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:

Evaluating the Acceptance of GenAI in Italian Academies of Fine Arts (AFAM)

(519-528)

Francesco Filippo, Francesco Paolo Lagrasta, Barbara Scozzi

Abstract

Generative Artificial Intelligence (GenAI) is increasingly entering higher education, offering new opportunities for teaching, learning, content generation and creative experimentation. In artistic and design-oriented education, however, the acceptance of GenAI cannot be explained only through traditional efficiency-based criteria. Within Italian Academies of Fine Arts (AFAM), creative work is closely linked to identity, authorship, aesthetic judgment, expressive autonomy and cultural meaning. For this reason, understanding the factors that shape students’ and educators’ intention to use GenAI requires a contextualized extension of established technology acceptance models.
This paper investigates the acceptance of GenAI in Italian Academies of Fine Arts by developing and preliminarily validating a survey instrument grounded in the Technology Acceptance Model (TAM) and enriched with constructs specific to creative contexts. The study builds on a systematic literature review conducted on Scopus, which identified 274 articles at the intersection of artificial intelligence, creativity and technology adoption. A core dataset of 31 studies was then examined to identify recurrent theoretical frameworks, constructs and measurement items. Based on this analysis, TAM was selected as the reference model and extended with four context-specific constructs: trust in GenAI systems, aesthetic satisfaction, creative autonomy and subjective norm. The methodological design combines literature-based construct development, questionnaire design and qualitative validation through a cognitive interviewing approach. The questionnaire is structured around a seven-point Likert scale and includes items adapted from prior acceptance studies and reformulated for GenAI-enabled creative practices. A preliminary validation phase was conducted with participants familiar with artistic and creative domains, to assess semantic clarity, item interpretability, cognitive load and potential response biases. The paper contributes to the literature by proposing a TAM-based model tailored to creative higher education. It also offers practical implications for AFAM institutions, educators and curriculum designers interested in supporting responsible and context-sensitive GenAI integration. The findings suggest that GenAI acceptance in artistic education depends not only on usefulness and ease of use, but also on trust, perceived control over the creative process and satisfaction with the aesthetic quality of AI-generated outputs.

Explainable AI as Decision Support in Automated Assessment: An Exploratory Study

(529-536)

Gabriele Creta, Andrea De Mauro

Abstract

This paper investigates how rubric-based Explainable Artificial Intelligence (XAI) outputs influence instructor trust and grading behavior in AI-assisted academic assessment. As AI technologies become increasingly integrated into educational evaluation workflows, understanding the cognitive mechanisms through which instructors interact with AI-generated scores is critical for designing effective human–AI collaboration in grading and for the broader governance of knowledge assets in higher education (Khosravi et al., 2022). Two competing hypotheses motivate the study: explanations may either mitigate automation bias by enabling critical evaluation, or amplify it by providing a rationalization for uncritical acceptance of AI recommendations. We present a within-subject pilot experiment in which university-level evaluators (n = 3) assessed 12 short student responses to Computer Science questions under three conditions: (a) AI-only, where only a numerical AI score was displayed; (b) AI + Explanation, where the AI score was accompanied by a four-criterion rubric breakdown and a textual justification; and (c) Anchoring, where evaluators first graded independently and then were exposed to the AI output with an option to revise. AI grading outputs were generated using a Retrieval-Augmented Generation (RAG) pipeline calibrated against three open-access essay scoring datasets (GRE Analytical Writing, DREsS, and ASAP2) and indexed in a FAISS vector store for similarity retrieval. Preliminary results from this pilot are consistent with rubric-based explanations reducing deviation between human and AI grades (mean |delta| = 1.2 vs. 1.9 for AI-only), doubling the observed revision rate in the anchoring condition (67% vs. 33%), and shifting all observed revisions toward the AI score. Qualitative evidence indicates that evaluators cite specific AI observations when revising with explanations but resist adjustment without them. A discrepancy between self-reported and observed AI influence further suggests a metacognitive blind spot consistent with the automation-bias literature. These preliminary findings are consistent with the hypothesis that structured XAI outputs may enhance evaluator reliance on AI grading systems and reshape evaluative judgment through anchoring mechanisms, with implications for the governance of human–AI assessment workflows in higher education.

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.

