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Evidence of a Collective Intelligence Factor in the Performance of Human Groups


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Meeting of Minds The performance of humans across a range of different kinds of cognitive tasks has been encapsulated as a common statistical factor called g or general intelligence factor. What intelligence actually is, is unclear and hotly debated, yet there is a reproducible association of g with performance outcomes, such as income and academic achievement. Woolley et al. (p. 686 , published online 30 September) report a psychometric methodology for quantifying a factor termed “collective intelligence” ( c ), which reflects how well groups perform on a similarly diverse set of group problem-solving tasks. The primary contributors to c appear to be the g factors of the group members, along with a propensity toward social sensitivity—in essence, how well individuals work with others.
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DOI: 10.1126/science.1193147
, 686 (2010);330 Science , et al.Anita Williams Woolley
Human Groups
Evidence for a Collective Intelligence Factor in the Performance of
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task (correct, error, inserted error, and corrected
error) to allow typists to distinguish sources of errors
and correct responses and, therefore, provide a
stronger test of illusions of authorship. We asked 24
skilled typists (WPM = 70.7 T16.4) to type 600
words, each of which was followed by a four-
alternative explicit report screen. Typists typed
91.8% of the words correctly. Mean interkeystroke
intervals, plotted in Fig. 3A, show post-error slow-
ing for incorrect responses (F
= 117.7, p< 0.01)
and corrected errors (F
= 120.0, p<0.01),but
not for inserted errors (F< 1.0), indicating that
inner-loop detection distinguishes between actual
errors and correct responses.
Explicit detection probabilities, plotted in Fig.
3B, show good discrimination between correct
and error responses. For correct responses, typists
said correctmore than error[t(23) = 97.29,
p< 0.01]; for error responses, typists said error
more than correct[t(23) = 8.22, p< 0.01]. Typ-
ists distinguished actual errors from inserted errors
well, avoiding an illusion of authorship. They
said errormore than insertedfor actual errors
[t(23) = 7.06, p< 0.01] and insertedmore than
errorfor inserted errors [t(23) = 14.75, p<
0.01]. However, typists showed a strong illusion
of authorship with corrected errors. They were
just as likely to call them correct responses as
corrected errors [t(23) = 1.38].
The post-error slowing and post-trial report
data show a dissociation between inner- and outer-
loop error detection. We assessed the dissociation
further by comparing post-error slowing on trials
in which typists did and did not experience
illusions of authorship (21). The pattern of post-
error slowing was the same for both sets of trials
(fig. S6), suggesting that the pattern in Fig. 3A is
representative of all trials.
The three experiments found strong dissocia-
tions between explicit error reports and post-error
slowing. These dissociations are consistent with
the hierarchical error-detection mechanism that we
proposed, with an outer loop that mediates ex-
plicit reports and an inner loop that mediates post-
error slowing. This nested-loop description of error
detection is consistent with hierarchical models
of cognitive control in typewriting (9,10,1517)
and with models of hierarchical control in other
complex tasks (2, 8,22). Speaking, playing music,
and navigating through space may all involve
inner loops that take care of the details of per-
formance (e.g., uttering phonemes, playing notes,
and walking) and outer loops that ensure that in-
tentions are fulfilled (e.g., messages communi-
cated, songs performed, and destinations reached).
Hierarchical control may be prevalent in highly
skilled performers who have had enough practice
to develop an autonomous inner loop. Previous
studies of error detection in simple tasks may
describe inner-loop processing. The novel con-
tribution of our research is to dissociate the outer
loop from the inner loop.
The three experiments demonstrate cogni-
tive illusions of authorship in skilled typewriting
(1114). Typists readily take credit for correct
output on the screen, interpreting corrected errors
as their own correct responses. They take the
blame for inserted errors, as in the first and sec-
ond experiments, but they also blame the com-
puter, as in the third experiment. These illusions
are consistent with the hierarchical model of error
detection, with the outer loop assigning credit
and blame and the inner loop doing the work of
typing (10,17). Thus, illusions of authorship
may be a hallmark of hierarchical control systems
References and Notes
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material on Science Online.
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23. R. Cooper, T. Shallice, Cogn. Neuropsychol. 17, 297 (2000).
24. We thank J. D. Schall for comments on the manuscript.
This research was supported by grants BCS 0646588
and BCS 0957074 from the NSF.
