Blockchain technology is increasingly recognized as a transformative tool for improving agri-food supply chain (ASC) performance, particularly in enhancing transparency and traceability. Nonetheless, it has still not been widely adopted in developing nations like Pakistan. This study investigates the impact of blockchain adoption on ASC performance, incorporating transparency as a mediating variable and the Internet of Things (IoT) as a moderating factor within the Unified Theory of Acceptance and Use of Technology (UTAUT) framework. Data were collected from 333 professionals in Pakistan's agri-food sector and analyzed using smart PLS, with mediation and moderation effects. The findings reveal that blockchain adoption significantly improves supply chain performance. Transparency partially mediates this relationship, while IoT strengthens the effect by enabling real-time data sharing and monitoring. The study offers new insights into literature by extending UTAUT through the integration of blockchain and IoT, and by empirically validating their combined impact in a developing economy context. In practice, the results provide practical information to policy makers and industry players with a view to improving efficiency, accountability, and sustainability in the agri-food supply chains.

This study examines how circular value is created under conditions of structural fragility, disruption, and resource scarcity, where prevailing circular supply chain theories offer limited explanatory power. While prior research emphasizes efficiency and optimization, it provides limited insight into how circular supply chains function when stable infrastructure, predictable flows, and formal coordination mechanisms are absent. Drawing on dynamic capabilities theory and insights from humanitarian logistics, this study proposes a conceptual framework that reconceptualizes circular value creation as an adaptive and capability-mediated process. The framework posits that digital circular capabilities, sensing, seizing, and reconfiguring, enable organizations to develop adaptive reverse logistics systems, which serve as the primary mechanism through which circular value is realized under constrained conditions. Structural fragility and disruption intensity are introduced as boundary conditions shaping the effectiveness of these relationships. The study contributes by reframing circular supply chain theory beyond efficiency-based assumptions, extending dynamic capabilities to disrupted and resource-constrained environments, and positioning reverse logistics as a central adaptive mechanism for circular value recovery. By integrating insights from humanitarian logistics and circular supply chain research, the study advances a more context-sensitive understanding of supply chain functioning under disruption.

Artificial Intelligence (AI) technologies have revolutionized maritime logistics and supply chain operations as much as they have revolutionized the manner in which other basic services are provided and exported in the maritime world, especially for the export-oriented Micro, Small and Medium Enterprises (MSMEs). This study delves into how the adoption of AI affects maritime supply chain resilience for MSME exporters in the India–Thailand trade corridor. The research combines the Technology Acceptance Model (TAM) and Dynamic Capability Theory, aiming to analyse the influence of perceived usefulness, technological readiness, digital integration and organizational adaptability on AI adoption and resilience of the maritime supply chain. The research method utilized was a cross-sectional survey which uses quantitative empirical research design. The primary data obtained were 268 responses from MSME exporters of maritime trade between India and Thailand. The collected data were analysed using a software tool called a Partial Least Squares (PLS) and a Software Statistical Package (SPSS) version 29 (SmartPLS 4). The results indicate that the use of AI has a positive and significant impact on maritime supply chain resilience. Perceived usefulness and technological readiness emerged as significant factors affecting AI adoption, and digital integration and organization's adaptability as factors affecting maritime supply chain resilience. AI-powered solutions like predictive analytics, intelligent cargo tracking, route optimization, and automated logistics systems are pivotal in bolstering the operational continuity and resilience of maritime supply chains, as emphasized in the study. The research has theoretical contributions by building on TAM and Dynamic Capability Theory and applying them to the maritime logistics context and implications for MSMEs, policy makers and maritime authorities on digital transformation and the resilient maritime trade systems. The study provides a comprehensive overview of the role of AI in maritime supply chains for achieving greater competitiveness and sustainability in new global trade contexts.

