The proposed framework analyzes technology acceptance, algorithm transparency, human–AI collaboration, and privacy protection as interconnected components of an intelligent management architecture and develops an implementation framework that combines employee participation, explainable decision mechanisms, and data-governance strategies.
Abstract
The increasing adoption of artificial intelligence in organizational management requires effective integration of intelligent decision support, human-centered design, and secure information governance. This study investigates the key determinants affecting human-centered AI adoption in human resource management and develops an implementation framework that combines employee participation, explainable decision mechanisms, and data-governance strategies. The proposed framework analyzes technology acceptance, algorithm transparency, human–AI collaboration, and privacy protection as interconnected components of an intelligent management architecture. Furthermore, a dual-track decision model is introduced to balance automated analytical efficiency with human judgment in high-impact management scenarios. To support trustworthy deployment, a governance structure incorporating hierarchical access control, privacy-preserving computation, ethical review mechanisms, and transparent information management is established. The framework also emphasizes continuous feedback, interpretability, and employee engagement to improve system acceptance and operational reliability. By integrating intelligent information processing, explainable decision support, and secure data governance, the proposed approach provides an engineering-oriented methodology for trustworthy AI deployment and human–AI collaborative management in data-intensive organizational environments.
The rapid adoption of Generative Artificial Intelligence in organizational information systems has created new opportunities for improving productivity, decision-making, service innovation, and knowledge management. However, its implementation also introduces critical risks related to data privacy, information security, inaccurate outputs, algorithmic bias, ethical misuse, and declining user trust. Objective: This study aims to develop a conceptual model of Generative AI risk governance by integrating AI governance readiness, information security control, ethical AI awareness, user digital trust, and AI adoption effectiveness. The model is proposed to explain how organizations can adopt Generative AI in a secure, ethical, responsible, and trusted manner. Methodology: This study employed a conceptual research design using an integrative literature review approach. Data were collected from secondary academic sources, including peer-reviewed journal articles, reputable conference proceedings, and official technical reports relevant to Generative AI, information systems, cybersecurity, AI ethics, responsible AI governance, and digital trust. The data were analyzed through thematic synthesis to identify conceptual domains, relationships among constructs, and research propositions. Findings: The findings indicate that AI governance readiness serves as a foundational construct that strengthens information security control and ethical AI awareness. These two mechanisms contribute to user digital trust, which subsequently supports the effectiveness of Generative AI adoption in organizational information systems. Implications: This study implies that organizations should not adopt Generative AI solely based on technological benefits. Organizations need to establish governance policies, security controls, ethical guidelines, user education, and trust-building strategies to ensure that Generative AI implementation is safe, accountable, and aligned with organizational objectives. Originality: The originality of this study lies in its integrated conceptual framework, which connects technology adoption, information security, AI ethics, responsible AI governance, and digital trust into a single model for responsible Generative AI implementation in organizational information systems.
Nurdiyanto Yusuf· International Journal for Sc...· 0 citations
The study argues that collaborative intelligence should be viewed as an organizational capability rather than merely a technological outcome, requiring deliberate management of human judgment, ethical responsibility, and organizational design.
M. R· International Journal of Phi...· 0 citations
AI capability is a new strategic capability in the organization that goes beyond operational efficiency and can support the quality strategic decision-making, sustainable performance of an organization, and high decision quality. Though AI capability is evolving, current research remains disparate in how to transform an AI capability to a organizational value with the role of governance, leadership, and organizations capability. To solve this, in this study, a integrated conceptual framework grounded in the theory of resource-based view(RBV), dynamic capabilities theory(DCT) and the AI Governance literature is developed and empirically tested. In the model, the sequential relation between AI capability, AI governance, strategic decision quality, organizational agility, and organizational performance was proposed and the moderating role of digital leadership was examined. An explanatory sequential mixed-methods research design was used. The empirical analysis includes two phases. In the first phase, a cross-sectional survey of 446 senior executives and strategic decision makers of public and private organizations was conducted to empirically test the proposed integrated model using Partial Least Squares Structural Equation Modeling (PLS-SEM). In the second phase, qualitative data from 30 semi-structured interviews with senior executives was collected to gain a deep understanding of AI governance, digital leadership and organizational agility practices. Multi-group analysis further revealed differences in the proposed relationships for public and private organizations. Findings revealed that AI capability not only significantly strengthens the AI governance, and consequently the strategic decision quality, but it also improve the organizational agility, resulting in improved performance. Furthermore, digital leadership has a positive effect on reinforcing the association between AI governance and the strategic decision quality. Overall, this study integrates the technology capability, the organizational capability and the leadership capability to establish an AI-enabled strategic decision-making and performance management framework, and provides strategic insights for organizations that aim to realize greater value from their AI investments.
Dareen Alshamsi, Dr. Mohamed Manea Almansoori, Dalal S. Almansoori et al.· Journal of Intelligent Decis...· 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 application of artificial intelligence (AI) technologies in the public sector has led to improved public services, enhanced administrative performance, and strengthened automated decision-making. However, the increasing reliance on AI systems has raised concerns regarding accountability, ethical compliance, privacy protection, transparency, human oversight, and risk management. This study, employing both conceptual and qualitative research methodologies, examines the governance factors and requirements for responsible AI implementation in the public sector. The research methodology includes a comparative analysis of international AI governance frameworks and regulations. The study identifies key dimensions influencing responsible AI implementation, such as accountability, human oversight, ethical governance, legal compliance, risk management, and transparency. The findings demonstrate that the adoption of responsible AI cannot be achieved through technological means alone but also requires a commitment to comprehensive governance mechanisms. Furthermore, the sequential interaction and interdependence of governance factors reduce operational and societal risks, increase transparency and explainability, and foster public trust in the systems. This study contributes to enriching the culture and knowledge of AI governance, and the proposed framework helps government sector leaders develop responsible AI governance in accordance with international standards and regulations.
Ghazwan Hani Hussein, Faiza Mohamed, A. Abuzreda· Journal of Technology and Sy...· 0 citations
This paper reconceptualizes managerial rationality in artificial intelligence (AI)-augmented decision-making through the notion of algorithmic-bounded rationality (ABR). It argues that AI does not remove boundedness but relocates it into algorithmic constraints related to data volatility, model opacity and governance maturity.
Building on bounded rationality and socio-technical systems theory, this conceptual study develops an ABR framework linking three decision modes (AI-led, human-first and collaborative) to mechanisms of algorithmic boundedness. The framework is further extended through propositions on mode–task fit and governance conditions for sustaining hybrid decision architectures.
The analysis shows that human–AI collaboration represents a distinct rationality configuration rather than a midpoint between automation and human judgment. Under ABR, each decision mode becomes effective under different combinations of data intensity, contextual ambiguity and accountability demands.
Managers should treat AI integration as a redesign of decision governance rather than a technological upgrade, emphasizing appropriate authority allocation and oversight mechanisms.
The study reframes rationality in the AI era by showing how boundedness shifts from human cognition to socio-technical decision infrastructures. It contributes a mechanism-based framework linking decision modes, task conditions and governance arrangements in AI-augmented decision systems.
Z.-S. Chen· Management Decision· 1 citation
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