INTRODUCTION:

Construction activity is an integral part of a country’s infrastructure and industrial development. The Indian construction industry is the second largest employer and contributor to economic activity after agriculture. It employs over 31 million people and contributes about 10% to the nation’s GDP. The Indian construction sector also accounts for second highest inflow of FDI after the services sector. 50% of the demand for construction activity in India comes from the infrastructure sector, the rest comes from industrial activities, residential and commercial development etc. The Indian construction industry is valued at over USD 126 Billion [1]. However, in recent years, construction projects in India have been facing number of problems including time and cost overruns, low productivity, poor safety, poor quality, lack of skilled labor, and low level of technology adoption. Productivity is a major factor that affects overall performance of any small or medium or large construction organization.

Productivity is generally defined as the ratio of outputs to inputs. In engineering terms, productivity may be conceptualized as a measure of the technical or engineering efficiency of production. Numerous factors influence construction productivity. As many of the construction activities are labour-intensive, it can be argued that construction productivity is predominantly dependent on human effort and performance [2].

METHODOLOGY:

Research articles with any of the mentioned keywords: productivity, labor, construction productivity, labor productivity, construction labor productivity, crew productivity, workforce and personnel management were searched initially. Journal of Construction Engineering and Management (JCEM), Construction Management and Economics (CME) and International Journal of Project Management (IJPM), Journal of Management in Engineering, Journal of Professional Issues in Engineering, Automation in Construction, Engineering, Construction and Architectural Management, International Journal of Productivity and Performance Management are identified as the top international journals in publishing construction productivity research. However, about 90% of research publications on construction productivity are from JCEM, CME and IJPM [3, 4].Hence, this review study is primarily carried based on the research papers/ articles available from these three journals.

About 136 relevant research papers/ articles from JCEM, CME, IJPM and other mentioned journals between 1985 and 2014 are reviewed for this study. Major areas of construction labor productivity research at industry, project and activity-level are identified. Significant areas of research include factors affecting construction labor productivity, estimating, modeling and forecasting labor productivity, baseline and benchmarking labor productivity, impact of critical factors on labor productivity, impact of new management practices and technology on labor productivity, and worker motivation. For the sake of brevity, three major areas in industry and project-level which includes estimating, modeling and forecasting labor productivity, baseline and benchmarking labor productivity, impact of critical factors on labor productivity will be discussed in this paper. In addition to that, a review on measuring and improving productivity at activity-level is also discussed. Review of these research areas in relation to Indian construction context is discussed in this paper.

Estimating, modeling and forecasting construction labor productivity

Numerous factors impact the variations in construction labor productivity. The association between the influential factors and the resulting productivity can be quantified through productivity models. These models play significant roles in planning, scheduling and estimating functions of the project. Also, they aid in decision making for planning future projects [5]. Numerous modeling techniques have been put forward to study the relationship between the influential factors and construction labor productivity which includes statistical and regression models, factor model, expectancy model, action-response model, expert systems, and artificial neural networks (ANNs) [6]. Some significant research efforts in this area are discussed below.

Thomas et al. [7] presented a philosophical argument for developing construction labor productivity models based on actual factors affecting productivity. The factor model and the expectancy model of motivation were the two models proposed in this study. The factor model accounts for the project, site and management factors affecting productivity. The expectancy model describes why a crew exerts an effort to perform and how this effect relates to productivity. It is suggested that the models can be integrated into a single comprehensive model to quantify the factors affecting productivity and to forecast performance.

From the time when the theoretical concept of modeling construction labor productivity was discussed [7, 8], the use of statistical methods to estimate, model and forecast productivity was developed. Sanders and Thomas [9] developed a productivity forecasting model considering the effect of project-related factors. They developed an additive regression model based on data collected from 11 masonry projects. This was among the first labor forecasting models developed considering the effects of project-related factors. One other forecasting model developed by Thomas and Sakarcan [10] compared two approaches for forecasting labor productivity with data from 22 masonry projects. In the first approach, the current work-hour total is divided by the percent complete (PC) of the activity. The second method used the factor model [which was developed by Thomas et al. [7]) and developed a predicted labor-productivity curve. The error range of about 50% was observed in the PC approach. It was concluded that the factor model isa more reliable method of forecasting labor productivity.

Artificial neural networks in construction productivity has been an area of research recently. Abou Rizk et al. [11] discussed an approach based on artificial neural networks for estimating labor production rates of industrial construction tasks. The model developed in this study is a two-stage artificial neural network, which is used to predict an efficiency multiplier (an index) based on input factors identified by the user. The multiplier is then used to adjust an average production rate given in man-hours/unit for use on a specific project. The study compared the estimates of production rates from the developed approach with that of the existing estimating practices.

