Business Big Data Research Articles

Research on the Issues and Effects of Big Data on Business Economic Management

With the advent of the big data era, the application of big data has become more and more common and mature, gradually changing social production and lifestyle, and also has a certain impact on business and economic management. With the support of big data technology, enterprises can comprehensively analyze and process relevant data and extract valuable data from massive amounts of data, thereby providing a scientific reference for economic management and improving the effectiveness and science of business decision-making. At this stage, although enterprises also realize the importance of big data, there are many problems in the specific application process, and the role of big data in business and economic management has not been achieved.to perform effectively. Therefore, enterprises must conduct in-depth research on big data, fully understand the problems of integrating big data into business economic management, and put forward targeted suggestions on this basis to ensure the efficient use of big data. This article discusses big data’s issues and roles in business economic management.

An end-to-end technology management model in cross-border M&A transactions

Subject. This article examines an adapted management model based on the use of end-to-end technologies in key business processes for finding target companies and deciding on the feasibility of implementing mergers and acquisitions. Objectives. The article aims to present an author-developed model for managing end-to-end technologies in cross-border mergers and acquisitions. Methods. For the study, we used empirical and logical constructions, analysis and synthesis, generalization, formalization, systems approach, and the graphic and tabular methods of visualization. Results. The article identifies trends in the use of artificial intelligence in the main elements of the developed management model along with traditional ways of managing mergers and acquisitions. The proposed system management integrator helps use machine learning algorithms and business process controlling to increase the accuracy and efficiency of decisions and maximize the synergy of the buyer and the target company after the implementation of mergers and acquisitions, which is verified using mathematical algorithms and developed indicators for the use of artificial intelligence and big data business process management. Conclusions. The management model of cross-border mergers and acquisitions of companies determines the use of end-to-end technologies to improve the time and quality of management decision-making.

Business ecosystem model innovation based on Internet of Things big data

The research paper presents a novel big data technique for conceptual modelling of Internet of Things (IoT) networks. This method enables the development of models that incorporate IoT based devices into its activities to give a business for the company while taking into account the business operational activities. This study's major goal is to assist management in making quicker effective options that depend on the structured output from their input to develop IoT networks and resources. In the big data business ecological model based on the Internet of Things, 47.5% of the integrated enterprises started to innovate the enterprise ecological model, which can better help enterprises make decisions and plan. In order to include IoT into business model innovation programs to ensure that IoT is taken into account in the overall strategy business planning and decision-making at all levels, such that the modelling and formal descriptions must be used. A number of interviews with workers from various companies and with their experts, throughout such platform were done to further illuminate this research area. Future challenges will create a constant challenge to create and adopt fresh IoT-related business models for large manufacturing organisations. The high-level strategic steps of business model innovation (BMI) are an intriguing topic for current research. These results represent a first step in the exploration of IoT adoption and serve as a springboard for promising new research directions.

ESG quality management: Тhe role of digital business transformation

The article explores the prospects for the most complete elucidation of the potential of Russian business products for improving their quality through the ESG quality management in the context of digital business transformation based on artificial intelligence and big data. Econometric modeling of the dependence of the quality management effectiveness in business is carried out by using the method of regression analysis. Two alternative models are constructed and compared. The first model is compiled in isolation from the effectiveness of ESG management. The second model is compiled together with the effectiveness of ESG management and the active use of artificial intelligence and big data in business. The prospects for increasing the effectiveness of quality management in Russian business along with maximizing the effectiveness of ESG management are evaluated based on the best international experience and the obtained regression models. Comparison of the results in the alternative models allowed the authors to justify the advantages of ESG quality management based on artificial intelligence and big data. The results include the development of a forward-thinking approach to ESG quality management in the context of digital business transformation. This approach provides a number of advantages for ESG quality management including firstly, broader opportunities for obtaining state (tax, subsidiary) support for the introduction of artificial intelligence and big data the flexibility to raise funding through a combination of sustainable investments in ESG and investments in artificial intelligence and big data. Secondly, more stable consumer preferences, more sustainable competitive advantages, and higher and more permanent loyalty. Thirdly, reliance on dynamically developing and increasingly accessible technologies for intellectual support in making managerial decisions based on artificial intelligence and big data (technologies are being introduced into the activities of the business itself).

