Search Results (661)

The circular economy (CE) has emerged as a central policy framework for advancing sustainable urban development; however, empirical evidence regarding the role of micro, small, and medium-sized enterprises (MSMEs) in metropolitan CE transitions remains limited, particularly in arid regions. This study examines how private sector firms and MSMEs engage with CE practices within an arid metropolitan context, using the Dammam Metropolitan Area (Saudi Arabia) as an illustrative case study. Adopting a place-based and governance-sensitive analytical perspective grounded in urban studies scholarship, the research employs a structured quantitative survey of 180 firms across key urban–industrial sectors. The analysis investigates levels of CE awareness, adoption patterns, perceived barriers, support needs, and future expectations. The findings indicate that MSMEs primarily engage in resource-based and efficiency-oriented circular practices, while more systemic models, such as supply-chain integration and platform-based circular solutions, remain limited. Moreover, capability-related factors, particularly skills and technological capacity, exert a stronger influence on adoption than awareness alone. Importantly, the study identifies a high level of latent willingness among MSMEs to invest in circular practices under supportive policy and institutional conditions. The discussion reframes CE transitions as governance-mediated urban development processes, emphasizing the importance of metropolitan coordination, institutional capacity-building, and shared spatial infrastructure. By grounding the analysis in the case of the Dammam Metropolitan Area, the study contributes to urban studies and CE scholarship by positioning MSMEs as conditionally willing system-building actors whose engagement is essential for advancing inclusive and place-sensitive circular transitions in arid metropolitan regions. Full article

The South African Government prioritises Small, Micro-, and Medium Enterprises (SMMEs) as catalysts for employment creation, in alignment with Sustainable Development Goal 8 (SDG 8), Decent Work and Economic Growth, which advocates for sustained, inclusive, and sustainable economic growth. However, the extent to which agro-processing SMMEs translate this policy ambition into measurable socio-economic gains remains contested due to persistent structural, financial, and operational constraints. This study develops a comprehensive, data-driven business support framework tailored to agro-processing SMMEs in the Free State province of South Africa. Employing a mixed-methods approach, survey data from 88 agro-processing SMMEs were analysed across 18 business performance dimensions. Average agreement scores and performance gaps were utilised to diagnose strengths and vulnerabilities within the sector. While overall performance was relatively strong (average agreement score: 86.7%), a critical weakness emerged in operational cost management (76.1%), revealing a 14.2% gap relative to the highest-performing dimension, equipment selection (90.3%). Based on these empirical insights, the study proposes a three-tiered business support architecture: (i) maintaining and leveraging high-performing dimensions (≥85% agreement), (ii) targeted enhancement for moderate-performing areas (80–84.9%), and (iii) crisis intervention for critical weaknesses (<80%). The framework integrates cross-cutting support services, including financing, regulatory guidance, and technology access, delivered through a phased implementation strategy comprising crisis intervention, system establishment, and optimisation and scaling. A multi-channel delivery mechanism, combining a hub-and-spoke model, mobile support units, and a digital platform, ensures provincial accessibility. By translating performance diagnostics into differentiated policy action, the framework promotes efficient resource allocation, supports both high-potential and vulnerable agro-processing SMMEs, and embeds a robust monitoring and evaluation system to track key performance indicators. The study contributes to the SMME development literature by demonstrating how structured, tiered, and context-specific support models can strengthen resilience, competitiveness, and sustainable agro-industrial growth in developing-country settings. Full article

As industrial internet platforms increasingly play a central role in the digital transformation of manufacturing, they have become crucial areas for manufacturing enterprises to pursue digital innovation. Current academic research has paid relatively little attention to the digital innovation of participating enterprises within industrial internet platforms, failing to fully reveal the driving mechanisms of such innovation in this context. Based on the TOE framework and adopting a platform participant perspective, this study employs fuzzy set qualitative comparative analysis (fsQCA). By surveying 169 manufacturing enterprises participating in industrial internet platforms, it integrates seven key antecedents—technology availability, technology fit, digital leadership, organizational structural flexibility, resource orchestration, policy support, and competitive pressure—to systematically explore the complex influence pathways of multi-factor concurrent interactions on digital innovation. The research results show that the high digital innovation of manufacturing enterprises on the industrial internet platform includes precise implementation type, exploration-oriented type and co-evolution type, while the non-high digital innovation paths include technology blocking type, dual-core absence type and system disorder type. These conclusions expand the theoretical framework for digital innovation in manufacturing enterprises within industrial internet platforms and offer practical recommendations for their digital innovation practices. Full article

