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The purpose of this study is to investigate efficiency and productivity of Turkey’s both brokerage sector and intermediary institutions (IIs) that have been active in Turkish capital markets.

Data envelopment analysis (DEA) and Malmquist total factor productivity index (MPI) are used to analyze efficiency and productivity of Turkey’s both brokerage sector and 51 Turkish IIs constantly operated between the years 2008 and 2018. Paid-in capital, administrative expenses and trading volumes are used as input, while net trading commissions and net profit/loss are used as output in analysis. The calculations of this analysis are made with DEAP 2.2 program and Python.

The results reveal that during the analysis period, percentage of efficient institutions among 51 IIs was between 18% and 39% while the sector’s mean efficiency score ranged between 52% and 65%. While 2009 is the year with the highest number of efficient institutions, 2013 is observed to be the least. Finally, the results of productivity analysis indicate that all types of IIs are not fully productive during the related period. The striking finding obtained is that though there is a decrease in total productivity change, the technological change has a positive effect on their productivity change.

This study is a double-layered research paper that includes efficiency analysis by DEA in the first step and productivity analysis by using MPI in the second step.

A review is made of corporate practices in reporting performance so as to extract elements for constructing a productivity calculus for reflecting corporate performance. A communications strategy reflecting critical value‐added productivity changes is then explained. We propose multi‐directionality embedded in corporate value‐added performance changes be communicated through symbolism of “bread” or “wooden rice box”. The Internet is now streamlined as part of standard office systems. It is therefore timely to enable the e‐versions of annual reports with manipulative controls for in‐depth strategic analyses of directional changes in corporate value‐added performances.

Culture has been identified as one of the main drivers of the “competitive productivity” (CP) of nations. However, research studies examining the relationships between culture, competition and productivity are highly fragmented across different streams of literature, leaving researchers with a lack of a holistic view of the topic. This study reviews research studies that examined the relationships between culture and productivity and between culture and competitiveness, as well as the joint relationships between culture, productivity and competitiveness in leading economic, business and management journals in the period 2009–2018 in order to identify research gaps and opportunities for future research.

The authors used a combination of bibliometric analysis using VOSviewer, text analysis using Leximancer and systematic review by expert reviewers to analyze 293 articles that consider culture, productivity and competitiveness published in leading business, management and economics journals in the period 2009–2018.

The findings indicate that, although productivity and competitiveness are often discussed jointly in some policy circles, research studies on the roles of culture on productivity and on competitiveness take place in quite different streams of academic literature, drawing on different sets of concepts and theoretical frameworks. The concept of innovation appears prominently in both sets of the literature as an antecedent of both productivity improvement and international competitiveness.

The findings highlight the need for more research studies which jointly examine culture, productivity and competitiveness and the relationships between them.

To the best of the authors’ knowledge, this study is among the first attempts to systematically analyze the literature on the relationship between culture and CP.

The purpose of this work is twofold: on the one hand, recent methodologies will be used to estimate efficiency and productivity in Spain's non‐specialized retail sector for the period of 1997‐2007. In particular, the order‐m approach proposed by Cazals et al., which is based on the concept of expected minimum input function. On the other hand, the results obtained applying the methods mentioned in the Spanish retail sector can contribute to opening up a new field of analysis since the results may be compared by means of the methodologies proposed as well as those which already exist in the literature.

The paper used data envelopment analysis stochastic (order‐m) and bootstrapping Malmquist index to measure productivity and efficiency in 12 sectors in Spanish retail trade 1997‐2007.

In order to illustrate the methodology proposed in this paper different phases involved; first, we have estimated the efficiency in 12 sectors of the retail sector four digits NACE, we found high levels of inefficiency in most of the sectors analyzed over the period of analysis. Next, we will deepen and simplify the analysis by concentrating on food‐predominant sectors in non‐specialised shops (5211). The evolution of the efficiency of firms belonging to this sector decreases over the period of analysis. Analyzing the relationship between firms and size, the results obtained in this work shows that the firm's size have a positive influence on efficiency that suggest that the management may have incentives to grow in order to improve their efficiency levels. Our second contribution has to do with the use of bootstrapping Malmquist productivity indices. Productivity decreased at an average rate of −4.1 percent over the entire period of 1997‐2007. On average, this deterioration was due to efficiency change −6.1 percent. Technical progress is increased at an average rate of 2.1 percent. All rates at global level are statistically significant at 95 percent.

