Impact Of Big Data Analytics Research Articles

The Impact of Big Data Analytics on Investment Efficiency and Financial Performance: Evidence from Saudi Stock Market

The Impact of Big Data Analytics on Investment Efficiency and Financial Performance: Evidence from Saudi Stock Market

Data analytics impacts in the field of accounting

This research study has focused on the impact of business intelligence and data analytics in the field of accounting. Based on the information collected from scholarly sources, data analytics, and business intelligence is helpful for business organizations in the improvement of their accounting processes and enhancing accuracy in real-time data storing and analysis. The research paper has analyzed the possible impact of big data analytics and business intelligence regarding the transformation of accounting practices. This research paper has also highlighted three main benefits or advantages of data analytics and business intelligence adoptions in accounting systems which include: increasing accuracy, enhancing efficiency, and improvement in the decision-making processes. Based on the research findings we suggest a significant impact of data analytics on the accounting industry which has enabled accounting professionals to draw appropriate decisions based on real-time data. However, some challenges and issues are associated with the implementation and adoption of advanced analytics and business intelligence in accounting systems. These challenges and issues are required to be focused on by the business managers while implementing technological advancement plans in business organizations, especially accounting departments. This research paper has also highlighted the possible issues and challenges with those analytics in accounting systems. The overall objective of this research paper is to review existing literature containing secondary and primary research information on similar research topics.

Understanding the Impact of Big Data Analytics and Knowledge Management on Green Innovation Practices and Organizational Performance: The Moderating Effect of Government Support

Understanding and predicting the relationship between big data analytics (BDA) and knowledge management (KM) and how they complement each other is still an ambiguous and strategically crucial issue. This study aims to investigate the interrelationships between BDA and KM in fostering green innovation practices (GIP) and organizational performance and, in addition, to explore the mediation effect of KM and how it can boost the impact of BDA on GIP. Based upon the underpinning theories that cover the study’s research model, several hypotheses were proposed and then examined. Using a cross-sectional research design, 174 questionnaires were collected from medium and large Algerian manufacturing firms. The study applied smart PLS-SEM for data analysis. The sorted results show that BDA positively impacts KM and GIP. The results also indicate that GIP positively influences and increases firm performance. In addition, the findings reveal that government support plays a significant part in leveling up firms’ GIP. Furthermore, the study emphasizes the critical impact of KM to polish the impact of BDA on GIP. This study highlights the necessity of improving the technological and managerial aspects of BDA by determining valuable capabilities, such as KM processes, to enhance organizational performance. Tracking business opportunities and predicting their relevant threats has become a severe concern to knowledge-extensive firms. Therefore, BDA can enable managers to absorb a large amount of data to increase the efficiency of a business decision to ensure survival and advancement. The study discusses both practical and theoretical implications.

Significant Impact of Big Data Analytics on the Organizations

The researcher provides an overview of one of the most significant technologies in the current era which is "Big data analytics". Also describe the definition of big data, the types of big data, the importance of this technology, the application of this technology, and the difficulties that can be faced during its implementation. Also, how the new generation of organizations depends on this big data for its several features.

Big Data Analytics and Firm Performance in the Hotel Sector

Big data (BD) analytics play a key role in helping hotel firms gain competitive advantages and achieve superior performance. The purpose of this study was to determine which factors encourage the use of big data analytics (BDA) by hotel firms and the impact of BDA on hotel firms’ performance. Understanding the impacts of big data analytics in the hotel sector is important to help hotel managers use big data for creating business value by increasing hotel performance. A research model was developed and tested with data collected through a questionnaire sent to hotel managers in a European country and analysed with PLS. The results indicate that organisational readiness and competitive pressure encourage the use of BDA through the mediating role of top management support. The findings also indicate that the use of BDA can create business value by increasing the main dimensions of hotel performance: financial performance, customer retention rate, and hotel reputation.

