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The concept of productivity is central to performance management and decision-making, although it is complex and multifaceted. This paper aims to describe a methodology based on the use of Big Data in a cluster analysis combined with a data envelopment analysis (DEA) that provides accurate and reliable productivity measures in a large network of retailers.

The methodology is described using a case study of a leading kitchen furniture producer. More specifically, Big Data is used in a two-step analysis prior to the DEA to automatically cluster a large number of retailers into groups that are homogeneous in terms of structural and environmental factors and assess a within-the-group level of productivity of the retailers.

The proposed methodology helps reduce the heterogeneity among the units analysed, which is a major concern in DEA applications. The data-driven factorial and clustering technique allows for maximum within-group homogeneity and between-group heterogeneity by reducing subjective bias and dimensionality, which is embedded with the use of Big Data.

The use of Big Data in clustering applied to productivity analysis can provide managers with data-driven information about the structural and socio-economic characteristics of retailers' catchment areas, which is important in establishing potential productivity performance and optimizing resource allocation. The improved productivity indexes enable the setting of targets that are coherent with retailers' potential, which increases motivation and commitment.

This article proposes an innovative technique to enhance the accuracy of productivity measures through the use of Big Data clustering and DEA. To the best of the authors’ knowledge, no attempts have been made to benefit from the use of Big Data in the literature on retail store productivity.

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.

Labour productivity in construction has fallen behind other industries in most of the world and has declined continuously for decades. Although several scholarly research projects have been conducted to salvage the prevalent low labour productivity in construction, contractors in the construction industry have continued to grapple with the devastating impact of low productivity. The purpose of this study is to determine key areas of focus necessary to promote productivity growth in construction.

Bibliometric and scientometric assessments were conducted to map the existing construction labour productivity (CLP) studies and establish key focus areas in the research domain. The keywords “Construction Productivity” OR “Construction Labour Productivity” OR “Construction Labor Productivity” OR “Construction Worker Productivity”.

Emerging trends in the CLP research field are reported. The study also determined the most productive authors and collaboration among authors, most productive journals, most active regions and publications with the highest impact in CLP research.

Documents published in the Scopus database were considered for analysis because of the wider coverage of the database. Journal and conference articles written in English language represent the inclusion criteria, while articles in press, review, book chapters, editorial, erratum, note, short survey and data paper were excluded from analysis. The study is also limited to documents published from 2012 to 2021.

The study brought to the awareness of the industry practitioners and other construction stakeholders, the key knowledge areas that are critical to promoting productivity growth in construction.

Except bibliometric analysis, previous research studies have used different approaches to investigate productivity in construction. The study presented future research directions through the emerging knowledge areas identified in the study.

Total factor productivity (TFP) has become a prominent concept in agriculture economics and policy over the last three decades. The main aim of this paper is to obtain a detailed picture of the field via bibliometric analysis to identify research streams and future research agenda.

The data sample consists of 472 papers in several bibliometric exercises. Citation and collaboration structure analyses are employed to identify most important authors and journals and track the interconnections between main authors and institutions. Next, content analysis based on bibliographic coupling is conducted to identify main research streams in TFP.

Three research streams in agricultural TFP research were distinguished: TFP growth in developing countries in the context of policy reforms (1), TFP in the context of new challenges in agriculture (2) and finally, non-parametric TFP decomposition based on secondary data (3).

This research indicates agenda of future TFP research, in particular broadening the concept of TFP to the problems of policy, environment and technology in emerging countries. It provides description of the current state of the art in the agricultural TFP literature and can serve as a “guide” to the field.

Health risks associated with coronavirus disease 2019 (COVID-19) have severely affected the financial stability of airline companies globally. Recapturing financial stability following this crisis depends heavily on these companies’ ability to attain efficient and productive operations. This study uses several empirical approaches to examine key factors contributing to carriers sustaining high productivity prior to, during and after a major recession. Findings suggest, regardless of economic conditions, that social distancing which requires airline companies in the Asia Pacific region to fly with a significant percentage of unfilled seats weakens the performance of those companies. Furthermore, efficient operations do not guarantee the avoidance of productivity declines, especially during a recession.

This study aims to measure the productivity of 21 major shipyards in China, South Korea and Japan.

Data envelopment analysis was applied to measure the productivity of shipyards. The contemporaneous and intertemporal productivity scores of each shipyard were measured. Additionally, the technical gaps among shipyards in China, South Korea and Japan were measured and compared.

