ABSTRACT. The increasing regulatory complexity surrounding corporate sustainability reporting—particularly under the EU Corporate Sustainability Reporting Di-rective (CSRD) and the Corporate Sustainability Due Diligence Directive (CSDDD)—is transforming how companies manage environmental, social, and governance (ESG) data. This article explores the evolution of sustaina-bility reporting, the integration of digital platforms into ESG governance, and the implications of mandatory due diligence and third-party assurance. Through the case study of Pattern Group, a luxury fashion manufacturer headquartered in Italy, the paper illustrates how digital tools can support data collection, traceability, and validation processes. The company’s adoption of the INTERACTA platform demonstrates a scalable model of “compliance by design,” whereby decentralized departments collect and validate ESG data in a structured and auditable environment. The analysis emphasizes the strate-gic value of digital infrastructures in ensuring reporting accuracy, facilitating regulatory compliance, and embedding sustainability into corporate decision-making processes.

ABSTRACT. This paper examines the informational value of the Euro Area Economic Sentiment Indicator (ESI) in predicting macroeconomic conditions and supporting decision-making under uncertainty. The ESI is a composite index aggregating confidence signals from industry, services, consumers, retail, and construction, and is published monthly by the European Commission. Despite its wide use in policy commentary, its empirical relevance in a structural macroeconomic framework has received limited attention. We investigate whether the ESI contains leading information for real economic variables—specifically industrial production and consumer confidence—and how this relationship evolves under different levels of economic policy uncertainty. Using a Vector Autoregressive (VAR) model with monthly data from 1991 to 2025, we estimate impulse response functions to evaluate the dynamic effects of ESI shocks. We further implement a Threshold VAR (TVAR), using the European Economic Policy Uncertainty (EPU) Index as a regime-switching variable. Our results show that ESI shocks significantly impact both industrial production and consumer sentiment, and that the magnitude and persistence of these effects are stronger during periods of elevated uncertainty. These findings are consistent with the literature on the role of sentiment as a substitute for hard data during times of limited visibility (Barsky & Sims, 2012; Stiglitz, 2000), and suggest that the ESI can serve as a valuable informational asset in economic forecasting and policy design.

ABSTRACT. The integration of Artificial Intelligence (AI) into venture capital (VC) is reshaping how startup risk is analyzed and investment decisions are made. This paper explores the application of AI as a decision-support system within VC, focusing on how it enhances startup risk evaluation, improves decision-making efficiency, and redefines investor–founder dynamics. Traditional VC decision processes have long been influenced by biases, intuition, and reliance on personal networks—factors that can limit objectivity and accessibility. By contrast, AI introduces data-driven methods that increase consistency, scalability, and speed. The paper builds its foundation on a structured literature review of recent academic contributions covering AI adoption in VC, startup risk modeling, and the evolving relationship between human judgment and algorithmic evaluation. The review is complemented by insights gathered from the Capital Networking Virtual Conference (April 2025), where leading investors discussed the role of AI in investment workflows. The study also includes a qualitative case analysis of NoCap, the first AI-based angel investor. Through direct engagement with its founders, we examine how NoCap leverages natural language processing and machine learning to evaluate early-stage startups, identify risks, and enable faster, bias-aware funding decisions. Our findings show that AI systems like NoCap can compress deal cycles, democratize capital access, and support more consistent risk assessment. At the same time, they raise new questions around model transparency, bias mitigation, and the evolving role of human oversight. This work offers both theoretical and practical insights into how AI is redefining VC investment practices in an increasingly data-centric era.

ABSTRACT. This paper investigates the causal impact of renewable energy subsidies on the deployment of solar photovoltaic (PV) systems in Italy, using the Fourth “Conto Energia” as a case study. While Italy’s feed-in tariff scheme played a pivotal role in accelerating PV diffusion, questions remain regard-ing its effectiveness under fiscally constrained designs. Drawing on region-al data from 2008 to 2013, we implement a Difference-in-Differences (DiD) approach that exploits heterogeneity in regional uptake intensity. Although the policy was implemented nationwide, substantial differences in responsiveness enable a quasi-experimental identification strategy. Diagnostic tests reveal violations of the parallel trends assumption, which we address using an extended DiD model with region-specific linear trends. This correction reduces bias and yields a more credible estimate of the policy’s impact, which remains positive and statistically significant. Our findings con-firm the effectiveness of calibrated subsidies even under declining tariffs, especially when paired with institutional credibility and local readiness. The study also highlights the value of rigorous ex-post evaluation in improving the equity and efficiency of clean energy policies.

ABSTRACT. This study conducts a systematic literature review on the “twin transition,” defined as the simultaneous integration of digitalization and sustainability. Following PRISMA guidelines, we analyze 35 open-access articles retrieved from Scopus and Web of Science to address four research questions: (i) how the twin transition is defined, (ii) which digital technologies are most associated with it, (iii) what benefits are reported, and (iv) what challenges emerge. The findings indicate a shared view of the twin transition as the convergence of environmental and digital dimensions, enabled primarily by artificial intelligence, big data, cloud computing, and the Internet of Things. Reported benefits include efficiency gains, reduced environmental impact, and improved competitiveness, while challenges relate to technological complexity, unequal access, and indirect environmental costs. Evidence is predominantly European, underscoring the need for comparative studies in non-European contexts to broaden theoretical understanding and inform policy design. Despite growing interest in twin transition, the literature has yet to provide a comprehensive framework that encompasses technologies, benefits, and challenges. This study fills these gaps by offering a systematic synthesis to guide future research and policy.

ABSTRACT. This study examines how Artificial Intelligence (AI) is reshaping strategic leadership and governance in intermediary organizations undergoing digital transformation. It argues that AI enhances decision-making, operational effectiveness, and adaptive governance while upholding human accountability and ethical standards. Rooted in hybrid intelligence and the dynamics of automation versus augmentation, the study conceptualizes AI adoption as a co-evolutionary process between human and machine capabilities. It introduces a four-phase framework: (1) Awareness and Strategy, (2) Experimentation and Prototyping, (3) Integration and Governance, and (4) Scalability and Continuous Innovation. Early stages foster AI literacy and tools such as internal dashboards and prompt-engineering systems. Phase three surfaces strategic and ethical concerns - including bias, reskilling, and vendor dependence - while phase four anticipates institutional scaling supported by multi-stakeholder governance. The methodology involves a qualitative, exploratory case study of a single intermediary institution. Sources include internal documentation, semi-structured interviews with key personnel, and a targeted literature review on augmented leadership and ethical AI governance. This triangulated approach enables a nuanced view of the socio-technical dynamics of AI integration. The case contributes to the literature by extending theories of human-AI collaboration to intermediary contexts, which are typically underexplored. Through a structured and iterative lens, the framework supports both replicability and scalability in multi-actor environments. The study enriches academic discourse on digitally enabled leadership and responsible AI adoption by offering theoretical and practical insights on co-evolving governance mechanisms, strategic agency, and ethical resilience.

ABSTRACT. This study explores the role and effects of Artificial Intelligence (AI) applications in the field of human resources management (HRM), specifically regarding the recruitment process within companies. In the last two decades, we have observed the increasing evolution of HRM practices and policies, mostly recent trends and challenges in the context of digital recruitment, due to AI applications. Starting from a brief review of the most recent contribu-tions in the literature and the industry on the topic, that is the role and func-tion of AI applications in the digital recruitment process, this paper presents a qualitative study Based on dataset, the following three main emerging themes related to digital recruitment and AI, were identified: digital recruit-ment using social media, digital recruitment using gamification, and digital recruitment using AI-based technologies. The paper provides a qualitative study, using semi-structured interviews and archival documentation, which is designed to answer to the following main research objectives: (1) how practitioners perceive and reflect on the integration of AI technology, and (2) to identify which AI-based tools are integrated in digital recruitment process by the recruiter. The qualitative design is suitable because the integration of AI into recruitment is quite new, and a limited number of studies have been conducted so far. The preliminary results show the relevant role played by AI applications in digital recruitment, although some issues require more atten-tion, such as ethical factors, candidate and recruiter experience, as well as the need to personalize and make AI applications more inclusive and sustainable.

ABSTRACT. Research on the integration of artificial intelligence (AI) in work processes has mainly focused on the reactions of first parties: those that immediately benefit from the AI and that have the prerogative to bypass its suggestions. Second parties – those people whose lives are affected by AI but that have no say in its uses and functions – have been neglected so far in the study of AI in the workplace. This research explores the relation between second parties and AI. Drawing from a study of 20 knowledge workers faced with seven HR scenarios where AI plays the role of the HR consultant, we develop a model of second-party emotional reactions to AI decisions. Our findings show that AI opacity interacts with HR opacity to explain the reactions of second parties. We develop a matrix that identifies four categories of reaction to AI by second parties, namely perceived stability, securing control, losing grasp, and deep powerlessness. We ultimately show that, for second parties, the transparency of managerial processes is just as important as the transparency of AI.

