The impact of the ‘open’ workspace on human collaboration

Abstract

Organizations’ pursuit of increased workplace collaboration has led managers to transform traditional office spaces into ‘open’, transparency-enhancing architectures with fewer walls, doors and other spatial boundaries, yet there is scant direct empirical research on how human interaction patterns change as a result of these architectural changes. In two intervention-based field studies of corporate headquarters transitioning to more open office spaces, we empirically examined—using digital data from advanced wearable devices and from electronic communication servers—the effect of open office architectures on employees' face-to-face, email and instant messaging (IM) interaction patterns. Contrary to common belief, the volume of face-to-face interaction decreased significantly (approx. 70%) in both cases, with an associated increase in electronic interaction. In short, rather than prompting increasingly vibrant face-to-face collaboration, open architecture appeared to trigger a natural human response to socially withdraw from officemates and interact instead over email and IM. This is the first study to empirically measure both face-to-face and electronic interaction before and after the adoption of open office architecture. The results inform our understanding of the impact on human behaviour of workspaces that trend towards fewer spatial boundaries.
This article is part of the theme issue ‘Interdisciplinary approaches for uncovering the impacts of architecture on collective behaviour’.

Full text

rstb.royalsocietypublishing.org
Research
Cite this article: Bernstein ES, Turban S. 2018
The impact of the ‘open’ workspace on human
collaboration. Phil.Trans.R.Soc.B373: 20170239.
http://dx.doi.org/10.1098/rstb.2017.0239
Accepted: 3 May 2018
One contribution of 11 to a theme issue
‘Interdisciplinary approaches for uncovering the
impacts of architecture on collective behaviour’.
Subject Areas:
behaviour, ecology
Keywords:
interaction, transparency, collaboration,
communication, spatial boundaries,
collective intelligence
Author for correspondence:
Ethan S. Bernstein
e-mail: ebernstein@hbs.edu
The impact of the ‘open’ workspace on
human collaboration
Ethan S. Bernstein1and Stephen Turban2
1
Harvard Business School, Boston, MA, USA
2
Harvard University, Cambridge MA, USA
ESB, 0000-0001-9819-0639
Organizations’ pursuit of increased workplace collaboration has led managers
to transform traditional office spaces into ‘open’, transparency-enhancing
architectures with fewer walls, doors and other spatial boundaries, yet there
is scant direct empirical research on how human interaction patterns change
as a result of these architectural changes. In two intervention-based field
studies of corporate headquarters transitioning to more open office spaces,
we empirically examined—using digital data from advanced wearable devices
and from electronic communication servers—the effect of open office
architectures on employees’ face-to-face, email and instant messaging (IM)
interaction patterns. Contrary to common belief, the volume of face-to-face
interaction decreased significantly (approx. 70%) in both cases, with an
associated increase in electronic interaction. In short, rather than prompting
increasingly vibrant face-to-face collaboration, open architecture appeared
to trigger a natural human response to socially withdraw from officemates
and interact instead over email and IM. This is the first study to empirically
measure both face-to-face and electronic interaction before and after the
adoption of open office architecture. The results inform our understanding
of the impact on human behaviour of workspaces that trend towards
fewer spatial boundaries.
This article is part of the theme issue ‘Interdisciplinary approaches for
uncovering the impacts of architecture on collective behaviour’.
1. Introduction
Boundaries between ‘us’ and ‘them’ have long captured human interest. Yet
even as social scientists continue to study the value of a vast array of bound-
aries [1], in an era in which the nature of work is changing [2– 4], managers
and organizational scholars have increasingly framed boundaries as barriers
to interaction that ought to be spanned [5–8], permeated [9] or blurred [10]
to increase collaboration. In the most physically salient and concrete example,
‘spatial boundaries’ [11] at work—such as office or cubicle walls—are being
removed to create open ‘unbounded’ offices in order to stimulate greater
collaboration and collective intelligence. Does it work?
