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Measuring Team Creativity Through Longitudinal Social Signals
Peter A. Gloor, Adam Almozlino, Orr Inbar
MIT Center for Collective Intelligence
5 Cambridge Center, Cambridge MA 02139, USA
pgloor@mit.edu
Wei Lo
Computer Science and Technology department
Zhejiang University, Hangzhou, P.R. China
Shannon Provost
McCombs School of Business, University of Texas at Austin
Austin, TX, USA
Summary
Research into human dynamical systems has long sought to identify robust signals for human
behavior. We have discovered a series of social network-based indicators that are reliable
predictors of team creativity and collaborative innovation. We extract these signals from
electronic records of interpersonal interactions, including e-mail, and face-to-face interaction
measured via sociometric badges. The first of these signals is Rotating Leadership, measuring the
degree to which, over time, actors in a team vary in how central they are to team’s communication
network’s structure. The second is Rotating Contribution, which measures the degree to which,
over time, actors in a team vary in the ratio of communications they distribute versus receive. The
third is Prompt Response Time, which measures, over time, the responsiveness of actors to one
another’s communications. Finally, we demonstrate the predictive utility of these signals in a
variety of contexts, showing them to be robust to various methods of evaluating innovation.
Introduction
In this paper we introduce a series of longitudinal, network-based measures of social interaction patterns
that predict collaborative innovation. Innovation is a universal, emergent human behavior. According to
noted evolutionary biologist E.O Wilson “…it was necessary for the evolving populations to acquire an
ever higher degree of intelligence. They had to feel empathy for others, to measure the emotions of
friends and enemy alike, to judge the intentions of all of them, and to plan a strategy for personal social
interactions” [1]. Innovation is a universal, emergent human behavior, one that rarely occurs through the
actions of a single individual, but rather through collaboration among individuals [2]. Here we focus on
the predictive utility of observing this collaboration at the level of interpersonal interaction events.
Recently, researchers have had success in identifying reliable, quantitative indicators of phenomena in
human systems. Among these indicators are “honest signals” [3][4][5], which signify the presence of
social influence. This name captures both the separation of these signals' from the subjectivity that often
plagues other methods for measuring human behavior, and the robustness of these signals to a variety of
behavioral contexts. Understanding these “honest signals” can convey a significant advantage. To quote
E.O Wilson again, “…social intelligence was therefore always at a high premium. A sharp sense of
empathy can make a huge difference and with it an ability to manipulate, to gain cooperation, and to
deceive” [1]. Robust, quantitative measures for collective human behavior may serve as the quantitative,
larger-scale analog for individual social intelligence.
Previous work studying collective creativity and innovation has been primarily qualitative, focused on the
creativity of individuals, or both [6][7]. Other research has been restricted to a particular interpretation of
creativity, studying for example patent production [8], or to a particular setting, studying for example
large corporations [9]. Therefore, this research has failed to identify reliable signals of collective
innovation.
Part of the reason previous work has had limited success may lie in the difficulty of understanding
innovation itself. A formal definition of innovation remains elusive, as does the boundary between
incremental improvements and innovative change. If a certain dependent variable, such as creativity, is
difficult to formally define, it may be difficult to identify a quantitative and reproducible independent
variable that indicates the dependent.
Our Approach
We have attempted to work around this issue by evaluating several different proxies for creativity across
several different scenarios, and identifying measures that reliably signal the presence of these proxies
across the scenarios. Using a wide selection of proxies in a variety of context, we have identified
reproducible independent variables that strongly correlate with the proxies. We term these variables (1)
Rotating Leadership, (2) Rotating Contribution, (3) Prompt Response Time.
From these variables, Rotating Leadership and Rotating Contribution show positive correlation in
“creative” work scenarios, but strong negative correlation with “non-creative” scenarios, suggesting that
Rotating Leadership and Rotating Contribution are a good “honest signal” for team creativity. This
corresponds with the intuition that creative work requires innovation and breaking known patterns of
thought and behavior, while breaking known patterns may disrupt non-creative work. Prompt Response
Time, on the other hand, shows positive correlation across all scenarios, suggesting that it is a better
indicator of team productivity. This corresponds with the intuition that it is, in general, better to have a
more promptly communicating team.
