Hubs, Brokers, and Structural Holes

These slides are from a Chicago Booth course, “Strategic Leadership,” research papers, and books recent, Neighbor Networks (2010), and forthcoming, Structural Holes in Virtual Worlds

(2015). All rights are reserved (© Ronald S. Burt 2014). The course syllabus, course slides, book overviews, and related research papers can be downloaded from http://chicagobooth.edu/fac/

ronald.burt, (download draft chapter, “Network Structure of Advantage,” for summary overview).

How Social Networks Create Competitive Advantage

Stra

tegi

c Le

ader

ship

Net

wor

k B

roke

rage

(pag

e 3)

Sociogram of Senior Leadership in the Healthcare OrganizationLines indicate frequent and substantive work discussion; heavy lines especiallyclose relationships.

CEO

C-Suite

Heir Apparent

Other Senior Person

B

B

B

B

B

B

B

B

BB

Figure 1.2 in Burt (2015, Structural Holes in Virtual Worlds).

Stra

tegi

c Le

ader

ship

Net

wor

k B

roke

rage

(pag

e 11

)

BEFORE

1

2

3

4

5

2

1

3

4

5

The employee AFTER is more positioned at the crossroads of communication between social clusters within the firm and its market, and so is better positioned to craft projects and policy that add value across clusters.

Here is the core network for a job BEFORE and AFTER the employee expanded the social capital of the job by reallocating network time and energy to more diverse contacts.

Research shows that employees in networkslike the AFTER network,spanning structural holes,are the key to integratingoperations across functional and business boundaries. In research comparing senior peoplewith networks like these BEFORE and AFTER networks, it is the AFTER networks that are associated with more creativity, fasterlearning, more positive individual and teamevaluations, faster promotions,and higher earnings.

*Network scores refer to direct contacts.

It is the weak contact connections (structural holes) in the AFTER network that provides the expanded social capital.

AFTER

53.6 constraint

20.0 constraint*

From Figure 1.4 in Burt (1992, Structural Holes)and Figure 1.2 in Brokerage and Closure. See Appendix I on survey network data, Appendix II on measuring network constraint.

To Create Value, Bridge

Structural Holes

STICKY INFORMATIONInformation expensive to move because: (a) tacit, (b) complex, (c) requires other knowledge to absorb, or (d) interaction with sender, recipient, or channel.

STRUCTURAL HOLEdisconnection between two groups

or clusters of people

BRIDGErelation across structural hole

NETWORK ENTREPRENEURor "broker," or "connector:" a person who coordinates

across a structural hole

BROKERAGEact of coordinating across

a structural hole

information breadth, timing, arbitrage

Figure 2.3 Brokerage for Detecting and Developing Opportunities

Graph A shows idea quality increasing with more access to structural holes. Circles are average scores on the vertical axis for a five-point interval of network constraint among supply-chain managers in a large electronics firm (Burt, 2004:382, 2005:92). Bold line is the vertical

axis predicted by the natural logarithm of network constraint. Graph B shows performance increasing with more access to structural holes. Circles are average scores on the vertical axis for a five-point interval of network constraint within each of six populations (analysts, bankers,

and managers in Asia, Europe, and North America; Burt, 2010:26, cf. Burt, 2005:56).

Network Constraintmany ——— Structural Holes ——— few

Aver

age

Z-Sc

ore

Idea

Val

ue

Z-Sc

ore

Res

idua

l Per

form

ance

(eva

luat

ion,

com

pens

atio

n, p

rom

otio

n)

B. Yielding Performance Scores Higher than Peers(r = -.58, t = -6.78, n = 85)

A. Brokers Are More Likely to Detect & Articulate Good Ideas

(r = -.80, t = -9.67, n = 54)

Stra

tegi

c Le

ader

ship

Net

wor

k B

roke

rage

(pag

e 8)

