Expanding Kelman’s vision for digital transformation in public procurement

Public procurement management has experienced a significant digital transformation over recent decades. This study revisits Steven Kelman’s influential 1990s vision advocating flexible, trust-based procurement. Also, it explores how Artificial Intelligence (AI) and Big Data implement Kelman’s results-oriented framework. The review identifies three key changes: (1) evidence-based trust replacing procedural compliance through algorithmic transparency and real-time monitoring; (2) machine learning improving decentralized decision-making; and (3) efficiency shifting from cost minimization to dynamic optimization via predictive analytics. However, barriers such as algorithmic opacity, data security concerns, and organizational readiness remain. Findings highlight three critical paradoxes: AI enhances results focus while narrowing it to algorithmic metrics; blockchain transparency remains unrealized as machine learning produces “black box” decisions; and AI’s “decision support” role systematically erodes official discretion. The study concludes that AI transforms rather than strengthens Kelman’s humanistic values, requiring careful management of this value transformation and adaptive procurement models harmonizing AI with public values.