New frontiers in Organizational Aesthetics

(537-544)

Giovanna Afeltra, Maria Menshikova, Maria Pia Ciano

Abstract

Over the past decades, Organizational Aesthetics (OA) has evolved from a marginal research stream into a vibrant interdisciplinary field addressing the sensory, effective, and material dimensions of organizing. Despite this growing interest, the literature remains fragmented across disciplines, methodologies, and levels of analysis, leaving key theoretical and empirical questions unresolved. This study responds to these challenges by offering a comprehensive Bibliometric-Systematic Literature Review (B-SLR) of OA research to map its intellectual structure, methodological trends, and emerging frontiers.
Drawing on a dataset of 82 peer-reviewed journal articles indexed in Scopus, the study combines bibliometric techniques (using Bibliometrix) with an in-depth qualitative analysis of selected contributions from leading journals. The findings show a steady growth of OA research since the mid-1990s, with strong contributions from the United Kingdom and the United States, alongside increasing global diversification. Thematic analysis highlights core areas such as embodiment, emotions, and aesthetic labour, while identifying emerging domains including workplace aesthetics, employee well-being, and environmental design.
The review reveals that OA has shifted from a focus on beauty and artefacts toward a broader understanding of aesthetics as a constitutive, embodied, and political dimension of organizational life. Methodologically, the field remains dominated by qualitative and interpretive approaches, though recent studies signal a move toward quantitative and mixed-methods approaches. Importantly, the analysis uncovers a growing “aesthetic turn” in digital and hybrid work environments, where aesthetic experience extends beyond physical spaces into virtual and platform-mediated contexts.
Building on these insights, the paper proposes a future research agenda centered on theoretical integration, ethical and responsible organizing, methodological advancement, digital aesthetics, and cross-cultural perspectives. Overall, the study positions OA as a critical lens for understanding contemporary organisations and highlights its relevance for both scholars and practitioners seeking to design more human-centred, inclusive, and sustainable work environments.

Organisational AI Literacy and Readiness: Enabling Factors and Barriers

(545-554)

Riccardo Michele Colangelo, Eleonora Petrazzuoli, Maria Menshikova

Abstract

The spread of artificial intelligence (AI) within organisations is reshaping how they operate, leading to deep and systemic transformations across processes, structures and decision-making practices.
In this context, the distinct concepts of AI literacy and AI readiness are increasingly central to understanding organisational AI-driven transformation.
This study investigates the relationship between AI literacy and AI readiness within organisations, also examining which enabling factors and barriers influence these constructs across multiple organisational levels.
By adopting an integrated bibliometric and systematic approach, this study aims to provide an overview of how these constructs are addressed in the literature, followed by a systematic analysis of enabling factors and barriers to AI literacy and AI readiness, with specific regard to 61 Q1 journal articles in the business, management, and accounting domain.
The bibliometric analysis, conducted using Biblioshiny, maps publication trends, geographical distribution and keyword co-occurrence networks.
The systematic analysis further explores enabling factors and barriers to AI literacy and AI readiness on a targeted selection of relevant peer-reviewed publications, using a specific purpose-built analytical framework, across micro (individual), meso (team), and macro (organisational) levels.
The key findings indicate that AI literacy is primarily associated with individual-level capabilities, including AI knowledge, self-efficacy and critical thinking skills, while AI readiness is predominantly linked to organisational dimensions such as technological infrastructure, data availability, leadership support and strategic alignment. Key barriers include skill gaps, cognitive biases, resource constraints, legacy systems, organisational rigidity, and regulatory uncertainty.
The study also identifies cross-cutting factors, such as trust in AI systems, ethical considerations and algorithmic bias, which influence both constructs and reflect the socio-technical nature of AI adoption.
Overall, the findings suggest that AI literacy and AI readiness are strongly interdependent constructs: they are closely interconnected and often mutually reinforcing in shaping how organisations engage with artificial intelligence, although they remain distinct constructs.

Project Tailoring as a Knowledge Legitimacy Process in Hybrid Intelligence Environments

(555-563)

Anikó Csepregi, Coşkun Akça

Abstract

Organizational change is increasingly implemented through projects that operate as temporary organizational settings where actors interpret uncertainty, negotiate priorities, and enact change through coordinated action. While project tailoring is traditionally understood as a technical adaptation of project management methods, recent research suggests that tailoring decisions emerge through collective sensemaking processes embedded in organizational and technological contexts. However, existing literature has paid limited attention to how organizational culture and digital technologies jointly shape knowledge processes through which tailoring decisions are enacted.
This paper explains how organizational culture and hybrid intelligence jointly shape knowledge legitimacy processes through which decisions are enacted in project-based organizations. This conceptual paper develops an integrative framework explaining project tailoring as a knowledge enactment process emerging from collective sensemaking and knowledge legitimacy processes. More specifically, the paper conceptualizes project tailoring as a knowledge legitimacy process emerging from collective sensemaking in hybrid intelligence environments. The framework integrates literature on project-based organizing, sensemaking, organizational culture, and sociomateriality, and introduces hybrid intelligence as a socio-technical context that shapes the visibility, measurability, interpretation, and legitimacy of knowledge in project environments.
The paper argues that project tailoring decisions should be understood as outcomes of culturally and sociomaterially embedded knowledge processes rather than purely technical adjustments. Organizational culture is conceptualized as an interpretive and knowledge logic that shapes how actors evaluate and legitimize knowledge, while hybrid intelligence environments influence what knowledge becomes visible, measurable, and actionable in decision-making processes. Project tailoring is therefore interpreted as an enacted outcome of interacting cultural, interpretive, and technological processes.
The paper contributes to knowledge management and project studies by reframing project tailoring as a culturally embedded knowledge process, introducing knowledge legitimacy as a key mechanism linking collective sensemaking and project decision making, and by conceptualizing hybrid intelligence as a sociomaterial context influencing organizational knowledge processes and decision outcomes. The paper concludes by outlining implications for organizational change and knowledge-based decision making in hybrid intelligence environments.

