ABSTRACT. This study explores how Proof-of-Concept (PoC) frameworks align with sustainability goals to drive innovations that are both technically feasible and environmentally sound. Through separate and combined bibliometric analyses of Scopus literature, it identifies convergent clusters, carbon neutrality, governance, and emerging technologies, shared by both domains. Results show PoC frameworks validate new solutions while gauging their ability to tackle challenges such as resource efficiency and ethical impact. By tracing the interplay among governance, technological change, and social behaviour, the study demonstrates PoC’s role in shaping sustainable practices across sectors. These insights lay a conceptual base for future research and for policy and managerial action in responsible innovation.

ABSTRACT. This paper reimagines digital transformation in the public sector by investigating how Agile methodologies can drive systemic innovation in structurally rigid and institutionally com-plex environments. Using the Italian judiciary - a prototypical loosely coupled bureaucracy - as an empirical lens, it examines a pioneering initiative that challenges the limitations of traditional top-down, waterfall-style IT development. Centered on a new digital platform for performance evaluation in courts, the initiative was co-designed with end-users through iterative cycles, continuous feedback, and embedded training, resulting in a highly contex-tualized and adaptable system. Rather than bypassing bureaucratic rigidity, the initiative engaged directly with it, levera-ging participatory co-design, local adaptation, and multi-level stakeholder involvement to embed Agile logic into daily practices. This iterative approach fostered not only more func-tional digital systems but also more resilient and responsive organizational dynamics. Based on an extensive qualitative inquiry encompassing immersive fieldwork, in-depth interviews, and longitudinal observation, the study introduces a multi-layered model of “Mediated Agile Innovation” that captures the causal dynamics driving transformation in decentralized public organizations. The model reframes fragmentation and formality not as obstacles but as fertile ground for bottom-up innovation when supported by legitimacy, iterative learning, and co-owned development. The findings challenge dominant narratives of digital failure in the public sector, offering a new perspective: transformation is possible not despite bureaucracy, but through its creative reconfiguration. Agile, when thoughtfully adapted to institutional contexts, becomes more than a development method - it becomes an enabler of cultural change, sustained engage-ment, and enduring public value creation.

ABSTRACT. The fast-paced digitalization of products and services across all indus-tries creates volatile and rapidly changing market environments. In such a cli-mate, many organizations are struggling due to a lack of dynamic digital capabil-ities: the abilities to both create and react to new digital business models in a timely fashion. To develop dynamic digital capabilities, organizations need to rethink the role of the IT-organization and its position in the digital value chain. Based on the in-sights from three Danish companies that successfully compete in the digital econ-omy, we identify organizational templates that can guide managers in the process of reshaping IT organizations for digital succes

ABSTRACT. As generative AI tools like Amazon’s Rufus and ChatGPT increasingly shape consumer decision-making, questions remain about when and for whom consumers are willing to trust AI-generated product recommendations. This research investigates how relational context, specifically, whether consumers are choosing for themselves or for others, shapes acceptance of GenAI recommendations. Across two experimental studies, we find that consumers are equally likely to accept recommendations when choosing for themselves versus for a close friend, suggesting psychological overlap and preference projection in socially proximate relationships. However, when choosing for a socially distant and emotionally significant other, such as a grandmother, consumers exhibit significantly lower acceptance. The research contributes to the emerging literature on consumer–AI interactions by demonstrating how social distance shapes the acceptance of AI-generated advice.

ABSTRACT. Generative artificial intelligence (GAI) systems, such as chatbots like ChatGPT, are increasingly embedded in employees’ everyday interactions, offering real-time assistance and personalized support across a wide range of contexts. As these systems grow more prevalent, questions arise regarding the neutrality of their responses and the potential influence of the perceived user’s characteristics, such as gender, on their communicative behavior. This study investigates whether and how ChatGPT adapts its tone, language, and the type of support it offers based on the user’s gender as perceived by the machine. We conducted a qualitative experiment consisting of two phases: in the first, the user’ gender is omitted, while it is explicitly stated in the second. Through a thematic comparative analysis of linguistic choices and content, we identify subtle yet notable differences in the nature of the assistance provided. Preliminary results suggest that, despite the model’s training objectives aimed at ensuring neutrality, gendered interaction variations do emerge. These findings underscore the need for critical reflection on the design and deployment of GAI systems, particularly concerning implicit biases and their broader social implications.

ABSTRACT. This paper investigates how generative artificial intelligence (Gen-AI) is reshaping creative work within organizational settings. Creativity literature reveals that Gen-AI systems are not only accelerating content production but actively co-constructing creative outputs, leading to shifts in task orchestration, role distribution, and epistemic authority. These developments demand new forms of skill acquisition, such as prompt engineering and algorithmic curation, while also surfacing tensions around authorship, authenticity, and job displacement. At the emotional level, professionals report ambivalence, balancing enthusiasm for AI-augmented creativity with anxieties related to identity erosion and ethical ambiguity. This study adopts a qualitative research methodology, based on fifteen semi-structured interviews conducted with professionals operating within the creative industry. The results explore four interrelated domains: the redefinition of human-AI collaboration in the idea generation; the transformation of workflows and professional competencies; the evolution of authorship and evaluation standards; and the ethical and emotional impacts on creative identity. The paper contributes to ongoing debates in human resource management, organizational studies, and the future of creative labor by synthesizing current evidence and outlining implications for theory, practice, and policy.