The Returns to Brokerage Aggregate to Companies, Industries, and Communities

People with phone networks that span structural holes live in communities higherin socio-economic rank Networks are defined by land-line & mobile phone calls (map to left). Socio-economic rank is UK government index of multiple deprivation (IMD) based on local income, employment, education, health, crime, housing, and environmental quality (graph below). Units are phone area codes.

figures from Eagle, Macy, and Claxton (2010, Science), “Network diversity and economic development”

110

12

127

138

143146

156

157

163

172

196

21

337

34

344

352

354

44

503

526

559

56

591

608

694

695

696

697

698

699

700

702

704

707

711

712

715

716

717

719

720

721

722

723

724

9

98

Bold lines indicate relations in which three of four connections are strong, one of which is supervision (other threeare frequent email, 360 evaluations, and close contacts cited in the network survey).

Light lines indicate relations in which at least one of the three non-authority connections are strong.

BU1

BU2

BU3

BU4

These are the 509 senior people in the senior population. Symbol shape indicates job rank (triangle for level 1

[white are C-suite), circle for level 2, square for level 3 or lower).

Largest symbols indicate hiers apparent incurrent program cohort (random ID numbers),

slightly smaller symbols indicate people in the previous cohort.

Network Map of Senior Leadership in a Large Finance Organization

538

718

1

10

100

101

102

103

104

105

106

107

108

109

11

110

111

112

113

114

115

116

117

118

119

12

120

121

122 123

124

125

126

127

128

129

13

130

131

132

133

134

135

136

137

138

139

14

140

141

142

143

144

145

146

147

148

149

15

150

151

152

153154

155

156

157

158

159

16

160

161

162

163

164

165

166

167

168

169

17

170

171

172

173

174

175

176

177

178

179

18

180

181

182

183

184

185

186

187

188

189

19

190

191

192

193

194

195

196

197198

199

2

20

200

201

202

203

204

205

206

207

208

209

21

210

211

212

213

214

215

216

217

218

219

22

220

221

222

223

224

225

226

227

228

229

23

230

231

232

233

234

235

236

237 238

239

24

240

241

242

243

244

245

246

247

248

249

25

250

251

252

253

254

255

256

257

258

259

26

260

261

262

263

264

265

266

267

268

269

27

270

271

28

29

3

30

31

32

33

34

3536

37

38

39

4

40

41

42

43

44

45

46

47

48

49

5

50

51

52

53

54

55

56

57

58

59

6

60

61

62

6364

65

66

67

68

69

7

70

7172

73

74

75

7677

78

79

8

80

81

82

83

84

85

86

87

88

89

9

90

91

92

93

94

95

96

97

98

99

IT

BU1

BU2

FIN

HR

BU3

Network Map ofSenior Leadership

in a Large FinancialOrganization

Gold is C-Suite. Red is program participant.

Bold lines are strong connections. Light lines are weak connections. Numbers are random identification codes.

BU1BU2BU3

FINITHROther

Organization

Executive Function

Corporate Operations

Insurance Retail

Other Divisions WholesaleWhat are the implications of such a dense network for bank

operations? Customer service? Employee engagement? Bank adaptation to the changing business environment?

These are the senior leaders in a large bank.

Lines indicate people who have frequent and substantial face-to-face contact. Average such connection is embedded in 28 mutual friends (0 minimum, 63 maximum).

Figure 15.

Sociogram of

Directors in

Chicago Index

Companies

1,380 Chicago directors.

Gold indicates Commercial Club

member, concentrated in center (13.2 t-test).

818 isolates sit on one Chicago board or one Chicago board plus

outside boards containing no other

Chicago elites.

Stra

tegi

c Lea

ders

hip

Deliv

erin

g Va

lue:

Net

work

Clo

sure

(pag

e 4)

Network Constraint

Z-Sc

ore

Com

pens

atio

nN

etw

ork

Stat

us(e

igen

vect

or s

core

/ m

ean

scor

e)

Sales Regional OpsProduct SupportAdministration

Regions indexedby shading, functions by shape

Network Constraint

R = .742

r = .96 forhigh status

r = .03 forlow status

Audience Effect II: Broker's Social Standing Reassures or Concerns the Target Audience

Network status is on the vertical axis of the top graph. It is computed like price in the general equilibrium model: Si = Sj zji Sj, where Si is status of person i, and zji is connection from j to i (see Appendix V for details). Like price, status is only meaningful in reference to the status of some numeraire benchmark person. Here, status is normalized at the mean, so a score of 1.0 indicates a person of

average status in the network.

