Abstract
Companies hold particular interest in group generative interactions - the conception of novel ideas and solutions through group exchanges. They are a root-cause of innovation and thus are important to companies’ survival. Enterprise Social Media (ESM) offer a unique opportunity to study generative group interactions, due to the transparent nature of activities on these platforms. In this research-in-progress paper, we conduct a preliminary analysis to develop a method that could identify the instances of ESM-based generative group interactions, where we focus on distinguishing generative versus non- generative group interactions. To do this, we used the text from all group interactions from an ESM platform of a multinational organization. We implemented machine learning models to learn and classify the text as generative or non-generative. As a result, we produced the top important term features from the best performing model. These features will help us understand the nature of discussions that occur in these interactions in future studies.
Recommended Citation
Averkiadi, Elisavet; Van Osch, Wietske; and Liang, Yuyang, "Developing a Method for Identifying Instances of Group Generative Interactions in Enterprise Social Media" (2019). SIGHCI 2019 Proceedings. 8.
https://aisel.aisnet.org/sighci2019/8