ABSTRACT. As organizations rush to invest in Generative Artificial Intelligence (GenAI), a deeper transformation is unfolding where few are looking: at the edges of formal systems, in the hands of employees who are quietly reshaping their work. This paper exposes GenAI Job Crafting (GenAI JC), the unsanc-tioned, informal, and profoundly creative ways in which workers adapt GenAI tools to redesign their tasks, interactions, and outputs. Far from being a side effect of digital transformation, we argue that GenAI JC is its hidden epicenter. This phenomenon disrupts traditional boundaries between design and execu-tion, human and machine, formal role and lived experience. Yet, it remains largely invisible, frequently concealed due to fear of judgment, ethical uncer-tainty, or lack of organizational support. In such a context, it is not technology but culture that determines whether GenAI JC thrives or withers. Where organi-zations foster psychological safety, curiosity, and shared meaning, GenAI JC be-comes a catalyst for distributed innovation. Where they do not, it becomes a missed opportunity buried beneath metrics and compliance. We challenge the prevailing logic of AI adoption as a matter of infrastructure or skills. Instead, we call for a cultural turn in digital strategy—one that fore-grounds the informal, the experimental, and the human. If organizations are to meaningfully transform, they must stop asking only what GenAI can do, and start asking: What kind of organization do we need to be for people to craft the future of work with it?

ABSTRACT. The expansion of hybrid work models, especially after the COVID-19 pandemic, has redefined how organizations structure work and support employee well-being. While hybrid arrangements offer flexibility and increased autonomy, they also introduce challenges that may impact employees differently. This study aims to explore how workers perceive hybrid work environments and how these perceptions influence their well-being. Drawing on Organizational Support Theory and a multidimensional approach to well-being—including psychological, social, and financial dimensions—we developed and administered a questionnaire to employees of an Italian public organization that has adopted hybrid work since 2020. The data, collected from 266 hybrid workers, was analyzed using cluster analysis to identify distinct employee profiles based on perceived well-being and work-related experiences. The findings reveal five differentiated clusters, ranging from highly satisfied workers to those reporting significant difficulties in hybrid settings. The study highlights the importance of organizational support, work-life integration, and social dynamics in shaping employees' hybrid work experiences. These insights underscore the need for organizations to personalize hybrid work models and support mechanisms to accommodate diverse employee needs. The paper concludes by discussing managerial and theoretical implications, acknowledging limitations, and proposing directions for future research.

ABSTRACT. . Burnout is one of the most complex phenomena currently affecting the healthcare sector, defined as an emotional state that involves fatigue, a sense of detachment from work and a reduction in personal fulfillment, through this study we investigate how the use of artificial intelligence (AI) can impact the mitigation of this phenomenon through a mixed research method. A phase A quantitative analysis of bibliometric records from 22 ABS-listed journals (524 Dimensions.AI records) was conducted using Voyant Tools, which revealed five recurring themes: work, health, employment, stress and employees. These themes were identified as significant links between workload, psychological well-being and technology. A Scopus search identified 845 records, with VOSviewer highlighting the nodes "patient", "time", "stress" and "exhaustion", thus confirming the impact of time pressure in clinical contexts. The qualitative phase forms the basis for the subsequent analysis. 17 semi-structured interviews were conducted with doctors, psychologists and managers from Campania, Lazio and Lombardy. Thematic analysis of transcribed interviews was performed using Voyant Tools. Five recurring themes emerged through this analysis: work, communication, collegiality, burnout, and patients. Using the Job Demands-Resources (JDR) model, and extending it to AI, which was considered both as a work resource and as a tool to reduce work demand. The results indicate that excessive administrative burden, time constraints, and clinical complexity contribute to emotional exhaustion, while relational and technological resources remain underutilized.

ABSTRACT. Legacy systems continue to remain crucial for the core business operations of most financial service providers around the globe. However, increasing operational risk due to knowledge loss and lack of skills because of demographic change, rising maintenance and run costs, as well as changing business requirements, put organizations under pressure to modernize them. By applying the case study research methodology, this paper derives the core elements of a socio-technical system design methodology for the stepwise managed evolution of a large, mainframe-based and historically grown legacy application. It demonstrates why this approach could be superior to frequently favored "lift-and-shift" or replace strategies, with respect to cost efficiency, risk mitigation and solution quality. In addition, a system architecture and implementation strategy are proposed and evaluated to demonstrate, how this is achieved.

