How Collective Intelligence Impacts Team Performance:
Exploring Transactive Memory System as a Mechanism
Young Ji Kim (Massachusetts Institute of Technology)
Ishani Aggarwal (Brazilian School of Public and Business Administration)
Anita Williams Woolley (Carnegie Mellon University)
Recent research has demonstrated that teams exhibit “collective intelligence” (CI) (Woolley, Chabris,
Pentland, Hashmi, & Malone, 2010) defined as a team’s basic capacity to perform a wide variety of tasks,
which is consistently predictive of future performance (Engel, Woolley, Jing, Chabris, & Malone, 2014;
Kim et al., 2015; Woolley et al., 2010). Previous research has shown that CI is predicted by group
compositional factors and group interaction process (Aggarwal, Woolley, Chabris, & Malone, 2015;
Engel et al., 2014; Kim et al., 2015; Woolley et al., 2010). Yet, it remains unclear how exactly CI leads to
greater performance. In this paper, we theorize CI as the basic capacity of a group to perform but which
needs to be expressed via team coordination and communication mechanisms. Here we specifically focus
on transactive memory system (TMS), or a group’s shared memory of “who knows what,” as more likely
to develop when a group has high CI as well as a mechanism via which CI affects performance. We
examine this hypothesis from data collected on 41 MBA teams.
Collective Intelligence
Psychologists have repeatedly shown that a single statistical factor emerges from the correlations among
individual people's performance on a wide variety of cognitive tasks (e.g., Deary, 2012; Spearman, 1904).
This factor is often referred to as “g” or “general intelligence.” Until recently, however, none of the
research on group performance had systematically examined whether a similar kind of “collective
intelligence” exists for groups of people (Woolley et al., 2010).
To examine this idea in groups, Woolley et al. (2010) had almost 200 lab groups work on a series of
different tasks together that required a range of qualitatively different collaboration processes (McGrath,
1984). In a factor analysis of all the groups’ scores, the first factor accounted for 43% of the variance in
performance on all the different tasks. This is consistent with the 30–50% of variance typically explained
by the first factor in a battery of individual cognitive tasks (Chabris, 2007). They called this first factor
“collective intelligence,” (CI) analogous to general intelligence in individuals. Each group’s CI predicted
how they performed on a more complex criterion task at a later point in time. Furthermore, while CI was
a significant predictor of group performance, the average individual intelligence of group members was
not. In addition, higher CI groups also had more female members, higher average social perceptiveness,
and equal distribution of turn taking in communication than lower CI groups, which has been replicated
with groups working online (Engel et al., 2014), and in groups in multiple cultures (Engel et al., 2015).
Transactive Memory System and Collective Intelligence
Transactive memory system (TMS) is defined as a shared system or network of encoding, storing, and
retrieving information that develops through interactions among individuals in dyads or groups
(Hollingshead, 1998; Wegner, 1987). Wegner (1995) explains that a TMS develops when members know
other members’ expertise, use the expertise directory to share new information with the person who has
the relevant expertise, and seek information from the group’s designated expert. Past empirical research,
done in both laboratory and field settings, has found a positive relation between TMS and group
performance such as increased efficiency and effectiveness in accomplishing tasks as well as innovation
(Austin, 2003; Lewis, 2003; Moreland, 1999; Moreland & Myaskovsky, 2000). The main benefit of TMS
for team performance lies in that TMS enables teams to fully coordinate and leverage knowledge
embedded in team members, which is especially important for performing tasks that require the
combination of knowledge across various areas (Lewis, 2004).
