Proceedings IFKAD 2026

PROCEEDINGS e-books

Proceedings IFKAD 2026

Intelligent Knowledge for Sustainable Organizations

Corvinus University of Budapest, Budapest, Hungary 1-3 July 2026
ISBN: 978-88-96687-21-5
ISSN: 2280-787X

List of Included Articles:

The Cascade Paradox: Information Refraction in Hierarchical Organisations, AI-Driven Power Shifts, and the Future of Knowledge Cascades

(978-987)

Noémi Szilvia Lőrincz, Márta Kiss

Abstract

In knowledge-intensive organisations, the cascade — the sequential relay of strategic information through management layers — is the dominant mechanism by which corporate direction is translated into individual action. Yet cascade practices are rarely neutral conduits: information bends as it travels, shaped by the cognitive, structural, and political properties of each relay agent. This paper introduces information refraction as a theoretical construct to capture this systematic transformation and presents empirical evidence from two complementary datasets spanning the European automotive industry.
The first dataset comprises original mixed-method research within a large European automotive manufacturing organisation: 61 survey respondents stratified across four hierarchical levels (working level, first-line managers, middle managers, and senior managers / divisional heads), supplemented by three focus groups. The second dataset is a multi-organisational AI adoption survey with 37 respondents drawing respondents from OEMs, Tier 1 and Tier 2 suppliers, and adjacent industry actors across Europe, Japan, Germany, Hungary, Portugal, and the United Kingdom.
Together, these datasets reveal a cascade paradox: the actors most responsible for relaying strategy — middle managers — exhibit the lowest strategic comprehension and the highest self-reported limitations in cascade execution; while across the broader automotive ecosystem, AI adoption remains shallow (65% of organisations at pilot or early-exploration stage), fragmented (80%+ limiting AI to less than 20% of production processes), and impeded by identical barriers — lack of internal expertise (73% of respondents) and resistance to change (62%). We argue these two findings are structurally connected: the cascade problem and the AI readiness problem are expressions of the same underlying dynamic — organisations that cannot transmit knowledge faithfully cannot absorb transformative technologies effectively.
We advance a prospective argument with significant implications for knowledge management theory: AI-driven data centralisation will fundamentally alter the cascade’s role as a power mechanism. For organisations that invest in AI literacy and knowledge infrastructure, the cascade paradox resolves through genuine democratisation — employees with strong analytical capacity gain direct data access, bypassing refraction. For those that do not, distortions compound. In an automotive sector navigating simultaneous disruption across electrification, supply chain fragility, and geopolitical volatility, cascade quality and AI readiness are not separate strategic priorities — they are the same priority.

Integrating Knowledge and Relational Capital to Improve Elderly Care: An Interventionist Approach

(988-992)

Francesca Dal Mas, Ludovica Schiavone, Giuseppe Pezzotti, Alberto Cavazza

Abstract

The aging population is a major systemic challenge of the 21st century, reshaping healthcare demand, organizational structures, and knowledge dynamics. In highly aged countries like Japan, demographic pressures expose fragmentation in care pathways and the limits of purely structural or technological solutions. This paper sees Knowledge Management (KM) and Relational Capital (RC) are strategic assets that enable healthcare organizations to turn aging from a liability into a catalyst for learning, coordination, and innovation.
The study examines the interplay between KM and RC within a university-hospital ecosystem, specifically analyzing Kansai Medical University (KMU) and its affiliated hospital in Japan. It employs an Interventionist Research approach, using six months of participant observation, institutional documentation, and interviews to deepen the integration of knowledge translation into routine organizational processes.
The results indicate that KMU functions as a relational nexus where clinical, organizational, and community knowledge converge. Formal procedures, such as interprofessional rounds, digital repositories, and structured training, enhance knowledge generation and dissemination, whereas informal, trust-based networks promote the exchange of tacit knowledge. Collectively, these processes strengthen coordination, resilience, and continuity of care in response to population shifts.
The study emphasizes that traditional Japanese caregiving concepts are culturally ingrained in senior care, yet are increasingly implemented through modern knowledge and relational frameworks. In this setting, RC links hospital services with community-based care, facilitating integrated models in which clinical transitions are processes of knowledge translation supported by relational continuity.
Digital technologies, such as telemedicine, digital platforms, and AI-driven tools, augment these dynamics by improving service delivery and enabling trust-based interactions in virtual settings. Nonetheless, the research shows that technology is effective only when integrated within robust relational and knowledge frameworks, functioning as an enabler rather than a replacement.
The university serves as a pivotal coordinator of the healthcare knowledge ecosystem, integrating education, research, and care delivery while ensuring the stability of knowledge flows across many stakeholders. The study proposes the relational hub as an empirically grounded configuration that renders KM and RC observable and actionable within healthcare ecosystems. It implies that effective responses to aging require organized knowledge-sharing systems, intentional development of research collaborations beyond hospital confines, and a university-led coordination role to promote innovation.

