Journal of Organizational and End User Computing

Published by IGI Global
Online ISSN: 1546-5012
Print ISSN: 1546-2234
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As market competition intensifies, companies recognize the value of attracting customers to participate in activities and loyalty programs (LPs) that encourage repeat purchases and maintain customer loyalty. Literature on LP design explores the positive impact of program structure and rewards on the acquisition of customers. However, research is lacking on the role of LP information transparency on customer participation intention. This study uses 280 college students in China as the survey object to explore the influence of LP information transparency on willingness to participate in such programs. Using experimental design methods, the authors verify whether the type of merchant and channel customers select affect willingness to participate when customers redeem rewards. This study also explains the internal psychological mechanism of information transparency, merchant and channel types, and customer participation intention from the perspective of perceptual psychological distance in construal level theory (CLT) and the elaboration likelihood model (ELM). Both information visibility and accessibility have a positive impact on customer intention to participate in LPs. When a customer redeems a reward from a LP operator, information visibility has a more positive impact on willingness to participate than redeeming a reward from an alliance partner. Moreover, when a customer redeems a reward from online channels, the positive impact of information accessibility on willingness to participate is greater than redeeming from offline channels. Under the influence of multiple psychological distance effects, the synergistic effect of merchant type and channel type is not significant in the relationship between information transparency and willingness to participate in LPs. This article will provide design strategies and management suggestions for retail managers to attract customers to participate in LPs.
At present, internet celebrity marketing has become a driving force for the growth of mobile e-commerce; however, it has also become more apparent that the credibility and authenticity of the internet celebrity is directly correlated to the success of the marketing model. Therefore, in order to entice consumers into purchasing products, cooperations and internet celebrities must be deemed trustworthy. In addition, there are several factors that influence the trust between internet celebrities and consumers. To highlight these factors, this paper constructed an internet celebrity marketing model from the perspective of trust and takes internet celebrity features, marketing character, and product factor as three constructs. Furthermore, eight independent variables are defined, and the corresponding items are designed. Through a quite large data survey and analysis, they have three findings: all eight independent variables have significant influence on trust, and internet celebrities' popularity, interactivity, and professionalism are the top three important factors.
This paper utilizes nonfinancial information disclosure to develop a measure of text-based competition network. Using the data of China's listed firms, the authors adopt the textual analysis method to identify a unique group of competitors for the focal firm and construct the text-based competition network. In the whole network, leading firms receive increasing attention from competitors, and they play a vital role for the dynamic changes in the whole market. Moreover, the interactions between the focal firm and competitors in the text-based competition network are shown by some financial indicators. The characteristics of the text-based competition network have a significant impact on the future performance of the focal firm. Finally, economic links in the competition network are discussed by varying the number of competitors, which shows the impact of various competitors on economic similarities. The text-based competition network shows the relative importance of competitors for the focal firm and explains firms' decision-making from the perspective of dynamic competition.
The E-CLSC (E-closed-loop supply chain) game model dominated by manufacturer is set, and information value about asymmetry fairness concern of E-platform (E-commerce platform) is calculated for manufacturer, recycler, and E-platform. By signaling model under various signal costs, the authors study the condition for E-platform to transmit real information about fairness concern so as to reduce profit loss for all parties in E-CLSC. The authors prove that E-platform has the motivation to disguise or exaggerate fairness concerns in order to obtain more profit, and manufacturers must try to identify the E-platform’s real fairness concern to avoid profit loss. Besides, only when different types of E-platforms need significantly different signaling cost, both of them would like to send real fairness-concern signal, and thus manufacturer can effectively identify E-platform’s real information about fairness concern so as to improve recycling rate and optimize the whole E-CLSC operation.
The influence of leaders on employees' innovative behavior is a new problem. Based on the relationship culture and digital technology situation in China, with reference to information processing and other theories, this study constructs a double intermediary model of the impact of e-leadership on employee innovation behavior from the perspective of “self” and “relationship,” and introduces employee power distance as the boundary condition. The results show that psychological capital and affective commitment to leadership play a mediating effect between e-leadership and employee innovation behavior, and employee power distance weakens the positive impact of e-leadership on employee innovation behavior through psychological capital and emotional commitment. The research conclusion of this paper provides theoretical basis and practical enlightenment for enterprise leaders to promote employee innovation behavior by improving their e-leadership level and understanding the relationship between themselves and employees.
