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All Journal Jurnal Ekonomi, Akuntasi dan Manajemen Indonesia(JEAMI) Indonesian Journal of Accounting and Governance
Lidya Christine Wattileo
Universitas Agung Podomoro
Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Environmental Science Law, Crime, Criminology & Criminal Justice Social Sciences

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Book-Tax Differences as an Indicator of Earnings Quality: A Study of Tax Accounting Literature Ratna Dina; Sri Handayani; Lidya Christine Wattileo
Jurnal Ekonomi, Akuntasi dan manajemen Indonesia (JEAMI) Vol. 4 No. 02 (2026): Jurnal Ekonomi, Akuntasi dan Manajemen Indonesia (JEAMI) 2026
Publisher : SEAN Institute

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Abstract

This study aims to systematically examine the role of Tax-Book Differences (BTD) as an indicator of earnings quality and a detection tool for earnings manipulation practices from a tax accounting perspective. This research uses a Systematic Literature Review (SLR) approach with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines on scientific articles of national and international repute published in the period 2015-2024. From a systematic selection process, 12 key articles were thematically synthesised. The results show that BTD not only reflects technical differences between accounting standards and tax regulations, but also contains information about management's discretionary and opportunistic behaviour. BTD, especially those that are abnormal in nature, are shown to be associated with reduced earnings quality characterised by low earnings persistence and increased information uncertainty. The decline in earnings quality further increases the tendency of earnings manipulation practices, both through discretionary accounting policies and real activity manipulation. This study develops a conceptual framework that integrates the direct and indirect relationships between BTD, earnings quality, and earnings manipulation, and confirms the role of earnings quality as the main transmission mechanism. The research findings provide conceptual contributions to the development of tax accounting literature and practical implications for regulators in utilising BTD as an indicator of risk-based supervision, particularly in the Indonesian context.
The Impact of AI-Driven Performance Systems on Employee Performance and Turnover: The Moderating Role of Organizational Culture Lidya Christine Wattileo; Dheny Biantara
Indonesian Journal of Accounting and Governance Vol. 10 No. 1 (2026): JUNE
Publisher : School of Accountancy, University of Agung Podomoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36766/8qwfj772

Abstract

This study investigates the impact of AI-driven performance management systems on employee performance and turnover intention, and examines the moderating role of organizational culture. As organizations increasingly adopt AI and algorithmic management for performance evaluation, understanding employee reactions is critical. This research addresses how these systems influence employee performance and their intention to leave and whether organizational culture can mitigate or amplify these effects. An AI-driven performance system is an artificial intelligence – powered performance measurement system. Although AI is used for decision-making within the system, it remains under human oversight. Employing a quantitative, cross-sectional survey design, data were collected from 200 employees with experience in using AI-driven performance systems. The data were analyzed using Structural Equation Modeling (SEM) to test the proposed hypotheses. The findings revealed that AI-driven performance systems have a significant direct impact on both employee performance and turnover intention. Furthermore, this study confirms the significant moderating role of organizational culture. A supportive organizational culture was found to enhance the positive effects of the AI system on performance, while buffering the negative impact on turnover intention. In conclusion, the effectiveness and acceptance of AI-driven performance systems are not absolute and are significantly influenced by prevailing organizational culture. This suggests that, for the successful implementation of new technologies, organizations must cultivate a culture of trust and transparency to maximize benefits and minimize negative employee outcomes. This study extends the application of Social Exchange Theory to the context of AI in the workplace, providing valuable insights for both theory and practice.
Co-Authors Dheny Biantara Ratna Dina Sri Handayani