Knowledge Transfer and Innovation Spillovers in Aerospace

(1019-1027)

Antonio Limongiello, Andrea Tomo

Abstract

The Space Economy is one of the most strategic and innovative sectors of the 21st century, characterized by advanced technologies and new market opportunities (Yablonsky, 2018). Its value chain encompasses public and private actors, spanning upstream and downstream activities (Binci et al., 2022). This innovative environment provides a setting for analysing how knowledge generated in aerospace contexts can be transferred to other industries (Smith, 2013). In this sense, Knowledge Management (KM) provides both a process and an organisational perspective through which knowledge integration and transfer can be understood (Cristache et al., 2025). This paper aims to map the KM practices and innovation processes developed in the aerospace industry and to assess their applicability to other industries. Attention is devoted to the mechanisms through which scientific and technical knowledge is translated into innovations capable of generating market impact through new products, services or organisational models. The study adopts a systematic literature review based on the Web of Science database. The search query combined terms related to aerospace, knowledge management and knowledge transfer. The initial search identified 494 papers. After applying the filters “Article & Proceeding Papers”, “Management & Business” and “English”, the dataset was reduced to 97 publications. The PRISMA protocol ensured transparency and replicability. Papers were excluded when not aligned with KM or when knowledge was treated as a secondary element within engineering-oriented analyses. The final dataset consists of 57 papers. This study develops a framework for qualitative content analysis based on three dimensions (Snyder, 2019): object of transfer (knowledge, technologies, innovation processes), transfer mechanisms (human-relational, organisational, technological) and sectoral proximity (related, distant, unrelated). The objective is to: a) identify points of convergence between aerospace and other industries; b) classify the main strategies of KM (Ardito et al., 2021); c) provide an overview of good practices supporting learning and innovation dynamics across industries.

Evolving Competences through HR Projects: Knowledge Brokering Mechanisms in the SKiLL-PA Implementation Pathway

(1028-1036)

Paolo Canonico, Ernesto De Nito, Vincenza Esposito, Mario Pezzillo Iacono

Abstract

Competence-based HRM reforms in public administrations often remain trapped in formal compliance because competence taxonomies, templates, and platform inscriptions must be translated into locally credible and administratively governable routines. This paper examines how inter-organisational knowledge brokering enables such translation in SKiLL-PA, a national, platform-mediated reform programme designed to institutionalise strategic, competency-based HRM and to connect competence modelling to workforce planning and development. We adopt a qualitative, process-oriented single-case study of UNI-1, a mid-sized public university in inland Southern Italy, drawing on interviews, documentary analysis, and direct observation. Findings show that implementation effectiveness hinged on a formally mandated academic coordinator acting as the primary knowledge broker across organisational interfaces and internal domains. Brokerage unfolded through a patterned repertoire of mechanisms: sense-giving and shared framing to reduce equivocality; translation and reframing of competence language to align behavioural competences with situated work; artefact-mediated alignment through versioned templates and traceable decision logs; cross-domain convening to integrate HR modelling with budgetary sustainability; negotiated bounded flexibility to reconcile standardisation with role hybridity; data management to secure informational integrity; legitimacy work with governance and social partners; and iterative learning routines that stabilised interpretations over time. The findings extend knowledge management research by showing how brokering work aligns interpretive, socio-technical, and institutional layers in platform-mediated competence reforms, framing inter-organisational knowledge transfer as negotiated translation rather than a top-down transmission.

