Aug 2026· Global Journal of Purchasing and Procurement Management· Vol 5, pp. 55-68· 0 citations· 20 references
TL;DR
The findings indicate that Artificial Intelligence-driven procurement systems significantly enhance procurement performance in public institutions by improving efficiency, transparency, supplier selection, decision-making, and cost management through automation and data-driven processes.
Abstract
Purpose: The aim of the study was to analyze the influence of artificial intelligence-driven procurement systems on procurement performance in public institutions
Methodology: This study adopted a desk methodology. A desk study research design is commonly known as secondary data collection. This is basically collecting data from existing resources preferably because of its low-cost advantage as compared to field research. Our current study looked into already published studies and reports as the data was easily accessed through online journals and libraries.
Findings: The findings indicate that Artificial Intelligence-driven procurement systems significantly enhance procurement performance in public institutions by improving efficiency, transparency, supplier selection, decision-making, and cost management through automation and data-driven processes. However, their successful implementation depends on organizational readiness, adequate digital infrastructure, skilled personnel, high-quality data, and supportive regulatory frameworks. The literature also reveals limited empirical evidence on the influence of AI-driven procurement systems in public institutions within developing countries, highlighting the need for further context-specific research.
Unique Contribution to Theory, Practice and Policy: The technology–organization–environment (TOE) framework, resource-based view (RBV) & dynamic capabilities theory may be used to anchor future studies on analyze the influence of artificial intelligence-driven procurement systems on procurement performance in public institutions. Procurement professionals should receive continuous training in artificial intelligence, data analytics, and digital procurement to strengthen their ability to utilize AI technologies effectively. Governments should formulate comprehensive national policies and regulatory frameworks that guide the adoption, governance, and ethical application of Artificial Intelligence within public procurement.
: Artificial Intelligence (AI) has increasingly emerged as an important technology for transforming public-sector operations and improving organizations' capacity to make timely, accurate, and evidence-based decisions. Despite its growing adoption, concerns about technological readiness, skills, data availability, automation, and effective integration continue to influence the extent to which public institutions realize the benefits of AI. This study examined the effect of Artificial Intelligence adoption on decision-making effectiveness in the public sector. The study was guided by the Technology Acceptance Model (TAM) and adopted a positivist research philosophy and a descriptive correlational research design. The target population comprised employees in ministries and extra-budgetary institutions in Kenya, estimated at 236,700 employees in 2024. Using Yamane's formula at a 5% level of precision, the study determined a sample size of 399 respondents. Of the 399 questionnaires considered issued, 327 were completed and returned, representing an illustrative response rate of 82.0%. Data were collected using a structured questionnaire and analyzed using descriptive statistics, Pearson correlation, and simple linear regression. Artificial Intelligence adoption was assessed using AI data analysis, predictive analytics, process automation, and AI decision-support systems, while decision-making effectiveness was assessed in terms of accuracy, timeliness, quality, and efficiency. Descriptive findings indicated a relatively high level of Artificial Intelligence adoption, with an overall mean of 3.79 and standard deviation of 1.16. AI-generated data analysis providing useful information for organizational decisions recorded the highest mean (4.22), while the use of AI to automate routine administrative tasks recorded the lowest mean (3.25). Inferential findings established a strong positive relationship between Artificial Intelligence adoption and decision-making effectiveness (r = 0.746, p < 0.01). Regression analysis indicated that Artificial Intelligence adoption explained 55.7% of the variation in decision-making effectiveness (R² = 0.557). The regression model was statistically significant, F (1, 325) = 408.65, p < 0.001. Further, Artificial Intelligence adoption had a positive and statistically significant effect on decision-making effectiveness (β = 0.746, t = 20.195, p < 0.001), leading to rejection of the null hypothesis. The study concluded that Artificial Intelligence adoption significantly enhanced decision-making effectiveness in the public sector. It recommended increased investment in AI technologies, digital infrastructure, employee training, and responsible AI governance, while maintaining appropriate human oversight. The study contributed empirical evidence on the importance of AI adoption in strengthening decision-making effectiveness within public-sector organizations.
