Managing contributor performance in knowledge-sharing communities : a dynamic perspective

Knowledge sharing has become an important practice in the era of knowledge economy. This paper concerns the management of contributor performance in social knowledge-sharing communities. Based on ability-motivation-opportunity theory, we propose a hidden Markov model to characterize the change in a knowledge contributor's latent state, which then determines the performance of knowledge sharing in terms of quantity and quality. The proposed model is calibrated using data from a social question-and-answer community. Three latent states are identified: unmotivated, exploratory, and sophisticated. Several factors influence the state-transition process. Specifically, the increase in followers encourages contributors in the unmotivated state to transition to the motivated states and, therefore, contribute knowledge. When the contributors are in the exploratory state, observing the behavior of their followees increases the probability of their transitioning to the sophisticated state, in which they will make high-quality contributions. These results suggest that followers influence mainly the quantity of contributions, while followees help mainly to increase the quality of contributions. This study contributes to the literature by revealing the dynamics of contributor performance in the context of knowledge sharing and by showing the roles of different social factors in influencing contributor performance. The modeling framework and findings of this study can help managers to identify the latent contribution states and then intervene in the performance of knowledge contributors in a variety of settings.