Dynamic Capabilities and Economies of Scale: Rethinking Intelligent Knowledge in Sustainable Innovation

(1037-1045)

László Kállay

Abstract

This paper develops an economic perspective on dynamic capabilities by conceptualizing them as investments in intelligent knowledge under uncertainty. The dynamic capabilities literature has made substantial progress in explaining how firms sense opportunities, seize them, and reconfigure resources, yet it has paid less systematic attention to the cost structure of capability development and to the possibility that organizations may invest either too little or too much in dynamic capabilities. Building on a formal transaction cost optimization model previously developed by the author, the paper argues that dynamic capability investment follows an analogous optimization logic: organizations incur direct and quasi-fixed costs in building sensing, seizing, and reconfiguring mechanisms, while these investments reduce the expected losses associated with misadaptation, missed opportunities, strategic errors, or delayed transformation. The central claim is that there is a scale-dependent optimum of dynamic capability investment. As organizational exposure increases – through size, volume, scope, technological interdependence, or ecosystem involvement – the potential losses from weak adaptation increase, making higher capability investment economically justified. However, the optimum remains bounded because excessive investment may generate coordination burdens, cognitive overload, bureaucratic routines, and declining flexibility. The paper contributes to dynamic capability theory by integrating economies and diseconomies of scale into the analysis of intelligent knowledge investments and by deriving propositions for SMEs, large firms, and sustainability-oriented innovation ecosystems.

The Possible Effects of Digital Transformation on Social Enterprises and NGO

(1046-1052)

Maria Vittoria Prudentino, Paola Canestrini, Federica Bandini

Abstract

Existing research offers limited insight into how digital transformation unfolds in Social Enterprises (SEs). This study examines what drives SEs to engage in digital transformation, the facilitating and hindering factors, and work-related risks. Drawing on a theoretical framework integrating Dynamic Capabilities, Digital Social Innovation enablers and barriers, and Corporate Digital Responsibility, it adopts a qualitative multi-method case study based on a group interview and a focus group with managers from Italian SEs. The findings show that digital transformation is filtered through SEs’ anthropocentric orientation. In the SEs studied, it is triggered by concrete organizational and social needs and leads to a reconfiguring process tailored to mission preservation. More specifically, digital transformation unfolds through processes of sensing, seizing, and reconfiguring, in which organizations identify unmet social and organizational needs, adopt technologies through gradual and context-sensitive approaches, and adapt internal structures, competences, and external partnerships over time. Digital transformation depends on a combination of human, organizational, and environmental factors, with hybrid competences, bottom-up experimentation, and ecosystem collaboration emerging as key enablers, while resource constraints, uneven digital literacy, and resistance to change pose obstacles. These barriers are not only structural but also reflect concerns related to service quality, inclusion, and mission coherence. The implications of digital transformation for work are actively filtered through mission-oriented values and responsibility-oriented practices, while digital technologies are predominantly framed as tools for mission augmentation rather than work substitution. In this respect, digital transformation in SEs appears to be governed through embedded responsibility-oriented logics, which shape how risks such as dehumanization and job displacement are anticipated and managed. Overall, the study contributes to research on digital transformation by showing how anthropocentric, mission-driven organizations interpret and enact digital change. It also suggests that other sectors may learn from SEs that maintaining a human-centered approach can help mitigate ethical and organizational risks associated with digital transformation.

Governing Adaptation: Family Councils, Dynamic Capabilities, and Company Performance in Family Firms

(1053-1060)