Song and AbouRizk [5] studied measuring and modeling labor productivity using historical data. The study used artificial neural network and discrete-event simulation to model labor productivity using historical data. The study was also validated using actual data collected from a steel fabrication company. Heravi and Eslamdoost [6] also investigated the influential factors on labor productivity and developed an artificial neural network to measure and predict labor productivity using the Bayesian regularization and early stopping methods. Results of their study proved that a better prediction performance for Bayesian regularization than early stopping was observed.

Both quantitative and qualitative factors affect construction labor productivity. However, until recently research on estimating, modeling and forecasting construction labor productivity have been focused on quantitative factors affecting construction labor productivity. Quantitative factors have been studied by regression and multivariate statistical modeling, factor analysis, structural equation modeling etc. Qualitative factors include worker coordination, quality of supervision, worker motivation etc. Recently, the application of ANN was developed to study the impact of qualitative factors on construction labor productivity [6]. Hence, the need for incorporating qualitative factors for modeling and forecasting construction labor productivity and introduction of new modeling tools such as ANN, fuzzy logic is prospective.

Baseline and bench marking construction productivity

Although a number of publications exist on construction productivity, there is no agreed upon definition of work activities nor a standard productivity measurement system. In many studies, researchers have concluded that it is difficult to obtain a standard method to measure construction labor productivity because of project complexity and the unique characteristics of construction projects [12].

Thomas and Zavrski [13], presented a theoretical basis for construction labor productivity measurement by examining masonry construction productivity database of 23 projects. Various hypothesis on design complexity, management and craft skills and the use of technology were tested with respect to baseline productivity. Two measures were proposed to measure the performance of individual projects: the disruption index (DI) and the project management index (PMI). Cumulative probability distributions of the disruption index and the project management index were evaluated with 23-project database and also compared with other databases. It was concluded that the DI and PMI were found to have correctly identified the best and worst performing projects and both were concluded as reliable indicators of project performance.

Research initiated by Park et al. [12] established a common set of construction productivity metrics and their corresponding definitions. The Construction Productivity Metrics System (CPMS), contain a list of direct, indirect accounts and 56 data elements were grouped into seven major construction metrics categories as a result of this research effort. CPMS provides a framework for continuous construction productivity improvement through benchmarking.

Over the years, new statistical methods and use of Key Performance Indicators (KPI’s) to benchmark productivity at industry-level are seen. Lin and Huang [14] introduced data envelopment analysis (DEA) as a new statistical method for benchmarking construction productivity. Five different benchmarking methods, which were available including the DEA are compared. It was found that both Thomas’s method and DEA were consistent. As for the other methods concerned, it was suggested that more research is required before they can be further practiced in the construction industry.

Over the past decade, many researchers have applied the concept of key performance indicators (KPIs) to conduct benchmarking studies in the construction management discipline (Cox et al. [15]; Lee et al. [16]; Yeung et al. [17]). However, few others debated the use of KPI’s in construction as they do not offer the opportunity for organizational change (Beatham et al. [18], Costa et al. [19]). Yeung et al. [20] attempted to incorporate both leading and lagging KPIs and applied the reliability interval method (RIM) to formulate a benchmarking model to assess project success in Hong Kong. The results of their study revealed the top 10 KPIs to evaluate the success of construction projects in Hong Kong. The top five includes safety performance, cost performance, time performance, quality performance, and client’s satisfaction. The study also developed a composite performance index (CPI) which helps managers to objectively evaluate and compare construction projects in Hong Kong.

Recently, researchers have proposed improved baseline method to calculate the lost construction productivity. Zhao and Dungan [21] has compared the various existing methods on baseline productivity, and identified the advantages and weaknesses of the existing methods. The method proposed in their study calculate productivity based on contractor’s normal operating performance and aid in calculating lost construction productivity.

To summarize the progression of research conducted in this area, a few studies have attempted to develop common definitions and a standard productivity system, however, those were not based on the consensus of academia and industry as many companies have developed their own productivity tracking systems based on their experiences and accounting systems. However, none have been successful in establishing common definitions and developing a survey tool that collects standard productivity data at the appropriate levels [12]. Need for the establishment of KPI’s as a benchmarking model to assess the performance of the industry has been debated for long time, hence the need for more research in this area is evident.

Impact of critical factors on construction labor productivity

Numerous factors affect construction productivity. There is substantial amount of literature available on factors affecting construction productivity in different countries. It is one of the most discussed topics in construction productivity domain. Several researchers have published on major and critical factors affecting construction productivity in different countries. Some of them include Dai et al. [22] (US), Horner et al. [23] (UK), Hughes and Thorpe [24] (Australia), Lim et al. [25] (Singapore), Kaming et al. [26] (Indonesia), Zakeri et al [27] (Iran), Jarkas and Bitar [28] (Kuwait), and Soham and Rajiv [29] (India-South Gujarat). Some common factors affecting construction productivity identified in these studies include material shortage and delivery, poor site layout, poor construction methods, effect of change orders, buildability, overtime and shiftwork, quality of supervision, skill of labor, crew size, composition and interference, absenteeism and turnover of workers, language and communication, extreme weather conditions, and management influence.

