Indonesia Accounting Research Journal

Financial performance of islamic commercial banks: Integrating liquidity, profitability, and Maqashid al-Shariah framework

Muhammad Ryan Fahlevi — 2026-06-30

This study examines whether improvements in the financial performance of Islamic Commercial Banks are accompanied by progress in achieving maqashid al-shariah objectives. Previous studies have generally assessed Islamic banking performance using either conventional financial indicators or maqashid-based measures separately, with limited evidence on their relationship at the industry level. This study addresses this gap by integrating financial and maqashid performance within a single analytical framework. A descriptive quantitative approach was employed using secondary data from Islamic Banking Statistics published by the Financial Services Authority (OJK) for the period 2021–2025. Financial performance was evaluated using the Financing to Deposit Ratio (FDR) and Return on Assets (ROA), while maqashid performance was measured using the Maqashid Sharia Index (MSI). The findings indicate that FDR increased from 70.12% to 84.62% and ROA improved from 1.55% to 2.03%, reflecting stronger financial intermediation and operational efficiency. However, MSI fluctuated throughout the observation period, suggesting that financial improvement does not necessarily translate into proportional progress in maqashid achievement. Among the maqashid dimensions, Promoting Welfare showed the strongest performance, whereas Educating the Individuals and Establishing Justice remained relatively weaker. These findings highlight that financial and maqashid performance represent complementary but distinct dimensions of Islamic banking evaluation.

Determinants of earnings persistence in Indonesian food and beverage firms

Juli Ismanto — 2026-06-30

There has been a great deal of mixed empirical evidence on the determinants of earnings persistence, largely because previous research has looked at individual variables in isolation rather than within a comprehensive framework. Also, there is no evidence from non-cyclical industries in the wake of the post-pandemic recovery. This research aims to examine the joint effect of operating cash flow, tax planning strategies and debt levels on earnings persistence of Indonesian food and beverage companies listed on the Indonesia Stock Exchange in the period 2020-2024. This study adopted a quantitative approach using secondary data from 21 firms, providing 105 firm-year observations. Panel data regression analysis with the fixed-effects model was employed to test the hypotheses in the study. The results show that cash flow from operation has positive effect on earnings persistence (β = 0.254, p = 0.015) and tax planning has the most positive effect (β = 0.500, p = 0.014). Conversely, debt levels do not have a statistically significant impact (β = 0.002, p = 0.988) and the model explains 71.5% of the variance in earnings persistence (adjusted R² = 0.715). This study provides a wide analytical framework that shows that operational efficiency and fiscal prudence are more important drivers of earnings quality in industries with stable demand than decisions about capital structure. The results challenge the generalisability of the debt monitoring hypothesis and point to operational transparency as an alternative to debt-based governance mechanisms in sectors that are insulated from the economic cycle.

Moderating effect job satisfaction: Investigation transformational leadership on turnover intention

Didit Haryadi — 2026-06-30

The identified gaps arose because previous research showed inconsistent results and rarely included job satisfaction as a moderator in the model of the influence of transformational leadership on turnover intentions. This study seeks to examine the influence of transformative leadership on employee turnover intentions, including variations in work satisfaction. This study employs quantitative methodologies pertinent to the objective of 103 employees at PT BFI Sector Tangerang Financing. The sample was meticulously gathered, comprising a total of 103 respondents. The data gathering was a questionnaire survey employing a point scale from 1 (strongly disagree) to 10 (strongly agree). The data analysis employed the SEM-PLS methodology, integrating external models, internal models, and bootstrap testing to validate three hypotheses. The results indicate that transformative leadership exerts a significant negative influence on turnover intention (b-0.353 sig 0.013). Moreover, job satisfaction exerts a substantial negative influence on turnover intention (b-0.341 sig 0.001). Moreover, work satisfaction serves as a moderated in the association between transformative leadership and turnover intention (b 0.369 sig 0.000). This study underscores the significance of management approaches that boost employee well-being and retention rates by improving leadership quality and fostering a supportive work environment. This research seeks to offer valuable insights for human resource management by integrating leadership components and work satisfaction to develop effective employee retention strategies. The moderating role of job satisfaction as a strengthening factor increases the influence of transformational leadership on turnover intentions, thus clarifying the mechanism of its influence in the organizational context.

AI Adoption and accounting efficiency in Indonesia and Malaysia

Rima Sundari — 2026-06-30

This study extends the artificial intelligence (AI) accounting literature by examining how AI adoption translates into measurable accounting efficiency rather than focusing solely on technology adoption intention. It also reconceptualizes organizational readiness as a moderating mechanism influencing post-adoption performance. A quantitative research design was employed using survey data collected from 500 accounting professionals in Indonesia and Malaysia. The data were analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM), including moderation and multi-group analyses. The findings indicate that AI adoption significantly improves accounting efficiency, with employee competence emerging as the strongest predictor, followed by data quality. Organizational readiness significantly strengthens the positive relationship between AI adoption and accounting efficiency, demonstrating that infrastructure, leadership commitment, and innovation-oriented culture are essential for maximizing AI benefits. The structural model explains 37.2% of the variance in accounting efficiency (R² = 0.372), while multi-group analysis reveals no significant structural differences between Indonesia and Malaysia. By integrating the Technology Acceptance Model, Resource-Based View, and Contingency Theory into a unified framework, this study advances a capability-performance perspective of AI-enabled accounting transformation. The findings suggest that sustainable accounting digitalization requires not only AI investment but also workforce upskilling, robust data governance, and strong organizational readiness.

Automated Z-Score method for fraud detection in rural credit banks

Nano Suyatna — 2026-06-30

This research proposes a new transaction monitoring framework using AIS, integrating Z-Score and Interquartile Range methods into a real-time system for fraud detection and early warning in Rural Credit Banks. The previous studies on fraud detection have been mainly oriented towards analytical models and machine learning approaches. Very little attention has been paid to integrating lightweight statistical anomaly detection approaches in operational Accounting Information Systems. Unlike traditional AIS-based monitoring systems that rely on periodic audits and manual supervision, the proposed framework enables continuous statistical anomaly detection in a real-time AIS environment based on computationally efficient methods. The main contribution of this work is not the development of new statistical techniques, but the operational integration of statistical anomaly detection methods into an AIS-based monitoring environment. The framework was conceptually designed using an Agile Software Development approach to classify transactions as normal or suspicious based on Z-Score and IQR calculations. A preliminary empirical validation was conducted on a synthetic banking transaction dataset with 852 transactions. The framework also achieved detection accuracy of 98.00%, precision of 92.38%, recall of 100%, and F1-score of 96.04%. The findings indicate that the incorporation of statistical anomaly detection methodologies within AIS environments can improve internal control, boost the efficiency of transaction monitoring, and support the prevention of fraud in Rural Credit Banks and similar financial entities.