Understanding Users' Ask Intention on Paid Q&A Platform from the Perspective of Impression Management

Peiyao Liang, Mingyue Zhang, Baojun Ma

Abstract: With the widespread fusion of social media and knowledge economy, paid Q&A platform has become one of the most prominent ways for people to seek knowledge online. More and more users enroll as answerers to answer questions for gaining extra money, so it is necessary for them to find out ways to attract more askers in this competitive market. When investigating influential factors on the ask intention, most previous research focuses on the objective data displayed on answerers’ accounts such as number of followers and price per question, while largely ignores the information that was voluntarily disclosed by answerers. In this regard, we draw from impression management theory to explore the role of answerers’ self-image which is built through active information disclosure in this Q&A process. We constructed a cross-sectional dataset with 9,887 answerers on Weibo Q&A platform to test hypotheses. Using zero-inflated negative binomial model, we find that the congruence between answerers’ self-image and their provided knowledge has a significant positive impact on knowledge seekers’ ask intention. Particularly, the larger the congruence between those actively disclosed text information (i.e., biography) and picture information (i.e., avatar) and provided knowledge, the higher the askers’ willingness to ask questions. Managerial implications on paid Q&A platforms are thus provided.