The Science of Workplace Collaboration

The Science ofWorkplace Collaboration

Gabor Nagy, Ph.D.

Collaboration and the physical work environment

Quantifying and visualizing F2F interactions

Social and spatial implications

The “Innovation Potential” or “Innovation Index”

Group / Departmental adjacencies

Interactive visualization (blocking & stacking)

“It’s the People, stupid!”Dr. Jay Brand

Workplace impacts:

Job satisfactionWork attitude

Collaboration

Team Dynamics

Innovation

CreativityPerformance

It’s not rocket science…

Well-defined,“hard sciences”

Fluid mechanicsAstrodynamicsStatisticsMathematicsElectrotechnology

Materials scienceSolid mechanicsAeroelasticityAeroacousticsetc…

It can be more complex!

We are dealing with PEOPLE !!!

The Workplace Ecosystem

Soc

iolo

gyMatrix

Algebra NetworkTheory

wel

l-def

ined

“Har

d Sc

ienc

e” ambiguous

“Soft Science”

…

Soft Sciences+

Hard Sciences = interdisciplinaryill-definedambiguouscontradicting

Recipe for Failure?

The “soft stuff” is the hard stuff !

Why bother?

ΣIndividualPerformance

OrganizationalPerformance≠

NOT hard science!

1+1+1 ≠ 3

It can be (or should be) 5 or even 10!

The “magic” ingredient

We need to measure / assess / quantify collaboration, too!

IndividualPerformance

Collaboration(ONA)

OrganizationalPerformance+

Again: why bother?

Quantifying collaboration:

Organizational Performance (the very bottom line $$$)

Innovation Potential

Proximities – Space Configuration

Planned Serendipitous Encounters

Group Adjacencies

Blocking / Stacking

Collaboration Matrix

same timesynchronous

sam

e pl

ace

colo

cate

d

different timeasynchronous

face-to-face interactions

diffe

rent

pla

cere

mot

e

remoteinteractions

(e.g. videoconf.)

communication / coordination(e.g. email)

continuoustasks(e.g. in project

rooms)

Richest media channel – critical for transfer of complex knowledgeTacit – difficult to codify Quickly resolves ambiguities

collaborationworking together toward a shared goal

communicationmeaningful interaction

Face-to-Face Interaction

60 - 90%

Verbal

Face-to-Face Interaction

body

lang

uage

“Emoticons”

F2F mirrored by email!

Electronic communication patterns mirror our face-to-face networks (70-80% overlap)

More accentuated in open workplace environments

“…underlines the importance of the physical spaces we inhabit and their spatial configuration, since this (partially) drives who we talk to most often.…while we theoretically could overcome spatial boundaries with electronic communication, it seems we hardly ever do (statistically speaking).”

Sailer, 2014

Planning for the unplanned

Face-to-Faceinteractions

Planned(dept. adjacencies)

Unplanned(serendipitous)

Blocking&

Stacking

Plan for these, too!(by providing appropriate spaces

for “chance” interactions)

HOW?

Organizational Network Analysis

Social Capital

Social CapitalPhysical Capital Cultural / Human Capital

Just as your Mac or your university education can increase your productivity, so do your social contacts – people you interact with!

P0 (Len)

P1 (Chris)

P10 (Marife)

P11 (Meg)P2 (Isadora)

P3 (Mindy)

P4 (Megan)

P5 (Jackie)

P6 (John)

P7 (Alexa)

P8 (Rob)

P9 (Janna)

Organizational Network Analysis (ONA):Visualizes interactions – seeing patterns are powerfulQuantifies interactions – “connectedness”

Social Networks

Measuring Face-to-Face Interaction at the Workplace

Self-Report Surveyssubjectiveself-serving biasnon-intrusive

Observations

Using Technology

subjectiveobserver biassomewhat intrusive

objectivemore intrusivepeople adjust quickly

Visualizes and quantifies communication patterns and collaboration rates through Organizational Network AnalysisAlso measures time / space utilization

Business Microscope

1. Node (IRID Badges)

2. Antenna (Beacons)

3. Base Station

Business Microscope

G

Node

Infrared emitter & sensorMemory, microprocessor3D accelerometerVoice power detectorNO microphone or camera!

