Dynamics of Content Contributors’ Performance: Evidence from a Social Question-and-Answer Community

Abstract

This paper concerns the dynamics of contributor performance in a social content-sharing community. Based on ability-motivation-opportunity theory, we propose a hidden Markov model to characterize the change in a contributor’s latent state, which then determines performance 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, motivated but incompetent, and motivated and competent. Several factors influence the state-transition process. Specifically, the increase in followers encourages contributors in the unmotivated state to transition to the motivated states. When the contributors are motivated but incompetent, observing the behavior of their followings increases the probability of their transitioning to the motivated and competent state. In addition, direct experience boosts all contributors to higher states. The modeling framework and findings of this study have implications for contributor performance management in a variety of settings.