This study focuses on the knowledge fusion model of e-government information resources that supports user decision-making information needs, it discusses the user decision-making information needs model, the knowledge fusion service model, and the relationship between them. The inter-layer mapping matching mechanism realizes the ultimate value of knowledge fusion. Therefore, this paper analyses and studies the mapping mechanism between the user information demand model and the knowledge fusion service model. A semantic, similarity-based knowledge fusion service matching method for e-government information resources is proposed to address the problem of lack of semantics in traditional web service matching methods. This method uses the ontology description language OWL-S to map information requirement documents of user decisions and knowledge fusion service function documents into an ontology tree structure. The authors then use this as the basis to calculate the concept similarity and relationship similarity measures, and the service matching based on semantic similarity can be realized.
At present, society has entered the era of digital finance, and the information management system (IMS) of financial services has been developing rapidly, so the security of data has become particularly important. Firstly, some security techniques in IMS of financial services are introduced. Secondly, this study analyzes how to combine secure muti-party computation with blockchain technology to enhance the security of IMS. Finally, the feasibility and reliability of the scheme are verified by a comparative test. The experimental results reveal that the evaluation index score of the optimized scheme is higher than that of the traditional scheme. Meanwhile, in the comparative experiment of information data encryption, it can be seen that the running time of all schemes will improve with the increase of data. However, the increase rate of the optimized model in this study is much slower than that of the traditional model.
The aim is to improve small and medium-sized enterprises (SMEs)' core competitiveness and financing attainability using deep learning (DL) under economic globalization. Accordingly, this work constructs a supply chain symbiosis system based on DL, economics, and Stackelberg game theory following a status quo analysis of the financing status of SMEs. Afterward, a structural framework of supply chain financing (SCF) is designed. Further, it verifies the effectiveness of the proposed back propagation neural network (BPNN) credit evaluation model through specific enterprise data. The results show that the proposed internet of things (IoT)-based SCF SMEs-oriented BPNN credit evaluation model reaches a prediction accuracy of 91.4%. It effectively eliminates information asymmetry between banks and various capitals. As a result, banks can guarantee operation funds for the supply chain SMEs and help them minimize project risks by lowering financing leverage and through information transparency.
The current development of remote monitoring technology (RMT) has become increasingly mature. The key to implementing this technology lies in the user's willingness to use it. In order to study the influencing factors of using RMT in green operation and service-oriented manufacturing enterprises, based on organizational behavior, this exploration discusses the reasons that affect the introduction of new technologies into enterprises from the perspectives of perceived risk, conformity and technology acceptance. Moreover, a series of data is obtained through the questionnaire and the results are obtained by analyzing the data. Suggestions to improve the use of RMT in enterprises are put forward. The results show that technology itself, external environment and organizational characteristics can all affect the decision-making of enterprises on new technology.
The continuous upsurge of tourism consumption activities has promoted economic development, but at the same time, it has also produced numerous problems, such as low-quality service and high admission prices at scenic spots, which are not conducive to the sustainable development of tourism. In this paper, in view of the phenomenon of low-quality service of scenic spots, a three-party evolutionary game model of scenic spots, tourists, and government is constructed under the participation of tourists and the reward-subsidy mechanism and punishment mechanism, and a simulation analysis is performed using the NetLogo platform. The results show that, under the reward-subsidy and punishment mechanisms, the service strategy selection of scenic spots will eventually evolve to provide high-quality services, tourists will eventually choose the no-complaint strategy, and the government will eventually evolve to provide active supervision.
Based on the perspectives of social risk amplification and the knowledge-attitudes-practice model, this study aimed to test how the level of knowledge about COVID-19 and information sources can predict people's behavioral changes and to examine the effect mechanisms through the mediating roles of attitude, risk perception, and negative emotions in a survey of 498 older Chinese adults. The results showed that (1) older people had a lower level of factual knowledge regarding the variant strains and vaccines; (2) in the information sources-behavior, information sources had a critical influence on elderly individuals' coping behaviors; and (3) in the knowledge-behavior, factual knowledge had a significant effect on elderly individuals' coping behaviors. Specifically, for prevention behaviors, both risk perception and negative emotions played full mediating roles. The findings have significant implications for the development of an effective COVID-19 prevention program to older adults coping with pandemic conditions.
