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The necessity to determine optimal teaching strategies for the development of moral education in the context of implementing 5G technologies has motivated the goal of this research: to study the personal aspects of students’ value orientations in the dynamics of their formation depending on the pedagogical strategies of teachers in the context of education based on 5G and big data technologies. The study was conducted at Shandong University of Science and Technology (SDUST), involving 279 instructors and 582 undergraduate students in the full-time program. A survey of the instructor group was carried out using a specially designed questionnaire, and a longitudinal study of students’ value orientations in the first year of undergraduate studies was conducted using the Morphological Test of Life Values (Sopov and Karpushina), This allowed assessing how teaching strategies influence indicators of students’ moral development. Identifying the leading pedagogical strategies of moral education in one of China’s largest universities revealed how general principles are refracted through ethnocultural peculiarities, evaluating the effectiveness of these strategies in shaping the personalities of future professionals who will work with large databases in the 5G network. [ABSTRACT FROM AUTHOR]

This research investigates the impact of Artificial Intelligence (AI) and Big Data technologies on the accounting profession, explicitly focusing on accountants who serve as educators. The rapid development of AI and Big Data technology has changed teaching approaches and curricula in accounting education.. The research methodology involves surveys and interviews with accounting educators affiliated with the Indonesian Institute of Accountants, specifically within the Educator Accountants Department, from various higher education institutions in Indonesia. Data obtained from these interviews are qualitatively analyzed to identify their perspectives on the impact of AI and Big Data technologies in the context of teaching and accounting practices. The findings reveal that accounting educators widely recognize the importance of integrating AI and Big Data concepts into accounting curricula. This research provides valuable insights for educators to be able to design responsive curricula and equip students with the skills necessary to succeed in an increasingly interconnected and digitally transformed work environment.

Objectives: This paper aims to comprehensively explore the implications of Big Data within the realm of accounting, dissecting both its potential advantages and the hurdles it presents. The primary goal is to introduce and delineate the potential benefits of Big Data integration in accounting practices. Additionally, it seeks to identify and thoroughly examine the challenges impeding the seamless assimilation of Big Data into accounting methodologies. By delving into diverse applications, including Auditing, Cost Management, and financial reporting, this study aims to shed light on the multifaceted nature of Big Data's role in accounting. Methods/Analysis: This paper commences with an introduction to the concept of Big Data and its anticipated advantages for accounting practices. It proceeds to conduct a meticulous review and synthesis of existing literature, dissecting the intricate relationship between Big Data and accounting. Through this review, it emphasizes the stumbling blocks encountered in integrating Big Data. Subsequently, it offers a detailed exploration of Big Data's applications in accounting. Findings: Big Data exhibits the potential to substantially transform accounting practices, offering avenues for superior decision-making and analysis. However, the challenges related to data management and analysis pose substantial barriers for accountants in effectively integrating Big Data. Novelty/Improvement: This study offers a comprehensive exploration, dissecting both the potential advantages and challenges presented by Big Data within accounting practices. It provides detailed insights into specific applications of Big Data in accounting, going beyond a surface-level understanding and focusing on domains like Auditing, Cost Management, and financial reporting. Doi: 10.28991/ESJ-2024-08-03-024 Full Text: PDF

Big data technologies (BDT) are the latest instalments in a long line of technological disruptions credited with advancing the field of supply chain management (SCM) from a purely clerical function to a strategic necessity. Yet, despite the wave of optimism about the utility of BDT in SCM, the origins of value in a BDT-enabled supply chain are not well understood. This study examines the generative mechanisms of value creation in such a supply chain by a two-pronged approach. First, we interrogate the theoretical raisons d'être of BDT in SCM. Second, we examine the evidence that support the value-added potential of BDT in SCM informed by extant empirical and quantitative studies (EQS). Taken together, our analyses reveal three key findings. First, in extending the dynamic capabilities perspective, we deduced that micro-founded rather than macro-founded studies tend to be more instructive to practice. Second, we discovered that the generative mechanisms of value in a BDT-enabled supply chain operate at the level of supply chain processes. And thirdly, we found that resilience and agility are the most important dynamic capabilities that have emerged from current BDT-enabled SCM research. Insights for policy, practice, theory, and future research are discussed. [ABSTRACT FROM AUTHOR]