The rapid growth of Internet-of-Things (IoT) deployments has substantially expanded the attack surface of modern cyber–physical systems, making accurate and computationally feasible malware detection essential for enterprise and industrial environments. This study presents a large-scale, systematic comparison of 27 machine learning (ML) and 18 deep learning (DL) models for IoT malware detection across eight major malware categories: Trojan, Botnet, Ransomware, Rootkit, Worm, Spyware, Keylogger, and Virus. A realistic dataset was constructed using 50,000 executable samples collected from the Any.Run platform, including 8000 malware instances (1000 per class) and 42,000 benign samples. Each sample was executed in a sandbox to extract detailed static and behavioral telemetry. A targeted feature-selection pipeline reduced the feature space to 47 diagnostic features spanning static properties, behavioral indicators, process/file/registry activity, debug signals, and network telemetry, yielding a compact representation suitable for malware detection in IoT settings. Experimental results demonstrate that ensemble tree-based ML models consistently dominate performance on the engineered tabular feature set as 7 of the top 10 models are ML, with CatBoost and LightGBM achieving near-ceiling accuracy and low false-positive rates. Per-malware analysis further shows that optimal model choice depends on malware behavior. CatBoost is best for Trojan/Spyware, LightGBM for Botnet, XGBoost for Worm, Extra Trees for Rootkit, and Random Forest for Keylogger, while DL models are competitive only for specific categories, with TabNet performing best for Ransomware and FT-Transformer for Virus. In addition, an end-to-end computational time analysis across all 45 models reveals a clear efficiency advantage for boosted tree ensembles relative to most DL architectures, supporting deployment feasibility on commodity CPU hardware. Overall, the study provides actionable guidance for designing adaptive IoT malware detection frameworks, recommending gradient-boosted ensemble ML models as the primary deployment choice, with selective DL models only when category-specific gains justify additional computational cost. Full article

The digital divide between large enterprises and SMEs (Small and Medium-sized Enterprises) within industrial clusters poses a significant challenge to achieving collective digital transformation, exacerbated by the quasi-public goods, attributes of digital inclusion ecosystems, and the prevalence of free-riding behavior. This paper investigates whether platform enterprises, as core actors occupying structural holes in cluster networks, can foster the co-construction of a digitally inclusive ecosystem. We developed a complex network public goods game model, incorporating performance feedback into a modified Fermi learning to capture firms’ adaptive decision-making based on historical and social aspirations. The model simulates strategic interactions on both small-world and scale-free networks, characteristic of industrial clusters. Numerical simulations reveal that: (1) The core driver of co-construction is the investment return coefficient; (2) Performance feedback amplifies individual rationality, accelerating the formation or collapse of cooperation depending on the investment return coefficient; (3) Platform empowerment—specifically, selectively connecting and incentivizing cooperative firms—effectively promotes ecosystem co-construction, with this strategy proving most impactful when investment returns are moderate. Furthermore, while this selective empowerment strategy benefits the cluster overall, its effect on the platform’s own revenue is network-dependent, showing a more pronounced decline in small-world structures. This study provides a novel analytical framework for understanding strategic interactions in digital inclusion and offers practical insights for policymakers and platform leaders in orchestrating collaborative digital transformation. Full article

