Ebook: Modern Management Based on Big Data IV
The concept of Big Data has become increasingly familiar in recent years, and it is already an indispensible tool in the management of everything from supply chains and transport to health and education.
This book presents the proceedings of MMBD 2023, the 4th International Conference on Modern Management based on Big Data, held in Seoul, South Korea, from 1-4 August 2023. The 50 papers included here were selected from total of around 160 submissions after a rigorous review process. Papers delivered at the conference were divided into 3 main categories: Big Data, Modern Management, and a special session devoted to Big Data-driven manufacturing and service-industry supply-chain (SC) management, but in addition to these general topics, there were also a number of papers related to lifelong education. Topics covered in the book include innovation in online education management with big data; digital transformation in lifelong education; big data analysis in lifelong education management; green supply chain management; big data analytics in supply chains; policy and strategy for new energy and the environment; smart grid load and energy management; decision-making on sustainable transport policies; modern healthcare management; and social strategy to manage human relationships. Of particular interest are papers concerning big-data analysis and emerging applications.
Presenting innovative original ideas and methods, together with significant results, and supported by clear and rigorous reasoning and compelling new evidence, the book will be of interest to all those who use Big Data to support their management strategies.
Organized by Keimyung University, and co-organized by Beijing Wuzi University and the University of Messina (UniME), the 4th International Conference on Modern Management based on Big Data (MMBD2023) was held from 1–4 August 2023 in Seoul, South Korea. With MMBD2023, the conference series Modern Management based on Big Data (MMBD) completed its fourth edition.
The general topics of MMBD2023 fall into three main categories: 1) Big Data, 2) Modern Management and 3) a Special Session on big data-driven manufacturing and service industry supply chain (SC) management.
Apart from the general topics, MMBD2023 attracted a number of papers related to lifelong education. Topics at the conference included but were not limited to: innovation in online education management with big data; digital transformation in lifelong education; big data analysis in lifelong education management; green supply chain management; big data analytics in supply chains; policy and strategy for new energy and environment; smart grid load and energy management; decision making on sustainable transport policies; modern healthcare management; and social strategy to manage human relationships.
The most popular topics, both at the conference and in this book, concern big data analysis and emerging applications.
All of the papers were exhaustively reviewed by program committee members and peer-reviewers, who took into account the breadth and depth of the research topics that fall under the scope of MMBD. The 50 most promising and FAIA mainstream-relevant contributions were selected from about 160 submissions for presentation and inclusion in this book, which presents innovative original ideas or results of general significance supported by clear and rigorous reasoning and compelling new evidence, as well as methods.
I would like to thank all the keynote and invited speakers, authors, program committee members and anonymous reviewers for their efforts in making MMBD a conference of the highest standard.
Antonio J. Tallón-Ballesteros
University of Huelva (Spain)
Huelva city, Spain
Intelligent marketing and recommendation are a core business of commercial companies, and accurate prediction of sales is the premise and foundation for greater efficiency of smart marketing and recommendation. In order to predict product sales, deep neural network (DNN), convolutional neural network (CNN), time series analysis and other methods have been put forward, but most of which only focus on the temporal or spatial characteristics of data. According to modeling and analyzing sales of products, they are closely related to the spatial location and time of the corresponding merchants. The goal is to predict the sales of products accurately at a given time and place, we advance a hybrid model of CNN-LSTM to forecast sales. Firstly, a large-scale knowledge graph system based on merchants is constructed, which describes the sales data and the relevant interaction scenarios of the corresponding business, merchants and users through the data model of a graph, and add the spatial and data characteristics of the business data on the graph model to describe the temporal and spatial characteristics of the merchants. Based on the constructed business knowledge graph, graph convolutional neural network (GCN) is used to aggregate information and obtain spatial features. Correspondingly, long short-term memory (LSTM) is used to extract time features. Researchers combine the two characteristics to make the sales forecast. In this study, neural network and GCN-LSTM algorithm are respectively used to carry out experiments on two kinds of product regulations. The result shows that the sales predicted by hybrid model of GCN-LSTM is almost as equal as the actual sales. The average accuracy of the proposed model is 89%.
