Network Position and Crowdsourcing Innovation Contribution Behavior: The Moderating Role of Knowledge Absorption Capacity

Purpose. The purpose of this study is to examine the relationship between network position and crowdsourcing innovation contribution behavior and the moderating effects of knowledge absorption capacity on the aforementioned relationship. Design/Methodology/Approach. Focusing on the Chinese context, the study conducts empirical research with the user’s knowledge‐sharing network of the MIUI community to test the research model. The negative binomial regression model which is suitable for processing discrete data is used to examine the main effects of the network position, knowledge absorption capacity, and crowdsourcing innovation contribution behavior. Findings. The findings reveal that the closer the user gets to the center of the network, the more likely they will contribute. The users’ knowledge absorption capacity can help stimulate the users’ crowdsourcing innovation contribution behavior, and the users with stronger knowledge absorption capacity are more likely to transform their network position advantages into innovative contribution behaviors. Practical Implications. The study provides evidence that network position has a positive impact on their crowdsourcing innovation contribution behavior, and knowledge absorption capacity promotes the crowdsourcing innovation behavior of users. Managers should encourage users to occupy a favorable network position and increase knowledge exchange with other users, while at the same time continuously improving their own knowledge absorption capacity. Originality/Value. This study combines social network theory and the individual mindset to introduce knowledge absorptive capacity into the relationship model of the user’s network position and crowdsourcing innovation contribution behavior, thereby constructing a complete path of “knowledge supply‐knowledge acquisition‐knowledge application‐knowledge output.” The study contributes to provide a theoretical basis for an in‐depth understanding of the influence relationship between network position and crowdsourcing innovation contribution behavior. Also, it provides a reference for enterprises to carry out practical crowdsourcing innovation community governance and improve innovation performance.