Sociogram is Figure 3.2 in Neighbor Networks and the graphs are from Figures 1 and 2 in Burt & Merluzzi discussion of the link between brokerage and network status as a reputation measures (2013, "Embedded brokerage," Research in the Sociology of Organizations)

Stra

tegi

c Lea

ders

hip

Sign

ifica

nt C

ontin

genc

ies (p

age 2

4)

Less Generic Work Requires Social Acceptance of a Would-Be Broker: Reputation & Network Integration Tests

To the extent that a broker advocates something new, there is no guarantee that the proposal will work in our market, with our company processes, staffed by our people. The proposal involves uncertainty, so it requires trust; the more uncertain the proposal, the more trust required. Would-be brokers needed to be reputable.

These are data averaged across a few hundred investment bankers in the mid-1990s sorted by

reputation into those with above-median reputation (solid dots), versus those with median or

below (hollow dots).

Graph is from Figure 2.8 in Burt, "Network Structure of Advantage" (2013 manuscript).

The boutique investment bank, Moelis — "Best Global Independent Investment Bank" in 2010 and "Most Innovative Boutique of the Year" in 2011 — nicely

illustrates the competitive advantage of reputation as an entrée to brokerage opportunities (case at www.sbs.

oxford.edu/reputation/cases).

“Differences in detail aside, most social scientists agree upon two aspects of reputation: first, knowing a business

partner’s past behavior mitigates uncertainty about his future performance; second, reputation demonstrates the

person’s credibility as an honest business partner and reduces the uncertainty associated with trusting him.”

(Hilllmann and Aven, 2011, AJS, page 485)

Z-Sc

ore

Com

pens

atio

n(to

tal a

nnua

l) Top 50%

Bottom 50%

BankerReputations:

Network Constraint (C)many ——— Structural Holes ——— few

B. Regardless of a Banker’s StatusPositive Reputation Is Sufficent

to Get High Returns to Brokerage

r = -.86

r = -.28

Stra

tegi

c Lea

ders

hip

Mana

ging

Bar

riers

to C

oord

inat

ion:

Stra

tegi

c Par

tner

s (pa

ge 8)

NetworkIntegration Tests

Returns to social capital are diagnostic of social barriers to coordination. Recall that reputation is critical to successful brokers. Every network broker is probably suspect from time to time so as not to benefit from brokerage, but when a category of people are systematically denied returns to social capital, we have likely found a social barrier to coordination.

Former Dean Witter executive on integration after merger with Morgan Stanley: "They treated us like we were the Clampetts. We would have meetings with them, and they would ask to present first and then just leave. They wouldn't stay for us. Maybe they had somewhere to go." It is a story the drips with irony: Here is a union engineered by some of the world's foremost experts in the art of mergers and acquisitions. They made huge personal fortunes putting companies together, collecting their fees, then walking away. But this time they had to live with the combination they created. (Fortune, 2005 May 2, Bethany McLean & Andy Serwer)

Z-Sc

ore

Rel

ativ

e C

ompe

nsat

ion

Z-Sc

ore

Rel

ativ

e C

ompe

nsat

ion

Acquiring Managementr = -.40, t = -4.92, P < .001

Acquired Managementr = .11, t = 1.06, P = .29

Network Constraint

Z-Sc

ore

Rel

ativ

e C

ompe

nsat

ion

Z-Sc

ore

Rel

ativ

e C

ompe

nsat

ion

All But One Division of Firmr = -.36, t = -5.66, P < .001

The One Other Divisionr = .09, t = 1.05, P = .30

Network ConstraintNetwork Constraint

M&A Integration Leader Development

see Appendix VI for network metrics identifying people treated as outsiders in an organization.