(1) Rotating Leadership (RL)
Rotating Leadership (RL) measures the degree to which, over time, the members in a team vary in how
“central” they are to the team’s communications. The advantage of centralized leadership for creative
tasks was for instance observed among Wikipedians [10], where it was found that Wikipedia articles
authored by more centrally communicating editors became articles of the highest quality (featured
articles) more rapidly. RL can be observed in a visualization of a network when distinct nodes appear,
over time, to oscillate between central and peripheral positions in the network. Intuitively, RL evaluates
how much, across time and the team members, team members switch between being highly central to the
overall communications of the team, and being peripheral to those communications. Formally, RL
measures oscillations in Betweenness Centrality (BC) over time among actors in the team.
The effects of the centrality of team’s actors to the team’s performance was first observed among teams
of Eclipse open source developers communicating electronically [12]. It was subsequently observed in a
study of a marketing team in a bank communicating face-to-face [13], and in a study of nurses
communicating in a hospital [14]. In this last scenario, quantitative measures were compared with
personality characteristics such as openness, as measured by the Neo-FFI [15], and group creativity was
measured through peer and management/instructor assessment, based on the premise that experts can
identify creativity [7]. Note that teams composed of highly intelligent individuals are not necessarily
intelligent as a whole [16], while measures such as RL were dependably correlated with team creativity.
Betweenness centrality [11] (BC) is a global measure of how centrally located a node is in the structure of
a network. For a given node, it is measured by evaluating the shortest paths in the network, specifically,
the proportion of all shortest paths in the network that pass through the node of interest. Mathematically,
BC of a node v is defined as:
where is the total number of shortest paths from node to node and is the number of those
paths that pass through v.
In order to calculate RL, it is necessary to aggregate measures of BC, which occur at the scale of an
individual actor at an individual time step, to the scale of the whole network over the full time frame. In
order to do this in a fashion that indicates variation in BC we counted the number of local maxima and
minima in the vector of each actor’s BC over time, and then summed this number across the actors in a
team.
Formally, we count the local maxima of function f(t)=g(t) within time interval [t1,t2]. There is a local
maximum for time t at point t*, if there exists some ε > 0 such that f(t*) ≥ f(t) when |t – t*| < ε. Similarly,
we count the local minima at t*, if f(t*) ≤ f(t) when |t – t*| < ε.
RL for actor i over time window ws is therefore:
RLi = #local minimai
ws + #local maximai
ws
𝑅𝐿 =
1
𝑛
𝑅𝐿!
!
!!!
Figure 1: RL visualized through oscillations in BC over time [17]
This figure illustrates Rotating Leadership (RL) for two teams, one with high RL, and one with low RL.
Actors are placed along the Y-axis, while the X-axis encodes time, and the Z-axis the Betweenness
Centrality (BC) of actors for each hour, sorted, each hour, by the decreasing BC of actors. The back
plane, which rises and falls, represents the set of actors who rotate taking the lead in the team’s
communication.
The left picture illustrates an example of a team with high RL. This example was drawn from a 6-day long
graduate student seminar, and communications were measured using sociometric badges. This image
includes 15 actors, and has had BC oscillation computed every hour using a time window of 12 hours,
with a date range 6/13/2010 12:37 pm to 6/19/2010 23:37 pm.
The right picture illustrates an example of a team with low RL. This example was drawn from the
customers and employees of a large service provider serving one customer, and communications were
measured using the email archive of the service provider. This image includes 2857 actors, and has had
BC oscillation computed every day using a time window of 7 days, with a date range between 6/13/2012
to 12/30/2012. The high back represents the key account managers who are consistently taking the lead in
team communication.
(2) Rotating Contribution (RC)
Rotating Contribution (RC) measures the degree to which, over time, actors in a team vary in how much
they broadcast communications versus listen to communications. RC can be observed in a visualization of
a network when distinct nodes appear, over time, to vary widely in how many incoming versus outgoing
links they have. Intuitively, RC evaluates how much, across time and the team members, team members
switch off between broadcasting many communications and listening to may communications. Formally,
RC measures oscillations, over time, of the Contribution Index (CI) of actors in a team.
