How to efficiently and greenly dismantle abandoned buildings and reuse them is a dilemma facing the building material industry’s low-carbon objective. However, relevant studies ignore the influence mechanism of altruistic preferences of enterprises on green dismantling technology in supply chains. Driven by filling this theoretical gap, this paper firstly integrates reciprocal altruism theory and the Stackalberg game method and constructs a construction and demolition waste (CDW) recycling supply chain system consisting of a recycler and a remanufacturer, in which enterprises’ altruistic preferences are considered. The main theoretical outcomes of this paper are as follows. (1) In the case of unilateral altruism, enterprises’ altruistic preference behaviors help in increasing the green dismantling technological level and the amount of CDW recycling. Under the mutual altruism model, the influence of the recycler’s altruistic preference degree on the amount of CDW recycled hinges on the remanufacturer’s altruistic preference degree. (2) The utility of the enterprises and the green dismantling technological level are optimized under the mutual altruism model. (3) In a system of unequal power, unilateral “goodwill” by the follower will have a negative effect on their own interests; the leader plays a crucial role in facilitating equal cooperation and realizing win–win situations. This paper enriches the reciprocal altruism theory in waste management. It also helps in providing guidance for the recycler and remanufacturer in making operational decisions.
{"title":"Operational Decisions of Construction and Demolition Waste Recycling Supply Chain Members under Altruistic Preferences","authors":"Junlin Zhu, Hao Zhang, Weihong Chen, Xingwei Li","doi":"10.3390/systems12090346","DOIUrl":"https://doi.org/10.3390/systems12090346","url":null,"abstract":"How to efficiently and greenly dismantle abandoned buildings and reuse them is a dilemma facing the building material industry’s low-carbon objective. However, relevant studies ignore the influence mechanism of altruistic preferences of enterprises on green dismantling technology in supply chains. Driven by filling this theoretical gap, this paper firstly integrates reciprocal altruism theory and the Stackalberg game method and constructs a construction and demolition waste (CDW) recycling supply chain system consisting of a recycler and a remanufacturer, in which enterprises’ altruistic preferences are considered. The main theoretical outcomes of this paper are as follows. (1) In the case of unilateral altruism, enterprises’ altruistic preference behaviors help in increasing the green dismantling technological level and the amount of CDW recycling. Under the mutual altruism model, the influence of the recycler’s altruistic preference degree on the amount of CDW recycled hinges on the remanufacturer’s altruistic preference degree. (2) The utility of the enterprises and the green dismantling technological level are optimized under the mutual altruism model. (3) In a system of unequal power, unilateral “goodwill” by the follower will have a negative effect on their own interests; the leader plays a crucial role in facilitating equal cooperation and realizing win–win situations. This paper enriches the reciprocal altruism theory in waste management. It also helps in providing guidance for the recycler and remanufacturer in making operational decisions.","PeriodicalId":36394,"journal":{"name":"Systems","volume":"1 1","pages":""},"PeriodicalIF":1.9,"publicationDate":"2024-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142225993","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"社会学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Samer Al-Rabeei, Michal Hovanec, Volodymyr Tymofiiv, Juraj Horkay
In this article, we point out that luggage disinfection is a key step in stopping the spread of infectious diseases that can be contracted at airports due to viruses and bacteria, which can spread through passenger luggage at airports. To prevent the spread of respiratory infections at airports, in this research study, we compare two types of baggage disinfection system. The first method uses UV light to disinfect luggage while selectively checking it for the presence of bacteria and viruses. The second system uses non-selective disinfection, taking into account the possibility of the spread of disease from the aircraft’s home country. An analysis and simulation of a specific airport security procedure was carried out on the model under study, which is an automated system for disinfecting baggage at airports in two variants. The aim is to reduce the transmission of harmful diseases and, at the same time, to ensure the accuracy of passenger security screening by efficiently exhausting each of the proposed models. This research shows that the suggested measures to stop the spread of infectious diseases that travelers’ luggage can bring in will enhance screening procedures and, in particular, boost overall security while lowering the risk of infection transmission at airports.