ABSTRACT. What if remote work, often seen as a desirable work arrangement, is deliberately refused, even when fully accessible? This study investigates the subjective and cultural factors influencing telework preferences among profes-sional, technical, and administrative (PTA) staff in a large-sized Italian public university. Using open-ended responses collected through a questionnaire, we explore the reasons upholding the acceptance of remote work, as well as the meanings underlying its rejection. Rather than assuming flexibility as a uniformly positive option, our research adopts a relational lens, attentive to how work arrangements are shaped by symbolic needs, identity concerns, and cultural expectations. By shifting the focus from structural enablers to subjective interpretations, the study contributes to ongoing debates about the future of work, calling for more nuanced understandings of employee preferences. In doing so, it highlights the importance of organizational contexts in shaping the lived experience of flexibility.

ABSTRACT. Recently, resilience has become increasingly important for individuals, or-ganizations and society to flourish in the world characterised as uncertain and dynamic. Resilient employees not only sustain through challenging situa-tions but also experience better wellbeing and demonstrate higher perfor-mance. Given the relevance of employee resilience, one of the questions re-fers to whether and how employee resilience differs based on individual characteristics. The current paper aims to reveal whether and how demo-graphic factors (gender and generation) and employment characteristics (job tenure, sector type (private and public), sector (healthcare and wellness), and calling versus job) shape the employee resilience. In doing this, the survey in healthcare institutions and wellness centres in Lithuania was conducted, re-sulting in 758 completed questionnaires. The findings revealed that greater resilience was demonstrated by employees in the private sector, men, well-ness sector workers, those with 3–5 years' job tenure and older workers.

ABSTRACT. The present study investigates the acceptance of digital technologies by civil servants, focusing on the potential role played by digital competences. The theoretical reference framework underpinning this study is the UTAUT (Unified Theory of Acceptance and Use of Technology) model. The study goes beyond the core constructs of the model by integrating the role of digital competences in digital technology acceptance. The goal is to assess whether and how digital literacy influences individual intention to adopt and use digital tools in everyday work practices. The methodology adopted involves the administration of an online questionnaire addressed to employees of the Italian public administration. The responses, collected using a seven-point Likert scale, will undergo analysis to provide empirical validation of the proposed model. The expected results are intended to provide useful evidence for policymakers, digital transformation managers and trainers, highlighting the strategic value of digital literacy programmes and innovation-friendly organisational environments.

ABSTRACT. As urban populations grow and cities confront complex governance challenges, local e-Government assessments have emerged as critical tools for enhancing the efficiency, responsiveness, and inclusivity of smart city initiatives. This paper investigates how structured evaluation frameworks embedded within municipal digital governance systems can optimize public service delivery and promote sustainable urban innovation. Through a comparative case study approach, the research analyses six cities, Amsterdam, Milan, Barcelona, Seoul, Cape Town, and New York, each recognized for domain-specific leadership in AI ethics, environmental sustainability, civic engagement, data governance, digital inclusion, and operational efficiency. Findings reveal that well-designed local assessment mechanisms significantly contribute to improvements in transparency, accountability, citizen trust, and real-time decision-making. Common success factors include the integration of quantitative metrics with qualitative feedback, iterative service optimization, and clear reporting protocols. Each city’s approach demonstrates how digital governance can be tailored to local socio-political and infrastructural contexts while maintaining agility and impact. The study highlights that the use of e-Government assessments not only strengthens policy alignment and performance monitoring but also empowers stakeholders by institutionalizing participatory and data-informed governance. While challenges such as data integration, digital literacy gaps, and institutional continuity remain, the evidence suggests that adaptable, ethically grounded assessment frameworks are essential for long-term smart city success. These insights provide a practical roadmap for policymakers, urban planners, and civic technologists aiming to replicate or scale such frameworks in other urban contexts globally.

ABSTRACT. Urban dashboards (or city dashboards) have emerged as tools that are able to help both citizens and local policymakers understand the state of a city and act accordingly, to address service disruptions and risks, and seize opportunities. However, at present, urban dashboards remain evolving tools whose effectiveness for local public administration is still largely unknown. The research question supporting the present work addresses the effectiveness and usability of urban dashboards with respect to their various scopes, as well as which features are most important for their effectiveness. To study this topic, the research proposes an analysis framework to investigate the features and critical success factors for effective urban dashboards addressed to the local public management. A selection of 21 urban dashboards was analyzed through this framework, leading to a discussion of key insights and recommendations on how to define and implement a robust, effective, and useful urban dashboard for city governance.

ABSTRACT. The rigorous assessment of soft skills is still undeveloped both conceptually and methodologically, mainly due to their inherent dynamic and context-sensitive nature. Predominant reliance on self-report instruments and expert ratings continues to limit objectivity and validity of the assessment results. This study introduces the Soft Lab model, a novel assessment method that integrates Mixed Reality (MR), neurophysiological monitoring and advanced analytics to capture real-time, multimodal data on soft skills enactment. By considering soft skills as emerging self-adapting behaviors to ecologically valid MR-based environments, the Soft Lab model addresses core challenges related to subjectivity, measurement precision, and replicability.

