Kolawole G. Akintola, Bukola A. Ifedayo-Ojo, Olukemi V. Olatunde, Oluwadamilola E. Muritala, Saidi O. Lawal
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引用次数: 0
Abstract
The rapid generation of structured and unstructured data—commonly referred to as big data—offers great opportunities for financial institutions and systems, such as electronic banking (e-banking), electronic commerce (e-commerce) and electronic Payment systems, to extract valuable insights that enhance operational efficiency, profitability and enhanced customer experience. These data are obtained from customers, sellers, shopping, and business activities. In this work, a review of big data analytics techniques applied to e-commerce and digital financial services (DFS) in this era of digital economy is carried out. The review attempts to demonstrate the role of big data analytics in financial data analysis and ecommerce in the contemporary digital economy. The research also examines the role of big data analytics in enhancing customer experience in ecommerce. Three hypotheses were formulated as: effect of big data analytics on predicting users’ experience on different platforms leading to customer satisfaction, effect of big data analytics on predicting users’ experience based on operational efficiency on different platforms. Effect of big data analytics in predicting customers retention based on operational efficiency on different platforms. The results obtained showed that big data analytics can actually have impact on users’ experience, but it may not be the only reason as there some other factors that determine users experience in ecommerce platforms.
期刊介绍:
Annals of Data Science (ADS) publishes cutting-edge research findings, experimental results and case studies of data science. Although Data Science is regarded as an interdisciplinary field of using mathematics, statistics, databases, data mining, high-performance computing, knowledge management and virtualization to discover knowledge from Big Data, it should have its own scientific contents, such as axioms, laws and rules, which are fundamentally important for experts in different fields to explore their own interests from Big Data. ADS encourages contributors to address such challenging problems at this exchange platform. At present, how to discover knowledge from heterogeneous data under Big Data environment needs to be addressed. ADS is a series of volumes edited by either the editorial office or guest editors. Guest editors will be responsible for call-for-papers and the review process for high-quality contributions in their volumes.