Prior theory is divided—and empirical evidence mixed—on the effect that
removing spatial boundaries has on human behaviour in the space previously
within those boundaries (e.g. [12,13]). On the one hand, sociological theory pre-
sents a strong argument that removing spatial boundaries to bring more people
into contact should increase collaboration and collective intelligence. The notion
that propinquity, or proximity, predicts social interaction [14]—driving the for-
mation of social ties and therefore information exchange and collaboration—is
one of the most robust findings in sociology [15,16]. It has been observed in
contexts as diverse as the US Congress [17,18], nineteenth-century boarding
houses [19], college dormitories [14], laboratories [20], co-working spaces [21]
and corporate buildings [22]. When spatial boundaries—such as walls—are
removed, individuals feel more physically proximate, which, such theory
suggests, should lead to more interaction. Such interaction is a necessary
&2018 The Authors. Published by the Royal Society under the terms of the Creative Commons Attribution
License http://creativecommons.org/licenses/by/4.0/, which permits unrestricted use, provided the original
author and source are credited.

foundation for collective intelligence—a form of distributed
intelligence that arises from the social interaction of individ-
uals [23] and that predicts, more so than the intelligence of
individual members, a group’s general ability to perform a
wide variety of tasks [24 –26]. Much like the swarm intelli-
gence observed among cognitively simple agents such as
social insects and other animals [27 –29], collective intelli-
gence for groups of humans requires interaction [30]. If
greater propinquity drives greater interaction, it should
generate greater collaboration and collective intelligence.
On the other hand, some organizational scholars, espec-
ially social psychologists and environmental psychologists,
have shown that removing spatial boundaries can decrease
collaboration and collective intelligence. Spatial boundaries
have long served a functional role at multiple levels of
analysis, helping people make sense of their environment
by modularizing it [31], clarifying who is watching and
who is not, who has information and who does not, who
belongs and who does not, who controls what and
who does not, to whom one answers and to whom one
does not [32]. This school of thought, like theories of organ-
izational design and architecture [29], assumes that spatial
boundaries built into workspace architecture support collab-
oration and collective intelligence by mitigating the effects of
the cognitive constraints of the human beings working within
them. Like social insects which swarm within functionally-
determined zones ‘partitioned’ by spatial boundaries (e.g.
hives, nests or schools) [29], human beings—despite their
greater cognitive abilities—may also require boundaries to
constrain their interactions, thereby reducing the potential
for overload, distraction, bias, myopia and other symptoms
of bounded rationality. Research as far back as the founda-
tional Hawthorne Studies [33,34] shows that being walled
off can therefore increase interaction within the separated
group [33]. Similarly, subsequent workplace design research
(for reviews, see [35 –38])—though mixed in its findings—
suggests that open offices can reduce certain conditions
conducive to collaboration and collective intelligence,
including employee satisfaction [39,40], focus [41– 44],
psychological privacy [45,46] and other affective and
behavioural responses [40,41,43,47,48]. Such negative psycho-
logical effects of open offices conceivably may lead to less,
not more, interaction between those within them [49],
reducing collaboration and collective intelligence.
To our knowledge, no prior study has directly measured
the effect on actual interaction that results from removing spatial
boundaries to create an open office environment. Past work-
place design research, rather than directly and objectively
measuring behaviours, has relied heavily on survey-based
or activity-log methodologies, which provided self-reported
measures, or on social observation studies, which provided
an observer’s subjective interpretation of human interactions.
Several decades ago, when much of the workplace design
research was conducted, measuring actual interaction patterns
of individuals at work in both traditional and open office
environments would have been prohibitively difficult, but
new ‘people analytics’ technology has made it quite feasible.
Using two field studies of organizations transforming
their office architecture by removing spatial boundaries to
become more open, we empirically measure the effect on inter-
action, carefully tracking face-to-face (F2F) interaction before
and after the transition with wearable sociometric devices
[50,51] that avoid the imprecise and subjective survey-based
self-reported measures typical of previous office collaboration
studies [52,53]. We also measure two digital channels of inter-
action—email and instant messaging (IM) [54– 56]—using
information from the organizations’ own servers.
In the first study, we focus on the most basic set of empiri-
cal questions: what is the effect of transitioning from cubicles
to open workspaces on the overall volume and type of interac-
tion, with what implications for organizational performance
based on the company’s own performance management sys-
tem? In the second study, we replicate the first study’s results
and then consider two more-targeted empirical questions:
how does spatial distance between workstations moderate
the effect of transitioning from cubicles to open workspaces
and how do individual employee interaction networks, both
F2F and electronic, change differentially? While the first
study considers interactions involving individuals, the second
considers interactions for dyads (both sides of the inter-
action), allowing a more precise but limited investigation of
the effects.