ABSTRACT. In a context marked by rapid and pervasive technological evolution, artificial intelligence (AI) is significantly impacting human resource management (HRM) practices, reshaping their structures, functions, and operational modalities. This study aims to critically examine the evolution of scientific literature concerning the application of AI within HRM processes, with the objective of mapping the current state of the art, identifying major research streams, and clarifying the nature of the role attributed to AI, understood in terms of either the augmentation or automation of HR practices, affecting in different ways the human involvement. Through a bibliometric analysis conducted on a corpus of articles retrieved from the Scopus database, and complemented by thematic and methodological classification, the study identifies the HRM functional areas most frequently explored in relation to AI. The findings offer valuable insights for advancing academic discourse and supporting HR professionals in addressing the challenges associated with the integration of intelligent systems within organizational settings.

ABSTRACT. This study explores the influence of artificial intelligence on candidate screening in HR selection processes. Three AI chatbots – ChatGPT-4o, DeepSeek V3, and LLaMA 3.1 8b instruct - were prompted to evaluate 60 anonymised LinkedIn profiles against five real job advertisements. Each chatbot assigned scores for education, experience, and overall profile quali-ty, resulting in 900 evaluations. Results analysed using descriptive statis-tics, mixed-effect logistic regression, and fsQCA reveal that AI chatbots se-lect candidates based on different hidden logics, with low levels of agree-ment. Regression models confirm that higher education and work experi-ence increase the chance of selection, while gender shows no significant ef-fect. In the fsQCA analysis, gender appears to be a causal factor in both the general model and the specific models for females (for ChatGPT) and males (for DeepSeek and LLaMA). These findings also suggest that applying iden-tical prompts across different models can produce inconsistent shortlists, posing challenges for transparency, fairness, and organisational continuity when technological stacks change. Although they do not seem to discrimi-nate systematically, they have internal causal mechanisms that do not ex-clude women even without some of the required parameters. The study pro-vides empirical evidence on AI applications in stochastic automation with-in HRM and raises important implications for both theory and practice.

ABSTRACT. This study investigates the interplay between e-governance (EGOV), education (EDU), and institutional quality (IQ), along with control variables on ecological footprints (EFP). Leveraging the time series data of Italy from 2000 to 2022, we employ a Quantile Regression (QR) to capture heterogeneous effects across the distribution of environmental impact. However, the findings reveal a significant negative relationship between ecological footprint and e-governance, indicating that digital governance, equipped with digitalization in public services, governance, and policy implementation, can enhance environmental sustainability by promoting resource efficiency, reducing paper-based processes, and supporting green innovations. The negative relationship between EDU and EFP shows that quality education plays a vital role in fostering environmental awareness, informed decision-making, and sustainable behaviors, thereby serving as a catalyst for innovation and the transition toward greener economies. Furthermore, the negative value of institutional quality and renewable energy consumption indicates their critical roles in reducing ecological footprints. Conversely, total natural resource rent exhibits a positive association with ecological footprint, highlighting the environmental risks of resource dependence. Policy recommendations emphasize strengthening e-government services, improving educational quality, enhancing institutional governance, and accelerating the adoption of renewable energy to reduce Italy's ecological footprint and promote sustainable development effectively.

ABSTRACT. The rise of awareness about climate change and its possible consequences has increased the interest in the impact of meteorological stressors on the sustainability of complex socio-economic systems. Sustainability can have many facets, ranging from economic to political and societal issues. This work focuses on political and humanitarian crises in fragile contexts. Spe-cifically, it aims at assessing the impact of droughts on conflicts and dis-placements in developing countries. To reach our goal, we proceeded as fol-lows. We first provided a theoretical model framing the problem and pro-posing hypotheses on the mutual relationships among variables. Hypothe-ses are then tested by estimating the parameters of a multi-variate linear cor-relation model. Finally, we propose an operational implementation of the model to forecast crises by training a machine learning model. Empirical tests have provided consistent and encouraging results supporting the abil-ity of the model to predict conflicts and displacements. We studied the im-pact of droughts on both phenomena, highlighting their role as an exacerbat-ing factor rather than a triggering one. The value of our work lies in the con-ceptual framework we provided, which is flexible and reusable to account for other stressors and impacts. Moreover, the system that we developed could be employed to perform crisis management in a variety of real-world applications.

ABSTRACT. Digital platform dominance is reshaping competition policy. We use an agent-based simulation to examine how three families of regulatory levers affect generativity, a proxy for innovation: (1) switching costs for customers and complementors, (2) customer and complementor inertia, and (3) caps on customer share, complementor share, and platform revenue share. We evaluate outcomes with four metrics: dominant-platform revenue share, average service complexity, requirement-fulfilment rate, and market concentration (Herfindahl-Hirschman Index). Switching costs and complementor inertia move these metrics little, and lowering customer inertia can backfire. By contrast, calibrated share caps deliver the most consistent gains. Two settings perform well across all four metrics: limiting a platform to 80% customer share or 70% revenue share, with no cap on complementor share; overly tight limits dampen innovation and competition. Regulators whose goals align with these outcomes should prioritize market- or revenue-share ceilings and deprioritize interventions in switching costs or inertia.

ABSTRACT. The current paper formulates an interesting framework to scrutinize the impact of geopolitical risk, climate policy uncertainty, environmental policy stringency, and financial institution's efficiency on renewable energy investment in the USA. For empirical analysis, this study utilizes modern econometric approaches such as the recently developed novel dynamic simulated ARDL and the frequency domain causality approach, harnessing the annual time series data spanning from 1990 to 2022. The obtained results explain that geopolitical risk negatively affects renewable energy investment, suggesting that higher geopolitical risk hinders renewable energy investment. Contrary to this, climate policy uncertainty, environmental policy, financial integration, and financial institution's efficiency have a significant positive impact on renewable energy investment. In addition, the frequency domain causality test provides evidence of long- medium- and short-term causal connections between variables. The outcomes of the study are further supported by the robustness analysis. Based on the outcomes, the USA should continue to implement strict environmental policies and strengthen financial integration and financial institution efficiency that can further stimulate renewable energy investment.

ABSTRACT. Public organisations are going smart, developing the potential of digital technologies and information and communication to support a collaborative approach to work processes, enabling the smart work practices as an innovation-led process that supports collaborative views and frameworks for engendering better quality of life and work. Technology is shaping the forms of working beyond time and spaces, opening new opportunities for rethinking work organisation and drive into the future public organisations that are embracing a smart view that facilitates innovation as a process that supports smart work initiatives that contribute to social and organisational changes, and innovation. Smart work enables smart public administration as public value driven organisation. Smart working helps shape the work relationships in a way that enhances collaborative work spaces opening to organisational innovation. Smart working public organisations are adopting the smart working mode and mind set as a way to support collaborative work relationships enabled by digital technologies, enhancing the human and social side of working collaboratively, and strengthening the networking. Smart working introduction, experimentation and developments provide an opportunity that concerns public organisations that aim at driving digital modernisation and transformation, empowering people at work who behave in autonomy and flexibility in order to improve job satisfaction and quality of life within communities.

ABSTRACT. The ongoing redefinition of teamwork has given rise to Hybrid Virtual Teams (HVTs), which blend in-office and remote work and challenge established notions of e-leadership. As HVTs possess distinct characteristics to virtual/traditional teams, we adopted a three-phase research design to examine leadership in this context. In Phase 1, survey data from 158 professionals revealed three types of HVTs: fixed, flexible, and total flexible. In Phase 2, we interviewed 20 HVT leaders to understand how leadership is exercised in the different HVT types, identifying a unique “adaptive” leadership style and four key responsibilities for HVT leaders: (a) fostering relational connections through purposeful face-to-face interactions; (b) building trust via intra-team and organizational visibility; (c) tailoring hybrid work practices to individual and team needs; and (d) leveraging digital technologies to sustain spontaneity and authenticity. Phase 3, currently underway, adopts a case study approach with a global financial organization to investigate Cultural Intelligence (CQ), a competence recognized in other team types, as a key factor in adapting leadership practices. This research aims to advance understanding of global collaboration by contributing to the literatures on hybrid work, virtual teams, leadership, and specifically e-leadership in HVTs.

ABSTRACT. This study examines the growing role of innovation brokers in promoting the transition to circular business models (CBMs) within the Italian agri-food sector. Building on prior research that focused on organisational networks and circularity, our empirical exploration was initially designed to examine CBMs through semi-structured interviews. However, during fieldwork, an unexpected theme emerged: the central role of a consultancy agency functioning as an innovation broker, actively enabling early-stage collaboration among various players in the supply chain. This evidence reframed our analytical lens, highlighting the strategic facilitation performed by innovation brokers. These players exemplify the Tertius Iungens approach, actively bridging structural holes, cultivating trust among heterogeneous stakeholders, and orchestrating collaboration across fragmented value chains. In this paper, we present preliminary results through Gephi-based visualisations of the one-to-one relationships established by the innovation broker (consultancy agency) with individual organisations (players in the agri-food supply chain). These initial mappings provide the basis for developing a comprehensive network representation that aims to identify emerging sub-projects and collaborative clusters within the major initiative. By shedding light on the relational mechanisms underpinning early circular transitions, this study contributes to the understanding of how innovation brokers enable systemic change across organisational and inter-organisational levels (potentially catalysing the twin transition in complex ecosystems). This work aims to offer actionable implications for practitioners and policymakers seeking to foster resilient, inclusive, and circular agri-food ecosystems.

