Aug 2026· International Journal of Public Sector Management· pp. 1-26· 0 citations· 71 references
TL;DR
Empirical evidence is provided for the high relevance of procurement for public sector AI services via supplier-related governance perspectives and for the need to refine existing AI readiness and adoption models, particularly for regulated or defense-related procurement environments.
Abstract
Artificial Intelligence (AI) promises major performance gains in the public sector. However, as AI is primarily procured from private suppliers, adequate governance is crucial, particularly regarding AI risk perceptions. This study links existing AI governance frameworks and make-or-buy considerations to empirically examine perceived AI benefits and risks and their effects on supplier performance.
A novel workshare governance construct is established, capturing the allocation of digital development responsibilities and is adapted to a public sector setting, connecting dynamic capabilities with individual AI-related factors like motivation and risk awareness. A partial least squares structural equation model (PLS-SEM) is applied to test hypotheses between governance, AI readiness and performance with survey data from 104 questionnaires.
The necessity of workshare governance and its role as a mitigator of negative effects is empirically validated. AI risk awareness significantly reduces supplier performance, while AI motivation shows no significant effect. The results indicate that motivation alone is insufficient; risk awareness and governance determine whether AI leads to measurable improvements in supplier performance.
The study challenges the assumption that AI adoption automatically yields transformation. It highlights workshare governance and risk awareness as potential explanatory mechanisms, suggesting the need to refine existing AI readiness and adoption models, particularly for regulated or defense-related procurement environments.
Policy makers and procurement managers should prioritize governance frameworks addressing individual barriers to AI use and rigorously assess the value of potential AI products and services.
This study provides empirical evidence for the high relevance of procurement for public sector AI services via supplier-related governance perspectives.
The rapid diffusion of generative artificial intelligence (GAI) is transforming organizational knowledge processes, yet existing research offers limited insight into its governance implications. This study addresses this gap by conceptualizing GAI as a mechanism of knowledge governance and examining its relationship with digital trust, information security maturity, and decision quality. Data from 300 firms across Poland, Germany, and Spain were collected using a CAWI survey of top management teams. Structural equation modeling (SEM) and multilevel analysis were applied to test relationships between AI-mediated knowledge practices, digital trust, information security maturity, and perceived decision quality. The results show that AI use significantly increases digital trust, which in turn improves decision quality. Digital trust acts as a mediator, while information security maturity moderates this relationship. The findings indicate that the impact of generative AI on decision-making depends on governance conditions rather than technology alone.
Justyna Żywiołek· European Conference on Knowl...· 0 citations
The results suggest that the sustainability benefits of AI emerge when contextual readiness and psychological assurance jointly enable organizations to move beyond adoption intention toward sustained AI utilization, encompassing economic, operational, and environmental dimensions.
Artificial intelligence (AI) is increasingly transforming banking, yet responsible adoption depends not only on technical deployment but also on organizational readiness, governance capacity, monitoring practices, and the capacity to scale AI responsibly. This study examines AI adoption, governance readiness, maturity, perceived benefits, adoption barriers, and scaling intention in the Albanian banking system. Based on a cross-sectional survey of 85 professionals from 15 institutions, including all 12 banks operating in Albania and 3 additional financial institutions, the study applies the Technology–Organization–Environment framework together with principles of responsible AI governance. The analysis uses reliability and validity diagnostics, common-method diagnostics, robust OLS regressions, institution-clustered inference, bootstrap confidence intervals, PLS-SEM robustness analysis, and sensitivity checks. The findings show that AI adoption is visible but uneven: more than half of respondents reported active or pilot AI use, while integration, monitoring, and benefit measurement remain less developed. Data and Technology Readiness and Environmental/Regulatory Pressure were positively associated with Capability-Governance Readiness, which was positively associated with Perceived Benefits. Perceived Benefits were positively associated with Intention to Invest in or Scale AI, whereas Adoption Barriers showed no statistically significant association with scaling intention. The study provides exploratory evidence from a small banking system, indicating that responsible AI development requires the alignment of technological foundations, organizational capability, governance structures, monitoring routines, responsible-use orientation, and benefit-measurement practices.
Laureta Domi, Majlinda Godolja, K. Sevrani· Administrative Sciences· 0 citations
The digital transformation of corporate finance, driven by artificial intelligence (AI), presents a significant opportunity for the capital-intensive chemical industry. Success stories from other sectors, particularly banking, manufacturing and healthcare, have already demonstrated that AI can enhance forecasting, risk assessment, process efficiency and data-driven decision-making. This strengthens its strategic relevance for corporate finance in the chemical industry. This study systematically analyses the key barriers and enablers that determine the nature and success of AI implementation in the industry’s corporate finance functions. A synthesis of contemporary academic literature and industry analytical sources was conducted to develop a diagnostic conceptual model, comprising a Barrier Index (BI) and an Enabler Index (EI). The proposed model was tested on a sample of 94 publicly listed chemical companies using publicly available corporate data. The results of the empirical analysis reveal a strong and statistically positive relationship between the BI and EI indices, indicating the systemic role of organizational enablers—strategic leadership, mature data infrastructure, and organizational agility—in mitigating institutional and operational barriers to digital transformation. The application of cluster analysis to companies according to their digital maturity levels has resulted in the identification of four qualitatively distinct profiles. It is essential to note that each of these profiles requires a tailored strategic development trajectory. The principal contribution of the study lies in operationalizing the concept of dynamic capabilities into an applied diagnostic tool, thus providing managers with a structured approach to assessing AI readiness and prioritizing strategic investments for its sustainable integration.
Dariia Drozd, B. Sloka· International Scientific Con...· 0 citations
It is argued that AI should be understood as a socio-technical system rather than a neutral technical upgrade and a human-centered governance framework based on educational purpose, proportionality, contestability, transparency, stakeholder participation, vendor accountability, professional development, and continuous audit is proposed.
This study examines the role of Artificial Intelligence (AI) in enhancing public financial management and budgeting systems, with particular emphasis on its implications for efficiency, transparency, accountability, and fiscal governance. The increasing complexity of government financial operations and the growing demand for evidence-based decision-making have created the need for innovative technological solutions capable of improving budgeting processes and resource allocation. The study adopts a qualitative approach based on an extensive review of scholarly literature, policy documents, institutional reports, and empirical studies relating to AI applications in public finance. The theoretical foundation of the study is anchored on the Technology Acceptance Model (TAM) and Public Choice Theory. TAM explains the factors influencing the adoption of AI technologies by public officials, while Public Choice Theory highlights how AI can reduce inefficiencies, corruption, and waste through automated monitoring and transparent decision-making mechanisms. Findings reveal that AI significantly improves budget forecasting, expenditure monitoring, fraud detection, financial reporting, and policy evaluation through the use of machine learning, predictive analytics, and intelligent automation. The study also identifies challenges such as inadequate digital infrastructure, cybersecurity risks, poor data quality, limited technical expertise, and resistance to organisational change. The study concludes that AI possesses substantial potential to transform public financial management by promoting fiscal discipline, strengthening accountability, and enhancing the effectiveness of budgeting systems. It recommends increased investment in digital infrastructure, human capacity development, data governance frameworks, cybersecurity measures, and ethical AI policies to ensure successful implementation. The study contributes to the growing body of knowledge on digital governance and provides practical insights for policymakers, public administrators, and development practitioners seeking to leverage AI for improved financial management and sustainable public sector performance.
Ukpong Johnson, O. Duke, Bayo Lekara et al.· Science Journal of Business...· 0 citations