Systems, Volume 12, Issue 5 (May 2024) – 39 articles

The capacity for climate governance is crucial for sustainable advancement, with data elements being a pivotal production factor in contemporary governance. This study examines the trajectory and strategy of digital transformation in climate governance, creating a three-dimensional dataset encapsulating 11 primary and 36 secondary indicators to facilitate the assessment of digital climate governance. Employing spatiotemporal analysis and coupling coordination models, this study evaluates the digitalization levels in climate governance across 30 regions in China, examining how to progress digital integration from governmental and market perspectives. Findings reveal a consistent improvement in China’s regional digital climate governance, bolstering economic and social progress. Nonetheless, regional disparities and developmental lags persist, with convergence analysis indicating a divergence trend in provincial climate governance capabilities. Moreover, kernel density and Markov chain analyses suggest an ongoing evolution in regional digital climate governance efforts, aiming at achieving a higher development plateau. The study emphasizes the dual role of government and market dynamics in boosting digital governance levels, deducing from two-stage regression that effective government-market interplay is vital for elevating governance quality and fostering new productive forces, recommending an integrated governance mechanism for optimal synergy. Full article

Engineering managers leverage the expertise of engineers in their teams to inform decisions. Engineers may convey their expertise in the form of opinions and/or judgements. Given a decision, it is common to elicit and aggregate the expertise from various engineers to capture a broader set of experiences and knowledge. Establishing an internally and externally consistent aggregation framework is therefore paramount to yield a meaningful aggregation, that is, to make sure that the expertise of each engineer is accounted for reasonably. However, we contend that most de facto aggregation techniques lack such consistency and lead to the inadequate use and aggregation of engineering expertise. In this paper, we investigate the consistency or lack thereof of various expertise aggregation techniques. We derive implications of such inconsistencies and provide recommendations about how they may be overcome. We illustrate our discussion using safety decisions in engineering as a notional case. Full article

This study aims to experimentally confirm whether knowledge that has been challenging to transfer through traditional on-the-job training (OJT) can be effectively transferred by introducing a formalized OJT approach that describes the improvement process knowledge of skilled production systems consultants, facilitating imitation by unskilled consultants. We adopted the Digital Triplet (D3) concept, an extension of the authors’ digital twin framework to intelligent activities, aligning with our study objectives. Recognizing the difficulty and inadequacy of knowledge transfer in production systems consulting OJT, we propose an OJT support method integrating a decision-making modeling approach for skilled consultants’ processes based on the Generalized Production Systems Consulting Process Model (GCPM) from prior literature into traditional OJT methods involving self-learning and direct instruction. This method enables the construction of a domain-specific GCPM, formalizing the improvement process flow implemented by skilled consultants and linking it to production improvement expertise and tools. In a case study focused on energy-saving improvement, we constructed and tested a domain-specific GCPM’s efficacy in facilitating the transfer of difficult-to-transfer knowledge. The results indicate that domain-specific GCPM facilitates such knowledge transfer, including specialized improvement, knowledge utilization, rationale, and adaptation to specific cases. Full article

Urban Air Mobility (UAM) is an emerging industry marked by technological advancements, new operational contexts, and regulatory frameworks. This article examines how to improve safety management in UAM operations by adopting a just culture approach from a system of systems perspective. Acknowledging the critical role of front-line workers, especially in the early stage with piloted vehicles, the ecosystem-level approach comprehends multiple providers, operators, and services. Employing an enterprise architecture methodology, we address the challenge of fostering a learning-oriented environment amidst diverse organizational perspectives and stakeholders’ interests. This study identifies key capabilities, functions, and resource exchanges within and across organizations by strategically leveraging architectural views and systemic visualizations. A unified safety committee is discussed and recommended to facilitate consensus among stakeholders, including regulatory bodies, thus paving the way for industry-wide improvements. Findings contribute to evolving safety protocols in UAM operations and serve as a blueprint for integrating cutting-edge methodologies to drive systemic enhancements. Full article

