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Conventional data envelopment analysis (DEA) models permit each decision-making unit (DMU) to assess its efficiency score with the most favorable weights. In other words, each DMU selects the best weighting schemes to obtain maximum efficiency for itself. Therefore, using different sets of weights leads to many different efficient DMUs, which makes comparing and ranking them on a similar basis impossible. Another issue is that often more than one DMU is evaluated as efficient because the selection of weights is flexible; therefore, all DMUs cannot be completely differentiated. The purpose of this paper is to development a common weight in dynamic network DEA with a goal programming approach.

In this paper, a goal programming approach has been proposed to generate common weights in dynamic network DEA. To validate the applicability of the proposed model, the data of 30 non-life insurance companies in Iran during 2013-2015 have been used for measuring their efficiency scores and ranking all of the companies.

Findings show that the proposed methodology is an effective and practical approach to measure the efficiency of DMUs with dynamic network structure.

The proposed model delivers more knowledge of the common weight approaches and improves the DEA theory and methodology. This model makes it possible to measure efficiency scores and compare all DMUs from multiple different standpoints. Further, this model allows one to not only calculate the overall efficiency of DMUs throughout the time period but also consider dynamic change of the time period efficiency and dynamic change of the divisional efficiency of DMUs.

Data envelopment analysis (DEA) is a mathematical method for the evaluation of the relative efficiency of a set of alternatives, which produces multiple outputs by consuming multiple inputs. Each unit is evaluated on the basis of the weighted output over the weighted input ratio with a free selection of weights and is allowed to select its own weighting scheme for both inputs and outputs so that the individual evaluation is optimized. However, several situations can be found in which the variability between weighting profiles is unsuitable. In those cases, it seems more appropriate to consider a common vector of weights. The purpose of this paper is to include a systematic revision of the existing literature regarding the procedures to determine a common set of weights (CSW) in the DEA context. The contributions are classified with respect to the methodology and to the main aim of the procedure. The discussion and findings of this paper provide insights into future research on the topic.

This paper includes a systematic revision of the existing literature about the procedures to determine a CSW in the DEA context. The contributions are classified with respect to the methodology and to the main aim of the procedure.

The discussion and findings of the literature review might insights into future research on the topic.

This papers revise the state of the art on the topic of models with CSW in DEA methodology and propose a systematic classification of the contributions with respect to several criteria. The paper would be useful for both theoretical and practical future research on the topic.

Neuromarketing, which is an interdisciplinary area, concentrates on evaluating consumers’ cognitive and emotional reactions to different marketing stimuli. In spite of advantages, neuromarketing still requires development and lacks a strong theoretical framework. Techniques that are used in neuromarketing studies have different superiorities and limitations, and thus, there is a need for the evaluation of the relevance of these techniques. The purpose of this study is to introduce a novel integrated approach for the neuromarketing research area.

The proposed approach combines 2-tuple linguistic representation model and data envelopment analysis to obtain the most efficient neuromarketing technique. It is apt to handle information provided by using both linguistic and numerical scales with multiple information sources. Furthermore, it allows managers to deal with heterogeneous information, without loss of information.

The proposed approach indicates that functional magnetic resonance imaging (fMRI) is the best performing neuromarketing technology. Recently, fMRI has been widely used in neuromarketing research. In spite of its high cost, its main superiorities are improved spatial and temporal resolutions. On the other hand, transcranial magnetic stimulation (TMS) and positron emission tomography (PET) are ranked at the bottom because of their poor resolutions and lower willingness of participants.

This paper proposes a common weight data envelopment analysis (DEA)-based decision model to cope with heterogeneous information collected by the experts to determine the best performing neuromarketing technology. The decision procedure enables the decision-makers to handle the problems of loss of information and multi-granularity by using the fusion of 2-tuple linguistic representation model and fuzzy information. Moreover, a DEA-based common weight model does not require subjective experts’ opinions to weight the evaluation criteria.

