Search Results (26)

AI-enabled quality control (AI-QC) tools are increasingly available to manufacturing SMEs in emerging economies, yet the firm-level conditions associated with their adoption remain underexamined. Building on the Technology–Organization–Environment (TOE) framework of Tornatzky and Fleischer, integrated with the perceived usefulness and perceived ease-of-use constructs of the Technology Acceptance Model (TAM), this study examines the determinants of AI-QC adoption intention, and its association with operational performance improvement, in 284 manufacturing SMEs from Turkey, Malaysia, and Egypt. The focal dependent construct is adoption intention rather than realized adoption. The AI-QC technologies considered are machine learning defect detection, computer vision inspection, predictive maintenance, and digital twin integration. Three complementary analytical procedures are applied to the same data: partial least squares structural equation modeling (PLS-SEM) to estimate the strength of the modeled associations, importance–performance map analysis (IPMA) to identify high-importance but low-performance predictors, and fuzzy-set qualitative comparative analysis (fsQCA) to identify combinations of conditions jointly sufficient for high adoption intention. The PLS-SEM estimates indicate positive associations for the technological, organizational, and environmental predictors, with top management support, perceived usefulness, and organizational readiness showing the largest coefficients and data security concern showing a negative association; effect magnitudes varied considerably, and several were small. The IPMA results indicate that the two most important predictors exhibit comparatively low performance scores in the sample. The fsQCA results identify three configurations associated with high adoption intention. Because the design is cross-sectional and based on self-reported, single-respondent data, the findings are interpreted as associations rather than causal effects. The paper concludes with guidance for SME managers, AI technology vendors, and industrial policymakers. Full article

This study examines experiential and financial factors associated with Metaverse readiness in Lebanon. Drawing on a socio-technical readiness perspective informed by selected concepts from technology adoption literature, the study explores how interest in immersive technologies, remote work experience, and perceived financial security in decentralized digital assets relate to individual readiness and perceived organizational expectations. Using an exploratory cross-sectional survey of 231 respondents, multiple regression analyses were conducted to examine these associations. The findings indicate that interest in VR/AR technologies and positive remote work experience are positively associated with individual willingness to use the Metaverse for work, education, or professional activities. Perceived financial security in decentralized digital assets is also positively associated with perceived organizational benefit expectations. The results suggest that Metaverse readiness is linked not only to technological interest but also to prior digital collaboration experience and financial trust. By focusing on Lebanon as a developing and crisis-affected economy, the study contributes a context-sensitive and perception-based understanding of readiness for immersive digital ecosystems. Practical implications are discussed for organizations and policymakers seeking to support responsible digital transformation. Full article

China’s low-altitude economy (LAE) is moving from policy experimentation to coordinated industrial deployment, yet existing assessments often treat the LAE as a homogeneous sector or equate aircraft capability with deployment readiness. This study develops a scenario-gated sustainability readiness framework for six representative LAE and urban air mobility (UAM) scenarios in China: emergency medical logistics and disaster response, infrastructure inspection and public-service monitoring, urban instant logistics, airport shuttle and intermodal passenger transfer, urban air taxi, and low-altitude tourism. The proposed framework consists of a scenario layer, an eight-dimensional readiness layer, and a decision layer integrating 0–4 ordinal scoring, evidence-confidence tagging, non-compensatory gate conditions, and readiness classification. The eight dimensions cover mission and demand fit; airspace and traffic controllability; infrastructure and site readiness; digital communication, navigation, surveillance, and data security; vehicle, energy, and environmental performance; weather and route-environment robustness; workforce and organizational readiness; and social acceptance and legal legitimacy. The illustrative application indicates that infrastructure inspection is the only routine scaling candidate; emergency medical logistics and urban instant logistics are suitable for bounded routine operation; airport shuttle and tourism should remain controlled pilot candidates; and open-network urban air taxi is still at the pre-pilot stage. The study contributes a scenario-based deployment logic for sustainable aviation and UAM governance. Full article