Organizations are integrating big data technologies with Enterprise Resource Planning (ERP) systems with an aim to enhance ERP responsiveness (i.e., the ability of the ERP systems to react towards the large volumes of data). Yet, organizations are struggling to manage the integration between the ERP systems and big data technologies, leading to lack of ERP responsiveness. For example, it is difficult to manage large volumes of data collected through big data technologies and to identify and transform the collected data by filtering, aggregating and inferencing through the ERP systems. Building on this motivation, this research examined the factors leading to ERP responsiveness with a focus on big data technologies. The conceptual model which was developed through a systematic literature review was tested using Structural equation modelling (SEM) performed on the survey data collected from 110 industry experts. Our results suggested 12 factors (e.g., big data management and data contextualization) and their relationships which impact on ERP responsiveness. An understanding of the factors which impact on ERP responsiveness contributes to the literature on ERP and big data management as well as offers significant practical implications for ERP and big data management practice. [ABSTRACT FROM AUTHOR]

In order to better improve students’ Japanese language performance and help them progress, this paper proposes an analysis of personalized Japanese language teaching in a big data technology environment. We build a model of learner characteristics and use collaborative filtering techniques to push learning information from learners with the same or similar interest and preference characteristics. Adjust teaching strategies based on visual user information in the information panel. Build a personalized teaching model based on big data, extract and visualize the data of each student’s learning behavior, find out the gap between the teaching objectives and the preset ones, and record the students’ learning methods. Based on data to dynamically update the teaching content, assign the most appropriate learning tasks, determine the distance of teaching objectives, and improve students’ learning ability and learning effectiveness. Through inductive learning inference on the training dataset, the probabilities of all subsets are calculated, the relevant data subset intervals are merged, and the business sensitivity index is introduced to measure the whole source, which makes the improved decision tree gradually reduce the uncertainty of the division and decrease the weight degree of the attributes, which makes the personalized Japanese teaching better. The analysis results showed that the improved decision tree model reached 71% accuracy in 80 cases and stayed above 73% afterwards, and the ratio of students’ mastered knowledge to the number of knowledges learned was 0.4. Personalized Japanese language teaching under this method led to a significant improvement in students’ performance.

This paper examines big data technology through the use of big data clustering algorithms and examines the factors that influence the transformation of financial accounting in the digital economy. Separate the clustering center to rectify it, finish accurate clustering of the original data set, and normalize the data. The data center point results are output after dividing the data set, and the final clustering results of the data set are obtained according to the similarity coefficient through the continuous iterative execution process. The results show that the comprehensive scores after the transformation of financial accounting have all risen sharply, and the score of the work content indicator in financial risk avoidance has risen from 4 points before the transformation to 9.5 points. It shows that big data technology can effectively promote the transformation of financial accounting in the digital economy era.

The article examines the functioning of the tourism sector in Spain, which is a competitive, highly profitable national business whose efficiency directly depends on the information database and digital technologies. It is substantiated that the accelerated digital transformation of tourism in Spain will help to stimulate economic growth, increase labor productivity and increase employment of the population. The seven most important forms of digital technological solutions used in the Spanish tourism industry are allocated: 1) mobile technologies today perform a whole range of services in the digitization of the tourism sector; 2) augmented reality (AR) has also entered the world of travel in Spain owing to the opportunities it can offer to those who wish to travel virtually; 3) the Internet of Things (IoT) promises to bring significant updates to the tourism industry and will become a major transformational factor in personalizing the customer experience over the next few years; 4) virtual assistants Siri and Alexa are being used by hotels to provide digital comfort for tourists; 5) the multiplier effect of the use of Big Data technologies in the Spanish tourism industry allows to build effective interaction between the government and business communities, improve and personalize travel services; 6) travel companies that rely on Blockchain functions to develop their loyalty and reward programs, certainly create a competitive advantage over other entities in the tourism market; 7) travel technology is becoming even more powerful thanks to the 5G networks. It is justified that in the future, the digitalization of tourism will be accompanied by a further process of squeezing the traditional companies with offline offices out of the tourism market along with the development of tour design according to the parameters individually set by each specific client.