Supporting Online Material
Materials and Methods
SOM Text
Figs. S1 to S6
5 April 2010; accepted 13 September 2010
Evidence for a Collective Intelligence
Factor in the Performance of
Human Groups
Anita Williams Woolley,
*Christopher F. Chabris,
Alex Pentland,
Nada Hashmi,
Thomas W. Malone
Psychologists have repeatedly shown that a single statistical factoroften called general
intelligence”—emerges from the correlations among peoples performance on a wide variety of cognitive
tasks. But no one has systematically examined whether a similar kind of collective intelligenceexists for
groups of people. In two studies with 699 people, working in groups of two to five, we find converging
evidence of a general collective intelligence factor that explains a groups performance on a wide variety
of tasks. This cfactoris not strongly correlated with the average or maximum individual intelligence
of group members but is correlated with the average social sensitivity of group members, the equality in
distribution of conversational turn-taking, and the proportion of females in the group.
As research, management, and many other
kinds of tasks are increasingly accom-
plished by groupsworking both face-
to-face and virtually (13)it is becoming ever
more important to understand the determinants of
group performance. Over the past century,
psychologists made considerable progress in
defining and systematically measuring intelli-
gence in individuals (4). We have used the sta-
tistical approach they developed for individual
intelligence to systematically measure the intelli-
gence of groups. Even though social psycholo-
gists and others have studied for decades how
well groups perform specific tasks (5,6), they have
not attempted to measure group intelligence in the
same way individual intelligence is measured
by assessing how well a single group can perform
a wide range of different tasks and using that
information to predict how that same group will
perform other tasks in the future. The goal of the
research reported here was to test the hypothesis
that groups, like individuals, do have character-
istic levels of intelligence, which can be measured
andusedtopredictthegroupsperformance on a
wide variety of tasks.
Although controversy has surrounded it, the
concept of measurable human intelligence is based
on a fact that is still as remarkable as it was to
Spearman when he first documented it in 1904
Carnegie Mellon University, Tepper School of Business, Pitts-
burgh, PA 15213, USA.
Union College, Schenectady, NY
12308, USA.
Massachusetts Institute of Technology (MIT)
Center for Collective Intelligence, Cambridge, MA 02142, USA.
MIT Media Lab, Cambridge, MA 02139, USA.
MIT Sloan School
of Management, Cambridge, MA 02142, USA.
*To whom correspondence should be addressed. E-mail:
29 OCTOBER 2010 VOL 330 SCIENCE www.sciencemag.org686
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(7): People who do well on one mental task tend to
do well on most others, despite large variations in
the testscontents and methods of administration
(4). In principle, performance on cognitive tasks
could be largely uncorrelated, as one might expect
if each relied on a specific set of capacities that
was not used by other tasks (8). It could even be
negatively correlated, if practicing to improve one
task caused neglect of others (9). The empirical
fact of general cognitive ability as first demon-
strated by Spearman is now, arguably, the most
replicated result in all of psychology (4).
Evidence of general intelligence comes from
the observation that the average correlation among
individualsperformance scores on a relatively
diverse set of cognitive tasks is positive, the first
factor extracted in a factor analysis of these scores
generally accounts for 30 to 50% of the variance,
and subsequent factors extracted account for
substantially less variance. This first factor extracted
in an analysis of individual intelligence tests is
referred to as general cognitive ability, or g,andit
is the main factor that intelligence tests measure.
What makes intelligence tests of substantial prac-
tical (not just theoretical) importance is that in-
telligence can be measured in an hour or less,
and is a reliable predictor of a very wide range
of important life outcomes over a long span of
time, including grades in school, success in many
occupations, and even life expectancy (4).
By analogy with individual intelligence, we
define a groups collective intelligence (c)asthe
general ability of the group to perform a wide
variety of tasks. Empirically, collective intelligence
is the inference one draws when the ability of a
group to perform one task is correlated with that
groups ability to perform a wide range of other
tasks. This kind of collective intelligence is a prop-
erty of the group itself, not just the individuals in it.
Unlike previous work that examined the effect on
group performance of the average intelligence of
individual group members (10), one of our goals is
to determine whether the collective intelligence of
the group as a whole has predictive power above
and beyond what can be explained by knowing
the abilities of the individual group members.