In response to the COVID-19 outbreak, the governments of different countries adopted, such as locking down cities and restricting travel and social contact. Online health communities (OHCs) with specialized physicians have become an important way for the elderly to access health information and social support, which has expanded their use since the outbreak. This paper examines the factors influencing elderly people’s behavior in terms of the continuous use of OHCs from a social support perspective, to understand the impact of public health emergencies. Research collected data from March to April 2019, February 2020, and August 2021, in China. A total of 189 samples were collected and analyzed by using SmartPLS. The results show that (1) social support to the elderly during different stages has different influences on their sense of community and (2) the influence of the sense of community on the intention to continuously use OHCs also seems to change over time. The results of this study provide important implications for research and practice related to both OHCs and COVID-19.
In the period of public health crisis, effective and efficient transmission of crisis information to the public through social media is an important support for achieving social stability and orderly online public engagement. From the perspective of public value management, this study systematically investigated how local government agencies in China used social media to promote public engagement and raise public sentiment during the COVID-19 crisis. Using data captured from the “Wuhan Release” Sina Weibo account, the authors studied the factors that influence public engagement, including information sources, language styles, and media types. Further, it explores the influence of the interactive effects of public value with information sources, language styles, and media types on public engagement and public sentiment. The results show that the consistency of government response content and public value promotes public engagement and raises public sentiment. This research provides enlightenment and ideas for cognition, understanding and governance of public opinion in practice.
To reveal the influence mechanism of e-banking channel selection of elderly customers, according to the analysis of elderly customers’decision-making process, a threshold model is proposed by using small world customer relationship network and variable setting in this study. The multi-agent simulation of e-banking channel selection behavior of elderly customers is carried out from the perspectives of channel diffusion speed and customer channel selection proportion in the context of Covid-19 pandemic. The research shows that channel performance and individual differences of customers affect the adoption of e-banking by elderly customers. This study also has found that network size and network density can regulate the impact of channel performance on the selection behavior of elderly groups. However, they could play a regulatory role under certain conditions. Finally, this study puts forward some suggestions to improve the channel diffusion efficiency, such as building an elderly friendly e-financial service channel and construction of elderly business market culture.
In this paper, Artificial Intelligence assisted rule-based confidence metric (AI-CRBM) framework has been introduced for analyzing environmental governance expense prediction reform. A metric method is to assess a level of collective environmental governance representing general, government, and corporate aspects. The equilibrium approach is used to calculate improvements in the source of environmental management based on cost, and it is tailored to test the public sector-corporation for environmental shared governance. The overall concept of cost prediction or estimation of environmental governance is achieved by the rule-based confidence method. The framework compares the expected cost to the environment of governance to determine the efficiency of the cost prediction process.
This study focuses on the restorative effects of immersive virtual reality (VR) forest experiences on elderly people during the COVID-19 lockdown. A field experiment with 63 elderly participants was conducted in an elderly care institution in China. The results showed that a five-minute VR forest experience with three minutes of subsequent reliving can bring immediate psychological improvements (i.e., increased positive affect, decreased negative affect, and enhanced stress recovery) to elderly individuals. The negative affect decrease and stress recovery enhancement were more obvious among introverted individuals. Furthermore, participating in three VR forest experiences over three consecutive days can bring continuous psychological improvements. Moreover, short VR forest experiences were unable to significantly decrease the blood pressure of participants. The effects of three VR experiences over three days on blood pressure improvement were also nonsignificant. Additionally, VR forest experiences can increase elderly participants' intentions to undertake real forest therapy.
Healthcare insurance fraud influences not only organizations by overburdening the already fragile healthcare systems, but also individuals in terms of increasing premiums in health insurance and even fatalities. Identifying the behavioral characteristics of fraudulent claims can help shed light on the development of artificial intelligence and machine learning technologies to detect fraud in health information system research. In this paper, a theoretical model of medical insurance fraud identification is proposed, which characterizes the judgment variables of fraud from the three dimensions of time, quantity, and expenses. The model is verified with large-scale, real-world medical records. Our study shows that, in comparison with claims made by normal people, fraudulent claims usually have a greater frequency of hospital visits, and more medical bills, accompanied by higher amounts of medical expenses. An interesting discovery is that the price per bill for fraudulent cases is not statistically different from the normal cases.
Patients’ emotions toward health IT can play an important role in explaining their usage of it. One form of health IT is self-managing care IT, such as activity trackers that can be used by chronic patients to adopt a healthy lifestyle. The goal of this study is to understand the factors that influence the arousal of emotions in chronic patients while using these tools. Past studies, in general, tend to emphasize how IT shapes emotions, underplaying the role of the individual user’s identity and, specifically, how central health is to the user’s self in shaping emotions. In this research, the authors argue that patients’ health identity centrality (i.e., the extent to which they consider health as central to their sense of self) can play an important role in forming their dependence on health IT by affecting their use of it directly and shaping their emotions around it.
