Showing papers on "Business analytics published in 2022"

IoT data visualization for business intelligence in corporate finance

Abstract: Business intelligence (BI) incorporates business research, data mining, data visualization, data tools,infrastructure, and best practices to help businesses make more data-driven choices.Business intelligence's challenging characteristics include data breaches, difficulty in analyzing different data sources, and poor data quality is consideredessential factors. In this paper, IoT-based Efficient Data Visualization Framework (IoT- EDVF) has been proposed to strengthen leaks' risk, analyze multiple data sources, and data quality management for business intelligence in corporate finance.Corporate analytics management is introduced to enhance the data analysis system's risk, and the complexity of different sources can allow accessing Business Intelligence. Financial risk analysis is implemented to improve data quality management initiative helps use main metrics of success, which are essential to the individual needs and objectives. The statistical outcomes of the simulation analysis show the increasedperformance with a lower delay response of 5ms and improved revenue analysis with the improvement of 29.42% over existing models proving the proposed framework's reliability.

IoT data visualization for business intelligence in corporate finance

Abstract: Business intelligence (BI) incorporates business research, data mining, data visualization, data tools,infrastructure, and best practices to help businesses make more data-driven choices.Business intelligence's challenging characteristics include data breaches, difficulty in analyzing different data sources, and poor data quality is consideredessential factors. In this paper, IoT-based Efficient Data Visualization Framework (IoT- EDVF) has been proposed to strengthen leaks' risk, analyze multiple data sources, and data quality management for business intelligence in corporate finance.Corporate analytics management is introduced to enhance the data analysis system's risk, and the complexity of different sources can allow accessing Business Intelligence. Financial risk analysis is implemented to improve data quality management initiative helps use main metrics of success, which are essential to the individual needs and objectives. The statistical outcomes of the simulation analysis show the increasedperformance with a lower delay response of 5ms and improved revenue analysis with the improvement of 29.42% over existing models proving the proposed framework's reliability.

Big Data Analytics in Supply Chain Management: A Systematic Literature Review and Research Directions

Abstract: Big data analytics has been successfully used for various business functions, such as accounting, marketing, supply chain, and operations. Currently, along with the recent development in machine learning and computing infrastructure, big data analytics in the supply chain are surging in importance. In light of the great interest and evolving nature of big data analytics in supply chains, this study conducts a systematic review of existing studies in big data analytics. This study presents a framework of a systematic literature review from interdisciplinary perspectives. From the organizational perspective, this study examines the theoretical foundations and research models that explain the sustainability and performances achieved through the use of big data analytics. Then, from the technical perspective, this study analyzes types of big data analytics, techniques, algorithms, and features developed for enhanced supply chain functions. Finally, this study identifies the research gap and suggests future research directions.

Social media data analytics for business decision making system to competitive analysis

Abstract: For the past few years, business intelligence has been a major field that uses data analysis to produce key information as part of business decision-making. Data collected from social media sites and blogs are analyzed to make business decisions, a process called social media analytics (SMA). This method, which goes beyond ordinary monitoring or a basic analysis of retweets, develops an in-depth insight into the social consumer. After reading the whole report, add the pertinent figures to the table. Add pertinent data from the Brand24 report to the table. During a social media audit, any followers, impressions, engagement, copy/traffic, and brand mentions are key parameters to analyze. For companies and research institutions, the great interest is to analyse and gain knowledge from user-produced data. These data contain useful knowledge, including customer perceptions feedback and product/service suggestions. Due to content saturation, social media's true meaning regarding business data is hardly ever found. Therefore, in this paper, the business decision making system (BDMS) has been proposed to develop business using social media data analytics. BDMS provides a clear understanding of the key principles, issues and functionality, and big social data developments. Besides, BDMS concentrates on marketing and describes an operational approach for obtaining valuable information from social data. BDMS performs a short and precise description of current use scenarios from the evidence, as per the help of decisions and investment opportunities companies get when using social data analytics. The experimental result shows that BDMS achieves the highest competitive results. With greater accuracy, system dependability, F-1 measurement, and deviation rate of 85.5%, the BDMS system guarantees 93.7%, 86.8%, and 7.0%.

