Artificial intelligence-enabled diagnostics promise to transform hearing healthcare, yet real-world adoption remains limited. This study identifies and prioritizes barriers to AI integration in clinical audiology through a three-phase mixed-methods approach. Phase I reviewed literature, surfacing 20 obstacles across workflow, infrastructure, culture, and ethics. Phase II involved expert interviews, refining these into nine context-specific barriers. In Phase III, a fuzzy-DEMATEL survey and thematic coding revealed a causal hierarchy: algorithmic inaccuracy, privacy concerns, and lack of training erode clinician trust and widen the knowledge gap. Cost, integration issues, and resource limitations add systemic stress, while ethical concerns emerge downstream. Cluster analysis groups the barriers into three blocs: Clinical Workflow, Governance and Trust, and Tech Infrastructure. This is the first study to apply fuzzy-DEMATEL to AI barriers in audiology, producing a causal map and cluster framework that offer both theoretical insights and practical guidance for adoption strategies.

Moving towards a circular economy needs development of digital platforms and organizational capabilities for innovation, reconfiguration and capture for sustainable value. This study enquires the influence of institutional pressures (namely coercive, normative, and mimetic) on the circular performance of firms, resulting in better ecosystem innovation synergy as well as dynamic circular reconfiguration, circular value capture and circular financial performance. The goal of this study is to develop a theory-based model that links institutional theory, dynamic capabilities, and circular economy with circular economic value added. The data comprises 207 valid responses collected from companies in the logistics and supply chain sector. SmartPLS 4 is used to analyze data. The three institutional pressures have a significant impact on the circular economy capabilities. Coercive pressure and normative pressure influence ecosystem innovation synergy while mimetic pressure predicts dynamic circular reconfiguration. Moreover, dynamic circular reconfiguration is associated with measurable improvements in cost efficiency and revenue generation, thereby strengthening circular financial performance and circular economic value added. The study reveals that circular value capture has a significant impact on circular economic value added. The data indicates that ecosystem innovation synergy does not have a significant effect on circular value capture or circular financial performance. Ecosystem collaboration does not produce changes in firm economic performance unless it brings about internal reconfiguration. The findings suggest that both dynamic circular reconfiguration and circular performance became significant for circular economic value added in the context of circular finance. The routes of ecosystem innovation synergy and circular value capture, by their nature, are not sources of pressure. The findings enhance the understanding of how institutional pressures and potential capabilities enabled by digital platforms facilitate the transition to the circular economy. This paper finally provides theoretical and managerial insights for achieving sustainable value creation.

This paper explores the key enablers driving clinical adoption of AI-enabled audiology tools. Despite rapid technological maturity, real-world uptake remains uneven. Using a three-phase mixed-methods design, the authors identified and prioritised adoption drivers. Phase I involved a targeted literature scan (1991–2025), yielding 21 candidate factors. Phase II refined these through interviews with audiologists, physicians, engineers, and health-IT managers, distilling 10 core drivers. Phase III applied a decision-making trial and evaluation laboratory survey with 27 clinicians, revealing a causal chain where data accuracy, real-time analytics, and seamless integration enhance workflow efficiency, clinician confidence, and patient personalisation. Robust technical support and structured training further amplify adoption. Cluster analysis grouped drivers into technological, human-capital, and process domains, suggesting distinct tactical interventions. This study provides the first domain-specific causal influence map for AI adoption in hearing healthcare.