Systems, Volume 13, Issue 5 (May 2025) – 92 articles

As digital platforms reshape the commercial landscape, brands increasingly collaborate with these platforms to enhance product sales. Many adopt livestream as a strategic tool to attract more traffic, typically choosing between Artificial Intelligence (AI) or Key Opinion Leader (KOL) approaches. Meanwhile, platforms operate under either an agency or a resale mode. However, the relative effectiveness of these strategies remains unclear. This study investigates an e-commerce supply chain comprising a single brand and platform, examining how AI and KOL livestream influence supply chain decisions across different sales modes and identifying optimal strategies for the brand and platform. Results show that when the platform’s revenue sharing rate is low, the agency mode consistently yields a Pareto improvement over resale, regardless of the livestream scheme. Moreover, when the KOL promotion fee rate is low, KOL livestream outperforms AI livestream under both sales modes. When the revenue sharing rate is high, the brand’s optimal strategy is “resale mode and KOL livestream”, while the platform prefers “agency mode and KOL livestream”. Conversely, when the revenue sharing rate is low, the platform’s best strategy is “resale mode and KOL livestream”, while the brand favors the agency mode, with livestream preferences shaped by KOL promotion fee rate. Full article

In an era of rapid digital transformation, organisations must cultivate dynamic capabilities that promote innovation and continuous learning. This study examines how self-determined motivation and innovation adoption are crucial enablers in developing the digital competencies essential for employees to navigate digital transformation. Grounded in Self-Determination Theory and the Diffusion of Innovation framework, our research underscores the systemic role of individual agency, technological advancements, and organisational structures in facilitating workforce adaptation. Employing a quantitative approach with 152 survey participants, our findings reveal that self-determined motivation alone is inadequate, while adopting innovation significantly influences digital competence. We demonstrate that human-centred factors must align with systemic digital transformation efforts. Moreover, we highlight the necessity of integrating employee capabilities into broader enterprise and government digital innovation strategies. The implications of this study are both theoretical and practical. We stress the need for organisations to design change processes that support digital knowledge acquisition and adaptability in evolving workplaces. Our research offers a systemic perspective on digital transformation, reinforcing that successful organisational innovation requires structured learning environments that empower employees. By fostering an ecosystem where digital competencies are nurtured, organisations can enhance agility, resilience, and sustained competitiveness in the digital age. Full article

Decarbonizing production activities is a critical task in the transition towards carbon neutrality. Traditional carbon footprint accounting tools, such as life-cycle assessment (LCA) and the Greenhouse Gas Protocol, primarily quantify direct and indirect emissions but offer limited guidance on actionable reduction strategies. To address this gap, this study proposes a comprehensive life-cycle carbon footprint optimization framework that integrates LCA with a mixed-integer linear programming (MILP) model. The framework, while applicable to various production contexts, is validated using a university campus as a case study. In 2023, the evaluated university’s net carbon emissions totaled approximately 24,175.07 t CO₂-eq. Based on gross emissions (28,306.43 t CO₂-eq) before offsetting, electricity accounted for 66.09%, buildings for 15.55%, fossil fuels for 8.67%, and waste treatment for 8.46%. Seasonal analysis revealed that June and December exhibited the highest energy consumption, with emissions exceeding the monthly average by 19.4% and 48.6%, respectively, due to energy-intensive air conditioning demand. Teaching activities emerged as a primary contributor, with baseline emissions estimated at 5485.24 t CO₂-eq. Optimization strategies targeting course scheduling yielded substantial reductions: photovoltaic-based scheduling reduced electricity emissions by 7.00%, seasonal load shifting achieved a 26.92% reduction, and combining both strategies resulted in the highest reduction, at 45.95%. These results demonstrate that aligning academic schedules with photovoltaic generation and seasonal energy demand can significantly enhance emission reduction outcomes. The proposed framework provides a scalable and transferable approach for integrating time-based and capacity-based carbon optimization strategies across broader operational systems beyond the education sector. Full article

