Teams are increasingly used as a basic building block of work in organizations, as well as the locus of innovation in academic and business settings. However, our understanding of the key factors that influence team performance is still far from complete. I have been developing three lines of research focused on different aspects of team performance. In my first line of research, I focus on the existence of a stable and measurable collective intelligence in groups. We conceptualize collective intelligence as the capability of a group that emerges from the coordination and collaboration of members and enables them to perform on a wide range of tasks. My recent work focuses on the development of a battery to measure team collective intelligence, as well as explores features of a group that affect its emergence.

In research on individual intelligence, psychological researchers have repeatedly demonstrated that a single statistical factor—often called “general intelligence” or “g”— emerges from the correlations among individuals’ performance on a wide variety of cognitive tasks. The emergence of a general performance factor appears to be a common property of complex nervous systems, whether animal or human; performance is positively correlated across cognitive tests. This raises the possibility that, far from being an artifact of human society or culture, general intelligence factors may be a characteristic of complex systems, whether made up of brain modules, groups of humans, or even humans with computers. My first line of research explores the existence of a general collective intelligence factor in human groups. In our first paper on this topic (Woolley, Chabris, Pentland, Hashmi, & Malone, 2010), we used the same approach used by researchers in the construction of IQ tests, and administered a range of different kinds of tasks to groups. ­­Our selection of tasks was guided by existing taxonomies categorizing tasks on the basis of the various group processes needed to perform successfully. Our goal was to select a wide variety of tasks, such that a group could not rely on a well-learned routine, or depend too heavily on the same member(s) to accomplish them all successfully. Thus we included tasks that required creativity or brainstorming, along with tasks that had singular correct solutions, as well as tasks that required the careful coordination of inputs from all members for successful completion. Our analysis of groups’ scores on the tasks demonstrated a collective intelligence or c factor for groups, and its ability to predict group performance on more complex tasks. We also explored variables that might be predictive of groups’ ability to develop c. One interesting finding was that the proportion of females in a group is significantly correlated with c. Moreover, member social perceptiveness appears to play a mediating role. Social perceptiveness is significantly positively correlated with c, and consistent with existing research, men in our sample had significantly lower scores than women on our social perceptiveness measure.

In further developing our understanding of c, in two additional studies we have explored its connection to group learning. Conceptually, we predicted that the same coordination and collaboration abilities that enable a group to adapt and perform a wide variety of tasks (i.e., develop high levels of c) would also enable them to assimilate new information and improve at a faster rate with experience on the same task. In addition, we also explored the connection between c and group synergy, or the ability of a group to perform better than its best member. While prior work documents the limited conditions under which groups can achieve synergy, no research has looked at what enables certain groups to do so more consistently than others. In a study of MBA student teams, we measured c at the beginning of the academic term, and found that it significantly predicted the rate of group improvement across a series of group-based exams. We also found that c was a strong predictor of a group’s ability to consistently outperform their smartest member (Woolley, Aggarwal, Chabris, & Malone, under review). In a laboratory study, we further find that c significantly predicts the rate at which a group learns to coordinate their individual choices in a tacit coordination game across a series of 10 rounds, above and beyond the variance explained by individual intelligence (Aggarwal, Woolley, Chabris, & Malone, in prep).

We have also continued to develop more efficient methods of measuring c and have explored their generalizability in two additional papers. In a laboratory study (Engel, Woolley, Jing, Chabris, & Malone, under review), we randomly assigned teams to collaborate face-to-face versus online, and used our newly developed, web-based tool to administer a short battery of tasks. We find that our new shorter battery of tasks performs similarly to the longer set of tasks administered in the lab in our original study, and is moderately predictive of groups’ future performance, both in the groups working face-to-face and those working online. Surprisingly, we also found that our “Reading the Mind in the Eyes” measure of social perceptiveness is as predictive of collective intelligence in online groups communicating only via text-based chat as it is in face-to-face groups. In a second paper, we build on these findings by combining them with additional studies conducted in online lab groups in Japan and field-based project teams in Germany working online and face-to-face (Engel, Woolley, Aggarwal, Chabris, & Malone, under review). We find that our shortened, web-based measure operates similarly in all of these settings.

Taken together, these preliminary results offer encouraging support for our basic approach. Continuing to develop our understanding of c is important for a number of reasons. First, on a conceptual level, understanding c as a measurable and stable attribute of a team, demonstrably separate from individual ability, will change the importance placed on team composition versus other team design and process elements that can be more effectively managed. Until now, group capability has been thought about primarily as the aggregate of individual member abilities. Second, on an empirical level, one major factor hampering the advancement of knowledge in teams research is the use of different criterion variables in different team studies, making the accumulation of and comparison among findings difficult to impossible. A reliable and generalizable measure of c would enable researchers to more readily evaluate the effects of team design or process variables in a manner we could expect to generalize to other settings. My collaborators and I have received funding for future work on this topic from the Army Research Office and NSF as well as the PNC Center for Financial Services Innovation. With the funding we have received we have further adapted the original collective intelligence test battery into an online tool that enables us to administer a collective intelligence test in a standardized way to all groups, whether working face-to-face or online. We enhanced the battery to span a wide and comprehensive range of group processes, building on existing task taxonomies, and have incorporated tasks that tap into both verbal and nonverbal domains. We are using the battery to conduct additional studies in the lab, as well as with even larger groups online and in organizational settings. In the near future we also hope to make it available to other researchers. In our ongoing work, we intend to further explore a number of questions, including: (1) Does status hierarchy affect the development of collective intelligence in teams? (2) How does collective intelligence relate to group size? At what point do we see declines in collective intelligence as group size increases? (3) What tools or practices can be used to support group processes to enable groups to be more collectively intelligent?

