People give inputs to the organizations and receive outputs instead. Each individual compares his/her inputs and received outputs with others and may feel dissatisfied. This comparison forms the foundation of the equity theory introduced by Adams. Despite the widespread acceptance of Adams’ equity theory and the consensus on its ability to explain the distributive equity perceived by employees, the difficulty in quantifying the concept of equity has made its use challenging to some extent. This study seeks to propose an approach to estimate the degree of equity perceived by each employee compared to others and determine the number of changes in inputs/outputs of the employees to increase their sense of equity while causing the least dissatisfaction.

The output-oriented CCR model, as one of the Data Envelopment Analysis (DEA) methods, was used with two different scenarios of optimism and pessimism. It provided an estimate of the degree of equity perceived by each employee. DEA is a mathematical model that is conceptually similar to equity theory. This technique compares homogeneous decision-making units that consume similar inputs and produce similar outputs. It distinguishes the efficient and inefficient units. To illustrate the applicability of the proposed approach, it was used to estimate the equity perceived by the faculty members of an educational group. This example estimated the equity perceived by nine professors working in an educational institution, by considering two inputs (i.e., scores achieved for educational activities and research activities) and two outputs (i.e., financial compensation and supporting facilities).

Solving the formulated models showed that optimistic and pessimistic views have different effects on the ranking of employees in terms of the degree of equity they perceive. The results also showed that the degree of equity perceived by an employee does not necessarily correspond to the degree of equity estimated using both optimistic and pessimistic views because these two perspectives measure the extreme values of these approaches. Instead, the degree of equity perceived by employees is more in line with the average of equities obtained from both optimistic and pessimistic approaches.

The results showed that the real equity values perceived by employees fall within the interval values obtained from the approach proposed in this study, indicating its validity. Therefore, it is possible to estimate the equity perceived by each employee as an interval value and, accordingly, prepare some facilities for employees who have the potential to feel inequity based on DEA models to reduce the intensity of this feeling. Since equity theory is a reactive theory that investigates the perceived equities in an exchange relation, it does not have the ability to predict and cannot be applied until the exchange relation is finished. DEA, the same as the equity theory, is also a reactive model. However, it can be considered a futuristic model due to its capability of estimating how the change in each person’s inputs and received outputs will affect his/her satisfaction and the satisfaction of his/her peers i.e. changing the inputs and outputs values of a person will change the degree of satisfaction perceived by others. DEA can predict such changes; it can predict the satisfaction degree of individuals in advance by considering different values for their inputs and outcomes.

Venture capitalists have unique capabilities in dealing with uncertain conditions. Despite the increasing level of risk in the development of innovative startups based on emerging technologies, investing in these emerging areas and startups is a strategic measure for venture capitalists. This highlights the need for comprehensive carrying out evaluation and analysis in this regard. The need for a benchmark and a model to identify high-potential success options among the masses of startups is a challenging issue for which this research seeks to provide a solution. The main purpose of this research is to develop a benchmark for identifying high-potential success options.

The main research question of the present study was answered by cooperating and interacting with ten experts in the venture capital industry. First, the components of collaboration were extracted through library studies. Next, the extracted components were evaluated and analyzed based on Markov analysis, the average duration of multidimensional analysis in decision-making. Then, based on the findings of Markov analysis, two-dimensional evaluation and screening of identified agents were performed based on the fuzzy Delphi method.

This study showed that out of 62 indicators of mutual participation between investors and startups, 41 indicators were key, and 21 indicators were not applicable. In the next step, the indicators were evaluated based on the FBWM method.

The achieved model was tested on six successful startups in capital attraction using fuzzy multi Moora. The model could rank the investment priority in these six startups.

This research has been carried out to identify the effective factors in the formation of the technology-based entrepreneurship ecosystem in the technical faculties of the University of Tehran.