Cold vaccine delivery is often known as a high-cost logistic process, which forces many pharmaceutical manufacturers, particularly small- and medium-sized enterprises (SMEs), to subcontract logistics operations of vaccines to third-party logistics (3PL). It is clear that maintaining the traceability and trackability of vaccines in this dynamic collaborative environment is fundamental for guaranteeing the safety of product. However, the lack of a unified vaccine logistics platform holds back comprehensive supervision and traceability, posing significant challenges to the development of useful cold chain logistics systems. To address these challenges, in this study we propose a blockchain-enabled platform for the evaluation and selection of 3PL providers in vaccine supply chains. We leveraged consortium blockchain technology to guarantee data integrity, transparency, and decentralization, facilitating trust among four main players of vaccine supply chain. We utilized smart contracts as a main part of this platform, which are responsible for automating key operational processes, including 3PL evaluation, contract execution, and monitoring. In this respect, the Fuzzy Analytic Hierarchy Process (FAHP) engine is integrated into the proposed platform to enable a data-driven, multi-criteria decision-making framework for selecting the most suitable 3PL providers. We evaluated the proposed platform through case study and gas consumption analysis; the results of the case study validate high operational accuracy (93.21%), precision (90.23%), recall (94.50%), and an F1-score of 92.32% for the platform, which offers a robust solution to enhance accountability, reliability, and decision-making in vaccine distribution networks. Full article

This study analyzes 1441 industry–university–research (I-U-R) collaborative invention patents (2004–2023) in China’s new energy vehicle (NEV) industry using social network analysis. We propose the “Proximity–Industry Life Cycle” Fit Theory to systematically investigate the influence mechanisms of industrial proximity, geographical proximity, and technological proximity on the evolution of the industry–university–research collaborative innovation network of the new energy vehicle industry across three industry life cycle stages. Key findings include: (1) the network scale expanded significantly while density declined; (2) State Grid Corporation emerged as the core node after 2010; (3) all three proximity dimensions positively influence network evolution, with varying effects across stages—industrial proximity dominates in the emergent stage, while technological proximity becomes the primary driver in later stages. Policy implications: Governments should formulate stage-differentiated policies—encouraging industrial chain collaboration in early stages while promoting technology alliances in mature stages. Core enterprises should be supported to strengthen I-U-R collaboration, and cross-regional innovation platforms should be established to optimize proximity-driven knowledge transfer. Full article

This study investigates the determinants of financial performance among Micro, Small, and Medium Enterprises (MSMEs) in Indonesia, addressing the critical issues of low accountability and limited access to capital. Grounded in the Resource-Based View and Dynamic Capabilities Theory, the research examines the impact of accounting information systems, management knowledge capability, and digital platform capability on financial performance, mediated by digital innovation. A quantitative approach was employed, utilizing a cluster random sampling survey of 403 MSME owners across Indonesia’s major islands. Data were analyzed using Structural Equation Modeling (SEM) with AMOS software. The results reveal that accounting information systems, management knowledge capability, and digital platforms significantly enhance financial performance. Notably, digital platform capability emerged as the most potent driver. Furthermore, digital innovation proved to be a vital mediator, transforming management knowledge and platform capabilities into tangible financial outcomes. The study concludes that while digital tools provide essential infrastructure, innovation serves as the critical mechanism for unlocking value. These findings suggest that MSMEs must transition from passive technology adoption to active digital innovation to achieve sustainable financial success in the digital economy. Full article

Public cultural services are traditionally viewed as welfare provisions. However, this perspective overlooks their productive externalities as critical social infrastructure. This study treats China’s National Public Cultural Service System Demonstration Zone program as a quasi-natural experiment to examine its economic performance. The analysis utilizes panel data from 280 prefecture-level cities between 2008 and 2021 and employs a multi-period difference-in-differences model. Results show that the policy successfully increased employment in the cultural sector. This was achieved by enabling flexible labor opportunities through digital platforms and government procurement, rather than through significant growth in formal enterprises. We term this structural divergence De-organized Growth. Mechanism analysis confirms that Fiscal-Digital Synergy drives this phenomenon. Effective collaboration between government funding and digital technology activates cultural consumption on the demand side and facilitates disintermediation on the supply side. Crucially, we identify a nonlinear Digital Exclusion Trap. In this trap, fiscal support is ineffective or even counterproductive in regions falling below a critical digital infrastructure threshold. The findings suggest that the equalized provision of public culture serves as a productive input for achieving UN Sustainable Development Goal 8 regarding decent work. We advocate for a shift in governance paradigms from traditional administration to a strategic purchaser role. This role leverages digital platforms to foster a more inclusive labor market. Full article