АНАЛИЗ СЕРВИСНОЙ СТРАТЕГИИ УПРАВЛЕНИЯ БОЛЬШИМИ ДАННЫМИ НА МАЛЫХ И СРЕДНИХ ПРЕДПРИЯТИЯХ

Goal. The use of big data technology can significantly improve the efficiency of enterprise management and strengthen its viability. The development of the Internet based on the use of big data technologies is an effective means to overcome the financial problems faced by small and medium-sized enterprises. Service platforms using new information technologies such as the Internet, big data and the sharing economy have unique service advantages that can provide cost savings due to the scale and volume of services provided. Such platforms are of great practical importance for the healthy and rapid development of small and medium-sized enterprises. Tasks. Technology-based enterprise development is difficult to achieve on their own, and they require targeted and professional knowledge provided by internal knowledge service departments, intermediary service agencies and government agencies. The purpose of this knowledge is to improve the knowledge reserves of small and medium-sized technological enterprises and stimulate their development and innovation. This paper analyzes the relationship between small and medium-sized enterprises and big data technologies, and raises issues related to awareness, technology, politics, and talent. In addition, appropriate strategies are proposed that can contribute to the development of small and medium-sized enterprises. Methodology. The authors used such research methods as statistical analysis, observation, generalization, description and graphical modeling. Results. In order to overcome these problems and promote the development of small and medium-sized enterprises in the context of big data technologies, it is necessary to implement appropriate strategies. First, it is necessary to conduct information campaigns and educational programs aimed at raising awareness about the possibilities of using big data in business. Secondly, government agencies and regulatory organizations should develop policies and legislation that promote the use of big data technologies and ensure the protection of enterprise data. Thirdly, it is necessary to support the development of affordable and easy-to-use data analysis solutions that will be available to small and medium-sized enterprises. Finally, it is important to invest in the education and training of qualified specialists in the field of data analysis and big data technologies. Conclusions. The implementation of these strategies will help create a favorable environment for the development of small and medium-sized enterprises based on big data technologies. This, in turn, will lead to an increase in the efficiency of enterprise management, strengthen its viability and stimulate innovation, contributing to the economic development of the country as a whole.

Analysis of Big Data Business Intelligence Tools using Technology Acceptance Model in a Healthcare

To study and analyse the Big Data BI Tools in Healthcare using TAM and to suggest the ways to improve the efficiency and effectiveness of Big Data Big Data BI tools are an important aspect relating to analysing data quicker for any organization or sector. Business Intelligence (BI) tools are application software which helps in analysing large volumes of data quicker. Once the BI tools are implemented, companies or user feel difficult to get most benefit from them due to lack of user knowledge leading to user acceptance, this led to motivation of the study. To analyse the technology acceptance factors influencing the end users of big data BI Tools, this study is conducted in a reputed hospital located in Bangalore. This study was initiated by having a study on literature reviews based on TAM models in healthcare, which gradually helped in identifying important factors influencing the acceptance and satisfaction of healthcare BI Tools users. The questionnaire was framed based on the factors identified and obtained data was analysed using IBM Statistics SPSS 25 and SMARTPLS 3 data analysis tools, Tests like reliability, factor analysis, descriptive statistics, correlation test, Regression analysis, Bootstrapping, PLS algorithm tests were done. The test depicted in five main factors such as perceived ease of use, perceived usefulness, attitude, perceived risks, intended outcomes, that helped in influencing the acceptance and satisfaction of end users using Big data BI Tools, This study also revealed that most of the responses use BI Tools on daily basis, but still failed to use most of the features of Big Data BI Tools, This enables us to know that Training and development programmes must to given to users, Management should get involve the end users in Big Data BI tools by educating them the importance of Big data BI Tools, also with the help of Information technology, user technology and perceived ease of use has to be improved.

