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Review Open access Aug 2026

Rethinking the future

The HCSAIGF contributes to AI governance research by providing an integrated explanatory architecture and offers a conceptual basis for future empirical research and more coherent governance practices.

Emre İmamoğlu · 0 citations
Conference Open access 2025

Embedding AI Risk Governance into Knowledge-Based Organizations: A Path Towards Systemic Sustainability

As artificial intelligence (AI) systems become increasingly embedded in the structures of knowledge-based organizations, the governance of AI-related risks is emerging as a critical factor for long-term systemic sustainability. This paper explores how AI risk governance can be effectively integrated into the epistemological and structural foundations of such organizations through the lens of fourth-order cybernetics. This theoretical framework emphasizes reflexivity, ethical co-construction, and multilevel feedback involving both human and technical agents. Rather than treating governance as a static set of compliance measures, the proposed model presents it as a dynamic and participatory process. Four core principles are introduced: multilevel feedback, contextual ethics, recursive governance, and the inclusion of marginalized perspectives. These principles support the embedding of AI governance into decision-making and knowledge management systems. The paper contributes to responsible innovation discourse and offers a conceptual pathway for resilient and ethically aligned AI implementation in complex organizational environments.

Ludmila Jiříčková, Petr Doucek · 0 citations
Open access Jul 2026

Exploring AI governance for sustainable decision-making: evidence from six case studies

This study aims to examine how artificial intelligence (AI) governance supports sustainable decision-making across organizational contexts in Europe, focusing on six Portuguese firms in energy, urban mobility and finance. Adopting a sociotechnical perspective, this research uses a qualitative multiple case study design with semi-structured interviews of Chief Information Officers across diverse organizational contexts. It integrates technical and social dimensions to capture how digital infrastructures, governance practices and human factors interact in decision-making processes. The findings reveal that governance increasingly aligns with formal frameworks through policies, dedicated structures, human oversight and Environmental, Social and Governance (ESG) oriented indicators, enhancing transparency and reliability. However, maturity varies by sector, resources and technology and challenges such as data limitations, organizational resistance and regulatory uncertainty persist. Furthermore, AI governance emerges as an adaptive, iterative capability for navigating sustainability complexities. This study provides original insights by linking AI governance to sustainable decision-making through a sociotechnical lens, an area still underexplored in empirical research. It advances theory by integrating ESG considerations into AI governance and offers practical value by identifying mechanisms that enhance transparency, accountability and sustainability outcomes.

Fernando Almeida · 0 citations
Review Open access Jul 2026

Artificial Intelligence, Sustainable Human Resource Management, and Organisational Sustainability: A Multi-Level Integrative Framework

Purpose – This study proposes a multilevel integrative framework explaining how AI capabilities are transformed into sustainability outcomes through HRM architectures and employee mechanisms under institutional and governance contingencies. Design/methodology/approach – A systematic literature review (SLR) was conducted following PRISMA guidelines. This study identified 326 records, of which 36 studies met the inclusion criteria and were included in the final review. Finding/Results –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. From a socio-technical perspective, effective AI implementation depends on the alignment between technological systems and human factors. Originality/Value – This study provides theoretical and practical implications by demonstrating that the integration of the AMO framework, dynamic capabilities, and socio-technical systems strengthens the understanding of how AI-driven HRM contributes to sustainability has implication for managers, policy makers and regulators.

Masyhuri Masyhuri, Iqbal Lhutfi, Siswanto Siswanto et al. · 0 citations
Open access Aug 2026

Sustainable Digital Governance of AI-Based Decision Support Systems in Public Administration: A Socio-Technical Capacity Framework

Artificial intelligence (AI)-based decision support systems (DSSs) increasingly shape how public organizations classify cases, rank risks, allocate attention, and interpret administrative information. Although these systems may improve administrative performance, their contribution to sustainable digital governance depends on institutional arrangements that preserve accountability, adaptability, inclusiveness, and public justification. This conceptual article develops a lifecycle-oriented socio-technical governance capacity framework through a structured synthesis of public administration, digital government, decision support systems, responsible AI, socio-technical systems, sustainability, and risk governance scholarship. The framework distinguishes five interacting layers—technical, organizational, legal–ethical, societal, and adaptive—and eight cross-layer capacities: data governance, algorithmic accountability, human oversight, legal and ethical assurance, organizational learning, inter-organizational coordination, public justification and contestability, and adaptive monitoring and response. It further identifies four system-level relationships concerning capacity alignment, lifecycle variation, distributed responsibility, and adaptive feedback. Isolated safeguards provide limited assurance when they are institutionally disconnected or unsupported by the authority to learn and intervene. By conceptualizing responsible AI-based decision support as a configuration of interdependent capacities, the framework connects AI governance with the institutional resilience, accountability, and adaptability required for sustainable public administration. It provides a diagnostic basis for comparative research and organizational assessment throughout the lifecycle of AI-based DSSs.

Cihan Necmi Günal · 0 citations
Open access Jul 2026

Sustainable Governance in the Age of Artificial Intelligence

This international scientific monograph explores contemporary challenges and opportunities in governance, sustainability, and digital transformation in an era of rapid technological advancement. Bringing together contributions from researchers across different countries and disciplines, the volume examines how artificial intelligence, ESG frameworks, corporate responsibility, innovation, regulatory developments, and digital technologies are reshaping organizations, institutions, and society. Particular attention is given to sustainable governance, responsible decision-making, organizational adaptation, and the creation of long-term value in increasingly complex environments. The chapters combine theoretical insights, empirical research, and case studies, offering interdisciplinary perspectives on the evolving relationship between technology, sustainability, and governance. By bridging academic knowledge and practical implications, the monograph contributes to current debates on sustainable development, responsible innovation, and governance transformation. It is intended for researchers, academics, students, policymakers, and practitioners interested in the future of governance, sustainability, and digital change.

Sunčica Oberman Peterka · 0 citations