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Intra-Organisational Networks - Spatially Embedded

The document explores the relationship between intra-organizational networks and spatial embedding, emphasizing how workplace design affects collaboration and interaction. It discusses various models and empirical results relating to distance and interaction frequency, highlighting the importance of proximity for effective communication. Additionally, the study covers the impact of organizational structure, team affiliation, and transparency on collaboration dynamics within different office environments.

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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
Intra-Organisational Networks – Spatially Embedded June 2017 Introduction “How to Collaborate” British Airways Business Life Magazine March 2015
Intra-Organisational Networks – Spatially Embedded June 2017 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?
Intra-Organisational Networks – Spatially Embedded June 2017 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]
Intra-Organisational Networks – Spatially Embedded June 2017 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)
Intra-Organisational Networks – Spatially Embedded June 2017 The Role of Workplace Design FACE TO FACE INTERACTION NETWORKS construct affects Layout of office building as affordance to face-to-face interaction networks
Intra-Organisational Networks – Spatially Embedded June 2017 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?
Intra-Organisational Networks – Spatially Embedded June 2017 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)
Intra-Organisational Networks – Spatially Embedded June 2017 Three examples from my work Effects of openness Effects of floors as barriers Effects of distance
Intra-Organisational Networks – Spatially Embedded June 2017 Effects of distance
Intra-Organisational Networks – Spatially Embedded June 2017 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
Intra-Organisational Networks – Spatially Embedded June 2017 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 Effects of Distance on Interaction Networks in Offices Own illustrations after: Knoke and Yang (2008)
Intra-Organisational Networks – Spatially Embedded June 2017 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) Effects of Distance on Interaction Networks in Offices
Intra-Organisational Networks – Spatially Embedded June 2017 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 Effects of Distance on Interaction Networks in Offices
Intra-Organisational Networks – Spatially Embedded June 2017 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 Control Model Spatial Models 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 Effects of Distance on Interaction Networks in Offices
Intra-Organisational Networks – Spatially Embedded June 2017 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 Control Model Spatial Models 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 Effects of Distance on Interaction Networks in Offices
Intra-Organisational Networks – Spatially Embedded June 2017 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 Control Model Spatial Models 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 Effects of Distance on Interaction Networks in Offices
Intra-Organisational Networks – Spatially Embedded June 2017 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; Effects of Distance on Interaction Networks in Offices
Intra-Organisational Networks – Spatially Embedded June 2017 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 Distance on Interaction Networks in Offices
Intra-Organisational Networks – Spatially Embedded June 2017 Effects of floors as barriers
Intra-Organisational Networks – Spatially Embedded June 2017 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
Intra-Organisational Networks – Spatially Embedded June 2017 Floors as barriers University 2005 Building 2 Building 1 University 2008 Building 2 Building 1
Intra-Organisational Networks – Spatially Embedded June 2017 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?
Intra-Organisational Networks – Spatially Embedded June 2017 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?
Intra-Organisational Networks – Spatially Embedded June 2017 Effects of openness
Intra-Organisational Networks – Spatially Embedded June 2017 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]
Intra-Organisational Networks – Spatially Embedded June 2017 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
Intra-Organisational Networks – Spatially Embedded June 2017 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
Intra-Organisational Networks – Spatially Embedded June 2017 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*
Intra-Organisational Networks – Spatially Embedded June 2017 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
Intra-Organisational Networks – Spatially Embedded June 2017 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 Spatial and transpatial solidarities correspond Locally strong, exclusive and hierarchical with pronounced boundaries → Strength of weak ties (Granovetter 1973) Effects of Openness – Solidarities
Intra-Organisational Networks – Spatially Embedded June 2017 Non- Correspondence Model Spatial and transpatial solidarities 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 – Solidarities
Intra-Organisational Networks – Spatially Embedded June 2017 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
Intra-Organisational Networks – Spatially Embedded June 2017 Effects of Openness: The case of a Retailer HQ PRE POST
Intra-Organisational Networks – Spatially Embedded June 2017 Effects of Openness: The case of a Retailer HQ 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
Intra-Organisational Networks – Spatially Embedded June 2017 Conclusions text Effects of Openness: Positive and Negative Effects
Intra-Organisational Networks – Spatially Embedded June 2017 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