{"title":"Innovating Airport Luggage Disinfection Systems with Advanced Technologies and Automation","authors":"Samer Al-Rabeei, Michal Hovanec, Volodymyr Tymofiiv, Juraj Horkay","doi":"10.3390/systems12090345","DOIUrl":"https://doi.org/10.3390/systems12090345","url":null,"abstract":"In this article, we point out that luggage disinfection is a key step in stopping the spread of infectious diseases that can be contracted at airports due to viruses and bacteria, which can spread through passenger luggage at airports. To prevent the spread of respiratory infections at airports, in this research study, we compare two types of baggage disinfection system. The first method uses UV light to disinfect luggage while selectively checking it for the presence of bacteria and viruses. The second system uses non-selective disinfection, taking into account the possibility of the spread of disease from the aircraft’s home country. An analysis and simulation of a specific airport security procedure was carried out on the model under study, which is an automated system for disinfecting baggage at airports in two variants. The aim is to reduce the transmission of harmful diseases and, at the same time, to ensure the accuracy of passenger security screening by efficiently exhausting each of the proposed models. This research shows that the suggested measures to stop the spread of infectious diseases that travelers’ luggage can bring in will enhance screening procedures and, in particular, boost overall security while lowering the risk of infection transmission at airports.","PeriodicalId":36394,"journal":{"name":"Systems","volume":"1 1","pages":""},"PeriodicalIF":1.9,"publicationDate":"2024-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142225889","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"社会学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
A system of incentives can be established to encourage several parties to unite as a community of interest and become jointly committed to the platform economic governance. The platform economy involves progressively more complex subjects of interest and relationships, which are not the typical principal–agent one-time cooperative relationship. This study investigates the problem of regulatory incentives in the platform economy, specifically focusing on the relationship between the government, platform enterprises, and merchants. It analyzes this issue under conditions of asymmetric information by constructing and solving a dual principal–agent model. The findings indicate the following: (1) the government’s incentives and regulatory mechanisms can be considered as interchangeable to some extent, with decisions made by evaluating their respective costs; (2) the government’s optimal incentives and regulations ultimately shape the self-regulatory behavior of merchants through platform enterprises; and (3) the optimal level of incentives for both the government and the platform enterprise is influenced by factors such as the ability coefficient, the social transformation coefficient, and the merchants’ reliance on the platform enterprise. Additionally, the optimal effort level of the platform enterprise and the merchants increases with higher levels of the regulatory effort, risk sensitivity coefficient, and ability coefficient. A win–win scenario and a long-term, stable cooperative partnership can be reached by the three parties under the ideal incentive intensity. The study’s conclusions can serve as a theoretical foundation and support for the creation of incentive contracts for platform economy regulation.
{"title":"A Study on the Design of Incentive Contracts for Platform Economy Regulation Based on Dual Principal–Agents","authors":"Ruibi Zhang, Jinhe Zhu, Ming Lei","doi":"10.3390/systems12090343","DOIUrl":"https://doi.org/10.3390/systems12090343","url":null,"abstract":"A system of incentives can be established to encourage several parties to unite as a community of interest and become jointly committed to the platform economic governance. The platform economy involves progressively more complex subjects of interest and relationships, which are not the typical principal–agent one-time cooperative relationship. This study investigates the problem of regulatory incentives in the platform economy, specifically focusing on the relationship between the government, platform enterprises, and merchants. It analyzes this issue under conditions of asymmetric information by constructing and solving a dual principal–agent model. The findings indicate the following: (1) the government’s incentives and regulatory mechanisms can be considered as interchangeable to some extent, with decisions made by evaluating their respective costs; (2) the government’s optimal incentives and regulations ultimately shape the self-regulatory behavior of merchants through platform enterprises; and (3) the optimal level of incentives for both the government and the platform enterprise is influenced by factors such as the ability coefficient, the social transformation coefficient, and the merchants’ reliance on the platform enterprise. Additionally, the optimal effort level of the platform enterprise and the merchants increases with higher levels of the regulatory effort, risk sensitivity coefficient, and ability coefficient. A win–win scenario and a long-term, stable cooperative partnership can be reached by the three parties under the ideal incentive intensity. The study’s conclusions can serve as a theoretical foundation and support for the creation of incentive contracts for platform economy regulation.","PeriodicalId":36394,"journal":{"name":"Systems","volume":"63 1","pages":""},"PeriodicalIF":1.9,"publicationDate":"2024-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142225886","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"社会学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Nowadays, the focus of flow shops is the adoption of customized