By applying the “safety atmosphere” measurement theory to Chinese management companies engaged in road transportation, a corporate and individual safety risk assessment system was established that is consistent with the management and cultural climate in China, thereby reducing the driving safety risk of truck drivers. The system realizes the safety risk assessment of enterprises, fleets and individuals in the form of test scales by constructing a structural model of the enterprise safety atmosphere, including the management, communication, and supervision of enterprises, fleets, and individuals. The safety climate was modeled using a two-level framework, at the organizational level and fleet level, and three dimensions of test items for each level were obtained by exploratory factor analysis. The three dimensions of safety management, safety supervision, and safety priority at the organizational level, and the three dimensions of positive communication, safety awareness, and self-discipline at the fleet level, respectively, passed a valid factorial test (p < 0.01). Finally, the validity of the system evaluation results was verified by relying on the actual in-vehicle monitoring data and accident records of the corporate transportation fleet. The results show that the total test scores at the organizational level and the fleet level are significantly correlated with their driving risk behaviors, and both are linearly and negatively correlated with the number of accidents per thousand kilometers. This indicates a high degree of consistency between the system’s test results and actual risky accidents. Full article

The application of artificial intelligence (AI) in programming assistance has garnered researchers’ attention for its potential to reduce learning costs for users, increase work efficiency, and decrease repetitive coding tasks. However, given the novelty of AI Coding Assistant Tools (AICATs), user acceptance is currently limited, and the factors influencing this phenomenon are unclear. This study proposes an expanded model based on the Technology Acceptance Model (TAM) that incorporates the characteristics of AICAT users to explore the key factors affecting college students’ willingness to use AICATs. Utilizing a survey methodology, 303 Chinese participants completed the questionnaire. Factor analysis and Structural Equation Modeling (SEM) results indicate that users’ dependence worry (DW) about AICATs positively affects perceived risk (PR), which in turn negatively impacts perceived usefulness (PU) and perceived ease of use (PEOU), thus reducing user willingness to use. Dependence concerns also negatively impact perceived trust (PT), while PT positively affects PU and PEOU, thereby enhancing willingness to use. Additionally, a user’s self-efficacy (SE) negatively impacts DW and positively affects PEOU. This study discusses the potential significance of these findings and offers suggestions for AICAT developers to foster and promote widespread use. Full article

This paper delves into regional airport system economics in Central Europe, with a particular focus on Slovakia, Czechia, Poland, Hungary, and Croatia. This research aimed to identify key indicators that shape optimal business models for regional airport systems by analyzing data from 24 airports between 2016 and 2019. Through cluster analysis, airports were categorized based on performance metrics, economic indicators, and ownership structures. The findings reveal distinct groupings among regional airports and shed light on critical factors influencing their operational and financial dynamics. By offering insights into the relationships between airport system characteristics and business model effectiveness, this paper aimed to provide valuable guidance for stakeholders, policymakers, and airport management teams. It facilitates informed decision-making and strategic planning for sustainable aviation infrastructure development in the region. Full article

The ultimate goal of China’s tourism industry is to create a flourishing sector that brings happiness. It is of immense theoretical and practical importance to investigate the impact of tourism development (TD) on urban welfare (UW) and uncover its spatial spillover characteristics from a macro perspective. Utilizing panel data from 41 cities in the Yangtze River Delta region from 2000 to 2021, this study applies the spatial panel Durbin model to explore the direct and spillover effects of TD on UW. The results show that TD significantly boosts UW in both local and neighboring areas, with the spillover effects taking a dominant position in the total effects. Examining the sub-dimensions of UW, the local welfare effects of TD primarily stem from economic welfare, whereas the spillover effects are characterized by the “three-wheel drive” of economic, social, and environmental welfare. This study can provide practical insights into the coordinated and sustainable development of the regional tourism industry. Full article

