درخت حوزه‌های تخصصی

Purpose: The purpose of the current study was to identify, quantify, and prioritize the components of reducing the resistance of managers of government organizations in the administrative transformation plan. Methodology: The mixed study method is based on exploratory mixed study. The statistical population in the qualitative section was university professors, managers, and experts of executive systems, in the quantitative section, managers and experts of government agencies, and in the prioritization section, high and middle managers of executive systems of selected organizations. In-depth interviews were used in the qualitative part, while questionnaires were used in the quantitative part, and matrix questionnaires were used in the prioritization one. Acceptability (expert review) and confirmability (expert review) were used to determine the validity and reliability in the qualitative stage, while in the quantitative stage, the validity of the questionnaires was confirmed by three methods: form, content, and structure. Reliability was estimated and confirmed by three methods of determining factor loadings of items, Cronbach's alpha of components (between 0.760 and 0.888) and combined reliability (between 0.842 and 0.923). In the prioritization section, the content of the matrix questionnaire was approved by five academic and organizational experts in terms of comprehensibility and expressiveness, and the rate of inconsistency of the criteria was calculated and confirmed as 0.05 to 0.07. Qualitative data were analyzed with the ground theory technique, while quantitative data with structural equation modeling technique, exploratory and confirmatory factor analysis tests in SPSS and Smart PLS software, and the prioritization section, data were analyzed through hierarchical analysis (AHP) in EXPERT CHOICE software. Findings: The results of the qualitative part indicated that the paradigm model includes 10 dimensions (categories) and 22 components (concepts) in the form of causal, contextual, interventional, strategy, and consequence conditions. Conclusion: The results of the quantitative part showed that all the components of the study model were confirmed. The results of the prioritization section show the greater importance of the outcome and organizational productivity component.

Profit is one of the most important factors influencing economic decision making, the changes of which depend on various factors. Banks, as one of the most important business units, have a special reliance on the concept of profitability and their performance is significantly influenced by macroeconomic conditions. Government economic policies are placed. This paper aims at applying the panel regression model to study the effect of monetary policy on the profitability of banks from 2006 to 2018 using data from 30 provinces to find appropriate policy recommendations for decision-making in the banking system. The research method was the use of GMM regression technique in the context of combined data. According to the results, the performance and profitability of banks are improved by the implementation of expansionary monetary policy. However, bank lending and price inflation have a negative effect on banks' performance. According to the results of this study, control variables such as the amount of overdue claims and GDP also had a positive effect on the performance of banks. Also at the macro level, with regard to the negative effects of Expansion monetary policy and the growth of liquidity, with the controlled implementation of Expansion monetary policy, it helps to improve the performance of banks in the banking system.

Financial markets play a key role in economic development, and the insurance industry as a financial institution can be the bedrock of economic growth. Thus, risk and performance appraisal are very important in the insurance industry. There are several methods for evaluate risk and efficiency in financial markets, but since the performance of insurance companies is different from other financial institutions for the risk acceptance of other organizations and individuals, it is necessary to rank factors affecting efficiency and risk in insurance companies separately based on the performance of companies prior to focus on the calculation method. This paper discussed factors affecting the financial risks of insurance companies and efficiency and their rankings using the Delphi qualitative method and collecting expert’s opinions as well as data from domestic and forign papers. The statistical population of the research is experts and specialists in the field of risk and insurance. Spss, Eviews and Excell 2013 were used to review the questionnaires and estimate the results. The results of this paper identified the factors affecting each of the risks of financial wealth, liquidity, credit, operations and efficiency, and in the ranking obtained through Friedman test, efficiency is the highest rank, followed by liquidity, operational and credit risk in the rankings.

Services are important major element of the economy in today's societies, and banks as one of the most important service organizations direct and support many of the community's economic activities. The purpose of this study was to develop an optimal model for East Azarbaijan banks' performance based on organization risk management using the standardized questionnaire of Kosovo 2017. In order to achieve this purpose, the director or assistant director, head or deputy head, bank managers and experts of banks were selected for statistical sampling and structural equation Modeling approach was used for estimating the model and tests. Organizational risk management factors including "written job descriptions and resources to describe personnel duties, fraud risk assessment with regard to how management and other employees participate" were assessed as factors af-fecting bank performance. Therefore, the structural problems of the banking sys-tem should be resolved so that this system can function and develop in the future, and consequently, in order to resolve the crisis of the banking system, it is neces-sary to reform the banking system.

Allocating fixed costs with undesirable data has recently been one of the most important issues for managers to discuss. Lack of attention to undesirable data may lead to incorrect cost allocation. Considering and determining undesirable inputs and outputs, data envelopment analysis (DEA) technique can be significantly helpful in determining the cost allocation strategy. In-puts and outputs are divided into two desirable and undesirable groups. Obviously, desirable inputs and undesirable outputs must be reduced and undesirable inputs and desirable outputs must be increased to improve performance. This manuscript presents two strategies for allocating fixed costs with undesirable data. In the first strategy, each decision making unit (DMU) first determines the minimum and maximum shares that it can receive from the fixed resources while the efficiency of that DMU and other DMUs re-mains the same after receiving the fixed resources. Finally, the decision maker chooses the fixed cost for each DMU between the minimum and maxi-mum cost values proposed. In the second strategy, the allocation of fixed costs is done using the CCR multiplicative model with undesirable data. The effectiveness of both methods is examined by an applied study on the commercial banks.