mansour soufi

The ever-increasing changes in the business world and the new requirements of production and trade in the present era have provided the basis for the emergence of new attitudes. The supply chain in the current environment is complex and diverse. Based on the change in the market environment, the demand for the supply chain has also created a lot of uncertainty. Also, with transformative technologies in the new era, managing supply chain uncertainties has taken on a new face. One of these technologies is blockchain technology. The importance of drug safety, always one of the biggest concerns, cannot be overstated as it directly affects the public health of society. It is a shared responsibility of all stakeholders in the pharmaceutical industry to establish a reliable and traceable pharmaceutical system. For this reason, in this research, an effort has been made to provide a model for sustainable supply chains in a state of uncertainty, emphasizing blockchain technology as a tool to fulfill this responsibility, fostering a sense of collective duty and commitment in the audience.

The aim of this research is to develop a stochastic data envelopment analysis (SDEA) model to assess the sustainability of active companies of concrete industry in Guilan province based on the sustainability indicators. At the first step, economic, social, and environmental indicators for evaluating the companies were identified through the literature review. At the next step, these indicators were evaluated using the fuzzy Delphi method and more important indicators were identified. These indicators, were categorized into the controllable, uncontrollable, desirable and undesirable inputs and outputs for assessing concrete companies. Finally, a new stochastic DEA model was developed for sustainability assessment of concrete companies in Guilan province in both linear and nonlinear equations. In the proposed models, errors are incorporated into the model via a stochastic component, which is specified by considering the constraints probabilistically. Based on the results of implementing the presented model, at an alpha level of 0.1, the companies Darvishan and Zahmatkesh, at an alpha level of 0.3, the companies Darvishan, Takht-e-Jamshid, and Zahmatkesh, and finally at an alpha level of 0.5, only the company Darvishan are sustainable. So, the only sustainable efficient company is Darvishan which should be considered as a benchmark for other units for achieving sustainability efficiency.

This research aims to develop data envelopment analysis (DEA) for predicting the supply chain sustainability of the concrete industry using stochastic variables. In the first phase, the mines and companies that organized the four supply chains of the Guilan concrete industry based on competitive elements were identified. After that, based on economic, social, and environmental indexes, the sustainability of the supply chains was chosen using the Fuzzy Delphi model and input and output of mines and concrete companies, which include controllable, uncontrollable, and undesirable inputs and outputs. Finally, data envelopment analysis was used to measure the supply chain sustainability of the concrete industry employing crisp data and a linear model. The errors were entered into the model with a stochastic element based on the probability of the errors. In other words, according to stochastic data envelopment analysis, all input and output data were considered randomly. The output data shows that none of the four supply chains of the Guilan concrete industry is sustainable.

Background & Decisions related to the supply chain and the selection of supply chain channel members are one of the main and strategic issues in supply chain and play an important role in the company's success in responding to customer demand. The aim of this research is to analyze the performance of the supply chain of dairy products under conditions of uncertainty.
The current research is an applied research in terms of its purpose and a mixed exploratory research in terms of the method of data collection. The statistical population of the research in the qualitative part included senior managers of dairy companies as executive experts, 15 of whom were selected by snowball sampling. In the quantitative part, the statistical population included managers and employees (experts and above) of one of the country's dairy companies, 106 of whom were selected based on Morgan's table and available sampling method. For data analysis, thematic analysis was used in the qualitative part and structural equations were used in the quantitative part.
The findings from the qualitative data analysis indicate the identification of 28 damages in the form of three overarching themes (supervisory factors, organizational factors and environmental factors) and 8 organizing themes (internal supervision, external supervision, structural factors, process factors, human factors, transportation conditions , economic factors and market conditions). The results of the confirmatory factor analysis also indicated the validity of the findings of the qualitative section.
Improving the performance of the dairy products supply chain network requires the company managers to focus on the identified damages. Then, setting goals in strategic plans and formulating appropriate strategies around supply chain performance in uncertain conditions can be effective.

