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:

Rethinking Product Data Management Through Digital Product Passports: Data Governance for Global Supply Chains

(357-365)

Martina De Giovanni, Antonella Marsano, Mariangela Lazoi

Abstract

Increasing regulatory pressures and societal expectations have made digital transparency a critical requirement, positioning DPPs as key instruments to support traceability and supply chain-based decision-making. However, their effectiveness depends not only on technological infrastructure (e.g., blockchain, RFID, IoT sensors, and system integration), but also on robust data governance frameworks.
This study aims to explore the role of data in enabling the effective implementation of Digital Product Passports (DPPs) as strategic governance tools for transparency, accountability, and sustainable value creation in global supply chains. The research is based on a Systematic Literature Review (SLR) conducted through Scopus and Web of Science, focusing on the evolution of Product Data Management (PDM) and its transition toward the emergence of DPPs. While early PDM approaches emphasized manufacturer-controlled data for operational efficiency, more recent developments show a shift toward multi-actor data ecosystems incorporating environmental, social, and end-of-life information. This transition has been accelerated by European policy initiatives, particularly the Circular Economy Action Plan (CEAP) and the Ecodesign for Sustainable Products Regulation (ESPR), which are transforming DPPs from voluntary tools into mandatory regulatory instruments.
DPPs are especially relevant in high-impact sectors (e.g., electronics, textiles, construction), where they contribute to reducing environmental footprints and enhancing supply chain accountability.
The literature review identifies forty-two data typologies (referred to as factors) required for DPP implementation and organizes them into ten key data categories. The proposed data mapping framework is linked to the Environmental, Social, and Governance (ESG) criteria, emphasizing the integration of data into governance processes to support strategic decision-making. DPPs enable continuous monitoring and lifecycle traceability fostering alignment between circularity and sustainability practices. Moreover, they facilitate bidirectional information flows: consumers gain access to reliable product data, while their feedback can inform producers and regulators, promoting adaptive governance and collective learning.
Despite these opportunities, significant challenges remain, including data asymmetries, privacy concerns, lack of standardization, and unclear stakeholder responsibilities. Without well-defined governance structures, DPPs risk reinforcing existing power imbalances rather than enhancing transparency.
The study concludes that governance is a critical determinant of DPP success. It calls for the development of scalable, harmonized, and compliant data governance models capable of supporting DPP adoption across sectors while ensuring data continuity along the value chain. By framing governance as a strategic enabler, DPPs can evolve beyond compliance tools into foundational mechanisms for circular and sustainable value creation.

Accounting-Mediated Knowledge Management in Agrifood Supply Chains: Insights from Participant Observation on How Quality, Traceability and Cost Knowledge is Produced and Coordinated

(366-372)

Gaetano Rago

Abstract

Agrifood supply chains are characterised by high levels of complexity, biological variability and increasing regulatory and societal pressures for transparency, traceability and sustainability. In such contexts, knowledge management (KM) plays a critical role in enabling coordination among heterogeneous actors, including farmers, processors, quality managers, accountants and auditors. However, existing literature has largely conceptualised KM in supply chains in abstract terms, focusing on information sharing and integration, while paying limited attention to the everyday practices through which knowledge is produced, translated and made actionable.
This study addresses this gap by examining how knowledge is managed in practice within an agrifood supply chain and by exploring the role of accounting and auditing artefacts in mediating this process. Drawing on an ethnographic research design based on one year of participant observation within a large Italian agrifood processing firm and its network of partners, the study provides a fine-grained analysis of the micro-practices through which knowledge is transformed into organizationally relevant forms.
The findings show that knowledge management is not a linear process of data transfer, but a situated and ongoing accomplishment. Three key mechanisms are identified. First, translational practices convert tacit and experiential knowledge into standardized categories and inscriptions, such as quality classifications, traceability records and cost analyses. Second, accounting and auditing artefacts operate as boundary objects that enable coordination across different communities of practice while shaping visibility and accountability. Third, governance routines, including audits, meetings and performance reviews, serve as arenas in which knowledge claims are validated and stabilised.
The study contributes to knowledge management literature by demonstrating that knowledge conversion is materially mediated by calculative and documentary practices. It also extends accounting research by showing that accounting functions as a knowledge infrastructure linking quality, traceability, cost and sustainability. Overall, the paper advances an accounting-mediated view of KM, highlighting the central role of accounting and auditing artefacts in making knowledge visible, credible and actionable in complex agrifood supply chains.

