Peer Recognition and Content Provision Online

Abstract

Abstract This paper provides both theoretical and empirical studies on the incentive effect of peer recognition on online content provision. Our theoretical model illustrates how an influencer strategically reacts to various platform policies. Using unique data from the largest Chinese Question-and-Answer platform, we analyze the content provisions of all the influencers with more than 10,000 followers on the platform over two years. Using an instrumental variable approach, we find that a simple OLS method is likely to under-estimate the incentive of peer recognition for content provision because the reputational and privacy concerns make the badge policies counterproductive. Our findings suggest that while badges in the Q&A platform make it easier for users to identify the quality of an influencer, those badges that include a strong connotation may also limit the content contribution since influencers have concerns about their reputation management and privacy. The paper indicates from an empirical perspective that platforms’ attitudes towards real-name policies may depend on the trade-off between incentives for content creation and content regulation. Policies based on reputation and privacy may have a backlash against policies that encourage traffic.