The first question we examined was whether
collective intelligencein this senseeven exists.
Is there a single factor for groups, a cfactor, that
functions in the same way for groups as general
intelligence does for individuals? Or does group
performance, instead, have some other correla-
tional structure, such as several equally important
but independent factors, as is typically found in
research on individual personality (11)?
To answer this question, we randomly as-
signed individuals to groups and asked them to
perform a variety of different tasks (12). In Study
1, 40 three-person groups worked together for up
to 5 hours on a diverse set of simple group tasks
plus a more complex criterion task. To guide our
task sampling, we drew tasks from all quadrants
of the McGrath Task Circumplex (6,12), a well-
established taxonomy of group tasks based on the
coordination processes they require. Tasks in-
cluded solving visual puzzles, brainstorming,
making collective moral judgments, and negoti-
ating over limited resources. At the beginning of
each session, we measured team membersindi-
vidual intelligence. And, as a criterion task at the
end of each session, each group played checkers
against a standardized computer opponent.
The results support the hypothesis that a
general collective intelligence factor (c)existsin
groups. First, the average inter-item correlation
for group scores on different tasks is positive (r=
0.28) (Table 1). Next, factor analysis of team
scores yielded one factor with an initial eigen-
value accounting for more than 43% of the
variance (in the middle of the 30 to 50% range
typical in individual intelligence tests), whereas
the next factor accounted for only 18%. Confir-
matory factor analysis supported the fit of a
single latent factor model with the data [c
1.66, P= 0.89, df = 5; comparative fit index
(CFI) =.99, root mean square error of approxi-
mation (RMSEA) = 0.01]. Furthermore, when
the factor loadings for different tasks on the first
general factor are used to calculate a cscore for
each group, this score strongly predicts perform-
ance on the criterion task (r= 0.52, P= 0.01).
Finally, the average and maximum intelligence
scores of individual group members are not
significantly correlated with c[r= 0.19, not
significant (ns); r= 0.27, ns,respectively] and
not predictive of criterion task performance (r=
0.18, ns; r=0.13, ns, respectively). In a regres-
sion using both individual intelligence and cto
predict performance on the criterion task, chas
a significant effect (b= 0.51, P= 0.001), but
average individual intelligence (b= 0.08, ns) and
maximum individual intelligence (b=.01, ns) do
not (Fig. 1).
In Study 2, we used 152 groups ranging from
two to five members. Our goal was to replicate
these findings in groups of different sizes, using a
broader sample of tasks and an alternative mea-
sure of individual intelligence. As expected, this
study replicated the findings of Study 1, yielding
a first factor explaining 44% of the variance and a
second factor explaining only 20%. In addition, a
confirmatory factor analysis suggests an excel-
lent fit of the single-factor model with the data
= 5.85, P=0.32,df=5;CFI=0.98,NFI=
0.89, RMSEA = 0.03).
In addition, for a subset of the groups in Study
2, we included five additional tasks, for a total of
ten. The results from analyses incorporating all
ten tasks were also consistent with the hypothesis
that a general cfactor exists (see Fig. 2). The
scree test (13) clearly suggests that a one-factor
model is the best fit for the data from both studies
[Akaike Information Criterion (AIC) = 0.00 for
single-factor solution]. Furthermore, parallel anal-
ysis (13) suggests that only factors with an eigen-
value above 1.38 should be retained, and there is
only one such factor in each sample. These conclu-
sions are supported by examining the eigenvalue s
both before and after principal axis extraction,
which yields a first factor explaining 31% of
Table 1. Correlations among group tasks and descriptive statistics for Study 1. n= 40 groups; *P
0.05; **P0.001.
12345 6 789
1 Collective intelligence (c)
2 Brainstorming 0.38*
3 Group matrix reasoning 0.86** 0.30*
4 Group moral reasoning 0.42* 0.12 0.27
5 Plan shopping trip 0.66** 0.21 0.38* 0.18
6 Group typing 0.80** 0.13 0.50** 0.25* 0.43*
7 Avg member intelligence 0.19 0.11 0.19 0.12 0.06 0.22
8 Max member intelligence 0.27 0.09 0.33* 0.05 0.04 0.28 0.73**
9 Video game 0.52* 0.17 0.38* 0.37* 0.39* 0.44* 0.18 0.13
Minimum 2.67 9 2 32 10.80 148 4.00 8.00 26
Maximum 1.56 55 17 81 82.40 1169 12.67 15.67 96
Mean 0 28.33 11.05 57.35 46.92 596.13 8.92 11.67 61.80
SD 1.00 11.36 3.02 10.96 19.64 263.74 1.82 1.69 17.56
Fig. 1. Standardized regression coefficients for
collective intelligence (c) and average individual
member intelligence when both are regressed to-
gether on criterion task performance in Studies
1 and 2 (controlling for group size in Study 2).
