AI adoption for procurement transparency in Nigeria's public construction projects

Abstract

The research objective is to explore the barriers and opportunities for artificial intelligence (AI) adoption and examine how these factors interact to improve transparency in procurement processes. A qualitative research design was employed, using 25 semi-structured interviews with procurement officers, engineers, contractors, and policy stakeholders involved in public construction projects. The study finds that AI has significant potential to improve procurement transparency through fraud detection, real-time monitoring, and data analytics. However, its effectiveness is contingent on the alignment of technological readiness (clean data, digital infrastructure, user-friendly systems), organisational capacity (skills, leadership, coordination), and institutional support (clear regulations, enforcement, trust). Key barriers include fragmented digital infrastructure, limited technical skills, leadership commitment, bureaucratic rigidity, and informal practices that undermine accountability. This study integrates the technology-organisation-environment framework with institutional theory to develop a conditional model of AI-enabled transparency in public procurement. The findings advance scholarship by demonstrating that AI adoption is a socio-technical process, where technology alone cannot deliver transparency without supportive organisational and institutional conditions. The proposed framework provides practical guidance for policymakers, public agencies, and technology developers in emerging economies.