Articles in IFKAD Proceedings

The following database includes exclusively articles from IFKAD Proceedings

2226
Concetta Lucia Cristofaro, Marzia Ventura, Anna Maria Melina, Walter Vesperi
Organizational Capabilities for Knowledge Ecosystems in Public Healthcare: Towards Intelligent and Sustainable Transformation
  • In: Proceedings IFKAD 2026: Intelligent Knowledge for Sustainable Organizations,
  • pp. 964-970,
  • 2026,
  • ISBN: 978-88-96687-21-5,
  • ISSN: 2280-787X

In the current context, public health systems are facing an unprecedented challenge, namely, being able to manage increasing complexity resulting from demographic aging, chronicity and the need to integrate frontier technologies in contexts of limited resources. In this scenario, the healthcare organization evolves from a simple bureaucratic structure to a “knowledge ecosystem”. the healthcare organization becomes a dynamic network in which the value lies in individual services, but also and above all in the ability to generate, share and apply multidimensional knowledge. This study was born with the aim of exploring how organizational capabilities act as a fundamental pillar to support and, above all, make these ecosystems sustainable in the long term. The central objective of the research is to identify the key organizational capabilities necessary to overcome information fragmentation and promote real integration between the different players in the system (clinicians, managers, patients and technological partners). To achieve this aim, the study adopts a qualitative methodology based on interviews with strategic decision-makers, highlighting three macro-categories of essential capabilities.
First, the absorptive capacity emerges, therefore, the organization’s propensity and ability to identify, assimilate and apply all those external innovations and complex clinical data to translate them into efficient operational protocols. Secondly, relational and collaborative skills, essential for breaking down functional silos and promoting more widespread leadership models. These capabilities allow the transition from hierarchical personnel management to a community of practice that enhances human capital as the driver of the ecosystem. Finally, strategic digital capabilities, understood not only as infrastructural equipment, but as the organization’s ability to manage data interoperability and mitigate phenomena of resistance to change and technostress. The results suggest that the sustainability of a knowledge ecosystem does not depend exclusively on technological investment, but also on a strategic alignment between organizational culture and knowledge management processes. Without a solid foundation of organizational capabilities, digital infrastructures risk becoming mere repositories of unused data. The research contributes to the debate on healthcare management by proposing a model in which performance is directly proportional to the company’s ability to continuously regenerate its knowledge assets.

2225
Fabrizio Rossi, Paola Rossi, Michele Bertoni, Domenico Celenza
Legality Rating and Performance of Italian Companies: Face or Mask?
  • In: Proceedings IFKAD 2026: Intelligent Knowledge for Sustainable Organizations,
  • pp. 956-963,
  • 2026,
  • ISBN: 978-88-96687-21-5,
  • ISSN: 2280-787X

With Decree-Law of January 24, 2012, Italy introduced the Legality Rating to encourage small and medium-sized enterprises to adopt ethical behaviours and comply with principles of legality. Awarded by the Italian Competition Authority (AGCM) on a scale from one to three stars, the Rating provides certified firms with several advantages, including preferential conditions in accessing bank credit, reputational improvements, and access to public funding. Within a civil-law jurisdiction characterised by concentrated ownership and weak formal investor protection, the Rating has been theoretically conceived as a credible signalling and governance device that should reduce information asymmetries, constrain managerial discretion, and align corporate conduct with ethical standards.
Prior empirical evidence on the Legality Rating remains mixed: some studies document better profitability, lower insolvency risk, and improved earnings quality among certified firms, while others suggest that weaker or less profitable firms may use the Rating as a symbolic management tool. Drawing on agency theory, stakeholder theory, and signalling theory, this study revisits this debate by jointly examining default risk, operational risk, and agency costs.
Using a sample of 1,925 Italian firms holding the Legality Rating, for a total of 7,700 firm-year observations over the period 2020–2023, we apply a fixed-effects econometric model to test the relationship between the Rating score and firm performance. Performance is measured through Altman’s Z_SCORE (default risk), the standard deviation of ROA over five years (operational risk), and the ratio of operating expenses to total assets (agency costs), while controlling for size, age, ownership concentration, profitability, leverage, industry, and year effects. We investigate whether firms holding the Rating, and thus considered more virtuous, actually mitigate agency costs and reduce financial distress risk, or whether they hide behind it to pursue window-dressing strategies.
Contrary to expectations, the results do not support the formulated hypotheses. Higher Rating scores are systematically associated with higher operating expenses, greater ROA volatility, and lower Z_SCORE values, suggesting that more highly rated firms exhibit higher operational risk, higher agency costs, and greater proximity to the financial distress zone. These findings raise concerns about the effectiveness of the Rating as a governance and risk-mitigating mechanism and are consistent with a window-dressing interpretation, whereby financially fragile firms strategically use the certification to enhance reputation, attract preferential credit conditions, and conceal underlying weaknesses. The study contributes to the literature on ethical certifications, signalling, and corporate transparency, and offers practical insights for investors, creditors, and minority shareholders.

