European Conference on Knowledge Management

Knowledge Management in Nuclear Waste Disposal: Approaches and Challenges in Germany

Gunnar Hoefer — 2026-08-25

Knowledge management (KM) in the field of nuclear waste disposal in Germany was largely neglected in all predecessor organisations and existed mostly within a loose framework of a substandard data archiving system. A reliable KM structure was only established since 2019. The KM team has gradually established various KM methods and tools to meet the current and future needs of colleagues. KM work is, and continues to be, marked by multitude challenges such as an ongoing major company reorganisation for the last years. Reduced KM workforce, loosening interest on KM and the longtime awaited, still missing implementation of Artificial Intelligence (AI) are making the work increasingly difficult.

From Knowledge Retrieval to Execution: Designing Executable Knowledge Systems

Sara Michelazzo — 2026-08-25

Generative AI is changing the role of knowledge in organisations. Traditional knowledge management (KM) systems have primarily supported storage, access and retrieval, assuming that knowledge is interpreted and applied by human users. In AI-enabled environments, however, organisational knowledge increasingly becomes a direct input into execution, shaping generated proposals, analyses, summaries, recommendations and other workflow outputs. This shift exposes a limitation of retrieval-oriented KM: fragmented, outdated or weakly governed knowledge can be amplified through AI-generated outputs, reducing consistency, reliability and trust. This paper introduces executable knowledge systems as a conceptual model for structuring organisational knowledge to support reliable human and AI-assisted execution. The term executable is used in a socio-technical sense. Knowledge does not necessarily become code, but is curated, validated and embedded into workflows so that it can guide outputs, decisions and actions. The paper distinguishes this concept from prior work on executable knowledge graphs and executable knowledge bases, which primarily focus on deterministic execution through rules, scripts or formalised representations. The paper further develops a framework of decay and compounding loops to explain how AI-enabled knowledge systems evolve over time. In decay loops, AI-generated outputs re-enter the knowledge environment without sufficient validation, allowing inconsistency and low-quality knowledge to accumulate. In compounding loops, curated knowledge assets are refined through governed feedback, domain ownership and controlled reuse, enabling improvements in reliability over time. The framework is informed by an exploratory case study within a global professional services organisation, where a curated knowledge environment was introduced to support AI-assisted workflows in the Retail, Consumer Products, Travel and Transportation domain. The evaluation compared outputs generated from a controlled, subject matter expert (SME)-validated knowledge dataset with outputs generated from an unconstrained organisational knowledge base. Findings indicate improved retrieval relevance and output quality when AI systems operate on validated knowledge assets. The paper contributes to KM research by reframing KM as a system design challenge for AI-enabled execution and by positioning governance, validation and feedback control as central mechanisms for reliable organisational knowledge use.

Knowledge as the Energy Flow of the Project Ecosystem

Tiziano Villa — 2026-08-25

An ecosystem is a community of organisms and their physical environment interacting together. Ecosystem involves both biotic (living) organisms (plants, animals, microbes) and abiotic (non-living) physical surroundings (air, water, soil, sunlight). A project is an ecosystem because it is a dynamic, interdependent community of living parts (stakeholders) and non-living parts (OPA – Organizational Process Assets and EEF – Enterprise Environmental Factors). The parts of an ecosystem are linked together through an energy flow, essential for its survival. Energy flow relies on a source, mostly solar. If energy flow stops, an ecosystem will rapidly and completely collapse. Without a continuous flow of energy to maintain complex structures, biological organization breaks down into simple components. Similarly, knowledge is the energy flow that assures the life of the project ecosystem. Without knowledge project naturally moves toward disorder, entropy and chaos. The local profile of the single stakeholder (history, values, and culture, reputation, distinctive skills, organizational structure, and, last but not least, its interest and power over the project), prevails over the global dimension of the project (common goal, synergies, collective way of working, contamination of ideas, points of view, perspectives). This paper is aimed at discussing how to address the peculiarities of a project ecosystem through the energy flow of knowledge. Project peculiarities are: limited life cycle; interdependent stakeholders; fragile relationships; adaptation and learning; emergent value; agency for change. First of all, each peculiarity is briefly explained in terms of meaning, strengths and weaknesses. Then, the practical use of knowledge is discussed for enhancing strengths and reducing weaknesses. Finally, some advices are provided to project key roles, such as project manager, subject matter experts, agile coach, in order to become more familiar with knowledge as the energy flow of the project ecosystem. The topics of this paper may effectively feed the integration of knowledge management other business disciplines, project management first and foremost.

Integrating Organizational Knowledge Sources into a Knowledge Management Portal

Negar Erfani Jazi — 2026-08-25

This paper presents a practical (non-academic) case study of organisational knowledge-resource sharing through the corporate knowledge management portal at Mobarakeh Steel Company in Isfahan, Iran. It explains how a structured portal was designed and implemented to integrate dispersed knowledge assets, improve access to organisational experience, and support learning across operational and managerial domains. In many industrial organisations, valuable knowledge is created through operations, maintenance and repair, projects, audits, training, and continuous improvement activities; however, retrieval and reuse are often difficult because knowledge artefacts are distributed across multiple units and information systems. The experience reported in this paper shows how the portal was developed as a pragmatic response to this dispersion challenge. The portal was designed to provide a single point of access to a portfolio of knowledge resources, while explicitly considering organisational structure, content ownership, and access-control policies. The initiative was informed by established knowledge management concepts as well as practical frameworks for assessing and developing knowledge management capabilities (Alavi and Leidner, 2001; Bhatt, 2001; Davenport and Prusak, 1998; APQC, 2025). The paper also describes how the organisational context influenced the portal’s evolution. Prior experience in technology transfer, the implementation of management systems, the development of quality procedures and document control, training and empowerment mechanisms, and organisational excellence initiatives provided the foundation for more structured management of knowledge resources. Therefore, the portal emerged not as a standalone digital tool, but as part of a broader organisational effort to capture, reuse, and share knowledge. In this process, institutional standards and approaches, including ISO-based practices, contributed to structuring and consolidating the initiative (ISO, 2019).

AI-Enabled Digital Knowledge Systems: Implications for Organizational Learning and Strategic Decision-making

Peter Mkhize — 2026-08-27

Artificial intelligence (AI) is transforming digital knowledge systems (DKS), reshaping how organizations generate, interpret, and apply knowledge in support of organizational learning and strategic decision-making. While AI-enabled systems enhance analytical capability, predictive insight, and information processing speed, their implications for learning quality and epistemic judgment remain insufficiently understood. This study addresses this gap through a systematic literature review that synthesises research across knowledge management, information systems, and organizational learning. Adopting a concept-centric approach, the review analyses peer-reviewed studies published between 2000 and 2026, focusing on how AI-enabled digital knowledge systems are conceptualised and how they influence knowledge processes, organizational learning, and strategic decision-making. The findings indicate a shift from viewing digital knowledge systems as passive repositories toward conceptualising them as mediating infrastructures that augment, generate, and orchestrate knowledge. These systems expand knowledge creation through data-driven insights, enhance knowledge sharing through algorithmic mediation, and increasingly shape how knowledge is applied in decision contexts. However, the analysis also reveals a tension between informational efficiency and authentic learning. While AI-enabled systems support sensemaking and improve decision support under conditions of uncertainty, they may encourage overreliance on algorithmically generated outputs, reduce opportunities for critical reflection, and narrow interpretive perspectives. These dynamics highlight the importance of governance conditions, including epistemic transparency, trust calibration, and learning-oriented organizational practices. The study develops an integrative framework that positions AI-enabled digital knowledge systems as mediating infrastructures linking knowledge processes to organizational learning and strategic decision-making under specific governance and learning conditions. The findings contribute to knowledge management and information systems research by providing a coherent conceptualisation of how AI-enabled systems reshape the epistemic foundations of learning and decision-making in contemporary organizations.

Reclaiming Organizational Knowledge in an Age of Post-truth Disruption

Marta - Christina Suciu — 2026-08-27

The paper is dedicated to the post-truth mini track. There are unprecedented challenges in terms of the reliability of data, knowledge, information that address important questions regarding traditional KM. The paper main goal was to investigate the systemic mechanics of organizational communication and strategic clarity, by isolating the behavioral patterns of the institutional “bullibility” and the cognitive exhaustion. From a methodological point of view, the paper uses mixed-methods that support an interdisciplinary approach. Psychometric scale evaluations demonstrated internal consistency for both the Corporate Bullshit Receptivity Score (CBRS; α = 0.935) and the Operational Clarity Baseline (OCB; α = 0.801). Pearson’s r correlations revealed a significant, positive relationship between jargon receptivity and operational data baselines (r = 0.433, p < 0.001), alongside a significant link between high reliance on operational clarity and heightened crisis-induced jargon exhaustion (r = 0.220, p = 0.011). The paper argues that, in order to navigate post-truth complexity knowledge-based organizations have to move further the pseudo-profound administrative jargon. We highly recommend that organizations have to apply a more human-oriented transformational leadership model, capable of driving cooperation and collaborative dialogue through decentralized networks of competent teams and communication frameworks rooted in empathy and transparency.

Integration of AI-Generated Information Into Organizational Knowledge Through Coherent HRM- and KMM Processes

Marco Wiersma — 2026-08-27

This position paper argues that the integration of AI-generated information has the potential to disrupt existing human agencies in organizational routines. This disruption of agency is a result of displacement of judgement particularly when routine automation reduces human agency to higher dependencies on these AI outputs leading to lower sense-making. Reinforcement of agency requires conscious moves between different epistemic modes translated into Human Resource Management (HRM) and Knowledge Management (KMM) frameworks and processes. Such a robust epistemological framework epistemological framework relies on orchestrating coherence between high-level exploratory knowledge and low-level pragmatic knowledge that enables justifying, validating by human agents.

Knowledge Management and Public Value in Municipal Mergers: The Nuova Pescara Case

Fabio Zuccarini — 2026-08-27

Municipal mergers are commonly examined through their effects on administrative efficiency, expenditure, and service delivery. Yet, their success also depends on the capacity to integrate the knowledge, competences, routines, and institutional memories embedded within previously autonomous organizations. This paper reconceptualizes municipal amalgamation as a knowledge-intensive organizational transformation and investigates how Knowledge Management (KM) can support organizational integration and Public Value co-creation. The study adopts a conceptual research design supported by an exploratory case study of Nuova Pescara, one of the largest municipal merger processes currently underway in Italy. Drawing on documentary and institutional evidence, the analysis interprets the merger’s as-is, to-be, and gap analysis phases through the lenses of Knowledge Management, Organizational Learning, and Public Value. The findings suggest that administrative integration should be understood as the outcome, rather than the starting point, of a progressive knowledge integration process. Building on these insights, the paper develops a Knowledge Integration Governance Framework in which knowledge transfer, combination, and creation, supported by organizational learning and Strategic Human Resource Governance, transform distributed knowledge into shared organizational capabilities. The study contributes to Knowledge Management research by positioning knowledge integration as a strategic governance capability enabling institutional transformation and creating the organizational conditions for Public Value co-creation.

Understanding Healthcare Professionals' Adoption and Sustained Use of AI Medical Technologies in Saudi Arabia's Vision 2030 Context

Khawlah Alyami — 2026-08-25

Despite Saudi Arabia’s Vision 2030 AI strategies, a significant gap remains between policy measures and the sustained clinical integration of AI in healthcare. This research employs a sequential exploratory mixed methods design to investigate the transition from initial adoption to continuous usage among practitioners. Currently, the qualitative phase is complete, with thematic analysis underway for interviews conducted with physicians, radiologists, and nurses across the Kingdom. Emerging findings reveal that trust in autonomous AI is not a static construct but evolves through distinct phases: from initial distrust to hesitant reliance, and finally to trustful integration. Preliminary data further suggests that workflow complexity and organisational readiness pose more significant barriers to sustained use than the technical performance of the AI itself. Theoretically, this study proposes a framework for "Adaptive Clinical Integration" that identifies key acceptance factors and explores how Agile project management practices can be adapted to manage the unique complexities of AI-Based Medical Devices (ABMDs). By bridging individual acceptance models with agile science, this research provides a context-specific roadmap for Saudi policymakers to ensure the successful, sustained assimilation of high-stakes healthcare AI.

The Adoption of Green Knowledge Management in Startups: A Comparative Landscape

Furong Cai — 2026-08-25

In the context of the global twin transition towards digitalisation and sustainability, startups increasingly rely on Green Knowledge Management (GKM) to integrate environmental objectives into their core operations. Although GKM is widely recognised as a relevant enabler of sustainability-oriented entrepreneurship, the existing academic literature remains fragmented. It is often limited to single-country empirical settings and frequently treats GKM as a monolithic construct rather than a complex set of distinct organisational routines. This study addresses this critical gap by providing a comparative analysis of GKM adoption between startups operating in China and Europe. Grounded in Institutional Theory and the Dynamic Capabilities View, the study conceptualises GKM as a multidimensional construct comprising five sub-dimensions: green knowledge acquisition, storage, sharing, application, and creation. To examine whether these dimensions exhibit comparable patterns across the two distinct regions, the study utilises a cross-sectional survey of 679 startups, comprising 302 firms in China and 377 in Europe, employing measurement invariance assessment to ensure robust cross-group comparability. The findings indicate statistically significant differences across all five sub-dimensions, with Chinese startups reporting consistently higher average maturity scores than their European counterparts. Crucially, these results should not be interpreted as universal performance hierarchies. Rather, they highlight region-specific baseline profiles that may reflect differences in institutional environments, support structures, and entrepreneurial ecosystems. While the observed pattern is consistent across all knowledge dimensions, the study acknowledges the descriptive nature of these capability gaps without claiming strict causal identification of the underlying drivers. This study contributes by establishing an initial comparative benchmark for multidimensional GKM across contrasting institutional contexts. Furthermore, it provides an empirical foundation for future work to investigate the mechanisms linking institutional pressures and GKM development using advanced structural modelling. From a practical perspective, the findings suggest that sustainability support for startups should be tailored to regional conditions and to the specific knowledge processes that require strengthening.

