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Adoption of Artificial Intelligence in Supply Chain Management: Barriers and Enablers in Developing Economies

Year: 2026

Type: article

Abstract: Artificial Intelligence (AI) has emerged as a transformative technology capable of enhancing efficiency, decision-making, and resilience in supply chain management. However, its adoption in developing economies remains constrained by several technological, organizational, and environmental challenges. This study examined the adoption of Artificial Intelligence in supply chain management, with emphasis on the barriers and enablers in developing economies, using Nigeria as the study context. A cross-sectional survey research design was adopted, and data were collected from 235 employees, managers, and supply chain professionals in selected manufacturing firms, logistics companies, retail organizations, and small and medium-sized enterprises (SMEs) in Nigeria using a structured questionnaire titled Artificial Intelligence Adoption in Supply Chain Management Index (AIASCMI). Data were analyzed using descriptive statistics and Pearson Product Moment Correlation. The findings revealed that identified barriers, including inadequate digital infrastructure, high implementation costs, shortage of skilled personnel, and organizational challenges, have a significant negative relationship with the adoption of Artificial Intelligence in supply chain management. The study also found that identified enablers, such as top management support, technological readiness, employee competence, and government support, significantly influence the successful adoption of Artificial Intelligence. The study concludes that overcoming existing barriers while strengthening organizational and technological capabilities is essential for the successful adoption of Artificial Intelligence in supply chain management. It is recommended that organizations invest in digital infrastructure, workforce development, and supportive management practices to maximize the benefits of Artificial Intelligence and improve supply chain performance.

Source: Zenodo (CERN European Organization for Nuclear Research)

DOI: 10.5281/zenodo.23855201

Author: DR SHAIBU, OGWUCHE GABRIEL1*, DR EMEM AKANINYENE SAMPSON2, . DR MBAEGBU R. E. V.3, V/REV DR CHIKEZIE IHEOMA4, OKEBARAM HUMPHREY CHIBO5, DR IBRAHIM MUSA6

Language: English

Topic: Impact of AI and Big Data on Business and Society

Subfield: Management Science and Operations Research

Field: Decision Sciences

Domain: Social Sciences

SDGs: Industry, innovation and infrastructure, Decent work and economic growth

Open Access status: green

OpenAlex ID: W7225058218

FWCI:

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