Aug 2026· Journal of Knowledge Management· pp. 1-21· 0 citations· 60 references
TL;DR
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.
Abstract
This study aims to advance knowledge management (KM) scholarship by theorizing how generative artificial intelligence (GenAI) reshapes knowledge practices in management education. Prior work on GenAI in higher education has been largely descriptive, mapping faculty experiences onto generic technology-adoption constructs while leaving its KM consequences under-theorized.
The authors address this gap by developing a process model of GenAI-mediated knowledge practices organized around three interrelated dynamics, namely knowledge integration, knowledge orchestration and epistemic risk. This study uses Dual Factor Theory and the Unified Theory of Acceptance and Use of Technology 2 as sensitizing frameworks rather than as the contribution itself.
The findings show that GenAI catalyzes knowledge integration by fusing artificial intelligence (AI)-generated content with disciplinary expertise, exposes weaknesses in knowledge orchestration where policy voids and uneven digital literacy fragment institutional coordination and generates qualitatively new epistemic risks such as hallucinated citations, opaque authorship and selective disclosure of AI use.
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. Also, they extend KM scholarship on knowledge risk by identifying a class of AI-specific risks that conventional governance instruments are not designed to absorb. Finally, they offer practitioner-relevant guidance for academic leaders seeking to orchestrate responsible GenAI integration in knowledge-intensive institutions.
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.
Generative artificial intelligence (GenAI) is increasingly used in knowledge-intensive work, yet empirical evidence remains limited on how it influences organizational knowledge processes and innovation. Although the rapid diffusion of GenAI technologies has attracted significant attention from researchers and practitioners, there is still a limited understanding of how these tools are integrated into everyday knowledge work and how they contribute to the generation and implementation of innovative ideas. This study examines how knowledge workers in Norwegian organizations use GenAI, the factors that encourage or constrain adoption, and the influence of leadership and organizational culture on its effective use. An exploratory qualitative study was conducted through ten semi-structured interviews with knowledge workers from public- and private-sector organizations. Participants represented a variety of professional roles and organizational contexts, providing insights into both individual and organizational experiences with GenAI. The interview data were analyzed thematically to identify recurring patterns related to knowledge activities, innovation processes, and organizational conditions. The findings show that GenAI supports innovation by facilitating knowledge discovery, synthesis, translation, recombination, idea generation, and concept development. Participants associated its use with faster access to information, accelerated learning, improved problem-solving, and broader exploration of alternatives. Many described GenAI as a valuable cognitive support tool that enhanced creativity and enabled more efficient knowledge sharing across teams. However, they also raised concerns about inaccurate outputs, weak source traceability, privacy, cybersecurity, and excessive reliance on AI-generated content. Additional concerns related to the potential erosion of critical thinking skills and professional expertise if human judgment is not actively maintained. Trust, employee autonomy, and supportive leadership encouraged experimentation, while GDPR compliance, transparency, and professional accountability shaped responsible use. The study conceptualizes GenAI as a conditional knowledge-integration mechanism whose contribution to innovation depends on human validation, organizational learning, and responsible governance. The findings contribute to knowledge management and innovation research by highlighting the organizational capabilities required to harness GenAI while mitigating associated risks
Aurilla Aurelie Bechina Arntzen, Tone Aud Vold, Neno Ruseno· European Conference on Knowl...· 0 citations
As artificial intelligence (AI), especially Generative AI (GenAI), becomes more ubiquitous, its adoption in higher education requires the critical review of all knowledge management (KM) processes, likely transforming how individuals create, acquire, share, transfer, and retain knowledge. As such, the use of GenAI in higher education poses significant benefits but also entails noteworthy risks. Key benefits encompass enhanced academic support, an increase in productivity, innovative pedagogical approaches, tailored learning experiences, and improved student engagement, all of which facilitate more effective knowledge creation and transfer. GenAI tools are also recognized for their ability to act as tutors, assist with the writing of assignments, aid with research, or take on creative, developmental, or administrative tasks. These tools facilitate knowledge acquisition and retention while broadening access to educational resources which theoretically improve learning outcomes. However, these technologies also bear considerable risks that can undermine trust in GenAI technologies, which is crucial for their responsible and sustainable use in higher educational settings. Some cited examples of risks include academic dishonesty, ethical concerns, data privacy issues, misinformation, hallucinations, and over-reliance on AI tools, which can be argued to lead to the decline of critical thinking, social engagement, and the ability to support sustainable KM practices. When higher education institutions lack structured approaches or guidelines for the use of GenAI among their instructors, students, and staff, potential benefits may not be leveraged, and risks may worsen if they go unmonitored. As such, a thorough understanding of the benefits and risks associated with GenAI use is critical to promoting its responsible use in higher education, as well as to developing trust in it, as ethical standards, transparency, and responsible implementation are critical to ensuring constructive engagement. Since an understanding of these benefits and risks currently remains fragmented and constrained in scope, the main objective of this paper is to identify and analyze the perceived benefits and risks of GenAI use in higher education. The overarching goal being to foster trustworthiness in the technology, and trust in the use of these systems. This paper is intended to provide help to higher education institutions seeking to take advantage of the transformative potential of GenAI in a way that leverages benefits, mitigates risks, and fosters trust in these systems.