The Impact of Big Data Analytics on Stock Price Prediction in the Bangladesh Stock Market: A Machine Learning Approach

The stock market is a volatile and complex environment impacted by various unpredictable factors, making accurate stock price prediction challenging. This research paper explored the potential and capability of big data analytics and machine learning techniques in terms of enhancing stock price prediction accuracy in the setting of the Bangladesh stock market. The methodology adopted in the study entailed a data gathering process, which comprised collecting financial data from the Bangladesh stock market, such as news articles, financial statements, macroeconomic indicators, and historical stock prices. Based on a literature review, various fundamental and technical indicators are chosen as predictive features. The research paper employed a combined methodology that consolidates technical calculations and sentimental analysis to predict and forecast stock market patterns. By adopting machine learning and sentiment analysis techniques, this technique provides future predictions for the stock market while considering the impact of political events, economic factors, and dynamics in social media. The consolidation of big data analytics enables real-time predictions of stock market movements. The sentiment analysis algorithm facilitates prompt and extensive evaluations of tweets and news articles. As a result, the integration of technical and sentiment analyses greatly enhances the accuracy of stock market predictions.

Beyond the Numbers: Exploring the Multifaceted Impact of Big Data Analytics on Auditing

Beyond the Numbers: Exploring the Multifaceted Impact of Big Data Analytics on Auditing

BIG DATA ANALYTICS: INTEGRATION, IMPORT SUBSTITUTION, OPERATIVENESS, RELIABILITY AND AVAILABILITY

Big data analytics faces the complexities of dealing with big, unstructured, and rapidly changing data that traditional methods of managing are faltering. The exponential growth of the internet and the digital economy has led to a huge increase in demand for data storage and data analysis. This new type of information, often referred to as unstructured data or big data, includes documents, images, audio, video, and social media content, rather than the traditional structured data found in databases. Big Data Analytics aims to extract valuable insights from these vast amounts of information. The article explores diverse technologies, such as Apache Flume, Pig, Hive and ZooKeeper, along with the open-source MongoDB NoSQL database, which collectively make up the big data analytics system. This system is used to predict volumes, gain ideas, take proactive actions and make effective strategic decisions. The article delves into the implementation, use and impact of Big Data Analytics on the value of an enterprise, taking advantage of algorithms designed for large data sets such as Hadoop and MapReduce. In addition, he provides illustrative examples of big data processing, specifically leveraging the integration of Hadoop and Matlab to solve handwritten number recognition tasks with a neural network trained using the MNIST dataset. Moreover, the article briefly touches on software import substitution in the context of the Russian Railways company, noting the use of Postgres Pro and Greenplum DBMS, and the loginom analytical platform. The article also provides a brief description of approaches to predictive assessment of the operation, reliability and availability of Big Data Processing Systems.

Assessing the Impact of Big Data Analytics in the Telecommunications Sector

Big data analytics is a significant way of improving telecommunications operators’ Quality of Service (QoS) and improve their businesses. This study was aimed at determining the impact of big data analysis in telecommunication industry and also to arouse their interest towards big data. The study used four domestic telecommunication companies as case study, namely MTN, GLO, 9Mobile and AIRTEL. The study uses survey method of data collection by using structured questionnaire. Sixteen network nodes were used in the study from four clusters. Analysis of Variance (ANOVA), Cronbach’s Alpha Coefficient using Smart – PLS, DBSCAN Algorithm was employed using google python as data analysis method. The study revealed that there is high traffic data from the telecoms and this is as a result of various user activities like internet usage and highly dense network clusters, which happened at a specific time or within a particular duration at a specified location. The findings led to a conclusion that the big data analytics can be considered as a solution to directional small cells in millimeter wave cellular networks to improve QoS. The study recommends that big data should be given due consideration and be used in making national budget and policy formulation. The study further recommends that tertiary institutions should create big data analytics training to create a platform for training and development of manpower mainly on big data analytics as is obtainable in developing and IT-driven countries.

