Narges Mohseni dehkalani - Ph.D. Candidate, Accounting Department, Islamic Azad University, Qeshm Branch, Qeshm, Iran
Fereydon Rahnamay Roodposhti - Department of Financial Management, Science and Research Branch, Islamic Azad University, Tehran, Iran
HAMIDREZA KORDLOUIE - FACULTY OF MANAGEMENT AND ACCOUNTING, ISLAMIC AZAD UNIVERCITY, ISLAMSHAHR BRANCH
Mohammad Hamed khanmohammadi - Accounting Department, Islamic Azad University, Damavand Branch
Shadi Shahverdiani - Assistant Professor, Department of Business Administration, Shahr-e-Qods Branch, Islamic Azad University, Tehran, Iran

This study evaluated the relationship between behavioral bias with an emphasis on perception bias and earnings management incentives among financial analysts, accountants, and auditors. The sample of this study included ۱۰ experts in the fields of accounting and auditing. In terms of the technical dimension, the fuzzy method was used to include uncertainty in the research. Instead of using a pairwise comparison of factors, the non-rank comparison of the ELECTRE III method with a systematic review was used to study and rank the different dimensions of perception bias factors affecting the types of earnings management through the Delphi method and the opinion of the elite. The results of the study indicated that out of ۶۵ variables of perception bias affecting the role of earnings management, ۲۷ top biases were identified and prioritized. The obtained results showed that the highest effect on earnings management types was related to overconfidence bias, regret aversion, and self-serving bias being at the first to third ranks. Besides, based on the scenario-building in developing an optimal model, the model with a significant relationship between perception bias and all types of earnings management (real, accrual, efficient, and opportunistic earnings management) significantly was more accurate than those in which the relationship between perception bias with some types of earnings management was considered.

Behavioral Finance, Perception bias, Attribution bias, earnings management