In explaining how collective intelligence affects performance, we predict that collectively intelligent
teams are more capable of developing a well-functioning TMS, which in turn enables them to translate CI
into team performance. A team’s CI is indicative of its latent ability to work together, and has been shown
to predict a team’s ability to implicitly coordinate (Aggarwal et al., 2015). Coordination is defined as
“managing dependencies between activities” (Malone & Crowston, 1994, p. 90). In team collaboration,
coordination involves managing “synchronous and/or simultaneous activities, as well as information
exchange and mutual adjustment of action” (Summers, Humphrey, & Ferris, 2012, p. 315). TMS involves
the ability of a team to understand how to marshal the resources of team members and to combine these
resources in a well-coordinated fashion, consistently across time and task domains. For instance, an
effective TMS relies upon group members to “actually know who is good at what” (Brandon &
Hollingshead, 2004, p. 639). Teams with high CI may be at an advantage in developing an accurate
cognitive representation of group members’ expertise because of their greater information processing
ability. At a team’s inception, members typically rely on various signals to form perceptions of one
another’s expertise (Hollingshead & Fraidin, 2003; Yoon & Hollingshead, 2010). Over time, team
members must modify their initial perceptions based on new information gleaned from observations and
interactions. This process requires team members to be highly perceptive of one another’s cognition, a
trait that collectively intelligent teams are more likely to have than others (Woolley et al., 2010). In
addition, for a team to actually participate in the TMS beyond being aware of it, the team should be
capable of coordinating its members’ activities, such as sequencing and tracking activities by multiple
members and engaging in mutual adjustment so as to prevent omission or redundancy. Therefore, the
basic ability to coordinate which is associated with CI should enable a team, with experience, to develop a
TMS which will help to translate CI into performance.
Method
Forty-one student teams enrolled in a 7-week long MBA course at a U.S. business school participated in
this study. The teams varied in size from three to five, resulting in a total of 190 individuals in the sample.
A significant part of the grade for the course was determined based on team projects. Teams were formed
in the first week through the instructor’s random assignment.
CI was measured during the first week of the semester, using the online CI battery developed by Engel et
al. (2014) and Woolley et al. (2010). The battery contains 11 tasks, capturing various general categories
of group processes, sampled from well-known group task taxonomies by McGrath (1984) and Larson
(2009), and was administered via an online platform for synchronous group collaboration (Engel et al.,
2015, 2014). A team’s CI was then scored following the method used by Woolley et al. (2010): we
performed a factor analysis using all the teams’ standardized task scores, and a team’s score on the first
factor served as the team’s CI score. A team’s TMS was measured using the scale developed by Lewis
(2003), which contains 15 items (on a 5-point Likert scale). We aggregated scores on all the items for a
single TMS score whose reliability was acceptable (Cronbach’s alpha = .81). Since TMS is
conceptualized as a team-level construct, we aggregated team-member responses to the team level (ICC(1)
=.17, F = 52.117, p < .001). Team performance was measured by the grade the teams got for their team
assignment from the course instructor. The team assignment consisted of developing a case about a senior
manager, which entailed finding an interesting subject, conducting an interview, and generating a report
that utilized concepts learned in class. Finally, we controlled for team size because team size likely affect
the other variables.
Results
Table 1 reports the results of a hierarchical regression analysis with performance as the outcome variable.
Team size (control), CI, and TMS were added sequentially. As expected, CI was a positive, and
significant predictor of performance controlling for team size (Model 2). In Model 3, which included
TMS, the effect of CI was no longer significant while TMS had a significant, positive effect on
performance controlling for the other variables. This result supports our hypothesis that the relationship
between collective intelligence and performance is mediated by TMS development. We tested this
mediation model using PROCESS macro (Hayes, 2013). The bootstrapping analysis showed that there
was a significant indirect effect of CI on performance via TMS development (a 90% bias-corrected 5000
bootstrap confidence interval ranged from .0158 to 1.2085, not containing zero).