Nodes are communicating with one another, picking up IR signals

SpeakerListener

Nodes also detect the direction of communication

ListenerSpeaker

P0 (Len)

P1 (Chris)

P10 (Marife)

P11 (Meg)

P2 (Isadora)

P3 (Mindy)

P4 (Megan)

P5 (Jackie)

P6 (John)

P7 (Alexa)

P8 (Rob)

P9 (Janna)

Red = SpeakerGreen = ListenerSize of bubble shows intensity

Direction of communication

Intensity of communicationTotal time of f2f interaction during measurement period

0 minute1 minute3 minutes5 minutes

10 minutes

filter “chitchat” from more meaningful interactions

Thresholds

less meaningful, picks up everythingstill picking up “noise”optimal threshold

Thresholds & number of ties

…picking up signals from nodes

Antenna

Antennas are “anchoring” places of communication…

… in meeting rooms


… at individual workstations


… in social spaces

What Antennas Measure

How many involved 5 people

Who was involved 2 people from Sales & 3 from Marketing

Type of meeting active (e.g. brainstorming)

passive (e.g. presentation)

Percentage of utilization 35% (Space / Time Utilization)

Base Station

How It Works…

Face-to-face interaction yes (1) or no (0)

What We Can Quantify

Duration of interaction 67 minutes

Frequency of interaction 4 times on Tuesday

Direction of interaction “listener” or “speaker”

Place of interaction meeting rooms, workstations, kitchen, etc.

Intensity / activity level “passive” (observing) or “active” (involved)

(also “Flow Theory”)

What We Can’t Quantify

Importance of face-to-face interaction (as opposed to e.g. virtual)

Content of interaction (work related or non-work related)

Type of interaction (besides face-to-face: email, phone, etc.)

Sharing new ideas with …Getting new ideas from …Non-work related social interactions (any type, incl. virtual)

Auxiliary Data Collection –Value Network Analysis (VNA)

Network-level analysis

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All Departments – 1 min Threshold

“Listener”“Speaker”

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All Departments – 10 min Threshold

Strong ties are formed between subgroups of 2-3 people

All Departments – 60 min ThresholdB3B4

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“Ego”-level analysis

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“Outliers”

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Bridge between Group F and rest of org.

Social implications:

“connectors” – important role“bottlenecks” – hinder communication flow

Spatial implications:

Providing spaces for serendipitous interactions (café, kitchen, lounge, etc,) can help form more bridges in traditionally compartmentalized organizations.

“Bridge”

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Innovation = “connecting the dots” Finding and filling structural holes can give competitive advantage


Spatial separation can cause structural holes:

separate floorsseparate buildingsseparate geographical locations

“Structural Hole”


Organizations with dept. silos naturally exhibit large distances that hurt capability to innovate


Provide apt spaces for cross-functional collaboration (formal & serendipitous)Encourage use of such spaces

A – B:7 degrees of separation

A B

“Distance”

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Connector / “Listener”

Connectors (Central Figures)

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Connector / “Speaker”

Connectors (Central Figures)

Group-level analysis

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“Density”

High density group (51%)

Low density group (24%)

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Groups with high density typically stick together over timeImportant predictor for job satisfaction and retention


Group density can be positively affected by providing appropriate group “beak-out” or social spacesOrganizational density can be positively affected by:

careful layout of groupsproviding apt social spaces in the boundaries of groupsproviding appropriate support spaces (gym, cafeteria, etc.)

“Density”

“Cliques & Tie Strength”Social implications:

Small groups with strong bondsCommunication is non-hierarchical and ad-hoc


Members should be closely co-located to help communication flowCliques are territorial, so they should have their own space with clear boundaries (with full control over it)

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“Isolate”

“Clique” - subgroup with high density (high interconnectedness)

“Cliques” and “Isolates”

Which Comes First?

The chicken…

Providing dense spaces with clear boundaries helps groups create strong bonds

…or the egg

Groups with strong bonds require their own space with clear boundaries

Spatial implications

Working on same floor - higher propinquityThe “30 meters rule”

Having a workspace near staircases -higher propinquity with different floorsThe “one floor up, one floor down rule”

The tendency to have more ties with geographically close others

“Propinquity”

“Colocation in the same building and on the same floor has significant, positive effects on collaboration formation.”

Owen-Smith et al., 2013

Social implications

Social ties form based on spatial position, thus can be influenced with appropriate space design

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2nd Floor

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Some departments are fragmented on multiple floors

“Propinquity” – 1 min Threshold

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Weak connections through a few bridges between floors

