Aug 2026· European Conference on Knowledge Management· Vol 27, pp. 422-428· 0 citations· 47 references
TL;DR
This study embarks on conceptualizing knowledge sufficiency in the era of contemporary digital technologies and elaborating the degree of technological assistance required to reach the elusive levels of knowledge sufficiency in complex, non-routine organizational tasks and processes, to overcome the hurdle related to “algorithmic aversion”.
Abstract
Knowledge management (KM) and the related literature on intellectual capital (IC) have a long-standing tradition of emphasizing the fundamental role of human knowledge, including know-how, reasoning, and judgement, as the central pillar of organizational decision-making and value creation. It represents one of the key aspects of what has been traditionally considered as “sufficient” knowledge utilized in knowledge work and to successfully conducting complex knowledge-intensive work tasks. In this school of thought, humans are seen as reliable agents while non-human sources, such as digital technologies, pose technical and institutional risks. Simultaneously, a corpus of scholarly work in management and information systems has argued and provided empirical evidence on the superiority of digital technology enabled data-driven decisions in terms of their quickness and accuracy, claiming that algorithms consistently outperform humans in decision-making. In this school of thought, the cognitive limitations of human agents and the risk of potential occurrence of human error are underlined, while the processing power and consistency of digital technologies considered a gold standard. To overcome the hurdle related to “algorithmic aversion” yet mitigating the risk of falling to the trap of “artificial certainty”, this study embarks 1) conceptualizing knowledge sufficiency in the era of contemporary digital technologies and 2) elaborating the degree of technological assistance required to reach the elusive levels of knowledge sufficiency in complex, non-routine organizational tasks and processes. Through conceptual work and empirical snippets, this study helps organizations maximize the benefits of technology-driven knowledge while safeguarding the uncertainties of critical decisions, helping them to successfully navigate the landscape of contemporary digital technologies.
In the present day, the idea of personal knowledge, which was introduced by the philosopher of science Michael Polanyi in the 20th century as a critique of logical positivism, serves as the cornerstone for various contemporary lines of research in the field of the humanities and social practices. This became possible by incorporating the individual as a fundamental element in pedagogical, economic, and sociological studies. The theory of knowledge management developed by I. Nonaka, which is widely recognized in both academia and practice, is rooted in the concept of personal knowledge, effectively adapting it to the context of the emerging knowledge economy. Nevertheless, the advent of large language models (LLMs) fundamentally alters the dynamics between cognitive abilities and knowledge, prompting us to rethink the future of the job market and education as a societal institution. In this context, knowledge management theory must be revisited, considering the capabilities of LLMs and the exponential growth in the quantity of diverse information. In this context, we envision a potential path for sustaining and advancing higher education through the adoption of tailored knowledge management frameworks. However, it is crucial to acknowledge that these frameworks do not fully encompass the essential elements of M. Polanyi’s concept. The transformative nature of technological and societal advancements has necessitated a reevaluation of aspects that were once considered secondary. The aim of this paper is to delve into the intricacies of M. Polanyi’s theory, which holds the potential to enrich and revitalize the field of knowledge management in light of the increasing prevalence of large language models.
V. Konoplev· Bulletin of Liberal Arts Uni...· 0 citations
The rapid diffusion of artificial intelligence technologies is reshaping the foundations of organizational development, creating new challenges for knowledge management and innovation management. The relevance of the study is determined by the growing need to rethink the role of human capital in an environment where intelligent algorithms increasingly participate in information processing, decision support, and innovation activities. The purpose of the research is to identify the key directions of human capital transformation under the diffusion of artificial intelligence and to determine their implications for knowledge management systems and innovation management practices in contemporary organizations. The methodological framework of the study is based on an interdisciplinary approach that combines the concepts of human capital theory, knowledge-based management, innovation management, and digital transformation. The research applies methods of theoretical generalization, comparative analysis, synthesis, abstraction, and systems thinking to reveal the relationships between human competencies, organizational knowledge resources, and artificial intelligence technologies. The use of a conceptual modeling approach made it possible to identify the mechanisms through which digital technologies influence the creation, dissemination, and practical application of knowledge within organizations. The findings demonstrate that the diffusion of artificial intelligence contributes to the emergence of new forms of interaction between employees and intelligent systems, transforming traditional approaches to knowledge creation and innovation processes. Human capital is increasingly characterized not only by the possession of professional expertise but also by the ability to integrate digital capabilities into decision-making and problem-solving activities. The study reveals that organizational competitiveness is becoming strongly dependent on the effective combination of human creativity, critical thinking, learning capacity, and algorithmic support. It is substantiated that knowledge management evolves from a process focused primarily on information accumulation toward a dynamic system that facilitates continuous learning, knowledge integration, and collaborative value creation. Simultaneously, innovation management is shifting toward more flexible and adaptive models that encourage experimentation, rapid knowledge exchange, and the development of innovation ecosystems supported by digital technologies. The practical value of the article lies in the development of conceptual guidelines for organizations seeking to strengthen their innovative potential and enhance the effectiveness of knowledge management in the era of artificial intelligence. The obtained results may be used for designing managerial strategies aimed at developing human capital, improving organizational learning processes, promoting knowledge sharing, and creating conditions for sustainable innovation-driven growth in the digital economy.
