo lường năng suất lao động [13][14][15][16], (d) các giải pháp tăng năng suất lao động [4,8,17,18]. ...
Năng suất lao động các công tác xây dựng ảnh hưởng lớn đến tiến độ và hiệu quả sử dụng vốn của công trình. Nghiên cứu này đề xuất một phương pháp phân tích cấu trúc các công tác này thông qua lấy mẫu công việc từ hình ảnh trích xuất từ các máy quay giám sát. Phương pháp này giảm bớt yêu cầu thủ công của người thu thập dữ liệu cũng như các ảnh hưởng đến tâm lý người lao động. Phương pháp trên được áp dụng đối với một số công tác trên một công trình nhà cao tầng bê tông cốt thép toàn khối. Kết quả vừa thể hiện được tính mạch lạc, dễ áp dụng của phương pháp, vừa cung cấp một số tính chất của các công việc được chọn. Hiểu rõ cấu trúc thành phần và cấu trúc sử dụng thời gian của các công việc giúp người sử dụng lao động có cơ sở để đưa ra các quyết định quản lý làm tăng năng suất.q Từ khóa: năng suất; lấy mẫu công việc; cấu trúc công việc; máy quay giám sát.
... (UK Government, 2017); having the availability of up-to-date Key Performance Indicators (KPIs) data, for most projects in the sector, whether, small, medium, or large in scale. Furthermore, Vogl and Abdel-Wahab (2015) in their review study on international comparisons of construction productivity performance stated for UK construction statistics reporting; difficult decisions are required when deciding upon the list of KPIs to measure and monitor given reliable data is vital for determining the industry's productivity performance. Although they warned, there is always the trap of focusing on performance indicators, that are more accessible, even if they are not akin to the most relevant indicators. ...
... Whilst further adding, that a substantial effort is required to determine, a dependable evaluation to establish a knowledgeable orientation towards the dynamics that disturb industry productivity and performance (e.g. workforce skills) (Vogl and Abdel-Wahab, 2015). ...
The UK historic building repair and maintenance (R&M) sector generates £9.7 billion in output. However, challenging delivering quality R&M are project under-performance, a lack of collaborative project practices, resulting in poor communication, underpinned by persistent skills shortages. These are not solely UK concerns; various international studies have echoed similar issues, however, to maintain focus, the scope of the research is within the UK context, in particular Scotland’s stone-built heritage. Adopting a four-stage qualitative participatory exploratory action research strategy; this research aims to develop a framework, to support an effective integrated multi-disciplinary, collaborative, structured, and digitised Project Management and on-site practice approach, to aid increased efficiencies.
Firstly, a synthesis of the relevant academic literature and industrial reports enabled direction towards the acquisition of appropriate intelligence, in order to guide and inform the study’s theoretical foundation. Secondly, 14 semi-structured interviews with Scottish SMEs were executed, which bounded the key findings under three main themes; senior management, human resource, and technical. Concurrently, through co-operative industry engagement, the generation of a best practice historic building SME R&M four-phase process map was undertaken. Thirdly, a common structured collaborative process-standard framework was developed and finally, validated through active industry participation; a demonstration project, four semi-structured interviews, and two focus groups of six industry practitioners. The validation feedback confirmed that the developed framework is valid, credible, acceptable, and applicable as a process standard designed to offer a process model, map, and management tool.
Keywords: Historic Building, Repair, and Maintenance, Process Improvement, Demonstration Projects, SME, Stonemasonry.
... Nevertheless, SFP includes only one input leaving out information about many different factors that affect the transformation of inputs into an output. There are other methods that counter the use of one input and reveal more information about the process, e.g. total factor productivity (TFP) (Vogl and Abdel-Wahab 2015). TFP intends to measure the performance of the production by incorporating several factors affecting the transformation of inputs into an output, hence requiring large amount of robust data. ...
... Still, despite the potential of PIs, existing measures seem to be in limited use in building production (Vogl and Abdel-Wahab 2015). If used, Beatham et al. (2004) state that they tend to be KPOs, giving the house builders little chance to act in time. ...
... Productivity is one of the prime indicators of economic growth and prosperity, with higher productivity converting to higher gains, incomes, and tax revenue, in addition to discounted products and services for consumers (Vogl and Abdel-Wahab 2014). Thus, higher productivity can benefit society as a whole (Pekuri et al. 2011). ...
... If construction productivity were to catch up to that of the total world ec , he d value-added could rise by US$1.6 trillion a year (Barbosa et al. 2017). The construction industry productivity is one of the key drivers of economic production activities (Pekuri et al. 2011, Riratanaphong 2013, Tangen 2005, Vogl and Abdel-Wahab 2014. ...
Since the late 1990s, the construction industry in the Kingdom of Saudi Arabia (KSA) has lagged behind the rest of the world in actual levels of productivity and productivity growth. This is despite the KSA Government’s tremendous support for the construction industry and the expectation that, in the coming decades, the KSA construction industry will continue to develop and grow to achieve the targets of Saudi Vision 2030. This paper explores in depth the concept of productivity to gain insights through answering two questions contextualized within the KSA construction industry: 1) How do managers perceive productivity? and 2) How is productivity measured? Semi-structured interviews were conducted with 13 senior managers with 10-27 years’ experience in project management in the KSA construction industry. Qualitative analysis of interview results found that around 40% of interviewees had little or an incorrect understanding of the concept of productivity and its relevance to production processes. This poor understanding of productivity and its measures extends to the lack of clear specific procedures for regular measurement of productivity.
... � Time performance: This measures the adherence to project schedules, encompassing the duration required to complete activities and milestones. Delays significantly impact overall productivity, often cascading into increased costs and diminished quality (Vogl and Abdel-Wahab 2015). � Cost performance: This assesses the alignment of project expenditures with the budget, ensuring efficient allocation and utilization of financial resources. ...
The synergy between Building Information Modelling (BIM) capabilities and Lean Construction (LC) methods, through waste reduction, can have a positive impact on the productivity performance (PP) of construction projects. In view of this, the current paper aims to review empirical evidence and develop a conceptual communication model to assess the synergistic effects of BIM capabilities and LC methods on the PP of Iranian construction companies. To achieve this goal, Confirmatory Factor Analysis (CFA) and Partial Least-Squares Structural Equation Modelling (PLS-SEM) approach, incorporating factor analysis and path analysis, were employed to elucidate the conceptual relationship among these three constructs. Following the development of the conceptual relationship model, confirmatory factor analysis was utilized to validate the factor structure of a set of observational variables. Subsequently, PLS-SEM was applied to examine the proposed hypotheses. The findings support the hypothesized model, indicating a positive and direct correlation between BIM capabilities and LC methods. Moreover, BIM capabilities exhibit a positive influence on PP, with LC methods serving as an intermediary between BIM capabilities and PP indicators.
... The inspiration behind doing something and the intention to want to do work. [6,17,25,35,36] Health risk concern This fear for the health and safety of oneself on the job is due to factors beyond an individual's control. [19,30,37,38] Job satisfaction The feeling that comes with the accomplishment of a job well done and the need to celebrate a milestone with your co-workers. ...
The COVID-19 pandemic brought challenges such as social distancing, health fears, reduced interaction, and increased stress for construction workers. Understanding their changing social and psychological states is crucial for effective management and performance. This study investigated the impact of the pandemic on the managers’ and laborers’ social and psychological well-being states and identified the changes in their social and psychological well-being states affecting project performance before and after the pandemic. Construction professionals, including construction managers, superintendents, and laborers, participated in a survey exploring thirteen social and psychological well-being variables and three performance variables. Data analysis involved paired t-tests and multiple regression. The findings revealed increased levels of anxiety and depression among both managers and laborers after the pandemic, with laborers more severely affected. Managers considered a broader range of variables, while laborers primarily focused on social factors influencing project performance. These disparities suggested that managers should prioritize health and safety measures, fair compensation, team cohesion, and stress management, while laborers’ motivation, work environment, knowledge acquisition, and sense of belonging should receive priority attention. This study contributes to providing managerial implications and guidance for improving the construction workforce, including managers’ and site laborers’ performance in the post-pandemic period.
... It is impossible to aggregate all outputs and enhance performance to ensure that organisational goals are being achieved [12]. Therefore, it is essential to understand the heterogeneous results and develop ways to analyse them [13]. While enhancing performance outcomes is considered helpful, there are arguments on how, why, and when it should be utilised [14]. ...