Studies have examined the influence of each critical factor on productivity/ labor productivity in detail. Some of those studies and their outputs are discussed below.

Process planning

Salim and Bernold[30] demonstrated the concept of design-integrated process planning in a six-storey building project. Field experiments were conducted on same crew with rebar delivered and staged in traditional as well as on placement sequences. Placement oriented delivery and staging method showed a productivity improvement of about 30% over the traditional method. Percentage of effective working and crew effectiveness was also improved. This study is one of the initial studies demonstrated the effect of process planning in construction.

Overtime

Thomas and Raynar[31] analyzed 121 weeks of labor productivity data from four industrial projects to study the effect of scheduled overtime on labor productivity. The results of their study shows that 10-15% loss of efficiency observed for 50-h and 60-h work weeks. They conclude by saying that the loss of efficiency was caused by the inability to provide materials, tools, equipment, and information at an accelerated rate.

Hanna et al. [32] analyzed the impact of extended overtime (beyond 40-h scheduled work week) on construction labor productivity. They studied about 88 projects in the US and developed a statistical model. They suggested that the model can be reactively used to determine the amount of productivity loss resulting from an unplanned period of overtime. Proactively, it was suggested that the model can be used to estimate the additional cost of labor when extended overtime is planned for a project.

Material delivery methods

A study by Thomas et al. [33] demonstrated the different types of material delivery methods and their impact on labor productivity. Three structural steel erection projects that used different methods of delivering the structural steel members were compared. Results of their analysis show that the most efficient delivery method is to erect the steel directly from the truck. Double-handling of material and indiscriminate deliveries resulted in a productivity loss of about 9% and 16% respectively. Significant losses of productivity occurred because of snow and cold temperatures was 41% and 32% respectively as observed in their study. Studies have also been conducted on specific materials delivery methods such as concrete batch plant truck mixer operations and its impact on productivity [34].

Change orders and change’s timing

Hanna and Gunduz[35] studied the impact of change orders on labor efficiency on small labor-intensive projects. They developed a statistical model that estimates the amount of labor efficiency lost due to change orders by studying 34 selected small projects in the US. The variables in their final model includes percent design related changes, percent owner initiated changes, the ratio of actual peak labor to estimated peak labor, the ratio of actual project duration to estimated project duration, and project manager’s percent time on the project. The proposed model was validated using cross-validation procedures.

Ibbs [36] studied the impact of change’s timing on labor productivity. He studied about 162 projects and developed three curves that represented the impact of early, normal and late timings of change orders on labor productivity. He suggested that if changes are necessary, then they should be recognized and incorporated as early as possible as late change is more disruptive of project productivity than early change.

Equipment technology

Goodrum and Hass [37] examined the long-term impact of equipment technology on labor productivity in the US on 200 construction activities between 1976 and 1998. They developed a technology index consisting of five technology change factors which examined the long-term substantial impact of equipment technology on labor productivity. The study revealed that changes in energy, control, and functional range are significantly and positively correlated with improvements in labor productivity.

Overmanning

Hanna et al. [38] analyzed the impact of overmanning on construction labor productivity. They studied about 54 mechanical and sheet metal projects across the US. The study indicates that 0-41% productivity loss was observed depending on the level of overmanning. They suggest that a schedule with less overmanning is preferable to avoid productivity losses. The study recommends that if schedule acceleration is required, overtime or shift work may be the more favorable work acceleration technique due to the higher initial productivity losses experienced from overmanning.

Shiftwork

Shiftwork is an effective schedule compression technique in labor intensive construction if it is used for a short duration. Hanna et al. [39], studied the labor efficiency associated with working in a second shift. The study derived a relationship between the length of the shift and the labor efficiency. Productivity loss of -11 to 17% was observed by them depending on the amount of shift work used.

Buildability

Buildability is one of the most significant factor affecting construction labor productivity. However, the tangible benefits of buildability is difficult to measure. A study by Jarkas [40] analyzed data from 72 residential, office, and commercial buildings in Kuwait and explored relevant buildability factors. The factors explored in the study includesvariability of beam sizes in the floor, usable floor area, number of beams used to support the floor area, number of individual slab panels formed within the floor due to beam-framing plan, number of joints formed due to beam intersections, floor configuration repetition criteria, and number of angles formed around the floor perimeter. Relationship between each factor and formwork labor productivity was modeled by the categorical regression method. The study concluded that the effect of identified buildability factors with labor productivity was significant. The study also substantiated the importance of applying the buildability principles to the design stage of building construction.