Enhancing Competitive Capabilities of Healthcare SCM through the Blockchain: Big Data Business Model’s Viewpoint

The reasons why supply chain management (SCM) needs blockchain technology include simplification of transaction procedures, time and cost reductions, and reliability improvement. This study emphasizes the necessity of introducing a blockchain-based joint logistics system to strengthen the competency of medical SCM and proposes a healthcare supply chain management (HSCM) competency measurement item through an analytic hierarchy process. The variables needed for using blockchain-based joint logistics are the performance expectations, effort expectations, promotion conditions, and social impact of the unified theory of acceptance and use of technology (UTAUT) model, as well as the HSCM competency results in increased reliability and transparency, enhanced SCM, and enhanced scalability. By analyzing the importance of securing reliability based on blockchain technology in the establishment of a supply chain network for HSCM competency, we reveal that joint logistics can be achieved, and synergistic effects can be created by implementing the integrated database to secure HSCM competency. Strengthening partnerships, such as joint logistics, will eventually lead to HSCM competency. In particular, HSCM should seek ways to upgrade its competitive capabilities through big data analysis based on the establishment of a joint logistics system.

A framework for IoT and Blockchain Based on Marketing Systems with an Emphasis on Big Data Analysis

In today's world, huge amounts of information affect all aspects of our lives and play an important role in our decisions. The importance of big data in business, like other key processes in the organization, has not hidden from experts. Access to big data can play an important role in understanding audience behavior, planning advertising campaigns, deciding on a marketing mix such as changing a product and selecting its distribution channels, implementing e-marketing strategies, content production strategies, and many other micro and macro decisions. The Internet of Things as a transformative technology is one of the most important sources of big data production. The use of social networks and online platforms in marketing, allows companies to interact with consumers in a targeted manner and provide the required information as soon as possible. In addition to high volume, IoT data also has high accuracy and purity, based on performance. Despite all the positive features of IoT-based smart marketing and the presence of big data, the disclosure of private information is the main problem. Therefore, it is necessary to pay attention to methods, approaches and technologies that can help eliminate this complication. It seems that blockchain technology with features such as tracking, transparency and security enhancement, can help to eliminate this problem and improve the performance of the digital marketing industry. For this reason, this chapter of the book provides a conceptual framework for demonstrating the causal relationships of the elements that make up an intelligent marketing system based on these evolving technologies. Understanding this framework will help implement a smart and capable marketing system.

The impact of artificial intelligence, big data analytics and business intelligence on transforming capability and digital transformation in Jordanian telecommunication firms

The major purpose of this research is studying the impact of artificial intelligence, big data analytics and business intelligence on transforming capability and digital transformation in Jordanian telecommunication firms. 303 samples were gathered after the sample checking process was completed. The PLS program was utilized in the research study to process all the collected data. The findings of the research demonstrate a set of relationships and linkages that can enhance transforming capability and digital transformation. Finally, this study found that artificial intelligence, big data analytics and business intelligence have good effects on developing and enhancing the transforming capability and digital transformation.

Expand and Renewal of Analyzed Satellite Image and Service

In this study, additional satellite analysis data in 2019, 2020, and 2021 were generated using Landsat-8 and Sentinel-2 satellite images. We additionally employed 19 types of satellite analysis methods, and generated totally 57 cases of satellite analysis data for three years. In addition, 34 types of satellite analysis data were updated using 2021 satellite data. In conclusion, a total of 91 cases of satellite analysis data were generated. The coverage of the study is the entire South Korea. The spatial resolution and the coordinate system were 30 m and UTM-K (EPSG: 5179) respectively. The products are provided as the entire South Korean and regional data, respectively. In addition, it is provided in three data types: ASCII, ArcGIS Grid, and GeoTIFF as same as last distribution. All satellite image analysis data can be downloaded free of charge from the Environmental Big Data website (www.bigdata-environment.kr), an environmental business big data platform.

