Assessing Critical Success Factors for Implementing Artificial Intelligence (AI) in Public Procurement

@article{kasim2026assessing,
  author    = {Kasim, Sani Ibrahim and Ahmadu, Hassan Adaviriku and Muhammad, Saidu Minin and Sani, Amina Bello},
  title     = {{Assessing Critical Success Factors for Implementing Artificial Intelligence (AI) in Public Procurement}},
  journal   = {Journal of Agriculture and Food Science Applications},
  year      = {2026},
  volume    = {2},
  number    = {1},
  pages     = {1--6},
  doi       = {10.36108/cbpm/2026.0110},
  url       = {https://doi.org/10.36108/cbpm/2026.0110},
  issn      = {1234-5678},
  publisher = {International Academics Publishers (IAP)}
}

TY  - JOUR
AU  - Kasim, Sani Ibrahim
AU  - Ahmadu, Hassan Adaviriku
AU  - Muhammad, Saidu Minin
AU  - Sani, Amina Bello
TI  - Assessing Critical Success Factors for Implementing Artificial Intelligence (AI) in Public Procurement
JO  - Journal of Agriculture and Food Science Applications
JA  - JAFSA
PY  - 2026
DA  - 2026/10/17/
VL  - 2
IS  - 1
SP  - 1
EP  - 6
DO  - 10.36108/cbpm/2026.0110
UR  - https://doi.org/10.36108/cbpm/2026.0110
AB  - Artificial intelligence (AI) offers significant potential to enhance efficiency and accountability in public procurement, yet its implementation remains limited, particularly in developing economies. Drawing on Institutional Theory and the Technology-Organisation-Environment (TOE) framework, this study identifies and clusters critical success factors (CSFs) for AI implementation in Nigerian public procurement. A quantitative survey of 71 procurement professionals across federal Ministries, Departments, and Agencies (MDAs) assessed 38 CSFs derived from literature. Data were analysed using descriptive statistics and exploratory factor analysis (EFA) with principal component extraction and Varimax rotation. Results identified appropriate team skills and knowledge, adequate technical know-how in AI tools, and adequate knowledge of AI applications as the top three CSFs. EFA revealed a ten-component model explaining 76.13% of variance, comprising: internal process coordination, data infrastructure, strategic leadership, vendor partnership, early-stage planning, user competence, data governance, project clarity, executive support, and technical compatibility. All components demonstrated acceptable reliability (Cronbach's α = .623–.891). The study contributes an empirically derived ten-factor model to guide evidence-based policymaking for AI adoption in public procurement. Limitations include the exploratory nature of the findings and the Nigeria-specific context, requiring validation in other settings.
SN  - 1234-5678
PB  - International Academics Publishers (IAP)
ER  -