Stra

tegi

c Lea

ders

hip

Mana

ging

Bar

riers

to C

oord

inat

ion:

Stra

tegi

c Par

tner

s (pa

ge 9)

Network Integration Tests (continued)

Diversity

Early

Pro

mot

ion

(in y

ears

)

C. Diversity

Women and Junior Menr = .30

t = 3.38P < .01

Senior Menr = -.40

t = -5.56P < .001

Network Constraint

Early

Pro

mot

ion

(in y

ears

)"That's an excellent suggestion, Miss Triggs. Perhaps one of the men here would like to suggest it." (Punch, 8 January, 1988)

Stra

tegi

c Lea

ders

hip

Sign

ifica

nt C

ontin

genc

ies (p

age 2

4)Four Summary Points

CONTEXT: Network Structure Is a Proxy for the Distribution of InformationFor reasons of opportunity, shared interests, experience — and simple inertia — organizations and markets

drift toward the bridge-and-cluster structure known as a “small world.” Over time, information becomes “sticky” within clusters, diverent between clusters.

EMPIRICAL FACT: In Which Network Brokers Have a Competitive Advantage Bridge relations across the structural holes between clusters provide information breadth, timing, and

arbitrage advantages, such that network brokers managing the bridges are at higher risk of “productive accident” in detecting and developing good ideas. Network brokers tend to be better compensated than peers, more widely celebrated than peers, and promoted more quickly to senior rank relative to peers; in

short, brokers do better.

MECHANISM: But Personal Engagement is Essential to the AdvantageThere is no advantage or disadvantage to affiliation with network brokers. Advantage comes from personal

access to structural holes. Advantage does not result from exclusive access to the information of diverse contacts so much as it results from personal skills developed from translating information between diverse

contacts. Brokers develop skills of analogy and metaphor for seeing and communicating across diverse ways of thinking and behaving. Network advantage affects performance less for who you know than for who you are. In other words, social capital is a forcing function for human capital, transforming network

brokers into people stronger than they would otherwise be.

CONTINGENCY: Broker Reputation Is Critical, Which Creates Analytic Tests for Network IntegrationReputation is critical to benefiting from advantage. The organization depends on able leaders exploring

networks to search out and implement ways to add value, but too much uncertainty puts the enterprise at risk. Chains of command broken in service of company interests can just as easily be broken in service of

personal interests, or in service of well-intentioned but strategy-eroding interests. Reputation is the way would-be brokers overcome the suspicions with which brokers can be viewed. So what kinds of people are

not trusted to be network brokers?

Appendix Slides

Stra

tegi

c Lea

ders

hip

Coor

dina

ting

acro

ss th

e Ent

erpr

ise: F

indi

ng th

e Bala

nce b

etwe

en B

roke

rage

and

Clos

ure (

page

12)

1

3

2

10

1112

65

498

7

1

3

2

10

1112

65

498

7

1

3

2

10

1112

65

498

7

1

3

2

10

1112

65

498

7

1

3

2

10

1112

65

498

7

Bob Bob Bob Bob Bob

February OctoberJune AugustApril

1

3

2

10

1112

65

498

7

1

3

2

10

1112

65

498

7

1

3

2

10

1112

65

498

7

1

3

2

10

1112

65

498

7

1

3

2

10

1112

65

498

7

Deb Deb Deb Deb Deb

1

3

11

58

Bob

NonRedundantContacts(thinsolidline)

Network Density& Constraint

(bold lineis constraint)

Bob IsAlways aBroker

Deb Buildsvia

PunctuatedBrokerage

(Metricsoscillatethrough

reversals)

December Network Survey

1

3

11

58

Deb

Building Your Network: A Broker Network Can Resultfrom Always Being a Broker or from Punctuated Brokerage

Figure 6 in Burt & Merluzzi (2013),"Path Dependent Network Advantage"

