Systems doi: 10.3390/systems12030104
Authors: Carlos Agualimpia-Arriaga José Vuelvas Carlos-Iván Páez-Rueda Carlos Adrián Correa-Flórez Arturo Fajardo
In contemporary mobile communications markets, various agents or players interact to pursue welfare. Regulatory policies enacted by governments in certain markets aim to maximize social welfare. However, some countries, both least developed and developing, often adopt successful models from developed nations without local market validation. Therefore, network economics serves as a pertinent framework for analyzing such policies. This paper introduces a novel scheme based on constrained optimization problems, where the constraints represent multilevel economic game equilibria within a system model involving three agents: the central planner, the mobile network operator, and the mobile data users. These agents strategically optimize their payoff functions by considering benefit factors and decision variables such as the subsidization factor, pricing, and data consumption. To this end, a three-stage dynamic game is proposed to model the players’ interactions, employing the backward induction method to ascertain the subgame perfect equilibrium from the Nash equilibrium. A case study is presented, demonstrating a 31.16% increase in social welfare between scenarios involving no adoption of the subsidization factor and its adoption at the optimal value when the central planner enacts it to other players in the game, even if they do not necessarily attain maximum payoff values. In countries aligning with this proposed model, social welfare is maximized through a subsidization scheme.
]]>Systems doi: 10.3390/systems12030103
Authors: Varun Gupta
Technologies, such as Chat Generative Pre-Trained Transformer (ChatGPT, Smart PLS version 4), are prime examples of Generative Artificial Intelligence (AI), which is a constantly evolving area. SMEs, particularly startups, can obtain a competitive edge, innovate their business models, gain business value, and undergo a digital transformation by implementing these technologies. Continuous but gradual experimentation with these technologies is the foundation for their adoption. The experience that comes from trying new technologies can help entrepreneurs adopt new technologies more strategically and experiment more with them. The urgent need for an in-depth investigation is highlighted by the paucity of previous research on ChatGPT uptake in the startup context, particularly from an entrepreneurial perspective. The objective of this research study is to empirically validate the Generative AI technology adoption model to establish the direction and strength of the correlations among the adoption factors from the perspectives of the entrepreneurs. The data are collected from 482 entrepreneurs who exhibit great diversity in their genders, the countries in which their startups are located, the industries their startups serve, their age, their educational levels, their work experience as entrepreneurs, and the length of time the startups have been on the market. Collected data are analyzed using the Partial Least Squares Structural Equation Modeling (PLS-SEM) technique, which results in a statistical examination of the relationships between the adoption model’s factors. The results indicate that social influence, domain experience, technology familiarity, system quality, training and support, interaction convenience, and anthropomorphism are the factors that impact the pre-perception and perception phase of adoption. These factors motivate entrepreneurs to experiment more with the technology, thereby building perceptions of its usefulness, perceived ease of use, and perceived enjoyment, three factors that in turn affect emotions toward the technology and, finally, switching intentions. Control variables like age, gender, and educational attainment have no appreciable effect on switching intentions to alternatives of the Generative AI technology. Rather, the experience factor of running businesses shows itself to be a crucial one. The results have practical implications for entrepreneurs and other innovation ecosystem actors, including, for instance, technology providers, libraries, and policymakers. This research study enriches the Generative AI technology acceptance theory and extends the existing literature by introducing new adoption variables and stages specific to entrepreneurship.
]]>Systems doi: 10.3390/systems12030102
Authors: Fei Yu Xiuchuan Jia Xiaowei Zhao Jing Li
The reasonable application of cross-domain knowledge tends to promote the generation of radical innovation. However, it is difficult to accurately capture the cross-domain knowledge needed for radical innovation. To solve this problem, this paper proposes a method for inspiring radical innovative design based on FOS and technological distance measurement. First, the functional analysis of the problem product is carried out to determine the target function. Second, the patent sets of problem domain and target domains are constructed based on FOS. Then, this study optimizes the method of technological distance measurement and uses it to determine the optimal target domain. After further categorizing and screening the patents contained in the optimal target domain, specific cross-domain knowledge is pushed to designers. This method can help firms select the most appropriate cross-domain knowledge to design solutions for different problems, thus increasing the possibility of generating radical innovation. In the end, the method is validated in the design of a stovetop cleaning device.
]]>Systems doi: 10.3390/systems12030101
Authors: Philippe J. Giabbanelli Andrew Page
In responding to population health challenges, epidemiologists want to identify causal associations between an exposure (e [...]
]]>Systems doi: 10.3390/systems12030099
Authors: Lei Nie Xueli Bao Shunfeng Song Zhifang Wu
China’s digital economy has been expanding rapidly in the past decade. This expansion is having a profound impact on the country’s economy. Using panel data on 97 prefecture-level cities in the Yellow River Basin from 2011 to 2020, this study investigates the multifaceted relationship between the digital economy and total-factor carbon emission efficiency. The research yields three key findings: (1) The digital economy positively enhances overall carbon emission efficiency. This conclusion is drawn with robustness tests. (2) Green technology innovation serves as a partial mediator between the digital economy and total-factor carbon emission efficiency, and this mediation role is influenced by government intervention, which negatively moderates the relationship between the digital economy and green technology innovation but positively impacts the mediation role of green technology innovation between the digital economy and total-factor carbon emission efficiency. (3) The positive impact of the digital economy on total-factor carbon emission efficiency is more significant in the upper reaches, lower reaches, and resource-based cities of the Yellow River Basin. These findings provide new perspectives and empirical evidence for better understanding the relationship between digital economy development and total-factor carbon emission efficiency. They also provide policy recommendations for achieving strategic objectives, including digital economy development, carbon emission reduction, carbon peaking, and carbon neutrality.
]]>Systems doi: 10.3390/systems12030100
Authors: Rossella Pozzi Violetta Giada Cannas Tommaso Rossi
Research in lean production has recently focused on linking lean production to Industry 4.0 by discussing the positive relationship between them. In the context of Industry 4.0, data science plays a fundamental role, and operations management research is dedicating particular attention to this field. However, the literature on the empirical implementation of data science to lean production is still under-investigated and details are lacking in most of the reported contributions. In this study, multiple case studies were conducted involving the Italian manufacturing sector to collect evidence of the application of data science to support lean production and to understand it. The results provide empirical proof of the link and examples of a variety of data science techniques and tools that can be combined to support lean production practices. The findings offer insights into the applications of the traditional lean plan–do–check–act cycle, supporting feedback on performance metrics, total productive maintenance, total quality management, statistical process control, root cause analysis for problem-solving, visual management, and Kaizen.
]]>Systems doi: 10.3390/systems12030098
Authors: Junjie Liu Junxian Liu Mengmeng Zhang
System-of-systems (SoS) evolution is a complex and unpredictable process. Although various principles to facilitate collaborative SoS evolution have been proposed, there is a lack of experimental data validating their effectiveness. To address these issues, we present an Agent-Based Model (ABM) for SoS evolution in the Internet of Vehicles (IoV), serving as a quantitative analysis tool for SoS research. By integrating multiple complex and rational behaviors of individuals, we aim to simulate real-world scenarios as accurately as possible. To simulate the SoS evolution process, our model employs multiple agents with autonomous interactions and incorporates external environmental variables. Furthermore, we propose three evaluation metrics: evolutionary time, degree of variation, and evolutionary cost, to assess the performance of SoS evolution. Our study demonstrates that enhanced information transparency significantly improves the evolutionary performance of distributed SoS. Conversely, the adoption of uniform standards only brings limited performance enhancement to distributed SoSs. Although our proposed model has limitations, it stands out from other approaches that utilize Agent-Based Modeling to analyze SoS theories. Our model focuses on realistic problem contexts and simulates realistic interaction behaviors. This study enhances the comprehension of SoS evolution processes and provides valuable insights for the formulation of effective evolutionary strategies.
]]>Systems doi: 10.3390/systems12030097
Authors: Samuel Plečko Barbara Bradač Hojnik
This study examines the relationship between digital transformation and sustainable practices within enterprises against the backdrop of global transformative forces framed within the holistic paradigm of systems thinking. It examines the extent to which digital advances either facilitate or impede the sustainable development of companies, while also considering the systemic impact of demographic variables (such as gender, age, education), national income levels, and geographical regions on business sustainability. Using data from the Global Entrepreneurship Monitor (GEM), which encompasses 26,790 entrepreneurs in 47 countries, this research uses multinomial regression to assess how these factors influence companies’ commitment to social and environmental goals. A key finding is that the strategic use of digital technologies in sales processes significantly increases the likelihood that entrepreneurs will integrate social and environmental considerations into their decision-making. Notably, this conscientious approach to business is most prevalent among entrepreneurs in Latin America and the Caribbean. Our findings underscore the central role of digital technologies in driving sustainable business transformation while also highlighting the significant influence of regional socio-environmental contexts on business sustainability orientations.
]]>Systems doi: 10.3390/systems12030096
Authors: Neal Wagner
Modeling and simulation of complex systems frequently requires capturing probabilistic dynamics across multiple scales and/or multiple domains. Cyber–physical, cyber–social, socio–technical, and cyber–physical–social systems are common examples. Modeling and simulating such systems via a single, all-encompassing model is often infeasible, and thus composable modeling techniques are sought. Co-simulation and closure modeling are two prevalent composable modeling techniques that divide a multi-scale/multi-domain system into sub-systems, use smaller component models to capture each sub-system, and coordinate data transfer between component models. While the two techniques have similar goals, differences in their methods lead to differences in the complexity and computational efficiency of a simulation model built using one technique or the other. This paper presents a probabilistic analysis of the complexity and computational efficiency of these two composable modeling techniques for multi-scale/multi-domain complex system modeling and simulation applications. The aim is twofold: to promote awareness of these two composable modeling approaches and to facilitate complex system model design by identifying circumstances that are amenable to either approach.
]]>Systems doi: 10.3390/systems12030095
Authors: Yizhuo Zhou Jianjun Zhang Yundan Yang
The dynamics of medical resource demand during public health crises pose significant challenges to emergency supply chain management, particularly within an evolving and complex social environment. To explore this, the interactive effects of information diffusion and virus spreading on medical resource demand are investigated using a novel three-layer coevolution “information–epidemic–resource” model through Markov process simulations. The study firstly identifies eight factors influencing demand fluctuations in terms of some city characteristics, such as media exposure, consistency of public opinion, self-protection level, and restrictive protection level, while categorizing resources into individual holdings and centralized storage. Then, extensive simulations are examined to elucidate the impact of these factors. The results reveal that various city characteristics can affect fluctuation in demand for both individual holdings and centralized storage. Inaccurate media information tends to inflate fluctuations, while higher public opinion consistency can reduce it. Reinforcing self-protection decreases the demand fluctuations of individuals, and effective restrictive protections can reduce fluctuations in centralized resource storage. Moreover, an analytical simulation of various city scenarios, underpinned by statistical data from selected Chinese and German cities, demonstrates that distinct city characteristics significantly influence medical resource demand changes during epidemics. This underscores the importance of tailoring emergency medical supply strategies to the specific developmental traits of different countries and cities. This study provides valuable insights to researchers, governments, and enterprises, enhancing their preparedness and response for emergency supply chain disruptions.
]]>Systems doi: 10.3390/systems12030094
Authors: Montserrat Jiménez-Partearroyo Ana Medina-López
This study contextualizes the transformative role of Business Intelligence (BI) over the past two decades, emphasizing its impact on business strategy and competitive advantage. Employing a dual-method approach, it integrates a bibliometric analysis using SciMAT with a qualitative examination of six key articles from the Web of Science (WoS), analyzed through the Gioia methodology, focusing on BI and competitiveness. The aim is to examine the metamorphosis of Business Intelligence (BI) and how it has evolved from a traditionally supporting role to a central strategic player in shaping corporate strategy and business competitive advantage over the past two decades. It discusses the overall transformation of BI and provides an in-depth examination of the specific ways in which Business Intelligence tools have redefined the landscape in contemporary business practices. Key findings reveal BI’s pivotal role in enhancing knowledge management, innovation, and marketing capabilities. Challenges in BI implementation, such as the necessity for skilled personnel and adaptability to swift technological shifts, are also highlighted. Results advocate for a dynamic BI approach, adaptable to market trends and technological evolutions. The research demonstrates that BI tools, especially when integrated with technologies like AI, IoT, and machine learning, significantly enhances decision making and efficiency in socio–technical and management systems, leading to a paradigm shift in handling complex systems and adapting to changing environments.
]]>Systems doi: 10.3390/systems12030093
Authors: Filippo Maria Ottaviani Alberto De Marco Carlo Rafele Gabriel Castelblanco
Project risk management (PRM) involves identifying risks, assessing their impact, and developing a contingency plan. A structured contingency management (CM) approach prevents subjective biases in analyzing risks and developing responses. Previous studies have either focused on improving the accuracy of risk estimates or analyzed, from a qualitative perspective, the relationships between perceived risk and project performance. This study aimed to improve PRM by providing a risk-perception-based contingency management framework (CMF). The CMF guides contingency depletion based on two short- and long-term cost overrun indicators and their respective thresholds. Thresholds and the initial contingency reserve amount are determined by applying the Monte Carlo method to a stochastic, discrete-event, finite-horizon, dynamic project simulation model. The study developed the CMF through a structured approach, validating the simulation model on eight specific project configurations. The results prove that the framework can be applied to any project, shaping the risk response strategy. This study contributes to PRM by explaining the relationships between risk perception and risk responses and providing a prescriptive CM tool.
]]>Systems doi: 10.3390/systems12030092
Authors: Peng Wu Yisheng Liu
In the face of increasingly severe global climate change, achieving zero-carbon development goals has gradually become a consensus across various industries. Enhancing the electrification level of building energy use and increasing the proportion of renewable energy applications are primary means to achieve zero-carbon development in the construction sector, which also imposes higher demands on energy system planning and operation. This study focuses on urban building clusters and district energy systems, proposing coordinated optimization methods for energy supply and demand. On the demand side, strategies such as utilizing energy storage from electric vehicles are applied to enhance the flexibility of building energy use, along with methods to improve building load leveling rates and increase renewable energy penetration rates. On the supply side, a dual-layer planning method is proposed for the optimal configuration and operation of district energy systems considering the construction of shared energy storage stations. Results indicate that the optimization methods for urban building clusters significantly improve the flexibility of building energy use, and different functional compositions of building clusters can enhance load leveling and renewable energy penetration rates to a certain extent. The dual-layer optimization method for district energy systems can further exploit the potential of building energy flexibility, thereby achieving a balance between economic and environmental benefits.