Team Size
Model 1
Model 2
Model 3
.11
.08
.11
CI
.30
*
.20
.36*
TMS
R2
.01
.10
.22
F
.22
2.07
3.50*
Discussion
In this paper, we demonstrate that the team’s collective intelligence positively predicts future performance,
and transactive memory system development is one mechanism that explains this relationship. TMS is
known to positively influence team performance (e.g., Austin, 2003; Faraj & Sproull, 2000; Lewis, 2003;
Liang, Moreland, & Argote, 1995; Michinov, Olivier-Chiron, Rusch, & Chiron, 2008). Accurate expertise
recognition improves team performance because it facilitates the division of cognitive labor among
members, the search and location of required knowledge, the match of problems with the person with the
requisite expertise to solve the problems, the coordination of group activities, and better decisions through
the evaluation and integration of knowledge contributed by group members (Moreland, 1999; Ren &
Argote, 2011). We found that teams with high CI to begin with are able to develop a stronger TMS during
team collaboration, and in turn perform well on a long-term, knowledge intensive task. Although our data
was not collected in an organizational setting, several attributes of the setting (use of management
program students, working on a long-term knowledge intensive project) lead us to have greater
confidence that our findings would generalize (consistent with recent meta-analysis on teams found that
no reliable differences between lab and field settings, e.g., Hoever, van Knippenberg, van Ginkel, &
Barkema, 2012; van Dijk, van Engen, & van Knippenberg, 2012). Thus, we believe that patterns similar
to the ones found in this study should emerge in organizational teams as well.
References
Aggarwal, I., Woolley, A. W., Chabris, C. F., & Malone, T. W. (2015). Cognitive diversity, collective intelligence,
and learning in teams. In Proceedings of Collective Intelligence 2015. Santa Clara, CA.
Austin, J. R. (2003). Transactive memory in organizational groups: The effects of content, consensus, specialization,
and accuracy on group performance. Journal of Applied Psychology, 88(5), 866–878.
https://doi.org/10.1037/0021-9010.88.5.866
Brandon, D. P., & Hollingshead, A. B. (2004). Transactive memory systems in organizations: Matching tasks,
expertise, and people. Organization Science, 15, 633–644.
Chabris, C. F. (2007). Cognitive and neurobiological mechanisms of the law of general intelligence. In M. J. Roberts
(Ed.), Integrating the Mind: Domain General versus Domain specific Processes in Higher Cognition (pp.
449–491). Hove, UK: Psychology Press.
Deary, I. J. (2012). Intelligence. Annual Review of Psychology, 63, 17.1–17.30.
Engel, D., Woolley, A. W., Aggarwal, I., Chabris, C. F., Takahashi, M., Nemoto, K., … Malone, T. W. (2015).
Collective intelligence in online collaboration emerges in different contexts and cultures. In CHI ’15
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems. Seoul, Korea.
https://doi.org/10.1145/2702123.2702259
Engel, D., Woolley, A. W., Jing, L. X., Chabris, C. F., & Malone, T. W. (2014). Reading the mind in the eyes or
reading between the lines? Theory of Mind predicts collective intelligence equally well online and face-toface. PLoS ONE, 9(12). https://doi.org/10.1371/journal.pone.0115212
Faraj, S., & Sproull, L. (2000). Coordinating expertise in software development teams. Management Science, 46,
1554–1568.
Hayes, A. F. (2013). Introduction to Mediation, Moderation, and Conditional Process Analysis: A RegressionBased Approach. Guilford Press.
Hoever, I. J., van Knippenberg, D., van Ginkel, W. P., & Barkema, H. G. (2012). Fostering team creativity:
Perspective taking as key to unlocking diversity’s potential. Journal of Applied Psychology, 97(5), 982–
996. https://doi.org/10.1037/a0029159
Hollingshead, A. B. (1998). Communication, Learning, and Retrieval in Transactive Memory Systems. Journal of
Experimental Social Psychology, 34(5), 423–442. https://doi.org/10.1006/jesp.1998.1358
Hollingshead, A. B., & Fraidin, S. N. (2003). Gender stereotypes and assumptions about expertise in transactive
memory. Journal of Experimental Social Psychology, 39(4), 355–363. https://doi.org/10.1016/S00221031(02)00549-8
Kim, Y. J., Engel, D., Woolley, A. W., Lin, J., McArthur, N., & Malone, T. W. (2015). Work Together, Play Smart:
Collective Intelligence in League of Legends Teams. In Proceedings of Collective Intelligence 2015. Santa
Clara, CA.
