Anmerkungen:
Nach Informationen von SSRN wurde die ursprüngliche Fassung des Dokuments December 17, 2021 erstellt
Beschreibung:
Online question-and-answer platforms allow consumers to learn different perspectives of information and knowledge from content producers. Such platforms’ performance critically depends on both quantity and variety of knowledge contents generated by the crowd. This paper studies how early-stage knowledge productions influence the future crowd’s knowledge production behavior. Using a novel data set from one of the largest question-and-answer platforms, we construct measure of knowledge variety using an unsupervised learning method. Our empirical analysis suggests that early-stage knowledge contents have substantial influences on the quantity and variety of the future knowledge contents. Specifically, we document that (1) lengthier early-stage knowledge contents decrease the quantity of future knowledge contents but increase the variety; (2) higher numbers of upvotes of early-stage knowledge contents lead to more future knowledge contents and do not affect the variety. Moreover, we find that the identity of the early knowledge producer (e.g., expert v.s. non-expert) moderates the interrelationship between early-stage knowledge contents and future knowledge contents under the same question on a Q&A platform. Our results and simulations on hypothetical scenarios shed light on how platforms can utilize the early stage knowledge contents’ influences on the direction of future knowledge content growth and further optimize platform strategies