Social media data analytics for business decision making system to competitive analysis

Abstract: For the past few years, business intelligence has been a major field that uses data analysis to produce key information as part of business decision-making. Data collected from social media sites and blogs are analyzed to make business decisions, a process called social media analytics (SMA). This method, which goes beyond ordinary monitoring or a basic analysis of retweets, develops an in-depth insight into the social consumer. After reading the whole report, add the pertinent figures to the table. Add pertinent data from the Brand24 report to the table. During a social media audit, any followers, impressions, engagement, copy/traffic, and brand mentions are key parameters to analyze. For companies and research institutions, the great interest is to analyse and gain knowledge from user-produced data. These data contain useful knowledge, including customer perceptions feedback and product/service suggestions. Due to content saturation, social media's true meaning regarding business data is hardly ever found. Therefore, in this paper, the business decision making system (BDMS) has been proposed to develop business using social media data analytics. BDMS provides a clear understanding of the key principles, issues and functionality, and big social data developments. Besides, BDMS concentrates on marketing and describes an operational approach for obtaining valuable information from social data. BDMS performs a short and precise description of current use scenarios from the evidence, as per the help of decisions and investment opportunities companies get when using social data analytics. The experimental result shows that BDMS achieves the highest competitive results. With greater accuracy, system dependability, F-1 measurement, and deviation rate of 85.5%, the BDMS system guarantees 93.7%, 86.8%, and 7.0%.

Can big data analytics improve the quality of decision-making in businesses?

Abstract: Big data analytics (BDA) initiatives are crucial for changing conventional firms' decision-making (DM) directly into a data driven one that helps achieve the firm's objectives. Nevertheless, prior information methods analysis has not given sufficient interest in the effect of BDA use on DM merit. Using the computational ability of information analytics, this analysis examined the effect of BD on DM merit and evaluated the arbitrating impact of data analytics abilities. We gathered information through 480 software companies in America. The empirical methods demonstrated that BDA implementation had a strong effect on DM merit, where BDA had an arbitrating role in the relationship between BDA use and DM merit. Thus, companies would not only increase BDA use in business DM, but also take steps to boost the data analytics abilities, which will boost the DM merit in the direction of acquiring competitive advantage.

Big data-assisted social media analytics for business model for business decision making system competitive analysis

Abstract: Business is based on manufacturing, purchasing, selling a product, and earning or making profits. Social media analytics collect and analyze data from various social networks such as Facebook, Instagram, and Twitter. Social media data analysis can help companies identify consumer desires and preferences, improve customer service and market analytics on social networks, and smarter product development and marketing investments. The business decision-making process is a step-by-step process that enables employees to resolve challenges by weighing evidence, evaluating possible solutions, and selecting a route. In this paper, Big Data-assisted Social Media Analytics for Business (BD-SMAB) Model increases awareness and affects decision-makers in marketing strategies. Companies can use big data analytics in many ways to enhance management. It can evaluate its competitors in real-time and change prices, make deals better than its competitors' sales, analyze competitors' unfavorable feedback and see if they can outperform that competitor. The proposed method examines social media analysis impacts on different areas such as real estate, organizations, and beauty trade fairs. This diversity of these companies shows the effects of social media and how positive decisions can be developed. Take better marketing decisions and develop a strategic approach. As a result, the BD-SMAB method enhance customer satisfaction and experience and develop brand awareness.

Big data-assisted social media analytics for business model for business decision making system competitive analysis

Abstract: Business is based on manufacturing, purchasing, selling a product, and earning or making profits. Social media analytics collect and analyze data from various social networks such as Facebook, Instagram, and Twitter. Social media data analysis can help companies identify consumer desires and preferences, improve customer service and market analytics on social networks, and smarter product development and marketing investments. The business decision-making process is a step-by-step process that enables employees to resolve challenges by weighing evidence, evaluating possible solutions, and selecting a route. In this paper, Big Data-assisted Social Media Analytics for Business (BD-SMAB) Model increases awareness and affects decision-makers in marketing strategies. Companies can use big data analytics in many ways to enhance management. It can evaluate its competitors in real-time and change prices, make deals better than its competitors' sales, analyze competitors' unfavorable feedback and see if they can outperform that competitor. The proposed method examines social media analysis impacts on different areas such as real estate, organizations, and beauty trade fairs. This diversity of these companies shows the effects of social media and how positive decisions can be developed. Take better marketing decisions and develop a strategic approach. As a result, the BD-SMAB method enhance customer satisfaction and experience and develop brand awareness.