Contribution Index (CI) is a measure of how much an actor disseminates versus receives communications.
For a given node, it is equal to ratio of incoming to outgoing links incident upon that node. An actor that
only sends messages will have a CI of 1, an actor that sends and receives an identical number of messages
will have a CI of 0, and an actor that only receives messages will have a CI of -1 [18]. Formally, the CI of
an actor over a given time frame is:
CI =messages _sent −messages _received
messages _sent +messages _received
In order to calculate RC, it is necessary to aggregate measures of CI, which occur at the scale of an
individual actor at an individual time step, to the scale of the whole network over the full time frame. In
order to do this in a fashion that indicates variation in CI we counted the number of local maxima and
minima in the vector of each actor’s CI over time, and then summed this number across the actors in a
team.
Formally, we count the number of local maximum points of function f(t)=c(t) within time interval [t1,t2].
There is a local maximum for time t at point t* if there exists some ε > 0 such that f(t*) ≥ f(t) when |t – t*|
< ε. Similarly, we count the local minima at t*, if f(t*) ≤ f(t) when |t – t*| < ε. RCi
ws for actor i and time
window ws is therefore
RCi
ws = #local minimai
ws + #local maximai
ws
RC= !
!
𝑅𝐶!
!
!!!
Figure 2: RC visualizing though CI oscillations over time [17]
This figure illustrates Rotating Contribution (RC) for two teams, one with high RC, and one with low RC.
Actors are placed along the Y-axis, while the X-axis encodes time, and the Z-axis the Contribution Index
(CI) of actors for each hour, sorted, each hour, by the decreasing CI of actors. The back plane, which
rises and falls, represents the set of actors who rotate taking the lead as most vocal contributors.
The left picture illustrates an example of a team with high RC; RC oscillates highly among time steps and
the actors of the team. This example was drawn from a 6-day long graduate student seminar, and
communications were measured using sociometric badges. This image includes 15 actors, and has had
BC oscillation computed every hour using a time window of 12 hours, with a date range 6/13/2010 12:37
pm to 6/19/2010 23:37 pm.
The right picture illustrates an example of a team with low RC; CI oscillates relatively little among time
steps and the actors of the team. This example was drawn from the customers and employees of a large
service provider serving one customer, and communications were measured using the email archive of
the service provider. This image includes 2857 actors, and has had CI oscillation computed every day
using a time window of 7 days, with a date range between 6/13/2012 to 12/30/2012. The high back
represents the key account managers who are consistently the most vocal by sending more emails than
they receive.
(3) Prompt Response Time (PRT)
Prompt Response Time (PRT) measures the degree to which, over time, actors are prompt at
communicating to those who have communicated to them. Intuitively, PRT corresponds with how fast,
across actors in a network, actors are at “getting back” to each other’s communications. In order to
capture this formally, PRT is defined in terms of the Communication Frame (CF), which groups
communication events between pairs of actors which may “get back” to each other, and Frame Nudges,
which measure the number of communication events in a CF, and Elapsed Time, which measures the time
duration of a CF.
A Communication Frame (CF) groups a set of time-adjacent communications between a pair of actors.
Suppose a pair of actors X and Y in a network, with a set of communication events, or directional, time-
stamped edges, between them. A single CF defines all communication events from X to Y, prior to and
including a communication event from Y to X. Intuitively, this is all the messages your colleague has sent
you since you last messaged her, prior to and including the first message you send back to your colleague.
In this framework, you, actor X, are the “source” actor in the CF, corresponding with the origin of the
first communication in the CF, and your colleague, actor Y, is the “target” actor in the CF, corresponding
with the origin of the last communication in the CF. The Elapsed Time (ET) for this CF is the difference
between the first communication in the frame and the last communication in the frame. The Frame
Nudges (FN) for this CF is the number of communication events in the CF, intuitively FN is the number
of “pings” X sends until Y responds.