Rethinking Help-Seeking in AI-Mediated Workplaces: Organizational and Individual Antecedents Through a Socio-Technical and Resource-Based Perspective

(564-571)

Filomena Pagnozzi, Helen Zhao

Abstract

Artificial intelligence (AI) systems are increasingly embedded in contemporary workplaces, offering employees new sources of assistance for obtaining information and receiving support during work tasks. This transformation is reshaping how help is accessed, as employees can now turn not only to colleagues and supervisors but also to AI-based tools such as chatbots and generative AI systems.
However, the help-seeking literature has traditionally conceptualized this behavior as inherently interpersonal, shaped by relational dynamics such as reputational concerns and power relations. While this perspective has generated important insights, it offers only a partial account of help-seeking in AI-mediated workplaces, where sources of assistance extend beyond human interactions.
In these contexts, help-seeking decisions are shaped by the interaction between socio-technical infrastructures, including availability, accessibility, characteristics of AI systems and individuals’ evaluations of potential resource gains and losses related to help sources. Despite the relevance of this shift, existing research lacks a framework that conceptualizes help-seeking as a multi-level phenomenon emerging from the interaction of organizational and individual factors.
To address this gap, this paper integrates Socio-Technical Systems theory and Conservation of Resources theory to examine how organizational and individual antecedents jointly shape help-seeking behaviors. Drawing on this integrated perspective, we propose a conceptual framework that identifies organizational infrastructure conditions and individual intrapersonal and interpersonal factors of three help-seeking strategies: AI-based, human-based, and hybrid help-seeking.
By combining these perspectives, we reconceptualize help-seeking in AI-mediated workplaces as a multi-level resource-regulation process and provide a foundation for future research on help-seeking in AI-mediated workplaces.

The Hidden Cost of Flexibility: Knowledge Visibility in Flexible Work Arrangements

(572-579)

Elina Pentjusa, Elina Gaile-Sarkane

Abstract

Flexible work arrangements have become a defining feature of contemporary organizations, yet their implications for organizational knowledge processes remain insufficiently theorized. Existing research has primarily examined flexible work through employee autonomy, wellbeing, productivity, retention, and work-life balance. While these perspectives are important, they provide only a partial account of how flexible work reshapes the creation, sharing, retention, and renewal of organizational knowledge. This paper argues that flexible work should be understood not only as a human resource management or work design issue, but also as a knowledge governance issue.
The paper develops a conceptual framework explaining how flexible work arrangements influence knowledge visibility in organizations. Flexible work is treated as a multidimensional construct comprising spatial, temporal, and organizational flexibility rather than as a single uniform practice. Drawing on tacit knowledge theory, organizational knowledge creation, situated learning, communities of practice, technology affordance theory, transactive memory systems, and knowledge governance, the paper argues that the knowledge effects of flexibility are conditional. Flexible work may strengthen knowledge processes by supporting autonomy, focused work, digital traceability, and access to distributed expertise, but may also weaken tacit knowledge transfer, newcomer learning, shared interpretation, and expertise visibility when knowledge processes are not intentionally governed.
The main contribution is the development of a flexibility-knowledge visibility framework. The framework explains how different forms of flexibility affect organizational knowledge through four mechanisms: tacit knowledge transfer, digital and social visibility, transactive memory, and participation in learning communities. The paper develops five propositions for future empirical research and outlines governance mechanisms for maintaining knowledge visibility in flexible work contexts. The argument is not that flexibility is inherently harmful. Rather, knowledge fragmentation emerges when organizations redesign where and when work occurs without redesigning how knowledge becomes visible, accessible, interpretable, and renewable.