First, by reviewing the extant literature, the effective factors in the formation of the technology-based academic entrepreneurship ecosystem were identified. Then, the collected items were completed by conducting in-depth and exploratory interviews with 17 specialists and experts in the research community. Using an open and axial coding method, nine main factors affecting the formation of the technology-based university entrepreneurship ecosystem were obtained. By distributing paired questionnaires among experts and with the help of the structural-interpretive modeling method, the components of the model were configured in six levels.

A six-level model was developed. The first level included the infrastructure components of entrepreneurship ecosystem development, policy and management of financial resources, intensity and level of design preparation, development and commercialization, creation and development of supporting institutions, and business development in the region. The second level included the university policy and rules component.  The market component stood in the third level. The fourth level embraced the economic factors and the business environment. The fifth level included the governance factors and the last level included the cultural component of the academic technological entrepreneurship ecosystem. It was also shown that the cultural component is the most influential factor and the infrastructure components of entrepreneurship ecosystem development, policy, and financial resource management, intensity and level of preparation for design, development, and commercialization, creation and development of supporting institutions, and business development in the region were the most effective factors in the formation of the technology-based academic entrepreneurship ecosystem in the University of Tehran.

The development of entrepreneurial culture and spirit is the base of the formation of the university entrepreneurship ecosystem. Relying on technology, it can be implemented by applying correct policies and competent management at both micro and macro levels.

Attention to environmental issues in supply chain activities has been taken into consideration due to the increase in public awareness and strict laws related to environmental protection. Initially, only the economic aspects of the supply chain were considered in the network configuration, but with increasing concerns about environmental issues, reverse logistics and closed-loop supply chains were developed. Designing a closed-loop supply chain network plays an important role in reducing costs, improving service levels, and responding to environmental issues. Therefore, the purpose of this study is to design a closed-loop supply chain network taking into account hybrid uncertainties and flexibility in constraints.

In most of the conducted studies about supply chain network design, the types of cognitive and random uncertainties, as well as the flexibility of soft constraints, have not been investigated simultaneously, while the conducted modeling is not able to consider hybrid uncertainty in supply chain parameters in the real world. In this study, to simultaneously consider the hybrid uncertainties and flexibility in constraints, a novel model of robust stochastic, possibilistic, and flexible programming based on Me measurement was developed. In this model, the convex combination of optimistic and pessimistic attitudes of decision-makers was considered in the form of the Me measure, and the modeling was more flexible and realistic.

In the proposed approach, a convex combination of optimistic and pessimistic spectra was considered in the model. The need for subjective and repetitive reviews by decision-makers was eliminated in the model and the level of satisfaction was calculated optimally after solving the problem. On the other hand, due to the robustness of the model, possible deviations, scenario deviations, non-fulfillment of demand and capacity, and deviations of soft constraints were minimized. In the proposed approach based on the Me measure, the problem-solving approach was reduced and there was no need for a two-step solution to find solutions.

A case study was conducted in the supply chain of stone paper production to evaluate the efficiency of the proposed model. The results of sensitivity analysis, robustness analysis, and simulation with the realization model showed that the proposed model was able to provide robust and realistic solutions. The proposal of a realistic and flexible solution for designing problems of the supply chain network by creating a trade-off between the objective function and the risk-taking level of decision-makers and managers through changing the justified space in the Me criterion in the proposed approach was one of the achievements of the present study. As its other achievement, the present study could provide a combination of different viewpoints of decision-makers’ risk-taking through changing the justified space based on different values of the parameter λ in measuring Me and propose flexible and realistic solutions according to the results of numerical simulation in the proposed approach.

The purpose of this research was to provide a mathematical model for smart supply chains with Vendor Managed Inventory (VMI) through the Internet of Things (IoT) technology to overcome traditional supply chain challenges and solve problems such as uncertainty, high costs, and changes in customer demands. Creating an intelligent and agile supply chain is essential to tackle these challenges and problems. Generally, customers are still skeptical and doubtful of the smart supply chain, and this skepticism is more likely to arise in the financial context. To improve efficiency, all organizations and institutions need to adapt themselves to external and internal changes, maintain supply chain inventory, and be flexible with customer demands.

