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Data envelopment analysis (DEA) calculates the efficiency of a business unit if all the inputs are creating outputs within a “black box.” Under traditional DEA, the detailed process of that business unit is ignored. However, a network DEA can explain the black box structure and provide efficiency results for sub-sections within any business process. This study aims to propose a network DEA model that explains a bank's total operation.

Earlier studies have focused only on bank efficiency ignoring this breakdown. This study departs from them by using a slack-based two-stage network DEA under a novel banking business perspective.

The results reveal that network DEA provides better benchmarking insights than the traditional DEA. As such, better benchmarking can guide both the banking industry managers and policy makers in Bangladesh.

The major contribution of this study includes dividing a bank's total operation efficiency into two sub-operations: “core operations – collecting deposits and giving loans” and “additional operations – fees, commissions and other services.”

The purpose of this paper is to analyze the bank efficiency in three developing countries, namely Angola, Brazil and Mozambique, aiming to infer differences given that they belong to the same cultural tradition. The underlying idea is to control for the cultural background, thus allowing the discussion on how different socio-economic and historical variables maybe impacting different levels of banking efficiency and returns to scale results within the ambit of these three countries.

Due to the presence of latent inefficiency, the authors have to modify the technique to accommodate simulation by importance sampling; therefore, in effect, the authors use a local maximum simulated likelihood approach.

The results reveal that Brazil has the highest level of output-oriented efficiency, followed by Angola and then Mozambique. The same ranking is observed in returns to scale, except that vis-à-vis technical change, Brazil and Angola rank first. Finally, inefficiency derived from technical change is highest in Mozambique, followed by Angola and then Brazil. Therefore, these results reveal that the countries with the highest degree of development are higher in efficiency.

Previous studies have identified factors such as legal tradition, accounting conventions, regulatory structures, property rights, culture and religion as possible explanations for cross-border variations in financial development and economic growth. This is the first time banking efficiency is assessed in light of a common cultural background by selecting a group of countries that share the same language and colonial past. Since results are controlled for the same background, it is possible to affirm that the findings are purely related to scale size and economic/political background issues of each country.

This paper aims to examine the efficiency of Islamic vs conventional banks in Malaysia by unveiling the traditional efficiency concept – black box – with a three-stage network structure of bank operations.

This paper applies data envelopment analysis (DEA) for examining bank efficiency. An adaptive three-step network DEA (NDEA) model is demonstrated for redefining the traditional black box of banking operations. Slack-based variable returns to scale approach is used. Data from all 43 commercial banks in Malaysia are examined over a six-year study period (2010-2015). Inputs and outputs of the model are selected based on CAMELS rating. Undesired output is also considered in time of examining bank efficiency in Malaysia.

The empirical results of this study signify that only a few banks in Malaysia have been performing well in converting deposits and equities into profit as well as minimizing loan loss provisions. Islamic banks in Malaysia have performed better both in production (converting deposits and equities into earning assets) and profitability (converting loans into net income). Conventional banks, however, have over scored in intermediation (converting earning assets into loans).

An adaptive NDEA approach proposed in this paper defines the core banking process instead of traditional approaches in examining bank efficiency based on individual functions (nodes in the network model). This approach has proven to provide better benchmark capacity.

The COVID-19 epidemic has brought attention to the variables that influence the mental health of health workers who are entrusted with nursing individuals. Despite the fact that many articles have examined the effects of social media usage on mental health, there is a lack of research synthesizing learning from this body of research. The purpose of this study is to use text mining and citation-based bibliometric analysis to conduct a detailed review of extant literature on health workers’ mental health and social networking habits.

This study conducts a full-text analysis of 36 articles selected on health workers' mental health and social media using text-mining techniques in R programming and a bibliometric citation analysis of 183 papers from the Scopus database in VOS viewer software. But the limitations of the methods used in this study are that the bibliometric analysis was limited to the Scopus database because the VOS viewer program did not support any other database and the text-mining approach caused the natural processing redundancy.

