The impact of big data analytics on the effectiveness of management decisions

Big data and data analytics are currently the buzzwords in both academia and industry to become data driven. Big data has been the trending topic in the accounting industry also. Big data and data analytics will have an important impact on accounting and accountants. Big data will improve the quality of accounting information and the accounting profession will continue to provide real-time and dynamic information to assist in decision-making. The purpose of this research is to investigate the impact of big data and big data analytics in accounting. Data analytics is one of the most recent developments in the accounting context. This study is qualitative in nature and adopted a literature review methodology to gain a better understanding of the study area. This literature review seeks to provide a description and evaluation of the impact of big data analytics on accounting. This research found that big data presents great opportunities for decision making in accounting and risks analysis, which indicated that companies could improve their performance, measure performance, manage risks and allow effective real-time decision-making with data analytics. This research revealed that accountants can create more value in a world of big data analytics and encourages accountants to get started with big data to find answers to risks in business operations as well as understand financial performance. It shows that relying on big data analytics will open new possibilities for accountants. This study contributes to the research literature in the area of big data analytics on accounting. The limitations of this study are that it utilizes few recent peer reviewed articles in the general accounting practice, therefore not exhaustive in describing how big data and big data analytics impacts accounting.

Big Data is defined as high-volume, high-velocity, high-variety (and more recently, high-veracity) information assets that demand cost-effective, innovative forms of capturing, storing, distributing, managing and analyzing that information (Gartner IT Glossary, n.d.; TechAmerica Foundation’s Federal Big Data Commission 2012). Big Data Analytics (BDA) refers therefore to the “application of statistical processing, and analytics techniques to big data for advancing business” (Grover et al. 2018, p. 360). In marketing, despite much interest to the concept of Big Data and analytics, there is a lack of empirical evidence of the benefits associated with BDA. More surprisingly, little attention has been paid to the empirical investigation of the impact of BDA for market purposes on financial performance, despite the critical importance of such a relationship to reach strategic objectives.Using the resource-based theory (RBT) framework (Barney 1991; Lee and Grewal 2014), this study fills that void in the literature by adopting an inter-disciplinary perspective to assess the impacts of BDA for marketing purposes on firm financial performance. More specifically, the research involves a large-scale study of organizations that are part of the S&P 500 (Standard & Poors 500) in the USA, and of the S&P/TSX 60 (Standard & Poors/Toronto Stock Exchange 60), in Canada, to identify to what extent the implementation of BDA, in the marketing function, forms a competitive advantage that materializes through financial performance.Overall, the findings suggest that BDA has a significant and extensive impact on corporate performance. Second, while descriptive analytics contribute positively to profit-related performance indicators (i.e., share price), prescriptive analysis load more significantly on revenue and profit-related performance indicators. Furthermore, the contribution of BDA to the revenue performance of the manufacturing industry is greater than in other industries.This study contributes uniquely to past research and professional practice by providing an exploratory research on the impact of particular big data analytics (i.e., descriptive, predictive, and prescriptive) on the financial performance of 560 large capitalization companies (i.e., S&P500 and S&P/TSX60 stock indices).KeywordsBig dataMarketingFinancial performanceBig data analytics

This study aims to evaluate the impact of big data analytics on the performance of companies in Asia's digital technology industry, as well as the role that knowledge management plays in influencing that impact. The study adopts a quantitative research methodology and collects data from a number of regional firms. According to the findings, big data analytics have a favorable effect on corporate performance in the digital technology industry, and the adoption of efficient knowledge management systems strengthens this connection. In addition, the study found that knowledge management functions as a moderator between big data analytics and business performance. This highlights how important it is to effectively manage knowledge in order to use big data analytics to enhance company performance. The findings have significance for regional managers and policymakers, underlining the need to invest in big data analytics and knowledge management to increase business performance in the digital technology industry. This study contributes to our comprehension of the role that big data analytics and knowledge management play in enhancing the performance of Asian companies in the digital technology sector. In conclusion, the findings show that regional companies may benefit from a strategic approach to knowledge management in order to optimize the positive impact of big data analytics on their overall performance

We present a new approach to study big data in finance (specifically, in limit order books), based on stochastic modelling of price changes associated with high-frequency and algorithmic trading. We introduce a big data in finance, namely, limit order books (LOB), and describes them by Lobster data-academic data for studying LOB. Numerical results, associated with Lobster and other data, are presented, and explanation and justification of our method of studying of big data in finance are considered. We also describe various stochastic models for mid-price changes in the market, and explain how to use these models in practice, highlighting the methodological aspects of using the models.