To reduce the incidence of cerebrovascular disease and mortality, identifying the risks of cerebrovascular disease in advance and taking certain preventive measures are significant. This article was aimed to investigate the risk factors of cerebrovascular disease (CVD) in the primary prevention and to build an early warning model based on the existing technology. The authors use the information entropy algorithm of rough set theory to establish the index system suitable for the early warning model. Then, using the limited Boltzmann machine and direction propagation algorithm, the depth trust network is established by building and stacking RBM, and the back propagation is used to fine-tune the parameters of the network at the top layer. Compared with the LM-BP early-warning model, the deep confidence network model is more effective than traditional artificial neural network, which can help to identify the risk of cerebrovascular disease in advance and promote the primary prevention.
Structural design of a WSN
Simulation model for routing protocols using MANETs
The end-to-end delay (CBR) of routing protocols
Medical sensors are implanted within the vital organs of human body to record and monitor the vital signs of pulse rate, heartbeat, electrocardiogram, body mass index, temperature, blood pressure, etc. to ensure their effective functioning. These are monitored to detect patient’s health from anywhere and at any time. The Wireless Sensor Networks are embedded in the form of Body Area Nets and are capable of sensing and storing the information on a digital device. Later this information could be inspected or even sent to a remotely located storage device specifically (server or any public or private cloud for analysis) so that a medical doctor can diagnose the present medical condition of a person or a patient. Such a facility would be of immense help in the event of an emergency such as a sudden disaster or natural calamity where communication is damaged, and the potential sources become inaccessible. The aim of this paper is to create a mobile platform using Mobile Ad hoc Network to support healthcare connectivity and treatment in emergency situations.
Adoption and user perceptions are dominant on personal health records literature and have led to a better understanding of what individuals' behaviors and perceptions are about the adoption of personal health records. However, these insights are descriptive and are not actionable to allow creating personal health records that will overcome the adoption problems identified by users. This study uses action design research to provide actionable knowledge regarding user perceptions and adoption and their application in the case of the digital allergy card. To achieve this, we conducted interviews with patients and physicians as part of the evaluation of the digital allergy card mock-up and the first prototype. As results, we provided some research proposals regarding the benefits of, levers for, and barriers to adoption of the digital allergy card that can be tested for several other personal health records.
This article reports on an investigation into how to improve problem formulation and ideation in Design Science Research (DSR) within the mHealth domain. A Systematic Literature Review of problem formulation in published mHealth DSR papers found that problem formulation is often only weakly performed, with shortcomings in stakeholder analysis, patient-centricity, clinical input, use of kernel theory, and problem analysis. The study proposes using Coloured Cognitive Mapping for DSR (CCM4DSR) as a tool to improve problem formulation in mHealth DSR. A case study using CCM4DSR found that using CCM4DSR provided a more comprehensive problem formulation and analysis, highlighting aspects that, until CCM4DSR was used, weren’t apparent to the research team and which served as a better basis for mHealth feature ideation.
As M-Health apps become more popular, users can access more mobile health information (MHI) through these platforms. Yet one preeminent question among both researchers and practitioners is how to bridge the gap between simply providing MHI and persuading users to buy into the MHI for health self-management. To solve this challenge, this study extends the Elaboration Likelihood Model to explore how to make MHI advice persuasive by identifying the important central and peripheral cues of MHI under individual difference. The proposed research model was validated through a survey. The results confirm that (1) both information matching and platform credibility, as central and peripheral cues, respectively, have significant positive effects on attitudes toward MHI, but only information matching could directly affect health behavior changes; (2) health concern significantly moderates the link between information matching and cognitive attitude and only marginally moderates the link between platform credibility and attitudes. Theoretical and practical implications are also discussed.
Online medical communities have revolutionized the way patients obtain medical-related information and services. Investigating what factors might influence patients' satisfaction with doctors and predicting their satisfaction can help patients narrow their choices and increase their loyalty towards online medical communities. Considering the imbalanced feature of dataset collected from Good Doctor, the authors integrated XGBoost and SMOTE algorithms to examine what factors can be used to predict patient satisfaction. SMOTE algorithm addresses the imbalanced issue by oversampling imbalanced classification datasets. And XGBoost algorithm is an ensemble of decision trees algorithm where new trees fix errors of existing trees. The experimental results demonstrate that SMOTE and XGBoost algorithms can achieve better performance. The authors further analyzed the role of features played in satisfaction prediction from two levels: individual feature level and feature combination level.