Plasma processing for advanced microelectronics beyond CMOS

The scientific study of plasma discharges and their material interactions has been crucial to the development of semiconductor process engineering and, by extension, the entire microelectronics industry. In recent years, the proliferation of the big data business model has led to heightened interest in technology candidates with the potential to supplant CMOS architectures in critical metrics such as computational capacity or power consumption. These novel technologies share many common material elements with existing logic and memory devices, but the impact of mass fabrication techniques on their performance is largely unknown due to differences in the underlying physics of their operation. Two components are thus vital to this endeavor: fundamental evaluation of any emerging plasma process interactions and the ability to tailor any aspect of the plasma process necessary to produce the desired specifications. In this article, we review relevant advances in the study of plasma-induced damage mechanisms as well as characterization methods such as diagnostic probes and simulation tools. We also provide an outlook for the application of techniques such as plasma doping, area-selective etch/deposition, and heterogeneous integration. The frontiers of any new computing paradigms can only be explored through a focus on atomic scale engineering, and progress in the field of plasma science supplies the necessary toolset.

RETRACTED: Business big data analysis based on microprocessor system and mathematical modeling

Big data analysis requires examining large amounts of data. This is, so discover the hidden patterns, and give the insight to make decisions on the correct business. Companies, because it uses the power of data and technology be a more objective data-driven. Big data of human resources, faced with various problems from big data storage security problems. These issues have been discussed in the next four categories based on the Contextual Data Analyzing Technique (CDAT) using FPGA tool. Data were collected and finally to analysts that are eligible to determine the problems caused by direct analysis, on security issues, and to find the problem. Companies that big data is presented to improve the company's strategy, to the best of time, various opportunities to make the right decisions. Human resources are one of the substantive departments of any company that needs big data benefits in a different process. The main purpose is to describe the big human data analysis method to improve company performance. Also, the problem is, all of the human resources department is that it is facing using big data.

Big Data Projects and Business Benefits: Empirical Evidence from Extensive Data Survey

Big Data Projects and Business Benefits: Empirical Evidence from Extensive Data Survey

Research on FUI Style Design of Big Data Information Visualization

The purpose of the article is as follows, studying the FUI style of the interface for big data information visualization design among various styles of visualization interface in the 21st century. There are three research methods in this paper. Firstly, combing the concept of FUI and summarizing the design features of FUI style interface of big data information visualization from the perspective of design psychology. Secondly, the characteristics of government business big data information visualization and the needs of users are analyzed. Finally, the FUI design style is used in the government business big data information visualization display interface design. The FUI style presentation mode of big data information visualization and the dynamic interaction mode of FUI style interface are discussed. The conclusion is as follows, FUI is more in line with the people's psychological expectation of "future technology visual style" in big data information visualization display, highlighting the FUI data display focus on the prediction function of future events; FUI achieves visual balance through reasonable typesetting, comfortable color matching, uniform decorative shapes, etc. In terms of visual display, it not only meets the requirements of data display design, but also achieves the visual unity of partial data display design and overall data display. FUI mostly adopts flat design and simple dynamic interaction design, which conforms to users' gesture operation and people's visual browsing behavior habits in the era of mobile Internet, and meanwhile improves the utilization rate of single screen display of government departments.

Construction of Analyzed Satellite Image and Service

In this study, 17 types of satellite analysis maps were generated using Landsat-8 and Sentinel-2 satellite images acquired at 2019 and 2020. Totally, 68 of satellite analysis data were produced. The scope of deployment is South Korea as a whole, with a resolution of 30 meters, and the coordinate system is UTM-K coordinates. The established data will be provided in both South Korean and regional data respectively. In addition, it is provided by three data format: ASCII, ArcGIS Grid, and GeoTIFF for enhancing accessibility of the data. All these satellite analysis data can be downloaded free of charge from the Environmental Big Data website (www.bigdata-environment.kr), an environmental business big data platform.