While investigating how environmental factors affect the performance of construction enterprises has remained a topical issue in the literature, many concerns about these factors, particularly in developing nations, are still unresolved. This study empirically examines specific environmental drivers affecting construction firms' performance and establishes the role of institutional pressures on construction firms' performance outcomes. Primary data were gathered from a sample of 197 respondents working in Syria's construction industry and were analysed using partial least squares structural equation modelling (PLS-SEM). When considering the impact of some environmental factors (economic environment, human capital, leadership, communication culture, socio-cultural environment, and political environment) on construction firm performance, the findings revealed that institutional pressures (coercive, mimetic, and normative) play a mediating role. However, the mediating influence of institutional pressures on the technological environment and performance of construction firms was not significant, thus indicating the need for more research. The findings of this study make a substantial addition to the current discussion about the most important environmental factors impacting construction firm performance. This study adds to the current debate about the causes of poor performance in construction firms by assisting managers in recognising the impact of the mentioned factors above on a firm's performance.
... The measures for cyberslacking activity and device Computer Security Posture (CSP) are anchored in prior literature. Productivity has been used as an indicator of an organization's success, and continues to expand as a key performance measure, incorporating strategic organizational goals along with financial considerations [8], [30], [50], [51]. The studies conducted by Eldridge and Pabilonia [14], Ferreira and Du Plessis [16], and Syed et al. [43] utilized employee's engagement in work activities within a respective period of time as the measurement for productivity. ...
Cyberslacking is conducted by employees who are using their companies’ equipment and network for personal purposes instead of performing their work duties during work hours. Cyberslacking has a significant adverse effect on overall employee productivity, however, recently, due to COVID19 pandemic move to remote working also pose a cybersecurity risk to organizations networks and infrastructure. In this work-in-progress research study, we are developing, validating, and will empirically test taxonomy to assess an organization’s remote workers’ risk level of cybersecurity threats. This study includes a three-phased developmental approach in developing the Remote Worker Cyberslacking Security Risk Taxonomy. With feedback from cybersecurity Subject Matter Experts (SMEs) on the taxonomy and measures, we then plan to use the taxonomy to assess organizational remote workers’ risk level of cybersecurity threats by using actual system indicators of productivity measures to estimate their cyberslacking along with assessing the computer security posture of the remote device being used to access organizational resources. Anticipated results from 125 anonymous employees will then be assessed on the proposed novel taxonomy where recommendation to the organizational cybersecurity leadership will be provided.
... The measures for cyberslacking activity and device Computer Security Posture (CSP) are anchored in prior literature. Productivity has been used as an indicator of an organization's success, and continues to expand as a key performance measure, incorporating strategic organizational goals along with financial considerations [8], [30], [50], [51]. The studies conducted by Eldridge and Pabilonia [14], Ferreira and Du Plessis [16], and Syed et al. [43] utilized employee's engagement in work activities within a respective period of time as the measurement for productivity. ...
Cyberslacking is conducted by employees who are using their companies’ equipment and network for personal purposes instead of performing their work duties during work hours. Cyberslacking has a significant adverse effect on overall employee productivity, however, recently, due to COVID19 pandemic move to remote working also pose a cybersecurity risk to organizations networks and infrastructure. In this work-in-progress research study, we are developing, validating, and will empirically test taxonomy to assess an organization’s remote workers’ risk level of cybersecurity threats. This study includes a three-phased developmental approach in developing the Remote Worker Cyberslacking Security Risk Taxonomy. With feedback from cybersecurity Subject Matter Experts (SMEs) on the taxonomy and measures, we then plan to use the taxonomy to assess organizational remote workers’ risk level of cybersecurity threats by using actual system indicators of productivity measures to estimate their cyberslacking along with assessing the computer security posture of the remote device being used to access organizational resources. Anticipated results from 125 anonymous employees will then be assessed on the proposed novel taxonomy where recommendation to the organizational cybersecurity leadership will be provided.
... The concept of productivity is an important performance indicator, but this term is often misunderstood, inadequately measured, compromising its use (Berlak et al., 2021). Improving productivity is a prime factor for the economic growth and wealth of any nation, contributing to better wages, higher profits, and cheaper items, benefiting society (Vogl and Abdel-Wahab, 2015). According to Fu et al. (2021), construction productivity is a key indicator in assessing the efficiency of the production process. ...
... labor productivity), TFP has been regarded as an ideal concept for an in-depth productivity analysis (X. Wang et al. 2013, Vogl and Abdel-Wahab 2015, Chancellor and Lu 2016, X. Hu and Liu 2017, Zhan and Pan 2020 because it can take other resources (e.g. materials and capital) into the calculation process (Zhan and Pan 2020). ...
Industrial agglomeration (IA), a common industrial phenomenon, has been verified to have a significant impact on total factor productivity (TFP) in many industries. However, the impact of IA on TFP is seldom investigated in the construction industry, despite the existence of the industrial agglomeration phenomenon in the construction industry. As such, this study aims to probe into the impact of IA on TFP in the construction industry, so as to provide new insights into the industry development and improvement of TFP in the construction industry. Based on the competing results of the agglomeration effect and congestion effect caused by IA, this study proposed three hypotheses on the impact mechanism of IA on TFP in the construction industry. Then, the non-linear regression model and linear regression model were developed to test the hypotheses based on the provincial panel data from 2002 to 2017 in China. The empirical results reveal that IA has a positive linear impact on TFP in the construction industry, and the impact of IA on TFP in the Chinese construction industry during the observed period is in the embryonic stage. Besides, both the firm scale and economic development level have positive impacts on TFP, whereas the specialization structure has a negative impact. Hence, the government can encourage industrial agglomeration in the construction industry to enhance TFP, in order to leverage the knowledge spillovers, labor pool, and other benefits from IA.
... In most cases, productivity is measured as the ratio of outputs to inputs. The variables that influence productivity are numerous, complex, interconnected, and dynamic (Vogl and Abdel-Wahab, 2015). If the only input considered is labor, then it is referred to as labor productivity, and if all possible inputs are considered, it is referred to as total factor productivity. ...
Purpose
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.
Design/methodology/approach
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).
Findings
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.
Originality/value
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.
... Unfortunately, it is lopsided to suggest that the coordinated development of the new urbanization and construction industry can be viewed through a single system. In addition, measurement of the development level of the construction industry has primarily been studied from the perspectives of sustainability (Karji et al., 2019;Xu et al., 2019), development potential (Ozkan et al., 2012), competitiveness (Castells-Quintana, 2017), and production efficiency (Chancellor and Lu, 2016;Vogl and Abdel-Wahab, 2015). These studies provide many reference indicators for the development of the construction industry. ...
Purpose
The coordinated development of the urbanization and construction industry is crucial for the sustainable development of cities. However, the coupling relationship and coordination mechanism between them remain unclear. To bridge this gap, this study attempts to explore the level of coupling coordination between new urbanization and construction industry development and investigate the critical driving factors influencing their coupling coordination degree.
Design/methodology/approach
By referring to the existing literature, two index systems were established to evaluate the development level of the new urbanization and construction industry. The spatiotemporal characteristics of the coupled coordinated development of the new urbanization and construction industry in China from 2014 to 2020 were investigated using the coupling coordination model. The Markov chain and geographic detector were adopted to understand the transition probability and driving factors of the coupling coordination degree.
Findings
The results indicate that the coupling degree of China's new urbanization and construction industry is high, and the two systems exhibit obvious interaction phenomena. However, the construction industry in most provinces lags behind the new urbanization. A positive interactive relationship and coordination mechanism has not been established between the two systems. Furthermore, the coupling contribution degree of the driving factors from high to low is as follows: market size > labor resource concentration > government investment ability > economic development level > industrial structure > production efficiency > technology level. Accordingly, a driving mechanism including market, policy, economic, and production technology drivers was developed.
Originality/value
This study contributes to the existing body of knowledge by providing a set of scientific analysis methods to address the deficiency of coordination mechanism research on new urbanization and the construction industry. The results also provide a theoretical basis for decision makers to develop differentiated sustainable development policies.
... It is essential to note that these construction industry measures are not homogeneous, and their outcomes cannot be measured using one physical measurement unit. The heterogeneous findings must be recognized, and ways must be created to interpret them [19]. Furthermore, construction firms' performance as a discipline, is growing in depth [20]. ...
