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All Journal EL-MUHASABA JAKPP : Jurnal Analisis Kebijakan & Pelayanan Publik JPPUMA: Jurnal Ilmu Pemerintahan dan Sosial Politik Universitas Medan Area Ilomata International Journal of Tax and Accounting Jurnal Sains Teknologi Transportasi Maritim International Journal of Social Science and Religion (IJSSR) Journal of Financial and Behavioural Accounting The Indonesian Journal of Computer Science
Gbadebo, Adedeji Daniel
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Religion Humanities Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Electrical & Electronics Engineering Energy Engineering Environmental Science Law, Crime, Criminology & Criminal Justice Social Sciences Transportation

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Audit Attributes and Financial Reporting Quality in Nigeria: Evidence from Deposit Money Banks Ibrahim, Majeed Ajibola; Gbadebo, Adedeji Daniel
EL MUHASABA: Jurnal Akuntansi (e-Journal) Vol 17, No 1 (2026): EL MUHASABA
Publisher : Jurusan Akuntansi Fakultas Ekonomi Universitas Islam Negeri Maulana Malik Ibrahim Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18860/em.v17i1.33817

Abstract

Purpose: The issue of high-quality financial reporting is of concern to financial report users and the entire economy since it influences financial decisions. This paper examines the relationship between audit attributes and the financial reporting quality of deposit money banks (DMBs) in Nigeria. Method: The paper applied the Generalized Least Square (random effects) regression to analyze how audit fees, audit firm independence, auditor tenure, and other controlled variables affect the quality of financial reporting of DMBs during 2014–2022. Results: The findings reveal that the main variables—audit fees, auditor tenure, and audit firm independence: have positive and significant impacts on financial reporting quality. Specifically, a unit change in audit fees, auditor tenure, and audit firm independence increases earnings quality by 0.104, 0.081, and 0.223, respectively. When client asset size, audit firm type, and firm growth are controlled for, they also exert positive and significant effects on financial reporting quality. Implications: The findings have implications for DMBs, capital market stakeholders, and the broader economy. The study recommends measures to ensure enhanced financial reporting quality for Nigerian DMBs, including the need for management and regulatory bodies to place strong emphasis on the independence of audit firms in all facets of auditors’ work. Novelty: This study contributes to the financial reporting literature by empirically demonstrating how specific audit attributes improve financial reporting quality in Nigeria’s banking sector, offering evidence from a developing economy context that has been underexplored in prior research.
Gendered Pathways of Labour Integration: Migrant Workers in Urban Economic Zones in Ethiopia Gbadebo, Adedeji Daniel
International Journal of Social Science and Religion (IJSSR) 2026: Volume 7 Issue 1
Publisher : Indonesian Academy of Social and Religious Research (IASRR)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.53639/ijssr.v7i1.387

Abstract

This study investigates the labour market integration of migrant workers in Ethiopia’s urban economic zones, with a focus on how gender shapes employment outcomes, occupational mobility, and access to social protection. Drawing on neoclassical and structural migration theories, gendered migration frameworks, and labour market segmentation literature, the study examines the intersections of migration status, gender, and urban economic structures. Using evidence from industrial parks, special economic zones, and informal urban markets, the research highlights patterns of formal and informal employment, wage disparities, skill utilization, and occupational segregation. It further explores institutional, social, and structural barriers, including legal restrictions, discrimination, care responsibilities, housing precarity, and vulnerability to exploitation, demonstrating how these disproportionately affect women and other marginalized groups. The study concludes by proposing gender-responsive policies, inclusive urban planning, and skills recognition frameworks to enhance equitable labour integration.
Optimizing Berth Allocation at Lekki Deep Sea Port: A Predictive Model for Efficiency and Growth Godwin Nwachukwu Nkem; Gbadebo, Adedeji Daniel
Nama Jurnal Akmi Vol 7 No 2 (2025): Jurnal Sitektransmar November 2025
Publisher : LPPM AKMI SUAKA BAHARI CIREBON

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51578/j.sitektransmar.v7i2.112

Abstract

Abstract Seaports are essential for global trade, acting as vital hubs within vast freight transport networks. Efficient berth allocation is critical for smooth port operations, minimising vessel wait times, and optimising resource use written. Purpose – This study analyzed berth utilization, vessel service times, traffic seasonality, and revenue at Lekki Deep Seaport. Methodology –This study uses Python-based simulation and data visualisation to analyze berth allocation at Lekki Deep Sea Port, considering factors like vessel arrival rates (averaging one every 2.5 days), service times (1.5 to 2.5 days based on vessel size), berth utilisation under different traffic scenarios, revenue, idle costs, and congestion management via predictive modelling. Findings indicate that the current berth infrastructure is sufficient under present traffic conditions. Findings – Findings indicate that the current berth infrastructure is sufficient under present traffic conditions. However, to prepare for future challenges, proactive measures like optimizing service times and implementing machine learning models are recommended as traffic grows to maintain efficiency. This study offers valuable insights for optimizing port operations and ensuring Lekki Deep Sea Port’s continued contribution to West African economic growth. Originality – Simulation techniques replicate port operations, helping identify bottlenecks and test allocation scenarios
Predicting Container Delivery Dates Using Machine Learning Techniques: A Regression Approach Godwin Nwachukwu Nkem; Gbadebo, Adedeji Daniel
Nama Jurnal Akmi Vol 7 No 2 (2025): Jurnal Sitektransmar November 2025
Publisher : LPPM AKMI SUAKA BAHARI CIREBON

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51578/j.sitektransmar.v7i2.113

Abstract

Abstract Inland container delivery constitutes a critical component of the global maritime logistics chain, acting as the final phase that connects international ports to inland destinations. Accurate prediction of inland container delivery times is crucial for enhancing operational efficiency, minimizing demurrage and detention costs, and improving customer satisfaction across global supply chains. Purpose –. This study leverages historical container movement data across key international ports to develop a robust machine learning model for predicting inland container delivery timelines. Methodology –. Using a Random Forest Regressor, the model was trained to forecast the total inland delivery time based on features such as container size, type, shipping line, dispatch weekday, and temporal patterns. Findings – The findings have practical implications for shipping lines, freight forwarders, port authorities, and inland terminal operators seeking to optimize logistics planning, reduce uncertainty, and improve supply chain. Evaluation of the model's performance yielded a Mean Absolute Error of 4.59 days, a Root Mean Squared Error of 10.55 days, and a coefficient of determination of 0.68, indicating moderate predictive accuracy. Supporting visualizations - including learning curves, gain curves, feature importance plots, residual distributions, and prediction bands - illustrate the model's strengths and areas for further refinement. Originality – The study contributes to the growing field of intelligent logistics and maritime informatics by providing a data-driven framework for improving inland delivery predictability
Co-Authors Abdulrauf, Lukman Adebayo-Oke Dada, Olawale Bamidele Emmanuel Imuede Oyasor Godwin Nwachukwu Nkem Ibrahim, Majeed Ajibola