Organizational Unlearning in Angolan Ports: A Path Toward Digitalization and the Blue Economy

Osvaldo Cruz — 2026-08-25

Studies on organizational unlearning show that, although researchers recognize its importance and research on the topic has increased substantially in recent years, significant gaps remain in the literature. Early research neglected its potential as an autonomous organizational capacity, and there is a scarcity of studies in critical infrastructure sectors, such as ports, while the African context remains virtually unexplored. In this regard, the present study analyzes the role of organizational unlearning as a critical factor in the digital transformation of Angolan ports, within the context of the blue economy. The research adopted a quantitative, descriptive, and correlational approach, based on a sample of 248 employees at different hierarchical levels in Angolan ports, who responded to a structured questionnaire. The data were analyzed using descriptive statistics, reliability and validity tests (Cronbach’s alpha, AVE, and CR), Pearson’s correlation, and linear regression models to test the proposed hypotheses. The empirical results reveal that organizational culture exerts a positive and statistically significant influence on organizational unlearning practices, confirming its role as the primary antecedent of this process.

Knowledge-based Analysis of Open Data

Marius Marian Dinu — 2026-08-25

There is a shortage between the emerging availability of time stamped open data and the capacity of extents in the application sphere to turn it into a reliable source of knowledge without the help of data scientists. Time series now concern every aspect in our daily life, from environmental monitoring to music, or the telemetry of our actions on social media. This struggle of understanding the underlaying data behaviour deepens when considering the heterogeneity problem of the scattered series, that must be handled consistently to produce a reliable source of knowledge. The current approach proposes to target this bottleneck by generating a versatile and domain-neutral representation of data, containing statistics, designed to be consumed both by people (via a brief interaction) and by systems (via a standardized enriched structure). This method has the aim of converting tacit data into explicit artefacts, prioritizing time series knowledge representations. The paper will present a knowledge-based approach to time series interpretation, which takes a dataset from an open data source, without domain assumptions, transforms it into a common format, with semantic enrichment of metadata and includes analytical results. The open data are mapped onto a sub-part of the SemTS (Semantic Time Series) ontology, so that series characteristics can be stored and reused as knowledge artefacts. The final output is a knowledge specialized JSON, partitioned into insightful sections (e.g. dimensions, feature families), plus explanations in natural language, with the aim of reducing comprehension barriers for non-specialists and facilitating subsequent integration into knowledge management flows, like cataloguing, searching or comparing. The proposed format encompasses insights regarding the series, drawing a profile for the series that includes quality indications (e.g. missingness, gaps, outliers), trend patterns, seasonality (cyclicality). The contribution is a practical knowledge aspect for time series analysis, a tool to convert raw time-stamped open data into reusable knowledge artefacts, that could guide downstream decisions in the data discovery procedure. The intent is to assist with accessible diagnostics, rather than to replace analytical expert judgment.

Belonging and Knowledge Articulation in Epistemically Fragile Communities of Practice

Kirsten Garbini — 2026-08-25

Organisations operate under conditions of social and technological acceleration‚ contested knowledge, and epistemic instability. Knowledge articulation is made difficult not only because knowledge is tacit but because the legitimacy of the knower is uncertain․ This paper draws upon qualitative research conducted with two women-centered Communities of Practice (CoPs) showing how such spaces , were experienced by the women who participated in them less as formal, professional‚ knowledge-sharing communities and more as relational spaces for recognition‚ belonging and epistemic legitimacy․ In particular‚ it reflects on how the spiritually-inflected relational practices of these CoPs enabled women community members to articulate knowledge and experience in ways that they generally found more difficult to do in other more formal settings in their organisations which were characterised by hierarchy‚ alienation and risk of testimonial injustice․ This paper adds to KM scholarship through showing how relational recognition and spiritually-inflected belonging in these two CoPs created conditions in which knowledge became articulate․

A Taxonomy and Reference Architecture for AI-Enabled Industrial Knowledge Management

Mohamed Amine Guedria — 2026-08-25

Much of the knowledge that keeps an industrial operation running is never written down: it sits with individual employees, scattered across incompatible systems, and cannot be found in time—a risk that becomes acute whenever people change roles or retire. Artificial intelligence (AI) is widely proposed as a remedy, but existing approaches concentrate on documentary, white-collar work; embodied, blue-collar work is comparatively underserved, particularly by large language models with no native grounding in physical activity. Existing work also treats AI as a single undifferentiated capability, leaving practitioners without a principled basis for choosing among technologies or integrating their outputs. This paper proposes a taxonomy of five AI paradigms (perceptive, dialogic, interpretive, structural and contextual), each defined by its contribution to one of three knowledge processes (capture, structuring, transfer) and by the type of work it serves, with a boundary marked where collective tacit knowledge resists codification. A separate orchestration layer, realised by autonomous agents and enabling technologies (knowledge graphs, retrieval-augmented generation, augmented reality), connects the paradigms into a pipeline. Applied diagnostically to three tools in one manufacturer’s training programme, an assembly-guidance system, a structured interview system, and a RAG-based conversational assistant, the taxonomy shows all three occupy the capture or transfer columns while structuring goes unserved, leaving each tool’s knowledge inaccessible to the others. A pump-assembly scenario shows how an agent-orchestrated pipeline over a shared knowledge graph, with human validation, could unify their outputs. The tools’ reported gains, a 29 per cent onboarding-time reduction and an over 90 per cent retrieval-time reduction for 3,000+ daily users, are taken at face value; whether integration compounds them, and for whom, is a working hypothesis, not a demonstrated result. The paper concludes with a staged evaluation strategy measuring cross-tool retrieval coverage and validation throughput to isolate the structural layer’s impact.

Tax Incentives and Knowledge Heterogeneity: Short‑Term Stimulus or Long‑Term Stock?

Hao He — 2026-08-25

This paper unpacks the black box of knowledge accumulation by examining how tax incentives affect three distinct types of knowledge: high‑quality (highly cited) patents, AI patents, and green patents. We find that the long‑term accumulation effect is not uniform across knowledge types. High‑quality and AI patents respond significantly only in the short term (lag 1), with no long‑term accumulation; green patent quantity expansion generates bubbles without quality improvement. These findings suggest that durable knowledge accumulation is primarily driven by ordinary, low‑depreciation patents. From a knowledge management perspective, this implies that policies should distinguish between "short‑shelf‑life" and "long‑shelf‑life" knowledge, and avoid over‑incentivizing short‑lived innovation at the expense of durable knowledge infrastructure.

Working with AI: A Practice-Based Study of Knowledge Sharing in a Multinational Enterprise (MNE)

Xiang Li — 2026-08-25

Knowledge sharing is central to intraorganisational collaboration in multinational enterprises (MNEs), where employees work across functions, locations and linguistic contexts. Existing research has highlighted the potential of AI to enhance knowledge management and organisational performance, but there is still limited understanding of how employees use AI in their everyday knowledge sharing practices in MNEs. This study therefore examines how AI influences these practices within an anonymised MNE, referred to as GlobalMFG. The study adopts a qualitative single-case study design and draws on six semi-structured interviews with managers and knowledge workers from different organisational functions. The findings demonstrate that AI supports knowledge sharing by improving access to information, assisting communication across geographically dispersed teams and supporting meeting follow-up. Its effectiveness remained dependent on employees’ ability to formulate appropriate prompts and assess how AI-generated outputs could be applied within specific organisational contexts. AI was primarily used before and after interpersonal interactions. Experienced colleagues continued to be the preferred source of specialist and contextual knowledge. Although organisational documents reflected a strong strategic commitment to AI adoption, interview data revealed inconsistencies in access to AI tools, system integration, and governance, resulting in uneven implementation across the organisation. These findings highlight the need to connect AI investment with clear governance, consistent access to approved tools and task-specific employee support. Establishing ongoing employee feedback mechanisms can further support the responsible, effective, and contextually appropriate adoption of AI-enabled knowledge sharing practices within such MNEs.

Rethinking Knowledge Creation in the Age of Digital Innovation: insights from IT Sector

Giuseppe Liccardo — 2026-08-25

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.

Tracing Knowledge Flows in Financial Markets with Hybrid Sentiment AI

Peter Németh — 2026-08-25

Financial markets are commonly described as information-efficient, yet the phrase conceals more complexity than it clarifies. Between the arrival of information and the adjustment of prices lies a process that is neither fully observable nor mechanically uniform. Interpretation mediates how signals are received, credibility shapes their perceived relevance, and responses unfold unevenly across participants. From a knowledge management perspective, this raises a central question: how does dispersed information consolidate into knowledge that meaningfully informs decisions? The framework we developed does not seek to model the entire market mechanism. We concentrate instead on a measurable segment of that transformation. Media sentiment is examined using a hybrid analytical approach that combines the lexicon-based VADER model with the domain-specific transformer model FinBERT. The two models do not always converge, and this divergence provides additional insight rather than methodological weakness. Their integration enables us to capture both surface polarity and contextual financial nuance within a single structure. Because informational inputs differ in reliability, sources are weighted by credibility and filtered through an entity-based relevance mechanism. Sentiment scores are aggregated across adjustable time windows, enabling us to detect whether informational pressure accumulates before a price adjustment. Using selected S&P 500 firms characterised by sustained media exposure and high liquidity as the empirical setting, we conduct structured backtesting to evaluate directional alignment and interval coherence across multiple horizons. The main objective of this paper is to assess whether consistent relational patterns can be identified. Our results indicate that such patterns are present, although their strength varies across horizons and informational density. In some instances, aggregated sentiment precedes adjustment, while in others it dissipates without measurable impact. This unevenness suggests that sentiment analysis may be more appropriately understood as a mechanism for tracing knowledge flows within financial ecosystems rather than as a deterministic forecasting device. From this perspective, our study contributes to knowledge management by offering a computational approach for examining how dispersed information becomes actionable knowledge in high-velocity environments.

Reframing Substantive Privacy Compliance as Dynamic Capabilities – An Integrated Conceptual Model

Uyen Hong Nguyen — 2026-08-25

Despite the widespread introduction of privacy protection laws globally, many organizations continue to treat privacy compliance as an administrative practice focusing on compulsory policies and documentations. However, many organizations have treated privacy protection compliance as a way to enhancing organizational dynamic capabilities from effectively using technology, people and processes required to implement privacy protection. Drawing on the Technology-Organization-Environment (TOE) model, Institutional Theory, and the Dynamic Capabilities View (DCV), the paper proposes that developing Substantive Privacy Compliance (SPC) through meeting regulatory, organizational, and technological requirements enables organizations enhance their dynamic capabilities. Theoretically, it fills the gap between the institutional legitimacy view and the dynamic capabilities lens, providing a comprehensive explanation of how external pressures can develop adaptive organizational capabilities. Practically, the model emphasizes the critical role of integrating privacy governance in both strategic and operational activities in enriching the firm’s resilience and sustainable competitiveness.

A Sociomaterial Framework for Orchestrating Knowledge Co-Creation in Expert Communities

Niina Elina Salo — 2026-08-25

Complex societal challenges increasingly require knowledge co-creation that unites diverse expertise across organisational and sectoral boundaries. Such collaboration does not usually arise spontaneously but requires orchestration practices to coordinate actors from different fields. Previous research on orchestration has examined networks, expert communities, digital platforms, collaborative processes, and material artifacts largely as separate streams of research. However, less attention has been paid to sociomaterial orchestration in voluntary expert communities on digital collaboration platforms. Therefore, the research question is “How is knowledge co-creation orchestrated in expert communities on a digital collaboration platform?” The empirical setting is based on the Fast Expert Teams model developed at LUT Business School, which invites volunteer experts from different sectors to engage in pro bono work on complex societal challenges through a four-week digitally mediated collaboration process. The study adopts a Participatory Action Research (PAR) approach and qualitative methods, through which the research team both implemented FET initiatives and conducted research. The empirical data consist of six FET collaboration processes, ranging from approximately 50 to over 100 participants, from which rich longitudinal qualitative data were collected. The study draws on process analysis and practice-based theorizing. The study develops a sociomaterial framework for orchestrating knowledge co-creation in expert communities. Sociomaterial orchestration aligns social, processual, technological, and material dimensions to enable collective outcomes. The framework is scalable and suitable for orchestrating initiatives across different forms of collaboration, from small-scale local projects to global networks.

From Compliance to Integration: A Knowledge Management-Based Maturity Model for Quality Culture in Portuguese Higher Education

Sílvia Santos — 2026-08-25

Higher Education Institutions (HEIs) are increasingly required to move beyond formal compliance with Quality Management Systems (QMS) towards the development of genuine Quality Cultures — organisational environments in which improvement is collectively owned, evidence-based, and knowledge-driven. Yet, despite growing theoretical interest in the intersection of Knowledge Management (KM) and Quality Culture (QC), empirical investigation of this relationship remains notably scarce, particularly within the European context, where quality assurance research has historically prioritised structural and regulatory dimensions over the cultural and knowledge processes that sustain them. This paper presents the conceptual framework and preliminary empirical findings of an ongoing doctoral research project. The study makes two contributions: it empirically investigates the mediating role of KM processes — namely knowledge creation, acquisition, sharing, sensemaking, retrieval, and utilisation — in the relationship between TQM frameworks and QC within Portuguese HEIs; and it introduces the Quality Culture Maturity Model (QCMM), an inductively derived four-stage developmental framework grounded in the qualitative analysis of all 97 Portuguese HEIs' institutional evaluation reports. The study is grounded in Organisational Psychology, anchored in ISO 30401:2018 as a practical implementation reference, and addresses four core research questions concerning the extent to which KM processes are integrated into Portuguese HEIs' QMS, and how they shape QC. A sequential mixed-methods design is employed, comprising: (a) a Systematic Literature Review (SLR), registered in PROSPERO and following PRISMA protocol; (b) quantitative data collection via validated psychometric instruments administered to quality managers across all 97 Portuguese HEIs; and (c) qualitative triangulation through semi-structured interviews with institutional leadership, focus group with quality teams, and documentary analysis of strategic plans and institutional evaluation reports. Two sets of preliminary findings are reported. First, the SLR confirms that empirical research on the KM-QM relationship in HEIs remains geographically concentrated in non-European contexts, validating the relevance of this study. Second, documentary analysis of all 97 institutional evaluation reports from the Agency for Evaluation and Accreditation of Higher Education (A3ES) reveals a consistent pattern: institutions rated 'Good' or 'Very Good' by A3ES demonstrate a greater capacity for evidence-based decision-making, underpinned by more sophisticated — though often unrecognised — KM practices. From this analysis, a four-stage Quality Culture Maturity Model (QCMM) is inductively proposed, tracing institutional progression from Compliance-Driven through Structural and Aligned to Integrated Quality Cultures. A particularly distinctive finding is the prevalence of what we term 'unlabelled KM' — knowledge-sharing and sensemaking practices structurally embedded in institutional routines but neither formally recognised nor strategically leveraged. These findings suggest that the transition toward an integrated QC is fundamentally a knowledge challenge: institutions must develop the organisational capacity to transform raw data into actionable knowledge that informs strategic decision-making. This research contributes an original empirical and conceptual framework to a field marked by theoretical abundance but empirical scarcity, with direct practical implications for institutional leadership, quality managers, and European quality assurance policy.

