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All Journal International Journal of Educational Review, Law And Social Sciences (IJERLAS) Journal of Governance Risk Management Compliance and Sustainability
Adedeji Daniel Gbadebo
Walter Sisulu University
Religion Humanities Environmental Science Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Social Sciences Other

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HARNESSING ARTIFICIAL INTELLIGENCE FOR GOVERNANCE EFFICIENCY IN FCT, ABUJA Adedeji Daniel Gbadebo
International Journal of Educational Review, Law And Social Sciences (IJERLAS) Vol. 5 No. 4 (2025)
Publisher : CV. RADJA PUBLIKA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54443/ijerlas.v5i4.2701

Abstract

The study investigates the use of artificial intelligence (AI) to improve government efficiency in the Federal Capital Territory (FCT), Abuja. The study, based on Socio-Technical Systems (STS) Theory, investigates the interaction between technical breakthroughs and social systems in governance. The study used a survey method where a structured questionnaire was given to 385 respondents by stratified random sampling, with data analyzed using the chi-square statistical method. The findings show that AI greatly enhances decision-making and service delivery, with 71% of participants believing in its revolutionary potential. Inadequate infrastructure and a lack of technical skills are among the key concerns cited. The study emphasizes the necessity for strong digital frameworks, capacity building, and ethical concerns in AI integration. Infrastructure improvements, focused AI training for public authorities, and small-scale pilot projects in healthcare, transportation, and urban management are among the recommendations made to foster citizen trust and inclusion. The findings provide policymakers with concrete insights into using AI as a catalyst for successful governance in Abuja, promoting transparency, responsiveness, and economic growth.
The Estimation and Power of Alternative Discretionary Accruals Models Adedeji Daniel Gbadebo; Ahmed Oluwatobi Adekunle; Joseph Olorunfemi Akande
Journal of Governance Risk Management Compliance and Sustainability Vol. 3 No. 1 (2023): April Volume
Publisher : Center for Risk Management & Sustainability and RSF Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31098/jgrcs.v3i1.1197

Abstract

Discretionary accruals remain decade’s long measures to detect earnings management in empirical accounting research. The correctness of the specifications and test power of the information content for the models remains unexplored based on samples of most emerging market firms. Yet, country’s-based researchers have increasingly used different Jones-based discretionary accruals to proxy earnings management. The paper aims to evaluate four discretionary accruals models and to decide the most appropriate one for the detection of earnings management. For the aim, we apply regression methods to estimate and evaluate four Jones-type discretionary accruals models – simple Jones, modified Jones, extended Jones cash flow model and working capital accruals – based on evidence of a final sample of 1,852 firm-year of 102 firms in Nigeria during 2001–2020. The results disclose that all models are well-specified such that the likelihood of Type I errors is minimum and below the significance level of 5%. In order to demonstrate the power of the test, the simulations completed identify that the modified Jones model exhibits the highest power capability. The implication of this finding is that the modified Jones model is the most appropriate model to detect earnings management based on the Nigerian sample.
Co-Authors Ahmed Oluwatobi Adekunle Joseph Olorunfemi Akande