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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 and understand the performance of countries in individual Olympic Games, specifically focusing on the Tokyo 2020 Olympics. Employing cluster analysis and decision trees, the research aims to categorize countries based on their representation, participation and success.

This research employs a data-driven approach to comprehensively analyze and enhance understanding of countries' performances in individual Olympic Games. The methodology involves a two-stage clustering method and decision tree analysis to categorize countries and identify influential factors shaping their Olympic profiles.

The study, analyzing countries' performances in the Tokyo 2020 Olympics through cluster analysis and decision trees, identified five clusters with consistent profiles. Notably, China, Great Britain, Japan, Russian Olympic Committee and the United States formed a high-performing group, showcasing superior success, representation and participation. The analysis revealed a correlation between higher representation/participation and success in individual Olympic Games. Decision tree insights underscored the significance of population size, GDP per Capita and HALE index, indicating that countries with larger populations, better economic standing and higher health indices tended to perform better.

The study has several limitations that should be considered. Firstly, the findings are based on data exclusively from the Tokyo 2020 Olympics, which may limit the generalizability of the results to other editions.

The research offers practical implications for policymakers, governments and sports organizations seeking to enhance their country's performance in individual Olympic Games.

The research holds significant social implications by contributing insights that extend beyond the realm of sports.

The originality and value of this research lie in its holistic approach to analyzing countries' performances in individual Olympic Games, particularly using a two-stage clustering method and decision tree analysis.

This study aims to provide an overview of the state-of-the-art in efficiency measurement within higher education (HE). Specifically, it seeks to gather all relevant articles on the topic and subsequently categorize these studies using a flowchart based on two core aspects of the topic.

This study employs bibliometric and content analyses to conduct a systematic literature review. The Preferred Reporting Items for Systematic Review (PRISMA) framework is used to identify the search protocol, followed by analyses to classify and categorize articles.

The bibliometric analysis identifies prominent themes, methodologies and literature gaps. The content findings highlight key insights on higher educational institution (HEI) efficiency, including organizational structures, services and operational activities.

This research contributes to the existing knowledge by synthesizing global literature on HEI’s efficiency. Utilizing the flowchart developed by the authors, the study captures the state-of-the-art based on two critical aspects: methodologies and content. Insights from the analysis and subsequent classification of previous literature provide valuable directions for future research.

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.

Intellectual capital (IC) plays a crucial role in today’s volatile business landscape, yet its measurement remains complex. To better navigate these challenges, the authors propose the Integrated Intellectual Capital Measurement (IICM) model, an innovative, robust and comprehensive framework designed to capture IC amid business uncertainty. This study focuses on IC measurement models, typically reliant on secondary data, thus distinguishing it from conventional IC studies.

The authors conducted a systematic literature review (SLR) and bibliometric analysis across Web of Science, Scopus and EBSCO Business Source Ultimate in February 2023. This yielded 2,709 IC measurement studies, from which the authors selected 27 quantitative papers published from 1985 to 2023.

The analysis revealed no single, universally accepted approach for measuring IC, with company attributes such as size, industry and location significantly influencing IC measurement methods. A key finding is human capital’s critical yet underrepresented role in firm competitiveness, which the IICM model aims to elevate.

This is the first SLR focused on IC measurement amid business uncertainty, providing insights for better management and navigating turbulence. The authors envisage future research exploring the interplay between IC components, technology, innovation and network-building strategies for business resilience. Additionally, there is a need to understand better the IC’s impact on specific industries (automotive, transportation and hospitality), Social Development Goals and digital transformation performance.

This study aims to present an innovative predictive methodology that transitions from traditional efficiency assessment techniques to a forward-looking strategy for evaluating working capital management(WCM) and its determinants by integrating data envelopment analysis (DEA) with artificial neural networks (ANN).