Larson, J. R. (2009). In search of synergy in small group performance. New York, NY: Psychology Press.
Lewis, K. (2003). Measuring transactive memory systems in the field: Scale development and validation. Journal of
Applied Psychology, 88(4), 587–604.
Lewis, K. (2004). Knowledge and performance in knowledge-worker teams: A longitudinal study of transactive
memory systems. Management Science, 50, 1519–1533.
Liang, D. W., Moreland, R. L., & Argote, L. (1995). Group versus individual training and group performance: The
mediating role of transactive memory. Personality and Social Psychology Bulletin, 21(4), 384–393.
Malone, T. W., & Crowston, K. (1994). The interdisciplinary study of coordination. ACM Computing Surveys,
26(1), 87–119.
McGrath, J. E. (1984). Groups: Interaction and Performance. Englewood Cliffs, NJ: Prentice-Hall.
Michinov, E., Olivier-Chiron, E., Rusch, E., & Chiron, B. (2008). Influence of transactive memory on perceived
performance, job satisfaction and identification in anaesthesia teams. British Journal of Anaesthesia,
100(3), 327–332. https://doi.org/10.1093/bja/aem404
Moreland, R. L. (1999). Transactive memory and job performance: Helping workers learn who knows what. Shared
Cognition in Organizations: The Management of Knowledge, Mahwah, NJ: Lawrence Erlbaum Associates,
Inc, 3–32.
Moreland, R. L., & Myaskovsky, L. (2000). Exploring the Performance Benefits of Group Training: Transactive
Memory or Improved Communication? Organizational Behavior and Human Decision Processes, 82(1),
117–133. https://doi.org/10.1006/obhd.2000.2891
Ren, Y., & Argote, L. (2011). Transactive memory systems 1985–2010: An integrative framework of key
dimensions, antecedents, and consequences. The Academy of Management Annals, 5(1), 189–229.
Spearman, C. (1904). General intelligence, objectively determined and measured. American Journal of Psychology,
15(2), 201–293.
Summers, J. K., Humphrey, S. E., & Ferris, G. R. (2012). Team Member Change, Flux in Coordination, and
Performance: Effects of Strategic Core Roles, Information Transfer, and Cognitive Ability. Academy of
Management Journal, 55(2), 314–338. https://doi.org/10.5465/amj.2010.0175
van Dijk, H., van Engen, M. L., & van Knippenberg, D. (2012). Defying conventional wisdom: A meta-analytical
examination of the differences between demographic and job-related diversity relationships with
performance. Organizational Behavior and Human Decision Processes, 119(1), 38–53.
https://doi.org/10.1016/j.obhdp.2012.06.003
Wegner, D. M. (1987). Transactive memory: A contemporary analysis of the group mind. In B. Mullen & G. R.
Goethals (Eds.) (pp. 185–208). New York: Springer-Verlag.
Wegner, D. M. (1995). A Computer Network Model of Human Transactive Memory. Social Cognition, 13(3), 319–
339. https://doi.org/10.1521/soco.1995.13.3.319
Woolley, A. W., Chabris, C. F., Pentland, A. S., Hashmi, N., & Malone, T. W. (2010). Evidence for a collective
intelligence factor in the performance of human groups. Science, 330(6004), 686–688.
Yoon, K., & Hollingshead, A. B. (2010). Cultural Stereotyping, Convergent Expectations, and Performance in
Cross-Cultural Collaborations. Social Psychological and Personality Science, 1(2), 160–167.
https://doi.org/10.1177/1948550610362597