Big Data Analytics For Smes' Performance Sustainability In Post-Covid-19 Kenya

  • Musyoka C., Wanjohi. P. Kirinyaga University, Kenya
Keywords: Big Data Analytics, Data Analytics, Business Intelligence, Machine Learning, Predictive Analytics


Small and medium-sized businesses (SMEs) play a critical role in a nation's economy, contributing significantly to its wealth and fostering innovation. Globally, they account for up to half of all jobs and 90% of all businesses. However, SMEs often grapple with limited access to credit from suppliers, compounded by liquidity challenges, decreased sales, and defaults, as supply chains struggle to secure credit. Factors like advance payments, penalties for delayed credit payments, and recurring expenses further exacerbate the vulnerability of Smashing the context of Kenya's post-COVID-19 landscape. This study aimed to explore the potential of Big Data Analytics and Data Science in sustaining SMEs' performance. Drawing from theories such as Complex Adaptive System and Strategic Choice Theory, a descriptive survey design was employed, on a target population of 287 managers of SMEs in each sub County, in Nairobi. Employing a stratified sampling method, a total of 260 respondents were interviewed. Data was analyzed using descriptive statistics, including frequencies, percentages, mean, and standard deviation, while inferential statistics like multiple regression and Pearson correlation were used to examine relationships between variables. The study revealed that Business Intelligence, with a mean score of 3.9 (std. dv = 0.851), and Machine Learning, with a mean of 3.7 (std. dv = 0.928), both had a positive impact on SMEs' sustainability, with an overall average mean of 3.8 (std. dv = 0.8895). Similarly, Data Analytics, comprising Predictive Analytics (mean = 3.73, std. dv = 0.850) and Prescriptive Analytics (mean = 3.85, std. dv = 0.684), positively influenced SMEs' performance, with an average mean of 3.79 (std. dv = 0.767). These findings underscore the potential of Data Science drivers like Business Intelligence and Machine Learning in helping SMEs tackle unforeseen challenges in competitiveness. The study further highlights the importance of implementing a robust legal framework to safeguard data in the context of Data Analytics, particularly in predictive and prescriptive analysis, as a means to enhancing SMEs' performance, survival, and growth in the post-COVID-19 era.

How to Cite
Musyoka C., Wanjohi. P. (2024). Big Data Analytics For Smes’ Performance Sustainability In Post-Covid-19 Kenya. AFRICAN JOURNAL OF SCIENCE, TECHNOLOGY AND ENGINEERING (AJSTE) , 4(2), 81-91. Retrieved from