Which feedback matters? The role of expressions and valence in continuous high-quality knowledge contribution in the online Q&A community

• Published in: Decision Support Systems Vol. 156; p. 113750
• Main Authors: Wang, Ning, Liu, Yang, Xiao, Shengsheng
• Format: Journal Article
• Language: English
• Published: Amsterdam Elsevier B.V 01-05-2022; Elsevier Sequoia S.A
• Subjects: Community development; Continuous knowledge contribution; Empirical analysis; Feedback expressions; Feedback mechanism; Feedback valence; High-quality knowledge; Knowledge; Negative feedback; Online Q&A community; Positive feedback; Feedback expressions; Feedback valence; Continuous knowledge contribution; Feedback mechanism; High-quality knowledge; Online Q&A community
• ISSN: 0167-9236; 1873-5797
• DOI: 10.1016/j.dss.2022.113750

With the growing popularity of online Q&A communities, motivating continuous high-quality knowledge contribution is a key challenge for sustainable community development. Although feedback mechanisms are designed to meet this challenge, there is still no comprehensive understanding of the impact of feedback. Most previous research focuses on feedback valence (i.e., positive or negative) but ignores potential differences associated with the expression of feedback (i.e., textual or nontextual). To address this research gap, we classify community feedback into four types on the dimension of expression and valence to examine their effects on continuous high-quality knowledge contribution. Using a longitudinal knowledge contribution dataset collected from a leading online Q&A community, our empirical results with a series of robustness checks show that (1) positive nontextual feedback is negatively correlated with the continuous contribution of high-quality knowledge, whereas the other three types of feedback generate positive effects; (2) textual and nontextual positive feedback generates significantly opposite effects, but there is no statistically significant difference between textual and nontextual negative feedback. The additional analyses generate a deeper understanding of feedback effects, including disassembling feedback effects on the knowledge quantity and quality, and verifying the moderating effects of user reputation values on feedback effects. This study extends research concerning feedback and continuous knowledge contribution and generates important managerial implications for the online Q&A community.
•Uncover the feedback effects on continuous high-quality knowledge contribution under difference expressions and valence.•Feedback intervention theory and self-regulation theory are applied to understand the feedback effects.•Positive feedback generates reverse effects under different expressions, while negative feedback does not.•Recipient's reputation can weaken both positive and negative feedback effects.•Provide managerial insights on online Q&A communities.