In the digital age, data has emerged as a critical resource, often referred to as the "new oil." Governments worldwide are grappling with the challenges of safeguarding this resource while ensuring its effective utilization. Data localization has been heralded as a technological "fix" to address growing concerns over cybersecurity, privacy, and national sovereignty. By mandating the storage and processing of data within national borders, data localization seeks to bolster security and minimize risks associated with foreign interference. However, this approach also introduces significant operational complexities and economic challenges, particularly for multinational organizations that depend on the free flow of data.

This paper explores the intricate relationship between data localization and cybersecurity, analyzing how localization policies aim to mitigate threats such as data breaches, cyber espionage, and Distributed Denial of Service (DDoS) attacks. It also delves into the unintended consequences of these measures, including the centralization of data and fragmented global cybersecurity strategies. Through case studies and a comparative analysis of international data localization laws, the article aims to evaluate whether these measures effectively address cybersecurity concerns or concerned vulnerabilities.

Further, framed within the broader discourse of technological "fixes," the paper aims to analyse the rhetoric surrounding data localization as a one-size-fits-all solution. Also, the nuanced approach that balances national security with global collaboration and innovation, highlighting the need for robust infrastructure and harmonized regulatory frameworks to achieve meaningful cybersecurity outcomes.

In this paper, the contextual, multiple (re)configurations of AI-powered business models, being enacted in diverse entrepreneurial conditions are conceptualised as liminal innovation process instances of iterative experimentation and exploitation.

Arguably, the different forms of AI technology bearing a multiplicity of automation, autonomy and learning capacities, when aimed at different fields and distinct levels of the business operation and management activities, instigate correspondingly, distinctive concerns and corporate uncertainties. ΑΙ is most often perceived in line with a simplified narrative of algorithmic systems progressively substituting or augmenting established or newly defined organizational roles and routines.

An AI-driven liminal innovation process is henceforth theorized as a multiplicity bearing and generating process, purposefully enacted to unveil, leverage and reconcile AI emergence tensions, coupled in distinct value creation and capture logics and associated management and governance “apparatuses”. An integrated, multi-level approach that examines the complex interplay of macro, meso and micro level influencing factors of AI strategizing is initially adopted.

We view an AI driven innovation process, embedded in multiplicity-ridden business and market orientations, as a mutually constitutive process of AI technology and re-negotiated organizing norms and practices. We elaborate AI entrepreneurship in terms of its inherently paradoxical institutional reinforcement and displacement dynamics and its emergence and performativity aptitude. The entrenched innovation and strategic management perspectives adopted aim at offering a hybrid viewpoint for the new fundamentals of AI strategizing.

Digital transformation often fails not because of the technology itself, but due to gaps in people, strategy, and internal alignment. Too many efforts focus only on the latest tools, without preparing the organisation for how change actually works in practice.

The 4D-DT Framework takes a broader view. It explores readiness across four essential areas: Organisational systems, Technology infrastructure, Strategic direction, and People empowerment. Each area includes measurable indicators, 305 in total, that help companies understand where they stand and what to improve.

What makes this approach different is how it works at every level. It supports leadership with strategic insights, while also giving managers and employees specific, role-based actions. The model uses AI to process results and suggest steps, but its logic is grounded in years of practical experience.

Initial results from case studies show that the framework helps reduce resistance, connect digital tools to business goals, and support long-term adaptability. In the shift from Industry 4.0 to 5.0, where technology and human values must align, this kind of balanced approach becomes essential.

Career guidance plays a crucial role in equipping students with the skills and knowledge needed to navigate complex career landscapes. This study explores the impact of career guidance on fostering vocational clarity among Human Resource Management (HRM) students. Vocational clarity refers to the ability to articulate career aspirations and understand the pathways to achieve them. Using Social Cognitive Career Theory (SCCT) as a framework, the research examines how structured career guidance initiatives influence students' career decision-making, self-efficacy, and outcome expectations. A quantitative methodology was employed, collecting data via self-administered online surveys from HRM students. Statistical analysis using SPSS version 29.0 revealed a significant positive relationship between career guidance and vocational clarity. The findings emphasize the importance of tailored career counseling, interactive workshops, and mentorship programs in bridging academic training and career readiness. This study contributes to ongoing efforts to enhance employability through targeted career interventions and offers practical recommendations for higher education institutions to implement proactive and student-centered career services.