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This study aims to examine the technical efficiency of the chemical-free farming system in India using a hybrid combination of data envelopment analysis (DEA) and machine learning (ML) approaches.

The study used a two-stage approach. In the first stage, the efficiency scores of decision-making units’ efficiency (DMUs) are obtained using an input-oriented DEA model under the assumption of a variable return to scale. Based on these scores, the DMUs are classified into efficient and inefficient categories. The 2nd stage of analysis involves the identification of the most important predictors of efficiency using a random forest model and a generalized logistic regression model.

The results show that by using their resources efficiently, growers can reduce their inputs by 34 percent without affecting the output. Orchard's size, the proportion of land, grower's age, orchard's age and family labor are the most important determinants of efficiency. Besides, growers' main occupation and footfall of intermediaries at the farm gate also demonstrate significant influence on efficiency.

The study used only one output and a limited set of input variables. Incorporating additional variables or dimensions like fertility of the land, climatic conditions, altitude of the land, output quality (size/taste/appearance) and per acre profitability could yield more robust results. Although pineapple is cultivated in all eight northeastern states, the data for the study has been collected from only two states. The production and marketing practices followed by the growers in the remaining six northeastern states and other parts of the country might be different. As the growers do not maintain farm records, their data might suffer from selective retrieval bias.

Given the rising demand for organic food, improving the efficiency of chemical-free growers will be a win-win situation for both growers and consumers. The results will aid policymakers in bringing necessary interventions to make chemical-free farming more remunerative for the growers. The business managers can act as a bridge to connect these remote growers with the market by sharing customer feedback and global best practices.

Although many developments have happened to the DEA technique, the present study used a traditional form of DEA. Therefore, future research should combine ML techniques with more advanced versions like bootstrap and fuzzy DEA. Upcoming research should include more input and output variables to predict the efficiency of the chemical-free farming system. For instance, environmental variables, like climatic conditions, degree of competition, government support and consumers' attitude towards chemical-free food, can be examined along with farm and grower-specific variables. Future studies should also incorporate chemical-free growers from a wider geographic area. Lastly, future studies can also undertake a longitudinal estimation of efficiency and its determinants for the chemical-free farming system.

No prior study has used a hybrid framework to examine the performance of a chemical-free farming system.

This study aims to propose a refined dynamic network slacks-based measure (DNSBM) to evaluate the efficiency of China's regional green innovation system which consists of basic research, applied research and commercialization stages and explore the influencing factors of the stage efficiency.

A two-step procedure is employed. The first step proposes an improved DNSBM model with flexible settings of stages' input or output efficiency and uses second order cone programming (SOCP) to solve the non-linear problem. In the second step, least absolute shrinkage and selection operator (LASSO) and Tobit models are used to explore the influencing factors of the stage efficiency. Global Dynamic Malmquist Productivity Index (GDMPI) and Dagum Gini coefficient decomposition method are introduced for further discussion of the productivity change and regional differences.

On average, Chinese provincial green innovation efficiency should be improved by 24.11% to become efficient. The commercialization stage outperforms the stages of basic research and applied research. Comparisons between the proposed model and input-oriented, output-oriented and non-oriented DNSBM models show that the proposed model is more advanced because it allows some stages to have output-oriented model characteristics while the other stages have input-oriented model characteristics. The examination of the influencing factors reveals that the three stages of the green innovation system have quite diverse influencing factors. Further discussion reveals that Chinese green innovation productivity has increased by 39.85%, which is driven mainly by technology progress, and the increasing tendency of regional differences between northern and southern China should be paid attention to.

This study proposes an improved dynamic three-stage slacks-based measure (SBM) model that allows calculating output efficiency in some stages and input efficiency in the other stages with the application of SOCP approach. In order to capture productivity change, this study develops a GDMPI based on the DNSBM model. In practice, the efficiency of regional green innovation in China and the factors that influence each stage are examined.

This study empirically examines the impact of climate change and agricultural research and development (R&D) as well as their interaction on agricultural productivity in 12 selected Asian and Pacific countries over the period of 1990–2018.

Various estimation methods for panel data, including Fixed Effects (FE), the Feasible Generalized Least Squares (FGLS) and two-step System Generalized Method of Moments (SGMM) were used.