The aim of this research is to design scenarios based on cooperative game theory models to investigate the observed rewards and penalties resulting from knowledge sharing or non-sharing among companies. The research population includes managers and experts from 30 top private companies active in the field of oil and gas projects. The research methodology is based on Kumar's classification, which is applied, interpretive, exploratory, and includes both quantitative and qualitative aspects. This study underscores the importance of efficient knowledge sharing as a means to foster collaboration among investors in the portfolios of the National Iranian Oil Company to compete with foreign competitors during sanctions.Upon reviewing the results of four scenarios derived from the game, it is observed that the rewards perceived by companies from knowledge sharing depend on the behavior resulting from knowledge sharing by other companies. The more capable an organization is in the influential factors of knowledge transfer and higher innovation, the greater the returns companies will obtain through cooperation. Sensitivity analysis regarding the costs of project cooperation, which arises due to the difficulty of implementing joint projects, shows that as costs increase, companies shift from a cooperation strategy towards a retaliation strategy and then towards a non-cooperation strategy. These strategies are determined based on aligning the results of the profit equation designed with three interrelated cooperative games: the prisoner's dilemma, the hawk-dove game, and assurance game.

In this study, the relationship between memorable TV advertisement and consumers’ audiovisual emotions has been investigated using neuromarketing approach. Considering research objective, it’s an applied study and regarding method it’s an experimental study. The statistical population of the study includes audience of TV commercials and the statistical sample includes 4 male and 5 female in the range of 22 to 55 years old which selected using purposive sampling method. In data gathering phase, customers’ brain reactions were recorded using electroencephalogram applying OpenBCI processing software with a sampling rate of 512, in the range of 7-13 HZ and 30 frames per second using FP1. After elimination of the artifacts and calculation of the data average of these in the Excel software, TV commercials were divided into three selected and normal messages. Analyzing the research data using SPSS software indicated that, there is a significant correlation between the emotions and feelings while watching the selected commercial and the remembered words, sounds and images. Furthermore, there isn’t any significant relation between the emotions and feelings while watching the lower rank commercial and the remembered words, sounds and images.

One of the most difficult decisions for private/intuitional investors of stock exchange market is finding the best combination of shares (portfolio) which meets their financial expectations. Therefore, a systematic performance evaluation of companies in stock exchange is a critical prerequisite for their investment. By developing a combined multi, criteria decision analysis (MCDA) approach; this research aims to help investors in evaluating Tehran stock exchange industries. To this regard, after determining the weight of the five main criteria in financial performance evaluation of the stock exchange listed companies using analytical network process (ANP), WASPAS, PROMETHEE II and ELECTRE III methods, the listed industries in Tehran stock exchange were ranked during a 10 years period (2011 to 2020). Next, the results of the applied methods combined for each year using Borda aggregating method and the ranks of the industries in each year were calculated. Finally, using Spearman's rank correlation coefficient, the outputs of the applied methods compared with the rank of Borda method. According to the results, ranking of the ELECTRE III method has the highest correlation with Borda method. Therefore, ranking of the ELECTRE III method selected as the best ranking.

Clustering algorithms are useful tools for understanding data structure and classifying them into different data sets. Due to the importance of using these algorithms in analyzing financial market data that have a high volume and scope, this study in order to select the best clustering algorithm for clustering companies listed on the Tehran Stock Exchange in the field of finance from It has used different clustering algorithms and evaluated the validity of these algorithms and selected the best algorithm.

This research is applied in terms of purpose and descriptive in terms of implementation method and is of quantitative type (mathematical modeling). The statistical population of the research includes 403 companies listed on the Tehran Stock Exchange in 2019, whose performance has been evaluated based on four financial criteria.

After clustering the surveyed companies by five clustering algorithms, namely K-means, EM, COBWEB, density-based algorithm and ward method, seven indicators RS, DB, Dun, SD, Purity, Entropy and Time were used to evaluate the algorithms. Finally, the total performance of the algorithms was analyzed based on TOPSIS, VICOR and DEA methods. Based on the results, K-means has a better performance in clustering based on the financial data sets.

Originality/Value:

 Since no clustering algorithm can have the best performance in all measurements for each data set, this study uses a combination of multiple criteria to analyze data clustering algorithms related to the field of financial performance appraisal. Companies have provided suggestions and the results of this study have been used effectively for investors in the field of finance, which leads to the optimal choice of investment portfolio.

