The value of expert vs user generated information in online health communities

Abstract

Online health communities are becoming an important source of information whereby users of these platforms, especially patients, participate for knowledge sharing and emotional support. This research examines the perceived value of expert generated information and user (patient) generated information and how it is influenced by different types of information that a user seeks. First, we propose a framework to classify the types of information based on the different types of information patients typically seek out on online health communities using a knowledge based perspective. Then based on this framework, we provide a set of propositions on the perceived value of expert generated versus user driven responses derived by patients. We expect that expert generated responses to have greater perceived value by patients as compared to community driven responses depending on the type of information patients are asking. Specifically, information uncertainty requiring tacit knowledge and high affect such as treatment experiences has greater value when generated by other patients. On the other hand, information uncertainty requiring explicit knowledge and low affect such as understanding the nature of diseases has greater value when generated by expertise. The proposed framework can help to extend the line of research on online health communities and inform health professionals, health organizations or developers of such communities.