spatial and organisatinonal parameters in social network structures of teams
DESCRIPTION
Patterns of interaction within organisations are driven by job roles, reporting lines and organisational culture. In addition to these organisational parameters, it has been shown that the design and layout of workplaces plays an important role, too. For instance, spatial proximity between colleagues has a measurable impact on the frequency of face-to-face interaction. Thus both organisational dimensions as well as spatial configuration can be argued to jointly shape the structure of intra-organisational networks. Previous research on intra-organisational networks has mostly focused on investigating single cases or small samples. A comparative analysis across cases is interesting, since it provides an opportunity to understand how one case compares against others and whether results of one case can be inferred to other cases. It also allows mapping top and bottom ranges of phenomena, and understanding the strength and consistency of a relationship between a set of variables across cases. However, this also presents a challenging methodological problem: how is it possible to compare metrics between cases and how can these metrics be normalised? For instance, the E-I index measures group embedding according to an attribute of interest (e.g. team affiliation), yet the structure of an organisation (number and size of teams) will have an influence on the outcomes, too. Using a data set of 15 cases of different knowledge-based organisations (all studied separately from 2007-2013 with the same methodology of investigating social networks of interaction through self-reported surveys), this paper presents a larger scale cross-case analysis on the relationship between spatial configuration of a workplace and the emerging network structures of interaction. With a focus on team cohesion, clustering and embedding, it will provide a first sketch of different metrics and parameters (both organisational and spatial) to compare intra-organisational networks of interaction.TRANSCRIPT
Team Cohesion and Embedding Sailer, July 2014
Team Cohesion and Embedding – A Comparative
Analysis of Spatial and Organisational Parameters
Dr Kerstin Sailer
Bartlett School of Graduate Studies, University College London, UK
1st European Conference on Social Networks EUSN, 1-4 July 2014
@kerstinsailer
Team Cohesion and Embedding Sailer, July 2014
Introduction
ORGANISATION
Intra-organisational
networks of face-to-face
interaction in the workplace
Proximity
Shared
paths
Shared
workspace
Job roles
Reporting lines
Organisational
cultures
IMPACT IMPACT
Team Cohesion and Embedding Sailer, July 2014
Introduction
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
Team Cohesion and Embedding Sailer, July 2014
Introduction
WEEKLY INTERACTION DAILY INTERACTION
organisation team internal floor internal team internal floor internal
University School pre 42% 63% 65% 91%
University School post 47% 61% 54% 86%
Research Institute 48% 59% 64% 71%
Publisher C pre 32% 60% 37% 77%
Some results for a small sample of organisations (based on earlier work 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?
Team Cohesion and Embedding Sailer, July 2014
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?
Team Cohesion and Embedding Sailer, July 2014
Case Study Overview
16 knowledge-intensive organisations across different sectors (creative industry, information
business, retail, legal, software, media, NGO) in the UK, all studied separately between
2007 and 2014 as part of workplace consultancy undertaken by Spacelab Architects
Ranges:
Organisation size: 67 ↔ 1377 staff
Numbers of teams: 5 ↔ 51 teams
Average team size: 9.4 ↔ 32.5 staff
Office building: 1 ↔ 12 floors
Average size of floor plate: 200 ↔ 2800 sqm
Organisation Size
Average Team Size
0
200
400
600
800
1000
1200
1400
0.0
5.0
10.0
15.0
20.0
25.0
30.0
35.0
Team Cohesion and Embedding Sailer, July 2014
Methodology
SNA:
Online survey of each organisation; survey distributed to all staff
members; return quote: 49% (lowest) to 90% (highest);
Asked each participant to name top 25 contacts and indicate
frequency of face-to-face encounter and usefulness;
Analysis of network of strong ties (daily encounter, extremely useful);
Network attributes: team affiliation, floor where desk is
Calculating E-I index, Expected E-I index, Yule’s Q
Spatial Analysis:
Anaysis of spatial configuration: calculating levels
of spatial overall closeness centrality in the office
building using Space Syntax methods
[average mean depth of all paths];
Team Cohesion and Embedding Sailer, July 2014
Results
Calculation and analysis of various metrics for each organisation (using UCINET):
• Percentage of internal links as calculated by E-I index routine (%INT)
• Internal – external preference, i.e.
