علیرضا امیرتیموری

Banks play an important role in the growth and development of any economy. A profitable banking system enhances economic stability and efficiency to mitigate the impacts of sudden macroeconomic shocks. To be more efficient and profitable, banks need to recognize the factors underpinning their performance. Accrued liabilities are one of the factors that hinder the profitability of banks. There are several methods to assess banks' profitability with their own pros and cons. Among them, data envelopment analysis (DEA) has been recommended as one of the most common approaches to evaluate different efficiencies including cost efficiency, revenue efficiency, technical efficiency, and finally profitability. The availability of prices/weights of inputs and outputs provides financial managers with significant information for evaluating efficiencies and assists them in decision-making and strategy development processes. This study mainly aims to analyze banks' profitability by considering accrued liabilities resulting from undesirable factors, for which relevant data were collected from 33 branches of a commercial bank in Gilan province, Iran based on managerial and weak disposability. The results illustrated that only three branches were graded one in three dimensions of efficiency, namely technical, cost and revenue, and profitability. Besides, it was suggested that the lack of these efficiencies was not correlated with the branches' profitability.

Evaluating the performance of countries in international markets and measuring how they utilize allocated resources provides a lucid picture of the state of business management in countries also assessing performance sustainability in international markets, is one of the important issues in predicting international trade. In international markets knowledge of countries' performance leads to the recognition of their economic and non-economic management statuses even more it is used to allocate financial support of global organization. In the present article, an attempt was made to design a model capable of examining the performance of nations in international markets by both economic and non-economic dimensions in the presence of undesirable factors and two preparation and exploitation stages of existing platforms and also assessing the sustainability of countries' performance in the studied period which is quite innovative. Then, data from 21 countries, from 2010 to 2017 were evaluated to test the model. The results indicated that the model is capable of examining the performance of countries and assess the sustainability of the countries over a period of time. According to the results of this model, an attempt was made to evaluate Iran's performance status among homogeneous countries and provide a roadmap for enhancing Iran's performance

After assessing the efficiency of the units under evaluation, determining their ranking becomes a critical concern for decision-making. Ranking methodologies rely on specific unit inputs and outputs, but some variables affecting unit performance are not directly observable and are termed contextual variables. Thus, the primary objective of this research is to introduce a ranking methodology that incorporates these variables. To achieve this, a two-step approach is employed. Firstly, the Data Envelopment Analysis (DEA) technique calculates efficiency scores for each Decision-Making Unit (DMU), considering its unique inputs and outputs. Subsequently, the linear regression method is applied to assess the impact of contextual variables on efficiency. Finally, efficiency scores are modified by removing the impact of contextual variables, enabling unit ranking. To illustrate this proposed methodology, we conducted a performance evaluation of provincial gas companies in Iran. The evaluation covered four periods, from 2013 to 2016.

One important issue in data envelopment analysis (DEA) which has been studied by many researchers is returns to scale (RTS). The authors developed DEA models to evaluate the efficiency of two-stage networks in returns to scale variable and introduced a new definition to determine return to scale classification in two-stage networks. The current article proposed an approach for determining the stability region of returns to scale classification in two-stage network DEA. The data were collected from insurance companies in Iran in 2019. We consider the insurance industry process as a two-stage network; the stage of marketing and that of investment. The effectiveness of insurance companies was evaluated, and, after determining the classification of returns to scale, we found a sustainability interval to classify returns to their scale.