Construction method

Anand and Ramamurthy [41] carried productivity assessment of conventional and interlocking-block masonry with different construction methods. Using work sampling technique, output per productive hour of different masonry construction methods were analyzed. Results of their study show that productivity enhancement of 80–120% and 60–90% more than conventional masonry was observed for dry-stacked masonry and thin-jointed, mortar-bedded interlocking block masonry respectively. The interlocking-block masonry was observed to have indirect contributory work.

Successive studies have been conducted on factors affecting construction labor productivity and on examining the impact of critical factors on construction labor productivity. For the sake of brevity, only primary and most referred studies in this area is discussed above. Some of the common limitations observed in these studies include the following. It was observed that there is incongruence among the factors affecting construction labor productivity in different countries. Since most of the factors identified are country specific, the factors identified in one country have limited application in another country. Hence, more research on identifying the common factors affecting construction labor productivity adds theoretical value. Also, the mode of data collection in most of the reviewed studies included questionnaire survey, historic project databases and literature surveys. Only few studies have been conducted through field measurement data collection methods. Also, absence of follow-up studies was observed to validate the findings of studies conducted. Hence, more studies are needed to be conducted to generalize the findings of the conducted studies.

Measuring and improving productivity at the activity-level

Measuring and improving productivity at the activity-level is also one of the major research areas in construction productivity. However, less scholarly interest has been shown in this area. Some of the significant topics in this area includes measuring labor productivity at the activity-level, factors affecting labor productivity (for specific activities eg. formwork, rebar fixing etc.), crew interference between activities, learning effect of crews, workforce management practices and challenges in trade-level productivity studies. Some significant studies in this area and their outputs are discussed below.

Factors affecting construction labor productivity

The loss of construction productivity is usually attributed to various factors, rather than a single one. In addition, factors affecting construction labor productivity are rarely independent of the others; some factors may be the result of the same cause, or one factor may trigger the occurrence of others [22].Studies were conducted on factors affecting construction labor productivity on different activities in different countries. Sanders and Thomas [42] identified the factors affecting masonry labor productivity by analyzing data from 11 masonry projects in the US. Factors identified by them include work type, building element, construction methods, design requirements, and weather. Significant findings of their study includes repetitive designs can effect a 30% improvement in productivity and designs that require extensive layout and cutting can negatively affect productivity by as much as 40%.

A study by Dai et al. [22] analyzed the underlying structure of the factors affecting productivity from different trade workers perspectives. The study identified 83 factors affecting productivity through 18 focus group sessions on nine projects across the US. The factors were then surveyed by 1,996 workers to assess the impact of the factors. Principal factor analyses was then applied and 10 latent factors were identified. The 10 latent factors include Construction Equipment, Materials, Tools and Consumables, Engineering Drawing Management, Direction and Coordination, Project Management, Training, Craft Worker Qualification, Superintendent Competency, and Foreman Competency. The study also examined the differences in the perceived relative magnitude of productivity factors’ influence on construction productivity based on respondents’ union status and trade [43].

To understand the factors affecting construction labor productivity from the perspectives of workers and midlevel employees Rivas et al. [44] surveyed workers and midlevel employees of a Chilean construction company. Materials, tools, rework, equipment, truck availability, and the workers’ motivational dynamics were the critical factors identified in the study. The findings of the study were compared with the previous similar studies in the United States and in Chile. The authors claim that salary expectations were found to be the main reason for turnover in the studied Chilean company, which was an aspect not mentioned in any of the similar previous studies.

Construction crew design

Hassanein and Melin [45] presented the results of an investigation to determine the rules contractors use in designing the crews for their daily activities. Through a structured interview approach and a questionnaire, they captured the main criteria and rules the contractors follow in their crew design process. The study was investigated for reinforced concrete, masonry, mechanical and electrical trades and it was validated for reinforced concrete and masonry trades.

Crew interference between activities

The construction site is a complex system composed of interactions between individual crewmembers and crews. Congestion of crews often leads to lowered productivity [46].Thomas et al. [47] introduced the concept of symbiotic crew relationships. Symbiotic relationship occurs when the work pace of one crew depends on the pace of a preceding crew. They analyzed data from steel reinforcement activities of six projects in Brazil and demonstrated the negative effects of symbiotic relationships. Results of their analysis shows that crews that were symbiotically related did not perform as well as those that were sequentially related. Also, symbiotic relationships incurred 25% increase in labor resources compared to sequential relationships.

Watkins et al [46] proposed an agent-based modeling method to represent project site as a system of complex interactions. The model used a bottom-up approach and investigated impacts of space congestion on worker efficiency, and the impacts of varying efficiency on congestion. From results of their simulation, they concluded that congestion on a construction site can be studied as an emergent property due to interactions between individual crew members on site. The study provided a method that can be used to efficiently utilize construction space, and develop plans and schedules that account for congestion arising from crew interactions in space.