The bibliometric analysis reveals the thematic networks that exist in the literature of health workers’ mental health and social networking. The findings from text mining identified ten topic models, which helped to find the related papers classified in ten different groups and are provided alongside a summary of the published research and a list of the primary authors with posterior probability through Latent Dirichlet Allocation.

To the best of the authors’ knowledge, this is the first hybrid review, combining text mining and bibliometric review, on health workers’ mental health where social networking plays a moderating role. This paper critically provides an overview of the impact of social networking on health workers' mental health, presents the most important and frequent topics, introduces the scientific visualization of articles published in the Scopus database and suggests further research avenues. These findings are important for academics, health practitioners and medical specialists interested in learning how to better support the mental health of health workers using social media.

Over the past two decades, China’s outward foreign direct investment (OFDI) has risen remarkably. Whether such an increase affects the Chinese export diversification (ED) is a significant issue that has surprisingly remained unaddressed. This study aims to explain this issue that how OFDI plays a vital role in symmetric and asymmetric effects on its ED.

The authors introduce a robust nonlinear autoregressive distributed lag (NARDL) model. Ironically, the purpose of this study is to analyze the symmetric and asymmetric effect of OFDI on ED.

The authors propose that growing OFDI would be more advantageous to China, rather than the policies of contraction. Therefore, the study provides valuable policy insights to consider the long-run asymmetric momentum given to ED by China’s OFDI.

The results of this study may seem to be an important newsletter for further policy discussion on how China can catch up on the benefits of ED through OFDI.

Personal remittance and ready-made garments (RMG) export incomes have emerged as the largest source of foreign income for Bangladesh's economy. The study investigates their impact on income inequality and gross domestic product (GDP) as a control variable, using time-series yearly data from 1983 to 2018.

It employs the Autoregressive Distributed Lag (ARDL) estimation and the Toda-Yamamoto (T-Y) causality approach. The ARDL estimation outcomes confirm a long-run association among the above variables and validate the autoregressive characteristic of the model.

Personal remittances positively contribute to reducing the income gap among the people of the society and declining income inequality. In contrast, RMG export income and economic growth contribute to further income inequality. The T-Y causality analysis follows the ARDL estimation outcomes and authenticates their robustness. It reveals a feedback relationship between remittance inflow and the Gini coefficient, unidirectional causalities from RMG export income to income inequality and economic growth to income inequality.

The finding has important policy implications to limit the income gaps between low and high-income groups by channeling incremental income to the lower-income group people. The policymakers may facilitate further international migration to attract further remittances and may upgrade the minimum wage of the RMG workers.

The study is original. As far as the authors' knowledge goes, this is a maiden attempt to investigate the impact of personal remittances and RMG export income on income disparity in the case of Bangladesh.

Supplier selection (SS) is one of the prime competencies in a sourcing decision. Taking into account the key role played by suppliers in facilitating the implementation of green supply chain management (GSCM), it is somewhat surprising that very little research attention has been imparted to the development of a strategic sourcing model for GSCM. This research aims to develop a strategic sourcing framework in which supplier organizations are prioritized and ranked based on their GSCM performance. Accordingly, the benchmark organization is identified and its strategy is explored for GSCM performance improvement.

The research develops an innovative GSCM performance evaluation framework using six parameters, namely, investment in corporate social responsibility, investment in research and development, utilization of renewable energy, total energy consumption, total carbon-di-oxide emissions and total waste generation. An integrated multicriteria decision-making (MCDM) approach is proposed in which the entropy method calculates criteria weights. The Complex Proportional Assessment (COPRAS) and the Grey relational analysis (GRA) methods are used to rank supplier organizations based on their performance scores. A real-world case of green supplier selection (GSS) is considered in which five leading India-based automobile manufacturing organizations (Supplier 1, Supplier 2, Supplier 3, Supplier 4 and Supplier 5) are selected. Surveys with industry experts at the strategic, tactical, and operational levels are carried out to collect relevant data.