Economic growth of China in the past decades has caused rapid increase in energy consumption and environmental deterioration. Therefore, it is critical to make an environmental evaluation and to help decision makers to know each province’s distance to the optimal target and improve environmental performance.

In such background, the authors use a within-group common benchmarking model with data envelopment analysis for China’s environmental evaluation and target setting in this paper. This model considers a common treatment of decision-making units within groups but allows for the different circumstances across groups and gives a common reference set for benchmarking.

The results show that there are regional difference in economic development and environmental protection. The coastal area has the best average environmental efficiency, then followed by inland area and the lowest level is the western area. The target results show that in four inputs, namely, population, capital, energy consumption and water consumption, the water consumption is the significant variable which should be decreased largely. All provinces have room to improve their economic level under the condition of a better environment.

In this research, the authors consider the similarity in geography and economy for Chinese provinces and divide 30 provinces into eight economic zones; thus, provinces in the same zone are evaluated with the same weight. Environmental performance and efficiency for each province can be obtained. Efficient targets for those inefficient provinces are provided as a possible improvement direction as well.

Despite unanimity in the literature that eco-innovation (EI) leads to sustainable development, evidence remains limited on measuring EI efficiency with the Malmquist productivity index (MPI). In conventional data envelopment analysis (DEA) models, decision-making units (DMUs) are inclined to assign more favorable weights, even zero, to the inputs and outputs to maximize their own efficiency. This paper aims to overcome this shortcoming by developing a common set of weights (CSW).

Using goal programming, this study develops a CSW model to evaluate the EI efficiency of the organization for economic co-operation and development (OECD) countries and track their changes with MPI during 2010–2018.

Achieving a complete ranking of DMUs, findings show the higher discrimination power of the proposed CSW compared with the original DEA models. Furthermore, results reveal that Iceland, Latvia and Luxembourg are the only OECD countries that have incessantly improved their EI productivity (MPI > 1) from 2010 to 2018. On the other hand, Japan is the OECD country that has experienced the highest yearly EI efficiency during 2010–2018. This paper also found that Iceland has the highest MPI over 2010–2018.

More investment in environmental research and development (R&D) projects instead of generic R&D enables OECD members to realize more opportunities for sustainable development through minimizing energy use and environmental pollution in any form of waste and greenhouse gas emissions.

In addition to developing a novel common weights model for DEA-MPI to measure and evaluate the EI of OECD countries, this paper develops a CSW model by including the undesirable outputs for EI analysis.

The present study aims to suggest a new approach to hotel quality rating, specifically designed for the business travel segment, where the evaluation of surveyed consumers (business travelers) does not necessarily reflect the priority of customers (corporate travel departments [CTDs]).

Preliminarily, the authors defined key areas (domains), exploring what was done by quality certifiers recognized worldwide. Then, each domain quality was considered as a latent variable measured by a set of observable attributes (sub-domains) surveyed by a professional assessor. A continuous, fine-grained, composite indicator (CI) for quality was finally obtained by a weighted average of the domain (latent) quality measures. Weights were endogenously determined by data envelopment analysis.

The suggested CI shows both the existence of large quality disparities within the same star rating and a relevant bias in the internet reviews. A “soundproofed” room, a front desk open 24 h with sufficient staff and an adequate urban context are necessary features of any business hotel.

Data came from a professional assessor’s database; therefore, the authors could only consider a three-domains measurement model. The database is mainly composed of three- and four-star hotels in Italy; nonetheless, these accommodations are the most widespread in the Italian corporation hotel programs, preserving the practical utility of the results.

This study provides a transparent (replicable) evaluation protocol that is of potential use in the most popular models for quality measurement; any assessor can use it to underline its impartiality to CTD and assessed hotels.

Resource allocation has always been a critical problem with significant economic relevance. Many industries allocate the resources based on classical methods such as overall equipment effectiveness (OEE) and data envelopment analysis (DEA). The lack of OEE factors’ weight, how it is defined, analyzed, interpreted and compared in OEE and selection of unrealistic weights, self-appraisal and disability of complete ranking in DEA are challenges that are possible to occur. These defects may result in unfair allocation of the resources. This study aims to overcome the mentioned weaknesses.