Workforce management practices

Competent workforce management is considered as an important component of effective project management. Since workforce management lasts throughout the whole project, it is considered relatively more important than other factors affecting construction labor productivity [48]. Thomas et al. [48] studied the role of workforce management on labor productivity in bridge superstructure projects. The study presented the results of four case studies of highway bridge construction projects in the US. Some of their quantitative results indicate that loss of labor efficiency of 80, 75, 32, and 70%, was observed on the four case study projects. Also, schedule slippage between 127 and 329% was estimated on the four case study projects. The study also highlighted the differences in managing labor intensive projects and equipment-intensive projects. Specific workforce management problems observed by them include no alternate work assigned to crews, insufficient productive work available, and overstaffing for the size of the specific construction operations.

Fundamental principles of workforce management developed by Thomas and Horman [49] provides 19 fundamental principles to avoid poor workforce management practices and mitigate disruptions in site management. The authors suggested that the application of these principles will show immediate response in project savings through reduction of poor workforce management practices. Also, the principles have been intentionally left general so that it can applied in broad range of circumstances.

Challenges in trade-level productivity studies

Hwang and Soh [50] investigated the challenges faced by contractors and sub-contractors in measuring and assessing productivity at the trade-level. 17 critical challenges and 24 plausible solutions were identified and analyzed in the study. The top five identified challenges include a lack of a proper system to measure productivity, difficulties in obtaining accurate productivity data due to the nature of the trade, difficulties in measuring work hours of trades, the complicated nature of measuring productivity, and the time and manpower required to measure and analyze productivity data. The top four identified solutions include simplify productivity measurement methods and focus on important trades, conduct seminars on developing a productivity measurement system for firms, create a culture within contractor organizations for productivity measurement and documentation, and make standardized productivity measurement system.

Construction labor productivity research in Indian context

Before presenting the review of labor productivity research in Indian context, a brief description about the profile of construction labor in India is discussed.

Construction Labor in India

The Indian construction industry comprising infrastructure, industrial and real estate sectors employs over 29 million workers and is the country’s second largest employer after agriculture. The Planning Commission of India has projected that the construction sector will require another 47 million workers over the next decade [51]. The current pool of the construction workforce in India comprises mainly unskilled workers reported by the Working Group on Construction for the Eleventh Five Year Plan, Planning Commission, Government of India. (Table 1).

Table 1: Employment in Construction Sector by Education Level of Workers

Category	Percentage of employment	Total Employment
Unskilled workers	83%	25.6 million
Skilled workers	10%	3.3 million
Engineers	3%	0.8 million
Technicians and foremen	2%	0.6 million
Clerical	2%	0.7 million

According to an annual report by the Ministry of Labour and Employment, Government of India (2010) [52], most of the construction workers are seasonal, migrant workers from poorer agricultural states and constitute a large section of construction. These workers are mostly labor sub-contracted and are managed by the labor sub-contractor who recruit them from towns and villages. They lack basic education, formal training and usually pick up skills on the job, informally from peers or supervisors. Majority of the migrant construction workers are from West Bengal, Odisha, Bihar, Jharkhand, Uttar Pradesh, Chhattisgarh, Assam and Andhra Pradesh.

Indian construction contractors are classified as major, medium and small contractors. Small contractors are those which employ less than 200 people, medium contractors’ employs 200 – 500 people, and major contractors’ employs more than 500 people. Small contractors constitute about 96% of the total number of contractors in India [53].Construction productivity is a major factor that affects overall performance of any small or medium or large construction organization. Since most of the construction activities in Indian are labor-intensive, with little to no mechanization construction labor productivity is critically important. However, labor productivity is one of the least studied topics in the Indian construction [29, 54].

The major research areas in construction labor productivity is discussed so far. The following section identifies the gap in knowledge in relation to Indian construction at the industry, project and activity-level.

At the industry-level, factors affecting construction productivity were studied in different countries such as the US, UK, Australia, Singapore, UAE etc. and are discussed earlier. However, with respect to the Indian context, most of the studies are region specific including Soham and Rajiv in the south region of Gujarat [29], Raj and Kothai in Erode district in Tamil Nadu, [55], Shashank K et al. in Bangalore [56]. Moreover, studies in the past have not attempted to study the influencing factors from the perspectives of all the related stakeholders i.e. managers, engineers, foreman/ supervisors and workers. With respect to productivity benchmarking, although the Central Public Works Department’s (CPWD) Delhi Analysis of Rates (DAR) Indexes are considered in the Indian scenario, it is not regularly updated and needs adjustments for different regions/ states. Benchmarking productivity will aid to improve productivity.