While the mainstream of construction management research has paid attention to the causes of poor performance in construction projects, there is a dearth of empirical research that considers the influence of external environment and institutional pressures in the debate. The objective of this research was to examine the impact of external environmental factors and institutional pressures on the performance outcomes of construction firms. The role of institutional pressures as a mediator in that relationship was also evaluated. Using a self-administered questionnaire, primary data was collected from 250 executives working in construction firms. This was then analysed using partial least squares structural equation modelling. The results demonstrated that both external environmental factors and institutional pressures have an impact on the performance of construction firms. In light of the institutional theory, the findings addressed a genuine research gap, as institutional pressures were discovered to mediate the relationship between external environmental factors and construction firm performance outcomes. This study contributes to the current debate about the causes of poor performance in construction firms by assisting managers in recognizing the impact of the aforementioned factors on a firm's performance.
... The construction industry is one of the most important economic sectors in each country [1], and it makes a significant contribution to gross domestic product (GDP) across the world [2,3]. Although construction productivity is a vital factor in sustaining economic growth [4], it has barely increased and may have even decreased during the last five decades [5]. Moreover, it has been reported that productivity in the construction industry is lower than that in many other industries [6,7]. ...
The construction industry, as a large economic sector in industrialized countries, is falling behind other industries in terms of benefitting from automation and robotics to improve its productivity. Therefore, the need for implementing new technologies to resolve construction productivity issues is paramount. By dividing the main research activities of the Robotics and Automation in Construction (RAC) into two large groups, civil infrastructure and residential/commercial buildings, the purpose of this study is to prioritize the areas in which the RAC can be adopted to improve the productivity of civil infrastructure projects. First, different RAC adoption categories in heavy civil projects and main limiting factors were extracted by conducting a literature review. Thereafter, to prioritize the areas with higher potential and need of shifting to automated practices, a structured questionnaire survey was designed to elicit lived experiences of construction professionals with an average of 19 years of experience in heavy civil construction. The results show that the off-site automated prefabrication systems are perceived as the first effective category of RAC adoption, followed by using drones and autonomous vehicles on-site. In addition, top barriers and limiting factors were identified as reluctance of the construction industry to change, high cost of implementation, and lack of government incentive. Overall, it is expected that the findings of this study can help policymakers and construction companies prioritize the investments that are made in RAC implementation in civil infrastructure construction projects. Furthermore, the survey results can further provide insights into the main barriers and challenges in each RAC application area.
... Concerning the application of benchmarking and performance measurement in civil construction, several papers have been reported in the literature. The proposition of Key Performance Indicators (KPIs) and benchmarking models are presented by Beatham et al. (2004), Chan & Chan, (2004), Fang et al. (2004, Ramirez et al. (2004), Lee et al. (2005), Park et al. (2005), Costa et al. (2006), Cox et al. (2006), El-Mashaleh et al. (2007, Yeung et al. (2007), Yeung et al. (2008), Yeung et al. (2009a), Yeung et al. (2009b), Ahuja et al. (2010), Yeung et al. (2013), Abbasian-Hosseini et al. (2014) and Vogl & Abdel-Wahab (2015). However, studies which approach the proposition of flexible benchmarking models in the civil construction sector are scarce in the literature. ...
This paper aims at presenting a new flexible benchmarking model in civil construction. It describes an explanatory case study whereby data are collected through an internet benchmarking system with multi-criteria performance. This case study was based on the Production Arrangements Monitoring and Benchmarking System (SIMAP), the authors describe a flexible benchmarking model considering 46 criteria classified into 7 subsystems. The proposed benchmarking system can be used by companies from other countries with proper adaptations.
... The architecture, engineering, and construction (AEC) industry is experiencing low productivity than many industries (Abdel-Wahab and Vogl, 2011;Tookey, 2011;Vogl and Abdel-Wahab 2015). Why is low productivity in the AEC industry concern for governments? ...
Reducing energy consumption in agroecosystems alone is inefficient. Dependence of food security on agroecosystems in the globe and the high share of environment emissions from agricultural activities in the world, which is about 10%–12% of the world's carbon dioxide emissions, cause comprehensive optimization of agricultural systems to be important. Data envelopment analysis (DEA) and life cycle assessment (LCA) are among the most widely applied methods in optimizing and evaluating environment, respectively, which have been considered by many researchers in recent years. As such, the aim of this work is to evaluate the integration of LCA and optimization of energy consumption applying DEA. In this regard, methods of computing input–output energy, environmental emissions, energy optimization using the DEA method, and reducing the amount of environmental emissions by this method are fully described and results are then interpreted.
... The architecture, engineering, and construction (AEC) industry is experiencing low productivity than many industries (Abdel-Wahab and Vogl, 2011;Tookey, 2011;Vogl and Abdel-Wahab 2015). Why is low productivity in the AEC industry concern for governments? ...
This chapter addresses the joint use of Data Envelopment Analysis (DEA) and Life Cycle Assessment (LCA) as a source of synergistic frameworks for quantitative sustainability assessment and benchmarking of multiple similar entities. In addition to current progress in the growing field of research in (environmental) LCA + DEA, novel approaches further integrating social life-cycle indicators are proposed. This enhanced sustainability scope is expected to further promote the combined use of life-cycle approaches and DEA when assessing and benchmarking a large number of resembling entities.
... While the previous studies focused on the US market, Sriram et al. (2015) argued that worldwide construction productivity has decreased in the period from 1995 to 2012. In addition, scholars have argued that macro-economic (Rojas and Aramvareekul, 2003) and international (Abdel-Wahab and Vogl, 2011;Vogl and Abdel-Wahab, 2015) studies suffer from methodological issues that raise questions as to their conclusions. ...
Productivity is widely recognized as one of the main contributors to increased economic and societal wellbeing. Unfortunately, productivity has been extremely difficult to operationalize in a repeatable context in the construction sector. The result is a lack of consensus on the basic question of whether there has been improvement or decline in the productivity of the sector. This study focuses on productivity in the housing industry. Productivity is especially important in this industry, as in addition to providing shelter, the housing market is the primary source of wealth accumulation in the US. An individual’s ability to enter this market will be a function of affordability which will be effected by the productivity of the industry. The combination of academic and societal impacts suggests that there is a need to address a fundamental question: what is the status of productivity in the housing industry. In order to address this question a data base was compiled from the 10-Ks of the largest, long lived, US companies in the single family housing industry. The result is a panel data set that consists of information on 11 firms over a 15-year period. These 11 firms were responsible for approximately 25% of all new home sales in any given year. The data set was analysed with random effects GLS time series regression. The results indicate that, at best, the housing industry has seen negligible total productivity growth.
... BIM has been acknowledged as one of the most appropriate platforms for the Architecture, Engineering, and Construction industry, which is considered to be multi-organizational and multi-disciplinary as a solution to improve construction performance challenges during the planning, design, construction operation, and maintenance stage of entire project lifecycle [4] The construction industry at a global level is suffering from poor productivity and inefficiencies [1] and [5] Numerous research and development have been produced to minimize these performance challenges in the construction industry. ...
Building Information Modeling (BIM) has been recognized as one of the most efficient technological novelty in response to the challenges within the construction industry. Most of the developed countries have adopted BIM to improve the performance of their construction industry. The purpose of this study was to identify and cluster the benefits of BIM into the Planning, design, construction, and Operation and maintenance stages of the project lifecycle. It was also intended to identify the challenges of implementing BIM in the construction industry in Ethiopia. The challenges have been clustered into the process, technology, organization, human, and policy-related challenges. An online questionnaire was distributed to 235 participants to collect data from practitioners involved in the design, construction, and research activities from academia and industry. Relative importance index (RII) was used to analyses and rate the identified variables. Based on RII values, the top-ranked perceived benefits of BIM in the AEC sector of Ethiopia were easy quantity and cost estimates, Timely integration and data sharing, digital project documentation, and ease of producing as-built documents during planning, design, construction, and operation and maintenance stages respectively. On the other hand; Lack of collaborative initiatives from the industry, fragmented nature of the industry, none widespread of BIM are identified as process-related barriers to BIM adoption. Whereas; Insufficient ICT infrastructures, High cost of BIM technology, the longer time required to adopt BIM has been identified as technology-related barriers to BIM adoption. Organization related barriers to BIM adoption were: Lack of BIM-based workflow, Lack of comprehensive framework and implementation plan, and lack of senior management support and attention. Human or stakeholder related barriers to BIM adoption include Lack of adequately trained BIM professionals, lack of client awareness and knowledge about BIM, and unfamiliarity with BIM use. Finally; Standard and policy-related barriers to BIM adoption were found to be a lack of BIM National Standards and guidelines, Lack of legal framework for BIM application, and the Absence of standard BIM-based contract documents and contractual relationships. Finally, recommendations were forwarded to those stakeholders who have a significant role in avoiding the barriers in adopting BIM and to properly use benefit from technological innovation.
