With user-generated content, the network size (i.e., number of contributors to particular content) can vary. Research on prediction markets, virtual teams, and social networks suggests that the quality of aggregate information, number of ideas generated, and likelihood of a valuable answer increases with the number of participants. Because each contributor represents a unique source of knowledge, additional contributors can identify important missing information or factual inaccuracies. The more people who contribute, the more thorough and high-value information the content contains.
Yet additional contributors may be valuable only up to a point. Too much available information leads to information overload, making it dificult to decide what information is most valuable and salient. Less content is potentially more valuable in some settings, because the costs associated with finding the most valuable content decrease. New ideas have limited marginal value after a certain point, because they are redundant, and it is increasingly costly to filter out bad ideas. For collaborative user-generated content, more contributors also increase coordination costs and development time and possibly decrease the quality of the final product . Although larger and more diverse teams can enhance creativity, an increasing diversity of perspectives makes it harder for teams to reach consensus.
Hypothesis: The market value of collaborative user-generated content has a curvilinear (inverted U) relationship with the number of contributors to that content.
Although SNA provides important insights into how the relationships among content creators and content sources affect viewership, it refers to the potential for content to flow among nodes, rather than measuring the actual how. Additional research might examine the extent to which specific content and process knowledge gets transferred through collaboration networks. Although our data show that more and less prominent articles exhibit different network eects than do typical ones, limitations in our data set prohibit us from discerning whether these differences reflect the topic of the collaboration, time, or both.
Adapted from: Collaborative User-Generated Content, by Kane & Ransbotham here