2224
Domenico Celenza, Marco Sebastiano, Andrea Maturo, Antonella Fortuna
Research on AI and Sustainability Ratings: Evidence from the Noverim Case
  • In: Proceedings IFKAD 2026: Intelligent Knowledge for Sustainable Organizations,
  • pp. 948-955,
  • 2026,
  • ISBN: 978-88-96687-21-5,
  • ISSN: 2280-787X

The convergence between artificial intelligence, sustainability measurement and knowledge management is reshaping how organisations diagnose vulnerability, create value and build resilience. This paper examines the Noverim case as the basis for a study hypothesis: AI-enabled sustainability ratings may help identify organisational disequilibrium before conventional accounting data reveal it. The article does not present original empirical evidence. It develops a conceptual and case-based framework centred on an intelligent predictive system for assessing sustainability and business equilibrium. The reference to 500 Italian knowledge-intensive firms is used solely as a prospective field for future validation; it is not an observed sample, a tested dataset or evidence supporting the hypothesis. The study asks how heterogeneous knowledge assets, sustainability ratings and predictive analytics can be integrated to support early diagnosis, resilience-building and knowledge-driven innovation. The proposed framework interprets sustainability as an endogenous component of equilibrium, since ESG and organisational conditions affect risk exposure, adaptive capacity, stakeholder trust and the durability of value creation. The Noverim architecture combines supervised machine-learning techniques with unsupervised behavioural clustering to identify latent risk profiles and possible trajectories of imbalance. On this basis, the paper formulates three propositions: first, such systems may anticipate disequilibrium over a 12-to-18-month horizon; second, weak signals such as reduced intangible investment, turnover of key personnel, deteriorating internal sustainability indicators and strategy-conduct misalignment may reveal early fragility; third, intelligent ratings may help distinguish reversible from structural crises. These propositions require future empirical testing. The paper contributes to the literature on knowledge-based organisations by conceptualising predictive intelligence as a knowledge infrastructure for organisational learning, resilience mapping and accountable decision-making.

2223
Domenico Celenza, Alessia Nardecchia, Francesco Di Vona, Marco Sebastiano
The Experience of Error in Business Administration: The Case of Predictive Assessment in the Early Emergence of Corporate Distress
  • In: Proceedings IFKAD 2026: Intelligent Knowledge for Sustainable Organizations,
  • pp. 940-947,
  • 2026,
  • ISBN: 978-88-96687-21-5,
  • ISSN: 2280-787X

This paper examines the experience of error in business-administrative judgement as a constitutive dimension of knowledge creation and as a critical object of governance in intelligent and sustainable organisations. The central thesis is that error is not a residual disturbance to be eliminated by predictive technologies, but a structural component of organisational knowledge that must be governed through hybrid human-AI arrangements. The analysis focuses on predictive assessment in the early emergence of corporate distress, a field in which managerial judgement, financial information, organisational arrangements and algorithmic models interact under conditions of uncertainty.
The paper argues that organisational intelligence cannot be reduced to the adoption of digital technologies or artificial intelligence systems. It depends on the capacity of organisations to recognise fallibility, interpret weak signals, transform mistakes into shared knowledge and design decision routines that strengthen resilience over time. The duty to establish adequate organisational, administrative and accounting arrangements under the Italian Code of Corporate Crisis and Insolvency is interpreted as an institutional response to the structural limits of judgement. Artificial intelligence can reinforce this infrastructure by processing large volumes of heterogeneous data, detecting non-obvious patterns and supporting early-warning systems. At the same time, predictive technologies introduce new forms of error, including poor data quality, model opacity, automation bias and the displacement of responsibility from human decision-makers to technical systems.
The paper therefore proposes a detection-interpretation-decision-learning framework of hybrid predictive governance. In this framework, AI supports the identification of patterns, weak signals and anomalies; human judgement interprets the organisational and strategic context; boards and management assume responsibility for decisions; model and organisational errors are reviewed; and the system learns and improves over time. The contribution is conceptual. It advances the debate on intelligent organisations and communities by showing that technology does not make organisations intelligent by itself. Organisations become intelligent when they develop the cultural, informational and technological capacity to learn from error before it becomes irreversible corporate failure.

2222
Giovanni Maiuri, Domenico Celenza, Francesco Di Vona, Alessia Nardecchia
Research on Megatrends and the Evolution of ESG Standard
  • In: Proceedings IFKAD 2026: Intelligent Knowledge for Sustainable Organizations,
  • pp. 932-939,
  • 2026,
  • ISBN: 978-88-96687-21-5,
  • ISSN: 2280-787X

This paper examines how global megatrends can be transformed into actionable knowledge for the design of sustainability standards and the development of predictive environmental, social and governance reporting. The argument is grounded in the view that intelligent and sustainable organizations are not merely adopters of digital tools, but knowledge-processing systems that detect weak signals, translate them into governance priorities and combine human and artificial intelligence to support responsible value creation. The paper develops a conceptual framework linking megatrend research, European sustainability reporting traditions, knowledge management and artificial intelligence. The analysis identifies three historically rooted European trajectories: the German emphasis on measurement, comparability and procedural discipline; the French emphasis on public regulation, social responsibility and corporate accountability; and the Italian Economia Aziendale tradition, which connects economic performance, social embeddedness and systemic equilibrium. The paper argues that the next stage in the evolution of sustainability standards requires a shift from descriptive disclosure to predictive ESG intelligence, understood as the capacity to transform megatrend knowledge into forward-looking, scenario-based and accountable sustainability decisions. Artificial intelligence is interpreted as a cognitive infrastructure that can strengthen human interpretation by mapping weak signals, clustering heterogeneous information, modelling scenario-based risks and connecting sustainability variables with business continuity and organizational resilience. The contribution of the paper is twofold. First, it reframes ESG standards as dynamic knowledge artefacts rather than static compliance devices. Second, it proposes an AI-enabled pathway through which organizations can move from fragmented non-financial reporting to anticipatory, accountable and human-centred sustainability governance. An illustrative climate-transition example shows how a megatrend can be translated into dynamic ESG metrics, predictive outputs and strategic governance decisions. The paper concludes that predictive ESG intelligence can support intelligent organizations and communities only when embedded in ethical oversight, stakeholder dialogue and a relational view of value creation.