Educational Integration, Social Inclusion and Transition to Work: A Scoping Review of Conceptual Frameworks

Maj-Britt Tallbacka — 2026-08-25

This study examines frameworks that describe educational integration, social inclusion, and the transition to working life among students with immigrant backgrounds in higher education. A scoping review of 14 studies identified three framework clusters: social inclusion, educational support, and transition to working life. The findings show overlap but limited integration, particularly in linking pedagogy to labour market outcomes. A multi-level model (micro, meso, macro) is proposed, interpreted through the lenses of educational integration, social inclusion, and knowledge management, highlighting the need for more integrated approaches.

Deriving Uncertain Knowledge in Knowledge Graphs Under Theoretical Mapping of Heuristics

Simon von Oppenkowski — 2026-08-25

This paper proposes a deterministic, explainable framework for validating uncertain entities during Knowledge Graph (KG) expansion. The contribution is conceptual and formal: we present the framework and demonstrate its internal mathematical coherence, while empirical evaluation is explicitly deferred to future work. Since adding unverified nodes is trivial but removing structurally corrupted data afterwards is computationally hard, the multiphase Let's Build Knowledge (LBK) Framework acts as a gatekeeper that prevents such corruption at the point of entry. Drawing on cognitive heuristics, molecular data patterns, and meta-reasoning, LBK offers a deterministic alternative to prevalent stochastic and embedding-based approaches in Explainable AI (XAI): it integrates Formal Concept Analysis (FCA) with a localized adaptation of Byzantine Fault Tolerance (BFT) to scrutinize incoming structures, enabling loss-free, rule-based verification of new entities before global integration. We provide a precise specification of this hybrid model and demonstrate its formal capacity for deterministic uncertainty management, without claiming empirical validation.

From Vision to Knowledge Flow: How Transformational Leadership Fosters Knowledge Sharing in the Polish Film Industry

Tomasz Zontek — 2026-08-25

This study examines how transformational leadership fosters knowledge sharing in the Polish film industry, focusing on the mechanisms through which trust-based relationships, mentoring practices, and adaptive responses to disruption shape knowledge flows in film production teams. The research is grounded in the transformational leadership framework of Bass and Riggio (2006) and integrates Nonaka’s (1994) distinction between tacit and explicit knowledge with project-based organization research and developmental network theory. The methodology comprises eleven semi-structured interviews with film producers selected through purposive sampling from the membership directory of the Krajowa Izba Producentów Audiowizualnych (KIPA), and analysed through three-stage grounded coding, yielding 221 first-order codes and 18 second-order categories. The findings show that transformational leadership in film production is enacted primarily through relational mechanisms — role modelling, reputational sanctioning, vision-based partner selection, and leader presence — which form the foundation for knowledge sharing in long-cycle creative projects. Mentoring operates predominantly outside formal pathways and relies on distributed developmental networks and situated learning, while leadership behaviours adapt to technological disruption and project-based uncertainty. The study contributes to transformational leadership theory by extending its application to project-based creative industries and to the knowledge sharing literature by clarifying the conditions under which trust and adaptive leadership facilitate knowledge exchange.

Knowledge-based Smart City Management in the Context of Hofstede’s Culture Dimensions

Aleksandra Radziszewska — 2026-08-25

This study aims to examine the relationships between Hofstede's cultural dimensions and the adoption of knowledge-based solutions in city management. The cultural context of effective smart city management has been investigated based on a city’s position in the global smart cities ranking (the IMD Smart City Index). Multiple regressions were used to identify the relationships between the independent variables (the values of the culture dimensions defined by Hofstede: power distance, individualism, masculinity, uncertainty avoidance, and long-term orientation) and the dependent variable – the value of the IMD Smart City Index. The IMD Smart City Index was chosen for its thematic relevance to the issue of adapting knowledge-based solutions in smart city management, as it reflects current results across several indicators that are priority indicators of the city's technological and innovative development. Smart cities were selected based on their rankings in the IMD Smart City Index. This study demonstrates that elevated levels of long-term orientation and individualism, coupled with low uncertainty avoidance, positively impact the adoption of knowledge-based city management. In contrast, the anticipated relationships between power distance and the adoption of smart city solutions, as well as between masculinity and their adoption, alongside effective knowledge-based smart city management, were not substantiated and warrant further examination. The proposed connections between specific dimensions of national culture and effective smart city management are pertinent to the cities featured in the IMD Smart City Index. The literature pays little attention to the cultural determinants of smart city management, particularly organisational and managerial solutions, during the transition from a classic city to a smart city. The paper explored the role of cultural factors in city management and urban development. The motivation for this study is to identify the relationship between Hofstede's cultural dimensions and the level of adaptation of knowledge-based solutions in city management and to examine their influence on a city’s position in the global smart cities ranking. Future investigations should aim to explore additional variables that may affect the adoption of smart city solutions and the overall performance of city management.

Building Entrepreneurial Capacity in Universities Through Cross-Institutional Staff Training

Olga Rantanen — 2026-08-25

Universities are increasingly expected to act as entrepreneurial and innovation-driven organisations, yet the development of entrepreneurial capacity among academic and professional staff remains uneven and fragmented. To address this issue, this study examines a cross-institutional knowledge management intervention designed to reduce fragmentation in university entrepreneurship support and strengthen staff capacity in innovation ecosystems. The study is situated within a five-university EIT HEI project and draws on needs-assessment data from all partner institutions, while the empirical evaluation focuses on two staff training cases implemented in Finland and the United Kingdom. Building on a multi-site needs assessment, semi-structured interviews and partner competence mapping were thematically analysed and formalised using a DACUM-based competence map that codifies three duty areas: (1) commercialisation of research and innovations, (2) student entrepreneurship support, and (3) innovative teaching methods in entrepreneurship. The resulting competence architecture specifies role-relevant tasks, required knowledge/skills/attitudes, supporting tools, and performance evidence, enabling transfer across heterogeneous academic and professional staff groups. The mapped competences were operationalised through blended local training and mentoring. In the two evaluated cases, training participation was associated with improved self-reported confidence in navigating entrepreneurship ecosystems, stronger cross-silo collaboration, and early spillover effects such as staff acting as ambassadors and strengthening links to external ecosystem actors. However, the study also identified challenges related to staff engagement, knowledge institutionalisation, and sustaining learning beyond project-based interventions. The findings suggest that structured competence mapping combined with multi-channel delivery can function as an effective KM design for eliciting, codifying, and scaling entrepreneurial support knowledge in higher education, while highlighting the need for stronger incentives and more systematic evaluation to sustain participation and evidence impact.

Intellectual Capital for Monitoring Technology Transfer Maturity in National Innovation Systems

Vanessa Guimarães Ribeiro — 2026-08-25

This paper proposes a methodological framework for restructuring the innovation indicator system used to monitor Brazilian Scientific, Technological and Innovation Institutions (ICTs) through the lens of Intellectual Capital (IC). Traditional metrics of technology transfer, such as patent counts and licensing revenues, often suffer from an outcome bias, providing only a partial representation of innovation performance while overlooking the institutional capacities and organisational routines that enable knowledge generation and transfer. To address this limitation, the study reorganises 77 original metrics into the three core dimensions of Intellectual Capital: Human Capital, Structural Capital and Relational Capital. These dimensions underpin a causal analytical model linking intangible assets to innovation capacity, technology transfer processes and economic impact. Rather than relying on isolated outputs, the framework emphasises the role of systemic capabilities in shaping innovation performance. It further incorporates Absorptive Capacity (ACAP) as a contextual and mediating analytical dimension, enabling the assessment of institutional readiness to assimilate and exploit externally generated knowledge within the broader set of structural and relational conditions shaping transfer effectiveness. The methodology advances beyond descriptive measurement by establishing analytical relationships between indicators and by proposing a synthetic Technology Transfer Maturity Index (TTMI), designed to identify structural bottlenecks and institutional maturity profiles through cluster analysis. This approach enables policymakers to move from static reporting to a more structured interpretation of how knowledge is produced, mobilised and converted into value. The framework has been developed and submitted to a working group coordinated by the Brazilian Ministry of Science, Technology and Innovation (MCTI), which received it favourably as a basis for reconsidering the national data collection system for ICTs. By transforming fragmented metrics into structured analytical insights, the proposed approach provides a scalable model for national monitoring and evidence-based policy design. The study contributes to the Intellectual Capital and knowledge management literature by demonstrating how IC-based analytical structures can enhance the interpretation of innovation performance in complex national innovation systems.

From Tacit Knowledge to Structured Documents: A Framework for Knowledge Elicitation

Sylvain Roudiere — 2026-08-25

Recent advances in Large Language Model (LLM) “agent” systems have moved language models beyond single-turn generation toward goal-directed workflows that can plan, ask clarifying questions, and iteratively refine outputs. In parallel, retrieval-augmented generation (RAG) has become a practical way to ground these agents in enterprise knowledge—enabling models to leverage internal documentation and policies without expensive and recurring retraining. However, RAG is only as strong as the underlying corpus: when key information is missing, outdated, or fragmented, retrieval cannot fill the gaps. In many organizations, assembling high-quality, up-to-date documents remains a persistent bottleneck, limiting both the reliability of downstream applications and the speed at which knowledge can be operationalized. To address this, we introduce the Assistant-Scribe-Knowledge Checker (ASK) framework for generating structured specification documents through guided interviews. ASK decomposes the end-to-end workflow into three specialized LLM agents: an Assistant that asks questions to the user and steers the interview through adaptive follow-ups; a Scribe that continuously summarizes what has been said and records it into a schema-constrained specification document with explicit content requirements; and a Knowledge Checker that evaluates the evolving document against those specifications, detects gaps or weakly supported entries, and advises the Assistant on the next best questions to ask to reach the desired completeness and quality. This separation supports seamless document creation while improving quality control over content, structure, and coverage. By distributing responsibilities across three lightweight, role-specific agents, ASK reduces reliance on a single large model and enables modular scaling across teams and domains. We evaluated the ASK framework in a consulting-firm setting where consultants are required to produce project “return-of-experience” documents (successes, failures, challenges, and solutions) to capture reusable knowledge. Compared to documents authored manually, ASK-guided interviews consistently produced more complete, better-structured, and higher-quality specifications with less variability across authors. Beyond measurable quality gains, consultants also reported a clear preference for the interview-based workflow, citing lower effort and a more natural way to articulate tacit project knowledge.

Tacit Knowledge Sovereignty: Are Some Small Family Businesses Reluctant to Relinquish Their Smallness?

Sreejith S S — 2026-08-25

It is a conventional understanding that all businesses desire to grow and expand in terms of revenue, bottom-line profit margins, and employee count. In an apt environment, growth is natural, and all small businesses – including family businesses - desire to scale up into medium and eventually large firms. Nevertheless, despite supportive macroeconomic factors, some family businesses deliberately choose to remain small. This research aims to identify the reasons behind this counterintuitive but conscious decision of some small family businesses to remain small. Specifically, this paper challenges the conventional development framework of small family businesses and questions the assumption that all small family business owners desire their firms to grow. The purpose of this paper is to explore the reasons for maintaining such intentional resistance to expand and also to generate research propositions that support the implausible argument that at least some small family business owners deliberately remain small for varied reasons. A qualitative grounded theory method was used with small family business owners who were willing to participate in the study. Purposive sampling was used to identify small family business owners, and unstructured, in-depth interviews with 12 such participants were conducted, recorded, and transcribed. Open and axial coding identified two major and three minor themes. A major inference was an unwillingness to share tacit knowledge outside their family. The participants revealed that, as they did not want their embodied family knowledge to lose its boundaries and to maintain tacit knowledge sovereignty, they opted to stay small while remaining content with the growth of their business. Another major reason was to ensure the members’ well-being, making succession planning easier. In addition, three minor themes emerged to explain why they are reluctant to relinquish their smallness: core skill focus, relinquishing authority, and compliance overhead. The identified themes were validated through member checking, thereby enhancing the credibility of the findings. Research propositions are developed from these themes and could fuel further research.

A Model Integrating Organizational Culture into Knowledge Sharing and Employee Development

Ondřej Saglena — 2026-08-25

While knowledge sharing and employee development are recognized as important drivers of performance of an organization, existing research often examines organizational culture, structure, and knowledge processes separately. Currently there is an emerging need for integrated frameworks explaining how these organizational dimensions create conditions for effective knowledge sharing and continuous learning. This paper aims to create an integrated conceptual model linking organizational culture, management practices, organizational structure, knowledge sharing, and employee development. Drawing from literature oriented towards knowledge management (KM) and human resource management (HRM), the study proposes a framework that explains how characteristics of an organization, including trust, collaboration, communication openness, and learning, together with leadership approaches and structural arrangements shape knowledge related behaviors and learning opportunities for employees. The proposed model is primarily built to emphasize the interconnected nature of cultural and structural factors in enabling and improving knowledge flows within an organization. To evaluate the relevance and applicability of the proposed model, it is examined through empirical research conducted in selected organizations. This phase aims to explore the relationships between the key characteristics and to provide an initial evaluation of the model’s utility in practice. The study offers an integrated perspective that bridges organizational culture, structure, and management with knowledge sharing and employee development, addressing a gap in current KM and HRM research. By demonstrating how these factors interact, the model supports the streamlining of knowledge management processes. It is useful for managers and HR professionals who are trying to create or improve organizational environments that are more supportive of knowledge sharing and continuous employee learning. A comprehensive view of the factors that shape a knowledge-oriented work environment is a starting point for academic discussion and for further verification of the identified influences in business practice.