At the project-level, the impact of critical factors affecting construction labor productivity are discussed earlier. However, project conditions in India differ significantly when compared with other developed countries where mechanization and technology adoption is relatively higher. In the Indian context, at the project-level, studies were conducted on comparing different construction methods and its impact on productivity [41], comparing local and migrant construction workers productivity [57], productivity studies in precast construction [58]. However, less scholarly interest is seen in investigating the impact of critical factors such as overtime, overmanning, shiftwork, change orders buildability etc. on construction labor productivity

At the activity-level, analysis of labor productivity for specific activities such as formwork, reinforcement work, masonry work are studied in the past [59], [60]. Studies have also been conducted on factors affecting productivity variation in construction projects [61], [56] and productivity comparison of direct workers and sub-contractor workers [54]. Some of the important topics that need explicit attention at the activity-level in the Indian context includes factors affecting construction labor productivity from craft workers and supervisors perspectives, craft time utilization and impact of crew interactions on productivity, role of workforce management practices on productivity, and learning effect of crews on productivity [61, 62].

CONCLUSION:

Research on construction productivity has been generating substantial interest in both the academia and in the industry. In general, research topics in construction labor productivity can be classified into industry, project and activity-level [4]. About 136 relevant research papers/ articles from JCEM, CME, IJPM and other related journals between 1985 and 2014 are reviewed in this study. Major areas of construction labor productivity research at industry, project and activity-level are identified. Significant areas of research includes factors affecting construction labor productivity, estimating, modeling and forecasting labor productivity, baseline and benchmarking labor productivity, impact of critical factors on labor productivity, impact of new management practices and technology on labor productivity, and worker motivation. On close examination, it is found that the quantum of research in industry-level and project-level are relatively higher as compared to activity-level research. Also, in Indian construction as many of the construction activities are labor-intensive, research in activity-level is highly significant. Major research areas in construction labor productivity are reviewed in relation to Indian construction context and potential research topics are highlighted. The outcome of this study will be useful for both academia and industrial practitioners by providing insights about the developments, trends and the need for labor productivity research in different levels.

REFERENCES:

1. Makeinindia.com,. (2015). ‘Sector – Construction – www.makeinindia.com’. <http://www.makeinindia.com/sector/construction/> (Nov.5, 2015).

2. Jarkas, A. M. (2010). “The influence of buildability factors on rebar fixing labour productivity of beams.” J. of Constr. Mgmt and Eco., 28 (5) 527-543.

3. Yi, W. and Chan, A. (2014). “Critical Review of Labor Productivity Research in Construction Journals.” J. Manage. Eng., 10.1061/(ASCE)ME.1943-5479.0000194, 214-225.

4. Dolage, D. A. R. and Chan. P. (2013). “Productivity in Construction – A Critical Review of Research.” The Institution of Engineers., Vol. XXXXVI, No. 04, pp. [31-42].

5. Song, L. and AbouRizk, S. (2008). “Measuring and Modeling Labor Productivity Using Historical Data.” J. Constr. Eng. Manage.,134(10), 786–794.

6. Heravi, G. and Eslamdoost, E. (2015). “Applying Artificial Neural Networks for Measuring and Predicting Construction-Labor Productivity.” J. Constr. Eng. Manage., 141(10), 04015032.

7. Thomas, H., Maloney, W., Horner, R., Smith, G., Handa, V., and Sanders, S. (1990). “Modeling Construction Labor Productivity.” J. Constr. Eng. Manage., 116(4), 705–726.

8. Thomas, H. and Yiakoumis, I. (1987). "Factor Model of Construction Productivity." J. Constr. Eng. Manage., 10.1061/(ASCE)0733-9364(1987)113:4(623), 623-639.

9. Sanders, S. and Thomas, H. (1993). “Masonry Productivity Forecasting Model.” J. Constr. Eng. Manage., 119(1), 163–179.

10. Thomas, H. and Sakarcan, A. (1994). “Forecasting Labor Productivity Using Factor Model.” J. Constr. Eng. Manage., 120(1), 228–239.

11. AbouRizk, S., Knowles, P., and Hermann, U. (2001). “Estimating Labor Production Rates for Industrial Construction Activities.” J. Constr. Eng. Manage., 127(6), 502–511.

12. Park, H., Thomas, S., and Tucker, R. (2005). “Benchmarking of Construction Productivity.” J. Constr. Eng. Manage., 131(7), 772–778.

13. Thomas, H. and Završki, I. (1999). “Construction Baseline Productivity: Theory and Practice.” J. Constr. Eng. Manage., 125(5), 295–303.

14. Lin, C. and Huang, H. (2010). “Improved Baseline Productivity Analysis Technique.” J. Constr. Eng. Manage., 136(3), 367–376.

15. Cox, R. F., Issa, R. R. A., and Ahrens, D. (2003). “Management’s perception of key performance indicators for construction.” J. Constr. Eng. Manage., 129(2), 142–151.

16. Lee, S. H., Thomas, S. R., and Tucker, R. L. (2005). “Web-based benchmarking system for the construction industry.” J. Constr. Eng. Manage.,131(7), 790–798.

17. Yeung, J. F. Y., Chan, A. P. C., and Chan, D. W. M. (2009a). “A computerized model for measuring and benchmarking the partnering performance of construction projects.” Autom. Constr., 18(8),1099–1113.