Illegal, unreported, and unregulated (IUU) fishing activities have become one of the most critical challenges facing the global fish industry, particularly in developing countries, with the economic impact of fish fraud reaching billions of dollars annually. A major contributor to this problem is the limitation of conventional fish supply chain systems, which lack secure data sharing among stakeholders, fail to provide trusted product information to consumers, and offer insufficient transparency for regulatory authorities. These shortcomings facilitate fraud and weaken trust and oversight across the supply chain. Blockchain technology has demonstrated strong capability to address key cybersecurity challenges by enhancing traceability, transparency, and tamper-resistant data integrity across distributed supply chain stakeholders. In this paper, we present an enterprise-oriented prototype of a secure, permissioned blockchain-based fish supply chain system designed to enable trusted data sharing and end-to-end traceability across multi-stakeholder environments. Building upon our prior work in Ethereum-based seafood quality monitoring, this study contributes: (1) a modular, consortium-grade architecture implemented using Hyperledger Fabric and containerized via Docker, supporting scalable organizational participation; (2) formal UML-based system modeling of supply chain actors, assets, and lifecycle transitions; and (3) custom chaincode logic that enforces ownership transfer workflows and regulatory compliance policies. In addition, the architecture is designed as agent-ready, exposing standardized APIs that enable future integration of autonomous AI-driven client applications for proactive supply chain orchestration. By leveraging a private, permissioned network model, the functional prototype demonstrates the feasibility of improving data veracity and providing a practical foundation for mitigating fraud and enhancing regulatory oversight in the global fish industry. Full article

The increasing digitalization of smart circular economy (CE) systems intensifies reliance on interconnected cyber-physical infrastructures, thereby increasing exposure to cybersecurity risks that may affect operational continuity and regulatory compliance. This study proposes a Fuzzy Analytical Hierarchy Process (Fuzzy AHP)-based framework to systematically assess cybersecurity readiness in alignment with the ISO/IEC 27001:2022 Information Security Management System (ISMS) standard. The framework adopts a structured three-level hierarchy consisting of seven main criteria and 39 sub-criteria, derived from ISO/IEC 27001:2022 clause-based requirements and Annex A control families, and expanded with an additional regulatory criterion based on the Cyber Resilience Act (CRA) Requirements Standards Mapping. Expert judgments from ten specialists in cybersecurity and digital systems were elicited using linguistic assessments and converted into triangular fuzzy numbers to compute priority weights under uncertainty. The results indicate that ISMS governance and organizational context are the most influential determinants of cybersecurity readiness, followed by regulatory and compliance alignment, operational oversight, and technological controls, while organizational, human, and physical controls play supportive roles. Consistency and sensitivity analyses confirm the robustness and stability of the weighting structure. Overall, the framework provides a standards-aligned decision-support tool for prioritizing cybersecurity readiness in digitally intensive CE environments. Full article

Adaptive learning at scale requires explicit handling of uncertainty and information flow across diverse educational technologies. This paper proposes a TOGAF-conformant enterprise architecture for the University of Tabuk (UT) that embeds entropy- and uncertainty-aware requirements from the outset and aligns them with institutional goals in teaching, research, and administration. Using the Architecture Development Method (ADM), we map information-theoretic requirements to architectural artifacts across the architecture vision, business, information systems, and technology domains; formally specify core entropy-informed observables, including predictive entropy, expected information gain, workflow variability entropy, and uncertainty hot-spot severity; and define semantic and metadata standards for their near-real-time computation. These indicators are positioned explicitly across the TOGAF domains: business architecture identifies where uncertainty matters, information systems architecture defines the computable data and application representations, technology architecture operationalizes secure and scalable computation, and later ADM phases use the resulting metrics for prioritization and governance. The architecture also establishes governance that ranks initiatives by their expected uncertainty reduction through Architecture Review Board (ARB) decision gates. We address three research questions: (R.Q.1) how to design a TOGAF-conformant architecture for UT that natively encodes uncertainty-aware requirements and aligns with institutional needs; (R.Q.2) how to integrate dispersed data, achieve semantic harmonization, and deliver analytics-ready streams that support information-theoretic indicators for personalization without delay; and (R.Q.3) how to embed IT demand planning in opportunities and solutions and migration planning using uncertainty reduction and expected information gain as prioritization criteria. The resulting architecture offers a university-wide foundation for adaptive learning: it unifies learner and system interaction data under governed schemas, supports low-latency analytics, and formalizes decision processes that treat uncertainty as a primary metric. Though learner-level operational validation is future work, the design establishes the technical and organizational foundations for responsible, large-scale deployment of entropy-driven learner modeling, content sequencing, and feedback optimization. Full article