Digital ESG Communication in Sustainable Fashion Consumption: Comparing Italy and South Korea

(329-337)

Serena Strazzullo, Sara Ianniello, Jiyeonb Lee, Ilkyeong Moon

Abstract

The fashion industry is increasingly challenged to demonstrate credible sustainability commitments in response to its significant environmental and social impacts, globalised supply chains, and persistent concerns over unsustainable production and consumption patterns. In this context, Environmental, Social, and Governance (ESG) communication has become a central tool by which fashion companies disclose their sustainability efforts and engage consumers. However, the effectiveness of ESG communication in influencing consumer behaviour remains uncertain. This is particularly relevant in digital environments, where sustainability claims are often difficult to verify and where information asymmetry, consumer scepticism, and greenwashing concerns may weaken the credibility of corporate messages. Moreover, consumers’ interpretation of sustainability communication may differ across geographical contexts. Hence, the study adopts a narrative literature review approach, focusing on two main streams of research: ESG communication practices in the fashion industry and consumer behaviour studies related to sustainable consumption, with specific attention to Italy and South Korea, two relevant and contrasting contexts.
Building on signalling theory, the study develops an empirical model that integrates the insights emerging from the narrative review with cross-country evidence. In the proposed model, ESG communication practices are conceptualised as sustainability signals. Sustainability predisposition and digital and social influence are introduced as antecedents, shaping consumers’ interpretation of these signals. In line with the theory, the interpretation of sustainability signals is expected to influence consumers’ behavioural intention, which is linked to actual purchase behaviour, while behavioural barriers are considered as factors that may affect this relation.
In doing so, the study advances a model that can be empirically tested through multi-group analysis to assess whether the proposed relationships operate differently among Italian and South Korean consumers. Generally, the study offers a theoretical and practical foundation for understanding how digital ESG communication becomes meaningful, credible, and behaviourally relevant in different contexts.

Intelligent Knowledge, Corporate Crisis and Criminal Liability: Incentive Structures for Sustainable Governance in the Italian Framework

(373-380)

Alfonso Laudonia

Abstract

The paper investigates how intelligent knowledge systems reshape incentives and responsibilities in corporate crisis governance under the Italian legal framework. It focuses on the interaction between the Code of Business Crisis and Insolvency (CCII) and Legislative Decree 231/2001 on corporate criminal liability, analysing how legal duties of early detection and adequate organisational arrangements intersect with the emerging use of AI-enabled early-warning and monitoring tools. The central question is under which conditions these systems function as levers for sustainable and responsible governance, and when they instead generate new risks of moral hazard, scapegoating and purely formalistic compliance.
Theoretically, the paper bridges three strands of literature: knowledge management and organisational learning, which conceptualise knowledge as a strategic asset increasingly mediated by digital technologies (Nonaka and Takeuchi, 1995; Argote, 2013; Schiuma, 2011); law and economics of corporate crisis and insolvency, which examines how sanctions and incentives shape directors’ behaviour in the vicinity of insolvency (Jensen and Meckling, 1976; Coffee, 2006; Armour and Ringe, 2020); and socio-legal and criminological studies on corporate crime and compliance, which highlight the role of organisational cultures and control architectures in enabling or constraining wrongdoing (Clinard and Yeager, 1980; Braithwaite, 1989; Parker, 2002). Building on these perspectives, the paper proposes to conceptualise corporate crisis governance as a knowledge-based regulatory game in which legal rules, intelligent knowledge systems and organisational cultures jointly determine the information and payoff structures faced by corporate actors.
Methodologically, the study adopts a qualitative, analytical-conceptual design that combines doctrinal analysis of the CCII and Legislative Decree 231/2001 with theory-informed discussion of AI-enabled early-warning and HR-analytics-based frameworks, including prior empirical work by the author on predictive HR analytics for financial risk prevention (Laudonia et al., 2025).
The analysis shows that intelligently designed and well-governed knowledge systems can enhance the preventive aims of the CCII and the compliance-oriented incentives of the 231 regime by reducing information asymmetries, supporting timely escalation and enabling more informed restructuring decisions. However, it also warns against the risks of black-box solutions that may foster over-reliance on algorithms, diffusion of responsibility and symbolic compliance. The paper concludes by outlining design principles for intelligent knowledge systems in crisis governance—transparency, explainability, preservation of professional judgement, robust data-governance and learning-oriented feedback loops—and by deriving implications for regulators, boards and compliance officers concerned with aligning legal accountability, effective risk reduction and long-term organisational sustainability.

Smart Knowledge for Sustainable Artisanal Production in the Integration of Science, Technology and Local Knowledge in Rural Communities in Guanajuato

(381-389)

Fabiola Colmenero Fonseca, Daniel Ruíz López, Ramiro Rodríguez Pérez, Lauren Yolanda Gómez Zamorano, Amparo Borrell

Abstract

This study identifies and empirically validates a microfoundational mechanism based on the Intelligent Knowledge Management Model (MIGC) to explain how knowledge systems mediated by artificial intelligence (AI) enable the twin transition in artisanal production contexts. Although the literature has extensively analyzed the relationship between digitalization and sustainability, a critical gap persists in the understanding of the microfoundational processes through which these transformations emerge in environments of low technological intensity and strong cultural roots.
Through a combined approach of design science and participatory action research, an intervention in a rural ceramics system in Guanajuato, Mexico, is implemented and evaluated. The model integrates AI, energy monitoring and artisanal knowledge within an architecture aimed at the recombination of knowledge and the development of organizational capacities. The data were collected over 12 months through energy audits, production records and qualitative fieldwork.
The results show a 19.6% reduction in energy intensity and a 14.2% reduction in production defects, along with improvements in process stability, traceability and decision-making. Beyond these results, the study identifies a recursive microfoundational mechanism in which AI does not act as an automation tool, but as a cognitive infrastructure that facilitates the recombination of tacit and explicit knowledge, activating the reconfiguration of organizational capacities and generating sustainable results.
This finding allows us to reconceptualize the twin transition as a socio-cognitive process based on the articulation of knowledge, rather than as a direct consequence of technological adoption. Likewise, the study demonstrates that dynamics of innovation ecosystems can emerge at microterritorial scales and that their effectiveness depends on the interaction between technology, agency and knowledge structures.
Overall, the results establish that sustainability in contexts of low technological intensity does not depend on intensive automation, but on the design of knowledge architectures that integrate artificial intelligence, local knowledge and organizational agency, opening new lines of research on the scalability of these processes.