PurposeThis study empirically examines the impact of greenwashing practices on consumers’ intention to purchase green products. It also investigates whether greenwashing has any impact on economic and social risks. Moreover, this research captures the moderating impact of big data analytics (BDA) on the relationships between intention to purchase green products and its predictors.Design/methodology/approachWith the support of literature in the field of greenwashing, economics, consumer behavior, as well as emerging technologies and theories on consumer behavior, a conceptual model is developed. The model was validated using partial least squares (PLS) structural equation modeling (SEM) technique considering 356 respondents from European countries who are aware of greenwashing practices.FindingsThis study highlights that greenwashing practices negatively impact people both economically and socially. But greenwashing practices also have a profound impact on consumers’ intention to purchase ecofriendly products. It also demonstrates a significant moderating impact of big data analytics adoption on the relationship between intention to purchase green products with its predictors.Research limitations/implicationsThe study presents insights on greenwashing and its impact on society and the economy. It also shows that technologies like BDA could help minimize the challenges of greenwashing activities as well as the social and economic risks. Moreover, the study provides vital insights into the influence of greenwashing practices on the intention to purchase green products. Finally, as this is a cross-sectional study, there are some limitations.Originality/valueThis study takes a fresh look at greenwashing practices and BDA technology to overcome some of the challenges. It provides a unique research model that is (1) unique in this research domain; (2) provides a solid foundation for future researchers to further nurture; (3) extends the understanding of how using technology can minimize the risks caused by greenwashing; and (4) contributes to the body of literature on consumer behavior, economics, technology and social risks.

How can financial data be made more accessible and more secure, as well as more useful to regulators, market participants, and the public? As new data sets are created, opportunities emerge. Vast quantities of financial data may help identify emerging risks, enable market participants and regulators to see and better understand financial networks and interconnections, enhance financial stability, bolster consumer protection, and increase access to the underserved. Data can also increase transparency in the financial system for market participants, regulators and the public. These data sets, however, can raise significant questions about security and privacy; ensuring data quality; protecting against discrimination or privacy intrusions; managing, synthesizing, presenting, and analyzing data in usable form; and sharing data among regulators, researchers, and the public. Moreover, any conflicts among regulators and financial firms over such data could create opportunities for regulatory arbitrage and gaps in understanding risk in the financial system. The Big Data in Finance Conference, co-sponsored by the federal Office of Financial Research and the University of Michigan Center on Finance, Law, and Policy, and held at the University of Michigan Law School on October 27-28, 2016, covered a number of important and timely topics in the worlds of Big Data and finance. This paper highlights several key issues and conference takeaways as originally presented by the contributors and panelists who took part.