A slack-based measure (SBM) within DEA was used to evaluate the WCME of 1,388 firms in the Indian manufacturing sector across nine industries over the period from April 2009 to March 2024. Subsequently, a fixed-effects model was used to determine the relationships between selected determinants and WCME. Moreover, the multi-layer perceptron method was applied to calculate the artificial neural network (ANN). Finally, sensitivity analysis was conducted to determine the relative significance of key predictors on WCME.

Manufacturing firms consistently operate at around 50% WCME throughout the study period. Furthermore, among the selected variables, ability to create internal resources, leverage, growth, total fixed assets and productivity are relatively significant vital predictors influencing WCME.

The integration of SBM-DEA and ANN represents the primary contribution of this research, introducing a novel approach to efficiency assessment. Unlike traditional models, the SBM-DEA model offers unit invariance and monotonicity for slacks, allowing it to handle zero and negative data, which overcomes the limitations of previous DEA models. This innovation leads to more accurate efficiency scores, enabling robust analysis. Furthermore, applying neural networks provides predictive insights by identifying critical predictors for WCME, equipping firms to address WCM challenges proactively.

This work aims to analyse the relationship between public health efficiency and well-being considering a panel of 102 Italian provinces from 2000 to 2016 and evaluates if there are omitted variable biases and endogeneity biases and also evaluates if there are heterogeneous effects among provinces with different income levels.

We use a multi-input and output bootstrap data envelopment analysis to assess public health efficiency. Then, we measure well-being indices using the min-max linear scaling transformation technique. A two-stage least squares model is used to identify the causal effect of improving public health efficiency on well-being to account for time-invariant heterogeneity, omitted variable bias and endogeneity bias.

After controlling for important economic factors, the results show a significant effect of an accountable and efficient public health system on well-being. Those effects are concentrated in the North, the most economically, geographically and environmentally advantageous areas.

The use of the sample mean, probably the oldest and most used method for aggregating the indicators, could be affected by variable compensation, with consequent misleading results in the process of constructing the well-being index. Another limitation is the use of lagged values of the main predictor as an instrument in the instrumental variables setting because it could lead to information loss. Finally, the availability of data over a long period of time.

The findings could help policymakers adopt measures to strengthen the public health system, encourage private providers and inspire countries worldwide.

These results draw the attention of local authorities, who play an important role in designing and implementing policies to stimulate local public health efficiency, which puts individuals in the conditions of achieving overall well-being in their communities.

For the first time in Italy, a panel of well-being indices was constructed by developing new methodologies based on microeconomic theory. Furthermore, for the first time, the assessment of the relationship between public health efficiency and well-being is carried out using a panel of 102 Italian provinces.

The aim of this study was to evaluate the performance of fuel flow processes in a network of eight gas stations, located in the mesoregion of Alto Paranaíba and Triângulo Mineiro.

Two multi-criteria decision support methods were applied, respectively, of a statistical and mathematical nature, namely, Principal Component Analysis (PCA) and Data Envelopment Analysis (DEA). The research method used was quantitative, with a brief complement of qualitative research, and descriptive in purpose, supported by the inductive method. The data collection stage took place with the support of interviews, with the application of a structured questionnaire, and non-probabilistic sampling, for convenience.

It was possible to verify that the gas station that stood out the most was station 2 (GS2), which achieved maximum efficiency, a fact that can be justified by the analysis resulting from the application of PCA, as for the product purchase variable (PP), the GS2 is the one that buys the most fuel, and is also the one with the largest storage capacity (C), and the highest volume of product sales (PS), which suggests signs of balance between supply and demand for this station, justifying its prominence.

The limitations of the study were related to the DEA technique, which requires a number of variables/indicators three times smaller than the number of DMUs considered, and the difficulty in obtaining financial data on the DMUs analyzed. Considering the security and anonymity of the gas station network, it was not possible to use this data.

The performance assessment of fuel flow processes carried out in this study promotes the efficient use of available resources as well as identifying efficient DMUs that represent benchmarks for improving management processes and performance of inefficient DMUs.

