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All Journal JABE (Journal of Accounting and Business Education) Journal of Accounting Science
Essia Ries Ahmed
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Economics, Econometrics & Finance Education

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How Significantly to Emerging Economies Benefit From Board Attributes and Risk Management in Enhancing Firm Profitability? Seberapa Signifikan Keuntungan Ekonomi Berkembang Dari Atribut Dewan dan Manajemen Risiko dalam Meningkatkan Profitabilitas Perusahaan? Tariq Tawfeeq Yousif Alabdullah; Essia Ries Ahmed; Mohammed Almashhadani; Sara Kadhim Yousif; Hasan Ahmed Almashhadani; Raghad Almashhadani; Eskasari Putri
Journal of Accounting Science Vol. 5 No. 2 (2021): July
Publisher : Universitas Muhammadiyah Sidoarjo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21070/jas.v5i2.1530

Abstract

Recently, the literature review represented by its previous studies have witnessed obvious development that has been become the reason to create different trends. This paper aims to considerably contribute to the area of corporate governance to be then involved in the new trends testing the role of board attributes as mechanisms of corporate governance to know whether non-financial companies in the developing economies will benefit from these mechanisms in their impact of firm profitability. Thus, the present study tested 100 non-financial companies based on their annual reports in the year of 2020 as a cross sectional study. The results of testing the variables of the current study revealed that there is a negative link between board of directors size and profitability. On the other hand, the results showed that the managers independency has no relationship with profitability. Likewise, the results revealed that risk management has no effect on profitability. This study probably could be considered as a unique study due to its new contribution that fills the gap of what have been done in the previous studies in the area of corporate governance (CG) and profitability because it tested the link between risk management and growth. Hence, according to the researchers’ knowledge, there is no research that has been dealt with the two variables that were dealt by the current study. The current study introduces evidence to many parties, such as shareholders, scholar, executives and policy makers.
A Qualitative Exploration of Auditor Trust in Artificial Intelligence Audit Systems Lutfi Ardhani; Tariq Tawfeeq Yousif Alabdullah; Essia Ries Ahmed
JABE (JOURNAL OF ACCOUNTING AND BUSINESS EDUCATION) Volume 10, Issue 3, March 2026
Publisher : Universitas Negeri Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.17977/jabe.v10i3.65156

Abstract

This study examined auditors' trust in AI-based audit tools that function as "black box" systems in various Public Accounting Firms (KAPs) in East Java, Indonesia. Based on theories of trust in technology and technology adoption, this study proposes that auditor trust is shaped by cognitive trust in reliability and explainability, social norms within the audit team, and the organizational structure that guides AI implementation. A qualitative approach using the Interpretative Phenomenological Analysis (IPA) methodology was chosen, using in-depth interviews with auditors from small, medium, and internationally affiliated firms to investigate how they interpret, negotiate, and even reject AI output recommendations in their audit practices. The findings indicate that auditors often place conditional trust in AI output. They position AI tools as mere tools that support traditional audit procedures and professional judgment. The higher the auditor's level, the lower the level of trust in AI. AI adoption by public accounting firms is influenced by their business models, strategic risks, and reputations. This study also reveals infrastructure gaps, differences in AI access capabilities, and a strong relational culture in AI integration. This study advances the literature on AI in auditing by presenting an empirically grounded conceptual model of the development of auditor trust in AI-based audit tools operating in a "black box.".
Co-Authors Hasan Ahmed Almashhadani Lutfi Ardhani Mohammed Almashhadani Putri, Eskasari Raghad Almashhadani Sara Kadhim Yousif Tariq Tawfeeq Yousif Alabdullah