Effect of Data Analytics on Tax Evasion Among Small and Medium Enterprises in Nairobi Central Business District, Kenya: The Moderating Role of Firm Size

Tax evasion remains a major challenge to domestic revenue mobilization despite the increasing adoption of digital technologies by tax authorities. Small and Medium Enterprises (SMEs), which constitute a significant proportion of economic activity in Kenya, continue to exhibit substantial tax compliance gaps. This study examined the effect of data analytics on tax evasion among SMEs in Nairobi Central Business District, Kenya, while assessing the moderating role of firm size in this relationship. The study was guided by Economic Deterrence Theory, Resource Dependency Theory, and Laffer Curve Theory. A cross-sectional explanatory research design was adopted and data were collected from 391 SME owners and managers using structured questionnaires. Descriptive statistics, Pearson correlation analysis, multiple regression analysis, and hierarchical moderated regression were employed, with data analysed using SPSS version 27. The findings revealed that descriptive analytics had a positive and significant effect on tax compliance (β = 0.175, p = 0.033), leading to the rejection of H01. Due to severe multicollinearity among diagnostic, predictive, and prescriptive analytics (correlations ranging from 0.831 to 0.862), these three variables were combined into a composite Advanced Analytics Index, which demonstrated a stronger and statistically significant effect on tax compliance (β = 0.408, p = 0.003), leading to the rejection of H02, H03, and H04. The results further established that firm size significantly moderated the relationship between data analytics and tax evasion, with interaction terms for descriptive analytics × firm size (β = -0.198, p = 0.000) and advanced analytics × firm size (β = -0.229, p = 0.000) being statistically significant, leading to the rejection of H05a, H05b, H05c, and H05d. These findings indicate that larger firms benefited more from analytics-based compliance strategies compared to smaller firms. The study concludes that investments in data analytics substantially strengthen tax compliance and reduce tax evasion among SMEs. It recommends that the Kenya Revenue Authority enhance the adoption of integrated analytics platforms, strengthen digital record management among SMEs, and implement firm-size-specific compliance strategies to maximize the effectiveness of data-driven tax administration.