The incorporation of Information and Communication Technology (ICT) into higher education has profoundly revolutionized the manner in which students engage with educational resources and acquire knowledge. Personal Learning Environments (PLEs) facilitate a tailored and adaptable learning approach, empowering students to take charge of their own educational experiences. Nonetheless, to effectively leverage PLEs in higher education, a robust ICT framework is imperative. This paper explores the development of an ICT framework specifically designed for PLEs within the context of higher education. The framework encompasses diverse ICT attributes, including accessibility, flexibility, data analysis, extensive capacity, automation, personalization, and security. Analyzing interview data led to the formulation of a PLE-ICT framework prototype, comprising five dimensions: ICT hardware, ICT software, ICT services, project development, and support team. The findings of this study enrich the theoretical comprehension of ICT integration in PLEs and offer valuable insights for the future elaboration of more intricate PLE-ICT scales. The suggested ICT framework delivers a comprehensive and pragmatic blueprint for the implementation of PLEs in higher education institutions, with the potential to augment student learning experiences and outcomes.
In the upcoming age of AI, smart education reshapes not only the macro modality of society and education but also the micro modality of knowledge and learning. The top priority of smart education is the timely learning disability diagnosis and feedback, the assessment and improvement of students’ mental quality, in which affective data is the crucial index. Putting in perspective the working of positive and negative affects in smart teaching, this research proposes the affective decision tree model drawing on the affective data collected from the PANAS test. Based on the C5.0 algorithm, classification rules are extracted from the calculation of the information gain ratio of affective variables and the construction of decision tree is realized through SPSS 26.0. The research findings indicate that 1) negative affects took precedence over positive affects in the formulation of participants’ smart learning strategy; 2) the architecture of the affective decision tree constructed via learners’ affective judgments and ratings represents the affective filtering process which optimizes teachers’ selection and organization of smart teaching and learning activities. Accordingly, the affective decision tree model could be used as the efficient prediction model and affective profiling model and teaching support system in smart teaching, prompting the affective and cognitive connectivity between teachers and students.
In the midst of the COVID-19 pandemic, the employment and education sectors have shifted significantly toward online platforms. However, the increased reliance on these digital spaces has raised concerns about personal security information. Scholars have taken note of this issue and have explored its implications, with some employing the extended knowledge, attitude, and behavior (KAB) model to investigate the moderating effects of societal education level on the relationship between knowledge and attitude. Hong et al.  conducted a study to examine undergraduates’ KAB regarding personal data sharing in Chinese higher education institutions during the pandemic. Using a questionnaire, the study recruited 156 participants from three universities in West and East China. Using SPSS 23.0, data analysis revealed a widespread lack of awareness, a positive attitude, and proper behavior among college students regarding online personal information leakage during the pandemic. Notably, disparities were observed in KAB among students of different grades, majors, and genders. Students in their sophomore, junior, and senior years were found to be more concerned than freshmen about the availability of their personal information online; what’s more, science majors were more concerned than students of other majors. There appear to be significant gender differences in personal information sharing, ie., males are more concerned about the security of personal information online than females. Through this study, we aim to emphasize that college students’ awareness of personal information protection needs to be improved and suggest that university administrators and policymakers increase information security training. The findings of this study contribute to the theoretical and practical efforts to improve information security in higher education. Future studies should broaden the survey sample and examine the primary factors that influence college students’ KAB of personal information security to ensure the generalization of findings.
Live streaming has brought new opportunities for e-commerce development. As the most distinctive feature of live e-commerce is the virtual presence. How it creates a more immersive and engaging online shopping experience for consumers is a concern for academics and businesses alike. This paper reviews the relevant literature and analyzes the impact of the evolution from social presence to virtual presence on consumer behavior since the development of e-commerce, summarizing the interactions and main differences between social presence and virtual presence. The research results help e-commerce brands to better understand the differences and maximize their potential in different e-commerce models, so as to gain a deeper understanding of consumer needs and improve positive behaviors such as consumer purchase decisions and loyalty.
The accreditation and evaluation of undergraduate majors is one of the most important elements in the monitoring of China’s higher education quality. with the combination of quantitative and qualitative research methods, this paper illustrates the stages of development, problems of undergraduate majors operation in China, and proposes an evaluation framework for undergraduate majors from the fourth paradigm perspective. The development of undergraduate major evaluation in China are divided into four stages: the budding period (1985–1998), the rising period (1998–2009), and the booming period (2010–2022). The forms of evaluation are divided into major accreditation, major ranking and major assessment. Major evaluation mode includes independent evaluation mode, comprehensive evaluation mode and appraisal mode. Continuous collection of sample data, customized indicators, multiple fusion calculation analysis, visual feedback are the typical features of big- data-based intelligent education evaluation.