Coefficient for maximum member intelligence is
also shown for comparison, calculated in a separate
regression because it is too highly correlated with
individual member intelligence to incorporate both
in a single analysis (r= 0.73 and 0.62 in Studies
1 and 2, respectively). Error bars, mean TSE. SCIENCE VOL 330 29 OCTOBER 2010 687
on January 17, 2011www.sciencemag.orgDownloaded from
the variance in Study 1 and 35% of the variance
in Study 2. Multiple-group confirmatory factor
analysis suggests that the factor structures of
the two studies are invariant (c
= 11.34, P=
0.66, df = 14; CFI = 0.99, RMSEA = 0.01).
Taken together, these results provide strong
support for the existence of a single dominant
cfactor underlying group performance.
The criterion task used in Study 2 was an ar-
chitectural design task modeled after a complex
research and development problem (14). We had
a sample of 63 individuals complete this task
working alone, and under these circumstances,
individual intelligence was a significant predictor
of performance on the task (r=0.33,P= 0.009).
When the same task was done by groups,
however, the average individual intelligence of
the group members was not a significant predictor
of group performance (r= 0.18, ns). When both
individual intelligence and careusedtopredict
group performance, cis a significant predictor (b=
0.36, P= 0.0001), but average group member
intelligence (b= 0.05, ns) and maximum member
intelligence (b= 0.12, ns) are not (Fig. 1).
If cexists, what causes it? Combining the find-
ings of the two studies, the average intelligence of
individual group members was moderately cor-
related with c(r=0.15,P= 0.04), and so was the
intelligence of the highest-scoring team member
(r=0.19,P= 0.008). However, for both studies, c
was still a much better predictor of group per-
formance on the criterion tasks than the average or
maximum individual intelligence (Fig. 1).
We also examined a number of group and indi-
vidual factors that might be good predictors of c.We
found that many of the factors one might have ex-
pected to predict group performancesuch as group
cohesion, motivation, and satisfactiondid not.
However, three factors were significantly cor-
related with c. First, there was a significant corre-
lation between cand the average social sensitivity
of group members, as measured by the Reading
the Mind in the Eyestest (15)(r= 0.26, P=
0.002). Second, cwas negatively correlated with
the variance in the number of speaking turns by
group members, as measured by the sociometric
badges worn by a subset of the groups (16)(r=
0.41, P= 0.01). In other words, groups where a
few people dominated the conversation were less
collectively intelligent than those with a more
equal distribution of conversational turn-taking.
Finally, cwas positively and significantly
correlated with the proportion of females in the
group (r= 0.23, P= 0.007). However, this result
appears to be largely mediated by social sensitiv-
ity (Sobel z=1.93,P= 0.03), because (consistent
with previous research) women in our sample
scored better on the social sensitivity measure
than men [t(441) = 3.42, P= 0.001]. In a regres-
sion analysis with the groups for which all three
variables (social sensitivity, speaking turn vari-
ance, and percent female) were available, all had
similar predictive power for c, although only
social sensitivity reached statistical significance
(b= 0.33, P=0.05)(12).
These results provide substantial evidence for
the existence of cin groups, analogous to a well-
known similar ability in individuals. Notably, this
collective intelligence factor appears to depend
both on the composition of the group (e.g., aver-
age member intelligence) and on factors that emerge
from the way group members interact when they
are assembled (e.g., their conversational turn-
taking behavior) (17,18).
These findings raise many additional questions.
For example, could a short collective inteligence
test predict a sales teams or a top management
teams long-term effectiveness? More important-
ly, it would seem to be much easier to raise the
intelligence of a group than an individual. Could
a groups collective intelligence be increased by,
for example, better electronic collaboration tools?