... Given the diverse nature of the construction industry, it is impossible to aggregate all types of outputs and measure them with one physical measurement unit. It is essential to understand the heterogeneous results and develop ways to analyze them [26]. ...
Organizations have been trying to increase their efficiency and improve their performance in order to achieve their goals. Various factors determine organizational success. The construction industry is a project-based industry which is exceptionally dynamic. The need to identify the weak points and search for solutions to improve the performance of the construction organization is extremely crucial. The industry has always focused on the measure of project success. Previous research works have primarily focused on the measurement of financial or tangible assets. However, there is a lack of understanding of qualitative factors and their combined effect on organizational performance. Therefore, the objectives of this paper are to identify and study the success factors—both financial and non-financial factors. The potential success factors are collected from the literature review and construction experts through a questionnaire to evaluate their effect on organizational performance. The collected data have been analyzed using the Analytic Hierarchy Process (AHP) to shortlist the critical success factors. Thereafter, the Hierarchical Fuzzy Expert System has been used to build a prediction model based on the selected factors. The developed research/model benefits both researchers and practitioners to predict accurate company performance.
In the present research, we explore the differential effects of 'intellectual capital' (IC) on the financial performance of 100 Indian SMEs listed on the BSE SME, focusing on the pre-COVID (2013-2014 to 2019-2020) and during the COVID (2020-2021 to 2022-2023) periods, using pooled OLS, static panel regression, followed by the robust GMM estimation. The findings demonstrate varying impacts of IC under different economic phases. The results from Pre-pandemic phase reveal that profitability of Indian SMEs is predominantly driven by the efficiencies of capital employed and structural capital, while each IC component constitute the core drivers of productivity. The analysis of the COVID period indicates that human, structural, and financial capital were identified as primary factors in fostering profitability, whereas, human capital, financial capital and relational capital are key drivers in optimising productivity. This novel comparative analysis highlights the evolving role of IC under different economic conditions, offering practical insights for policymakers, investors and SMEs. The findings pinpoint the need for adaptive IC strategies to enhance resilience and performance during economic disruptions. These results provide a framework to harness IC in navigating economic downturns, offering lesson for SMEs globally in leveraging IC for sustainable growth.. He has more than 17 years of teaching and research experience, specialising in intellectual capital management, disclosure, and risk management. He published several research papers in various national and international journals of repute and presented papers in national and international seminars and conferences. He is also a reviewer of national and international journals.
Purpose: Construction Labour Productivity (CLP) has been identified as one of the causes of the decline in the construction sector's growth. The decline has mainly been caused by a lack of full exploration of the constraint factors, strategies and parameters used in the prediction improvements have not been fully explored. The study aimed at identifying and assessing factors contributing to labour productivity in construction projects in Zambia. Design/Methodology/Approach: The research adopted a cross-sectional descriptive design using quantitative methods. The study data was collected using 150 structured questionnaires administered to critical site personnel such as Project Managers, Site Engineers, General Foremen, Bricklayers and Handymen with a response rate of 81% obtained. Out of 150, about 122 respondents confirmed the extent of the impact of the factors through a survey questionnaire whose scale was tested using Cronbach's Alpha which was found to be > 0.8. The primary data which was obtained from questionnaires was analyzed using descriptive statistics. Findings: The research identified 36 key constraint factors of labour productivity and grouped them as project-related, management-related, labour-related and industry-related. The top five ranked constraint factors included: challenging working heights, poor time planning and scheduling, poor sequencing of work, lack of experience and skill, and poor working environment. The study confirmed through 74.60% of the participants that productivity is highly affected negatively by the presence of Project-related, Management-related, Industry-related and labour-related factors. Based on the findings, there is a need for construction firm managers to develop a strategy to measure, monitor and document data on the labour productivity of every project activity and site, to increase organizational efficiency. Implications/Research Limitations: The research did not focus on productivity from projects dependent on plant and equipment but rather focused on labour-intensive projects. Furthermore, the present study focused on specific trades such as bricklaying and concreting. Similar studies aimed at enhancing productivity in plant and equipment or other trades would provide further information in coming up with more comprehensive policies. Practical Implications: The research findings are key to improving productivity in labour-intensive construction projects and generating data for decision-making by policymakers and construction managers. Originality/Value: With the identified decline in the growth of the construction sector, enhancing labour productivity is being encouraged though there is evidence that strategies and parameters for predicting improvements have not been fully explored. Thus, the current study is among the few to be undertaken in Zambia and provides critical industry information for planning and enhancing labour productivity in the construction sector.
Purpose: Labour productivity has been identified as one of the causes of the decline in the construction sector's growth, mainly because factors, strategies and parameters for predicting improvements have not been fully explored. The study aimedat identifying and assessing factors contributing to labour productivity in construction projects in Zambia. Design/Methodology/Approach: The study adopted a cross-sectional descriptive design using quantitative methods. Data was collected from 150 structured questionnaires administered to Project Managers, Site Engineers, General Foremen, Bricklayers and Handymen with a response rate of 81%obtained. 122 respondents confirmed the extent of the impact of the factors through a survey questionnaire whose scale was tested using Cronbach's Alpha which was found to be > 0.8. The primary data which was obtained from questionnaires was analyzed using descriptive statistics. Findings: The study identified 36 key factors of productivity and grouped them as project-related, management-related, labour-related and industry-related. The top five ranked factors included: working heights, poor time planning and scheduling, poor sequencing of work, lack of experience and skill, and poor working environment. The study confirmed through 74.60% of the participants that productivity is highly affected negatively by the presence of Project-related, Management-related, Industry-related and labour-related factors. Based on the findings, there is a need for construction firm managers to develop a strategy to measure, monitor and document data on the labour productivity of every project activity and site, in order to increase organizational efficiency. Implications/Research Limitations: The study did not focus on productivity from projects dependent on plant and equipment but rather focused on labour-intensive projects. Furthermore, the present study focused on specific trades such as bricklaying and concreting. Similar studies aimed at enhancing productivity in plant and equipment or other trades would provide further information in coming up with more comprehensive policies. Practical Implications: This study's findings are key to improving productivity in labour-intensive construction projects and generating data for decision-making by policymakers and construction managers. Originality/Value: With the identified decline in the growth of the construction sector, enhancing labour productivity is being encouraged though there is evidence that strategies and parameters for predicting improvements have not been fully explored. Thus, the current study is among the few to be undertaken in Zambia and provides critical industry information for planning and enhancement of labour productivity in the construction sector.
Job satisfaction is significant multifactor influencing workforce productivity. Job satisfaction is termed as positive emotional circumstances of an employee experience with particular job. Particular analysis is accomplished to examine the effects of job satisfaction towards increasing professional employee’s productivity levels in the construction organizations and to determine factors correlated with productivity decrease among professional construction employees with special reference to various construction industries in Chennai. In this study, 500 respondents selected on various job levels in construction industry are participated. Data was collected using demographic questionnaire and job performance-centric questionnaire as job descriptive index to investigate the job satisfaction level. Further questionnaire investigates effectiveness of the professional employee to increase the company productivity on managing the employee in the study. The job satisfaction level was considered as low, moderate and high, respectively. Also, their productivity was computed as moderate. Furthermore, positive association among between job satisfaction and work productivity indices was considered as significant with t-value as 2.624. The statistical analysis using SPSS software represents that productivity was significantly correlated with dimension of job satisfaction such as work autonomy and fringe benefits.
Purpose
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.
Design/methodology/approach
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.
Findings
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.
Practical implications
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.
Originality/value
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.
Purpose
The purpose of this paper is to understand if and how industrialized house building (IHB) could support productivity developments for housebuilding on project and industry levels. The take is that fragmentation of construction is one explanation for the lack of productivity growth, and that IHB could be an integrating method of overcoming horizontal and vertical fragmentation.
Design/methodology/approach
Singe-factor productivity measures are calculated based on data reported by IHB companies and compared to official produced and published research data. The survey covers the years 2013–2020 for IHB companies building multi-storey houses in timber. Generalization is sought through descriptive statistics by contrasting the data samples to the used means to control vertical and horizontal fragmentation formulated as three theoretical propositions.