The results indicate that Japan led the global shipbuilding industry in 2014 and South Korea dominated in 2015. Additionally, from 2014 to 2015, shipyards in South Korea and Japan maintained their levels of productivity. Comparatively, major shipyards in China made substantial progress from 2014 to 2015, revealing their strong ambition to improve productivity.

This study first used a metafrontier framework to measure the technical gap of shipyards among major shipbuilding countries. The model and approach objectively analyze the productivity of major shipyards and considers their nationalities. Additionally, this study is the first to measure changes in the productivity of shipyards. By decomposing the metafrontier Malmquist productivity index, major shipyards were categorized into eight sets. The results of this study can provide a clear direction for shipyards to improve their productivity.

Taiwan’s hotel industry was adversely impacted by the COVID-19 pandemic. This study aims to examine the effect of strategic choices by Taiwanese international tourist hotels before and during the pandemic environments.

A data envelopment analysis (DEA)-based Malmquist methodology is used in this study to provide a mechanism to assess Taiwanese hotel strategy performance. Changes in the productivity and performance of Taiwanese international tourist hotels were analyzed in the periods before and during the pandemic to uncover insights useful should a similar crisis occur in the future. Panel data were obtained from the annual report of international tourist hotels published by the Taiwan Tourism Bureau from 2017–2020. Two groups of hotels were analyzed in this study: city hotels and scenic hotels.

The findings of this study reveal that chain hotels tended to perform better than independent hotels in both city and scenic areas during the global pandemic. Specifically, the crisis caused a substantial decline in productivity and profitability for international tourist hotels in Taipei City during the COVID-19 period. Compared to city hotels, findings also indicate that most international tourist hotels in scenic areas were able to maintain better productivity, including larger-sized scenic hotels.

The DEA-based analysis provides unique and valuable insights for hotel firm leaders on how to better identify and make strategic choices when responding to future crises.

Global climate change characterized by an increase in temperature has become the focus of attention all over the world. China is a sensitive and significant area of global climate change. This paper specifically aims to examine the association between agricultural productivity and the climate change by using China’s provincial agricultural input–output data from 2000 to 2019 and the climatic data of the ground meteorological stations.

The authors used the three-stage spatial Durbin model (SDM) model and entropy method for analysis of collected data; further, the authors also empirically tested the climate change marginal effect on agricultural productivity by using ordinary least square and SDM approaches.

The results revealed that climate change has a significant negative effect on agricultural productivity, which showed significance in robustness tests, including index replacement, quantile regression and tail reduction. The results of this study also indicated that by subdividing the climatic factors, annual precipitation had no significant impact on the growth of agricultural productivity; further, other climatic variables, including wind speed and temperature, had a substantial adverse effect on agricultural productivity. The heterogeneity test showed that climatic changes ominously hinder agricultural productivity growth only in the western region of China, and in the eastern and central regions, climate change had no effect.

The findings of this study highlight the importance of various social connections of farm households in designing policies to improve their responses to climate change and expand land productivity in different regions. The study also provides a hypothetical approach to prioritize developing regions that need proper attention to improve crop productivity.

The paper explores the impact of climate change on agricultural productivity by using the climatic data of China. Empirical evidence previously missing in the body of knowledge will support governments and researchers to establish a mechanism to improve climate change mitigation tools in China.

Using a large firm-level data set, this paper examines total factor productivity (TFP) and its determinants in the Ecuadorian manufacturing sector in the period 2007–2018.

I analyze the role played by traditional TPF determinants, including internal firm characteristics, international trade activities, financial constraints and competition intensity. I contribute to the literature by presenting quantile regression results. Moreover, I analyze industry patterns, distinguishing between industries according to their technological intensity (following the organisation for economic co-operation and development classification).

My results confirm that firm age is positively related to TFP level but negatively related to TFP growth. I also find that being an exporter and an importer at the same time is associated with higher TFP levels and that this effect is higher than when being only an exporter or an importer. Additionally, l find that credit is positively related to TFP levels. Finally, I find that more competition is positively related to productivity in lower quantiles of output.

The results are the source of tools to propose policy recommendations, which are stated in the present document.

This paper aims to reopen the debate of firm productivity determinants in a developing country such as Ecuador. The authors use a set of covariates less analyzed in this issue.