Intra-Organisational Networks - Spatially Embedded

Intra-Organisational Networks – Spatially Embedded June 2017
Intra-Organisational Networks –
Spatially Embedded
Dr Kerstin Sailer
Space Syntax Laboratory,
Bartlett School of Architecture,
University College London, UK
SSNAR, Statistical Social Network Analysis in R, Summer School 2017, Birkbeck University, 22nd June 2017
@kerstinsailer

Introduction
“How to Collaborate”
British Airways Business
Life Magazine
March 2015

Introduction
Advertising Agency, Frankfurt
Very frequent face-to-face encounter
(several times a week)
Colour of nodes: Teams
Shape of nodes: Floor
How are organisational
networks of interaction and
collaboration embedded
spatially?

Introduction – Social Capital and Team Performance
Strength of
weak ties
[Granovetter]
Ties across
teams &
brokerage
[Burt]
Strength of
strong ties
[Krackhardt] [Hansen]
[De Montjoye]
Within team ties
[Cummings & Cross]
Serial
closure
[Burt]

The Role of Workplace Design in Interaction and Collaboration
Floors as barriers: Being located on
different floors of an office
building forms a significant barrier
to frequent face-to-face
communication (Allen and Fustfeld 1975)
Small distances matter, as
being on a different floor can have
a dramatic effect on team
performance, which drops with
dispersion of team members
(Siebdrat, Hoegl and Ernst 2009)
Propinquity effect: Co-workers with desks located closer to each other have a higher
probability of frequent face-to-face communication (Allen and Fustfeld 1975)
Colleagues with a higher degree of path overlap in a research laboratory collaborate
more often (Kabo et al. 2015)

The Role of Workplace Design
FACE TO FACE
INTERACTION
NETWORKS
construct affects
Layout of office building as affordance to face-to-face interaction networks

A Brief Introduction to Space Syntax
Conceived in 1970’s at UCL by Bill Hiller, Julienne Hanson and colleagues as theory to think
about relationship between spatial structure and social life
Is there any relationship between the spatial design of cities or buildings, and the way they
work socially?

A Brief Introduction to Space Syntax
(Hillier 1996)
Spatial configuration: The way in which spatial elements are
put together to form an interconnected system of spaces
(Sailer 2010)

Three examples from my work
Effects of
openness
Effects of floors
as barriers
Effects of
distance

Effects of distance

Ways of measuring distance: actual cost (metric), perceived distance (axial), cognitive
distance (angular)
Horizontal distances Vertical distances
Effects of Distance on Interaction Networks in Offices

Exponential Random Graph
Modelling / P* Models:
Probability of a current graph (daily
interaction network) as a function of
network properties (mutuality,
transitivity) and other independent
variables (spatial depth networks)
D=27.3m
D=5.8m
Own illustrations after: Knoke and Yang (2008)

Systematic statistical test of network dependence between spatial network and social
network using Exponential Random Graph Modelling (in 4 different cases)
Dependent Variable:
• Interaction frequency: dichotomous network of daily interaction among agents
Control Variables:
• Network structures: edges, mutuality, geometrically weighted edgewise shared
partners (gwesp)
• Organisational and social control mechanisms: self-reported usefulness, team
affiliation, floor of office or desk of agents
→ BASE MODEL
Independent Variable
• Depth networks of routes between agents (axial topology, segment topology, angle
change, metric distance, euclidean distance)