Results show that both proxies of climate change – temperature and precipitation – have negative impacts on agricultural productivity. Notably, agricultural R&D investments not only increase agricultural productivity but also mitigate the detrimental impact of climate change proxied by temperature on agricultural productivity. Interestingly, climate change proxied by precipitation initially reduces agricultural productivity until a threshold of agricultural R&D beyond which precipitation increases agricultural productivity.

The findings imply useful policies to boost agricultural productivity by using R&D in the context of rising climate change in the vulnerable continent.

This study contributes to the literature in two ways. First, this study examines how climate change affects agricultural productivity in Asian and Pacific countries – those are most vulnerable to climate change. Second, this study assesses the role of R&D in improving agricultural productivity as well as its moderating effect in reducing the harmful impact of climate change on agricultural productivity.

This study investigates the effect of intellectual capital (IC) and its components on the technical efficiency of Indian commercial banks after controlling the influence of bank-specific and macroeconomic variables.

The study selects a sample of 37 listed Indian commercial banks from 2005 to 2019 and uses the two-step data envelopment analysis (DEA) approach. Banks' technical efficiency scores are first estimated, while the relationship between IC and technical efficiency is examined in the second stage using the panel data Tobit model.

This study's findings suggest a fluctuating trend in the technical efficiency of Indian banks. Notably, from 2015 onwards, a declining technical efficiency trend is observed for all banks. However, private-sector banks outperform public-sector banks in terms of technical efficiency. This study's regression analysis indicates a positive relationship between IC and banks' technical efficiency scores. Further, by decomposing IC into its components like human capital, structural capital and capital employed, the study's findings show that human capital and structural capital enhance banks' technical efficiency. Notably, capital employed reduces technical efficiency. Moreover, bank size, diversification, capitalization, net interest margin and the country's growth rate significantly drive Indian banks' efficiency. In contrast, their operating cost ratio and the country's inflation negatively influence the same.

This study makes a novel endeavor to examine the IC and bank's technical efficiency nexus in the Indian context, encompassing a period of landmark banking reforms.

The purpose of this paper is to ascertain if practising healthy lifestyles improves the technical efficiency of farms in Kwara state, Nigeria. In theory, all deviations from the optimum level of output are due to random effects and inefficiency of producers in which their health plays a key part and is dependent on the kind of lifestyle practiced whether healthy or unhealthy.

Cross-sectional data were employed through a three-staged sampling technique to pick 320 arable crop farmers across the state using a well-defined questionnaire. Data analysis was carried out using descriptive statistics, healthy lifestyles index (HLI), stochastic production frontier (SPF) and propensity score matching (PSM).

First, the analysis showed that about one-third of the sampled arable crop farmers practised healthy lifestyles. Second, the average technical efficiency of arable crop production for farmers who practised a healthy lifestyle was 0.893, and the level of technical inefficiency of the farms was determined by health-related lifestyle status, number of day's illness and educational level. Third, technical efficiency was improved by 0.00431067 for farms whose farmers practised a healthy lifestyle.

Rather than seeing that technical efficiencies of farms are attributed to farm characteristics, inputs used and socioeconomic characteristics alone, the findings suggest that technical inefficiencies of arable crop farmers were also due to the kind of lifestyle practised, which was evidenced in the increased efficiency for farmers who practised healthy lifestyle.

The peer review history for this article is available at: https://publons.com/publon/10.1108/IJSE-05-2023-0353

The authors test the weak-form efficiency in cryptocurrency markets using the most recent and comprehensive data as of 2021. The authors apply various technical indicators to take a long or short position on 99 cryptocurrencies and compare the 10-day returns based on the technical trading strategies to the simple buy-and-hold returns. The authors find that the trading strategies based on single indicators or the combination of two indicators do not generate higher returns than buy-and-hold returns among cryptos. These findings suggest that cryptocurrency markets are weak-form efficient in general.

This paper aims to define the efficiency achieved by urban transport companies in Spain concerning the resources they use, considering the type of management used for implementation, public-private, and size.

This study consisted of an analysis of the efficiency of 229 public-private urban transport operators during the period 2012–2021 using Data Envelopment Analysis, the Malmquist Index and inference estimators to determine productivity, efficiency change into Pure Technical Efficiency Change (PTECH), and scale efficiency change.

