Impacts of big data on accounting

Purpose- This paper aims to discuss the impact of big data analytics on financial reporting and accounting within the institutional framework. Methodology- The paper’s design is a qualitative study based on in-depth interviews with accounting professionals in different industries to determine and analyze potential uses and impacts of big data analytics in financial reporting and accounting. Findings- The nature of accounting and financial reporting do not alter but traditional methods of recording, collecting and analyzing accounting information change. The revenue recognition in many industries including airways and telecommunication companies needs to process a large volume of data because of the complex billing systems. Furthermore, the big data and analytics present a pressing issue and in the same time a real opportunity for accountants working in forensic and valuation areas. Conclusion- We concluded that the companies are tracking the policies and procedures of BDA adopted by other firms in trying to assure the legitimacy of their firm and to maintain and enhance their competitive advantages. In addition, financial reporting and accounting professionals try to adapt to the challenges regarding using and mastering new technologies and applications, they need to improve their capabilities and skills in the areas of big 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

Advances in the field of technology enabled individuals and businesses to collect large amounts of data (structured and unstructured) from various sources like never before. Data from social media, user-generated, internet, health care, manufacturing, supply chain, financial institution, and sensors have grown exponentially. This paper’s objective is to review how big data drive and impact innovation, competitive advantage, productivity, and decision support. Methodology: A comprehensive literature review on big data and identifying the impact of big data analytics on innovation, competitive advantage, productivity, and decision support are studied. The reviewed literature created the foundation for studying, a model that was developed based on an extensive review of literature as well as case studies and future forecast by market leaders. Big data is the latest buzzword among businesses. A new model is suggested identifying big data and the correlation between innovation, competitive advantage, productivity, and decision support. Findings: A review of scholarly literature and existing case studies finds that there is a gap between existing frameworks and the integration of big data into various business and management functions and objectives. The findings are interesting that literature is rich with concepts and frameworks for achieving the end goal for business or management function along with framework but very little is available in the literature on the question of how to integrate big data analytics into those frameworks. This paper finds that a key question is missing i.e., what are the essential steps that businesses should perform to implement and integrate big data analytics into existing frameworks to fully exploit the big data potential. Research Limitations/Implications: The research was limited to a review of selective literature focused on in-depth understanding of big data. Additionally, it focuses on how big data leads to innovation, competitive advantage, productivity, and decision support. Although there are many other related fields of studies where big data impact can be studied but is not part of this study effort. Future studies can lead to more in-depth studies of other related areas of studies. Practical Implications: The review of the literature suggested that “Big Data” is playing an important role in innovations, creating competitive advantage, enhancing productivity, and assisting in data-driven decisions. Businesses are taking advantage of the customer insights that are innovating products and services which are very customer-centric, keeping the competition on the run, improving productivity at all levels, and making educated decisions every day. The future will be driven by smarter big data solutions and insights. Originality/Value: The study provides evidence that big data is the catalyst for innovation, creates competitive advantage, enhances productivity, and assists in decision making. The methodology is to review scholarly literature and case studies. It supports the need for developing new models, implementation frameworks for better insights, and patterns. The big data implementation methodologies, framework, and governance have been ignored in empirical research.

In the last half decade or so, the buzz phrase in the business and research arena is Big Data and Big Data Analytics. One industry that can reap substantial benefits from big data and analytics is the mobile phone industry. Nigeria in the West Africa is one country where mobile phone market is considerably big. Over 90 per cent of individuals and corporate businesses completely rely on the mobile industry for their day-to-day transactions. Embracing advanced analytics of big data by the mobile operators in Nigeria will impact positively on revenue and reflect on GDP. In this paper, we discuss the business impact of big data analytics in the light of telecom industry. We point out the various ways the industry could leverage on big data and analytics to render customer-centric service and reap a good return on investment.

Purpose This 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/approach With 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. Findings This 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/implications The 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/value This 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.

In order to scrutinize or evaluate an extremely high quantity of an ever-present and diversified nature of data, new technologies are developed. With the application of these technologies, called big data technologies, to the constantly developing various internal as well as external sources of data, concealed correlations between data can be identified, and promising strategies can be developed, which is necessary for economic growth and new innovations. This chapter deals with the analysis of the real-time uses of big data to both individual persons and the society too, while concentrating on seven important areas of key usage: big data for business optimization and customer analytics, big data and healthcare, big data and science, big data and finance, big data as enablers of openness and efficiency in government, big data and the emerging energy distribution systems, and big data security.

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.

Big Data Analytics (BDA) has emerged as a critical enabler of project management practices enabling quick decision-making and improvement of projects’ performance. This study aims to examine the effects of Big Data Analytics (BDA) on arrayed aspects of project success such as planning, decisions and others like budget, schedule and stakeholders’ engagement. Quantitative data is collected from 200 project managers and stakeholders from different sectors through a cross-sectional survey. Additionally, 15 face-to-face semi-structured interviews are conducted to gather qualitative data. The quantitative data is analyzed using descriptive statistics and regression analysis to establish the level and manner in which BDA affects the success of the projects. The qualitative data on the other hand is analyzed using thematic analysis to get the recurrent themes and experiences of the participants. The findings indicate that the benefits of adopting BDA enhance project success in aspects such as the schedule and resources. However, it is also shown that difficulties in integrating data and addressing gaps in expertise are crucial. The study adds to the body of knowledge of how BDA is applied within contemporary project management and resource suggestions for mitigating the barriers to its integration. Based on the findings of the research implications for both BDA applications in specific sectors and the long-term impact of BDA implementation on project management practices are recommended and directions for further research are provided.