ABSTRACT. Despite increasing global participation of women in STEM, substantial gender disparities persist in both employment and wage outcomes, particularly in fields linked to emerging technologies like Artificial Intelligence (AI). While existing research has explored the wage impacts of industrial automation, fewer studies have examined how AI-related occupational structures affect gender-based wage inequality. This study contributes to the literature by analyzing the cross-national relationship between AI workforce development and gender wage gaps. Using fuzzy-set Qualitative Comparative Analysis (fs-QCA), we analyze data from 30 OECD countries from 2021 to 2023. We employ a longitudinal dataset incorporating temporal logic to trace the onset and evolution of AI implementation and its labor market effects. Our findings reveal two consistent and contrasting causal configurations. Countries with low AI talent concentration, low female AI participation, and low cultural masculinity repeatedly show lower gender wage gaps, even when women’s presence in AI is limited. Conversely, high AI talent concentration, low female representation in AI, and high cultural masculinity are jointly associated with persistent wage inequality. These configurations remained stable across the observed years, suggesting that the diffusion of AI-related employment reinforces existing structural and cultural dynamics. The study highlights how gender wage disparities are not merely a consequence of technology adoption but also its intersection with occupational inclusion and societal norms. These results underscore the need for policy interventions integrating gender inclusivity into AI workforce development to mitigate inequality and ensure more equitable labor market outcomes in the digital age.

ABSTRACT. Prior literature emphasizes the role of digital technologies and Information Systems (IS), mainly in supporting financial reporting. Technologies such as cloud computing, Artificial Intelligence (AI), blockchain and data analytics enhance the accuracy, efficiency and integration of accounting processes. Accounting Information Systems (AIS) are central to managing data flows and enabling seamless communication across organizational functions. In recent years, the importance of Non-Financial Disclosure (NFD) has increased significantly, especially following the introduction of Directive 2014/95/EU and the more recent Corporate Sustainability Reporting Directive (2022/2464/EU), which expands mandatory sustainability reporting standards in the EU. These developments, reinforced by the 2025 Omnibus Decree, highlight growing demands for transparency and reliability in ESG reporting. Given that limited research has explored the role of IS in supporting NFD, we investigate how IS is helping organizations adapt to evolving regulatory and stakeholder demands on the sustainability issues, building on institutional change theory. In fact, in response to institutional pressures, companies may invest in IS to better manage data related to sustainability and to disclose sustainability-related information. Through an online survey conducted among Italian managers in 2025, this study explores: a) the role of IS in producing, disclosing and assuring sustainability information; b) if and how companies’ sustainability focus has influenced IS investments; c) to which extent stakeholder demands drive such investments. Special attention is given to Small and Medium Enterprises (SMEs), which may face distinct challenges in adopting digital technologies and complying with new reporting standards. Findings offer insights for both managers and policymakers.

ABSTRACT. see pdf attached

ABSTRACT. This research investigates corporate decarbonization reporting, a critical facet of contemporary sustainability driven by European directives targeting Net Zero by 2050. We analyze 2022 sustainability reports from energy sector companies listed on the Milan Stock Exchange, a segment identified by the International Energy Agency (IEA) in 2022 as a paramount global polluter. Our study employs a novel mixed-methods approach, integrating preliminary manual content analysis with the innovative application of NotebookLM, a Large Language Model (LLM). The primary objective is to demonstrate the profound analytical value derived from leveraging such an LLM for nuanced social research. We explore how custom prompts, coupled with NotebookLM's capabilities for rapid pattern identification and intricate data extraction, inherently surpass purely manual limitations. This dynamic interplay between human interpretation and sophisticated tool-driven insights enables unparalleled analytical depth in comprehending corporate climate action. Our findings contribute significantly to both the literature on corporate social responsibility and methodological advancements in computational social science, offering a robust evaluation of corporate climate commitments within stringent regulatory environments.

ABSTRACT. In a multitude of publicly funded universalistic national healthcare systems, New Public Management-driven reforms have resulted in a progressive increase in the involvement of doctors heading operational units in the management of public healthcare organizations (PHOs). These “doctor- managers”, who are required to address both “new” budgetary responsibilities and their traditional clinical duties, are exposed to goal ambiguity risks, which may hinder their effective involvement in the budgeting process. To address this issue, the improvement of the budgeting system, in terms of enhanced characteristics of budgetary information systems, as supported by the digitalization processes, may play a critical function. Adopting the lenses of Person-Environment Fit theory, this paper aimed at analysing the role of end- user’s perceptions of budgetary information systems characteristics, as experienced by doctor-managers, in arising their predisposition to be involved in the budgeting process, through the indirect effect of perceptions of informational justice experienced in the hierarchical relation with top management and controllers. Survey data were collected from 131 doctor-managers working in Italian PHOs. Hypotheses were tested through regression analysis. Findings showed that doctor-managers-end-users’ perceptions of budgetary information systems characteristics positively influence both their perceptions of informational justice and their involvement in the budgeting process. Furthermore, the mediating role of perceptions of informational justice is highlighted. The study attempted to contribute to the psychology-based budgeting research by enriching the empirical evidence on the psychological antecedents of doctor-managers’ involvement in the budgeting process, a subject which has received scant attention from researchers to date.

ABSTRACT. This paper explores the strategic role of intermediary bodies within the regional innovation ecosystem of Abruzzo, using Innovation Systems Theory, particularly the Regional Innovation Systems (RIS) framework, as its main interpretative lens. Intermediaries are conceptualized as boundary-spanning actors that connect fragmented institutional domains and facilitate multi-stakeholder coordination. The study employs a qualitative methodology based on semi-structured interviews with key regional stakeholders. A purposive sampling strategy was adopted, targeting actors with institutional embeddedness. A SWOT analysis reveals strengths such as territorial embeddedness, multi-level connectivity, and alignment with EU policy frameworks, alongside weaknesses like limited absorptive capacity among SMEs, fragmented governance, and reliance on public funding. External threats, including economic volatility and demographic decline, further constrain the operational capacity of intermediaries. Despite these challenges, findings show that intermediary bodies play a crucial role in aligning top-down policy frameworks (e.g., RIS3 2021–2027, Abruzzo Prossimo) with bottom-up innovation dynamics. Their strategic positioning is key to leveraging opportunities from the European Green Deal, Horizon Europe, and Italy’s National Recovery and Resilience Plan. While not claiming statistical representativeness, the study offers contextually grounded insights. Its methodological rigor lies in transparent sampling, coherent coding, and source triangulation. However, excluding certain stakeholder categories may limit the ability to capture the full pluralism of the innovation system. Future research could address this gap and extend the design to other Italian regions, enhancing theoretical generalizability and contributing to a comparative taxonomy of regional innovation ecosystems.

ABSTRACT. This study examines the transformative impact of fibre optic networks on enhancing information systems within community district firms, thereby directly contributing to sustainable development. We propose fibre optics as a novel and highly efficient tool for industrial communication, capable of significantly improving inter-firm data exchange and collaborative processes. A compelling case study from an Italian industrial district illustrates the practical application and significant benefits of integrating fibre optics. Our findings demonstrate how this advanced connectivity not only optimises individual firm information systems but also fosters a more robust and sustainable information ecosystem, driving long-term industrial efficiency and resilience. This research highlights the crucial role of modern communication infrastructure in promoting economic and environmental sustainability in industrial settings.

ABSTRACT. This paper examines the economic risks and distributional consequences of implementing Artificial Intelligence (AI) systems in public tax collection, focusing on the Italian case. While AI-powered solutions offer significant potential for improving administrative efficiency and revenue recovery, they introduce substantial challenges regarding algorithmic fairness, transparency, and social equity. The study first evaluates the adequacy of existing regulatory frameworks, including the EU AI Act and Italian legislative reforms, finding significant gaps in addressing algorithmic discrimination in fiscal contexts. Through a theoretical model of taxpayer–tax authority negotiations, the paper demonstrates how AI systems relying on biased training data or latent discriminatory variables can generate market failures in the form of adverse selection and moral hazard. The model reveals that algorithmic discrimination creates perverse incentives: Honest taxpayers from disadvantaged groups may face higher rejection rates for settlement offers due to artificially deflated enforcement probability estimates, while strategic actors exploit these biases to secure favourable terms through manipulative strategies. This leads to Pareto-suboptimal equilibria that reduce fiscal efficiency and exacerbate economic inequality. Findings show that even taxpayers with identical observable characteristics may face unequal treatment when latent bias indicators penalise certain demographic groups, violating principles of vertical equity and administrative impartiality. This research contributes to the literature on AI governance in public finance by integrating economic theory with legal analysis to propose policy solutions that balance technological efficiency with distributional justice. The study concludes with recommendations for developing transparent, auditable, and contestable algorithmic models in tax enforcement, supported by regulatory frameworks codifying fairness principles.

ABSTRACT. This paper presents the MEJA Decision Support System (MEJA-DSS), a machine learning–based framework for the early detection of financial and economic crises. The system integrates macroeconomic and behavioural indicators to capture complex, non-linear dynamics often missed by traditional econometric models. A comparative evaluation of Random Forest and Gradient Boosting is conducted, with Random Forest consistently outperforming Gradient Boosting in both current-state classification (accuracy 0.87) and three-month-ahead forecasting (0.80). These results are explained by Random Forest’s robustness to noisy, moderately dimensional, and imbalanced datasets. On this basis, Random Forest was adopted as the operational backbone of the system, while Gradient Boosting serves as a benchmark for robustness checks. The MEJA-DSS delivers actionable outputs—including probabilistic crisis scores, composite risk indices, and multi-tier risk alerts—through both APIs for technical users and dashboards for non-technical stakeholders, supported by automated real-time data ingestion and daily alert mechanisms. By embedding behavioural finance insights such as investor sentiment and the Adaptive Markets Hypothesis, the system provides an adaptive perspective on systemic risk. The MEJA-DSS thus contributes methodologically and operationally to next-generation early warning systems, while future developments will focus on deep learning architectures, natural language processing, and enhanced institutional customisation.

