Role Of Big Data Analytics Research Articles

Leveraging big data analytics to combat emerging financial fraud schemes in the USA: A literature review and practical implications

The rise of emerging financial fraud schemes in the USA has presented significant challenges for financial institutions, regulators, and law enforcement agencies. As fraud tactics become increasingly sophisticated, traditional methods of detection and prevention are proving insufficient. This literature review explores the role of big data analytics as a critical tool in combating financial fraud, focusing on its practical applications and effectiveness. By leveraging machine learning, predictive analytics, and artificial intelligence, big data analytics enables real-time monitoring and detection of fraudulent activities, offering a more dynamic and adaptive approach to fraud prevention. The review synthesizes key studies, case reports, and industry practices, highlighting the implementation of big data in analyzing large volumes of transactional and behavioral data to identify suspicious patterns. It further discusses the challenges associated with integrating big data technologies into existing systems, including issues related to data privacy, regulatory compliance, and the high cost of implementation. Additionally, the paper assesses the impact of big data solutions on fraud prevention, comparing their effectiveness to traditional methods and identifying best practices for institutions looking to adopt these technologies. Finally, the review proposes future directions, emphasizing the potential of emerging technologies such as artificial intelligence and blockchain to enhance fraud detection capabilities. It offers recommendations for financial institutions, regulators, and policymakers to collaborate in developing more robust strategies to safeguard the financial system from evolving fraud threats. This paper provides a comprehensive analysis of the current state of big data analytics in combating financial fraud and its practical implications for the financial services industry in the USA.

The role of big data analytics in the footwear supply chains: a multiple case study perspective

Purpose This paper aims to analyze the importance of big data analytics (BDA) and supply chain analytics for sustainable supply chain management in all types of enterprises in the footwear industry. The importance and implementation of BDA in supply chain management have been discussed in this research paper, and a conceptual framework is proposed for the implementation. Design/methodology/approach The previous research indicates that the application of BDA and supply chain analytics is the key point to establishing sustainable supply chain management. Even though there are several types of research on the implications of BDA, there is still a gap in practical implications in organizations. For a better understanding, comparison of frameworks has been done for literature support. Further, 11 in-depth, semi-structured interviews have been conducted with four organizations from the footwear industry, and a conceptual framework has been proposed. Findings This research demonstrates an important gap in the literature through a thorough investigation of how firms can improve operational efficiency and productivity through the framework application and whether it is feasible to apply this within the global footwear industry or not. In conclusion, the research has denoted that despite lack of understanding related to sustainability agendas and not having proper ERP or BDA systems, the organizations operating in Pakistan still met some standards of sustainability and are moving toward achieving more. Research limitations/implications The research has pointed out the importance of the integration of all stakeholders in the supply chain through a proposed framework that aligns and guides the stakeholders so that the policies and other constraints may be taken into consideration while revising sustainable supply chain strategies. Practical implications This study will help increase sustainability through the application of BDA technology, which will benefit various stakeholders, particularly those in the footwear industry. Originality/value This study has proposed a framework based on three stages of the implementation of BDA in the existing supply chain to maintain and achieve sustainability in the organization in the footwear industry. This three-stage framework covers many aspects that were not covered in the literature before.

Role of big data analytics and hyperspectral imaging in waste management for circular economy

The accumulation of waste has a profound impact on the environment, prompting a crucial discussion about effective waste management strategies aligned with Oman Vision 2040’s sustainability goals. The consequences of municipal solid waste generation have multifaceted impacts on the environment, public health, and overall well-being of communities. Addressing these consequences requires a holistic approach that includes the integration of sustainable waste management technologies to foster a circular economy. This paper emphasizes the necessity for a paradigm shift in waste management methodologies, emphasizing the importance of Hyperspectral Imaging and Big Data Analytics into municipal solid waste management processes. This paper explores the synergistic relationship between hyperspectral imaging which is capable of precise material identification, and big data analytics to facilitate comprehensive data analysis. The integration aims to optimize waste segregation, resource recovery, and recycling processes. The utilization of data-driven insights enables predictive modeling and the identification of trends thereby facilitating more efficient and sustainable waste management practices. The harnessing of big data analytics empowers stakeholders to make informed decisions in waste management to achieve long-term environmental, and economic sustainability.

