Systems

Dong, Ying;Sun, Zhongyuan;Qiu, Luyi

doi: 10.3390/systems13030151pmid: N/A

The tripartite evolutionary game model focuses on the strategic choices and evolutionary laws of three parties in dynamic interaction. By constructing a tripartite evolutionary game model involving the government, Enterprise A, and Enterprise B, this paper analyzes the strategic choices of enterprise data sharing from the perspective of government regulation and uses the simulation method to assign and simulate the parameters of the model. Furthermore, the evolutionary trends of the behavioral strategies of the three parties are analyzed under the changes of factors such as the government’s regulation costs, government penalties, government rewards, and the compensation fees for enterprises to obtain shared data. The findings indicate that when the benefits obtained by enterprises from data sharing are relatively high, and the compensation fees incurred by enterprises to obtain the other party’s data are sufficient to compensate for the losses caused by the other party’s data sharing, enterprises will tend to choose “data-sharing”. At this time, the combined strategy of “no-regulation, data-sharing, data-sharing” reaches an equilibrium point. In this combination strategy, the initial willingness of the government and enterprises will not affect the final evolutionary result. The government’s regulation costs, government penalties, and government rewards will not affect the final behavioral strategy evolutionary result for the government and enterprises. However, the compensation fees for enterprises to obtain shared data will affect the final evolutionary direction of the three parties. When the compensation fees for enterprises to obtain shared data are low, enterprises are more inclined toward “no-data-sharing”.

Purcărea, Theodor Valentin;Ionescu, Ştefan-Alexandru;Purcărea, Ioan Matei;Purcărea, Irina;Ionescu, Alexandra Georgiana

doi: 10.3390/systems13030152pmid: N/A

The rise of the experience economy, driven by disruptive technologies delivering innovative experiences, has transformed the interactions between tech-enabled shoppers and the phygital retail complex system. An important knowledge gap is addressed in our study by evaluating shoppers’ perceptions of disruptive technologies and the adaptive challenges that retailers face in securing consistency within a highly complex e-commerce landscape shaped by transformative interactions. A quantitative analysis was carried out using structural equation modeling (SEM) and survey data from an international supermarket chain integrating physical and digital retail spaces. We propose a novel framework to explore how retailers can harness data-driven insights and disruptive technologies to optimize the phygital shopping experience and adapt to the shift from multichannel and omnichannel strategies to optichanneling, as well as respond to societal shifts, including the role of digital natives and the expanding influence of the metaverse. This framework integrates key principles such as emergence, feedback, and criticality. The research reveals key findings about transformative shopper experiences across phygital retail touchpoints that influence shoppers’ perceptions and behaviors. Based on these identified key insights, as shoppers increasingly expect seamless interactions, the framework includes practical recommendations for retailers relating to several key areas, including leveraging the metaverse for refined shopper engagement.

Huang, Xiaotong;Zhan, Wentao;Qi, Tianjiao;Guo, Yu;Bai, Rui;Hong, Tao

doi: 10.3390/systems13030153pmid: N/A

In the current context of increasingly severe global environmental problems, green innovation policies have attracted much attention as an important means to promote sustainable economic development, achieve efficient resource utilization, and be environmentally friendly. Since green innovation involves various factors such as technology research and development and policy support, active cooperation and coordination among governments at all levels are required. Therefore, the theoretical analysis of the game strategy of green innovation among regional governments is particularly important. This paper focuses on inter-governmental collaboration, constructs a tripartite evolutionary game model between the central government and different local governments, analyzes the revenue situation of each party under different policy tendencies, and studies the impact of changes in different factors such as local government green innovation revenue and central government tax revenue on the stability of green innovation policy through simulation analysis. The results show that the adoption of punitive policies by the central government is more conducive to the formation of a stable collaborative governance mechanism. In addition to direct governance costs and benefits, the tax coefficient of local governments and the reduction in local enterprise profits are also key factors affecting regional collaborative governance. On this basis, this paper discusses the game strategies of different regional governments in promoting green innovation from the perspectives of the central government and local governments and puts forward policy recommendations to promote regional collaborative green innovation at the mechanism construction and specific operational levels, providing theoretical guidance for inter-governmental green innovation cooperation.