INSPIRE PM as a Human–AI Hybrid Knowledge Ecosystem for Sustainable Project-Based Organizations

(390-396)

Antonio Bassi

Abstract

Project-based organizations are increasingly exposed to a combination of uncertainty, accelerated technological change, and rising sustainability expectations. In this context, many organizations are investing in digital platforms, analytics capabilities, and artificial intelligence applications, yet they often struggle to transform dispersed project information into reliable and actionable knowledge. The spread of generative AI has intensified this tension: while organizations can now generate answers, summaries, and recommendations at scale, they frequently lack adequate governance mechanisms for knowledge provenance, accountability, privacy, and ethical use. Starting from the INSPIRE PM framework and its broader grounding in Project Management Evolutive (PME), this paper argues that sustainable project performance depends on the ability to build Human–AI Hybrid Knowledge Ecosystems (HA-HKE), that is, socio-technical configurations in which human actors and AI systems collaboratively generate, validate, circulate, and apply project knowledge under explicit governance rules. The paper develops a conceptual architecture for such ecosystems, identifies the key knowledge assets and flows required across the project lifecycle, and proposes a design-oriented research agenda centered on three artifacts: a hybrid knowledge ecosystem blueprint, a decision intelligence loop, and a lightweight governance checklist for responsible GenAI use in project contexts. The paper contributes conceptually by reframing project management as a knowledge ecosystem challenge, methodologically by outlining a design science approach with multiple-case evaluation, and managerially by offering a practical pathway for PMOs and project leaders seeking to combine AI-enabled decision support with sustainability, traceability, and human accountability.

AI-Enabled Knowledge Ecosystems: Conditions for Learning and Deskilling Across Economic Sectors

(397-406)

Maria Cristina Manocchio, Francesca Di Virgilio, Gianluigi Mangia, Antonia Puccio

Abstract

In modern organizations, the integration of artificial intelligence is revolutionizing how knowledge is created, shared, and utilized, while also altering the dynamics of organizational learning, skill enhancement, and governance frameworks. Generative AI is pivotal in making explicit knowledge more accessible and in optimizing both automation and decision-making processes. Nevertheless, it introduces challenges, such as the risk of diminishing tacit knowledge and an increasing reliance on algorithm-driven insights. Thus, AI is gradually becoming both a technological foundation and an active player in organizational knowledge environments. The effectiveness of AI in enhancing knowledge creation and innovation, or conversely, in causing skill mismatches and deskilling, is largely influenced by organizational learning capabilities, culture, and governance mechanisms. Although there is increasing academic interest, there remains a lack of empirical evidence on the influence of AI on organizational knowledge ecosystems beyond individual companies. This study addresses this gap by examining how various AI adoption configurations affect organizational knowledge ecosystems and learning dynamics at the sectoral level. The research employs secondary data from the 2025 Survey on Information and Communication Technologies in Enterprises by ISTAT, concentrating on Italian companies with at least 10 employees. Data at the firm level is aggregated by economic sector, allowing for a meso-level analysis of AI adoption patterns. From a capability-based viewpoint, the study distinguishes between AI technologies that are cognitively oriented, aiding in knowledge creation, and those that are automation-oriented, primarily focused on task substitution. A sector-level cluster analysis is conducted to pinpoint distinct AI adoption patterns and their integration within organizations. This research aims to reveal various organizational knowledge ecosystems, each defined by a unique balance between AI applications focused on learning and those centered on automation. By employing a configurational and sector-specific approach, the study enhances the knowledge management literature by clarifying the circumstances under which AI adoption promotes organizational learning and the development of sustainable capabilities, rather than leading to a reduction in skills.