Impact of Big Data Analytics on Banking Sector

Impact of Big Data Analytics on Banking Sector

The role of big data analytics and decision-making in achieving project success

Big data analytics and decision-making are critical to project success. Therefore, this study aims to investigate the impact of big data analytics on project success and the moderating effect of decision-making from the resource-based view perspective. The study adopted a survey instrument and collected data from 135 respondents engaged in big data analytics in the IT and telecommunications sector of Pakistan. The findings identify implications based on the significant positive impact of big data analytics on project success as well as enhancement of relationships through the interaction of decision-making between big data analytics and the three dimensions of project success.

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

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.

How can big data analytics be used for healthcare organization management? Literary framework and future research from a systematic review

BackgroundMultiple attempts aimed at highlighting the relationship between big data analytics and benefits for healthcare organizations have been raised in the literature. The big data impact on health organization management is still not clear due to the relationship’s multi-disciplinary nature. This study aims to answer three research questions: a) What is the state of art of big data analytics adopted by healthcare organizations? b) What about the benefits for both health managers and healthcare organizations? c) What about future directions on big data analytics research in healthcare?MethodsThrough a systematic literature review the impact of big data analytics on healthcare management has been examined. The study aims to map extant literature and present a framework for future scholars to further build on, and executives to be guided by.ResultsThe positive relationship between big data analytics and healthcare organization management has emerged. To find out common elements in the studies reviewed, 16 studies have been selected and clustered into 4 research areas: 1) Potentialities of big data analytics. 2) Resource management. 3) Big data analytics and management of health surveillance systems. 4) Big data analytics and technology for healthcare organization.ConclusionsIn conclusion is identified how the big data analytics solutions are considered a milestone for managerial studies applied to healthcare organizations, although scientific research needs to investigate standardization and integration of the devices as well as the protocol in data analysis to improve the performance of the healthcare organization.

Impact of big data analytics on supply chain performance: an analysis of influencing factors

This paper aims to understand the impact of big data analytics on the retail supply chain. For doing so, we set our context to select the best big data practices amongst the available alternatives based on retail supply chain performance. We have applied TODIM (an acronym in Portuguese for Interactive Multi-criteria Decision Making) for the selection of the best big data analytics tools among the identified nine practices (data science, neural networks, enterprise resource planning, cloud computing, machine learning, data mining, RFID, Blockchain and IoT and Business intelligence) based on seven supply chain performance criteria (supplier integration, customer integration, cost, capacity utilization, flexibility, demand management, and time and value). One of the intriguing understandings from this paper is that most of the retail firms are in a dilemma between customer loyalty and cost while implementing the big data practices in their organization. This study analyses the dominance of the big data practices at the retail supply chain level. This helps the newly emerging retail firms in evaluating the best big data practice based on the importance and dominance of supply chain performance measures.

Critical analysis of the impact of big data analytics on supply chain operations

Undoubtedly, due to the increasingly competitive pressures and the stride of varying demands, volatility and disturbance have become the standard in today’s global markets. The spread of Covid-19 is a prime example of that. Supply chain managers are urged to rethink their competitive strategies to make use of Big Data Analytics (BDA), due to the increasing uncertainty in both demand and supply side, the competition among the supply chain partners and the need to identify ways to offer personalised products and services. With many supply chain executives recognising the need of ‘improving with data’, supply chain businesses need to equip themselves with sophisticated BDA methods/techniques to create valuable insights from big data, thus, enhancing the decision-making process and optimising the efficiency of Supply Chain Operations (SCO). This paper proposes the building blocks of a theoretical framework for understanding the impact of BDA on SCO. The framework is based on a Systematic Literature Review (SLR) on BDA and SCO, underpinned by Task-Technology-Fit theory and Institutional Theory. The paper contributes to the literature by building a platform for future work on investigating factors driving and inhibiting BDA impact on SCO.