In the context of global efforts to address climate change and pursue sustainable development, green finance (GF) and energy efficiency (EE) have become key issues of focus for academics and policymakers. This study explores the spatiotemporal coupling characteristics and driving factors of China’s green finance and energy efficiency from 2011 to 2022, aiming to help China achieve its dual carbon goals. This study used a three-dimensional framework to assess 30 provinces, considering factor inputs, expected outputs, and undesirable outputs. The study employed the global benchmark super-efficiency EBM model, entropy method, coupling coordination model (CCD), Dagum Gini coefficient decomposition, and spatiotemporal geographic weighted regression model (GTWR). Key findings include a “high in the east, low in the west” gradient distribution of both green finance and energy efficiency, expanding regional disparities, and a strong synergistic effect between technological innovation and energy regulation. Based on the findings, this paper proposes a three-tier governance framework: regional adaptation, digital integration, and institutional compensation. This study contributes to a deeper understanding of the coupling theory of environmental financial systems and provides empirical support for optimizing global carbon neutrality pathways. Full article

The current research presents an extension of the Pit-Stop Manufacturing framework. It addresses the challenges of managing complexity and uncertainty in the production ramp-up phase of manufacturing systems, bridging the gap in existing approaches that lack comprehensive, quantitative, and system-level solutions. This research integrates state-of-the-art methodologies, utilising such metrics as Overall Equipment Effectiveness and Effective Throughput Loss to enhance ramp-up management. The developed framework is represented by a conceptual model, which is translated into a digital product combining multiple artefacts for comprehensive ramp-up research, namely a digital twin of the production system, a Custom Experiment Manager for multiple simulation runs, and a Graph Solver that uses the stochastic dynamic programming approach to address the decision-making issues during the production system ramp-up evolution. This work provides a robust decision-support tool to optimise production transitions under dynamic conditions by combining stochastic dynamic programming and discrete event simulation. The framework enables manufacturers to model, simulate, and optimise system evolution, reducing throughput losses, improving equipment efficiency, and enhancing decision-making precision. This paper demonstrates the framework’s potential to streamline ramp-up processes and boost competitiveness in volatile manufacturing environments. Full article

The digital economy is a key area for nurturing new productivity and a strategic high ground for innovative development and international competitiveness. This paper innovatively constructs the PCT (Policy–Coordination–Talent) analysis framework to systematically analyze the U.S. digital economy development model from three core dimensions: policy guidance, coordination mechanisms, and talent strategy. Through empirical analysis, the research develops three matrices: a leading policy intensity strategy matrix, a coordination mechanism intensity strategy matrix, and a digital talent cultivation strategy matrix. The findings reveal that the U.S. government has formed a resilient digital economy development paradigm through forward-looking policy guidance, precise coordination mechanisms, and systematic talent strategies. The theoretical contributions include developing a multi-dimensional PCT framework for understanding digital economy development models and constructing three strategy matrices based on real data. The research provides theoretical insights and policy implications for China to improve its digital economy governance system and promote high-quality development. Full article

China is presently placing significant emphasis on sustainable urbanization as a means to facilitate the shift towards high-quality economic development. The concept of sustainable urbanization has gained added intricacy and depth within the framework of ‘carbon peak and carbon neutrality’. A primary concern among practitioners is that while carbon emissions adhere to specific standards, the term sustainable urbanization remains vaguely defined, encompassing multifaceted objectives. The absence of a well-defined evaluative standard for sustainable urbanization creates challenges in driving comprehensive progress. Additionally, owing to the spatially heterogeneous nature of urbanization, its influence on carbon emissions can vary across different cities. This study delves into the shared factors influencing the performance of sustainable urbanization, introducing a Principal Component Analysis (PCA)-based evaluation system to gauge sustainable urbanization performance. Furthermore, we employ spatial regression analyses to explore the spatial differences in the impact of these factors on carbon emissions. Our investigation centers on data from nine cities in the Pearl River Delta, allowing for the ranking of these cities based on their sustainability performance. The outcomes reveal that the key factors influencing carbon emissions differ among cities due to variations in sustainable urbanization characteristics. Notably, our research integrates sustainable urbanization with the parameters of a low-carbon economy. In the realm of policymaking, we offer a quantifiable approach for assessing sustainable urbanization. Furthermore, we assert that cities at distinct stages of sustainable urbanization should prioritize different factors to attain carbon neutrality. Full article

