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The paper analyzes the relative technical efficiency of the transition economies of the Western Balkans in the period 2007–2021, in comparison with the former countries with a socialist state system, today members of the European Union (EU), based on selected macroeconomic indicators and panel data.

Data envelopment analysis (DEA), i.e. its extension, DEA Window analysis, is applied. Total technical efficiency, as a prerequisite of economic efficiency, is decomposed into pure technical efficiency (PTE) and scale efficiency (SE). Bootstrapping method and Mann–Whitney U test were used to check the robustness of the obtained results, i.e. efficiency values.

The results show that in 2020, all observed countries recorded a significant drop in economic efficiency as a result of a general, disproportionate drop in the value of selected macroeconomic variables, which occurred due to the global economic crisis and the slowdown in economic activity caused by the COVID-19 pandemic. This drop in efficiency was significantly greater in the former socialist states, now members of the European Union, which showed their greater sensitivity to global crises. None of the observed economies in the observed period was relatively efficient, that is, at the level of best practice, which occurred primarily as a consequence of the inefficiency of business conditions expressed in the economies of scale.

The main limitation of this study stems from the very nature of the concept of DEA efficiency, which is relative in nature. Also, the results and their interpretation are also significantly influenced by the choice of model variables, as shown by Lábaj et al. (2013), as well as a small number of decision-making units (DMUs). The mentioned limitations prevent unambiguous interpretation and generalization of the obtained results.

The study may be of importance to economic policy makers in macroeconomic decision-making. The application of the DEA concept in measuring the technical efficiency of national economies is a useful tool in the analysis of macroeconomic performance and a benchmarking approach for positioning and achieving competitive advantage on the international market.

Since research of this type is very limited, the results of this study make a theoretical and empirical contribution to the literature, creating a basis for future research and reexamination. The application of the DEA concept in measuring the technical efficiency of national economies is a useful tool in the analysis of macroeconomic performance and a benchmarking approach for positioning and achieving competitive advantage in the international market.

The purpose of this study is to measure the technical efficiency of 20 Algerian insurance companies from 2016 to 2020, by using slacks-based measure (slacks-based measure [SBM] data envelopment analysis [DEA]) model. This research aims to provide a comprehensive assessment of this companies' efficiency, taking into account both the desirable and undesirable outputs.

The study uses a nonoriented, SBM model with the assumption of constant returns to scale to estimate the technical efficiency of commercial insurance companies over a five-year period. The inputs used are labor expense, agent expense and investments, while the outputs included are gross premiums and investment income as desirable outputs and gross claims as undesirable output.

Among 20 insurance companies evaluated, only 5 companies consistently achieved technical efficiency during the study period (Caisse Nationale de Mutualité Agricole [CNMA], MACIR, CARDIF, MUTUALISTE and AGLIC); so they represent the best practices in the Algerian insurance sector, with overall average of the technical efficiency is 81%. However, the reference sets analysis showed that CNMA and AGLIC had high robustness. Also, the results demonstrate the impact of ignoring the undesirable outputs on the accuracy of the assessment.

The sample of the study consists of the active insurance companies in Algeria, based on the Annual Insurance Reports of Algeria; there are 20 companies as shown in the table. The data are taken from the annual reports of insurance companies during the 2020 period, issued by the Algerian Ministry of Finance.

The challenge for insurance company is how to find a balance between reducing claims paid and simultaneously improving the quality of insurance services. In fact, it is observed that studies evaluating their efficiency ignore claims in the analysis process. Therefore, the study highlights the importance of considering undesirable outputs within the DEA framework; this allows for a more accurate assessment of the company's performance and helps in improvement. Furthermore, although the insurance sector plays a crucial role, it has not received enough research attention compared to other financial sectors, especially in Arab and developing countries.

The literature on efficiency assessment in the insurance companies shows a lack of addressing undesirable outputs (such as claims) within the DEA framework; so this study aims at bridging this research gap. Also, the study provides an overview of the efficiency of Algerian insurance companies.

This study aims to evaluate the efficiency of the three telecommunications companies in Saudi Arabia: Saudi Telecom Company (STC), Mobily and Zain over the period of 2010–2019. This evaluation is a step toward improving the performance of the Saudi telecommunications sector.

Three multicriteria decision-making (MCDM) techniques were used to calculate technical efficiency. These techniques include the traditional data envelopment analysis (DEA), window DEA and analytical hierarchy process (AHP). The three inputs used were total assets, operating expenses and capital expenditures, whereas the two outputs were sales revenue and total stockholders’ equity.

STC was ranked first using the three techniques, followed by Zain, and then Mobily. According to the DEA window analysis, these three companies were all efficient only in 2012. The efficiency was high in the initial years, 2010–2013, when it was above 0.90, and it dropped below 0.90 in the subsequent years, 2014–2019. In addition, the efficiency of STC remained high, with an average of 0.990. However, the average efficiencies of Zain and Mobily during this period were 0.807 and 0.804, respectively.

This is the first study to use the three MCDM techniques to evaluate the performance of telecommunications providers. The results show that window DEA is better than the other two techniques at evaluating performance over time, as it has a higher discrimination power than either the traditional DEA or AHP.

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.

The study evaluates the accident-adjusted dynamic efficiency of public bus operators providing bus transportation services in eight major metropolitan cities of India.

The slack-based measure (SBM)–undesirable window analysis approach is used to gauge the dynamic efficiency levels and identify the sources of inefficiency in bus transportation services. This innovative approach integrates the SBM model developed by Tone (2001, 2004) and the window analysis approach of Charnes et al. (1985). The main advantage of this approach is that one can explicitly incorporate the number of accidents in the production technology specification as an undesirable (bad) output and potently handle the issue of the “curse of dimensionality” in a small sample like ours.