G. Akrong, M. Evans, A. Wensley· European Conference on Knowl...· 0 citations
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
Recent literature highlights that generative artificial intelligence (GenAI) is expected to play a central role in knowledge-intensive work, particularly in sectors strongly exposed to technological innovation. Beyond facilitating existing knowledge management (KM) practices, GenAI appears to introduce a fundamentally new epistemic dimension that challenges traditional models of organizational knowledge creation, most notably Nonaka’s SECI model. While the SECI framework has long explained knowledge creation through the dynamic interaction between tacit and explicit human knowledge, it does not explicitly account for knowledge processes emerging from human-AI interaction. Drawing on these premises, the aim of this research is to investigate how GenAI alters knowledge creation dynamics and to identify new knowledge conversion mechanisms emerging from human-machine interaction. To achieve this, the study adopts an exploratory single case study within the IT sector, an environment characterized by high innovation intensity and rapid technological change. The selected case organization was chosen based on its positioning along three key dimensions known to influence technology adoption: orientation to innovation, technological culture and organizational complexity. Data were collected over a period of one year and five months through thirty semi-structured interviews with employees across R&D, operations and senior management. Data were triangulated by direct observation and analysis of project documentation. The qualitative data were analysed using content analysis supported by inductive coding techniques inspired by grounded theory. This process led to the construction of an inductive coding tree capturing core themes and their relationships. Findings reveal that GenAI generates knowledge dynamics that cannot be fully mapped onto the traditional model. In particular, new knowledge conversion modes emerge between human knowledge, machine-generated data and artificial knowledge. These include transformations from human tacit and explicit knowledge into data, from data into artificial knowledge and from artificial knowledge back into human explicit knowledge. Alongside these conversion modes, several conceptual domains were identified. Based on these results, the study proposes a novel conceptual framework that integrates the SECI model with a machine dimension, offering a more comprehensive representation of contemporary knowledge creation in organizations which integrate GenAI into their processes. The framework highlights the importance of strategic integration of GenAI into KM processes and the value of human-in-the-loop validation. This research contributes to KM theory by extending classical models to account for artificial knowledge generation and provides a foundation for future empirical studies on human-machine knowledge ecosystems.
G. Liccardo· European Conference on Knowl...· 0 citations
The rapid evolution of Generative AI (GenAI) has transformed the ways in which knowledge is created, shared, interpreted, and applied in organization and educational contexts. While earlier studies have often focused on the technical capabilities of GenAI or on the detection of synthetic content, less attention has been given to how these tools influence knowledge management processes at the individual and group levels. This study addresses that gap by examining how GenAI affects the production, transfer, validation, and use of knowledge among individuals and within collaborative settings. The aim of this study is to explore the current state of 'AI vs AI' by generating a dataset comprising texts, images, audio and video, utilizing a set of freely available AI tools. This pilot illustrative study was based on a two-stage strategy: in the first stage synthetic media was generated with different GenAI tools to mimic human creativity. A second stage, which involved a rigorous evaluation of AI-detection tools for each modality separately. Texts were created in three different GenAI tools and Images were prompted to mimic Renaissance paintings by Michelangelo and Raphael. Audio was generated with the purpose of mimicking interviews with the Renaissance painters. Videos, finally, were created to be deceptive deepfakes, placing persons in environments and situations where they never have been in real life. Findings provide a comparative analysis of tools' accuracy and false-positive rates that could act as valuable guidelines in the increasing flood of AI-generated content. This research contributes to the growing discussion on how organizations and institutions can manage the opportunities and risks associated with GenAI in knowledge-intensive environment. The study highlights the need for critical AI literacy, transparent knowledge practices, and governance mechanisms that ensure the responsible integration of GenAI into individual and collaborative knowledge work.
Anastasiia Iufereva, Peter Mozelius· European Conference on Knowl...· 0 citations
We use cookies to run the site and, with your consent, for analytics and to show ads.
See our Cookie Policy.