With the ongoing reform of higher education, the University Practice Education Community (UPEC) has become a crucial platform for advancing collaborative education and innovating talent cultivation models. However, research remains insufficient on the influencing factors of UPEC’s sustainable development and, in particular, on how these factors interact with one another. From a complex systems perspective, this study conceptualizes UPEC as a dynamic and interconnected system in which multiple factors jointly shape sustainability outcomes. Accordingly, the overall objective is to (i) identify key influencing factors, (ii) model and quantify their interrelationships, and (iii) pinpoint critical factors and interaction pathways that structure UPEC sustainability. Adopting this holistic view, we integrate literature review, expert interviews, questionnaire surveys, and social network analysis (SNA) to systematically identify and analyze twenty influencing factors. SNA, as a systems-oriented analytical tool, enables the mapping of structural relationships and interaction pathways among factors, revealing how these interdependencies collectively form the governance ecosystem of UPEC. The results identify eight key factors—including willingness for multi-stakeholder collaboration, stability of cooperation mechanisms, policy and institutional support, effectiveness of communication and coordination mechanisms, feedback and improvement mechanisms, enthusiasm of industry and enterprise participation, local government support, and influence of public opinion—along with five critical paths linking subsystems through chain effects. Based on this diagnostic evidence, this study further outlines strategy implications to support practice-oriented improvement, while the primary contribution remains the identification of key factors and critical interaction structures underlying UPEC sustainability. Full article

Against the backdrop of the rapid development of the platform economy, gig workers’ mental health and behavior impact both individual well-being and the long-term sustainability of platform operations. Based on the cognitive appraisal theory of emotion, this study reveals the nonlinear psychological mechanism through which perceived algorithmic management influences gig workers’ behavior. Using hierarchical regression and Bootstrap analysis on data from 385 Chinese gig workers, we examine mediating and moderating effects. The findings indicate that a U-shaped relationship between them: both excessively low and high algorithmic control intensify counterproductive behaviors, while moderate control suppresses them. Negative emotions mediate this effect, uncovering the mechanism by which algorithmic control influences behavior through emotional pathways. Locus of control moderates this relationship: externally controlled workers are more sensitive to algorithmic changes, amplifying the U-shaped effect, while internally controlled workers buffer negative emotions, reducing counterproductive behaviors. This study extends the cognitive appraisal theory of emotion to the context of algorithmic management, revealing the threshold effect of perceived control and the moderating role of individual attribution tendencies. It provides theoretical guidance for platform enterprises to optimize algorithmic design and guide gig workers’ behavior, thereby facilitating the coordinated development of dual sustainability for both gig workers and platform operations. Full article

Digital transformation (DT) is reshaping manufacturing, with core enterprises (CEs) leveraging their resources to build industrial Internet platforms (IIPs) that support ordinary enterprises (OEs) in adopting DT. Differences in enterprise roles lead to varying impacts of government subsidies, necessitating careful policy design. Crucially, IIP adoption involves higher-order, multi-player interactions beyond conventional pairwise relationships—a dimension often overlooked in existing quantitative studies. This research employs hypergraph theory to model these complex interactions on IIPs and applies evolutionary game theory to analyze how enterprise decisions and government subsidies shape DT dynamics in manufacturing supply chains. The findings reveal that: (1) The network effect is the primary driver for DT via IIPs, but its promotional impact exhibits diminishing marginal returns. (2) Governments should prioritize subsidizing CEs for platform establishment, as subsidies directed at OEs for DT adoption are less effective. (3) Before withdrawing subsidies, governments must ensure a sufficiently high IIP adoption rate to sustain DT autonomously. This study introduces a novel methodology for examining DT and offers theoretical insights to guide enterprise strategy and policy implementation. Full article