ABSTRACT. This paper analyzes how the introduction of emerging technologies and artificial intelligence (AI) is transforming the role and competencies of the executive board, with particular attention to gender dynamics. Through a review of the literature and an analysis of data from the Excelsior Information System (Unioncamere – Italian Ministry of Labor) for the years 2017–2023, the study investigates the evolution of the competency profile required of top managers in recent years in response to digital transformation and Industry 4.0. The findings show that, despite some signs of progress, the preference for new female entries is lower than that for males, although organizations often do not express preferences or different expectations based on gender when recruiting individuals into top management positions. This paper aims to explore differences in the competency profiles of men and women in top management, highlighting that women remain underrepresented in top-level roles requiring advanced digital skills, while both genders display a significant propensity for soft skills, increasingly fundamental in the age of AI, in order to ensure more effective, transparent, and conscious governance.

ABSTRACT. Generative AI often fails to deliver on its promise because organizations treat it as a tool for automating/augmenting isolated tasks while overlooking the interdependencies that define work in real-world contexts. This paper argues for a shift in perspective. We introduce a conceptual model grounded in Organizational Information Processing Theory that separates GenAI's capabilities into two distinct areas: Task Execution (its ability to perform a core function) and Information Orchestration (its ability to manage the information flows required for coordination). This distinction carries significant implications. First, it redefines “AI autonomy." A system isn't truly autonomous if it cannot manage its own information inputs and outputs within a process, exposing the "plug-and-play" approach as a myth for many real organizational contexts. Second, it necessitates the co-design of human roles alongside AI. Humans must evolve from being mere AI users to becoming orchestration managers, bridging the coordination gaps the technology creates. Ultimately, successful GenAI integration requires redesigning entire workflows to balance both execution and orchestration, a critical step for achieving systemic transformation rather than isolated productivity gains.

ABSTRACT. Artificial intelligence (AI) is transforming personnel selection processes significantly by introducing automated tools that promise greater efficiency, speed, and impartiality. However, the adoption of such systems raises concerns about candidates' emotional experiences, the transparency of decision-making criteria, and the quality of human–machine interaction. This study takes a socio-technical approach, based on Socio-Technical Systems (STS) theory, to analyse the emotional impact of automated recruitment systems compared with traditional face-to-face interviews. A systematic literature review and bibliometric analysis of the Scopus database identified 35 relevant contributions from the fields of business, management, and accounting; social sciences; and economics, econometrics, and finance. Citation analysis enabled the identification of key research streams, influential authors, and emerging thematic connections.

ABSTRACT. This article explores how legal professionals are crafting their job as they integrate generative AI (GenAI) in their practices. Recent research has as-sessed the technical capabilities and ethical concerns surrounding adoption of GenAI chatbots in legal practices, but there is little evidence on if and how their work has been reshaping. Drawing on the concept of job crafting, this study ex-amines how legal professionals proactively modify their tasks, routines, and workflows in response to GenAI chatbots’ growing presence in their practice. The paper contributes a theoretically grounded and empirically informed account of these micro-level adaptations, emphasizing the agency of legal professionals in a rapidly evolving technological landscape. It proposes a mixed-methods re-search design involving qualitative interviews and a large-scale survey to inves-tigate the dynamics of GenAI chatbots integration across law firms and legal spe-cializations and the related practical implications for digital strategy, skill devel-opment, and ethical governance.

ABSTRACT. This paper proposes a conceptual Cyberbiosecurity model (CBS4SDE) tailored to the Eastern Mediterranean Region (E-MED), recognizing the interplay be-tween digital vulnerabilities, fragmented governance, and public health chal-lenges. Drawing from a socio-technical perspective and One Health principles, we highlight organizational and technological gaps in cross-border data sharing, pathogen surveillance, and cybersecurity infrastructure across E-MED coun-tries. By synthesizing regional assessments, global governance frameworks, and empirical literature, the study advocates for a participatory CBS model that integrates stakeholder coordination, interdisciplinary education, and secure digital infrastructures. The model aims to foster trusted data ecosystems, improve outbreak response capabilities, and enable equitable, resilient health systems across the contextually constrained E-MED. This work fills a critical gap in CBS scholarship by contextualizing global strategies for regional implementation, offering scalable frameworks applicable to similarly fragile health ecosystems.

ABSTRACT. While the benefits of automation are widely known, public understanding of current developments in Robotic Process Automation (RPA) and Intelligent Process Automation (IPA) approaches is still limited. Our narrative literature review aims to address this gap by analyzing $n=57$ research articles selected from six major scientific databases. Following the PRISMA guidelines on systematically reporting literature insights, our findings reveal a peak in RPA and IPA publications between 2020 and 2022, with finance, manufacturing, and energy as the leading application sectors. It was also found that researchers primarily use case studies and semi-structured interviews as their investigation methods. Furthermore, while RPA and IPA benefits have been widely recognized, studies have also addressed important drawbacks and suggested improvement strategies. Overall, however, we found that a better understanding of the complexities attached to RPA and IPA applications is still needed so as to fully realize the technology's potential.