The inherent uncertainty and information asymmetry in financial markets create significant challenges for accurate price forecasting. Although investor sentiment analysis has gained traction in recent research, the temporal dimension of sentiment dynamics remains underexplored. This study develops a novel framework that enhances stock price prediction by integrating time-partitioned investor sentiment, while improving model interpretability via Shapley additive explanations (SHAP) analysis. Employing the ERNIE (enhanced representation through knowledge integration) 3.0 model for sentiment extraction from China’s Eastmoney Guba stock forum, we quantitatively distinguish intraday and post-market investor sentiment then integrate these temporal components with technical indicators through neural network architecture. Our results indicate that temporal sentiment partitioning effectively reduces uncertainty. Empirical evidence demonstrates that our long short-term memory (LSTM) model integrating intraday and post-market sentiment indicators achieves better prediction accuracy, and SHAP analysis reveals the importance of intraday and post-market investor sentiment to stock price prediction models. Implementing quantitative trading strategies based on these insights generates significantly more annualized returns for representative stocks with controlled risk, outperforming sentiment-agnostic and non-temporal sentiment models. This research provides methodological innovations for processing temporal unstructured data in finance, while the SHAP framework offers regulators and investors actionable insights into sentiment-driven market dynamics. Full article

The manufacturing industry serves as critical carrier for the empowerment of digital technologies and is the cornerstone of digital innovation and development. Smart transformation (ST), propelled by technological advancements, has become a prominent area of academic research, but its role in fostering the co-development of manufacturing industries has been overlooked. This study employs an empirical approach to examine the causal mechanisms linking ST with regional economic development (RED), particularly emphasizing the mediating effects exerted by regional innovation capacity (RIC). Leveraging panel data from 29 Chinese provinces spanning the period from 2009 to 2023, we constructed an econometric model for analysis. The findings reveal that ST has a direct effect on RED, knowledge innovation capacity (KIC), and innovation infrastructure (II) partially mediated, while technology innovation capacity (TIC) completely mediated the relationship. Theoretical contributions manifest in three dimensions: First, drawing on the sociotechnical system theory and technology diffusion theory, this paper establishes a multidimensional framework of ST, deepening the theoretical underpinnings of smart technology application in the manufacturing industry from three aspects: smart base input, smart applications, and smart market benefits. Second, it extends regional innovation theory and endogenous growth theory by conceptualizing RIC in three sub-capabilities (KIC, TIC, and II). Third, it contributes to the RED literature by exploring the coupling effect between manufacturing industry clusters and the development of RIC and ultimately concludes with targeted policy recommendations for optimizing ST strategies to foster RED in different manufacturing industries. Full article

Green supply chain integration (GSCI) has emerged as a significant technique for improving sustainable performance by promoting collaboration with supply chain partners and breaking down organizational barriers to utilize complementary resources. This study investigates the relationships among GSCI, supply chain agility (SCA), digital orientation (DO), and sustainable performance, grounded in the Natural Resource-Based View (NRBV) and Contingency Theory (CT), based on survey data from 288 Chinese pharmaceutical manufacturing enterprises. Using mediation, moderation, and moderated mediation analyses, the findings indicate that SCA serves as a mediator between GSCI and sustainable performance. Significantly, DO strengthens both the direct effect of SCA on sustainable performance and the overall mediating pathway; nevertheless, it does not substantially boost the association between GSCI and SCA. This study’s innovation lies in elucidating the significance of GSCI as a resource for sustainable performance within the pharmaceutical enterprises, while further delineating the pathways and contingent elements for achieving sustainable performance in a digital context. This study offers valuable implications for both academic research and managerial practice. Full article

The industrial landscape has undergone rapid conceptual evolution in recent years, marked by the successive emergence of Industry 4.0, Industry 5.0, and the nascent Industry 6.0. This study explores the emergence of Industry 6.0 as a prospective industrial paradigm, characterized by intelligent, autonomous, and sustainable systems, which builds upon the digital foundations of its predecessors. Using management fashion theory as a theoretical lens, we analyze how these industrial concepts arise, diffuse, and potentially become institutionalized within management discourse. The study reveals that the adoption and dissemination of these paradigms are influenced not only by technological advancements but also by the discursive efforts of a fashion-setting community comprising academics, policymakers, consultants, and media actors. Industry 6.0, while still largely speculative, continues a broader trend of using numbered industrial revolutions to frame ongoing innovation. The findings suggest that such paradigms serve both practical and rhetorical purposes, driving organizational change while also reflecting shifting societal and managerial values. The study concludes with reflections on the implications for managers and policymakers as they navigate the evolving industrial landscape. Full article

