Next, participants were asked whether the RIMS they used supported asking and answering questions, and whether they answered questions received from other researchers. One hundred fifty-five participants indicated that at least one RIMS they used supported question answering. Fifty-five participants revealed that they answered questions from other members of a RIMS. To identify the underlying structure of researchers’ motivations for answering other members’ questions, the study used a factor analysis. The analysis treated each motivation question item as a variable. The initial model included 16 variables. The study used PCA to extract factors. The component factor matrix was rotated using the Varimax rotation algorithm with Kaiser normalization. A scree plot suggested selecting the first four eigenvalues. Factor loadings of 0.75 and above were identified as significant based on the total number of cases. Variables that had an MSA lower than 0.5 or that were cross-loaded on more than one factor were removed from the model one by one, and the loadings were recalculated until no such variable was found. The resultant version of the model consisted of 10 variables and 4 factors. The MSA of each of the variables was higher than 0.5, with the overall MSA equal to 0.57 and the Bartlett test of sphericity significant at the 0.0001 level. The model captured 80% of the total variance of the data.