Attila Wieszt, Noémi Szilvia Lőrinczz, Amitabh Anand

Abstract

Family firms often rely on resources such as long-term orientation, trust, patient capital, ownership commitment and accumulated knowledge, yet these resources may generate rigidity, risk aversion and resistance to renewal. This paper examines how this paradox is shaped by family councils, understood as governance forums operating at the boundary of family, ownership and business. While family governance, dynamic capabilities and family firm performance have each received scholarly attention, their interrelationship remains underdeveloped. In particular, little is known about how family council characteristics and functioning influence the development of dynamic capabilities and condition their translation into company performance.
The paper develops a review-based conceptual model through a structured integrative literature synthesis. It connects three literature streams: family council and family governance research, dynamic capabilities research in family firms, and studies on family firm performance. Rather than presenting a systematic review, the study uses these streams to build a causal conceptual model of the family council–dynamic capabilities–company performance relationship.
The model proposes that family council characteristics and functioning influence the development of sensing, seizing and transforming capabilities. This relationship may be mediated or moderated by variables such as socioemotional wealth balancing, family social capital, intergenerational knowledge transfer, legitimacy creation, conflict processing and ambidextrous governance. Second, the model argues that family councils moderate the relationship between dynamic capabilities and company performance by shaping whether adaptive capabilities receive family support, ownership legitimacy and resource commitment. Third, the model includes recursive feedback loops: dynamic capabilities and performance outcomes may reshape the characteristics, legitimacy and functioning of the family council over time.
The paper contributes by positioning the family council not merely as a communication body, but as a governance infrastructure for firm-level adaptation. It also identifies future research directions on the family council–dynamic capabilities link, the dynamic capabilities–performance black box, contextual conditions and feedback processes.

Absorptive Capacity in Complex Public Organizations: The Role of AI

(1061-1068)

Silvia Oliveira, Nathalia Werlang

Abstract

Artificial Intelligence has emerged as a relevant enabler of Knowledge Management, particularly in complex public organizations exposed to large volumes of external information and increasing demands for innovation and public value creation. In such contexts, international missions constitute strategic channels for accessing external knowledge through cooperation networks, foreign institutions, and professional interactions. However, while organizations may effectively identify and acquire valuable knowledge through these initiatives, significant challenges often remain in converting it into institutional learning and sustained organizational outcomes. Accordingly, the objective of this article is to understand how AI can act as a facilitating element of Absorptive Capacity in public organizations characterized by high organizational complexity and intense cooperation with international networks. The study adopts a qualitative single-case study design conducted in a federal public organization. Empirical evidence was generated through documentary analysis and 18 semi-structured interviews with public servants assigned to international missions in different countries and international organizations. Data were analyzed through content analysis supported by Atlas.ti, using the refined AC model proposed by Todorova and Durisin (2007) as the analytical framework. The findings indicate that AC is manifested in the context of international missions, particularly in the stages of recognition of value, knowledge acquisition and assimilation. Public servants assigned abroad perform functions analogous to gatekeepers, connecting the organization to strategic knowledge sources and international networks. Prior knowledge, technical expertise, and relational embeddedness were identified as critical antecedents supporting these early absorptive processes. However, significant bottlenecks emerge in the later stages of transformation and exploitation. The results reveal weaknesses in knowledge diffusion routines, fragmented feedback cycles, dependence on individual actors, and institutional memory losses associated with personnel turnover. Based on these findings, the article argues that AI may strengthen organizational learning processes through intelligent scanning, information filtering, knowledge repositories, pattern recognition, and decision-support systems. Such mechanisms may help public organizations reduce information overload, improve knowledge circulation, and convert externally acquired knowledge into innovation and sustained public value. Nevertheless, AI should complement rather than replace human judgment, contextual interpretation, and relational capabilities.

Microfoundations of Dynamic Capabilities: Intelligent Knowledge for Sustainable Internationalisation in Higher Education

(1069-1077)