The current landscape indicates that sustainability is gaining traction as one of the core business strategies. The use of data analytics to monitor and improve sustainability measures in organizations has remained one of the most effective approaches. Thus, this study examines the impact of Big Data Analytics (BDA) capabilities on process eco-innovation and sustainability performance across industries. We focus on four core capabilities—information technology, personnel expertise, management, and BDA—and their role in achieving sustainability goals. Our results reveal that predictive analytics can significantly reduce carbon emissions by 15% over five years, with emissions projected to drop from 100 metric tons in 2024 to 65 metric tons by 2030. Additionally, energy consumption accounts for 33% of overall resource usage, followed by carbon emissions (33%), water usage (24%), and waste generation (10%). Comparative metrics indicate a 30-40% reduction in carbon emissions, water consumption, and waste generation after adopting sustainability practices, underscoring the importance of data-driven innovation. Our findings highlight the varying needs across industries: the financial sector demands real-time decision- making, healthcare focuses on cost optimization, and retail prioritizes customer satisfaction and operational efficiency. Furthermore, regulatory compliance and resource heterogeneity shape BDA adoption, influencing organizational performance. This study offers practical insights into how industries can align analytics with eco-innovation, driving sustainable growth and operational excellence. These results emphasize the transformative potential of predictive analytics in enhancing sustainability, making BDA a critical component of future industrial strategies. The current landscape indicates that sustainability is gaining traction as one of the core business strategies. The use of data analytics to monitor and improve sustainability measures in organizations has remained one of the most effective approaches. Thus, this study examines the impact of Big Data Analytics (BDA) capabilities on process eco-innovation and sustainability performance across industries. We focus on four core capabilities—information technology, personnel expertise, management, and BDA—and their role in achieving sustainability goals. Our results reveal that predictive analytics can significantly reduce carbon emissions by 15% over five years, with emissions projected to drop from 100 metric tons in 2024 to 65 metric tons by 2030. Additionally, energy consumption accounts for 33% of overall resource usage, followed by carbon emissions (33%), water usage (24%), and waste generation (10%). Comparative metrics indicate a 30-40% reduction in carbon emissions, water consumption, and waste generation after adopting sustainability practices, underscoring the importance of data-driven innovation. Our findings highlight the varying needs across industries: the financial sector demands real-time decision- making, healthcare focuses on cost optimization, and retail prioritizes customer satisfaction and operational efficiency. Furthermore, regulatory compliance and resource heterogeneity shape BDA adoption, influencing organizational performance. This study offers practical insights into how industries can align analytics with eco-innovation, driving sustainable growth and operational excellence. These results emphasize the transformative potential of predictive analytics in enhancing sustainability, making BDA a critical component of future industrial strategies.

Purpose This study aims to examine the impact of big data analytics (BDA) on innovation performance (IPF) in the IT sector, with a focus on the mediating roles of team innovation culture (TIC), team reflexivity (TR), and team entrepreneurial passion (TEP). Design/methodology/approach A survey was conducted among 422 senior IT professionals in Peru. Data were analyzed using partial least squares structural equation modeling to test both direct and indirect relationships between BDA and IPF, incorporating TIC, TR, and TEP as mediators. Findings Results show that BDA has a significant positive effect on IPF. While BDA also positively impacts TIC and TR, these factors do not significantly mediate the relationship between BDA and IPF. However, TEP emerges as a significant partial mediator, highlighting its role in translating data insights into innovation outcomes. Research limitations/implications The cross-sectional design limits causal inference. Future studies should investigate the longitudinal effects and explore additional mediators, such as organizational readiness and knowledge-sharing practices. Practical implications IT firms should foster entrepreneurial passion within teams and align BDA strategies with innovation goals to drive performance. Originality/value To the best of the authors’ knowledge, this study is among the first to examine TEP as a mediator between BDA and IPF, combining resource-based view and social learning theory to explain how team dynamics shape data-driven innovation.

While the healthcare landscape continues to evolve, rural-based hospitals face unique challenges in providing quality patient care amidst resource constraints and geographical isolation. This study evaluates the impact of big data analytics in rural-based hospitals in relation to service delivery and shaping future policies. Evaluating the impact of big data analytics in rural-based hospitals will assist in discovering the benefits and challenges pertinent to this hospital. The study employs a positivist paradigm to quantitatively analyze collected data from rural-based hospital professionals from the Information Technology (IT) departments. Through a comprehensive evaluation of big data analytics, this study seeks to provide valuable insights into the feasibility, infrastructure, policies, development, benefits and challenges associated with incorporating big data analytics into rural-based hospitals for day-to-day operations. The findings are expected to contribute to the ongoing discourse on healthcare innovation, particularly in rural-based hospitals and inform strategies for optimizing the implementation and use of big data analytics to improve patient care, decision-making, operations and healthcare sustainability in rural-based hospitals.

Strategic decisions require the use of analytics and big data technologies. Previous study has focused on big data applications, ethics, benefits, drawbacks, and analytical viewpoints, among other things. The goal of this study is to conduct a comprehensive literature assessment in these areas and to fill any research gaps on the impact of big data analytics on digital marketing approaches. We attempted to cover as many as 200 articles, news, publications, and other portals to investigate studies conducted from the past to the present. As a result, this study evaluated the current literature on big data applications and discovered that digital marketing is a vast sector in which big data has a considerable influence on the creation of digital advertising strategies and how advertising is influenced by big data. Using the greatest big data applications, according to past research, can assist selected organizations in overcoming severe limits during one of the world's most catastrophic pandemics. This outcome will benefit academics and industry in two ways: first, the experimental output will navigate the state of mind on the relationship between digital marketing, digital advertising, and big data analytics; second, the data-based result will improve the ability to think more creatively in the future with other industry affiliations.