Findings
According to the results, IHB in timber is on average more productive than conventional housebuilding at the company level, project level, in absolute and in growth terms over the eight-year period. On the company level, the labour productivity was on average 10% higher for IHB compared to general construction and positioned between general construction and general manufacturing. On the project level, IHB displayed an average cost productivity growth of 19% for an employed prefabrication degree of about 45%.
Originality/value
Empirical evidence is presented quantifying so far perceived advantages of IHB. By providing analysis of actual cost and project data derived from IHB companies, the article quantifies previous research that IHB is not only about prefabrication. The observed positive productivity growth in relation to the employed prefabrication degree indicates that off-site production is not a sufficient mean for reaching high productivity and productivity growth. Instead, the capabilities to integrate the operative logic of conventional housebuilding together with logic of IHB platform development and use is a probable explanation of the observed positive productivity growth.
Model of productivity estimation and monitoring for manufacture of metal structure components Resumo Neste artigo, busca-se avaliar a viabilidade de um modelo para estimar e monitorar a produtividade global, que considera os tempos produtivos e improdutivos, na pré-fabricação de componentes, em uma empresa construtora de estruturas metálicas. O modelo é constituído das funções "Ociosidade" e "Produtividade Intrínseca". A Produtividade Intrínseca considera apenas o desempenho dos procedimentos operacionais. O processamento de dados utilizou como ferramenta o "Método de Monte Carlo" via software @risk 7.5 da Palisade Corporation. Os resultados revelam que o método proposto é viável, apresenta grau de precisão adequado e baixo custo de implementação. Adicionalmente, o processamento dos dados via Método de Monte Carlo, gera como subprodutos gráficos de Tornado, que permitem identificar, quais variáveis devem ser priorizadas para implementação de ações visando à melhoria da produtividade. Palavras-chave: Produtividade, Estruturas Metálicas, Método de Monte Carlo, Ociosidade. Abstract This article aims to evaluate the feasibility of a model to estimate and monitor global productivity, which considers productive and unproductive times, in the prefabrication of components, in a company that builds metallic structures. The model consists of the functions "Idleness" and "Process Productivity". Process Productivity considers only the performance of operational procedures. Data processing used as a tool the "Monte Carlo Method" via software @risk 7.5 from Palisade Corporation. The results reveal that the proposed method is feasible, presents a degree of precision adequate and low cost of implementation. Additionally, the processing of data via the Monte Carlo Method, generates as sub-products tornado graphics, which allow to identify, which variables should be prioritized for the improvement of productivity.
Construction is one of the key parameters for social and economic growth for any nation, but it is also named as one of the major wastes generating process. However, it has sufficient potential to minimize or reduce any waste generation. Advance planning and monitoring start from the project till the end of construction can reduce waste generation up to a large extent. “Zero waste” is one of the most visionary approaches to overcome waste problems. In this research, the zero-waste approach is correlated for building construction work to assess the practical applications on a design and process throughout the life cycle. A state of zero waste can be reached by holistically implementing the revolutionary concept of prefabrication, demonstrability, circular economy, green rating tools and the principle of reducing, reuse and recycle. Further practices like smart waste audit, smart waste collection,high value mixed waste processing, and the collaborative platform further help towards the prime target of zero waste construction.
Focuses on applications and advantages of eco-friendly construction materials.
Discusses latest advances in landslide mitigation technologies.
Contains the selected contributions from the international conference ICCME 2020.
The achievement of economic growth depends on productivity in economic contributing sectors. Governments are encouraging architecture, engineering, and construction (AEC) industry professionals to adopt digital solutions in their work process to solve productivity problems in the AEC industry. A primary question guides the development of this chapter. Can digital transformation alone be enough to improve productivity in the AEC industry? This chapter’s main argument is that digital solutions should be applied to speed the process of effectively doing a risk assessment and eliminating or reducing the intensity of the root cause of a problem instead of the problem as they are currently adopted in the sustainable building delivery process. Whereas, lean thinking helps put a system, based on risk assessment, in place to reduce waste. Lean thinking can help project managers to effectively do problem analysis, identify the root cause of a problem, and provide knowledge on how the solution can be provided to eliminate or reduce the root cause’s intensity. The role of project managers in charge of the entire building delivery process is emphasised. The knowledge put forward in this chapter is that the digital transformation of a sustainable building delivery process can only lead to high value-oriented high productivity if it is grounded in lean thinking.
This experimental study aims at evaluating the performance of coal bottom ash (CBA) mortar after exposure to elevated temperatures. Two mortar mixes were prepared in which one was conventional natural sand (NS) based mortar and other mix contains CBA as 100% replacement of NS. The test specimens were cured for 28 days at ambient condition and then was heat treated at different elevated temperature (200, 400, 600 and 800 °C) using an electric controlled high-temperature furnace. The effect of elevated temperature on the mass loss, ultrasonic pulse velocity (UPV) and compressive strength of mortar specimens was evaluated after the high-temperature treatment. In addition, empirical equation derived from regression analysis was proposed to predict the residual mechanical strength of mortar incorporating CBA as fine aggregate at different elevated temperature. The test results signify that both mass and UPV decreases with increases in temperature, however, residual compressive strength of CBA mortar was better or comparable to NS mortar up to 400 °C, but above 400 °C; strength reduction rate in CBA mortar was higher than NS mortar.
Purpose
Since China's accession of the World Trade Organization (WTO), its construction industry has attained unprecedented growth. However, for the sources of this enormous growth, a controversy regarding the total factor productivity growth (TFPG) still remains in production practice and extant studies. In view of this, the purpose of this paper is to measure TFPG and to explore its sources in the industry post-WTO accession.
Design/methodology/approach
This study presents an innovative source analysis of TFPG. Stochastic frontier approach is adopted to measure TFPG and to explore its sources by decomposing TFPG into technical progress (TP), technical efficiency change (TEC), allocative efficiency change (AEC) and scale efficiency change (SEC). Although China joined WTO in 2001, to provide an effective baseline, the study period is from 2000 to 2017.
Findings
The empirical results reveal that TFPG presented an overall downward evolutionary trend, but it still maintained a high growth post-WTO accession. From the perspective of decomposition, TP was the main source of TFPG. Furthermore, as a neglected source, interaction effects among TP, TEC, AEC and SEC have been demonstrated to have a significant influence on the cumulative TFPG.
Practical implications
To make the results be reliable, the authors discuss the empirical findings mainly by revealing the reasons behind the evolutions of TFPG and its sources. Based on these revealed reasons, government and policy makers can further refine and summarize some more detailed and targeted policy implications to improve TFPG.
Originality/value
By providing many empirical evidences to solve the aforesaid TFPG controversy, this paper, therefore, enriches the body of knowledge on growth theories, especially at the level of industrial economics.
Concrete pouring activity is essential for the schedule and quality of the structural work construction. In practice, the process of concrete pouring is frequently congested and interrupted due to many unforeseeable reasons. The primary purpose of this study is to explore the productivity of concrete trucks for multistory building projects. The interview technique and work sampling method have been employed to collect the necessary data. Based on the literature review and experts’ opinions, twenty-five factors affecting the productivity of pouring concrete have been found and discussed. Among them, there are seven factors identified as different from previous studies. Through two case studies of the hospital project, the actual average productivity of one concrete truck used to pour concrete into columns and walls is 0.184 m3/min by using a truck-mounted pump and 0.087 m3/min by using a tower crane. These productivities have been then determined based on discrete event simulation (DES). The simulation results indicated that the simulated productivity is higher than the actual productivity of approximately 16% and 13% for truck-mounted pump and tower crane, respectively. It is concluded that DES is a handy simulation tool for construction operations before the period of implementation. Based on the relationship between events of the process of concrete pouring, two relevant solutions have been proposed to enhance the productivity of concrete trucks. The results of this study may help practitioners manage the concreting activities in their projects with higher productivity.
In recent years there has been a rising interest in the level of productivity and efficiency of the New Zealand construction industry. This interest has meant that there has been an increased use of statistical techniques to determine the productivity and efficiency of the overall industry. The purpose of this paper is to review the various measures that have been used to gauge the levels of productivity and efficiency in the New Zealand construction industry; as well as analyse some of the results to date. Finally, it considers potential areas for future research, including possible improvements to measurements techniques that maybe used.