Judit Fortvingler

Abstract

As global competition intensifies, international higher education institutions must develop strong organisational agility to remain competitive. However, the sector’s traditional emphasis on volume-driven massification frequently generates bureaucratic rigidities that constrain adaptation within international operations. While dynamic capabilities theory addresses such environmental volatility, its predominantly macro-level focus offers limited insight into how universities adapt internally.
To address this gap, this paper examines the cognitive microfoundations of dynamic capabilities, within the context of internationalisation, focusing on how the intelligent knowledge of international academic leaders facilitates strategic renewal and sustainable internationalisation. Building on this perspective, the paper proposes a conceptual framework that links individual-level cognition to organisational-level adaptation.
The research adopts a qualitative, comparative methodology, examining three structurally distinct Hungarian universities governed by different operational models: a traditional state university, a centralised public institution, and a market-oriented private university. Data were collected via in-depth interviews with senior leaders responsible for international affairs. Using the proposed framework, the analysis maps leaders’ cognitive capabilities onto the key stages of dynamic capabilities: sensing, seizing, and transforming.
The findings suggest that managerial cognition plays a critical role in enabling agility in international higher education. Process-oriented expertise allows executives to detect emerging global shifts, while experience-based judgement helps reconcile financial pressures with academic values under conditions of uncertainty. In addition, contextual awareness allows leaders to interpret external demands in ways that reduce internal resistance during organisational change. The results also indicate that governance structures significantly shape these processes: while market-oriented institutions rely on rapid structural adjustments, state-funded universities depend more heavily on individual leadership to overcome institutional inertia and build organisational resilience.
From a practical perspective, the study highlights the importance of developing multidimensional cognitive competencies among international academic leaders, rather than relying solely on formal administrative procedures. To ensure continuity in internationalisation strategies, individual expertise should be embedded within institutional processes and practices.
By introducing a micro-level perspective on dynamic capabilities, the paper shows how managerial cognition can support sustainable internationalisation in institutions outside the global elite tier. The study contributes to a more context-sensitive understanding of academic agility, demonstrating that institutional resilience in international operations can be built without uncritically importing corporate management models.

The Key Factors in RTO Spin-off Development: Reconfiguring Organizational Knowledge for Spin-off Ventures

(1078-1085)

Guan-Ting Chen, Sirirat Sae Lim

Abstract

Research and Technology Organizations (RTOs) are under growing pressure to convert publicly funded knowledge assets into market-facing ventures, yet the mechanisms through which RTOs reconfigure knowledge across the parent-spin-off boundary remain insufficiently theorized. Drawing on the Knowledge-Based View, knowledge-creation theory, and intellectual-capital theory, this paper asks how RTOs combine intellectual property (IP), tangible assets, tacit know-how, human capital, and relational capital during spin-off development, and how Resource Coordination Capability enables such reconfiguration. The study uses a qualitative, theory-elaborating design based on semi-structured interviews with twenty-two respondents, including thirteen investors and nine policymakers with direct exposure to RTO spin-off cases. This external stakeholder sample was selected because investors and policymakers observe multiple reconfiguration attempts and evaluate whether ventures are investment-ready or policy-ready. A six-phase thematic analysis generated two linked theoretical artifacts. First, the Knowledge Asset Reconfiguration (KAR) Model shows that licensing, assignment, and joint development are normally combined across different subsets of the knowledge-asset portfolio rather than chosen as mutually exclusive alternatives. Second, the Progressive Separation Model explains spin-off emergence as a three-phase process of incubation, acceleration, and independent operation, with different capital forms moving at different speeds. Findings indicate that structural capital can be formalized relatively quickly, human capital migrates in clusters around formation, and relational capital remains the slowest and most consequential asset to rebuild. Across both models, Resource Coordination Capability acts as the meta-capability that allows RTOs to assemble, sequence, and reallocate heterogeneous resources across a changing boundary. The paper contributes to RTO spin-off research by replacing a single-transfer view with a boundary-reconfiguration view. This processual framing treats the venture not as an entity born at incorporation but as an evolving arrangement through which codified IP, people, routines, facilities, and relationships are progressively recombined. It is especially relevant for publicly funded technologies, where market uncertainty, policy accountability, and knowledge-leakage concerns must be balanced. The paper offers managers, investors, and policymakers practical diagnostics for judging reconfiguration readiness.

