What makes helpful online mental health information? Empirical evidence on the effects of information quality and responders’ effort

Abstract

Although online health communities are popular in supporting mental health, factors leading to the helpfulness of mental health information are still under-investigated. Based on the elaboration likelihood model and motivation theory, we incorporate two types of health information-related constructs, i.e., information quality (central route) and responders’ effort (peripheral route), and adopt reputation as an extrinsic motivation to build our model. We crawl data from a Chinese online mental health community and extract 11 key variables, and then analyze the model with negative binomial regression. The empirical results indicate that the effect of the length of health information on its helpfulness votes is positively significant, while the effect of readability of health information on its helpfulness votes is relatively negative. In terms of responders’ effort, both the timelines of the response and interactive feedback have a significant positive impact on helpfulness of health information votes, while these effects are negatively moderated by the online reputation of responders. This study contributes to the literature on information evaluation mechanisms in online health communities.