demand in the context of service-oriented manufacturing. Since production tasks are often characterized by multi-variety, low volume, and a short lead time, it becomes an indispensable factor to include supporting logistics in practical scheduling decisions to reflect the frequent transport of jobs between resources. Motivated by the above background, a hybrid method based on dual back propagation (BP) neural networks is proposed to meet the real-time scheduling requirements with the aim of integrating production and transport activities. First, according to different resource attributes, the hierarchical structure of a flow shop is divided into three layers, respectively: the operation task layer, the job logistics layer, and the production resource layer. Based on the process logic relationships between intra-layer and inter-layer elements, an operation task–logistics–resource supernetwork model is established. Secondly, a dual BP neural network scheduling algorithm is designed for determining an operations sequence involving the transport time. The neural network 1 is used for the initial classification of operation tasks’ priority; and the neural network 2 is used for the sorting of conflicting tasks in the same priority, which can effectively reduce the amount of computational time and dramatically accelerate the solution speed. Finally, the effectiveness of the proposed method is verified by comparing the completion time and computational time for different examples. The numerical simulation results show that with the increase in problem scale, the solution ability of the traditional method gradually deteriorates, while the dual BP neural network has a stable performance and fast computational time.
如今，流程车间的重点是在以服务为导向的制造过程中采用定制需求。由于生产任务通常具有多品种、小批量和短交货期的特点，因此在实际调度决策中纳入配套物流以反映资源间频繁的作业运输成为一个不可或缺的因素。基于上述背景，本文提出了一种基于双反向传播（BP）神经网络的混合方法，以满足实时调度的要求，实现生产与运输活动一体化的目标。首先，根据不同的资源属性，将流水车间的层次结构分为三层，分别是操作任务层、作业物流层和生产资源层。根据层内要素和层间要素之间的流程逻辑关系，建立了操作任务-物流-资源超网络模型。其次，设计了一种双 BP 神经网络调度算法，用于确定涉及运输时间的作业序列。利用神经网络 1 对操作任务的优先级进行初步划分，利用神经网络 2 对优先级相同的冲突任务进行排序，从而有效减少了计算时间，大大加快了求解速度。最后，通过比较不同实例的完成时间和计算时间，验证了所提方法的有效性。数值模拟结果表明，随着问题规模的增大，传统方法的求解能力逐渐下降，而双 BP 神经网络性能稳定，计算时间快。
{"title":"A Flow Shop Scheduling Method Based on Dual BP Neural Networks with Multi-Layer Topology Feature Parameters","authors":"Hui Mu, Zinuo Wang, Jiaqi Chen, Guoqiang Zhang, Shaocun Wang, Fuqiang Zhang","doi":"10.3390/systems12090339","DOIUrl":"https://doi.org/10.3390/systems12090339","url":null,"abstract":"Nowadays, the focus of flow shops is the adoption of customized demand in the context of service-oriented manufacturing. Since production tasks are often characterized by multi-variety, low volume, and a short lead time, it becomes an indispensable factor to include supporting logistics in practical scheduling decisions to reflect the frequent transport of jobs between resources. Motivated by the above background, a hybrid method based on dual back propagation (BP) neural networks is proposed to meet the real-time scheduling requirements with the aim of integrating production and transport activities. First, according to different resource attributes, the hierarchical structure of a flow shop is divided into three layers, respectively: the operation task layer, the job logistics layer, and the production resource layer. Based on the process logic relationships between intra-layer and inter-layer elements, an operation task–logistics–resource supernetwork model is established. Secondly, a dual BP neural network scheduling algorithm is designed for determining an operations sequence involving the transport time. The neural network 1 is used for the initial classification of operation tasks’ priority; and the neural network 2 is used for the sorting of conflicting tasks in the same priority, which can effectively reduce the amount of computational time and dramatically accelerate the solution speed. Finally, the effectiveness of the proposed method is verified by comparing the completion time and computational time for different examples. The numerical simulation results show that with the increase in problem scale, the solution ability of the traditional method gradually deteriorates, while the dual BP neural network has a stable performance and fast computational time.","PeriodicalId":36394,"journal":{"name":"Systems","volume":"36 1","pages":""},"PeriodicalIF":1.9,"publicationDate":"2024-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142227757","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"社会学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Uzayr Karimulla, Kapil Gupta, Daramy Vandi Von Kallon
Lean methodologies, a system widely used in the manufacturing industry, have demonstrated numerous benefits and added significant value to the industry. In the same way that other industries have benefitted from the application of Lean techniques, the engineering projects sector can also realise significant improvements by integrating Lean principles into its processes and operations. The study aims to gain a comprehensive insight into the application of the Lean concept, with a focus on the South African engineering projects sector. The findings of a thorough literature review, focussing on Lean techniques, technology, and the projects sector, examined key variables that have an impact on the effective implementation of Lean methodologies within this industry. The variables studied are skills and expertise, active work methods, and leadership style. The study also explores the impact of digital technologies on Lean implementation, exploring how these technologies enhance the integration of Lean practices. The study includes descriptive statistical analysis, exploratory factor analysis, and a confirmatory factor analysis of the data. A significant relationship is found between variables influencing Lean implementation and the effectiveness of Lean practices within the South African engineering projects sector.