This article explores the impact of Big Data analytics on the evolution of future trends within the telecommunication services sector. With the exponential growth of data generated in the digital age, telecommunications companies are increasingly leveraging Big Data analytics to gain valuable insights, enhance operational efficiency, and improve customer experiences. This paper reviews recent advancements in Big Data analytics technologies and methodologies and examines their implications for telecommunication services. Additionally, it analyzes how Big Data analytics is shaping future trends such as network optimization, personalized services, predictive maintenance, and customer engagement in the telecommunication industry. This paper also provides valuable insights into the transformative potential of Big Data analytics and its implications for the future of telecommunication services. Additionally, it discusses the challenges and opportunities presented by the adoption of Big Data analytics in the telecommunications sector and provides insights into the potential future trajectory of the industry. Keywords—Big Data Analytics, Telecommunication Services, Future Trends, Network Optimization, Customer Experience, Predictive Maintenance, Personalized Offerings

This study examines the impact of big data analytics, specifically its dimensions of descriptive, predictive, and prescriptive analysis on sustainable auditing practices in Jordanian commercial banks. A descriptive analytical approach was employed to achieve the research objectives. The study population consisted of 12 Jordanian commercial banks with participants including data analysts, auditors, IT staff, and sustainable auditing consultants. 300 questionnaires were distributed, of which 277 were successfully retrieved and analyzed using SPSS. The results indicate that big data analytics significantly influences sustainable auditing practices in Jordanian commercial banks. The findings suggest that as technological advancements accelerate, increased investments in big data analytics are essential for enhancing auditing processes and achieving sustainability objectives in the banking sector. This underscores the growing importance of big data as a critical tool in modern financial management and auditing. Adopting BDA in sustainable auditing faces challenges, including data security concerns, high implementation costs, and the need for specialized expertise. Overcoming these obstacles requires strategic investments in technology, workforce training, and regulatory support. The findings of this study provide valuable insights for auditors, financial institutions, and policymakers seeking to enhance sustainable auditing practices through big data analytics (BDA). The integration of BDA in Jordanian commercial banks can lead to several practical benefits such as enhanced audit efficiency and accuracy, improved regulatory compliance, cost reduction and resource optimization.

Big data analytics is becoming a key to success for many organization as it extracts the productive value from a huge amount of raw data. This data helps in strategic decision making for continuous process improvements and advancement. This study also focusses on the impact of big data analytics on one of the most important process of an organization which is service supply chain process. ERP (Enterprise Resource Planning) is the tool which is used for big data analytics and based on that study was initiated for pre- and post-implementation of ERP for the years 2015 and 2017 respectively. Null hypothesis and H1 hypothesis are formed. Then, ten major factors have been identified which act as performance indicators for service supply chain process. Based on these factors data has been collected and analyzed. After obtaining the values one-way ANNOVA technique has been applied for testing of hypothesis. It has been observed that F calculated value comes out to be very high in comparison to the F table value which rejects the null hypothesis and proves that Big data analytics has an impact on service supply process.

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

This study aims to examine the impact of Big Data Analytics on Audit Quality using the Structural Equation Modeling-Partial Least Squares (SEM-PLS) approach. The research involved 120 respondents consisting of auditors from Big Ten public accounting firms in Indonesia. The Big Data Analytics variable was measured based on five main dimensions: volume, velocity, variety, veracity, and value. The results indicate that Big Data Analytics has a positive and significant effect on Audit Quality. This relationship is demonstrated by a path coefficient in the moderate category, with a significance level below the five percent threshold. The coefficient of determination shows that nearly half of the variation in Audit Quality can be explained by Big Data Analytics. These findings confirm that effective implementation of Big Data Analytics can enhance the effectiveness, efficiency, and reliability of the audit process. The study also supports the application of the Technology Acceptance Model framework, where perceived usefulness and ease of use of technology contribute to improved audit quality. The practical implications of this research highlight the importance of data-driven strategies in enhancing audit quality in today's digital era.