Previous research work concerning construction productivity has often been related to labour productivity. Although easy to measure and understand, labour productivity may be misleading in measuring the efficiency of utilization of resources. An alternative concept — total factor productivity — is preferred. However, this concept has seldom been used in empirical research of construction productivity due to problems of measurement and availability of data. This paper presents a method of indirectly measuring the total factor productivity of the construction industry using various construction cost and price indices and other statistics. Although special reference has been made to the construction industry of Hong Kong, the same approach should also be applicable to other countries.
Construction productivity trends carry immense consequences for the economy as a whole. However, there is little scholarly consensus concerning even the direction of such trends. The main objectives of this paper are to (1) present an approach to studying long-term productivity trends in the U.S. construction industry; and (2) provide a preliminary indication of such trends over the past 25-30 years. Subsequent, extended statistical studies are suggested that may be based on the approach of the selected work presented here. Labor cost and output productivity trends are tracked for tasks that represent different trades and differing levels of technological intensity within the building construction sector. Specific tasks dealt with a range from a zero technology impact task, such as hand trenching, to compaction with a sheepsfoot roller. Means's cost manuals were used to trace the benchmark values for these tasks. These values reflect productivity trends. Unit labor costs in constant dollars and daily output factors were compared over decades for each task. Direct work rate data from 72 projects in Austin, Tex., over the last 25 years were also examined. Increasing the direct work rate usually increases construction productivity. The combined data indicate that productivity has increased in the 1980s and 1990s. Depressed real wages and technological advances appear to be the two biggest reasons for this increase. The data also indicate that management practices were not a leading contributor to construction productivity changes over time. Subsequent studies are required to add weight to these observations and can be based on the approach presented here.
Construction productivity has been generating significant interest in both the construction industry itself and academia.
Productivity management is currently recognized as a formal project management process in construction. However, most previous
studies focused on defining factors that influence productivity and on measuring limited parts of activities at a micro level
to investigate the relationship between factors and productivity. Construction productivity rates differ between projects
because of the varying environments, characteristics, and project management efforts for each project. This study performed
an extensive liter ature review on productivity in construction to support the rationale of a proposed conceptual productivity
estimation model. The conceptual model is proposed for estimating productivity: expected productivity based on both project
environment factors and management efforts. It also presents a comparison between project productivity expected, given the
project environment and level of management efforts, and the raw (observed) productivity measure in the field.
EBS’s estimates of relative productivity in construction are as follows:
1.
The US is about 25-35% ahead of the UK and Germany in terms of average labour productivity (ALP).
2.
The UK is ahead of Germany in ALP on an output per worker basis, but not on an output per hour worked basis (this is due to Germans working fewer hours per week on average).
These results are largely unchanged under various sensitivity tests, for example, using GDP PPP exchange rates instead of construction PPP exchange rates to convert national currencies to a common currency.
The EBS estimate for the US-UK comparison is supported by UCL/DL (who estimate a US lead in ALP of 42% in 1999).
UCL/DL’s estimates for the Germany-UK comparison are also similar to those of EBS, since they show Germany level with the UK in ALP on an output per worker basis, but ahead on an output per hour worked basis.
Productivity comparisons of the UK with France are subject to difficulties. Depending on the exchange rates they use for conversion purposes, EBS find that France is well ahead of Britain on some measures of ALP (and indeed is close to the US) but on other measures French ALP is much the same as in Britain.
UCL/DL argue strongly for using an exchange rate, which shows French construction ALP to be well ahead of the UK. They state that: ‘The French construction PPPs have been controversial for some time….[and tend to lead to] underestimates of French construction output’.
There are difficulties in conducting this type of analysis that are hard to surmount, for example it is unlikely that labour inputs are well measured in any country because of illegal immigration, ‘ the hidden economy’, etc. However, unless there is reason to think that the proportion of uncounted construction workers is significantly higher or lower in Britain as compared to, say, the United States, then it seems reasonable to accept that ALP in the US construction industry is some way ahead of the UK.
In many ways cross-country comparisons of productivity levels across the whole construction industry are not comparing like with like since the composition of construction output differs greatly from country to country. It is therefore hard to construct reliable national rankings based on aggregated data for construction given the present state of the data.
Benchmarking is a systematic process of measuring and comparing an organization's performance against that of other similar organizations in key business activities. The lessons learned from other companies can be used to establish improvement targets and to promote changes in the organization. The benchmarking process can create a fertile ground for ideas, but only in a receptive environment; companies that share good practices and compare their performance against others benefit most. Recently, industry groups in several different countries have initiated benchmarking programs focused mainly on construction performance measures. This paper describes the scope of these initiatives and discusses the lessons learned and improvement opportunities that were identified in their design and implementation. This investigation is focused on four initiatives, carried out in Brazil, Chile, the United Kingdom, and the United States. This paper concludes by recommending some further directions on this research topic.
The nature of the labour process in the British construction industry is different from that in Germany. The rationale of the British system is based on controlling costs through overseeing contract relations, themselves circumscribing a range of narrow, clearly defined and priced tasks. The production process has become secondary and production expertise restricted. In contrast, in Germany cost aspects are incorporated into, rather than separated from, the production system, built on the interaction of capital and labour and on a high level of production expertise. Employment relations rather than contract relations predominate and circumscribe a set of skills drawn from the potential of the labour force and dependent on broad-based vocational education. A detailed investigation of social housebuilding projects and the firms involved in Britain and Germany reveals the need for more skills, and a qualitatively different constellation of skills, professional and operative, in Britain.
Construction is an increasingly global industry, and benchmarking initiatives that are restricted to a single country run the risk of complacency, as national best practice falls out of line with international best practice. The aim of this article is to report the results of a detailed comparative analysis of the on-site performance of the UK and French divisions of a major UK construction corporation. It focuses on structural concrete because this is potentially a major element in the budget and programme of any construction project, and an area of known French strength. It deploys an innovative computerized productivity measurement tool based on activity sampling, 'CALIBRE', in order to provide detailed comparative data. The results show that the UK productivity performance is poorer than in France, and that the poor UK performance compared with the French is explained by elaborate divisions of labour, lack of investment in plant, and less effective work organization. However, as will be argued in the conclusions, these differences cannot be understood without reference to the overall contracting system and the constraints upon action that it imposes.
International productivity comparisons can be built up with micro and macro data. Studies of firms or groups of firms producing similar outputs reveal the deeper causes of differences in productivity across countries. The studies find that such differences often depend on patterns of organization within firms, the motivations of managers and the like. The intensity of domestic and international competition can have a large impact on productivity. The case of retailing illustrates the importance of industry evolution. High productivity retailing formats drive out traditional retailers, unless restrained by land-use restrictions or regulations.
A contrast of site productivity levels for an in situ concrete operation (reinforcement fixing) on a high-rise project amongst construction contractors from Germany, France and the UK is given. The productivity rates provided by contractors' planning engineers for a model construction project form the basis of this evaluation. Conclusions drawn, based on relatively small samples, are considered approximations of the actual productivity levels in each international location. An analysis of variance based on international origin indicates significant differences between these productivity rates. Generally, amongst the sample surveyed, UK and German contractors exhibit the most efficient levels of labour productivity for the operations observed, whilst French contractors are by far the least productive. For the model building, UK contractors are the most productive, requiring less labour input than those from Germany and France. The UK contractors also demonstrate a high degree of performance variation. Leading on from these analyses, a construction (labour) cost comparison indicates the UK to be the most economic location. A comparison with previous research indicates contrasting findings. It is concluded that the performance ranking of French, German and UK contractors will vary depending upon the construction operations concerned, and therefore, assumptions regarding national contracting industries should not be based on individual operations. Contractors could benefit from developing closer links with their international counterparts since this would facilitate dissemination of European ‘best practice’.
A report of the Construction Research and Innovation Strategy Panel (CRISP) addresses the conceptual issues and practical aspects to understand the social and economic value of construction. Some of the factors that need to be considered include the performance of the construction industry, additional indicators to access the industry's medium and long-term viability such as economic competitiveness, industry health and well-being, and the factors of robustness, capabilities, social and economic factors. The scope has been broadened to include economic competitiveness, health, and well-being. The report has provided beginning for substantial contributions to research in construction and to expose ideas to international research and policy communities.
To overcome data deficiencies in measuring trends in U.S. nonmanufacturing productivity, the Bureau of Labor Statistics, the Bureau of Economic Analysis, and the Census Bureau have expanded and improved the measurement of service sector and other data; even with these changes, many nonmanufacturing industries continue to exhibit negative productivity trends.