{"title":"An Investigation into Lean Implementation Preparedness in the Engineering Projects Sector","authors":"Uzayr Karimulla, Kapil Gupta, Daramy Vandi Von Kallon","doi":"10.3390/systems12090335","DOIUrl":"https://doi.org/10.3390/systems12090335","url":null,"abstract":"Lean methodologies, a system widely used in the manufacturing industry, have demonstrated numerous benefits and added significant value to the industry. In the same way that other industries have benefitted from the application of Lean techniques, the engineering projects sector can also realise significant improvements by integrating Lean principles into its processes and operations. The study aims to gain a comprehensive insight into the application of the Lean concept, with a focus on the South African engineering projects sector. The findings of a thorough literature review, focussing on Lean techniques, technology, and the projects sector, examined key variables that have an impact on the effective implementation of Lean methodologies within this industry. The variables studied are skills and expertise, active work methods, and leadership style. The study also explores the impact of digital technologies on Lean implementation, exploring how these technologies enhance the integration of Lean practices. The study includes descriptive statistical analysis, exploratory factor analysis, and a confirmatory factor analysis of the data. A significant relationship is found between variables influencing Lean implementation and the effectiveness of Lean practices within the South African engineering projects sector.","PeriodicalId":36394,"journal":{"name":"Systems","volume":"188 1","pages":""},"PeriodicalIF":1.9,"publicationDate":"2024-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142227758","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"社会学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The importance of architecture design keeps increasing as the complexity of systems and system-of-systems (SoSs) continues rising. While the architecture frameworks such as the Department of Defense Architecture Framework (DoDAF) are commonly used to guide architecture design, many perspectives are still hindering their effective use. Instead of generating a set of architecture description models probably only for satisfying the milestone review, the architecture frameworks should be used to organize the vague or incomplete information, identify and formulate the decision-making problem, and guide the architecture decision-making. Unfortunately, the decision points are hidden in the architecture models and the ambiguity often leads to a confusion of whether the architecture models are built incorrectly due to the lack of modeling experience or the lack of adequate decision analysis. Therefore, this paper identifies the key decision points and decision types during the architecture model development based on the DoDAF. Plus, this paper proposes a set of decision patterns and a guide to their use to provide qualitative decision analysis for developing architecture models and generating alternatives. An illustrative example to anti-submarine SoSs demonstrates the process of applying the decision patterns to the DoDAF model’s development and the generated architecture alternatives.
