Member Awareness of Expertise, Information Sharing, Information Weighting, and Group Decision Making

Abstract

One of a group’s most valuable resources is the expertise of its members. How this expertise is (or is not) used has a major impact on group performance. However, determining expertise is often difficult. Thus the issue of how many group members need to be aware of expertise before the benefits of recognition accrue is of great importance. For example, do all members have to be aware of expertise prior to discussion for the group to benefit, or is a subset of members sufficient? If a subset is sufficient, how large must it be? To address these questions, we manipulated the number of group members possessing foreknowledge of member expertise. We then analyzed perceived expertise, information sharing, information weighting, and group decision making using a series of planned contrasts representing common social combination models. Discussion of unique information followed a majority wins model (i.e., a shift occurred when greater than half of members were made aware of expertise prior to discussion). For weighting of unique information, several models, including majority wins, fit when examining regression-based estimates of weighting whereas only the majority wins model fit when examining self-reported weighting. None of the models tested adequately explained rated expertise.