ABSTRACT. This paper presents a novel AI-based information system for analysing how drivers interact strategically at roundabouts, conceptualised as natural ultimatum games. While game theoretic interpretations of roundabout behaviour have been proposed, the literature still lacks the methodological and architectural foundations required to enable large-scale and systematic experimental studies. The proposed system integrates deep learning based vehicle detection (YOLOv8), multi-object tracking (DeepSORT), and rule-based behavioural modelling within a modular and scalable framework. It automatically extracts vehicle trajectories, determines right-of-way status, and interprets interactions as strategic exchanges by processing standard urban traffic camera footage. Although the system has not yet been deployed or validated on experimental data, it establishes a robust foundation for future real-world and simulation-based studies. By operationalising game theoretic concepts in an automated and non-intrusive pipeline, this work advances research in transportation science, behavioural economics, and artificial intelligence, and opens the way to large-scale analyses of strategic driving behaviour in complex traffic environments.

ABSTRACT. The adoption of Artificial Intelligence (AI) in workplace is rapidly transforming the labour market: skills demand is reshaping job roles across industries, new opportunities arise for workers and companies, new strategies for skill acquisition take place in the AI era. This study investigates skill complementarity in the AI labour market, with a specific focus on Italy. Drawing on the theoretical framework of Technologies-Skills Complementarity, it explores how AI adoption reshapes skill demand and how skill combinations can influence workforce training and HR development in terms of AI-driven upskilling and reskilling strategies. Using a large dataset of over 7 million job postings in Italy (2022–2023), sourced from Lightcast (www.lightcast.io - already known before as Burning Glass), Natural Language Processing and Network Theory are applied to quantify the interplay between AI-related technical competencies and other skills. Four different skills-sets are identified: (1) fundamental analytical and practical skills, (2) computational linguistics, (3) Industry 4.0 skills for engineering and maintenance, (4) skills related to the area of marketing and sales. Next, skill sets differ significantly by educational level following the European Qualifications Framework (EQF). AI adoption is thus not limited to purely technical domains but is embedded in broader organisational functions, highlighting the need for cross-functional, interdisciplinary training strategies at the different levels of expertise. The findings provide a structural view of skill interdependence, offering insights for education providers, policymakers, and HR professionals. The study contributes to labour market intelligence by offering a novel, network-based indicator of skill value through complementarity.

ABSTRACT. While younger generations were introduced to computers, smartphones, and the internet from a young age, older workers may not have had the same exposure to these tools. As a result, they might not be as comfortable or confident in using newer technologies. The speed at which technology evolves can be overwhelming. Older workers who might have learned a specific software or tool in the past may struggle to keep up with newer versions or entirely different tools that have become industry standards. This article presents a systematic literature review (SLR) of 154 peer-reviewed studies to map the academic discourse on digital skills development and digi-tal fluency among older workers. The study seeks to identify key research themes, theoretical frameworks, and existing gaps in literature, thereby providing a comprehensive overview of how senior employees are navigating the demands of digital transformation in the workplace. The results that have emerged from the literature suggest four important strands, both for academics and practitioners: “Digital skills as a success factor in aging”,”Psychosocial Barriers and Stereotypes”, “Training, Development and Transfer of Skills”, “Adaptation Strategies and Digital Fluency”. By synthesizing current knowledge, this study contributes to the understanding of how older workers can be supported in developing digital competencies and highlights directions for future research and practical interventions.

ABSTRACT. Artificial Intelligence (AI) is increasingly integrated into the educational landscape. However, many gaps remain in our understanding of the role of AI in education and how it interacts with educational approaches, includ-ing the gamification of the educational process. To address these gaps, this study offers insights into the benefits that AI brings to education by foster-ing gamification, as well as the advantages it provides for stakeholders of education. The research is based on a case study approach and expands our knowledge of how widely the practices of combining AI and gamification are applied in education, as well as allowing for a deeper exploration of the AI phenomenon in educational settings.

ABSTRACT. This systematic literature review explores how task crafting - defined as the proactive modification of work tasks by employees - is evolving in the context of artificial intelligence (AI) and generative AI technologies. Drawing upon 102 peer-reviewed articles published between 2010 and 2024, the review synthesizes emerging evidence across disciplines. The findings reveal that AI technologies not only augment task execution but increasingly participate in task definition, positioning AI as a co-crafting agent. Four dominant themes emerge: augmentation of task boundaries, AI-human co-construction of work, task frag- mentation risks, and the organizational conditions that enable or constrain AI- enabled crafting. The review proposes a theoretical reframing of task crafting that incorporates distributed agency and technological mediation. Implications are discussed for job design theory, organizational policy, and the ethical deployment of AI in the workplace. The review contributes to understanding how intelligent systems reconfigure the meaning, structure, and ownership of human work.

ABSTRACT. This study explores the early-stage development of an information technology (IT) artefact designed to enhance the social impact of Renewable Energy Communities(RECs), with a particular focus on addressing energy poverty. Within the scope of an applied research initiative targeting three REC pilot sites in Southern Italy, the project is structured around a Design Science Research (DSR) approach, specifically addressing the initial two phases of the research protocol: problem identification and requirement definition. The initiative aims to support the creation of RECs as socially-oriented entities capable of delivering measurable benefits to the targeted communities. To this end, a digital dashboard system is anticipated, intended to monitor and foster positive social outcomes while guiding REC managers in aligning operational practices with broader community needs. The conceptual foundation integrates two key domains: recent scholarly contributions on social impact assessment, and contemporary research on the intersection between RECs and energy poverty. Through a structured collaboration involving both institutional stakeholders and an IT development partner, the project bridges theoretical insight and technical feasibility. The preliminary result contributes to the advancement of a conceptual framework for artefact design that reflects both empirical specificity and methodological rigour. Ultimately, this research contributes to strengthening the social mission of RECs and offers a replicable model for data-informed community energy governance.

ABSTRACT. This paper investigates the roles played by digital leaders in adopting emerging technologies through a pilot test applying the Q-methodology. In this mixed-method approach, we conducted interviews with seven experts to develop the Q-sample. These experts, together with seven additional practitioners, formed the Q-sort. The analysis reveals five distinct niches concerning digital leaders’ roles across pre-adoption, adoption and post-adoption phases. Findings show how participants attribute specific digital leadership roles to different phases of the adoption process, highlighting how role relevance shifts across phases. By mapping these role configura-tions across the adoption process, the study advances theoretical knowledge of digital leadership while providing guidance for incentivizing organizations to foster digital leadership throughout the adoption journey, with particular attention to how roles are played in each phase.

ABSTRACT. This study examines how the use of ICT in work dynamics and group relationships triggers organisational change processes (Avgerou, 2000; Hanelt et al., 2021; Volkoff et al., 2007). In different work contexts, face-to-face (traditional), online (Microsoft Teams) and virtual reality (VR - Metaverse), working methods and group relationship systems change. The change in teamwork within organisations implies a change in team management dynamics in organisations. Using a mixed qualitative and quantitative methodology, the study observes the learning, integration and collaboration process among 200 employees from three Italian organisations. In particular, the study compares the dynamics, organisation and management, as well as the approach of team members. By examining these aspects, although each requires specific skills, the results reveal different requirements and objectives in the three environments, highlighting the importance of integrating these modes to improve overall performance. The focus is on the emergence of a collaborative climate, increased creativity and the development of positive attitudes, knowledge and self-efficacy (Kaganer et al., 2023). These findings highlight the need to plan new human resource management strategies in a process of organisational change towards digital transformation in the workplace (Karimi & Walter, 2015). The potential opportunities and risks arising from the growing integration of new technologies, such as the metaverse, have a broader social impact, including effects on relational dynamics in work processes (Sabherwal & Grover, 2024). The study clarifies how teamwork in a mix of different contexts promotes integration, including different dynamics of collaboration and interaction within teams.

ABSTRACT. IS plays an important role in addressing the challenges associated with achieving the SDGs, as digital technology is a key enabler of human development around the world. Since the Sustainable Development Goals (SDGs) are interdependent, six broad SDG Transformations have been defined to focus sustainable devel-opment efforts while reducing the risk of unintended trade-offs. Although achiev-ing the SDG Transformations requires solving complex sociotechnical challeng-es, which is a strength of the IS field, most of the current research on SDG Transformations is taking place in different disciplines. To introduce the topic to IS and provide a foundation for future research, we review the currently unsys-tematic and insufficiently integrated literature on SDG Transformations. In par-ticular, we focus on the fourth SDG transformation, which poses the greatest risk of negative trade-offs. Thereby, we introduce the SDG Transformations to the IS, integrate existing knowledge, and outline promising areas for future research.

