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.

This study explores how the digital business ecosystem enhances purchasing and supply management performance in the hospitality industry, focusing on the mediating role of service innovation capabilities and the moderating roles of collaborative network capabilities and knowledge sharing. Data were gathered through surveys from 401 managers of four- and five-star hotels in Türkiye, including purchasing managers and supply management professionals, and analyzed using structural equation modeling and moderated mediation analysis based on Hayes’ PROCESS macro. Digital business ecosystems positively influence purchasing and supply management performance, both directly and indirectly, via their service integration capability. Collaborative network capability and knowledge sharing serve as moderators in the research model. The findings confirm that integrating digital infrastructure, innovation capabilities, and collaborative networks improves purchasing efficiency, supplier collaboration, and overall supply management effectiveness in service-intensive environments.

The question of cybersecurity has turned into a necessity of credible digital finance, defining the expansion capacity of fintech sectors to increase access to secure and safe financial services. The phenomenon of fintech inclusion is becoming subject to the impact of institutional and corporate protection factors, which minimize perceived risk, and safeguard users, as well as confidence in digital financial channels. This paper is based on institutional and risk governance views and explores the influence of cybersecurity practices on fintech inclusion and suggests digital policies as a mediating factor that converts cybersecurity preparedness to inclusive fintech performance. Although increased attention is paid to cybersecurity and the modernization of regulations, there is still a lack of empirical data on the impact of cybersecurity practices on the inclusion of fintech based on policy-driven processes in a unified analytical framework. The 300 respondents who worked in the positions of information security managers, compliance and risk officers, fintech product managers, IT governance specialists, and senior executives were sampled to gather the data across the fintech-enabled organizations. The proposed hypothesized relationships were tested using Structural Equation Modeling based on the PLS-SEM. These results suggest that cybersecurity practices affect considerably digital policies and fintech inclusion, whereas digital policies affect considerably fintech inclusion. The mediation analysis ensures that the effect of cybersecurity practices on fintech inclusion is partly transmitted through the digital policies, which indicate that a better result is observed when backed by the clear governance structure and policy frameworks. The findings have practical implications to managers and policymakers who aim to improve the inclusion of fintech using secure and policy-driven digital finance environments.

The digital transformation has been a main engine of economic growth by transforming the organizational structures, operations and strategic decisions with the help of sophisticated digital technologies. The mediating role of Big Data Analytics in this transformation is that it helps organizations to transform digital initiatives into real economic performance. It will utilize the Technology Acceptance Model and recent literature on the concept of digital transformation and the capabilities of data to identify the effects of digital transformation on economic growth in the mediator Big Data Analytics. The research paper is based on three aspects of digital transformation: preparations of digital infrastructure, process integration based on technology and data-driven decisions support. A sample of 300 respondents was taken, who are representatives of digitally intensive organizations, including senior executives, technology managers, and analytics specialists. Structural Equation Modeling on the Partial Least Squares algorithm was used to test the research framework. The results prove that digital transformation has a positive impact on economic growth and improves the capabilities of Big Data Analytics to a large extent. Big Data Analytics, in its turn, can help improve economic outcomes due to its contribution to enhancing efficiency, the ability to innovate, and responsiveness. The mediation analysis supports the claims and confirms that Big Data Analytics is a major transmission channel upon which the digital transformation initiatives create sustainable economic value. The research has its implications on organizational leaders, technology strategy, and policymakers. Digital transformation can only be maximized by strategic investment in digital infrastructure, development of analytics capability and data governance. To create the digital policies that can support both the data-driven innovation and inclusive economic growth, policymakers can rely on these findings.

This study diagnoses the systemic sustainability challenges in Iran's national automotive supply chain using Soft Systems Methodology to analyze the interrelated economic, environmental, social, technological, and managerial dimensions. The goal is to develop a conceptual model that reflects these complexities, validate it with real-world case data, and propose practical and desirable changes to improve sustainable supply chain management practices. The research addresses a critical gap in sustainability strategies for emerging markets with structurally constrained and politically sensitive industrial ecosystems. The study adopts an integrated approach combining systemic methodology and thematic analysis, utilizing semi-structured interviews with senior automotive industry experts and a targeted literature review. Rich pictures, conceptual modeling, and iterative validation ensured alignment between systemic challenges and practical realities, with data coded to identify barriers and enablers across the supply chain. Comparing conceptual and real-world models revealed key areas for intervention, including blockchain-enabled traceability, water recycling, supplier diversification, and digital integration to enhance sustainability. Findings highlight issues such as reliance on foreign suppliers, currency volatility, limited domestic manufacturing capacity, inefficient logistics, weak governance, and slow adoption of green technologies. Environmental concerns include water scarcity, inadequate vehicle scrappage systems, and high emissions from diesel transport. The study provides actionable recommendations for policymakers and industry managers, such as diversifying suppliers, implementing digital platforms, and introducing transparent governance. These measures can improve efficiency, reduce environmental impact, and increase resilience against political and economic disruptions. The research also offers future research needed to test the proposed interventions and explore policy frameworks for large-scale adoption.

This study develops a generalized finite-horizon inventory model that integrates complex demand patterns, deterioration, seasonal effects, and stochastic variability within a Monte Carlo–based simulation framework in MATLAB. The model incorporates fixed, holding, procurement, deterioration, transportation, backordering, and lost sales costs, enabling comprehensive cost evaluation. Simulation results show that deterioration and seasonality significantly affect optimal replenishment cycles and order quantities, and that the model yields robust cost estimates under uncertainty. Sensitivity analysis further indicates that the base demand rate has the strongest effect on cost outcomes, followed by procurement and backorder costs, whereas seasonality and holding cost parameters exhibit negligible influence.