Influence of Artificial Intelligence (AI) on Organizational Knowledge Creation (OKC) in Workplace Meetings

(407-414)

Shuyun Zhao, Krishna Venkitachalam

Abstract

Workplace meetings are important to organizations’ daily operations, and knowledge creation is a valuable outcome of these meetings. Despite the increasing use of AI in meetings, its influence on OKC remains unexamined. This study uses the SECI model as the theoretical framework and aims to explore how AI influences the OKC process before, during, and after workplace meetings.
This study adopted a qualitative interpretive approach to explore and understand a new and complex phenomenon. Eight semi-structured interviews were conducted between March and April 2026 with employees from 3 countries and across different industries who regularly use AI in work-related meetings. Data were analyzed using the thematic analysis method with deductive-inductive coding.
The findings show that AI encourages people to engage in social interaction. In externalization, AI enables the articulation process via several mechanisms. AI enhances combination by connecting large amounts of existing codified knowledge from multiple sources. In the last mode, AI empowers individuals in internalizing knowledge. A conceptual framework is proposed that maps AI’s influence in the SECI model and indicates the primary work meeting phases.
The study’s qualitative nature and narrow scope restrict how broadly its findings can be statistically generalized. Second, it focuses solely on the SECI model as the analytical lens. Third, the rapid evolution of AI means the findings are time-bound. Future research can focus on AI-facilitated meetings across a wider array of organizations, apply diverse theoretical models to OKC, and scrutinize the dynamic effects of AI on this phenomenon.
The study extends the SECI model by including AI’s influence in the four knowledge conversion modes in workplace meetings. Essentially, it sheds light on how AI in meetings reshapes the way organizations create knowledge, possibly enabling more effective AI application and use.

Beyond Facilitation: Ethical and Behavioral Tensions in Technology-Enabled Knowledge Management

(415-421)

Emilia Romeo, Valerio Giampaola, Giuliano Marolla, Alberto Ferraris, Angelo Rosa

Abstract

This study examines the ethical and behavioural implications of technology-enabled knowledge management (KM) systems, challenging the dominant view of digital technologies as neutral tools that merely facilitate knowledge processing. Instead, it conceptualises KM systems as sociotechnical infrastructures that actively shape how knowledge is produced, shared, evaluated, and legitimised within organisations. Building on insights from the knowledge management, organizational behaviour, and information systems literature, this study develops an integrated conceptual framework that links technological design features to employee behaviour and knowledge-sharing outcomes through embedded ethical risks.
Adopting an integrative and theory-building methodology, this research synthesises interdisciplinary literature to identify recurring mechanisms through which digital KM infrastructures influence organisational dynamics. The analysis highlights three interrelated dimensions of ethical risk: (1) surveillance and visibility, (2) control and standardisation, and (3) power asymmetries and transparency. These dimensions are conceptualised as embedded governance mechanisms that regulate not only the flow of knowledge but also the behavioural and epistemic conditions under which employees engage in knowledge-sharing practices.
The findings show that increased visibility and traceability, while enhancing coordination and accountability, may reduce psychological safety when associated with monitoring and evaluation, leading to self-censorship and selective participation. Similarly, standardisation and codification processes tend to privilege explicit and measurable knowledge, contributing to epistemic narrowing and limiting organisational knowledge diversity. Finally, algorithmic opacity and information asymmetries undermine perceptions of procedural fairness and organisational trust, weakening employees’ willingness to contribute. These dimensions interact to produce reinforcing effects that encourage defensive behaviours, such as knowledge hiding and risk-averse participation, ultimately constraining organisational learning.
By linking these technological features to micro-level mechanisms, such as trust, intrinsic motivation, perceived fairness, and psychological safety, this study demonstrates that ethical risks are not external constraints but endogenous components of KM systems that shape knowledge-sharing outcomes. This study advances four theoretical propositions that explain how these mechanisms operate individually and in combination.
This study contributes to KM theory by integrating behavioural and ethical perspectives into the analysis of digital infrastructures, reframing KM systems as instruments of knowledge governance. It also provides practical implications, suggesting that organisations should prioritise transparency, participatory design, and psychologically safe environments to foster more inclusive and sustainable knowledge-sharing practices.

A Knowledge-Based Life-Cycle Framework for Sustainable Restoration Decision-Making

(422-428)

Stefano Abbate, Anna Scotto di Santolo

Abstract

Cultural heritage represents a key driver of social identity, cultural continuity, and local economic development, yet it is increasingly exposed to environmental, anthropogenic, and climate-related threats. In this context, restoration and conservation interventions require selecting materials and techniques that ensure technical performance while addressing broader sustainability objectives. However, decision-making in cultural heritage restoration is often based on fragmented criteria, with limited integration of environmental, economic, and social dimensions across the entire life cycle. This study addresses this gap by developing a multi-criteria life-cycle framework to support the selection and prioritisation of sustainable restoration techniques in cultural heritage. The research adopts a systematic literature review (SLR) methodology to identify and analyse contributions on Life Cycle Assessment (LCA), Life Cycle Costing (LCC), and Social Life Cycle Assessment (S-LCA) within the heritage conservation domain. A final sample of 45 peer-reviewed articles was examined through descriptive and content analyses to extract relevant indicators, methodological approaches, and decision variables. The output is a structured multi-criteria life-cycle framework that integrates sustainability indicators across environmental, economic, and social dimensions, including, for example, Global Warming Potential (GWP), natural resource depletion, water footprint, equipment and energy costs, health and safety, employment stability, and community well-being. This set of indicators constitutes a coherent, multidimensional taxonomy that underpins a flexible, replicable decision-support framework, enabling transparent, systematic comparison of alternative restoration strategies. The study offers relevant implications for both practice and policy, supporting more informed resource allocation and the development of sustainability-oriented guidelines for cultural heritage conservation. The proposed framework is explicitly designed to provide a solid knowledge base for future empirical applications, making it an operational tool for decision-makers. By integrating multi-criteria decision-making techniques, it will be possible to weight the heterogeneous criteria identified and prioritise restoration interventions under conditions of uncertainty. Future developments should also focus on empirical validation through case studies and on incorporating stakeholder perspectives to enhance the framework’s applicability and robustness.