2221
Domenico Celenza, Marcella Caradonna, Francesca Francioli, Stefano Gazzani
The Italian Crisis and Insolvency Code (CCII): Adequate Organisational Frameworks and AI Predictive Judgments
  • In: Proceedings IFKAD 2026: Intelligent Knowledge for Sustainable Organizations,
  • pp. 925-931,
  • 2026,
  • ISBN: 978-88-96687-21-5,
  • ISSN: 2280-787X

The Italian Crisis and Insolvency Code has moved corporate governance from a reactive idea of crisis management to a preventive model in which firms are expected to organise information, responsibilities and controls before distress becomes irreversible. This paper develops a conceptual framework for interpreting adequate organisational, administrative and accounting arrangements as an intelligent and sustainable governance infrastructure. It argues that Artificial Intelligence (AI) can enhance such arrangements not simply by improving insolvency prediction, but by transforming dispersed organisational data into shared knowledge for timely, accountable and human-centred decisions. The analysis is positioned at the intersection of the knowledge-based theory of the firm, management control, corporate governance and the literature on algorithmic decision support. It considers adequate arrangements as dynamic knowledge architectures that connect monitoring, interpretation and intervention, and it examines how predictive analytics, machine learning and explainable AI may support early warning systems, business continuity and organisational resilience. The contribution of the paper is threefold. First, it reframes legal adequacy as a managerial and knowledge-management capability, rather than as a formal compliance exercise. Second, it proposes an AI-enabled model in which data governance, predictive judgement, human oversight and ethical accountability are treated as mutually dependent elements of the same control environment. Third, it links crisis prevention to the broader agenda of intelligent and sustainable organisations, showing that predictive systems can help boards and managers reconcile continuity, stakeholder protection and sustainable value creation. The paper also discusses critical risks associated with AI adoption, including model opacity, poor data quality, bias, cybersecurity vulnerability and excessive delegation of judgement to automated systems. These risks require boards to preserve interpretive responsibility and to build validation, auditability and learning routines into the design of intelligent arrangements. The paper concludes that AI may strengthen the practical effectiveness of adequate arrangements only when embedded in relational governance, organisational learning and explicit ethical safeguards. Its main implication is that the future of crisis governance depends less on technological sophistication alone than on the capacity of organisations to combine human and artificial intelligence in a coherent knowledge system.

2220
Alexandra Zbuchea, Monica Bira, Mauro Romanelli
Cultural Organizations and Young Audiences: A Collaborative View through Artificial Intelligence
  • In: Proceedings IFKAD 2026: Intelligent Knowledge for Sustainable Organizations,
  • pp. 919-924,
  • 2026,
  • ISBN: 978-88-96687-21-5,
  • ISSN: 2280-787X

Cultural organizations are developing and implementing Artificial Intelligence (AI) and digital interactive technologies in order to improve cultural and social value production, meeting and engaging the young audiences in cultural heritage landscape. AI is increasingly shaping the strategies and operations of cultural organizations such as museums, galleries, libraries, and arts institutions. AI helps cultural organizations to improve the interaction with young people who act as agents of cultural heritage. AI helps enable cultural and heritage institutions and organizations as collaborative spaces that function as socio technical environments where communities, professionals, and technologies interact to co-create cultural value. AI supports cultural organizations to shape cultural heritage environments as collaborative spaces and meeting places for social innovation development. AI aims to facilitate collaborative interpretation and contribute to cultural knowledge. The study aims at investigating the relationships between cultural organizations, artificial intelligence and young audiences. AI supports cultural heritage sites as collaborative spaces for social innovation. AI helps drive cultural organizations to play a significant role in cultural value and knowledge creation, acting as agents of social change and value. Engaging young audiences in becoming proactive actors who contribute to social value creation within cultural heritage ecosystems supports social and technological innovation within cultural organizations that are rediscovering new sources and energies in order to behave as social value-driven and knowledge-led intelligent organizations that contribute to public value and better quality of life, driving social change within communities. The challenge of cultural organizations is to use AI to engage young people and make them proactive actors and responsible co-producers of social and cultural value. Cultural organizations enable youth to shape narratives and contribute to increasing the youth engagement, positioning themselves as platforms of social exchange and dialogue, enabling cultural heritage sites as digital and onsite meeting collaborative places that contribute to supporting cultural value creation.

2219
Fabrizio Rossi, Alessandro Sicali
Do Renewable Energy Communities Create Value? Evidence from a Systematic Literature Review
  • In: Proceedings IFKAD 2026: Intelligent Knowledge for Sustainable Organizations,
  • pp. 910-918,
  • 2026,
  • ISBN: 978-88-96687-21-5,
  • ISSN: 2280-787X

