The main objective of this research focused on the effects of key innovation performance indicators on growth of retail enterprises in Oyugis Municipality of Homa Bay County in Kenya. The constructs for the independent variable included product, process, organization, and market whereas that of the dependent variable were market share, revenue, number of employees, and equity.  Design/Methodology/Approach The target population was 288 Small and Medium Enterprises of which 52 of them formed the sample. Data was successfully collected using questionnaire, interviews, desk study, and observation from 34 respondent retail enterprises representing 65% response rate. Data processing was done by use of SPSS.  Findings Results from the test shows that process was the leading predictor of enterprise growth with adjusted R 2 =0.047 and p =0.01. The other independent variables, market, organization, and product, with adjusted R 2 of 0.018, 0.002, and 0.001 followed respectively indicating lower prediction rates. To check for multicollinearity, variance inflation factors for all the variables were examined and all were found to be lower than 4 with tolerance values below 0.99, thus well below the recommended cut-off of 10 and 1 respectively, confirming that multicollinearity was not a problem in these results. Both the Kolmogorov-Smirnov test and Shapiro-Wilks test for normality revealed probabilities of more than 0.05 meaning that the data sets were normally distributed and all the four research questions were positively answered.  Originality and Value Some studies undertaken in the past indicate varied results whereas this research has yielded clearly the leading constructs that have high prediction rates in relation to key innovation performance indicators for growth of the retail enterprises in general. The outcome of this survey adds value to a pool of knowledge available to the entrepreneurs, scholars, and policy makers focusing on where to lay greater emphasis to realize growth and development in their economies.

Keywords : Equity, Market, Multiple Regression, Organization, Predictor, Retail.