Aug 2026· International Journal of Public Sector Management· 0 citations· 36 references
TL;DR
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.
Abstract
This study explores how artificial intelligence (AI) adoption in public procurement emerges through dynamic capabilities across individual and organisational levels.
A qualitative study was conducted with 20 professionals from 13 Finnish public organisations engaged in AI-related procurement initiatives. Using the Gioia methodology, interviews and supplementary documents were analysed to identify enablers, barriers and transformation mechanisms.
The findings show that dynamic capabilities – sensing, seizing and transforming – are distributed across levels and shaped by bottom-up and top-down mechanisms. Individual experimentation feeds organisational learning, while leadership and governance structures enable or constrain scaling. Most organisations remain in the early phases, with transformation hampered by silos, vendor lock-in and weak orchestration.
The study advances dynamic capabilities theory by demonstrating its multilevel nature in the public sector. It provides actionable insights into aligning individual initiatives with organisational structures to achieve sustainable, AI-enabled procurement transformation.
The findings indicate that AI enhances sustainable HRM by strengthening employee abilities, motivation, and opportunities, while simultaneously enabling organisational dynamic capabilities such as sensing, seizing, and transforming.
Masyhuri Masyhuri, Iqbal Lhutfi, Siswanto Siswanto et al.· Journal of Economics, Entrep...· 0 citations
Public procurement is adopting artificial intelligence (AI) more slowly than private‐sector procurement, despite similar operational opportunities. We analyze this gap through Moore's Strategic Triangle (ST), focusing on public value, operational capacity, and legitimacy and support. Based on 38 expert interviews and a follow‐up resonance questionnaire, the study identifies three tensions: (i) contested performance gains versus social and environmental costs; (ii) data access for model performance versus stewardship duties for public and supplier data; and (iii) opacity versus accountability and contestability requirements. We elaborate the ST for AI‐enabled procurement by showing that AI changes how the three existing conditions must be held together. The tensions surface as practical questions about whether gains can be evidenced as public value, who controls procurement data and vendor learning, and whether AI‐supported recommendations can be explained and challenged. The strongest mitigation levers sit in agencies' contracting and decision practices, including work design, data rights and portability, audit trails, and supplier challenge mechanisms.
G. Culot, Matteo Podrecca, Andrea S. Patrucco et al.· Journal of Business Logistic...· 0 citations
The public sector is undergoing a profound digital transformation characterised by the increasing integration of artificial intelligence into its management control practices. Concurrently, the optimisation of public performance and accountability has emerged as a central concern among scholars, particularly within the broader context of digital transition. This period of structural change calls for renewed forms of governance and organisational steering grounded in innovative and adaptive approaches. Research on artificial intelligence in public administration has developed significantly since the early 2020s, although the effective adoption of these technologies continues to present substantial challenges. This article offers a systematic review of the literature published between 2020 and 2025, conducted in accordance with the PRISMA guidelines to ensure methodological rigour and reliability. The content analysis is based on a corpus of twenty-five selected articles. The findings indicate that the adoption of artificial intelligence in public management control remains a critical issue. AI has contributed to innovation across five principal domains: financial auditing, budget forecasting, operational optimisation, public health, and legal support. These developments open promising avenues for enhancing the quality of public management, particularly through the effective implementation of technological innovations, the strengthening of institutional adaptability and resilience, the development of essential digital competencies, the cultivation of critical engagement with algorithmic systems, and the reinforcement of digital literacy among public officials. Nevertheless, significant ethical challenges persist, including algorithmic opacity, discriminatory bias, and tensions between efficiency and equity. Addressing these concerns requires the establishment of appropriate governance frameworks and the consolidation of broader societal acceptance.
A systematic review of recent literature identifies and categorises the key challenges hindering the successful implementation of AI in both public and private sector procurement in Nigeria, providing a coherent framework for policymakers and organisational leaders to develop targeted intervention strategies.
C. K. Kabiri· IIARD International Journal...· 0 citations
The study demonstrates that the successful deployment of AI Builder is contingent more on organizational readiness and accountability than technological maturity, and offers an informed blueprint to organizations adopting low-code AI platforms.
By integrating five theoretical perspectives, the review develops a model of the AI strategic lifecycle, offering both a consolidated foundation for future research and a forwardlooking agenda for managers seeking to leverage AI as a strategic asset.
J. Lambert, O. Garanina· Review of business and econo...· 0 citations
We use cookies to run the site and, with your consent, for analytics and to show ads.
See our Cookie Policy.