]]>Systems doi: 10.3390/systems12030091
Authors: Min Wan Haibo Kuang Peng Jia Sue Zhao
This paper aims to solve the decision-making problem of value chain reconstruction of Chinese port enterprises under the background of the Free Trade Zone policy. Based on value chain theory and system dynamics method, this paper constructs a simulation model that can simulate the value-added change process of port enterprises under different combination input conditions. Furthermore, it conducts simulation case studies. The research indicates that the Free Trade Zone policy has a significant promoting effect on the restructuring of port enterprise value chains and the transformation and upgrading of enterprises. Moreover, considering the impact of the Free Trade Zone policy and limited resources, the overall benefits to port enterprises from combined investments are superior to those from single-factor investments. According to the value chain theory, the business segments of a port are decomposed into ancillary value activities, basic value activities, and external value activities. The investments in these three types of value activities play roles, respectively, in enhancing the operational efficiency of port enterprises, expanding the business scope of port enterprises, and strengthening the core competitiveness of port enterprises. From the overall perspective of the system, Shanghai Port can obtain the maximum operating profit when the endogenous factor input rate is 13%, the basic factor input rate is 4%, and the exogenous factor input rate is 13%. The findings of this research provide a decision-making reference for Chinese port enterprises to realize value reconstruction, transformation, and upgrading in the context of the Free Trade Zone policy.
]]>Systems doi: 10.3390/systems12030090
Authors: Rees Hill David Slater
In an increasingly complex world there is a real, urgent need for methodologies to enable engineers to model complex sociotechnical systems, as these now seem to describe the majority of systems in use today. This is, of course, exacerbated by the increasing involvement and augmentation with “black box” AI contributions. Hollnagel produced a methodology (FRAM) which did allow the analyst insights into these systems’ behaviour, but the model-based system engineering applications demand numbers and a quantitative approach. In the last 10 years, this original approach, developed to model systems as sets of interactive, interdependent “functions” (abstracted from agent or component details), has been further developed to the point where it can take the basic data and structures from the current component-focussed system engineering “models”, and can pull them all together into dynamic models (as opposed to the static, fixed System Theoretic Process Accimaps) from which analysts can discern how they really work in practice, and predict the emergent behaviours characteristic of complex systems. This paper describes how the FRAM methodology has now been extended to provide these extra, essential attributes. It also describes its implementation using an open-source software, freely available for use and verification on the GitHub site.
]]>Systems doi: 10.3390/systems12030089
Authors: Alvaro Paricio-Garcia Miguel A. Lopez-Carmona
Addressing urban traffic congestion is a pressing issue requiring efficient solutions that need to be analyzed regarding travel time and pollutant emissions. The traffic weighted multi-maps (TWM) method has been proposed as an efficient mechanism for congestion mitigation that enables differential traffic routing and path diversity by strategically distributing different network views (maps) to the drivers. Previous works have focused on TWM generation by creating optimal edge weights, but the complexity exponentially increases with the network size and traffic group diversity. This work describes how congestion and emissions can be addressed using TWM maps based on the k-shortest paths for the traffic flows (instead of individuals) that are optimally assigned and distributed to the components of the traffic flow. The map allocation strategies optimal TWM (OTV), optimal TWM per path flow with linear constraints (LCTV), and its variant unconstrained optimal TWM per path flow (UCTV) are described. They use maps generated from the k-shortest paths of the traffic flows (kSP-TWM). The heuristic solution obtained is compared with the theoretical static traffic assignment estimation baseline with different configurations, regarding congestion reduction, total travel time enhancement, and pollutant emissions. Experiments are developed using a synthetic traffic grid network scenario with a mesoscopic simulation. They show that the solution provided is adequate for its proximity to the theoretical equilibrium solutions and can generate minimum emissions patterns. The presented solution opens new possibilities for further congestion and pollutant management studies and seamless integration with existing traffic management frameworks.
]]>Systems doi: 10.3390/systems12030088
Authors: Travis R. Moore Nicholas Cardamone Helena VonVille Robert W. S. Coulter
Complex systems science (CSS) and community-based research (CBR) have emerged over the past 50 years as complementary disciplines. However, there is a gap in understanding what has driven the recent proliferation of integrating these two disciplines to study complex and relevant issues. In this review, we report on the results of a scoping review of articles that utilized both disciplines. After two levels of reviewing articles using DistillerSR, a web-based platform designed to streamline and facilitate the process of conducting systematic reviews, we used two forms of natural language processing to extract data. We developed a novel named entity recognition model to extract descriptive information from the corpus of articles. We also conducted dynamic topic modeling to deductively examine in tandem the development of CSS and CBR and to inductively discover the specific topics that may be driving their use in research and practice. We find that among the CSS and CBR papers, CBR topic frequency has grown at a faster pace than CSS, with CBR using CSS concepts and techniques more often. Four topics that may be driving this trend are collaboration within social systems, business management, food and land use and knowledge, and water shed management. We conclude by discussing the implications of this work for researchers and practitioners who are interested in studying and solving complex social, economic, and health-related issues.
]]>Systems doi: 10.3390/systems12030087
Authors: Xinyu Yang Lizhen Shen Xia Wang Xiao Qin
In the knowledge economy era, innovation has become a key emphasis for urban competitions. This paper constructs a theoretical research framework that integrates the basic understandings, influencing factors and ensuing results of intercity innovative competition relations. On the basis of data from the general programs of the National Natural Science Foundation of China from 2005 to 2019, this paper constructs intercity innovative competition relations in China, analyses their spatial distribution and quantitative characteristics, and quantitatively investigates the impact of urban innovation capacity and multidimensional proximity (e.g., geographical proximity, institutional proximity and cognitive proximity) on intercity innovative competition relations through a negative binomial model. The study obtained the following findings: (1) In terms of the overall intercity innovative competition relations, the intensity of China’s intercity innovative competition relations gradually increased from 2005 to 2019, with a spatial clustering towards cities with high administrative ranks (e.g., municipalities directly under the central government, sub-provincial cities and provincial capitals); Beijing is always at the centre of innovative competition relations, but its standing has slightly slipped in recent years. (2) From the perspective of disciplines, cities can become benchmarks in particular fields of innovative competitions by competing according to their disciplinary strengths; intercity innovative competition relations in China vary across various academic disciplines. (3) In terms of influencing factors, urban innovation capacity has significant positive effects on intercity innovative competition relations; geographical proximity, institutional proximity and cognitive proximity all have significant positive effects on innovative competition relations; and interactions occur between multidimensional proximities, including a complementary effect between geographical proximity and institutional proximity, a substitutive effect between cognitive proximity and geographical proximity, and a substitutive effect between cognitive proximity and institutional proximity.
]]>Systems doi: 10.3390/systems12030086
Authors: Bing Li Xiaoduan Sun Yulong He Meng Zhang
Expressways in urban agglomerations are important in connecting cities, thus attracting great attention from researchers in the expressways risk assessment. However, there is a lack of safety assessment models suitable for the characteristics of expressways in Chinese urban agglomerations, and the nature and mode of dynamic risks on Chinese highways are still unclear. Therefore, this study adopts the Adaptive Neural Fuzzy Inference System (ANFIS) and the method of decision tree, combined with data from the Beijing section of the Beijing Harbin Expressway, to model the risk of accident-prone highways in urban agglomerations. To determine the optimal model, we evaluated the model’s bias at different time intervals. In addition, key factors affecting highway safety were analyzed, providing scientific support for the risk prevention of highways in urban agglomerations in China.
]]>Systems doi: 10.3390/systems12030085
Authors: Lei Nie Yuanyuan Wang
Elucidating the impacts of service industry’s agglomeration on the optimization of industrial structures holds paramount significance in advancing urban economic growth and fostering the coordinated and sustainable development of city economies. This study leverages panel data encompassing 251 prefecture-level cities spanning from 2003 to 2019, employing a spatial Dubin model to scrutinize the influence of distinct types of service industry agglomeration on industrial structure optimization. The results show that specialized agglomeration within the service sector significantly inhibits the rationalization of industrial structures and their underlying fundamentals. Conversely, heightened levels of agglomeration in diversified service industries facilitate the rationalization of industrial structure, predominantly driven by regional spatial spillover effects. Further analysis reveals heterogeneity in service industry agglomeration across cities of varying sizes concerning industrial structure optimization, notably accentuating underutilized spatial spillover effects in smaller cities. In light of these insights, this paper advocates for cities to capitalize on the agglomeration and spillover effects between the service industry and other sectors, strategically selecting optimal service industry agglomeration modes to propel industrial structure optimization.
]]>Systems doi: 10.3390/systems12030084
Authors: Osama Younis Kamal Jambi Fathy Eassa Lamiaa Elrefaei
Intelligent systems are being proposed every day as advances in cloud systems are increasing. Mostly, the services offered by these cloud systems are dependent only on their providers, without the inclusion of services from other providers, specialized third parties, or individuals. This ‘vendor lock-in’ issue and the limitations related to offering tailored services could be resolved by allowing multiple providers or individuals to collaborate through intelligent task scheduling. To address such real-world systems’ limitations in provisioning and executing heterogeneous services, we employed Blockchain and Deep Reinforcement Learning here; the first is used for the token-based secured communication between parties, and the latter is to predict the appropriate task scheduling; hence, we guarantee the quality of not only the immediate decision but also the long-term. The empirical results show a high reward achieved, meaning that it accurately selected the candidates and adaptably assigned the tasks based on job nature and executors’ individual computing capabilities, with 95 s less than the baseline in job completion time to maintain the Quality of Service. The successful collaboration between parties in this tokenized system while securing transactions through Blockchain and predicting the right scheduling of tasks makes it a promising intelligent system for advanced use cases.
]]>Systems doi: 10.3390/systems12030083
Authors: Hao Wang Tao Zhang Xi Wang Jiansong Zheng You Zhao Rongjiang Cai Xia Liu Qiaoran Jia Zehua Zhu Xiaolong Jiang
Numerous organizational researchers have acknowledged that COVID-19 reduced the profit in the tourism industry. Some tourism firms decreased the cost by reducing the investment of CSR in order to increase the profit. However, the relevant literature remains scarce. The main purpose of this study is to explore the effect of COVID-19 on CSR investment in the tourism industry. This study fills the gap between stakeholder and cost stickiness theories. Based on a quasi-experiment of listed Chinese tourism companies from 2017 to 2021, the study finds that COVID-19 caused tourism firms to increase strategic CSR and decrease a responsive one. In addition, tourism firms that adopted cost leadership strategies trimmed responsive CSR more than strategic CSR. Tourism firms with differentiation leadership strategies increased strategic and decreased responsive CSR. Tourism firms with higher levels of political connections increased responsive CSR, while tourism firms with higher organizational resilience increased strategic CSR. At the theoretical level, this study reveals the theoretical mechanism of COVID-19 on tourism firms’ adjustment of CSR from the perspective of cost stickiness. On a practical level, it helps inform tourism firms’ decision-making regarding CSR adjustments for sustainable development when they face widespread crisis scenarios.
]]>Systems doi: 10.3390/systems12030082
Authors: Cosmin Florin Lehene Mohammad Jaradat Răzvan Liviu Nistor
Industrial Organization, the Resource-Based View, and the Relational View are some classical, well-established, and widely accepted theories in the strategic management domain regarding the understanding, explanation, and prediction of competitive advantage of firms and above-average firm performance. Recent evidence of economic geography and regional economics added to this stream of research new perspectives like cluster theory and microeconomic competitiveness. Despite the high enthusiasm with which companies and policymakers embraced the new advancements, there is some contradictory evidence regarding the positive effect of local conditions on firm performance. Thus, in this paper, we aim to empirically test some aspects of a modern regional development theory, proposed mainly by Michael Porter and collaborators, and the impact of these aspects on firm performance. External determinants considered at three levels of analysis (local economy, local clusters, and industry) will be investigated in relation to firm performance. We will analyze empirical data through detailed correlational analyses and by building multilinear regression models. After the statistical analysis of the answers provided directly by 67 medium and large manufacturing companies operating in Romania, we will provide empirical support for some external determinants, while for other determinants, we will show that the data rejected the proposed associations. The main conclusion derived from this study is that different combinations of external determinants, considered at all three levels of analysis, have a positive and significant effect on different measures of firm performance. The findings in our paper are important for both regional economics and the strategic management literature, suggesting the importance of creating local or urban conditions depending on the type of performance that the firms in the local economy are underperforming.
]]>Systems doi: 10.3390/systems12030081
Authors: Antonio Juan Briones-Peñalver Francisco Campuzano-Bolarin Francisco Acosta Hernández José Rodrigo Córdoba-Pachón
In the context of public administrations after COVID-19, this paper formulates and validates a digital model of tutoring (e-tutoring) for small and medium enterprises (SMEs) by public administrations or PAs to help the former reduce their risks to fold in their first few years of existence and with the support of private professionals (economists, accountants, business advisors, managers, etc.). The model draws on ideas about relational administration (RA), a concept that is yet to be fully exploited or assessed in the literature. Several hypotheses derived from the model are formulated and tested using a polytomic-nominal logistic regression. A questionnaire was sent to and returned by 236 small and medium entrepreneurs in Spain facing insolvency proceedings to identify main reasons for business failure and if or how they would accept online tutoring from private professionals associated with PAs. Findings suggest that SM entrepreneurs agree with receiving selected forms of tutoring, requiring public administrations to enhance capabilities for joint information provision and decision making through the use of information and communication technologies or ICTs. These findings have important implications for the potential restructuring of public administrations, their collaborations with professionals, and the future co-design and implementation of e-government services by PAs
]]>Systems doi: 10.3390/systems12030080
Authors: Bernard P. Zeigler
Paratemporal methods based on tree expansion have proven to be effective in efficiently generating the trajectories of stochastic systems. However, combinatorial explosion of branching arising from multiple choice points presents a major hurdle that must be overcome to implement such techniques. In this paper, we tackle this scalability problem by developing a systems theory-based framework covering both conventional and proposed tree expansion algorithms for speeding up discrete event system stochastic simulations while preserving the desired accuracy. An example is discussed to illustrate the tree expansion framework in which a discrete event system specification (DEVS) Markov stochastic model takes the form of a tree isomorphic to a free monoid over the branching alphabet. We derive the computation times for baseline, non-merging, and merging tree expansion algorithms to compute the distribution of output values at any given depth. The results show the remarkable reduction from exponential to polynomial dependence on depth effectuated by node merging. We relate these results to the similarly reduced computation time of binomial coefficients underlying Pascal’s triangle. Finally, we discuss the application of tree expansion to estimating temporal distributions in stochastic simulations involving serial and parallel compositions with potential real-world use cases.
