Many applications such as hiring and university admissions involve evaluation and selection of applicants. These tasks are fundamentally difficult, and require combining evidence from multiple different aspects (what we term "attributes"). In these applications, the number of applicants is often large, and a common practice is to assign the task to multiple evaluators in a distributed fashion. Specifically, in the often-used holistic allocation, each evaluator is assigned a subset of the applicants, and is asked to assess all relevant information for their assigned applicants. However, such an evaluation process is subject to issues such as miscalibration (evaluators see only a small fraction of the applicants and may not get a good sense of relative quality), and discrimination (evaluators are influenced by irrelevant information about the applicants). We identify that such attribute-based evaluation allows alternative allocation schemes. Specifically, we consider assigning each evaluator more applicants but fewer attributes per applicant, termed segmented allocation. We compare segmented allocation to holistic allocation on several dimensions via theoretical and experimental methods. We establish various tradeoffs between these two approaches, and identify conditions under which one approach results in more accurate evaluation than the other.

When and why do information dashboards enhance team productivity? We explore this question in the context of project teams whose members have to work across multiple teams at the same time. We expected multi-teaming, especially that involving a high variety in team members, would have a negative effect on team performance by impairing a team's transactive memory system (TMS), a critical form of shared cognition allowing members to keep track of who knows what. Further, we expected the use of an information dashboard with member skills could combat the harmful effects of high-variety multi-teaming. To test our hypotheses experimentally, we developed an online game (Knowledge Worker's Dilemma) that simulates the coordination decisions that professionals face in app development teams. Results show multi-teaming hurt team performance by reducing TMS; use of an information dashboard benefited high-variety teams and, surprisingly, hurt the performance of low-variety teams. Potential explanations and implications are discussed.

Collective intelligence (CI), a group's capacity to perform a wide variety of tasks, is a key factor in successful collaboration. Group composition, particularly diversity and member social perceptiveness, are consistent predictors of CI, but we have limited knowledge about the mechanisms underlying their effects. To address this gap, we examine how physiological synchrony, as an indicator of coordination and rapport, relates to CI in computer-mediated teams, and if synchrony might serve as a mechanism explaining the effect of group composition on CI. We present results from a laboratory experiment where 60 dyads completed the Test of Collective Intelligence (TCI) together online and rated their group satisfaction, while wearing physiological sensors. We find that synchrony in facial expressions (indicative of shared experience) was associated with CI and synchrony in electrodermal activity (indicative of shared arousal) with group satisfaction. Furthermore, various forms of synchrony mediated the effect of member diversity and social perceptiveness on CI and group satisfaction. Our results have important implications for online collaborations and distributed teams.

Recent research has demonstrated that (a) groups can be characterized by a collective intelligence (CI) factor that measures their ability to perform together on a wide range of different tasks, and (b) this factor can predict groups' performance on other tasks in the future. The current study examines whether these results translate into the world of teams in competitive online video games where self-organized, time-pressured, and intense collaboration occurs purely online. In this study of teams playing the online game League of Legends, we find that CI does, indeed, predict the competitive performance of teams controlling for the amount of time played as a team. We also find that CI is positively correlated with the presence of a female team member and with the team members' average social perceptiveness. Finally, unlike in prior studies, tacit coordination in this setting plays a larger role than verbal communication.

Collective intelligence (CI) is a property of groups that emerges from the coordination and collaboration of members and predicts group performance on a wide range of tasks. Previous studies of CI have been conducted with lab-based groups in the USA. We introduce a new standardized online battery to measure CI and demonstrate consistent emergence of a CI factor across three different studies despite broad differences in (a) communication media (face-to-face vs online), (b) group contexts (short-term ad hoc groups vs long-term groups) and (c) cultural settings (US, Germany, and Japan). In two of the studies, we also show that CI is correlated with a group's performance on more complex tasks. Consequently, the CI metric provides a generalizable performance measure for groups that is robust to broad changes in media, context, and culture, making it useful for testing the effects of general-purpose collaboration technologies intended to improve group performance.