محمدحامد خان محمدی

Resource-based view (RBV) theory analyzes and interprets company's resources in order to find out how organizations gain a competitive advantage. This theory fo-cuses on the implications of the complicated features of a company as the resources for excellent performance and comparative advantage. According to this view, capa-bilities and resources of the company are the main factors in explaining the func-tional results or competitive advantage. Resources can be considered as inputs which enable companies to do their activities. The purpose of this research is to introduce a resource-based theory for calculating the efficiency score of accepted companies in Tehran Securities Exchange by using Data Envelopment Analysis (DEA). In this regard, the financial statements of 190 companies accepted in the exchange for the 2009 – 2018 period have been analysed. Efficiency indicators, which include 4 categories of resources (10 inputs) and 5 outputs, have formed the axis of the mentioned technique. The results of implementing this model for compa-nies with the efficiency score of one, indicates first a minimal input consumption compared to competing companies in the same industry while producing more out-put, and second, using the resource-based view theory (integration of tangible and intangible resources) enables the company to push the boundaries of efficiency. Finally, it can be said that utilizing minimum and maximum resources simultaneous-ly leads to a focus strategy-type competitive advantage.

Strategic management accounting, including systems and processes in the modern era, is characterized by changes in the competitive environment that, Can be achieved through the participation of companies in the management accounting system, Assist financial managers to make decisions in the value chain and develop green accounting functions. Unfortunately, though, over time, strategic management accounting techniques are still unknown to corporate executives, especially in environmental processes, leading to a decline in corporate participation in strategic decision making. The Purpose of this research is Capacity Building Green Accounting Consequences Based on Explanation of Strategic Management Accounting Techniques by analysis of CARD and developed the theory of Rough (ERST). The methodology of this research is developmental, applied and hybrid and two groups of target population participated in this research. In the first part of 15 experts in the field of accounting at the university level an In the second part, 35 executives participated matrix Tehran Stock Exchange through a questionnaire that both the target population, sampling was homogenous. In this research, in the qualitative part, the components and indices related to the research variables were selected based on content analysis and cross-analysis and then based on Delphi analysis and hierarchical analysis in order to achieve theoretical adequacy. Then, in the quantitative section, through the analysis of the Rough Complex analysis, the decision tree determined the most effective green accounting function/outcome and strategic management accounting technique, and finally, the product life cycle costing technique is the most effective strategic management accounting technique for enhancing green accounting functions. In fact, the results showed that product cycle costing technique is a factor for enhancing the cost savings of recycling and waste recycling costs and increasing the competitiveness of the company as a component of green accounting competitive Consequences.

both academic and auditing firms have been searching for ways to detect corporate fraud. The main objective of this study was to present a model to detect financial reporting fraud by companies listed on Tehran Stock Exchange (TSE) using genetic algorithm. For this purpose, consistent with theoretical foundations, 21 variables were selected to predict fraud in financial reporting that finally, using statistical tests, 9 variables including SALE/EMP, RECT/SALE, LT/CEQ, INVT/SALE, SALE/TA, NI/CEQ, NI/SALE, LT/XINT, and AT/LT were selected as the potential financial reporting fraud indexes. Then, using genetic algorithm, the final model of fraud detection in financial reporting was presented. The statistical population of this study included 66 companies including 33 fraudulent and 33 non-fraudulent companies from 2011 to 2016. The results showed that the presented model with the accuracy of 91.5% can detect fraudulent companies. These findings extend financial statement fraud research and can be used by practitioners and regulators to improve fraud risk models.