18. Beatham, S., Anumba, C., Thorpe, T., and Hedges, I. (2004). “KPIs: A critical appraisal of their use in construction.” Benchmark. Int. J.,11(1), 93–117.

19. Costa, D. B., Formoso, C. T., Kagioglou, M., Alarcón, L. F., and Caldas,C. H. (2006). “Benchmarking initiatives in the construction industry:Lessons learned and improvement opportunities.” J. Manage. Eng.,22(4), 158–167.

20. Yeung, J., Chan, A., Chan, D., Chiang, Y., and Yang, H. (2013). “Developing a Benchmarking Model for Construction Projects in Hong Kong.” J. Constr. Eng. Manage., 139(6), 705–716.

21. Zhao, T. and Dungan, J. (2014). “Improved Baseline Method to Calculate Lost Construction Productivity.” J. Constr. Eng. Manage.,140(2), 06013006.

22. Dai, J., Goodrum, P., Maloney, W., and Srinivasan, C. (2009). “Latent Structures of the Factors Affecting Construction Labor Productivity.” J. Constr. Eng. Manage., 135(5), 397–406.

23. Horner, R. M. W., Talhouni, B. T., and Thomas, H. R. (1989). “Preliminary results of major labour productivity monitoring programme.”In Proceedings of the 3rd Yugoslavian Symposium on Construction Management., Cavtat (pp. 18-28).

24. Hughes, R., and Thorpe, D. (2014). “A review of enabling factors in construction industry productivity in an Australian environment.” Construction Innovation: Information, Process, Management., 14(2), 210-228.

25. Lim, E. C. and Alum, J. (1995). “Construction productivity: issues encountered by contractors in Singapore”. International Journal of Project Management., Vol. 13, No. 1, pp. 51-58.

26. Kaming, P. F., Olomolaiye, P. O., Holt, G. D., and Harris, F. C. (1997). “Factors influencing craftsmen's productivity in Indonesia.”International journal of project management., 15(1), 21-30.

27. Mahmood Zakeri , Paul O. Olomolaiye , Gary D. Holt and Frank C. Harris. (1996). “A survey of constraints on Iranian construction operatives' productivity.” Construction Management and Economics.,14:5, 417-426.

28. Jarkas, A. and Bitar, C. (2012). “Factors Affecting Construction Labor Productivity in Kuwait.” J. Constr. Eng. Manage., 138(7), 811–820.

29. Soham, M. & Rajiv, B. (2013). “Critical Factors Affecting Labour Productivity In Construction Projects: Case Study Of South Gujarat Region Of India.” International Journal of Engineering and Advanced Technology., Volume-2, Issue-4, 2249 – 8958.

30. Salim, M. and Bernold, L. (1994). “Effects of Design‐Integrated Process Planning on Productivity in Rebar Placement.” J. Constr. Eng. Manage., 120(4), 720–738.

31. Thomas, H. and Raynar, K. (1997). “Scheduled Overtime and Labor Productivity: Quantitative Analysis.” J. Constr. Eng. Manage.,123(2), 181–188.

32. Hanna, A., Taylor, C., and Sullivan, K. (2005). “Impact of Extended Overtime on Construction Labor Productivity.” J. Constr. Eng. Manage., 131(6), 734–739.

33. Thomas, H., Riley, D., and Sanvido, V. (1999). “Loss of Labor Productivity due to Delivery Methods and Weather.” J. Constr. Eng. Manage., 125(1), 39–46.

34. Zayed,T. M., Halpin, D. W. and Basha I. M. (2005), “Productivity and Delays Assessment For Concrete Batch Plant-Truck Mixer Operations.”Construction Management and Economics., Vol.23. pp. 839–850.

35. Hanna, A. and Gunduz, M. (2004). “Impact of Change Orders on Small Labor-Intensive Projects.” J. Constr. Eng. Manage., 130(5), 726–733.

36. Ibbs, W. (2005). “Impact of Change’s Timing on Labor Productivity.” J. Constr. Eng. Manage., 131(11), 1219–1223.

37. Goodrum, P. and Haas, C. (2004). “Long-Term Impact of Equipment Technology on Labor Productivity in the U.S. Construction Industry at the Activity Level.” J. Constr. Eng. Manage., 130(1), 124–133.

38. Hanna, A., Chang, C., Lackney, J., and Sullivan, K. (2007). “Impact of Overmanning on Mechanical and Sheet Metal Labor Productivity.” J. Constr. Eng. Manage., 133(1), 22–28.

39. Hanna, A., Chang, C., Sullivan, K., and Lackney, J. (2008). “Impact of Shift Work on Labor Productivity for Labor Intensive Contractor.” J. Constr. Eng. Manage., 134(3), 197–204.

