Abstract
Generative artificial intelligence (GenAI) promises substantial productivity gains for organisations, yet unresolved questions about data management and privacy continue to shape managers' and employees' confidence. This study examines workplace adoption of GenAI and shows how trust, conditioned by perceptions of data-management integrity, information transparency, and privacy risk, influences acceptance. This mixed-method study tests, using a survey-based structural equation model plus interviews focused on managerial practices among daily GenAI practitioners, two core insights: (i) trust is the strongest predictor of intention to use GenAI, and (ii) trust depends chiefly on manager's and employees' belief that organisational data are handled reliably and objectively through management routines. Perceptions of transparency or privacy risk exert no direct influence on either trust or usage. Building on these results, the study delineates four managerial domains: data-management process, information transparency, privacy risk, and trust, alongside twenty future research questions designed to understand how GenAI is linked to managerial practices. For practice, the findings recommend monitoring, calibrated disclosure, and adaptive privacy protocols as concrete managerial levers to strengthen GenAI acceptance. The evidence highlights trustworthy data governance, not abstract explainability, as the foundation of sustainable GenAI adoption. The study also provides a roadmap of actionable management practices to guide its implementation in modern workplaces.

Abstract
Context: Software testing is an often used method to ensure software quality, with developers spending a significant amount of time on it. However, software testing education is underrepresented in curricula, particularly with regard to Conceptual Modelling (CM), where model-level validation is important. As a consequence, graduate students are insufficiently prepared for industry-level testing. Objective: We want to understand students' behavioural patterns during model testing and their relationship to testing effectiveness.Method: We use process mining to analyze 554 interaction logs from students using a conceptual model simulation tool.Results: We identify three strategies: the coverage chaser (using feedback iteratively), the problem solver (balancing feedback with requirements focus), and the disconnected tester (avoiding feedback-driven iteration). High-coverage students integrated feedback into test loops; low-coverage students did not. Notably, high and average-coverage students achieved similar grades, suggesting a plateau where systematic testing enables sufficient educational performance without requiring maximum coverage.Conclusions: Our findings highlight the importance of teaching students to systematically use coverage feedback to guide test design in CM contexts.

Abstract

Abstract
The ability to assess molecular binding kinetics in real time is critical for advancing our understanding of molecular interactions in biochemical and biotechnological systems. This work presents a novel optical tweezer (OT)-based method to monitor molecular affinity in real time, focusing on the high-affinity streptavidin-biotin system as a model. Transparent poly(methyl methacrylate) (PMMA) microparticles functionalized with streptavidin were trapped before, during, and after binding with biotinylated bovine serum albumin (biotin-BSA), enabling the analysis of forward-scattered signals to detect nanoscale changes in particle size. By applying the Power Spectral Density method, the friction coefficient of individual particles was calculated, allowing for real-time tracking of binding dynamics and the estimation of the association rate constant (kon approximate to 106M-1s-1). These results are consistent with literature values and demonstrate the potential of this OT-based approach for non-invasive, label-free detection of molecular interactions. Compared to existing techniques, such as atomic force microscopy and cantilever-based sensors, this method offers significant advantages, including real-time monitoring, adaptability to different bioaffinity systems, and compatibility with miniaturized setups. This work establishes a foundation for using OT-based tools to monitor high-affinity molecular interactions in real time. While demonstrated here using biotinylated BSA as a model ligand, future studies will explore the method's applicability to smaller ligands and more subtle surface modifications.

Abstract

Abstract
The evolving landscape of software development demands that software testers continuously adapt to new tools, practices, and acquire new skills. This study investigates software testing competency needs in industry, identifies knowledge gaps in current testing education, and highlights competencies and gaps not addressed in academic literature. This is done by conducting two focus group sessions and interviews with professionals across diverse domains, including railway industry, healthcare, and software consulting and performing a curated small-scale scoping review. The study instrument, co-designed by members of the ENACTEST project consortium, was developed collaboratively and refined through multiple iterations to ensure comprehensive coverage of industry needs and educational gaps. In particular, by performing a thematic qualitative analysis, we report our findings and observations regarding: professional training methods, challenges in offering training in industry, different ways of evaluating the quality of training, identified knowledge gaps with respect to academic education and industry needs, future needs and trends in testing education, and knowledge transfer methods within companies. Finally, the scoping review results confirm knowledge gaps in areas such as AI testing, security testing and soft skills.