Intra-Organisational Networks – Spatially Embedded June 2017 References Allen, Thomas J. and Fustfeld, Alan R. (1975), 'Research laboratory architecture and the structuring of communications', R&D Management, 5 (2), 153-64. Burt, Ronald S. (2004), 'Structural Holes and Good Ideas', American Journal of Sociology, 110 (2), 349-99. Burt, Ronald S., Merluzzi, Jennifer L. , and Burrows, John G. (2013), 'Path dependent network advantage', Proceedings of the 2013 Conference on Computer Supported Cooperative Work (CSCW) (San Antonio, Texas, USA: ACM), 1-2. Cummings, Jonathon N. and Cross, Rob (2003), 'Structural properties of work groups and their consequences for performance', Social Networks, 25 (3), 197-210. De Montjoye, Yves-Alexandre, et al. (2014), 'The Strength of the Strongest Ties in Collaborative Problem Solving', Scientific Reports, 4. Granovetter, Mark S (1973), 'The Strength of Weak Ties', The American Journal of Sociology, 78 (6), 1360-80. Hansen, Morten T. (1999), 'The Search-Transfer Problem: The Role of Weak Ties in Sharing Knowledge across Organization Subunits', Administrative Science Quarterly, 44 (1), 82-111. Hillier, Bill (1996), Space is the machine (Cambridge: Cambridge University Press). Hillier, Bill and Hanson, Julienne (1984), The social logic of space (Cambridge: Cambridge University Press). Kabo, Felichism, et al. (2015), 'Shared Paths to the Lab: A Sociospatial Network Analysis of Collaboration', Environment and Behavior, 47 (1), 57-84. Knoke, David and Yang, Song (2008), Social network analysis, 2nd edition (Los Angeles: SAGE Publications). Krackhardt, David (1992), 'The Strength of Strong Ties: The Importance of Philos in Organizations', in N. Nohria and R. Eccles (eds.), Networks and Organizations: Structure, Form and Action (Boston, MA: Harvard Business School Press), 216-39. Krackhardt, David and Stern, Robert N. (1988), 'Informal Networks and Organizational Crises: An Experimental Simulation', Social Psychology Quarterly, 51 (2), 123-40. Sailer, Kerstin (2010), 'The space-organisation relationship. On the shape of the relationship between spatial configuration and collective organisational behaviours', PhD (Technical University of Dresden). Sailer, Kerstin and McCulloh, Ian A. (2012), 'Social Networks and Spatial Configuration - How Office Layouts Drive Social Interaction', Social Networks, 34 (1), 47-58. Siebdrat, Frank, Hoegl, Martin, and Ernst, Holger (2009), 'How to Manage Virtual Teams', MIT Sloan Management Review, 50 (4), 63- 68.

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Intra-Organisational Networks - Spatially Embedded

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    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
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    Intra-Organisational Networks –Spatially Embedded June 2017 Introduction “How to Collaborate” British Airways Business Life Magazine March 2015
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    Intra-Organisational Networks –Spatially Embedded June 2017 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?
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    Intra-Organisational Networks –Spatially Embedded June 2017 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]
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    Intra-Organisational Networks –Spatially Embedded June 2017 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)
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    Intra-Organisational Networks –Spatially Embedded June 2017 The Role of Workplace Design FACE TO FACE INTERACTION NETWORKS construct affects Layout of office building as affordance to face-to-face interaction networks
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    Intra-Organisational Networks –Spatially Embedded June 2017 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?
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    Intra-Organisational Networks –Spatially Embedded June 2017 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)
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    Intra-Organisational Networks –Spatially Embedded June 2017 Three examples from my work Effects of openness Effects of floors as barriers Effects of distance
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    Intra-Organisational Networks –Spatially Embedded June 2017 Effects of distance
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    Intra-Organisational Networks –Spatially Embedded June 2017 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
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    Intra-Organisational Networks –Spatially Embedded June 2017 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 Effects of Distance on Interaction Networks in Offices Own illustrations after: Knoke and Yang (2008)
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    Intra-Organisational Networks –Spatially Embedded June 2017 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) Effects of Distance on Interaction Networks in Offices
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    Intra-Organisational Networks –Spatially Embedded June 2017 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 Effects of Distance on Interaction Networks in Offices
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    Intra-Organisational Networks –Spatially Embedded June 2017 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 Control Model Spatial Models 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 Effects of Distance on Interaction Networks in Offices
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    Intra-Organisational Networks –Spatially Embedded June 2017 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 Control Model Spatial Models 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 Effects of Distance on Interaction Networks in Offices
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    Intra-Organisational Networks –Spatially Embedded June 2017 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 Control Model Spatial Models 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 Effects of Distance on Interaction Networks in Offices
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    Intra-Organisational Networks –Spatially Embedded June 2017 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; Effects of Distance on Interaction Networks in Offices
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    Intra-Organisational Networks –Spatially Embedded June 2017 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 Distance on Interaction Networks in Offices
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    Intra-Organisational Networks –Spatially Embedded June 2017 Effects of floors as barriers
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    Intra-Organisational Networks –Spatially Embedded June 2017 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
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    Intra-Organisational Networks –Spatially Embedded June 2017 Floors as barriers University 2005 Building 2 Building 1 University 2008 Building 2 Building 1
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    Intra-Organisational Networks –Spatially Embedded June 2017 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?
