Adoption of Big Data Analytics and Financial Performance of Listed Manufacturing Firms in Kenya

Abstract

Scholars and practioner’s have termed big data analytics (BDA) as the 4th paradigm of science. It is major frontier for innovation and creativity and has attracted substantive interest under academia and business standpoints. While BDA has been noted as a key attribute between excellent and poorly performing organizations, it’s not clear whether it had an impact on the big variation in performance among the NSE listed manufacturing firms. The research considered BDA and financial performance among listed manufacturing firms. Main objective of the study was to examine the effect of BDA adoption on financial performance of firms listed at the NSE under the manufacturing category. The study conducted a census on the 8 active manufacturing entities and used a mixed method research design. Primary and secondary data were deployed. Trailing 5 year financial performance (2017-2021) secondary data was collected from published financial reports using a MS Excel template. Primary data was obtained using a web enabled questionnaire. A single response was received from each of the eight targeted organizations translating to 100% response rate. Study adopted financial performance as the dependent variable with three indicators; NP margin, ROA and ROCE and three independent variables; BDA Insights, BDA Types and BDA technology capabilities. Descriptive statistics, correlation and multiple linear regression analysis revealed substantive evidence that there is value in BDA adoption, which is in line with the resource based theory. BDA adoption is founded on IT adoption theories, however, it should not pass as just another technology addition to a business. It should not be confused with other technologies that emerged, created a lot of expectations but upon application did not yield the expected results. BDA was found to be a major attribute between the high and low performers. Study revealed a strong positive correlation between financial performance indicators and BDA variables.NP margin was found to be the most appropriate measure of financial performance due to its ability to capture efficiency of whole business operations. It is a widely used measure of performance and to remain as a going concern, a business must turn a reasonable profit. BDA insights were found to be the best indicators of BDA adoption. Application of BDA related solutions sums up the value of all BDA activities. Analysis revealed a statistically significant relationship between NP margins and the two explanatory variables; BDA insights and BDA Technology. BDA insights had statistically significant positive association with all the financial indicators. Further, BDA adoption was found to be at very low levels with only two out of the eight active listed firms having specialized BDA technology in place. The two firms posted superior financial performance compared to their peers in the period under review. In light of these findings the study makes a recommendation to the business community to widely adopt BDA to realize better returns on their investments. Further, study highlights the importance of proprietary data in BDA and policy issues surrounding the new data protection and privacy act. The study thus makes a recommendation to policy makers to strike a balance in creation of a friendly regulatory environment that ensures a flourishing manufacturing sector and adequate protection of consumer’s private data.