随着系统和系统级系统（SoSs）复杂性的不断提高，架构设计的重要性也与日俱增。虽然《国防部架构框架》（DoDAF）等架构框架通常被用来指导架构设计，但仍有许多观点阻碍其有效使用。架构框架应该用来组织模糊或不完整的信息，识别和提出决策问题，并指导架构决策，而不是仅仅为了满足里程碑审查而生成一套架构描述模型。遗憾的是，决策点隐藏在架构模型中，其模糊性常常导致人们困惑，究竟是缺乏建模经验还是缺乏足够的决策分析而导致架构模型建立错误。因此，本文基于 DoDAF 确定了架构模型开发过程中的关键决策点和决策类型。此外，本文还提出了一套决策模式及其使用指南，为开发架构模型和生成备选方案提供定性决策分析。以反潜 SoS 为例来说明将决策模式应用于 DoDAF 模型开发和生成架构备选方案的过程。
{"title":"Architecture Design Space Generation via Decision Pattern-Guided Department of Defense Architecture Framework Modeling","authors":"Zhemei Fang, Xuemeng Zhao, Fengyun Li","doi":"10.3390/systems12090336","DOIUrl":"https://doi.org/10.3390/systems12090336","url":null,"abstract":"The importance of architecture design keeps increasing as the complexity of systems and system-of-systems (SoSs) continues rising. While the architecture frameworks such as the Department of Defense Architecture Framework (DoDAF) are commonly used to guide architecture design, many perspectives are still hindering their effective use. Instead of generating a set of architecture description models probably only for satisfying the milestone review, the architecture frameworks should be used to organize the vague or incomplete information, identify and formulate the decision-making problem, and guide the architecture decision-making. Unfortunately, the decision points are hidden in the architecture models and the ambiguity often leads to a confusion of whether the architecture models are built incorrectly due to the lack of modeling experience or the lack of adequate decision analysis. Therefore, this paper identifies the key decision points and decision types during the architecture model development based on the DoDAF. Plus, this paper proposes a set of decision patterns and a guide to their use to provide qualitative decision analysis for developing architecture models and generating alternatives. An illustrative example to anti-submarine SoSs demonstrates the process of applying the decision patterns to the DoDAF model’s development and the generated architecture alternatives.","PeriodicalId":36394,"journal":{"name":"Systems","volume":"1 1","pages":""},"PeriodicalIF":1.9,"publicationDate":"2024-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142225909","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"社会学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Shiwei Xu, Siyuan Zhang, Yilei Ren, Qijun Jiang, Dan Wu
Digital technology has the function of information governance, and digital transformation of enterprises may be the key way to identify and restrain ESG greenwashing. Based on the theory of digital empowerment, this study analyzes the influence and mechanism of digital transformation on restraining corporate green washing behavior from the perspective of internal and external factors. This study takes A-share listed companies in 2012–2022 as research samples and tests the effectiveness of digital transformation. Research has found that (1) digital transformation can significantly suppress corporate greenwashing behavior, and this conclusion still holds after a series of endogeneity and robustness tests. (2) In the context of high environmental awareness among executives, the inhibitory effect of digital transformation on corporate ESG greenwashing is more pronounced. (3) Mechanism analysis shows that digital transformation has inhibited the company’s greenwashing behavior by increasing the attention of investors. (4) Heterogeneity analysis shows that in state-owned enterprises, non-heavily polluting industries, high-tech industries, and enterprises located in the eastern region digital transformation has a more effective inhibitory effect on corporate greenwashing behavior. This study examines the impact of digital transformation on corporate ESG greenwashing, expands the research on the non-economic effects of digital transformation, and may provide empirical evidence for improving the quality of ESG information disclosure and sustainable development of enterprises.