Artificial intelligence and knowledge sharing: Contributing factors to organizational performance

Abstract: The evolution of organizational processes and performance over the past decade has been largely enabled by cutting-edge technologies such as data analytics, artificial intelligence (AI), and business intelligence applications. The increasing use of cutting-edge technologies has boosted effectiveness, efficiency and productivity, as existing and new knowledge within an organization continues to improve AI abilities. Consequently, AI can identify redundancies within business processes and offer optimal resource utilization for improved performance. However, the lack of integration of existing and new knowledge makes it problematic to ascertain the required nature of knowledge needed for AI’s ability to optimally improve organizational performance. Hence, organizations continue to face reoccurring challenges in their business processes, competition, technological advancement and finding new solutions in a fast-changing society. To address this knowledge gap, this study applies a fuzzy set-theoretic approach underpinned by the conceptualization of AI, knowledge sharing (KS) and organizational performance (OP). Our result suggests that the implementation of AI technologies alone is not sufficient in improving organizational performance. Rather, a complementary system that combines AI and KS provides a more sustainable organizational performance strategy for business operations in a constantly changing digitized society.

How Big Data Analytics Impacts the Retail Management on the European and American Markets?

Abstract: Many studies on big data analytics focused on specialized use cases in business environments. Studies have been performed on the use of big data analytics in order to learn about consumer associations and expertise, among others. Nevertheless, there is an absence of investigation within the retail industry contemplating the big data management, looking at the adverse effect on organizational performance and customer satisfaction. Merchants investigate analytics to obtain a unified picture of their operations and customers throughout online channels or stores and make strategic choices towards the management of retail. Thereof, this analysis was carried out by focusing heavily on the European and American retail sector to demonstrate the impact of big data analytics. A quantitative study technique was used to analyze 450 individuals in the European and American retail sector. The outcomes on the analysis mentioned that among the various big data analytics used inside the European and American retail sector, the individuals majorly emphasized social networking analytics. Future scientists can do research on the forthcoming retail fashion on the European and American markets, and the way the consequences of big data evaluation evolved within the previous couple of years and contend with the unpredicted long-term recessions within the European and American retail sector.

An Analysis of the Impact of Business Analytics on Progress

Abstract: Business analytics is as a rule progressively used to pick up data driven experiences to help basic leadership. We are finding a new technique which is help to less time-consuming process or effortless process. In Business analytics is based on big data, machine learning, data science which experts or professional people work. As historical data or consolidate data takes more time because it works on the basis of decision-making process. Here, we need to improve the decision-making technique by comparing all previous technologies we already have.

Using Business Analytics for SME Business Model Transformation under Pandemic Time Pressure

Abstract: The COVID-19 pandemic has had an unprecedented impact on many industry sectors, forcing many companies and particularly Small Medium Enterprises (SMEs) to fundamentally change their business models under extreme time pressure. While there are claims that technologies such as analytics can help such rapid transitions, little empirical research exists that shows if or how Business Analytics (BA) supports the adaptation or innovation of SMEs' business models, let alone within the context of extreme time pressure and turbulence. This study addresses this gap through an exemplar case, where the SME actively used location-based business analytics for rapid business model adaptation and innovation during the Covid-19 crisis. The paper contributes to existing theory by providing a set of propositions, an agenda for future research and a guide for SMEs to assess and implement their own use of analytics for business model transformation.