The main contribution of this paper is to provide an efficiency analysis using a non‐parametric approach with a robust estimator that has been suggested recently by Cazals et al. This methodology is that the first time that is applied in the analysis of retail sector. In addition, we analyze productivity growth using bootstrapping Malmquist indices. This methodology allows for a more careful analysis of what happens at firm level. Differences in conclusions between the original estimates and the bootstrap results are more evident when we scrutinize the sample firms and individual levels.

The high-quality development of the real estate industry is crucial to the transformation of China's economy. However, few studies apply the productivity to explore the development path of the real estate industry in China. To fill this gap, this study mainly investigates the total factor productivity (TFP) of the real estate industry of 30 sample provinces in mainland China from 2007 to 2016.

The Malmquist index is applied to estimate the productivity (i.e. TFP) of the real estate industry, based on the data envelopment analysis (DEA). Then, the truncated tobit regression analysis explores the external influencing factors on the TFP of the real estate industry.

Through empirical analysis, it is found that the high-quality development of the real estate industry depends on the technological innovation by the real estate enterprises and the targeted policies by the provincial government. Moreover, the development of the real estate industry has a positive correlation with the growth of China's economy but a negative correlation with the development of other industries.

TFP mainly reveals the development status of the provincial real estate industry and identifies the driving force for exploring the high-quality development mode of the real estate sector. Furthermore, the fluctuation rule of TFP can be applied to predict the development trend of the real estate industry in the future.

As an application, this study measures the TFP of the Chinese real estate industry in different provinces and periods. The results have meaningful policy implications for policymakers regulating the real estate industry.

The productivity of a port is a measure that is important to different stakeholders: port administrators (port authority), third-party logistics providers, manufacturers and consumers, among others. This study analyses productivity in terms of vessel movement efficiencies (loading/unloading of cargo) and container release from port facilities. Both factors add to the overall productivity in any port.

A comparative analysis of the productivity of three ports is measured using a Quality Function Deployment (QFD) and benchmarking analysis to help establish strategies that will help improve productivity. Considering the information confidentially the authors will call the ports according to their geographic location. The ports under study are the USA Southeast Port (Port of America), Central Asian Port (Port of Asia) and Central Europe Port (Port of Europe).

This study has established an analysis strategy that allows seeing points of sale in the ports. This study will compare three different continents, only to demonstrate the applicability of QFD and benchmarking. Still, the strategy can be used in ports that compete due to their proximity and location. Identifying the variables to be analyzed made it possible to establish a strategy to increase productivity.

There are many studies that analyze port productivity, but none try to standardize the variables to be compared in different scenarios. This study has compared three ports from three different geographical areas, using the same variables in all three cases. The study critically analyses the performance of three ports and proposes a strategy based on QFD and benchmarking research.

This paper aims to study the changes in productivity for 25 state‐owned coal‐fired power plants (CFPPs) over a period of seven years (2003‐2010).

The methodology that is utilized in the study follows a non‐parametric approach of data envelopment analysis (DEA) and uses the Malmquist index to estimate the change in productivity of panel samples. In the calculations, the study considers installed capacity, fuel, labour, electricity used, and average operational time as inputs and considers net electricity produced as output.

The results indicate that the average total factor productivity regressed during the investigation period at an annual rate of 2 percent. The decrease in productivity is equally attributed to the technical efficiency change and technological change components, with an average decline in productivity of 1 percent per year. A plant‐wise analysis demonstrates that the Parichha plant recorded an average increase in productivity of 3.9 percent per year that was mainly driven by the technical efficiency change component (4.2 percent).There is little variation in the productivity of small‐size plants when compared with medium and large‐size plants. The productivity of multivaried plants is comparatively lower than BHEL (Bharat Heavy Electricals Limited) make plants.

The impact of size, make and region on change in productivity is examined. This study recommends specific policies that can be implemented to increase the productivity of power plants. The study also provides a contemporary overview of Indian CFPPs that can aid energy planners and plant operators in the monitoring and detection of changes in productivity.