Advancing Foresight and Futures Literacy by Generative Artificial Intelligence

(1086-1094)

Tamás Kristóf

Abstract

In the era of accelerating technological change and systemic uncertainty, foresight has become a critical capability for organizations seeking to anticipate and navigate complex futures. At the same time, advances in generative artificial intelligence (GenAI) are rapidly transforming how foresight is conducted, enabling new forms of future exploration, data synthesis, scenario generation, and forward-looking analysis. Despite this growing relevance, the role of GenAI in foresight remains conceptually underdeveloped, with existing research predominantly framing artificial intelligence (AI) as a tool for augmenting human cognition. This paper addresses this gap by conceptualizing the role of GenAI in reshaping foresight practices and contributes to the development of futures literacy – the capacity to understand, imagine, and use the future in decision-making. Drawing on insights from futures studies, knowledge management, and socio-technical systems theory, this paper develops a conceptual framework that positions GenAI as a knowledge infrastructure mediating foresight processes. The paper’s main contribution is the identification of a dual-path transformation in AI-supported foresight. The first, the augmentation paradigm, conceptualizes GenAI as a cognitive collaborator that enhances human sensemaking, supports participatory foresight processes, and fosters futures literacy as a learning-oriented capability. The second, the autonomous foresight paradigm, reflects an emerging trajectory in which GenAI systems increasingly perform foresight tasks with limited human intervention, prioritizing efficiency, scalability, and accessibility. The paper highlights a fundamental tension between these two paradigms. While automation expands access to foresight and reduces costs, it may also weaken the reflexive and participatory processes that underpin futures literacy. This raises critical questions regarding the relationship between foresight production and anticipatory learning, as well as the role of human agency in AI-mediated knowledge creation. The paper contributes to the literature by advancing a novel conceptualization of AI-supported foresight as a socio-technical transformation characterized by competing epistemic logics. It further outlines implications for organizational practice, emphasizing the need for hybrid foresight models that integrate AI-driven capabilities with human-centered sensemaking, and identifies key directions for future research on governance, ethics, and human–AI co-creation in foresight.

Between Heritage and Change: Value-Oriented Digital Transformation in European Theatres

(1095-1101)

Dora Horvath

Abstract

This study examines how theatres interpret, govern, and strategically integrate digital technologies and knowledge practices in the context of mission-driven value creation. While digital transformation is often associated with efficiency gains and technological change, less attention has been paid to how digital knowledge is embedded in organizational practices and contributes to cultural and social value in non-profit contexts. Drawing on 28 semi-structured interviews with theatre professionals across Europe, the study applies an inductive qualitative approach inspired by grounded theory to explore how digitalization unfolds in practice. The findings show that digitalization is primarily embedded in operational and support activities, where digital tools function as knowledge infrastructures that enhance communication, coordination, and audience engagement. Rather than being driven mainly by efficiency, these practices contribute to public value by extending access, sustaining relationships, and reinforcing theatres’ cultural missions. At the same time, digital transformation remains incremental and selective. More advanced forms of transformation – defined as the strategic integration of digital knowledge into governance and value creation – are limited, as digital practices are typically embedded within existing institutional frameworks shaped by legacy structures and public value orientations. Digital experimentation supports organizational learning but remains largely project-based and weakly institutionalized. Overall, the study shows that digital transformation in theatres primarily reinforces existing value creation logics rather than redefining them. It contributes to the literature by conceptualizing digital transformation as a knowledge-based, value-oriented process shaped by governance, institutional logics, and organizational capacities, offering insights into how mission-driven organizations navigate digital change beyond market-based contexts.

Measuring the Unmeasurable? A Systematic Literature Review of University Technology Transfer Efficiency

(1102-1110)