The role of the social and technical factors in creating business value from big data analytics: A meta-analysis

Abstract: • The impact of big data analytics (BDA) on firm performance is conceptualized in a research model. • BDA is viewed as a sociotechnical system consisting of social and technical components. • A meta-analysis is conducted to examine the impact of BDA on firm performance. • Both BDA technical and social factors strongly impact firm performance. • More advanced BDA concepts contribute more to enhancing firm performance. Big data analytics (BDA) has recently gained importance as an emerging technology for handling big data. The use of advanced techniques with differing levels of intelligence, such as descriptive, predictive, prescriptive, and autonomous analytics, is expected to create value for firms. By viewing BDA as a sociotechnical system, we conduct a meta-analysis of 107 individual studies to integrate prior evidence on the role of the technical and social factors of BDA in creating BDA business value. The findings underline the predominant role of the social components in enhancing firm performance, such as the BDA system’s human factors and a nurturing organizational structure, in contrast to the minor role of the technological factors. However, both the technical and social factors are found to be strong determinants of BDA business value. Through the combined lens of sociotechnical theory and the IS business value framework, we contribute to research and practice by enhancing the understanding of the main technical and social determinants of BDA business value at the firm level.

What translates big data into business value? A meta-analysis of the impacts of business analytics on firm performance

Abstract: The main purpose of this study is to examine the factors that are critical to create business value from business analytics (BA). Therefore, we conduct a meta-analysis of 125 firm-level studies spanning ten years of research from across 26 countries. We found evidence that the social factors of BA, such as human resources, management capabilities, and organizational culture show a greater impact on business value, whereas technical aspects play a minor role in enhancing firm performance. Through these findings, we contribute to the ongoing debate concerning BA business value by synthesizing and validating the findings of the body of knowledge.

Big Data Analytics Capability and Business Alignment for Organizational Agility: A Fit Perspective

Abstract: This paper investigates the impact of big data analytics capability (BDAC) on organizational agility under the moderating effect of BDAC–business alignment and its impact on performance through organizational agility. Data from a matched-pair survey of business, data technology, and financial executives in 161 organizations were used to examine the proposed research model. This paper used partial least squares–structural equation modeling and hierarchical component analysis to examine the data. The results suggest a positive mediation role of organizational agility in the relationship between big data analytics capability and organizational performance, except that the mediation effect of operational adjustment agility on BDAC and market performance is not statistically significant. This study also finds that alignment between the business strategy and the big data analytics strategy enhances the relationship between BDAC and market responsiveness agility. It proposes a new perspective which is to realize the value of BDAC in enhancing agility and performance.

Smart Urban Transport and Logistics: A business analytics perspective

Abstract: New technologies and innovative business models are leading to connected, shared, autonomous, and electric solutions for the tomorrow of urban transport and logistics (UTL). The efficiency and sustainability of these solutions are greatly empowered by the capability of understanding and utilizing the tremendous amount of data generated by passengers, drivers, and vehicles. In this study, we first review the innovative applications in UTL and several related research areas in the operations management (OM)/operations research (OR) literature. We then highlight the sources, types, and uses of data in different applications. We further elaborate on business analytics techniques and software developed to facilitate the planning and management of UTL systems. Finally, we conclude the paper by reflecting on the emerging trends and potential research directions in data-driven decision making for smart UTL.

Business Decision Making by Big Data Analytics

Abstract: Information is the key component towards success when it comes to controlling the decision-makers performance with the quality of a decision. In the modern era, an absolute amount of data is available to organizations for analysis usage. Data is the most important component of the business in the 21st century and a significant number of devices are already equipped with the internet. Based on this the solutions should be studied in order to control and capture the knowledge value pair out of the datasets. Following this, the decision-makers should have access to insightful and valuable data based on the dynamic high volume & velocity using big data analytics. Our research focuses on how to integrate big data analytics into the decision-making process. The B-DAD (big data analytics and decision) framework was created to map the big data tools, its architecture, and analytics for the several decision-making steps by the adoption of methodology based on design science. The ideal goal and offerings of the framework are adopting big data analytics in order to intensify & aid decision making for the organization using an integration of big data analytics into the corresponding decision-making process. Thus, the experiment was carried out in the retail domain to test the framework. As an end result, the results showcased the value-added if big data analytics is integrated with corresponding decision-making activity.