{"title":"Can Digital Transformation Restrain Corporate ESG Greenwashing—A Test Based on Internal and External Joint Perspectives","authors":"Shiwei Xu, Siyuan Zhang, Yilei Ren, Qijun Jiang, Dan Wu","doi":"10.3390/systems12090334","DOIUrl":"https://doi.org/10.3390/systems12090334","url":null,"abstract":"Digital technology has the function of information governance, and digital transformation of enterprises may be the key way to identify and restrain ESG greenwashing. Based on the theory of digital empowerment, this study analyzes the influence and mechanism of digital transformation on restraining corporate green washing behavior from the perspective of internal and external factors. This study takes A-share listed companies in 2012–2022 as research samples and tests the effectiveness of digital transformation. Research has found that (1) digital transformation can significantly suppress corporate greenwashing behavior, and this conclusion still holds after a series of endogeneity and robustness tests. (2) In the context of high environmental awareness among executives, the inhibitory effect of digital transformation on corporate ESG greenwashing is more pronounced. (3) Mechanism analysis shows that digital transformation has inhibited the company’s greenwashing behavior by increasing the attention of investors. (4) Heterogeneity analysis shows that in state-owned enterprises, non-heavily polluting industries, high-tech industries, and enterprises located in the eastern region digital transformation has a more effective inhibitory effect on corporate greenwashing behavior. This study examines the impact of digital transformation on corporate ESG greenwashing, expands the research on the non-economic effects of digital transformation, and may provide empirical evidence for improving the quality of ESG information disclosure and sustainable development of enterprises.","PeriodicalId":36394,"journal":{"name":"Systems","volume":"74 1","pages":""},"PeriodicalIF":1.9,"publicationDate":"2024-08-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142225911","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"社会学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This study investigates the factors influencing undergraduate students’ self-directed learning (SDL) abilities in generative Artificial Intelligence (AI)-driven interactive learning environments. The advent of generative AI has revolutionized interactive learning environments, offering unprecedented opportunities for personalized and adaptive education. Generative AI supports teachers in delivering smart education, enhancing students’ acceptance of technology, and providing personalized, adaptive learning experiences. Nevertheless, the application of generative AI in higher education is underexplored. This study explores how these AI-driven platforms impact undergraduate students’ self-directed learning (SDL) abilities, focusing on the key factors of teacher support, learning strategies, and technology acceptance. Through a quantitative approach involving surveys of 306 undergraduates, we identified the key factors of motivation, technological familiarity, and the quality of AI interaction. The findings reveal the mediating roles of self-efficacy and learning motivation. Also, the findings confirmed that improvements in teacher support and learning strategies within generative AI-enhanced learning environments contribute to increasing students’ self-efficacy, technology acceptance, and learning motivation. This study contributes to uncovering the influencing factors that can inform the design of more effective educational technologies and strategies to enhance student autonomy and learning outcomes. Our theoretical model and research findings deepen the understanding of applying generative AI in higher education while offering important research contributions and managerial implications.
{"title":"Unlocking Potential: Key Factors Shaping Undergraduate Self-Directed Learning in AI-Enhanced Educational Environments","authors":"Di Wu, Shuling Zhang, Zhiyuan Ma, Xiao-Guang Yue, Rebecca Kechen Dong","doi":"10.3390/systems12090332","DOIUrl":"https://doi.org/10.3390/systems12090332","url":null,"abstract":"This study investigates the factors influencing undergraduate students’ self-directed learning (SDL) abilities in generative Artificial Intelligence (AI)-driven interactive learning environments. The advent of generative AI has revolutionized interactive learning environments, offering unprecedented opportunities for personalized and adaptive education. Generative AI supports teachers in delivering smart education, enhancing students’ acceptance of technology, and providing personalized, adaptive learning experiences. Nevertheless, the application of generative AI in higher education is underexplored. This study explores how these AI-driven platforms impact undergraduate students’ self-directed learning (SDL) abilities, focusing on the key factors of teacher support, learning strategies, and technology acceptance. Through a quantitative approach involving surveys of 306 undergraduates, we identified the key factors of motivation, technological familiarity, and the quality of AI interaction. The findings reveal the mediating roles of self-efficacy and learning motivation. Also, the findings confirmed that improvements in teacher support and learning strategies within generative AI-enhanced learning environments contribute to increasing students’ self-efficacy, technology acceptance, and learning motivation. This study contributes to uncovering the influencing factors that can inform the design of more effective educational technologies and strategies to enhance student autonomy and learning outcomes. Our theoretical model and research findings deepen the understanding of applying generative AI in higher education while offering important research contributions and managerial implications.","PeriodicalId":36394,"journal":{"name":"Systems","volume":"114 1","pages":""},"PeriodicalIF":1.9,"publicationDate":"2024-08-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142225910","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"社会学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In this study, we analyzed the evolution of online public opinion on emergencies using a new Stochastic Petri Net modeling approach. First, an intuitive description of the emergency online public opinion development process was conceptualized from the life cycle evolution law perspective. Then, based on Petri net theory, a Stochastic Petri Net isomorphic Markov chain model was constructed to simulate the evolution of online public opinion on emergencies. Finally, four real-life cases were selected to validate and analyze the model, demonstrating that the evolutionary leaps, complexity, critical nodes, evolutionary rate, and execution time differ across different online public opinions on emergencies. The study results indicate that this modeling approach has certain advantages in examining the evolution based on multi-factor coupling and quantifying the evolution law in online public opinion on emergencies.