Retraction Note: Post-COVID-19's impact on green supply chain management and sustainable E-commerce performance: the moderating role of big data analytics.

Retraction Note: Post-COVID-19's impact on green supply chain management and sustainable E-commerce performance: the moderating role of big data analytics.

Role of big data analytics and information processing capabilities in enhancing transparency and accountability in e-procurement applications

Purpose Considering information processing theory, this study aims to examine how big data analytics (BDA) mediates the influence of e-procurement coordination (EPC) and e-procurement transactional (EPT) applications on transparency and accountability (TA) in the procurement processes of firms within the United Arab Emirates' private sector. Furthermore, it investigates the moderating role of information processing capabilities (IPCs) in the relationships among EPC, EPT and BDA to clarify their collective impact on enhancing TA and procurement performance. Design/methodology/approach Data were collected from procurement and information technology professionals in the UAE’s private sector through a Web-based survey. Established scales were used to assess e-procurement, BDA, TA, procurement performance and IPCs. Data were analyzed using partial least squares structural equation modeling. Findings Integrating e-procurement with BDA demonstrates the potential to improve TA and procurement performance in the UAE’s private sector. BDA is positively associated with EPC and EPT applications use, contributing to increased procurement TA and enhancing overall procurement performance. Practical implications Organizations can enhance procurement TA by adopting e-procurement and BDA technologies. Originality/value This study identifies the mediating role of BDA in the relationship between e-procurement and procurement TA. In addition, it investigates the moderating role of IPCs in the relationship between e-procurement and BDA.

Role of Big Data Analytics In Industry 4.0

Role of Big Data Analytics In Industry 4.0

The role of big data analytics in customer relationship management: Strategies for improving customer engagement and retention

This paper explores the transformative impact of Big Data Analytics on Customer Relationship Management (CRM), focusing on its role in enhancing customer engagement and retention. The study highlights how Big Data enables businesses to create personalized customer experiences, anticipate customer needs through predictive analytics, and respond promptly with real-time data analysis. Additionally, the paper discusses using sentiment analysis to refine engagement strategies and applying predictive models to prevent customer churn. The effectiveness of loyalty programs is also examined, demonstrating how Big Data can tailor incentives to boost customer loyalty. The conclusion emphasizes the need for future research into advanced machine learning techniques, ethical considerations, and developing more sophisticated real-time analytics tools. The findings underscore the pivotal role of Big Data in transforming CRM strategies, driving customer satisfaction, and ensuring long-term business success.

Exploring the Role of Big Data Analytics in Strengthening Financial Accounting for Promoting Sustainable Business Practices

Exploring the Role of Big Data Analytics in Strengthening Financial Accounting for Promoting Sustainable Business Practices

The Effect of Supply Chain Management Practices on Competitive Advantage in Manufacturing Industry in Pakistan: The Moderating Role of Big Data Analytics

The purpose of the current study is to examine that what big data analytics (BDA) may have a moderating effect on the relationship between supply chain management practices (SCMPs) and competitive advantage (CA) in Pakistani manufacturing companies. Data was gathered quantitatively from several manufacturing businesses located industrial in Karachi. The study hypothesis was tested using SPSS method and linear regression analysis, sample size is 154 persons working in small, medium and large sized manufacturing companies with multiple categorizations based on enterprise size, job & experience. The findings demonstrate that SCMPs have a major favorable impact on competitive advantage (CA). In particular, information quality (IQ), customer relationship management (CRM), and strategic supplier partnerships (SSP) all significantly improve competitive advantage (CA), information sharing (ISh) not significantly on competitive advantage (CA). Furthermore, investigates the association between SCMPs and CA in Pakistani manufacturing enterprises, as well as the moderating effect of big data analytics (BDA). The findings validate H1, H2, and H3 and demonstrate the beneficial effects of Supplier Strategic Partnership (SSP), customer relationship management (CRM), and information quality (IQ) on competitive advantage (CA). This study is to look at how big data analytics (BDA) may be able to moderate the relationship between supply chain management practices (SCMPs) and competitive advantage (CA) in Pakistani manufacturing companies. The findings demonstrate that SCMPs significantly improve CA. Information quality (IQ), customer relationship management (CRM), and strategic supplier partnerships (SSP) in particular significantly improve competitive advantage (CA). Information sharing (ISh), on the other hand, had no effect on competitive advantage (CA). Some flaw in the way the firms under investigation handle big data analytics (BDA). It emphasizes that for big data analytics (BDA) systems to produce the intended outcomes, managers must integrate them with other business systems and capabilities.

