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Networks, Teams and Space June 2015
Team Affiliation and Spatial Networks
A Comparative Analysis of Organisation, Space an...
Networks, Teams and Space June 2015
Introduction
“How to Collaborate”
British Airways Business
Life Magazine
March 2015
Networks, Teams and Space June 2015
Introduction
Advertising Agency, Frankfurt
Very frequent face-to-face encounter
(sever...
Networks, Teams and Space June 2015
Introduction
ORGANISATION
Intra-organisational
networks of face-to-face
interaction in...
Networks, Teams and Space June 2015
Introduction
ORGANISATION
Attribute:
Team affiliation
E-I index:
Comparing numbers of ...
Networks, Teams and Space June 2015
Introduction
WEEKLY INTERACTION DAILY INTERACTION
organisation team internal floor int...
Networks, Teams and Space June 2015
Research Problem
Organisation Structure A
100 staff, N=10 teams of S=10
50
50
10
10
10...
Networks, Teams and Space June 2015
Case Study Overview
21 knowledge-intensive organisations across different sectors (cre...
Networks, Teams and Space June 2015
Methodology
SNA:
Online survey of each organisation; survey
distributed to all staff m...
Networks, Teams and Space June 2015
Results
Calculation and analysis of various metrics for each organisation (using UCINE...
Networks, Teams and Space June 2015
Results
% INT
[team]
INT-EXT pref
[team]
Internalisation
[team]
Yules Q
[team]
% INT
[...
Networks, Teams and Space June 2015
Results
Two metrics seem (relatively) robust: %INT and Yule’s Q
→ calculating values f...
Networks, Teams and Space June 2015
Results – Analysing
single cases
CASE 7
Post prod
house
BENCHMARK
all org.
CASE 9
larg...
Networks, Teams and Space June 2015
Results – Exploring the Impact of Spatial Structure
Correlation between Yule’s Q [team...
Networks, Teams and Space June 2015
Outlook – Where to go from here?
• Growing the data and looking at different metrics
o...
Networks, Teams and Space June 2015
Dr Kerstin Sailer
Lecturer in Complex Buildings
Bartlett School of Architecture
Univer...
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Team Affiliation and Spatial Networks

Presentation at Sunbelt XXXV conference, International Network of Social Network Analysis, 25 June 2015

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Team Affiliation and Spatial Networks

  1. 1. Networks, Teams and Space June 2015 Team Affiliation and Spatial Networks A Comparative Analysis of Organisation, Space and Network Structure Dr Kerstin Sailer Space Syntax Laboratory, Bartlett School of Architecture, University College London, UK XXXV Sunbelt Conference of the International Network for Social Network Analysis, 23-28 June 2015 @kerstinsailer
  2. 2. Networks, Teams and Space June 2015 Introduction “How to Collaborate” British Airways Business Life Magazine March 2015
  3. 3. Networks, Teams and Space June 2015 Introduction Advertising Agency, Frankfurt Very frequent face-to-face encounter (several times a week) Colour of nodes: Teams Shape of nodes: Floor To which degree do organisational and spatial barriers hinder interactions?
  4. 4. Networks, Teams and Space June 2015 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
  5. 5. Networks, Teams and Space June 2015 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
  6. 6. Networks, Teams and Space June 2015 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?
  7. 7. Networks, Teams and Space June 2015 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?
  8. 8. Networks, Teams and Space June 2015 Case Study Overview 21 knowledge-intensive organisations across different sectors (creative industry, 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 Ranges: Organisation size: 67 ↔ 1377 staff Numbers of teams: 5 ↔ 83 teams Average team size: 8.5 ↔ 32.5 staff Office building: 1 ↔ 18 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
  9. 9. Networks, Teams and Space June 2015 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; Analysis of network of strong ties (daily encounter); 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 using VGA on eye level (visibility) [average mean depth];
  10. 10. Networks, Teams and Space June 2015 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 %𝐸𝑋𝑇 𝑒𝑥𝑝𝑒𝑐𝑡𝑒𝑑 %𝐼𝑁𝑇 𝑒𝑥𝑝𝑒𝑐𝑡𝑒𝑑 as per E-I index routine (INT-EXT pref) • Degree of internalisation, i.e. 𝐼𝐿 𝑁 𝑆 𝑎𝑣 , where IL is the total number of internal links, N is the total number of nodes in the network and 𝑆 𝑎𝑣 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)
  11. 11. Networks, Teams and Space June 2015 Results % INT [team] INT-EXT pref [team] Internalisation [team] Yules Q [team] % INT [floor] Yules Q [floor] # Staff 0.007 0.242* 0.114 0.168 0.088 0.059 # Teams 0.012 0.344** 0.003 0.278* 0.056 0.067 Av team size 0.012 0.114 0.703** 0.164 0.033 0.001 # Ties 0.002 0.292* 0.040 0.220* 0.080 0.067 Density 0.050 0.074 0.159 0.040 0.219* 0.188 Testing correlation between metrics and standard descriptors of organisation structure (number of staff, number of teams, average team size) and network structure (number of ties, density) DAILY INTERACTION FREQUENCY R2 values; significance at p<0.05 marked with * and p<0.01 with ** Note: correlation marked in purple is driven by one outlier;
  12. 12. Networks, Teams and Space June 2015 Results Two metrics seem (relatively) robust: %INT and Yule’s Q → calculating values for both attributes (team, floor) for daily interaction → plotting range of cases
  13. 13. Networks, Teams and Space June 2015 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
  14. 14. Networks, Teams and Space June 2015 Results – Exploring the Impact of Spatial Structure Correlation between Yule’s Q [team] and Maximum Mean Depth (R2=0.468**, p<0.003) (if outlier case 3 is excluded) Case14–Strategicvisibilityinoffice(closenesscentrality) → Offices with higher levels of maximum visibility tend to host more heterophilous interactions, i.e. allow more interactions between colleagues of different teams Integrated Segregated
  15. 15. Networks, Teams and Space June 2015 Outlook – Where to go from here? • Growing the data and looking at different metrics o Calculate %INT and Yule’s Q for weaker ties of weekly encounter? o Calculate %INT and Yule’s Q for usefulness ties? • Control for work flows and organisational purpose (the need to collaborate across team boundaries)… but how? o Calculate average In-Degree of usefulness as control variable? o Group by industry? o Break down analysis to team level? o Compare teams with similar tasks, e.g. Sales or Marketing? • Bring space back in more systematically o Are smaller or larger floor plates better? No obvious correlation… o Does the provision / distribution of attractors make a difference? o Does the average integration of a team workspace make a difference?
  16. 16. Networks, Teams and Space June 2015 Dr Kerstin Sailer Lecturer in Complex Buildings Bartlett School of Architecture University College London 140 Hampstead Road London NW1 2BX United Kingdom Thank you! k.sailer@ucl.ac.uk @kerstinsailer http://spaceandorganisation.wordpress.com/ http://tinyurl.com/kerstinsailer

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