2. Study 1
The first empirical study, a quasi-field experiment [57,58],
was conducted at the global headquarters of OpenCo1,
1
a Fortune 500 multinational. In a so-called war on walls,
OpenCo1 decided to use the latest open office workstation
products to completely transform the wall-bounded work-
spaces in its headquarters so that one entire floor was open,
transparent and boundaryless.
The redesign—which required people to move from
assigned seats on their original floor to similarly assigned
seats on a redesigned floor of the same size—affected employees
in functions including technology, sales and pricing, human
resources (HR), finance, and product development, as well as
the top leadership. Of those people, a cluster of 52 (roughly
40%) agreed to participate in the experiment. A comparison of
HR data for participants and nonparticipants provided no evi-
dence of nonresponse bias. Because of the nature of office
space,all employees moved fromthe old space to the redesigned
space at the same time, so the experiment wasstructured with an
interrupted time-series design [58].
To capture a full, data-rich picture of interaction patterns
both before and after the boundaries were removed, partici-
pants were asked to wear a sensor, known as a sociometric
badge [59], that recorded, in great detail, their F2F inter-
actions: an infrared (IR) sensor captured whom they were
facing (by making contact with the other person’s IR
sensor), microphones captured whether they were talking
or listening (but not what was said), an accelerometer cap-
tured body movement and posture, and a Bluetooth sensor
captured spatial location (figure 1). All sensors recorded
time-stamped data in 10 ms intervals. Based on prior research
using these sociometric badges [50], an F2F interaction was
recorded when three conditions were met: two or more
badges (i) were facing each other (with uninterrupted infra-
red line-of-sight), (ii) detected alternating speaking, and
(iii) were within 10 m of each other. The interaction ended
when any of the three criteria ceased to be true for more
than 5 s. While these criteria were based on precedent from
significant prior use of sociometric badges, sensitivity analy-
sis showed the results to be robust to reasonable alternative
assumptions (including shorter distances in 1 m increments,
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2

different lag times before concluding an interaction, and
different speaking patterns). This F2F data was combined
with email and IM data for the same time periods, collected
from the company’s servers, to create a full picture of these
professionals’ interactions before and after the redesign.
Data were collected in two phases: for 15 workdays (three
weeks) before the redesign and, roughly three months later,
for 15 workdays after the redesign. Three-week data collection
windows were chosen as a balance between the organiz-
ation’s desire to minimize the burden of the research study
on its employees and our need to control for the possibility
of idiosyncratic daily and weekly variations in employee
schedules. The three-month gap between phases was chosen
for two reasons. First, work at OpenCo1’s global head-
quarters followed quarterly cycles, so a three-month gap
allowed us to conduct the two data-collection phases at the
same point in the quarter. Second, it allowed just over two
months of adjustment after the move, enough for people to
have settled into their new environment but not so much
that the work they did could have changed much.
The dataset included 96 778 F2F interactions, 84 026 emails
(18 748 sent, 55012 received, 9755 received by cc and 511
received by bcc) and 25 691 IMs (consisting of 221 426 words).
The most straightforward and conservative empirical strategy
for analysing the intervention was to simply aggregate and
then compare pre-intervention and post-intervention volumes:
Yit ¼
a
þð
b
1Postit ÞþXperson fixed effects þ1it :ð2:1Þ
Y
it
, the dependent variable, is the amount of interaction—F2F
or electronic—where ‘i’ is the individual in question and ‘t’ is
the phase (pre- or post-redesign). Post
it
is an indicator variable
that equals 1 if the interaction occurred after the redesign. The
main estimation used ordinary least-squares (OLS) regressions
with person fixed effects, although all results were robust to
the exclusion of person fixed effects. Standard errors were cor-
rected for autocorrelation and clustered by individual [60]. If
the redesign increased F2F interaction, we should see a posi-
tive and significant
b
1
— the coefficient reported in the ‘Post’
column of table 1—when Y
it
is F2F interaction (the first row
of table 1). More generally, in table 1, the effect on a particular
kind of interaction due to the transition to more open architec-
ture is reported in the ‘post’ column, where a negative number
indicates reduced interaction and a positive number indicates
increased interaction.
