Digital leadership, not just e‑procurement software, drives better public purchasing: a 43‑study review shows leadership capabilities translate digital systems into improved transparency, data‑driven decisions, coordination and risk governance that raise procurement quality.

Main Finding

Digital leadership — beyond mere deployment of e-procurement technology — improves public procurement quality through five governance mechanisms (digital literacy/strategy, data-driven decisions, transparency/auditability, collaborative coordination, and value-for-money risk governance). The study synthesizes 43 studies into a unified conceptual framework showing how leadership capabilities translate digital systems into better procurement outcomes.

Key Points

• Study type: Systematic Literature Review (PRISMA 2020) of peer‑reviewed articles and selected conference proceedings (2015–2025).
• Evidence base: 43 studies from Scopus, ScienceDirect, Emerald Insight, and Google Scholar were included after rigorous screening and thematic synthesis.
• Five core governance mechanisms identified:
• Strengthening digital literacy and strategic direction — leaders define digital procurement strategy and build staff capabilities to use platforms effectively.
• Enabling data‑driven procurement decisions — leaders institutionalize data collection, analytics, and metrics for supplier selection and contract management.
• Enhancing transparency and auditability — digital documentation and governance practices enable traceability, reduce corruption, and support audits.
• Fostering collaborative coordination among procurement actors — leadership enables coordination across Procurement Working Units, Selection Working Groups, and other stakeholders to reduce fragmentation.
• Supporting value‑for‑money risk governance — leadership integrates risk assessment, supplier performance monitoring, and accountability into digital workflows.
• Target actors: Procurement Working Unit, Selection Working Groups, procurement officials operating in regulated digital systems.
• Novelty: Integrates digital leadership and procurement‑quality governance into a single conceptual framework, emphasizing leadership capacities beyond technical skill.
• Practical recommendations: institutionalize digital leadership development programs, provide data‑driven training, and mandate accountable digital documentation.
• Theoretical contribution: a framework suitable for empirical testing and informing capacity‑building beyond technology adoption.

Data & Methods

• Methodology: Systematic Literature Review following PRISMA 2020.
• Sources searched: Scopus, ScienceDirect, Emerald Insight, Google Scholar.
• Time window: 2015–2025.
• Inclusion: Peer‑reviewed journal articles and selected conference proceedings addressing digital leadership and public procurement quality.
• Screening and synthesis: Rigorous screening steps (identification, eligibility, inclusion) resulted in 43 studies; thematic synthesis used to extract governance mechanisms and build the conceptual framework.
• Output: A conceptual (theory) framework linking leadership mechanisms to procurement outcomes; no primary empirical testing conducted in this review.

Implications for AI Economics

• Procurement of AI systems depends critically on digital leadership, not just on procurement platforms. Leaders must combine technological competence with governance to procure AI responsibly and cost‑effectively.
• Data‑driven procurement practices enable better evaluation of AI vendors (performance, bias, reliability) and support ex post monitoring — improving value‑for‑money and reducing vendor lock‑in risk.
• Transparency and auditability are central for algorithmic accountability: digital documentation and audit trails allow economic researchers and regulators to assess procurement choices, contract terms, and downstream impacts of procured AI.
• Collaborative coordination reduces information asymmetries and can increase competition in AI tenders — potentially lowering prices and improving product fit. Leadership that coordinates across units can avoid fragmented, small tenders that favor incumbents.
• Risk governance mechanisms highlighted (e.g., supplier performance monitoring, risk assessment embedded in digital workflows) are directly relevant for managing AI‑specific risks such as model bias, privacy breaches, and systemic vendor failure.
• Market design and competition effects: well‑governed e‑procurement with capable leadership may increase market entry by clarifying requirements and providing standardized procurement data, enabling better matching between public demand and AI suppliers.
• Measurement and empirical research opportunities for AI economists:
  • Outcome metrics to track: procurement price, quality scores, time‑to‑award, number of bidders, vendor concentration, incidence of contract failures, audit findings, and realized service performance.
  • Research designs: difference‑in‑differences around leadership training rollouts, RCTs of decision‑support tools or documentation standards, and instrumental variables exploiting staggered adoption of digital leadership programs.
  • Use procurement audit logs and e‑procurement datasets (when available) to study causal effects of leadership interventions on procurement outcomes for AI and other digital goods.
• Policy relevance: investment in leadership capacity (training, institutionalized decision protocols, mandatory audit trails) should accompany procurement of AI to ensure efficiency, competition, accountability, and mitigation of algorithmic harms.
• Caveat: The review synthesizes literature and proposes a conceptual framework; empirical validation is needed to quantify magnitudes of effects in AI procurement contexts.