Climate change threatens human survival and development. Cities, as the main gathering places for human production and life, serve as the focal points for the implementation of the policies related to energy efficiency, energy transition, and environmental protection. This study constructs an index system for the evaluation of carbon-neutral cities from the perspectives of carbon sources and carbon sinks. The system includes thirteen indicators under six dimensions. It combines objective and subjective data (i.e., statistical data and expert evaluations) by integrating two approaches: the fuzzy analytic hierarchy process (fuzzy AHP) and vise kriterijumska optimizacija i kompromisno resenje with hesitant fuzzy sets (HFS-VIKOR). We verify the efficacy of the proposed approach through a case study of thirteen low-carbon pilot cities in China. The results indicate that among these cities, Shenzhen performs the best, followed by Guangzhou and Hangzhou, while Kunming, Baoding, and Tianjin show poor performance in terms of carbon neutrality. Kunming and Baoding exhibit shortcomings mainly in carbon sources, while Tianjin faces deficiencies in both carbon sources and carbon sinks. Sensitivity analysis and comparative analysis show the availability and effectiveness of the proposed method. The proposed radar chart further highlights the improvement directions for each city to achieve carbon neutrality. Full article

Product development is a complex process involving intricate components, dynamics and constantly evolving internal and external environments, as well as numerous influencing factors. In order to accurately simulate and predict the effectiveness of the development process, this paper proposes a system dynamics simulation method based on information maturity. Different types of development processes are simulated, and the discussion includes the impact of activity information correlation, information evolution coefficient, start time, and other parameters on the dynamic behavior of the process. This study examines a specific mold development process as a case study to validate the method’s feasibility, accurately predicting the duration and cost of the process. It also investigates dynamic fluctuations resulting from uncertain events such as changes in customer demand and resource shortages. The method provides support for process optimization and resource scheduling. Full article

In this research, an innovative state space-based Transformer model is proposed to address the challenges of complex system prediction tasks. By integrating state space theory, the model aims to enhance the capability to capture dynamic changes in complex data, thereby improving the accuracy and robustness of prediction tasks. Extensive experimental validations were conducted on three representative tasks, including legal case judgment, legal case translation, and financial data analysis to assess the performance and application potential of the model. The experimental results demonstrate significant performance improvements of the proposed model over traditional Transformer models and other advanced variants such as Bidirectional Encoder Representation from Transformers (BERT) and Finsformer across all evaluated tasks. Specifically, in the task of legal case judgment, the proposed model exhibited a precision of 0.93, a recall of 0.90, and an accuracy of 0.91, significantly surpassing the traditional Transformer model (with precision of 0.78, recall of 0.73, accuracy of 0.76) and performances of other comparative models. In the task of legal case translation, the precision of the proposed model reached 0.95, with a recall of 0.91 and an accuracy of 0.93, also outperforming other models. Likewise, in the task of financial data analysis, the proposed model also demonstrated excellent performance, with a precision of 0.94, recall of 0.90, and accuracy of 0.92. The state space-based Transformer model proposed not only theoretically expands the research boundaries of deep learning models in complex system prediction but also validates its efficiency and broad application prospects through experiments. These achievements provide new insights and directions for future research and development of deep learning models, especially in tasks requiring the understanding and prediction of complex system dynamics. Full article

In this paper, we study the dynamic meal delivery routing problem (MDRP) with time-sensitive customers. The multi-objective MDRP optimization model is developed to maximize customer satisfaction and minimize delay penalty cost and riding cost. To solve the dynamic MDRP, a novel waiting strategy is proposed to divide the dynamic problem into a series of static subproblems. This waiting strategy utilizes the decision threshold to determine rerouting points based on the number of dynamic meal orders. Meanwhile, time-sensitive priority is introduced to accelerate assignment and routing decisions for orders from customers with high time sensitivity. For each static subproblem, a hybrid AGA–ALNS algorithm that incorporates the adaptive genetic algorithm and adaptive large neighborhood search is developed to improve both the global and local search capabilities of the genetic algorithm. We validate the performance of the proposed waiting strategy and the AGA–ALNS algorithm through numerical instances. In addition, managerial insights are obtained from sensitivity analysis experiments. Full article

