ABSTRACT. Quality management has progressively evolved from inspection-based ap-proaches to Total Quality Management, and more recently toward Quality 4.0, where digital technologies such as artificial intelligence, IoT, big data analytics and predictive systems have reshaped quality control and deci-sion-making. However, the transition from Industry 4.0 to Industry 5.0 highlights the need to move beyond a predominantly technology-driven view of quality, integrating human-centricity, sustainability, resilience and ethical governance into quality management systems. This paper aims to explore how quality management has evolved from TQM to Quality 5.0 through the intermediate digital transformation enabled by Quality 4.0. A structured narrative literature review based on qualitative thematic analysis, supported by an exploratory bibliometric analysis, is adopted to examine emerging contributions on TQM, Quality 4.0, Industry 5.0 and Quality 5.0. The analysis identifies five emerging dimensions of Quality 5.0: intelligent quality, human-centric quality, sustainable quality, resilient quality and ethical quality governance. The paper contributes to the literature by pro-posing an evolutionary interpretation of quality management, according to which Quality 5.0 reconfigures traditional TQM principles and Quality 4.0 digital capabilities within a broader socio-technical paradigm integrating intelligent technologies, human-centricity, sustainability, resilience and ethical governance.

ABSTRACT. Clinical documentation is a central yet time-intensive component of pro-fessional practice in the Allied Health Professions, tightly coupled with clinical reasoning and the continuity and quality of care. Against the back-drop of advances in Automatic Speech Recognition, Natural Language Pro-cessing, and generative AI, we examine the state of AI-supported documen-tation in Allied Health Professions and the conditions under which it is ef-fective. We synthesise fourteen studies in a traceable concept matrix from which we inductively derive an integrative framework. The evidence base is fragmented and nascent: concentrated in pre-hospital and emergency set-tings, dominated by pilot studies, and only beginning to engage generative AI in the therapeutic professions. Reported benefits, improved complete-ness, context-dependent recognition accuracy, and selective time savings, are real but conditional, and they do not transfer automatically from the di-agnosis-centred settings in which most systems were developed. Our framework links technological, organisational, situational, professional, and regulatory dimensions to documentation outcomes, and explains why effectiveness in AHP depends on profession-specific reasoning and reim-bursement logics that generic automation overlooks. The study positions AHP as a distinct, underrepresented domain within digital health research.

ABSTRACT. This study examines how Artificial Intelligence (AI) is governed within the emerging Healthcare 5.0 (H5.0) paradigm. It asks which implementation challenges, strategic opportunities and operational domains characterise the explicitly labelled AI–H5.0 literature and how these dimensions can be integrated into an information-systems (IS) governance model for responsible adoption. A focused systematic review of 27 Scopus-indexed contributions and a combined deductive–inductive content analysis identify a literature dominated by cybersecurity, decision support, infrastructure and trust-related concerns. Rather than treating AI as a stand-alone dynamic capability, the study conceptualises AIenabled capability as the organisational orchestration of data, digital infrastructure, clinical workflows, decision rights, human oversight and learning routines. The resulting six-stage cycle—orient, govern, enable, embed, assure, and learn—illustrates how technological solutions can be integrated with managerial practices to support patient-centred outcomes. Its main contribution lies in explaining this configuration and showing how H5.0 moves beyond the efficiency-driven perspective commonly associated with Healthcare 4.0. At the same time, these conclusions should be interpreted with caution, given the limited evidence base of a narrowly focused, single-database review.

ABSTRACT. Digital competencies are increasingly regarded as essential for suc-cessful work in digitally transformed organizations. At the same time, emerging technologies such as artificial intelligence (AI) further reshape competency re-quirements and emphasize the need for adaptability, critical thinking, and con-tinuous learning. While prior research provides extensive conceptualizations of digital competencies, little is known about to what extent these competencies are considered and assessed within recruiting and personnel selection processes. To address this gap, this study investigates how organizations assess digital compe-tencies during recruiting and personnel selection. Drawing on a qualitative re-search approach, we conducted semi-structured interviews with twelve recruiting professionals from diverse industries and organizational contexts. The findings show that digital competencies are widely perceived as essential requirements in contemporary work environments. However, they are rarely operationalized as explicit assessment criteria during recruiting. Instead, recruiters often treat digital competencies as implicit baseline expectations and rely on indirect signals and intuitive judgments when evaluating applicants. Furthermore, the results indicate a shift from assessing specific technical competencies towards broader meta-competencies such as adaptability, learning ability, and openness towards emerg-ing technologies such as AI. This study contributes to research on digital compe-tencies and recruiting and personnel selection by highlighting the gap between conceptual competency frameworks and practical assessment processes. In addi-tion, the findings provide practical implications for designing more structured and strategically aligned approaches to assessing digital competencies.

ABSTRACT. Healthcare organizations are increasingly shaped by the integration of arti-ficial intelligence (AI), generating new demands and resources for profes-sionals. This work-in-progress study examines how healthcare profession-als in Italian public organizations proactively adapt to AI-driven change through job crafting behaviors, using the Job Demands–Resources (JD-R) framework as its theoretical foundation. A quasi-experimental pretest–posttest design involves approximately 80 healthcare professionals dis-tributed across intervention and control groups. The intervention consists of a structured job crafting me incorporating AI-related components and digital adaptation strategies. Data are collected through validated question-naires and semi-structured interviews. Preliminary qualitative findings re-veal that professionals perceive AI as both a source of complexity and an organizational resource and that proactive crafting behaviors mediated by psychological safety and leadership support may sustain well-being under technological change. The study contributes to the emerging literature on AI-driven organizational transformation, proactive work behavior, and workforce sustainability in healthcare.

ABSTRACT. The paper examines the role that GenAI chatbots play in the strategic decision-making processes of HR managers, arguing that their integration into decisional practice generates a cognitive extension dynamic, theorized through Extended Mind Theory, - in which the boundary between the manager’s own reasoning and the chatbot’s output becomes progressively difficult to locate. We propose the concept of aware delegation as the theoretically ideal condition in which an HR manager consciously allocates specific cognitive functions to a GenAI chatbot while retaining metacognitive recognition of that allocation, and we argue that this condition is systematically undermined by the very success of cognitive extension. The paper develops a theoretical framework grounded in the decision-making literature and proposes a qualitative empirical investigation to examine where, along the decisional process, HR managers are aware of what they are delegating.

ABSTRACT. Renewable Energy Communities (RECs) promise environmental, economic, and social benefits, yet governing their performance remains challenging due to persistent and interrelated tensions. In particular, REC decision-making involves balancing economic viability and social value creation, while simultaneously adapting established routines and governance arrangements over time. This paper adopts a Design Science Research (DSR) approach to develop prescriptive design knowledge for a Decision Support System (DSS) supporting impact governance in RECs. Drawing on paradox theory and DSS design foundations, the study abstracts recurring decision issues into meta-requirements and design principles intended to enable collective sensemaking through transparent indicators, scenario exploration, and longitudinal analysis. Although this work focuses on steps 1-3 in Peffers et al. [1], it also outlines a conceptual artefact demonstration and formative evaluation strategy. The paper contributes prescriptive guidance for designing DSS that support the navigation of persistent tensions in generating impact in community-based sustainability contexts.