]]>Systems doi: 10.3390/systems12030079
Authors: Rasheed Mohammad Faisal Saeed Abdulwahab Ali Almazroi Faisal S. Alsubaei Abdulaleem Ali Almazroi
Cybersecurity relies heavily on the effectiveness of intrusion detection systems (IDSs) in securing business communication because they play a pivotal role as the first line of defense against malicious activities. Despite the wide application of machine learning methods for intrusion detection, they have certain limitations that might be effectively addressed by leveraging different deep learning architectures. Furthermore, the evaluation of the proposed models is often hindered by imbalanced datasets, limiting a comprehensive assessment of model efficacy. Hence, this study aims to address these challenges by employing data augmentation methods on four prominent datasets, the UNSW-NB15, 5G-NIDD, FLNET2023, and CIC-IDS-2017, to enhance the performance of several deep learning architectures for intrusion detection systems. The experimental results underscored the capability of a simple CNN-based architecture to achieve highly accurate network attack detection, while more complex architectures showed only marginal improvements in performance. The findings highlight how the proposed methods of deep learning-based intrusion detection can be seamlessly integrated into cybersecurity frameworks, enhancing the ability to detect and mitigate sophisticated network attacks. The outcomes of this study have shown that the intrusion detection models have achieved high accuracy (up to 91% for the augmented CIC-IDS-2017 dataset) and are strongly influenced by the quality and quantity of the dataset used.
]]>Systems doi: 10.3390/systems12030078
Authors: Tingyang Huang Shuangjie Li Fang Liu Hongyu Diao
This paper introduces an improved slack-based game cross-efficiency measurement model that enhances the existing cross-efficiency framework and integrates it with the Data Envelopment Analysis (DEA) game cross-efficiency. The model ensures the fairness of its results through the implementation of a more stringent selection of frontier face weights. It accounts for the competitive relationships among Decision Making Units (DMUs), achieving a Nash equilibrium solution through continuous iterations. Furthermore, the model accounts for undesirable outputs and various strategic orientations, enhancing its applicability. The model’s effectiveness is validated through comparative analyses of diverse case studies. Additionally, the model’s practical utility is demonstrated through the analysis of industrial data from various Chinese provinces between 2010 and 2019. Analysis results show that the proposed model measures production efficiency with greater precision and comparability than alternative models.
]]>Systems doi: 10.3390/systems12030077
Authors: Aaron Pico Joaquin Taverner Emilio Vivancos Vicente Botti Ana García-Fornes
Emotion regulation is the human ability to modulate one’s or other emotions to maintain emotional well-being. Despite its importance, only a few computational models have been proposed for facilitating emotion regulation. None of them prepare a plan of all the actions necessary for emotion regulation customized to the needs of a specific individual. To address this gap, we propose a computational model for an intelligent agent which, grounded in a multidimensional emotion representation, facilitates emotion regulation in individuals. This computational model is based on J. Gross’s theoretical framework of emotion regulation. An intelligent agent selects the most appropriate regulation strategy to maintain an individual’s emotional equilibrium considering the individual’s personality traits. A dynamic planner prepares a plan of emotion regulation actions which is dynamically adapted according to the emotional changes observed in the individual after applying the previous emotion regulation actions. This refinement of the initial regulatory action plan allows the proposed emotion regulation agent to adapt the plan to the specific characteristics of the individual, facilitating the individual to improve their emotion regulation capabilities and improve their emotional health.
]]>Systems doi: 10.3390/systems12030076
Authors: HyoungSuk Cho NakHyeok Choi
The capacity of local governments to act as frontline disaster management agencies is crucial to urban sustainability and disaster risk management systems. However, vulnerabilities in the management systems can hinder the effectiveness of disaster risk management, affecting the resilience and sustainable development of urban areas. This study examines vulnerable areas of disaster risk management from a practical perspective, based on audit findings conducted by the Board of Audit and Inspection (BAI). The stages of disaster risk management are classified as prevention, preparedness, response, and recovery. The disaster risk management activities that local governments should undertake at each stage have been identified and summarized. The vulnerabilities and associated cases related to disaster risk management were comprehensively analyzed by compiling the results of local government disaster risk management audits conducted after 2015. This study revealed vulnerabilities in areas such as disaster management funds, prevention facilities, safety inspections, forecasting and warning systems, and resident evacuation, all of which are integral to maintaining urban sustainability. To avoid the recurrence of these issues, this study suggests that local governments should develop and implement improvement measures for each vulnerable area. The findings of this study can serve as valuable guidelines for local governments on ways to enhance their disaster risk management systems.
]]>Systems doi: 10.3390/systems12030075
Authors: Nan Chen Dan Bai Na Lv
Public hospitals in China are working to build an integrated online–offline healthcare system that combines telehealth and traditional healthcare to better serve patients. This study aims to explore Chinese patients’ preferences for online versus offline outpatient follow-up visits after the COVID-19 pandemic and to inform healthcare providers in designing optimal service delivery programmes. A discrete choice experiment was designed to elicit respondents’ stated preferences. A total of 311 valid respondents were recruited. Analysis of the full sample showed that respondents preferred traditional, offline outpatient follow-up visits. Nevertheless, a class of respondents was identified who preferred online outpatient follow-up visits. Our results show that Chinese patients are currently generally cautious about online outpatient follow-up visits since there is proportion of potentially targeted patients who stated a preference for online visits while the overall preference is still offline, in-person follow-up visits. Online outpatient follow-up visits could be attractive alternatives to traditional visits if they could meet potential users’ preferences for shorter waiting time for appointments, lower service cost, and continuity of follow-up visits. This study also suggests that it is necessary and worthwhile for healthcare providers to further explore the optimal integration of telehealth services with traditional healthcare.
]]>Systems doi: 10.3390/systems12030074
Authors: Xi-Hui Jia Jui-Che Tu
In the aftermath of the COVID-19 pandemic, college students have faced various challenges that could negatively impact their critical thinking abilities due to disruptions to education, increased stress and anxiety, less social interaction, and the advancement of distance learning relying more heavily on digital tools. With the increasing integration of AI technology across sectors, higher education institutions have deployed various AI capabilities for intelligent campuses and modernized teaching. However, how to fully utilize AI capabilities to promote students’ thinking awareness on learning effectiveness is still not clear, as critical thinking is an essential skill set holding significant implications for college students’ development. This research adopts the resource-based theory (RBT) to conceptualize the university as a unified entity of artificial intelligence (AI) resources. It aims to investigate whether AI capabilities can foster critical thinking awareness among students by enhancing general self-efficacy and learning motivation. In particular, it examines the causal relationships between AI capabilities, general self-efficacy, motivation and critical thinking awareness. Primary data was collected through a questionnaire administered to 637 college students. Structural equation modeling was employed to test hypotheses pertaining to causality. The results showed that AI capabilities could indirectly enhance students’ critical thinking awareness by strengthening general self-efficacy and learning motivation, but the effect on critical thinking awareness was not significant. Meanwhile, general self-efficacy significantly affected the formation of learning motivation and critical thinking awareness. This indicates that AI capabilities are able to reshape the cognitive learning process, but its direct influence on thinking awareness needs to be viewed with caution. This study explored the role of AI capabilities in education from the perspective of organizational capabilities. It not only proves how AI facilitates cognition, but also discovered the important mediating role of general self-efficacy and motivation in this process. This finding explains the inherent connections between the mechanism links. Furthermore, the study expands research on AI capabilities research from the technical level to the educational field. It provides a comprehensive and in-depth theoretical explanation theoretically, guiding the practice and application of AI in education. The study is of positive significance for understanding the need for the future development of the cultivation of critical thinking awareness talents needed for future development through AI capabilities in education.
]]>Systems doi: 10.3390/systems12030073
Authors: Zhu Wang Shenglei Hao Leqi Yuan Ke Hao
Driven by the growing threat of natural disasters caused by climate change, there is an urgent need to strengthen the emergency rescue logistics network. However, insufficient research has been conducted on optimizing both pre-disaster preparation and post-disaster response, resulting in lower resilience and inefficiency of emergency logistics management. To this end, this study explores the optimization of emergency rescue resource allocation and transportation network design, considering the uncertainty and multi-period nature of natural disaster rescue. By employing a lateral transshipment strategy, a three-stage stochastic programming model is established, which aims to balance economic benefits with the need for devastations, thereby enhancing the resilience of the logistics network. Numerical experiments verify the effectiveness of the proposed model with different instances and the performance of the lateral transshipment strategy by comparing it with a two-stage stochastic programming model. Sensitivity analysis is performed on the costs of constructing a depot and the penalties for unmet needs. The analysis yielded valuable insights that can be used to enhance emergency rescue operations, supply chain network design, and logistics network design. The research outcome can benefit emergency responders and logistics professionals in optimizing their operations.
]]>Systems doi: 10.3390/systems12030072
Authors: Eun-jung Hyun Brian Tae-Seok Kim
This paper investigates how historical inter-firm syndication networks influence venture capitalists’ (VCs) propensity to invest in startups pursuing novel, uncertain technologies, with a focus on artificial intelligence (AI). We theorize that VCs’ positional attributes within cumulative syndication networks determine their access to external expertise and intelligence that aid AI investment decisions amidst informational opacity. Specifically, reachability to prior AI investors provides referrals and insights transmitted across short network paths to reduce ambiguity. Additionally, VC brokerage between disconnected industry clusters furnishes expansive, non-redundant information that is pivotal for discovering and assessing AI opportunities. Through hypotheses grounded in social network theory, we posit network-based mechanisms that equip VCs to navigate uncertainty when engaging with ambiguous innovations like AI. We test our framework, utilizing comprehensive historical records of global venture capital investments. Analyzing the location information of VC firms in this database, we uncovered a history of 14,751 investments made by Korean and Japanese firms. Using these data, we assembled an imbalanced panel dataset from 1984 to 2022 spanning 230 Korean and 413 Japanese VCs, with 4508 firm-year observations. Negative binomial regression analysis of this dataset reveals how historical relational patterns among venture capital firms foster readiness to evaluate unfamiliar innovations.
]]>Systems doi: 10.3390/systems12030071
Authors: Gabriel Koman Dominika Toman Radoslav Jankal Patrik Boršoš
This literary review examines the current state of research in the field of e-recruitment within the framework of smart government and its implementation in the context of modern public services. We elucidate the concepts of “smart government” as a concept of efficient, technologically supported public administration, and “electronic recruitment” as a process utilizing digital tools in the search and acquisition of suitable candidates for job vacancies. The objective of this review is to provide a brief overview of the current state of smart government, e-HRM (electronic human resource management), and e-recruitment, and analyze their interconnection. The selection of relevant sources followed the PRISMA method. In the context of defining the key functional module of e-HRM, the Grounded Theory Method (GTM) was employed. The final part of the methodological approach involved designing a research problem for future research. Specifically, the review focuses on defining the key functional module of e-HRM and proposes an orientation for future research that should concentrate on the impacts of e-recruitment on the efficiency of human resources within public services. The results of this study can serve as a foundation for future research aimed at optimizing and utilizing e-recruitment in the specific field of smart government.
]]>Systems doi: 10.3390/systems12030070
Authors: Qiang Zhang Jihong Liu Xu Chen
The digital thread, as a crucial technology for industrial digitization and the realization of smart manufacturing, has garnered extensive attention and research in recent years. Furthermore, there is a growing interest in the key technologies supporting the implementation of the digital thread. Given the diversity of product lifecycle models, various definitions, reference architectures, and implementation methods have been proposed to study the digital thread. Thus, this study systematically investigates the current definition, key technologies, and applications of the digital thread. A comprehensive analysis of 94 articles spanning from 2015 to 2023 was conducted, clarifying the definition of the digital thread and its relationship with related terms. Building upon this foundation, this study delves into the research methodologies concerning pivotal technologies in implementing the digital thread (such as authoritative sources of truth, data linkage, and model integration) and scrutinizes various application scenarios of the digital thread, providing a comprehensive summary. Finally, this study presents the research findings along with recommendations for future research endeavors.
]]>Systems doi: 10.3390/systems12030069
Authors: Byung Duk Song Sungbum Jun Seokcheon Lee
Disaster management requires efficient allocation of essential facilities with consideration of various objectives. During the response and recovery phase of disaster management (RRDM), various types of missions occur in multiple periods, and each of them needs different support from facilities. In this study, a bi-objective mathematical model was derived to support multi-period RRDM by optimal allocation of required facilities such as drone stations, shelters, emergency medical facilities, and warehouses according to the mission life cycle. Connectivity between facilities was considered to ensure inter-facility complementarity. For efficient derivation of Pareto solutions, a modified epsilon-constraint algorithm for bi-objective optimization was developed. The algorithm was tested with a realistic disaster simulation scenario using HAZUS 4.0 as a demonstration of the benefits of the proposed approach. With the simulation experiments, the proposed approach was expected to provide efficient operational plans and guidelines to decision makers for the bi-objective optimization problem in RRDM systems. In addition, the consideration of inter-facility connectivity can play an important role in the RRDM, especially for robustness and readiness.
]]>Systems doi: 10.3390/systems12030068
Authors: Pujunqian Fan Qianling Jiang
With the continuous evolution of artificial intelligence technology, AI drawing tools have emerged as highly esteemed instruments in the modern design industry. These tools, owing to their exceptional performance and innovative features, offer creators an unprecedented artistic experience. However, the factors influencing designers’ continuance intention to use AI drawing tools remain ambiguous. This study is grounded in the expectation–confirmation model–information systems continuance (ECM-ISC) model, which is further refined and hypothesized in light of the characteristics of AI drawing tools. Using structural equation modeling, we analyzed 398 valid questionnaire responses. The results elucidated the relationships of key constructs, such as perceived usefulness, perceived ease of use, satisfaction, expectation confirmation, perceived playfulness, perceived switching cost, subjective norms, and perceived risk, on designers’ continuance intention. Notably, perceived ease of use, traditionally considered vital, did not result in a significant influence on continuance intention or perceived usefulness in this research. This insight offers new perspectives for AI drawing tool developers and designers, suggesting that while pursuing user friendliness, broader considerations affecting user decisions should be taken into account. This study not only enriches the theoretical framework but also provides valuable guidance for the practical field.
