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This paper aims to measure and unveil the relationship between customer satisfaction and efficiency levels in the most relevant Brazilian airports.

The authors utilize a two-stage network DEA (data envelopment analysis) and AHP (analytic hierarchy process) model as the cornerstones of the study. The first stage of the network productive structure focuses on examining the infrastructure efficiency of the selected airports, while the second stage assesses their business efficiency.

Although the results indicate that infrastructure and business efficiency levels are heterogeneous and widely dispersed across airports, controlling the regression results with different contextual variables suggests that the impact of efficiency levels on customer satisfaction is mediated by a set of socio-economic and demographic (endogenous) and regulatory (exogenous) variables. Furthermore, encouraging investment in airports is necessary to achieve higher infrastructural efficiency and scale efficiency, thereby improving customer satisfaction.

There is a scarcity of studies examining the relationships among customer satisfaction, privatization and airport efficiency, particularly in developing countries like Brazil.

This study aims to explain the market-oriented transformation dilemma of Chinese urban investment and development companies (UIDCs; also known as local government investment and financing companies) and objectively evaluate their transformation efficiency from both static and dynamic perspectives. The results of the research provide methodological bases for improving the transformation efficiency of UIDCs, thus pointing out the direction for the rational planning of their transformation path.

This study takes Chinese UIDCs in market transformation during 2015–2019 as the research object and uses principal component analysis to screen the index system for measuring the efficiency of market transformation. It then uses a three-stage data envelopment analysis model and the Malmquist productivity index to evaluate the market transformation efficiency of these companies during 2015–2019 and comprehensively analyzes the influence of external environmental factors on the market transformation of Chinese UIDCs.

Research results show that the transformation efficiency of Chinese UIDCs is low and slow overall and that large spatial and temporal differences exist. The transformation efficiency of UIDCs located in eastern China is higher than that of UIDCs in central and western China. The higher the external environmental factors of regional GDP, local debt service pressure and credit rating, the more likely they are to cause input redundancy in the transformation process of Chinese UIDCs, which is not conducive to their market-oriented transformation. In addition, the higher the urbanization rate, the more effective it is to improve the efficiency of market-oriented transformation of UIDCs. If the influence of environmental factors is stripped away, both the overall efficiency value and pure technical efficiency value of market-oriented transformation of Chinese UIDCs will increase while the scale efficiency value becomes smaller.

This research measures the transformation efficiency of Chinese UIDCs and comprehensively analyzes the influence of external environmental factors on their market-oriented transformation. The goal is to enrich the study of the market-oriented transformation efficiency evaluation index system of Chinese UIDCs at the theoretical level and provide important reference values for improving the efficiency of market-oriented transformation of Chinese UIDCs at the practical level.

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.

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.

This paper aims to present an example of the measurement of airport efficiency, a topic of great interest in civil aviation today. The methodology used is data envelopment analysis (DEA) and the Malmquist Index. The calculation of airport efficiency with up-to-date data for each period is of great importance in the context of sustainability. The study selected ten airports with high air traffic in Turkey as the sample set. The objective of this study is to evaluate the current state of airport efficiency, identify the sources of inefficiency and make appropriate policy recommendations based on the findings obtained.

The study is based on DEA and Malmquist Index Analysis. The number of personnel and terminal size data of ten selected decision-making units (DMU) are used as inputs, while passenger, cargo and aircraft traffic data are used as outputs. A five-year period, spanning from 2018 to 2022, is considered as the data set in the study.

Upon analysis of the data from the ten airports included in the study, it was found that the current input-output combination yielded efficient results, with the exception of certain characteristics, such as the impact of seasonal conditions or tourism. Concurrent with the growth in aircraft, passenger and freight traffic, the number of personnel employed at these airports has also increased. It was concluded that technological efficiency is of paramount importance for each airport, and that investments in technology should be increased.

A separate assessment was conducted for each of the ten airports included in the study sample. Each airport was evaluated in terms of its strengths and weaknesses, and areas of low efficiency were identified. Consequently, more general conclusions were reached than airport-specific evaluations.