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    Intra-Organisational Networks –Spatially Embedded June 2017 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?
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    Intra-Organisational Networks –Spatially Embedded June 2017 Effects of openness
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    Intra-Organisational Networks –Spatially Embedded June 2017 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]
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    Intra-Organisational Networks –Spatially Embedded June 2017 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
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    Intra-Organisational Networks –Spatially Embedded June 2017 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
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    Intra-Organisational Networks –Spatially Embedded June 2017 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*
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    Intra-Organisational Networks –Spatially Embedded June 2017 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
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    Intra-Organisational Networks –Spatially Embedded June 2017 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 Spatial and transpatial solidarities correspond Locally strong, exclusive and hierarchical with pronounced boundaries → Strength of weak ties (Granovetter 1973) Effects of Openness – Solidarities
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    Intra-Organisational Networks –Spatially Embedded June 2017 Non- Correspondence Model Spatial and transpatial solidarities 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 – Solidarities
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    Intra-Organisational Networks –Spatially Embedded June 2017 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
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    Intra-Organisational Networks –Spatially Embedded June 2017 Effects of Openness: The case of a Retailer HQ PRE POST
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    Intra-Organisational Networks –Spatially Embedded June 2017 Effects of Openness: The case of a Retailer HQ 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
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    Intra-Organisational Networks –Spatially Embedded June 2017 Conclusions text Effects of Openness: Positive and Negative Effects
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    Intra-Organisational Networks –Spatially Embedded June 2017 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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    Intra-Organisational Networks –Spatially Embedded June 2017 References Allen, Thomas J. and Fustfeld, Alan R. (1975), 'Research laboratory architecture and the structuring of communications', R&D Management, 5 (2), 153-64. Burt, Ronald S. (2004), 'Structural Holes and Good Ideas', American Journal of Sociology, 110 (2), 349-99. Burt, Ronald S., Merluzzi, Jennifer L. , and Burrows, John G. (2013), 'Path dependent network advantage', Proceedings of the 2013 Conference on Computer Supported Cooperative Work (CSCW) (San Antonio, Texas, USA: ACM), 1-2. Cummings, Jonathon N. and Cross, Rob (2003), 'Structural properties of work groups and their consequences for performance', Social Networks, 25 (3), 197-210. De Montjoye, Yves-Alexandre, et al. (2014), 'The Strength of the Strongest Ties in Collaborative Problem Solving', Scientific Reports, 4. Granovetter, Mark S (1973), 'The Strength of Weak Ties', The American Journal of Sociology, 78 (6), 1360-80. Hansen, Morten T. (1999), 'The Search-Transfer Problem: The Role of Weak Ties in Sharing Knowledge across Organization Subunits', Administrative Science Quarterly, 44 (1), 82-111. Hillier, Bill (1996), Space is the machine (Cambridge: Cambridge University Press). Hillier, Bill and Hanson, Julienne (1984), The social logic of space (Cambridge: Cambridge University Press). Kabo, Felichism, et al. (2015), 'Shared Paths to the Lab: A Sociospatial Network Analysis of Collaboration', Environment and Behavior, 47 (1), 57-84. Knoke, David and Yang, Song (2008), Social network analysis, 2nd edition (Los Angeles: SAGE Publications). Krackhardt, David (1992), 'The Strength of Strong Ties: The Importance of Philos in Organizations', in N. Nohria and R. Eccles (eds.), Networks and Organizations: Structure, Form and Action (Boston, MA: Harvard Business School Press), 216-39. Krackhardt, David and Stern, Robert N. (1988), 'Informal Networks and Organizational Crises: An Experimental Simulation', Social Psychology Quarterly, 51 (2), 123-40. Sailer, Kerstin (2010), 'The space-organisation relationship. On the shape of the relationship between spatial configuration and collective organisational behaviours', PhD (Technical University of Dresden). Sailer, Kerstin and McCulloh, Ian A. (2012), 'Social Networks and Spatial Configuration - How Office Layouts Drive Social Interaction', Social Networks, 34 (1), 47-58. Siebdrat, Frank, Hoegl, Martin, and Ernst, Holger (2009), 'How to Manage Virtual Teams', MIT Sloan Management Review, 50 (4), 63- 68.