ABSTRACT. This study seeks to contribute to the knowledge on how technological inno-vations are adopted and normalized within public healthcare settings, and what promotes or hinders the integration of new practices into routine organ-izational processes. The innovations refer to the range and extent to which digital artefacts are introduced across different organizational domains and functions within the public healthcare system. The Normalization Process Theory is used to understand the innovation adoption in the healthcare con-text. To gain an in-depth understanding of the adoption of technological in-novations in public healthcare organizations, a multiple case study design was employed, and collected data were deductively analyzed based on the four generative mechanisms of the Normalization Process Theory: coher-ence, cognitive participation, collective action, and reflexive monitoring. The study concludes that successful normalization of technological innovations in public healthcare depends largely on addressing systemic and organiza-tional barriers rather than focusing solely on the innovations themselves. Without aligning institutional structures, leadership, and learning processes, even well-designed innovations struggle to become part of routine practice.

ABSTRACT. The rise of user-driven digital practices has long challenged organiza-tional IT governance, with Shadow IT emerging as a widely studied phenomenon reflecting the unauthorized use of technologies within firms. However, the rapid advancement of Artificial Intelligence (AI) - and more recently Generative AI (GenAI) - has introduced new forms of shadow activity that are not yet fully captured in existing literature. This paper addresses this gap by offering a con-ceptual extension of the Shadow IT paradigm, arguing that Shadow AI and Shadow GenAI represent qualitatively distinct yet evolutionarily linked develop-ments in shadow technology use. We conduct a Systematic Literature Network Analysis (SLNA) using Scopus-indexed data and bibliometric tools such as VOSviewer and Pajek to examine how the academic discourse on Shadow IT has evolved, and how emerging re-search is beginning to address the challenges posed by unauthorized AI and GenAI adoption. Our analysis reveals a deepening of risks: from governance and data privacy in Shadow IT, to ethical opacity and knowledge erosion in Shadow GenAI. The paper contributes to the literature by theorizing Shadow AI and GenAI as part of a continuum rooted in organizational strain and user rationalization. In doing so, we highlight the need for new governance strategies that go beyond infrastructure control to address the epistemic and cultural implications of AI-driven autonomy in the workplace.

ABSTRACT. With the pervasive adoption of Generative Artificial Intelligence (GenAI) in the workplace, practitioners may need to rethink in previously unimagined ways how they accomplish their organizational tasks. Recent literature has identified Information Technology Identity (ITID) archetypes which capture different reactions to Information Systems (IS) adoption. In this research-in-progress paper, we seek to add to this literature by enabling a temporal understanding of how users with different ITID archetypes experience the process of IS adoption in different organizational contexts. To achieve this, we present a multiple case study with three organizations in three industries suitable for studying multiple applications of similar GenAI tools. Our preliminary findings suggest that as GenAI tools are increasingly adopted in the workplace, adoption increases in two types of situations: (a) when employees already experiment with similar tools in their personal lives, reducing individual resistance; and (b) when organizations roll out such tools through cen-trally mandated, coordinated programs rather than ad-hoc efforts.

ABSTRACT. This preliminary study investigates how explainability features in Generative AI, particularly intelligibility and stability, influence causability, the user's ability to understand and reconstruct AI reasoning. Additionally, it examines how perceptions of GenAI humanness moderate the relationship between causability and distinct types of trust (human-like and system-like). Drawing on Social Cognitive Theory, the research further explores how these trust dimensions impact user self-efficacy, defined as confidence in interacting effectively with GenAI systems. Employing Partial Least Squares Structural Equation Modeling (PLS-SEM) on data from professionals experienced with GenAI tools, this study aims to provide empirical insights into how clear and interpretable explanations enhance user confidence. By addressing the opacity and complexity characteristic of deep learning-based AI systems, the research contributes to developing responsible and transparent AI solutions, enhancing interpretability, trust, and effective user interaction.

ABSTRACT. The growing maturity of large language models (LLMs) has led to a growing interest in applying generative AI (GenAI) to automate information-heavy communication workflows. This study investigates the use of prompting techniques to classify business related email responses in a real world setting. Based on expert interviews and operational data from a multinational organization, five prompting strategies were prototypically implemented and evaluated on a labelled dataset of 200 emails. The evaluation followed a multi-criteria decision analysis, considering performance, reliability, scalability, and adaptability. Results show that detailed zero-shot prompts outperform both simple baselines and more complex approaches like few-shot or Chain-of-Thought prompting. Unlike many prior studies based on synthetic data or benchmark tasks, this research demonstrates the practical viability of prompt only GenAI solutions under realistic operational constraints. The study contributes to applied GenAI research by demonstrating that effective prompt design is a key enabler for LLM-based automation in communication heavy business processes, without requiring training data or model adaptation. Furthermore, the study offers practical guidance for organizations seeking to integrate LLM-based automation into shared communication channels.