The impact of freight on the transportation system is accentuated by the fact that trucks consume a greater roadway capacity than other vehicles and therefore cause more significant problems including traffic congestion, traffic delays, crashes, and pavement damage. Evaluating the actual repercussions of truck traffic becomes paramount in locales where roadway expansion is unfeasible. Trucks are vital to the economy, providing essential services to commerce and industry, and yet it is crucial that their operation does not contribute to the deterioration of infrastructural quality or compromise public safety. Currently, we lack methodologies in practice for the real-time management of traffic, specifically for truck routing, to minimize travel times and prevent delays due to non-recurrent congestion, such as traffic incidents. Accordingly, this study aimed to devise a truck routing strategy utilizing a traffic micro-simulation model (VISSIM) and to assess its effects on reducing travel delays. This involved the development of real-time truck re-routing simulation models that take into account non-recurrent congestion and the resulting travel delays and fuel consumption. The VISSIM model was applied to the I-75 corridor in Marion County, Florida, focusing on non-recurrent congestion effects on travel delays and fuel consumption. The initial findings suggest that the implementation of a dynamic truck re-routing system can significantly alleviate traffic congestion, resulting in a marked decrease in travel delays and fuel usage, demonstrating the potential for such strategies to enhance the overall efficiency of the transportation system. Full article

As companies increasingly undergo digital transformation, the role of talent management processes becomes pivotal in enhancing overall organizational performance. The objective of this research is to assess the extent to which greater digitalization in the talent management process is linked to company size. The research has addressed four research questions in order to explore the significance of talent management in corporate digital transformation, examining whether variations in the digitalization of these processes can be attributed to company size. A qualitative approach was employed, utilizing a questionnaire, and collecting responses from 202 organizations across diverse sectors. The findings reveal disparities in digitalization throughout the talent management process, with pronounced presence in the attracting, selecting, and rewarding phases, but diminishing in deployment and development, and further declining in planning. A positive correlation between company size and the adoption of specific digital platforms was observed. Larger enterprises exhibit greater utilization of digital platforms in talent deployment and development. Moreover, corporate communication tools are consistently utilized in the rewarding phase, irrespective of company size. These findings offer practical insights for organizations aiming to optimize their digitalization strategies based on their scale, thereby contributing to more effective and tailored digitalization endeavours. The uniqueness of this research lies in its exploration of the influence of company size on the digitalization of talent management processes and its potential to explain variations across different stages of these processes. Full article

This study evaluates the propagation impact of three risk categories (hazard and exposure, socio-economic vulnerability, and lack of coping capacity) and their associated factors on vaccination uptake policy decisions in Pakistan. This study proposed Bayesian influence diagrams using expert elicitation and data-driven approaches. The Bayesian network (BN) approach uses the best policy algorithm to determine the expected utility of decisions. The study found that the government’s firm vaccine uptake decisions had a positive effect in Pakistan. The findings on hazard and exposure-related factors show that people living in rural areas were more susceptible to COVID-19 than people living in urban areas. Among socio-economic vulnerability factors, household characteristics were affected due to household economic situations, fear of using health facilities due to the spread of COVID-19, lack of public transportation services, food insecurity, a temporary halt in education, and weak governance, which affected the vaccination uptake decision. The factors linked with coping capacity show that the government’s financial assistance and development of digital platforms raised digital health literacy and increased vaccine uptake decision utility. The proposed methodology and results of this study can be used to develop contingency planning for any future potential pandemic situations. Full article