Can Ethics Become Isomorphic? Informational Power in GenAI Governance

(580-586)

Marco Di Giovanni

Abstract

The rapid diffusion of Generative AI (GenAI) across organizational contexts is transforming not only knowledge work and creative practices, but also the way ethical questions are defined, structured, and legitimized at the field level. Despite the growing scholarly attention devoted to AI ethics, relatively little is known about how the moral boundaries of GenAI are stabilized and diffused across dominant providers. This paper argues that the publicly available governance artifacts produced by leading GenAI providers — including usage policies, safety frameworks, transparency statements, and model documentation — can be understood as moral ordering infrastructures that shape what is permitted, what is prohibited, which harms are foregrounded, and how responsibility is framed.
Drawing on Information Ethics and the concept of the infosphere, the paper proposes that those who govern the infrastructures of generative intelligence exercise a form of informational power capable of orienting the moral grammar through which organizations interpret and manage ethical questions. Integrating this perspective with neo-institutional theory, the paper introduces the concept of ethical isomorphism: a process through which coercive, mimetic, and normative pressures, combined with the concentration of informational power, foster convergence in ethical governance scripts across dominant providers.
The paper develops this conceptual argument through a focused comparative reading of ten publicly available governance documents produced by five leading providers — OpenAI, Anthropic, Google, Microsoft, and Meta. The comparison uses a structured analytical template covering ethical principles, harm taxonomies, stakeholder framing, trade-offs, and enforcement and accountability mechanisms. This approach allows the paper to examine how abstract AI ethics principles are translated into observable governance scripts across the GenAI provider field.
The comparative analysis reveals a pattern of partial but meaningful convergence. The strongest alignment emerges in harm taxonomies, prohibited-use structures, and governance architectures, while the main divergences concern governance style, the breadth of stakeholder framing, and the degree to which moral trade-offs are explicitly acknowledged. The paper concludes that the public governance artifacts of leading providers make visible an emerging form of ethical isomorphism — not as full normative identity, but as a progressive standardization of the way ethics is operationalized and institutionalized. Such convergence may narrow the ethical pluralism available to adopting organizations, which may find themselves importing preconfigured moral assumptions alongside GenAI tools. The paper offers relevant implications for research, organizations, and AI governance policy.

MatchIS: A Digital Platform Designed to Address Critical Gaps in Industrial Symbiosis Marketplaces

(587-595)

Giulia Lippi, Luca Colomba, Paolo Garza, Enrica Vesce

Abstract

Industrial Symbiosis is widely recognised as a key strategy for fostering the ecological transition of production systems. This model allows companies to reduce their environmental impacts by exchanging unused resources, whilst maintaining their economic competitiveness and lowering their operational costs. Despite the promising outcomes, its adoption is still hindered by several barriers. Informational gaps related to the lack of data-sharing among companies, technological lock-ins, unclear legislation, and difficulties in assessing the economic viability of symbiotic exchanges are some of the obstacles that companies face when engaging in Industrial Symbiosis. Within this context, digitalisation and, in particular, Information and Communication Technologies, may help to widen symbiotic possibilities and connect companies across broader geographical boundaries. This is testified by the rise of digital platforms promoting Industrial Symbiosis, which can be classified into three main groups according to their core functionalities: good practice repositories, marketplaces, and training solutions. However, most Industrial Symbiosis platforms are little used and often tend to become static spaces, especially in the case of open and public platforms.
The aim of this paper is to present the design of the MatchIS platform, which aims to foster Industrial Symbiosis adoption and help companies address their main challenges. The design of MatchIS was developed through a literature review of IS barriers and semi-structured interviews with representatives of business associations and Chambers of Commerce, which provided both theoretical grounding and empirical insights into the limitations of existing platforms. Building on these results, MatchIS is designed as an open, public, and integrated platform that combines marketplace functionalities with a multidisciplinary support system. The platform enables firms to exchange information on production residues and valorisation technologies, while also providing access to legal, economic, environmental, and certification-related guidance. Distinctively, the platform explicitly incorporates multiple symbiotic roles — producers, utilisers, and transformers — thereby overcoming the binary perspective that characterises most existing solutions.
The paper contributes to the literature by proposing an integrated and accessible platform model that addresses multiple IS barriers simultaneously, demonstrating that advanced support services typically associated with private platforms can be delivered within an open and publicly accessible environment. However, this study is conceptual and design-oriented, focusing on how the identified barriers to IS have been translated into specific platform features. Empirical validation of the platform’s effectiveness is left for future research.