To get the network-level measure of PRT from the edge-level measure of ET in CFs it is necessary to
aggregate measures. We accomplished this by using an intermediate actor-scale measure, where we
evaluated the “responsiveness” of actors through their Responsiveness in Communication Frames (RCF).
Intuitively, we measure how quickly actors got back to people who messaged them.
This can be accomplished either by measuring the ET or the FN of CFs. We define RCF via ET (RCF-
ET) for an actor as the mean ET for all CFs in which the node is the “target” node. We define RFC via
FN (RCF-FN) for an actor as the mean FN for all CFs in which the node is the “target” node. For actor i,
where 𝑓 is a given CF in the set of CF denoted as 𝑓∈F, and ∆T
!!is the time elapsed for frame 𝑓∗, such
that 𝑓∗∈ F∩i, where F∩i represents the set of all frames that actor i is a member, RCF -ET is:
RCF-ET i =
∆!!
|!∩!|
!!!
!∩!
For actor i, where 𝑓 is a given CF in the set of CF denoted as 𝑓∈F, and FN!is the number of edges in
frame 𝑓∗, such that 𝑓∗∈ F∩i, where F∩i represents the set of all frames that actor i is a member, RFC-FN
is:
RCF-FN i =
!!!
|!∩!|
!!!
!∩!
We then aggregate this actor-scale measure to the networks-scale by averaging RCF for all actors in the
network. This procedure is the same for RCF-ET and RCF-FN. For a network, where 𝑛! denotes the
number of communications of each actor i, PRT is therefore:
PRT= !"#!!∗!!!
!
!!!
!!
!
!!!
!
Analysis and Results
We extracted signals of team creativity and productivity from electronic records of interpersonal
interactions, including e-mail, and face-to-face interaction measured via sociometric badges [28]. Some of
our samples have quite a small N (<10) because of the difficulty of obtaining the type of small group
communication data we are analyzing - small team communication networks which are associated with a
measure of creativity and/or performance. This is compensated for by the comparability of the 5 datasets
that allow for cross-comparative validation across a wide range of small group and larger organizational
settings. For each scenario, we measured the Rotating Leadership, Rotating Contribution, and Prompt
Response Time measured by Elapsed Time and Frame Nudges, for the teams recorded (Table 1). We
studied the following five scenarios, captured via the described datasets:
Dependent
variable
Interaction
type
#actors
#interaction
records
duration
Global Virtual
Course
creativity
e-mail
161
3782
3 months
Co-located Course
creativity
sociometric
badges
15
265,160
5 days
Eclipse developers
Creativity,
performance
mailing list
1371
6405
6 months
Medical
researchers
creativity
e-mail
22,523
117,027
12 months
Service Provider
performance
e-mail
85,680
7,640,016
7 months
Table 1. Basic parameters of 5 datasets employed to verify “honest signals”
(1) (COINscourse2012 [19]) – An e-mail archive of a multinational, distributed graduate student seminar.
Contains 161 actors and 3782 messages. 50 students were divided into 10 student teams at five in three
countries on two continents. These students worked together as distributed virtual teams over 14 weeks.
The dependent variable for creativity was taken as the mean of peer-ratings of students, and from an
instructor rating.
(2) (CGSseminar2010 [20]) – A face-to-face interaction archive of a co-located course, gathered through
sociometric badges at a doctoral seminar. Contains 15 participants, who worked on different projects in
nine teams during one week. The dependent variable for creativity was measured through peer ratings
from participants.
(3) (Eclipse2005 [12]) - A mailing list archive of 26 working groups of Eclipse open source developers.
Contains 1371 actors and 6405 messages over a period of six months. The dependent variables for
performance and creativity were measured as the volume of bugs fixed (normalized by team size) and the
volume of new features (normalized by of the count of fixed bugs), respectively.
(4) (ChronicCareTeams2012 [21]) – An e-mail archive with a core team of 30 clinicians and health
services researchers. Contains 22,523 different actors and 117,027 messages, working on 10 different
medical innovations over the period of one year. The dependent variable for creativity was measured
through ratings from a senior project management team.