This study aims to analyse the literature on the economic performance of Renewable Energy Communities (RECs). Specifically, the objective of this work is to identify how existing research assesses REC profitability and to shed light on whether they represent financially viable investment vehicles. The distribution mechanisms employed to allocate economic benefits among REC participants are also investigated. To address these questions, a systematic literature review was conducted following the PRISMA 2020 guidelines, based on a search of the Scopus database. A final sample of 40 peer-reviewed articles published between 2018 and 2026 was analysed. The findings show that capital budgeting tools, such as NPV, IRR, and Payback Period, are the most widely adopted instruments for assessing REC economic performance. The empirical evidence suggests that RECs generally represent profitable investments capable of generating positive returns. Italy emerges as the most extensively studied context, notably in the residential sector, driven by a robust incentive system that has proven decisive in sustaining profitability. Indeed, economic performance tends to deteriorate and NPV can turn negative where regulatory support is insufficiently developed. Furthermore, photovoltaic technology emerges as the primary renewable energy source, while alternative configurations show potential but remain financially constrained by high CAPEX. Public and private involvement consistently generates positive economic outcomes by leveraging the complementarity of energy demand profiles. Regarding benefit distribution, a delicate balance exists between prosumers and consumers that can lead to participant dropout when allocation mechanisms prove ineffective. Minimum return guarantees, fairness-based criteria, and technology-driven tools have been shown to improve distributional outcomes and community stability. This study adds to the existing literature by providing systematic evidence on the key drivers of REC profitability and benefit distribution, while stimulating future research to further examine RECs through a corporate finance lens. The insights suggest that participation by private citizens, SMEs, and public administrations represents both an economically sustainable choice and a positive contribution to the energy transition. At the same time, this study offers practical implications for policymakers, identifying incentive system as a key driver of REC development, and for REC managers, highlighting the role of advanced benefit distribution systems in preserving community cohesion.

2218
Mauro Romanelli
Smart Healthy Cities Advancing
  • In: Proceedings IFKAD 2026: Intelligent Knowledge for Sustainable Organizations,
  • pp. 905-909,
  • 2026,
  • ISBN: 978-88-96687-21-5,
  • ISSN: 2280-787X

Cities are adopting a smart city strategy in order to support and improve smart urban health to achieve better quality of life within smart healthy urban communities. Cities are rediscovering the smart urban health as a driver of urban growth and innovation. Cities are going into the future, encouraging urban stakeholders’ engagement and collaboration, and embracing a smart city mind set and urban health view to urban value creation. The study aims to elucidate how the European cities are rethinking on the future of urban communities, adopting a smart view and focusing on urban health to improve the wealth of urban communities. The study relies on qualitative research analysis. The research analyzes two case studies drawn from policy documents related to European cities that are rethinking on future design of the city, and planning a smart view to urban growth, leading to smart health as a driver of urban innovation. Brussels and Wien have designed a smart healthy city strategy, promoting urban health, and driving smart urban innovation. Smart urban health planning helps shape sustainable and wealthy cities, leading to responsible urban innovation and sustainable growth, supporting multi-actor collaborative innovation, and focusing on community development. Smart urban health and innovation support collaborative spaces in order to drive smart and responsible urban innovation. Introducing the smart urban health view within urban planning is enriching the approach to smart city vision, enabling the smart mind set as a way to design and implement organizational and collaborative spaces for innovation. Driving smart urban health refers to collaborative frameworks that involve a variety of urban stakeholders and actors that contribute to shaping the city a smart innovative community which is able to contribute to sustainable growth and social innovation. The smart city vision supports a collaborative platform that enhances smart health, leading to social innovation within urban communities.

2217
Sara Gigli, Anna Turchetta
How European Living Labs Contribute To the Creation of Ecosystems of Innovation: A Social Network Analysis (SNA) of Project-Based Collaboration
  • In: Proceedings IFKAD 2026: Intelligent Knowledge for Sustainable Organizations,
  • pp. 898-904,
  • 2026,
  • ISBN: 978-88-96687-21-5,
  • ISSN: 2280-787X

In recent years, increasing attention has been devoted to the role of collaboration and innovation in addressing sustainability challenges. Sustainable innovation has become a key driver of long-term value creation. Technological progress and knowledge exchange are crucial enablers of such innovation, especially within the open innovation framework, where firms integrate internal and external knowledge sources.
Within this evolving landscape, living labs have emerged as pivotal actors in innovation ecosystems. Based on principles of co-creation, experimentation, and user involvement, living labs facilitate the development and testing of innovative solutions in real-life contexts. By bringing together multiple stakeholders, they foster collaborative processes to tackle complex societal challenges and advance sustainability transitions.
This study addresses the following research question: How do knowledge collaboration networks among living labs contribute to territorial innovation ecosystems? To answer this question, we adopt a Social Network Analysis (SNA) approach, which is particularly suitable for examining patterns of collaboration and knowledge exchange. Data were manually collected from the websites of living labs listed in the European Network of Living Labs (ENoLL) database. The analysis focuses on four European countries (France, Germany, Italy, and Spain) selected due to their high concentration of living labs and the availability of comparable data.
In the constructed network, nodes represent organizations involved in living lab activities, and edges represent project-based collaborations. Each tie is characterized by attributes such as the number of shared projects, collaboration intensity, domain, and interaction type. Using RStudio, we analyse both global network properties and node-level centrality measures to assess the structural role of living labs within the ecosystem.
Preliminary findings reveal a partially connected network characterized by heterogeneous collaboration patterns. While the overall network density is relatively low, a very small number of living labs act as central hubs. This study contributes to the literature on knowledge management and innovation ecosystems by providing empirical evidence on the relational mechanisms underpinning knowledge-driven innovation. However, limitations related to data availability suggest the need for future research integrating longitudinal and qualitative approaches to better capture the evolution of living lab networks.