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N5

K7

H2

N11

J1

G14

N6

N7

G15

G12

G7

A7

N3

A9

G13 K5

I4N4

D44

E33 D7

E34E35

N8

G9

D10

K6

N9

N10

D9E3

I5

E5

H1 I3 G5I2

G11

K2

G6

K3

N12

K4

D11

D12

D14

D16

E6D17

D4

D20

D21

2nd Floor

3rd Floor 4th Floor

Structural holes between floors


D41

E28E29E31

D42

B3

B4B9J4K1

D38

B10F4O1D46E2E20L7L8

D35

D36

J5L9F3B2E11B1E12B8F2D33E16A3E19C2E9C1D24

D27

D28

E1B7

D29

D30

A2

D15

O3A1J6L4E7

D1

D2

D3

E8E23E24E25E26

D37

A6E27D39D40

D32

D34

E17E18E36E21

D6

E22A5

D22

D23

E10A4

D25D26

E13E14

D31

E15O2

D8

L1L2J2E4L3E37M1D13E30O5E32L10

D43

F1

D45

L11L12B5J3B6D18L5D19

D5

M2L6O4N1N6D44E33

D7

E34E35D10N9

D9

E3E5

D11

D12

D14

D16

E6

D17D4

D20

D21

3rd Floor

4th Floor

R&D Fragmented on 2 Floors

More connections on the same floor than across floors

Network Measures – Intra-GroupSize Density Centrality

Collaboration Rate –Innovation Potential?

Hypothetically derived algorithm from the function of:

SizeDensityCentrality

N=180 (100%)

n=152 (84% - RR)

All ties = 2,086

Total mtg time=34,898 min

Avg mtg time*=16.73 min

MIN=0 min (1 min)

MAX=553 min

STDEV=13.84

STDEV*=35.26

MEDIAN*=3

* removing zero-ties

Powerful if mapped on a floor plan!

Visualizing Collaboration

Chris (49.3%)

Isadora (18.5%)

Mindy(35.2%)

Jackie (41.7%)

John(26.0%)

Alexa(46.7%)

Rob(37.9%)

Jenna(51.9%)

Marife(44.1%)

Meg (23.0%)

Megan(44.7%)

Len(6.9%)

Social Network & Space Utilization

Mapping ON diagram on floor plan

Inter-Group Relations:No ConnectionsWeak ConnectionsStrong Connections

àR&DHC=7

EngineeringHC=13

Product MktgHC=7

Density on Group LevelIndividual level analysis:

Data are anonymous (less useful)Intra-group density shows cohesion within groups (SNA)

Group level analysis:

Data can be revealedInter-group density informs adjacencies (bubble diagram)

F4

L7

L8

L9

F3

F2

L4

L1L2L3

L10

F1

L11

L12

L5

L6

“Structural hole”(lack of connections)

No Connection: Finance & Tech. S.

E28

E29

E31

E2

E20

E11E12

E16

E19

E9

E1

E7

E8

E23

E24

E25

E26

E27

E17E18

E36

E21

E22

E10

E13

E14

E15

E4

E37

E30

E32

G1

G2

G10

G3

G4

G8

G14

G15

G12

G7

G13

E33E34

E35

G9

E3

E5

G5

G11

G6

E6

Weak connections through a few bridges

Weak Connection: R&D & CS

I1

G1

G2

G10

G3

G4

G8

G14

G15

G12

G7

G13

I4

G9

I5

I3

G5I2

G11

G6

Strong Connection: WW Ops & CS

Two groups are intertwined

Inter-Group Collaboration Matrix

Adjacency Thresholds

Empirically derived thresholds

Group Adjacency Matrix

2 – Strong Connections (Necessary or Critical Adjacency)1 – Weak Connections (Optional or Less Critical Adjacency)

Adjacency (“Bubble”) Diagram

Adjacency Map (demo)

Dual purpose

Architectural planning tool

Interconnected elements - reducing errors“Sanity check”

Visual communication vehicle

Interactive space-related organizational dataReal-time changes / simulations - reducing time

Reducing time & errors = Reducing costs

AdjacencyDiagram

BlockingStacking

Collaborative Planning Process

Planning mtg(presentation)

Planningrevision

AsynchronousIterative

Update & re-present

Executive / client feedback

Planning mtginteraction

AdjacencyMapSimulation

Present scenariosMake instant changesSimulate ideasBuild consensus

SynchronousCollaborative

Organizational data(HC scenario planning)

Spatial data – density, area(arch. programming)

Floor plans(schematic design)

Adjacency data

Garbage In, Garbage Out (GIGO)

AdjacencyMap

INPUT OUTPUT

Adjacency (“Bubble”) Diagram

Blocking Diagram

Stacking Diagram

Isometric Stack-BlockDiagram

Revit area plans

Blocking and Stacking (Example)

Empirical data for evidence-based planning

Twin Towers in Beijing

Time & Space Utilization

Active / Passive

(more heads-down work)

All Departments Avg.

(Client predicted 15%!)

35%

Antennas–Space Utilization

Space utilization – individual workstation (A4)

11/17 (Thu) Daily Average Utilization: 53.8%

Average Utilization: 35.2%(7 days avg.)