Mapping Circular Employability Skills in Agri-Food Value Chains from Large-Scale Labour-Market Data

(596-602)

Francesco Smaldone, Benedetta Esposito, Daniela Sica, Stefania Supino

Abstract

The twin transition, combining circular economy and digital transformation, is reshaping the food industry and generating new skill requirements. While previous studies have examined circular and digital technologies in agri-food systems, limited attention has been paid to the employability skills needed to support their implementation. This study addresses this gap by profiling the skills required for the twin transition in the food processing phase of the agri-food supply chain. The research adopts a big-data-driven approach based on 6,473 job advertisements collected from major online recruitment platforms. Starting from ESCO occupations, relevant food processing job profiles were selected and integrated with keyword-based searches. The textual corpus was analysed through natural language processing, BERTopic, hierarchical clustering, machine learning and SHAP analysis. Skills were classified into three semantic topics – hard, soft and transversal skills – and three cognitive clusters: industry-specific, circular and digital competences. Findings show that labour demand is dominated by hard and industry-specific skills, especially production, quality management, safety standards and packaging. Circular competences are increasingly present, whereas digital skills remain comparatively underrepresented, despite their strategic role in traceability, monitoring and efficiency. Advanced traceability, LCA-guided eco-design and industrial symbiosis emerge as high-impact but still underdeveloped competence areas. The study contributes to the literature by framing employability skills as strategic assets for enabling the twin transition. It also provides practical implications for firms, policymakers and education providers, highlighting the need to invest in targeted skill portfolios able to translate circular and digital innovation into sustainable performance.

Knowledge Dissemination Strategies for Circular Economy: Evidence from Italian Local Governments

(603-611)

Nicola Capolupo, Miriana Ferrara

Abstract

The circular economy (CE) has emerged as a central paradigm in the governance of urban sustainability, assigning local governments a crucial role in the transition towards regenerative models of production and consumption. However, the success of CE initiatives depends not only on the design of regulatory strategies and instruments, but also on the ability of public institutions to disseminate knowledge effectively to citizens and stakeholders. However, the literature has devoted greater attention to the design of CE policies rather than to the processes through which such policies are made accessible and socially embedded.
This study investigates the knowledge dissemination strategies adopted by three Italian regional capitals (Florence, Turin and Genoa) through their institutional channels. The analysis draws on the framework of Green et al. (2009), which articulates the dissemination process into five constituent dimensions: the source, the content, the medium, the user and the context. The research design is based on a multiple-case approach, involving the collection of qualitative secondary data, primarily institutional websites, project documentation and official reports, analysed through direct qualitative content analysis.
The findings reveal that, while sharing a formal commitment to the circular transition as signatories of the European Circular Cities Declaration, the three cities are developing structurally distinct dissemination strategies. Florence adopts a civic dissemination strategy towards citizens’ behavioural compliance. Turin develops a business-oriented ecosystemic strategy, in which knowledge is co-produced within a permanent urban innovation platform. Genoa presents the most complex architecture, characterized by a dual-track system that explicitly differentiates the registers intended for citizens and economic actors.
The study contributes to the literature by extending the application of Green et al’s framework to the domain of CE governance in local public administration, demonstrating how choices across the five dimensions do not operate independently but constitute coherent epistemic architectures that encode distinct governance philosophies.

Climate Change Adaptation and Mitigation through Digital Technologies in the Extra Virgin Olive Oil Supply Chain

(612-618)

Giulia Padovani, Dario Barberini, Maria Giovina Pasca, Gabriella Arcese

Abstract

The study aims to analyse the role of digital technologies across the extra virgin olive oil (EVOO) supply chain, with particular reference to their contributions to climate change adaptation and mitigation. The research is developed through a systematic literature review, following the approach proposed by Tranfield et al. (2003), to examine the existing scientific literature on the topic in a structured manner. The analysis was conducted in January 2026 using the Scopus database, applying specific inclusion criteria for document type, language, and full-text availability. The final corpus consists of 20 articles, selected through a full-text screening and evaluation process conducted by two researchers.
The results focus on three main themes: I) digitalisation and operational optimisation in olive cultivation; II) predictive models based on artificial intelligence and digital twins; III) digital technologies for climate change adaptation and mitigation. The results highlight a clear prevalence of applications aimed at climate adaptation, especially in the upstream stages of the supply chain, through the use of IoT sensors, open-source platforms, GIS systems, remote sensing, drones, and AI models for monitoring water stress, forecasting yields, managing plant health, and optimising irrigation. The findings on climate change mitigation primarily focus on improving input efficiency, reducing the carbon footprint, and valorising by-products within a circular bioeconomy framework.
The study highlights how digital technologies serve as a strategic lever to strengthen resilience, traceability, and competitiveness in the sector. At the same time, the findings underscore the need to develop more integrated approaches between adaptation and mitigation, as well as more advanced predictive and prescriptive models that systematically support decision-making processes and future climate reporting across the supply chain.