Ecological management zoning plays a significant role in optimizing resource utilization, improving ecosystem service function, and promoting coordinated regional development. Taking Hexi Corridor as a representative region of the Mountain–Oasis–Desert composite system in arid regions of Asia, this study analyzed the spatial and temporal evolution of ecosystem service values and explored the influencing mechanism based on the optimal parameters-based geographical detector model. We have comprehensively divided ecological management zones and proposed corresponding control strategies. The results show that (1) the Hexi Corridor is characterized by regional differentiation, which is composed of three systems: The southern mountain system, central oasis system, and northern desert system. The mountain system is mainly composed of forestland and grassland, the oasis system is mainly composed of cropland, and the desert system is mainly composed of unused land. The conversion of land use mainly involves the conversion of unused land to cropland and grassland, while grassland is mainly converted to cropland. (2) The ecosystem service value of the Hexi Corridor increased significantly and demonstrated agglomeration characteristics in space. The highest value areas are mainly distributed in the southern mountain, with higher value and medium areas mainly distributed in the central oasis, and the lowest value areas are mainly located in the northern desert. (3) Socio-economic factors greatly influence the spatial differentiation of ecosystem service values in the Hexi Corridor, with natural environmental factors having less impact. Additionally, the internal interaction of natural environmental factors is the most significant. (4) The Hexi Corridor is divided into three ecological management zones: Ecological function protection zone, ecological and agricultural coordinated development zone, and ecological and urbanization coordinated development zone. This research has important reference value for global ecological management in arid regions. Full article

This work explores the integration and effectiveness of artificial intelligence in improving the security of critical energy infrastructure, highlighting its potential to transform cybersecurity practices in the sector. The ability of artificial intelligence solutions to detect and respond to cyber threats in critical energy infrastructure environments was evaluated through a methodology that combines empirical analysis and artificial intelligence modeling. The results indicate a significant increase in the threat detection rate, reaching 98%, and a reduction in incident response time by more than 70%, demonstrating the effectiveness of artificial intelligence in identifying and mitigating cyber risks quickly and accurately. In addition, implementing machine learning algorithms has allowed for the early prediction of failures and cyber-attacks, significantly improving proactivity and security management in energy infrastructure. This study highlights the importance of integrating artificial intelligence into energy infrastructure security strategies, proposing a paradigmatic change in cybersecurity management that increases operational efficiency and strengthens the resilience and sustainability of the energy sector against cyber threats. Full article

In the digital transformation era, digitalization integrates deeply into production, bolstering output efficiency and economic value. Through stochastic frontier analysis (SFA), this research positions digitalization as an input in the production function, dissecting its elasticity impact on capital, labor, and output. The effect of digitalization on total factor productivity change (TFPC) is explained by comparing TFPC with and without digitalization. Findings reveal that digitalization’s integration into economic growth displays a U-shaped trajectory, with initial productivity setbacks transitioning to long-term benefits as industries adapt. The periodic complementarity and substitution between digitalization and labor, along with a weak substitution relationship with capital, illustrate that, as a production factor, digitalization dynamically interacts with other factors, both complementing and substituting them. This dynamic interplay highlights the intricate role that digitalization plays within the production function. Furthermore, digitalization has played a crucial role in China’s TFP growth, which also highlights the lack of other technological progress. Meanwhile, the pace of digital transformation presents scalability challenges, evident in the fluctuating scale efficiency change (SEC). Policymakers are advised to address these early stage challenges through supportive measures, ensuring smoother digital transitions. Concurrently, industries should embrace this non-linear transformation, emphasizing adaptability to maximize digitalization’s long-term advantages. Full article

In this paper, we explore the known connection among sustainability, resilience, and well-being within the framework of active inference. Initially, we revisit how the notions of well-being and resilience intersect within active inference before defining sustainability. We adopt a holistic concept of sustainability denoting the enduring capacity to meet needs over time without depleting crucial resources. It extends beyond material wealth to encompass community networks, labor, and knowledge. Using the free energy principle, we can emphasize the role of fostering resource renewal, harmonious system–entity exchanges, and practices that encourage self-organization and resilience as pathways to achieving sustainability both as an agent and as a part of a collective. We start by connecting active inference with well-being, building on existing work. We then attempt to link resilience with sustainability, asserting that resilience alone is insufficient for sustainable outcomes. While crucial for absorbing shocks and stresses, resilience must be intrinsically linked with sustainability to ensure that adaptive capacities do not merely perpetuate existing vulnerabilities. Rather, it should facilitate transformative processes that address the root causes of unsustainability. Sustainability, therefore, must manifest across extended timescales and all system strata, from individual components to the broader system, to uphold ecological integrity, economic stability, and social well-being. We explain how sustainability manifests at the level of an agent and then at the level of collectives and systems. To model and quantify the interdependencies between resources and their impact on overall system sustainability, we introduce the application of network theory and dynamical systems theory. We emphasize the optimization of precision or learning rates through the active inference framework, advocating for an approach that fosters the elastic and plastic resilience necessary for long-term sustainability and abundance. Full article