(a) Study 1 results
(i) Volume of interaction
Although OpenCo1’s primary purpose in opening up the
space had been to increase F2F interactions, the 52 participants
now spent 72% less time interacting F2F. Prior to the redesign,
they accumulated 5266 min of interaction over 15 days, or
roughly 5.8 h of F2F interaction per person per day. After the
redesign, those same people accumulated only 1492 min of
interaction over 15 days, or roughly 1.7 h per person per day.
Even though everyone on the floor could see everyone else
all the time (or perhaps because they could), virtual interaction
replaced F2F interaction in the newly boundaryless space.
After the redesign, participants collectively sent 56% (66)
more emails to other participants over 15 days, received 20%
(78) more emails from other participants, and were cc’d
on 41% (27) more emails from other participants. (For the
received and cc’d volumes, emails sent are counted once for
each recipient.) Bcc: activity, which was low in volume and
limited to a small subset of individuals, did not significantly
change. IM message activity increased by 67% (99 more mess-
ages) and words sent by IM increased by 75% (850 more
words). Thus—to restate more precisely—in boundaryless
space, electronic interaction replaced F2F interaction.
(ii) Performance outcome
Should we be concerned about these effects? One indication
of the meaningfulness of this shift in behaviour was its
effect on performance. In an internal and confidential
management review, OpenCo1 executives reported to us
qualitatively that productivity, as defined by the metrics
used by their internal performance management system,
had declined after the redesign to eliminate spatial bound-
aries. Consistent with research on the impact of a decline in
media richness on productivity [54,55] and on the particular
challenges of email [61], it is not necessarily surprising that
productivity declined due to a substitution of email for F2F
interaction. What is surprising is that more open, transparent
architecture prompted such a substitution.
3. Study 2
Given the findings from Study 1, another organization was
recruited to further this research. Our goal was to conduct a
conceptual replication of the first study with a longer time
window. This second empirical study was also a quasi-field
experiment at a Fortune 500 multinational and was conducted
at the global headquarters of OpenCo2.
2
At the time of the
study, OpenCo2 was in the process of a multi-year head-
quarters redesign, which—as in Study 1—involved a
transformation from assigned seats in cubicles to similarly
assigned seats in an open office design, with large rooms of
desks and monitors and no dividers between people’s desks.
We again collected F2F data using sociometric badges
and email data from company servers, this time for 100
employees from a single floor, which was roughly 45% of
the employees on that floor. As in Study 1, data were
microphone
infrared
acceleromete
r
bluetooth
Figure 1. Sociometric badge. (Online version in colour.)
rstb.royalsocietypublishing.org Phil. Trans. R. Soc. B 373: 20170239
3

collected in two phases: for eight weeks starting three months
prior to the redesign of this particular floor and for eight
weeks starting two months after the redesign. But for this
study, we also collected detailed data on the participants;
namely, three employee attributes—gender, team assignment
and role—and one architectural attribute—desk location. In
the first phase, desks were in cubicles, so seats were roughly
2 m apart and directly adjacent to one another. In the second
phase, seats still lay roughly 2 m apart and directly adjacent
to one another, but were grouped at undivided and unwalled
tables of six to eight. Seat location allowed us to calculate the
physical distance between dyads of employee workstations
before and after the redesign, such that we could include
physical distance, as well as the other employee attributes, as
control variables. The OpenCo2 dataset included 63 363 min
of F2F interaction and 25 553 emails, all generated by
1830 dyads—those with interaction—of the 100 employees
involved. Mindful of Study 1’s consistent results across
multiple forms of electronic communication, Study 2 only
collected email data to measure electronic interaction. The
empirical strategy was similar:
Yjt ¼
a
þð
b
1PostjtÞþXdyad fixed effects þ1jt ð3:1Þ
and
Yjt ¼
a
þð
b
1PostjtÞþð
b
2Physical DistancejtÞþ
ð
b
3GenderjÞþð
b
4TeamjÞþð
b
5RolejÞþ1jt:ð3:2Þ
In equation (3.1), as in equation (2.1), Y
jt
, the dependent
variable, is the amount of interaction, F2F or electronic. How-
ever, because the physical-distance control variable was
dyadic, Y
jt
must also be specific to a particular dyad ‘j’
(rather than to an individual ‘i’, as in Study 1). As in
Study 1, ‘t’ refers to the phase (pre- or post-redesign). Post
jt
is an indicator variable that equals 1 if the dyadic interaction
occurred after the redesign. In equation (3.2), we investigate
specific control variables—characteristics of each dyad—
rather than just dyad fixed effects. Physical Distance
jt
is the
distance between the dyad’s workstations, measured as the
shortest walking path (in metres). Gender