Business Intelligence and Analytics (BIA) Usage in the Banking Industry Sector: An Application of the TOE Framework

Abstract: This study aims to examine the factors that influence business intelligence and analytics (BIA) usage in the banking sector. Based on a comprehensive literature review, a theoretical model was developed to explore the impact of three key factors on business intelligence and analytics adoption and usage in the banking sector, namely technological, organizational, and environmental factors. The study used the Statistical Package for the Social Sciences (SPSS) to analyze data collected from 120 employees of Jordan Arab bank. The results revealed the critical impact of not only the existence of data and technology infrastructure but also the importance and availability of management and human resources support and capabilities. This study suggests that, more importantly, successful planning for business intelligence and analytics should go beyond the technology aspects to gain the full benefits of such technology, especially in the banking sector. Yet, we argue that more research needs to be conducted, especially in the context of developing countries, to fully understand how banking sectors can successfully implement and utilize business intelligence and analytics.

Impact of big data analytics on distributed manufacturing: does big data help?

Abstract: Big data (BD) analytics has brought progressive improvement in the business environment. It provides businesses with optimized production, personalization and improvement in the way production is dispersed. Nevertheless, conflicts arise in the use of these methods in certain industries, like retail items, which usually basis on large-scale production and prolonged supply chain. The study develops a theoretical structure to investigate if big data coupled with different production solutions can provide for a dispersed production system. Through investigation of twenty-one buyer products business instances applying secondary and main data, the study investigated changing production processes, the inherent catalyst, the function of analytics, and its effect on distributed production. The study discovers several uses of distributed manufacturing principles to evaluate the current production processes worked for larger customer product solutions by using analytics and industry analysis. The evaluation’s suggested structure mentioned in this research has a deeper impact on planning, comprehension relationships, among factors of data analytics and distributed production.

Introduction to Business Analytics Using Simulation

Abstract: Introduction to Business Analytics Using Simulation employs an innovative strategy to teach business analytics. It uses simulation modeling and analysis as mechanisms to introduce and link predictive and prescriptive modeling. Because managers can't fully assess what will happen in the future, but must still make decisions, the book treats uncertainty as an essential element in decision-making. Its use of simulation gives readers a superior way of analyzing past data, understanding an uncertain future, and optimizing results to select the best decision. With its focus on the uncertainty and variability of business, this comprehensive book provides a better foundation for business analytics than standard introductory business analytics books. Students will gain a better understanding of fundamental statistical concepts that are essential to marketing research, Six-Sigma, financial analysis, and business analytics. Winner of the 2017 Textbook and Academic Authors Association (TAA) Most Promising New Textbook Award. Teaches managers how they can use business analytics to formulate and solve business problems to enhance managerial decision-making Explains the processes needed to develop, report, and analyze business data Describes how to use and apply business analytics software

Customer Analytics Capabilities in the Big Data Spectrum

Abstract: Customer analytics plays a vital role in generating insights from big data to improve service innovation, product development, personalization, and managerial decision-making; yet, no academic study has investigated customer analytics capability through which it is possible to achieve sustainable business growth. To close this gap, this chapter explores the constructs of the customer analytics capability by drawing on a systematic review of the literature in the big data spectrum. The chapter's interpretive framework portrays a definitional aspect of customer analytics, the importance of customer analytics, and customer analytics capability constructs. The study proposes a customer analytics capability model, which consists of four principal constructs and some important sub-constructs. The chapter briefly discusses the challenges and future research direction for developing the customer analytics capability model in the data rich competitive business environment.

Role and Application of Artificial Intelligence in Business Analytics: A Critical Evaluation

Abstract: The commercial adoption of artificial intelligence in business analytics tools across multiple industries is being driven by the rising volume and complexity of company data. Business organizations are being helped by the widespread application of artificial intelligence and machine learning in business intelligence to glean meaningful insights from sizable and complicated datasets and provide business recommendations that are clear to any business user. Within the industry of information technology, the business analytics is used to refer the usage of computing to gain the insights from data. Such data can be acquired from the internal sources of company like from its enterprise resource planning application, warehouse and mart data, providers of third-party data, or from other public sources. Sample of 198 respondents from different business sectors were to know the role, application and impact of artificial intelligence in business analytics. It is found that there is a significant role of artificial intelligence in business analytics. This paper provides a rigorous examination of the literature in an effort to illustrate the value-creating procedures and to elucidate how organizations may employ AI technologies in their operations. In this study, the forms of AI use in the organizational setting, first- and second-order impacts, and usage typologies are highlighted along with the significant enablers and inhibitors of AI adoption and use. Our analysis synthesizes the current literature.