Аннотация:
purpose: the credibility of auditors is directly linked to the added value of confidence in communication between the auditee and their audience. consequently, the credibility of auditors takes precedence due to the significance of audited financial statements in facilitating transactions in capital markets. this research aims to measure auditors' credibility using artificial neural networks. methodology: initially, research variables were identified using grounded theory, and factor analysis was employed to analyze research questions. the final model for measuring auditors' credibility was then presented, and matlab software was utilized to measure auditors' credibility. this research was conducted in the year 1401 (solar hijri calendar, equivalent to 2022-2023 in the gregorian calendar). findings: the results revealed that various factors influence the evaluation of auditors' credibility, including examining independence, acceptance or continuation of work, correspondence file, permanent file, understanding the unit under scrutiny and its environment (including internal controls), content tests, work planning, control and supervision, checklists and reports, the execution of duties by the second manager, overall assessment of audit files, the general status of the audit institution, human resource status within the audit institution, functions of the audit institution, compliance with auditing profession rules and regulations, and the external appearance of the audit institution. conclusion: the weed optimization algorithm outperforms the particle swarm optimization algorithm in predicting auditors' credibility. based on the findings of this study, stakeholders in the auditing profession, especially the iranian institute of certified accountants responsible for assessing auditors' credibility, should pay attention to the identified factors that experts and professionals believe to have an impact on evaluating auditors' credibility.
Автоматическая аннотация:
Findings: The results showed that the components of reviewing independence, acceptance or continuance of engagement; correspondence files; permanent files; understanding the entity under audit and its environment including internal controls; substantive tests; planning, control, and supervision of work; checklists and reports; how the second manager performs duties; overall evaluation of audit files; overall status of the audit firm; status of the audit firm's human resources; functions of the audit firm; compliance with accounting profession laws and regulations; and the apparent status of the audit firm affect the assessment of auditor credibility.
Therefore, the research hypothesis based on "comparing the ability of two algorithms, Weed Optimization and Particle Swarm Optimization, to predict auditors' credibility assessment using artificial neural networks, emphasizing the components of review of independence, acceptance or continuance of work; correspondence file; permanent file; understanding the entity being audited and its environment including internal controls; substantive tests; planning, control and supervision of work; checklists and reports; how the second manager performs duties; overall evaluation of audit files; overall status of the audit firm; status of the audit firm's human resources; functions of the audit firm; compliance with auditing profession laws and regulations and the apparent status of the audit firm" is confirmed.
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