With the demographic dividend disappearing, the key to achieving high-quality development in China is to promote full employment of the workforce. Women are a significant group in the job market, but they frequently face greater pressure and higher employment thresholds. Ensuring high-quality employment for women will be one of the most important tasks in the future. Based on the China Family Panel Studies data, this paper uses two-way fixed effects models, causal stepwise regression analysis, and structural equation models to study the impact of digital transformation of households on female employment and how it works. The empirical results show that digital transformation of households significantly promotes female employment. For low-security employment and high-security employment, the promotion effect of digital transformation is significant. Further mechanism analysis shows that digital transformation of households mainly increases women’s human capital, improves their search for information, and stimulates improvements in social skills, thus effectively eliminating employment-related gender discrimination and ultimately promoting women’s employment. This paper can provide a significant reference for alleviating female employment pressure, promoting full employment, and achieving high-quality development in the context of digital transformation. Full article

The purpose of this study was to establish a spatial structural framework to explore how cross-border mergers and acquisitions (M&As) in emerging markets can enhance long-term productivity and select the appropriate host country market structures. Utilizing cross-border M&A data from Chinese companies from 2008 to 2016, we developed a moderated U-shaped mediation model. Employing Two-Stage Least Squares and the Generalized Method of Moments for endogeneity analysis, we offer robust empirical insights. Our findings illustrate that enterprise productivity progression from cross-border M&As is significantly influenced by a U-shaped mediation of the host country’s market size effect, which is further moderated by the technological distance between the home and host countries. A high technological distance intensifies the U-shaped mediation of market size effects on enterprise productivity, while low technological distances result in an inverted U-shaped curve, indicating that such markets may boost short-term productivity but limit long-term growth. Conversely, larger markets with greater technological distances better support sustained productivity increases, even requiring persistent technological absorption efforts. This study underscores the necessity of selecting appropriate host country market structures and effectively managing the acquisition timeline to positively impact both short- and long-term productivity. By conceptualizing firm-level technological absorption efforts as the technological gap between the home and host countries, this study highlights the crucial moderating role that the technological gap plays in influencing long-term productivity at the macro level, providing new insights into the economic geographic strategic decisions and spatial planning for emerging market enterprises in cross-border acquisitions. Full article

Cardiovascular diseases (CVDs) represent the leading cause of death globally. Romania recorded the highest mortality rate due to CVDs in the EU in 2022, with 162,984 deaths, while the number of registered patients with CVDs surpassed 4 million. This study aims to measure the population’s potential spatial accessibility to cardiovascular hospitals in Romania, as timely access to such healthcare facilities is crucial to minimise avoidable mortality due to CVDs. Although distance is an essential parameter of spatial accessibility, time-based analysis is more reflective of real-world scenarios due to the unpredictability of travel. The potential spatial accessibility was measured using the Application Program Interface (API) offered through the Google Maps platform and a personal car as the transportation mode. The country’s cardiovascular hospital network comprises 161 units, of which 84 can provide complex care. Because all of them are located in urban areas, three different time slots were considered to distinguish between high and low traffic congestion situations. We created hierarchies of ten-minute and five km intervals for travel time and distance, respectively, to emphasize the population percentages with better or low potential spatial accessibility. Results showed that only 15% of the population can reach the nearest cardiovascular hospital in less than 20 min, and 23% must travel for over 60 min, while 45.7% live farther than 20 km from a cardiovascular hospital. Inhabitants living in remote areas, especially rural ones, are the most vulnerable, having to travel for the longest time and distance. Actions like improving the existing transport infrastructure and upgrading healthcare facilities and equipment are needed to ensure better medical care and an adequate response to population needs. This study can support local authorities in optimising spatial accessibility to cardiovascular care by identifying the most burdened hospitals in the context of low medical specialised staff and large catchment areas. Full article

