Strategic Sustainable Procurement Practices and Competitive Project Performance of Listed Construction Firms in Abuja (Published)
This study investigates the impact of Strategic Sustainable Procurement (SSP) practices on competitive project performance among listed construction firms in Abuja, Nigeria. Drawing on Institutional Theory, the research explores how environmental, social and economic dimensions influence project outcomes such as cost efficiency, quality delivery, and stakeholder satisfaction. Using a descriptive survey design, data were collected from 343 respondents across Julius Berger Nigeria Plc and Arbico Plc and analysed using SPSS regression techniques. Findings reveal a statistically significant positive relationship between all three SSP components and Timely Completion, with innovation and compliance exerting the strongest influence. Environmental and economic considerations also showed strong correlations with enhanced project outcomes, while social considerations had a moderate but meaningful effect. The study underscores that integrating SSP into procurement frameworks enables firms to meet sustainability goals, comply with regulations, and gain strategic advantages in Nigeria’s competitive construction industry. It recommends that construction firms institutionalize SSP practices through policy reform, stakeholder collaboration, and continuous capacity building. This research contributes to bridging the empirical gap on sustainable procurement in developing economies and provides actionable insights for both industry practitioners and policymakers committed to driving sustainable development through responsible construction practices.
Keywords: Cost Efficiency, quality delivery, stakeholder satisfaction, sustainability goals
AI-Driven Cloud Optimization for Cost Efficiency (Published)
AI-driven cloud optimization represents a transformative approach to addressing the significant challenges of cloud resource management and cost efficiency. As global cloud expenditure continues to grow at a rapid pace, organizations face increasing pressure to optimize their cloud investments while maintaining performance standards. This article examines how artificial intelligence technologies are revolutionizing cloud resource management through dynamic allocation, predictive analytics, and automated workload optimization. The integration of machine learning algorithms with cloud infrastructure enables unprecedented levels of accuracy in resource forecasting, automated scaling, and workload classification. These capabilities allow organizations to significantly reduce both over-provisioning and under-provisioning scenarios that plague traditional threshold-based management approaches. The economic benefits of these technologies are substantial and multifaceted, extending beyond direct cost reduction to include improved application performance, reduced downtime, and decreased operational overhead. As the complexity of cloud environments continues to increase, the strategic value of AI-driven optimization becomes increasingly apparent across diverse industry sectors, from financial services to healthcare and e-commerce.
Keywords: Artificial Intelligence, Cloud optimization, Cost Efficiency, Resource Allocation, predictive analytics