ABSTRACT. Traditional enterprise automation systems often lack the contextual intelligence and flexibility required in logistics-intensive environments, particularly for small and Medium-Sized Enterprises (SME). This paper proposes a five-phase implementation framework for Cognitive Enterprise Hubs (CEH), emphasizing role-sensitive deployment and continuous alignment between human and AI. The model combines architectural planning, AI integration, and cultural adaptation to support scalable and adaptive collaboration across federated ecosystems. Empirical validation is provided through two cross-sector case studies and a multi-role survey with 18 participants from IT, cybersecurity, and telecommunications sectors. Findings reveal notable perceptual differences across organizational roles, especially between IT leaders, transformation strategists, and frontline employees, regarding CEH impact on productivity, support, and future opportunities. The study underscores that CEH success depends not only on technical orchestration but also on socio-cultural alignment. To address this, we offer practical deployment guidelines tailored for SME and logistics-driven operations. By integrating technical and organizational perspectives, this work advances the practical deployment of intelligent enterprise systems, positioning CEH as enablers of inclusive, cognitively enhanced coordination frameworks.

ABSTRACT. Emerging technologies like chatbots are employed across multiple domains to support both external and internal stakeholders and impact organizational cultures. In our research, we explore how a chatbot designed for leadership training facilitates actionable insights on inclusion, a core challenge in digital transformation, translating abstract concepts into tangible practices. We designed a chatbot to guide leaders in practicing inclusive behaviors, then tested it with real-world leaders. Using Natural Language Processing (NLP) methods on responses from leaders who interacted with the chatbot, we uncover multi-level discourse on inclusion and examine how these exchanges shape team inclusion climate and, by extension, impact organizational culture, providing data-driven insights into whether and how technology can foster inclusion in teams. Our findings contribute to the growing research by investigating whether "digital" tools like chatbots enable transformative change. Additionally, we redefine the "user" as an active participant in co-creating inclusive cultures. Furthermore, by guiding leaders through structured interaction, our chatbot helps develop concrete, actionable skills that can be applied across different settings to strengthen diversity, equity, and inclusion efforts in organizations. This study bridges the gap between emerging technologies and team inclusion by demonstrating how chatbot applications can be effectively integrated into existing HRM practices, particularly in leadership training and development, to provide systematic, empirically validated support for digital transformation initiatives.

ABSTRACT. This paper explores how digital transformation unfolds within the structurally rigid and highly institutionalized environment of Italian prison organizations. Drawing on a qualitative single-case study conducted at the Casa di Reclusione di Padova, we examine the bottom-up development and implementation of a digital registry system for managing disciplinary reports. Anchored in a sociomaterial perspective, the study highlights how even low-complexity technologies can generate cultural disruption, reshape organizational routines, and reconfigure authority structures. The findings show that digitalization in this context does not merely automate tasks but acts as a catalyst for new professional norms, accountability mechanisms, and forms of coordination. Through thematic analysis of interview data triangulated with internal documents, the research identifies three key dynamics: cultural adaptation through informal learning, infrastructural realignment enabling transparency and traceability, and a rearticulation of hierarchical roles mediated by technology. This study contributes to the literature on sociomateriality and public sector innovation, while also offering practical insights for designing context-sensitive digital tools in institutional settings typically resistant to change.

ABSTRACT. The intersection of Artificial Intelligence (AI) and Public Administration (PA) is a field of exponential growth, yet it is widely perceived as fragmented and lacking a coherent intellectual core. This study addresses this tension by providing a bibliometric-based thematic analysis of the research landscape. Analyzing a corpus of 820 documents, we employ performance analysis and keyword co-occurrence to map the field's evolution and thematic structure. Our findings reveal a "post-2018 explosion" of research, creating a two-speed intellectual economy where PA functions as a net importer of ideas from adjacent disciplines. We identify five distinct thematic clusters—from technical work on Explainable AI to normative debates on AI Governance—that mostly operate as functional silos. We argue that the central challenge is not a lack of research but a need for intellectual integration. This map provides a foundational tool for scholars to build conceptual bridges and foster a more cumulative body of knowledge.

ABSTRACT. This paper presents a systematic literature review examining shared respon-sibility in human-AI collaboration across various domains. As AI systems increasingly work alongside humans in decision-making processes, under-standing how responsibility is distributed between human users and AI agents becomes critical for ensuring proper accountability and trust. Through analysis of 40 peer-reviewed studies published between 2019-2025, we synthesize current conceptualizations of shared responsibility, identify key mechanisms for implementation, and examine associated out-comes. Our findings reveal three primary mechanisms for effective respon-sibility sharing: human-in-the-loop control, explainability/transparency features, and organizational processes. Well-designed responsibility frameworks promote calibrated trust, enhanced decision quality, and ap-propriate blame attribution, while poorly implemented ones create account-ability gaps. Emerging trends include shifts toward multi-actor accountabil-ity models, new challenges posed by increasingly autonomous AI, and technical solutions for structurally enforcing responsibility parameters. This review provides valuable insights for researchers and practitioners on designing AI systems that not only enhance decision efficiency but also en-sure fair distribution of responsibility in human-AI partnerships.