An Optimisation Approach for Prioritising Actions to Mitigate Collaborative Knowledge Management Barriers

Raquel Sanchis — 2026-08-25

Collaborative Knowledge Management (CKM) initiatives often struggle in practice because organisations face everyday managerial challenges such as limited coordination, misaligned objectives, uneven skills, and unclear documentation practices. Although many studies identify these barriers, managers still lack simple tools that help them decide which improvement actions should be implemented first when time, budget, and organisational capacity are limited. This paper proposes an optimisation-based approach designed to support these decisions. The model helps organisations select the combination of actions that is expected to reduce management-related knowledge barriers the most, while considering real-world constraints such as budget limitations, implementation capacity, feasibility conditions, and logical relationships between actions, including prerequisites and mutually incompatible initiatives. Besides suggesting which actions to implement, the approach also shows how much each barrier is likely to decrease, helping managers clearly understand the expected benefits and making the results easier to explain and discuss in decision-making processes. Finally, a real application illustrates how the proposed approach supports managerial decision-making by identifying practical actions, such as training initiatives, coordination mechanisms, and clear documentation practices, that can simultaneously reduce several management barriers and prepare the organisation for more advanced collaborative knowledge initiatives in later implementation phases. Future research may extend the proposed approach by incorporating uncertainty in the estimation of action effectiveness, multi-period planning considerations, and dynamic learning mechanisms that update decision recommendations as new implementation data becomes available.

Generative AI and Knowledge Creation: Comparing Proposals for Revising the SECI Model

Enrico Scarso — 2026-08-25

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.

Business Model Canvasses Revisited: From Static Stagnation towards Dynamic Agile Feedback Cycles

Ulrich Schmitt — 2026-08-25

While the Business Model Canvas (BMC) is a ubiquitous tool for strategic visualization, it remains fundamentally a ‘static snapshot’ unsuitable for the high-velocity requirements of market dynamics and knowledge management alertness. This paper proposes a transition from static stagnation toward a dynamic, cybernetic framework by extending the traditional nine-element structure into a 12-Slot Unified Matrix (SUM). Building on the previous synthesis of sixteen dynamic models of knowledge creation (ECKM 2025), this research introduces three distinct Cybernetic Loop Agile Systemic Processes (CLASP) that transform the canvas into a dynamic iterative cycle. The model repurposes the ninth slot as a Validation Benchmark—a ‘Higher Ground’ sensor—to trigger feedback cycles. These cycles flow through three novel control layers: Organizational Cohesion (Slot 10) for process adaptation, Evolutionary Agility (Slot 11) for perimeter adjustment, and Scalable Integrity (Slot 12) for project adaptation and trust engineering. By integrating Axiomatic Design principles, the paper demonstrates how these loops manage goals and outcomes and strengthen the institutional knowledge heritage for organizational learning. The result is a dynamic governance system where the ‘Functional Intent’ of the venture is continuously re-engineered through active feedback, ensuring that the business model evolves in lockstep with the knowledge assets it seeks to leverage.

Scaling Human Capabilities: A Learning Architecture for Knowledge Management

Joachim Schmitt — 2026-08-25

Artificial intelligence is fundamentally changing the nature of knowledge work by providing greater support for the generation, organisation and retrieval of knowledge. Consequently, organisational performance is becoming more dependent on human capabilities such as ownership, collaboration and responsible judgement. While existing learning approaches address individual aspects of this challenge, they rarely combine effectiveness, sustainability and scalability. This study therefore asks which learning architecture can systematically develop collaborative human capabilities while enabling organisation-wide implementation. The development methodology combines a literature review with an action research project and an exploratory pilot evaluation to develop and evaluate the proposed Learning Architecture. The literature review examined peer learning, learning psychology, neuroscience, organisational learning and adult education in order to identify design principles for the development of collaborative capabilities. These findings were then translated into an iterative action research process involving researchers, executives and trainers. The resulting learning approach was then evaluated in six pilot projects involving participants from different organisations. The study developed a Learning Architecture comprising four complementary design elements: Learning Circles as the collaborative learning format. A Learning Cycle as the didactic process. A shared but adaptable curriculum. The Learning Journey as the organisation-wide implementation framework. Together, these elements combine structured organisational orientation with decentralised self-organisation. Participants gave positive evaluations of the learning environment, particularly the self-organisation of the Learning Circles. Knowledge acquisition, reflection, practice and peer feedback were also evaluated positively, whereas workplace tasks were seen as the weakest. Participants further reported perceived improvements across all outcome dimensions, with the strongest effects observed for collaboration within work teams. The study contributes to contemporary knowledge management by proposing a Learning Architecture that systematically connects individual collaborative learning with organisation-wide capability development. It argues that, as explicit knowledge and increasing parts of professional tacit knowledge become more accessible through digital systems, competitive advantage increasingly depends on collaborative capabilities through which knowledge is interpreted, evaluated and responsibly applied in organisational practice. The study provides a structured and scalable mechanism that links knowledge management and organisational learning while preserving decentralised collaborative practice. Although the exploratory pilot does not permit conclusions about long-term behavioural or cultural change, it provides initial empirical support for the proposed conceptual framework and establishes a foundation for future longitudinal research.

Developing Knowledge-Based Dynamic Capabilities for Shared Value Creation in Circular Supply Networks

Anna Sibińska — 2026-08-25

This paper examines the development of knowledge-based dynamic capabilities (KBDCs) within a circular supply network and their role in enabling shared value creation. It analyses the interplay among knowledge acquisition, knowledge generation, and knowledge combination capabilities as they emerge through interorganizational collaboration and investigates how these capabilities contribute to economic, environmental, and social value outcomes. An exploratory case study was conducted within a circular supply network in Poland comprising grain producers, distributors, feed manufacturers, farms, and processors engaged in circular exchanges of materials, services, and by-products. Data were collected through in-depth interviews, focus groups, study tour observations, and internal documents. The findings indicate that all forms of KBDCs, such as knowledge acquisition, generation, and combination, are actively developed through collaboration among network actors. High relational embeddedness, manifested through trust, joint problem-solving, and mutual commitment, is identified as the central enabler of KBDC development. Knowledge acquisition is enhanced through interactions that enable actors to assimilate strategic, operational, and contextual knowledge. Knowledge generation arises from collective efforts orientated toward circularity, whereas knowledge combination is facilitated through the integration and reconfiguration of knowledge flows across the network. These processes foster shared value creation, manifested in economic efficiency and supply chain resilience, environmental restoration through regenerative production and closed-loop practices, and social benefits such as local skill development and community education. The study advances understanding of how KBDCs support shared value creation within circular supply networks by elucidating the interplay among distinct knowledge capabilities and demonstrating that collaborative interactions underpin their development. Contributes to the knowledge management literature by showing how KBDCs emerge from joint interactions and are reinforced through industrial network relationships in a circular economy context. The study illustrates how organisations can embed environmental and social objectives into their strategic core by leveraging KBDCs to develop circular solutions and simultaneously achieve economic, environmental, and social value.

Development of a Structure of User Rights in a Knowledge-Sharing Environment

Sebastian Skolik — 2026-08-25

The issue of knowledge sharing in projects connected to the free-culture movement is linked to egalitarianism and the lack of formal social roles. This should distinguish them from bureaucratic organizations, where the source of authority is position, not knowledge. However, on Wikipedia, the most recognizable project of this movement, the initially flat user-rights structure has become greater over time. This article presents the variation within the user-rights structure in the largest language versions of Wikipedia. Previous research into the power structure within the Wikipedia community has focused on the effectiveness of teamwork, the formation of network structures and the influence of cultural factors on the activity of individuals in administrative roles. This article compares the user-rights structure across 42 language versions of Wikipedia. For this purpose, publicly available statistical data were analysed concerning the number of user groups (UGs) with different types of user rights and the activity of administrators. The next stage of the research involved comparing the relationships between these variables and the Power-Distance Index, as defined by Hofstede. Analysis of statistical data has shown that larger language versions of Wikipedia with a high number of administrators tend to have a talled organizational structure. Both the number of bureaucrats — individuals formally responsible for assigning Wikipedians to specific UGs — and the number of UGs were increasing. A comparison of variables relating to a power structure with Hofstede’s Power-Distance Index (PDI) allowed to accept the hypotheses that a higher PDI correlates with 1) less frequent use of blocking and 2) less frequent removal of user pages by administrators. Furthermore, a weak negative correlation was observed between the PDI score and the number of users with administrator rights, though only in the case of smaller Wikipedia communities. This may imply that a greater power distance limits interference in people’s actions, or that in cultures with a higher PDI, behaviour requiring intervention by those with the power to exclude are less common.

Enacting Explainability: Knowledge Practices Around Generative AI in a Consulting Firm

Magnus Erga Skreden — 2026-08-25

As generative AI becomes embedded in knowledge-intensive work, organizations face a central paradox: AI systems produce influential outputs while their inner workings remain opaque. Although explainability research has largely focused on technical methods, less attention has been given to how organizational actors practically make sense of and govern AI-generated knowledge. This study adopts a knowledge-practice perspective to examine how explainability is enacted in everyday work within a mid-sized consulting firm. The study is based on a qualitative case design involving nine semi-structured interviews with AI practitioners, clients, and an AI expert. Rather than treating explainability as a technical property, the analysis explores how consultants interpret, validate, and operationalize AI outputs in the absence of formal governance structures. The findings show that explainability is enacted as a situated and role-dependent knowledge practice. First, employees assess outputs pragmatically, focusing on whether they “make sense” in context rather than seeking insight into model internals. Second, prompting emerges as a form of tacit expertise developed through trial-and-error learning, functioning as an informal mechanism for influencing and interpreting outputs. Third, actors rely on local validation routines such as re-running prompts and cross-checking information to manage uncertainty and maintain trust. Finally, the absence of internal guidelines results in individualized and uneven practices, indicating that knowledge about AI use is created but not institutionalized. From a Knowledge Management (KM) perspective, the study contributes by conceptualizing explainability as an emergent knowledge practice shaped by situated sensemaking, tacit skill development, and informal governance. It extends knowing-in-practice research by illustrating how organizations cope with opaque digital systems when formal knowledge infrastructures lag behind technological adoption. For KM practice, the findings highlight the need for lightweight governance mechanisms, shared prompting norms and role-adapted guidelines that support collective knowledge development around AI use.

Factors Influencing Knowledge Sharing, Cooperation, and Relationship Building Between Academia and Business

Urszula Słupska — 2026-08-25

Building relationships between businesses and research centres enables the creation of synergies that stimulate development and the flow of best practices. A key element of cooperation and the development of science-business relationships is the sharing of knowledge, which leads to the transformation of intellectual capital into organizational and market value; Technology Transfer Centres (TTCs) serve as intermediaries in the flow of knowledge between the academic community and the business sector. The aim of this article is to identify factors that facilitate cooperation between enterprises and scientific research centres, as well as factors limiting such cooperation, with a focus on the Kuyavian-Pomeranian region in Poland. The research conducted was part of a larger research project dedicated to improving the process of transferring and commercializing innovative solutions between science and the economy. The study covered enterprises from the Kuyavian-Pomeranian region in Poland and TTC units operating in Poland. Data were collected through computer-assisted telephone interviews (CATI), and the research tools consisted of questionnaires developed based on literature reviews. The study determined the significance for both groups of respondents of the factors identified by the research team as either facilitating or limiting cooperation between enterprises and scientific research centres. The findings can be used by businesses, universities, and research centres alike to better tailor their offerings and improve cooperation processes.

From Unstructured Procedural Text to Knowledge Graphs: Comparing LLM-Based Extraction Strategies

Erik Sörqvist — 2026-08-25

As experienced workers retire across industrialized economies, organizations risk losing procedural expertise that often remains tacit, undocumented or scattered across unstructured documents. Translating this knowledge into structured, machine-readable representations is difficult to scale, labor-intensive, and prone to inconsistency when done manually. This paper addresses the automated construction of knowledge graphs from natural language procedural descriptions, developing a generic approach for transforming unstructured expert knowledge into structured knowledge graphs that support downstream retrieval and question-answering applications. Three text-to-knowledge-graph approaches were designed, implemented, and systematically evaluated. The first employed a large general-purpose language model (Qwen3-32B) with a single-stage zero-shot extraction prompt, the second applied the same strategy using a smaller base model (Llama2-13B), and the third combined supervised fine-tuning of the smaller model on synthetic extraction data with a decomposed extraction architecture targeting one to two ontology elements per phase. All approaches were evaluated across six procedural descriptions spanning multiple technical domains, with chunk size, model temperature, and ontology detail as configuration parameters. Results were assessed for intrinsic quality and extrinsic fitness for use, measured by question-answering accuracy in a Graph-RAG application. The results demonstrate that extraction strategy is a more decisive factor than model capacity. The fine-tuned model achieved a Question Answering (QA) pass rate of 55.3%, compared to 46.8% for the large model and 33% for the small base model, outperforming the general-purpose model on both intrinsic quality metrics and downstream performance. Average node degree, duplication rate, and ontology simplicity emerge as the strongest predictors of retrieval performance. Shorter ontologies consistently outperformed richer ones, suggesting that ontology design should be driven by the intended downstream application rather than semantic completeness. Over-extraction of procedural steps did not degrade performance but instead improved retrieval robustness by providing additional entry points for graph traversal. The findings offer practical guidance for designing scalable, locally deployable knowledge graph construction pipelines for procedural texts under computational and confidentiality constraints.

Comparative Analysis of the Efficiency Indicators of the Formation of Intellectual Capital in the Field of Humanities and Arts Sciences

Inese Spica — 2026-08-25

In the present situation, a comparative analysis of the efficiency of the formation of intellectual capital (IC) in the humanities and arts sciences at scientific institutions (SIs) in Latvia is an actual research topic. The object of the research is the formation of IC of humanities and art sciences at SIs, while the subject of the research is the comparative analysis of the efficiency of the formation of IC of humanities and art sciences at SIs of Latvia. The objective of the research is to compare various efficiency indicators of the formation of IC of humanities and art sciences at SIs in Latvia in the period from 2013 to 2018. The following tasks were determined to reach the objective: to study the formation of IC of humanities and art sciences at SIs; to identify various efficiency indicators of the formation of IC of humanities and art sciences; to calculate various indicators thereof; and to carry out the comparative analysis of various efficiency indicators that characterise the formation of IC of humanities and art sciences at SIs in Latvia. Research methods used in the paper are content analysis, economic analysis, financial analysis, and economic experiment.