In this paper, an approach using a set of various DEA models and Nash bargaining solution (NBS) is designed to solve the resource allocation problem based on OEE, among a set of comparable and uniform DMUs (decision-making units) in a fair way.

The results show that a unique Pareto optimal allocation solution is obtained by the proposed DEA–NBS model among the DMUs. This allocation is more acceptable for players, because the allocation results are commonly determined by all DMUs rather than a specific one. Furthermore, the rankings achieved by the utilized methods and TOPSIS (technique for order preference by similarity to ideal solution) are compared by Spearman’s rank correlation coefficient to validate the resource allocation plan. The findings indicate that the DEA–NBS method has the best correlation with the TOPSIS approach.

To the best of authors’ knowledge, no research has considered the use of DEA and NBS with OEE.

This study models cost-efficiency against revenue for hotels in the Pearl River Delta (PRD) – in Guangzhou, Hong Kong and Macau – by considering regional differences and weight restrictions on revenue output.

The authors modified and applied a context-dependent assurance region data envelopment analysis (CAR-DEA) model in assessing the performance of 41 hotels in the PRD. The model considers the relationships among output variables and sets the revenue composition of the hotels as weight restrictions in accounting for the relative importance of different revenue sources.

When assessing the 41 hotels all together, those in Guangzhou outperformed the hotels in other two cities by showing better pure technical efficiency (PTE), while those in Macau had the best scale efficiency (SE). When the assurance region (AR) restriction was imposed, the hotels in Macau outperformed those in the other two cities by showing better SE. When considering regional differences, the Macau hotels ranked first in terms of both the average efficiency score and the overall ranking. All the sample hotels in Guangzhou and half of the sample hotels in Hong Kong and Macau exhibited increasing, constant and decreasing returns to scale.

The research results are limited by data quality and the variables included in the models.

The study helps hotel practitioners in the PRD better assess their cost-efficiency performance by considering regional differences and operational parameters so as to strategically improve their performance.

This study improves upon previous hotel efficiency studies by considering the influence of different operational parameters across different localities. It can be extended to examine the performance of different calibers of hotels, restaurants or tourism entities located in various localities and possessing different operational characteristics.

Software development projects are known for inaccuracies associated with elapsed time and total cost estimates. Attempts have previously been made to provide tools to facilitate estimation of just how much effort will be required. One such tool is the estimation of project size (and therefore effort and time required) using function point counts. This benchmarking tool facilitates measures of productivity relating this size to the person‐hours required. The problem with this is that such relative productivity measures assume labour hours to be homogenous and that the only measure of output is the size of the project. This paper investigates the use of data envelopment analysis as a method of benchmarking which overcomes these issues. The end result is a set of simple tools that can be used to determine whether a given project or project plan is efficient.

The purpose of this paper is to consider the following problem; if the manager of the parallel network systems wants to add new sub-decision making units (sub-DMUs) to each parallel network system, he/she wants to know how much new fuzzy inputs allocate to new sub-DMUs and how much outputs these new sub-DMUs produce such that the efficiency of each parallel network system improve or preserve.

Resource allocation and target setting is a famous topic in management science, therefore many managers attention to this field. Data envelopment analysis is one approach to apply the resource. Resource allocation and target setting is a famous topic in management science, therefore many managers attention to this field. Data envelopment analysis is one approach to apply the resource allocation and target setting. In real application the structure of many DMUs are network and the data of them are imprecise. In this work first the authors calculate the fuzzy efficiency of parallel systems by common set of weights method, after that the authors propose an approach to find how much fuzzy inputs allocate to new sub-DMUs, and how much new fuzzy outputs produce, where the efficiency of each parallel network system improve or preserve.

It is found the value of inputs and outputs of new sub-DMUs, where the efficiency of each system not worse than before.

The method can be used in many organizations such as banks, chain stores, car factory, etc.

For the first time the authors allocate new sub-DMUs to each system, and the data of paper are fuzzy numbers.