Aurelio Ravarini

The rapid rise of Generative AI (GenAI) chatbots such as ChatGPT and Claude presents organizations with a strategic dilemma: adopt powerful external solutions or invest in developing secure, internal alternatives. This study conceptualizes the "GenAI adoption dilemma" and examines it through the lens of internal data ecosystems. Based on qualitative case study research, including 46 interviews with senior professionals in a multinational consultancy firm, we identify three strategic pathways: internal, external, and hybrid chatbot adoption. We analyze them across five criteria: cost, risk, time to market, data ecosystem integration, and competitiveness. We develop a framework to guide decision-makers in navigating this emerging challenge. Our findings primarily contribute to GenAI chatbot adoption literature by framing it as a systemic organizational decision rather than a purely technological, they extend data ecosystem theory by empirically linking GenAI chatbots adoption to internal infrastructure, governance, stakeholders, and data practices, and provide insights for IT adoption and outsourcing literature.

The adoption of Generative AI chatbots in organizations is transforming how employees perform cognitive work, enhancing efficiency and supporting augmentation. Perceived social presence can make these systems feel more human-like and trustworthy; however, it may also lead employees to overestimate their capabilities and delegate cognitive effort to them. This study investigates how trust, shaped by perceived social presence, contributes to higher-order cognitive offloading—the tendency to shift critical thinking to Generative AI chatbot tools. To explore this phenomenon, we propose to conduct an inductive qualitative case study supported by thematic analysis of interviews. By collecting insights from employees and managers, the study will examine how Generative AI chatbots are used in the workplace and the triggers and consequences involved. The goal is to develop an empirically grounded framework explaining the mechanisms linking perceived social presence, trust, and cognitive offloading in organizations adopting Generative AI chatbots, offering contributions to IS research and guidance for practitioners.

As AI reshapes manufacturing, understanding how workers adapt and develop new competencies is critical. This study offers a grounded, step-by-step model of task crafting in a digitally evolving manufacturing company, highlighting how employees respond to misalignments by redesigning tasks, tools and routines. Based on interviews and observations, the findings reveal a recursive process driven by experiential knowledge, collaboration, and local innovation. Generative AI chatbots (e.g., ChatGPT) augment this process by supporting ideation, automation and informal learning, without displacing human agency. Generative AI supports micro-reskilling and hybrid competencies as a digital companion. This study contributes to job crafting literature by framing competence development as emergent and situated, and to Generative AI-in-manufacturing debates by showing how human-AI collaboration unfolds in practice. It offers implications for workforce transformation strategies aligned with Industry 5.0, where AI and human expertise co-evolve in dynamic, socially embedded ways.

As Generative artificial intelligence (GenAI) tools become increasingly integrated into clinical practice, their anthropomorphic features raise important questions about their role, reliability and implications for human-centred care. This discussion article examines the concept of anthropomorphic GenAI, highlighting how its human-like traits may influence trust, professional judgement and the dynamics of patient care. While such technologies can enhance efficiency and support clinical decision-making, their design often blurs the line between simulation and authentic human understanding. This paper identifies key gaps in the existing literature, particularly around emotional intelligence, ethical reasoning, specialisation-specific adoption and the long-term impact of AI on clinical practice and proposes a structured research agenda to guide future inquiry and responsible innovation. By drawing attention to the promises and pitfalls of anthropomorphic AI in healthcare, this discussion invites interdisciplinary reflection on how such systems should be designed, evaluated and integrated into healthcare and clinical contexts.

The rise of user-driven digital practices has long challenged organizational 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 conceptual extension of the Shadow IT paradigm, arguing that Shadow AI and Shadow GenAI represent qualitatively distinct yet evolutionarily linked developments 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 research 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.

This paper explores the implementation of Game-Based Learning (GBL) in higher education, with a particular focus on the dual role of students as developers and players of educational games. In particular, the study investigates the extent to which GBL leverages intrinsic motivation, fosters engagement, consolidates prior knowledge, and cultivates essential 21st-century skills in undergraduate students that designed, developed, and played interactive digital gamebooks on STEM disciplines. A mixed method based on surveys and focus groups was leveraged to assess the diverse learning outcomes and the educational impact of the project. Findings highlightsthat theGBL approach significantly increased student engagement, fostered deeper learning in IT-related skills, and provided valuable experiences in competences such as project management and teamwork. However, the integration of advanced academic content in Mathematics and Statistics was perceived as less effective, posing challenges to knowledge acquisition and consolidation. This study shows that while GBL is highly effective in promoting motivation and skill development, further refinement is needed to align content complexity with learning objectives.

This study explores how prompt engineering training influences student performance and adaptability when using Generative AI (GenAI) chatbots in education. Focusing on first-year engineering management students, we examine the impact of targeted instruction on learning outcomes, linking AI interaction to academic self-efficacy, goal orientation, self-monitoring, study skills, and technology engagement. Using a mixed-methods approach, results show that prompt engineering significantly enhances academic performance and critical engagement with AI tools. Findings highlight the importance of integrating AI literacy into curricula to maximize GenAI’s educational potential and prepare students for AI-driven professional environments.

This study offers a comprehensive understanding of Generative AI’s role in knowledge management and knowledge retention within SMEs, proposing a robust framework that integrates social and technical factors to promote continuous learning and innovation. Despite substantial research on the general impact of AI on knowledge management, there is a notable scarcity of literature focusing specifically on Generative AI and its role in enhancing knowledge retention in SMEs. This gap is critical, given SMEs’ unique challenges, such as limited resources and high employee turnover rates. The research question guiding this study is: “To what extent does Generative AI impact knowledge retention in SMEs?”.
The article employs a socio-technical systems framework to analyze the variables influencing knowledge sharing, emphasizing the interplay between individual motivation, organizational support and culture, and technological capability. This approach is complemented by the concept of human-centric digital technology, which focuses on enhancing human capabilities and experiences through technology. Generative AI enhances knowledge management practices by automating processes, analyzing large datasets, and generating new content, thus improving the efficiency and quality of knowledge sharing. It also reduces the administrative burden on employees, fosters a collaborative culture, and provides personalized feedback, thereby increasing individual motivation for knowledge sharing. Furthermore, Generative AI can address the inherent challenges SMEs face in knowledge retention by making critical knowledge easily accessible, reducing the learning curve for new employees, and maintaining operational efficiency. However, it is crucial to manage employees’ perceptions of Generative AI to avoid resistance or sabotage due to fears of redundancy.

This study investigates how Generative AI (GenAI) chatbots mediate sensemaking in organizations, particularly in contexts characterized by equivocality. Drawing on the enactment model by Weick and colleagues (2005), we analyze how GenAI technology interacts with workers during sensemaking. Our findings reveal that GenAI chatbots act as assistants, collaborators, and facilitators, by both introducing as well as reducing equivocality in an organizational context through iterative dialogue and contextual adaptation. The chatbots do not only transform cues into actionable insights, they also introduce novel ways of structuring and interpreting information. Furthermore, workers’ presumption of the chatbot behavior shapes their interaction with it, influencing its role in navigating equivocality during sensemaking. By extending theoretical boundaries of technology-mediated sensemaking, this research provides actionable insights into leveraging GenAI for organizational resilience and adaptability in dynamic environments.