]]>Systems doi: 10.3390/systems12030067
Authors: Niu Gao Linchi Qu Yuantao Jiang Jian Hou
Environmental responsibility and economic benefits have promoted the development of closed-loop supply chains (CLSCs), and shortages and channels are considered to be two important issues in a CLSC. This paper explores the ordering and channel decisions in a CLSC with new and differentiated remanufactured products; considers the price and sales-effort-dependent demands, as well as the proportion of emergency orders determined by emergency order costs and backorder losses; and establishes integrated and decentralized CLSC game models. We introduce a stochastic sales effort, which affects two types of products. The numerical results show that sales effort and the order quantity of new and remanufactured products exhibit concave and convex functions, respectively. The upper limit of sales effort has a greater impact on supply chain decisions. High sales efforts can serve as a means of coordinating dispersed supply chains. Moreover, in different cases, the decisions of an integrated channel are better than those of a decentralized channel. Finally, whether the supply chain adopts an emergency order strategy depends on the relative cost of emergency orders and out-of-stock costs. According to this research, some management insights are also provided.
]]>Systems doi: 10.3390/systems12030066
Authors: Jihong Yan Xiang Li Siyang Ji
In an era where countries worldwide are emphasizing the process of educational informatization, traditional experimental teaching methods encounter inherent limitations, such as constraints related to the availability of experimental sites and the amount of experimental equipment. Consequently, it becomes a challenge to provide students with an unconstrained, open, and collaborative experimental environment. The digital twin, as a pivotal technology for achieving interactive mapping of the physical world and the information world, possesses attributes characterized by real time interactivity and the seamless fusion of virtual and tangible elements. As a result, various virtual simulation experimental teaching platforms grounded in digital twin technology are now extensively integrated into talent development initiatives. In the context of the “Production Planning and Control” course at Harbin Institute of Technology (HIT), the virtual simulation experimental teaching method is examined, leading to the creation of a virtual simulation instructional platform that blends the virtual and tangible realms, thanks to the foundation of digital twin technology. The virtual simulation experimental teaching platform detailed in this paper, specifically designed for optimizing scheduling within assembly line workshops, represents a pioneering model practice in the integration of digital twin technology into workshop-based experimental instruction and assumes an influential role in enhancing students’ grasp of theoretical knowledge and their capacity for practical innovation.
]]>Systems doi: 10.3390/systems12020065
Authors: Wensheng Wang Zhiliang Liang
This paper aims to tackle the problem of low accuracy in predicting financial distress in Chinese industrial enterprises, attributable to data imbalance and insufficient information. It utilizes annual data on systemic risk indicators and financial metrics of Chinese industrial enterprises listed on the China’s A-share market between 2008 and 2022 to construct the adaptive weighted XGBoost-Bagging model for corporate financial distress prediction. Empirical findings demonstrate that systemic risk indicators possess predictive potential independent of traditional financial information, rendering them valuable non-financial early warning indicators for China’s industrial sector; moreover, they help to enhance the predictive accuracy of various comparative models. The adaptive weighted XGBoost-Bagging model incorporating systemic risk indicators effectively addresses challenges arising from data imbalance and information scarcity, significantly improving the accuracy of financial distress prediction in Chinese industrial enterprises under the 2015 Chinese stock market crash, the Sino-US trade friction, and the COVID-19 epidemic; as such, it can be used as an efficient risk early warning tool for China’s industrial sector.
]]>Systems doi: 10.3390/systems12020064
Authors: Guihua Lin Jiayu Zhang Qi Zhang
This paper considers the agency selling channel in a supply chain under platform service investment. We construct Stackelberg game models to study the impact of the manufacturer’s encroachment strategy on supply chain members. Research results indicate that the encroachment strategy always has a positive impact at the levels of the manufacturer and platform service, which should dynamically change in response to the manufacturer’s action; the platform may actively implement a service strategy without encroachment, while the platform should be cautious in providing services to avoid backlash when encroachment occurs; the high commission rate may prompt the platform to increase the service effort and hinder manufacturer encroachment; when the channel substitution rate is high, both the manufacturer and platform may suffer from it and hence they should slow down their strategy implementation and consider cooperation; when the elasticity coefficient is large and the service cost is high, it may hinder the platform from providing services and the manufacturer may take the opportunity to encroach and thus seize the market.
]]>Systems doi: 10.3390/systems12020063
Authors: Kena Mi Zetao Cui Xinyi Zhu Rulong Zhuang
With the promotion of the “carbon neutrality” and “carbon peak” initiatives, green credit plays an important role in helping enterprises to change their high-pollution, high-energy-consumption production methods and establishing a sound green, low-carbon, and circular economic system. This study used spatial correlation analysis and a fixed effects SDM model to examine the spatiotemporal and causal relationship between green credit levels and enterprise green technology innovation in 271 prefecture level cities in China from 2013 to 2021. It found that (1) green credit and green technology innovation levels are both highest in the eastern region, followed by the central region, and exhibit spatial correlation characteristics. The main types of agglomeration are high–high and low–low agglomeration. (2) Green credit has a significant enhancing effect on green technology innovation in enterprises, and this conclusion still holds after robustness and endogeneity tests. (3) There is significant regional heterogeneity in the impact of green credit on green technology innovation, mainly concentrated in the central and western regions. (4) Green credit can significantly increase enterprise R&D investment and enhance the level of green technology innovation through this channel. Finally, some policy implications are provided to the decision-making departments that can be used for reference.
]]>Systems doi: 10.3390/systems12020062
Authors: Zhen Yue Meisha Zhang Shuran Yang Kai Zhao
In response to Boschma’s concern that the implications of relatedness- and unrelatedness-based diversification strategies lack empirical evidence at disaggregated levels and in the context of the Global South, this study generates a unique dataset at the city level and explores how these smart specialization strategies (S3) may explain digital industry innovations within a specific regional innovation system, i.e., the Yangtze River Delta, China. The findings reveal that both relatedness density and knowledge complexity play a positive role in explaining digital industry innovations. However, the relationship between relatedness and knowledge complexity and its interactive effects on innovation performance are less straightforward. In our study, we found that efficient cooperation between relatedness and complexity can only be achieved if the level of government intervention is moderate. Therefore, the discussion of S3 focuses on more than the dichotomous argument between relatedness and unrelatedness. Many socio-economic factors also impact the effectiveness of these theoretical components within different innovation systems, which are largely overlooked by present studies.
]]>Systems doi: 10.3390/systems12020061
Authors: Yi Zhu Xiaofei Ye Xingchen Yan Tao Wang Jun Chen Pengjun Zheng
Shared autonomous electric vehicles (SAEVs) can offer safer, more efficient, and more environmentally friendly real-time mobility services with advanced autonomous driving technologies. In this study, a multi-agent-based simulation model considering SAEVs’ vehicle range and charging behavior is proposed. Based on real-world datasets from the Luohu District in Shenzhen, China, various scenarios with different fleet sizes, charging rates, and vehicle ranges are established to evaluate the impact of these parameters on parking demand, charging demand, vehicle miles traveled (VMT), and response time in the era of SAEVs. The results show there would be much more charging demand than parking demand. Moreover, a larger fleet size and longer vehicle range would lead to more parking demand, more charging demand, and more VMT while increasing the charging rate can dramatically reduce the charging demand and VMT. Average response time can be reduced by increasing the fleet size or the charging rate, and a larger vehicle range leads to longer response time due to the longer time spent recharging. It is worth noting that the VMT generated from relocating from the previous request destination to the origin of the upcoming request accounts for nearly 90% of the total VMT, which should be addressed properly with appropriate scheduling. A charging policy considering current requests and the availability of charging stations was proposed and verified in terms of reducing the response time by 2.5% to 18.9%.
]]>Systems doi: 10.3390/systems12020060
Authors: Jianxin Zhao Pengbin Gao
Although previous studies have predominantly dealt with innovation ambidexterity, they have only focused on a single innovation activity and overlooked the interaction of innovation activities. Drawing on organizational ambidexterity theory, this study established four types of innovation configurations: dual exploration (technology exploration and business model exploration), business model leveraging (technology exploration and business model exploitation), technology leveraging (technology exploitation and business model exploration), and dual exploitation (technology exploitation and business model exploitation). Using the panel data of 613 listed manufacturing firms in China, this study examined whether and how configurations of ambidextrous innovation affect firm performance in the context of digital transformation. Empirical results provide evidence that a dual exploration and technology leveraging strategy has a positive impact on firm performance, while a dual exploitation and business model leveraging strategy has the opposite effect and is subject to the moderating influence of the level of digitalization. Under high levels of digitalization, the positive effect of the dual exploration strategy on firm performance becomes more significant, while the effects of others are weakened. This study contributes to the organizational ambidexterity literature by providing a finer-grained understanding of the effect of ambidextrous innovation from a configurational perspective. This study also contributes to the digitalization transformation literature by revealing the moderating role of digitalization.
]]>Systems doi: 10.3390/systems12020059
Authors: Kübra Akyol Özcan
Through the examination of the ecological consequences of human actions, policymakers are able to distinguish certain areas in which resource use can be increased and the generation of waste diminished. This study examines the effects of foreign direct investment, gross domestic product, industrialization, renewable energy consumption, and urban population on the ecological footprints in 131 countries between 1997 and 2020. The objective of this study is to establish a thorough understanding of the relationship between these variables and ecological footprints while considering temporal changes from economic and environmental aspects. The analysis of a substantial dataset encompassing many countries aims to uncover recurring patterns and trends that can provide valuable information for the formulation of policies and strategies pertaining to sustainable development on a global level. The study fills a significant gap in the knowledge on the ecological impact of different variables, providing a nuanced understanding of the interdependencies among these factors, thus guiding sustainable development strategies, and promoting global sustainability. The study utilizes quantile regression analysis, a nonparametric estimator, to estimate consistent coefficients. The statistical analysis reveals that FDI, urbanization, and GDP have statistically significant and positive effects on ecological footprints. Industrialization and renewable energy consumption show significant and negative relationships with ecological footprints. The findings of this study contribute to the understanding of the relationships among these variables and provide insight to inform policy and decision-making efforts focused on reducing ecological consequences and advancing sustainable development goals.
]]>Systems doi: 10.3390/systems12020058
Authors: Tetiana Hovorushchenko Ivan Izonin Hakan Kutucu
At the current stage of development and implementation of information technology in various areas of human activity, decisive changes are taking place, as there are powerful technical resources for the accumulation and processing of large amounts of information [...]
]]>Systems doi: 10.3390/systems12020057
Authors: Yan Zhao Qiuying Li Jianlin Lyu
Embedding collaboration networks in the context of open innovation can facilitate firm innovation. Previous studies have not considered the impact of multilevel network structural embedding on firm innovation. In this study, organizational collaboration networks, knowledge networks, and urban collaboration networks are viewed as systems to explore their impact on innovation quantity and innovation quality. We validate the research hypotheses using data from Chinese high-tech firms in the field of artificial intelligence and intelligent manufacturing equipment. The results indicate that structural holes occupied by firms in organizational collaboration networks can increase the innovation quantity and have a U-shaped effect on innovation quality. Knowledge network structural holes and urban collaboration network structural holes moderate the relationship between organizational collaboration network structural holes and innovation quantity and quality. Our findings will help firms to efficiently utilize the advantages of multilevel network structural holes to improve the innovation quantity and innovation quality.
]]>Systems doi: 10.3390/systems12020056
Authors: Federica Asperti Emanuela Foglia Giovanni Pirovano Rossella Pozzi Tommaso Rossi Maurizia Punginelli Fabrizio Schettini
Drug distribution performed through hospital pharmacies facilitates public expenditure savings but incurs higher social costs for patients and caregivers. The widespread presence of community pharmacies could support patient access while also improving drug distribution. The implementation of prescriptive data analyses as constrained optimization to achieve specific objectives, could be also applied with good results in the healthcare context. Assuming the perspective of the Italian National Healthcare Service, the present study, built upon existing research in this field, proposes a decision support tool that is able to define which self-administered drugs for chronic diseases should be distributed by community pharmacies, answering to critical challenges in the case of future pandemics and healthcare emergencies, while also providing suggestions for the institutional decision-making process. Moreover, the tool aids in determining the optimal setup of the drug distribution network, comparing centralized (hospital pharmacies) and decentralized (community pharmacies) approaches, as well as their economic and social implications.
]]>Systems doi: 10.3390/systems12020055
Authors: Yang Shen Xiuwu Zhang
Air pollution severely threatens people’s health and sustainable economic development. In the era of the digital economy, modern information technology is profoundly changing the way governments govern, the production mode of enterprises, and the living behavior of residents. Whether digital technology can bring ecological welfare needs to be further studied. Based on panel data from 269 Chinese cities from 2006 to 2021, this study empirically examines the impact of digital technology on air pollution by using the two-way fixed effect model. The results show that digital technology will significantly reduce the concentration of fine particles in the air and help protect the atmospheric environment. The results are still valid after using the interactive fixed effect model and the two-stage least square method after the robustness test and causality identification. Digital technology can also reduce the air pollution by promoting green innovation, improving energy efficiency, and easing market segmentation. The effect of digital technology on reducing the concentration of fine particles in the air is heterogeneous. Digital technology plays a more substantial role in reducing pollution in resource-based cities and areas with a high degree of modernization of the commodity supply chain. The positive effect of digital technology in reducing air pollution is affected by the amount of air pollutants emitted. When the concentration of PM2.5 in the air is high, the role of digital technology in protecting the atmosphere will be strongly highlighted. This research is a beneficial exploration of protecting the atmospheric environment by using digital technology while building an ecological civilization society. The conclusion will help urban managers, the public, and business operators entirely use modern equipment such as 5G, remote sensing, and the Internet of Things in their respective fields to protect the atmospheric environment.
]]>Systems doi: 10.3390/systems12020054
Authors: Giulio de Felice
In psychotherapy research, the first applications of dynamical systems research (DSR) date back to the 1990s. Over time, DSR has developed three main lines of research: the study of oscillations in synchronization; the study of oscillations between stability and flexibility of process variables (S–F oscillations); the mathematical modeling to analyze the evolution of psychotherapy process. However, the connections among the empirical results and their implications for psychotherapy practice are unclear. For this reason, for the first time in the literature, this work carries out a comprehensive review of all three lines of research, including the main scientific contributions from the 1990s to the present day. For each line of research, the work critically analyzes the results, proposes future developments, and underlines the connections between empirical results and implications for psychotherapy practice. Furthermore, the work highlights the model of change that emerges from the empirical results, and its clinical correlates. In the conclusions, the author summarizes the results and the evolution of psychotherapy process in accordance with the DSR.
