Digital health technologies, including mobile applications, clinical decision-support systems, software-based medical technologies and artificial intelligence (AI)-enabled tools, are increasingly integrated into healthcare delivery. Within the UK National Health Service (NHS), these technologies offer opportunities to improve patient outcomes, operational efficiency and data-driven clinical decision-making. Their implementation, however, introduces complex challenges relating to medical device qualification and classification, clinical safety, cybersecurity, interoperability, data governance and regulatory compliance.This study examines the regulatory and governance frameworks applicable to AI-enabled digital health and medical device systems within the NHS, with particular emphasis on the Clinical Risk Management Standards DCB0129 and DCB0160, the Digital Technology Assessment Criteria (DTAC), and relevant UK medical device regulatory requirements. A structured literature review was conducted using PubMed, covering publications between 2015 and 2025. Sixty studies were included and analysed using a narrative synthesis approach to identify recurring regulatory, technical and governance challenges associated with the implementation and lifecycle management of digital health technologies.The findings indicate that existing NHS frameworks provide an important foundation for clinical safety, data protection, cybersecurity and organisational assurance. However, their application becomes increasingly complex for interconnected and adaptive AI-enabled systems. Key challenges include uncertainty in medical device qualification and regulatory classification, fragmentation across governance requirements, algorithmic bias and explainability, data integrity, cybersecurity vulnerabilities, interoperability, limitations in conventional evaluation approaches, and the need for continuous monitoring of system performance following implementation.Drawing on these findings, this study proposes a Unified NHS AI Governance and Compliance Model that integrates medical device qualification and regulatory classification with clinical safety, data protection, cybersecurity, usability and accessibility, interoperability, and data and AI workflow governance. The model provides a structured lifecycle approach to support early determination of regulatory requirements, clearer allocation of organisational responsibilities, proportionate risk assessment, implementation assurance and post-deployment monitoring.The study concludes that governance of AI-enabled digital health and medical device technologies should evolve towards a more integrated, adaptive and risk-based approach that aligns medical device regulation with wider digital-health assurance requirements. Although developed within the NHS context, the proposed framework may provide a transferable governance approach for healthcare organisations implementing complex, interoperable and AI-enabled digital technologies.
The aim of this study is to analyze the influence of territorial factors associated with infrastructure, strategic location, and connectivity (ISC) on the territorial competitiveness of the state of Michoacán (Mexico) in the context of nearshoring. The research was conducted using a quantitative, explanatory approach with partial least squares structural equation modeling (PLS-SEM). Data was obtained through a survey administered to companies and strategic stakeholders involved in productive activities and investment in the state. Results show that the ISC construct exerts a positive influence on territorial competitiveness. The study thus demonstrates the importance of territorial factors for the state’s level of competitiveness and concludes that strengthening infrastructure, connectivity, and territorial conditions are key strategic elements for increasing Michoacán’s competitiveness and improving its capacity to capitalize on the opportunities that may arise from nearshoring.
This conceptual article analyses core issues in the South African higher education sector, focusing on the mismatch between graduates’ skills and post-graduation employability. It proposes a theoretical framework examining how impostor syndrome undermines performance and the demonstration of soft skills, mediated by self-esteem and academic self-efficacy. Impostor syndrome has emerged as a growing concern for capable graduates who feel self-disqualified and fear being exposed as frauds (Clance & Imes, 1978; Chatterjee, 2023). They attribute success to luck or undeserved factors (Langford & Clance, 1993), when in fact they have succeeded despite impostor feelings. This manifests indirectly through lowered self-efficacy, reduced goal-setting, and challenges with avoidance (Abdelaal, 2020), which contrast with work readiness expectations. In educational settings, this conflict heightens aversion to job preparation, clashing with self-interests in workplace needs (Gadsby, 2022). Acquiring adaptive skills—such as communication, teamwork, and flexibility—is essential to enhancing employability. However, impostor syndrome undermines learning, often leading to a perception of fraudulence. The framework identifies self-esteem and academic self-efficacy as mediators in combating impostor syndrome, emphasising learning, work skills, adaptability, and teamwork to build readiness. Low self-esteem leads to self-dysfunction, disrupting social integrity and perceptions of trust (Bowen & Botha, 2023). Diminished self-efficacy hampers task engagement and peer association (Haghighi & Ghorbali, 2022). By bridging cognitive-affective theories with outcome measures, this paper refines the understanding of impostor syndrome’s impact on the acquisition of soft skills and informs strategies to advance graduate employment.
Background: Total quality management (TQM) philosophy is managerial approach illustrating pathway to achieve business excellence and objectives. There are many models for TQM implementation as Deming, European foundation of quality management (EFQM) and Malcolm Baldrige national quality award (MNBQA) models. Sustainable development goals (SDGs) demonstrated through social, environmental and economic pillars however, sustainable outcomes are preferred to be categorized as social, environmental and governance categories. Purpose: The study focuses on impact of TQM implementation on business sustainability from its three pillars with demonstration the mediators as green practices and dynamic capabilities. The study was conducted in Egyptian healthcare sector.Design: Online survey with quantitative and qualitative questions was conducted in August 2025. Purposive sample is taken from top and middle management in Egyptian private healthcare organizations. Quantitative data were analyzed through regression and mediation analysis and qualitative data are thematically analyzed.Findings: 31 participants participated, results demonstrate strong direct relation between TQM implementation and business sustainability with significantly positive mediators’ effects by dynamic capabilities and green practices.Originality & Discussion: Unfortunately, studies are limited in merging between TQM implementation and sustainable outcomes especially in Egyptian healthcare industry, in addition to the limited researches on discussion the mediating factors. Green practices are representing the implementation of green innovations. Dynamic capabilities are representing ability of organization to change and respond toward external circumstances. Ultimately, TQM activities offer roadmap to accomplish sustainable outcomes.
This systematic literature review examines how Artificial Intelligence (AI) is reshaping business administration, with particular emphasis on consumer decision-making. Following PRISMA guidelines, the review synthesizes evidence from 85 peer-reviewed studies published between 2010 and 2024 and retrieved from Scopus, Web of Science, and ScienceDirect. The analysis identifies four dominant mechanisms through which AI influences consumer decisions: personalization and recommender systems, predictive analytics, dynamic pricing, and AI-enabled customer service. While these applications can improve decision efficiency, relevance, and convenience, the literature also reports persistent risks, including algorithmic bias, limited transparency, perceived unfairness, and potential erosion of consumer autonomy. To integrate these insights, the study proposes an enhanced Trust-Transparency-Governance (TTG) framework that conceptualizes responsible consumer-facing AI as the interaction of (a) trust in algorithmic outputs, (b) transparency of models and decision logic, and (c) organizational governance structures that ensure accountability and ethical alignment. Extending prior AI ethics frameworks, the enhanced TTG model specifies mechanism-based linkages, how transparency shapes trust, how governance operationalizes transparency, and under which conditions these components jointly support responsible AI deployment. The review concludes with implications for managers and policymakers and highlights research gaps, notably the need for empirical studies on consumer responses to transparency interventions, fairness trade-offs, and algorithmic influence on autonomy across diverse market contexts.