在本研究中，我们采用一种新的随机 Petri 网建模方法分析了突发事件网络舆情的演变过程。首先，从生命周期演化规律的角度，对突发事件网络舆情的发展过程进行了直观的概念化描述。然后，以 Petri 网理论为基础，构建了随机 Petri 网同构马尔可夫链模型，模拟突发事件网络舆情的演化过程。最后，选取了四个真实案例对模型进行了验证和分析，证明了不同的突发事件网络舆情在演化跃迁、复杂度、关键节点、演化速率和执行时间等方面存在差异。研究结果表明，该建模方法在研究基于多因素耦合的演化、量化突发事件网络舆情演化规律方面具有一定的优势。
{"title":"A New Stochastic Petri Net Modeling Approach for the Evolution of Online Public Opinion on Emergencies: Based on Four Real-Life Cases","authors":"Chen Guo, Yinghua Song","doi":"10.3390/systems12090333","DOIUrl":"https://doi.org/10.3390/systems12090333","url":null,"abstract":"In this study, we analyzed the evolution of online public opinion on emergencies using a new Stochastic Petri Net modeling approach. First, an intuitive description of the emergency online public opinion development process was conceptualized from the life cycle evolution law perspective. Then, based on Petri net theory, a Stochastic Petri Net isomorphic Markov chain model was constructed to simulate the evolution of online public opinion on emergencies. Finally, four real-life cases were selected to validate and analyze the model, demonstrating that the evolutionary leaps, complexity, critical nodes, evolutionary rate, and execution time differ across different online public opinions on emergencies. The study results indicate that this modeling approach has certain advantages in examining the evolution based on multi-factor coupling and quantifying the evolution law in online public opinion on emergencies.","PeriodicalId":36394,"journal":{"name":"Systems","volume":"30 1","pages":""},"PeriodicalIF":1.9,"publicationDate":"2024-08-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142227759","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"社会学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Valerio Muto, Simone Luongo, Martina Percuoco, Mario Tani
The rise of artificial intelligence is fundamentally transforming the competitive landscape across various sectors, offering visionary enterprises new pathways to innovation development and to get a competitive edge. AI leverages data, analysis, and observations to perform tasks without hard coding, and benefits from self-learning and continuous improvement. We use Systems Thinking to frame how managers may adopt and integrate AI in business activities. We also investigate the motivations driving entrepreneurs to adopt AI solutions, and how they may impact on sustainable business model innovation, by administering a questionnaire to a sample of innovative Italian SMEs to get a comprehensive overview of the dynamics influencing AI adoption in business. This study sheds light on the intricate relationship between technology, sustainability, and corporate innovation. It offers both valuable insights for future research and for strategic managerial decisions on AI integration. Furthermore, it helps the development of innovative, sustainable business models in the evolving landscape of the Great Reset.
{"title":"Artificial Intelligence and the Great Reset: Impacts and Perspectives for Italian SMEs Business Model Innovation","authors":"Valerio Muto, Simone Luongo, Martina Percuoco, Mario Tani","doi":"10.3390/systems12090330","DOIUrl":"https://doi.org/10.3390/systems12090330","url":null,"abstract":"The rise of artificial intelligence is fundamentally transforming the competitive landscape across various sectors, offering visionary enterprises new pathways to innovation development and to get a competitive edge. AI leverages data, analysis, and observations to perform tasks without hard coding, and benefits from self-learning and continuous improvement. We use Systems Thinking to frame how managers may adopt and integrate AI in business activities. We also investigate the motivations driving entrepreneurs to adopt AI solutions, and how they may impact on sustainable business model innovation, by administering a questionnaire to a sample of innovative Italian SMEs to get a comprehensive overview of the dynamics influencing AI adoption in business. This study sheds light on the intricate relationship between technology, sustainability, and corporate innovation. It offers both valuable insights for future research and for strategic managerial decisions on AI integration. Furthermore, it helps the development of innovative, sustainable business models in the evolving landscape of the Great Reset.","PeriodicalId":36394,"journal":{"name":"Systems","volume":"10 1","pages":""},"PeriodicalIF":1.9,"publicationDate":"2024-08-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142225912","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"社会学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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