ABSTRACT. Digital Product Passports (DPPs) are expected to make circular value chains more transparent by sharing data on product composition, lifecycle events, provenance, and recovery options. Yet the sustainability of critical raw material recovery from e-waste rests on inter-organisational power dynamics among policymakers, producer, recyclers, and raw material producers, as well as on the availability of information itself. This paper outlines, at a logical level, how a simulation could be structured to study the impact of the DPP, as an inter-organisational information system, on the power dy-namics shaping the economic, environmental, and social sustainability of the circular value chain. Building on three prior simulation studies, we develop a framework that couples macro power dynamics, meso-level impacts (circular value chain), and micro dynamics of heterogeneous actors. The simulation framework offers a starting point for studies on how DPP-enabled information infrastructures may redistribute power and produce uneven sustainability outcomes among actors and across levels of analysis.

ABSTRACT. This paper presents a preliminary investigation into the roles played by digital leaders in the adoption of emerging technologies within SMEs. While prior research has identified several roles associated with digital leaders, limited research has examined how these roles evolve throughout the organizational adoption of emerging technologies. Drawing on semi-structured interviews with 13 participants from 11 SMEs, the study examines how digital leaders support emerging technology adoption throughout the pre-adoption, adoption, and post-adoption phases. The findings show that different digital leaders’ roles become particularly relevant across the different phases of the adoption process, while some roles operate transversally across phases. By adopting a process-oriented perspective, the study contributes to the literature on digital leaders by showing how different roles gain relevance throughout the adoption of emerging technologies, with implications for supporting SMEs throughout the adoption process.

ABSTRACT. Digital transformation is reshaping how work is organized and performed, yet its success depends not only on the implementation of new technologies but also on how employees adapt to changing work demands and proactively redesign their roles. This study examines strengths-based job crafting as a form of employee adaptation during technology-driven organizational change, focusing on the role of employees’ readiness for change, job satisfaction, and attitudes toward information and communication technologies. Drawing on the Job Demands–Resources perspective, we examine how individual and work-related resources may foster employees’ proactive efforts to align their tasks with their personal strengths, while considering technology as an environmental factor that may simultaneously enable and constrain employee agency. Data were collected from administrative employees working in four Italian healthcare organizations undergoing digital transformation. Results show that readiness for change and job satisfaction are positively associated with strengths-based job crafting. In contrast, attitudes toward ICT are not significantly associated with strengths-based job crafting, despite the negative direction of the relationship. This finding suggests that positive attitudes toward digital technologies do not necessarily translate into greater proactive work redesign. Rather, employee adaptation during digital transformation appears to depend more strongly on psychological readiness and positive work-related resources than on technology orientation alone. The study highlights the role of employees as active participants in technological change and shows that the relationship between technology and proactive work redesign is more nuanced than a simple technology-enablement perspective would suggest.

ABSTRACT. The integration of Artificial Intelligence (AI) into Human Resource Man-agement (HRM) is redefining the borders of organizational design and per-sonnel selection processes (Marler & Boudreau, 2017). From recent studies on the AI-HRM intersection (Tambe et al., 2019; Strohmeier, 2020), this study analyzes how the adoption of AI in personnel selection processes simplifies the work of HR managers, freeing up time to be dedicated to higher-value-added activities. Adopting a qualitative approach based on narrative interview, the study analyzes the empirical case of “PeopleNest AI”, software used for resume screening and skills mapping. The results confirm that AI reduces transactional workloads through prompt engineer-ing guided by objective institutional frameworks. However, the algorithm does not replace the human evaluator, who, acting as a technological coach-ing tool, converts masses of data into narrative and actionable reports. The implications highlight how a human-centered approach is fundamental to balance algorithmic coordination (Burton et al., 2021) and the guarantee of procedural fairness.

ABSTRACT. As digital technologies reshape workplaces, their relationship to employee well-being remain unclear. This study examines how digital tools enabling be-havioural visibility affect well-being and whether flexible work arrangements mitigate these effects. Drawing on Self-Determination Theory and Conserva-tion of Resources Theory, we propose that behavioural visibility creates both motivational benefits and psychological costs, and that work flexibility and the ability to disconnect moderate these effects. We analysed data from 5,529 Eu-ropean knowledge and service workers from the 2024 European Working Con-ditions Survey, using hierarchical OLS and negative binomial regression to ex-amine work engagement and mental health problems. Behavioural visibility is positively associated with both work engagement and mental health problems, indicating a trade-off. Flexible work arrangements show limited and sometimes unexpected moderating effects. In particular, greater flexibility in working hours may exacerbate mental health problems, while the ability to disconnect provides a protective effect. Digital behavioural visibility offers motivational benefits but also carries mental health costs. Flexible work arrangements alone may not sufficiently protect employee well-being, highlighting the importance of maintaining clear boundaries between work and non-work time.

ABSTRACT. Information systems (IS) implementation continues to face resistance despite substantial organizational investment in digital transformation initiatives. While prior research has extensively examined the technical, political, and cognitive antecedents of resistance, comparatively less attention has been given to the role of culture in shaping how resistance emerges, circulates, and escalates during implementation processes. This study examines the initial implementation phase of an automated routing system within a large consumer goods distribution organization. Drawing on grounded theory analysis of 25 semi-structured interviews, the study develops a process model explaining how culturally embedded authority norms, communication structures, and trust perceptions influenced resistance dynamics during implementation. The findings demonstrate that culture played a central role in shaping how organizational actors interpreted operational disruption and leadership actions. Hierarchical authority norms constrained upward voice and reinforced managerial passivity, while high-context communication patterns distorted feedback circulation and amplified interpretive ambiguity. At the same time, uncertainty surrounding implementation outcomes contributed to growing distrust among frontline actors, intensifying resistance escalation. Leadership responses characterized by directive enforcement rather than deliberative engagement further reinforced these culturally conditioned dynamics and contributed to the suspension of the initial implementation phase. By conceptualizing resistance as a culturally structured and socially mediated process rather than merely a reaction to technical disruption, this study advances process-oriented understanding of culture and resistance in IS implementation.

ABSTRACT. This paper examines how internal communicators in large organizations perceive the introduction of generative AI, and what their perceptions reveal about a culture in transformation. Organizational communication operates on two entangled levels: an operative-communicative level, through which texts circulate and action is coordinated, and a cultural-meaning level, through which symbolic order, identities and organizational languages are sustained. Internal communication occupies a liminal position across these levels, gardening organizational culture by translating, selecting and stabilising the symbolic order in moments of technological change. Rather than treating generative AI as a neutral technology for content generation, this study approaches it as a sociotechnical innovation that interferes with organizational talk, sensemaking and the mediation of meaning. The empirical material consists of ten semi-structured interviews with internal communication professionals working in large organizations operating in Italy. The interpretive work of the researchers, combining thematic analysis and Gioia-inspired coding, is enriched by a comparative reading with two large language models, GPT 5.5 and Claude Opus 4.7, used as co-analytical lenses. Three tensions emerge. Augmented multitasking, where AI redistributes time across micro-decisions rather than freeing it for strategic judgement. Narrative homologation and qualitative flattening, produced by texts that are fluent yet increasingly indistinguishable. AI as a mirror, returning to the organization an image of its own assumptions, vulnerabilities and competencies. We designate the resulting asymmetry as the paradox of the privileged observer: internal communicators see the shift in meaning, while the organization frames it as a change of technology rather than of culture.