From a social perspective, this study promotes the improvement of the quality of flow processes and effective management of the fuel supply chain, ensuring the safe storage and transportation of fuels to customer supply. Performance management in this sector moves other sectors of the economy, since an efficient unit represents a balance between supply and demand, and consequently, boosts the regional economy, promoting economic growth of the population. Hiring qualified labor for this purpose also represents one of the implications of the study. From an environmental perspective, optimizing flow processes generates a reduction in greenhouse gas emissions and encourages the formulation of public policies aimed at consolidating sustainable practices.

Performance management applied to the context of the fuel supply chain is a relevant topic that has been little explored in scientific research, with a low level of information detail. This study using the inductive method allows the generalization and replication of this management pattern in other organizations in the sector in order to increase the efficiency of the fuel distribution system, with the perspective of maximizing outputs and reducing input consumption. In this aspect, the study introduces possibilities for advancement in social and environmental perspectives based on the effective management of fuel logistics.

The interplay between individual and collective creativity and its translation into innovation is a critical yet complex challenge in the ever-evolving innovation landscape. This study delves into the intricate relationship between managerial ability, intellectual property rights (IPRs) and research and development (R&D) investments contextualized within the dynamics of leverage, firm life stages and tangibility for pharmaceutical firms in the Asia-Pacific region. By exploring how micro-level factors influence macro-level innovation processes, this study aims to contribute to the broader understanding of creativity and innovation, a theme at the heart of addressing contemporary global challenges.

Econometric methodologies were used to analyse a data set comprising 2,660 firm-year observations spanning the decade from 2011 to 2020.

A key finding was that companies with lower managerial prowess strategically leverage R&D intensity to signal their value to the market and accrue reputational currency. The research unearths a significant positive relationship between managerial ability, IPRs and R&D investment. In environments characterized by strong managerial acumen and robust IPR safeguards, firms exhibit a heightened propensity to allocate resources to R&D endeavours. This underscores the role of intellectual leadership and legal protections in shaping R&D strategies within the pharmaceutical domain. Incorporating firm life stages as a moderating factor reveals that firm maturity fundamentally influences the interplay between managerial ability, IPRs and R&D expenditure.

These findings’ implications resonate profoundly within policy-making circles and pharmaceutical firms’ day-to-day operational strategies, underscoring the pivotal role of intellectual capital and legal safeguards in shaping the future of innovation in the Asia-Pacific pharmaceutical sector.

The paper aims at addressing the following research questions: does institutional quality improve countries' environmental efficiency? And which pillars of institutional quality improve countries' environmental efficiency?

By specifying a directional distance function in the context of stochastic frontier method where GHG emissions are considered as the bad output and the GDP is referred as the desirable one, the work computes the environmental efficiency into the appraisal of a production function for the European countries over three decades.

According to the countries' performance, the findings confirm that high and upper middle-income countries have higher environmental efficiency compared to low middle-income countries. In this environmental context, the role of institutional quality turns out to be really important in improving the environmental efficiency for high income countries.

This article attempts to analyze the role of different dimensions of institutional quality in different European countries' performance – in terms of mitigating GHGs (undesirable output) – while trying to raise their economic performance through their GDP (desirable output).

1. The paper aims at addressing the following research question: does institutional quality improve countries' environmental efficiency?

2. We adopt a directional distance function in the context of stochastic frontier method, considering 40 European economies over a 30-year time interval.

3. The findings confirm that high and upper middle-income countries have higher environmental efficiency compared to low middle-income countries.

4. The role of institutional quality turns out to be really important in improving the environmental efficiency for high income countries, while the performance decreases for the low middle-income countries.

The paper aims at addressing the following research question: does institutional quality improve countries' environmental efficiency?

We adopt a directional distance function in the context of stochastic frontier method, considering 40 European economies over a 30-year time interval.

The findings confirm that high and upper middle-income countries have higher environmental efficiency compared to low middle-income countries.

The role of institutional quality turns out to be really important in improving the environmental efficiency for high income countries, while the performance decreases for the low middle-income countries.