j
, Team
j
and Role
j
are indicator variables that equal 1 if the two individuals in
the dyad were of the same gender, on the same team, or in
the same role, and equal 0 otherwise. The main estimation
used OLS regressions with either dyad fixed effects (2) or
distance, gender, team and role controls (3). Standard errors
of the coefficients were corrected for autocorrelation and clus-
tered by dyad [60]. If the redesign increased F2F interaction,
we should see a positive and significant
b
1
—the coefficient
reported in the ‘post’ row of table 2—when Y
it
is F2F
interaction. More generally, in table 2, we report the effect of
the transition to open architecture on particular types of inter-
action in the ‘post’ row, where a negative number indicates
reduced interaction and a positive number indicates increased
interaction. For the control variables, we report the coefficient
for the entire sample without regard to whether the office
architecture involved cubicles or open spaces, as our purpose
in including those variables is to remove gender, team and
role effects from the variable of interest, Post. For example,
the significant and positive coefficient for Team means that
those on the same team communicated more than those on
different teams (for both cubicles and open spaces), and the
significant and positive coefficient for Role means that those
in the same role communicated more than those in different
roles (for both cubicles and open spaces).
Table 1. Impact of open offices on interaction at OpenCo1. Models are OLS with person fixed effects and with standard errors clustered by individual in
parentheses. Coefficients represent minutes of face-to-face (F2F) interaction, number of email messages or IM messages, or number of words in IM between a
member of the study and all others at work during the period of the study. *p,0.05; **p,0.01; ***p,0.001.
type of interaction post constant obs.
volume:
F2F interaction
minutes of F2F interaction time (indicated by proximity of individuals
combined with spoken words by at least one party)
23774*
(1607)
5266***
(1136)
104
email interaction (sent)
total number of emails sent by participants to other participants
66***
(19)
118***
(13)
104
email interaction (received: To)
total number of emails received by participants from other participants, where
the recipient appeared in the ‘To:’ field
78***
(21)
394***
(15)
104
email interaction (received: cc)
total number of emails received by participants from other participants, where
the recipient appeared in the ‘Cc:’ field
27***
(8)
66***
(6)
104
email interaction (received: bcc)
total number of emails received by participants from other participants, where
the recipient appeared in the ‘Bcc:’ field
21
(1)
6***
(1)
104
IM interaction (number of messages)
total number of instant messages sent by participants to other participants
99**
(30)
147***
(21)
104
IM interaction (cumulative word count of messages)
total number of words sent in instant messages by participants to other participants
850***
(218)
1140***
(154)
104
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4

(a) Study 2 results
(i) Volume of interactions
As a result of the redesign, 643 dyads decreased their F2F
interaction and 141 dyads increased it. At the same time,
222 dyads decreased their email interaction and 374 dyads
increased it. Like OpenCo1, OpenCo2 had hoped, by opening
up the space, to increase F2F interactions, but the results did
not bear this out. The 100 employees—or 1830 dyads—we
tracked spent between 67% (Model 1, 12.79/17.99) and 71%
(Model 2, 9.81/14.63) less time interacting F2F. Instead, they
emailed each other between 22% (Model 3, 1.24/5.75) and
50% (Model 4, 1.54/3.07) more.
As one might suspect, dyads on the same team or with the
same role communicated more, both F2F and by email, relative
to dyads on different teams or in different roles. Gender, in con-
trast, had no significant effect on the volume of either form of
interaction. Physical distance did show a small inverse effect
on F2F interaction (Model 2): the nearer the two workstations,
the more F2F interaction. This effect was notable both for its
small size relative to the size of the effect of the open office
and for the fact that it was limited to F2F interaction (not
email). We investigate this in further detail next.
(ii) The effect of physical distance on F2F versus email
Model 2 of table 2 shows that the effect of physical distance on
F2F interaction is small—and the effect on email insignificant—
relative to that of openness. The relatively small effect of
distance on F2F interaction was surprising given that repeated
studies have shown that people talk more to those who are
physically closer to them [62,63]. When others are physically
proximate, it is easier to be aware of them [64], start conversa-
tions with them [64,65], unexpectedly encounter or overhear
them [66], and manage their impressions of our collaborative
work behaviour [67]. Nonetheless, our review of these prior
studies found none that directly measured interaction volumes,
and thus perhaps—while present—the effect of distance on F2F
interaction may be far more minimal than previously thought.