Digitalisation is reshaping shipyard production, yet its methodological foundations remain fragmented across simulation, optimisation, Artificial Intelligence (AI), and Digital Twin (DT) research streams. This paper presents a domain-specific methodological review of shipyard production modelling from 2010 to 2025, synthesising advances in Discrete-Event Simulation (DES), multi-objective optimisation, hybrid simulation–optimisation architectures, Machine Learning (ML), reinforcement learning (RL), and DT-enabled cyber-physical systems. Using an explicit evaluative framework based on integration depth, validation basis, and decision scope, the review differentiates between analytically mature but execution-decoupled DES/optimisation approaches and integration-rich yet variably validated DT and AI-driven systems. The analysis shows that hybrid DES-optimisation frameworks currently represent the most operationally credible class of methods, delivering measurable production improvements under structured conditions, whereas many DT and AI contributions prioritise architectural integration and data synchronisation over longitudinal yard-wide KPI validation. A comparative assessment of simulation platforms, optimisation engines, and manufacturing execution system/enterprise resource planning/product lifecycle management infrastructures highlights the central role of structured product–process–resource data and execution-layer connectivity, while severe confidentiality constraints and the scarcity of openly available industrial datasets continue to limit reproducibility and benchmarking. Overall, shipyard production research is progressing toward increasingly integrated and cyber-physical systems, but sustained yard-scale validation and shared benchmark development remain critical prerequisites for translating architectural sophistication into demonstrable operational impact. Full article

The rapid expansion of algorithmic systems has transformed cognition into an increasingly distributed and collective enterprise, giving rise to what can be described as cognitive assemblages, dynamic constellations of humans, institutions, data infrastructures, and artificial agents. This paper traces the historical and conceptual evolution that has led to this shift. First, we show how cognition, once conceived as the property of autonomous individuals, has progressively become embedded in socio-technical networks in which algorithmic processes participate as co-agents. Second, we revisit the progressive awareness of human cognitive limits, from bounded rationality to contemporary theories of extended mind. These frameworks anticipate and help explain today’s hybrid cognitive ecologies. Third, we assess the philosophical implications for Enlightenment ideals of autonomy, rationality, and self-governance, showing how these concepts must be reinterpreted in light of pervasive algorithmic intermediation. Finally, we examine global initiatives that seek to integrate augmented cognitive capacities into large-scale cybernetic forms of societal coordination, ranging from digital platforms and data spaces to AI-driven governance systems. These developments offer new opportunities for steering complex societies under conditions of globalization, environmental disruption, and the rise of autonomous intelligent systems, yet they also raise profound questions regarding control, accountability, and democratic legitimacy. We argue that understanding cognitive assemblages is essential to designing socio-technical systems capable of supporting collective intelligence while preserving human values in an era of accelerating complexity. Full article

Blockchain technology can enhance traceability and trust in contract farming supply chains, yet high implementation costs deter adoption by supply chain participants. This study examines the synergistic mechanisms between blockchain adoption strategies and government subsidy policies. We develop a multi-stage Stackelberg game model involving an agricultural enterprise, an e-commerce platform, and a government, and comparatively analyze six decision-making scenarios across non-subsidy, unilateral subsidy, and full-chain subsidy settings. Three key findings emerge. First, blockchain investment has a cost–effect threshold below which consumer traceability preferences do not translate into profit gains. Second, well-designed subsidies overcome investment inertia and yield Pareto improvements in both profits and social welfare, with the full-chain subsidy model (Model BG) maximizing social welfare; however, subsidies exhibit distinct efficiency boundaries, and over-subsidization causes resource misallocation. Third, both supply chain parties tend to free-ride on the other’s investment, creating strategic conflicts that necessitate differentiated subsidy mechanisms tailored to specific dominance structures. These findings provide policy guidance for facilitating agricultural digital transformation and enhancing supply chain coordination. Full article