Community-based question-and-answer (Q&A) websites have become increasingly popular in recent years as an alternative to general-purpose Web search engines for open-ended complex questions. Despite their unique contextual characteristics, only a handful of Q&A websites have been successful in sustaining members' active participation that, unlike lurking, consists of not only posting questions but also answering others' inquiries. Because the specific design of the information technology artifacts on Q&A websites can influence their level of success, studying leading Q&A communities such as Yahoo! Answers (YA) provides insights into more effective design mechanisms. We tested a goal-oriented action framework using data from 2,920 YA users, and found that active online participation is largely driven by artifacts (e.g., incentives), membership (e.g., levels of membership and tenure), and habit (e.g., past behavior). This study contributes to the information systems literature by showing that active participation can be understood as the setting, pursuit, and automatic activation of goals. > >
Reputation systems have been recognized as successful online review communities and word-of-mouth channels. Our study draws upon the elaboration likelihood model to analyze the extent that the characteristics of reviewers and their early reviews reduce or worsen the bias of subsequent online reviews. Investigating the sources of this bias and ways to mitigate it is of considerable importance given the previously established significant impact of online reviews on consumers' purchasing decisions and on businesses' profitability. Based on a panel data set of 744 individual consumers collected from Yelp, we used the Markov chain Monte Carlo simulation method to develop and empirically test a system of simultaneous models of consumer review behavior. Our results reveal that male reviewers or those who lack experience, geographic mobility, or social connectedness are more prone to being influenced by prior reviews. We also found that longer and more frequent reviews can reduce online reviews' biases. This paper is among the first to examine the moderating effects of reviewer and review characteristics on the relationship between prior reviews and subsequent reviews. Practically, this study offers businesses effective customer relationship management strategies to improve their reputations and expand their clientele.