Aug 2026· Human Systems Management· 0 citations· 28 references
TL;DR
The study concludes that generative AI-enabled KM requires a balanced socio-technical approach integrating technology, human expertise, organizational practices, and governance, and contributes to understanding AI-enabled KM in emerging economies.
Abstract
Generative artificial intelligence (AI) is increasingly transforming knowledge management (KM) in knowledge-based organizations (KBOs). This study examines the integration of generative AI into KM within the United Arab Emirates (UAE), focusing on the technological, human, organizational, cultural, and regulatory factors influencing adoption.
The study aims to identify the key enablers and barriers affecting generative AI adoption in KM and to examine how UAE-specific factors, including national AI initiatives, public–private partnerships, workforce diversity, and SME characteristics, shape adoption pathways.
A mixed-methods approach combined thematic analysis of government strategies, organizational reports, industry publications, and peer-reviewed literature with ML-assisted conceptual coherence assessment. The procedure examined framework relationships without empirical validation, causal testing, or prediction. The framework was informed by Knowledge-Based View, Socio-Technical Systems, and Technology Acceptance Models.
Effective AI-enabled KM depends on technological capabilities, human expertise, organizational culture, leadership, and regulatory conditions. UAE-specific factors, including the national AI agenda, institutional environment, workforce characteristics, and organizational differences, influence adoption, indicating that technological readiness alone is insufficient without effective socio-technical alignment.
The study concludes that generative AI-enabled KM requires a balanced socio-technical approach integrating technology, human expertise, organizational practices, and governance. The proposed framework supports responsible AI adoption in UAE organizations and contributes to understanding AI-enabled KM in emerging economies.
Generative artificial intelligence (Gen AI) and large language models (LLMs) offer substantial potential to improve how organisations capture, organise, retrieve and reuse knowledge. Existing knowledge management (KM) frameworks, however, seldom integrate Gen AI/LLM-specific processes, data governance, and ethical requirements in highly regulated public-sector settings, and few have been empirically evaluated. This study assesses the feasibility of adopting GenAI and LLMs for organisational KM and develops a corresponding Knowledge Management and Artificial Intelligence (KMAI) framework for a national communications regulator. Guided by Diffusion of Innovation (DOI) theory, the study employed a multi-method qualitative design comprising a literature review, semi-structured interviews with six KM representatives, a focus group discussion involving fourteen participants using the LEIQ™ model for SWOT analysis, and a content-validity evaluation by three experts. Interview data were analysed thematically, SWOT findings were transformed into strategies through TOWS analysis, and framework relevance was assessed using item- and scale-level content validity indices. The findings indicate that adoption is feasible in this setting: participants perceived clear relative advantage, compatibility, trialability and observability, while complexity was manageable when supported by adequate skills, data classification, secure infrastructure and governance. The principal risks concerned data quality, privacy, security, misinformation, over-reliance on AI, and organisational resistance. The proposed KMAI framework achieved acceptable content validity across all clusters, with S-CVI/Ave values ranging from 0.89 to 1.00, and was refined into four strategic thrusts: People, Process, Technology and Data. Because the evidence derives from one organisation and a three-member expert panel, the framework is presented as an empirically grounded and internally validated proposition whose extension to other agencies is analytic rather than statistical; the boundary conditions governing such extension are stated explicitly, and confirmatory validation with a larger expert panel and independent organisational sites is identified as the necessary next step. The study extends DOI-based adoption analysis by showing that data governance and ethics condition Gen AI/LLM adoption in organisational KM and provides a practical, staged framework for regulated public-sector organisations.
Surya Sumarni Hussein, Nur Azaliah Abu Bakar, S. Hamidi et al.· International Journal of Adv...· 0 citations
Generative artificial intelligence (GenAI) is rapidly transforming organisational knowledge management (KM) processes. Yet limited research has examined how cultural and institutional contexts shape GenAI-enabled KM adoption across developed and developing economies. This study investigates the organisational, cultural, and institutional factors influencing GenAI-enabled KM practices in Jordan and Europe through a mixed-methods approach. Quantitative data were collected from 456 respondents across higher education institutions, SMEs, public-sector organisations and large enterprises. Qualitative data was collected through 36 semi-structured interviews. The findings reveal notable variations in the extent of GenAI adoption, readiness of organisational structures, maturity governance and trust in AI systems across cultures. Structural model analysis showed that technological infrastructure, AI literacy, leadership support, financial resources, and data governance positively affected GenAI adoption. Power distance and uncertainty avoidance negatively moderated adoption effectiveness. Qualitative results revealed that GenAI has high potential for supporting explicit knowledge processes, including externalisation and combination. Tacit knowledge processes such as socialisation remain dependent on people, trust and collaborative organisational culture. The study advances knowledge management theory by extending the SECI model through a GenAI-enhanced perspective that differentiates AI effects across tacit and explicit knowledge conversion processes. The study advances knowledge management theory by: (1) extending the SECI model through a GenAI-enhanced perspective that differentiates AI effects across tacit and explicit knowledge conversion processes—demonstrating process-contingency not previously specified; (2) reconceptualising GenAI capabilities as context-sensitive dynamic capabilities within the Knowledge-Based View, requiring complementary organisational resources; and (3) integrating cultural moderators into AI adoption theory, showing that cultural dimensions function as effectiveness moderators rather than just direct predictors. The study makes a novel contribution to cross-cultural AI adoption literature through a context-based model combining organisational readiness, cultural dimensions, and institutional maturity.