The aim is to bring a fresh perspective to the construction productivity research agenda, which is congruent with the new demands in the construction industry and its ever changing nature. The articles which had ‘construction productivity’ as a keyword in the abstract, and were published in each of the three journals (JCEM, CEM and IJP) from the earliest year that the articles had been uploaded to the respective official website of each journal were identified. Out of 5862 articles searched, only 121 articles fulfilled the selection criteria, the titles of which were examined. The past decade has witnessed the continuation of the same relentless research interest in productivity studies. The findings revealed that, in the studies: five types of productivity have been examined; five data collection methods have been deployed; research objects can be classified under seven categories. The research objects in a high number of studies are devoted to ‘measurement of productivity’ and ‘examining the casual relationships with productivity’. The study ascertained that the main drawbacks of past productivity studies are the strong empirical inclination of methodologies adopted and the overwhelming positivist approach to examining productivity issues. The absence of follow-up studies to investigate the validity of productivity measurement techniques and the models and to test the claims made in productivity improvement studies, is a striking feature. Another impressive finding is the lack of scholarly attention to incorporate blue-collar worker perspective, employee involvement, and social dimension into productivity research. DOI: http://dx.doi.org/10.4038/engineer.v46i4.6808 ENGINEER, Vol. 46, No.04, pp. 31-42, 2013
The construction industry is labor intensive by nature; consequently, the measurement of trade-level productivity is a critical component that can be used to assess the overall productivity of the industry. Despite its importance, little research has been conducted to investigate the challenges that contractors and subcontractors face in measuring productivity accurately at the trade level. As a result, this study aims to identify critical challenges in the measurement of trade-level productivity and to explore plausible solutions for these challenges in order to improve productivity assessment in the industry. On the basis of an extensive literature review and a survey and interviews with industry experts, a total of 17 critical challenges and 24 plausible solutions were identified and analyzed statistically. The findings from this study will help to shed light on the difficulties that contractors and subcontractors encounter during trade-level productivity measurement, and will identify solutions that can be used effectively to deal with these challenges.
Since the 1960s, researchers have provided short-term and long-term explanations for low productivity growth in the construction industry. In retrospect, the main challenge appears to be the measurement of changes in heterogeneous input and output qualities. The aim here is to review earlier construction productivity research and to compare it with more recent approaches to quality measurement used when analysing services productivity, ultimately intending to provide guidance for using performance data from construction projects. Relying on the EU KLEMS database, industries with similar patterns of productivity growth are identified, primarily the business services industry. In services productivity analyses, the attempts to introduce output quality measures reflecting customer satisfaction are particularly interesting, as this creates a link to productivity effects on clients. A conclusion is that it should be possible to use the increasing volume of performance indicator data collected for construction project benchmarking for extending the range of output quality variables. However, resource constraints imply that it is infeasible to base industry productivity statistics on project level data reflecting customer satisfaction and customer productivity effects.
A significant body of literature has been dedicated to research studies on construction labor productivity (CLP) and related issues, and many underlying theories and industrial practices on CLP application have been reported. However, research topics about CLP are highly diversified and there is a lack of systematic analysis in CLP-related issues. Through a systematic review of selected papers from well-known academic journals in construction management, major research areas are identified, such as factors affecting CLP, modeling and evaluation of CLP, methods and technology for CLP improvement, trends and comparisons of CLP, effects of change/variation on CLP, and baseline/benchmarking CLP. Critical reviews on these areas are presented by focusing on industry, project, and activity levels to investigate the state of the art and trends in CLP research. Gaps in research and practices are discussed and future research directions are proposed. The outcome of this paper may provide a platform for both researchers and industrial practitioners to appreciate the latest developments and trends in productivity research.
This paper presents an exploratory study to assess the efficiency level of construction companies worldwide, exploring in particular the effect of location and activity in the efficiency levels. This paper also provides insights concerning the convergence in efficiency across regions. The companies are divided in three regions (Europe, Asia and North America), and in the three main construction activities (Buildings, Heavy Civil and Specialty Trade). We analyze a sample of 118 companies worldwide between 1995 and 2003. Data envelopment analysis is used to estimate efficiency, and the Malmquist index is applied for the evaluation of productivity change. Both methods were complemented by bootstrapping to refine the estimates obtained. A panel data truncated regression with categorical regressors is used to explore the impact of location and activity in the efficiency levels. The results reveal that the efficiency of North American companies is higher than the European and Asian counterparts. Other important conclusion points to a convergence in efficiency levels across regions as in North America productivity remains stable, whereas in Asia and Europe productivity improves.
Because of the difficulties in finding homogeneous final outputs, the procedure in general use for the deflation of construction involves the pricing of inputs rather than outputs and consequently ignores changes in productivity. The present paper proposes: (a) a re-definition of output in terms of intermediate products; and (b) because of the inapplicability of a direct method, two indirect methods for obtaining a measure of the change in the average price of intermediate products. One of the two methods combines an index of input prices and a measure of productivity changes, the latter of which may be of some independent interest.* I am indebted to Richard Moorsteen of the RAND Corporation and Raymond P. Powell of Yale University for helpful comments on this paper and discussions of the underlying issues.
The paper provides an overview of methods used to measure productivity in the construction industry. The advantages and disadvantages of average labour productivity and total factor productivity measures are discussed in detail, and the relationship between these two measures is established both theoretically and in an application at the industry level. The usefulness of any productivity measurement framework for policy-makers and industry practitioners alike depends crucially on the extent to which it enables the identification of the underlying drivers of productivity. This requirement necessitates an approach that involves formally describing the production process and explaining as much as possible of construction output in terms of the quantity and quality of inputs that are used to generate it. Whilst it is accepted that data requirements are a major constraint to such an approach, it is suggested that by establishing a robust measurement framework, data deficiencies can be defined more easily. Guidance on areas where improvements are needed is provided and it is considered that the focus of future research should be in creating new and improving existing datasets.
Despite a decrease in industry level measures in construction productivity, there has been a steady increase in construction productivity at the activity level. This research examines equipment technology as one factor that may explain that increase. The relationship between changes in equipment technology and partial factor productivity is examined for 200 activities over a 22 year time period. Specifically, the paper examines the relative impact of different types of equipment technology for five technology factors: energy, control, functional range, information processing, and ergonomics. Through ANOVA and regression analyses, it is found that activities that experienced a significant change in equipment technology also witnessed substantially greater long-term improvements in partial factor productivity than those that did not experience a change.
Factors that can be fairly easily identified and modified and can lead to significant improvements in production rates for activities in construction are considered in this paper. These factors are divided into four work categories. Two of the four work categories in which each construction activity was subdivided were idle and waiting times. Productivity measurements generally do not distinguish between the idle and waiting times. Conclusions can therefore be misleading. and more importantly, the attention of management is only vaguely and imprecisely directed to the cause of the inefficiencies. The breakdown of nonproductive time into two factors is therefore very important in directing the attention of management to the root causes of inefficient time. The variation in production rates used by contractors' estimators are given and compared with actual on-site production rates. The frequency of different sources of information used by contractors when estimating production rates, and the percentage use of production monitoring methods. are also given. A prototype expert system. using the Personal Consultant Plus shell program of 1987. was developed to assist in the acquisition and management of knowledge and data for the estimation of production rates.
Macroeconomics data suggest that labor productivity declined significantly in the construction industry during the 1979-1998 period. However, microeconomic studies indicate the contrary. This paper critically examines the construction labor productivity macroeconomic data in the United States from 1979 to 1998 to determine their validity and reliability. Data collection, distribution, manipulation, analysis, and interpretation are reviewed and problems are identified. The paper also presents a comparison of construction and manufacturing labor productivity during this period. The main conclusion of the study is that the raw data used to calculate construction productivity values at the macroeconomic level and their further manipulation and interpretation present so many problems that the results should be deemed unreliable. The uncertainty generated in the process of computing these values is such that it cannot be determined if labor productivity has actually increased, decreased, or remained constant in the construction industry for the 1979-1998 period.
Traditionally businesses have measured their performance solely in financial terms. This limited approach has been challenged, with the introduction of the concept of key performance indicators (KPIs) for non-financial results. In response to the Latham and Egan Reports, the UK construction industry has developed its own set of KPIs. However, their effective use has been limited. This paper reviews these and other construction KPIs and concludes that most of the KPIs used are post event, lagging measures that do not provide the opportunity to change. Their results are not validated and thus are open to interpretation. The result is that KPIs are being used within the industry as a marketing tool, and not as an integral part of business management. This paper distinguishes between three types of measure and suggests a framework for their effective use within an overall performance measurement system based on change action driven by results.