Intelligent Knowledge Ecosystems for the Dual Transition, Curricular Innovation, and Sustainable Production in Artisan Communities of Mexico

(429-438)

Juan Francisco, Palomino Bernal, Fabiola, Colmenero Fonseca, Rosa María, López Salinas, Elsa Ivette, Aldana Ornelas, Javier, Cárcel Carrasco

Abstract

The dual transition, understood as the convergence of digital transformation and environmental sustainability, has emerged as a central strategic paradigm for territorial development and innovation policy. However, existing scholarship has predominantly focused on urban, industrial, and technologically mature environments, leaving rural and artisanal production systems underexplored. This gap is theoretically significant because conventional innovation ecosystem frameworks insufficiently explain how innovation emerges in contexts where tacit, culturally embedded knowledge constitutes the dominant productive resource. To address this limitation, this study introduces the concept of Intelligent Knowledge Ecosystems (IKEs), defined as socio-technical and territorially embedded configurations in which collective intelligence, knowledge recombination, and accessible digital-environmental capabilities interact to generate adaptive, inclusive, and sustainable innovation processes. Unlike conventional innovation ecosystems, which frequently emphasize formal institutional arrangements and market-oriented value creation, IKEs position tacit community knowledge as a strategic innovation asset and conceptualize technology as an enabler of collective learning rather than as an exogenous driver of transformation. Methodologically, the research adopts a participatory action research design implemented over a 12-month intervention in an artisan ceramic production community in Dolores Hidalgo, Guanajuato, Mexico. The study involved 48 participants, including artisans, university students, researchers, and institutional actors. Data were collected through semi-structured interviews, co-creation workshops, and field observations, and analyzed using thematic coding and process tracing to identify causal mechanisms underpinning ecosystem emergence and innovation dynamics. The findings indicate that innovation in this context does not emerge through isolated technological adoption, but through structured processes of knowledge recombination between traditional artisanal expertise and contemporary digital and sustainability-oriented tools. These interactions improved productive efficiency, supported more sustainable production practices, strengthened local capabilities, and reinforced cultural identity. The study contributes in three principal ways. First, it advances innovation ecosystem theory by proposing the IKE construct as a more inclusive and context-sensitive framework. Second, it provides empirical evidence from a traditionally underrepresented setting. Third, it offers a transferable model for integrating universities, communities, and accessible technologies in support of territorially grounded dual transition strategies.

Transforming Cyberbullying Prevention Guidelines into Actionable Workflows through AI-Based Detection and BPMN Modeling

(439-445)

Alessandro Massaro, Giuseppe Loseto, Filippo Gramegna, Giovanni Schiuma, Francesco Santarsiero

Abstract

Cyberbullying represents a pervasive and evolving threat within digital environments, exacerbated by the scale, anonymity, and immediacy of online interactions. While recent advances in Artificial Intelligence (AI), particularly in Natural Language Processing (NLP), have significantly improved the ability to automatically detect harmful content, a critical gap persists between detection and effective intervention. Existing approaches primarily focus on identifying cyberbullying instances, but they often lack mechanisms to operationalize prevention guidelines and translate them into consistent, actionable responses. This paper addresses this gap by proposing a novel framework that integrates AI-based cyberbullying detection with Business Process Model and Notation (BPMN) to transform textual prevention guidelines into executable workflows. The framework leverages AI models to analyze user-generated content and identify potential cyberbullying signals, which are then used to trigger BPMN-based processes representing structured intervention strategies. A key contribution of this work lies in the systematic mapping of unstructured policy documents into BPMN constructs, allowing organizations to bridge the gap between high-level guidelines and operational procedures. The proposed approach is illustrated through a detailed modeling of cyberbullying prevention guidelines, demonstrating how complex policies can be translated into actionable pipelines. By combining the strengths of AI-driven detection and process-oriented modeling, this work contributes to the development of more effective and scalable cyberbullying prevention systems. Ultimately, the framework aims to support the creation of safer digital environments by enabling not only the identification of harmful behavior but also the consistent execution of appropriate and timely responses, paving the way for future research at the intersection of AI, process management, and digital safety.

