Abstract

Online knowledge communities greatly rely on users' voluntary knowledge contribution activities (KCAs) to achieve success and sustainability, which has been a focus area of IS research. Drawing on self-presentation theory and prior research on online knowledge communities, this study investigates the effects of constructive and destructive criticisms in peer comments on users' future knowledge contribution patterns. To accurately identify criticisms in comments, we develop a deep learning-based method that incorporates BERT with an emotion analysis. We then use the identified criticisms to examine whether constructive and destructive criticisms affect users’ future KCAs, while reducing the endogeneity problem of independent variables. Our empirical results reveal that receiving comments with constructive criticisms has no impact on the celerity of a user’s future KCA, but the more destructive criticisms a user receives from peer comments on his or her previous KCAs, the sooner he or she will engage in the next KCA again.

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Paper Number 1585; Track Platforms; Short Paper

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