Due to the increasing ageing population, how can caregivers effectively provide long-term care services to meet the older adults' needs with finite resources is emerging. In addressing this issue, nursing homes are striving to adopt smart health with the internet of things and artificial intelligence to improve the efficiency and sustainability of healthcare. This study proposed a two-echelon responsive health analytic model (EHAM) to deliver appropriate healthcare services in nursing homes under the internet of medical things environment. A novel care plan revision index is developed using a dual fuzzy logic approach for multidimensional health assessments, followed by care plan modification using case-based reasoning. The findings reveal that EHAM can generate patient-centred long-term care solutions of high quality to maximise the satisfaction of nursing home residents and their families. Ultimately, sustainable healthcare services can be within the communities.
As artificial intelligence technique is widely used in the automatic driving system, the safety evaluation of automatic vehicles is considered to be the most important demand. Under this context, in this paper, an evaluation system, which is composed of several important evaluation projects is proposed based on big data. These indicators reflect the performance of the automatic driving system. Besides, the principle of the evaluation index and the data management scheme are explained. In terms of the evaluation projects, the online test and the offline test are included, when the former focuses on the function design that is as expected, while the latter aims to ensure the actual driving experience of the automatic driving system. The evaluated results provide optimization direction of the algorithm index. Furthermore, based on AI technology and user big data management, the system saves lots of test cost and guarantees algorithm performance and system stability.
Recent years, many online network communities, such as Facebook, Twitter, Tik Tok, Weibo, etc., have developed rapidly and become the bridge connecting physical social world and virtual cyberspace. Online network communities store a large number of social relationships and interactions between users. How to analyze diffusion of influence from these massive social data has become a research hotspot in the applications of big data mining in online network communities. A core issue in the study of influence diffusion is influence maximization. Influence maximization refers to selecting a few nodes in a social network as seeds, so as to maximize influence spread of seed nodes under a specific diffusion model. Focusing on two core aspects of influence maximization, i.e., models and algorithms, this paper summarizes the main achievements of research on influence maximization in the computer field in recent years. Finally, this paper briefly discusses issues, challenges and future research directions in the research and application of influence maximization.
Data interaction scenarios involving multiple parties in network communities have problems of trust, data security, and reliability of the parties, and secure multiparty computation(SMPC) can effectively solve these problems. To address the security and fairness issues of SMPC, this study considers that semi-honest participants can lead to deviations in the security and fairness of the protocol, and combines information entropy and mutual information to present an n-round information exchange protocol in which each participant broadcasts a relevant information value in each round without revealing other information. The uncertainty of the correct outcome value is blurred by the interaction information in each round, and each participant is not sure of the correct outcome value until the end of the protocol, which effectively prevents malicious behavior and ensures the correct execution of the protocol. Security and fairness analysis shows that our protocol guarantees the security and relative fairness of the output obtained by the participants after completing the protocol.
The outbreak of COVID-19 led to rapid development of the mobile healthcare services. Given that user satisfaction is of great significance in inducing marketing success in competition markets, this research explores and predicts user satisfaction with mobile healthcare services. Specifically, the current research aimed to design a machine learning model that predicts user satisfaction with healthcare services using big data from Google Play Store reviews and satisfaction ratings. By dealing with the sentimental features in online reviews with five classifiers, the authors find that logistic regression with term frequency-inverse document frequency (TF-IDF) and XGBoost with bag of words (BoW) have superior performances in predicting user satisfaction for healthcare services. Based on these results, the authors conclude that such user-generated texts as online reviews can be used to predict user satisfaction, and logistic regression with TF-IDF and XGBoost with BoW can be prioritized for developing online review analysis platforms for healthcare service providers.
This paper aims to provide an overview of academic research within the field of digital transformation. The authors conduct a bibliometric analysis using VOSviewer, Harzing's Publish or Perish, and SciMAT to evaluate and visualize the bibliographic materials. The analysis focuses on journals, papers, researchers, institutions, and countries, using bibliometric indicators such as productivity, citations, H-index values, and TLS values. Graphical analyses illustrate co-authorship, co-occurrence of keywords, evolution of research topics, and network of influential researchers within digital transformation. The results complement each other and show that Germany, the United States, and the Russian Federation are the most influential countries in digital transformation research. Additionally, the results suggest that collaboration within this field is still weak, and many research topics are just beginning to emerge. This research provides a summary of most of the key aspects in digital transformation research and helps lay the groundwork to shape the future of this growing field.