ABSTRACT. In this article we examine the impact of artificial intelligence (AI) on organizational culture, proposing a multilevel algorithm synergy framework. Although AI is valuable to many organizations, we argue that the role of synergy between AI and organizational culture remains undertheorized. Drawing from the competing values framework and the relational models theory of social exchange, we argue the effectiveness of AI is only partially determined by the quality of algorithms because employees will interpret AI in light of their cultural understanding. Demonstrating this logic, this paper maps four distinct AI functions onto four distinct organizational cultures through relational models. We articulate overlooked properties of various AI functions –conversational AI, biometric AI, generative AI, and algorithmic management that make each uniquely positioned to not only activate but influence social exchange within organizations, catalyzing distinct synergies that correspond to various cultural archetypes. We suggest the strategic implementation of AI can result in a jolt of synergy enhancing collaboration, control, innovation, or financial effectiveness at both the individual and organizational level. This research enriches the discourse on human-AI interaction by offering a more sophisticated understanding of AI’s role in shaping organizational culture and performance.

ABSTRACT. Organizations are increasingly adopting Low-Code/No-Code Artificial Intelligence (LCNC AI) platforms to accelerate and democratize the development of AI-enabled applications beyond the scope of traditional IT departments. While these platforms can enhance innovation and agility, they also introduce technical, organizational, and ethical challenges. In particular, decentralized development by business users with limited technical and ethical expertise may lead to integration complexity, quality issues, shadow IT, algorithmic bias, and limited transparency. However, existing governance approaches for traditional LCNC platforms and AI systems only partially address these challenges, providing limited guidance for governing LCNC AI development in organizational settings. Against this backdrop, this paper investigates which design principles (DPs) can be derived to support the safe and effective governance of LCNC AI platforms within organizations. To this end, a systematic literature review was conducted to synthesize opportunities and challenges of LCNC AI along technical, organizational, and ethical dimensions. Based on these findings, eight DPs were derived and grouped into structural, procedural, and relational governance mechanisms. These DPs offer prescriptive guidance for balancing innovation and agility with oversight and responsibility in LCNC AI development.

ABSTRACT. Blockchain ecosystems have been described both as engines of trustlessness and as new architectures of trust. Recent work has shown that distributed-ledger technologies do not eliminate trust but redistribute it across the protocol, the network, and the institutional actors who deploy it. What this literature has not yet examined is how trust is produced when the same blockchain artifact is approached simultaneously by populations of actors who mobilize different cognitive resources. We address this question through a qualitative cross-sector analysis of 23 interview transcripts from four sectoral clusters in Italy: public infrastructure, circular economy traceability, agrifood supply chain, and regulated digital finance. The analysis identifies three trust regimes that coexist around the same blockchain infrastructure: trust by inspection, in which developers and auditors grant trust by reading the code; trust by label, in which enterprise clients and end users grant trust to the word blockchain as a category marker; and trust by reputation, in which regulators grant trust on the basis of the institutional stand-ing of the deploying actor. The cross-case evidence indicates that the three regimes remain decoupled, and that this decoupling is not a deficit of communication but a structural condition under which the ecosystem operates. The paper introduces the trust regime as a unit of analysis that couples a population of actors to a cognitive ground, and reframes decoupling as a productive condition rather than as a residual asymmetry.

ABSTRACT. Digital technologies are reshaping nearly every aspect of our daily existence, including education and work. The higher education sector is undergoing a significant transition, in which educators are increasingly integrating advanced technologies to optimise student academic performance while prioritising the long-term viability of their professional careers. This study aims to assess the competent use of digital systems in human-technology interaction based on the Conservation of Resources Theory (COR). This study explored the relationship among the usage of digital technologies, digital self-efficacy (DSE), and perceived sustainable employability (PSE) of university teachers at public-sector universities, using a sample of 260 participants. Data was analysed using the Structural Equation Modelling (SEM) approach through Smart PLS (v 4.1.1.8). Results indicated that the usage of digital technologies enhances digital self-efficacy, which in turn promotes sustainable employability among university teachers. However, the mediating role of information and data literacy has not been found to be statistically significant in this study. Based on these findings, recommendations and future research directions have also been provided.

ABSTRACT. Research on Electronic Design Automation (EDA) and the design of heterogeneous electronic systems is characterized by a rapidly expanding body of literature, high technical complexity, and continuously evolving terminology. These characteristics make it increasingly difficult for researchers to acquire a comprehensive understanding of the field, particularly when operating under severe time constraints or with limited prior domain expertise. Traditional literature review approaches, which typically rely on predefined search strategies and linear review processes, may therefore prove insufficient in contexts where the research perimeter must be progressively discovered and refined throughout the review. This paper addresses this challenge by proposing a practical and AI-assisted literature review methodology designed to support rapid knowledge acquisition in complex technological domains. The methodology combines expert knowledge, large language models, citation analysis, keyword extraction, thematic clustering, and iterative literature retrieval to progressively identify key concepts, research challenges, and relevant scientific contributions. Rather than treating the literature review as a static and predefined process, the proposed approach views it as an adaptive knowledge discovery process in which the research scope evolves through continuous interaction between human expertise and artificial intelligence. The methodology is applied to the domain of AI-assisted design automation for heterogeneous electronic systems, a field characterized by strong interdisciplinarity and fragmented knowledge sources. The results suggest that the proposed approach can significantly accelerate domain familiarization, improve the identification of relevant literature, and support the construction of a coherent conceptual framework in emerging and rapidly evolving research areas.

ABSTRACT. Cognitive offloading is an increasingly relevant phenomenon for understand-ing how university students manage cognitive demands through external tools, resources, and digital technologies. While cognitive offloading can re-duce cognitive load and support task completion, it may also become prob-lematic when students rely on external tools in ways that replace, rather than support, their own reasoning, reflection, and knowledge construction. This issue has become particularly important with the diffusion of GenAI chatbots in higher education. While traditional forms of cognitive offloading mainly involve the externalization of memory support or information retrieval, GenAI chatbots may extend this process to more complex cognitive activi-ties, including comprehension, problem-solving, aspects of critical thinking, and evaluations of students’ own cognitive capabilities. Through a systemat-ic literature review, this paper examines cognitive offloading in higher edu-cation by synthesizing how the concept has been conceptualized, identifying its main antecedents, and analysing its potential consequences for students’ learning. Our analysis reveals that cognitive offloading is encouraged by ef-fort minimization, metacognitive judgments, immediate feedback and ready-made solutions, and overreliance on external tools. The review also indicates that cognitive offloading may be associated with surface-level cognitive en-gagement, reduced critical thinking, and weakened metacognitive confi-dence. Overall, this study contributes to the Information Systems literature by positioning GenAI-mediated cognitive offloading within a broader conceptu-al continuum and by highlighting the conditions under which cognitive of-floading may become a risk for university students’ cognitive development.