ABSTRACT. This paper examines the vital role that open educational resources (OER) and digital tools play in integrating green skills into academic curricula. Based on UNESCO's Open Science paradigm, including the Open Scientific Knowledge and Open Infrastructures components, this paper shows how openly available information and cooperative online resources enable teachers and students to jointly develop sustainability-focused solutions. OER platforms make high-quality sustainability content more accessible to all, allowing educational institutions to quickly modify their courses to meet changing environmental issues and overcome resource limitations. The study also examines the application of Learning Management Systems (LMS) like Moodle, Canvas, and Blackboard for the incorporation of sustainability material via interactive modules, gamification features, and practical project-based learning. Platforms such as Miro and Padlet cultivate collaborative settings in which interdisciplinary teams address real-world sustainability issues, hence improving critical thinking and problem-solving abilities. These platforms not only promote active learning but also function as essential repository for digital badges, e-portfolios, and micro-credentials that validate sustainable competencies. Institutional support mechanisms essential for scaling these techniques are emphasised significantly. Initiatives, like the Piedmont Project, provide instructors with novel teaching techniques to effectively include sustainability topics. The paper concludes by presenting a strategic framework for higher education institutions to institutionalise sustainability education driven by open educational resources, including recommendations for investing in green digital infrastructure, establishing cross-sector partnerships, and utilising international frameworks such as Erasmus+ to promote global collaboration.

ABSTRACT. Water stress is a global issue, and it’s getting worse with climate change and the growing demand for water in farming, industry, and cities. Traditional models often fall short—they can’t capture the complex, unpredictable nature of how water stress develops. To tackle this challenge, we need more advanced tools that can predict water stress more accurately and help guide better decisions. This research used five Machine Learning (ML) methods—Support Vector Machine (SVM), Decision Tree (DT), Random Forest (RF), Extreme Gradient Boosting (XGBoost), and integrates a stacked ensemble model as novelty—to see how well they can forecast water stress. Data preprocessing was used, such as median imputation for missing values and feature selection. The overall best performance was achieved by the stacking meta-model that stacked RF, XGBoost, and neural networks with Mean Absolute Error (MAE) = 2.4149, MAE = 1.0388, and R2 = 0.9883. A close second was XGBoost with Root Mean Square Error (RMSE) = 2.4191, MAE =1.0385, and R2 = 0.9883, which was slightly better than RF (RMSE =2.9819, MAE = 1.5158, R2 = 0.9837). These results show that ensemble methods, in particular stacking configurations, significantly enhance predictive power over baseline and individual-approach models, by defining best methods for estimating global water stress.

ABSTRACT. This paper proposes an Artificial Intelligence (AI)-based method for predicting global water scarcity, which would help make better sustainable water management decisions. Gradient Boosting, Random Forest (RF), Support Vector Machine (SVM), Logistic Regression, and K-Nearest Neighborss (KNNs) are some of the supervised machine learn ing techniques used in the model. To address the natural class imbalance of the dataset itself, as novelty, the method incorporates Synthetic Minority Over-Sampling Technique (SMOTE) to generate synthetic samples, and weighted classification to enhance model fairness. Among the models evaluated, ensemble methods were found to perform better, the most accurate (97.33%) and highest scoring (96.92%) being Gradient Boosting in the F1 score, followed very closely by Random Forest at 96.00% accuracy and 94.37% F1 score. These results verify the efficiency of ensemble methods to handle structured, complex data of skewed distribution. On the other hand, SVM and KNN had poorer performance, where KNN had the worst results (accuracy: 80.67%, F1 score: 78.58%), revealing the incompetence of KNN in learning patterns from the minority classes despite the use of SMOTE. Logistic Regression was then a strong candidate, having achieved an accuracy of 93.33% and an F1 score of 92.45%. This study emphasizes municipal (domestic) and agricultural water use which are the most significant effect on water scarcity classification, followed by larger spatial and temporal measures. This work demonstrates the potential for a complete data pipeline—ranging from preprocessing to validation and deployment of models—of delivering actionable insights into global water stress.

ABSTRACT. In the era of digital transformation quantum technologies represent a new challenge for the society, industries, governmental sector. The benefits and consequences of the adoption of quantum technologies have yet to be fully explored. In this context the role these technologies will play in urban con-text is essential to explore. The present study examines the European Un-ion’s quantum initiatives and its vision of the possible applications of quan-tum solutions. This study applies these considerations to the European urban context and explores it through the prism of public values created by the smart cities and smart city dimensions. The findings of this research highlight the socio-technical implications and transformative potential of quantum technologies on European smart urban environments and offer strategic in-sights for urban planners, policymakers, and city managers to prepare for the opportunities presented by quantum technologies.

ABSTRACT. This study examines how ethical design influences AI acceptance through cognitive and affective pathways. Using experimental data from 278 participants exposed to high versus low ethical AI designs, we uncover dual processing mechanisms: direct visceral responses and sequential cognitive-affective elaboration. Remarkably, all 16 sequential mediation paths were statistically significant, while simple mediations showed partial success. The equivalence of direct and sequential indirect effects implies that the processing of ethical AI features involves both intuitive and analytical systems. Different dimensions require distinct strategies: privacy needs cognitive scaffolding, autonomy triggers immediate responses, and fairness engages both routes. These findings establish ethical design as a central determinant of AI acceptance, requiring a complementary mediation model where cognitive understanding enables rather than competes with emotional engagement.

