Abstract
Virtual production (VP) is becoming central to film and television education, with universities offering degree programs, minors, tracks, electives, and short-term credentials. This review of 115 English-language sources, including 55 curricula from 49 higher education institutions (HEI), shows VP as a socially uneven, tool-weighted formation clustered in well-resourced Anglophone systems. Curricula overwhelmingly foreground real-time workflows, engine-driven pipelines, and stage operations over story development, audio design, and game-adjacent or interactive practices. The core tools include the Unreal Engine, motion-capture systems, and LED volumes, framed as prestige infrastructure rather than collective capacity. Programs emphasize employability, production-style blocks, and 'learning by doing real jobs', supporting industry transition but compressing experimentation, critique, and cross-cultural perspectives. Competency stacks map robust technical cores but reveal structural gaps in leadership, narrative, sound, and AI/ML literacy. The findings argue that evaluating VP education requires analyzing how programmes distribute technological and symbolic capital, organize human-machine networks, and produce learning spaces. Future research should model VP curricula as sociotechnical networks, measure AI integration maturity, test transferability, track longitudinal outcomes, map non-English ecosystems, and formalize stage pedagogy frameworks.

Abstract
Artificial intelligence (AI) is transforming how organizations develop human potential, offering scalable and data-driven support for coaching and capability building. This study proposes and validates a conceptual framework for integrating AI into organizational coaching processes to enhance competence development and strategic alignment. AI-supported coaching in this research is treated as an emerging organizational technology whose potential organizational value depends less on model capability and more on governance design, decision rights, and auditable evaluation outputs. Following a mixed-methods, multi-phase design, the research combined a Systematic Literature Review (SLR) with the construction of a layered design architecture in which OSCAR serves as the primary coaching-process scaffold, complemented by KSA for competency specification, Situational Leadership for adaptive guidance, and KPIs for monitoring and governance. The framework structures AI-supported coaching across 10 interrelated phases, from contextual anchoring to review and measurement, while preserving iterative re-entry to earlier phases whenever review evidence, contextual change, or insufficient progress makes adjustment necessary. Prototyping demonstrated feasibility and coherence across models, while the focus group provided qualitative expert feedback on the framework's clarity, governance needs, and perceived usefulness for competence development. At this stage, however, the KPI structures generated by the framework and the descriptive comparison across AI tools should be interpreted as prototype-level outputs rather than as empirically validated performance measures or evidence of added value over baseline approaches. Because the evaluation relied on two fictional prototyping scenarios and a small expert-oriented focus group (n = 6), the findings should be interpreted as evidence of prototype demonstration and qualitative refinement rather than of real-world effectiveness or organizational impact. The study also does not include a control group or comparison with traditional human coaching, so the added value of the AI-supported framework over alternative coaching arrangements remains a question for future empirical testing. Findings suggest that AI can usefully support organizational coaching by personalizing dialogue, structuring reflection, and generating auditable development artefacts, provided ethical safeguards and human oversight remain integral. The research contributes a preliminarily validated, ethics-informed, and governance-aware framework for AI adoption in organizational coaching and offers practical insights for embedding AI-enabled development in learning organizations.

Abstract
Web frontends are ubiquitous, from Web pages and single-page applications to hybrid mobile apps, Web frontends play a crucial role in today’s digital economy. At their core, they rely on JavaScript, whose single-threaded nature poses significant challenges to delivering smooth and responsive user experiences as complexity rises. In–browser parallelism promises responsiveness and throughput improvements, but spans several mechanisms ( Web Workers , Worklets , OffscreenCanvas , WebAssembly threads ) with diverse coordination models. We conducted a systematic review of primary studies on using Web Workers for parallel JavaScript in browsers, extracting design and scope choices, to collate the resources necessary for a generic ES5-compatible parallel enumeration system capable of type introspection. Such a system would enable widespread in-browser parallelization without any external plug-ins or experimental JavaScript specifications, and spare developers from managing work-splitting and result-merging.

Abstract
Some of the main challenges faced by organizations when applying Continuous Business Process Improvement are data fragmentation, limited explainability, weak governance, and the isolated use of Artificial Intelligence in Business Process Management. This study initially conducts a Systematic Literature Review on the topic of business process improvement enabled by Large Language Models or Artificial Intelligence in organizations, presenting a comprehensive analysis of prevailing research trends, conceptual frameworks, and persistent limitations, identifying seventeen recurring gaps that affect the effectiveness of integrating the capabilities of Large Language Models and other Artificial Intelligence technologies throughout the entire lifecycle of Continuous Business Process Improvement. As a result, we propose a Framework and its gap-oriented reference architecture that, through modular components, facilitates data integration, reasoning, validation, execution, and monitoring within a closed loop of continuous business process improvement. The framework is operationalized through six phases: Process Understanding, Process Diagnosis, Process Redesign, Process Validation, Process Execution Support, and Continuous Monitoring. The results suggest that designing the framework and architecture directly from the identified gaps creates a coherent foundation for AI-driven process improvement, enabling more reliable, explainable, and easily governed and managed solutions. The study improves the current state of the art by creating a cohesive framework for intelligent, scalable, lifecycle-integrated, and operationally deployable process optimization systems.

Abstract
In an era of data-driven organisational transformation, ensuring high data quality is critical to sustaining service excellence and effective decision-making. In the banking sector, where data integrity underpins trust, regulatory compliance, and operational performance, data quality is experienced not only as a technical attribute but also as a socio-organisational phenomenon shaped by user perception and governance structures. Addressing limitations in generic data quality frameworks, this study develops and analytically grounds a user-focused, context-specific data quality framework tailored to regulated banking environments. Guided by the Design Science Research methodology, the study integrates a systematic literature review with empirical insights from a structured survey of employees at a Eurozone bank. The results provide an interpretive understanding of how user perceptions of data quality influence day-to-day banking operations, revealing governance and accessibility-related patterns that affect service processes and decision-making. By explicitly positioning user perception as a central explanatory mechanism linking data systems, governance arrangements, and business processes, the proposed framework extends user-perceived data quality theory to regulated financial contexts. The study contributes to data quality and service research by demonstrating how perception-informed assessment can support continuous improvement, organisational learning, and more resilient data management practices in banking institutions.

Abstract
In recent years, the research community, but also the general public, has raised serious questions about the reproducibility and replicability of scientific work. Since many studies include some kind of computational work, these issues are also a technological challenge, not only in computer science, but also in most research domains. Computational replicability and reproducibility are not easy to achieve due to the variety of computational environments that can be used. Indeed, it is challenging to recreate the same environment via the same frameworks, code, programming languages, dependencies, and so on. We propose a framework, known as SciRep, that supports the configuration, execution, and packaging of computational experiments by defining their code, data, programming languages, dependencies, databases, and commands to be executed. After the initial configuration, the experiments can be executed any number of times, always producing exactly the same results. Our approach allows the creation of a reproducibility package for experiments from multiple scientific fields, from medicine to computer science, which can be re-executed on any computer. The produced package acts as a capsule, holding absolutely everything necessary to re-execute the experiment. To evaluate our framework, we compare it with three state-of-the-art tools and use it to reproduce 18 experiments extracted from published scientific articles. With our approach, we were able to execute 16 (89%) of those experiments, while the others reached only 61%, thus showing that our approach is effective. Moreover, all the experiments that were executed produced the results presented in the original publication. Thus, SciRep was able to reproduce 100% of the experiments it could run.