Evaluating the role of big data analytics in enhancing accuracy and efficiency in accounting: A critical review

Evaluating the role of big data analytics in enhancing accuracy and efficiency in accounting: A critical review

Developing an integrated framework for risk management in policing: crisis management using big data analytics as a case study

Objective: The study seeks to develop an integrated framework for enhancing risk management and crisis management in policing through the utilization of big data analytics, using the Dubai police force as a case study. Theoretical Framework: This research is grounded in the intersection of big data analytics and risk management within the field of policing, focusing on how data-driven approaches can innovate and improve security crisis management. Method: Data for the study was collected via questionnaires distributed to 450 police officers across all departments in Dubai. The analysis was conducted using AMOS software within a Structural Equation Modeling framework to assess the impact of big data analytics on police risk and crisis management. Results and Discussion: The results indicate that big data analytics significantly enhances risk management and crisis management in policing. It was found that police risk management serves as a mediating factor between big data analytics and security crisis management. These findings suggest that the application of big data can substantially improve knowledge and operational performance in police organizations. Research Implications: The study emphasizes the crucial role of big data analytics in revolutionizing police risk and crisis management processes. It highlights the potential for these technologies to provide substantial improvements in the efficiency and effectiveness of police operations. Originality/Value: This research is valuable as it provides empirical evidence of the benefits of big data analytics in the context of policing. It offers practical guidelines to police departments, particularly within the UAE, on leveraging big data to enhance their operational performance in managing risks and crises. The study contributes to the broader understanding of integrating advanced data analytics into public safety and emergency response strategies.

The Impact of Big Data Analytics on The Detection of Errors And Fraud in Accounting Processes

Purpose: This study aims to discuss and investigate the role of big data analytics (BDA) in promoting error detection and preventing fraud in accounting operations. Methodology: It uses a secondary method of data collection (desk study) to explore the potential impact of BDA in enhancing error and fraud prevention on six key considerations including data quality and integrity; data privacy and security; real-time monitoring and alerts; integration with internal controls; ethical implications; and human experience. Finding: The analysis shows that the BDA enhances fraud detection by integrating data from multiple sources, using sophisticated algorithms to identify anomalies. Reduces false positives and improves accuracy. However, human expertise is essential for ethical standards and transparency. Implications: It has significant implications for the accounting profession, as it provides an addition in both theoretical knowledge and practical applications, theoretical implications include developing accounting knowledge, developing data-driven models, establishing ethical frameworks, and promoting interdisciplinary insights. On a practical level, it provides guidance for improving financial accuracy, fraud prevention, regulatory compliance, data-driven decision-making, and professional development for accountants. Contribution: It contributes to bridging the research gap in the aspect related to the analysis of big data and its impact on the quality of accountants' work, as this topic is of high importance to researchers, governments, policymakers, industries, companies, investors, and regulators, bridging the gap between accounting and data analytics. This interdisciplinary approach is critical in understanding the evolving landscape of the impact of big data analytics on financial transparency and accuracy of financial reporting. Article Type: Research Paper.

Analyzing the Efficacy of the Relative Strength Indicator of Capital Inflows and Outflows Based on Big Data Analysis in Achieving Abnormal Returns Evidence from the Chinese Stock Market

This study originates from an analysis of market microstructure and introduces a novel statistical indicator of relative strength in capital flows through the application of big data analytics. This indicator effectively captures the impact of capital movements on future stock prices by integrating variations in stock prices with the volume of transactions within a corresponding timeframe. Building upon this foundation, the research develops an innovative momentum investment strategy based on the relative strength indicator of capital inflows and outflows, extending beyond the traditional fixed holding period momentum strategy framework. Empirical analysis reveals that this new indicator significantly demonstrates momentum effects in the Chinese A-share market; notably, in portfolios of stocks with smaller market capitalizations, the momentum strategy grounded on the new indicator achieves higher excess returns compared to traditional momentum strategies. These findings not only validate the efficacy of the new indicator but also underscore the critical role of big data analytics in enhancing the analysis of financial markets and the efficiency of investment decision-making.