Many previous studies have addressed ques-
tions like these for specific tasks, but by measur-
ing the effects of specific interventions on a groups
c, one can predict the effects of those interventions
on a wide range of tasks. Thus, the ability to
measure collective intelligence as a stable property
of groups provides both a substantial economy of
effort and a range of new questions to explore in
building a science of collective performance.
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19. This work was made possible by financial support from the
National Science Foundation (grant IIS-0963451), the
Army Research Office (grant 56692-MA), the Berkman
Faculty Development Fund at Carnegie Mellon University,
and Cisco Systems, Inc., through their sponsorship of the
MIT Center for Collective Intelligence. We would especially
like to thank S. Kosslyn for his invaluable help in the
initial conceptualization and early stages of this work and
collection and analysis. We are also grateful for comments
and research assistance from L. Argote, E. Anderson,
J. Chapman, M. Ding, S. Gaikwad, C. Huang, J. Introne,
F. Sun, E. Sievers, K. Tenabe, and R. Wong. The hardware
and software used in collecting sociometric data are the
subject of an MIT patent application and will be provided for
academic research via a not-for-profit arrangement through
A.P. In addition to the affiliations listed above, T.W.M.
is also a member of the Strategic Advisory Board at
InnoCentive, Inc.; a director of Seriosity, Inc.; and chairman
of Phios Corporation.
Supporting Online Material
Materials and Methods
Tables S1 to S4
2 June 2010; accepted 10 September 2010
Published online 30 September 2010;
Include this information when citing this paper.
Fig. 2. Scree plot demonstrating
the first factor from each study ac-
counting for more than twice as
much variance as subsequent fac-
tors. Factor analysis of items from
dividual intelligence administered
to 642 individuals is included as a
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... Another line of thinking lies in how the collective intelligence with more diverse teams improves with the increased diversity overall, resulting in stronger conferences and science (Hall et al., 2018;Woolley et al., 2010). However, even though there is a selection of SPC members by conveners, this does not guarantee that something beyond the "token" numbers of a few women is sufficient. ...
... However, even though there is a selection of SPC members by conveners, this does not guarantee that something beyond the "token" numbers of a few women is sufficient. Even if we reach critical mass (>15%) of women representation for effective teams (Cain & Leahey, 2014), the collective intelligence of the team is lower if a few people dominate rather than turn-taking (Woolley et al., 2010). Interpersonal relationships are also involved so that all have a voice to counteract the chilly climate if critical mass is not on the team from the start. ...
Full-text available
Science conferences have increasingly come under a spotlight for inclusion and representation of marginalized groups. Here, we report on our analysis of the representation of women in conference leadership with regard to internal structure and dynamics at the Chapman conference series, spanning a period from 2007 to 2019. Chapman conferences are small, focused meetings in the Earth and space sciences, under the umbrella of the American Geophysical Union (AGU). They follow a two‐leveled scientific leadership model, starting at conference inception by the organizing conveners and their selection of an invited science program committee. Our main findings were: (a) The average women proportion was less for the conveners (17%) than for the Science Program Committee (SPC) (24%), which is in line with the AGU demographics of attrition, assuming a different mix of career stages among conveners and SPC. At the individual conference level, the unfavorable case that convener or SPC teams were comprised only of men was nonetheless frequent. (b) On average, mixed convener teams, as opposed to all‐men convener teams, selected a higher women representation among the SPC members (18% vs. 28%). (c) There were fewer all‐men SPC teams when at least one woman was in the convener team (21% vs. 7%). In conclusion, while there was evidence that equitable representation can be achieved in the leadership, it still lagged in a consistent fashion for individual conferences. Targeted efforts for increased representation–especially at the convener level of the two‐leveled conference model–are recommended, as increased women representation at the convener level may improve women representation of the SPC.
... Emerging themes in recent literature such as psychological safety (Edmondson, 1999;Anderson & West, 2019) and collective intelligence (Woolley et al., 2010) suggest a potential overlap between organizational culture and team dynamics. Yet, there is still a discernable gap in understanding how these two domains interact in a holistic manner. ...