This research evaluates the psychometric properties of a modified instrument designed to examine green logistics implementation, organizational outcomes, and the regulatory influence of government. Drawing from validated 5-point Likert scales in current literature, the study utilized a pilot sample of 50 participants, with data processed via SPSS v30.0. The refinement of the scale was achieved through internal consistency checks and exploratory factor analysis (EFA). Findings confirmed robust reliability, as Cronbach's alpha coefficients for all constructs surpassed the 0.7 threshold. The final 40-item scale, organized into five theoretically aligned dimensions, demonstrated strong factor loadings and sampling adequacy (KMO > 0.7). By validating these core constructs, this work establishes a rigorous foundation for future empirical inquiries into the environmental performance of the trucking industry.

Green supply chain management has emerged as a critical strategic approach for improving environmental sustainability and organizational performance in manufacturing firms. However, existing literature remains limited and missed the mechanisms and contextual conditions that shape these relationships. This study examines the relationship between green supply chain management and organizational performance through green operational capability as mediating mechanism, while considering perceived governmental environmental signaling intensity as moderated mediator. The study employed a quantitative research approach with explanatory research design. The data were collected from 327 respondents who are working in manufacturing factories. Data were analyzed using Smart PLS to test both the measurement and structural models. The findings show that green supply chain management has significant effect on organizational performance and green operational capability. Green operational capability also significantly enhances organizational performance and mediated the relationship between green supply chain management and organizational performance. Furthermore, perceived governmental environmental signaling intensity significantly strengthen the relationship between green supply chain management and green operational capability then influence the organizational performance which confirm moderated mediation effect. This study suggests that manufacturing firms institutionalize green supply chain management as a strategic capability. Policymakers should strengthen clear and consistent environmental signaling to enhance firm responsiveness. Managers should develop green operational capabilities to effectively translate sustainability practices into improved organizational performance and long-term competitive advantage.

The purpose of this study is to prioritize key environmental sustainability dimensions and indicators within the pharmaceutical sector to support informed decision-making and policy formulation. The study adopts a quantitative methodology using RIDIT analysis to compare multiple sustainability indicators against a reference distribution and derive priority rankings. Survey data collected from 516 respondents were analyzed across environmental impact, environmental sustainability, impact investing, and adoption of green pharmaceutical practices. The findings reveal that environmental sustainability and environmental impact indicators receive the highest priority, indicating strong stakeholder emphasis on long-term ecological outcomes. Impact investing plays a moderate facilitative role, while green practice adoption ranks lowest, highlighting implementation gaps. The study offers practical implications for policymakers and industry managers by identifying areas requiring targeted interventions and resource allocation. The novelty of this research lies in the application of RIDIT analysis to systematically rank sustainability dimensions in the pharmaceutical context, providing a robust and interpretable prioritization framework.

The rapid growth of healthcare information systems has led to an increase in the volume of clinical data stored in electronic medical record systems. This data contains valuable information such as patient demographics, vital signs, diagnoses, and medical procedures, which can be used to support clinical decision-making. Determining the appropriate clinical procedure remains a challenge due to the complexity of patient conditions and the potential need for multiple procedures simultaneously. This study aims to develop a robust model for predicting clinical procedures using a multi-label classification approach based on electronic medical record data. The proposed methodology integrates data preprocessing, feature engineering, followed by the implementation of several Machine Learning methods such as LightGBM, XGBoost, CatBoost, Deep Learning Models and combined using an Ensemble Learning method. Various Ensemble Learning techniques, including Simple Average, Machine Learning Average, and Optimized Weight-based combinations, are applied to improve predictive performance. Evaluation is performed using the Micro F1 score and Macro F1 score to assess overall performance. Experimental results show that individual models achieve competitive performance, with LightGBM, XGBoost, CatBoost outperforming the deep learning approach on tabular clinical data. The Ensemble Learning approach further improved performance, with the Optimized Weight with Grid Search Optimization ensemble achieving the highest Micro F1 score of 0.7788 and Macro F1 score of 0.7674. These results also demonstrate that combining multiple models effectively reduces bias and variance while improving generalization. The small difference between Micro and Macro F1 scores indicates balanced performance across labels. In conclusion, the proposed Ensemble Learning-based multi-label prediction model demonstrates robust capabilities in handling complex clinical data and improving prediction accuracy. This study highlights the importance of model combination strategies rather than increasing model complexity. These findings have practical implications for supporting clinical decision-making systems in healthcare settings.