The key empirical findings suggest wide variations in average efficiency levels across sample bus operators in metropolitan cities. The Chennai Transport Corporation is observed as the most efficient and consistent bus operator due to its most stable efficiency performance. The results additionally unveil that the role of managerial inefficiency was diminutive, and the scale-related issues were the real cause of sub-optimal or supra-optimal behaviour of sample bus operators in the resource-utilisation process.

There is an urgent requirement for effective policy intercessions to mitigate the sizeable observed inefficiency in the production process and resolve scale-related issues of public bus operators offering transit services in major metropolitan cities of India.

This paper is maybe the first to assess the dynamic efficiency of public bus transit systems in India's major metropolitan cities after treating accidents.

Decree Law No. 663 introduced a decentralized organizational structure and administration pertaining to Turkish public hospitals in November 2011. This study aims to explore the effects of the public hospital unions (PHUs), which were a result of Decree Law No. 663, on the efficiency and productivity of public hospitals.

Data envelopment analysis (DEA) and DEA-based Malmquist total factor productivity (TFP) index were used from 2011 to 2016. Raw materials and supply expenses, salaries and fringe benefits, other service costs, general administrative expenses, total number of beds, number of specialists, number of residents, number of general practitioners, number of nurses and midwives and other medical officials were used as input variables. Working capital turnover, number of inpatients, number of outpatients and number of surgical operations for Groups A, B and C were used as output variables.

According to the DEA scores, the percentage of efficient hospitals showed a declining trend from 2011 to 2016. The TFP results also showed a decreasing trend from 2011 to 2016.

Providing administrative and financial autonomy to public hospital managers may cause efficiency and productivity losses, which is contrary to expectations.

This study is the first to reveal the impact of decentralization of public healthcare providers on their performance levels in Turkey.

The purpose of this paper is to provide scientific guidance for the integration of industrialization and information (TIOII). In recent years, TIOII has promoted the development of intelligent manufacturing in China. However, many enterprises blindly invest in TIOII, which affects their normal production and operation.

This study establishes an efficiency evaluation model for TIOII. In this paper, entropy analytic hierarchy process (AHP) constraint cone and cross-efficiency are added based on traditional data envelopment analysis (DEA) model, and entropy AHP–cross-efficiency DEA model is proposed. Then, statistical analysis is carried out on the integration efficiency of enterprises in Guangzhou using cross-sectional data, and the traditional DEA model and entropy AHP–cross-efficiency DEA model are used to analyze the integration efficiency of enterprises.

The data show that the efficiency of enterprise integration is at a medium level in Guangzhou. The efficiency of enterprise integration has no significant relationship with enterprise size and production type but has a low negative correlation with the development level of enterprise integration. In addition, the improved DEA model can better reflect the real integration efficiency of enterprises and obtain complete ranking results.

By adding the entropy AHP constraint cone and cross-efficiency, the traditional DEA model is improved. The improved DEA model can better reflect the real efficiency of TIOII and obtain complete ranking results.

This paper aims to examine the relationship between economic freedom and Islamic rural banks' efficiency in the case of Indonesia.

The study covers 40 Islamic rural banks in 34 Indonesian regions from 2014 to 2020. Tobit regression is utilized to expose the impact of economic freedom on the efficiency of Islamic rural banks, and nonparametric frontier data envelopment analysis is used to acquire banks' technical efficiency.

The findings reveal that overall economic freedom has a strong favorable impact on the efficiency of Islamic rural banks. The study’s breakdown components suggest that business freedom, government spending and investment freedom are favorable indicators, whereas government integrity and tax burden are negative indicators, and all indicators agree with previous studies.

This research can serve as a guideline for Islamic rural bank management in terms of maintaining financial efficiency. The government should think about the ramifications of financial sector liberalization and reforms, according to these findings. When financial intermediaries operate in a less constrained environment, they are more likely to pursue competitive practices that increase their operating rate and other efficiency metrics. Finally, academics might utilize this information to investigate the economic flexibility of Islamic rural banks.

The novelty of this study is in using data envelopment analysis and Tobit regression to identify economic freedom and Islamic rural banks' efficiency. To the best of the authors' knowledge, the study of the role of economic freedom in Islamic rural bank's efficiency is limited, particularly in the context of Indonesia.

This study aims to examine in depth the impact of merger activities on banks in Saudi Arabia.

Event study, financial ratio and efficient frontier analyses with a mixture of parametric and non-parametric tests are used for the sample period 2016Q1–2022Q4.

Event study analysis shows that merging banks (bidders) have higher positive cumulative abnormal returns than merged banks (targets), indicating that investors believe that bidding banks will benefit the most from the merger strategy. It was also found that the efficiency measures of the combined banks of Saudi British Bank and Alawwal Bank deteriorated, while they improved for the combined banks of National Commercial Bank and Saudi American Bank in the post-merger period, confirming investors' views.

Although the study focuses on the Saudi banking sector, its findings could be generalized to other banks in the region, as the Saudi banking sector is one of the largest in the Middle East region and is expected to grow further in the future.

The mere act of merging two banks does not guarantee the realization of cost synergies or efficiency gains. This research shows that mergers are not automatically cost-effective and that their success depends on good integration and restructuring strategies.

To the best of the author's knowledge, this is the first study to provide a comprehensive analysis of the short- and long-term impacts of merger activities in the Saudi banking sector.

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.