| Summary: | The study sought to assess the importance of classifying incubators based on the programs offered for optimum performance. Client selection criteria were assessed through three constructs namely: models that fit program goals, uniqueness of ideas, and standard selection tool. A mixed cross-sectional and causal design was adopted and a census was carried out targeting all the 51 incubators. Primary data was collected with an incubator program as a grouping/ cluster variable yielding a multilevel data structure with incubator centres nested in programs. Linear mixed effect models were fitted using Stata to assess the study objective taking into account the fixed effects for the incubator centre level (level-1) and random effects for the program level (level-2). The uniqueness of ideas was found to have a significant fixed effect on performance at level one while at level two, the study found significant random intercepts of incubator centre performance across the programs. Models that match program goals and standard selection tools were also found to have significant random slopes as level two random covariates in the model. Based on the findings of significant random slopes, the study concluded that incubator classification is key for client selection criteria and enhances incubator performance.
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