Data is one of the most valuable assets in the digital era because it may conceal hidden valuable insights. Diverse organizations in diverse domains overcome the challenges of the big data value chain by employing a wide range of technologies to meet their needs and achieve a variety of goals to support their decision-making. Due to the significance of data-oriented technologies, this paper presents a model of the big data value chain based on technologies used in the acquisition, storage, and analysis of data. The following are the paper’s contributions: First, a model of the big data value chain is developed to illustrate a comprehensive representation of the big data value chain that depicts the relationships between the characteristics of big data and the technologies associated with each category. Second, in contrast to previous research, this paper presents an overview of technologies for each category of the big data value chain. The third contribution of this paper is to assist researchers and developers of data-intensive systems in selecting the appropriate technology for their specific application development use cases by providing examples of applications and use cases from prominent papers in a variety of fields and by describing the capabilities and stages of the technologies being presented so that the right technology is used at the right time in the big data collection, processing, storage, and analytics tasks.

The use of artificial intelligence, along with its various components, is rapidly increasing in various fields of study today, going beyond the traditional domains of computer science and mathematics. To gain insights into how artificial intelligence is being applied in the air transport industry, uncover underlying correlations and trends in the literature, and identify potential research gaps, we conducted a systematic literature review supplemented with bibliometric elements such as keyword co-occurrence and author influence. The key findings of our research shed light on the most prolific institutions and authors globally involved in generating knowledge about AI applications in air transport. Additionally, we identified five research clusters that dominate the overall research direction: prediction and optimisation (constituting 65% of the articles), inter- industry collaborations (17% of the articles), human experience (9% of the articles), safety, risks, and ethical considerations (6% of the articles), and ecology and sustainable development (3% of the articles). Overall, further research is needed to explore the ethical implications, legal considerations, integration processes, and impact on employment and the environment in the air transport industry.

This article describes mobile robot systems and their information management processes that generate the use of Big Data technologies. The features of the application of the group of simultaneous localization and mapping (SLAM) methods for navigating the terrain of mobile robots are considered. An overview of approaches to the implementation of SLAM methods and their implementations on the platform of robot operation system packages is given. The possibility of using convolutional neural networks to improve the accuracy of the SLAM method is substantiated. In addition, this article presents conceptual diagram of the navigation algorithm for a mobile robotic system. [ABSTRACT FROM AUTHOR]

The world has been observing unprecedented circumstances with the outbreak of the COVID-19 pandemic. The upsurge of infection cases has left hospitals overwhelmed, education quality compromised, caused unemployment issues, and affected the international economy, tourism sector, and financial markets. The number of confirmed cases and deaths has been increasing on daily basis. However, a vaccination drive has been conducted globally to reduce the transmission. The purpose of this study was to present the basic system configuration of the Big Data technology called Spark and to performe an analysis of Thailand’s COVID-19 pandemic data. Data were collected from an open Thai government data center between January 2020 and 28. May 2021. Bangkok province has the most COVID-19 cases followed by Samut Sakhon province. It has been observed that the ages between 20 and 40 were the most infected by COVID-19 in Thailand.