ABSTRACT. As the academic world strives to reduce environmental and social impact of international conferences, the digital turn has been for a while perceived as the to-go solution, but is showing recurring fragilities. However, the assumption that ‘virtual equals sustainable’ should be critically examined. This paper explores how digital sustainability can be implemented in the design of academic conferences, questioning both the environmental effectiveness and the social inclusiveness of current practices. Through a literature review, it investigates the tension between stated sustainability goals and the persistent reliance on high-carbon event formats, highlighting contradictions in offsetting strategies and the lack of standardized impact measurement tools. It also addresses inequalities in access to academic events, particularly in terms of country of origin, institutional prestige, and site selection processes, emphasizing how digital transitions risk reproducing exclusions if not strategically governed. The empirical section presents the case study of the 2024 EGOS Colloquium, illustrating a quantitative survey of participants’ travel and accommodation choices, showing behavioural patterns consistent with criticisms from the literature review, confirming the need to explore multi-hub and hybrid models as a promising – though underutilized – alternatives for integrating digital, environmental and social sustainability into event design. The paper calls for a new paradigm: from distrust, hypocrisy and surface-level technological fixes to systemic change. It encourages a more critical engagement with how sustainability is defined, measured, and enacted in digital transitions, suggesting that conference design should be rethought through an (E)ESG lens balancing face-to-face interaction with impact reduction.

ABSTRACT. This interdisciplinary study investigates the role of citizen science (CS) as a methodological and participatory approach to addressing today’s grand challenges, particularly those related to social and organizational sustainability. Leveraging a systematic review of 100 projects extracted from 3,219 EU-funded projects from the CORDIS database, the research explores how citizen science initiatives align with the UN Sustainable Development Goals (SDGs), particularly those focusing on health, urban resilience, clean energy, and innovation. In contrast, social justice goals were underrepresented, and while SDG17 (on cooperation) was frequently cited, it rarely translated into lasting governance or open-data frameworks. By using natural language processing, relevance scoring, SDG mapping, and digital technology usage analysis, the study identifies dominant patterns, gaps, and systemic limitations across the projects. Results reveal that while SDGs such as Climate Action, Sustainable Cities, and Good Health are prominently addressed, key social goals like Gender Equality and No Poverty remain underrepresented. The findings also highlight the critical role of digital technologies in enabling citizen-driven data collection, but underscore the fragility of project sustainability post-funding and the lack of standardized KPIs, hindering cross-project coordination and synergic alignment to the SDGs.

ABSTRACT. Port automation is reshaping maritime logistics, improving operational efficiency and reducing costs in the main ports of Northern Europe, supporting the transition to intelligent and competitive models. However, this change has a profound effect on the social dynamics of port work, reducing traditional jobs and increasing the demand for specialised skills. This study analyses four categories of automation: decision automation, process automation, digitalisation and robotics, with particular attention to their impacts on work, skills required and organization in European ports. Through a qualitative approach, based on scientific literature, secondary sources and case studies, each cluster was examined in terms of its social and transformative effects. The results show that the impact of automation is not uniform, but varies according to technology, territorial context and governance strategies adopted. Alongside the benefits in terms of efficiency and safety, major critical issues emerge, such as the polarisation of the labour market, digital exclusion and the professional identity crisis. Among the clusters, digitisation is the most socially impactful, requiring a high level of digital skills and generating the greatest risks of inequality and marginalisation. The study highlights the need for active policies for training, retraining and inclusion, as well as strong involvement of social partners. Only through participatory governance will it be possible to ensure a fair and sustainable digital transition, in line with the principles promoted by the European Union.

ABSTRACT. Various methods have been proposed to identify promising AI use cases. However, it is not known if these methods work under real business conditions (e.g., limited time and personnel for method execution). We address this issue by taking an alternative approach to method development: Action Design Research at Germany’s leading e-commerce company “OTTO”. We co-designed our method with practitioners through iterative cycles of building, organizational intervention, and evaluation. Our method synergizes Design Thinking and Fit-Viability Theory to identify AI use cases that are relevant and realistic for the business. We make two contributions. First, we provide a method that accounts for resource constraints faced in business practice. Practitioners can use the method “as is” or adapt it for their purposes. Second, we abstract our learnings into four principles for identifying promising AI use cases under real business conditions: Human-Centered Technorealism, Co-Creative Pragmatism, Adaptive Standardization, and Innovative Continuity.

ABSTRACT. Despite widespread digitalization in healthcare, resident scheduling remains a manual process in many hospitals, often relying on spreadsheet-based sys-tems such as Excel. This qualitative study investigates the digital transfor-mation of resident scheduling across three hospitals in Sweden. Guided by socio-technical systems (STS) theory, the paper explores how digital sched-uling can enhance efficiency, workflow coordination, and staff satisfaction while also examining the barriers to successful implementation. Data was collected through semi-structured interviews and guided observations involv-ing administrators and medical residents and analyzed with thematic analy-sis. Findings suggest a strong stakeholder interest in digital solutions that of-fer automation, transparency, and adaptability and highlight the need for us-er-centered design, staff training, and effective change management. In con-clusion, the paper identifies and discusses socio-technical challenges and provides practical insights for healthcare institutions in the transition from manual to digital scheduling, highlighting the importance of aligning tech-nical innovation with human and organizational factors.

ABSTRACT. The deployment of AI in high-risk domains demands nuanced governance strategies that move beyond static, supervisory oversight. Current regulatory frameworks, such as the EU AI Act, often conceptualize human oversight through rigid paradigms like Human-in-the-Loop (HITL), Human-on-the-Loop (HOTL), or Human-in-Command (HIC), reducing complex socio-technical interactions to predefined interventions. This paper introduces the concept of authomation—a novel framework rooted in Japanese manufacturing philosophy (jidōka) and hybrid intelligence theory—which integrates human discretion dynamically within automated processes. Unlike hierarchical models, authomation emphasizes relational, situated, and co-constructed human-AI interactions.

We conduct a normative-analytical review of policy documents, technical standards, and interdisciplinary literature in HCI, AI ethics, and decision theory, supplemented by real-world case insights. The authomation model foregrounds continuous calibration, symbolic mediation, and mutual adaptation, proposing oversight as an evolving relationship rather than a fixed requirement.

By embedding agency across both human and machine actors, authomation reframes responsibility as a shared, emergent property of human-AI assemblages. It aligns with techno-vigilance and controllable AI paradigms, addressing critical risks such as model drift, opacity, and automation bias. Moreover, it redefines AI not as an alien form of agency but as an extension of collective human “documanity”—the accumulated intelligence of documents, data, and discourse.

Authomation advocates a shift toward adaptive governance protocols that foster ethical, transparent, and resilient AI ecosystems. This approach empowers Europe to lead a fairer and more human-centered path in AI innovation, grounded in co-evolution and shared responsibility.

ABSTRACT. This paper introduces an agent-based model developed as a computational laboratory to simulate and analyze the diffusion of fake news under realistic and heterogeneous conditions. The model integrates several conceptual building blocks: a SEIR-inspired contagion framework adapted for informational diffusion; a bounded-confidence opinion dynamics module, capturing belief evolution influenced by homophily, stubbornness, and exposure; and a role-based influence system grounded in the tipping point theory, enabling differentiated behaviors for Connectors, Mavens, and Salesmen. Network structures can be synthetically generated (e.g., scale-free, small-world) or empirically imported from real social graphs, offering both generalizability and ecological validity. The model supports the simulation of both endogenous interactions and exogenous interventions—such as algorithmic visibility reduction and targeted immunization based on centrality measures—which reflect the logic of real world platform governance. A key strength of the model lies in its ability to reproduce emergent phenomena, including cascades, echo chamber stabilization, polarization, and tipping point dynamics. These features position the model as a generative theoretical tool as well as a practical environment for testing policy interventions. The system is currently undergoing structured internal validation, where each building block is tested in isolation before being integrated. Upon completion, the model will be calibrated using empirical data from observed disinformation campaigns. The proposed model aims to bridging the gap between theoretical modeling and digital platform governance, sup-porting the design of context-aware moderation strategies and resilient in-formation systems. Additionally, it can offer a data-rich environment to support the training of AI-driven, adaptive responses to misinformation in complex online ecosystems.

ABSTRACT. Technological development and new economy concepts have created complex, and often, challenging work environments for employees. The new career metaphors are emerging along with increasing pressure to be more resilient and tuned to the expectations imposed by the labor market and employers. The concept of employee resilience and the perception of the career as being sustainable have become central issues in today’s workplaces.. The goal of the research was to explore the relationship between the level of employee resilience and the career perception among employees from Romania, Hungary, Serbia, Greece and Lithuania. The sample consisted of 202 respondents who completed a survey using three Likert-type scales. Results show that occupation might have a central role in defining workplace behavior, incorporating both behavioral traits and the soft skills influenced by how individuals perceive their careers.. The findings indicate that the relationship between resilience as the behavioral concept at work, and the more abstract perception of the core sustainability values, is rather complex and requires further investigation.