0

2

4

6:00 9:00 12:00 15:00 18:00Num

ber

of p

eopl

e

Individual workstations

Time range: 8:00 – 5:00 incl. lunch


DepartmentsAntennas–Space Utilization

32% 43%

Touch-down spacesAntennas–Space Utilization

0

2

4

6

8

6:00 9:00 12:00 15:00 18:00

Num

ber

of p

eopl

e

Average Utilization: 67.1%(7 days avg.)Active: 35% / Passive: 65%

31%(Sales)

43%(Sales+Mktg)

26%(Sales+Mktg+R&D)

Meeting rooms

11/16 (Wed) Meeting room 101 | Daily Average Utilization: 60.3%


Participants:

31%69%

16%

84%

active

passive

active

passive

Meeting rooms

Cubicles Game Room

Labs

Kitchen

Private Offices

Conf rm

Conf rm

Antennas – Activity Levels

Workspace utilization average percentage:

35%

Thank you for your attention!

QA&Gabor Nagy, Ph.D.

[email protected]

“Flow” Analysis of a Department

FlowAnxiety

Relaxation

High Challenge

High Skill

Apathy

Low Challenge

Low Skill

Flow analysis algorithm wasdeveloped based on Prof. Mihaly

Csikszentmihalyi’s work

Year

Month

Day

Time(From) 9 12 15 18 9 12 15 18 9 12 15 18 9 12 15 18 9 12 15 18 9 12 15 18 9 12 15 18 9 12 15 18 9 12 15 18

Time(To) 12 15 18 21 12 15 18 21 12 15 18 21 12 15 18 21 12 15 18 21 12 15 18 21 12 15 18 21 12 15 18 21 12 15 18 21

Len Pilon 3 3 2 0 2 2 3 0 2 3 2 0 2 1 0 0 0 0 0 0 0 0 0 0 3 1 2 0 2 4 1 0 2 4 1 0

Datema, Chris 3 3 3 0 1 4 2 0 2 3 2 0 1 1 1 0 0 0 0 0 0 0 0 0 2 3 2 0 0 0 0 0 1 1 1 0

Marife Vander Schuur 3 3 2 0 1 4 1 0 1 4 0 0 0 2 2 0 0 0 0 0 0 0 0 0 1 1 0 0 2 1 1 0 1 4 2 0

Meg Zerfas 1 4 4 0 0 0 0 0 1 3 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1 4 1 0 0 0 0 0 0 0 0 0

Isadora Godley 3 4 4 0 0 0 0 0 1 1 1 2 1 0 0 0 0 0 0 0 0 0 0 0 2 1 2 0 0 0 0 0 4 1 1 0

Mindy Heyboer 4 4 4 0 1 1 2 0 1 1 4 0 3 2 2 0 0 0 0 0 0 0 0 0 2 3 2 0 0 0 0 0 0 0 0 0

Megen Murray 3 3 4 3 1 4 1 0 2 2 2 2 1 1 0 0 0 0 0 0 0 0 0 0 3 3 1 0 1 4 1 0 2 2 2 0

Jackie Neerken 4 4 1 0 2 3 2 0 3 1 3 0 3 2 2 0 0 0 0 0 0 0 0 0 3 1 1 0 1 1 1 0 3 4 2 0

John Scott 2 3 3 0 3 2 2 0 2 1 1 0 3 2 4 0 0 0 0 0 0 0 0 0 4 3 2 0 2 2 2 0 1 3 2 0

Alexa Smith 1 4 4 0 3 3 2 0 1 3 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 4 4 2 0 1 4 2 0

Rob Standish 0 0 0 0 4 4 1 0 4 4 4 0 4 3 2 0 0 0 0 0 0 0 0 0 2 2 1 0 0 0 0 0 2 1 1 0

Janna Szotko 4 4 1 0 2 2 4 0 2 2 2 0 2 3 3 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 4 0 1 1 2 0

8 9 10

2010

February

2 3 4 5 6 7

Len

Chris

Marife

Meg

Isadora

Mindy

Megan

Jackie

John

Alexa

Rob

Janna

3 + Hz

2 – 3 Hz

1 -2 Hz

0 – 1 Hz

Not in Use

Experimental Flow Analysis

2/2 (Tue) 2/3 (Wed) 2/4 (Thu) 2/5 (Fri) 2/9 (Tue) 2/10 (Wed)2/8 (Mon)

“Flow” Pattern Diagnosis for All Departments

3 + Hz

2 – 3 Hz

1 -2 Hz

0 – 1 Hz

Not in Use


Size of circle indicates meeting area utilization rateColor of circle indicates the degree of intensity of interaction

Intensity of Interaction (Hitachi High-Tech)


The Science of Workplace Collaboration

Documents