ERGM Results
Office 1
M1 M2 M3 M4 M5 M6 M7 M8
Edges -4.75 -4.78 -5.81 -4.97 -5.74 -4.97 -5.92 -5.96
Mutual Insig Insig Insig Insig Insig Insig Insig Insig
Gwesp 1.59 1.55 1.40 1.47 1.46 1.45 1.33 1.29
Usefulness 1.67 1.72 1.30 1.39
Team 1.07 0.95 1.04 0.91
Floor 1.66 1.60 1.58 1.63
AIC 879.79 858.54 844.83 839.64 838.36 830.93 807.63 789.49
Table 1: Base models for 2005 University
Control Model Spatial Models
Terms Edges Gwesp Usefulness Team Floor Metric Angular
Change
Seg Topo Axial
Topo
Euclidean
Estim
ate
-5.9590 1.2925 1.3929 0.9123 1.6344 -0.0584 -0.1341 -0.1447 -
0.0229
-0.0595
Std.
Error
0.0586 0.0251 0.1591 0.0521 0.0274 0.0017 0.5741 0.0204 0.0064 0.0014
p-
value
<<0.0001 <<0.0001 <<0.0001 <<0.0001 <<0.0001 <<0.0001 <<0.0001 <<0.0001 0.0004 << 0.0001
AIC 789.49 789.49 789.49 789.49 789.49 760.08 793.66 780.7 819.28 780.2
Table 2: ERGM for 2005 University

ERGM Results
Office 2
M1 M2 M3 M4 M5 M6 M7 M8 M9
Edges -3.66 -3.46 -3.69 -4.54 -4.82 -4.66 -4.84 -4.64 -5.38
Mutual 2.70 2.85 2.70 2.13 1.97 2.10 1.97 0.81 0.52
Gwesp Insig Insig Insig 1.28 1.04
Usefulness 1.99 1.58 2.00 1.63 1.77 1.41
Team 0.86 0.63 0.83 0.59 0.62
Floor 2.15 2.11 2.14 2.12 1.18
AIC 1339.2 1325.3 1312.3 1232.8 1210.0 1194.4 1184.6 952.87 915.18
Table 3: Base models for 2008 University
Table 4: ERGM for 2008 University
Terms Edges Mutual Gwesp Useful-
ness
Team Floor Metric Angular
Change
Seg
Topo
Axial Topo Euclidean
Estim
ate
-5.3821 0.5204 1.0445 1.4111 0.618 1.1838 -0.0117 -0.0912 -0.0539 -0.0437 -0.0089
Std.
Error
0.0684 0.0334 0.0446 0.1655 0.0319 0.0096 0.0069 0.0211 0.0015 0.0005 0.0033
p-
value
<<
0.0001
<<
0.0001
<<
0.0001
<<
0.0001
<<
0.0001
<<
0.0001
<<
0.0001
<< 0.0001 <<
0.0001
<< 0.0001 0.007
AIC 915.18 915.18 915.18 915.18 915.18 915.18 864.79 890.28 897.1 907.48 902.88

ERGM Results
Office 3
M1 M2 M3 M4 M5 M6 M7
Edges -4.82 -4.78 -4.77 -4.90 -4.98 -4.95 -4.87
Mutual 2.46 2.47 2.46 2.17 2.17 2.17 2.17
Gwesp 0.99 0.98 0.99 0.91 0.91 0.91 0.91
Usefulness 2.20 2.21 2.21 2.21
Team 0.16 0.16 0.13 0.13
Floor 0.11 0.11 0.16 0.16
AIC 2241.8 2240.5 2240.1 2073.1 2070.5 2070.0 2067.7
Table 5: Base models for Research Institute
Table 6: ERGM for Research Institute
Terms Edges Mutual Gwesp Usefulness Metric Angular
Change
Seg Topo Axial Topo Euclidean
Estimate -4.8674 2.1702 0.9085 2.2064 -0.0156 -0.1662 -0.0352 -0.1313 -0.0022
Std. Error 0.0338 0.0798 0.0112 0.0773 0.0007 0.0036 0.0014 0.0027 0.0002
p-value << 0.0001 << 0.0001 << 0.0001 << 0.0001 << 0.0001 << 0.0001 0.0001 << 0.0001 << 0.0001
AIC 2067.7 2067.7 2067.7 2067.7 2010.1 2064.2 2025.6 2035.6 2069.9