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.

One of the most complex decision issues for investors of stock exchange is evaluation of companies’ performance that can be done based on different approaches. The aim of this study was to evaluate the performance components of firms in stock exchange with a combination of fuzzy Delphi method and fuzzy cognitive mapping. Although numerous studies have been recorded in evaluating the performance of companies, but a few of them have used a Balanced Score Card (BSC) to identify performance evaluation criteria. Therefore, in this paper, first, the key elements of performance evaluation of companies were identified from investors ' perspectives using fuzzy Delphi method, among 82 of the performance evaluation elements that extracted from research literature, and then the relationships between them were examined using fuzzy cognitive mapping method, and components were ranked. Based on the findings of the five earnings growth metrics ( OPG ) , the ratio of price per share ( P / E ) ratio is the ratio of the operating interest to sales , the ratio of sales interest to sales , and the firm 's sales growth rate , which all examine firms from financial perspective , are the most important criteria in terms of investors .

Estimating the revenue efficiency of entities under evaluation is one of the most important evaluations that can give valuable information about organizations provided that the output prices are known . In this research, a new definition of optimal scale size ( OSS ) based on maximizing the average revenue efficiency (ARE) is presented . Also , the ARE is defined in both convex and non - convex sets, which is independent of returns to scale and the assumption that the vector of input-output prices of units is uniform . Next, due to the presence of uncertain data in many real applications, the introduced ARE model is extended to evaluate systems with random inputs and outputs , and approaches are provided to calculate it . Finally , the proposed method is used in an experimental example and the ARE is calculated for a data set of postal areas in Iran .

According to the importance of public organizations such as municipalities in Iran, few researches conducted on benefits of e-government and discrepancy among the results of the researches, its necessary to develop analytical research. The aim of this study is to evaluate the benefits of the e-government from the perspective of municipal employees. Considering research objective, it is an applied study and regarding to the data collection and data analysis method, it is a descriptive- correlational study. The statistical population of the study consists of 2003 employees of the Rasht municipality, includes the five urban areas, the center municipality and its related organizations which according Morgan-Krejcie, sample size was estimated 322. After distribution of 390 questionnaires using stratified random sampling method, 330 questionnaires were collected and analyzed using structural equation modeling (SEM) and based on partial least squares approach. Results of the research hypotheses indicated that all three types of quality (information, system and services) have significant relationship with network benefits and user satisfaction. On the other hand, the results on the relationship between network benefits and user satisfaction also showed that the network benefits significantly affect user satisfaction. Finally, the results showed the network benefits and user satisfaction had a positive and significant effect on intention to use. Since the most indirect path coefficient is resulted from the effect of information quality on intention to use, through the network benefits, it’s suggested to pay more attention to improve information quality and network benefits in order to increase intention to use of e-government.

Considering the importance of employees' behavior in service organizations and its significant impact on their success and failure, the present study investigates the environmental and ergonomic factors affecting on mental health and behavior of employees in one of the branches of Islamic Azad University.

The present study is applied in terms of purpose and descriptive-correlational in data gathering and analysis. The statistical population of the survey includes: 315 employees of the Rasht Branch of Azad University, out of the 196 people were selected by random sampling method and the necessary data were collected using a questionnaire. SPSS 21 software was used to describe the research variables and structural equation modeling and SMART PLS3 software were used to test the hypotheses.

According to the findings, workplace layout affects the use of work equipment (0.389), use of office chairs (0.252), and environmental conditions (0.538), but the impact of workplace layout on the posture of employees did not confirm in 5 percent significance level. Also, the impact of posture (0.586), layout (0.440), work equipment (0.458), office chair (0.345), and environmental conditions (0.550) on public health and also the effect on general health (0.690) and environmental conditions (0.158) on employees’ behavior is significant. Examining the indirect relationships of the research model also shows that workplace layout affects employee behavior through the general health and environmental conditions. Workplace layout influences employees’ behavior through work equipment, office chair, environment conditions, and general health.