Table 2, however, does not allow us to compare the rela-
tive effects of physical distance on F2F interaction and on
email interaction. To do so, we used a latent space model
called the Latent Position Clustering Model [68] to take into
account clustering and to control for other covariates. We
find that physical distance affected F2F interaction twice
as much as it did email interaction. As a robustness check,
we used several machine learning algorithms, such as a
Random Forest, to see if changes in F2F networks prompted
by changes in physical distance predicted changes in email
networks. Across all models, we find that F2F networks
and email networks respond very differently to changes in
the built environment, with changes in one type of network
failing to predict changes in the other.
This variance between the adaptation of F2F and elec-
tronic networks in response to a change in physical space is
an important finding for future research on collaboration and
collective intelligence. In several notable cases, past research
has relied on email alone [69,70] to study topics ranging from
the Enron debacle to the relationship between office layout
and interaction, basing claims about F2F interaction on
findings from electronic interaction data. Our finding that
changes in workplace design affect electronic and F2F
interaction networks differently (and, on some measures, in
opposite directions) should make future researchers wary of
using one network as a proxy for the other.
4. Discussion
We began with a specific research question: does removing
spatial boundaries at work to create open, unbounded offices
Table 2. Impact of open offices on interaction at OpenCo2. Models are OLS with standard errors clustered by dyad in parentheses. Models 1 and 3 include dyad
fixed effects. In Models 1 and 2, coefficients represent minutes of F2F interaction between a particular dyad during the period of the study. In Models 3 and 4,
coefficients represent number of emails between a particular dyad during the period of the study. *p,0.05, **p,0.01, ***p,0.001.
type of interaction
123 4
F2F with fixed
effects
F2F with
controls
email with fixed
effects
email with
controls
change in volume:
post
0 if before redesign, 1 if after
212.79***
(1.39)
29.81***
(1.27)
1.24***
(0.31)
1.54***
(0.32)
physical distance
walking distance (in metres) between desks
20.01
(0.02)
20.07***
(0.02)
20.00
(0.01)
20.01
(0.01)
gender
0 if different genders, 1 if same
2.08
(1.37)
0.08
(1.02)
team
0 if different teams, 1 if same
41.02***
(2.53)
33.86***
(1.80)
role
0 if different roles, 1 if same
9.59***
(1.91)
3.12*
(1.42)
constant 17.99***
(1.27)
14.63***
(1.47)
5.75***
(0.28)
3.07***
(0.85)
observations 3660 3660 3660 3660
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5

increase interaction? Our two empirical field studies were
consistent in their answer: open, unbounded offices reduce
F2F interaction with a magnitude, in these contexts, of about
70%. Electronic interaction takes up at least some of the slack,
increasing by roughly 20% to 50% (as measured by ‘To:’
received email).
Many organizations, like our two field sites, transform their
office architectures into open spaces with the intention of
creating more F2F interaction and thus a more vibrant work
environment. What they often get—as captured by a steady
stream of news articles professing the death of the open office
[71– 73]—is an open expanse of proximal employees choosing
to isolate themselves as best they can (e.g. by wearing large
headphones [74]) while appearing to be as busy as possible
(since everyone can see them). Recent studies [75] and earlier
research [40,41,43,47,48] have investigated the self-reported dis-
satisfaction of employeesin openoffices, but to our knowledge,
we are the first to empirically study the direct behavioural
impact of open office space on the volume of F2F and electronic
interaction. Our results support three cautionary tales.
First, transitions to open office architecture do not
necessarily promote open interaction. Consistent with the
fundamental human desire for privacy [76] and prior evi-
dence that privacy may increase productivity [32,45], when
office architecture makes everyone more observable or ‘trans-
parent’, it can dampen F2F interaction, as employees find
other strategies to preserve their privacy; for example, by
choosing a different channel through which to communicate
[39]. Rather than have an F2F interaction in front of a large
audience of peers, an employee might look around, see that
a particular person is at his or her desk, and send an email.