Big data analytics in the financial services industry: Trends, challenges, and future prospects: A review

This paper provides a comprehensive review of the evolving role of big data analytics within the financial services industry, highlighting current trends, inherent challenges, and future prospects. The objective is to synthesize existing research and practices to understand how big data analytics is transforming financial services, including banking, insurance, and investment sectors. Through a systematic examination of peer-reviewed articles, industry reports, and case studies, the research methodology encompasses both qualitative and quantitative analyses to offer insights into the adoption, implementation, and impact of big data analytics. Key findings reveal that big data analytics is pivotal in driving innovation, enhancing customer experiences, and improving operational efficiency in financial services. It plays a critical role in risk management, fraud detection, personalized financial advice, and algorithmic trading, among other areas. However, the industry faces significant challenges, including data privacy concerns, regulatory compliance, technological infrastructure requirements, and the need for skilled professionals. The paper concludes that while big data analytics offers substantial opportunities for the financial services industry, realizing its full potential requires addressing existing challenges through strategic investments in technology, human capital, and data governance. The future prospects of big data analytics in financial services are promising, contingent upon the industry's ability to adapt to rapid technological advancements and evolving regulatory landscapes. This review underscores the importance of continued research and collaboration between industry stakeholders to leverage big data analytics for sustainable growth and competitiveness.

Big data analytics-artificial intelligence and sustainable performance through green supply chain practices in manufacturing firms of a developing country

Purpose This study aims to examine the role of big data analytics (BDA) powered by artificial intelligence (AI) in improving sustainable performance (SP) through green supply chain collaboration (GSCC), sustainable manufacturing (SM) and environmental process integration (EPI). Design/methodology/approach Data was collected from 249 supply chain professionals working at various manufacturing firms, and hypotheses were tested through a quantitative method using PLS-SEM with the help of SmartPLS version 4 to validate the measurement model. Findings This study identified that BDA-AI significantly and positively affects GSCC, SM and EPI. Similarly, the results showed that GSCC significantly and positively affects SP. At the same time, SM and EPI have an insignificant effect on SP. The GSCC found a significant relationship between BDA-AI and SP for mediation. However, SM and environmental performance integration did not mediate the relationship between BDA and AI and SP. Originality/value This research evaluated a second-order model and tested SP in conjunction with the dynamic capability theory in the manufacturing industry of Pakistan. Therefore, this research could be beneficial for researchers, manufacturers and policymakers to attain sustainable goals by implementing the BDA-AI in the supply chain.

Exploring the role of big data analytics and dynamic capabilities in ESG programs within pharmaceuticals

Achieving the United Nations’ Sustainable Development Goals (SDGs) requires environmental, social, and governance (ESG) programs in the pharmaceutical industry. Using the Millennium Development Goals, the 2030 agenda aims to transform European Union companies toward sustainability. In pharmaceuticals, in particular, ESG programs come with complexities such as employee skills, corporate goals, and management expectations. Managing these programs effectively requires advanced technologies such as big data analytics (BDA) and dynamic capabilities (DC). In this study, DC theory is used to develop an architecture for managing ESG criteria, focusing on provenance, traceability, and availability. BDA’s role in ESG programs is explored, along with its use cases and benefits, and how DC drives success in ESG implementation. The study examined five pharmaceutical companies in Germany, Portugal, and Switzerland, all consulting the same firm for BDA systems, to identify the characteristics of effective BDA implementation. The research explores how BDA and DC jointly enhance ESG efforts, the essential skills needed, and how DC aids in real-time decision-making in BDA projects aligned with ESG standards. It highlights the BDA system’s accuracy and effectiveness in managing ESG programs, with DC as a pivotal facilitator. Findings reveal BDA’s value in operational efficiency and aligning business models with ESG goals, underscoring the need for diverse skills in BDA implementation and DC’s importance in integrating various managerial capacities into effective strategies. The study promotes a dynamic, data-driven approach in the pharmaceutical industry for managing complex ESG initiatives. It stresses continuous learning, adaptation, and integrating technological advances with ethical business practices. The research concludes by emphasizing BDA and DC’s vital roles in advocating ethical, socially responsible, and environmentally sustainable practices in the pharmaceutical sector, marrying technology with ethical business strategies.