ABSTRACT. Public sector organizations around the world adopt digital technologies aiming to increase their efficiency and enhance the quality of their services. Through a review and simplified qualitative comparative analysis method, this paper exam-ines academic articles focused on investigating cases of digital transformation initiatives and new ways of working in public sector organizations across dif-ferent countries. The purpose of this study is to identify and discuss successful practices of public sector digital change efforts. To achieve this objective, an enriched Leavitt’s model encompassing five interconnected organizational ele-ments: task, structure, technology, people, and environment was followed. The findings illustrate that technology-driven change in the public sector requires careful planning, investments, and continuous support. Good practices include establishing transparent communication with stakeholders and involving them in the design and implementation stages of digital projects. Investing in developing technical talents internally in the long term can help public sector institutions reduce costs and enhance their ability to innovate. The study makes a contribu-tion to theory by advancing the understanding of the complexities of digital change and new work-related practices in the public sector. By examining a range of case studies, this research also provides valuable insights for public sector practitioners and policymakers and offers strategies to strengthen both in-ternal processes and public service provision.

ABSTRACT. The guest experience in the hotel sector is increasingly influenced by online opinions, which represent a valuable source for analyzing customer satis-faction and expectations. This study presents an applied test on the cases of Rome and Milan, two of Italy‘s main tourist hubs, through the systematic anal-ysis of reviews published on Booking.com. Sentiment analysis methods based on DistilBERT and topic modeling using Latent Dirichlet Allocation (LDA) were employed to objectively identify the main sources of customer satisfaction and dissatisfaction. The dataset, consisting of 52.033 reviews, underwent a rigorous pre-processing phase, including translation, normalization, and lemmatization of the texts. Sen-timent analysis revealed that 60,4% of cases were positive, 37,7% negative, and 1,9% neutral. The words most commonly associated with positive judgments were ―room‖, ―breakfast‖, ―staff‖, ―location‖, ―clean‖, ―comfortable‖, ―friend-ly‖, and ―helpful‖—terms reflecting appreciation for the quality of rooms, breakfast, the professionalism of the staff, and the location of the hotels. Topic modeling on negative reviews, on the other hand, highlighted recurring critical issues related to ―room‖, ―small‖, ―bathroom‖, ―shower‖, ―noise‖, ―price‖, and ―staff‖. These words indicate dissatisfaction regarding the size and comfort of rooms, maintenance problems, breakfast quality, noise, and value for money. The results provide empirical evidence of both the strengths and critical points perceived by guests, offering practical tools for improving the hotel offering in highly competitive urban destinations

ABSTRACT. The increasing frequency and severity of natural and human-induced disasters have highlighted the need to integrate quality, health, safety and environmental (QHSE), corporate social responsibility (CSR) and environ- mental, social and governance (ESG) frameworks into the risk management strategies of organisations. Although extensive research exists on these frameworks, most studies focus on large corporations, creating a significant gap in our understanding of their application within small and medium-sized enterprises (SMEs). This work examines an Italian SME in the energy sector, emphasising its transition from QHSE compliance to a structured ESG strategy that incorporates CSR principles. Using a multilayered network approach, we develop a conceptual model to analyse the overlapping relationships between these frameworks, mapping risk factors and certification structures. In the model, we construct a four-layer risk network, where intra-layer links represent the relevance of risk factors and inter-layer links are based on international certifications acquired by the firm. Consequently, QHSE provides operational support for ESG reporting, while CSR serves as the ethical foundation for both. Therefore, this work stresses the importance of a holistic approach to sustainability, offers further insights into SMEs' engagement in responsible corporate governance, and promotes economic development through integrated risk and sustainability management science.

ABSTRACT. The rapid advancement of Artificial Intelligence, particularly in deep learning architectures, has created a paradox of performance versus interpreta bility. While modern AI systems achieve unprecedented accuracy across do mains from medical diagnosis to criminal justice and predictive policing, their decision-making processes remain largely opaque. This opacity has generated significant concern among stakeholders, including regulators, practitioners, and end-users, leading to the emergence of Explainable Artificial Intelligence (XAI) as a critical research area. Without integrating well-established cognitive limita tions into their design, we contend that XAI systems risk impairing rather than promoting genuine human understanding. This might lead to problematic, undesirable outcomes and sometimes create detrimental effects, resulting in unwarranted overreliance on AI decisions and a corresponding aversion to algorithmic systems. While these cognitive limitations are well-documented, they are not always widely acknowledged within the AI research community. Therefore, we consider it highly useful and necessary for researchers in XAI to be aware of the cognitive pitfalls that may limit or hinder the adequate comprehension of explanations provided for the outcomes produced by artificial intelligence systems. In particular, this concerns local explanations. Accordingly, in addition to addressing general aspects, this work includes an advanced exposition of certain cognitive limitations of the human mind—some of which are well-known, while others remain relatively underexplored. We connect them to XAI and, in doing so, propose design directions for explanatory systems that truly enhance human understanding.

ABSTRACT. Information Systems, such as search engines, recommendation platforms, and online rating systems, are susceptible to strategic manipulation. Since these are software systems that rely heavily on algorithms, particularly machine learning (ML) and data analysis, this paper introduces a conceptual framework founded on a manipulability index, drawn from game-theoretic and voting-theoretic models, to investigate the potential repercussions of strategic behaviors. Integrating concepts from equilibrium computation, complexity theory, perturbation analysis, and evolutionary learning, the framework demonstrates how minor modifications in user behavior or system inputs can significantly affect outcomes. The paper lays the groundwork for future investigations into the design of manipulation-aware systems and the development of adaptive mechanisms in complex digital environments.

ABSTRACT. Artificial intelligence (AI) is profoundly reshaping organizational training, offering strategic opportunities, particularly in healthcare, where continuous professional development is crucial to ensure service quality and effective-ness. This study examines the role and functions of AI in training and professional growth, with a specific focus on the healthcare sector, through a qualitative methodology based on selected case studies (techniques: semi-structured interviews, data archival analysis, and direct observations). The research investigates how the integration of AI into training processes can enhance the ef-fectiveness of interventions, personalize learning pathways according to indi-vidual needs, support more informed decision-making, and foster continuous and adaptive learning. Several applications are considered, including intelligent systems for performance evaluation, adaptive learning platforms, train-ing chatbots, and AI-based simulators for complex clinical scenarios. The findings highlight both the tangible benefits and the challenges associated with AI adoption, such as ethical concerns, the demand for digital skills, and resistance to change. Overall, the study provides a critical reflection on the transformative potential of AI in healthcare training, emphasizing the need for rapid, effective, and sustainable implementation. By doing so, it underscores the capacity of AI to enhance human capital while promoting long-term organizational development.

ABSTRACT. In the evolving landscape of artificial intelligence (AI), critical thinking (CT) has become essential for navigating its opportunities and challenges, particularly in strategic HRM decision-making. This study evaluates the effectiveness of a targeted AI-based training intervention in enhancing AI-specific CT skills among graduate students. Adopting a mixed-methods convergent design, it combines a quasi-experimental pre- and post-test approach with qualitative focus groups. The quantitative component measures changes in CT levels using a performance-based assessment grounded in the Watson–Glaser framework and the International Performance Assessment of Learning (iPAL). The qualitative phase analyses how the adoption of AI during the training impacted students’ learning experiences and CT outcomes. Findings are expected to demonstrate improvements in AI-specific CT and inform the design of AI-based training programmes for HRM students and practitioners. The ultimate aim is to strengthen critical engagement with AI tools and support fairer, more reflective decision-making in HRM.

ABSTRACT. Generative Artificial Intelligence (GenAI) is changing higher education with new content creation and personalized learning tools. However, educators' adoption of GenAI is inconsistent due to various challenges. This study investigates the factors affecting GenAI adoption, focusing on perceptions, task alignment, institutional conditions, and readiness.Using an integrated framework of the Task-Technology Fit (TTF) model and the Technology Acceptance Model (TAM), the study analyzes how technology, tasks, and individual traits affect perceived alignment with academic tasks. The model includes two important contextual factors: Institutional Support and GenAI Readiness. Institutional Support refers to organizational resources like training, while Readiness indicates educators' confidence and skills with AI technologies. The research employs a quantitative, cross-sectional method, analyzing survey data from educators through Structural Equation Modeling (PLS-SEM). Findings indicate that perceived task fit, usefulness, and institutional support promote GenAI adoption, while AI readiness strengthens the link between perceived fit and actual use. The study enhances understanding of AI integration in education at multiple levels. It provides actionable recommendations for institutions to effectively implement GenAI, such as aligning tools with educator tasks, assessing readiness, and developing targeted support strategies for responsible adoption.

ABSTRACT. In the context of the digital trasnformation, organizations fase the challenge of training their employees in more complex, dynamics and technological environment, gamification and artificial Intelligence (AI) have emerged as innovative approaches to promote motivation, personalization of training experiences and meaningful learning. The objective of this review article is to analyze the conceptual and empirical evolution of gamification as a complement to AI-assisted learning processes in training contexts with an emphasis on applications in systems engineering; a structure exploratory review was conducted base don a database built from literatura indexed in Scopus, ScienceDirect, and Google Scholar, prioritizing studies published between 2013 and 2024 in Q1 and Q2 journals. The analysis integrated theorical foundation, applied methodologies, key definitions and findings regarding the impact of AI-supported gamification; the results reveal significant feedback Systems, the applications focus on Development soft skills decisión-making ands digital competences in simulated environments. In the field of Systems engineering is natable practices include simulators, intelligent gamified platforms and the use of serious game; the combination of gamification and AI constitutes a powerful approach to transform organizational training-especially in technical domains such as enginieering. Future progress requires the development of hybrid models that integrate learning theory, user-centered design, and intelligent adaptive capabilities.