The second caution relates to the impact of a transition to
open office architecture on collective intelligence. We still
have much to learn about how collective intelligence works
[77], as we borrow from and distinguish parallel work on
swarm intelligence among social insects and some other ani-
mals. While the earliest work assumed open spaces would
promote collective intelligence among humans, our findings
support more recent work that has begun to suggest otherwise.
Kao & Couzin, in modelling the presence of multiple cues and
the possibility of observing them, find that intermediate (rather
than maximal) levels of cues produce higher levels of collective
intelligence [78]. We see a close relationship between our find-
ing that open, ‘transparent’ offices may be overstimulating
and thus decrease organizational productivity and Kao &
Couzin’s demonstration that finitely bounded, and often
small, group size maximizes decision accuracy in complex,
realistic environments. Similarly, recent collective intelligence
work suggests that, like our open offices, too much information
from social data can be problematic, partly because of
challenges focusing attention [74,79], but also for reasons that
extend to more general functions of human cognition. For
example, by connecting human cognition and collective intelli-
gence with the behaviour of eusocial insects, Toyokawa et al.
found that richness in social information was detrimental to
collective intelligence outcomes, with performance being best
when social learning opportunities were constrained [80].
Similarly, in a study involving human subjects, Bernstein et al.
found that intermittent rather than constant social influence
produced the best performance among humans collectively
engaged in complex problem solving [81]. As we are reminded
in Hight & Perry’s article on collective intelligence and architec-
tural design, ‘collective intelligence is not simply technical, but
also explicitly social, political, and by extension, professional’
[2, p. 6]. Our findings empirically reinforce their caution that
the relationship between architectural design and collective
intelligence extends beyond technical considerations.
The third caution is that transitions to open office archi-
tecture can have different effects on different channels of
interaction. In our studies, openness decreased F2F inter-
action with an associated increase in email interaction. In
the digital age, employees can choose from multiple channels
of interaction [54] and a change in office architecture may
affect that choice.
Complementing prior research on media richness sug-
gesting that substituting email for F2F interaction can lower
productivity [53], our studies highlight two other conse-
quences. First, because fundamentally different mechanisms
drive F2F and email interaction, the physical propinquity that
redesigned offices seek to achieve has a direct effect only on
F2F interaction, not on email, yet drives interaction from F2F
to email. Adopting open offices, therefore, appears to have
the perverse outcome of reducing rather than increasing pro-
ductive interaction. Second, F2F and email networks differ.
Although prior studies have investigated one or the other
[56,82], none has empirically linked F2F and email network
interaction to discern how good a proxy one is for the other.
We find that they are poor proxies for each other. Therefore,
an intervention that redirects interaction from one network to
another, like the open office redesigns studied here, not only
changes the channel of interaction, but also skews whom a
person interacts with. That can have profound consequences
for how—and how productively—work gets done.
In summary, because the antecedents of human interaction
at work go beyond proximity and visibility, the effects of
open office architecture on collaboration are not as simple as
previously thought. While it is possible to bring chemical
substances together under specific conditions of temperature
and pressure to form the desired compound, more factors
seem to be at work in achieving a similar effect with humans.
Until we understand those factors, we may be surprised to
find a reduction in F2F collaboration at work even as we architect
transparent, open spaces intended to increase it.
Data accessibility. We are unable to provide open access to our data owing
to their sensitive nature and the nondisclosure and confidentiality
agreements that surround them. Please contact the corresponding
author for more information.
Authors’ contributions. E.S.B. carried out all work on Study 1 and drafted
the manuscript. S.T. carried out all work on Study 2 and helped draft
the manuscript. Both authors gave final approval for publication.
Competing interests. We declare we have no competing interests.
Funding. Funding for these studies was provided by the Division
of Research and Faculty Development at the Harvard Business School.
Acknowledgements. The authors thank Editor Steve Fiore and two anon-
ymous reviewers for developmental, insightful and encouraging
comments throughout the review process, as well as Senior Commis-
sioning Editor Helen Eaton for her guidance. We also thank Ben
Waber, Taemie Kim, Laura Freeman and the rest of the team at
Humanyze, without whom we would have been unable to collect
the unique datasets underlying these studies.
Endnotes
1
OpenCo1 is a pseudonym for the corporation’s real name, which has
been anonymized.
2
OpenCo2 is a pseudonym for the corporation’s real name, which has
been anonymized.
rstb.royalsocietypublishing.org Phil. Trans. R. Soc. B 373: 20170239
6

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