The results reveal that total carbon dioxide emission is the most influential parameter, as it gains the highest weight. On the contrary, investment in research and development, and total waste generation have no significant impact on GSCM performance. Results show that Supplier 5 secures the top rank. Hence, it is the benchmark organization.

The proposed methodology offers an easy and comprehensive approach to sourcing decisions in the field of GSCM. The entropy weight-based COPRAS and GRA methods offer an error-free channel of decision-making and can be proficiently used to outrank various industrial sectors based on their GSCM performances. This research is specific to the automobile manufacturing supply chain. Therefore, research outcomes may vary across supply chains with distinct characteristics.

The basic propositions of this research are based on a real-world case. Hence, the research findings are practically feasible. The less significant parameters identified in this study would enable managers to impart more attention to vulnerable areas for improvement. This research may help policymakers identify the influential parameters for effective GSCM implementation. As this research considers all aspects of sustainability, the strategies of the benchmark supplier have a direct impact on organizations' overall sustainability. The study would enable practitioners to make various strategies for GSCM performance improvement and to develop a cleaner production system.

The originality of this research lies in the consideration of both economic, social, environmental and operational aspects of sustainability for assessing the GSCM performance of supplier organizations. Quantitative criteria are considered so that vagueness can be removed from the decision. The use of an integrated grey-based approach for developing a strategic sourcing model is another unique feature of this study.

The purpose of this research is to identify and study the contextual relationships of the significant lean enablers in the Indian coal mining industry using the application of interpretive structural modeling (ISM), matrice d' impacts croisés-multiplication appliquée á un classement (MICMAC) and structural equation modeling (SEM).

In this study, a conceptual model based on ISM was developed forming a hierarchy and contextual relationships of significant enablers for lean implementation in the Indian coal mining industry using a literature review and eliciting expert opinion, which is followed by MICMAC for grouping of enablers and questionnaire survey to validate the ISM based conceptual model using SEM.

The study modeled and analyzed ten significant enablers of lean implementation in the Indian coal mining industry. The findings suggest that the most important lean enablers in the Indian coal mining industry are employee empowerment, employee motivation and commitment, consistent financial performance measurement and management support.

Judgmental sampling was used for selecting the respondents for conducting the questionnaire survey in this research work as there are a few numbers of coal mines implementing lean principles in India. Although the study was not restricted to a particular part of India with the sample representing the heterogeneous population, the study represents more data from the coal mines in eastern India.

The model on lean enablers would help the researchers, decision-makers and practitioners to anticipate potential lean enablers in the Indian coal mines and rank the enablers for improved and efficient usage of the available resources creating value to customers with lean and to sustenance academic research on lean.

Studies on lean enablers in the mining sector are scarce in the literature, and this study is a novel contribution of exploring lean enablers in the Indian coal mining industry using an integrated approach of ISM–MICMAC and SEM.

This paper aims to present a citation-based bibliometric review of Islamic banking efficiency literature from 2000 until August 2020 for analyzing the content of the literature and mapping future research directions.

Using bibliometric citation analysis, this study reviews and examines a total of 278 documents from Web of Science (WoS) indexed sources coupled with content analysis.

The results identified that the growth of Islamic banking efficiency literature has begun to rise since 2008 with an annual growth rate of 12.5% while identifying the most influential aspects of Islamic banking efficiency literature in terms of topics, papers, authors and keywords. The outcomes of science mapping (i.e. co-citation network, bibliometric coupling and co-authorship network) reveal that there are three underlying research streams in Islamic banking efficiency literature: growth of Islamic banking efficiency literature, stability and genuineness of Islamic banking operations and the methods in Islamic banking efficiency literature.

To the best of the knowledge, this is the first bibliometric review paper on Islamic banking efficiency literature. It successfully unveils the scientific mapping of published WoS documents with a future research agenda also proposed based on the review outcomes. These findings can be used in efficiency-related initiatives by managers or regulators for managing Islamic bank operations worldwide.