Institutional, policy, and management scholars and practitioners are increasingly interested in leveraging network perspectives, methods, and data to understand complex social phenomena, including the various stages of the policy process, community mobilization, and coupled natural and human systems. Viewing these phenomena through the lens of system-oriented networks can be valuable for understanding and intervening within complex policy arenas. However, currently, there is no clear consensus on who and what constitutes a relevant actor in a system-oriented network. Furthermore, numerous conceptual and methodological traditions for conceptualizing, measuring, and analyzing system-oriented networks have arisen, and each is linked to different disciplinary traditions. In this paper, we showcase six approaches from the public policy and public management literature for conceptualizing and analyzing system-oriented networks. We offer a conceptual framework for characterizing different approaches which considers differences in their focal system of interest, analytical focus, theoretical orientation, and approach for determining network boundaries. We review these elements with an eye toward helping scholars and practitioners interested in system-oriented networks to make informed decisions about the array of available approaches. Full article

This work presents two mixed-integer programming models that intend to minimize the costs of the picking and storage operation through better planning and organization of the places occupied by the products in the warehouse. A large customer that stores frozen goods in a Portuguese cold chain logistics company was selected for the analysis of the allocation of the products in the warehouse and of the corresponding outbound movements. Data with 8525 movements that occurred during 2021 were collected for 228 different product references. For this case study, the products that had a picking place in the initial scenario now have pallets with all the goods in the reserve area, and vice versa. The mathematical models were permitted to obtain savings for the logistics operator costs of around 30.9%. The proposed models can, in the future, be applied in other warehouse scenarios to companies in completely different sectors of activity. Full article

Population migration continues to reshape the spatial pattern of China’s population and regional economic development. During this internal migration process, production and consumption patterns often change, ultimately leading to changes in green total factor productivity. This paper, based on the Chinese population census data and 1% sampling survey data from 2005 to 2015, utilizes social network analysis methods to measure the labor mobility network indicators of 284 prefecture-level cities. Further, this paper analyzes the impact and mechanisms of regional network status on green total factor productivity using a panel fixed effects model. We find that as network density increases, the interpersonal connections between regions become closer, and the network exhibits a clear pattern of “concentrated inflows” and “dispersed outflows”, with the trend of forming strong alliances becoming increasingly apparent. Regions positioned centrally either in terms of network in-degree or out-degree exhibit higher green total factor productivity. Among these, the labor mobility network plays a crucial role in enhancing green total factor productivity through the channel of technology diffusion effects, which improve investment efficiency via knowledge exchange and material capital accumulation. The promotive effect of labor network status on green total factor productivity is more pronounced in the eastern regions, where talent quality is higher, and in areas with fewer restrictions from the household registration system. Full article

The healthcare industry is confronted with the challenge to offer an increasing variety of healthcare services while in the meantime controlling rapidly increasing healthcare costs. Mass customization has been proven to be an effective strategy to fulfill customers’ individual specific needs with high efficiency and low cost in the manufacturing industry. This paper investigates the theoretical feasibility and practical applicability of adopting mass customization as a conceptual framework for designing a healthcare service delivery system. The nature of healthcare delivery systems and their evolution are discussed relative to those of manufacturing systems. Recent research in personalized medicine, consumer-driven healthcare, consumer healthcare informatics, and integrated healthcare delivery is reviewed as enabling technologies towards mass customization of healthcare services. By synthesizing these scattered efforts in different streams of literature, this paper concludes that mass customization can contribute to the redesign of healthcare service systems, and delineates a roadmap for future research. Full article