A Multidimensional KPI-Based Framework for Evaluating AI-Driven Cybersecurity Solutions

(446-453)

Mariangela Lazoi, Marianna Lezzi, Nicolò Gianmauro Totaro

Abstract

Artificial Intelligence (AI)-powered cybersecurity is increasingly adopted in complex and data-intensive environments, particularly in Internet of Things (IoT) settings, where traditional rule-based approaches struggle to cope with dynamic and unforeseen threats. While AI-based solutions demonstrate high detection accuracy, responsiveness, and operational efficiency, their evaluation remains dominated by technical performance indicators such as precision, recall, false-positive rates, and computational efficiency. This focus provides a partial view of the suitability of AI-based cybersecurity solutions, ignoring relevant issues related to energy consumption, carbon footprint, organizational impact, accountability, and regulatory compliance. To addresses this limitation, the paper adopts a conceptual and design-oriented research approach to propose a multidimensional key performance indicators (KPI)-based framework for comparing AI-driven cybersecurity solutions, considering technical, environmental, social, economic, and governance dimensions. The framework operationalises evaluation through a structured pipeline that combines three main elements: the AI-based cybersecurity solutions under evaluation, a baseline set of KPI, and expert judgements on the relative importance of the five evaluation dimensions. The framework operationalises evaluation through a structured pipeline. Heterogeneous KPIs are classified according to their direction of preference and normalised onto a common scale to ensure comparability. Normalised indicators are aggregated within each dimension to generate dimensional performance scores. Expert judgements are processed to derive dimension weights reflecting contextual priorities. Dimensional scores and weights are combined through weighted aggregation to produce both a composite sustainability-performance index and a multidimensional performance profile for each solution. The contribution of the paper lies in moving beyond accuracy-centric benchmarking by providing a transparent and replicable evaluation logic that makes trade-offs across dimensions explicit. Rather than treating sustainability as an external factor or an additional descriptive element, the framework integrates it directly into the evaluation process, enabling more balanced and context-sensitive comparison among AI-driven cybersecurity solutions. The proposed framework provides a methodological foundation for future empirical applications and supports more informed decision-making in the selection and governance of AI-based cybersecurity technologies.

Integrating ESG Principles into Production Processes for Shaping Sustainable Industry

(471-478)

Mattia Arcuti, Vito Del Vecchio, Mariangela Lazoi, Giorgia Specchia

Abstract

The integration of Environmental, Social, and Governance (ESG) principles into organizational processes has become a critical challenge for firms seeking to align sustainability commitments with operational decision-making, regulatory compliance, and long-term value creation. Although Enterprise Resource Planning (ERP) systems represent core infrastructures for data integration, process standardization, and organizational memory, their role in supporting ESG integration remains fragmented and underexplored. This paper investigates how ESG principles and data can be integrated into ERP systems from a knowledge management perspective, with particular attention to the management of green knowledge. The study adopts a Systematic Literature Review (SLR), performed on Scopus, and collect 507 results. The findings, based on the resulting 29 pepers, show that ESG–ERP integration address specific technologies, indicators, sectors, or implementation issues, while limited attention is paid to how ESG-related data, practices, and responsibilities are transformed into structured, reusable, and actionable green knowledge within ERP systems. To address this gap, the paper proposes a research agenda organized around three dimensions: human, technological, and organizational. The paper also develops a set of best practices for knowledge-based ESG–ERP integration. These practices suggest that organizations should start from existing ERP processes, define shared ESG data meanings, embed ESG controls into workflows and decision-making, and support users through training, communication, and continuous learning. The study contributes to the literature by reframing ESG–ERP integration as a knowledge management process and provides practical guidance for organizations seeking to integrate sustainability principles into ERP systems in a gradual, coherent, and non-disruptive way.

Leading Intelligent Knowledge Management for Data-Driven Sustainable Innovation: Micro-Practices that Build Human–AI Complementarities

(479-483)

Vittoria Magrelli, Isotta Vazzoler, Dario Natale Palmucci, Luca Giraldi

Abstract

Intelligent Knowledge Management (IKM) represents a shift from static knowledge repositories toward dynamic socio-technical arrangements in which data, human expertise, and algorithmic inference are continuously combined to support organisational action. While existing research has examined knowledge creation processes, analytics capabilities, and the implications of AI for decision-making, less attention has been paid to how leaders make these elements work together in practice—particularly in the context of sustainability-oriented innovation. Addressing this gap, this study explores how leadership is enacted through everyday practices that transform AI- and data-driven outputs into actionable organisational knowledge. Adopting a practice-theory perspective, the study conceptualises leadership as a set of situated micro-practices through which problems are framed, insights are interpreted, and learning is stabilised over time. Empirically, it draws on a qualitative, comparative case study of organisations engaged in AI-enabled knowledge work for sustainability. The research combines semi-structured interviews with senior leaders and analytics professionals, analysis of internal artefacts such as knowledge governance protocols and AI guidelines, and observational material where accessible. An abductive analytical approach is used to iteratively connect empirical patterns with theoretical insights from knowledge management, organisational learning, and human–AI augmentation literatures. The findings are expected to show that IKM becomes effective not through technology adoption alone, but through leadership practices that makes knowledge usable. These include framing sustainability-relevant problems in ways that guide analytical attention, curating and translating AI-generated outputs for diverse organisational audiences, fostering productive contestation that balances critical scrutiny with integration of experiential knowledge, and embedding insights into routines that sustain learning beyond individual projects. From a recombinant innovation perspective, these practices enable the continuous recombination of heterogeneous knowledge—spanning past and present, human and machine—thereby supporting cumulative rather than episodic innovation. By foregrounding leadership as a governance mechanism of intelligent knowledge, the study contributes to bridging knowledge management, digital transformation, and sustainability research, offering both theoretical refinement and practical guidance for organisations operating in complex, data-rich environments.