A Regenerative Framework for Socio-Ecological Resilience: The Case of Armani’s Apulia Regenerative Cotton Project

(637-642)

Marta Menegoli, Luigi Jesus Basile, Roberto Mauriello

Abstract

In recent years, increasing stress on socio-ecological systems has highlighted the limits of traditional sustainability approaches, fostering growing interest in regenerative perspectives aimed at restoring natural assets and supporting long-term resilience. These approaches may be particularly relevant for the fashion industry, given the environmental pressures associated with production activities and the complexity of global supply chains. However, regenerative approaches are still nascent in the fashion industry, and the literature offers limited insight into how such models emerge and consolidate over time. To address this gap, this study analyses the development of a regenerative initiative in the fashion industry through the lens of Institutional Theory. This perspective is particularly suitable to examine how coercive, normative, and mimetic pressures affect the different actors involved and how these pressures are interpreted and translated into practices over time, contributing to the institutionalization of new business models based on regenerative approaches.
From a methodological perspective, the study builds on a longitudinal single embedded case study of the Apulian Regenerative Cotton Project promoted by Armani, a leading luxury fashion brand. The analysis considers the key actors involved in the initiative, including the focal firm, scientific partners, and local agricultural firms across the supply chain, following the project from its inception to garment production. The findings suggest that normative pressures play a central role in orienting actors’ behaviours and in defining appropriate practices throughout the initiative. Mimetic pressures emerge as the initiative gains visibility, while coercive pressures remain relatively limited, although potentially evolving in connection with emerging regulatory frameworks. Overall, the institutionalization process appears as gradual and non-linear, shaped by the continuous interpretation and translation of multiple pressures into concrete practices through the interaction among actors. Overall, this study contributes to the literature by providing an empirical analysis of a regenerative initiative in the fashion industry through the lens of Institutional Theory, showing how regenerative business models may emerge and diffuse. From a practical perspective, the findings highlight the importance of recognising and managing multiple institutional pressures, as well as supporting coordination among heterogeneous actors involved in the development of such initiatives.

UX-Driven Digital Architecture for Knowledge Integration in Social-Ecological Systems

(643-652)

Anna Maria Crespino, Marco Lucio Sarcinella, Lorenzo Quarta

Abstract

In the polycrisis triggered by Xylella fastidiosa in the Salento area, Italy, the regeneration of social‑ecological systems depends not only on data availability, but on the ability to integrate, interpret and operationalize heterogeneous knowledge produced by multiple institutional and territorial actors. Despite the abundance of environmental data and scientific outputs, a persistent information–action gap limits the capacity of stakeholders to make coordinated and informed decisions.
This paper investigates how UX-Driven Digital Architecture can support knowledge integration and decision-making in complex socio-ecological contexts. The study adopts a qualitative case study approach combined with a research-through-design methodology, focusing on the development of the DAJS Atlas, a web-based decision-support platform for landscape regeneration.
The findings show that structuring information through actor–action–goal pathways enables users to navigate complex datasets more effectively, reduces informational fragmentation, and supports coordination among stakeholders with different objectives and knowledge frameworks. By organizing heterogeneous resources into decision-oriented flows, the platform aligns scientific, institutional, and local knowledge within shared scenarios of territorial regeneration.
The contribution of the study is twofold. Empirically, it presents a practical model for designing digital platforms that support regenerative strategies in social–ecological systems. Conceptually, it advances the understanding of User Experience as a strategic component of digital architectures, positioning UX as a systemic layer for knowledge integration, coordination, and long-term transformation in polycrisis contexts.

Life Cycle Costing in Regenerative Systems: A Knowledge Integration Approach

(653-658)

Francesca Lerario, Achille Claudio Garavelli

Abstract

Post-extractive landscapes, such as abandoned quarries, represent complex socio-ecological systems in which environmental degradation, material flows, regulatory constraints, and local development dynamics intersect. Within this context, these environments can be interpreted as manifestations of a broader polycrisis, where multiple ecological, economic, and institutional pressures interact and reinforce one another. Regenerative approaches respond to this condition by moving beyond impact reduction toward restoration-oriented value creation processes that actively contribute to ecosystem and community recovery. At the same time, the implementation of regenerative systems requires lifecycle-oriented evaluation tools capable of supporting decision-making under conditions of uncertainty, long-term dynamics, and multi-stakeholder involvement. This paper develops an analytical framework, grounded in socio-ecological and regenerative system perspectives, for the application of Life Cycle Costing (LCC) to restoration systems based on circular materials and additive manufacturing. The proposed approach structures cost evaluation across lifecycle phases, including material processing, manufacturing, installation, monitoring, maintenance, and end-of-life, and considers alternative production configurations at different scales. The study highlights how lifecycle cost structures can support knowledge integration and stakeholder coordination, particularly in configurations where monitoring, maintenance, and data-driven activities play a central role. It also emphasizes the relevance of lifecycle-based analysis in capturing the temporal dimension of value creation and in supporting the alignment of technical and organizational decisions across system phases and improving transparency in complex, multi-actor restoration settings. Expected results suggest that LCC can enable the comparison of alternative restoration configurations and provide insights into the temporal distribution of activities and responsibilities across lifecycle phases. At the same time, the study acknowledges the limitations of LCC in capturing broader ecological and social value, pointing to the need for complementary evaluation approaches. The paper contributes to the literature on regenerative socio-ecological systems and lifecycle evaluation by proposing a structured framework that integrates economic analysis with knowledge-intensive and multi-stakeholder restoration processes.