Previous studies discussed intelligent supply chains, IoT, and their combination and described the advantages and disadvantages of using these technologies. Despite much research in this field, no mathematical modeling has been done for a smart supply chain that incorporates technology and automation. Based on such needs and objectives, this study sought to design a two-objective mathematical model, with a four-level supply chain. In this model, there is a direct relationship from suppliers to manufacturers, from producers to retailers, and from retailers to customers. The supply chain was intelligently designed, and the chain levels used Wireless Sensor Network (WSN) technologies, Radio Frequency Identification (RFID), blockchain, and Internet sales.

The designed model in this research was validated by GAMS software. The researchers identified 10 problems of small and medium dimensions in the model. They presented the results in the form of objective function values ​​and solution time. The basic problem was solved using the -constraint method. The results obtained from solving the model and the Pareto diagram were presented in detail. The optimal implementation of the mentioned technologies, as well as the time and cost response to this implementation, proved the efficiency of the model.

The present study put forward a two-objective mathematical planning model. The first and second objectives of this model were to minimize the cost and decrease the time of the supply chain, respectively. By solving the basic problem, using the -constraint method, the values ​​of the objective functions and the Pareto diagram showed desired results. Solving the model in small and medium-sized dimensions using GAMS software, the values of the objective functions and the solution time were achieved. Also, the results proved the validity of the provided model suggesting that it can be used in larger dimensions. However, due to the lack of memory in GAMS software, for large dimensions, the suggested mathematical model for the smart supply chain needs to be checked using MATLAB software and meta-heuristic algorithms.

To evaluate the achievement degree of an organization to its goals, its productivity must be measured. Since an organization is composed of different groups, the productivity of its groups should be examined. This paper aims to introduce a suitable index to evaluate group cost productivity changes and identify factors affecting it. To explain the applicability of the proposed index, a study on the productivity evaluation of a bank's branches in different regions is presented.

A suitable distance function is created to measure the cost efficiency of a group, and based on that, the group cost Malmquist index is introduced. Then, the influencing factors on the productivity growth of the groups are examined by focusing on intra-group and extra-group sections.

An indicator was presented to evaluate the cost productivity changes of a group of decision-making units and the factors that affect the cost productivity changes of the groups were identified. The proposed group Malmquist index breaks down into four components: Pure efficiency changes, scale efficiency changes, allocative efficiency changes, and cost technological changes. The pure efficiency change measures the optimal use of inputs to produce output indicators. The scale efficiency changes reflect the effect of changes in the size of branches of a region on its productivity growth. The allocative efficiency changes indicate the achieved changes in the optimal combinations of inputs considering the prices of each period. The cost technological changes component reflects changes in cost technology frontiers during two periods. All of these components were examined from two perspectives: intra-group and extra-group. In the intra-group perspective, the internal group frontier is considered, and in the extra-group perspective, the common frontier of all groups is focused. To explain the applicability of the proposed index and calculate the impact of its components on productivity growth, a real case study was presented. This case study evaluated the cost productivity changes of a bank's branches in eight different regions. The results showed that three regions have cost productivity growth. One area has almost no changes in cost productivity, and the other regions have productivity regressions. The results of measuring the group cost Malmquist productivity index on bank data provide meaningful and useful information about bank productivity changes in different areas.

Most organizations consist of different groups and departments. Sometimes, it is necessary to examine a group of decision-making units instead of evaluating the performance of several decision-making units to analyze the role of environmental conditions on the performance assessment of the decision-making units. The results are shown that the proposed group cost Malmquist productivity index provides a suitable tool for evaluating the cost productivity changes of groups. Also, it prepares a clear perspective for managers of organizations for future policies by identifying the intra-group and extra-group factors affecting group cost productivity changes.