Generative AI is rapidly becoming a key part of knowledge work, offering opportunities to increase productivity, creativity, job satisfaction, and improve learning outcomes, while also raising concerns about potential skill erosion as tasks are increasingly delegated to GenAI systems. Despite growing research on productivity, ethics, and governance, limited attention has been given to how knowledge workers manage their interaction with GenAI to sustain professional skills, which are critical for personal development, organizational efficacy, and societal resilience.

This thesis aims to enhance understanding of how GenAI impacts professional skills and whether knowledge workers self-regulate their use to counteract skill changes. Using a qualitative research approach, 38 semi-structured interviews were conducted with knowledge workers across ten sectors. Thematic analysis revealed that GenAI use can result in four distinct skill outcomes: development, maintenance, erosion, and revaluation. Crucially, these outcomes depend on the way workers engage with GenAI rather than the technology alone, with skill revaluation showing that erosion is not always perceived as harmful.

Knowledge workers employ self-regulatory strategies before, during, and after GenAI use, including setting boundaries, tailoring usage to specific situations, critically reviewing outputs, and rewriting or documenting results. Engagement in self-regulation is shaped by drivers across interactional, organizational, and societal contexts, yet organizational support for mitigating risks of skill erosion remains limited, leaving responsibility with individual workers.

By integrating insights on skill outcomes, strategies, and drivers, this study highlights how self-regulation unfolds within a complex sociotechnical system. The findings provide implications for knowledge workers, organizations, and policymakers to design interventions that foster responsible adoption of GenAI, protect professional competencies, and strengthen societal resilience. Future research should further explore organizational-level interventions, governance structures, and profession-specific experiences to ensure skill preservation in the age of GenAI.

This study investigates whether Large Language Models (LLMs) exhibit victim blaming tendencies when analyzing traffic accident scenarios. Using a systematic factorial design, 144 road safety scenarios were tested across ChatGPT-4o and DeepSeek-V3, varying risk behavior, demographics, injury severity, and driving context. Through mixed-methods analysis of 288 responses, the research reveals that LLMs do not exhibit traditional victim blaming but demonstrate compliance-based attribution. They adapt their analytical approach based on question framing rather than maintaining consistent safety principles. When asked about prevention, models provided comprehensive systems analysis with 89.5% of suggestions targeting systemic factors. However, responsibility attribution followed context-driven patterns: 100% driver blame in private scenarios versus 69.4% company blame in work-related scenarios, regardless of demographics. The convergence of these patterns across different AI architectures suggests fundamental challenges in analytical consistency rather than model-specific biases. These findings have critical implications for AI deployment in safety-critical contexts, revealing that sophisticated responses can mask inappropriate analytical frameworks. The study contributes new theoretical concepts to AI ethics and provides evidence-based guidance for ensuring AI systems support rather than undermine advances in safety science.

Robotic-assisted surgical methods are advancing rapidly, offering potential advantages such as improved precision, accuracy, and reduced patient recovery times. However, widespread adoption depends not only on technological advancements but also on patient acceptance and willingness to consent. Informed consent plays a crucial role in the successful integration of robotic-assisted surgery into clinical practice, making it essential to understand the factors influencing patient decision-making.

This study investigates the key factors influencing patient willingness to consent to robotic-assisted total hip replacement surgery in the Netherlands, using a direct-effects model and an extended Technology Acceptance Model (eTAM). A questionnaire-based survey was conducted to assess trust, perceived usefulness (PU), perceived complexity (PC) and willingness to consent across five different surgical scenarios with varying levels of robotic involvement and surgeon presence.

Results indicated that trust and PU showed the strongest association with willingness to consent across all scenarios, emphasizing the need for healthcare providers to prioritize transparency, building trust and discussion risks and benefits. Testing the mediating effect of showed that PU did not act as a consistent mediating variable in any of the scenarios. ANOVA results revealed significant differences between surgical scenarios, with robotic-assisted surgery performed by a surgeon receiving the highest ratings in PU, trust, and willingness to consent. Remote surgery was perceived as the most complex surgical scenario. Participant preferred autonomous robotic surgery over conventional (manual) surgery, despite higher trust and willingness scores for the latter.

Participants also had varying views on the role of technicians in robotic surgery. While many saw their presence as essential for system reliability, others associated it with potential system flaws, emphasizing the need for clear communication about their role. Additionally, most participants mentioned the need for full disclosure regarding surgical methods, reinforcing the importance of patient autonomy.

These findings provide valuable insights for healthcare providers and policymakers to improve informed consent procedures and increase the adoption of robotic-assisted surgery. By addressing patient concerns through education, transparency and building trust, healthcare providers can improve patient comfort and willingness to consent to robotic (assisted) surgery.

This thesis explores the phenomenon of greenwashing, particularly its systemic origins and contributory factors within corporate and regulatory frameworks. Through the analysis of three prominent case studies—Volkswagen, DWS, and Shell—it demonstrates that greenwashing is not merely the result of isolated corporate actions, but a complex, multi-layered symptom of wider sociotechnical influences. The research adopts Rasmussen’s risk management framework alongside Salmon’s systems-thinking approach to identify and map out systemic failures leading to greenwashing. Findings indicate that the most influential factors originate from management policies and regulatory oversight, showcasing the critical role of internal and external governance structures. The study’s conclusions emphasize the need for stricter regulatory audits and the cultivation of transparent corporate cultures. The analysis also provides insights into the ethical implications for sustainability-focused business practices, offering a taxonomy of systemic influences that contribute to greenwashing. This work aims to inform future strategies for regulatory bodies and businesses to mitigate greenwashing and promote genuine environmental responsibility.