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Data envelopment analysis (DEA) is one of the best mathematical techniques to compute the overall performance of units with some inputs and outputs. The original DEA methods are developed to tackle the information based on the crisp number but no ability to handle the indeterminacy, impreciseness, vagueness, inconsistent, and incompleteness information such as triangular neutrosophic numbers (TNNs). This study attempts to establish a new model of DEA, where the information on decision-making units is TNNs. Initially, the concept and features of a conventional DEA model and the comparative TNNs are discussed. Besides, some new ranking functions of TNNs are presented. Furthermore, based on the mentioned ranking functions, an algorithm for solving the new model has been established. A comparison of the new model with an existing method and other kinds of uncertainty tools has been provided. In comparison with the existing methods, the significant characteristic of the new model is that it can handle the triangular neutrosophic information simply and effectively. Finally, the implementation of this strategy for an example has been applied for various models of DEA.

All the basic models in data envelopment analysis (DEA) divide decision making units (DMUs) in two groups: efficient DMUs and inefficient DMUs, and lack of discrimination of efficient units is a serious problem. Also in spite of completely ranking units in analytical hierarchy process (AHP), the process of making pairwise comparison matrix is based on experts’ choices and it causes error and inconsistency in resulted matrix. In this paper first a combined method is suggested for ranking the units and it will use benefits of both AHP and DEA methods to present a rational ranking, also will covers the problem of last methods noticeably and then properties and advantages of suggested method compare with another methods will be explained. Finally, for better comparison some numerical examples will be explained.

For many companies, green product development has become a key strategic consideration due to regulatory requirements and market trends. In this paper, the life cycle assessment technique is used to develop an innovative multi-criteria group decision-making approach that incorporates power aggregation operators and a TOPSIS-based QUALIFLEX method in order to solve green product design selection problems using neutrosophic linguistic information. Differences in semantics as well as the risk preferences of decision-makers are considered in the proposed method. The practicality and effectiveness of the proposed approach are then demonstrated through an illustrative example, in which the proposed method is used to select the optimum green product design, followed by sensitivity and comparative analyses.

Hesitancy is the most common problem in decision making, for which hesitant fuzzy set can be considered as a suitable means allowing several possible degrees for an element to a set. In this paper, we study the aggregation of the hesitancy fuzzy information. Several series of aggregation operators are proposed and the connections of them are discussed. To reflect the correlation of the aggregation arguments, two methods are proposed to determine the aggregation weight vectors. Based on the support degrees among aggregation arguments, the weight vector of decision makers are obtained more objectively. To deal with the correlation of criteria, we apply the Choquet integral to get the weights of criteria. A method is also proposed for group decision making under hesitant fuzzy environment.

Purpose The purpose of this paper is to provide a systematic and comprehensive review of the existing literature on the determinants of firms reporting practices. Design/methodology/approach Following a systematic method, the sample literature of 135 studies was collected from the Scopus database. These studies were evaluated in terms of the theoretical lenses applied in the literature, yearly trend, regional distribution, research settings and prior studies finding to provide some recommendations for further research. Findings The investigation revealed that the literature was more interested in the agency theory in investigating the drivers of voluntary reporting such as company size, age, leverage, liquidity, profitability, corporate governance and ownership structure. Although firm-specific determinants were the most examined in the previous studies, however, the result is still inconclusive. Also, limited work was found on the country-related factors, while internal audit impact has yet to be explored. Originality/value Being the first of its kind, this research provides a comprehensive review of the current research landscape on the drivers of environmental or social disclosure and highlights several interesting opportunities for future research.

One of the problems with data envelopment analysis (DEA) is that it results in too many decision making units (DMUs) as efficient. This leads to a problem of discrimination among the efficient units. Model misspecification and unrestricted weight flexibility are two main reasons for the discrimination problem. In this paper, we propose and test a model averaging ensemble approach that results in unique DMU rankings. We also prove that ensemble based ranking of DMUs will always result in equal or fewer efficient DMUs than any other single DEA model considered in the ensemble.

This study investigates how a fundamental group decision-making bias referred to as group polarization can influence boards' acquisition premium decisions. The theory suggests that when prior premium experience would lead directors on average to support a relatively high premium prior to board discussions, they will support a focal premium that is even higher after discussions; but when directors' prior premium experience would lead them on average to support a relatively low premium prior to board discussions, they will support a focal premium that is even lower after discussions. Results provided strong support for the theory. Moreover, group polarization was reduced by demographic homogeneity among directors and by minority expertise but increased by board influence. This study introduces a fundamental group decision-making bias into governance research and explains how group processes can influence network diffusions.

Given the key role ports play in the trade and economic growth of countries, port managers are looking for novel ways to reduce inefficiencies and improve port performance. Input congestion (defined below) is one of the inefficiency factors in ports and calculating and identifying it is one of the most important keys to improving port performance. In this study, we are addressing input congestion via Data envelopment analysis (DEA). As a case study, the one-stage DEA model is used to calculate the efficiency scores and input congestion of Adriatic ports in the period 2020–2023. The model is proposed as a tool that container terminal managers can use dynamically to calculate and plan the optimal allocation of resources. Inputs include terminal area, length of quays, and two other inputs introduced in this study for the first time, namely the degree of connectivity and the level of terminal equipment. Financial and labor data were not available for all ports and were thus not included in the analysis. Output is represented by port throughput. In this paper, inefficiency and input congestion are assessed simultaneously. The results identify ports that are relatively inefficient compared to their competitors due to input congestion. The results are then compared with 12 Mediterranean ports to contextualize our findings.

We investigate the impact of political institutions on corporate risk-taking. Using a large sample of non-financial firms from 77 countries covering the period from 1988 to 2008, we find that sound political institutions are positively associated with corporate risk-taking, and that this relation is stronger when government extraction is higher. In a subsample of 45 countries, we also find that politically connected firms engage in more risk-taking, which suggests that close ties to the government lead to less conservative investment choices. Our results are economically significant, and are robust to alternative risk-taking measures, various political institution proxies, cross-sectional and country-level regressions, and endogeneity concerns of political institutions. Our results have important implications for governments and corporate managers by providing direct relevance of political institutions to the corporate decision-making process. To encourage investment at the firm level, and hence innovation and overall growth, governments need to undertake the necessary reforms to control corruption and enforce contracts better, and thus decrease government predation and extraction.

The assessment of performance or efficiency is an essential step in the production process. Performance and efficiency evaluations can be done using parametric or non-parametric approaches. When analysing the effectiveness of similar organisations, also known as Decision Making Units (DMUs), with many inputs and outputs, one of the most widely used non-parametric methodology is Data Envelopment Analysis (DEA), which is based on a linear programming approach. Inefficient units are benchmarked (peers) and efficiency scores are provided by DEA for each DMU. Based on the number of peers, the Decision Making Units might then be ranked. A Tie between ranks, however, can happen. Andersen-Peterson’s (1993) model aids in breaking the tie. The super-efficiency model produces an infeasible result, which is confirmed by applying the prerequisites for infeasibility identification put forth by Seiford & Zhu (1999). In this paper, we present the results of our empirical investigation of Public Sector Banks in India during the 2019–20 period, focussing on three inputs and two outputs. We used variable return to scale (VRS) assumption based Banker Charnes and Cooper (BCC) and super-efficiency models, and also identified the infeasibility problem and performed sensitivity analysis. Finally, a stability region was constructed.