In order to ensure the long-term sustainability of the sector, it is essential that the efficiency measurements of airports are calculated using up-to-date data on a regular basis. The results obtained from these calculations provide guidance for the strategic plans to be implemented in the long term, as well as for the solution proposals for operational problems. In this context, this study not only provides information to policymakers and airport managers about the current situation, as it includes recent data, but also contributes to the literature in this sense, as it includes policy recommendations.

Today’s one of the most important difficulties is tackling climate change and its effects on the environment. The Paris Agreement states that nations must balance the amount of greenhouse gases they emit and absorb until 2050 to contribute to the mitigation of greenhouse gases and to support sustainable development. According to the agreement, each country must determine, plan and regularly report on its contributions. Thus, it is important for the countries to predict and analyze their net zero performances in 2050. Therefore, the aim of this study is to evaluate European Continent Countries' net zero performances at the targeted year.

The European Continent Countries that ratified the Paris Agreement are specified as decision making units (DMUs). Input and output indicators are specified as primary energy consumption, freshwater withdrawals, gross domestic product (GDP), carbon-dioxide (CO₂) and nitrous-oxide (N₂O) emissions. Data from 1980 to 2019 are obtained and forecasted using autoregressive integrated moving average (ARIMA) until 2050. Then, the countries are clustered based on the forecasts of primary energy consumption and freshwater withdrawals using k-means algorithm. As desirable and undesirable outputs arise simultaneously, the performances are computed using Pure Environmental Index (PEI) and Mixed Environmental Index (MEI) data envelopment analysis (DEA) models.

It is expected that by 2050, CO₂ emissions of seven countries remain constant, N₂O emissions of seven countries remain stable and five countries’ both CO₂ and N₂O emissions remain constant. While it can be seen as success that many countries are expected to at least stabilize one emission, the likelihood of achieving net zero targets diminishes unless countries undertake significant reductions in emissions. According to the results, in Cluster 1, Turkey ranks last, while France, Germany, Italy and Spain are efficient countries. In Cluster 2, the United Kingdom ranks at last, while Greece, Luxembourg, Malta and Sweden are efficient countries.

In the literature, generally, CO₂ emission is considered as greenhouse gas. Moreover, none of the studies measured the net-zero performance of the countries in 2050 employing analytical techniques. This study objects to investigate how well European Continent Countries can comply with the necessities of the Agreement. Besides CO₂ emission, N₂O emission is also considered and the data of European Continent Countries in 2050 are estimated using ARIMA. Then, countries are clustered using k-means algorithm. DEA models are employed to measure the performances of the countries. Finally, forecasts and models validations are performed and comprehensive analysis of the results is conducted.

This study examines the effects of CLS and DS on companies' WCME and analyses the differences in WCME at company and market levels.

This study adopts the DEA approach, regression, differences, and additional analyses to achieve its objectives. This study employs 235 non-financial companies and 1,175 company-year observations from eight active industries in the United States from 2016 to 2020.

The findings indicate that CLS and DS strategies positively influence companies' WCME. Additionally, WCME differed across size categories and industries, with large companies and those operating in the communication services industry showing better WCME. By contrast, WCME did not differ between the periods before and during the COVID-19 pandemic.

This study scrutinizes the impact of CLS and DS strategies on companies' WCME to bridge the gap in this field. It extends the investigation of competitive strategies as explanatory variables for a company's WCME and examines the differences in companies' WCME at the company and market levels, which may assist decision-makers in improving their strategies and efficiencies for continuous improvement.

This study enhances current knowledge by uncovering the influence of CLS and DS strategies on improving companies' WCME, an underexplored topic. It also explores companies' WCME trends and patterns regarding company size, industry type, and the pandemic period to draw interesting conclusions about the essence of WCME.

The purpose of this paper is to understand if and how industrialized house building (IHB) could support productivity developments for housebuilding on project and industry levels. The take is that fragmentation of construction is one explanation for the lack of productivity growth, and that IHB could be an integrating method of overcoming horizontal and vertical fragmentation.