ABSTRACT. Our study investigates how users engage with and make sense of the integration of the new shopping functionality into ChatGPT. Using a netnographic approach inspired by ethnographic research, we examine the cultural, functional, and symbolic meanings users assign to this feature. Data was collected from Reddit, a platform known for active dis-cussions around technology. The findings reveal a dual response. On one hand, many users view the feature positively, describing it as intuitive, seamless, and well-suited to their needs. On the other hand, some express concern, raising issues such as distrust in the platform, fears of "enshittification" (a perceived decline in platform quality), and resistance to commercial integration. Rather than approaching these discourses as silos, we interpret them as coexisting perspectives that speak to deeper anxieties and desires regarding the handling of this new technology, particularly a new feature that might sig-nificantly shape how users search and decide on products to buy. With our study, we contribute to ongoing discussions in AI and marketing on the everyday cultural meanings that shape how users experience and adapt to technological change.

ABSTRACT. Tourism literature has long debated in favour of sustainable practices, where early sustainability scholars aimed to promote the concepts of justice, equity, and fairness. While sustainability is a well-researched phenomenon within tourism marketing literature, there are very few studies approaching the dis-course from a social-political or philosophical perspective. The need for such ethical discourse is far more crucial than ever before, given that the rapid in-tegration of digital innovation is further thickening the fog, raising new eth-ical challenges for businesses as well as society, especially related to knowledge management, stakeholder engagement, and sustainable destina-tion management practices.

ABSTRACT. The enthusiasm surrounding the use of artificial intelligence (AI) enabled digital solutions in healthcare is tempered by uncertainty around how it will change the working lives and practices of clinicians and healthcare professionals. This study examines an under-researched topic: the impact, as well as the perceptions, of digital automation via the implementation of AI-enabled technology (AIET) on doctors, nurses and allied health professionals (AHPs) who provide or facilitate healthcare to patients in high-volume low-complexity care pathways. Our findings shed light onto some perceived challenges and benefits of AIET implementation as well as capturing the early impacts of AI adoption in this particular care pathway.

ABSTRACT. In this paper, we investigate how user demographics– age, gender, and nationality– influence perceptions of generative AI, focus ing on ChatGPT as a case study. We draw on a survey of 472 ChatGPT users and analyse their responses about the AI’s perceived gender, the emotional sentiment of adjectives used to describe the AI, and their satisfaction with the AI’s performance. Using a socio-technical systems framework, we interpret these findings to understand the human-centric integration of AI. Statistical tests reveal no significant direct relationship between user demographics and their attribution of a gender to Chat GPT (p > 0.05). However, user gender, age, and cultural background collectively exhibit underlying influences on whether the AI is seen as “male” or “female”. Sentiment analysis of descriptive adjectives indicates that participants generally view ChatGPT positively– descriptors like “helpful,” “intelligent,” and “friendly” are common– and sentiment scores skew strongly toward the positive range. We find that sentiment and sat isfaction levels vary across demographic groups, especially by nationality and gender, suggesting cultural and identity-based differences in user–AI interaction. Through a socio-technical systems lens, we discuss how these perceptions reflect broader socio-cultural narratives and stereotypes. The study highlights that even ostensibly “neutral” technologies like Chat GPT are perceived through a human cultural lens, underscoring the need for human-centred AI design that accounts for diverse user identities.

ABSTRACT. Predictive Analytics encompasses methodologies that leverage statistical algorithms and data mining to forecast future outcomes based on historical data. Even though, analytics is a growing field its technology adoption is more prominent in big tech companies and young startups. This paper submission is to explore the design, development, implementation and adoption of predictive forecasting applications as human-centric digital technologies and with socio-technical alignment. This study also examines business applications which are defined as software systems that automate and support at least one business process or logically related task to execute or achieve specific business objectives and their application of analytics in data-driven decision-making. We start with a structured literature review which was conducted to gain an overview of analytics business applications in the current literature. After that, we conducted qualitative expert interviews which investigated the factors of technical adoption. The study design discusses technology adoption theory, the Technical Acceptance Model, Algorithmic Aversion and the Technology-Organization-Environment framework. We conduct an extensive qualitative study which shows the factors which facilitate predictive algorithms on business application data and which develops a comprehensible framework with practical guidelines for the use of Predictive Analytics and cloud-based Business Analytics.

ABSTRACT. This systematic review explores the impact of artificial intelligence (AI) on the well-being and burnout of healthcare professionals. With the increasing integration of AI into clinical settings, it is essential to understand its potential benefits and challenges for the healthcare workforce. This review identifies four key areas: AI for detecting burnout, AI-based interventions to reduce administrative workload, AI in patient interaction and workflow optimization, and ethical concerns. AI has the potential to alleviate clinician burnout by automating administrative tasks and supporting decision-making. However, challenges such as over-reliance on AI, erosion of clinical autonomy, and ethical issues related to data privacy and algorithmic bias must be carefully addressed. This review offers a foundation for future research and practical insights for developing AI tools that can enhance clinician well-being while aligning with human-centred care values.