Designing an auditors' Employability Model Based on Organizational Learning withan Interpretive Structural Modeling (ISM) Approach

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Abstract

Nowadays, learning employability skills is recognized as a solution to overcome organizational problems of the auditing profession such as high turnover rate, job abandonment and job instability. For this purpose, the aim of the present study was to present an employability model of auditors based on organizational learning. The research method in this study was of mixed exploratory type and the statistical population was auditors who were members of the Iranian Society of Certified Public Accountants. First, in the qualitative part, using the content analysis method, dimensions, components and indicators of employability were counted through systematic review and semi-structured interviews with 15 experts who were members of professional accounting organizations in Iran, which were classified into four main dimensions and eleven components. In the quantitative part, the Interpretive- Structural Modeling approach and MICMAC analysis were used to validate and analyze the relationships between the dimensions. The results showed that the employability model experts are formed based on a hierarchical and systematic structure including learning cognitive, specialized, emotional and job skills. According to these findings, learning cognitive and specialized skills, the underlying foundation, and learning emotional skills constitute the basis for employability transformation that should be considered as a first priority. The results of this study can provide necessary insight for policymakers and managers of organizations, especially audit firms and professional accounting organizations, in determining learning priorities to overcome employability challenges.

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Dehnavi, M. A. ., Moradi, M., Jafaei Rahni, M. ., & Karami, A. . (1406). Designing an auditors’ Employability Model Based on Organizational Learning withan Interpretive Structural Modeling (ISM) Approach. Accounting, Finance and Computational Intelligence, 1-18. https://jafci.com/index.php/jafci/article/view/499