Internet-based programs and communication techniques have become widely used and respected in the IT industry recently. A persistent source of "big data," or data that is enormous in volume, diverse in type, and has a complicated multidimensional structure, is internet applications and communications. Today, several measures are routinely performed with no assurance that any of them will be helpful in understanding the phenomenon of interest in an era of automatic, large-scale data collection. Online transactions that involve buying, selling, or even investing are all examples of e-commerce. As a result, they generate data that has a complex structure and a high dimension. The usual data storage techniques cannot handle those enormous volumes of data. There is a lot of work being done to find ways to minimize the dimensionality of big data in order to provide analytics reports that are even more accurate and data visualizations that are more interesting. As a result, the purpose of this survey study is to give an overview of big data analytics along with related problems and issues that go beyond technology.

The development of big data technologies, which have been applied extensively in various areas, has become one of the key factors affecting modern society, especially in the virtual reality environment. This paper provides a comprehensive survey of the recent developments in big data technologies, and their applications to virtual reality worlds, such as the Metaverse, virtual humans, and digital twins. The purpose of this survey was to explore several cutting-edge big data and virtual human modelling technologies, and to raise the issue of future trends in big data technologies and the Metaverse. This survey investigated the applications of big data technologies in several key areas—including e-health, transportation, and business and finance—and the main technologies adopted in the fast-growing virtual world sector, i.e., the Metaverse. [ABSTRACT FROM AUTHOR]

The use of artificial intelligence, along with its various components, is rapidly increasing in various "elds of study today, going beyond the traditional domains of computer science and mathematics. To gain insights into how arti"cial intelligence is being applied in the air transport industry, uncover underlying correlations and trends in the literature, and identify potential research gaps, we conducted a systematic literature review supplemented with bibliometric elements such as keyword co-occurrence and author in#uence. fie key "ndings of our research shed light on the most proli"c institutions and authors globally involved in generating knowledge about AI applications in air transport. Additionally, we identi"ed "ve research clusters that dominate the overall research direction: prediction and optimisation (constituting 65% of the articles), interindustry collaborations (17% of the articles), human experience (9% of the articles), safety, risks, and ethical considerations (6% of the articles), and ecology and sustainable development (3% of the articles). Overall, further research is needed to explore the ethical implications, legal considerations, integration processes, and impact on employment and the environment in the air transport industry. [ABSTRACT FROM AUTHOR]

Internet-based programs and communication techniques have become widely used and respected in the IT industry recently. A persistent source of "big data," or data that is enormous in volume, diverse in type, and has a complicated multidimensional structure, is internet applications and communications. Today, several measures are routinely performed with no assurance that any of them will be helpful in understanding the phenomenon of interest in an era of automatic, large-scale data collection. Online transactions that involve buying, selling, or even investing are all examples of e-commerce. As a result, they generate data that has a complex structure and a high dimension. The usual data storage techniques cannot handle those enormous volumes of data. There is a lot of work being done to find ways to minimize the dimensionality of big data in order to provide analytics reports that are even more accurate and data visualizations that are more interesting. As a result, the purpose of this survey study is to give an overview of big data analytics along with related problems and issues that go beyond technology. [ABSTRACT FROM AUTHOR]

Big data applications are at the epicentre of recent breakthroughs in digital health. However, controversies over privacy, security, ethics, accountability, and data governance have tarnished stakeholder trust, leaving health-relevant big data projects under threat, delayed, or abandoned. Taking the notion of big data as social construction, this work explores the social representations of the big data concept from the perspective of stakeholders in Kenya's digital health environment. Through analysing the similarities and differences in the way health professionals and information technology (IT) practitioners comprehend the idea of big data, we draw strategic implications for restoring confidence in big data initiatives. Respondents associated big data with a multiplicity of concepts and were conflicted in how they represented big data's benefits and challenges. On this point, we argue that peculiarities and nuances in how diverse players view big data contribute to the erosion of trust and the need to revamp stakeholder engagement practices. Specifically, decision makers should complement generalised informational campaigns with targeted, differentiated messages designed to address data responsibility, access, control, security, or other issues relevant to a specialised but influential community. [ABSTRACT FROM AUTHOR]

