Organizations are increasingly using open innovation communities (OICs) to gain external ideas. The success of OICs in promoting innovation, however, depends not just on posting activity by participants, but, crucially, on whether or not responses are subsequently received. Drawing on signaling theory, our study tries to explore how to leverage linguistic signals expressed in idea descriptions to influence feedback from two key parties: the moderator and peers. Linguistic features are divided into affective signaling (i.e., linguistic style matching, negative emotion, and impoliteness) and informative signaling (i.e., post length and quality). The research model is empirically tested on a large dataset collected from the Huawei community. Results show that both affective and informative signaling are effffective in invoking feedback from the moderator. We also find that only negative emotion is positively associated with feedback from peers, while the effects of other signals show different trends. This research provides practical insights into how to maintain the viability of OICs. Keywords: feedback, signaling theory, ideas, open innovation communities
Hu, Suya; Xu, Di; and Li, Yan, "Leveraging Linguistic Signaling to Prompt Feedback in Open Innovation Communities" (2021). WHICEB 2021 Proceedings. 22.