Miklós Verseghi-Nagy, András Nemeslaki

Abstract

Innovation has become central to achieving high-value growth, particularly in economies seeking to escape the middle-income trap where competitiveness still rests on low wages and process cost-cutting. In triple helix innovation ecosystems, technology transfer is seen as a crucial link enabling public–private value co-creation. Yet the efficiency of these processes remains poorly understood. University technology transfer efficiency (UTTE) — the relationship between inputs devoted to transferring research into commercial applications and resulting outputs — poses significant conceptual and measurement challenges. Inputs are ambiguous and multidimensional (research expenditure, technology transfer office resources, organizational capabilities); outputs are heterogeneous (patents, licenses, spin-offs, licensing income, broader impacts); and no consensus exists on which efficiency concept should formally link them. Despite a substantial body of empirical literature, UTTE measurement remains fragmented across incompatible units, anchored to transactional proxies, and disconnected from the economic outcomes that justify the investment. What is being measured is not what matters; what matters is not being measured.
This paper investigates whether the existing literature offers an adequate conceptual and empirical basis for measuring UTTE — and if not, what is missing. Our systematic review examines 71 peer-reviewed studies published between 2006 and 2026, analyzing: (a) how inputs and outputs are defined and operationalized; (b) which methodological frameworks relate inputs to outputs; (c) how studies address data limitations and cross-institutional comparability; and (d) to what extent emerging metric frameworks from professional associations (e.g., ASTP) are reflected in recent empirical work.
We find that DEA-type non-parametric frontiers dominate methodologically, with stochastic frontier analysis providing a parametric alternative; that data limitations severely constrain cross-institutional and cross-country comparability; and that emerging professional frameworks have only partially penetrated empirical practice. Taken together, these findings reveal a field built around what is measurable and institutionally available rather than what is theoretically adequate — and point toward a reoriented research agenda that moves upward in aggregation level, from institutions to ecosystems; forward in the value chain, from transfer outputs to enterprise outcomes; and deeper in explanatory mechanism, from quantitative proxies to qualitative process understanding.

Intelligent Knowledge Management Systems for Human-AI Collaboration in Financial Decision-Making: A Behavioural Finance Perspective on Value Creation Beyond Profit

(1111-1119)

Navini Perera, Erika Jáki

Abstract

This theoretical paper examines how intelligent knowledge management systems (IKMS) enable human–AI collaboration in financial decision-making and how such collaboration contributes to value creation beyond profit. The study addresses a theoretical gap at the intersection of behavioural finance, artificial intelligence in finance, knowledge management, and organisational innovation. Behavioural finance has demonstrated that financial judgement is shaped by biases, heuristics, emotions, and bounded rationality, while AI research in finance has largely emphasised automation, predictive accuracy, efficiency, and performance optimisation. However, less attention has been paid to how human cognition and algorithmic intelligence interact within organisational knowledge processes, and how this interaction reshapes financial decision-making in ways that affect trust, accountability, sustainability, and innovation. Using a conceptual framework-building approach, the paper positions IKMS as the central organisational layer through which human and AI cognition are integrated. Financial decision-making is conceptualised as a knowledge-intensive organisational process involving knowledge retrieval, knowledge integration, knowledge interpretation, and knowledge application. Within this process, AI contributes computational capacity, pattern recognition, prediction, and scalable knowledge synthesis, while human actors contribute contextual judgement, ethical reasoning, experience-based interpretation, and responsible oversight. The framework argues that human–AI collaboration becomes valuable not when AI replaces human judgement, but when IKMS enable complementarity, augmentation, validation, and continuous learning between human and machine intelligence. A central contribution of the paper is to show that AI does not eliminate behavioural bias in financial decision-making. Rather, biases may be redistributed, amplified, or transformed within hybrid human–AI knowledge systems. For example, AI-supported financial recommendations may reduce some forms of human overconfidence while creating new risks of automation bias, anchoring on algorithmic outputs, or excessive trust in model-generated classifications. The framework therefore identifies governance, explainability, trust calibration, ethical alignment, and feedback mechanisms as critical boundary conditions for responsible and effective human–AI collaboration. The paper conceptualises value creation beyond profit across four interrelated dimensions: cognitive value, reflected in improved decision quality, bias recalibration, and enhanced risk insight; institutional value, reflected in transparency, accountability, and stakeholder trust; societal value, reflected in ethical alignment, financial inclusion, sustainability, and long-term stability; and organisational innovation, reflected in improved knowledge recombination, adaptive learning routines, and more responsive financial decision processes. By integrating behavioural finance with intelligent knowledge management systems, the study contributes to a more comprehensive understanding of how AI-supported financial institutions can create trustworthy, sustainable, and innovation-oriented value beyond narrow economic performance.

