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· Journal of Perspectives in M...· 0 citations
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· International Scientific Con...· 0 citations
Examining how artificial intelligence (AI) governance supports sustainable decision-making across organizational contexts in Europe reveals that governance increasingly aligns with formal frameworks through policies, dedicated structures, human oversight and Environmental, Social and Governance oriented indicators, enhancing transparency and reliability.
Fernando Almeida· Journal of Ethics in Entrepr...· 0 citations
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.· Journal of Economics, Entrep...· 0 citations
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 is developed.
The volume examines how artificial intelligence, ESG frameworks, corporate responsibility, innovation, regulatory developments, and digital technologies are reshaping organizations, institutions, and society in an era of rapid technological advancement.