ABSTRACT. Social media are widely envisioned as environments for expanded democratic participation, yet online deliberation often remains fragmented and weakly reciprocal. Such limitations are often architectural consequences of the socio-technical environments hosting these interactions. Building on this premise, this study examines whether empirical studies conceptualize social media as active, constitutive architectures or merely as passive observational settings. We conduct a systematic, taxonomy-based review using an LLM-assisted coding methodology, which integrates both conceptual-to-empirical and empirical-to-conceptual iterations. A systematic Scopus search identified 154 records that, after rigorous cleaning and screening, resulted in a final corpus of 38 empirical studies. The taxonomy classifies study-level operationalizations across five dimensions of deliberative quality Rationality, Relevance, Equality, Reciprocity and Politeness/Civility), three socio-technical dimensions (Identifiability, Seed Comments,Additional Platform / Interface Architectural Functionalities, and one platform role dimension, Platform as Passive Context). The LLM assisted coding was human validated against the all corpus achieving a Global Cohen's kappa of 0.909. The results reveal a significant methodological imbalance. Empirical studies focus on discourse quality dimensions. Conversely, platform-level conditions remain under-theorized. Cluster analysis further identifies three profiles: Deliberative Quality Studies, Platform Design Studies, and a dominant Platform as Context profile.

ABSTRACT. This paper reports an ongoing Design Science Research project that is developing an LLM/RAG-based application to support Learning and Develop-ment professionals in transforming heterogeneous corporate documents into mi-crolearning paths and assessment materials. While Large Language Models offer new opportunities for automating training design activities, their adoption in or-ganizational contexts raises challenges related to hallucinations, source unfaith-fulness, pedagogical inconsistency, and error propagation across generative workflows. Using a Design Science Research approach, the study derives design knowledge from three complementary sources: collaboration with a company op-erating in corporate training, iterative artifact development through agile experi-mentation, and prior literature on HRM, instructional design, Retrieval-Aug-mented Generation, document understanding, human-AI interaction, and auto-matic question generation. The paper proposes five meta-requirements and five design principles for designing AI systems that make generated learning outputs traceable, instructionally usable, and subject to professional validation. These principles are being instantiated through a digital artifact called “Maieutica”, an evolving LLM/RAG-augmented authoring environment currently under iterative refinement with the partner company. The study contributes to Design Science Research and AI-enabled HRM by framing LLMs as augmentation technologies that support, rather than replace, professional judgement in training design.

ABSTRACT. The study examines how higher education trainers allocate and reconfigure training tasks, decision rights, and responsibilities between human actors and AI-enabled systems across the training lifecycle. It integrates delegation framework of agentic IS artifacts (Baird & Maruping, 2025) with five inter-dependent training phases - needs assessment, training design, delivery, assessment, and feedback/follow-up - thereby providing a process lens for ex-plaining the human-AI collaboration in the higher educational context. We conducted an exploratory qualitative study based on semi-structured inter-views with nine participants: senior trainers and faculty, junior training de-signers, and professionals developing AI-enabled educational software. Data was analyzed through a two-cycle procedure combining inductive and framework informed concepts. Findings show that delegation expands from bounded information processing in needs assessment to generative, learner-facing, evaluative, and persistent support roles in later phases. However, trainers retain or reclaim problem framing, pedagogical approval, contextual interpretation, relational facilitation, validity checks, and ultimate accountability. Three recurring configurations emerge: selective delegation, interactive augmentation, and regulated delegation. AI provides speed, broader content generation, personalization, accessibility, and feedback capacity, while also creating tensions between acceleration and epistemic depth, performance democratization and capability formation, expanded agency and accountability, and personalization and relational substitution. Across these tensions, Artisanal Intelligence emerges as the situated human capacity to orchestrate delegation, integrate contextual and relational knowledge, evaluate AI outputs, and intervene when educational quality or responsibility is at stake.

ABSTRACT. Public sector organizations are increasingly expected to improve perfor-mance, enhance service quality, and create public value while responding to digital transformation, workforce turnover, and growing organizational com-plexity. In Italy, the 2025 Zangrillo Directive promotes a strategic approach to human capital development by linking training to organizational perfor-mance. However, its organizational implications remain largely unexplored. This study investigates how the integration of Strategic Human Resource Management (SHRM), Knowledge Management (KM) and training policies supports the transformation of individual knowledge into organizational ca-pabilities and ultimately contributes to organizational performance and pub-lic value creation. An exploratory qualitative design was adopted, based on semi-structured interviews with senior public executives and documentary analysis. The findings reveal a significant gap between the strategic ambi-tions of the Directive and its operational implementation. Although training is increasingly recognized as a strategic organizational resource, bureaucratic rigidities and limited knowledge-sharing practices continue to hinder organi-zational capability development. The study shows that training generates or-ganizational value only when embedded within broader SHRM and KM pro-cesses that institutionalize individual learning into organizational routines, thereby supporting innovation, organizational performance, and public value creation.

ABSTRACT. Algorithmic systems are increasingly used by platform-based organizations to evaluate workers’ performance. The data collected through these systems often serve both developmental and administrative purposes. However, plat-forms rarely disclose how evaluation data are used or for which purpose. This opacity leaves workers uncertain about whether the evaluation serves their growth or their control. Drawing on the challenge-hindrance framework and the performance appraisal literature, this paper examines how different types of disclosure (administrative, developmental, or both) shape how workers perceive the evaluation purpose and its consequential effects on worker outcomes. We argue that disclosure shapes workers’ perceived eval-uation purpose, which determines whether algorithmic evaluation is experi-enced as a “challenge” leading to work engagement and organizational commitment, or as a “hindrance” generating emotional exhaustion and re-sistance behaviors. Finally, five research propositions are derived, and impli-cations for platforms, policymakers, and HR managers are discussed.

ABSTRACT. Conversational agents (CAs) show promise for supporting people with develop-mental disabilities (IDD), but their feasibility in low resource environments re-mains underexamined. This study examines the ecological requirements for in-clusive CAs in Lebanon, a low‑ and middle‑income country (LMIC) marked by infrastructural fragility, economic precarity, and fragmented disability services. Using an ecological systems perspective, we synthesize multidisciplinary literature and analyze interviews with practitioners, educators, therapists, and organizational actors. Findings show that CA inclusivity depends on the interaction of interdependent contexts shaping feasibility in LMICs. The study contributes an ecologically grounded frame for designing equitable, resilient, and culturally relevant CAs in LMICs, advancing IS debates on responsible AI and offering ac-tionable guidance for developers and policymakers.

ABSTRACT. The forest industry is undergoing profound changes driven by climate change, biodiversity loss, digitalization, the transition towards a circular bio-economy and evolving stakeholder expectations. These developments challenge traditional timber-oriented business models and require forest enterprises to redesign the ways in which they create, deliver and capture value. However, existing studies mainly focus on individual transformation processes, such as servitization, digitalization or ecosystem services, without offering an integrated perspective on business model transformation in the forest sector. Therefore, this paper develops a conceptual framework explaining business model transformation in the forest industry. Drawing on the literature on business models, sustainable business models and business model innovation, the paper identifies six interrelated transformation pathways: from timber-based production to sustainable value creation; from linear value chains to circular bioeconomy business models; from product-oriented logic to servitized business models; from firm-centric models to collaborative ecosystem business models; from traditional operations to digital and smart forest business models; and from economic value capture to multidimensional sustainable value creation. The proposed framework contributes to the literature by explaining how sustainability transitions reshape value proposition, value creation, value delivery and value capture in forest-based industries.