The transformative potential of Artificial Intelligence (AI) in urban management is severely constrained by pervasive systemic fragmentation. While AI applications demonstrate high efficacy within isolated domains, they rarely achieve the cross-domain integration necessary for realizing systemic benefits. Our prior research identified this fragmentation paradox, revealing that 91.5% of urban AI implementations operate at the lowest levels of integration. While the Urban Systems Artificial Intelligence Framework (UAIF) offers a technical blueprint for integration, realizing this vision is contingent upon organizational readiness. This paper addresses this critical gap by introducing the Urban AI Governance Maturity Model (UAIG), developed using a Design Science Research methodology. Distinguished from generic maturity models, the UAIG operationalizes Socio-Technical Systems theory by establishing a direct Governance-Technology Interlock that specifically links organizational maturity levels to the engineering requirements of cross-domain AI. The model defines five maturity levels across five critical dimensions: Strategy and Investment; Organizational Structure and Culture; Data Governance and Policy; Technical Capacity and Interoperability; and Trust, Ethics, and Security. Through illustrative applications, we demonstrate how the UAIG serves as a diagnostic tool and a strategic roadmap, enabling policymakers to bridge the gap between technical possibility and organizational reality. Full article

Retailers play a pivotal role in advancing digital commerce in emerging markets, yet their adoption of digital payment systems remains inconsistent. Drawing on the Technology–Organization–Environment (TOE) framework, this study examines how technological readiness, organizational capability, and environmental influences shape retailers’ adoption behavior in Saudi Arabia. Survey data from retailers in Riyadh indicates that transaction speed, convenience, and perceived usefulness function as key enablers, while transaction fees, limited digital skills, and security concerns act as barriers. Although institutional trust and supportive government initiatives encourage adoption, many retailers remain unaware of formal compliance expectations, illustrating a pattern of compliance without awareness. To account for this dynamic, the TOE framework is extended by incorporating regulatory literacy and institutional trust as contextual dimensions. The study therefore offers theoretical refinement to TOE in policy-driven adoption settings and provides practical implications for improving merchant training, usability support, and regulatory communication to better sustain digital payment usage in emerging digital commerce ecosystems. Full article

The rapid advancement of AI, especially large language models (LLMs), brings opportunities and challenges to industries. In construction, LLMs can enhance project coordination, support decision-making and reduce workload, but adoption is limited by hallucination, data security and domain complexity. This study investigates the current state of LLM adoption in China’s construction industry through a four-step approach, including a comprehensive literature review to identify potential drivers and barriers, questionnaire design and data collection for empirical analysis, and the application of the Entropy Weight Method (EWM) to quantify and rank the relative importance of each factor. The findings reveal that the top drivers originate at the company level, including strategic partnerships, internal research teams, and staff training—highlighting the central role of organizational readiness in enabling LLM integration. Conversely, the most critical barriers are embedded in the construction domain itself, including knowledge gaps, workflow integration, and data heterogeneity, which reflect structural limitations in the sector. Although LLM implementation remains in its early stages, survey responses show widespread optimism among stakeholders regarding its future potential. The study proposes several actionable strategies for both construction firms and policymakers to facilitate effective LLM adoption. Moreover, the identified drivers and barriers are not exclusive to construction but are also relevant to other digitally transforming sectors—such as manufacturing, healthcare, and energy—offering broader implications for AI adoption in complex, project-based environments. Full article

