Alireza Rahrovi Dastjerdi, Ph.D. ,

Assistant Professor

Department Of Accounting
Faculty Of Administrative Sciences And Economics

Email

a.rahrovi@ase.ui.ac.ir

Address

University of Isfahan Azadi square Isfahan, Iran
Postal Code : 8174673441

Overview

Research Output

3

All

3

Article

Articles

2025

Isolation, characterization, and genomic analysis of three novel Herelleviridae family lytic bacteriophages against uropathogenic isolates of Staphylococcus saprophyticus

( Article . )

Shirzad-Aski H., Yazdi, M., Mohebbi A., Rafiee M., Soleimani-delfan, A., Tabarraei A., Ghaemi, E.A., Asadi, P., Abbasi jondani, J., Rahrovi dastjerdi, A., Taymouri, S., Yazdkhasti, F., Saadatseresht, M., Foroghi, D., Khodarahmi, G., Abedi, A., Varshosaz m., M., Kiani, G.H., Jalali, H., Zaker, H., Sadeghi-aliabadi, H., Dinari, M.

Publication Date: 2025

Virology Journal (1743422X)(1)pp. 1358-1366

2022

Conceptual structure of balanced scorecard research: A co-word analysis

( Article . )

Faraji, O., Ezadpour, M., Rahrovi dastjerdi, A., Dolatzarei, E.

Publication Date: 2022

Evaluation and Program Planning (01497189)94

2019

Detecting manager’s fraud risk using text analysis: evidence from Iran

( Article . )

Rahrovi dastjerdi, A., Foroghi, D., Kiani, G.H.

Publication Date: 2019

Journal of Applied Accounting Research (09675426)20(2)pp. 154-171

Purpose: In accounting and finance, researchers have used many ways to detect manager’s fraud risk. Until now, many researchers have used some data mining methods in these two fields to detect this risk. The purpose of this paper is to compare the precision of two data mining methods in detecting such a risk. Design/methodology/approach: For this purpose, this paper analyzed the texts of board’s reports and used two methods including the convex optimization (CVX) method and least absolute shrinkage and selection operator (LASSO) regression method. In this way, the words of these reports, which have the greatest power in explaining the manager’s high fraud risk index, were identified. Using these words, this paper presented a model that could detect manager’s high fraud risk index in companies. Findings: The results indicated that both methods can detect the manager’s high fraud risk index with a precision between 82.55 and 91.25 percent. The LASSO method was significantly more precise than the CVX method. Research limitations/implications: The lack of access to an official and reliable list of firms suspected to fraud and the lack of access to the Microsoft Word (MS Word) file of board’s reports were two of the most important limitations of this study. Practical implications: Regulatory bodies and independent auditors can consider the proposed methods in this study for assessing the fraud risk for a firm or other legal parties. Originality/value: This paper avoided using merely financial statements data to detect the manager’s fraud risk index and focused on texts of board’s reports for the detection process. The capabilities of data mining and text mining methods for detecting the manager’s fraud risk index using board’s reports were tested in this paper. By comparing CVX and LASSO results, this paper indicated that methods with a binary-dependent variable have more power and are more precise than methods with continuous-dependent variables for detecting fraud. © 2019, Emerald Publishing Limited.