Unknown authors· State and regions Series Eco...· 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 proliferation of various artificial intelligence (Al) technologies in both organisational and academic ecosystems has irrevocably transformed the way institutions produce, interpret, and leverage information for decision-making. Paradoxically, the increasing complexity of financial systems, research environments and knowledge infrastructures has strained fragmented disciplinary approaches. This paper presents an interdisciplinary conceptual framework that links the fields of Artificial Intelligence, Financial Management and Library & Information Science (LIS) to explore how financial intelligence and scholarly communication through Al-enabled systems are engaged in digital libraries and institutional governance. From the paper; Al should not simply be understood as a technological tool but rather also as a strategic and epistemic force capable of determining what we consider risk, how we allocate resources, who has access to knowledge, how users interact with these systems, and ultimately, evidence-based decision-making. Based on theories in financial governance, technology adoption and usage, knowledge management, and information behavior the paper a) synthesizes major scholarly streams to propose an innovative conceptual model that ties together Al capability, digital knowledge infrastructure, information trust, decision intelligence and institutional performance The piece goes on to explain that the future of research support, funding strategy, and institutional performance will increasingly depend upon how institutions can harness intelligent systems in combination with ethical governance, human expertise, and knowledge shareability. At a conceptual level, the paper contributes by connecting three pieces that researchers rarely connect and providing a publication-oriented resource for future empirical research. This research speaks to academics, financial professionals, librarians, policymakers and leaders in higher education who strive to construct resilient, data-informed or ethical institutions amid an age of intelligent automation.
Paras, Mukesh Kumar, Sapna· International Journal of Inf...· 0 citations
The use of Generative Artificial Intelligence (GenAI) is deeply impacting all Knowledge Management (KM) processes. In particular, due to its ability to generate new content in different forms, the new technology is deemed capable of deeply transforming the knowledge creation process, which is considered the highest and most impactful stage of KM processes. Despite this, a comprehensive understanding of how companies can leverage GenAI to create organizational knowledge is lacking, both empirically and theoretically. Regarding the latter, scholars have recently underlined that prior research has yet to focus on the transformation of the SECI model and Ba theory, the most widely used conceptual frameworks for interpreting the organizational knowledge creation process, in the era of human-intelligence interaction. However, in the last two years, some studies have examined whether the SECI model needs to be revised in light of GenAI. Based on a review of the 19 systematically identified articles on Scopus, the present paper identifies, discusses, and compares the three different conceptual approaches adopted by scholars in dealing with the topic in question: a) applying the SECI model in its original version; b) adapting the original SECI model with small adjustments; c) developing a new SECI-based model. The paper compares the three approaches, highlighting how they assume different notions of the role of GenAI in the knowledge creation process and the types of knowledge involved. The academic and practical implications that arise from the study are discussed in the conclusions.
E. Scarso, K. Kirchner· European Conference on Knowl...· 0 citations
It is argued that successful digital HRM in Romania goes well beyond the simple adoption of new technologies, and depends on sound governance, effective human control, sustained investment in digital skills and genuine inclusion measures, all aligned with European standards.
Ionuț Drăgulescu, Daniel Danilov, Maria Dumitrache et al.· The Annals of the University...· 0 citations
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