Scientific experiments cover a wide range of fields—from basic to applied scientific research. Chemical experiments are the basis for cultivating chemical knowledge in scientific experiments and are an important way to cultivate scientific thinking and methods. However, due to the toxicity or flammability of the chemical substances in the experiments, hazardous events often lead to personal injuries and environmental damage. Exactly assessing risk factors and reducing the risk of hazards to protect the experimenters and ensure environmental safety are crucial in chemical experiments. However, while the traditional risk evaluation method cannot consider the weight of risk evaluation criteria, it also cannot effectively address problems through hierarchical analysis, as well as imprecise and ambiguous information inherent in human cognition. Therefore, this paper proposed an approach based on failure mode and effects analysis (FMEA) to assess the risk of chemical experiments in a fuzzy information environment. The approach combines the typical analytic hierarchy process (AHP), the risk priority number (RPN) of FMEA, and the intuitionistic fuzzy set (IFS) methods to evaluate risks associated with chemical experiments and consider the damage recovery in chemical experiments. This study applied the case of a university chemistry experiment, “preparation of hydrogels”, to validate the reasonableness and correctness of the proposed approach and compare its numerical verification results with those from the typical RPN, the AHP-RPN, and the AHP-fuzzy risk priority number (AHP-FRPN) methods. The finding demonstrates that the proposed method can more effectively address risk evaluation problems in chemical experiments than the other methods. This result serves as an important reference for reducing chemical experiment risk occurrences. Full article

Facing the increasingly complex and uncertain external environment, the reasonable control of investment risk is the key to realizing the sound operation and high-quality development of enterprises. Based on the innovation perspective, this paper takes A-share non-financial listed companies from 2007 to 2021 as the research sample to explore the impact of the corporate risk-taking level on the high-quality development of enterprises and examines the transmission mechanism of the relationship between the two from the perspectives of innovation efficiency, innovation input, and innovation output. It is found that enterprise risk-taking significantly inhibits the high-quality development of enterprises, by reducing innovation efficiency; innovation efficiency plays a mediating role in the influence of the relationship between the two, which is mainly due to the fact that enterprise risk-taking increases the innovation inputs of enterprises but reduces the innovation outputs of enterprises, and then reduces the innovation efficiency of enterprises and inhibits the high-quality development of enterprises. This heterogeneity study finds that the inhibitory effect of corporate risk-taking on the high-quality development of firms is more pronounced among manufacturing firms, small-scale firms, and firms with higher capital intensity. The findings of this study provide both guidance to help enterprises to reduce risky investment decision-making behaviors and experience for regulators to effectively promote the formulation of policies related to the high-quality development of the real economy. Full article

In this paper, we consider a multiperiod, two-location inventory system with unknown demand distributions and perishable products. Products can be transshipped from the location with excess inventory to the other with excess demand to better fulfill customer demand. The demand distributions are assumed to follow a family of parametric distributions and can only be learned on the fly. To address the challenge, we propose a data-driven inventory management algorithm called DD2LI that achieves a good performance in terms of regret. This algorithm, DD2LI, employs maximum likelihood estimation to approximate the unknown parameter and determines the order quantity based on these estimations. In addition, we emphasize a key assumption that tightens regret bound. Finally, we test the effectiveness of our proposed algorithm by conducting numerical experiments for two scenarios. Full article

During the 2008 financial crisis, central banks (such as the Fed) adopted a quantitative easing (QE) policy to stimulate their countries’ economies and overcome severe economic and financial recessions. However, apart from stimulating the economy by issuing a substantial amount of currency to purchase long-term bonds and suppress interest rates, QE policy also contributed to a boom in the real estate and construction sectors. Therefore, this study employs data envelopment analysis to measure the business performance (BP) of construction companies, and explore the impact of QE policy on the BP of construction companies, between 2004 and 2015, using hierarchical regression. We also examine the moderating role of the debt ratio on the relationship. Focused on publicly listed construction companies in Taiwan, this research reveals three encouraging findings. Firstly, QE policy indeed enhanced the BP of Taiwanese construction companies. Secondly, performance improvements in construction companies due to QE policy show a time-diminishing trend, suggesting the importance of seizing the initial policy benefits of QE implementation. Lastly, construction companies with appropriate financial leverage may exhibit better BP. These findings can provide valuable insights for relevant government entities and decision-makers in the industry for policy and investment decisions. Full article