A bibliometric review of a decade of research: Big data in business research – Setting a research agenda

The last several years have witnessed a surge of interest in artificial intelligence (AI). As the foundation of AI technologies, big data has attracted attention of researchers. Big data and data science have been recognized as new tools and methodologies for developing theories in business research (George, 2014). While several qualitative reviews have been conducted, there is still a lack of a quantitative and systematic review of big data in business research. Our review study fills this gap by depicting the development of big data in business research using bibliometric methods, such as publication counts and trends analysis, co-citation analysis, co-authorship analysis and keywords co-occurrence analysis. Based on the sample of 1366 primary focal articles and 55,718 secondary references, we visualize the landscape and evolution of big-data business research and capture the developmental trajectory and trends over time (between 2008 and 2018). Furthermore, based on our analyses, we provide several promising directions for future research. In doing so, we provide scholars with a systematic understanding of the development and panoramic roadmap of big data research in business.

Bigdata Business Model’s Viewpoint on the Necessity of the Physical Distribution Cooperation to Enhance Competitive Capabilities of Healthcare SCM

Bigdata Business Model’s Viewpoint on the Necessity of the Physical Distribution Cooperation to Enhance Competitive Capabilities of Healthcare SCM

Большие данные и цифровая экономика

In this paper, Big Data is considered as an "umbrella" term that combines various concepts, technologies and methods of data processing in distributed information systems that provide a qualitatively new useful information (new knowledge). The stages of "standard" research in the Big Data approach are described. A brief description of the Big Data ecosystem, which consists of several main categories, is given. Various projects and initiatives at the national and international levels are considered, as well as examples of the use of Big Data in business, economy, and society. As concrete examples of the construction and use of analytical platforms for Big Data, successful socio-economic research carried out by the authors as part of research teams at the Plekhanov Russian University of Economics is presented. The Big data metaphor is certainly successful, since it naturally connects a complex of concepts, technologies and methods of Big data with the economy by hinting at a connection with other well-known metaphors –"Big oil", "Big ore", etc.

The big data-business strategy interconnection: a grand challenge for knowledge management. A review and future perspectives

Purpose Designing knowledge management (KM) systems capable of transforming big data into information characterised by strategic value is a major challenge faced nowadays by firms in almost all industries. However, in the managerial field, big data is now mainly used to support operational activities while its strategic potential is still largely unexploited. Based on these considerations, this study proposes an overview of the literature regarding the relationship between big data and business strategy. Design/methodology/approach A bibliographic coupling method is applied over a dataset of 128 peer-reviewed articles, published from 2013 (first year when articles regarding the big data-business strategy relationship were published) to 2019. Thereafter, a systematic literature review is presented on 116 papers, which were found to be interconnected based on the VOSviewer algorithm. Findings This study discovers the existence of four thematic clusters. Three of the clusters relate to the following topics: big data and supply chain strategy; big data, personalisation and co-creation strategies and big data, strategic planning and strategic value creation. The fourth cluster concerns the relationship between big data and KM and represents a ‘bridge’ between the other three clusters. Research limitations/implications Based on the bibliometric analysis and the systematic literature review, this study identifies relevant understudied topics and research gaps, which are suggested as future research directions. Originality/value This is the first study to systematise and discuss the literature concerning the relationship between big data and firm strategy.

Analytics, accounting and big data: enhancing accounting education

The role of Analytics and Big Data in business has been the subject of considerable research and discussion. Members of the accounting profession have recognized the importance of both and recognize that the emergence of these technologies require employees who possess a higher level of data analysis capabilities. Thus, industry is demanding that institutions of higher education place a greater emphasis on developing curricular elements, which will adequately address the needs of the ultimate consumers of these capabilities. This paper examines the data analysis skills relevant to accountants and how and where data analytics and big data have impacted undergraduate accounting education. This research examines the degree to which selected undergraduate Accounting programs have embraced Analytics and Big Data to the greatest extent and proposes a set of curricular elements the provide the appropriate level of learning within the current framework of undergraduate accounting education.