ERGM Results
Office 4
M1 M2 M3 M4 M5 M6 M7 M8 M9
Edges -4.89 -4.98 -4.98 -5.00 -5.00 -5.12 -5.14 -5.11 -5.12
Mutual -1.16 -1.16 -1.16 -1.16 -1.20 -1.20 -1.20 -1.20
Gwesp 1.38 1.49 1.49 1.49 1.49 1.40 1.40 1.40 1.40
Usefulness 3.56 3.56 0.12 3.55
Team Insig Insig -0.09 0.03
Floor -0.06 -0.06 0.05 0.05
AIC 3693.2 3639.2 3637.6 3637.5 3635.4 3211.0 3210.0 3209.6 3208.5
Table 7: Base models for Publisher
Table 8: ERGM for Publisher
Terms Edges Mutual Gwesp Usefulness Metric Angular
Change
Seg Topo Axial Topo Euclidean
Estimate -5.121 -1.1975 1.4004 3.5533 -0.0283 -0.1665 -0.0547 -0.1344 -0.0032
Std. Error 0.0712 0.0745 0.0211 0.1505 0.0003 0.0017 0.0006 0.0015 0.0012
p-value << 0.0001 << 0.0001 << 0.0001 << 0.0001 << 0.0001 << 0.0001 0.0001 << 0.0001 <<0.0001
AIC 3208.50 3208.50 3208.50 3208.50 3089.3 3078.1 3063.4 3059.4 3205.4

Summary of results of ERGM:
• Negative coefficient ‘edges’: transaction cost; utility-driven interactions;
• Positive coefficient ‘mutual’: reciprocity;
• Positive coefficient ‘gwesp’: transitivity in interaction patterns;
• Positive coefficient ‘usefulness’: agents more likely to interact daily with those deemed
useful;
• Positive coefficient ‘team’: agents tend to interact more with direct team colleagues;
• (Mostly) positive coefficient ‘floor’: agents tend to interact more with those co-located
on the same floor;
• Team and floor covariates do not always improve the model (Cases 3&4);
• All spatial coefficients significant and show better fit of model (lower AIC);
• Euclidean distances worst predictor, metric walking distance (cases 1-3) and axial
topology (case 4) best predictor;

Summary of results of ERGM
• Significant Space Syntax
measurements:
→ People are more likely to interact
with those who have desks in
close proximity to them
• Metric distance as best predictor
(cellular offices) versus axial
topology as best predictor (open-
plan)
Research Institute: cellular
office
Publisher: open-plan office

Effects
of
floors
as
barriers

Floors as barriers
ORGANISATION
Attribute:
Team affiliation
E-I index:
Comparing numbers of ties within
groups and between groups
(Krackhardt and Stern 1988)
Attribute: floor where
desk is located

Floors as barriers
University 2005
Building 2
Building 1 University 2008
Building 2
Building 1

Floors as barriers
WEEKLY INTERACTION DAILY INTERACTION
organisation team internal floor internal team internal floor internal
University School 2005 42% 63% 65% 91%
University School 2008 47% 61% 54% 86%
Research Institute 48% 59% 64% 71%
Publisher C pre 32% 60% 37% 77%
Results for a small sample of organisations (based on earlier work first presented at 5th
UKSNA conference in 2009 and published in Sailer 2010):
(Based on E-I index calculations of face-to-face interaction networks)
→ But how do we control for intervening variables such as structure of an organisation?

Research Problem
Organisation Structure A
100 staff, N=10 teams of S=10
50
50
10
10
10
10
10
10
10
10
10
10
Organisation Structure B
100 staff, N=2 teams of S=50
Maximum number of internal and external ties vary depending on number and size of
subgroups (Krackhardt and Stern 1988)
E∗ = S2 𝑁 (𝑁−1)
2
and I∗ =
𝑁𝑆 (𝑆−1)
2
→ E*= 4500; I*= 450 → E*= 2500; I*= 2450
→ How can we compare across organisations and understand the degree of team
cohesion and structural embedding in the light of diverse organisational structures?

