Journal article
User’s online status and knowledge contribution behavior in the Q&A community–Based on core and non-core contributors
Information & management, Vol.63(1), 104260
01/2026
DOI: 10.1016/j.im.2025.104260
Abstract
While knowledge contribution studies have offered insights into status effects, how online status shapes knowledge contribution behavior across different user categories remains unclear. This study combines the entropy weight method and the Technique for Order Preference by Similarity to Ideal Solution method to categorize contributors, and uses a negative binomial model for deeper analysis. Results reveal that high-status core contributors tend to reduce their contributions, avoid low-quality contributions, and selectively answer high-quality questions to maintain their online status. Conversely, high-status non-core contributors increase both the quantity and quality of their contributions to boost their online status. Topic popularity moderates these processes by weakening the negative online status effect on core contributors’ contribution quantity, strengthening the positive effect of online status on their contribution quality, and amplifying online status effects on both contribution quantity and quality for non-core contributors. Additionally, compared with high-status male contributors, high-status female contributors are more likely to target high-quality questions; they contribute lower-quality content in core roles, but they contribute more and higher-quality content in non-core roles.
Details
- Title: Subtitle
- User’s online status and knowledge contribution behavior in the Q&A community–Based on core and non-core contributors
- Creators
- Mi ZhouMengmeng SongWeiguo Fan
- Resource Type
- Journal article
- Publication Details
- Information & management, Vol.63(1), 104260
- DOI
- 10.1016/j.im.2025.104260
- ISSN
- 0378-7206
- eISSN
- 1872-7530
- Publisher
- Elsevier
- Grant note
- National Natural Science Foundation of China: 71872142
This work was supported by the National Natural Science Foundation of China [71872142] .
- Language
- English
- Electronic publication date
- 10/08/2025
- Date published
- 01/2026
- Academic Unit
- Business Analytics
- Record Identifier
- 9985014804902771
Metrics
6 Record Views