Descriptive statistics of respondents' characteristics
Convergent validity analysis results
Discriminant validity: Fornell-Larcker criterion
This purpose of this study is to develop a research model by extending the theory of planned behavior in a new application context and apply it to investigate the extrinsic factors influencing people's attitudes towards donating to medical crowdfunding projects appearing on mobile social networking sites (MSNS) and their intention to donate. A survey of 356 Chinese users was conducted and structural equation modeling was used to validate the proposed model and hypotheses. The results indicate that project information, retweeter information, and MSNS information all have a significant effect on the general attitude towards donating to medical crowdfunding projects, and general attitude positively affects people's donation intention. In addition, perceived behavioral control also has a positive effect on people's donation intention, while experienced donating to medical crowdfunding projects has a negative effect on people's donation intention. The research findings provide important theoretical and practical implications.
In the era of big data-driven digital economies, technology commercialization capability has become the lifeblood of high-tech enterprises to shape competitive advantage and achieve multiplier growth, while the related research is still limited. Drawing on the dynamic capability theory, this study asserts that external user engagement provides an imperative way to enhance technology commercialization capability. Although the highly complicated external environment may weaken this link, high-tech enterprises’ own big data analytics capability contributes to effectively coping with the unpredictable changing environment, thereby amplifying the brighter side of user engagement. The moderated moderation model and hypotheses were supported by the unique surveys of 216 high-tech enterprises. Further, the findings broaden the vision of related research fields, and provide meaningful practical guidance for strategic decision-making and dynamic capability constructing of high-tech enterprises in the new era.
The Mobile Chronic Disease Management Service (MCDMS) is an emerging medical service for chronic disease prevention and treatment, but limited attention has been paid to the factors that affect users' intention to adopt the service. Based on the unified theory of acceptance and use of technology and the protection motivation theory, the authors built an MCDMS adoption model. The authors also verified the differentiating age effect on the service adoption intention from experiential distance perspective of the construal level theory. Empirical results showed that the young group focused more on the impact of effort expectancy, whereas the elderly group focused more on performance expectancy, imitating others, and perceived severity. Furthermore, the young group focused more on the impact of perceived vulnerability, and offline medical habits showed no significant influence on either group's intention to adopt, which were not consistent with the original hypotheses. The findings can aid MCDMS providers in selecting marketing strategies targeted toward different age groups.
This paper studies the motivation of learning law, compares the teaching effectiveness of two different teaching methods, e-book teaching and traditional teaching, and analyses the influence of e-book teaching on the effectiveness of law by using big data analysis. From the perspective of law student psychology, e-book teaching can attract students' attention, stimulate students' interest in learning, deepen knowledge impression while learning, expand knowledge, and ultimately improve the performance of practical assessment. With a small sample size, there may be some deficiencies in the research results' representativeness. To stimulate the learning motivation of law as well as some other theoretical disciplines in colleges and universities has particular referential significance and provides ideas for the reform of teaching mode at colleges and universities. This paper uses a decision tree algorithm in data mining for the analysis and finds out the influencing factors of law students' learning motivation and effectiveness in the learning process from students' perspective.
Most of the existing influence maximization problem in social networks only focus on single relationship social networks, that is, there is only one relationship in social networks. However, in reality, there are often many relationships among users of social networks, and these relationships jointly affect the propagation of network information and its final scope of influence. Based on the classical linear threshold model and combined with various relationships between network nodes, in this paper MRSN-LT propagation model is proposed to model the influence propagation process between nodes in multiple relationships social networks. Then, MRSN-RRset algorithm based on reverse reachable set is proposed to solve the problem of low computational performance caused by greedy algorithm in the research process of traditional influence maximization. Finally, the experimental results on real data sets show that the proposed method has better influence propagation scope and greater computational performance improvement.
Many real-world problems can be transformed into multimodal functional optimization. Each of these problems may include several globally optimal solutions, rendering the solution of the problem progressively more difficult. In our study, we present a crowding artificial bee colony, called IABC, which exploits the concepts of crowding and explores search solutions. A crowding approach formed in niches is used to make it capable of tracking and maintaining multiple optima, resulting in good convergence of the search space with a better chance of locating multiple optima. Two new solution search mechanisms are proposed to increase population diversity and explore new search spaces. Experiments were carried out on 14 benchmark functions selected from previous literature. The results of our experiments show that our method is both effective and efficient. In terms of the quality of the success rate, the average number of optima found, and the maximum peak ratio, IABC performs better, or at least comparably, to other cutting-edge approaches.