Big data analytics, firm risk and corporate policies: Evidence from China

PurposeThe purpose of this paper is to investigate the effects of Big Data analytics, data security and service supply chain innovation capabilities on services supply chain performance.Design/methodology/approachThe paper draws on the relational view of resource-based theory to propose a theoretical model. The data were collected through survey of 145 service firms.FindingsThe results of this study found that the Big Data analytics has a positive and significant relationship with a firm’s ability to manage data security and a positive impact on service supply chain innovation capabilities and service supply chain performance. This study also found that most service firms participating in this study used Big Data analytics to execute existing algorithms faster with larger data sets.Practical implicationsA main recommendation of this study is that service firms empower a chief data officer to establish the data needed and design the governance of data in the company to eliminate any security issues. Data security was a concern if a firm did not have ample data governance and protection as the information was shared among members of service supply chain networks.Originality/valueBig Data analytics are a useful technology tool to forecast market preference based on open source, structured and unstructured data.

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.

Supply chain and logistics are critical to the retail industry as they directly impact cost efficiency, customer satisfaction, and overall profitability. The rise of big data analytics (BDA) has transformed industries across the globe, and retail is no exception. With the increasing integration of BDA into retail supply chains and logistics, this literature review aims to explore a range of impacts of big data analytics on retail supply chains, including enhanced decision-making capabilities, customer experience improvements, inventory management optimization, and increased operational efficiency. By synthesizing recent studies and conceptual frameworks, this review highlights key findings and lays the groundwork for future research in this area.

ObjectiveMuch has been invested in big data analytics to improve health and reduce costs. However, it is unknown whether these investments have achieved the desired goals. We performed a scoping review to determine the health and economic impact of big data analytics for clinical decision-making.Materials and MethodsWe searched Medline, Embase, Web of Science and the National Health Services Economic Evaluations Database for relevant articles. We included peer-reviewed papers that report the health economic impact of analytics that assist clinical decision-making. We extracted the economic methods and estimated impact and also assessed the quality of the methods used. In addition, we estimated how many studies assessed “big data analytics” based on a broad definition of this term.ResultsThe search yielded 12 133 papers but only 71 studies fulfilled all eligibility criteria. Only a few papers were full economic evaluations; many were performed during development. Papers frequently reported savings for healthcare payers but only 20% also included costs of analytics. Twenty studies examined “big data analytics” and only 7 reported both cost-savings and better outcomes.DiscussionThe promised potential of big data is not yet reflected in the literature, partly since only a few full and properly performed economic evaluations have been published. This and the lack of a clear definition of “big data” limit policy makers and healthcare professionals from determining which big data initiatives are worth implementing.

BackgroundThe field of “Big Health,” which encompasses the integration of big data in healthcare, has seen rapid development in recent years. As big data technologies continue to transform healthcare, understanding emerging trends and key advancements within the field is essential.MethodsWe retrieved and filtered articles and reviews related to big data analytics in health research from the Web of Science Core Collection, including SCI Expanded and SSCI, covering the period from 2009 to 2024. Bibliometric and co-citation analyses were conducted using VOSviewer and CiteSpace.ResultsA total of 13,609 papers were analyzed, including 10,702 original research and 2,907 reviews. Co-occurrence word analysis identified six key research areas: (1) the application of big data analytics in health decision-making; (2) challenges in the technological management of health and medical big data; (3) integration of machine learning with health monitoring; (4) privacy and ethical issues in health and medical big data; (5) data integration in precision medicine; and (6) the use of big data in disease management and risk assessment. The co-word burst analysis results indicate that topics such as personalized medicine, decision support, and data protection experienced significant growth between 2015 and 2020. With the advancement of big data technologies, research hotspots have gradually expanded from basic data analysis to more complex application areas, such as the digital transformation of healthcare, digital health strategies, and smart health cities.ConclusionThis study highlights the growing impact of big data analytics in healthcare, emphasizing its role in decision-making, disease management, and precision medicine. As digital transformation in healthcare advances, addressing challenges in data integration, privacy, and machine learning integration will be crucial for maximizing the potential of big data technologies in improving health outcomes.

Big data ultimately provide assistance in the security of online different processes like payments. It can also integrate many related payment functions into a centralized platform that enable the simple descriptive analytics. It is important to evaluate the regular organizational activities regarding collection and distribution of large amounts of data from many different sources to support organizations managers' knowledge and perspectives. A lot of e-commerce business in different sectors believe that Big Data Analytics (BDA) is the most effective method provide and reveal new insights and effectively formulate the business activities. Big data in th e e-commerce is a form leverage the BDA to enhance the consumer engagement, personalize the customer experience, and increase the sale volumes. Big data e-commerce also engages the customers in two ways: personalized recommendations and increased availability of the product information. The key objective of this study is to explore the effects of BDA on customers' online behaviours to examine whether the BDA is important as stated by firms.