Finding an optimum way to identify stocks with less delisting risk is critical for every investor in the stock market. However, this procedure is often done based on personal experience, which doesn’t fully utilize the historical delisting records. This convention of selecting stocks might result in a greater loss since it merely involves subjective judgment, especially for individual investors. Our research proposes a probabilistic approach for identifying the delisting risk associated with different industry sectors, given the P/B ratio level distribution. And this research offers a customized guide for individual investors to better choose the safer investment options related to the stocks’ industry sectors. The completion of our conditional probability matrix is operated under the high-rank assumption, together with the features of Bayesian matrices. The experimental results for our domestic delisting stocks supports the validity and usefulness of our method.
Big data technology drives the library alliance and shifts focus from the construction of cooperation mechanism to the construction of technology level. It integrates library big data by building a national shared service platform covering concepts, resources, technology, readers, mechanisms and then fully share the literature resources within the library alliance with the help of blockchains and alliance chains technology to explore the future development and direction of the library.
Objective: Investigate the development status of preparations in medical institutions in Chongqing, analyse the key factors restricting the development of preparations in medical institutions in Chongqing, and put forward targeted countermeasures and suggestions, so as to promote the sustainable and healthy development of preparations in medical institutions in Chongqing. Methods: Data were collected by means of literature review, expert interview, questionnaire surveys (Questionnaire Star, which is a platform providing functions equivalent to Amazon Mechanical Turk). SPSSAU was used for statistical analysis, and the scientific research method of classic Grounded Theory was used for data processing and theoretical construction . The in-depth situational grounded research was conducted on the preparations of medical institutions in Chongqing. Results and conclusion: Due to the insufficient attention paid by the government, hospitals and the market, the development of preparations in Chongqing’s medical institutions could not keep up with the development of the pharmaceutical industry and drug administration supervision. In view of this, the government should coordinate the deployment of “capital increase, staff increase, strength increase” and “integration and joint construction” to promote development, focus on the research and development of Chinese medicine preparation varieties that are in short supply in the market and are urgently needed clinically, and incubate new drugs in the process of inheritance and innovation. The preliminary theoretical summary of the current situation and development of pharmaceutical preparations in medical institutions in Chongqing can provide a basis for decision-making by management departments and promote its sustained and healthy development.
With the popularization of online shopping, more and more advertisements are being placed on online shopping platforms. The impact of advertisements on consumers’ online impulsive buying behavior has attracted much attention, and the phenomenon of consumers making online impulsive purchases is becoming more and more common. This field has become a hot research topic for scholars. This article aims to summarize the connotation of visible advertising, summarize indicators of consumer online impulse buying behavior, compare and analyze the impact of visible advertising on consumer online impulse buying behavior, and provide management decision-making reference for advertisers and e-commerce platforms.
The Industrial Internet can be used in enterprises for predictive maintenance, location tracking, workplace analysis, remote quality monitoring and energy optimization. To realize the development of the industrial Internet, a large number of high-quality industrial Internet talents are indispensable. These talents not only need to have a solid foundation of engineering technology and a good level of computer technology but also need to have a deep understanding and grasp of the industrial production process and be able to develop suitable industrial Internet solutions according to actual needs, to promote the steady development of the industrial Internet. Based on preliminary research and communication with enterprises, this paper designed some courses for Industrial Internet Talent Training.
Taking 28 listed companies in China’s A-share pharmaceutical manufacturing industry from 2018 to 2021 as samples, with tax incentives as an explanatory variable, R&D investment intensity as an intervening variable, enterprise size, enterprise establishment time, return on total assets, debt-to-assets ratio, and fixed asset density as control variables, and innovation performance as explained variable, a multiple linear regression model is established, and descriptive statistics, correlation analysis, multicollinearity test, and robustness test are carried out to empirically analyze the influence of tax incentives on the innovation performance of pharmaceutical manufacturing enterprises. The results show that tax incentives have a significant positive impact on the innovation performance of pharmaceutical manufacturing enterprises, and tax incentives can promote pharmaceutical manufacturing enterprises to increase the intensity of R&D investment, and the intensity of R&D investment has a certain mediating effect and long-term influence on the relationship 1between tax incentives and innovation performance. Combined with the empirical results, the paper puts forward the strategy of improving enterprise innovation performance to enhance the core competitiveness of enterprises.
