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All Journal Jurnal ASET (Akuntansi Riset) Dinasti International Journal of Digital Business Management Jurnal Pengabdian kepada Masyarakat Jurnal Akuntansi Bisnis Pelita Bangsa Journal Of World Science Jurnal Ilmu Manajemen Mulawarman (JIMM) JURNAL ABDIKARYASAKTI Business and Investment Review Research Horizon Baileo: Jurnal Sosial Humaniora Journal of Management, Economic, and Financial Equity
Nengzih Nengzih
Universitas Mercu Buana
Agriculture, Biological Sciences & Forestry Arts Humanities Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Engineering Environmental Science Health Professions Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Public Health Social Sciences Other

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The Effect of Ineffective Monitoring, Change in Director, Financial Stability, and Good Corporate Governance on Financial Statement Fraud Hayat, Hasbi Nur; Nengzih, Nengzih
Dinasti International Journal of Digital Business Management Vol. 6 No. 5 (2025): Dinasti International Journal of Digital Business Management (August - Septembe
Publisher : Dinasti Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.38035/dijdbm.v6i5.5548

Abstract

This study originated from the phenomenon of differences in interests between principals and agents.  Fraud occurs due to opportunities, justifications, and pressures, as indicated by the large number of financial statement manipulations that turn companies' losses into profits (window dressing). This study aims to analyze the influence of Ineffective Monitoring, Change in Directors, Financial Stability, and Good Corporate Governance on Financial Statement Fraud in State-Owned Enterprises listed in the period 2018-2023. The method used is a descriptive-verificative method with a quantitative approach, where data is obtained through the collection of secondary data in the form of company annual reports and analyzed using multiple linear regression with the help of the SPSS application. The research sample consists of 29 SOEs over 6 years of observation, resulting in a total of 174 panel data observations. The results of the study indicate that Ineffective Monitoring does not have a positive effect on Financial Statement Fraud. Change in Director has a positive effect on Financial Statement Fraud. Financial Stability does not have a negative effect on Financial Statement Fraud. Good Corporate Governance does not have a negative effect on Financial Statement Fraud. However, simultaneously, these four variables together have an effect on financial statement fraud
The Effect of Profitability, Corporate Social Responsibility Disclosure, and Capital Intensity on Tax Avoidance Edo, Rizki; Nengzih, Nengzih
Research Horizon Vol. 5 No. 5 (2025): Research Horizon - October 2025
Publisher : LifeSciFi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54518/rh.5.5.2025.807

Abstract

Tax avoidance remains a persistent issue in Indonesia’s manufacturing sector, which plays a major role in national GDP yet remains vulnerable to regulatory loopholes. This study aims to analyze the effects of profitability, capital intensity, and Corporate Social Responsibility (CSR) on tax avoidance, while examining firm size as a moderating variable. Using purposive sampling, the research selected 30 manufacturing firms listed on the Indonesia Stock Exchange (IDX) from 2019–2023, yielding 150 firm-year observations. Secondary data were obtained from annual reports, and analysis was conducted using panel data regression with the Fixed Effect Model (FEM) and Moderated Regression Analysis (MRA). The results reveal that profitability has a significant negative effect on tax avoidance, indicating that more profitable firms tend to comply with tax regulations to maintain legitimacy and reputation. In contrast, CSR and capital intensity show no significant influence, and firm size does not moderate any of these relationships. These findings suggest that financial performance plays a greater role than structural or disclosure factors in determining tax behavior. The study contributes to the literature by providing empirical evidence on the determinants of tax avoidance in Indonesia’s manufacturing sector and offers policy implications for tax authorities to strengthen oversight of low-profit firms vulnerable to aggressive tax planning.
Design of Using Activity Based Budgeting Methods for Controlling Employee Overtime Costs Novita Sari, Mega; Nengzih, Nengzih
Journal of World Science Vol. 2 No. 5 (2023): Journal of World Science
Publisher : Riviera Publishing

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58344/jws.v2i2.239

Abstract

This study aims to design and propose using overtime budgets in companies. This type of research is descriptive exploratory, and the research object is data related to employee overtime in July-December 2021. The financing method used is time-driven activity-based costing (TDABC). This study suggests that companies follow up on departments with over-capacity status to evaluate employee activities during overtime so that it does not affect company performance. The application of the activity-based budgeting method can increase the effectiveness of employee overtime costs because it can prove that there are savings in overtime costs and can increase profits. So that the ABB method can control the overtime budget more efficiently and better. The ABB method can control employee overtime budgets more efficiently. The ABB method can be proposed to companies to control their employees' overtime costs. This research has implications for identifying the need for overtime, purchasing efficient budget allocations, evaluating the impact on financial companies.
Artificial Intelligence and Sustainability Reporting: Performance Outcomes in ESG Investing Lin Oktris; Siti Fathimah Azzahra; Nengzih Nengzih; Nurhafifah Amalina; Maisarah Mohamed Saat
EQUITY Vol 28 No 2 (2025): EQUITY
Publisher : Department of Accounting, Faculty of Economics and Business, Universitas Pembangunan Nasional Veteran Jakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34209/equ.v28i2.13063

Abstract

The convergence of artificial intelligence and sustainable finance represents a fundamental transformation in investment decision-making, yet empirical evidence concerning effectiveness remains fragmented across diverse research domains. This study synthesises evidence from 43 peer-reviewed investigations spanning 2020 to 2024, examining artificial intelligence applications in environmental, social, and governance investing through systematic meta-analysis following PRISMA 2020 guidelines. Random-effects models demonstrate that artificial intelligence technologies significantly enhance risk-adjusted financial returns (standardised mean difference = 0.58; 95% confidence interval: 0.44-0.72; p<0.001), translating to approximately 5.2 per cent annual performance improvement, and environmental, social, and governance prediction accuracy (standardised mean difference = 0.53; 95% confidence interval: 0.38-0.68; p<0.001), representing 15 per cent error reduction compared with traditional methodologies. Ensemble machine learning demonstrates robust performance (standardised mean difference = 0.64; I²=45 per cent), whilst deep learning exhibits highest effects with substantial variability (standardised mean difference = 0.71; I²=68 per cent). Implementation success depends critically on data quality infrastructure (identified in 88 per cent of studies) and phased deployment strategies (effective in 64 per cent of cases). Moderate evidence certainty supports that artificial intelligence represents genuine capability advancement, though unexplained heterogeneity (I²=58-62 per cent) limits precise outcome prediction in specific contexts. Findings provide evidence-based guidance for investment managers adopting artificial intelligence technologies, policymakers developing regulatory frameworks, and researchers identifying future research priorities. Keywords: artificial intelligence; sustainable finance; ESG investing; meta-analysis; machine learning; investment decision-making; financial technology
Co-Authors Ahmad Bukhori Muslim Amalina, Nurhafifah Arlita, Dita Asteria Deden Tarmidi Drajat, Dimas Prawira Driastuti, Melani Edo, Rizki Hayat, Hasbi Nur Lin Oktris Maisarah Mohamed Saat Mayasari, Yetti Novawiguna Kemalasari Novita Sari, Mega Siti Fathimah Azzahra yola yuliani