Two hundred eighty-one participants indicated that they maintained a profile in a RIMS. The participants were given a closed-ended question consisting of 21 items to identify their reasons for engaging in the profile maintenance activities. Next, we applied a factor analysis with principal components analysis (PCA) to participants’ responses to extract the underlying factors. The component factor matrix was rotated using the Varimax rotation algorithm with Kaiser normalization. A scree plot suggested selecting the first seven factors. Factor loadings of 0.40 and above were identified as significant based on the total number of cases. Variables 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 18 variables and 6 factors. Results of a measurement systems analysis (MSA) indicated that each of the variables was higher than 0.59, with the overall MSA equal to 0.75 and the Bartlett test of sphericity significant at the 0.0001 level. The model captured 69% of the total variance of the data.