ABSTRACT. The digital transformation of healthcare systems is reshaping the organizational models of care delivery. Telemedicine is increasingly being integrated into care pathways, treatment processes, and diagnostic services, in line with the provisions of the Italian Ministerial Decree 77/2022. Within this context, telerehabilitation represents an innovative model capable of transferring part of the therapeutic pathway from the hospital to the patient’s home, thereby enhancing accessibility and continuity of care. However, its implementation requires substantial organizational redesign to ensure that the quality of care is maintained while simultaneously creating opportunities for greater effectiveness and efficiency. This study investigates the transformative potential of telerehabilitation within hospital organizations. Specifically, it explores whether and how technological readiness, together with the capacity to redesign processes, professional roles, and managerial models, may be associated with successful telerehabilitation initiatives. To address this objective, an exploratory study was conducted through both a theoretical analysis and a case study focusing on a Sport Therapy project involving pediatric patients affected by metabolic disorders. The project was developed to promote multidisciplinary coordination and the personalization of interventions. Nevertheless, several critical issues emerged, including the lack of interoperable information systems, the absence of formalized organizational governance, limitations in remote clinical monitoring, and the risk of exacerbating digital inequalities.

ABSTRACT. Modernizing legacy systems is a strategic imperative for organizations seeking to remain competitive, efficient, and adaptable in a rapidly evolving technological landscape. As most remaining mainframe experts in organizations are in the transition age shortly before (or even after) formal retirement, and the successful execution of such modernization efforts strongly relies on their active contribution, it is crucial to ensure their well-being and professional alignment with modernization. However, little is known so far about how modernization initiatives affect the well-being and work satisfaction of this peer group. This study adopts a qualitative, interpretive research design to investigate how mainframe specialists in the financial services sector experience and respond to modernization initiatives. Data were collected through semi-structured interviews with experienced mainframe professionals, selected via purposeful sampling to ensure relevance and depth of expertise. We then integrated these dimensions into a process model explaining how structural constraints and perceived contract violations jointly shape resistance or engagement.

ABSTRACT. Enterprise systems in high-reliability environments impose structures that frequently collide with acute clinical realities, where delays or missing data carry immediate consequences. While IS research recognizes workarounds as responses to structural misfits, it has largely examined them within single systems and as local fixes, leaving open what their recurring location reveals about misfit in the integration architecture itself. Using radiology as a theory-revealing case, we apply the Gioia methodology to analyze 26 qualitative datasets from frontline Radiologic Technologists. Our findings reveal a recurring process where structural misfits at system boundaries provoke locally rational workarounds. These practices sustain immediate patient care but at the same time displace vulnerability into the integration architecture. This manifests most dangerously as illusory safeguards—where systems record a fully compliant digital audit trail while masking bypassed clinical checks. Our primary contribution theorizes workarounds as diagnostic signals of architecture-level structural misfit. From this, we derive two implications: First, this diagnostic reading depends on the dual character of workarounds as continuity mechanisms and risk carriers. Second, this shifts the unit of fit analysis from the standalone application to the integration architecture.

ABSTRACT. Digital responsibility spans multiple levels, from the individual to the organisational to the international, and involves multiple interdependent actors whose contributions and exposure to the consequences of digital technologies are intertwined. Existing frameworks call for empirical accounts of how digital responsibility is observed in practice in settings where a distribution of responsibility among actors is visible. Digital platform ecosystems are one of such setting, bringing together, around a shared infrastructure, those who build, govern, create on, and even simply use a platform. This paper examines how Roblox, as a platform owner, constructs and frames digital responsibility in its communications with the developer community. We analyse public posts published by Roblox staff on its Developer Forum between 2020 and 2022. We conduct a thematic analysis and organise the findings around three interdependent dimensions of digital responsibility: accountability, obligation, and dependability. Our findings indicate that Roblox communicates actions and decisions, and gives reasons for them, showing itself accountable for the safety, security, and reliability of the ecosystem. It grounds this accountability in external expectations, regulatory requirements, and its own stated values, while passing to developers the obligations that its actions and decisions create. Dependability is instead communicated as a forward-looking promise. Together, these patterns show that Roblox presents responsibility as distributed across its relationship with its complementors, with implications for how digital responsibility can be studied, governed, and attributed in platform ecosystems like Roblox or in the metaverse more broadly.

ABSTRACT. The integration of Artificial Intelligence (AI) into organizational decision processes has produced a theoretically consequential paradox: as the availability of algorithmic recommendations increases, organizations often exhibit systematic patterns of operational inaction and responsibility avoidance. While existing research addresses this phenomenon through either a technological information-processing lens or a behavioral perspective on individual biases, both frameworks presuppose that informational sufficiency ensures decision enactment. We argue that this premise fails in culturally structured environments. To address this gap, we introduce a sociotechnical framework where the impact of AI disruptive properties (opacity, accountability displacement, and authority mimicry) on decision entitlement is fully mediated by the emergence of authority ambiguity. We operationalize authority ambiguity across structural, epistemic, and evaluative dimensions. Furthermore, we position cultural infrastructure, comprising epistemic transparency protocols, psychological safety, and blame-free accountability frameworks, as a critical moderating mechanism that alters this relationship and prevents operational paralysis. By re-conceptualizing decision architecture through a cultural lens, this paper provides a cohesive model to understand and mitigate algorithmic inaction in modern organizations.

ABSTRACT. This research presents the design and testing of a multi-dimensional predictive model for armed conflict events at a subnational level. While conflict prediction is a well-established research field, existing models often operate at the national level. In contrast, our study aligns with recent advancements that emphasize subnational granularity as crucial for both accuracy and operational deployment of Crisis Early Warning Systems (CEWS). A common key limitation in current CEWS is their poor performance during sudden escalations or extreme peaks in violence. To address this, we introduce and validate a set of quantitative indicators that are dynamic and timely. Our indicators are selected to operationalize a multi-dimensional model based on the OECD fragility framework with its 6 dimensions: economic, environmental, political, societal, human, and security. We defined roughly 70 indicators, covering all dimensions of fragility, using data from multiple sources, including satellite and OSINT, for all regions of Ethiopia from 2018 to 2024, at monthly intervals. Using these data, we have trained a XGBoost regression model to predict the abso-lute number of conflict events in the next month. The model has proved significant with R2=0.83. Previous conflict remains the strongest predictor, but indicators such as public sentiment on free speech and food availability (derived from satellite data) significantly enhance predictive power. These results demonstrate that a multi-dimensional, subnational approach can meaningfully improve early warning for conflict escalation.