University innovation plays an increasingly significant role in regional industrial development. In this paper, we study how local industrial initiatives affect the university innovation activities. We link preferred initiatives from Chinese provincial Five-Year Plans to university–industry patent data. Using difference-in-differences design, we document that local preferred industrial initiatives significantly enhance university innovation. These initiatives increase public Research and Development (R&D) funding (government-push effect) and facilitate regional university–industry collaboration (market-pull effect). The effects exhibit heterogeneity across university administrative affiliation, university research capacity, and industry technology intensity. Furthermore, regional industrial comparative advantages and university technology transfer capabilities strengthen the innovation-enhancing effects. Finally, this paper demonstrates that university research capacity strengthens the effectiveness of industrial initiatives on firm output. These findings underscore the synergy among industrial initiatives, university innovation, and local development. Full article

For mobile agent path planning, traditional path planning algorithms frequently induce abrupt variations in path curvature and steering angles, increasing the risk of lateral tire slippage and undermining operational safety. Concurrently, conventional reinforcement learning methods struggle to converge rapidly, leading to an insufficient efficiency in planning to meet the demand for energy economy. This study proposes LSTM Bézier–Double Deep Q-Network (LB-DDQN), an advanced path-planning framework for mobile agents based on deep reinforcement learning. The architecture first enables mapless navigation through a DDQN foundation, subsequently integrates long short-term memory (LSTM) networks for the fusion of environmental features and preservation of training information, and ultimately enhances the path’s quality through redundant node elimination via an obstacle–path relationship analysis, combined with Bézier curve-based trajectory smoothing. A sensor-driven three-dimensional simulation environment featuring static obstacles was constructed using the ROS and Gazebo platforms, where LiDAR-equipped mobile agent models were trained for real-time environmental perception and strategy optimization prior to deployment on experimental vehicles. The simulation and physical implementation results reveal that LB-DDQN achieves effective collision avoidance, while demonstrating marked enhancements in critical metrics: the path’s smoothness, energy efficiency, and motion stability exhibit average improvements exceeding 50%. The framework further maintains superior safety standards and operational efficiency across diverse scenarios. Full article

Sustainable development initiatives are essential for enhancing the social economy and environmental preservation in marginalised rural areas of Tanzania. This study examines the impact of an IoT micro-factory on sustainable development, addressing issues such as inadequate production techniques, agribusiness monopolisation practices, the shortage of small-scale factories, and the failure to leverage global market comparative advantages. It explores the mediating role of architectural innovation and the moderating role of industrial symbiosis. The study surveyed 196 participants, including 100 orange farmers, 96 industrial engineers in the beverage sector, and conducted interviews with 3 industrial managers and 3 industrial consultants. SmartPLS 4 was used to evaluate the relationships between constructs. The results indicate that both IoT micro-factories and global production networks (GPNs) have a direct influence on sustainable social-economic development. Architectural innovation mediates these relationships, while industrial symbiotic moderates the interaction between IoT micro-factories and architectural innovation. The findings emphasise the importance of IoT micro-factories for sustainable development, with industrial symbiotic relationships addressing gaps in knowledge, skills, and equitable trade. The industrial stakeholders should prioritise IoT micro-factories as small-scale factories to promote sustainable development in rural communities of developing countries. Full article

The term “safety climate” describes how workers perceive and observe safety within an organization. Workers are typically on the front lines, where they are immediately exposed to safety procedures and working hazards. Their thoughts offer a practical perspective on how safety is applied on a daily basis. Therefore, this study employed the Fuzzy DEMATEL methodology to investigate the critical factors influencing safety climate from workers’ perspective. The research involved nine experts evaluating eight worker-related factors that affect safety climate. The incorporation of fuzzy logic improved the accommodation of the ambiguity and subjectivity inherent in expert judgments, particularly when examining employee viewpoints on safety. The study revealed that the following factors were identified as primary drivers (causal factors) of safety climate: Workers’ safety competence, Workers’ freedom speech about safety matters, and Worker’s ability to perceive hazards. From the perspective of workers, these causal factors have a considerable impact on the other dimensions of safety climate, implying that focused changes in these areas could deliver substantial advantages throughout the full safety spectrum. This distinction provides valuable information for firms to prioritize their safety improvement initiatives and resource allocation. By identifying important cause elements and their relationships, the study offers organizations with a strategic path for improving their safety climate. Full article