Today digital economic environment penetrates deeply in different spheres and industries of economy. Now doubt that digital transformations deal not only with outer environment of company functioning but also with their internal business-processes. The outer elements of digital transformation include such business-spaces, which in their essence were born as global, i. e. they are not connected with geographical location or specific segment of buying audience. We mean the development of the so-called digital platforms – ecosystems of digital business, where participants of different profiles or lines of commercial activity can be present, such as logistics, production, finance, marketing, sales, etc. Due to active emergence of outer aspects of digital transformation internal businessprocesses in such digital spaces are also subjected to transformations. These transformations in some cases are inevitable because of rising competition among companies inside one industry on the level of product range being sold and among related industries on the level of services that determine the company value for customers. In the digital business environment classical process approach undergoes serious changes due to transformation of business-models of business running, altered information opportunities, emergence of new digital competences of experts and technological breakthrough. The goal of the research is to develop a competence model of manager for decision-making in conditions of digital environment with regard to factors influencing the process of decisionmaking and advanced methodologies of management, such as Agile. The article shows conditions of digital transformation penetration in the business environment of present day companies, identifies factors of digital transformation impact on decision-making in management within the frames of the process approach, substantiates opportunities and threats of business-process digitalization in managerial decision-making and demonstrates new competences necessary for efficient functioning of business-processes. The authors put forward a complex model of decision-making in digital business environment, which gives an opportunity for companies to make the most effective managerial decisions.

The object of the research are methods and algorithms of optimizing of the Big Data transformation to build a social profile model, the subject of the research are methods of constructing of a social profile. For decisionmaking person, the problem of scientific methodological and instrumental re-equipment is relevant for the effective fulfillment of a set of managerial tasks and confronting of fundamentally new challenges and threats in society. This task is directly related to the problem of building of a model of the social profile of both the individual and the social group as a whole. Therefore, the problem of optimizing of methods of constructing of a mathematical model of a social profile is certainly relevant. During the research, methods of the mathematical apparatus of graph theory, database theory and the concept of non-relational data stores, Big Data technology, text analytics technologies, parallel data processing methods, methods of neural networks' using, methods of multimedia data analyzing were used. These methods were integrated into the general method, called the method of increasing of the efficiency of constructing of a mathematical model of a social profile. The proposed method improves the adequacy of the social profile model, which will significantly improve and simplify the functioning of information systems for decision-making based on knowledge of the social advantages of certain social groups, which will allow dynamic correction of their behavior. The obtained results of testing the method make it possible to consider it as an effective tool for obtaining of an objective information model of a social portrait of a social group. This is because the correctness of setting and solving of the problem ensured that adequate results were obtained. Unlike the existing ones, the proposed modeling method, which uses an oriented graph, allows to improve significantly the quality and adequacy of this process. Further research should be directed towards the implementation of proposed theoretical developments in real decision-making systems. This will increase the weight of automated decision-making systems for social climate analysis

Implementation of the task of creating a digital platform for a full-featured system of information and analytical support for public administration at the federal, regional and municipal levels, requires a lot of resources and serious expertise.In this regard, the subject of the study was the experience and trends in the construction and operation of digital platforms for state and public administration. The aim of the paper was to summarize the international practice of creating, operating and developing digital public administration platforms. As part of the study, the authors carry out a systematic, content and comparative analysis of various sources: regulatory documents, scientific papers, analytical materials of a number of international organizations, etc.The article presents the results of a study of world experience in the application of various tools to support e-government and support digital governance platforms. The authors describe global trends in the use of information and communication technologies to support public administration. The paper presents a list of general principles for the operation of digital platforms, gives the results of the analysis on the compliance with these principles of the state digital platform of the European Union, highlights its distinctive properties.The article forms proposals and development directions, based on the results and available studies on the development and promotion of digital management platforms, that can help accelerate the spread of digital transformation of public administration in the Russian Federation. The results of the study can be applied to identify the potential of the existing platform, its strengths and weaknesses, including in the context of the possibilities of developing the process of digital transformation of governance, as well as effectively moving towards the formation of the digital platform of public administration in the Russian Federation.

