Abstract
With the growing popularity of DevOps culture among companies and the corresponding increase in Microservices architecture development-both known to boost productivity and efficiency in software development-an increasing number of organizations are aiming to integrate them. Implementing DevOps culture and best practices can be challenging, but it is increasingly important as software applications become more robust and complex, and performance is considered essential by end users. By following the Design Science Research methodology, this paper proposes an iterative framework that closely follows the recommended DevOps practices, validated with the assistance of expert interviews, for implementing DevOps practices into Microservices architecture software development, while also offering a series of tools that serve as a base guideline for anyone following this framework, in the form of a theoretical use case. Therefore, this paper provides organizations with a guideline for adapting DevOps and offers organizations already using this methodology a framework to potentially enhance their established practices.

Abstract
With the advent of Information and Communication Technologies in recent decades, organizations face several challenges today. Adopting Digital Transformation (DT) offers numerous opportunities for Small and Medium Enterprises (SMEs) to improve their efficiency and operations, reaching new markets, shareholders, and customers. However, there are potential risks associated with this process. With Digital Transformation (DT), the radius of connectivity and interconnection between devices and systems increases in Mozambique and worldwide, creating more significant space cyberattacks. As Small and Medium-sized Enterprises (SMEs) connect to the digital world and move forward with adopting innovative digital technologies, they become more vulnerable to digital security risks. Hence, managing digital security risks effectively is crucial to realizing the benefits of Digital Transformation (DT). This position paper proposes to present the research work that will culminate in the proposal to develop a framework that fits Mozambican Small and Medium Enterprises (SMEs) through a Design Science Research (DSR) methodology, which can help to assist Mozambican Small and Medium Enterprises (SMEs) in the Digital Transformation (DT) process.

Abstract
As technology continues to evolve, organizations face growing and complex challenges and opportunities that affect their ability to govern, manage and harness data as a key source of competitive advantage. Equally, data are considered a powerful and unique source of success for organizations, which in turn, can impact their decision-making capabilities and play a critical role in their success. Hence, this article aims to provide a detailed identification, analysis and discussion over the current data governance context and its existing frameworks, highlighting their commonalities, differences and gaps, including ones related to data governance relationship with Generative Artificial Intelligence (GenAI). This article conducts an extensive methodological and in-depth analysis over a set of sixteen data governance frameworks based on different key data governance attributes, denoting that although there are numerous frameworks, they hold weaknesses, limitations and challenges which prevent them from being capable of incorporating and governing the use and management of AI, particularly the demands originating from GenAI. Our findings provide and propose a new and enhanced data governance framework which integrates the best features and ideas from the existing ones and initiatives derived from the advancements and particularities of AI and GenAI models, systems, and overall usage.

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.