2216
Constantin Bratianu, Ruxandra Bejinaru
The New Organizational Knowledge Dynamics Model that Integrates Human and Artificial Knowledge
  • In: Proceedings IFKAD 2026: Intelligent Knowledge for Sustainable Organizations,
  • pp. 892-897,
  • 2026,
  • ISBN: 978-88-96687-21-5,
  • ISSN: 2280-787X

The emergence of Generative Artificial Intelligence (GenAI) and its explosive applications challenges the structure, functionality and efficiency of the Knowledge Management Systems (KMS). GenAI processes data, information, and human knowledge and generates artificial knowledge that belongs to the sciences of the artificial. KMS have been designed to create, share, transfer, and use human knowledge. The basic theory of organizational knowledge creation dynamics has been developed by Ikujiro Nonaka and Hirotaka Takeuchi and illustrated as the well-known SECI (Socialization, Externalization, Combination, Internalization) model. Knowledge is created through a series of transformations between tacit knowledge and explicit knowledge, and scaling up from individuals to teams and organizations. GenAI transformed KMS into hybrid systems composed of people and machines. The problem now is how to integrate human knowledge with artificial knowledge to enhance the efficiency of the KMS. From a practical viewpoint, the problem is how to augment the SECI model with artificial knowledge such that it can represent the new Hybrid Knowledge Management Systems (HKMS). The difficulty comes from the fact that human knowledge and artificial knowledge are two distinct constructs, and they cannot be mixed entirely. Human knowledge is a synthesis of rational, emotional, and spiritual knowledge, while artificial knowledge is exclusively rational. Therefore, the augmentation of each stage of the SECI model with artificial knowledge cannot be a solution because tacit or emotional knowledge is embodied. The models published so far focused on this solution, ignoring the fact that tacit knowledge cannot be integrated directly with artificial knowledge. The purpose of this research is to show a new perspective on augmenting the SECI model with artificial knowledge. We consider a separate cycle for artificial knowledge that mirrors the structure of the SECI cycle, and an interaction between them through the process of combination (i.e., the only process that is based exclusively on explicit knowledge). Therefore, the new complex model is composed of two cycles (i.e., the human knowledge and the artificial knowledge cycles), which are interconnected through the combination process.

2215
Gabriele Rizzo, Daniele Allega
Redefining Financial Advisory: Human-Machine Collaboration and Organizational Transformation Under the EU Financial Data Access Regulation
  • In: Proceedings IFKAD 2026: Intelligent Knowledge for Sustainable Organizations,
  • pp. 883-891,
  • 2026,
  • ISBN: 978-88-96687-21-5,
  • ISSN: 2280-787X

The convergence of artificial intelligence and the proposed European Financial Data Access Regulation (FiDA) is reshaping financial advisory in ways that affect both client relationships and the structural composition of financial institutions. The dominant discourse has long oscillated between two extreme narratives: technological utopianism, which forecasts the displacement of human advisors, and technological scepticism, which confines algorithms to instrumental roles. This paper articulates a third narrative, that of human-machine symbiosis, in which AI capabilities and human expertise are deliberately coordinated as complementary contributors to advisory decision-making, and shows that this view carries macro-financial consequences that have received insufficient attention. The argument unfolds in four steps. We first review four interconnected literatures: AI in financial advisory, human-machine collaboration, FiDA and Open Finance, and capital structure theory in banking. We then develop a conceptual framework in which client data, algorithmic insight, and advisor expertise are coordinated as knowledge assets, deriving two implementation scenarios that distinguish AI-as-empowerment from AI-as-autonomous-interface configurations. A design-science proof-of-concept operationalises the framework on FiDA-aligned synthetic data, evaluated against alignment, suitability-augmentation, and interpretability criteria. Finally, we develop the macro-financial implications. AI-mediated advisory under FiDA can serve as a demand-side mechanism that complements the supply-side capital pressures of Basel III/IV. Through three transmission channels, ranging from direct conversion of deposits into Tier 1 or Tier 2 capital to maturity-profile improvements, depositors are progressively transformed into stakeholders, lowering institutional leverage and enhancing resilience to adverse macroeconomic shocks. The contribution is fourfold: a theoretically grounded alternative to dominant narratives; a mapping of FiDA provisions onto AI-advisory operational requirements; a development of macro-financial implications linking micro-personalisation to capital structure dynamics; and an algorithm-agnostic proof-of-concept.

2214
Giuseppe Ambrosio, Maria Zifaro, Federica Bandini, Stefania D’Aprile, Giovanni Spatola, Marianna Mancino
Beyond Automation: Strategic Human-Machine Partnerships in Social Enterprise HRM. A Systematic Literature Review
  • In: Proceedings IFKAD 2026: Intelligent Knowledge for Sustainable Organizations,
  • pp. 875-882,
  • 2026,
  • ISBN: 978-88-96687-21-5,
  • ISSN: 2280-787X

The intersection of artificial intelligence (AI), machine learning technologies, and human resource management (HRM) represents a transformative shift in organisational development. This paper presents a systematic literature review (SLR) examining peer-reviewed evidence on human-machine collaboration in HRM within social enterprises and mission-driven organisations. Using the SPIDER framework and PRISMA guidelines, the review conducted a systematic search on Scopus retrieving 96 peer-reviewed articles, supplemented by backward snowballing.
A critical methodological finding emerged during string construction: incorporating AI-related terms (Cluster C) into the automated TITLE-ABS-KEY search reduced the corpus by 97%, from 245 results to fewer than 12. This reflects a publishing convention whereby AI is discussed substantively in article bodies without being foregrounded in searchable metadata. Cluster C was therefore applied as a manual inclusion criterion during screening rather than as an automated filter.
The review’s primary substantive finding is structural: the three-way intersection of AI, HRM, and the third sector is demonstrably scarce in the indexed peer-reviewed literature. The single article meeting all inclusion criteria — Alnamrouti, Rjoub and Ozgit (2022) — provides empirical evidence that AI positively influences sustainable organisational performance in an NGO context, functioning primarily as a mediator amplifying the effect of organisational learning. Backward snowballing on seven references yielded zero additional inclusions, independently corroborating the structural scarcity finding.
Three major research gaps are identified: (1) the absence of domain-specific empirical evidence on AI adoption in social enterprise HRM functions (recruitment, performance management, learning and development, workforce planning, employee engagement); (2) the complete absence of research on ethical and algorithmic risks in mission-driven HRM contexts; and (3) the lack of engagement with established human-machine collaboration frameworks in third-sector management research. These findings establish a documented evidence baseline for a nascent research field, grounding the broader theoretical paper on human-machine partnerships.