The present investigation utilizes a bespoke methodology to analyse and compare the productivity rates of contractors' planning engineers for concrete placing operations amongst three European construction industries, namely Germany, France and the UK. An analysis of variance (anova) was used to investigate differences between the productivity rates. Based on such rates, the analysis shows that German contractors achieve the most efficient levels of labour productivity for this particular operation, whilst amongst the sample surveyed, British contractors are less productive than French and German companies. Although leading British contractors can compete with the best on the continent, the least productive companies in the UK sample were inferior to the least productive in France and Germany. Using national all-in rates for labour, actual (labour) costs for this concrete operation were calculated to be lowest in France despite French wage rates being marginally higher than in the UK. This was because of the superior labour output of French contractors. The apparent lower productivity of British firms sampled in the present research concurs with the findings of two other international studies, indicating that the methodology utilized can provide meaningful and accurate productivity information.
International comparisons of contractor performance allow contractors in different countries to benchmark their own strengths and weaknesses and improve their competitiveness accordingly. Based on a survey of contractors in Japan, the UK and the USA, contractor quality performance is evaluated and compared. It is found that Japanese contractors complete their construction projects with fewer defects, provide longer defects liability periods and are called upon fewer times during the defects liability period than their UK and US counterparts. UK and US contractors do seek more regular feedback from their clients than Japanese contractors and generally similar levels of client satisfaction are achieved in the three countries (based on quality performance). The somewhat superior performance of Japanese contractors may be attributed to their deep-rooted quality consciousness, closer working relationships with their subcontractors, and more advanced total quality management systems and quality assurance procedures.
Productivity in the use of resource inputs is of critical importance to the construction industry. This paper is intended to discuss the relative merits of the most commonly used measures of productivity for the purpose of assessing the productive and allocative efficiency of construction in the 1980s.The paper concludes that the total factor productivity method is the ideal against which the other approaches should be judged. Both average labour productivity and average capital productivity suffer from serious problems in assessing the efficiency of contracting operations. However, under certain circumstances, either can provide an adequate alternative measure.Of the two main single-factor measures of productivity, capital productivity appears to be superior in most aspects to average labour productivity as a means of assessing the overall financial management of a construction firm. Notwithstanding the problems associated in contracting activities and also the difficulties inherent in obtaining suitable data, capital productivity is recommended for most circumstances when total-factor productivity measures cannot be applied.
The Pearce Report of 2003 drew attention to potential shortcomings in some of the existing statistics that were used to analyse the UK construction sector. The current study examines the broader statistical issues in greater depth to identify shortcomings that could cause some of the findings presented by Pearce, and similar studies, to need qualification and possible revision. Problems with reliable and accurate data collection and statistical analysis include defining the scope and coverage of the industry; measuring industry outputs and their allocation across different types of activity; identifying construction firms; and measuring capital formation and capital stock, inconsistencies in employment statistics and labour market variables, discrepancies in measuring productivity, and the lack of international comparison. Needed improvements include a wider definition of construction output and employment activities, a continuous review of the usefulness and reliability of the published construction statistics, and the resolution of the discrepancies between different registers. Industry stakeholders also need to become more engaged in the process of statistical revision and use. The creation of an international forum could improve the quality of statistics and deal with the more technical aspects of international comparisons.
Productivity is a key driver for economic growth and prosperity in any country. The pursuit of productivity growth requires an understanding of the factors affecting productivity. The trend of productivity growth, along with the possible factors underlying such growth across Europe, the US and Japan, is thus examined. In particular, there is a focus on comparing the productivity performance of the construction sector to that of other industries. Using the recently released EU KLEMS1 database,2 a growth accounting framework was adopted to assess the contribution of the following factors to productivity growth (during 1971-2005): capital, labour quality and total factor productivity (TFP). It was found that there is a general slowdown in labour productivity growth in total industries including construction across major OECD countries, with the exception of the UK. The differences in labour productivity growth between construction and total industries can be largely explained by construction's poor TFP performance. With the exception of the UK, TFP negatively contributed to labour productivity growth in the period 1990-2005, suggesting that the industry has become less efficient in combining the factors of production. That phenomenon seems to be consistent across all selected countries and warrants further investigation. Indeed a better understanding of the factors underlying productivity growth in OECD countries is a prerequisite for effective intervention of policy makers to support sustained productivity growth.
According to unpublished data compiled by BLS, productivity in the construction industry reached a peak in 1968 and, except for a brief and small upturn between 1974 and 1976, has been falling ever since. This paper examines the sources of this productivity decline between 1968 and 1978 by estimating a production function to assign weights to various factors responsible for productivity change and deriving a new price deflator for construction which does not rely on labor or material cost indexes, thus eliminating a systematic bias toward overstating the rate of growth of prices.The production function analysis indicates that productivity should have declined by 8.8 percent between 1968 and 1978,representing 41 percent of the observed decline. The biggest factor in this decline was the reduction in skilled labor intensity resulting from a shift in the mix of output from largescale commercial, industrial, and institutional projects to single-family houses. Other important factors include declines in the average number of employees per establishment, capital-labor ratio, percent union, and the average age of workers. The difference between the official deflator and the new deflator proposed here accounts for an additional 51 percent of the reported productivity decline, leaving only 8 percent of the decline unexplained.
Globalization of the world economy demands that performance comparisons are undertaken at an international level. A new research protocol has been developed for comparing contractor performance internationally by combining the appropriate characteristics of two established approaches in order to balance the requirements of comparability and representativeness. This new approach is used to reveal some important international performance characteristics among Japanese, UK and US contractors. Building costs in the UK, when adjusted for exchange rate fluctuations, are significantly higher than those in Japan and the USA. Furthermore, cost certainty and client satisfaction are higher in Japan than in the UK, but there is no significant difference between Japan and the USA. Disparities in cost performance between the three countries are believed to originate from differences in the relationships between contractors and clients and also in the construction process.
Discrepancies exist between aggregate and activity productivity measurements in the US construction industry. Multiple studies using aggregate industry measures suggest that construction productivity has declined over the long term. A longstanding problem with the aggregate measures concerns the difficulty of controlling for inflation so as to accurately measure real output. As an alternative, average activity productivity, measured by individual work activities, indicates that construction productivity has increased over the same time period. Activity measurement data have been collected for 200 construction activities over a 22-year time period from commercial estimation manuals used by contractors and owners to estimate the cost and time requirements for construction. This paper examines the discrepancies between aggregate and activity measurements and suggests possible reasons for their existence.
Surveys of the top 400 US contractors were conducted in 1979, 1983 and 1993 to identify the areas with potential for productivity improvement in the construction industry. The trends in the findings of these surveys are observed and interpreted. The results indicate that cost control, scheduling, design practices, labour training, and quality control are the functions that consistently over the years are perceived as having considerable room for productivity improvement, whereas materials packaging and foreign developments in construction technologies are perceived consistently as functions that do not have much effect on improving construction productivity. The functions that were identified as needing more improvement in 1993 compared with the previous surveys were prefabrication, new materials, value engineering, specifications, labour availability, labour training, and quality control, whereas those that were identified as needing less improvement than in the previous surveys were field inspection and labour contract agreements. Also, respondents indicated consistently over the years that they are willing to participate in activities related to improving construction productivity but are not interested in funding any such activities.
OECD purchasing power parities (PPPs) are used regularly in strategic governmental policy papers to compare the performance of construction industries among countries. These PPPs suggest that the relative competitiveness of the Dutch construction sector is fairly weak compared with surrounding countries. This contradicts the general view that the Dutch construction industry is very productive and efficient, especially in house-building. For the member countries of the European Union the OECD uses data from Eurostat, the statistical office of the European Union. In this paper the methodology followed by OECD/Eurostat in their calculation of PPPs for construction is reviewed. The data for five European countries (Netherlands, Belgium, UK, France and Germany) are analysed. Next, the Eurostat results are placed alongside the results of other international building cost comparisons. The differences are observed and the conclusion is that the Eurostat PPPs do not reflect the real construction price or cost differentials among the five EU countries. It appears that the basic construction price data used by Eurostat are not very accurate, that the comparison methodology applied by Eurostat itself is insufficient to express and explain building cost differences among countries, and that the Eurostat figures for construction are the result of a complex statistical weighting and processing procedure in which corrections are not applied for extreme deviations. The conclusion is that the Eurostat data cannot be used for comparison purposes. Proposals for improvement of the comparison methodology are reviewed.
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