]]>Systems doi: 10.3390/systems12020053
Authors: Zixin Dou Yanming Sun
High-tech manufacturing enterprises, as innovative entities, are a key focus of national attention. Currently, such enterprises are facing both internal governance pressure and external institutional pressure. Unlike traditional studies that mostly use regression equations, this article uses the fuzzy-set qualitative comparative analysis method to examine how high-tech manufacturing enterprises can coordinate their internal governance mechanisms and external institutional pressures to achieve optimal innovation. This improves the complex mechanism of the multiple factors jointly explaining corporate innovation, and also helps to elucidate the nonlinear relationship between internal governance factors, external institutional factors, and corporate innovation, effectively enriching research methods and results. However, there has not been any research on the issue of enterprise innovation from the perspective of coordinating the two, which urgently needs to be addressed. This article examines how high-tech manufacturing enterprises can reconcile their internal governance mechanisms with external institutional pressures to achieve optimal innovation. The results showed that (1) a single factor cannot constitute the necessary conditions for innovation in high-tech manufacturing enterprises, but executive and shareholder governance have universality in the innovation in high-tech manufacturing enterprises; (2) in the absence of political advantages, high-tech manufacturing enterprises should focus on the coordinated development of internal governance, making board, executive, and shareholder governance the core conditions for innovative development; (3) with political advantages as the main focus and market attention as a supplement, high-tech manufacturing enterprises promote innovative development by combining executive and shareholder governance. This finding indicates a significant substitution effect between government legitimacy and board governance, and confirms that the importance of obtaining government legitimacy for high-tech manufacturing innovation is higher than market legitimacy. This article enriches the research on enterprise innovation by linking internal corporate governance with external institutional pressure, expands the research on the coordination relationship between institutional pressure and corporate governance, and has enlightening significance in revealing the collaborative path for innovation in high-tech manufacturing enterprises.
]]>Systems doi: 10.3390/systems12020052
Authors: Tomislav Slavina Nedeljko Štefanić
Manufacturing companies are always looking for ways to outperform their competitors. They are constantly trying to improve their efficiency and reduce costs. One method that improves efficiency and maximises the availability of production equipment is total productive maintenance (TPM), which is a lean optimisation philosophy tool that focuses on the optimisation of maintenance. Although TPM is known for improving maintenance, there are many obstacles to its successful implementation. Failure to properly implement TPM can result in additional costs and lost time, and it can have a negative impact on employees. For these reasons, a survey was prepared and conducted among several companies, each involved in a different field of work and having a different number of employees. The main findings of this research are the key factors that can negatively impact the implementation of TPM and lean tools in general, as well as suggestions for improvements that can ensure their successful implementation and sustainability. An analysis was conducted based on the size of each company as well as the job roles within them. The study covers issues that may arise during the implementation of TPM and other lean tools at all levels of the hierarchy in an enterprise and provides guidance on how to manage situations that may prevent the successful application of TPM.
]]>Systems doi: 10.3390/systems12020051
Authors: Ewa Skorupińska Miloš Hitka Maciej Sydor
Furniture production is a specific industrial sector with a high human labor demand, a wide range of materials processed, and short production runs caused by high customization of end products. The difficulty of measuring the aesthetic requirements of customers is also specific to furniture. This review of academic papers identifies and explains effective quality management strategies in furniture production. The reviewed literature highlights a range of quality management methodologies, including concurrent engineering (CE), total quality management (TQM), lean manufacturing, lean six sigma, and kaizen. These strategies encompass a variety of pro-quality tools, such as 5S, statistical process control (SPC), quality function deployment (QFD), and failure mode and effects analysis (FMEA). The strengths of these quality management strategies lie in their ability to enhance efficiency, reduce waste, increase product diversity, and improve product quality. However, the weaknesses concern implementation challenges and the need for culture change within organizations. Successful quality management in furniture production requires tailoring strategies to the specific context of the furniture production industry. Additionally, the importance of sustainability in the furniture industry is emphasized, which entails incorporating circular economy principles and resource-efficient practices. The most important finding from the literature analysis is that early detection and correction of poor quality yields the most beneficial outcomes for the manufacturer. Therefore, it is essential to strengthen the rigor of quality testing and analysis during the early stages of product development. Consequently, a deep understanding of consumer perspectives on required furniture quality is crucial. The review identified two research gaps: (1) the impact of unnecessary product over-quality on the efficiency of furniture production and (2) the influence of replacing CAD drawings with a model-based definition (MBD) format on quality management in furniture production.
]]>Systems doi: 10.3390/systems12020050
Authors: Virginia Ramírez-Herrero Marta Ortiz-de-Urbina-Criado José-Amelio Medina-Merodio
Artificial intelligence, augmented, virtual, and mixed reality applications are improving business tools to increase their efficiency and ability to innovate. Technological innovation offers creative opportunities, but each generation values these advances differently. This study analysed the intergenerational differences and their leadership styles. The research questions are as follows: what are the main characteristics of each generation? And what leadership style is most appropriate for managing generational diversity in companies? Firstly, the main characteristics of each generation—Boomers, Generation X, Millennials, Generation Z, and Generation Alpha—were identified. Secondly, the most representative leadership styles of each generation were analysed. And thirdly, a proposal for a leadership style that can be used to better manage the intergenerational needs and technological demands of companies was presented. The development of leadership styles that take account of all generations can support economic growth and the creation of innovative and sustainable industries, as well as improve social welfare.
]]>Systems doi: 10.3390/systems12020049
Authors: Shupeng Huang Hong Cheng Meiling Luo
Nowadays, the importance of logistics management has been increasingly realized in industry and society. However, current educational approaches in logistics management seem unable to effectively equip students with the necessary skills to cope with practical issues after graduation. Recently, contest-based education has attracted logistics management educators’ attention, but how it can be effectively utilized in this discipline is largely unclear. To fill this gap, this study followed a system approach and analyzed the factors influencing student performance in logistics management contests in China using interpretive structural modelling (ISM) and Matrice d’ Impacts Croisés-Multiplication Appliquée á un Classement (MICMAC). The results suggest that the driving forces for improving student performance in contests are the instructors’ encouragement and their previous experience in instructing contests. Also, the contestants’ previous experience in academic contests, team leadership, and effectiveness of communication between instructors and contestants are critical influencing factors. Based on the results, the educational strategies for effective utilization of contest-based education in logistics management are discussed. This study contributes to the existing literature by using a system modeling approach to clarify the mechanisms of contest-based education adoption in logistics management as well as informing university teachers and higher education institutes about strategies to improve their education quality.
]]>Systems doi: 10.3390/systems12020048
Authors: Enrico Alberti Sergio Alvarez-Napagao Victor Anaya Marta Barroso Cristian Barrué Christian Beecks Letizia Bergamasco Sisay Adugna Chala Victor Gimenez-Abalos Alexander Graß Daniel Hinjos Maike Holtkemper Natalia Jakubiak Alexandros Nizamis Edoardo Pristeri Miquel Sànchez-Marrè Georg Schlake Jona Scholz Gabriele Scivoletto Stefan Walter
The advancements in human-centered artificial intelligence (HCAI) systems for Industry 5.0 is a new phase of industrialization that places the worker at the center of the production process and uses new technologies to increase prosperity beyond jobs and growth. HCAI presents new objectives that were unreachable by either humans or machines alone, but this also comes with a new set of challenges. Our proposed method accomplishes this through the knowlEdge architecture, which enables human operators to implement AI solutions using a zero-touch framework. It relies on containerized AI model training and execution, supported by a robust data pipeline and rounded off with human feedback and evaluation interfaces. The result is a platform built from a number of components, spanning all major areas of the AI lifecycle. We outline both the architectural concepts and implementation guidelines and explain how they advance HCAI systems and Industry 5.0. In this article, we address the problems we encountered while implementing the ideas within the edge-to-cloud continuum. Further improvements to our approach may enhance the use of AI in Industry 5.0 and strengthen trust in AI systems.
]]>Systems doi: 10.3390/systems12020047
Authors: Negin Moghadasi Rupa S. Valdez Misagh Piran Negar Moghaddasi Igor Linkov Thomas L. Polmateer Davis C. Loose James H. Lambert
Artificial intelligence (AI) is advancing across technology domains including healthcare, commerce, the economy, the environment, cybersecurity, transportation, etc. AI will transform healthcare systems, bringing profound changes to diagnosis, treatment, patient care, data, medicines, devices, etc. However, AI in healthcare introduces entirely new categories of risk for assessment, management, and communication. For this topic, the framing of conventional risk and decision analyses is ongoing. This paper introduces a method to quantify risk as the disruption of the order of AI initiatives in healthcare systems, aiming to find the scenarios that are most and least disruptive to system order. This novel approach addresses scenarios that bring about a re-ordering of initiatives in each of the following three characteristic layers: purpose, structure, and function. In each layer, the following model elements are identified: 1. Typical research and development initiatives in healthcare. 2. The ordering criteria of the initiatives. 3. Emergent conditions and scenarios that could influence the ordering of the AI initiatives. This approach is a manifold accounting of the scenarios that could contribute to the risk associated with AI in healthcare. Recognizing the context-specific nature of risks and highlighting the role of human in the loop, this study identifies scenario s.06—non-interpretable AI and lack of human–AI communications—as the most disruptive across all three layers of healthcare systems. This finding suggests that AI transparency solutions primarily target domain experts, a reasonable inclination given the significance of “high-stakes” AI systems, particularly in healthcare. Future work should connect this approach with decision analysis and quantifying the value of information. Future work will explore the disruptions of system order in additional layers of the healthcare system, including the environment, boundary, interconnections, workforce, facilities, supply chains, and others.
]]>Systems doi: 10.3390/systems12020046
Authors: Fuping Chen Rongyu Li
Research focuses mainly on the impact of automation on employment and wages but pays little attention to its impact on employee job satisfaction, especially in the context of the Global South. Using survey data from China, this article investigates the impact of automation on employee job satisfaction due to the effects of job improvement and position replacement stress. The results indicate that automation can improve the job satisfaction of individual employees but reduces the job satisfaction of employees with a position that can be replaced easily by automation. The improvement and replacement effects coexist within the impact of automation. Through a structural equation model, this article finds that the improvement effect arises from an increase in job income, safety, and ability, whereas replacement stress is produced through the mediating effect of job stress and boredom. The heterogeneity analysis shows that the improvement effect is present in young employees with low job skills, position competency, and experience requirements, while replacement stress occurs in middle-aged and elderly employees with high job skills and high position competency and experience requirements. Our study provides evidence for the construction of an internal labor market in enterprises and labor policy interventions in the digital age.
]]>Systems doi: 10.3390/systems12020045
Authors: Muhammad Ayyaz Ahmad George Baryannis Richard Hill
Despite a profusion of literature on complex adaptive system (CAS) definitions, it is still challenging to definitely answer whether a given system is or is not a CAS. The challenge generally lies in deciding where the boundaries lie between a complex system (CS) and a CAS. In this work, we propose a novel definition for CASs in the form of a concise, robust, and scientific algorithmic framework. The definition allows a two-stage evaluation of a system to first determine whether it meets complexity-related attributes before exploring a series of attributes related to adaptivity, including autonomy, memory, self-organisation, and emergence. We demonstrate the appropriateness of the definition by applying it to two case studies in the medical and supply chain domains. We envision that the proposed algorithmic approach can provide an efficient auditing tool to determine whether a system is a CAS, also providing insights for the relevant communities to optimise their processes and organisational structures.
]]>Systems doi: 10.3390/systems12020044
Authors: Jialing Chen Linfan Pan Ren Zhou Qianling Jiang
With the continuous development of digital technology, the widespread use of virtual spokespersons to promote city images is becoming increasingly prevalent. This study responds to this trend by employing a factor analysis and entropy weight methodology to explore the different dimensions and priorities in shaping the image of virtual city spokespersons in China. The aim is to offer insights into the design strategies and directions for shaping the image of virtual city spokespersons. For the research, we first conducted a literature review and semi-structured interviews to investigate the requirements of users in mainland China and Hong Kong regarding the image shaping of virtual city spokespersons. Building upon this groundwork, a questionnaire was designed and distributed, and it successfully gathered 512 valid responses. Subsequently, a factor analysis was utilized to identify eight key dimensions in shaping the images of Chinese virtual city spokespersons: “Design elements”, “Anthropomorphism”, “Evolutionary”, “Emotionalization”, “Narrativity”, “Culturalism”, “Interactivity”, and “Reliability”. Then, the entropy weighting method was applied to analyze the weights of each indicator within these dimensions. The results revealed that “Design elements” have the highest priority in shaping the image of virtual city spokespersons, followed by “Anthropomorphism”, “Emotionalization”, “Evolutionary”, “Culturalism”, “Narrativity”, “Reliability”, and “Interactivity”. Based on these findings, a series of design optimization strategies are proposed, including but not limited to shaping visually appealing images aligned with user perceptions, establishing emotional connections with users, and meeting the functional experience needs of users. These strategies not only contribute to the image shaping of virtual city spokespersons, but also provide vital guidance for innovative directions in promoting the publicity and marketing of Chinese cities.
]]>Systems doi: 10.3390/systems12020043
Authors: Kang Li Xiaer Xiahou Zhou Wu Peng Shi Lingyi Tang Qiming Li
When confronted with rainstorms and flood disturbances, the operational processes of urban metro systems demonstrate vulnerabilities to attacks, inadequate resistance, and sluggish recovery characteristics. The flood resilience of UMS operational processes requires urgent enhancements. This paper aims to enhance the flood resilience of urban metro operation processes by proposing a three-stage PEL resilience enhancement framework: prevention resilience, response resilience, and learning resilience. Additionally, it summarizes the influencing factors on UMS flood resilience from five dimensions: natural-physical-social-management-economic (NPSME). By employing system dynamics as a simulation tool, this study elucidates the logical interconnections among these influential factors. Furthermore, by utilizing economic change conditions as an illustrative example, it effectively simulates the response characteristics of both standardized benchmark scenarios and economic change scenarios. Based on these simulation results, corresponding strategies for flood resilience enhancement are proposed to offer valuable insights for metro operation management. The Nanjing metro system was taken as a case study, where relevant historical data were collected and strategies were simulated for different development scenarios to validate the effectiveness and rationality of the proposed method for enhancing resilience. The simulation results demonstrate that changes in economic conditions and population structure are the primary factors influencing the enhancement of flood resilience in UMS operations.
