Abstract
More and more companies have adopted social media platforms for supporting knowledge sharing among customers and employees, where individuals ask and answer questions among each other. Hence, it is important to understand the knowledge-sharing behavior of users on these systems. We propose a theoretically-grounded, dynamic structural model with endogenized knowledge-sharing behavior that takes into account “learning by sharing” and “knowledge spillover,” which are two salient features that are enabled by social platforms. Our model recognizes the dynamic and interdependent nature of knowledge-seeking and sharing decisions and allows them to be driven by knowledge increments and social-status building in anticipation of future reciprocal rewards. Applying this model to a unique panel of data from an expertise-sharing forum used to shore up customer support at a Fortune 500 firm, we illustrate the dynamic interdependency between individual decisions. We show that an individual is more willing to contribute to the community when her peers are more knowledgeable. We further demonstrate how a “core/periphery” knowledge sharing structure emerges, discourage users with low social status from participating, and creates a barrier to knowledge sharing and integration for the company. An exploratory sensitivity analysis shows that hiding the identity of the knowledge seeker breaks the core/periphery structure and improves the knowledge sharing by 20.46%.
Recommended Citation
Lu, Yingda; Singh, Param; and Sun, Baohong, "Learning from Peers on Social Media Platforms" (2011). ICIS 2011 Proceedings. 10.
https://aisel.aisnet.org/icis2011/proceedings/economicvalueIS/10
Learning from Peers on Social Media Platforms
More and more companies have adopted social media platforms for supporting knowledge sharing among customers and employees, where individuals ask and answer questions among each other. Hence, it is important to understand the knowledge-sharing behavior of users on these systems. We propose a theoretically-grounded, dynamic structural model with endogenized knowledge-sharing behavior that takes into account “learning by sharing” and “knowledge spillover,” which are two salient features that are enabled by social platforms. Our model recognizes the dynamic and interdependent nature of knowledge-seeking and sharing decisions and allows them to be driven by knowledge increments and social-status building in anticipation of future reciprocal rewards. Applying this model to a unique panel of data from an expertise-sharing forum used to shore up customer support at a Fortune 500 firm, we illustrate the dynamic interdependency between individual decisions. We show that an individual is more willing to contribute to the community when her peers are more knowledgeable. We further demonstrate how a “core/periphery” knowledge sharing structure emerges, discourage users with low social status from participating, and creates a barrier to knowledge sharing and integration for the company. An exploratory sensitivity analysis shows that hiding the identity of the knowledge seeker breaks the core/periphery structure and improves the knowledge sharing by 20.46%.