ABSTRACT. Augmented reality (AR) has become an integral part of e-commerce, offering interactive user experiences within online retail environments. Virtual try-on is a key AR application that online retailers have adopted to enhance customer engagement. This exploratory study investigates the affordances of virtual try-on technology within the furniture and home furnishings retail sector. The data for the study were collected in spring 2025 through interviews (N=10) examining user experiences of IKEA Kreativ virtual try-on application. The study identifies three key categories of affordances: (1) shopping process facilitation affordances, (2) spatial–visual affordances, and (3) exploratory design affordances. The findings indicate that virtual furniture try-on supports greater efficiency in the shopping process and decision-making, while also enhancing customers’ understanding and creativity. Based on the insights, the study introduces a framework that integrates the main affordance categories along with eleven corresponding sub-dimensions.

ABSTRACT. The diffusion of Artificial Intelligence (AI) is transforming organizations, creating new challenges related to governance, accountability, and ethical responsibility. While existing research has largely focused on the techno-logical dimensions of AI-driven transformation, limited attention has been devoted to understanding how responsible leadership is discussed within these contexts. This study explores responsible leadership in AI-driven Digital Transformation (DT) through a thematic analysis. Building on these findings, the study analyzes 166 LinkedIn posts published by 66 AI and technology professionals. The findings suggest that responsible leadership is increasingly framed through narratives emphasizing organizational readi-ness, governance, accountability, and human-centered transformation. The study contributes to the literature by providing empirical insights into how responsible leadership is framed within professional discourses on AI-driven DT, highlighting the central role of governance, organizational readi-ness, accountability, and human-centered transformation.

ABSTRACT. Mobile payment (MP) adoption increases rapidly due to its contactless and hygienic usefulness during natural disasters such as the COVID-19 pandemic. Usage of digital financial tools in such disasters offers psychological relief by reducing physical exposure risks. This study investigates the factors influencing Mobile Payment Adoption (MPA) among 456 smartphone users in an emerging economy. It examines its post-adoption associations with users' Mental Wellbeing (MWB), Customer Experience (CEX), and Intention to Recommend (ITR). It expands the Mobile Technology Acceptance Model (MTAM) by integrating Customer Mobile Innovativeness (CMI). The empirical results were analyzed using PLS-SEM. They demonstrate that all MTAM antecedents and CMI significantly relate to MPA. The results also show that MPA exhibits strong positive associations with both MWB and CEX, which in turn correlate with a higher ITR. These findings provide actionable insights for fintech practitioners and policymakers for designing resilient digital financial systems in developing markets.

ABSTRACT. Emerging technologies are revolutionizing organizational dynamics, em-phasizing the importance of adapting to and integrating these technologies for organizations to remain competitive. Despite growing scholarly interest, the literature remains fragmented, treating adoption as a single event rather than a complex and dynamic process. This study performs a meta-synthesis by developing a taxonomy of 51 empirical articles published in journals listed in the Academic Journal Guide 2024 ranking, discussing emerging technology adoption at the organizational level. Following Nickerson et al. [1] and Cipriano and Za [2], the taxonomy comprises eight dimensions that systematically characterize the emerging technology adoption process: Technological, Organizational, Environmental, Individual, Adoption Fac-tors, Adoption Stages, Organizational Level, and Adoption Outcomes. Drawing on Rogers [3] and Damanpour and Schneider [4], the findings are further reinterpreted through a temporal lens, articulating the adoption process across pre-adoption, adoption, and post-adoption phases. The results reveal that adoption is predominantly studied in the pre-adoption phase, highlighting a significant gap regarding the adoption and implementation phases. This study contributes to the IS literature by providing a structured and comprehensive understanding of the ET adoption process and identifying directions for future research.

ABSTRACT. The increasing use of Artificial Intelligence (AI) and Machine Learning (ML) methods in various fields has put a spotlight on their development process, while simultaneously challenging it. Unlike traditional software systems, AI systems are based on models that make decisions based on sta-tistical data. This results in specific characteristics in the development pro-cess of AI/ML models, such as the integration of data from heterogeneous data sources. In practical application, these characteristics often lead to challenges in the development of AI /ML systems as they typically consist of different data types that cannot be directly integrated or aggregated. This again requires different data types to be transferred so that they can be inte-grated and used in one method. Hence, the development process of AI/ML systems faces new questions of how to guarantee their adequate quality. Against this background, we investigated procedural evaluation methods for assessing the quality of the development processes for AI and ML meth-ods and compared different evaluation criteria and methods for the AI/ML development process. Based on a structured literature review the evaluation criteria and methods have been identified. A qualitative content analysis revealed the typical properties of different criteria and methods for evalua-tion so that recommendations for the most suitable evaluation criteria for different settings can be given.

ABSTRACT. This study maps the intellectual development of AI-enabled digital recruitment within Smart Human Resource Management (SHRM) and identifies the themes, tensions, and research opportunities that have shaped the field over the last decade. A bibliometric analysis was conducted on 86 peer-reviewed articles indexed in the Web of Science database and published between 2014 and June 2024, using bibliometrix/Biblioshiny and VOSviewer. The analysis identifies three interconnected streams (social-media recruitment, gamified assessment, and AI-enabled recruitment) and shows a progressive shift from technology adoption and efficiency toward fairness, explainability, candidate experience, governance, and responsible AI. The study contributes by integrating descriptive performance indicators with co-word analysis and interpretive content analysis to provide a research roadmap for AI-supported recruitment. The findings are relevant to scholars examining digital HRM, HR professionals designing technology-enabled selection systems, and policymakers concerned with fair, transparent, and accountable digital labour markets.

ABSTRACT. Immersive and interactive learning systems, increasingly combined with AI and behavioural analytics, allow organizations to move beyond knowledge transmission toward the observation of behaviour in situated experiences. Yet, behavioural traces do not automatically become learning or organizational knowledge. This study investigates how technology-mediated learning systems can transform behavioural evidence into interpretable feedback and reflective knowledge for individual and organizational development. The study adopts a two-layer qualitative multi-case design. Twelve organizational learning initiatives implemented between 2017 and 2025 are comparatively examined to identify different configurations in the relationship among experience, behavioural evidence, analytics, feedback, and knowledge generation. Four cases displaying the Integrated Behavioural-Evidence configuration are then analysed in depth to reconstruct the underlying design and knowledge-transformation processes. The findings distinguish Experience-Centered, Data-Augmented, and Integrated Behavioural-Evidence configurations and show that the latter share a problem-first rather than technology-first design logic. Their distinctive feature lies in the integration of meaningful situated experience, behavioural evidence generation, interpretation, and feedback within a coherent developmental process. Building on these empirical patterns, the paper conceptualizes Human-Centered Intelligent Learning Systems (HCILS) as socio-technical systems designed to transform behavioural evidence into developmental knowledge. The Reflective Knowledge Loop (RKL) explains this knowledge-transformation mechanism, while a Multidisciplinary Design Architecture identifies the conditions enabling its implementation across different organizational and technological contexts. The study contributes to Knowledge Management, reflective learning, and Human-Centered AI research by shifting attention from technological immersion and data collection toward the processes through which enacted behaviour becomes interpretable and developmentally meaningful knowledge.