]]>Systems doi: 10.3390/systems12020042
Authors: Ana Laroca Maria Teresa Pereira Francisco J. G. Silva Marisa J. G. P. Oliveira
The following work aims to show how a combination of continuous improvement (CI) and Lean tools can reduce waste and process variability along an air-conditioned pipe production line (PL), calculate its capacity, and improve its efficiency to achieve the expected productivity. A variability study focused on the PL’s balancing was conducted to identify and reduce possible bottlenecks, as well as to evaluate the line’s real capacity. Several layout improvements were made to upgrade the line’s operational conditions and reduce unnecessary movements from the workers. The Constant Work-In-Progress (CONWIP) methodology was also applied to ease the component’s production management in the preparation stage. Additional modifications were implemented to support production and to contribute to the increases in efficiency, quality, and safety on the line. The results revealed an increase in the line’s capacity, associated with an efficiency rise from 28.81% to 47.21% from February to June 2023. The overall equipment effectiveness (OEE) in the same period increased by 18%. This demonstrates that, by interactively applying a mix of tools and methodologies, it is possible to achieve better performance of production lines. This knowledge can help scholars and practitioners to apply the same set of tools to solve usual problems in cell and production lines with performance below expectations.
]]>Systems doi: 10.3390/systems12020041
Authors: Tingli Liu Qianying Wang Songling Yang Qianqian Shi
We adopt a multilayer networks approach to assess how network structural embeddedness affects corporate technological innovation. Our findings indicate an annual increase in both single-layer and multilayer networks, although adoption of the latter by Chinese listed companies is comparatively low. We found that structural embeddedness of multilayer networks positively impacts corporate technological innovation. By reducing uncertainty within the internal environment, these networks bolster technological innovation. Moreover, such embeddedness notably spurs innovation in non-state-owned companies and those with greater internal transparency and robust external oversight. Our analysis reveals an intermediate effect where structural embeddedness in multilayer networks influences innovation. Our work provides new insights into enhancing innovation capacity via network embeddedness and supplies empirical data on utilizing network resources for innovation. We also offer actionable guidance and policy advice for managers, investors, and policymakers, especially relevant amidst economic transformation and pursuit of technological self-reliance of China.
]]>Systems doi: 10.3390/systems12020040
Authors: Adekunle Mofolasayo
Human factors play a huge role in road traffic safety. Research has found that a huge proportion of traffic crashes occur due to some form of human error. Improving road user behavior has been the major strategy that has been emphasized for improving road traffic safety. Meanwhile, despite the training efforts, and testing for drivers, the global status of road traffic safety is alarming. This research highlights the seriousness of human factors on road traffic safety and provides actionable strategies to greatly reduce the negative impact of human factors on road traffic safety. Motor vehicle safety data that were made available online by the U.S. Bureau of Transportation Statistics were reviewed to evaluate the severity of traffic collisions. To evaluate the extent of human factors in motor vehicle traffic fatalities, data for Canadian motor vehicle traffic collision statistics were reviewed. The study confirms that human factors (such as driver distraction, fatigue, driving under the influence of drugs and alcohol etc.) play a huge role in road traffic fatalities. The need for a reasonable degree of automation to help reduce the impacts of human factors on road safety and recommendations aimed at providing widespread support for a reasonable degree of automation systems in driving tasks are presented. Actionable strategies that can be implemented by policymakers to reduce global road traffic fatalities are also presented.
]]>Systems doi: 10.3390/systems12020039
Authors: Henghao Fan Hongmin Li Xiaoyang Gu Zhongqiu Ren
Timely short-term spatial air quality forecasting is essential for monitoring and prevention in urban agglomerations, providing a new perspective on joint air pollution prevention. However, a single model on air pollution forecasting or spatial correlation analysis is insufficient to meet the strong demand. Thus, this paper proposed a complex real-time monitoring and decision-making assistance system, using a hybrid forecasting module and social network analysis. Firstly, before an accurate forecasting module was constructed, text sentiment analysis and a strategy based on multiple feature selection methods and result fusion were introduced to data preprocessing. Subsequently, CNN-D-LSTM was proposed to improve the feature capture ability to make forecasting more accurate. Then, social network analysis was utilized to explore the spatial transporting characteristics, which could provide solutions to joint prevention and control in urban agglomerations. For experiment simulation, two comparative experiments were constructed for individual models and city cluster forecasting, in which the mean absolute error decreases to 7.8692 and the Pearson correlation coefficient is 0.9816. For overall spatial cluster forecasting, related experiments demonstrated that with appropriate cluster division, the Pearson correlation coefficient could be improved to nearly 0.99.
]]>Systems doi: 10.3390/systems12020038
Authors: Abdelmoula Khdoudi Tawfik Masrour Ibtissam El Hassani Choumicha El Mazgualdi
In the context of Industry 4.0 and smart manufacturing, production factories are increasingly focusing on process optimization, high product customization, quality improvement, cost reduction, and energy saving by implementing a new type of digital solutions that are mainly driven by Internet of Things (IoT), artificial intelligence, big data, and cloud computing. By the adoption of the cyber–physical systems (CPSs) concept, today’s factories are gaining in synergy between the physical and the cyber worlds. As a fast-spreading concept, a digital twin is considered today as a robust solution for decision-making support and optimization. Alongside these benefits, sectors are still working to adopt this technology because of the complexity of modeling manufacturing operations as digital twins. In addition, attempting to use a digital twin for fully automatic decision-making adds yet another layer of complexity. This paper presents our framework for the implementation of a full-duplex (data and decisions) specific-purpose digital twin system for autonomous process control, with plastic injection molding as a practical use-case. Our approach is based on a combination of supervised learning and deep reinforcement learning models that allows for an automated updating of the virtual representation of the system, in addition to an intelligent decision-making process for operational metrics optimization. The suggested method allows for improvements in the product quality while lowering costs. The outcomes demonstrate how the suggested structure can produce high-quality output with the least amount of human involvement. This study shows how the digital twin technology can improve the productivity and effectiveness of production processes and advances the use of the technology in the industrial sector.
]]>Systems doi: 10.3390/systems12020037
Authors: Mohammad Arani Mohsen Momenitabar Tazrin Jahan Priyanka
This research aims to study a real-world example of the unrelated parallel machine scheduling problem (UPMSP), considering job-splitting, inventories, shortage, and resource constraints. Since the nature of the studied optimization problem is NP-hard, we applied a metaheuristic algorithm named Grey Wolf Optimizer (GWO). The novelty of this study is fourfold. First, the model tackles the inventory problem along with the shortage amount to avoid the late fee. Second, due to the popularity of minimizing completion time (Makespan), each job is divided into small parts to be operated on various machines. Third, renewable resources are included to ensure the feasibility of the production process. Fourth, a mixed-integer linear programming formulation and the solution methodology are developed. To feed the metaheuristic algorithm with an initial viable solution, a heuristic algorithm is also fabricated. Also, the discrete version of the GWO algorithm for this specific problem is proposed to obtain the results. Our results confirmed that our proposed discrete GWO algorithm could efficiently solve a real case study in a timely manner. Finally, future research threads are suggested for academic and industrial communities.
]]>Systems doi: 10.3390/systems12010036
Authors: Ziyan Wang Tianjian Yang
The platform owner promotes the transaction between independent sellers and consumers, while entering the marketplace of independent sellers to compete with them for consumers. Faced with the threat of platform encroachment, independent sellers establish their own competitive advantages through ex ante category quality selection and ex post product differentiation. This study discusses how independent sellers should determine product positioning (including vertical and horizontal dimensions) in the face of platform category encroachment. We establish a game model and determine the best strategy. In addition, we develop a multi-agent model to reach conclusions for more complex market situations. We show that when the consumer’s platform preference is low, the independent seller is willing to locate in the high-end product market; otherwise, the independent seller is willing to locate in the low-end product market. In a competitive environment, when consumers’ ideal preferences are concentrated, the independent seller vertically positions in the low-end product market and horizontally positions close to the concentrated area of consumers’ ideal preferences. Similarly, the platform owner is more likely to encroach on the low-end product market. However, the independent seller positions in the high-end product market with greater horizontal differentiation, and the platform owner’s motivation to encroach is weakened.
]]>Systems doi: 10.3390/systems12010035
Authors: Hao Zhang Weihong Chen Jie Peng Yuhan Wang Lianghui Zeng Peiao Gao Xiaowen Zhu Xingwei Li
Pricing decisions for construction and demolition waste recycling are severely hampered by consumer uncertainty in assessing the value of recycled building materials. This paper uses a construction and demolition waste (CDW) recycling utilization model that consists of a building materials manufacturer and a building materials remanufacturer and compares both the prices and the profits under different carbon tax scenarios, i.e., consumer risk-averse and risk-neutral scenarios. The main conclusions are as follows. (1) The optimal price of traditional products is always negatively correlated with consumer risk aversion. Unlike traditional products, the optimal price of recycled building materials is negatively related to the degree of consumer risk aversion in the case of a low carbon tax; the opposite conclusion is obtained in the case of a high carbon tax. (2) When the abatement cost coefficient is below the threshold and the carbon tax is low, the profits of the building materials manufacturer and remanufacturer show a U-shaped trend with consumer risk aversion; in the case of a high carbon tax, the profits of the two enterprises are positively correlated with consumer risk aversion. In addition, when the abatement cost coefficient is above the threshold, there is an interval in which the profits of the building materials manufacturer are positively correlated with consumer risk aversion in the case in which the carbon tax satisfies this interval. In all the other cases, there is a U-shaped trend in profits and consumer risk aversion levels for both the building materials manufacturer and the remanufacturer.
]]>Systems doi: 10.3390/systems12010034
Authors: Richard John Logan Robert Y. Cavana Bronwyn E. Howell Ian Yeoman
This research addresses the strategic issue of why key decision-makers fail to foresee potential extreme ‘black swan’ events. Following a review of the literature, a conceptual framework is developed that identifies two types of organisational blindness that are reflected in Tetlock’s hedgehog cognitive thinking style, being the oversimplification of uncertainty (e.g., inductive biases) and an unquestioned, top-down, reference narrative. This framework is tested using a case study approach and qualitative analysis of secondary data sources available from the Royal Commission of Inquiry and other published reports following the 2010 methane explosion at the Pike River Coal Ltd.’s mine (Pike) in New Zealand, that killed 29 miners and caused the loss of all funds invested. The results indicate that the combined effect of both blindnesses meant that Pike’s collective intelligence was limited, and for the three key decision-makers at the Pike River mine, some type of extreme ‘black swan’ event was apparently inevitable. This research provides theoretical and practical contributions to the analysis of business and public policy decision-making under uncertainty.
]]>Systems doi: 10.3390/systems12010033
Authors: Linlan Zhang
The e-books industry is mature, and audiobooks are becoming increasingly popular. More and more publishers are coming to realize that audiobooks could be a potential revenue driver and intend to release audiobooks. Considering that there is a certain substitutability between e-books and audiobooks, publishers need to decide how to release a book in its audible version and its e-book version into the market. In this paper, we incorporate the discount factor and the consumers’ acceptance level for audiobooks into the consumer utility by dividing consumers into two types, high-value type and low-value type, and construct two different release models: releasing the audiobook and the e-book simultaneously and releasing the audiobook after the e-book. Using an optimization tool, we investigate pricing strategies of a monopolistic publisher under two different release models. By comparing the theoretical results of the two models, we find that when the consumers’ patience exceeds a certain threshold, releasing a book in its audible version after its e-book version is better for the publisher, and the publisher should adopt a skimming (refers to decreasing markups over time) pricing strategy for the e-book in this case. Further, the publisher should set a higher price for the audiobook than the e-book, whether or not they release the audiobook after the e-book. In addition, we conduct a numerical analysis to investigate how the discount factor, the percentage of high-value consumers, and the high-value consumers’ acceptance level for audiobooks affect the consumer surplus and the social welfare. This study offers publishers some managerial insights into the complex issues involving pricing and release strategies.
]]>Systems doi: 10.3390/systems12010032
Authors: Mingli Zhang Yanan Wang Yijie Zhang
Supply chain coordination has been a research hot spot in supply chain management. This paper constructs a secondary supply chain system. Taking the abatement of the bullwhip effect and the double marginal effect as the coordination objective, a simulation study of supply chain decision coordination was conducted using system dynamics. First, by controlling the lead time, it was found that in the decentralized decision-making model, the profit of the supplier and the whole supply chain increases with the shortening of the lead time, and vice versa for the retailer. In the centralized decision-making model with the addition of information sharing and contract, it was found that the retailer’s profit is consistent with the trend of the supplier and the supply chain as a whole, and the supplier’s profit is lower than that of decentralized decision making in the pre-cooperation period. In addition, it is also found that adjusting the contract parameters can effectively improve the situation. Finally, the above models were analyzed for supply chain coordination decisions based on two scenarios: “cooperative stability” or “balance of effects”.
]]>Systems doi: 10.3390/systems12010031
Authors: Wenyu Chen Weimin Li Tao Zhang
With the increasing complexity and frequency of interactions among a large number of heterogeneous nodes within a combat system of systems (SoS), evaluating the capability of the SoS to withstand external attacks and interferences has become an increasingly challenging and urgent issue. However, the complexity of a combat SoS often brings about difficulties in quantitative analysis. This paper proposes a method to assess the resilience of a combat SoS. Firstly, a network model of a combat SoS is constructed, taking into account multi-functional composite nodes. Secondly, the combat capability model of the combat SoS is built based on the capabilities of the kill chains and kill networks. Lastly, an approach is proposed to calculate the combat capability of the system based on the network’s two-terminal connectivity. Moreover, based on the SoS’s combat capability, the resilience is analyzed from the dimensions of anti-destruction, survival, and recovery. The simulation experiments show that, compared to other methods, the method in this paper for calculating the combat capability of the combat SoS does not depend on the number of kill chains and aligns with the combat processes and actual patterns. Additionally, the resilience measurement method can effectively analyze its capability to cope with external interferences.
]]>Systems doi: 10.3390/systems12010030
Authors: Wei Zhou Zhijie Lyu Shixiang Chen
The transformation of the government into a digital entity is imperative, serving not only as a catalyst for the modernization of China’s governance system and capacity but also as a cornerstone for advancing the digital economy and the establishment of a digital China. This paper presents a multi-level analytical framework designed to assess the digital transformation performance of local governments. Utilizing a dataset comprising macro-regional and micro-individual data from Hubei province, we conduct an extensive analysis to examine the underlying mechanisms that influence the digital transformation performance of local governments and employ the hierarchical linear model (HLM) as the primary analytical instrument. The results of our analysis show that individual-level government–citizen interactions, government image, and district-level department collaborative capacities exert substantial and positive influences on the digital transformation performance of local governments. Furthermore, it is worth noting that department collaborative capacity plays a significant and positive moderating role in the relationship between government image and the digital transformation performance of local governments. These findings not only offer valuable insights for optimizing policy formulation but also contribute to a more comprehensive understanding of the mechanisms underlying the digital transformation performance of local governments.
