Odunukwe, Ifeoma, Evangeline and Prince Okoli, Peter, Eziokwubundu and Dioha, Ifeanyichukwu, Rosemary (2025) Application of Artificial Intelligence in Predictive Analytics Among Brewing Firms in Southeast, Nigeria. American Journal of Business Practice, 2 (5). pp. 120-131. ISSN 2997-934X

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Abstract

This study investigates the effect of artificial intelligence (AI) application on predictive analytics and operational performance among brewing firms in Southeast Nigeria. Specifically, it examines how the types of AI techniques used, extent of AI integration, specific AI tools or systems adopted, level of AI sophistication, and scope of AI deployment influence enhanced operational performance within these firms. A structured questionnaire was administered to 117 respondents across selected brewing companies, and data were analyzed using the Ordinary Least Squares (OLS) regression technique to ascertain the relationships among variables. Findings reveal that all independent variables significantly influence operational performance. The types of AI techniques used (β = 0.245, p < 0.01) positively influence performance, indicating that diverse AI methods improve operational outcomes. The extent of AI integration (β = 0.198, p < 0.05) also shows a significant positive relationship, suggesting that greater AI adoption correlates with better operational results. Specific AI tools or systems adopted (β = 0.312, p < 0.01) have a substantial positive effect, emphasizing the importance of deploying advanced AI systems. The level of AI sophistication (β = 0.227, p < 0.01) significantly enhances operational efficiency, and the scope of AI deployment (β = 0.183, p < 0.05) contributes positively to operational performance. The model explains approximately 62% of the variance in operational performance (R² = 0.62). Based on these findings, it is recommended that brewing firms in Southeast Nigeria invest in diverse and advanced AI techniques, expand AI integration across production processes, and adopt sophisticated AI tools to optimize operational efficiency. Policymakers and managers should prioritize capacity building in AI technologies to leverage predictive analytics for sustainable growth and competitive advantage in the brewing industry.

Item Type:	Article
Subjects:	L Education > L Education (General)
Divisions:	Postgraduate > Master's of Islamic Education
Depositing User:	Journal Editor
Date Deposited:	07 Jul 2025 12:05
Last Modified:	07 Jul 2025 12:05
URI:	http://eprints.umsida.ac.id/id/eprint/16272

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