Economic Potential of ESCO Financing Models in Public Lighting: Evidence from Hungary

(484-492)

Nikolett Deutsch, László Berényi

Abstract

The technical capacity of LED street-lighting retrofits to achieve energy savings and reduce operating and maintenance costs is well established in the literature. This study contributes to the field of municipal energy-efficiency financing by presenting a two-sided discounted cash-flow analysis of public lighting modernization, comparing direct public financing with the ESCO-based model. Unlike most existing studies, which focus on either technical optimization or financing structures, this research models municipal and ESCO cash flows separately and examines their interaction within a unified financial framework for Hungarian municipalities. This issue is highly relevant in Hungary, where street lighting constitutes a major operational expense for municipalities with limited investment capacity and debt constraints. Regulatory and ownership limitations, lack of experience, uncertainty regarding realized savings, and weak procurement capacity further impede municipal retrofit activities. The analysis demonstrates that financial attractiveness depends not only on energy-saving potential as well as on tariff structures, contract duration, and contractor fees. The findings indicate that energy-efficient LED retrofits may remain financially unattractive for municipalities under direct self-financing, whereas the ESCO model can improve municipal outcomes by shifting upfront investment burdens, even if it reduces the provider’s return. The results show that key contractual parameters primarily redistribute economic value rather than alter the underlying savings potential, which helps explain the frequent emergence of conflicts of interest in ESCO contracts. By quantifying these trade-offs and identifying threshold values at which municipal and ESCO incentives diverge or align, this study improves understanding of when and under what conditions ESCO financing constitutes a viable solution for small Hungarian municipalities. These insights prove particularly relevant for policy design and contract structuring in contexts characterized by budget constraints, tariff uncertainty, and limited local investment capacity.

Analytics Maturity and AI Governance for Organizational Trust: A Swiss Capability Perspective

(454-460)

Viktoriia Apalkova

Abstract

This study examines how analytics maturity and AI governance are articulated as organizational capabilities that support trust in AI-driven and data-based decision-making. As organizations increasingly rely on artificial intelligence (AI) and advanced analytics, trust has become a critical concern, extending beyond technical performance to include reliability, responsibility, and institutional alignment. While existing research often treats analytics maturity as a linear progression and AI governance as a set of external constraints, this study adopts a configurational and capability-based perspective grounded in the Resource-Based View (RBV) and Dynamic Capabilities Theory (DCT).
Drawing on qualitative analysis of corporate website discourse from ten Swiss organizations across multiple industries, the study explores how AI applications, data infrastructures, governance mechanisms, and trust-related narratives are publicly communicated. The findings demonstrate that analytics maturity is not represented as a linear developmental path but as distinct configurations of interdependent resources, combining technical, organizational, and governance capabilities. AI governance emerges as an embedded organizational capability that supports trust by enabling reliability, accountability, and alignment with institutional expectations, rather than functioning solely as a compliance mechanism.
Trust is constructed differently across sectors, reflecting industry-specific logics such as clinical validation in healthcare, risk control in finance, data sovereignty in telecommunications, and operational safety in industrial and energy contexts. These findings highlight that trust in AI is not universal but context-dependent and institutionally embedded.
The study contributes by reconceptualizing analytics maturity as a configurational phenomenon, repositioning AI governance as an enabling capability, and demonstrating how trust is co-constructed through the alignment of organizational resources and institutional expectations. The results underline the importance for organizations to align technical capabilities with governance structures and communication practices to sustain trust in AI-enabled environments..

Knowledge as Experience: Embodied Artistic Practices, AI, and the Governance of Intelligent Knowledge for Sustainable Innovation

(461-470)

Daria Podmetina, Anne Berthinier Poncet, Peter Robbins, Jelena Hartsenko, Jani Kiljala