M. Taqatqa, Rami Aljbour· European Conference on Knowl...· 0 citations
The study contributed a validated lifecycle-integrated KD framework for AI initiatives; a taxonomy of ten systematically identified gaps in current AI KD practices; and a methodological demonstration of mixed-method CVI validation for framework development in information systems research.
Fitria Handayani, Finannisa Zhafira, D. Sensuse et al.· Jurnal Impresi Indonesia· 0 citations
This paper extends the author's Integrated Open Innovation and Knowledge Management (OIKM) framework, originally developed for a telecommunications operator, to account for generative and agentic artificial intelligence (AI), which the original model predates. Literature published between 2024 and 2026 on generative AI's role in knowledge management, absorptive capacity and intellectual capital is reviewed and used to formulate five theoretical propositions that recast AI as a boundary condition strengthening or conditioning the OIKM framework's existing mediating and moderating relationships, and, in one case, contributing a new direct effect on the knowledge management process itself. A mixed-methods design combining qualitative case analysis with survey-based PLS-SEM is proposed for future empirical validation. The paper offers an AI-Augmented OIKM (AI-OIKM) model comprising five propositions: AI-mediated open innovation, generative-AI-mediated knowledge flow, AI-enabled absorptive capacity, and AI/digital intellectual capital, each extending an original construct without altering its identity, plus a fifth proposition on generative AI's direct effect on the knowledge management process. As a conceptual paper, the propositions are not yet empirically tested; the proposed mixed-methods design offers a research agenda for future validation across telecommunication contexts. The paper builds a theory-driven connection between the knowledge-based view of the firm and the emerging literature on organizational AI capability, extending a previously validated framework while preserving comparability with the original study. The paper offers telecommunications executives a conceptual basis for integrating AI into innovation and knowledge processes as a core operating capability rather than a bolt-on efficiency tool.
Amirthanathan Prashanthan· Journal of Humanities and So...· 0 citations
The research re-specifies dual-factor dynamics for GenAI-mediated knowledge work, demonstrating that enablers and inhibitors operate as independent epistemic forces rather than as opposing poles and extends KM scholarship on knowledge risk by identifying a class of AI-specific risks that conventional governance instruments are not designed to absorb.
Wen-Dai Yang, Sarthak Singh, S. Alshibani et al.· Journal of Knowledge Managem...· 0 citations
This study investigates how organizational members concurrently perceive the benefits of artificial intelligence (AI) for knowledge management processes (KMPs) and the challenges involved in implementing AI within knowledge management systems (KMSs). Based on survey data from 378 respondents across diverse sectors and roles, the research employs validated instruments measuring perceptions of AI’s contribution to knowledge acquisition, documentation, sharing, and application, as well as perceived human, technological, financial, and ethical‑regulatory barriers. The results show a consistent positive relationship between perceived AI usefulness and perceived implementation barriers: individuals who attribute greater value to AI-enhanced knowledge processes also express heightened awareness of the complexities required to integrate AI into organizational systems. Knowledge documentation presents the strongest associations with all barrier categories, while knowledge sharing exhibits the weakest. Human‑related barriers emerge as the most pervasive across all processes, indicating the central role of employee readiness and organizational culture in shaping AI-enabled KM. These findings reveal a dual perception in which optimism regarding AI’s potential coexists with recognition of the organizational adjustments it demands. The study contributes to a more integrated understanding of AI adoption in KM, emphasizing that effective implementation requires aligning technological capabilities with human, cultural, and governance considerations.
M. Nakash, E. Bolisani· European Conference on Knowl...· 0 citations
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