Big Data and Analytics Adoption and Its Impact on Organizations: The Case of Ghanaian Banks.

Abstract

The emergence of the concept of Big Data (BD) has created significant value expectations for organizations with massive data collections. Banks, like all other organizations, understand the value potential of big data. Finance professionals view big data analytics as a powerful tool for improving risk analysis, improving fraud detection and prevention, and shifting the trade and investment paradigm from "of no moment" settlement to "real-time" settlement. Big data analytics is increasingly recognized as an essential component of agile organizations' decision-making processes, with the potential to yield impressive results across various industries. However, despite its growing importance, studies on big data analytics remain limited and inconsistent, particularly within the banking industry. Furthermore, practitioners are only beginning to embrace big data analytics, indicating a need for further research to understand the challenges associated with the adoption of big data technology in the banking sector. The purpose of this study is to investigate the adoption and impact of Big Data and Analytics (BDA) on Ghanaian banks. In addition, the study seeks to investigate the enablers and constraints of BDA adoption in Ghanaian banks and examine time as a moderating factor in the impact of big data and analytics in Ghanaian banks. The study uses qualitative research methodology and is exploratory. It uses a theoretical framework that is based on the integrative IS success model developed by Delone and Mclean, which is a popular method for evaluating IT adoption. To gather information, semi-structured interviews were used. The final respondents for the study were chosen using purposeful sampling. Based on the impact, adoption, and duration of use of the platform, a total of 10 respondents were used for the study.According to the study's findings based on the banks that participated, some foreign banks in Ghana have adopted BDA more than both local and private banks. BDA has been operational in some banking sectors for the last five (5) years, with most banks using the platform more frequently. Finally, the findings revealed a relationship between BDA Information Quality, BDA System Use, and BDA User Satisfaction. Furthermore, BDA Service Quality influenced BDA System Use and BDA User Satisfaction. Furthermore, BDA Time had a moderating effect on the relationship between BDA System Use and Net Benefits, as well as BDA User Satisfaction and Net Benefits. Net Benefits were directly affected by BDA System Use and BDA User Satisfaction. This study fills a gap in the body of knowledge regarding Big Data and Analytics, their application, and their impact on Ghanaian banks in the context of a developing economy. Although the Delone and Mclean success model is typically used in quantitative studies to access technology adoption, the study's originality lies in the researcher's adoption of it to qualitatively explore the study. The relevance of this study to Ghana can assist bank managers in their use and implementation of BDA as well as policy makers in using it to develop policies for BDA adoption. Also, the study offers a solid foundation for academics and students who desire to conduct research in the field not just in Ghana but also elsewhere in the globe, especially in other developing nations.