2213
Marianna Mancino, Maria Zifaro, Giuseppe Ambrosio, Giovanni Spatola, Stefania D’Aprile
Collective and Artificial Intelligence in the Challenge of Happy Degrowth
  • In: Proceedings IFKAD 2026: Intelligent Knowledge for Sustainable Organizations,
  • pp. 865-874,
  • 2026,
  • ISBN: 978-88-96687-21-5,
  • ISSN: 2280-787X

The current context is marked by systemic, ecological, social, and economic crises. These are accompanied by an increasing acceleration of technological change. In this scenario, the urgent need emerges to rethink development models based on unlimited economic growth. The degrowth paradigm proposes an alternative orientation centered on principles of sufficiency, equity, and collective well-being. It challenges dominant productivist logics and promotes new forms of social organization. At the same time, artificial intelligence is emerging as a powerful infrastructure capable of influencing organizational and decision-making processes in contemporary societies, reshaping the relationship between technology, work, and society in significant and long-term ways. This contribution explores the joint role of collective intelligence (CI) and artificial intelligence (AI) in supporting scenarios of convivial degrowth. It adopts a theoretical-exploratory and interdisciplinary approach based on the analysis of existing literature and documented case studies. The findings show that AI, if designed and governed according to ethical, inclusive, and democratic principles, can facilitate distributed coordination of activities, knowledge sharing, and collective participation in decision-making processes, thus strengthening local resilience and cooperation. Within this perspective, CI emerges as the cognitive engine of community cooperation and co-creation. Time is understood as a fundamental resource that must be redistributed to enable non-market social practices such as care, volunteering, and participatory deliberation. The paper proposes the Cooperative Socio-Technical Regeneration Model (CSTRM), a theoretical framework that integrates CI, AI, time, and sufficiency as key elements of a socio-ecological transformation oriented toward collective well-being and long-term sustainability. The model highlights that the relationship between AI and degrowth is not automatic. Rather, it is deeply mediated by design, institutional, and cultural choices. The contribution aims to enrich the debate on socio-ecological transition pathways, while acknowledging the limitations related to the theoretical nature of the analysis and the need for future empirical validation, as well as further comparative research across different territorial contexts.

2212
Daniele Allega, Gabriele Rizzo
From Paradoxes and Entropies to Sustainable Knowledge Ecosystems: Architectural and Governance Principles
  • In: Proceedings IFKAD 2026: Intelligent Knowledge for Sustainable Organizations,
  • pp. 858-864,
  • 2026,
  • ISBN: 978-88-96687-21-5,
  • ISSN: 2280-787X

Artificial Intelligence is often presented as a shortcut to organizational sustainability, yet many AI initiatives underperform because firms attempt to automate before they have structured, governed, and shared their knowledge assets. This paper addresses that gap by asking how organizations can move from fragmented, tool-centred AI adoption to sustainable knowledge ecosystems. We build a design-oriented conceptual framework through analytical generalization from four design artifacts previously developed in the legal-AI domain: the knowledge commoditization paradox (Allega, Puzio and Rizzo, 2026b), the MERL-T multi-expert architecture (Allega, Puzio and Rizzo, 2026c), the RLCF governance model (Allega, Puzio and Rizzo, 2026d), and the ALIS platform framework (Allega, Puzio and Rizzo, 2026a). The legal domain is treated as a revelatory setting because it concentrates stringent requirements for traceability, pluralism, accountability, and knowledge integrity that are increasingly relevant across knowledge-intensive organizations. The paper makes two main contributions. First, it extends the earlier notion of semantic entropy by identifying three entropic pressures that undermine the Knowledge Journey: coordination entropy, generated by fragmented repositories and inconsistent taxonomies; semantic entropy, generated by the decontextualization of knowledge during computational representation; and governance entropy, generated by opaque AI-mediated workflows that dilute authority and responsibility. Second, it translates these pressures into five design principles for sustainable knowledge ecosystems: commons-first knowledge architecture, provenance-preserving services, epistemically differentiated human-AI collaboration, community-anchored governance, and sustainability-by-measurement. These principles are then mapped onto the three phases of Planning, Acquisition, and Impact to show how organizations can reduce hidden costs such as search time, duplication of effort, hallucinated outputs, accountability gaps, and knowledge lock-in. Rather than treating AI as a stand-alone capability, the paper argues that sustainable value emerges when architectural choices and governance mechanisms are designed together. The result is a practical and theoretically grounded model for organizations seeking to transform knowledge management from a hidden cost centre into a resilient, auditable, and sustainability-oriented ecosystem.

