An Analysis of Financial Crimes in Iran Using Structural Equation Modeling

Subject Areas : Journal of Investment Knowledge

Ali Reza Amerian ¹ , Ebrahim Chirani ² , Mohammadhasan Gholizadeh ³ , Seyed Mozaffar MirbargKar ⁴

1 - Ph.d. student of financial management, Faculty of Management and Accounting, Rasht Branch, Islamic Azad University, Rasht, Iran.
2 - Assistant Professor, Department of Management, Faculty of Management and Accounting, Rasht Branch, Islamic Azad University, Rasht, Iran
3 - Associate Professor, Department of Management, Faculty of Literature and Human Sciences, University of Guilan.
4 - Assistant Professor, Department of Business Management, Faculty of Management and Accounting, Rasht Branch, Islamic Azad University, Rasht, Iran.

Received: 2019-11-13 Accepted : 2020-01-11 Published : 2022-03-21

Keywords: Confirmatory Factor Analysis, Financial Crime, structural equation modeling, Combined Research,

Abstract :

The increasing spread of financial crimes and the irreparable economic, and even social and cultural damage they cause, clearly illuminates the necessity of research and analysis in this area and to investigate the causes of such crimes. Therefore, the main issue in this research is the spread of financial crime and its concerns in Iranian society. The researcher seeks to understand financial crime economically and not from a sociological or criminological perspective. Therefore, the overall purpose of the present study is to design and test a model to explain and predict the causes of financial crime to prevent financial deviations. In this regard, a hybrid research approach was used which used the qualitative stage of the theory of grounded theory or the grounded theory and in the quantitative stage the descriptive-survey method. The qualitative findings were analyzed by MAXQDA software and at the end of the qualitative phase the conceptual model of financial crime was extracted. In the quantitative phase, the model was validated using partial least squares structural equation modeling with PLS software. Model fit indices indicate that the data fits well with the conceptual model. In other words, quantitative data fits well with the conceptual model of research and confirms the qualitative data.

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