ABSTRACT. AI-enabled learning systems increasingly support human resource develop-ment by diagnosing skill gaps, personalizing learning pathways, generating coaching feedback, simulating interpersonal situations, and producing learn-ing analytics. However, soft skills development cannot be reduced to effi-cient content delivery or automated assessment. Communication, collabora-tion, empathy, leadership, adaptability, and ethical judgement are relational capabilities that require vulnerability, feedback acceptance, reflection, exper-imentation, and contextual interpretation. This paper develops a focused conceptual argument on the role of trust in AI-mediated soft skills develop-ment. It argues that trust is the central mechanism through which human-centered organizational and governance conditions enable employees to en-gage authentically with AI-enabled feedback, coaching, simulation, and learning analytics. Drawing on conceptual theory-building, soft skills and human capital research, AI and HRD scholarship, organizational learning theory, responsible AI governance, and trust in automation and AI, the paper explains why the same AI-mediated learning system may be experienced as developmental support or as surveillance and control. It proposes a parsimo-nious trust-centered framework linking human-centered AI developmental conditions, trust, authentic developmental engagement, and soft skills out-comes, while accounting for boundary risks such as opacity, punitive use, biased feedback, overreliance, and lack of contestability. The paper contrib-utes to HRD, organizational learning, and human-centered AI scholarship by shifting attention from AI-enabled training tools to trustworthy developmen-tal infrastructure.

ABSTRACT. Systems that possess aspects including mechanics, electronics, computation, and control for the development of technological projects are called mechatronics. Mechatronics is an interdisciplinary field that is considered highly essential for the development of technology and the design of intelligent industrial systems through the integration and combination of software and hardware. Our objective is to understand how the adoption of AI has evolved within companies acting in the mechatronic sector and what benefits it may bring to the future of work. As preliminary stage, we collected our data (60 participants companies) using a live survey and obtained immediate feedback from participants through the interactive tool Mentimeter. The findings show that companies are still in the early experimental stages of using AI systems and need to invest more in employee skills in 2026, most responses were related to the Commercial Area. These preliminary findings show several concerns for industrial firms that are adopting AI in their workforce or are still evaluating doing so.

ABSTRACT. The progressive integration of Artificial Intelligence (AI) has pro-foundly transformed the academic landscape, prompting traditional educational models to evolve and introducing novel governance considerations that extend far beyond the initial, often cautious, institutional responses rooted in strict limi-tation. Today, universities are actively navigating a complex matrix of transform-ative factors encompassing the digital citizenship of students and the professional adaptation of faculty, alongside the promotion of socio-economic equity, ad-dressing challenges such as "pedagogical colonialism", and the crucial environ-mental sustainability necessitated by the significant resource requirements of modern AI infrastructures. In order to systematically frame these dynamics, this study conducts a qualitative and comparative documentary analysis of institu-tional policies across a broad selection of universities, evenly balancing an inter-national sample with a mirrored focus on the Italian context. The investigation reveals two distinctly complementary strategic orientations: whereas interna-tional institutions tend to favor a pragmatic and specialized approach aimed pri-marily at technical standardization and the management of operational frame-works, their Italian counterparts lean more heavily toward cross-cutting guide-lines firmly anchored in ethical and pedagogical values. Ultimately, aiming to harmonize these diverse institutional specificities through a proactive and inte-grated governance model, the paper culminates in a targeted operational proposal for the University of Messina, articulating a comprehensive framework of ten core principles designed to inaugurate a structured, transparent, and explicitly forward-looking path for AI integration within the academic community.

ABSTRACT. The rapid adoption of generative artificial intelligence (GenAI) in higher education has prompted universities to consider how to govern its use responsibly and consistently with academic standards. This study explores how Italian public universities translate Responsible AI (RAI) principles into governance practices through their institutional guidelines. Drawing on the RAI Governance framework, the paper analyzes guidelines from 16 Italian public universities using thematic analysis. Results indicate that these documents include essential RAI principles but translate them unevenly into governance practices. RAI is framed not only as regulatory compliance but also as a professional responsibility rooted in institutional values. Governance relies mainly on procedural and relational mechanisms, while structural arrangements remain less formalized, revealing a gap between stated principles and the organizational mechanisms needed for implementation. Faculty members emerge as key operational mediators, particularly in teaching. The analysis also shows that governance differs across teaching, research, and academic services, reflecting the specific responsibilities and risks associated with each domain. The effects identified are expected rather than empirically observed and converge on quality, institutional trust, and human judgment, with GenAI being framed as support rather than a substitute for human contribution. The study shows that these initial steps provide an important foundation but remain structurally underdeveloped, pointing to the need for clearer mechanisms for implementation, coordination, and oversight.

ABSTRACT. The rapid diffusion of generative artificial intelligence (GenAI) in education has sparked growing interest in its potential to transform learning. However, most research has focused on performance outcomes in narrow tasks, while studies on how GenAI tools are perceived and used by students remain limited, and it is still unclear how collaboration with AI may support students' knowledge construction. To address this gap, this study adopts a mixed-methods design involving 16 students enrolled in a bachelor’s degree in Industrial Design Engineering. Students, divided into groups, participated in a two-day product development workshop structured around the Double Diamond model and supported by KOBI, a GenAI tool specialized in arts and design. Student–GenAI interactions and questionnaire data were collected and analyzed using descriptive statistics and qualitative content analysis. The findings show that GenAI use varies according to the cognitive demands of each design phase. Two main interaction modes emerged: exploratory and executive. These modes differed in the form of interaction and the types of requests addressed to GenAI, shaping students' perceptions of usefulness and generating different experiential responses. The study contributes to research on GenAI in education by showing how students interact with GenAI across cognitively different activities and by proposing an interpretive model, inspired by the concept of absorptive capacity, that links interaction modes to knowledge construction and experiential responses within the design process.

ABSTRACT. Generative AI is increasingly used in sustained dialogue to develop arguments, explore alternatives, and form knowledge. Existing accounts of hybrid intelligence and human–AI complementarity largely ask how heterogeneous capabilities can be configured to improve joint performance. They explain less well how a locally plausible conversational trajectory becomes situated knowledge, or how a productive coupling is maintained and lost during an inquiry. This paper develops epistemic complementarity as an interactional accomplishment rather than a fixed division of cognitive labour. It derives the construct through four linked foundations: extended-mind theory explains cognitive coupling; phenomenology explains situated orientation and relevance; constructivism explains the recursive reorganisation of understanding; and situated socio-technical perspectives locate that process in practice. The resulting mechanism, recursive interpretive steering, comprises framing the inquiry, discriminating among generated trajectories, redirecting or reopening them, and provisionally stabilising meaning. A reflexive analytical vignette, reconstructed from a retained development dialogue, makes this mechanism visible while being explicitly delimited as an illustration rather than an empirical test. The paper also specifies conceptual drift and cognitive surrender as failure regimes. It contributes a process account of when human–AI dialogue becomes epistemically complementary and derives design principles for preserving substantive human interpretive agency.