Digital technology drives global industrial transformation. The synchronized development of organizational digital transformation and innovation systems is pivotal in corporate strategy and governmental governance. The dynamic interaction mechanisms among digital economy, government innovation policy, and regional innovation capacity remain insufficiently explored. This study employs panel data from 15 prefecture-level cities within the Yangtze River Delta urban agglomeration, spanning the years 2012 to 2020, and uses the panel vector autoregression (PVAR) model to investigate the interrelationships among the digital economy, government innovation preferences (the government’s supportive attitude and policy inclination towards innovative activities in the fields of science and technology as well as economic development), and regional innovation capacity. This research emphasizes the impact of the digital economy on regional innovation capacity and the influence of government innovation preferences on regional innovation capacity. The findings indicate that both the digital economy and government innovation preferences significantly enhance technological and product innovation, with this effect being particularly pronounced in the initial stages but diminishing over time. The three dimensions of the digital economy exert varying effects on technological and product innovation. Specifically, digital application has the most substantial impact on technological innovation, whereas infrastructure has a more pronounced effect on product innovation. Overall, the influence of government innovation preferences on technological and product innovation is less significant than that of the digital economy. The intensity of government innovation preferences has a greater impact than does the structure of government innovation preferences; however, in the long term, the structure of government innovation preferences can exert a more stable and sustainable influence. This study offers policy implications for constructing an innovation ecosystem driven by the synergy between government and market forces, particularly in optimizing data governance systems and planning sustainable transformation pathways, which hold practical value. Full article

A LoRaWAN application architecture comprises three functional components: (i) nodes, which convert and wirelessly transmit data as LoRaWAN messages; (ii) gateways, which receive and forward these transmissions; and (iii) network servers, which process the received data for application delivery. The nodes convert data into LoRaWAN messages and transmit them wirelessly with the hope that one or more LoRaWAN gateway will receive the messages successfully. Then, the gateways pass on the received messages to a distant network server, where various processing steps occur before the messages are forwarded to the end application. If none of the gateways can receive the messages, then they will be lost. Although this default behaviour is suitable for some applications, there are others where ensuring messages are successfully delivered at a higher rate would be helpful. One such scenario is the application in this paper: monitoring maritime vessels and fishing equipment in offshore environments characterised by intermittent or absent shore connectivity. To address this challenge, the Custodian project was initiated to develop a maritime monitoring solution with enhanced connectivity capabilities. Two additional features are especially welcome in this scenario. The most important feature is the transmission of messages created in offshore areas to end users who are offshore, regardless of the unavailability of the ground network server. An example would be fishermen who are offshore and wish to position their fishing equipment, also offshore, based on location data transmitted from nodes via LoRaWAN, even when both entities are far away from the mainland. The second aspect concerns the potential use of gateway-to-gateway communications, through gateways on various ships, to transmit messages to the coast. This setup enables fishing gear and fishing vessels to be monitored from the coast, even in the absence of a direct connection. The functional constraints of conventional commercial gateways necessitated the conceptualisation and implementation of C-Mesh, a novel relay architecture that extends LoRaWAN functionality beyond standard protocol implementations. The C-Mesh integrates with the Custodian ecosystem, alongside C-Beacon and C-Point devices, while maintaining transparent compatibility with standard LoRaWAN infrastructure components through protocol-compliant gateway emulation. Thus, compatibility with both commercially available nodes and gateways and those already in deployment is guaranteed. We provide a comprehensive description of C-Mesh, describing its hardware architecture (communications, power, and self-monitoring abilities) and data processing ability (filtering duplicate messages, security, and encryption). Sea trials carried out on board a commercial fishing vessel in Sesimbra, Portugal, proved C-Mesh to be effective. Location messages derived from fishing gear left at sea were received by an end user aboard the fishing vessel, independently of the network server on land. Additionally, field tests demonstrated that a single C-Mesh deployment functioning as a signal repeater on a vessel with an antenna elevation of $15m$ above sea level achieved a quantifiable coverage extension of 13 km (representing a 20% increase in effective transmission range), demonstrating the capacity of C-Mesh to increase LoRaWAN’s coverage. Full article

Basic public service (BPS) serves as a crucial connection between governments and citizens, impacting the standard of living and well-being of the populace. Can High-Speed Rail (HSR) service incentivize local governments to improve the fiscal competition model of prioritizing production over public service to expand the supply of public services? This study empirically examines the impact of HSR service on the provision of BPS based on panel data from 282 cities in China during the period from 2008 to 2020. The findings indicate that improvements in HSR service significantly stimulate the provision of BPS, a result that withstands various robustness tests. An analysis of mechanisms reveals that HSR service enhances the provision of BPS by mitigating tax competition and fostering fiscal expenditure competition among local governments. Furthermore, this study demonstrates that the positive impact of HSR is more pronounced in cities characterized by high levels of fiscal decentralization and financial autonomy. In western regions and peripheral cities, HSR service has a more pronounced effect on BPS provision. Ultimately, this study offers valuable policy insights for governments to optimize fiscal expenditure structures and bolster social governance capabilities. Full article

