Leveraging linguistic signalling to prompt feedback in open innovation communities
by Suya Hu; Di Xu; Yan Li
International Journal of Networking and Virtual Organisations (IJNVO), Vol. 26, No. 4, 2022

Abstract: The rise of open innovation communities (OICs) has enabled organisations to gain ideas from the outside. Although current studies mainly focus on idea generation behaviours among participants, little attention has been paid to the subsequent interactive feedback, which is equally important for the success of running OICs. Drawing on signalling theory, we empirically examine how to leverage signals expressed in idea descriptions to influence feedback from two key parties: the moderator and peers. Two linguistic features, i.e., affective signalling (linguistic style matching, negative emotion, and impoliteness) and informative signalling (post length and quality) are proposed. Analysing data collected from the Huawei community, we find that feedback from the moderator is indeed influenced by both affective and informative signalling. Furthermore, only negative emotion is positively associated with feedback from peers, while the effects of other signals show different trends. This study offers practical insights into how to maintain the viability of OICs.

Online publication date: Mon, 08-Aug-2022

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