(5) (ServiceProvider2012 [22]) – An e-mail archive of staff members working in 14 different accounts at
a global service provider. Contains 85,680 actors and 7,640,016 different messages, with an account
manager coordinating activities per corporate customer. The dependent variable for customer satisfaction
was measured through a Net Promoter Score [23].
COINscourse2012
RL
RC
PRT - FN
PRT - ET
creativity
Pearson Correlation
.830**
.928**
.796**
-.610
Sig. (2-tailed)
.003
.000
.006
.061
N
10
10
10
10
CGSseminar2010
creativity
Pearson Correlation
.707*
.733*
.368
.275
Sig. (2-tailed)
.033
.025
.370
.509
N
9
9
8
8
quality
Pearson Correlation
.277
.261
.882**
.954**
Sig. (2-tailed)
.470
.498
.004
.000
N
9
9
8
8
Eclipse2005
bugs_fixed
Pearson Correlation
-.092
-.200
-.366
-.546**
Sig. (2-tailed)
.654
.328
.078
.006
N
26
26
24
24
performance
Pearson Correlation
-.754**
-.698**
-.266
-.161
Sig. (2-tailed)
.000
.000
.220
.462
N
25
25
23
23
creativity
Pearson Correlation
.216
.246
.554**
-.084
Sig. (2-tailed)
.289
.226
.005
.697
N
26
26
24
24
ChronicCareTeams2012
creative
performance
Pearson Correlation
.753*
.751*
-.117
.262
Sig. (2-tailed)
.012
.012
.749
.465
N
10
10
10
10
creativity
Pearson Correlation
.231
.287
.730*
.571
Sig. (2-tailed)
.520
.422
.017
.085
N
10
10
10
10
ServiceProvider2012
performance
Pearson Correlation
-.589*
-.618*
.429
.629*
Sig. (2-tailed)
.034
.019
.164
.029
N
13
14
12
12
Table 2. Correlations between 3 Social-Network based indicators and Creativity for the five test
datasets. **. Correlation is significant at the 0.01 level (2-tailed). *. Correlation is significant at the 0.05
level (2-tailed)
Discussion
Dynamic Social Network Analysis (SNA) [24] provided us a common framework across these scenarios,
allowing us to extract the same measures. SNA represents people as nodes and their connections as links
which together form a network. The properties of the resulting network and its entities can be studied to
glean insights about the human collection represented. SNA has been previously used to study creativity
[25][26]. While adding time at the actor level is not new [27], our work complements existing methods by
measuring interaction over time among teams of individuals who must necessarily communicate,
allowing us to measure edge-dependent features of the network as well.
A main limitation of our study is the small N (<10) of some of our samples. This is caused by the
substantial effort of obtaining the type of small group communication data we are analyzing - team
communication networks which are associated with measures for creativity and performance. This
limitation is compensated for by the comparability of the 5 datasets, allowing for cross-comparative
validation across a wide range of small group and larger organizational settings. We also hope that the
far-reaching insights into human creativity possible through the approach proposed in this paper will
motivate other researchers to conduct similar studies, thus increasing the availability of data to validate
and extend our approach.
Rotating leadership RL and Rotating Contribution RC are a consistent indicator of creativity, we also find
that for a non-creative activity such as large account management at the global service provider RL and
RC maintain predictive power, however the direction of the correlation changes: for creative tasks, more
is better, for non-creative tasks, less rotation in leadership and contribution is better. The number of
nudges PRT-FN is a predictor of high creativity, while – counterintuitively – taking more time (PRT-ET)
for a reply leads to more satisfied customers of the service provider. PRT-ET is positively correlated to
the speed of fixing software bugs, which makes intuitive sense: the faster developers answer, the faster
they will also be in fixing bugs.
While the results presented are preliminary, they nevertheless illustrate that “honest signals” of
communication among team members predict the creativity and performance of the team. They are
therefore a first step towards defining a new science of collaboration, that delivers a novel way to measure
and even optimize creativity and performance of teams by coming up with recommendations for
increased communication. While the definition of “creativity” remains elusive, we have introduced a set
of robust dependent metrics that have the power to predict if humans working together in a team might be
engaged in a creative task.
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