Digital technologies are reshaping how firms create, deliver, and capture value, prompting growing interest in digital business model innovation (DBMI). Despite increasing scholarly attention, the existing research remains fragmented and often assumes stable environments, limiting its applicability in today’s complex and dynamic contexts. To address this gap, this study conducts a systematic literature review (SLR) to gather and critically synthesize the fragmented and evolving body of knowledge on DBMI. The review identifies key research perspectives, highlights their underlying assumptions, and reveals the limitations in addressing environmental and knowledge complexity. In response, the paper introduces the knowledge system perspective (KSP) as a novel lens that views DBMI as a knowledge-driven, adaptive process. This perspective advances the DBMI literature by integrating knowledge dynamics and contextual complexity, offering a more robust understanding of how firms navigate digital transformation. The study concludes by outlining future research opportunities and providing practical implications for managing DBMI in turbulent environments. Full article

Business model innovation (BMI) is a topic that has attracted significant interest from both researchers and academics. While research has suggested that there are associations between BMI and corporate social responsibility (CSR), as well as between BMI and firm performance (FP), we lack theoretical substantiation and empirical verification of these relationships. This study proposes that BMI mediates the link between CSR and FP. This research aims to investigate the mediating role of BMI between various aspects of CSR, introduced by Carroll, and the performance of medium-size enterprises (SMEs) in Iran. To achieve the research aim, 483 questionnaires were gathered from SMEs in Iran. To evaluate the conceptual model’s validity, structural equation modeling (SEM) was used. The results of this study show that BMI mediates the relationship between all the aspects of CSR introduced by Carroll and FP and only does not mediate the relationship between the environmental dimension and FP. As far as we know, this investigation is among the earliest to explore the relationship between BMI and CSR dimensions through mediation. In response to the increasing significance of environmental issues, this research incorporates a fresh element, namely environmental responsibility, into Carroll’s model. Managers can gain a better understanding of CSR and its impact on FP from the results of this research. This study reveals that managers emphasize certain aspects of CSR which can influence BMI and FP. Full article

Under the growing threat of global warming, green bonds have become a pivotal financial instrument to deal with climate change and promote sustainable development. However, the research on the affecting factors of green bond issuance remains scarce in the existing literature, particularly regarding the external influencing factors. In order to study the impact of climate risks faced by enterprises on green bond issuance and its influence mechanism, this paper takes A-share listed companies issuing green bonds in China as samples from 1 January 2000 to 31 December 2022, adopting the Probit model to study how climate risk faced by enterprises influences green bond issuance. The key findings of the research are as follows: the climate risk positively enhances green bond issuance through green transformation and green innovation. In addition, ownership concentration positively moderates the relationship between climate risk and green bond issuance, while managerial overconfidence negatively moderates the relationship. The effect of climate risk on green bond issuance is greater for larger firms, labor-intensive firms and firms with better environmental performance. Moreover, our research enriches green bond issuance theory, further supports the signal theory of green bonds, and provides theoretical guidance for the development of green bonds in China and other emerging market countries. Full article

Agent-based modelling (ABM) has revolutionised the simulation of complex systems, finding applications in diverse fields such as economic markets and traffic management. By modelling individuals as autonomous agents within a dynamic environment, ABM enables the exploration of system behaviours and the evaluation of interventions at various spatiotemporal resolutions. However, the computational intensity of ABM, particularly in large-scale simulations, remains a significant hurdle. This paper presents a novel approach to addressing these challenges through the development of a GPU-accelerated transport model, specifically applied to a road network. Utilising the FLAME-GPU framework, the proposed model demonstrates enhanced scalability and efficiency compared with traditional CPU-based simulations, such as Simulation of Urban MObility (SUMO). Through rigorous comparative analysis, this study highlights significant improvements in simulation speed and the capacity to manage larger vehicle populations. The research underscores the transformative potential of GPU acceleration in mitigating computational constraints within ABM, offering a practical framework for simulating transport systems with greater precision and depth. Extensive experimentation validates the model’s ability to realistically simulate the vehicle population of the Isle of Wight, achieving a balance between computational efficiency and the accurate representation of complex traffic dynamics. Full article