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The current study uses a mixed-methods research design to delve into the intricate relationship between organizational culture and team dynamics. Drawing on prior research that has individually explored the realms of organizational culture (Smith, 2019; Johnson & Williams, 2020) and team dynamics (Brown et al., 2018; Harris, 2021), this study seeks to bridge the gap by explicitly investigating how cultural factors within an organization affect key elements of team dynamics such as trust, communication, and conflict resolution. Through an analysis of survey data from 200 employees across five different organizations and in-depth interviews with 20 team leaders, the results compellingly demonstrate that organizational culture has a significant and multifaceted influence on team dynamics. Specifically, elements of culture, such as openness and inclusivity, are strongly correlated with enhanced trust, more effective conflict resolution strategies, and higher levels of overall team performance.
... Research by Takeda and Homberg concluded that gender-diverse teams are more effective, and male-dominant teams experience lower performance. Recent research indicates that having women in a team greatly enhances team collaboration, as measured by collective intelligence, regarding team processes (Bear & Woolley, 2011;Woolley et al., 2010). Additionally, Jehn et al.'s (1999) findings indicated that gender diversity positively affects team commitment and performance. ...
... Stereotype threat-one of the most widely studied topics in social psychology [146] across various domains [103,131,186]-has been implicated in long-standing racial and gender inequalities in academic performance [50,61,122], but has not been studied extensively in the context of online teamwork. Hence, the present research addresses this gap, recognizing that gender diversity creates better performing teams [63,177,187] and offering a theoretically grounded approach to mitigating stereotype threat and promoting more equitable online participation for women in technological team meetings. ...
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We investigate how gender-anonymous voice avatars influence women’s performance in online computing group work. Female participants worked with two male confederates. Voices were filtered according to four voice gender anonymity conditions: (1) All unmasked, (2) Male confederates masked, (3) Female participant masked, and (4) All masked. When only male confederates used masked voices (compared to all unmasked), female participants spoke for a longer period of time and scored higher on computing problems. When everyone used masked voices (compared to all unmasked), female participants spoke for a longer period of time, spoke more words, and scored higher on computing problems. Effects were not significant on subjective measures and one behavioral measure. We discuss the implications for virtual interactions between people.
... Raviv et al., 2022). From this follows the prediction that groups collaborating on complex problems will benefit from cognitive diversity (Aggarwal & Woolley, 2010;Aggarwal, Woolley, Chabris, & Malone, 2015;Fujisaki, Honda, & Ueda, 2018;Hong & Page, 2004;Woolley, Chabris, Pentland, Hashmi, & Malone, 2010). ...
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Capacities for abstract thinking and problem solving are central to human cognition. Processes of abstraction allow the transfer of experiences and knowledge between contexts helping us make informed decisions in new or changing contexts. While we are often inclined to relate such reasoning capacities to individual minds and brains, they may in fact be contingent on human-specific modes of collaboration, dialogue, and shared attention. In an experimental study, we test the hypothesis that social interaction enhances cognitive processes of rule-induction, which in turn improves problem-solving performance. Through three sessions of increasing complexity, individuals and groups were presented with a problem-solving task requiring them to categorize a set of visual stimuli. To assess the character of participants’ problem representations, after each training session they were presented with a transfer task involving stimuli that differed in appearance, but shared relations among features with the training set. Besides, we compared participants’ categorization behaviors to simulated agents relying on exemplar learning. We found that groups performed superior to individuals and agents in the training sessions and were more likely to correctly generalize their observations in the transfer phase, especially in the high complexity session, suggesting that groups more effectively induced underlying categorization rules from the stimuli than individuals and agents. Crucially, variation in performance among groups was predicted by semantic diversity in members’ dialogical contributions suggesting a link between social interaction, cognitive diversity, and abstraction.
... Dessa forma, se permitiria aos artesãos e designers-artesãos definir seus próprios significados e narrativas sem serem pressionados a reinventar a sua individualidade de forma intermitente. Uma ressignificação efetiva também pode garantir que objetos e processos tradicionais não sejam restritivamente classificados como peças culturais regionais (Woolley, 2010) e, adicionalmente, sejam identificados como caminhos testados ao longo do tempo para a construção de significado em contextos marginais e/ou desfavorecidos da sociedade e da economia contemporâneas. ...
