Aug 2026· WORLD JOURNAL OF INNOVATION AND MODERN TECHNOLOGY· pp. 280· 0 citations
TL;DR
The study concludes that AI capabilities constitute valuable strategic resources that enhance competitiveness, adaptability, and long-term organizational success and recommends increased investment in AI infrastructure, employee digital skills development, and data analytics systems to improve strategic outcomes in the Nigerian manufacturing sector.
Abstract
This study examined the effect of artificial intelligence (AI) capabilities on the strategic
performance of manufacturing firms in Nigeria. The study was motivated by the increasing
importance of AI technologies in enhancing organizational competitiveness, operational
efficiency, and decision-making. Specifically, the study investigated the influence of machine
learning capability, predictive analytics capability, intelligent automation capability, and data
management capability on strategic performance. The study was anchored on the Resource-Based
View (RBV) theory and adopted a survey research design. Data were collected from managers
and supervisors of selected manufacturing firms in Nigeria using a structured questionnaire.
Descriptive statistics, correlation, and multiple regression analyses were employed for data
analysis. The findings revealed that machine learning capability, predictive analytics capability,
intelligent automation capability, and data management capability significantly and positively
influence the strategic performance of manufacturing firms. The study concludes that AI
capabilities constitute valuable strategic resources that enhance competitiveness, adaptability,
and long-term organizational success. The study recommends increased investment in AI
infrastructure, employee digital skills development, and data analytics systems to improve
strategic outcomes in the Nigerian manufacturing sector.
This study examined the interrelationship among Artificial Intelligence (AI), strategic
management practices, cost reduction, and business improvement within the Nigerian oil and
gas industry, specifically concentrating on firms in Rivers State. Grounded in the Resource
Based View (Barney, 1991) and Dynamic Capabilities Theory (Teece et al., 1997), the research
explored how AI-driven tools, such as predictive maintenance systems, machine learning
algorithms, and intelligent supply chain analytics, combined with effective strategic
management practices, facilitate organizational attainment of operational efficiency, cost
reduction, and sustained competitive advantage amidst a volatile business environment. A
cross-sectional survey design was employed. Data were collected from 156 respondents across
fourteen selected oil and gas companies in Rivers State through the administration of a
structured questionnaire. Spearman’s rank correlation analysis was utilized to test four null
hypotheses at a 95% confidence interval. The results revealed significant positive relationships
between AI and cost reduction (ρ = .834, p = .000), AI and business improvement (ρ = .791, p
= .000), strategic management practices and cost reduction (ρ = .763, p = .000), and strategic
management practices and business improvement (ρ = .812, p = .000). All four null hypotheses
were rejected. The study concludes that both AI adoption and strategic management
sophistication are critical determinants of organizational efficiency and performance
improvement for oil and gas firms in Rivers State, and recommends that firms invest
strategically in AI infrastructure, governance, talent development, and evidence-based
management systems to achieve sustainable competitive advantage in an increasingly
digitalized energy sector.
Ernest Ifeanyi Eboigbe· INTERNATIONAL JOURNAL OF SOC...· 0 citations
This study investigated the impact of Artificial Intelligence (AI) on the performance of business in the Nigerian banks. The study was inspired by the proliferation of AI technologies among organizations that have identified enhancing their operational efficiency, decision-making, innovation, and customer service delivery as a key priority. In particular, the study explored how far AI is impacting business performance by integrating cutting-edge analytics, machine learning, automation, and data-driven processes. The research design used was quantitative and the data collected was structured questionnaires and a total of 305 employees were used. Data were analyzed using descriptive statistics (frequencies, percentages, mean scores) and regression analysis was used to check the correlation between adoption of AI and business performance. The results showed that the adoption of AI in business workflows can have a major impact on business efficiency, employee productivity, service quality, and overall performance. The study found that AI is a strategic technological tool that improves the functioning of organizations and their competitiveness. It suggests that organisations invest in AI-powered systems, increase the digital skills of their workforce, and create supportive policies to enable efficient adoption of AI technologies for long-term business growth and performance.
Amaihian Augusta Bosede, Harry Lydia Ineba Decster, O. Oluwasanmi et al.· Global Journal of Artificial...· 0 citations
The results indicate that SMEs with stronger AI-driven capabilities and entrepreneurial competencies are more likely to develop higher strategic intelligence, which in turn enhances overall organizational performance.
Shrooq A. Alsenan, W. Al-rahmi, I. Alyoussef et al.· Frontiers in Artificial Inte...· 0 citations
The study concludes that AI-driven analytics significantly enhances organizational performance through improved predictive analytics, decision automation, and data-driven strategic planning to maximize organizational benefits from AI technologies.
O. Enyinnaya, O. Onwuegbule, K. M. Amasiatu et al.· British journal of managemen...· 0 citations
The study concludes that AI adoption serves as a strategic organizational capability that significantly enhances strategic planning effectiveness and suggests that organizations leveraging AI technologies are more likely to develop effective strategies, improve decision quality, enhance forecasting accuracy, and strengthen organizational adaptability.
Mark Ian C. Abrias, Nerissa M. Revilla· World Journal of Advanced Re...· 0 citations
Artificial intelligence (AI) is used in logistics, but the mechanisms linking AI utilization to firm performance remain insufficiently differentiated. Drawing on the information technology business value perspective and dynamic capabilities theory, this study examines whether managers’ perceptions of logistics-oriented AI utilization are associated with perceived firm performance through innovation capability and logistics efficiency, with managerial support treated as a secondary boundary condition. Cross-sectional survey data from 254 middle- and senior-level managers in Chinese logistics firms were analyzed using IBM SPSS Statistics 27 and IBM SPSS Amos 29 (IBM Corp., Armonk, NY, USA), and the PROCESS macro version 4.2 (Andrew F. Hayes, Calgary, AB, Canada), with Model 83 and 5000 bootstrap samples. Perceived AI utilization was positively associated with innovation capability, logistics efficiency, and perceived firm performance. Both mediators showed significant indirect effects, and their sequential indirect effect was also significant. The two individual indirect effects did not differ significantly, but both exceeded the sequential indirect effect. The proposed sequential, reverse-sequence, and parallel-mediation models produced identical fit indices, whereas the restricted direct-effects model showed weaker fit. Neither the AI utilization–managerial support interaction nor the moderated mediation indices was significant. Exploratory item-level analyses showed differentiated associations for demand forecasting and order allocation and for AI infrastructure; the pattern remained stable among 194 respondents involved in AI- or digital transformation-related activities. Innovation capability and logistics efficiency appear to function as complementary mechanisms, with a smaller capability-to-process pathway. Their relative ordering cannot be determined from the cross-sectional data. As the data are self-reported, the findings represent associations among managerial perceptions rather than objective causal effects. Sustainability implications are limited to operational efficiency because environmental outcomes were not directly measured.