Singe-factor productivity measures are calculated based on data reported by IHB companies and compared to official produced and published research data. The survey covers the years 2013–2020 for IHB companies building multi-storey houses in timber. Generalization is sought through descriptive statistics by contrasting the data samples to the used means to control vertical and horizontal fragmentation formulated as three theoretical propositions.

According to the results, IHB in timber is on average more productive than conventional housebuilding at the company level, project level, in absolute and in growth terms over the eight-year period. On the company level, the labour productivity was on average 10% higher for IHB compared to general construction and positioned between general construction and general manufacturing. On the project level, IHB displayed an average cost productivity growth of 19% for an employed prefabrication degree of about 45%.

Empirical evidence is presented quantifying so far perceived advantages of IHB. By providing analysis of actual cost and project data derived from IHB companies, the article quantifies previous research that IHB is not only about prefabrication. The observed positive productivity growth in relation to the employed prefabrication degree indicates that off-site production is not a sufficient mean for reaching high productivity and productivity growth. Instead, the capabilities to integrate the operative logic of conventional housebuilding together with logic of IHB platform development and use is a probable explanation of the observed positive productivity growth.

The purpose of this study is to provide new insights into the relationship between fiscal policy and total factor productivity (TFP) while accounting for several economic and econometric issues of the phenomenon like non-stationarity, fiscal feedback effects, persistence in productivity, country heterogeneity and unobserved global shocks and local spillovers affecting heterogeneously the countries in the sample.

The paper is empirical. It builds an Error Correction Model (ECM) specification within a dynamic heterogeneous framework with common correlated effects and models both reverse causality and feedback effects.

The results of this study highlight some new findings relative to the existing related literature. The outcomes suggest some relevant evidence at both the academic and policy levels: (1) the causal effects going from fiscal deficit/surplus to TFP are heterogeneous across countries; (2) the effects depend on the time horizon considered; (3) the long-run dynamics of TFP are positively impacted by improvements in fiscal budget, but only if the austerity measures do not exert slowdowns in aggregate growth.

The main originality of this study is methodological, with possible extensions to related phenomena. Relative to the existing literature, the gains of this study rely on the way econometric techniques, recently proposed in the literature, are adapted to the economic relationship of interest. The endogeneity due to the existence of reverse causality is modelled without implying relevant performance losses of the models. Moreover, this is the first article that questions whether the effects of fiscal budget on productivity depend on the impact of the former on aggregate output growth, thus emphasising the importance of the quality of fiscal adjustments.

The purpose of this research is to address the complex multiobjective unrelated parallel machine scheduling problem with real-world constraints, including sequence-dependent setup times and periodic machine maintenance. The primary goal is to minimize total tardiness, earliness and total completion times simultaneously. This study aims to provide effective solution methods, including a Mixed-Integer Programming (MIP) model, an Epsilon-constraint method and the Nondominated Sorting Genetic Algorithm (NSGA-II), to offer valuable insights into solving large-sized instances of this challenging problem.

This study addresses a multiobjective unrelated parallel machine scheduling problem with sequence-dependent setup times and periodic machine maintenance activities. An MIP model is introduced to formulate the problem, and an Epsilon-constraint method is applied for a solution. To handle the NP-hard nature of the problem for larger instances, an NSGA-II is developed. The research involves the creation of 45 problem instances for computational experiments, which evaluate the performance of the algorithms in terms of proposed measures.

The research findings demonstrate the effectiveness of the proposed solution approaches for the multiobjective unrelated parallel machine scheduling problem. Computational experiments on 45 generated problem instances reveal that the NSGA-II algorithm outperforms the Epsilon-constraint method, particularly for larger instances. The algorithms successfully minimize total tardiness, earliness and total completion times, showcasing their practical applicability and efficiency in handling real-world scheduling scenarios.

This study contributes original value by addressing a complex multiobjective unrelated parallel machine scheduling problem with real-world constraints, including sequence-dependent setup times and periodic machine maintenance activities. The introduction of an MIP model, the application of the Epsilon-constraint method and the development of the NSGA-II algorithm offer innovative approaches to solving this NP-hard problem. The research provides valuable insights into efficient scheduling methods applicable in various industries, enhancing decision-making processes and operational efficiency.