Network emerging e-commerce refers to the development of wireless broadband technology, smart terminal technology, near-field network, etc. as the driving force. It is the emerging e-commerce represented by the continuous development of modern e-commerce and the integration of commerce. This paper proposes to use Michael Porter’s cluster theory method, income increasing algorithm, and spatial Gini coefficient method to sort out and analyze the research results of industrial agglomeration problems, further study the relationship of e-commerce industry agglomeration mechanism, and build agglomeration simulation model , the construction of the centripetal force model of the industrial agglomeration area, through the analysis of the production factors of the e-commerce industry, and then study the influence of each factor on the development of the e-commerce industry. Finally, this paper selects and uses 16 standard mechanical data sets to investigate and analyze the agglomeration mechanism of the e-commerce industry, which verifies the accuracy and overall applicability of the method.
Financial status and its role in the national economy have been increasingly recognized. In order to deduce the source of monetary funds and determine their whereabouts, financial information and prediction have become a scientific method that can not be ignored in the development of national economy. This paper improves the existing CNN and applies it to financial credit from different perspectives. Firstly, the noise of the collected data set is deleted, and then the clustering result is more stable by principal component analysis. The observation vectors are segmented to obtain a set of observation vectors corresponding to each hidden state. Based on the output of PCA algorithm, we recalculate the mean and variance of all kinds of observation vectors, and use the new mean and covariance matrix as credit financial credit, and then determine the best model parameters.The empirical results based on specific data from China's stock market show that the improved convolutional neural network proposed in this paper has advantages and the prediction accuracy reaches.
The study aims to establish a platform-based enterprise credit supervision mechanism, and combined with big data, accurately evaluate the credit assets of enterprises under the influence of social stability risk, and improve the ability of enterprises to deal with risks. Using descriptive statistical methods, the study shows that most local enterprises exist in the form of micro loans, which promotes the development of local economy to a certain extent, but it is a vicious cycle of economic development; The overall prediction accuracy of the single enterprise risk assessment model under the influence of social stability risk is 65%. Compared with the single algorithm, the prediction accuracy of the integrated algorithm model is significantly improved, and the prediction accuracy can reach 83.5%, the standard deviation of data prediction is small, and the stability of the model is high.
The purpose of this study was focused on exploring the relationship among the fans’ preferences, fans’ para-social interaction, and fans’ word-of-mouth. A survey consisted of 21 items based on the literature review and developed by this study. An online survey was distributed to the users of YouTube in Taiwan. A total of 606 valid samples was collected by survey. The instrument passed the reliability and validity test. Further, the data process applied the PLS (partial least squares) regression analysis methodology. The result shows that the ‘attractive’ impacted ‘para-social interaction’, ‘e-word-of-mouth’, and ‘preferences of fans’ positively. In addition, the para-social interaction plays an important role as a mediator between influencer’s attractiveness, w-word-of-mouth, and preferences of fans. Some suggestions were provided for social media influence’ related studies as reference.
For the taxed goods, the actual freight is generally determined by multiplying the allocated freight for each KG and actual outgoing weight based on the outgoing order number on the outgoing bill. Considering the conventional logistics is insufficient to cope with the rapid response of e-commerce orders to logistics requirements, this work discussed the implementation of data mining technology in bonded warehouse inbound and outbound goods trade. Specifically, a bonded warehouse decision-making system with data warehouse, conceptual model, online analytical processing system, human-computer interaction module and WEB data sharing platform was developed. The statistical query module can be used to perform statistics and queries on warehousing operations. After the optimization of the whole warehousing business process, it only takes 19.1 hours to get the actual freight, which is nearly one third less than the time before optimization. This study could create a better environment for the development of China's processing trade.
This article examines the policy implementation literature using a text mining technique, known as a structural topic model (STM), to conduct a comprehensive analysis of 547 articles published by 11 major journals between 2000 and 2019. The subject analyzed was the policy implementation literature, and the search included titles, keywords, and abstracts. The application of the STM not only allowed us to provide snapshots of different research topics and variation across covariates but also let us track the evolution and influence of topics over time. Examining the policy implementation literature has contributed to the understanding of public policy areas; the authors also provided recommendations for future studies in policy implementation.