Effects of
openness

Effects of Openness
Two potential effects:
1 2 Negative effectPositive effect
• Greater levels of visibility might
create opportunities for encounter
and allow for bridging ties between
departments.
• Spatial variable: mean depth
(global visibility, closeness
centrality, shortest paths)
• Interaction network variable: Yule’s
Q by department
(
𝐼𝐿×𝑁𝐸𝐿−𝐸𝐿×𝑁𝐼𝐿
𝐼𝐿×𝑁𝐸𝐿+𝐸𝐿×𝑁𝐼𝐿
), where IL: internal links, EL:
external links, NIL: non-links internally; NEL: non-
links externally
• Greater levels of visibility might
distract people from their jobs and
reduce their willingness to
connect with others.
• Spatial variable: connectivity
(local visibility, degree centrality)
and mean size of floor plates
• Proportion of ties brokering to a
different department located on a
different floor [%DDDF]

Effects of Openness – Case Study Overview and Method
21 knowledge-intensive organisations across different sectors (creative agency, information
business, retail, legal, technology, media, NGO) in the UK, all studied separately between
2007 and 2015 as part of workplace consultancy undertaken by Spacelab
Method:
• Online survey of each organisation separately; each participant in each organisation to
name top 25 contacts and indicate frequency of face-to-face encounter;
• Analysis of network of strong ties (daily encounter);
• Spatial analysis of visibility relations using Space Syntax methods;
Organisation Size
0
200
400
600
800
1000
1200
1400

Effects of Openness – Positive Effects
Correlation between Yule’s Q [dep] and Maximum Mean Depth (R2=0.468**, p<0.003)
→ Offices with higher levels of maximum visibility tend to host more heterophilous
interactions, i.e. allow more interactions between colleagues in different departments
Int.Seg Outlier case will be excluded

Effects of Openness – Negative Effects
Correlation between proportion of DDDF ties and size of floor plate / average connectivity:
→ In offices with smaller floor plates and less local visibility,
people tend to have a higher proportion of frequent interactions
with those on different floors and in different departments
LargeSmall
R2=0.33** R2=0.25*

Effects of Openness – Solidarities
Two mechanisms for social cohesion between people (Hillier and Hanson 1984):
1. Sharing same local world and coming together in physical space (spatial solidarity);
2. Shared interests or goals, which may overcome / transverse boundaries of physical
space (transpatial solidarity → ‘homophily’);
Spatial Solidarity: ‘WHERE WE ARE’ Transpatial Solidarities: ‘WHO WE ARE’
The Guildhall, City of London

Do spatial and transpatial solidarities correspond? (Hillier and Hanson 1984)
Non-
Correspondence
Model
Correspondence
Model
Spatial and transpatial solidarities
do not correspond
Openness, equality, inclusivity and
global strength
correspond
Locally strong, exclusive and
hierarchical with pronounced boundaries
→ Strength of weak ties
(Granovetter 1973)

Non-
Correspondence
Model
do not correspond
Openness, equality, inclusivity and
global strength
Counterintuitively, less open and smaller floor plates create higher
affordances for organisational cohesion and overall social strength
(Granovetter 1973)
→ Strength of weak ties

Effects of Openness: The case of a Retailer HQ
1
2
3
4
5
6
7
8
9
10
11
Dept
PRE POST236 staff, 11 departments, 2
floors, open plan but highly
partitioned
268 staff, 11 departments, single
floor, very open layout

PRE POST

PRE
Average number of contacts
POST
7.5 7.0
Average number of contacts between department 4 and 5
0.7 0.1
Average number of contacts within department 18.2 6.1
DAILY INTERACTION

Conclusions
text
Effects of Openness: Positive and Negative Effects

Dr Kerstin Sailer
Reader in Social and Spatial Networks
Bartlett School of Architecture
University College London
22 Gordon Street
London WC1H 0QB
United Kingdom
Thank you!
k.sailer@ucl.ac.uk
@kerstinsailer
http://spaceandorganisation.wordpress.com/
http://tinyurl.com/UCL-KS

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