Ibrahim, Atef;Gebali, Fayez

doi: 10.3390/systems13030154pmid: N/A

The emergence of the Internet of Things (IoT) technologies has greatly enhanced the lives of individuals with disabilities by leveraging radio frequency identification (RFID) systems to improve autonomy and access to essential services. However, these advancements also pose significant security risks, particularly through side-channel attacks that exploit weaknesses in the design and operation of RFID tags and readers, potentially jeopardizing sensitive information. To combat these threats, several solutions have been proposed, including advanced cryptographic protocols built on cryptographic algorithms such as elliptic curve cryptography. While these protocols offer strong protection and help minimize data leakage, they often require substantial computational resources, making them impractical for low-cost RFID tags. Therefore, it is essential to focus on the efficient implementation of cryptographic algorithms, which are fundamental to most encryption systems. Cryptographic algorithms primarily depend on various finite field operations, including field multiplication, field inversion, and field division. Among these operations, field multiplication is especially crucial, as it forms the foundation for executing other field operations, making it vital for the overall performance and security of the cryptographic framework. The method of implementing field multiplication operation significantly influences the system’s resilience against side-channel attacks; for instance, implementation using unidirectional systolic array structures can provide enhanced error detection capabilities, improving resistance to side-channel attacks compared to traditional bidirectional multipliers. Therefore, this research aims to develop a novel unidirectional systolic array structure for the Dickson basis multiplier, which is anticipated to achieve lower space and power consumption, facilitating the efficient and secure implementation of computationally intensive cryptographic algorithms in RFID systems with limited resources. This advancement is crucial as RFID technology becomes increasingly integrated into various IoT applications for individuals with disabilities, including secure identification and access control.

Li, Jiawei;Fu, Weigang;Huang, Gangjin;Liu, Kai;Zhang, Jiewei;Fu, Yaoming

doi: 10.3390/systems13030155pmid: N/A

To reduce airport operational costs and minimize environmental pollution, an increasing number of airports are transitioning from fuel-powered to electric ground-handling vehicles. However, the limited battery capacity of electric vehicles and the need for charging make the scheduling of these vehicles more complex. To address this scheduling problem, this paper proposes an electric ground-handling vehicle scheduling algorithm that combines the MAMBA model with an attention-based neural network. The MAMBA model is designed to process multi-dimensional features such as flight information, vehicle locations, service demands, and time window constraints. Subsequently, an attention mechanism-based neural network is developed to dynamically integrate vehicle states, service records, and operational and charging constraints, in order to select the most suitable flights for electric ground-handling vehicles to service. The experiments use flight data from Xiamen Gaoqi International Airport and compare the proposed method with CPLEX solvers, existing heuristic algorithms, and custom heuristic algorithms. The results demonstrate that the proposed method not only effectively solves the electric ground-handling vehicle scheduling problem and provides high-quality solutions, but also exhibits good scalability in different parameter settings and real-time scheduling scenarios.

Symasek, Luke;Yeazitzis, Taylor;Weger, Kristin;Mesmer, Bryan

doi: 10.3390/systems13030156pmid: N/A

Artificial intelligence (AI) technology has become one of the most frequently discussed subjects in the development of technology in recent years. Due to its incredible pattern recognition, it can help humans complete work much faster than before with little to no monetary cost. Despite the widespread impact that AI technologies have on various fields, acceptance and adoption of AI lag behind because of a wide range of factors among users. This paper outlines the results of a large literature review that attempts to tease out some of these factors by examining individual differences that may impact the acceptance and adoption of AI. This goal was achieved through an exploration of individual differences that play a role in the acceptance and adoption of new technologies more broadly, as well as AI technologies, to gain a more holistic understanding of the factors contributing to the lack of acceptance and adoption of AI. The main goal of this literature review was to find the individual differences (IDs) associated with the acceptance and adoption of AI technology and general technology. A secondary goal was to create a model based on the acceptance of general technology that could assist in future AI technology research, development, and implementation. This paper identifies several IDs that were found to play a role in the adoption and acceptance of AI technology, as well as 15 specific IDs that were commonly shown to play a role in the adoption and acceptance of general technology. Because of the rapid development of AI technologies in recent years, there is a lack of research examining the acceptance and adoption of AI technologies; however, there is a great deal of research examining the broader acceptance and adoption of technology, and there is significant overlap between the studies that examined general technology acceptance and adoption and those that examined AI-specific technology acceptance and adoption. Because of this, we believe that the research on general technology acceptance and adoption can be used as a foundation and inspiration for future research on AI technology in this area.