%𝐸𝑋𝑇
%𝐼𝑁𝑇%𝐸𝑋𝑇𝑒𝑥𝑝𝑒𝑐𝑡𝑒𝑑
%𝐼𝑁𝑇𝑒𝑥𝑝𝑒𝑐𝑡𝑒𝑑
as per E-I index routine (INT-EXT pref)
• Degree of internalisation, i.e.
𝐼𝐿
𝑁
𝑆𝑎𝑣, where IL is the total number of internal links, N is
the total number of nodes in the network and 𝑆𝑎𝑣 is the average size of teams, as per E-
I index routine (INTERNALISATION)
• Yule’s Q as calculated by the Homophily routine and derived from the odds ratio, which
maps perfect homophily (+1) and perfect heterophily (-1) by 𝐼𝐿×𝑁𝐸𝐿−𝐸𝐿×𝑁𝐼𝐿
𝐼𝐿×𝑁𝐸𝐿+𝐸𝐿×𝑁𝐼𝐿, where IL is
the number of internal links, EL the number of external links, NIL the number of non-links
internally and NEL the number of non-links externally (Yule’s Q)
Team Cohesion and Embedding Sailer, July 2014
Results
→ independent of org size, av team
size, # of ties and network density
→ independent of org size, av team
size, # of ties and network density
→ correlates with # of ties (r=-0.517*,
p<0.04)
→ correlates with av team size
(r=-0.814**, p<0.000) and network
density (r=-0.523*, p<0.038)
%INT
INT-EXT pref
Internalisation
Yule’s Q
Team Cohesion and Embedding Sailer, July 2014
Results
Two metrics seem robust: %INT and Yule’s Q
→ calculating values for both attributes (team, floor)
→ plotting range of cases
Team Cohesion and Embedding Sailer, July 2014
Results – Analysing
single cases CASE 7
Post prod
house
BENCHMARK
all org.
CASE 9
large retail
organisation
Percentage of internal ties
[%INT]: depicts patterns of
interaction and degree to which
they span team boundaries and
reach across floors
Yule’s Q [team]: depicts degree
of organisational structure as a
barrier
Yule’s Q [floor]: depicts degree of
spatial structure as a barrier
Team Cohesion and Embedding Sailer, July 2014
Results – Exploring the Impact of Spatial Structure
Correlation between Yule’s Q [team] and Average Mean Depth
(r=0.624*, p<0.017) (if outlier case 3 is excluded)
0.820
0.840
0.860
0.880
0.900
0.920
0.940
0.960
0.980
1.000
0.000 2.000 4.000 6.000 8.000 10.000
Yu
le's
Q [
team
]
Average MD
Cas
e 14
– S
trat
egic
vis
ibili
ty in
offi
ce (
clos
enes
s ce
ntra
lity)
→ Offices with higher levels of overall visibility tend to
host more heterophilous interactions, i.e. allow more
interactions between colleagues of different teams Integrated Segregated
Team Cohesion and Embedding Sailer, July 2014
Outlook
Investigated a large-ish sample of organisations in order to compare across cases and
develop a benchmark of interaction patterns in intra-organisational networks
Important to understand and map ranges of phenomena
Useful in consulting organisations and applying research knowledge in real life examples
Provided a first sketch of a way to benchmark interaction patterns, team cohesion and
embedding across different organisations of different sizes and different structures
Highlighted the impact of organisational structures and spatial structure in shaping
interaction patterns
“The spatial dimension of our lives has
never been of greater practical and political
relevance than it is today.” (Edward Soja, 1996: 1)
Team Cohesion and Embedding Sailer, July 2014
Dr Kerstin Sailer
Lecturer in Complex Buildings
Bartlett School of Graduate Studies
University College London
132 Hampstead Road
London NW1 2BX
United Kingdom
Thank you!
@kerstinsailer
http://spaceandorganisation.wordpress.com/
http://tinyurl.com/UCL-KS