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O Ciclo do Linho é uma tradição artesanal milenar, praticamente extinta em Portugal, que ainda se encontra viva na região da serra do Caramulo, Tondela, graças às habilidosas mãos das artesãs da associação AmaCastelões. Este artigo descreve a fase de observação participante na AmaCastelões inscrita numa investigação em design. Atualmente, a investigação concentra-se na identi-ficação dos atributos e das especialidades do design que poderão contribuir para um projeto de valorização, através da ressignificação, do patrimônio material e imaterial relacionado com o Ciclo do Linho de Castelões. Neste contexto, reflete-se sobre a ideia de "novo-com-tradição" enquanto vetor axiológico desse projeto, na perspetiva do design no território. Discutem-se os resultados inspirados numa abordagem fenomenológica, ontológica, autoetnográfica e hermenêutica que designamos "design centrado-no-ser". Para esta abordagem, a inovação pelo design não implica romper com a tradição, mas pode e deve emergir, enquanto "novo-com-tradição", de uma "fusão de horizontes" entre participantes, através de um diálogo hermenêutico intergeracional, conectando o passado ao futuro no sentido de nutrir um presente fértil. Palavras-chave investigação em design, ciclo do linho, novo-com-tradição, design centrado-no-ser, sustentabilidade cultural. The Cycle of Linen is an ancient tradition of linen making in Portugal that is practically extinct but which survives in the Caramulo mountain range, thanks to the skill and resilience of the artisans of the AmaCastelões association. This article describes the participant observation phase of a doctoral research project in design which focuses on identifying specialities and specificities that could contribute further to the valorisation of the craft through the re-signification of its tangible and intangible heritage. In this context, we reflect on the notion of "new-with-tradition", as an axiological vector of this project, from the perspective of design and territory. We discuss emerging results inspired by a phenomenological, ontological, hermeneutic, and autoethnographic approach to mediation following the principles of "Being-Centered Design". Within this approach, innovation by design does not imply breaking with tradition. However, it can and should emerge, as a consequence of "new-with-tradition", from a "fusion of horizons" among participants-through an intergenerational hermeneutic dialogue connecting the past to the future to nurture a fertile present.
We uncover the different patterns by which users on the open source intelligence platforms ThreatFox and MISP share information. We let these patterns inform a simulation model that describes how decentral users share indicators of compromise (IoC). The results suggest that both platform approaches have unique strenghts and drawbacks, and they highlight a trade-off between the speed with which IoC are shared and the reputational risk involved with this sharing. We find that single-community platforms such as ThreatFox let agents share low-value IoC fast, whereas closed-user communities such as MISP create conditions that enable users to share high-value IoC. We discuss the extent to which a combination of both designs may prove to be effective.
Purpose This study aims to explore the impact of board gender diversity on firms’ forward-looking risk, as perceived by both the firm’s management and its investors. The authors seek to understand whether the presence of female directors and the consequent enhancement of board dynamics can influence a firm’s risk profile. Design/methodology/approach The authors use firms’ cash holdings and option implied volatility as proxies for future risk. The approach involves a rigorous analysis that accounts for potential concerns related to selection bias, endogeneity, heteroskedasticity and serial correlation. The authors further substantiate the findings through robustness checks, including a dynamic panel system general method of moment test and a Heckman correction model. Findings The results reveal an inverse relationship between board gender diversity and firms’ expected risk. The findings suggest that the primary driver of this risk reduction is the improvement in the group dynamics of the board that comes with increased gender diversity. This implies that gender diverse boards can significantly influence a firm’s risk management and financial performance. Research limitations/implications The results indicate that gender diverse firms have economically and statistically significantly less expected risk and have better financial performance than firms with less board gender diversity. This has important implications for the organization of corporate boards. Practical implications If the addition of female directors alters the risk aversion of the board, then management may be compelled to alter their investment and production decisions that, ultimately, affects firms’ profitability. In addition, the authors investigate whether changes to firm risk is due to gender differences in risk preferences or to an improvement in the group dynamics of the board. Social implications The empirical results suggest that the effect of board gender diversity on firms’ expected risk and financial performance may be due to an improvement in the collective intelligence of the board, as a result of more gender diversity, and not due to gender differences in risk preferences. Originality/value To the best of the authors’ knowledge, this work is the first to study the effect of board gender diversity on firms’ future risk.
