مهدی زینلی

This study aims to develop a working capital management model for companies listed on the Tehran Stock Exchange. The proposed model determines the expected level of working capital for a company, enabling it to create the highest possible value. Additionally, this model can be used to assess the efficiency of working capital management. The discrepancy between the actual level of working capital and the expected level serves as an indicator of inefficiency in working capital management. Initially, based on theoretical foundations and expert opinions, 28 variables affecting working capital were selected. Then, using the operational working capital index, the research models were estimated using multiple regression and genetic algorithm techniques for data from 156 companies over the period from 2011 to 2022. Influential variables were identified and filtered. Finally, suitable working capital management models were identified based on two criteria: (1) the strong correlation between the errors of the fitted models and the working capital efficiency of the company, and (2) the model’s accuracy in identifying companies prone to excess or shortage of working capital. In total, after estimating 119 different models using regression and genetic algorithm methods, four suitable working capital management models were determined. The regression method resulted in models with an average accuracy of 77.27% and 79.54% for the dependent variable of working capital and the cash conversion cycle, respectively. The genetic algorithm method resulted in models with an average accuracy of 89.03% and 82.08%. The final model, with the cash conversion cycle as the dependent variable, was identified as the best model. It includes the variables of the previous year's cash conversion cycle, company-specific risk, gross profit margin, trade credit, growth opportunities, operating cycle, economic policy uncertainty, and exchange rate changes.

This research was conducted with the aim of explain and prioritize information disclosure factors related to sustainable development accounting with fuzzy approach" in 2019 of companies active on the Iranian Stock Exchange. Qualitative data were obtained through the study of research and credible sources in the field of sustainable development accounting and using coding through content analysis, the initial variables of the model were identified; 61 indicators were extracted from the initial codes in 4 dimensions including: environment, social factors. Economic and governance factors were categorized. In the quantitative section, the statistical population of the study included knowledgeable and professional and academic experts in the field of ac-counting. Using the targeted sampling method, 25 experts were selected as statistical samples. In the quantitative part, using fuzzy Delphi technique in one step, the indicators were screened. In the next step, prioritization of criteria and sub-criteria was done by hierarchical analysis method with fuzzy approach; among the main criteria, environmental dimension with weight 0.405 in rank 1 The social dimension with a weight of 0.296 was ranked 2nd, the economic dimension with a weight of 0.186 was ranked 3rd and the leadership dimension with a weight of 0.113 was ranked 4th. Finally, based on the calculated final weight, the strategic approach to environmental impacts with a weight of 0.955 in the first place, management and efficiency in consumption in the second place, social development and humanity in the third place and management Waste and waste came in fourth.