حسن قدرتی

Modeling the implementation of organizational ambidexterity as an organizational ability in alignment and efficiency in response to market demands and simultaneously adapting to environmental changes through innovation and discovery of new solutions can help improve the performance of the aforementioned organizations. The purpose of this research is to design a two-level organizational model in the branches of the Tejarat Bank of Isfahan province to identify the important indicators. in current research, the research method is a descriptive survey, and researcher-made questionnaires were designed based on the Fuzzy Dimetal method and the fuzzy network analysis process, these indicators have been prioritized by 36 senior employees of Isfahan Tejarat Bank using the combined technique of Fuzzy Dimetal and fuzzy network analysis process. based on the output of the above table, it indicates that the index of organizational managerial competence and loose relations, and finally attraction and employment were introduced as the most effective positive and negative indicators. the indicators of human resources of the organizational ambidexterity model, which mainly refers to the organizational and individual culture, indicate the influential role of the internals of individuals and organizations in the implementation of a new concept. In the strategic indicators, the existence of a flexible structure for the implementation of ambidexterity has been emphasized. From the aspect of social capital, the significant point is the high priority of the index of revision of social processes and components in the total indicators of ambidexterity in the organization. This means that in the initial stages of the implementation of dualism, it is necessary to review the processes and equipment to discover exploitation and exploration opportunities, plan to follow up on these opportunities at the same time, and formulate a model of ambidexterity in different parts of the bank.

In this research, first the financial criteria used in capital decision-making were identified and refined, then the most effective criteria were selected based on the deep learning algorithms including: RF, XGBoost, and LightGBM. In this stage, 11 factors were selected from the 35 factors found in previous research. In the next stage, based on the Forensic-Based Investigation algorithm (FBI), feasible investment options were identified and the internal rate of return was calculated over a 5-year period, and 42 companies that had an internal rate of return higher than the risk-free investment were selected as feasible investment options. During the next stage, different random combinations were used as investment portfolios using three methods: equal weight allocation, mean-variance model, and hierarchical risk preference model. Investment weights were determined for each invested share (combination) and investment returns were evaluated using different metrics. Finally, in order to validate the findings, the feasible investment options were divided into two categories of companies active in the financial industry and others, and the superiority of decision-making (higher returns) in a dynamic process was accepted.