Deliberating Sustainability Transitions: Citizen Assemblies as Knowledge Intermediaries

Raymond Andreas Stokke — 2026-08-25

Municipalities play a critical role in driving sustainability transitions, but they often face significant challenges when considering complex and contested technologies such as waste-to-energy with carbon capture and storage (WtE-CCS). This paper examines the use of a citizen assembly as a deliberative democratic method for exploring sustainability transitions. The study pays particular attention to how knowledge is generated, deliberated, and managed throughout this participatory process. Drawing on deliberative democratic theory, the research conceptualises the citizen assembly not only as a participatory governance mechanism but also as a temporary knowledge intermediary that bridges expert, experiential, and institutional forms of knowledge. Empirically, the study is based on close, participatory observation of an actual citizen assembly process, and in-depth interviews with participants after the assembly. The analysis examines how various types of knowledge are utilised within the deliberative setting. It also explores how these knowledge practices influence participants’ understanding of the WtE-CCS technology specifically and of broader sustainability trade-offs more generally. This combination of observation and interviews provides rich insight into the assembly dynamics and how participants process and reflect on information together. The findings indicate that citizen assemblies can strengthen the reflexive capacity of municipal governance. Through the process, participants and organisers developed shared framings of the problem, and the assembly helped legitimise a diversity of knowledge claims. However, the study also notes tensions that emerged, such as questions pertaining to the mandate, the uncertainty inherent in new technologies, and the limited time available for deliberation. Overall, the paper contributes to the literature on sustainability transitions by highlighting the role of deliberative democratic innovations, such as citizen assemblies, in local knowledge management. It argues that well-designed citizen assemblies, which explicitly focus on the processes by which knowledge is generated and exchanged, can support more inclusive transition pathways at the local level.

Unpacking KM Challenges for Building Resilient Destinations. A Qualitative Study from Poland

Ewa Stolarek-Muszyńska — 2026-08-25

Purpose: The tourism industry is fundamentally defined by volatility and exposure to diverse shocks. Considering its vulnerability, tourism requires a robust understanding of resilience to accelerate post-crisis recovery. With knowledge management established in the literature as a vital element in navigating crises and fostering long-term resilience, this study aims to explore KM challenges as emerging difficulties during crises on the pathway toward organisational resilience. Methodology: This study employs a qualitative in-depth analysis of data gathered from Polish destination management organisations (DMOs). The research involved semi-structured interviews with 13 key representatives. Following the analysis, identified KM challenges were mapped against a resilience framework, consisting of absorptive, adaptive, and transformative capacities. Findings: The results indicate that absorptive capacity of the organisations was primarily undermined by informational chaos, disrupted information flows, and inconsistencies, which complicated knowledge management in the face of high volatility. Regarding adaptive capacity, KM challenges pertained to the necessity of unlearning standard operating procedures, managing emergent knowledge, and generating novel ideas under crisis conditions. Finally, KM-related challenges for transformative capacity stemmed from a lack of codified post-crisis knowledge (lessons learned) and a deficiency in translating crisis experiences into strategic organisational protocols. Research limitations: As this is a qualitative study based on the analysis of a limited number of organisations, its findings cannot be generalised and do not represent the sector as a whole. Furthermore, the research was conducted within a single country and in the context of two specific crises, which limits its broader applicability. Practical implications: This study offers valuable insights for practitioners into the challenges arising during a crisis, serving as a baseline for proactive preparation and building the organisational resilience before a new crisis emerges. Originality/value: By integrating KM challenges with the resilience framework, it offers a more nuanced understanding of the role of knowledge management within the tourism crisis context and provides a food for thought for researchers focusing on KM and resilience.

Retaining Tacit Knowledge Through Dialogue: The AI Moderator in Work Processes

Pia Stürzebecher — 2026-08-25

Tacit knowledge is regarded as a central difficult-to-access resource for organizational learning. Drawing on Michael Polanyi (1985), this kind of knowledge is closely tied to individual action, experience, and contexts, and largely resists documentation capture. In complex work settings, tacit knowledge constitutes a basis for performance and quality assurance. Against the backdrop of demographic change and skills shortages, the challenge of securing, transferring, and making this knowledge usable for subsequent generations is intensifying: around one quarter of the workforce in Germany will reach retirement age within the next ten to fifteen years (Statistisches Bundesamt, 2025). This paper presents an AI-based assistance approach developed at the Fraunhofer Institute for Factory Operation and Automation IFF to support workers when additional guidance is needed, particularly during complex or non-routine tasks. The assistance enables hands-free interaction and goes beyond AI-driven dialogue management by addressing upstream and downstream processes, including preparing existing knowledge for AI use, structured content modelling, and linguistic adaptations. At its core, KIMO (short for AI Moderator) is a dialogue-based AI system that supports employees within their work processes. KIMO facilitates reflection on one’s actions and fosters the articulation of experiential knowledge that remains implicit in everyday practice. The approach builds on narrative methods that prompt reflection and the telling of experience-based stories, making implicitly applied heuristics and decision logics more visible (Erlach & Thier, 2004). These methods are complemented by an instructional dialogue design aligned with Bloom’s taxonomy of learning objectives (2004). Unlike conventional knowledge management systems, which focus on retrospective documentation and explicit knowledge repositories, KIMO emphasizes AI-based interaction at the moment when knowledge emerges. Through its dialogic mode, person-bound experiential knowledge is externalized, structured, and transformed into organizationally usable knowledge structures. The paper discusses the potential of dialogue-based AI systems for sustainable knowledge transfer and the long-term preservation of organizational memory. It concludes by outlining implications for the design of AI-enabled knowledge management systems, including required information and content infrastructures (e.g., a structured knowledge base).

Knowledge Management During Organizational Crisis: The Influence of Leadership

Ann Svensson — 2026-08-25

This qualitative case study investigates how leadership influences knowledge management (KM) and organizational learning (OL) within a Swedish manufacturing organization facing economic pressure. In an increasingly volatile environment, maintaining knowledge continuity is essential for being competitive and organizational resilient. Data were collected through 27 semi-structured interviews with 31 participants and then analyzed using thematic analysis. The study shows the importance of leadership styles and the leadership's ability to understand the organization from a holistic perspective as well as the digital maturity of the company. The results reveal a significant loss of knowledge resulting from downsizing in order to manage economic pressure, specifically regarding critical tacit knowledge described as "fingertip feeling". This personal, intuitive expertise is vital for navigating production variables and maintaining an old machine park that relies on specialized in-house competence. This means that even if workforce downsizing may be effective in the short-term for managing economic pressure, in the long-term it likely results in critical (tacit) knowledge loss and hence have a negative impact on effectiveness and competitiveness. The study finds that an authoritarian, top-down leadership style acts as a primary barrier to a healthy knowledge culture. Furthermore, it revealed that a lack of transparency regarding major leaderships decisions fosters a culture of silence. The study also revealed a digitalization maturity gap where leadership-driven tools are viewed as administrative burdens due to low digital maturity and a preference for analog methods suited to situated practice. Organizational learning is further hindered because employee improvement initiatives often run out in the sand due to a lack of systematic follow-up and structured feedback loops. The study confirms that leadership is the primary driver for building a culture of continuous learning. To maintain competitiveness and resilience during crises, leadership needs to adopt a holistic perspective that values tacit knowledge and organizational learning. Failing to integrate KM and OL into daily work processes directly compromises long-term organizational memory and the ability to adapt to adverse events.

Learning Through Experimentation: Growth Hacking in Organisational Development Strategy

Marta Szczepańczyk — 2026-08-25

This article attempts to explore the role of continuous learning through experimentation, considered within the context of the concept of growth hacking. The turbulence of the environment and the pace of change in this field mean that modern organisations are faced with the need to react quickly, including making immediate decisions and introducing innovations. In light of this, the first part of this article presents the theoretical foundations of growth hacking and the closely related practice of experimentation. A review of the literature led to the formulation of the following research question: Does an organisation’s general approach to experimentation influence its level of experimentation in technical terms? In the practical section, an attempt was made to verify the cause-and-effect relationship between these areas. To this end, the results of a survey conducted among 250 large and medium-sized IT companies in Poland in the second half of 2024 were utilised. The analysis was carried out in R, within which structural equation modelling (SEM) was developed. On this basis, it was found that a higher level of experimentation in general terms determines the level of technical experimentation. The final section of the article also highlights the limitations and directions for further research.

Future-proof Knowledge Management for Regenerative Value Ecosystems

Emőke Takács — 2026-08-25

Current societal developments increasingly prioritise social well-being and intergenerational responsibility, as evidenced by the growing emphasis on sustainability, circularity, and planetary stewardship. Since contemporary societies depend on knowledge accumulated over time, knowledge management has traditionally drawn on relatively stable repositories of past experience and established insights. However, the transition from linear, single-organisation models of value creation to regenerative, multi-partner value ecosystems challenges these assumptions. In such settings, relevant knowledge is no longer sourced solely from organisational memory but must be continuously generated, validated, and integrated across multiple contexts and stakeholders. This opinion paper argues that the resulting uncertainty amplifies the demands placed on knowledge management. The core challenge extends beyond capturing explicit and tacit knowledge: it lies in combining diverse, context-dependent perspectives into actionable understandings within an evolving social environment. As knowledge requirements become increasingly dynamic, organisations and networks must build resilience and develop the agility to respond to shifting conditions, emerging risks, and evolving societal expectations. We contribute by synthesising key societal developments shaping the knowledge landscape and its related requirements. Additionally, we propose revised principles for knowledge creation, valuation, and use that align with regenerative and circular approaches, and outline mechanisms to address the continuous emergence of new insights necessary for a well-functioning society. Finally, recognising that actors interpret and apply knowledge through the lens of their own contextual realities, we introduce the concept of a future-proof ecosystem orchestrator, also referred to scientifically as a circularity knowledge broker: a facilitating role that supports adaptive knowledge exchange, orchestrates multi-contextual sensemaking, and enables collaboration under conditions of uncertainty. This role is crucial for enhancing the collective capacity to navigate uncertain futures while maintaining social and ecological well-being.

Generative AI in Knowledge Management: A Comparative Mixed Methods Study of Jordan and Europe

Manal Taqatqa — 2026-08-25

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.

Integrating Generative AI into Knowledge-Intensive Environments: Insights from Agile Workers

Nima Taraghi — 2026-08-25

Generative AI (GenAI) is transforming how knowledge-intensive professionals work by generating new content and processing vast amounts of data, impacting knowledge creation. This phenomenon is widely acknowledged by scholars and practitioners, as evidenced by the growing body of literature on the topic. However, research remains in its early stages, and there is a pressing need to deepen our understanding of how these technologies are being adopted in specific contexts. This study addresses this gap by examining how agile practitioners who operate in highly knowledge‑intensive environments and are among the earliest adopters of GenAI integrate these tools into their work. We developed a questionnaire including questions about performance, effort, social influence, facilitating conditions, and risks. The survey was distributed among participants at the 2025 edition of the Italian Agile Days, a two-day event that gathers Italian agile professionals. Based on responses from 91 participants, we identified three distinct user clusters: sceptical GenAI explorers, confident adopters, and cautious power users. Our findings contribute to an understanding of how GenAI is being integrated into agile practices and what facilitates or hinders its adoption among these knowledge-intensive professionals. Finally, we discuss implications for how organizations might tailor support, training, and governance to the different clusters to allow more effective and responsible GenAI use.

Impact of Tacit Knowledge on Management Students by Experiential Learning Using Business Games

Silviu Mihail Tita — 2026-08-25

The purpose of this article is to observe how explicit knowledge acquired in a Management program is applied in an experiential learning exercise through the study of a business game. We are interested in identifying and analysing their perception of how students' explicit knowledge can be transformed into tacit knowledge through a simulated competitive environment and whether they consider this form of learning useful for developing managerial skills. The research is a replication of research conducted last year on another group of students, and we want to compare whether there are changes in perceptions regarding the accumulation of tacit knowledge between two generations of students (2025 vs 2026). Applying Kolb's learning cycle in such a context allows the study of different learning behaviours and the discovery of practical aspects that can allow the accumulation of a high volume of tacit awareness in an educational context in which technology generates the behaviour of rejecting classic forms of explicit knowledge transfer, students prefer role-playing games, games such as printable games, etc.

Is Artificial Intelligence a Better Researcher than Humans?

Eduardo Tomé — 2026-08-25

In the last few years Generative Artificial Intelligence (GenAI) has taken universities by storm. This does not only affect the way what and how to teach and how to assess knowledge and competences in exams, but it also impacts the way how to do research. On one hand, GenAI has fast access to a huge amount of wide-ranging (explicit) knowledge and the capability to extract and combine knowledge that seems to be relevant in the context of certain research. On the other hand, research is closely linked to the human capability of being creative showing curiosity, care, collaboration, and critical thinking. Against this background, the paper addresses a question of immense social and also ethical relevance: May we use (Gen)AI as tool for scientific research and if so to what extent? The paper starts analysing the problem from a theoretical and conceptual point of view by comparing capabilities of GenAI with those of a good researcher. Based on this the complete research cycle is investigated to understand which capabilities are more helpful to achieve research results of high quality and novelty, but in an effective way. To gather empirical data from a self-experiment, the setup of an unexperienced researcher (e.g. PhD student) with just a rough idea about the intentional research focus was chosen. Addressing the first and fundamental phase of any research process, idea generation, by comparing performance of an AI researcher with a human one, results demonstrate the usefulness of combining strength of both worlds mandatorily keeping the human researcher in the loop. With this, the paper focusses on chances and challenges coming with the highly dynamic evolution of AI within a complex, knowledge-intense application area where human experience, problem-solving capability and creativity is decisive. It contributes to discussions about necessary adjustments in how organizations (and particularly universities) manage their knowledge generating potential considering artificial and human intelligence.

Scientific Tacit Knowledge Among Researchers with Long Scientific Seniority: Towards a Four-Pillar Framework

Katarzyna H. Tomiczak — 2026-08-25

This study addresses the topic of scientific tacit knowledge (STK), specifically related to employees with Long Scientific Seniority (LSS), defined as those with more than 25 years of postdoctoral experience. STK serves as an internal resource for scientific institutions and contributes to high-quality research. Despite its importance, STK remains relatively underexplored in the literature; however, it is considered important in engineering sciences, such as construction. The research question focuses on understanding the characteristics and structure of the STK. An extensive literature review of STK definitions was conducted using multiple databases. An online survey was conducted among fourteen construction-related research institutions in Europe (five research institutes and nine universities) to understand how LSS personnel and their tacit knowledge are perceived within these institutions. Based on the survey findings, 51 in-depth interviews were conducted to gain a better understanding of STK. Participants were divided into three groups: LSS employees, their co-workers, and their leaders. The thematic analysis of the qualitative interview data led to the identification of recurring STK components, which were subsequently organised into four pillars. The cognitive pillar includes research intuition, pattern recognition, knowledge synthesis, and a deep understanding of scientific processes. The practical pillar consists of research skills, technical competence, complex problem-solving, historical knowledge, and academic writing skills. The ethos pillar includes work ethic, emotional engagement, and academic value. The relational pillar refers to academic culture, informal rules and practices, and the expert networks. This study had some limitations. First, it was conducted in scientific institutions within the construction field, and the interview data reflected the participants’ perspectives. Further research may include examining different disciplines, such as natural and social sciences, to determine which pillars are more universal and which differ across disciplines.