Up to 78% of oil and gas megaprojects fail to deliver on initial commitments, yet maintenance readiness is routinely deferred until after construction decisions are already irreversible. This study investigates the previously unexamined link between maintenance readiness and interface management across the capital-project life cycle through a systematic literature review combined with a 30-year longitudinal analysis of 941 anonymised interface lessons-learned records spanning 21 countries. Root causes were classified using ISO 14224 of 2016, and predictive models were validated through stratified coding (Cohen's κ = 0.81) and five-fold cross-validation. The analysis revealed that five root causes drove 81.2% of all failures, including slow interface identification (18.5%), uncertain technical definitions (17.4%), weak governance (17.4%), unclear scope and ownership (16.3%), and cross-discipline communication breakdowns (11.6%), with key effects being performance impact (41.8%) and schedule delay (25.7%), while 33.0% of records were classified as high or critical risk and approximately 40.3% originated in Front-End Engineering Design. The Interface Criticality Index (ICI) correlated strongly with high-risk classification (r = 0.83), and the Random Forest classifier achieved ROC-AUC = 0.848 and PR-AUC = 0.850 respectively. The findings conclude that maintenance readiness should be established as a distinct, front-end-loaded discipline rather than a post-handover activity, as embedding readiness controls upstream converts lessons-learned records from retrospective artefacts into proactive design constraints, enabling early intervention before costly lock-in.

This systematic review synthesizes 40 peer-reviewed studies to examine the role of inclusive finance and institutional governance in FinTech adoption in Pakistan's banking sector. While user-centric behavioural models are the dominant narrative in global discourse, this review highlights the underappreciated role of institutional trust, regulatory design, and structural inclusion in the scalability of FinTech ecosystems. The paper identifies four thematic clusters using PRISMA and TCCM frameworks, namely, user-level drivers, governance enablers, financial inclusion mechanisms, and integrated adoption frameworks. Findings suggest that inclusion should be reimagined not just as an objective but also as a prerequisite to digital finance, and quality of governance directly moderates trust and adoption behaviour. The study presents an original conceptual framework that links ecosystem-level conditions and adoption dynamics, highlighting the need for multi-level theoretical approaches that incorporate behavioural, institutional, and infrastructural dimensions. Implications are drawn for policymakers, FinTech innovators, and financial regulators who are looking for inclusive, trusted, and sustainable digital finance solutions in emerging markets.

This scientometric study provides a comprehensive analysis of the intellectual landscape surrounding Retrieval-Augmented Generation (RAG), a transformative paradigm that addresses fundamental limitations of Large Language Models by grounding generative outputs in external knowledge sources. Analyzing 200 highly cited Scopus-indexed publications from 2006 to 2026, this study employs bibliometric techniques including citation analysis, co-authorship network analysis, keyword co-occurrence analysis, and thematic evolution mapping to characterize the field's development trajectory, intellectual structure, and emerging frontiers. The findings reveal exponential growth in RAG research since 2020, with a 215.7% compound annual growth rate between 2020 and 2023, accelerating dramatically after 2024. The United States and China emerge as dominant contributors, supported by a collaborative “Triple Helix” network linking academia, industry, and government. Thematic analysis identifies six major research clusters: foundational RAG architectures, biomedical and healthcare applications, educational applications, legal and compliance applications, industrial and manufacturing applications, and multimodal RAG systems. Evaluation practices reveal fragmentation with over 40 distinct metrics employed across studies. Key challenges identified include hallucination mitigation, computational efficiency, retrieval quality optimization, and privacy preservation. Emerging directions include agentic RAG systems, graph-based retrieval, iterative retrieval-generation synergy, and personalized RAG applications. This study contributes a data-driven roadmap for navigating this rapidly evolving field and identifies priority areas for future investigation.