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.
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.
Results from the test shows that process was the
leading predictor of enterprise growth with adjusted R
=0.047 and p =0.01. The other independent variables,
market, organization, and product, with adjusted R
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.