Understanding Sustained Knowledge Contribution from a Motivation Crowding Perspective: A Case Study in a Chinese Q&A Community

Abstract

A Q&A community typically employs various types of external incentives to motivate knowledge contribution from their community members. This study aims to examine the effects of different external incentives, which are conceptualized as different types of motivational factors, on community participants’ sustained knowledge contribution. Drawing on motivation crowding theory, the present study proposes that different motivators interact and jointly influence knowledge contribution behavior. The panel data were collected from a Chinese Q&A community by using the Python Scrapy crawler, and the Poisson regression model with fixed effects was used to validate the integrative model. The results revealed that generalized reciprocity and social learning undermined the effect of online attractiveness on sustained knowledge contribution, whereas peer feedback strengthens this effect. The findings contribute to the extant research on sustained contribution behavior and provide practical insights into sustaining virtual communities.