]]>Systems doi: 10.3390/systems12010029
Authors: Srdjan Kesić
This article argues that complexity scientists have been searching for a universal complexity in the form of a “theory of everything” since some important theoretical breakthroughs such as Bertalanffy’s general systems theory, Wiener’s cybernetics, chaos theory, synergetics, self-organization, self-organized criticality and complex adaptive systems, which brought the study of complex systems into mainstream science. In this respect, much attention has been paid to the importance of a “reductionist complexity science” or a “reductionist theory of everything”. Alternatively, many scholars strongly argue for a holistic or emergentist “theory of everything”. The unifying characteristic of both attempts to account for complexity is an insistence on one robust explanatory framework to describe almost all natural and socio-technical phenomena. Nevertheless, researchers need to understand the conceptual historical background of “complexity science” in order to understand these longstanding efforts to develop a single all-inclusive theory. In this theoretical overview, I address this underappreciated problem and argue that both accounts of the “theory of everything” seem problematic, as they do not seem to be able to capture the whole of reality. This realization could mean that the idea of a single omnipotent theory falls flat. However, the prospects for a “holistic theory of everything” are much better than a “reductionist theory of everything”. Nonetheless, various forms of contemporary systems thinking and conceptual tools could make the path to the “theory of everything” much more accessible. These new advances in thinking about complexity, such as “Bohr’s complementarity”, Morin’s Complex thinking, and Cabrera’s DSRP theory, might allow the theorists to abandon the EITHER/OR logical operators and start thinking about BOTH/AND operators to seek reconciliation between reductionism and holism, which might lead them to a new “theory of everything”.
]]>Systems doi: 10.3390/systems12010028
Authors: Tanatorn Tanantong Piriyapong Wongras
Recruitment is a fundamental aspect of Human Resource Management to drive organizational performance. Traditional recruitment processes, with manual stages, are time-consuming and inefficient. Artificial Intelligence (AI), which demonstrates its potential in various sectors such as healthcare, education, and notable cases of ChatGPT, is currently reshaping recruitment by automating tasks to improve efficiency. However, in Thailand, where there is a growing demand for talents, the application of AI in recruitment remains relatively limited. This study focuses on human resources (HR) and recruitment professionals in Thailand, aiming to understand their perspectives on the integration of AI in recruitment. It utilized the Unified Theory for Acceptance and Use of Technology (UTAUT) model, customized to suit the specific requirements of Thailand recruitment practices. The study explores the factors influencing users’ intention to adopt AI in recruitment. Survey questionnaire items were created based on prior literature and refined with insights from HR and recruitment experts to ensure applicability in the context of recruitment in Thailand. A survey involving 364 HR and recruiting professionals in the Bangkok metropolitan area supplied comprehensive responses. The study reveals that several factors, including perceived value, perceived autonomy, effort expectancy, and facilitating conditions, significantly impact the intention to adopt AI for recruitment. While social influence and trust in AI technology do not have a direct influence on intention, social influence directly affects perceived value. Trust in AI technology positively influences Effort Expectancy. This study provides valuable benefits for HR and recruitment professionals, organizations, and AI developers by offering insights into AI adoption and sustainability, enhancing recruitment processes and promoting the effective use of AI tools in this sector.
]]>Systems doi: 10.3390/systems12010027
Authors: Daniel Dunbar Thomas Hagedorn Mark Blackburn Dinesh Verma
Individual model verification is a common practice that increases the quality of design on the left side of the Vee model, often before costly builds and prototypes are implemented. However, verification that spans multiple models at higher levels of abstraction (e.g., subsystem, system, mission) is a complicated endeavor due to the federated nature of the data. This paper presents a tool-agnostic approach to higher-level verification tasks that incorporates tools from Semantic Web Technologies (SWTs) and graph theory more generally to enable a three-pronged verification approach to connected data. The methods presented herein use existing SWTs to characterize a verification approach using ontology-aligned data from both an open-world and closed-world perspective. General graph-based algorithms are then introduced to further explore structural aspects of portions of the graph. This verification approach enables a robust model-based verification on the left side of the Vee model to reduce risk and increase the visibility of the design and analysis work being performed by multidisciplinary teams.
]]>Systems doi: 10.3390/systems12010026
Authors: Ikpe Justice Akpan Onyebuchi Felix Offodile
From the first to the fourth industrial revolutions (4IR) or Industry 4.0 (i4.0), the manufacturing sector has always been at the forefront of innovation and digital technology adoption. However, 4IR or i4.0 comes with diverse and integrated technologies that tend to sweep off all the old orders. This study undertakes a science mapping of research on the role of virtual reality simulation (VRSIM) in manufacturing in the 4IR, which reveals several roles and benefits. The analysis of the conceptual structure of relevant literature highlights the contexts, applications, and relevance of VRSIM in the i4.0 era, including its potent role in predictive modeling and simulation, digital twin and predictive maintenance, additive manufacturing and 3D printing, and virtual manufacturing and immersive virtual digital factory simulation. VRSIM also offers a realistic virtual environment for mapping human–robot collaboration in different manufacturing environments, such as aircraft and automotive assembly lines, evaluating processes, training factory workers on safety, and assessing workers’ ergonomics in digital production and operations. VRSIM in manufacturing offers active research activities, with increasing literature publications and impacts attracting core sources in industrial engineering, manufacturing systems, production and operations, and information technology.
]]>Systems doi: 10.3390/systems12010025
Authors: Ming Bai Yanru Chen Ye Hong Zhongqi Yang
Both executive corruption and corporate innovation are important factors affecting corporate development. This paper explores the impact of executive corruption on corporate innovation and examines the mechanism of their effects from the perspective of financing constraints. It is found that executive corruption significantly inhibits corporate innovation in general. In addition, financing constraints act as a mediator between executive corruption and corporate innovation, i.e., executive corruption exacerbates the financing constraints faced by firms and affects the access to and allocation of corporate resources, thus leading to a decrease in corporate innovation inputs and outputs. Further, the inhibitory effect of executive corruption on firm innovation is more pronounced in firms with low quality internal controls, strong professional background of executives, low quality external audit, low shareholding of institutional investors, strong political affiliation, and state-owned enterprises.
]]>Systems doi: 10.3390/systems12010024
Authors: Thi-Thu-Huyen Vu Tai-Woo Chang Haejoong Kim
The management of product quality is a crucial process in factory manufacturing. However, this approach still has some limitations, e.g., depending on the expertise of the engineer in evaluating products and being time consuming. Various approaches using deep learning in automatic defect detection and classification during production have been introduced to overcome these limitations. In this paper, we study applying different deep learning approaches and computer vision methods to detect scratches on the surface of microfasteners used in rechargeable batteries. Furthermore, we introduce an architecture with statistical quality control (SQC) to continuously improve the efficiency and accuracy of the product quality. The proposed architecture takes advantage of the capability of deep learning approaches, computer vision techniques, and SQC to automate the defect detection process and quality improvement. The proposed approach was evaluated using a real dataset comprising 1150 microfastener surface images obtained from a factory in Korea. In the study, we compared the direct and indirect prediction methods for predicting the scratches on the surface of the microfasteners and achieved the best accuracy of 0.91 with the indirect prediction approach. Notably, the indirect prediction method was more efficient than the traditional one. Furthermore, using control charts in SQC to analyze predicted defects in the production process helped operators understand the efficiency of the production line and make appropriate decisions in the manufacturing process, hence improving product quality management.
]]>Systems doi: 10.3390/systems12010023
Authors: Xiaozhen Liang Yingying Wang Mingge Yang
This paper introduces a hybrid framework for port container throughput forecasting, which is essential in global trade and transportation systems. It uses a multidisciplinary method that combines artificial intelligence, link prediction, and complex networks. To better grasp the interconnection and dynamics of port operations, time series data are first transformed using complex network theory into a network structure. The framework applies 13 similarity metrics, encompassing various aspects of network structural similarity, to form a feature set representing the complex port operation network. The most effective features are selected using the maximum relevance minimum redundancy (mRMR) method, adhering to systems theory’s efficiency principles. These features are processed through SVM, DNN, and LSTM models for link prediction, which is crucial for forecasting in port logistics. Finally, the methodology concludes with regression analysis to obtain container throughput forecasts, which is a key metric in port systems management. Case studies of Shanghai Port and Shenzhen Port validate the framework’s effectiveness, demonstrating a significant improvement in forecasting accuracy over the baseline models. This study contributes to systems analysis by showcasing a hybrid, AI-enhanced approach for managing and forecasting critical aspects of maritime trade systems.
]]>Systems doi: 10.3390/systems12010022
Authors: Ruiyue Lin Xinyuan Wang Yu Jiang
Due to persistent technological impacts on ecological efficiency (eco-efficiency) and variations in economic power and resource endowments among regions, considering regional and temporal heterogeneity becomes imperative. Ecosystems, often divided into economic production and environmental governance stages, necessitate a holistic assessment incorporating regional, temporal heterogeneity and stage distinctions. To address potential issues of a technology gap ratio (TGR) exceeding 1 within a two-stage network structure with dual heterogeneity, we introduce a segmented projection three-layer meta-frontier analysis method. In empirical study, we systematically examined eco-efficiency, emissions inefficiency and technology gaps across management, regional and temporal dimensions in 30 Chinese provinces from 2016 to 2020. Findings reveal disparities in management eco-efficiency, with the central provinces outperforming the east. Regional differences indicate advanced technology in the east, contributing to superior eco-efficiency. Temporal analysis highlights the positive role of scientific and technological development. Emissions inefficiency improvements are noted, necessitating attention toward management and regional technology levels. Eastern provinces exhibit superior emissions efficiency, emphasizing the role of regional and technological development. Recommendations include prioritizing environmental governance, strengthening regional collaborations and implementing policies to bridge technology gaps.
]]>Systems doi: 10.3390/systems12010021
Authors: Qinqin Chen Xingneng Xia Yuji Hui Sheng Zhang
Colleges and universities play a crucial role in fostering innovation, making it essential to explore effective strategies for promoting innovation at the institutional policy level. This paper focuses on the establishment of intellectual property model cities as a starting point and conducts an empirical analysis using innovation data from 234 cities and 942 colleges and universities between 2007 and 2017. By constructing a multi-temporal double-difference model, this study reveals that the establishment of intellectual property model cities effectively fosters innovation in colleges and universities. Further analysis demonstrates that this promotional effect is particularly significant in the western region, key cities, and key colleges and universities, as well as in the fields of invention and utility model patents. These conclusions withstand a series of robustness tests, confirming their validity. This study reveals that the national intellectual property pilot city policy has a significant influence on university innovation. It achieves this by encouraging investment in research and development and enhancing collaboration in innovation. The findings of this study provide important policy suggestions for maximizing the innovation potential of the intellectual property model city policy. This, in turn, can contribute to economic transformation, upgrading, and the promotion of innovation development in China.
]]>Systems doi: 10.3390/systems12010020
Authors: Nicholas Frick Jan Terwolbeck Benjamin Seibel Joachim Metternich
The value stream method, a key tool in industry to analyze and visualize value streams in production, aims to holistically optimize process steps, reduce waste, and achieve continuous material flow. However, this method primarily relies on data from a single on-site inspection, which is subjective and represents just a snapshot of the process. This limitation can lead to uncertainty and potentially incorrect decisions, especially in industries producing customer-specific products. The increasing digitization in production offers a solution to this limitation by supporting the method through data provision. The concept of the digital shadow emerges as a key tool that systematically captures, processes, and integrates necessary data into a model to enhance traditional value stream mapping. This addresses the method’s shortcomings, especially in heterogeneous IT landscapes and complex value streams. To effectively implement the digital shadow this study identifies concepts of digital shadows and their key components and evaluates them for their relevance in industrial environments using an expert study. Based on the results, a design model is defined. This model entails guidelines to support companies with the practical implementation of the digital shadow of a value stream. Lastly, the model is evaluated on a realistic value stream in a learning factory.
]]>Systems doi: 10.3390/systems12010019
Authors: Chia-Ching Tsai Chun-Ling Lin Yu-Huan Chen
The global economy has been profoundly affected by the COVID-19 pandemic. This impact is particularly evident in the restaurant industry, where restaurant traffic has dropped significantly, leading to a decline in revenue. In response to the impact of the pandemic, non-contact services, such as overseas delivery and door-to-door delivery, have been implemented to reduce interpersonal contact and minimize the spread of the virus. Contactless service not only provides consumers with more choices and convenience but is also an important means of livelihood for restaurant service staff during the pandemic. This study takes the Taiwanese chain restaurant Kura Sushi as an example to explore the impact of service contacts on authenticity consumption and experience value in the context of non-contact services. A total of 318 valid responses to a questionnaire were collected and analyzed using IBM SPSS 25.0 and IBM AMOS 25.0 software. This study made the following findings: (1) service staff performance has a significant positive impact on authenticity perception; (2) the physical restaurant environment has a positive impact on consumers’ perceptions of authenticity; (3) active interactions with other customers significantly enhance the sense of reality; (4) experience values significantly promote real consumption; and (5) experience values also significantly affect consumer satisfaction.
]]>Systems doi: 10.3390/systems12010018
Authors: Zirui Li Faizan Faheem Stephan Husung
Collaborative Model-based Systems Engineering between companies is becoming increasingly important. The utilization of the modeling possibilities of the standard language SysML v2 and the multilateral data exchange via Dataspaces open new possibilities for efficient collaboration. Based on systemic approaches, a modeling concept for decomposing the system into sub-systems is developed as a basis for the exchange. In addition, based on the analysis of collaboration processes in the context of Systems Engineering, an architectural approach with a SysML editor and Dataspace for the exchange is elaborated. The architecture is implemented on the basis of open-source solutions. The investigations are based on an application example from precision engineering. The potential and challenges are discussed.