This study investigates whether teamwork or individual effort is more effective in solving complex problems using a well-known business simulation game called ERPSim. ERPSim has been used in management education and training and has had widespread use in the last decade. Business decisions are highly dependent among departments, highly dynamic, and uncertain. However, what factors influence the team’s performance in solving these complex problems is yet to be determined. We conducted a qualitative study among students with different educational levels. We measured their complex problem-solving performance based on the business performance data generated from the ERPsim system. Our results show that teamwork was more effective than individual effort in solving complex problems. Through teamwork, participants could utilize diverse skills and perspectives to solve complex issues more efficiently.Additionally, teams could better manage their communication and collaboration, allowing them to be flexible and autonomous. The results also show that the impact of high individual intelligence on team performance was less significant compared to effective teamwork. These findings implied that besides developing individual cognitive skills (e.g., critical thinking, creativity), management education should focus more on social-emotional skills (e.g., communication, collaboration). Moreover, this study can consider an integrated approach to advancing knowledge and discovering new solutions to complex challenges.KeywordsERPsimTeam PerformanceIndividual Performance21st-Century Learning FrameworkBusiness Simulation GameActive Learning
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This study reports the results of several meta-analyses examining the relationship between four operational definitions of cognitive ability within teams (highest member score, lowest member score, mean score, standard deviation of scores) and team performance. The three indices associated with level yielded moderate and positive sample-weighted estimates of the population relationship (.21 to .29), but sampling error failed to account for enough variation to rule out moderator variables. In contrast, the index associated with dispersion (i.e., standard deviation of member scores) was essentially unrelated to team performance (-.03), and sampling error provided a plausible explanation for the observed variation across studies. A subgroup analysis revealed that mean cognitive ability was a much better predictor of team performance in laboratory settings (.37) than in field settings (.14). Study limitations, practical implications, and future research directions are discussed.
Part I: Teams Chapter 1: The Challenge Part II: Enabling Conditions Chapter 2: A Real Team Chapter 3: Compelling Direction Chapter 4: Enabling Structure Chapter 5: Supportive Context Chapter 6: Expert Coaching Part III: Opportunities Chapter 7: Imperatives for Leaders Chapter 8: Thinking Differently About Teams
Michaelsen, Watson, and Black (1989) argued that, by using experienced groups working on relevant tasks with real rewards, they were able to demonstrate an assembly bonus effect (Collins & Guetzkow, 1964)-group performance that is better than the performance of any individual group member or any combination of individual member efforts. Using computer simulations based on Michaelsen et al's findings and some recent data collected under circumstances similar to those used by Michaelsen et al, we demonstrate that is highly unlikely that they found an assembly bonus effect and that their results are typical of those obtained in standard laboratory experiments on group problem solving.
Nearly all research on the accuracy of individual versus group decision making has used ad hoc groups, artificial problems, and trivial or nonexistent reward contingencies. These studies have generally concluded that the knowledge base of the most competent group member appears to be the practical upper limit of group performance and that process gains will rarely be achieved. We studied individual versus group decision making by using data from 222 project teams, ranging in size from 3 to 8 members. These teams were engaged in solving contextually relevant and consequential problems and, in direct contrast with previous research, the groups outperformed their most proficient group member 97% of the time. Furthermore, 40% of the process gains could not be explained by either average or most knowledgeable group member scores. Implications for management practice are also discussed.
Nearly all research on the accuracy of individual versus group decision making has used ad hoc groups, artificial problems, and trivial or nonexistent reward contingencies. These studies have generally concluded that the knowledge base of the most competent group member appears to be the practical upper limit of group performance and that process gains will rarely be achieved. We studied individual versus group decision making by using data from 222 project teams, ranging in size from 3 to 8 members. These teams were engaged in solving contextually relevant and consequential problems and, in direct contrast with previous research, the groups outperformed their most proficient group member 97% of the time. Furthermore, 40% of the process gains could not be explained by either average or most knowledgeable group member scores. Implications for management practice are also discussed. (PsycINFO Database Record (c) 2012 APA, all rights reserved)
L. K. Michaelsen et al (see record 1990-04483-001) argue that, by using experienced groups working on relevant tasks with real rewards, an assembly bonus effect (group performance that is better than the performance of any individual group member or any combination of individual member efforts [B. E. Collins and H. Guetzkow, 1964]) was demonstrated. Using computer simulations based on the Michaelsen et al findings, the authors argue that it is highly unlikely that an assembly bonus effect was found and that the results are typical of those obtained in standard laboratory experiments on group problem solving. (PsycINFO Database Record (c) 2012 APA, all rights reserved)