Towards an Integrated Digital Twin Maturity Assessment Framework for Coastal Cultural Heritage Resilience

(619-627)

Sharmila Gope, Claudio Pascarelli, Mattia Mangia, Giovanni Coppini, Ivan Federico, Salvatore Causio, Marco Boetti, Federica Stella Blasi

Abstract

Coastal and underwater cultural heritage is increasingly exposed to climate-driven stressors, including sea-level rise, shoreline erosion, salinization, extreme weather events, and marine environmental change. These pressures are accelerating the degradation of vulnerable archaeological sites and historic coastal assets, while also exposing the limits of conventional documentation and conservation approaches based on periodic surveys and fragmented datasets. In this context, Digital Twins (DTs) are emerging as a promising paradigm for heritage monitoring and preservation, as they can integrate digital representation, sensor-based observation, environmental information, and predictive analysis within a dynamic and potentially interoperable system. However, current applications in cultural heritage remain uneven and frequently technology-centred, with limited attention to institutional embedding, environmental coupling, and long-term knowledge continuity. To address this gap, this study proposes a multidimensional maturity assessment framework for Digital Twins in coastal and underwater cultural heritage. The research adopts a sequential mixed-method design. First, a bibliometric analysis of 346 publications published between 2010 and 2025 was conducted using Scopus and Bibliometrix in RStudio to identify thematic trends, research fragmentation, and underrepresented dimensions in the literature. The results show a rapidly growing field, but still organized around weakly connected clusters, especially between digital representation, environmental modelling, and governance- or knowledge-related themes. Second, these findings were translated into a five-dimensional maturity framework comprising Technical, Structural, Environmental, Governmental, and Social dimensions. The proposed framework redefines DT maturity as a multidimensional preservation condition rather than a purely technological stage. It enables the assessment of whether a DT can function not only as a digital model, but also as an interoperable, governable, and sustainable preservation system. The findings indicate that current initiatives are advancing more strongly in technical and structural terms, while environmental, governmental, and social dimensions remain significantly underdeveloped. The framework, therefore, offers an evidence-based tool for identifying implementation gaps, supporting strategic planning, and guiding the more balanced development of Digital Twins for long-term heritage preservation.

Artificial Intelligence Applications in Biogas Systems: A Systematic Literature Review

(628-636)

Vincenzo Fiengo, Renato Passaro, Ivana Quinto

Abstract

The increasing importance of renewable energy and circular economy approaches has made biogas an important technology in sustainable energy generation and waste management (Uddin and Wright, 2023). In this respect, the application of artificial intelligence (AI) and machine learning (ML) in improving the efficiency and management of anaerobic digesters has become increasingly popular (Ananda et al., 2025). While there has been considerable research activity in this field, a systematic understanding of its progress has not yet been achieved. This research aims to fill this gap by performing a systematic literature review (SLR) based on PRISMA criteria (Page et al., 2021). A total of 131 articles has been selected from Scopus and Web of Science databases for analysis during the period 2010–2025. This paper presents a comprehensive overview of the literature using a combination of bibliometric and thematic analysis. Findings indicate a significant increase in the number of publications in recent years, confirming the growing scientific and practical interest in the topic. Moreover, there is a dominance of technical or engineering studies with a multidisciplinary yet fragmented body of literature, highlighting the evolving nature of the research field. From the thematic analysis, three major groups of studies were identified, which include: i) strategic and sustainability-oriented applications, focusing on techno-economic and environmental assessment; ii) operational optimisation, where AI is used to improve process efficiency, prediction, and control; iii) advanced computational modelling, centred on the development of hybrid and data-driven algorithms. The findings highlight that AI and ML are primarily used to enhance predictive accuracy and operational performance, while their potential as decision-support tools at strategic and organisational levels remains underexplored. Accordingly, the study proposes a research agenda emphasising the need to integrate AI into decision-making processes, improve data-driven management practices, and develop more holistic and interdisciplinary approaches to biogas system management.