ABSTRACT. Generative Artificial Intelligence (GenAI) tools have been found to produce creative outputs, for example, by unlocking an individual’s creativity or serving as assistants. Building on this emerging literature, in this paper, we are interested in exploring whether GenAI tools can also (reliably) assess creativity in a way similar to human evaluators. This is important as GenAI tools are increasingly used for content evaluation in addition to content generation, as it is the case in grading, recruitment, and content moderation on social media. We conducted a pilot study whereby we asked five human evaluators and five GenAI tools to independently evaluate seven human- and seven GenAI-written creative stories. Our preliminary findings suggest that GenAI tools evaluate creativity differently from human evaluators when using the same criteria, explaining how the two differ in language and format. Further, we discuss our follow-up study that is expected to better approximate real-world contexts in which creative content is produced and evaluated.

ABSTRACT. Hybrid and spatially flexible work reshaped organizational dynamics, necessitating a profound shift in managerial roles and a significant increase in workers' autonomous management, thus creating a pressing need for new coordination activities and routines. Recent literature highlights how coordination, trust, communication, and sociality are performative accomplishments, that is, situated sequences of activities that require constant daily work to sustain the organizational productivity and workers' wellbeing. Therefore, the office is no longer conceived as a static container but a performative space, created and recreated through the interactions with workers. This is particularly evident in the case of spatially flexible work, where the loss of a fixed workstation compels workers to continuously renegotiate their physical and social presence by actively performing actions that are managerial in nature, such as ensuring their visibility towards management, allocating spatial resources, and ensuring the functional coordination necessary for the office to operate.

ABSTRACT. This study examines how digitally enabled hybrid work reconfigures the conditions under which sensemaking unfolds in organizations. While existing research conceptualizes sensemaking as an interactional and relational process, it often assumes the stability of the relational conditions that sustain it. Drawing on a qualitative study of middle managers in a multinational organization undergoing hybrid work transformation, this paper shows how shifts in space–time affordances disrupt relational embeddedness and alter meaning construction processes. The findings identify a shift toward regulatory sensemaking, in which meaning is stabilized through codified and system-mediated cues rather than through ongoing dialogue. By integrating sociomaterial and process perspectives, the study conceptualizes sensemaking as a condition-dependent process and highlights how digital infrastructures reshape interaction, interpretation, and coordination in hybrid work environments.

ABSTRACT. This paper develops a theoretical extension of the information-processing view of organizational design in the context of AI-mediated systems. Within contingency theory, organizational effectiveness depends on the alignment between informational requirements and the organization’s capacity to process information. Existing organizational design models primarily focus on the fit between strategy, structure, coordination mechanisms, and informational requirements, while heterogeneity in work execution remains comparatively underdeveloped as an explicit source of organizational misfit. The paper argues that the increasing integration of AI-mediated coordination systems alters how organizations represent, process, and evaluate work activities. By relying on standardized representations and algorithmic forms of evaluation, AI-mediated systems expand computational capacity while potentially constraining the range of heterogeneous work conditions incorporated into organizational coordination processes. Under heterogeneous work conditions, this may generate forms of informational misfit, in which the representational capacity of the organizational system becomes misaligned with the actual informational requirements of work execution. To examine these dynamics, the paper develops a configurational analysis of organizational archetypes under AI-mediated systems and derives a set of theoretical propositions linking heterogeneity in work execution, algorithmic coordination, and organizational fit. The analysis contributes to contingency theory by showing how heterogeneous work conditions may alter informational requirements and by introducing informational misfit as a mechanism through which technologically advanced systems may remain misaligned with work conditions despite high computational capacity.

ABSTRACT. Manufacturing firms are capable of clearly articulating the value discipline they seek to pursue in their corporate strategy. However, as digitalization in-creasingly becomes a strategic pillar of corporate strategy, value disciplines, while not precluding digitalization, do not explicitly position it at their concep-tual core. This study, thus, reconceptualizes the concept of “Value Disciplines” into “Digital Value Disciplines,” positioning digitalization as a strategic mech-anism through which firms create value, sustain competitive advantage, and re-inforce market leadership. The findings reveal a hierarchical arrangement of the importance of Digital Value Disciplines in manufacturing firms: (1) Digital Operational Excellence, (2) Digital Customer Intimacy, (3) Digital Product Leadership, and (4) Sustainability & Resilience. In reality, failures in value creation stem from conscious decisions regarding the hierarchical arrangement of these Digital Value Disciplines within a firm’s corporate strategy and digi-talization implementation. Therefore, our study addressed the following re-search question: How is digitalization implemented across Digital Value Dis-ciplines in manufacturing firms to foster value creation? To address this question, drawing on the theories of strategic alignment and value disciplines, the study employs an exploratory qualitative research design that involves in-terviews with elite informants from 15 large manufacturing firms. Building on these insights, the study discusses how, during the implementation of digitali-zation, Digital Value Disciplines are created through the strategic fit and func-tional integration among actors.

ABSTRACT. Cities are adopting a smart city strategy in order to support and improve smart urban health to achieve better quality of life within smart healthy urban communi-ties. Cities are rediscovering the smart urban health as a driver of urban growth and innovation. Cities are going into the future, encouraging urban stakeholders’ engagement and collaboration, and embracing a smart city mind set and urban health view to urban value creation. The study aims to elucidate how the Europe-an cities are rethinking on the future of urban communities, adopting a smart view and focusing on urban health to improve the wealth of urban communities. The study relies on qualitative research analysis. The research analyzes two case stud-ies drawn from policy documents related to European cities that are rethinking on future design of the city, and planning a smart view to urban growth, leading to smart health as a driver of urban innovation. Brussels and Wien have designed a smart healthy city strategy, promoting urban health, and driving smart urban in-novation. Smart urban health planning helps shape sustainable and wealthy cities, leading to responsible urban innovation and sustainable growth, supporting mul-ti-actor collaborative innovation, and focusing on community development. Smart urban health and innovation support collaborative spaces in order to drive smart and responsible urban innovation.

ABSTRACT. This study examines how teleneurorehabilitation systems become embed-ded in healthcare organizations and how professional work changes during that process. The Technology-Organization-Environment framework (TOE) and the Technology Acceptance Model (TAM) are used as sensitizing theo-retical perspectives to examine organizational and contextual adoption conditions and healthcare professionals’ perceived usefulness and ease of use, while also highlighting their limited ability to explain how clinical work is transformed as digital systems become incorporated into care prac-tices. The study adopts an action research design within an Italian hospital or-ganization participating in a Horizon 2020-funded teleneurorehabilitation project. Empirical materials include 25 strategic documents and internal re-ports, four exploratory focus groups with medical directors, HRM officers and ward coordinators, and 38 semi-structured interviews with healthcare professionals and technical staff. Data were analyzed through a Gioia-informed abductive approach, moving iteratively between emerging empiri-cal patterns and existing theory. The analysis produced fifteen second-order themes and four aggregate dimensions. Three concern the conditions under which the platform became clinically workable, organizationally sustainable and institutionally legit-imate, and can be interpreted in relation to TOE and TAM. A fourth dimen-sion concerns the recomposition of professional work in mediated care: professionals reconstructed clinical assessment, mediated between system, patient and caregiver, interpreted system-generated information, renegotiat-ed responsibility at a distance and redefined professional competence. The paper conceptualizes this pattern as professional role reconfiguration and discusses its implications for HRM, competence development and ethical oversight in AI-enabled digital health.