Competence Model for Collaborative Workplaces in Industry 5.0: Initial Insight

(1128-1136)

Tadej Crnjac, Robert Ojsteršek, Marta Aranyossy, Zlatko Nedelko

Abstract

Industry 5.0 emphasizes the synergy between humans and technology, where employees’ competencies are critical for the effective implementation of collaborative workplaces. In this context, collaborative robots play a key role in modernizing organizational processes by increasing flexibility, usability, and reprogrammability (Segura et al., 2021; Ojsteršek et al., 2024). Research on human–robot collaboration (HRC), grounded in task analysis and ISO 10218-2 safety guidelines (International Organization for Standardization, 2011), highlights the need to align task requirements with human capabilities and competency profiles. Effective HRC requires not only technical knowledge but also cognitive and psychosocial competencies (Wilson & Salas, 2010; Villani et al., 2018; Salas and Rosen, 2020). Existing models typically distinguish four competency clusters: technical, safety, cognitive, and psychosocial. However, none provides a fully comprehensive framework for collaborative robotics (Werning et al., 2021; Simone et al., 2022; Wang et al., 2023; Potocan et al., 2024). This study addresses the gap by proposing a methodology for predicting competency weights, enabling structured learning approaches and supporting future empirical validation. Safety competencies are treated as embedded within technical, cognitive, and psychosocial dimensions, reflecting their integrative role in ISO-based HRC environments. The article develops an enhanced competency model based on task and job analysis aligned with ISO standards and applies the Analytic Hierarchy Process (AHP) to determine competency weights according to their impact on safety, efficiency, and work quality. The model integrates technical, cognitive, and psychosocial competencies, as well as ethical aspects, and emphasizes continuous professional development. To identify competencies, we combined a literature review with the Delphi method, collecting expert opinions through iterative rounds. This resulted in a comprehensive set of existing and additional competencies enabling safe and adaptive performance in collaborative environments. Employees were categorized into three groups: experts (Group A), students (Group B), and laypersons (Group C), reflecting different levels of prior knowledge and experience. Using ISO-based analysis, base competency weights were derived as follows: technical (0.30), cognitive (0.40), and psychosocial (0.30) (Romero et al., 2016; 2022). The model further combines AHP with TOPSIS, a robust multi-criteria decision-making approach (Opricovic & Tzeng, 2004). The main contribution lies in the methodological and normative development of a competency model for Industry 5.0 collaborative environments. The proposed framework establishes a foundation for empirical validation and practical application in personnel selection, training design, and task adaptation across different user groups.

Beyond Profit, Through Data: How AI-Driven Academic Spin-offs Enact Public Value in Knowledge Ecosystems

(1120-1127)

Silvana Revellino

Abstract

Artificial intelligence (AI) is increasingly positioned as a key driver of digital transformation, enabling organizations to enhance performance, decision-making, innovation and stakeholder engagement. However, for public, hybrid, and mission-driven organizations, the critical issue is not only how AI improves efficiency, but how it contributes to value creation beyond profit (Moore, 1995; 2013; Battilana and Lee, 2014). This paper investigates how AI-driven academic spin-offs enact public value within digital knowledge ecosystems.
Drawing on public value theory (Moore, 1995; 2013), the sociology of valuation (Stark, 2009; Dussauge et al., 2015; Kornberger et al., 2015), knowledge management literature (Davenport and Prusak, 1998; Zack, 1999), and research on AI and entrepreneurship (Shepherd and Majchrzak, 2022; Fossen et al., 2024), the study conceptualizes public value as a dynamic process of (dis)aggregation, where multiple and conflicting value regimes coexist and are negotiated through evaluative practices. This allows us to explain not only the multiplicity of value generated by such organizations, but also the tensions, asymmetries, and exclusions through which that value comes into being.
The paper is based on an exploratory qualitative case study of Kebula S.r.l., an academic spin-off operating in data engineering and AI. Adopting a valuographic approach (Dussauge et al., 2015), the analysis examines how value is constructed, justified, and governed through everyday practices. Findings show that AI systems act as infrastructures of valuation that reorganize what counts as legitimate impact, redistributing visibility across economic, social, and knowledge domains. Public value emerges as a contested and relational accomplishment rather than a stable outcome. The paper contributes to knowledge management, public value theory, and AI-entrepreneurship literature by showing that intelligent transformation shapes not only how value is created, but also how it is made visible, accountable, and legitimate. Empirically, the case highlights how digital knowledge systems mediate innovation, learning, failure, and infrastructural flexibility in a hybrid university-based organization over time and across organizational contexts.