Governing AI in Academic Innovation Ecosystems: A Process Model from Italian University Regulations

(659-666)

Lorella Cannavacciuolo, Carmine Passavanti, Pierluigi Rippa, Giustina Secundo

Abstract

Artificial intelligence is rapidly transforming how innovation is organised within university-centred ecosystems. As AI-enabled practices diffuse across teaching, research, and collaborative activities, universities are required to redefine their governance arrangements in order to preserve transparency, accountability, and institutional legitimacy. These tensions are particularly visible in Academic Innovation Ecosystems (AIEs), where universities act as orchestrators coordinating interactions, infrastructures, and knowledge flows among diverse internal and external actors. While the literature increasingly acknowledges the relevance of responsible AI in higher education, we still lack a process-oriented understanding of how universities translate abstract principles into operational governance configurations embedded in ecosystem activities.
This study develops a conceptual model of AI governance in AIEs through qualitative document analysis grounded in abductive theorising. The empirical basis consists of official AI regulations and policy documents issued by Italian universities in response to emerging European regulatory frameworks. Out of 95 universities, 14 had adopted formal AI-related guidelines. These documents were analysed alongside institutional strategies and mission-related materials to capture how AI governance is articulated across education, research, and third-mission domains. The university is treated as the unit of analysis in its role as ecosystem orchestrator, with attention to how governance provisions shape the coordination of data and decision processes across missions.
Preliminary findings reveal a clear concentration of governance efforts around generative AI in teaching and research, whereas third-mission and administrative applications remain comparatively underdeveloped. In educational contexts, universities primarily focus on academic integrity and disclosure practices, whereas research-related provisions tend to emphasise authorship, intellectual property, and compliance with data protection norms. Across cases, regulatory references to European frameworks are present but uneven, and the operationalisation of monitoring mechanisms often remains generic rather than process-specific.
These patterns are theoretically significant because effective ecosystem orchestration depends on governance arrangements that are embedded in concrete practices rather than articulated solely as high-level principles. When AI governance remains reactive and unevenly specified across missions, the coordinating role of the university risks becoming fragmented. The proposed model advances research on innovation ecosystems by linking institutional conditions, infrastructural arrangements, and mission-specific governance choices, offering a foundation for more accountable and sustainable AI adoption within academic settings.

Mentoring as a Sociodigital Ecosystem: Knowledge Sharing, Digital Communities and Student Support in an Engineering Department

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Demetrio Alessandro Trunfio, Ferdinando Potenza, Anna Mezzapesa, Carl Harvey Miguel, Giovanna Rotella

Abstract

Universities increasingly face the challenge of supporting diverse engineering students through critical transitions, while leveraging mentoring and digital infrastructures as strategic levers for knowledge management and student success. Building on mentoring and communities‐of‐practice literatures, this paper conceptualises mentoring in an engineering department as a sociodigital ecosystem that integrates formal, informal and peer‐based practices with digital communities and AI‐enabled tools. Empirically, the study focuses on the “continuous mentoring” initiative developed at the Engineering Department of a medium‐sized private university in Southern Italy, where a department‐wide digital community, cohort and event‐based groups, course‐specific chats and project teams have progressively evolved into a multi‐layered knowledge infrastructure.
The research adopts a qualitative single‐case design, drawing on internal project reports, departmental documents, social media analytics, extended abstracts and digital traces from student communities. These materials were coded inductively and organised into an ecosystem representation capturing layers, roles and knowledge flows, then interpreted through the lens of mentoring ecosystems and socio‐digital knowledge infrastructures. The findings show how a persistent core community, surrounded by cohort‐specific and project‐based groups, supports first‐year students by lowering the threshold for asking questions, sharing tacit tips and accessing timely guidance from faculty mentors, peer tutors and community administrators. The same ecosystem also amplifies participation in extracurricular and research‐oriented initiatives, such as the European Researchers Night and the Ingenium entrepreneurship team, enabling students to mobilise situated know‐how on how to prepare, present and learn from comparison with peers from other institutions.
Theoretically, the paper extends mentoring research by framing mentoring not as a single programme, but as an ecosystem of interconnected practices embedded in socio‐digital infrastructures, where human relations, community practices and AI‐enabled assistants jointly sustain knowledge creation and sharing. Practically, it identifies three design principles for universities seeking to build mentoring‐based knowledge infrastructures: anchoring mentoring in a persistent core community; cultivating flexible satellite communities around cohorts, courses and projects; and augmenting, rather than replacing, human mentoring with AI tools for routine queries. The contribution speaks to scholars and practitioners interested in mentoring, knowledge management and the design of learning ecosystems in engineering and beyond.