Theoretical Perspectives on Sustainable Supply Chain Management and Digital Transformation: A Literature Review and a Conceptual Framework

In an era where environmental and social pressures on companies are increasing, sustainable supply chain management is essential for the efficient operation and survivability of the organizations (members of the chain). Digital transformation and the adoption of new technologies could support the development of sustainable strategies, as they support supply chain processes, decrease operational costs, enable control and monitoring of operations and support green practices. The purpose of this paper is to explore the relationship between sustainable supply chain management and digital transformation through the adoption of specific technologies (Blockchain technology, big data analytics, internet of things). It aims at theory building and the development of a conceptual framework, enabling the explanation of under which circumstances the above combination could lead to the development of sustainable performances. It also aims to examine how companies can increase their competitive advantage and/or increase their business performance, contributing both to academics and practitioners. After conducting a literature review analysis, a significant gap was detected. There are a few studies providing theoretical approaches to examining all three pillars of sustainability, while at the same time analyzing the impact of big data analytics, internet of things and blockchain technology on the development of sustainable supply chains. Aiming to address this gap, this paper primarily conducts a literature review, identifies definitions and theories used to explain the different pillars of flexibility, and examines the effect of different technologies. It then develops a theoretical conceptual framework, which could enable both academics and practitioners to examine the impact of the adoption of different technologies on sustainable supply chain management. The findings of this research reveal that digital transformation plays an important role to companies, as the combination of different technologies may lead to the development of significant capabilities, increasing sustainable performances and enabling the development of sustainable strategies, which can improve companies’ position in the market.

The impact of Big Data Analytics in Developing Strategic Performance using Balanced Scorecard: A Field Study in Egypt

The impact of Big Data Analytics in Developing Strategic Performance using Balanced Scorecard: A Field Study in Egypt

The impact of big data analytics on organizational sustainability: The influencing factors of autonomous research and development and absorptive capacity

This study aims to see how the big data analytics capability (BDAC) and its dimensions affect the absorptive capacity and autonomous R&D of high-speed railways. As well as their impact on the organization’s long-term sustainability, using the mediation of absorptive capacity and autonomy in R&D. Employees and the heads of the planning, HR, R&D, and technical departments of high-speed railways were polled for information. For data analysis, we used PLS-SEM, and then we employed mediation analysis. BDAC increases the organization’s ability to tolerate change and gives management instructions on making decisions. This study adds to the body of knowledge on absorptive capacity development and autonomy over R&D activities by investigating the role of BDAC in the long-term development of high-speed trains.

Impact of big data analytics & machine learning on innovation of manufacturing companies

Developments in Business Analytics in the era of Big Data have furnished unprecedented possibilities for businesses to innovate. With insights gained from Business Analytics, businesses are in a position to cultivate new or even enhanced products/services. Nevertheless, not many scientific studies have examined the mechanism whereby Business Analytics plays a role in a firm 's innovation results. This particular analysis aims to deal with this gap by empirically and theoretically checking out the connection between Business Analytics as well as innovation. In order to do this aim, absorptive capability principle is employed as a theoretical lens to understand the improvement of a research version. Absorptive capacity theory describes a firm 's potential to understand the importance of fresh, external info, assimilate it and put it on to commercial ends. The study model covers the usage of Business Analytics, innovation, data-driven culture, environmental scanning, along with competitive advantage. The study design is examined by way of a questionnaire survey of 228 USA companies. The results suggest that Business Analytics specifically increases green scanning which helps you to improve a company 's development. Business Analytics also specifically enhances data driven culture which in turn impacts on green scanning. Data-driven culture plays another essential function by moderating the impact of green scanning on new product meaningfulness. The findings show the beneficial effect of company analytics on development and the pivotal roles of green scanning as well as data driven culture. Organizations wishing to realize the possibility of Business Analytics thus require changes in both their internal and external focus.

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

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.