The Role of Big Data Analytics in Understanding and Predicting Consumer Behavior

The Role of Big Data Analytics in Understanding and Predicting Consumer Behavior

The role of big data analytics for decision-making in projects: uses and challenges

ABSTRACT The utility of big data analytics (BDA) for data-driven decision-making is growing. However, its role in project decision-making remains under-researched though data is used in every phase of projects. Addressing the gap, data collected through twenty-five interviews was analysed using NVivo. The results show that there is a sense of confusion about the scope and utility of BDA for decision-making in projects. While BDA facilitates transparency in decision-making by enabling consistent evaluation of information and aids decision-making across a variety of domains, including risk, procurement and fraud management, it can also be used to mask inaccuracies, leading to poor decisions.

DATA GOVERNANCE IN GAMING INDUSTRY

This article examines the role of Big Data Analytics and Data Governance in the gaming industry. It shows how Big Data Analytics has changed game design and player interaction, focusing on trends and player preferences, especially in free-to-play games. The importance of Data Governance is stressed for handling data responsibly, focusing on challenges like data quality, security, and legal rules like GDPR. Examples from Ubisoft, SEGA, and Kolibri Games show these concepts in action. Metavibes is another example of research in field of Data Governance. The article also looks at how the gaming industry is dealing with data issues, including using strong policies and better security. It predicts future trends in AI and blockchain in gaming. The piece highlights the need for ethical practices, like protecting player privacy, as key for trust in the industry. The article points out the vital impact of these technologies in advancing and growing the gaming world.

BIG DATA ANALYTICS: A REVIEW OF ITS TRANSFORMATIVE ROLE IN MODERN BUSINESS INTELLIGENCE

In the dynamic landscape of modern business intelligence, Big Data Analytics has emerged as a transformative force, reshaping the way organizations derive insights from vast and diverse datasets. This paper provides a concise overview of the key themes explored in the comprehensive review of Big Data Analytics and its impact on modern business intelligence. Big Data Analytics represents a paradigm shift in decision-making processes, offering organizations the capability to harness the full potential of their data assets. The review delves into the multifaceted role of Big Data Analytics, emphasizing its significance in strategic planning, risk management, operational optimization, and customer-centric initiatives. Strategic planning takes a quantum leap forward as organizations leverage predictive analytics to anticipate market trends. The integration of analytics-derived insights aligns decision-making with overarching organizational objectives, driving a more informed and forward-thinking approach to strategic initiatives. Risk management becomes more proactive with the integration of Big Data Analytics, particularly in fraud detection and prevention. The ability to process large volumes of data in real-time enhances vigilance, mitigating financial risks associated with fraudulent activities cenario modeling further empowers organizations to assess and address potential risks before they materialize. Operational optimization becomes a focal point as analytics uncovers inefficiencies in manufacturing processes, supply chains, and retail operations. Real-time decision-making in retail, enabled by data analytics, ensures agility and responsiveness to changing market dynamics and customer preferences. Customer-centric initiatives are elevated through personalized marketing campaigns and predictive analytics in customer support. The review explores how Big Data Analytics enables organizations to craft personalized experiences, enhancing customer satisfaction and loyalty. The study encapsulates the transformative journey of Big Data Analytics in modern business intelligence, emphasizing its role in navigating strategic complexities, mitigating risks, optimizing operations, and placing the customer at the center of decision-making processes. The comprehensive review provides insights for organizations seeking to harness the transformative potential of Big Data Analytics in the data-driven era.
 Keywords: Big Data, Business Intelligence, Data Analytics, Modern Business, Review.