Almadani, Basem;Kaisar, Hunain;Thoker, Irfan Rashid;Aliyu, Farouq

doi: 10.3390/systems13030157pmid: N/A

The global Internet of Medical Things (IoMT) market is growing at a Compound Annual Growth Rate (CAGR) of 17.8%, a testament to the increasing demand for IoMT in the health sector. However, more IoMT devices mean an increase in the volume and velocity of data received by healthcare decision-makers, leading many to develop Distributed Decision Support Systems (DDSSs) to help them make accurate and timely decisions. This research is a systematic review of DDSSs in healthcare using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) framework. The study explores how advanced technologies such as Artificial Intelligence (AI), IoMT, and blockchain enhance clinical decision-making processes. It highlights key innovations in DDSSs, including hybrid imaging techniques for comprehensive disease characterization. It also examines the role of Case-Based Reasoning (CBR) frameworks in improving personalized treatment strategies for chronic diseases like diabetes mellitus. It also presents challenges of applying DDSSs in the healthcare sector, such as security and privacy, system integration, and interoperability issues. Finally, it discusses open issues as future research directions in the field of DDSSs in the healthcare sector, including data structure standardization, alert fatigue for healthcare workers using DDSSs, and the lack of adherence of emerging technologies like blockchain to medical regulations.

Hipólito, Francisco;Dias, Álvaro;Pereira, Leandro

doi: 10.3390/systems13030158pmid: N/A

This study examines the interplay of consumer trust, return policies, and risk perception in shaping satisfaction with online shopping experiences in a business to consumer e-commerce context. Employing a conceptual model tested through Partial Least Squares-Structural Equation Modeling (PLS-SEM), it evaluates how these factors individually and collectively influence consumer satisfaction. The findings confirm that consumer trust reduces perceived risk and enhances shopping satisfaction. Contrary to expectations, attractiveness of free returns has no significant impact on either risk perception or satisfaction, while clear return policies positively influence satisfaction but not risk perception. Additionally, the study reveals a quadratic relationship between risk perception and satisfaction, suggesting that an optimal level of perceived risk maximizes satisfaction, deviating from prior research advocating for minimal perceived risk. These results offer new insights into consumer behavior in online retail, highlighting the nuanced role of risk and emphasizing the strategic importance of trust and return policies. The paper concludes by discussing managerial implications and suggesting directions for future research.

Liao, Haihan;Wang, Chengmin;Gu, Yanzhang;Liu, Renhuai

doi: 10.3390/systems13030159pmid: N/A

Digital transformation (DT) has become an important engine for the development of the digital economy and an important means of reshaping corporate culture, business processes, management models, and so on. Different social communities at different levels have different needs and understandings of digital transformation. Therefore, this paper proposes to explore the communication themes of digital transformation on social media. This study’s main objective is to uncover underlying thematic structures and core ideas from large amounts of textual data in different social media communities to better understand the significance of the communication themes. This paper also aims to reveal the characteristics of diffusion patterns of DT themes by opinion-themed mining. This study uses text mining and social network analysis methods to mine DT themes, theme structure, and the statistical characteristics of hot words across various online communities. The main findings of this study are as follows. The Huawei forum discusses the technological drivers of the digital economy from a micro level. Sohu News explores business operation strategies at a macro level. The Zhihu forum discusses the elements of digital development at the micro level. Moreover, the hot words’ degree centrality and betweenness centrality across various online communities exhibited a power law distribution. In conclusion, this research paper studies and analyzes DT themes of different social media platforms to discover the opinions and attitudes of various social groups in the digital transformation era and deeply interprets social trends and public opinions in order to provide valuable decision-making theoretical support for managers, enterprises, and governments.

Song, Jingwei;Cong, Jianhui;Liu, Yuqing;Zhang, Weiqiang;Liang, Ran;Yang, Jun

doi: 10.3390/systems13030160pmid: N/A

In the context of sustainable development, water resources, energy, and carbon emissions are pivotal factors influencing the rational planning of economic development and the secure establishment of ecological barriers. As a core food production area, how can the Great River Basin balance the pressure on the “water–energy–carbon” system (WEC) to realize the coordinated development of “nature–society–economy”? Taking the Yellow River Basin in China as the research object, this paper explores the coupling characteristics and virtual transfer trends of WEC in the agricultural sector under the condition of mutual constraints. The results show the following: (1) On the dynamic coupling characteristics, W-E and E-C are strongly coupled with each other. The optimization of water resource allocation and the development of energy-saving water use technology make the W-E consumption show a downward trend, and the large-scale promotion of agricultural mechanization makes the E-C consumption show an upward trend. (2) On the spatial distribution of transfer, there is an obvious path dependence of virtual WEC transfer, showing a trend of transfer from less developed regions to developed regions, and the coupling strength decreases from developed regions to less developed regions. The assumption of producer responsibility serves to exacerbate the problem of inter-regional development imbalances. (3) According to the cross-sectoral analysis, water resources are in the center of sectoral interaction, and controlling the upstream sector of the resource supply will indirectly affect the synergistic relationship of WEC, and controlling the downstream sector of resource consumption will indirectly affect the constraint relationship of WEC. This study provides theoretical and methodological references for the Great River Basin to cope with the resource and environmental pressure brought by global climate change and the effective allocation of inter-regional resources.