ABSTRACT. Despite South Africa’s ambition to become a global 4IR leader, little re-search has been done to examine national AI capacity, capability, and com-petitiveness. An AI Hub in Cape Town offers the distinct opportunity to harness AI competitive advantage through aligning interventions with stra-tegically valuable national resources and capabilities. This study explores the critical success factors and competitive advantage of an AI Hub in Cape Town from the perspective of diverse South African AI stakeholders, guid-ed by Porter’s Diamond of National Advantage and Cluster Theory. Vision, bottom-up hub functions, collaboration, proximity, funding, leadership and location emerged as major critical success factors for an AI Hub in Cape Town. These critical success factors are moderated by government involve-ment and rapid AI change. In discussing the potential competitive ad-vantage, participants foregrounded the ability to solve African use cases us-ing artisanal capabilities, the need to create and safeguard South African da-ta, our diversity and culture, and the transferability of South African use cases. Using deductively derived themes, the study confirmed the relevance and interdependence of the constructs in Porter’s Diamond of National Ad-vantage in a developing context. The framework is enriched by the emergent sub-themes. The physical proximity inherent in Cluster Theory was extend-ed to include digital proximities. The study contributes to the literature on hubs in developing contexts and Porter’s frameworks, provides a starting point for the conceptualization of an AI Hub in Cape Town, and outlines actionable recommendations to specific stakeholders on their role in cata-lyzing the advancement of AI in Africa.

ABSTRACT. Digital transformation (DT) initiatives often underperform due to deficient coordination across heterogeneous stakeholders. Building on Organization-al Learning, Information Processing, and Cognitive Fit theories, this study problematizes the operational challenges of DT steering and proposes an approach towards a Digital Maturity Steering Model (DMSM). Adopting the Echelon Design Science Research (eDSR) methodology, we conduct a sys-tematic literature review, practitioner interviews to articulate a validated problem statement, requirements and objectives for the artifact. The result-ing design knowledge clarifies stakeholder roles, information needs, and communication flows, thereby laying a rigorous foundation for subsequent artifact development and evaluation.

ABSTRACT. The agri-food sector is under increasing pressure to adopt digital innovations like AI, automation, IoT, and sustainable practices to increase food production while reducing environmental impact and achieve long-term economic, environmental, and social sustainability. Digital technologies can improve efficiency, product quality, reduce waste, and support environmental goals. However, existing research often neglects social economic and political dimensions, which could reinforce existing inequalities. Sustainable agri-food development strategies, such as deploying renewable energy sources and reducing dependence on synthetic fertilizers, offer tangible pathways to cut greenhouse gas emissions. Industry 4.0 technologies, such as big data machine learning, deep learning, swarm intelligence, blockchain robotics, and autonomous systems, promise to enhance efficiency and environmental stewardship while also serving social and ecological objectives. SMEs face challenges such as limited adoption, economic and technological barriers, and the need for supportive policies and infrastructure. In Italy, infrastructural costs are higher than revenue, and young farmers face challenges in implementing digital innovations. The study will focus on how widely agri-food SMEs adopted digital and sustainable innovations, what factors drive or hinder adoption and if digital innovations correlate with measurable environmental benefits. The results can help tailor affordable digital solutions for these SMEs, enhance subsidies for green-tech adoption, and help SMEs prioritize innovations with dual benefits.

ABSTRACT. This research studies the impact of mission-driven strategies on the governance and orchestration of digital health ecosystems in Italy's private healthcare sector. Healthcare organizations frequently express socially oriented missions that highlight access, personalization, and empowerment; however, a significant gap persists between these normative commitments and the design of digital platforms. This research employs a qualitative, multiple-case study approach involving five healthcare entities to delineate three distinct pathways of mission-platform alignment: symbolic, structural, and emergent. Organizations that integrated their mission into governance mechanisms, such as partner onboarding, data-sharing protocols, and patient engagement interfaces, exhibited increased stakeholder trust and enhanced ecosystem coherence. In contrast, the use of symbolic missions correlated with fragmented governance and reduced legitimacy. This study utilizes ecosystem governance theory alongside Foucault’s concepts of governmentality and biopower to reconceptualize digital platform governance as a domain of ethical regulation rather than solely operational control. This paper advances the field of values-based digital transformation by presenting "mission–platform alignment" as an essential framework for assessing innovation legitimacy, stakeholder trust, and the design of inclusive ecosystems. Healthcare leaders, platform designers, and regulators are provided with practical implications to ensure that digital health ecosystems enhance institutional objectives rather than undermine them.

ABSTRACT. As Metaverse-enabling technologies are advancing rapidly, their combined impact in unified digital ecosystems poses new challenges for the responsible, and ultimately sustainable, development of the metaverse. Business actors play a crucial role in shaping how a metaverse ecosystem evolves, yet guidance on how they can foster its responsible development remains limited. Prior studies at the nexus of responsibility and the metaverse have largely focused either on risks to be mitigated or on opportunities to be seized. However, responsibility within the metaverse has received limited attention, and rarely with an ecosystem framing. To fill this gap, we systematically reviewed 62 studies on the metaverse and its enabling technologies in relation to business responsibility concepts. We inductively classified the practices identified in these studies into four dimensions of responsible business practices in a metaverse ecosystem. Our findings both echo and reconfigure dimensions of Corporate Digital Re-sponsibility (CDR) concerns in Information Systems (IS) research, linking metaverse-specific practices to broader IS debates while introducing an ecosystem perspective. Each of our dimensions also offers ways to address the risks and opportunities highlighted in prior metaverse research. Our study thereby contributes to metaverse scholarship and practice by outlining dimensions on which business actors can focus to advance the responsible, and thus sustainable, development of a metaverse ecosystem. We invite future research to test and further refine these dimensions, particularly by first mapping the roles and interdependencies among metaverse ecosystem stakeholders.

ABSTRACT. The concept of shared responsibility has become pivotal in the Infor- mation Systems (IS) community. This study addresses the gap in defining shared responsibility by developing a transdisciplinary synthesis model and exploring the impact of digital technologies, particularly AI and Generative AI, on this con- cept. Using the Gioia Methodology, we identified key dimensions: Technological Adoption and Resistance, Information Control, and Attitude to Knowledge Shar- ing. Our research highlights how traditional AI, while providing robust analytical capabilities, tends to centralize control due to its complexity. Generative AI, though democratizing access to advanced capabilities, poses challenges related to its opacity and unpredictability, impacting accountability and trust. We vali- dated these findings through semi-structured interviews with stakeholders from ten organizations, confirming the critical role of these technologies in shaping shared responsibility. Our study underscores the need for robust frameworks that support transparent and trustworthy relationships in increasingly complex digital ecosystems. Future research should focus on developing decentralized govern- ance mechanisms and methodologies for quantifying the shared value created by digital technologies. This work provides valuable insights for both researchers and practitioners, offering a comprehensive framework for understanding the evolving dynamics of human-technology interaction in the context of shared re- sponsibility.

ABSTRACT. This study examines the adoption of emerging technologies in organizations through an extended socio-technical systems (STS) framework, incorporating environmental factors. To identify relevant components, we first conducted a thorough literature review and conducted 28 interviews across diverse organizations and countries. The coding revealed limitations of the classical STS model, focusing on the role of environmental factors as critical for the successful implementation of emerging technologies (such AI based tools). This study emphasizes that a multi-dimensional approach, integrating organizational, technical, social, and environmental factors, is key for effective technology adoption. We also introduce the concept of "environmental design" as a vital component within the socio-technical ecosystem. This expanded framework provides a deeper understanding of technology adoption, contributing to improving the comprehensiveness of future studies and ensuring a more effective adoption of emerging technologies.

ABSTRACT. This study aims to model the relationships between Artificial Intelligence and technostress in organizations that are implementing, or intend to implement, AI-technologies, to better understand if they are, more or less, near the ideal organizational typology and to design the possible path that maximize AI-implementation, techno-eustress and organizational support and minimize techno-distress impacting positively on productivity. Starting from the literature on the topic of "Artificial Intelligence and technostress in organizations" and using the typology analysis as methodology, a model of organizational typologies was defined, representing theoretical organizational typologies based on the cases described and identified in different contributions emerging from the literature examined considering the particular business sector and considering, at this first level of analysis, both organizations operating in the public sector and those operating in the private sector, regardless of the size of the company. This work represents a framework for future research on the topic and offers a theoretical model for managers to understand which organizational typology is closest to their organization noting the level achieved by productivity with the possibility to understand what actions to realize on independent variables to divert them towards the ideal typology that maximizes productivity.

ABSTRACT. The fashion industry is undergoing a significant transformation by integrating artificial intelligence (AI) and the metaverse. These technologies are reshaping operational models and offering new economic and environmental sustainability opportunities by streamlining production, optimizing resource use, and reducing ecological impact. While such advancements are well documented, social sustainability remains relatively underexplored, despite its crucial relevance. Social sustainability in fashion touches on critical issues such as labor exploitation, unsafe working conditions, unequal market access, and the reinforcement of unattainable beauty standards. These concerns have broad implications for gender equity, traditional craftsmanship, and the preservation of local cultural heritage. This study examines the potential of AI and the metaverse to address these challenges and enhance social sustainability in fashion. It focuses on three key areas: AI-powered co-creation platforms, virtual communities as cultural exchange sites, and independent digital creators' influence in promoting decentralized and inclusive fashion practices. A qualitative methodology is adopted, using a multiple-case study design and thematic analysis. The cases of DressX and The Fabricant, two leading platforms in digital fashion innovation, are explored to understand how technology and consumer engagement can foster greater social inclusion within the industry.