Strategic PIAO communication for Public Value creation in Local Government

Alberto Trinchini — 2026-08-25

Public administrations are increasingly expected to create Public Value through transparency, participation, organizational alignment and trust, not only through service delivery. This paper examines how investments in communicating the Piano Integrato di Attività e Organizzazione - Integrated Activity and Organization Plan (PIAO) may contribute to the organizational conditions enabling Public Value creation. Building on Knowledge Management, Organizational Learning, Public Value Governance, Public Service Motivation and strategic communication literature, the study conceptualizes the PIAO as a Knowledge Management infrastructure whose effectiveness depends on whether its strategic content is understood and translated into everyday practices. Adopting a qualitative conceptual approach supported by an exploratory case study of the Municipality of Pescara (Italy), the paper develops the Public Value Promotion Investment Indicator (PVP-II), a managerial tool for estimating the organizational effort devoted to promoting Public Value through internal communication, external communication, training, stakeholder engagement and employee involvement. Rather than measuring Public Value itself, the PVP-II estimates investments in knowledge-sharing processes that facilitate strategic alignment, organizational learning, employee engagement, institutional trust and stakeholder awareness. The paper contributes to Knowledge Management research by positioning communication as a strategic organizational investment connecting integrated planning, knowledge processes and Public Value creation.

Transforming Environmental Knowledge Into Business Opportunity through Circular Economy Practices Mediation

Jowita Trzcielinska — 2026-08-25

In conditions of increasing environmental turbulence, organisations must continuously transform environmental information into strategic and operational responses. This study examines whether circular economy (CE) practices mediate the relationship between environmental scanning and business opportunity. Drawing on dynamic capabilities and the knowledge-based view, environmental scanning is conceptualised as a sensing capability, while CE practices represent organisational mechanisms transforming knowledge into value-creating actions. The study distinguishes between Sustainable Product Design and Circular Resource Management to explain how different CE dimensions contribute to business outcomes. Quantitative data were collected from 300 medium and large enterprises operating across 19 industrial sectors in Poland. Structural Equation Modelling (SEM) was used to examine direct and indirect relationships. The results show that environmental scanning does not directly influence business opportunity. Instead, its impact is primarily indirect and mediated through CE practices. The strongest effect was identified for Circular Resource Management, indicating that operational circular practices are critical for transforming environmental knowledge into business value. The study contributes to research on environmental scanning and circular economy by demonstrating that environmental knowledge creates value only when embedded in organisational capabilities enabling resource reconfiguration and operational implementation. The findings also provide managerial implications for firms seeking to translate environmental signals into competitive advantage.

Can Universities Forge Organizational Readiness for AI Adoption? Evidence from Meta-Analysis

Olga Tunkevichus — 2026-08-25

Nowadays, universities eagerly aim for artificial intelligence (AI), yet empirical evidence remains fragmented across different levels of analysis, AI system types, and implementation contexts, blurring cumulative conclusions. Empirical models remain limited, lacking big picture that could give complex understanding of how implementation processes really work out in modern universities. This study aims to identify determinants of organizational readiness for AI adoption in higher education via meta-analysis and consolidate existing predictors into semantic blocks. A systematic search of literature yielded 28 empirical HE studies using PLS-SEM. Following PRISMA, predictors were coded into three levels and random-effects meta-analysis was conducted in R; pooled effects with confidence intervals were estimated and heterogeneity assessed using Q, I², and τ². Twelve out of fourteen observed predictors showed statistically significant positive effects on AI adoption. The strongest effect is shown by individual determinants, which confirms the behavioural logic of technology adoption models in which intention (ES = 0.907; p <0.001) is the closest proximal predictor of the actual introduction of AI in HE. Cognitive predictors such as perceived usefulness and perceived ease of use also show comparable sustained effects, which confirms the importance of evaluating individual, cognitive-behavioural characteristics of personnel when implementing AI in HE processes The importance of capability-oriented factors was highlighted with AI literacy and self-efficacy, showing the importance of professional development programs for teachers to adopt AI successfully. Institutional determinants, such as social influence and competitive pressure have also shown significant results, indicating the importance of the external regulatory environment for universities. Organizational determinants also provided significant results. High heterogeneity for organizational readiness (I² = 0.84) indicated variation by context, AI solution type, and operationalization, supporting readiness as a dynamic capability. Given conceptual fragmentation and inconsistent modelling roles, future studies should standardize readiness measurement and test contingent and mediating mechanisms in HE AI implementation.

The Value of Knowledge Management and Digital Innovation in Brand Communication for Generation Z

Tabita Vagala — 2026-08-25

The development of digital technologies creates new opportunities for marketing communication while shaping the expectations of young consumers regarding how brands present their products, values, and innovative approaches. Generation Z represents one of the most active groups in the digital environment, and its consumer behaviour is closely connected with online content, interactivity, and the immediate availability of information. In this context, organisations increasingly need to understand the value of digital innovations in brand communication and how these innovations influence brand perception among young consumers. The value of knowledge management and digital innovations in brand communication with Generation Z lies in their ability to support a deeper understanding of young audiences and to transform this knowledge into authentic, interactive, and personalised digital communication. Insights derived from these interactions can represent an important source of organisational knowledge that may be utilised within knowledge management processes, particularly in identifying and applying insights about consumer behaviour when designing more effective marketing communication strategies. The aim of this paper is to examine the value of digital innovations in brand communication from the perspective of Generation Z. The study focuses on identifying which elements of digital communication are perceived as valuable by young users and how these elements influence their engagement, trust in brands, and overall perception of marketing communication. The research also considers the communication of ecological and sustainability values of brands in the digital environment, which has become an increasingly important component of contemporary marketing strategies. The empirical part of the study is based on a quantitative questionnaire survey conducted among respondents from Generation Z. The analysis focuses on selected aspects of digital communication, particularly preferred online content formats, factors influencing attention to digital advertising, and the perception of innovative elements in marketing communication. The methodological approach applies gap analysis to identify differences between the expected value of digital innovations from the perspective of Generation Z and their actual perception in brand communication. The findings highlight the importance of innovative communication elements that enhance user engagement and strengthen brand credibility in the digital environment. The identified gaps between expectations and perceived communication practices provide organisations with valuable insights for the more effective use of digital innovations in marketing communication. These insights may contribute to knowledge management practices by supporting organisations in better understanding the needs and expectations of Generation Z.

Why SMEs Don’t Use Academic Research: The Knowledge-to-Action Gap

Jaani Väisänen — 2026-08-25

Small and medium-sized enterprises (SMEs) often express baseline trust in academic research, yet report limited ability to translate research outputs into actionable decisions and routines. To address this phenomenon, this study examines a) the mechanisms that create a gap between trust in academic research and practical use, b) how SMEs move from accessing research to making sense of it and applying it in practice—and where this “research to action” process most commonly breaks down—and c) how differences in absorptive capacity and related routines explain variation in research utilization. Empirically, we draw on semi-structured interview data collected by the NOKKA project in southeastern Finland, comprising 23 SME representatives. The empirical analysis is grounded in three complementary perspectives. First, absorptive capacity explains firm-level differences in recognizing, assimilating, and exploiting external knowledge, offering a capability-based indicator of why some SMEs can convert research insights into practice more readily than others. Second, knowledge transfer stickiness provides a barrier-focused perspective on why knowledge—even when valued—tends to “stick” and fail to transfer into routines, highlighting frictions such as ambiguity and recipient-side constraints. Third, research utilization clarifies what “using research” can mean in practice (e.g., instrumental vs. conceptual use), while evidence-based management explains why and how research insights should be translated into actionable managerial practices to narrow the research–practice gap. Interview evidence indicates that SMEs typically rely on fast, familiar information channels, experience uncertainty and opportunity costs when assessing relevance and comprehensibility, and most frequently struggle at the point of converting insights into implementable actions. We further observe meaningful variation in “research literacy” and routines, suggesting different absorptive-capacity profiles across firms. This paper derives actionable guidance for designing low-barrier support that helps SMEs (i) find relevant research efficiently, (ii) reduce sensemaking costs, and (iii) operationalize evidence through case-based and action-oriented translation artefacts and facilitation.

Environmental Knowledge Management from the Perspective of Young Employees: An Economic Sector Comparison

Riccardo Valente — 2026-08-25

Currently, degradation of the natural environment led many companies to consider implementation of more sustainable practices. In the academic literature, environmental knowledge required and acquired by employees, moreover, is often considered part of knowledge management, with a growing number of researches focusing on Environmental Knowledge Management (EKM). In practice, however, EKM in firms from different sectors vary and interventions are sometimes lacking. The research objective of the current paper is to examine selected elements of the EKM in enterprises operating in different economic sectors. Three research questions connected with EKM functioning were thus enquired through a structured questionnaire, sent electronically to employees, using non-random, convenience sampling. Therefore, statistical analysis of the 297 collected questionnaires stressed a high relevance of environmental protection without differentiation between economic sectors, while the EKM assessment of employees varies between firms from different sectors. The evaluation of EKM, moreover, does not align with the relevance of environmental protection according to most of the employees, signalling – based on motivation expectancy theory – a moderate dissatisfaction with the functioning of EKM among young employees and the existence of an intensification potential of unused environmental protection policies in companies interested. These findings confirm that there is potential among staff to implement a more effective EKM system, yet in practice companies are not making use of it.

New Trends Towards AI, Agility and Maturity in Learning Organisations: A Scoping Review

Brenda Van Wyk — 2026-08-25

Technology adoption plays a central role in the success of organisations, where organisational maturity can only be achieved based on learning organisation principles and agile teams for cross-functional collaboration. These constructs are seldom examined in tandem, and are more often approached as separate aspects in the complex adaptive systems in today’s organisations. Recent AI developments necessitate a re-evaluation of how learning organisations are optimised in digitally transformed environments. Knowledge management (KM) has evolved into a strategic enabler of organisational learning and adaptive capacity. This paper reports on findings of a scoping review that explored trends in organisational maturity, conceptualised as the extent to which organisations institutionalise continuous learning, reflective practice, and knowledge integration across systems, culture, and leadership. Databases Google Scholar, Scopus, Web of Science and ProQuest Central were searched and sources published from 2009 to 2025 were selected for thematic analysis. Using the PRISMA-ScR reporting guidelines and Covidence for data extraction, 37 articles were selected and analysed to identify trends and gaps in research. Findings indicate that sustainable competitive advantage emerges when organisational maturity, AI integration, and continuous learning are aligned to support anticipatory, evidence-based decision-making in complex environments.

Knowledge Management in Times of Uncertainty: Barriers and Opportunities in Graduate Education

Aiko Varela Aguilar — 2026-08-25

In Higher Education Institutions (HEIs), graduate education is essential in the socialization, production, and transfer of knowledge and innovations for society. In this sense, graduate programs contribute to a knowledge society by training future researchers in collaboration with experts in a knowledge area, innovating in research with them, sharing these innovations through formal and informal practices, and ensuring learning through systematic reviews and discussions with research networks. These knowledge management (KM) dynamics can be hindered by a turbulent environment in which social, economic, political, and cultural demands call for changes to current KM practices. This paper examined two leading graduate research programs in the northwestern region of Mexico that underwent several changes in their institutional structures and scientific practices due to environmental shifts. We analyzed 1) the challenges faced by these programs from a public university in Mexico due to changes in policies, 2) how they managed to generate knowledge, and 3) what opportunities emerged during this period. Documentary analysis was conducted as a case study using a qualitative approach, which helped us conduct an in-depth analysis and understand the KM strategies adopted in both contexts. We sought participation from program directors and scholars who met certain leadership criteria. This study highlights that political change was both a barrier and an opportunity for knowledge production, as it imposed budget cuts on science but also introduced new perspectives that enabled wider impact in knowledge transfer. Nevertheless, the KM strategies that emerged from the pandemic, such as diversifying funding sources and adapting certain scientific practices, were solutions for advancing science, quality education, and research on social retribution.

Knowledge Management for Sustainable Multidimensional Wealth: Reframing the MSW Model

Jose-Manuel Vega-Baez — 2026-08-25

This paper interprets the RMS Model, translated as Multidimensional Sustainable Wealth (MSW) Model, through a functional knowledge management (KM) architecture in order to clarify how multidimensional organisational wealth depends on knowledge-related capabilities. The paper is derived from a doctoral study that designed an integral business management model aimed at fostering Sustainable Multidimensional Wealth (MSW) in firms. That study followed a predominantly qualitative and exploratory orientation grounded in theoretical inquiry, documentary analysis, and interpretive-inductive reasoning, and resulted in the MSW Model, structured around five categories, ten subcategories, and forty-four emerging themes. Rather than treating MSW as a KM framework in origin, this paper argues that a functional KM lens provides a useful analytical vocabulary for interpreting selected MSW subcategories. Recent literature increasingly portrays KM as a field concerned with knowledge creation, sharing, integration, application, governance, and continuity, while also linking it to organisational learning, ESG integration, sustainability, and intellectual capital. Building on this literature, the paper maps five MSW subcategories — Corporate Culture, Corporate Talent, Corporate Intelligence, Corporate Governance, and Corporate Legacy — onto core KM functions. This mapping is used to explain how knowledge-related capabilities can support organisational outcomes that go beyond operational efficiency and extend to broader forms of value creation. The paper makes three contributions. First, it specifies a functional KM architecture and uses it as an analytical device for examining the MSW Model. Second, it shows that selected MSW subcategories can be interpreted as organisational loci through which KM functions are enacted, coordinated, and sustained. Third, it argues that MSW extends current KM debates by linking knowledge-related capabilities to multidimensional organisational wealth, particularly through continuity, legacy, and long-term viability. In doing so, the paper positions MSW as a structured interpretive framework that connects KM with organisational design and sustainable business management. It also suggests a pathway for future empirical research on how organisations may align knowledge processes with broader human, functional, contextual, economic, and transcendent dimensions of wealth.