In recent years, with the acceleration of the process of economic globalization and the deepening of my country's financial liberalization, the scale of international short-term capital flows has been extremely rapid. This article mainly studies the deep learning digital economy scale measurement method and its application based on the big data cloud platform. This article uses the indirect method to estimate the stock of renminbi circulating abroad. The results show that the application of big data cloud platforms can increase the development share of digital media and digital transactions in the digital economy, and optimize the structure of China's digital economy.
The purpose is to solve the problems of sparse data information, low recommendation precision and recall rate and cold start of the current tourism personalized recommendation system. First, a context based personalized recommendation model (CPRM) is established by using the labeled-LDA (Labeled Latent Dirichlet Allocation) algorithm. The precision and recall of interest point recommendation are improved by mining the context information in unstructured text. Then, the interest point recommendation framework based on convolutional neural network (IPRC) is established. The semantic and emotional information in the comment text is extracted to identify user preferences, and the score of interest points in the target location is predicted combined with the influence factors of geographical location. Finally, real datasets are adopted to evaluate the recommendation precision and recall of the above two models and their performance of solving the cold start problem.
This paper takes the listed companies in China from 2008 to 2017 as the research sample to study the relationship between accounting information quality (AIQ) and company innovation investment efficiency. The results show that AIQ is negatively correlated with both the underinvestment and overinvestment of corporate innovation. Further, AIQ can alleviate financing constraints and reduce the lack of innovation investment; At the same time, AIQ can also alleviate the agency conflict and reduce the excessive investment in innovation. Finally, AIQ can promote the innovation investment efficiency of companies with low information environment.
Digital transformation contributes to enterprise supply chain resilience, but how to control the risks involved and whether this control contributes to supply chain resilience remains to be explored. This paper aims to clarify the relationship between risk control and resilience in the process of digital transformation and to construct a digital transformation supply chain risk (DTSCR) control process system. In this paper, we first use the SLRs method to retrieve 469 papers to construct a dimensional system of DTSCR from the theoretical perspective; we then test whether DTSCR control helps supply chain resilience through a structural equation model; finally, based on the case study of the institute of building materials of China Academy of Building Research, we use a Bayesian believe network to construct a risk control system. Our research contributes to existing literature by improving supply chain resilience from a risk perspective, and the risk control system innovatively constructed in this paper is also of significance for enterprises to carry out DTSCR control in practice.
Since DEMATEL can visualize the structure of complex causal relationships, it is widely used in decision making. One of the important steps in DEMATEL is normalization, and it has received a lot of attention in recent years. Maximum entropy is a universal principle, and it is an effective tool for determining the amount of information existed in evidence. In this paper, maximum entropy based DEMATEL, named as MaxEnt-DEMETEL is proposed, the greatest contribution in this paper is the use of maximum entropy principle to determine the normalized direct influence matrix, which makes it possible to obtain the normalized matrix with minimal information loss. Emergency management is illustrated to show the superiority of the proposed method.
Digital transformation has brought about great social changes, and individuals are constantly facing the challenge of using emerging technologies. This article, for the first time, combines the Diffusion of Innovation Theory and Contract Theory to build a decision model to solve the above challenge. The decision model is constructed according to the key factors that influence the individual decision process, including technological relative advantages, intrinsic motivation, risk-taking, use-cost, technological complexity and compatibility. Through the analysis of the cost utility of each party in Health CrowdSensing technology, the question of whether individuals use the technology is transformed into the question of cost utility. In the experiments, the validity of the decision model is verified by numerical analysis. The decision model proposed in this article provides theoretical basis and experimental verification for further research on how an individual decides whether to use technology or not.
Technology search is crucial to establishing competitive advantage and improving firm performance. However, it is still unclear how different technology search strategies affect competitive advantage and performance at the firm level and how to determine technology search strategies in dynamic environments. Therefore, based on resource-based theory, this study explores the relationship between digital technology search, competitive advantage, and new venture performance (NVP) in dynamic environments with a sample of 267 Chinese new ventures. The results show that the breadth and depth of digital technology search positively affect NVP. Environmental dynamism weakens the positive effect of digital technology search breadth on NVP but strengthens the positive relationship between digital technology search depth and NVP. Moreover, digital technology search breadth affects NVP via differentiated competitive advantage, and digital technology search depth affects NVP through differentiated and cost-leadership competitive advantage. Finally, implications and limitations are discussed.