]]>Systems doi: 10.3390/systems12010017
Authors: Leonel J. R. Nunes
This paper addresses the critical issue of managing biomass parks, a key component in the shift towards sustainable energy sources. The research problem centers on optimizing the management of these parks to enhance production and economic viability. Our aim was to bridge the gap in current research by developing and applying mathematical models tailored for biomass park management. The study commenced by constructing a basic model based on assumptions such as uniform biomass and steady input rates. Progressing from this initial model, we explored sophisticated control strategies, including Pontryagin’s maximum principle and dynamic programming, and employed numerical methods to tackle the nonlinearities and complexities inherent in biomass management. Our approach’s scope extended to predicting and managing biomass flow, highlighting each method’s distinct advantages. The simple model laid the groundwork for understanding, while optimal control techniques revealed the system’s intricate dynamics. The numerical methods provided practical solutions to complex equations. We found that while each method is beneficial on its own, their combined use can significantly improve decision-making in biomass park management. This research emphasizes the importance of aligning the chosen method with specific operational challenges and desired outcomes for optimal efficacy, offering both theoretical insights and practical applications in the field of renewable energy management.
]]>Systems doi: 10.3390/systems12010016
Authors: Fatemeh Ehteshami Rachel Cassidy Fabrizio Tediosi Günther Fink Daniel Cobos Muñoz
The burden of type 2 diabetes mellitus (T2DM) and hypertension (HTN) has increased worldwide in recent decades, particularly in low- and middle-income countries (LMICs). In these countries, health systems often struggle to provide effective health care services for the management of chronic conditions. We have developed a study protocol with the aim of conducting a realist review to delve into the complexities behind the management of T2DM and HTN in LMICs. First, we have developed a causal loop diagram (CLD) serving as the initial program theory to represent the health system drivers associated with the effective (or ineffective) management of T2DM and HTN. Next, we will search, select, appraise, extract and analyze the relevant evidence. This evidence will be used to refine and extend the initial program theory to transform it into a middle-range program theory. This will then be verified through Group Model Building (GMB) sessions. The evidence will be summarized applying RAMESES (Realist And MEta-narrative Evidence Syntheses: Evolving Standards). In combining a systems thinking approach with a realist approach to program evaluation, we aim to unravel the mechanisms that govern the management of T2DM and HTN, and the relation between health system-related factors, which lead to outcomes, in different contexts.
]]>Systems doi: 10.3390/systems12010015
Authors: Ana-Maria Zamfir Anamaria Beatrice Aldea Raluca-Mihaela Molea
Education is a complex system with implications for educational policy and management. Education systems that are more comprehensive generate more equal outcomes, fostering access to opportunities for all children. On the other hand, systems with early selection and tracking are more stratified and register higher inequalities in educational outcomes. Educational inequalities imply unequal access to education and, subsequently, career opportunities. The present study employs classification techniques, such as decision trees, in order to highlight lines of stratification and inequality in the upper secondary education system in Romania, focusing on the selection of students in general or vocational programs. Our results show that the education of parents and area of residence are factors that influence the stratification of students in the Romanian secondary education system, and the selection process contributes to the reproduction of social inequalities. The conclusions of this study are consistent with the cultural capital theory in education. Policy and strategic management implications are discussed in light of our results.
]]>Systems doi: 10.3390/systems12010014
Authors: Martin Tran Samuel Kreinberg Eric Specking Gregory S. Parnell Brenda Hernandez Ed Pohl George Gallarno John Richards Randy Buchanan Christina Rinaudo
Army installation commanders need timely weather information to make installation closure decisions before or during adverse weather events (e.g., hail, thunderstorms, snow, and floods). We worked with the military installation in Fort Carson, CO, and used their Weather Warning, Watch, and Advisory (WWA) criteria list to establish the foundation for our algorithm. We divided the Colorado Springs area into 2300 grids (2.5 square kilometers areas) and grouped the grids into ten microclimates, geographically and meteorologically unique regions, per pre-defined microclimate regions provided by the Fort Carson Air Force Staff Weather Officers (SWOs). Our algorithm classifies each weather event in the WWA list using the National Weather Service’s and National Digital Forecast Database’s data. Our algorithm assigns each event a criticality level: none, advisory, watch, or warning. The traffic network data highlight the importance of each road segment for travel to and from Fort Carson. The algorithm also uses traffic network data to assign weight to each grid, which enables the aggregation to the region and installation levels. We developed a weather dashboard in ArcGIS Pro to verify our algorithm and visualize the forecasted warnings for the grids and regions that are or may be affected by weather events.
]]>Systems doi: 10.3390/systems12010013
Authors: LynnDee Ford Atila Ertas
Systems engineering (SE) solves the most complex problems, bringing together societal issues, theoretical engineering, and the transformation of theory into products and services to better humanity and reduce suffering. In industry, the effort to transform theoretical concepts into practical solutions begins with the product life cycle concept stage, where systems engineering estimates and derives technologies, costs, and schedules. It is crucial to have a successful concept stage as today’s industries focus on producing the most capable technologies at an affordable cost and faster time to market than ever before. The research of this paper utilizes a transdisciplinary SE process model in the concept stage to develop and propose training for early-in-career engineers, effectively bridging the gap from university learning to industry practice. With a focus on the concept stage of the product life cycle and the industry’s demands of expeditiously proposing complex technical solutions, the paper aims to create an efficient learning program. The main objective of this research is to create a learning program to bring up-to-speed early-in-career engineers using a transdisciplinary SE process model, with six key components: (1) disciplinary convergence—creating a collective impact; (2) TD collaboration; (3) collective intelligence; (4) TD research integration; (5) TD engineering tools; and (6) analysis and TD assessment. The research will then conclude with a case study piloting the TD learning program and analyzing its effectiveness, ultimately aiming to enhance early-in-career engineers’ skills in proposing technical solutions that meet customer demands and drive business profitability.
]]>Systems doi: 10.3390/systems12010012
Authors: Elpida Samara Pavlos Kilintzis Nicos Komninos Athanasios Anastasiou George Martinidis
Innovation systems consist of different organisations from the quadruple helix, as well as the interactions and linkages between them. Smart technologies and ICT play a key role in the efficiency of systems. At the same time, regional scale is considered crucial for studying innovation in systems. However, the lack of many important data at the regional level compounds the efforts to study them. The paper proposes a novel methodological approach to the regionalisation of national-level indicators in order to address this issue. This is based on the model fit approach, using regressions to “regionalise” national-level indicators based on similar indicators that are available. The approach is tested on the data for Greek NUTS 2 regions and produces regional-level estimates for four innovation indicators, based on four available indicators that are found to be strongly correlated to them. However, the same approach can be used for any EU country or the whole of the EU. The results, their prospects for future research, and potential applications are considered. Overall, the availability of regional-level indicators is considered crucial for the formulation of impactful development policies.
]]>Systems doi: 10.3390/systems12010011
Authors: Jian Liu Qibin Wang Chaoyi Wei
The rapid development of digital technology has injected new vitality into green technological innovation within manufacturing enterprises. Proper application of digital technology during the innovation process can propel global sustainable development. Using Chinese publicly traded manufacturing firms as a sample, this study employed a constructed digital technology innovation network and OLS models to unveil the mechanisms through which digital technology application affects green technological innovation. This research reveals a significant positive impact of the breadth and depth of digital technology applications on companies’ green technological innovation performance. Green human resource allocation serves as an intermediary in this relationship. Furthermore, the embeddedness and structural embeddedness of the digital technology innovation network play a significantly positive moderating role in the relationship between digital technology applications and green human resource allocation. This discovery provides a theoretical foundation for how companies can harness digital technology to promote green innovation within China’s digital strategy. It aids manufacturing enterprises in optimizing digital technology applications, improving green human resource allocation, and facilitating the development of digital technology innovation networks, advancing more sustainable development and contributing to global environmental goals.
]]>Systems doi: 10.3390/systems12010010
Authors: Cezar Scarlat Daniela-Anca Sârbu Bărar
IT projects are becoming increasingly complex due to rapidly advancing technologies, the need to tackle more difficult problems, and the involvement of a larger variety of experts with different backgrounds and experiences from different countries and cultures. It is also common for these teams to often work remotely in virtual settings. In this context, besides conflicts between IT project team members, cross-functional and cross-hierarchical organizational conflicts might emerge as well. These conflicts can vary in terms of their origin, nature, and intensity. This paper is a qualitative study focused on understanding interpersonal communication-based conflicts in multicultural and multidisciplinary IT project teams. The purpose is to find a common approach that can mitigate and eventually resolve these conflicts, aiming to promote shared knowledge and ultimately reduce the gap in understanding and the likelihood of conflicts. Both secondary research (a literature survey) and primary research (involving experienced managers and experts from project teams in the Romanian IT industry) were conducted in order to reach the objectives, besides sets of lessons learned and recommendations, to develop a framework for systematic conflict analysis and to propose a practice for a transcultural framework of common team vocabulary. To achieve these, a number of conflicts were investigated in IT project teams and corresponding cases. Based on the research findings, the authors concluded that a more formal approach is needed to address the problem of conflicts. From a theoretical standpoint, this paper suggests the concept of management diversity and provides a typology of organizational conflicts. Nevertheless, the framework for systematic analysis of conflict typology (FACT) and the framework of common team vocabulary in the multicultural environment of IT organizational project teams, as well as the sets of lessons learned and recommendations, might be useful and inspiring for both scholars and managers, not only in the IT sector.
]]>Systems doi: 10.3390/systems12010009
Authors: Chengwei Yu Wenzhu Liao Leting Zu
With the implementation of AGV technology and automated scheduling, storage and retrieval systems have become widely utilized in warehouse management. However, due to the use of unidirectional channels, AGV movement is restricted, and detours may occur frequently. Additionally, as the number of AGVs increases, deadlocks can arise, which lead to delays in order packaging and a decrease in overall warehouse performance. Hence, this paper proposes a dynamic scheduling method for task assignment and route optimization of AGVs to prevent collisions. The routing optimization method is based on an improved A* algorithm, which takes into account the dynamic map as input. Moreover, this paper investigates highly complex collision scenarios in bidirectional channels. Through simulation experiments, it is evident that scheduling methods based on bidirectional channels offer a clear advantage in terms of efficiency compared to those based on unidirectional channels.
]]>Systems doi: 10.3390/systems12010008
Authors: Maurice Yolles Tuomo Rautakivi
Complex organisations require coherence to achieve adaptive goals through agency. This paper introduces Mindset Agency Theory (MAT), a metatheoretical framework designed for modelling and diagnosing agency within culturally diverse populations. MAT, a cybernetic multi-ontology framework, delineates five formative traits defining agency character. Its cognitive style trait (with bipolar values of Patterning–Dramatising) elucidates how agencies acquire information. Examining diverse agencies requires an appreciation of the social relationships that exist there, but MAT is currently devoid of this capability. Using the configuration approach to enable the integration of Tönnies’ social organisation theory into MAT, social relationships can be suitably explored, thus enhancing its capacity to investigate agency coherence. Tönnies’ theory of social organisation (with bipolar values of Gemeinschaft-Gesellschaft) that frames inter-agent interactions is configured within MAT. This integration births a new formative trait, pairing cognitive style with social organisation, and is thus capable of indicating the likelihood of operative coherence. Configuration is applied by relating propositional attributes of a holding metatheory framework such as MAT, with an entry theory such as Tönnies’ social organisation theory as determined from the literature. The elaborated MAT serves as a diagnostic tool, linking trait instabilities with agency pathologies that deliver dysfunction. A subsequent paper will apply this framework to ASEAN, a regional intergovernmental organisation addressing cultural diversity issues. The study aims to evaluate ASEAN’s mindset and diagnose its pathologies, such as narcissism and paradoxical behaviour.
]]>Systems doi: 10.3390/systems12010007
Authors: Liru Chen Hantao Zhao Chenhui Shi Youbo Wu Xuewen Yu Wenze Ren Ziyi Zhang Xiaomeng Shi
Visualization systems play a crucial role in industry, education, and research domains by offering valuable insights and enhancing decision making. These systems enable the representation of complex workflows and data in a visually intuitive manner, facilitating better understanding, analysis, and communication of information. This paper explores the potential of augmented reality (AR) visualization systems that enhance multi-modal perception and interaction for complex decision making. The proposed system combines the physicality and intuitiveness of the real world with the immersive and interactive capabilities of AR systems. By integrating physical objects and virtual elements, users can engage in natural and intuitive interactions, leveraging multiple sensory modalities. Specifically, the system incorporates vision, touch, eye-tracking, and sound as multi-modal interaction methods to further improve the user experience. This multi-modal nature enables users to perceive and interact in a more holistic and immersive manner. The software and hardware engineering of the proposed system are elaborated in detail, and the system’s architecture and preliminary function testing results are also included in the manuscript. The findings aim to aid visualization system designers, researchers, and practitioners in exploring and harnessing the capabilities of this integrated approach, ultimately leading to more engaging and immersive user experiences in various application domains.
]]>Systems doi: 10.3390/systems12010006
Authors: Meng Yi Renqian Zhang
In this paper, we provide a model to handle multiple replenishment cycles and the cross-selling of multiple major items with one minor item, while allowing partial late delivery. The optimization analytic expression of the model is finally obtained by utilizing the convexity of cost function for F and using the first-order conditions in optimization theory. Numerical examples and sensitivity analysis demonstrate the effectiveness of the model and algorithm, which offers a competent solution for practical applications.
]]>Systems doi: 10.3390/systems12010005
Authors: Weiting Xiong Jingang Li
Multi-scale urban innovation networks are important channels for intra- and inter-city knowledge spillovers and play an important role in urban industrial innovation and growth. However, there is a lack of direct evidence on the impact of multi-scale urban innovation networks on industrial development. Drawing upon the “buzz-and-pipeline” model, this paper analyzes the impact of multi-scale urban innovation networks on industrial development by taking the automobile manufacturing industry in China’s five urban agglomerations as an example. Firstly, based on the Form of Correlation between International Patent Classification and Industrial Classification for National Economic Activities (2018) and co-patents, we construct urban innovation networks on three different geographical scales, including intra-city innovation networks, inter-city innovation networks within urban agglomerations, and innovation networks between cities within and beyond urban agglomerations. Then, we employ the ordinary least squares model with fixed effects at the urban agglomeration level to explore the impact of urban multi-scale knowledge linkages on the development of the automobile manufacturing industry and the results showed that urban innovation networks at three different geographical scales have different impacts on industrial development. Specifically, intra-city innovation networks have a facilitating effect on industrial development, while both inter-city innovation networks within urban agglomerations and innovation networks between cities within and beyond urban agglomerations have an inverted U-shaped impact on industrial development. The interactions between urban innovation networks on three different geographical scales have a negative effect on industrial development. Simultaneously, the agglomeration level of urban industry plays a positive moderating role in the impacts of multi-scale urban innovation networks on industrial development.
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