Innovation is closely linked to a company's ability to survive and thrive. As a result, companies are becoming increasingly interested in group generative collaborations - the conception of novel ideas and solutions through group exchanges. This exploratory study investigates the prevalence of generative collaborations in ESM-based groups and identifies antecedents of such collaborations in groups. Additionally, the nature (i.e. language) of these collaborations is explored. A mixed methods approach is employed for this study, comprised of a content analysis of text-based data from an ESM platform, building a machine learning classifier model, and a predictive regression model. The results of this exploratory study show that approximately 44% of exchanges on an ESM platform contain elements of generative collaborations. Furthermore, the predictive negative binomial regression model illustrates that a group's visibility (closed or open), the bonding and bridging behaviors of group members, and the size of a group are significant antecedents for group generative collaborations. These outcomes are valuable insights that help advance our theoretical understanding of ESM-based group generative collaborations and could also help improve these collaboration behaviors in the context of ESM platforms, and possibly beyond.
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