Belvin Keitany, Stephene Oloo Magadi· International Journal of Sci...· 0 citations
The article examines the potential of artificial intelligence to optimize management processes and improve decision-making in large organizations and proposes a conceptual AI-based management framework that integrates data sources, analytical models, and decision support systems into a unified adaptive cycle with a feedback mechanism.
Serhii Kubitskyi, Yevhen Kozlovskyi, K. Balabukha et al.· Human Resources Management a...· 0 citations
Procurement processes at enterprise level require bona fide validation and risk evaluation for financial compliance, operational transparency, and streamlined approval workflows. In this study, ASAP (Agentic System for Automated Procurement), a decision support system powered by artificial intelligence has been designed to improve procurement approval processes in enterprise ERP spaces. The suggested Architecture gets the information of purchase requisition from SAP S/4HANA using secure OData APIs. It has supplier, quantity, pricing, and delivery schedule details. A deterministic rule-based AI engine analyzes the compliance policy of the organization, supplier indicators of reliability, budget utilization thresholds, and procurement anomalies to assess procurement risks. The system produces interpretable risk scores and generates explainable advisory recommendations that aid the procurement manager and finance controller in an informed approval decision. The tests showed that decisions are taken consistently, audits can be traced well and delays in procurement approval are minimized. The suggested ASAP framework performs transparent and scalable intelligent procurement risk assessment while maintaining the transactional integrity of ERP and adherence to the financial governance policies of the enterprise.
Arun Saravanan S, A. R. Kumar, V. J et al.· 2026 7th International Confe...· 0 citations
The findings show that dynamic capabilities – sensing, seizing and transforming – are distributed across levels and shaped by bottom-up and top-down mechanisms, which advances dynamic capabilities theory by demonstrating its multilevel nature in the public sector.
Mika Hanninen, M. Immonen, J. Hallikas· International Journal of Pub...· 0 citations
Artificial Intelligence (AI) is revolutionizing procurement and supply chain management through the use of predictive analytics, automation, and informed decision making. This research examines the impact of AI-based procurement optimization on strategic performance at Hydrogen Pharmaceutical PLC. A mixed methodological approach was employed, involving questionnaires to staff members in procurement, supply chain, finance, information technology, and management departments.
Out of 60 questionnaires issued, 45 valid responses were analyzed (75% response rate). Data analysis involved descriptive statistics, reliability test, correlation, and multiple regression analyses using SPSS. The findings reveal that AI implementation positively influences procurement efficiency, supplier performance, inventory management, and cost saving. Regression analysis suggests that AI accounts for 63% of the variance in strategic performance (R² = 0.63, p < 0.001).
The research suggests that AI is a strategic competency that positively influences procurement efficiency.
Keywords: Artificial Intelligence Procurement Optimization Strategic Performance Supply Chain Regression Analysis.
Tesfay Alemayeh· International Journal of Com...· 0 citations
The expansion of higher education has made university procurement systems increasingly complex, especially in the acquisition of high-value engineering equipment, digital infrastructure, and communication-related laboratory assets. Transparent and efficient p rocurement g overnance i s therefore important for supporting research platforms that rely on advanced sensing, network communication, and electromagnetic engineering equipment. This study develops a big data analytics solution to improve procurement decision efficiency and transparency in university procurement management. The proposed framework includes demand forecasting, intelligent supplier evaluation, anomaly detection and alerting, and end-to-end traceable oversight. A private Hyperledger Fabric blockchain with PBFT consensus was used to strengthen process traceability, while machine-learning models supported demand prediction and supplier risk evaluation. Empirical analysis across 20 representative universities and more than 15,000 procurement transactions shows that average procurement decision cycles were reduced by 50.8%, supplier evaluation accuracy reached 92.6%, the transparency score increased from 53.8 to 88.0, and anomaly detection achieved 90.9%. The results indicate that big-data-driven procurement systems can simultaneously improve efficiency, accountability, and institutional governance quality.
Zhou Li· Advanced Electromagnetics· 0 citations
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