40. Jarkas, A. (2015). “Effect of Buildability on Labor Productivity: A Practical Quantification Approach.” J. Constr. Eng. Manage., 10.1061/(ASCE)CO.1943-7862.0001062 , 06015002.

41. Anand, K. and Ramamurthy, K. (2003). “Laboratory-Based Productivity Study on Alternative Masonry Systems.” J. Constr. Eng. Manage., 129(3), 237–242.

42. Sanders, S. and Thomas, H. (1991). “Factors Affecting Masonry‐Labor Productivity.” J. Constr. Eng. Manage., 117(4), 626–644.

43. Dai, J., Goodrum, P., and Maloney, W. (2009). “Construction Craft Workers’ Perceptions of the Factors Affecting Their Productivity.” J. Constr. Eng. Manage., 135(3), 217–226.

44. Rivas, R., Borcherding, J., González, V., and Alarcón, L. (2011). “Analysis of Factors Influencing Productivity Using Craftsmen Questionnaires: Case Study in a Chilean Construction Company.” J. Constr. Eng. Manage., 137(4), 312–320.

45. Hassanein, A. and Melin, J. (1997). “Crew Design Methodology for Construction Contractors.” J. Constr. Eng. Manage., 123(3), 203–207.

46. Watkins, M., Mukherjee, A., Onder, N., and Mattila, K. (2009). “Using Agent-Based Modeling to Study Construction Labor Productivity as an Emergent Property of Individual and Crew Interactions.” J. Constr. Eng. Manage., 135(7), 657–667.

47. Thomas, H., Horman, M., and de Souza, U. (2004). “Symbiotic Crew Relationships and Labor Flow.” J. Constr. Eng. Manage.,130(6), 908–917.

48. Thomas, H., Minchin, R., Jr., and Chen, D. (2003). “Role of Workforce Management in Bridge Superstructure Labor Productivity.” J. Manage. Eng., 19(1), 9–16.

49. Thomas, H. and Horman, M. (2006). “Fundamental Principles of Workforce Management.” J. Constr. Eng. Manage., 132(1), 97–104.

50. Hwang, B. and Soh, C. (2013). “Trade-Level Productivity Measurement: Critical Challenges and Solutions.” J. Constr. Eng. Manage., 139(11), 04013013.

51. FICCI. (2010). The Skill Development Landscape in India and Implementing Quality Skills Training, New Delhi: Federation of Indian Chambers of Commerce and Industry.

52. Government of India (2010). Annual Report to the People on Employment, Ministry of Labour and Employment, Government of India.

53. CIDC. (2007). Indian Construction Industry (2006-2007): 13^th Asia Construct, Construction Industry Development Council, India, pp: 1-25.

54. Anu, V., Thomas. and J, Sudhakumar. (2013). “Labour productivity variability among labour force – a case study.” The International Journal of Engineering and Science., Vol.2, Issue. 5, pp 57-65.

55. Raj, V. A. and Kothai, P. S. (2014). “Improving the Labour Productivity through Other Resources in Construction Field.”International Journal of Engineering Research and General Science., Vol. 2, Issue 2.

56. Shashank, K., Hazra, S., and Pal, N., K. (2014). “Analysis of key factors affecting labour productivity in construction projects.” International Journal of Emerging Technology and Advanced Engineering., Vol. 4, Issue 5, pp 152-160.

57. Janagan, S. S. and Thirumalairaja, K. (2014). “Comparative Study of Local and Migrant Labours For Productivity Enhancement in Construction Field.” International Journal of Innovative Research in Science, Engineering and Technology., Vol. 3 Special issue 1.

58. Arifullah, Sherfudeen., Raghavan, N., Radhakrishna, G, Pillai., and Satyanarayana N, Kalidindi. (2015). “Productivity in Precast Construction Sites in India.” Proceedings of the first Indian Lean Construction Conference., pp. 195-205.

59. Kumar, Y., Kumar, G. H., Myneni, S. B. and Charan, S. (2013). “Productivity Analysis of Small Construction Projects in India.” Asian J. Apllied Sci., DOI: 10.3923.

60. Varma Santosh, and Apte, M. R. (2014). “Productivity in Building Construction.” J. Mechanical and Civil Engineering., Volume 10, Issue 5 (Jan. 2014), PP 64-71.

61. Santhosh, Loganathan. and Satyanarayana N Kalidindi. (2015). “Masonry labor productivity variation: an Indian case study.” Proceedings of the first Indian Lean Construction Conference., pp. 175-185.

62. Pratibha., B. and Gaikwad., P.G. (2015). “Analysis of Labour Productivity in Residential Construction Projects.” Int. J. of Modern Trends in Engineering and Research., e-ISSN No.2349-9745, pp. 937-941.

Received on 10.10.2015 Accepted on 11.11.2015

Int. J. Tech. 5(2): July-Dec., 2015; Page 91-99

DOI: 10.5958/2231-3915.2015.00003.6