It is essential to track the development of resource and pollution intensive industries such as textile, leather, pharmaceutical, etc., under burgeoning pressure of environmental compliance. Therefore, the purpose of this paper is to analyze the progress of Indian leather industry in terms of individual factors and total factor productivity.

This study applies and examines the various concepts of productivity such as labor productivity, capital productivity, material productivity and energy productivity. Further, it assesses and compares the performance of Indian leather industry in Tamil Nadu (TN), West Bengal (WB) and Uttar Pradesh (UP) based on productivity analysis, spatial variations determinants in productivity and technology closeness ratio.

The findings suggest that as per the productivity analysis, WB leather clusters have performed remarkably better in terms of partial factor productivity and technical efficiency (TE), followed by TN and UP. This can be attributed to shifting of leather cluster of WB to a state-of art leather complex with many avenues for resource conservation. Further, the findings reveal that the firm size and partial factor productivities have significant positive correlation with TE which supports technological theory of the firm.

The results of this study can be useful for the policy makers associated with the Indian leather industry especially to design interventions to support capacity building at individual firm level as well as cluster level to enhance the efficiency and productivity of overall industry.

The findings also support the resource dependence theory of firm according to which the larger size firms should reflect on resource conservation practices, for instance the concept of prevention is better than cure based upon 3R (reduce, recycle and reuse) principles.

The paper gives an explanation of the productivity in the leather industry in terms of its factor productivity and TE.

Work crew productivity and the application of limited resources are necessary elements in construction duration delay analysis. This study thus proposes a method to analyze construction delays and resource reallocation based on work crew productivity and resource constraints. The study also presents an economic feasibility analysis that maximizes economic effect by reducing construction duration, the cost of resource reallocation, delay liquidated damages (DLDs) and incentives for reducing contractual duration.

The proposed method involved three steps. First, work crew characteristics such as productivity, unit price and workload helped analyze delay information, including delay duration, reducible duration and daily reduced cost. Next, a goal programming method assessed resource reallocation based on the priority (as determined by decision-makers) of each constraint condition, such as the available number of workers, cost, goal workload and statutory working hours. Lastly, the level of reallocation was analyzed based on the results of the economic feasibility analysis and decision-makers’ delay attitudes.

A case study was performed to test the proposed method's applicability. Its involved sensitivity analysis indicated proposing to decision-makers a scenario based on the prioritization of economic feasibility. The proposed method's applicability proved high for decision-makers, as they can determine whether to reduce construction duration per the proposed data.

The proposed method's main contribution is the reallocation of resources to reduce construction duration based on work crew productivity and the prioritization of limited resources. The proposed method can analyze the differences in productivity between the plan and actual progress, as well as calculate the necessary number of workers. Decision-makers can then reduce the appropriate level of contractual duration based on their own delay attitude, constraint condition prioritization and results from daily economic feasibility analyses.

Currently, there is a dearth of research studies regarding macro analysis of the workforce productivity of the US construction industry. The purpose of this paper is to calculate the workforce productivity changes of the US construction industry from 2006 to 2016, with the number of laborers as input and value of construction industry as output.

The present study introduced the data envelopment analysis (DEA) based Malmquist productivity index model to measure the workforce productivity of the US construction industry from 2006 to 2016.

The results indicated that the workforce productivity of the US construction industry experienced a continuous decline, except for the increases from 2011 to 2013 and from 2014 to 2015. It was also shown that there were gaps in the workforce productivity development level among all states and nine regions in the US construction industry. Besides, the relationship between workforce productivity and four aspects, including real estate price, workforce, climate distribution and economic factors, was analyzed.

The calculation of the productivity of the US construction industry is based on the premise that the external environment is fixed and unchanged from 2006 to 2016, but the multi-level DEA model for further calculation is required for obtaining more effective conclusions.

This paper measures the workforce productivity of the US construction industry over the past 11 years, which added latest analysis and knowledge into the construction industry, providing decision-makers with advice and data support to formulate policies to improve workforce productivity.

This study provided both government decision-makers and industrial practitioners with important macro background environment information, which will facilitate the improvement of workforce productivity in the construction industry in different regions of the US.