ABSTRACT. Organizational learning (OL) is widely recognized as the engine that enables public institutions to sense turbulence, refine routines, and reinvent services. Yet the scholarship that should guide this engine is still conceptually splin-tered and empirically unbalanced. To take stock, we carried out a PRISMA-guided systematic review of peer-reviewed research published between 2005 and March 2025. Forty-nine studies met our inclusion criteria. Each was coded for the way it defines and measures OL, the drivers that foster it, the barriers that block it, and the outcomes it claims to deliver. The evidence shows that public-sector OL unfolds through three interlocking stages—knowledge acquisition, performance improvement, and strategic innovation rather than a single, monolithic process. Progress along this learning path-way is powered by four mutually reinforcing driver families: a leadership-culture nexus, knowledge-infrastructure systems, environmental turbulence with political mandates, and technology-resource signals. Learning stalls when three barrier constellations converge: bureaucratic inertia, chronic re-source scarcity, and fragmented information systems. Although these pat-terns are robust across the corpus, the empirical base is heavily skewed to-ward OECD settings and qualitative single-agency cases, limiting generaliza-bility to low- and middle-income contexts. The review yields a consolidated typology of OL constructs, an integrative driver–barrier framework, and a fu-ture research agenda that calls for Global-South comparisons, mixed-method designs, and outcome tracing to public value. For scholars, the framework supplies a cumulative scaffold for theorizing. For practitioners, it offers a di-agnostic roadmap for shifting agencies from compliance-centered routines to adaptive, learning-oriented governance.

ABSTRACT. Distributed Artificial Intelligence (AI) is transforming healthcare systems by enabling collaborative, privacy-preserving data analysis across institutions. Technologies such as Federated Learning and Edge Computing allow the development of high-performing AI models without centralized data storage, addressing regulatory constraints while opening new pathways for innovation. However, the adoption of distributed AI introduces multidimensional tensions involving technological, economic, legal, and ethical domains. This paper proposes an integrated analytical framework to assess and navigate these tensions in the context of healthcare governance. The framework encompasses four key axes: (1) Technology, focusing on interoperability, resilience, and performance; (2) Economy, addressing cost-efficiency, value distribution, and sustainability; (3) Law, analyzing compliance with GDPR, AI Act, and sector-specific regulations; and (4) Ethics, highlighting fairness, transparency, and patient autonomy. Each axis is detailed through specific constructs and evaluation metrics. We apply this model to real-world healthcare scenarios, identifying critical trade-offs—such as those between security and cost, innovation and regulation, or algorithmic performance and interpretability. The resulting tension matrix offers a tool to visualize interdependencies and prioritize governance actions. By integrating interdisciplinary expertise, the proposed framework supports adaptive governance strategies tailored to the dynamic nature of AI systems and healthcare environments. Our approach facilitates responsible innovation by aligning technological capabilities with legal requirements, ethical principles, and economic viability. The paper concludes by highlighting future research directions and policy implications for sustainable AI adoption in digital health.

ABSTRACT. This study combines experimental evidence and computational modelling to examine how institutional incentives influence cooperation in repeated public goods games. Building on a 2019 lab in the field experiment, we identify behavioural profiles through longitudinal clustering of participants’ decisions and embed them in a micro-founded agent-based model (ABM). The model simulates heterogeneous decision making under varying institutional regimes, including punishment and reward mechanisms, using a 70/30 calibration–validation split to avoid circular reasoning. The ABM reproduces key macro-level patterns observed in the experiments: Early rewards are more effective than punishment in sustaining cooperation, while delayed interventions rarely reverse free riding once it becomes dominant. Sensitivity analyses confirm the robustness of these dynamics to variations in group size, learning intensity, behavioural inertia, and alternative decision heuristics. Although the model underestimates long-term cooperation due to the exclusion of psychological and normative drivers, it provides a reliable tool for exploring institutional design and collective behaviour under bounded rationality. Overall, the results underscore the importance of early interventions and behavioural heterogeneity in sustaining cooperation in social dilemmas.

ABSTRACT. This paper studies how social media influences firms’ sustainability strategies through the lens of stakeholder theory and public goods economics. We develop a computational game-theoretical model in which firms act as players in a repeated Public Goods Game (PGG), weighing short-term private gains against long-term collective benefits. Social media is introduced as a mechanism of visibility and accountability, amplifying stakeholder pressures—particularly from consumers, activists, and institutional investors—who prioritise environmental and social responsibility. This digital visibility generates reputational incentives, shaping firms’ behaviour through rewards, sanctions, and the enforcement of social norms. Using agent-based simulations, we analyse how different levels of social media intensity, stakeholder responsiveness, and competitive dynamics affect firms’ contributions to sustainability, treated here as a public good. The results indicate that when supported by consistent and credible stakeholder feedback, reputational dynamics can significantly alter firms’ strategic incentives, encouraging more sustainable actions even in the absence of regulation. Our findings contribute to understanding how decentralised digital mechanisms foster collective action among self-interested actors facing global sustainability challenges. This study also advances the growing literature on the role of digital technologies—especially social media—in reshaping corporate decision-making by increasing the reputational costs of unsustainable behaviour and the benefits of transparent cooperation. Overall, we show how digital visibility can influence strategic behaviour beyond formal institutional frameworks.

ABSTRACT. Generative artificial intelligence is increasingly present in morally sensitive decision-making contexts, raising concerns about the values that guide its responses. This study explores AI’s potential as an ethical agent by s testing models on prosocial behaviours such as fairness and altruism. The study thus sits at the intersection of AI ethics, computational linguistics, and behavioural social sciences. The investigation is based on the premise that, although lacking intentionality, such models reflect the value structures embedded in their train-ing data and optimization processes. We develop a pilot evaluation parameter to assess ethical reasoning and its alignment with human values, considering both outcomes and procedural aspects. Our results reveal systematic differences across model architectures: reasoning-intensive models display stable and con-sistently prosocial behaviour, while other models exhibit context-dependent variability, adjusting their responses toward greater pro-sociality in more inter-active settings. These findings demonstrate that AI behaviour is neither value-neutral nor uniform. Assessing AI’s capacity for ethical decision-making will support the development of less biased systems and minimize unintended harm.

ABSTRACT. This study deconstructs prevailing myths about AI-driven labor displacement through a critical analysis of global case studies (Dukaan, IKEA, Mango) and interdisciplinary literature. Revealing a triple disconnect between early predictions and current realities, technological capabilities and institutional adaptability, Global North and Global South impacts, the research identifies multifaceted risks: erosion of entry-level roles ("junior crisis"), ethical threats like "digital slavery," cognitive biases in public perception, and legal liability gaps. Methodologically, it introduces an operationalizable matrix for hybrid intelligence (measuring cognitive synergy, task distribution, and techno-human balance) while acknowledging Western- centric limitations. The findings demonstrate that transformation, not replacement, defines AI's impact, with human soft skills (empathy, complex decision-making) becoming paramount comparative advantages. Geopolitical analysis highlights divergent adaptation trajectories: developed economies face migration pressures and reshoring, while export-oriented and resource-dependent nations risk technological dependency and premature deindustrialization. Mitigating these challenges requires urgent institutional innovation: context-sensitive labor regulations, human-centered AI design, and investment in psychosocial resilience infrastructure. The study concludes that sustainable labor market transitions depend on synergistic human-AI collaboration, enabled by cultural and organizational transformation.

ABSTRACT. The contemporary hospitality industry is characterized by rapid development and the implementation of new technologies, which necessitates the continuous updating of employees' professional competencies. This makes the industry particularly sensitive to the quality and effectiveness of educational platforms. The hospitality sector actively adopts digital technologies, including knowledge management systems, artificial intelligence, and virtual reality. This article presents the findings of a study on the impact of cognitive load and the usability of digital educational platforms on employee satisfaction in the hospitality industry. The study employed both quantitative and qualitative methods, including surveys and expert interviews. The empirical data were analyzed using descriptive analysis, regression analysis, factor analysis, and thematic analysis of qualitative data. The research revealed that the cognitive load of digital learning has a significant negative impact on employee satisfaction, while usability and engagement positively influence satisfaction. The authors identified three clusters of employees with different levels of cognitive load and satisfaction and proposed an integrated model of cognitive load, usability, and satisfaction in digital personnel training systems. The results can be used to develop digital educational platforms that take into account individual employee characteristics and reduce cognitive load

ABSTRACT. Technological advances have expanded collaboration between humans and artificial intelligence (AI) in the workplace, driven by the use of large data sets that enable more accurate analysis and predictions. It is far from expected that all organizations will automate and use AI, and efforts have been made to leverage the combined capabilities of humans and AI rather than relying solely on AI. However, this collaboration is fraught with ambiguity for business managers. This paper uses social presence theory, social response theory, and the IS Continuance Model to examine the consequences of professional user interaction with AI in an accounting context. In particular, the study examines how human-like (HL) and system-like (SL) trust can influence AI usage intention (AUI) and perceived usefulness (PU). We also examine the combined effect of satisfaction (ST) and frequency of use (FE) on these relationships.