2211
Davide D’Emiliano, Laura Nucera, Andrea De Mauro
Beyond Marketplace Reviews: Transformer-Based Sentiment Analysis of YouTube Influencer Ecosystems
  • In: Proceedings IFKAD 2026: Intelligent Knowledge for Sustainable Organizations,
  • pp. 850-857,
  • 2026,
  • ISBN: 978-88-96687-21-5,
  • ISSN: 2280-787X

Online reviews and influencer-centred social media conversations jointly shape product understanding in digital markets, yet they are still often studied as separate streams. This gap is especially relevant in the beauty sector, where product evaluation depends on subjective experience, perceived efficacy, tolerability, routine fit, and trust in peer-generated accounts. This study develops a cross-platform comparison of Amazon reviews and YouTube comments related to three active skincare products from the same brand, to examine whether the emotional profiles emerging from marketplace reviews differ from those found in influencer-centred comment spaces, and whether YouTube comments reveal needs, doubts, and advice-seeking signals that remain less visible in marketplace environments. The analysis is based on a replicable pipeline that combines data collection, filtering, text preparation, and transformer-based emotion detection using RoBERTa GoEmotions. The findings show that Amazon and YouTube do not produce equivalent emotional profiles. Across the three products, Amazon tends to concentrate emotional expression around admiration and approval, whereas YouTube comments display a broader and more fragmented emotional repertoire. The paper contributes theoretically by integrating review research and influencer ecosystems within a common comparative perspective, methodologically by proposing a transparent cross-platform workflow, and managerially by showing how YouTube-centred social listening can support the detection of consumer concerns that remain underrepresented in marketplace reviews.

2210
Ferdinando Sasso, Andrea De Mauro
Perturbation-First Robustness Evaluation of Automated Short Answer Grading
  • In: Proceedings IFKAD 2026: Intelligent Knowledge for Sustainable Organizations,
  • pp. 842-849,
  • 2026,
  • ISBN: 978-88-96687-21-5,
  • ISSN: 2280-787X

Automated Short Answer Grading (ASAG) is increasingly deployed in high-stakes educational contexts, yet the dominant evaluation paradigm focuses primarily on agreement with human raters, overlooking robustness as a complementary quality dimension. Robustness, defined as the consistency of grading behaviour under controlled, meaning-aware input transformations, bears directly on construct validity, the degree to which an assessment measures what it claims to measure, and conditions the trustworthiness of automated decisions in education.
We propose a perturbation-first evaluation framework that assesses robustness across three validity-theoretic dimensions: invariance to construct-irrelevant variation, sensitivity to meaning-altering changes, and resistance to adversarial gaming. The framework combines rule-based perturbation generators, two-gate quality validation, and self-referential metrics that compare the grader against its own output rather than against gold labels, isolating robustness from accuracy. A dual-protocol design contrasts within-question evaluation, the optimistic scenario typically reported in the literature, with cross-question evaluation, the realistic deployment scenario, capturing how trained graders degrade under distribution shift.
We apply the framework to a benchmark ASAG dataset of science short answers and compare a trained machine-learning baseline with zero-shot Large Language Model (LLM) graders evaluated at two prompting levels, with and without access to a reference answer. Three findings emerge. First, trained graders exhibit substantial robustness degradation under realistic cross-question evaluation, suggesting that standard within-question benchmarks overestimate operational reliability. Second, zero-shot LLM graders exhibit stronger robustness on invariance and sensitivity dimensions while remaining comparatively less vulnerable to adversarial manipulation, although residual sensitivity failures display a stable hierarchy across perturbation types. Finally, providing reference answers introduces a subtle trade-off, with improved consistency on meaning-preserving variations but increased susceptibility to keyword-based gaming, which is invisible to single-metric evaluation.
We argue that multi-dimensional robustness evaluation should complement accuracy-based metrics in ASAG systems intended for high-stakes educational use, and we release an open-source framework that operationalises the approach and is freely available to the research community.

2209
Paola Demartini, Alessandro Salvatori
The Digital Platform as Cognitive Infrastructure: Investigating Human-AI Synergy and Algorithmic Governance in Investment Management
  • In: Proceedings IFKAD 2026: Intelligent Knowledge for Sustainable Organizations,
  • pp. 833-841,
  • 2026,
  • ISBN: 978-88-96687-21-5,
  • ISSN: 2280-787X

Over the past two decades, the financial sector has undergone a profound transformation, firmly positioning information as its primary competitive asset and placing Artificial Intelligence (AI) at the core of this shift. By interpreting diverse data volumes at speeds beyond human capacity, AI serves as a critical supportive technology. Within this landscape, digital platforms like BlackRock’s Aladdin have emerged as essential cognitive infrastructures functioning as comprehensive operating systems for modern finance. These platforms seamlessly integrate the entire investment lifecycle into a unified data layer, reshaping organizational roles and business models by facilitating a vital interaction between human insight and algorithmic intelligence.
Despite AI’s well-documented technical capabilities, a critical gap remains in understanding the socio-technical dynamics of this human-AI synergy within institutional settings. Current literature predominantly focuses on the predictive accuracy of “black box” models or macro-level regulatory frameworks, overlooking how professionals navigate daily trade-offs between algorithmic performance and explainability. Furthermore, there is limited empirical exploration of the modern financial analyst’s “hybrid profile,” which requires combining computational literacy with ethical sensitivity within established platform constraints.
To address this gap, this study investigates how digital investment platforms facilitate human-AI synergy while managing structural risks of algorithmic opacity and bias. Employing a qualitative, exploratory approach, the research utilizes semi-structured interviews with senior European Union professionals involved in investment products, digital financial advisory, platform delivery, and risk-related decision-making. The investigation focuses on integrating AI-driven insights into fundamental analysis, perceptions of algorithmic opacity, the EU AI Act’s impact on governance, and platforms’ role in standardizing information.
Preliminary findings indicate that rather than replacing human experience, AI fundamentally reconfigures it, shifting professional judgment toward problem definition, exception management, and robustness testing. Integrated platforms provide a “common data language” that aligns internal functions, significantly reducing manual errors and operational risk. However, a persistent tension exists between maximizing predictive power and maintaining the transparency demanded by the EU AI Act, which frames AI as a corporate governance issue rather than mere efficiency. Ultimately, the study highlights a “new responsibility pact” requiring auditable decision-making, while warning that “algorithmic convergence” through dominant platforms could dangerously amplify market volatility. Sustainable innovation requires these cognitive infrastructures to be supported by robust data governance.