Development of a Human-centric Systemic Interdisciplinary Approach to Enterprise Content Management Institutionalisation

Louisa Venter — 2026-08-25

Enterprise Content Management (ECM) solutions are essential for organisations aiming to enhance efficiency, effectiveness, and productivity in their information management endeavours. It is widely accepted that ECM solutions must be fully institutionalised, yet full institutionalisation is globally problematic. Internationally, even technologically advanced countries struggle to implement and maintain sound information management practices enabled by ECM solutions. Human dimensions are arguably the primary contributing factor for the perceived lack of full institutionalisation of ECM solutions, necessitating a deep understanding of human behaviour during the implementation and institutionalisation phases. The study strives to focus on the effect of knowledge-sharing, learning, sensemaking, and organisational environments on human behaviour during ECM-institutionalisation. This study proposes a systemic interdisciplinary approach to understand human behaviour during ECM-institutionalisation and offers a comprehensive lens through which the complex interplay of human behaviour, processes, information, and technology can be examined. A literature review, results from a maturity assessment and change adoption survey as part of an ethnographic study provided findings indicating a multifaceted, dynamic system, leadership support and fostering a learning culture to be pivotal. Skilled change agents balancing autonomy and cooperation are vital. Digital literacy encompassing sociological awareness and emotional intelligence part of customised training lead to information behaviour change. By enhancing collaboration through interdisciplinary methods, this approach provides valuable insights into the human dimensions of change, facilitating more effective institutionalisation of ECM solution. This study contributes a robust approach for understanding and managing the human dimensions of ECM-institutionalisation and enhances collaboration by bridging boundaries between disciplines, fostering a deeper understanding of the human dimensions involved in ECM-institutionalisation. The findings have significant implications for future ECM projects, enabling organisations to navigate the complexities of change management more effectively.

Variation, Selection, and Retention in Knowledge Management: An Evolutionary Integrative Review

Alessandra Vitale — 2026-08-25

Abstract: The aim of this paper is to develop an interpretation of the temporal development of Knowledge Management (KM) through the lens of variation-selection-retention (VSR) logic. More specifically, it examines how KM arrangements emerge, gain support, become stabilised, and subsequently follow different post-retention trajectories over time. Although KM research has generated rich insights into knowledge creation, transfer, innovation, and technological enablement, it has been less explicit in theorising what happens after KM arrangements are introduced and retained within organisations. This paper addresses that gap by proposing an evolutionary reading of KM that extends beyond implementation and performance. The study adopts a PRISMA-informed, theory-driven integrative review of Scopus-indexed journal articles in the Business, Management and Accounting subject area published between 2000 and 2026, complemented by a focused VOSviewer term co-occurrence analysis. The final corpus of 25 studies was analysed through interpretive synthesis, with VSR used as a sensitising framework and bibliometric mapping employed to support the identification of recurring thematic configurations. The analysis identifies four connected configurations of KM scholarship: KM as routinised capability, ecologically embedded knowledge creation, sharing-driven innovation, and technology-mediated infrastructure. These configurations show that KM literature already contains important temporal and evolutionary insights, but that these remain dispersed across adjacent streams. Building on them, the paper develops an evolutionary conceptual model in which retention is not treated as an endpoint. Instead, retained KM arrangements may follow four post-retention trajectories: renewal, dormancy, replacement, or obsolescence. These trajectories are shaped by the interaction of functional relevance, organisational support, social legitimacy, and adaptive renewability. The paper contributes in three ways. First, it offers a temporally oriented reinterpretation of KM as an evolving organisational phenomenon. Second, it extends existing debate beyond implementation and performance by foregrounding differentiated post-retention trajectories. Third, it proposes a conditions-based framework that can guide future empirical research on the long-run viability, adaptation, and decline of KM practices and systems across different organisational settings.

Use of AI in Education to Support KM Processes in Organizations

Tone Vold — 2026-08-25

AI-tools are being used in some organizations and there is little doubt that AI-tools may be powerful aids in a stressful worklife. However, it is important to choose tools that will enable a productive use, and with the plethora of tools at hand today, it may be difficult to evaluate and decide on tools that will suit the needs of an organization. As an academic organization providing education also within the lifelong learning segment, we should integrate use of AI-tools in our courses. As many in the lifelong learning segment are already working in organizations, we should offer learning about AI-tools, as well as learn from our students regarding the use of AI-tools. Our project on using AI-tools to evaluate student assignments has been undertaken in cooperation with our students. After the students being provided feedback on their mandatory assignments from our faculty staff, the same assignments have received an evaluation from an AI-tool. The investigations have been threefold; about how the students evaluated their AI-evaluation (compared to the evaluation from the lecturer), about how the students predict or present similar AI-tools can be used back in their organizations, and from the perspective of the faculty staff; how lecturers can best integrate teaching AI-tools to students that are in a worklife. The paper presents results from the qualitative investigations and provide valuable insights that will define the further use of AI-tools for evaluation for their next courses.

Leadership as a Cultural Enabler of Knowledge Management in High-Reliability Organisations

Małgorzata Wiśniewska — 2026-08-25

In the context of high-reliability and high-consequence organisations, knowledge management initiatives are often impeded by cultural barriers that hinder the disclosure, sharing, and utilisation of sensitive knowledge. The present paper examines how leadership shapes organisational culture conditions that determine the effectiveness of knowledge management, drawing on empirical evidence from nuclear security culture (NSC) self-assessment processes conducted in medical organisations using radioactive sources. The study conceptualises NSC self-assessment not as a compliance exercise, but as a structured knowledge management intervention aimed at eliciting tacit knowledge, weak signals, and experiential insights related to organisational vulnerabilities and human performance. The present paper builds on qualitative and mixed-method evidence from the first Polish self-assessment of nuclear security culture in a medical facility, supported by the International Atomic Energy Agency. The paper analyses how leadership behaviours influence knowledge creation, psychological safety, and the translation of assessment findings into organisational learning and change. The findings of the study indicate that leadership plays a decisive role in reducing cultural barriers to knowledge management, including fear of blame, formalism, and low trust in governance structures. The presence of visible leadership engagement, non-punitive responses to reported weaknesses, and active sense-making of assessment results have been found to significantly enhance knowledge sharing and learning outcomes. Conversely, leadership approaches that prioritise regulatory compliance have been shown to impede the practical application of knowledge derived from assessments and to diminish the efficacy of change processes. The present paper contributes to the knowledge management literature by positioning leadership as a cultural enabler of KM effectiveness in contexts where knowledge is sensitive, risk-laden, and ethically constrained. The present study extends the existing research in the fields of knowledge management (KM) and organisational culture by demonstrating how assessment-based KM initiatives can support sustainable change when embedded within leadership-driven cultural frameworks. Practical implications are offered for leaders and KM practitioners designing and governing knowledge initiatives in high-risk organisational settings.

Diversity Wisdom: From Diversity Management to Value Creation

Miroslaw Wojcik — 2026-08-25

This study aims to position diversity wisdom as a subsequent, qualitatively distinct stage in the evolution of
organisational approaches to diversity, and to identify its key constitutive dimensions. The article responds to the need to
overcome the limitations inherent in existing approaches and to more fully leverage diversity as a sustainable source of
organisational value, extending beyond the logic of regulatory compliance and instrumental efficiency. Organisational
approaches to diversity have evolved from reactive efforts focused on legal compliance to strategic diversity management,
and now to DEI initiatives and ESG frameworks. This evolution reflects growing awareness of diversity's value-creating
potential. However, each stage has revealed significant limitations, underscoring the need to shift from managing diversity
to a reflective, normative approach grounded in organisational wisdom. The study is based on a systematic literature review
that covers the evolution of diversity management, various approaches, and existing conceptualisations of wisdom in
management and organisation studies. The synthetic analysis focuses on identifying the barriers that earlier approaches to
diversity management sought to address and on delineating areas in which a wisdom-based perspective offers a qualitative
advantage. The study is embedded within the theoretical frameworks of the Resource-Based View, the Deep Rational Theory
of Wisdom, and the Pragmatic Theory of Wisdom. Diversity wisdom constitutes a distinct organisational meta-capability that
integrates knowledge, values, experience, and the capacity for deep reflection on diversity in the context of long-term goals
and social responsibility. Three constitutive dimensions are identified: flexibility, reflexivity, and empathy. Flexibility enables
adaptive responses that transcend rigid compliance frameworks; reflexivity facilitates deep organisational learning and
continuous refinement of diversity approaches; empathy allows authentic understanding and the valuing of diverse
perspectives. Each dimension addresses specific barriers of previous approaches. From a wisdom-based perspective,
diversity is not a problem to be managed but a fundamental source of value that requires conscious, responsible, and
reflective cultivation.

Knowledge Management and Digitalization Potential in Employee Sickness Absence Management Processes

Mikołaj Zając — 2026-08-25

Employee sickness absence generates significant costs for organizations, and its reduction requires a structured management approach. Sickness absence management (SAM) constitutes a function of human resource management (HRM) that remains underdeveloped in organizational practice and insufficiently emphasized in the academic literature. By collecting, disseminating, and effectively utilizing knowledge on sickness absence to enhance managerial efficiency and effectiveness, SAM should also be considered an important component of organizational knowledge management. Accordingly, the first objective of this paper is to identify and systematize the key functions of SAM. The second objective is to assess the potential of digitalization to supplement SAM functions, which are currently reserved mainly for HRM, with additional functions in the area of knowledge management related to sickness absence (KM-SA). This, in turn, may facilitate its broader adoption among organizations that lack sufficient financial and human resources to sustain such management practices. The identification of SAM functions in HRM was conducted using a narrative literature review. The starting point for identifying KM-SA functions was a systematic literature review, the result of which was the identification of a research gap that provided the rationale for conducting empirical research based on a commercial IT platform, widely used in Poland by organizations facing high levels of sickness absence. In addition to systematizing SAM functions in HRM, the study provides examples of benefits resulting from their digitalization and extension to include KM-SA functions. The use of digital tools for absence monitoring enables partial cost reduction through improved workforce planning, earlier identification of adverse trends, and making both more informed staffing decisions and strategic decisions based on knowledge of the phenomenon of sickness absence not only within the organization but also in the labour market. Data derived from commercial platforms, such as sickness absence barometers, make it possible to identify absence patterns, segment problems by departments or employee groups, and link absence to working conditions and organizational policies. In practice, knowledge management of sickness absence (KM-SA) should contribute to increasing the accessibility of such solutions, particularly for organizations with limited resources.

Knowledge-intensive Activities and Regional Inequalities in Poland: A Spatial Centre–periphery Analysis

Patrycjusz Zarębski — 2026-08-25

This paper examines the spatial differentiation of knowledge-intensive activities in Poland within the centre–periphery framework. Contemporary regional development increasingly depends on knowledge-related processes; however, these are unevenly distributed across space. Despite extensive research on the knowledge-based economy, limited attention has been given to the joint spatial patterns of high- and medium-high-technology industries and knowledge-intensive services at lower territorial levels. The aim of the study is to identify the spatial distribution of these sectors and assess their role in shaping regional inequalities. The analysis is conducted at the county (powiat) level using data from the REGON register of the Central Statistical Office. An indirect approach is adopted, treating the sectoral structure of the economy as a proxy for regional knowledge systems. Spatial patterns are examined using descriptive measures and global Moran’s I. The results reveal a strong concentration of knowledge-intensive activities in metropolitan regions and their functional areas, confirming the persistence of the centre–periphery structure. High- and medium-high-technology industries exhibit stronger spatial clustering, while knowledge-intensive services show greater dispersion but remain concentrated in more developed regions. The findings also indicate increasing spatial autocorrelation over time, pointing cumulative development processes. The study contributes to the literature by integrating sectoral and spatial approaches and demonstrating the usefulness of structural indicators as proxies for knowledge processes at the regional level.

Perceptions of the use of the Internet of Things in Banking: The Perspective of Students in Poland

Marek Zborowski — 2026-08-25

The main objective of the article was to identify the perception of the use of Internet of Things technology in banking from the perspective of young people. The survey was conducted in 2025 using the CAWI method at the Faculty of Management of the University of Warsaw and covered 205 students. The survey form was divided into two parts. The first part examined the general perception of IoT technology. The second part focused on examining the perception of this technology in banking applications. The key conclusions include: respondents are concerned about the security of their data and, in the case of banking applications, financial losses as a result of data leaks and loss of privacy to government agencies. Data analysis aimed at improving the personalization of products and services are the main areas of IoT use in banking. They perceive the usefulness of speeding up the payment process as important. In addition, it can be concluded that there is a high level of knowledge about IoT technology. Theoretical implications include recognizing the use of Internet of Things technology in banking among young people in Poland and outlining how to study this phenomenon. Practical implications will be important for banks and financial institutions that use or plan to use IoT in their products and services. The limitation of the study was that the survey was conducted only among the academic community. Further research should focus on expanding the scope of the study to include a larger number of academic centres and deepening the understanding of the determinants of the use of Internet of Things technologies in banking in the context of knowledge management.

From Ad Hoc to Repeatable: A Knowledge-Centric Framework for Introducing API Performance Testing

Inga Zilinskiene — 2026-08-25

API performance testing is often introduced informally and remains dependent on individual expertise, which limits reuse and makes results difficult to repeat. This paper presents a lightweight framework and repository approach. It helps organizations establish a repeatable API performance testing process and capture knowledge for reuse. Using a Design Science Research (DSR) approach, a Knowledge-Centric API Performance Testing Framework with six components was developed: Scope & SLOs, Environment Setup, Data Preparation, Execution & Metrics, Analysis & Reporting, and Knowledge Capture & Sharing. A key distinguishing feature of the framework is its explicit inclusion of non-technical roles (e.g., Business Analysts and Product Owners), enabling them to contribute to performance testing through structured involvement in scope definition, governance, and knowledge capture. The framework was applied in a global IT organization during an initiative to validate API performance after a data source change. The case shows that a structured workflow and shared artefacts reduce repeated effort and support cross-functional collaboration. The paper contributes (1) a practical framework for organizations introducing API performance testing from scratch and (2) a knowledge management framing that treats performance testing outputs as reusable organizational assets.