This study examines the adoption and implementation of the Zero Trust (ZT) cybersecurity paradigm using the Technology–Organization–Environment (TOE) framework. While ZT is gaining traction as a security model, many organizations struggle to align strategic intent with effective implementation. We adopted a sequential mixed-methods design combining 27 semi-structured interviews with cybersecurity professionals and a survey of 267 experts across industries. The qualitative phase used an inductive approach to identify organizational challenges, whereas the quantitative phase employed Partial Least Squares Structural Equation Modeling (PLS-SEM) to test the hypothesized relationships. Results show that information security culture and investment significantly influence both strategic alignment and the technical implementation of ZT. Implementation acted as an intermediary mechanism through which these organizational factors affected governance and compliance outcomes. Strategic commitment alone was insufficient to drive effective implementation without strong cultural support. Qualitative insights underscored the importance of leadership engagement, cross-functional collaboration, and legacy infrastructure readiness in shaping outcomes. The findings emphasize the need for cultural alignment, targeted investments, and process maturity to ensure successful ZT adoption. Organizations can leverage these insights to prioritize resources, strengthen governance, and reduce implementation friction. This research is among the first to empirically investigate ZT implementation through the TOE lens. It contributes to cybersecurity management literature by integrating strategic, cultural, and operational dimensions of ZT adoption and offers practical guidance for decision-makers seeking to institutionalize Zero Trust principles. Full article

The leisure industry exerts adverse effects on the natural environment, creating critical challenges for achieving clean and sustainable environmental goals. However, the potential role of metaverse technologies in mitigating these challenges remains underexplored. Therefore, this study explores how metaverse technologies can support clean and safe environmental objectives in Turkey and Lithuania. Specifically, the research aims to identify the socio-cultural, institutional, and technological barriers that arise during the integration of these technologies into sustainable environmental strategies. Using purposive sampling, semi-structured interviews were conducted with forty experts from both countries, including policymakers, academics, and industry professionals. The results indicate that variations in technological infrastructure, levels of digital readiness, socio-cultural acceptance, and institutional governance capacity significantly shape the feasibility of applying metaverse technologies in the leisure industry. Furthermore, the findings highlight that local needs, stakeholder expectations, and organizational resources play a crucial role in determining the effectiveness of these technologies in promoting a cleaner and more sustainable environment. The recommendations particularly emphasize enhancing technological infrastructure, expanding institutional collaborations, implementing legal reforms related to metaverse technologies, and addressing data security concerns. Overall, the study provides a comprehensive perspective that connects technology, recreation, and environmental science. Full article

Background: Healthcare data interoperability faces significant barriers, including regulatory compliance complexities, institutional trust deficits, and technical integration challenges. Current centralized architectures demonstrate inadequate mechanisms for balancing data accessibility requirements with patient privacy protection, as mandated by HIPAA and GDPR frameworks. Traditional compliance approaches rely on manual policy implementation and periodic auditing, which are insufficient for dynamic, multi-organizational healthcare data-sharing scenarios. Objective: This study develops and proposes a blockchain-based healthcare data management framework that leverages Hyperledger Fabric, IPFS, and the HL7 FHIR standard and incorporates automated regulatory compliance mechanisms via smart contract implementation to meet HIPAA and GDPR requirements. It assesses the theoretical system architecture, security characteristics, and scalability considerations. Methods: We developed a permissioned blockchain architecture that employs smart contracts for privacy policy enforcement and for patient consent management. The proposed system incorporates multiple certification authorities for patients, hospitals, and research facilities. Architectural evaluation uses theoretical modeling and system design analysis to assess a system’s security, compliance, and scalability. Results: The proposed framework demonstrated enhanced security through decentralized control mechanisms and cryptographic protection protocols. Smart contract-based compliance verification can automate routine regulatory tasks while maintaining human oversight in complex scenarios. The architecture supports multi-organizational collaboration with attribute-based access control and comprehensive audit-trail capabilities. Conclusions: Blockchain-based healthcare data-sharing systems provide enhanced security and decentralized control compared with traditional architectures. The proposed framework offers a promising solution for automating regulatory compliance. However, implementation considerations—including organizational readiness, technical complexity, and scalability requirements—must be addressed for practical deployment in healthcare settings. Full article