This paper adopts the value-at-risk (VaR) method as the risk measurement framework, and establishes a series of VaR-GARCH models to measure the dynamic risk of short-term international capital random fluctuations. Then, the accuracy of the empirical results is measured by the Backing-Test. The results show that the measurements of VaR-GARCH(1,1) and VaR-GARCH-M models are relatively conservative; the measurement results of VaR-EGARCH model are closest to the true value of short-term international capital flows.
We study the periodic solutions of a discrete neuron model for period two or period three of the parameter (internal decay rate) unmapped: inline-formula unmapped: math unmapped: msubsup unmapped: mrow unmapped: mo (unmapped: msub unmapped: mrow unmapped: mi βunmapped: mrow unmapped: mi nunmapped: mo )unmapped: mrow unmapped: mi nunmapped: mo =unmapped: mn 0unmapped: mrow unmapped: mi ∞. The novelty of this research is finding a chaotic attractor for certain interval, outside the defined interval the solution goes to positive infinity or to negative infinity. The investigation can be useful in the design of chaos-based neural networks architecture.
This paper investigates the relationship between excess cash holdings, product market competition and corporate innovation by using an empirical research method with a sample of A-share manufacturing companies listed in Shanghai and Shenzhen in China from 2011 to 2021. The study finds that: manufacturing companies’ excess cash holdings have a positive impact on corporate innovation; product market competition positively regulates the relationship between excess cash holdings and corporate innovation, and the more intense the product market competition is, the more significant the regulating effect is. The research in this paper provides a reference for manufacturing firms to optimize corporate cash holdings and promote corporate innovation in different competitive market environments.
With the implementation of the “carbon peaking and carbon neutrality” target strategy, green, low-carbon development has become the main way of economic development, and the low-carbon economic operation and healthy development of state-owned enterprises not only play a leading role in the operation of the national economy as a whole, but also are economic entities that bear greater pressure for green and low-carbon development. This paper focuses on the construction of an evaluation system for the economic responsibility audit of the leaders of state-owned enterprises from a green and low-carbon perspective, using the hierarchical analysis method to determine the weights of each evaluation index, and selecting three commercial state-owned enterprises of Gansu province for verification. The evaluation system is feasible and contributes to the balanced development between the economic development of state-owned enterprises and green and low-carbon.
This paper takes the comprehensive system problem of charging station location and shortest path optimization faced by new energy vehicles in the distribution process as the research object. We hope to obtain a combination optimization method that can further support China’s use of new energy vehicles to complete urban distribution problems. This paper addresses the existing comprehensive challenges related to route selection and site allocation. It takes into account the short transportation mileage and low load capacity of new energy vehicles caused by charging constraints. At the same time, considering the timeliness of modern urban distribution, time window constraints and logic constraints are added. Taking the minimum number of charging stations and the minimum transportation cost as the multi-objective problem of combinatorial optimization, the mathematical model is finally established. After transforming the multi-objective problem into a single-objective problem, the genetic algorithm the genetic algorithm is used to solve the system problem comprehensively. Finally, an example is used to verify the feasibility and effectiveness of genetic algorithm in solving the new energy vehicle distribution – location path combination optimization problem. It provides theoretical support and development prospects for further promoting urban distribution of new energy vehicles in the future.
Economic responsibility auditing can help promote regional economic development, but whether it helps enhance regional innovation capacity and low carbon development needs to be further tested. This paper examines the objectives of authenticity, legality, and effectiveness of economic responsibility audit. The results show that the increased scrutiny of the authenticity, legality and effectiveness of financial records, policy formulation and implementation, and major project activities of government leaders can definitely enhance the awareness of environmental protection and innovation among cadres, thus improving regional innovation capacity and helping to reduce carbon emissions, achieving the goal of double balance between economic growth and carbon reduction.
The development of big data has profoundly changed not only the way of human production and life but also the way we learn and educate ourselves. This research conducted an empirical analysis on the influencing mechanism of the quality of higher education using the structural equation model, finding that five factors “Active and Collaborative Learning (ACL)”, “Student and Faculty Interactive (SFI)”, “Level of Academic Challenge (LAC)”, “Supportive Campus Environment (SCE)” and “Enriched Educational Experiences (EEE)” influence the indicators “Knowledge Obtaining (KO)” , “Ability Obtaining (AO)” and “Value Obtaining (VO)”. SCE has the largest impact on KO, while the influence of ACL is relatively weak. On this basis, this paper, by combining the features of big data, presented four methods for improving the quality of higher education: first, enhancing the construction of the education data platform; second, energizing energize education reform via big data; third, accurately identifying the level of academic challenge and selecting the appropriate education model by the aid of the big data technology; and fourth, based on the information technology, building a harmonic teacher-student relationship and peer relationship.