Based on the efficiency analysis, the authors concluded that of the 229 companies studied, more than 35 were inefficient in all analysed periods. Considering the sample used, direct management is considered significantly more efficient. It cannot be concluded that the size of these companies influences their efficiency, as the data show unequal development behaviours in the studied years.

This study provides arguments on whether there is a significant difference between the two types of management in the urban transport sector. It also includes firm size as a study variable, which has not been previously considered in other studies related to urban transport efficiency. Efficiency should be a crucial factor in determining funding allocation in this sector, as it encourages operators to optimize and improve their services.

This paper aims to investigate the impact of acquisition and organic growth on the operating efficiency and total factor productivity change of retailing networks.

The assessment uses low-frequency data of newly opened stores and acquired stores of a large supermarket (S/M) network in Athens, for a period (financial year 2014) where the network began to refocus on its organic growth after a two-year period of deep recession (financial years 2012–2013). To evaluate the performance effects of both strategies, the authors employ the innovative benchmarking tool of bootstrap data envelopment analysis (DEA) for measuring operational efficiency and the Malmquist productivity index DEA approach for measuring productivity change over time.

The short-run evidence indicates that compared to organic growth, acquisitions lead to lower operating efficiency. However, this difference gradually converges over time as acquired stores show a higher rate of productivity compared to newly opened stores. The authors interpret this as a result of the smooth integration of the acquired chain store into the organizational structure of the existing store network given their significant similarities in terms of products and customers.

The authors inform managers of store chains that during the process of organic growth, a general improvement in efficiency takes place while in the case of acquisitions, the required post-acquisition streamlining actions cause a short delay on the realization of efficiency gains. Therefore, managers should not take it for granted that acquisitions cause a long-term decrease in efficiency.

The study contributes to the literature on growth strategies and retailing performance in general, by offering new evidence regarding the comparative effect of the horizontal growth modes on the efficiency of store chains.

This study aims to benchmark the operational efficiency of fifty-eight public hospitals across Mexico between 2015 and 2018 and identifies the most critical inputs affecting their efficiency. In doing so, the study analyzes the impact of policy changes in the Mexican healthcare system introduced in recent years.

To measure the operational efficiency of Mexican public hospitals, data envelopment analysis (DEA) window analysis variable returns to scale (VRS) methodology using longitudinal data collected from the National Institute for Transparency and Access to Information (IFAI). Hospital groups are developed and compared using a categorization approach according to their average and most recent efficiency.

Results show that most of the hospitals in the study fall in the moving ahead category. The hospitals in the losing momentum or falling behind categories are mostly large units. Hospitals with initially low efficiency scores have either increased their efficiency or at least maintained a steady improvement. Finally, the findings indicate that most hospitals classified as moving ahead focused on a single care area (cancer, orthopedic care, child care and trauma).

This study examined the technical efficiency of the Mexican healthcare system over a four-year period. Contrary to conventional belief, results indicate that most public Mexican hospitals are managed efficiently. However, recent changes in public and economic policies that came into effect in the current administration (2018) will likely have long-lasting effects on the hospitals' operational efficiency, which could impact the results of this study.

To the best of authors’ knowledge, this is the first study that examines the efficiency of the complex Mexican healthcare system using longitudinal data.

This study aims to discuss climate change, by modifying the timing of several agricultural operations, reduce the efficiency and yield of inputs leading to a lower production level. The reduction of the effects of climate change on production yields and on farmers' technical efficiency (TE) requires the adoption of adaptation strategies. This paper analyses the impact of climate change adaptation strategies adopted on maize farmers' TE in Benin.

This paper uses an endogeneity-corrected stochastic production frontier approach based on data randomly collected from 354 farmers located in three different agro-ecological zones of Benin.

Estimation results revealed that the adoption of adaptation strategies improve maize farmers' TE by 1.28%. Therefore, polices to improve farmers' access to climate change adaptation strategies are necessarily for the improvement of farmers' TE and yield.

The results of this study contribute to the policy debate on the enhancement of food security by increasing farmers' TE through easy access to climate change adaptation strategies. The improvement of farmers' TE will in turn improve the livelihoods of the communities and therefore contribute to the achievement of Sustainable Development Goals 1, 2 and 13.

This study contributes to theoretical and empirical debate on the relationship between adaptation to climate change and farmers' TE. It also adapts a new methodology (endogeneity-corrected stochastic production frontier approach) to correct the endogeneity problem due to the farmers' adaptation decision.