Regional Knowledge Potential and Tourism Innovation in Poland: A Multi-Criteria Analysis

Zbigniew Zontek — 2026-08-25

The paper investigates the knowledge-based determinants of tourism innovation potential in Polish regions and their relevance for smart tourism development. The primary objective is to assess how regional knowledge resources, learning capabilities, and information-processing conditions influence innovation readiness within the tourism sector. Tourism innovation potential is conceptualized not only as a set of infrastructural or technological indicators but also as a component of regional knowledge ecosystems that enable the creation, diffusion, and practical application of knowledge in tourism-related activities. The analysis is based on statistical data from the Polish Central Statistical Office, including indicators related to the information society, research and experimental development, and enterprise innovation activity, complemented by selected tourism-related variables. Together, these indicators form an operational framework for assessing how regions accumulate, manage, and utilize knowledge resources relevant to tourism innovation. To measure this potential, the study applies a multi-criteria decision-making approach combining Hellwig’s method and the TOPSIS method. Eleven diagnostic variables were selected, including ten stimulants and one destimulant. Both methods generate standardized synthetic measures of knowledge-based tourism potential within the range [0,1]. Based on these measures, Polish regions were classified into four typological groups representing different levels of knowledge maturity, absorptive capacity, and innovation readiness. A further stage of the analysis examines the relationship between knowledge-driven innovation potential and actual tourism performance. Two indicators of tourist movement were used: the density of tourist movement (WG) and Schneider’s tourism intensity index (WS). Spearman’s rank correlation analysis indicates a strong positive relationship (rs = 0.90–0.95), suggesting that regions more effective in mobilizing and utilizing knowledge resources tend to attract higher tourist flows. At the same time, several regions with only moderate knowledge potential record relatively high tourism activity, which may reflect effective knowledge-management practices, institutional learning mechanisms, or strong regional cooperation networks. The findings reveal significant regional disparities in the creation and utilization of knowledge within the Polish tourism sector and highlight the importance of strengthening regional knowledge-management systems-such as digital infrastructure, innovation networks, and mechanisms of knowledge exchange - to enhance regional competitiveness and support smart tourism development.

Generative AI as a Knowledge Governance Mechanism: The Role of Digital Trust and Information Security

Justyna Żywiołek — 2026-08-25

The rapid diffusion of generative artificial intelligence (GAI) is transforming organizational knowledge processes, yet existing research offers limited insight into its governance implications. This study addresses this gap by conceptualizing GAI as a mechanism of knowledge governance and examining its relationship with digital trust, information security maturity, and decision quality. Data from 300 firms across Poland, Germany, and Spain were collected using a CAWI survey of top management teams. Structural equation modeling (SEM) and multilevel analysis were applied to test relationships between AI-mediated knowledge practices, digital trust, information security maturity, and perceived decision quality. The results show that AI use significantly increases digital trust, which in turn improves decision quality. Digital trust acts as a mediator, while information security maturity moderates this relationship. The findings indicate that the impact of generative AI on decision-making depends on governance conditions rather than technology alone.

Immersive Knowledge Transfer through VR Glasses: Designing and Using Instructional Video-Based Microlearning for Customer Self-Repair and Technical Support

Justyna Żywiołek — 2026-08-25

The growing complexity of technical products and increasing customer expectations for fast and autonomous problem resolution are driving the need for new approaches to knowledge transfer beyond traditional service channels. Virtual reality (VR), combined with immersive video-based microlearning, offers new possibilities for delivering procedural knowledge directly to end users. This study investigates the effectiveness of immersive, video-based microlearning delivered through VR glasses in supporting customer self-repair and technical support. The study adopts a quantitative research design based on a large-scale CAWI survey conducted among 11,318 users of technical products across three European countries. The analysis focuses on key constructs, including usability, learning effectiveness, task complexity reduction, customer confidence, and reduction in service interactions. The results indicate that immersive instructional content significantly enhances procedural understanding, reduces perceived task complexity, and increases customer confidence in performing basic maintenance and repair tasks. The findings also suggest a partial reduction in reliance on traditional service channels. This study contributes to the literature by extending immersive learning research to customer self-service contexts and by empirically validating relationships between usability, learning effectiveness, and customer confidence in VR-supported environments. The results offer practical implications for organizations seeking to redesign technical support models through immersive learning technologies.

From Adoption to Knowledge Utilisation: Understanding the Role of Value, Usage, and Social Perception in Generative AI

Hao-Fan Chumg — 2026-08-25

Generative AI (GAI) has fundamentally transformed how individuals acquire, generate, and apply knowledge in digital environments. While information systems research has extensively explained why individuals adopt new technologies, considerably less attention has been devoted to understanding how initial adoption develops into meaningful knowledge utilisation. Existing post-adoption research has primarily focused on continued system use; however, continued use does not necessarily indicate that AI-generated knowledge has become embedded in users' learning, decision-making, or problem-solving activities. This limitation highlights the need for a broader post-adoption perspective to explain how generative AI supports meaningful knowledge utilisation. To address this gap, this study develops a conceptual framework that extends post-adoption theory by explaining the mechanisms through which adoption intention evolves into knowledge utilisation intention. Drawing upon information systems, knowledge management, post-adoption theory, and social psychology, the proposed framework suggests that utilitarian value and hedonic value jointly influence adoption intention. Rather than assuming that adoption naturally leads to knowledge utilisation, this study argues that users gradually develop post-adoption perceived value through accumulated interaction with generative AI, which subsequently facilitates knowledge utilisation intention. Furthermore, usage frequency and pluralistic ignorance are introduced as behavioural and socio-cognitive boundary conditions that respectively strengthen and weaken this transformation. Together, these mechanisms explain why individuals with similar adoption intentions may demonstrate substantially different levels of knowledge utilisation. By distinguishing knowledge utilisation from continued technology use, repositioning perceived value as a post-adoption evaluative mechanism, and integrating behavioural reinforcement with social perception, the proposed framework offers a more comprehensive explanation of post-adoption behaviour in the context of generative AI. The framework contributes to both information systems and knowledge management research by providing a theoretical foundation for understanding how generative AI becomes meaningfully embedded in users' everyday knowledge practices and offers directions for future empirical research.

How Does Knowledge Sharing Within Communities of Practice Drive Innovation?

Anna Kleynhans — 2026-08-25

The link between Communities of Practice (CoP) and innovation is a topic that needs to be explored in more detail in organisations and how knowledge sharing within CoPs drives innovation. Knowledge is recognised as a critical resource in organisations that creates competitive advantage. Knowledge sharing practices and behaviours strengthen organisational capability to innovate, however, knowledge sharing does not come naturally to most individuals. A systematic literature review of 24 peer‑reviewed articles published between 2015 and 2025 identified three thematic areas that explore relationship between knowledge sharing in CoPs and innovation. The three key themes are 1) tacit knowledge sharing and its link to innovation, 2) knowledge sharing barriers and facilitators and 3) the role of CoPs as a knowledge sharing platform that drives innovation. The findings highlight that tacit knowledge is deeply embedded in individual experience and intuition. It plays a critical role in driving innovation through processes such as learning by doing, interaction and reflection. Motivational factors, such as reward systems, have also been identified in the literature as factors that can either facilitate or hinder knowledge sharing behaviours. The review also identified organisational climate and leadership behaviours as facilitators and/or barriers. Furthermore, CoPs emerge as valuable, low‑cost platforms for informal learning and tacit knowledge sharing, strengthening relationships and supporting innovation. However, a gap remains in the literature regarding how tacit knowledge within CoPs drives innovation as few empirical studies have explored this area. This study contributes to the Knowledge Management field by consolidating current insights and emphasising the need for deeper investigation into how CoPs can be leveraged as a knowledge sharing platform that drives innovation.

An Information Systems-Supported Strategy for Managing Tacit Knowledge in Organisations

Moritz Rau — 2026-08-25

Tacit knowledge represents one of the most strategically significant yet poorly managed resources in knowledge-intensive organisations. Existing research has documented the limits of codification-oriented approaches and the persistence of tacit knowledge problems, but the field lacks an integrated, practice-oriented strategy that connects detection, description, and safeguarding of tacit knowledge into one operational framework supported by information systems. This paper addresses that gap. Drawing on eight semi-structured interviews with senior professionals in knowledge-intensive consulting roles, analysed through qualitative content analysis with abductive logic, the study identifies recurring problems across three core functions: detecting where critical expertise resides; describing tacit and experience-based knowledge in forms that others can understand and reuse; and safeguarding such knowledge when projects end or personnel change. The findings show that organisations do not primarily fail because of missing documentation. The central challenges concern the limited visibility of expertise, the difficulty of making experience-based knowledge communicable without losing its practical meaning, and the fragility of knowledge retention when it is tied to individuals rather than stable organisational structures. Information systems and artificial intelligence can support all three functions, but only when they are aligned with actual work practices and backed by clear governance and ownership. Based on these findings, and grounded in the Knowledge-Based View, Organisational Learning Theory, Sociotechnical Systems Theory, and a Practice-Based Perspective, the study develops a lightweight, information systems-supported knowledge management strategy organised around three conceptual building blocks: Knowledge Hubs, Knowledge Capsules, and workflow-based safeguarding mechanisms, all supported by an enabling digital, governance and motivation layer. The strategy does not aim for complete codification of tacit knowledge but rather for practical and structured support for its detection, description, and safeguarding in everyday organisational work. The paper contributes both an empirically grounded analytical framework and an operationalisable strategy for knowledge-intensive organisations.

Adapting InCaS as an Acceleration Architecture for Emerging Organisations: A Work-in-Progress Report

Vanessa Guimarães Ribeiro — 2026-08-25

This paper reports on an ongoing research project investigating the adaptation and digital operationalisation of the InCaS (Intellectual Capital Statement) method as an acceleration architecture for emerging, knowledge-based organisations. The research is being conducted within the Impacta Júnior programme, a structured acceleration initiative promoted by Concentro and funded by the Coalizão pelo Impacto, involving ten university-based junior enterprises as the empirical field. The paper presents the theoretical grounding, the methodological design, and the full InCaS application process as implemented in this context, together with preliminary insights drawn from the first application cycle. The adapted process encompasses nine sequential stages, beginning with organisational pre-arrangement and business modelling, followed by intellectual capital factor identification, Quality-Quantity-Systematics (QQS) evaluation, and impact analysis. These stages feed into intervention potential mapping, indicator definition through Key Performance Indicators (KPIs), and structured strategic action planning, culminating in a pitch-based Demo Day as the final output. A purpose-built digital platform was developed to support this process at scale, enabling anonymised multi-stakeholder participation, automated composite scoring, and real-time visualisation of results. Twenty-four management factors were derived from organisational listening sessions and mapped across the three intellectual capital dimensions, using simplified terminology adapted to low-maturity organisational contexts. Early observations indicate that the adapted method fosters structured organisational self-reflection, makes latent knowledge gaps more visible, and encourages collective deliberation processes, even in organisations with limited prior exposure to formal management practices. Recurring challenges were observed in areas such as KPI definition, prioritisation of strategic factors, and the consolidation of shared organisational understanding. Full empirical analysis, including systematic pre-post comparisons of organisational maturity and longitudinal assessment of development trajectories, remains underway. The study contributes a documented and replicable model for adapting the InCaS method to acceleration environments, demonstrates the feasibility of its digital operationalisation, and offers initial evidence of its applicability for practitioners, programme designers, and policymakers working with knowledge-intensive and resource-constrained organisations.

Knowledge Management as a Determinant of Social Well-being: A Cognitive-based Composite Indicator

José-Luis Alfaro-Navarro — 2026-08-25

Knowledge management is a central element in contemporary processes of transformation across organizations, territories, and societies. Understanding its relationship with residents’ quality of life can contribute to progress toward models of sustainable human development. At the organizational level, knowledge management plays a pivotal role in enhancing the work environment by fostering autonomy, motivation, and creativity, as well as by promoting the development of employees’ skills and capabilities. These factors are strongly associated with key indicators of work-related and social quality of life. From a social perspective, knowledge management promotes more equitable access to educational, health, and labour opportunities, thereby reducing structural inequalities and strengthening civic participation, with positive effects on social well-being. Moreover, the consolidation of knowledge-based social ecosystems enhances collective resilience to complex challenges such as demographic ageing, climate change, and territorial inequalities, by improving communities’ adaptive and responsive capacities. In this study, we develop a methodological approach to measure individuals’ levels of knowledge using Likert-scale data obtained from a survey of Spanish citizens, which yielded a total of 4,147 validated responses. Specifically, we propose an individual knowledge indicator constructed using Item Response Theory (IRT) models and defined through three dimensions related to applied cognitive capacities: structural knowledge derived from formal education; digital competencies as an expression of applied knowledge; and access to and use of information as a measure of autonomy and informational capacity. Moreover, we examine the relationship between knowledge management and quality of life, measured from a subjective perspective. The main results highlight the importance of individual knowledge as a determinant of well‑being and reveal that the quality of life dimensions most strongly influenced by individual knowledge are the work environment, educational opportunities, and economic situation.

Assessing Knowledge Provenance Quality for CEO Decision Support: An OWL-DL Ontological Framework

Carolina De Oliveira — 2026-08-25

The ontology demonstrates approaches to enhancing the precision of knowledge provenance and outlines the systematic engineering of a knowledge provenance ontology designed to improve its reliability for executive decision-making. Utilizing architectural principles, we propose a formal model that evaluates knowledge provenance quality based on standardized coding systems such as PROV-O. The methodology follows a rapid prototyping lifecycle, employing the "Ontology 101" development process to transform domain expertise into a rigorous OWL-DL framework. By implementing constraints and functional properties, the ontology ensures data integrity and consistency, which are critical for CEOs navigating complex information environments. The results demonstrate that formal ontological reasoning, supported by tools such as Pellet, can effectively filter low-quality knowledge sources, providing a trustworthy basis for strategic corporate oversight.

Testing Evolutionary Psychology as a Foundation for Strategic Brand Building

Sami Lanu — 2026-08-25

This Work-in-Progress study conducts a theory-guided exploratory mapping of brand-following motives onto evolutionary motive systems (prestige/status, affiliation/belonging, resource security/trust, and leadership/followership). The purpose is to provide initial, preliminary evidence on whether brand-following motivations show patterns consistent with evolved coordination motives, and to outline implications for strategic knowledge management (KM). Building on the knowledge-based view, the study treats brands as socially distributed knowledge assets that compress reputational information and reduce uncertainty. Evolutionary psychology provides a candidate mechanism for why such symbolic knowledge structures scale and persist. Empirical evidence comes from a survey of marketing students (N = 113; Bachelor n = 37; Master n = 76). Respondents named a brand they actively follow and evaluated motivational drivers of that behavior. Motivations were grouped into four theory-derived clusters: resource security/trust, prestige/status, affiliation/belonging, and leadership/inspiration. Descriptive comparisons across educational levels and brand categories provide preliminary pattern evidence. Results suggest that brand following is not explained by security/trust motives alone: social-symbolic motives (prestige, belonging, and leadership) appear salient alongside trust/security. As a WIP contribution, the paper offers a focused empirical proof-of-concept and a lean theoretical bridge, with deeper KM integration planned after conference feedback.