This research aimed to determine the relative importance of four criteria for decision-making by using each one with a popular decision-making method and evaluating the outcomes and using these criteria to provide ranked alternatives (according to each criterion’s relative importance) for making an investment decision. The four criteria were obtained from a comprehensive literature review related to securities investment. The investment data analyzed were past investment data on trading securities under the Energy and Utilities category of the SET50 index in the Stock Exchange of Thailand. The analysis was done through an Analytic Hierarchy Process (AHP) and a Technique for Order of Preference by Similarity to the Ideal Solution (TOPSIS). Opinions of three experts with experience in giving securities investment advice were collected and arranged into pair-wise comparison matrices that were used in AHP. AHP and TOPSIS calculations were done in Microsoft Excel. The results of the study show that the most important criterion was financial fundamentals with a weight of 44.59%; the second rank criterion was technical factors with a weight of 20.15%; the third-rank criterion was risk factors with a weight of 19.64%; and the last rank criterion was fundamentals of structure and sustainable development with a weight of 15.62%. In addition, the outcome of security ranking by TOPSIS and the past security ranking data were significantly similar as analyzed by a hypothesis statistical test with two dependent samples.
With the rapid development of information technology, there is an emergence of the advancement in cultural digitalization and digital industrialization. By analyzing the implications of the value and technical routes of a cultural digitization strategy, the present study proposed a cultural digitization strategy that transforms the related works, knowledge and wisdom of cultural producers into quantifiable, traceable and tradable digital assets. Additionally, it promotes a more profound level of processing, including the processing and service of cultural resources, which further extends the supply chain of cultural digital resources, while promoting the integration and development within business sectors. This can assist the upgradation and transformation of the cultural resources to ensure an improvement of the digital service levels of cultural resources, along with stimulating new vitality norms of the cultural resources. Further, it can be a motivation factor to promote the creative development of a national culture. Libraries are cultural hub enriched with literature within diverse audience to drive the information circulation. The implementation of a cultural digitization strategy can effectively promote the transformation and upgradation of the library elements by providing a solid channel to focus on libraries as an institution resulting in strategic positions of cultural power in a changing global environment. By focusing on the key tasks and long term goals of cultural digitization, the active adoption and implementation of “enabling digital technology applications by integration and innovation” can be achieved.
The Yangtze River Delta (YRD) is an experimental location of high-quality holistic growth in China and an important area for realizing the “dual carbon” goal. Utilizing panel data from 27 important municipalities in the YRD urban agglomeration from 2010 to 2020, the fixed-effect model is used to evaluate the stage of advancement in the digital economy on urban emission levels. As an intermediary variable, industrial structure modification was tested to see how the digital economy affected local dioxide emissions. The results show that: (1) After an array of robustness tests, the overall impact on both core variables is inverted U-shaped, facilitating and then constraining emissions; (2) As part of its function in cutting emissions, the digital economy also impacts the industrial framework, which has an indirect, nonlinear, U-shaped effect on the intensity of regional dioxide emissions; (3) Looking further, regions with high levels of government spending and investment in innovation are those where carbon emissions are most clearly affected by digitalization. There is still opportunity for evolution and optimization of the YRD region’s energy consumption process. The study’s findings expand our comprehension of the factors that influence high-quality community growth as well as how the digital economy works to support it.
Under the background of the trend of social new retail, based on narrative transmission theory, this study collects 208 samples by questionnaire, integrating social media interaction and consumers’ purchase intention, and analyzes the conditional process of brand stories affecting how to affect consumers’ purchase intention by hierarchical regression method. The result shows that brand story has a positive impact on consumers’ purchase intention, and this process is realized through the intermediary of narrative transmission. Social media interaction negatively regulates the relationship between brand story and consumers’ purchase intentions. Finally, according to the research conclusion, it is suggested that enterprises and marketers pay attention to the role of brand story, make full use of various social media platforms, tell excellent brand stories, increase emotional resonance, and promote consumers’ purchase intention.
This paper expounds the connotation of high-quality development from the perspective of “five development concepts”, and analyzes the dialectical relationship among innovative development, coordinated development, green development, open development and shared development. On this basis, a relatively complete evaluation index system of intellectual property to promote high-quality development has been constructed, and finally, the countermeasures of intellectual property to promote high-quality development have been proposed.