Background: Transitions from hospital to long-term care (LTC) facilities are critical periods for older adults living with dementia, often involving complex medical, cognitive, and psychosocial needs. Virtual team-based care has emerged as a promising strategy to improve communication, coordination, and continuity of care during these transitions. However, there is limited evidence on how such approaches are implemented in practice, particularly with respect to inclusion, equity, and engagement of older adults and families. Objective: This study aimed to identify the enablers and barriers to delivering virtual team-based care to support older adults with dementia in transitioning from hospital to LTC. Methods: We conducted a qualitative study using semi-structured interviews, focus groups, and a policy review. Data were collected from 60 participants, including healthcare providers, older adults, and family care partners across hospital and LTC settings in British Columbia, Canada. Thematic analysis was conducted using a hybrid inductive and deductive approach. Eighteen institutional policies and guidelines on virtual care and dementia transitions were reviewed to contextualize findings. Results: Four themes were identified: (1) enhancing communication and collaboration, (2) engaging families in care planning, (3) digital access and literacy, and (4) organizational readiness and infrastructure. While virtual huddles and secure messaging platforms supported timely coordination, implementation was inconsistent due to infrastructure limitations, unclear protocols, and staffing pressures. Institutional policies emphasized privacy and security but lacked guidance for inclusive engagement of older adults and families. Many participants described limited access to reliable technology, a lack of training, and the absence of tools tailored for individuals with cognitive impairment. Conclusions: Virtual care has the potential to support more coordinated and inclusive transitions for people with dementia, but its success depends on more than technology. Structured protocols, inclusive policies, and leadership commitment are essential to ensure equitable access and meaningful engagement. The proposed VIRTUAL framework offers practical tips for strengthening virtual team-based care by embedding ethical, relational, and infrastructural readiness across settings. Full article

As governments pursue digital transformation to improve service delivery and administrative efficiency, AI chatbots have emerged as a promising innovation in smart public administration. However, their adoption remains limited, particularly in transitioning countries where institutional, organizational, and technological conditions are complex and evolving. This study aims to empirically examine the key aspects, challenges, and strategic implications of AI chatbots’ adoption in public administration of Uzbekistan, a transitioning government in Central Asia. The study offers a novel contribution by employing an extended technology–organization–environment (TOE) framework. Data were collected through a survey among 501 public employees and partial least squares structural equation modeling was used to analyze data. The results reveal that perceived usefulness, compatibility, organizational readiness, effective accountability, and ethical AI regulation are key enablers, while system complexity, traditional leadership, resistance to change, and concerns over data management and security pose major barriers. The findings contribute to the literature on effective innovation in public administration and provide practical insights for policymakers and public managers aiming to effectively implement AI solutions in complex governance settings. Full article

The advent of Industry 4.0 and the integration of Artificial Intelligence (AI) is transforming supply chain management (SCM), improving efficiency, resilience and strategic decision-making capabilities. This research study provides a comprehensive overview of AI applications in key SCM processes, including customer relationship management, inventory management, transportation networks, procurement, demand forecasting and risk management. AI technologies such as Machine Learning, Natural Language Processing and Generative AI offer transformative solutions to streamline logistics, reduce operational risk and improve demand forecasting. In addition, this study identifies barriers to AI adoption, such as implementation challenges, organizational readiness and ethical concerns, and highlights the critical role of AI in promoting supply chain visibility and resilience in the midst of global crises. Future trends emphasize human-centric AI, increasing digital maturity, and addressing ethical and security concerns. This review concludes by confirming the critical role of AI in shaping sustainable, flexible and resilient supply chains while providing a roadmap for future research and application in SCM. Full article