2208
Beren Çay, Mario Miozza, Andrea De Mauro
Retrieval-Augmented Generation in Business Applications: A Systematic Literature Review and Taxonomy
  • In: Proceedings IFKAD 2026: Intelligent Knowledge for Sustainable Organizations,
  • pp. 824-832,
  • 2026,
  • ISBN: 978-88-96687-21-5,
  • ISSN: 2280-787X

Retrieval-Augmented Generation (RAG) has become one of the most visible mechanisms for grounding large language model outputs in organizational knowledge sources. While the technical literature on RAG architectures is substantial, the evidence on how RAG is deployed inside organizations, and on the organizational consequences of those deployments, has not been systematically synthesized. This paper addresses that gap by treating RAG as a hybrid knowledge capability pairing a generative model with curated organizational knowledge and human oversight. The study reports a systematic literature review of peer-reviewed work on RAG in business and organizational contexts published between 2023 and 23 February 2026, covering Scopus and Web of Science in line with the PRISMA 2020 reporting framework. From 727 records after database-side filtering, 513 were screened and 41 retained. The studies were synthesized through the empirical-to-conceptual taxonomy development method of Nickerson, Varshney and Muntermann (2013) into a three-dimensional framework linking business application contexts, RAG architectures, and reported organizational consequences. Three findings stand out. First, adoption concentrates in knowledge-intensive support functions: enterprise knowledge retrieval, document intelligence, and decision support together account for around seventy percent of the corpus. Second, Classic RAG remains the most frequent single configuration, while Advanced and Agentic variants together appear in 18 studies and concentrate in deployments with more demanding task structures. Third, the most consistent finding is not a benefit but a limitation: residual output quality issues appear in 26 of 41 studies and recur across every architectural category and every application context, while data-quality dependence is the most frequent implementation challenge. The paper contributes a business-oriented taxonomy empirically distinct from architecture-first surveys, positioning RAG as a socio-technical capability whose organizational value depends on the surrounding data, governance, and oversight infrastructure. For practitioners, it supports deliberate decisions about where RAG delivers value, which architecture fits which task, and which conditions enable successful deployment.

2207
Davide Madonna, Simone Malacaria, Andrea De Mauro
Contextual–RFM (C-RFM): a Multimodal Framework for the Fusion of Transactional and Semantic Data Through LLMs for Interactive Customer Segmentation
  • In: Proceedings IFKAD 2026: Intelligent Knowledge for Sustainable Organizations,
  • pp. 815-823,
  • 2026,
  • ISBN: 978-88-96687-21-5,
  • ISSN: 2280-787X

This study examines the evolution of customer segmentation from rule-based systems to multimodal AI architectures, emphasizing the necessity for transparent and context-aware CRM strategies. Despite the widespread adoption of RFM models, organizations often struggle to synthesize transactional metrics with multifaceted qualitative feedback, creating a numerical paradox that this research addresses by capturing the authentic voice of the customer within high-dimensional analytical spaces. This paper presents the Contextual-RFM (C-RFM) framework, an innovative multimodal architecture aimed at bridging the representational divergence between quantitative metrics and qualitative feedback.
The research operationalizes a comprehensive five-phase pipeline to ensure methodological transparency and replicability: (1) establishment of a statistical baseline, (2) narrative synthesis for tabular serialization, (3) multimodal vectorization utilizing advanced embeddings, (4) semantic analysis through Latent Dirichlet Allocation (LDA) and (5) implementation of the C-RFM Expert chatbot. This entire workflow is implemented using KNIME Analytics and Langflow, providing a robust and replicable process for both scholars and practitioners. A key distinction of the framework is its early-fusion approach. Unlike traditional late-fusion models that integrate data streams only at the final analysis stage, early-fusion retains critical cross-modal correlations during the initial processing phase, effectively addressing the semantic opacity common in traditional numerical-only datasets.
Empirical findings indicate that the C-RFM framework adeptly organizes linguistic complexity, evidenced by a significant improvement in structural validity when transitioning from purely transactional environments to topic-validated contexts. Moreover, this study introduces the Discrepancy Detection Rate (DDR) as a novel performance metric to quantify the misalignment between transactional behaviour and qualitative feedback, thereby identifying critical churn risks that conventional quantitative analyses often overlook.
The primary contribution of this research lies in the synergy achieved between human expertise and Artificial Intelligence. By deploying dynamic persona chatbots grounded in Retrieval-Augmented Generation (RAG), the framework facilitates a shift from opaque analytical outputs to actionable, customized recovery strategies. This glass-box methodology transforms intricate numerical complexities into auditable business narratives, empowering decision-makers to transcend statistical constructs and foster a deeper, dialogue-based understanding of consumers. The C-RFM framework represents a significant advancement in human-centred AI, promoting strategic marketing intelligence that is not only transparent but also finely tuned to the authentic voices of real-world consumer archetypes, thereby paving the way for the next generation of customer-centric strategies.

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