cover
Article Per Year (5 Year)
All
Home Page
OAI Link
Editorial Team
Contact
Reviewer
Google Scholar
Contact Name
Andre Suryaningprang
Contact Email
inabajournals@inaba.ac.id
Phone
+62895405780777
Journal Mail Official
inabajournals@inaba.ac.id
Editorial Address
Jl. Soekarno Hatta No. 448, Batununggal, Bandung Kidul, Kota Bandung, Jawa Barat. 40266
Location
Kota bandung,
Jawa barat
INDONESIA
Journal of Accounting Inaba
Published by Universitas Indonesia Membangun
ISSN : 28297040     EISSN : 28295404     DOI : https://doi.org/10.56956/jai.v2i02
Core Subject : Economy,
Economics, Econometrics & Finance
Journal of accounting Inaba (JAI) comprises various topics of Accounting, all of those areas include; Financial Accounting and Auditing Management and Cost Accounting Taxation Accounting Information System and Information Technology Sharia Accounting Public Sector and Government Accounting Investment, Capital Market, Banking Financial Technology, Accounting for Cryptocurrency, etc. However, this journal warmly welcomes other issues to broaden accounting science
Arjuna Subject : Bisnis, Menejemen, dan Akutansi (semua kategori) - Bisnis, Menejemen, dan Akutansi (Lain-Lain)
Articles 42 Documents
 1   2   3   4   5 
An Empirical Analysis of Remittances, Foreign Direct Investment and Sovereign Debt on Economic Growth in Nigeria Oyamendan, Anthony
Journal of Accounting Inaba Vol. 4 No. 2 (2025): Volume 4 Number 2, December 2025
Publisher : Universitas Indonesia Membangun

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56956/jai.v4i2.572

Abstract

This study investigated the effects of remittances (REM), foreign direct investment (FDI), and sovereign debt (DEBT) on economic development (GDP) in Nigeria from 1990 to 2023. The objectives are to examine the long- and short-run impacts of REM, FDI, and DEBT on GDP, determine the direction of causality among these variables, and provide policy recommendations for sustainable economic growth. Descriptive statistics show that remittances averaged 5.41% of GDP, FDI 2.76%, and sovereign debt 35.82%, highlighting varying contributions to economic performance. Stationarity tests confirm mixed integration, validating the use of the ARDL bounds testing approach. The ARDL cointegration test reveals a long-run equilibrium relationship among the variables. Long-run ARDL results indicate that a 1% increase in remittances and FDI leads to approximately 0.31% and 0.22% increases in GDP, respectively, while a 1% increase in sovereign debt reduces GDP by about 0.18%. Short-run Error Correction Model results show that remittances and FDI contribute 0.12% and 0.09% to GDP growth per 1% change, respectively, with an ECM coefficient of -0.641, suggesting 64% of disequilibrium is corrected annually. Granger causality analysis confirms unidirectional causality from REM and FDI to GDP. The study concludes that remittances and FDI are significant growth drivers, whereas excessive debt hampers long-term development. Policy recommendations include optimizing remittance utilization, attracting productive FDI, and ensuring prudent debt management.
The Effectiveness Of Artificial Intelligence Techniques As An Approach To Improving The Quality Of Financial Reporting In The Iraqi Banking Sector Al-Obaidi, Rafid K. Nsaif
Journal of Accounting Inaba Vol. 4 No. 2 (2025): Volume 4 Number 2, December 2025
Publisher : Universitas Indonesia Membangun

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56956/jai.v4i2.653

Abstract

This study aims to investigate the impact of artificial intelligence (AI) technologies on the quality of accounting data by evaluating improvements in accuracy, processing time, and reliability after implementing AI in accounting processes. Statistical analysis was conducted using a questionnaire distributed to 179 respondents in branches of private banks in Babylon Governorate. T-tests and analysis of variance (ANOVA) were performed to measure statistical differences between data before and after AI implementation. Multiple regression analysis was also used to examine the relationship between data quality and independent variables. The results showed a significant improvement in the quality of accounting data after AI implementation, with increased data accuracy and reliability, as well as a decrease in processing time. Regression analysis also demonstrated that processing time directly affects data reliability, with statistical significance. These results indicate that the application of AI in accounting effectively contributes to improving the quality of accounting data, reducing operational errors, and increasing financial efficiency. One of the most important findings of the research is the reduction in financial data processing time through the adoption of intelligent automation, which contributes to faster financial decision-making and enhanced regulatory compliance. The statistical model also indicates that there are other variables that may affect the quality of the data, such as the size of the data, the experience of the accountants, and the accounting regulations that govern the use of artificial intelligence.

Page 5 of 5 | Total Record : 42

 1   2   3   4   5 

Filter by Year

2022 2025


Reset
Filter By Issues
All Issue Vol. 4 No. 2 (2025): Volume 4 Number 2, December 2025 Vol. 4 No. 1 (2025): Volume 4 Number 1, June 2025 Vol. 3 No. 2 (2024): Volume 3 Number 2, December 2024 Vol. 3 No. 1 (2024): Volume 3 Number 1, June 2024 Vol. 2 No. 2 (2023): Volume 2 Number 2, December 2023 Vol. 2 No. 1 (2023): Volume 2 Number 1, June 2023 Vol. 1 No. 2 (2022): Volume 1 Number 2, December 2022 Vol. 1 No. 1 (2022): Volume 1 Number 1, June 2022