Abstract

Knowledge management (KM) research has long recognised that organisational knowledge extends beyond codified information to include tacit, experiential, and relational forms of knowing. However, contemporary organisations increasingly rely on artificial intelligence (AI) and data-driven systems that privilege abstraction, codification, and optimisation. While these systems enhance efficiency and scalability, they risk marginalising embodied and contextual knowledge that is essential for creativity, innovation, and sustainability. This paper addresses this tension by examining how organisations can develop intelligent knowledge systems while preserving the embodied foundations of human knowing. Drawing on qualitative data from 21 semi-structured interviews with innovation leaders, managers, and artists across ten European organisations, the study investigates how arts-based initiatives (ABIs) generate and mobilise embodied knowledge in organisational contexts. ABIs, defined as structured engagements embedding artistic practices within organisational processes, provide a unique empirical setting to explore knowledge that is experiential, sensory, and difficult to codify. The findings reveal that embodied knowledge constitutes a distinct form of organisational knowledge that operates through experience rather than representation. Its integration into organisational processes depends on relational translation mechanisms, including intermediaries, boundary artefacts, and collaborative practices that bridge artistic and technical domains. At the same time, AI-enabled systems emerge as a structural pressure toward abstraction, privileging codified knowledge and reinforcing optimisation logics. This creates a tension between efficiency-driven knowledge systems and the forms of knowing required for innovation under uncertainty. The paper introduces the concept of embodied knowledge governance, which explains how organisations can recognise, integrate, and sustain experiential knowledge alongside codified and AI-mediated knowledge. This framework highlights the importance of translation, interaction, and organisational conditions that preserve contextual meaning. The findings further show that embodied knowledge plays a critical role in sustainable innovation by enabling long-term, systemic thinking and expanding value perspectives beyond short-term performance. This study contributes to KM, AI, and innovation research by advancing understanding of knowledge plurality in organisations and by highlighting governance as a key mechanism for balancing intelligent systems with human creativity and sustainability.

The Moderating Role of AI Self-Efficacy on the Effect of AI Anxiety on Unemployment Anxiety Among University Students: A Cross-Country Study

(493-501)

Coşkun Akça, Albena Antonova, Anikó Csepregi, Zsuzsa Darida

Abstract

Artificial intelligence (AI) is rapidly transforming labour market structures, raising concerns not only about employment prospects but also about the broader psychological implications of technological change. Among university students, these concerns often emerge as heightened uncertainty regarding future employability. In this context, AI-related anxiety can be understood as a forward-looking stressor that shapes how individuals interpret knowledge about future work.
This study examines the relationship between AI anxiety and unemployment anxiety, with a particular focus on the moderating role of AI self-efficacy. A quantitative, cross-sectional design was applied using survey data collected from 510 university students in Turkey, Hungary, and Bulgaria. Established measurement scales were used to assess AI anxiety, AI self-efficacy, and unemployment anxiety. Data were analysed using SPSS and Hayes’ PROCESS Macro (Model 1), alongside reliability, validity, and correlation analyses.
The results show that AI anxiety is positively associated with unemployment anxiety, suggesting that a large number of students tend to interpret AI-driven changes as potential threats to their future employment opportunities. Additionally, AI self-efficacy demonstrates a significant buffering effect: higher levels of perceived competence in using and understanding AI weaken the relationship between AI anxiety and unemployment anxiety. This points to the role of psychological resources in shaping how AI-related knowledge is cognitively and emotionally processed.
By integrating AI-related perceptions with perspectives on job insecurity, stress, and knowledge interpretation, the study contributes to the understanding of how technological change influences career-related anxiety. The findings suggest that AI anxiety should be conceptualised not merely as a technological attitude but as a mechanism through which individuals engage in sensemaking of AI-mediated knowledge about future employment.
From a practical perspective, the results underline the importance of strengthening AI-related competencies within higher education. Beyond technical skills, targeted interventions that enhance students’ sense of control and interpretive capacity may play a key role in mitigating employment-related uncertainty in AI-driven labour markets.

ChatGPT Acceptance among Higher Education Students: A Simplified Model

(502-509)

László Berényi

Abstract

Understanding and predicting the use of generative AI chatbots is an urgent task due to the rapid spread and rapid changes in technology. SEM models are popular in the field because they can account for the complex effects of multiple influencing factors. A serious problem is the data requirement of the analysis. Long surveys and large sample sizes are difficult to make feasible for the numerous targeted questions that arise in connection with the acceptance of generative AI use. The education system is significantly transformed by the emergence of AI. Strengthening its role in learning support and addressing academic misconduct requires actions grounded in readily accessible, up-to-date information. New framework models may support effective strategy building. The study aims to present a pilot framework model derived from existing models (Berényi et al., 2025) to measure the intention to use AI tailored to higher education students. The reflective measurement constructs are defined for PLS-SEM analysis. The limited number of items in the model enables rapid data collection by reducing the risk that respondents will lose interest while completing the survey. The pilot study for model validation used a sample of 281 students at the University of Miskolc (Hungary) and focused on ChatGPT use. SmartPLS4 was used for analysis. The pilot run confirmed acceptable reliability, with AVE between 0.626 and 0.814, HTMT <= 0.860, and VIF <= 1.974. Model fit is good (SRMR = 0.070). The model explained 55.9% of the total variance in intention to use and 47.2% of the total variance in usefulness. Path coefficients are significant, except for the impact of price-value. The results suggest that information quality impacts usefulness through usability; the direct impact is remarkably lower. The total impact of usability on intention to use is high (path coefficient is 0.629), partly through usefulness. Among the respondents, the impact of pricing on the intention to use was not significant, which may be due to free academic access to the chatbots or to the hoped-for profit exceeding the price. The results contribute to the body of knowledge on the acceptance of AI technologies among higher education students, but they can easily be extended to other purposes if it is assumed that intention to use depends on the quality of the information. The limited survey length increases the likelihood of completion, and the policymakers can work with more reliable information.