Global manufacturing economies have faced logistics performance challenges in recent decades. This study investigates the influence of industrialization, environmental technology, trade openness, foreign direct investment, and economic growth on the logistics performance index in the top 20 manufacturing economies from 2007 to 2023. This study used an advanced panel approach to obtain robust results, cross-section dependency, a unit root test and a panel cointegration test. The panel quantile regression (PQL) and panel quantile estimates based on income methods were employed to analyze long-run and short-run estimations. The empirical results show that industrialization accelerated across all the quantiles except at the 10th quantile, while environmental technology had a significantly positive impact on logistics performance across all quantiles (10th–90th). Moreover, our baseline model was further supported by the fact that we used Driscoll–Kraay and Prais–Winsten’s estimates and a panel causality test. Our findings reveal that in manufacturing countries, industrialization, environmental technology, and economic growth have a positive impact on logistics performance. This study proposes several recommendations to improve industrialization and environmental technology in manufacturing countries to promote logistics performance. At the same time, more resources should be allocated for industrialization as well as environmental technologies to promote logistics performance. Full article

The increasing digitalization of the world underscores the critical importance of both social and technical aspects in software engineering practice. While prior research links socio-technical congruence (STC) to positive workstream outcomes, the current convergence of digital products, technologies, and social systems introduces novel and often unpredictable results, driven by the complex interplay of leadership, organizational culture, and software engineering practices operating as a complex adaptive system (CAS). This paper proposes a novel model for adopting socio-cultural practices to bridge the social and technical divide through the lens of STC. The innovation of the model lies in its socio-technical evolutionary journey, built upon dual systems: (1) an analytical System-I focused on enhancing robustness via compliance with Lean and Agile socio-cultural practices, and (2) a holistic System-II emphasizing resilience through an acceptance of interdependence of system actors that requires sense-making techniques. A methodology based on this model was piloted across six case studies: three in an Enterprise IT organization and three in two business units undergoing transformations on Lean and Agile plus DevOps adoption. System-I’s robustness was evaluated through surveys and structured STC maturity assessments (self and guided ones). System-II employed sense-making techniques to foster resilience within the system of work (SoW), laying the groundwork for their evolutionary journeys. The findings reveal a significant need for greater alignment between management (as transformation agents) and software engineering practices. However, the study suggests actionable guidelines, grounded in new principles and mental models for operating within a CAS, to cultivate enhanced resilience and robustness in a VUCA world. Full article

In product development, costs play a major role in making design decisions. As such, it is desirable to have this information as early as possible in the product development process (PDP). The development of systems changes in the direction of Model-Based Systems Engineering (MBSE) with a system model containing all information. The product costs thus need to be considered in MBSE approaches and system models. In the early stages, cost calculation models are rough estimates and will become more refined over the course of the PDP. The flow of information between the product information and cost calculation models takes a long time, and multiple calculations need to be updated before the product cost can be calculated. In this contribution, we show how cost types like the manufacturing cost or purchase cost can be seamlessly integrated into a hierarchical product structure, and cost calculation models can be linked. We present how cost calculation models with different granularity can be changed over the course of the PDP and how the product cost calculation is affected when a specific factor of the product changes. In this contribution, the case of the electrohydraulic actuator (EHA) developed in the project “Dezentrale Hydraulik II” is used. Full article

In certain newly established or niche e-commerce platforms, user–item interactions are often exceedingly sparse due to limited user bases or specialized product lines, posing significant obstacles to accurate personalized recommendations. To address these challenges, this paper proposes an enhanced recommendation approach based on a latent factor model. By leveraging factorization to uncover the hidden features of users and items and incorporating both user behavioral data and item attribute information, a multi-dimensional latent semantic space is constructed to more effectively capture the underlying relationships between user preferences and item properties. The method involves data preprocessing, model construction, user and item vectorization, and semantic-similarity-based recommendation generation. For empirical validation, we employ a real-world dataset gathered from an e-commerce platform, comprising 4645 ratings from 3445 users across 277 items in nine distinct categories. Experimental results demonstrate that, compared with conventional collaborative filtering methods, this approach achieves superior precision and recall even in highly sparse settings, showing stronger resilience under low-density conditions. These findings offer objective and feasible insights for advancing personalized recommendation techniques in newly established or niche e-commerce platforms. Full article