A long history of research testifies to the importance of clear goals and work processes for individual and team performance. While the tacit assumption has been that clarity of task goals and clarity of work processes are mutually reinforcing, in my dissertation I investigated “process focus” and “outcome focus” in teams. Teams that are “outcome-focused” allow task goals to take precedence over work processes, while teams that are “process-focused” allow work processes to take precedence over task goals. A prototypical, outcome-focused team would be the group of friends who launched Apple Computer, Inc. by cobbling together some parts purchased on credit in borrowed space from friends and family to fill orders already received for a product they envisioned. In contrast, the development of the Post-It note is an example of high process focus, in which a low-tack, pressure sensitive adhesive was developed by accident at 3M, but a use for it was found and converted into a marketable product.

In my dissertation, I developed and tested the constructs of outcome and process focus in laboratory-based teams (Woolley, 2009a), testing and validating both a survey-based and observational measure. I then explored outcome and process focus in a field study with MBA student teams and at the American Red Cross (Woolley, 2009b). In both settings, I found that outcome focus enabled teams to more effectively adapt to changes in their task and environment and perform at a higher level on open-ended, creative tasks. My more recent work on team strategic orientation shows that a team’s position in a competitive environment is an important contextual antecedent of outcome or process focus and information use. I conceptualize team strategic orientation as encompassing the strategic direction and perceptions a team develops when operating against an opponent. Team strategic orientation can be characterized as offense, or focused on pursuing objectives whose attainment occurs at the expense of an opponent, or defense, or focused on preventing loss at the hands of an opponent. In a field study of teams in the U.S. intelligence community, I found that teams assigned an offensive strategic orientation became outcome-focused and concentrated more on surfacing the knowledge and skills of team members than searching their environment for information. In contrast, teams assigned a defensive strategic orientation became process-focused and established processes to thoroughly search the environment while overlooking the knowledge of other team members (Woolley, 2011). In two additional lab studies supported by a grant from the Army Research Institute, my collaborators and I replicated these effects in two experiments with teams whose strategic orientation was randomly assigned, and demonstrated that process focus mediates the effects of team strategic orientation on information search. We also explored the ability of teams to change orientation in response to changing events in their environment, and found that teams move more readily from offense to defense than the reverse, suggesting that defensive teams might become stuck in that posture longer than necessary (Woolley, Bear, Chang, & DeCostanza, 2013). In future work, I would like to explore the degree to which these ideas translate to business strategies, and the environmental antecedents that lead teams to adopt offensive or defensive strategic orientations in business settings.

In other work related to outcome and process focus, former doctoral student Ishani Aggarwal and I have explored cognitive styles and their implications for the task focus a team develops. Cognitive styles are psychological dimensions that represent consistencies in how individuals acquire and process information. In this line of work, we look at the cognitive style dimension that differentiates between object visualization and spatial visualization. Object visualization entails holistic processing and superior performance on tasks that require identifying global properties of shapes, whereas spatial visualization entails analytic processing, using spatial relations to arrange and analyze components of an image. Strong object visualizers are likely to work in the visual arts; strong spatial visualizers are often good at navigation and work in more mathematical and technical fields. In our first study, we found that due to their analytical style and propensity to break problems into parts, strong spatial visualizers exhibit a greater tendency to develop a high level of process focus, and that groups with higher levels of spatial visualization represented in members became more highly process focused. Furthermore, high levels of group diversity in object and spatial visualization disrupts a team’s ability to develop consensus about their performance strategy, leading teams to commit more errors and perform poorly on detail-oriented execution tasks (Aggarwal & Woolley, 2013). However, in a study of MBA student project teams working on a creative task, we find that the same level of cognitive diversity can be advantageous, as it strengthens the development of transactive memory systems and enhances creativity, so long as the team does not become highly process-focused (Aggarwal & Woolley, under review).

In another line of work with other collaborators, we explore the implications of outcome and process focus for team member deviance. We predicted that outcome focus would result in higher levels of team member deviance. We expected this because outcome focus is associated with higher levels of action identification (Woolley, 2009a), which refers to the level of abstraction at which individuals think about what they are doing. Higher levels of action identification permit individuals to perceive multiple ways of achieving the same goal, and possibly provide more latitude for rationalizing deviant behavior. However, contrary to our predictions, data collected from 389 team members across 68 teams and 15 organizations demonstrates that both teams’ outcome focus and process focus indirectly influence team members to engage in less workplace deviance as a result of raising team member’s level of action identification. In these longer-term, organizational teams, it appears that higher level action identities also permit individuals to experience more meaning in their work, which safeguards against deviance (Cruz, Pinto, & Woolley, under review).

While teams examined in the laboratory have the luxury of focusing on one project at a time, a reality for an increasing number of teams in organizations is the sharing of members with other project teams operating in parallel. Based on a survey of over 400 managers from a range of industries, we estimate that between 65% and 80% of workers are assigned to more than one team at a time (Mortensen, Woolley, & O’Leary, 2007). Building from this survey and a set of in-depth interviews and observations at a contract R&D firm, we have developed a model of the effects of multiple team membership at the individual, team, and organizational levels on productivity and learning at each level (O’Leary, Mortensen, & Woolley, 2011). We theorize that participating on a large number of similar projects aids individual and team productivity but impedes learning, while a broader variety of teams enhances individual and team learning but impedes productivity. We have recently combined findings based on a computational model with data supplied by a classroom exercise in which students were assigned to multiple teams (Woolley, Aven, Zhang, O’Leary, & Mortensen, 2014). In both the computational model and the exercise, we manipulated membership variety (operationalized as the amount of shared membership across different teams) as well as the autonomy members had to switch among teams to test parts of the theoretical model. Consistent with our theory, we find that membership variety impedes productivity but enhances learning, particularly in environments where members have autonomy to make their own choices about moving among teams.