The indirect effect of workplace layout on employees’ behavior through public health has the highest impact (0.304), therefore, making policy general on health, should improve employees’ behavior.

Due to the importance of blood as a vital element in the health system, in this study, the blood supply chain is modeled at three levels of donors, banks (blood centers) and hospitals in the form of a multi-objective model to minimize total costs, total delivery time of blood units and non-estimated demand of hospitals in each period.

The present study is applied in terms of purpose and descriptive and quantitative in terms of method. The data needed to implement the real problem in 2021 have been collected by through the regional office of the Tehran blood transfusion organization along with the Negareh system. Due to the Np-hard nature of the problem, the proposed model is solved using three algorithms of GA, NSGA-II and MOPSO in GAMS software.

In the proposed model, matching the blood type in meeting demand; blood type delivery and allocation system in laboratories and blood banks, blood wasting in laboratory, transfer of products between demand centers, sensitive and determinative parameters of the model such as; demand, blood donation and delivery time of blood products between network components are considered indefinitely. The findings show that the MOPSO algorithm has a better performance in problems 3, 7, 10 and 12 for the QM index, but generally, based on running times and their average, the NSGA-II algorithm is better.

Based on the results, the proposed model leads to a reduction in total costs, total delivery time of blood units and unapproved demand of hospitals.

Data envelopment analysis (DEA ) is a technique to evaluate the relative performance of a set of decision - making units ( DMUs ) . Corresponding to each inefficient DMU , an efficient benchmark on efficient frontier is determined and inefficient DMUs are projected to this benchmark by increasing their outputs and decreasing inputs . In this paper , a DEA - based procedure is proposed to determine an ideal benchmark to each inefficient unit . Our proposed benchmark dominates the unit under evaluation and it is a convex combination of projection points obtained from different aspects : cost, revenue efficiencies and the closest distance . A n important point is that although the obtained benchmark is not necessarily an efficient point, however , it dominates the unit under consideration . At the end of the paper , the proposed model would be implemented on a simple numerical example .

One of the most important issues in the field of financial management is how the investors distinguish between favorable investment opportunities and undesirable ones. One of the ways to help investors is to provide financial distress prediction models. According to the various studies have been made to develop these type of models, in this study the combination of artificial neural networks (ANN) and genetic algorithm (GA) techniques based on Zimensky prediction ratios is used for modeling financial distress. The research statistical population includes public companies in Tehran stock exchange which admitted between October 2013 to October 2015 and among them 66 distressed and 150 going concern companies were selected as the research sample using screening method. The results indicate that the power of both artificial neural network and genetic algorithm models in financial distress prediction are equal (95 percent), however, the prediction error of neural network is relatively low compared to genetic algorithm.

The rapid world economic growth has driven the businesses towards involvement in international marketplaces due to lower business risks, lower commitment of resources, and higher flexibility of these markets. However, evidence indicates that barriers to exportation vary across different stages of internationalization. Therefore, this empirical study was carried out to explore exportation barriers of agricultural commodities, especially honey, in Iran. Based on an explorative literature review, several factors were considered as export barriers of agricultural commodities. These barriers were provided to a sample of 20 honey exporters in Ardabil province using a questionnaire. After collecting and analyzing the data, 21 factors were identified as the important barriers to honey export. A graph theory-based technique (DEMATEL) with feedback structure was used to explore the direct and indirect effects of barriers on the other barriers and the whole system. The results indicate that adjusting export promotion activities, lack of home government assistance/incentives, shortage of working capital to finance exports, high tariff and nontariff barriers, different foreign customer habits/attitudes are the most critical barriers in the order of importance.

Production planning includes complex topics of production and operation management that according to expansion of decision-making methods, have been considerably developed. Nowadays, Managers use innovative approaches to solving problems of production planning. Given that the production plan is a type of prediction, models should be such that the slightest deviation from their reality. In this study, in order to minimize deviations from the values stated in the tea industry, two Particle Swarm optimization algorithm and genetic algorithm were used to solve the model. The data were obtained through interviews with Securities and Exchange Organization and those in financial units, industrial, commercial, and production. The results indicated the superiority of birds swarm optimization algorithm in the tea industry.