This paper utilizes AIS (Automatic Identification System) data to study the micro-level features of the international container capacity correlation network and their impact on shipping freight rates. It proposes, for the first time, constructing a capacity correlation network based on the correlation of operational capacity between different shipping routes. This approach captures micro changes in the shipping market by observing the “synchronized increase and decrease” in operational capacity across all routes, whereby “one decreases while the other increases” between routes. Secondly, a continuous synchronization method is introduced to construct a capacity correlation network feature index, reflecting trends in the structural changes in the capacity correlation network. This method establishes the capacity correlation network’s features without causing information loss, while capturing all detailed characteristics of the network and assigning “weights” based on the continuity of all features. Finally, the impact of the capacity correlation network feature index on shipping freight rates is examined. Experimental results indicate that the capacity correlation network feature index has a significant impact on shipping freight rates, which cannot be explained by factors such as supply, demand, or costs. This study is beneficial for revealing the price formation mechanism in the shipping market from a micro perspective. Full article

In the digital economy era, firms pursue innovation while also considering their environmental impact to ensure alignment with sustainability. However, existing research offers limited insights into how corporate environmental ethics influence the relationship between big data analytics capabilities (BDACs) and green radical innovation (GRI). This study investigates the impact of BDACs, environmental ethics, and GRI, using a sample of 291 firms and integrating resource-based theory with an environmental ethics perspective. Empirical results indicate that environmental ethics positively moderate the positive relationships between the three dimensions of BDAC—managerial, technical, and talent capability—and GRI. Moreover, there are differences in the moderating effects on this relationship. This study enriches boundary condition research on how BDACs impact GRI. Additionally, it contributes to understanding the mechanisms through which environmental ethics affect GRI, highlighting the combined effect of environmental ethics and BDAC. Furthermore, this study advances research on the heterogeneous role of environmental ethics, emphasizing the importance of enhancing corporate environmental ethics in transforming BDA technical capability into GRI. This contribution offers a new perspective on how firms can more effectively leverage their BDAC toward sustainable development. Full article

Research on the effects of digital transformation in micro and small enterprises (MSEs) is growing, yet remains underdeveloped, particularly in the context of emerging economies. While previous studies highlight the performance benefits of digital readiness, they often overlook how sector-specific challenges influence these outcomes. This study investigates the relationship between digital readiness and firm performance among MSEs in Bhutan, with a focus on the moderating roles of internal (operational) and external (supply chain) issues. Drawing on data from 217 survey responses collected from firm owners and operators, this study compares tourism and non-tourism sectors to reveal sectoral asymmetries in digital transformation outcomes. The results show that digital readiness is positively associated with firm performance across both sectors. However, the strength of this relationship is differentially moderated by contextual challenges: external issues negatively moderate the digital readiness–performance link in the tourism sector, while internal issues play a similar moderating role in the non-tourism sector. Additionally, firms in the tourism sector report higher levels of both digital performance and satisfaction with digitalization than their non-tourism counterparts. These findings contribute to the Diffusion of Innovation Theory by emphasizing the contingent and asymmetric nature of digital adoption effects across industry sectors. This study offers practical implications for managers and policymakers by underscoring the need for sector-sensitive digital strategies and support mechanisms. Full article

Future systems of systems (SoSs) must adapt rapidly to evolving environments and stakeholder needs, yet conventional system engineering approaches often lack the flexibility to accommodate such change without costly re-engineering. Addressing this gap, this study proposes a novel, generic architecture model for self-organized adaptive platform SoSs, emphasizing a modular, layered structure that enables dynamic integration and reconfiguration of sub-units for diverse missions. The research is grounded in a comprehensive review of complex SoS theory and platform system design, focusing on physical platforms with central management. Methodologically, this study develops a logical architecture for electronics and software, detailing the roles and interactions of each architectural layer and component. The model’s efficacy is demonstrated through its application to the F-35 Joint Strike Fighter, where it identified opportunities to enhance the aircraft’s adaptability and self-organization. Results indicate that early adoption of this generic architecture can significantly reduce design and redesign costs, prevent over-specification, and promote lifecycle adaptability across various platform types—including land, air, and sea systems. The proposed architecture thus offers a robust foundation for future adaptive SoSs, supporting efficient evolution in response to unpredictable operational demands. Full article