In educational institutions, E-learning has been known as a successful technology for enhancing performance, concentration, and thus providing higher academic success. Nevertheless, the conventional system for executing research work and selecting courses is a time-consuming and unexciting practice, that not only directly impacts the students' academic achievement, but also impacts the learning experience of students. In addition to that, there is an enormous number of various kinds of data in the E-Learning domain both structured and unstructured, and the academic establishments attempt to manage and understand big complicated data sets. To fix this problem, this paper proposes a model of an E-learning recommendation system that will suggest and encourage the learner in choosing the courses according to their needs. This system used big data tools such as Hadoop and Spark in order to enhance data collection, storage, analysis, processing, optimization, and visualization, furthermore based on cloud computing infrastructure and especially Google cloud services. [ABSTRACT FROM AUTHOR]

Several characteristics of insurance fraud including its chronic nature justifies the need for identifying feasible proposals which can be expected to bring about significant impacts. Recent statistics show that insurance fraud is now consistently on the increase. However, insurance fraud is highly fragmented and each offence is not significant enough to elicit active interest among the public or interventions from the police. Three problems have been identified and diagnosed. These were a lack of awareness, an absence of a national leadership and also limited attention directed to insurance fraud by the investigating authorities. Based on these, three recommendations have been suggested. (1) Embarking on and developing a national initiative by central government, (2) Taking a dynamic concentration approach to send deterrent threats to potential fraudsters, and (3) Using big data technologies to detect clandestine activities by organised groups.

The increased pressure on cities has led to a stronger need to build sustainable cities that can last. Planning sustainable cities of the future, educated by the lessons of the past and anticipating the challenges of the future, entails articulating a multi-scalar vision that, by further interplaying with major societal trends and paradigm shifts in science and technology, produce new opportunities towards reaching the goals of sustainability. Enabled by big data science and analytics, the ongoing transformative processes within sustainable cities are motivated by the need to address and overcome the challenges hampering progress towards sustainability. This means that sustainable cities should be understood, analyzed, planned, designed, and managed in new and innovative ways in order to improve and advance their contribution to sustainability. Therefore, sustainable cities are increasingly embracing and leveraging what smart cities have to offer in terms of data-driven technologies and applied solutions so as to optimize, enhance, and maintain their performance and thus achieve the desired outcomes of sustainability—under what has been termed "data-driven smart sustainable cities." Based on a case study analysis, this paper develops an applied theoretical framework for strategic sustainable urban development planning. This entails identifying and integrating the underlying components of data-driven smart sustainable cities of the future in terms of the dimensions, strategies, and solutions of the leading global paradigms of sustainable urbanism and smart urbanism. The novelty of the proposed framework lies in combining compact urban design strategies, eco-city design strategies and technology solutions; data-driven smart city technologies, competences, and solutions for sustainability; and environmentally data-driven smart sustainable city solutions and strategies. These combined have great potential to improve and advance the contribution of sustainable cities to the goals of sustainability through harnessing its synergistic effects and balancing the integration of its dimensions. The main contribution of this work lies in providing new insights into guiding the development of various types of strategic planning processes of transformative change towards sustainability, as well as to stimulate and inspire future research endeavors in this direction. This study informs policymakers and planners about the opportunity of attaining important advances in sustainability by integrating the established models of sustainable urbanism and the emerging models of smart urbanism thanks to the proven role and untapped potential of data-driven technologies in catalyzing sustainable development and thus boosting sustainability benefits. [ABSTRACT FROM AUTHOR]

Digital technologies expand opportunities for human development: life dynamics are increasing, digital platforms are being created for educational, scientific, and political purposes, new opportunities for control over government bodies, and communicative practices are being improved. At the same time, there are new dangers to which a person is exposed. The state and the civilian have both coinciding and differing tasks and priorities. Therefore, there is a need to identify priorities for civil society and the state in the context of technological improvements related to digitalization.