The Impact of Cognitive Style Diversity on Implicit Learning in Teams

Organizations are increasingly looking for ways to reap the benefits of cognitive diversity for problem solving. A major unanswered question concerns the implications of cognitive diversity for longer-term outcomes such as team learning, with its broader effects on organizational learning and productivity. We study how cognitive style diversity in teams-or diversity in the way that team members encode, organize and process information-indirectly influences team learning through collective intelligence, or the general ability of a team to work together across a wide array of tasks. Synthesizing several perspectives, we predict and find that cognitive style diversity has a curvilinear-inverted U-shaped-relationship with collective intelligence. Collective intelligence is further positively related to the rate at which teams learn, and is a mechanism guiding the indirect relationship between cognitive style diversity and team learning. We test the predictions in 98 teams using ten rounds of the minimum-effort tacit coordination game. Overall, this research advances our understanding of the implications of cognitive diversity for organizations and why some teams demonstrate high levels of team learning in dynamic situations while others do not. Keywords: teams; team learning; implicit coordination; collective intelligence; cognitive diversityNational Science Foundation (U.S.) (Grant IIS-0963451

Allocation Schemes in Analytic Evaluation: Applicant-Centric Holistic or Attribute-Centric Segmented?

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

From Crowds to Collaborators: Initiating Effort & Catalyzing Interactions Among Online Creative Workers

Online collaborative platforms have emerged as a complementary approach to traditional organizations for coordinating the collective efforts of creative workers. However, it is surprising that they result in any productive output as individuals often work without direct monetary incentives while collaborating with unknown others. In this paper, we distinguish the conditions necessary for eliciting effort from those affecting the quality of interdependent teamwork. We consider the role of incentives versus social processes in catalyzing collaboration. We test our hypotheses using a unique data set of 260 individuals randomly assigned to 52 teams tasked with developing working solutions to a complex innovation problem over 10 days, with varying monetary incentives. We find that levels of effort are driven by cash incentives and the presence of other interacting teammates. The level of collaboration, by contrast, was not sensitive to cash incentives. Instead, individuals increased their communication if teammates were also actively participating. Additionally, team performance is uniquely driven by the level of emergent interdependence, as indexed by the diversity of topics discussed and the temporal coordination of activity in short focused time periods. Our results contribute to the literature on how alternative organizational forms can be designed to solve complex innovation tasks

Reading the Mind in the Eyes or Reading between the Lines? Theory of Mind Predicts Collective Intelligence Equally Well Online and Face-To-Face

Recent research with face-to-face groups found that a measure of general group effectiveness (called “collective intelligence”) predicted a group’s performance on a wide range of different tasks. The same research also found that collective intelligence was correlated with the individual group members’ ability to reason about the mental states of others (an ability called “Theory of Mind” or “ToM”). Since ToM was measured in this work by a test that requires participants to “read” the mental states of others from looking at their eyes (the “Reading the Mind in the Eyes” test), it is uncertain whether the same results would emerge in online groups where these visual cues are not available. Here we find that: (1) a collective intelligence factor characterizes group performance approximately as well for online groups as for face-to-face groups; and (2) surprisingly, the ToM measure is equally predictive of collective intelligence in both face-to-face and online groups, even though the online groups communicate only via text and never see each other at all. This provides strong evidence that ToM abilities are just as important to group performance in online environments with limited nonverbal cues as they are face-to-face. It also suggests that the Reading the Mind in the Eyes test measures a deeper, domain-independent aspect of social reasoning, not merely the ability to recognize facial expressions of mental states.National Science Foundation (U.S.) (Grant IIS-0963285)National Science Foundation (U.S.) (Grant ACI-1322254)National Science Foundation (U.S.) (Grant IIS-0963451)United States. Army Research Office (Grant 56692-MA)United States. Army Research Office (Grant 64079-NS)Cisco Systems, Inc. Massachusetts Institute of Technology. Center for Collective Intelligenc

Translating Member Ability Into Group Brainstorming Performance: The Role of Collective Intelligence

Materials, data, and analysis code from this project may be found here

Data

Study data and analysis code

Materials

Idea coding material