Influence of Artificial Intelligence-Driven Procurement Systems on Procurement Performance in Public Institutions in United States
DOI:
https://doi.org/10.47604/gjppm.3895Keywords:
Artificial Intelligence-Driven Procurement Systems, Procurement Performance, Public InstitutionsAbstract
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.
Downloads
References
Aboagye, M. O., Agyei, S. K., Amankwah-Amoah, J., & Asamany, P. A. (2022). Enhancing procurement quality performance in a developing country: The roles of procurement audit and top management commitment. Public Performance & Management Review, 46(2), 283–294. https://doi.org/10.1080/01900692.2022.2116046
Adu-Gyamfi, G., Ahenkan, A., & Agyeman, F. O. (2024). Electronic procurement adoption and procurement performance: Does institutional quality matter? Business Process Management Journal, 30(6), 1783–1807. https://doi.org/10.1108/BPMJ-02-2024-0106
Andersson, P. E. (2025). Assessing the value of artificial intelligence in public procurement. Journal of Public Procurement, 25(1), 120–139. https://doi.org/10.1108/JOPP-05-2024-0057
Bag, S., Dhamija, P., Bryde, D. J., & Singh, R. K. (2021). Effect of artificial intelligence on supply chain resilience and procurement performance. International Journal of Production Research, 59(24), 7305–7325. https://doi.org/10.1080/00207543.2020.1844346
Baldus, B. J., & Hatton, L. (2020). U.S. chief procurement officers' perspectives on public procurement. Journal of Purchasing and Supply Management, 26(1), 100538. https://doi.org/10.1016/j.pursup.2019.05.003
Barney, J. (1991). Firm resources and sustained competitive advantage. Journal of Management, 17(1), 99–120.
Baryannis, G., Dani, S., Antoniou, G., & Widdowson, D. (2019). Predictive analytics and artificial intelligence in supply chain risk management. Computers & Industrial Engineering, 137, 106024. https://doi.org/10.1016/j.cie.2019.106024
Boykina, E., Lofaro, R. J., McCue, C., & Prier, E. (2024). Advancing the practice of public procurement performance measurement: A framework for conceptualizing efficiency and effectiveness. Public Money & Management, 45(4), 349–359. https://doi.org/10.1080/09540962.2024.2361832
Guida, M., Caniato, F., Moretto, A., & Ronchi, S. (2023). The role of artificial intelligence in the procurement process: State of the art and research agenda. Journal of Purchasing and Supply Management, 29(2), 100823. https://doi.org/10.1016/j.pursup.2023.100823
Guida, M., Caniato, F., Moretto, A., & Ronchi, S. (2023). The role of artificial intelligence in the procurement process: State of the art and research agenda. Journal of Purchasing and Supply Management, 29(2), 100823. https://doi.org/10.1016/j.pursup.2023.100823
Hallikas, J., Immonen, M., & Brax, S. (2021). Digitalizing procurement: The impact of data analytics on supply chain performance. Supply Chain Management: An International Journal, 26(5), 629–646. https://doi.org/10.1108/SCM-05-2020-0201
Harju, A., Hallikas, J., Immonen, M., & Lintukangas, K. (2023). The impact of procurement digitalization on supply chain resilience: Empirical evidence from Finland. Supply Chain Management: An International Journal, 28(7), 62–76. https://doi.org/10.1108/SCM-08-2022-0312
Herold, S., Heller, J., Rozemeijer, F., & Mahr, D. (2023). Dynamic capabilities for digital procurement transformation: A systematic literature review. International Journal of Physical Distribution & Logistics Management, 53(4), 424–447. https://doi.org/10.1108/IJPDLM-12-2021-0535
Johnson, N., Silva, E., Leon, H., Eslami, M., Schwanke, B., Dotan, R., & Heidari, H. (2025). Legacy procurement practices shape how U.S. cities govern AI: Understanding government employees’ practices, challenges, and needs. In Proceedings of the 2025 ACM Conference on Fairness, Accountability, and Transparency. https://doi.org/10.1145/3715275.3732049
Kero, C. A., & Bogale, A. T. (2023). A systematic review of Resource-Based View and Dynamic Capabilities of firms and future research avenues. International Journal of Sustainable Development and Planning, 18(10), 3137–3154. https://doi.org/10.18280/ijsdp.181016
Mathu, M. M., & Kihara, A. (2020). Factors influencing effective public procurement performance: The Ministry of Devolution and Planning. Academic Journal of Social Sciences and Education, 8(3). (No DOI available.)
Min, H. (2023). Artificial intelligence in supply chain management: Theory and applications. International Journal of Logistics Research and Applications, 26(8), 1001–1021. https://doi.org/10.1080/13675567.2022.2052505
OECD. (2023). Public procurement performance: A framework for measuring efficiency, compliance and strategic goals. OECD Publishing. https://doi.org/10.1787/0dde73f4-en
Patrucco, A. S., Agasisti, T., & Glasc, A. H. (2020). Structuring public procurement in local governments: The effect of centralization, standardization and digitalization on performance. Public Performance & Management Review, 44(3), 630–656. https://doi.org/10.1080/15309576.2020.1851267
Pettersen, I. J., Nyland, K., & Robbins, G. (2020). Public procurement performance and the challenge of service complexity: The case of pre-hospital healthcare. Journal of Public Procurement, 20(4), 403–421. https://doi.org/10.1108/JOPP-01-2020-0002
Sanchez-Graells, A. (2024). Responsibly buying artificial intelligence: A regulatory hallucination. Current Legal Problems, 77(1), 81–126. https://doi.org/10.1093/clp/cuae003
Spreitzenbarth, J. M., Bode, C., & Stuckenschmidt, H. (2024a). Artificial intelligence and machine learning in purchasing and supply management: A mixed-methods review of the state-of-the-art in literature and practice. Journal of Purchasing and Supply Management, 30(1), 100896. https://doi.org/10.1016/j.pursup.2024.100896
Spreitzenbarth, J. M., Bode, C., & Stuckenschmidt, H. (2024b). Designing an AI purchasing requisition bundling generator. Computers in Industry, 155, 104043. https://doi.org/10.1016/j.compind.2023.104043
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2026 Lauren Campbell
This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution (CC-BY) 4.0 License that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this journal.