Stra

tegi

c Lea

ders

hip

Coor

dina

ting

acro

ss th

e Ent

erpr

ise: F

indi

ng th

e Bala

nce b

etwe

en B

roke

rage

and

Clos

ure (

page

13)

Stra

tegi

c Lea

ders

hip

Crea

ting

Valu

e, Co

ntin

genc

ies: T

he S

ocial

Cap

ital o

f Bro

kera

ge (p

age 1

1)

Personality is not the slope adjustment here, see Appendix I, from Burt, "Network-relevant personality and the agency question" (2012 AJS)

In Sum: Individuals ReceiveDifferent Returns to Brokerage

Graph A is from Figure 2.3 showing achievement increasing with more access to structural holes in open networks. Circles are z-score residual achievement for 1,986 observations averaged within five-point intervals of network constraint in each of six management populations (analysts, bankers, and managers in Asia, Europe, and North America, see discussion of Figure 2.3 in Chapter 2; heteroscedasticity is negligible, X2 = 2.97, 1 d.f., P ~ .08). Bold line is the vertical axis predicted by the natural logarithm of network constraint. Graph B shows the raw data averaged in Graph A. Vertical axis is wider to accommodate more variable achievement. Heteroscedasticity is high due to achievement differences between advantaged individuals (X2 = 269.5, 1 d.f., P < .001), but the association between achievement and network advantage remains statistically significant when adjusted for heteroscedasticity (Huber-White, t = -8.49). Bold lines in graph B are hypothetical, distinguishing high-yield from low-yield network advantage.

A. Achievement Scores for People in Open Networks Are Higher than Peers on Average(r = -.58, t = -6.78, n = 85)

Network Constraintmany ——— Structural Holes ——— few

Z-Sc

ore

Res

idua

l Ach

ieve

men

t(e

valu

atio

n, c

ompe

nsat

ion,

pro

mot

ion)

B. But Vary Widely between the Advantaged Individuals(overall r = -.24, t = -9.98, n = 1,989)

from the second handout, "Creating Value, Contingencies"

Deb

Bob

Stra

tegi

c Le

ader

ship

Net

wor

k B

roke

rage

(pag

e 47

)

A A

A

A

B B

B B C C

C C

D D

D

D E E E

E

WHEEL (32.0 sec)

N NC Happy

A 1 100 37.5

B 1 100 20.0

C 4 25 97.0

D 1 100 25.0

E 1 100 42.5

Avg 1.6 85.0 44.4

Most Distributed Leadership

(slow, happy)

Most Centralized Leadership

(fast, unhappy)

Y-NETWORK (35.0 sec)

N NC Happy

A 1 100 46.0

B 1 100 49.0

C 3 33 95.0

D 2 50 71.0

E 1 100 31.0

Avg 1.6 76.7 58.4

CHAIN (53.2 sec)

N NC Happy

A 1 100 45.0

B 2 50 82.5

C 2 50 78.0

D 2 50 70.0

E 1 100 24.0

Avg 1.6 70.0 59.9

CIRCLE (50.4 sec)

N NC Happy

A 2 50 58.0

B 2 50 64.0

C 2 50 70.0

D 2 50 65.0

E 2 50 71.0

Avg 2.0 50.0 65.6

Appendix VII: Network Endogeneity

The four networks are from the Bavelas-Leavitt experiments on leadership in task groups. The WHEEL is a traditional bureaucracy in which C is in charge. The other three networks involve distributed leadership (all five people in the CIRCLE; B, C, and D in the CHAIN; C and D in the Y-NETWORK). More distributed leadership is associated with more messages (M), slower task completion, and greater enjoyment (E). Speed, messages, and enjoyment scores are from Leavitt (1951). Number of contacts (N) and network constraint (NC) are computed from binary ties in the sociograms (number of contacts equals number of non-redundant contacts in these structures).

Figure 2.5 in Burt, "Network structure of advantage" (2013 manuscript)

Stra

tegi

c Lea

ders

hip

Mana

ging

Bar

riers

to C

oord

inat

ion:

Stra

tegi

c Par

tner

s (pa

ge 10

)

You

2

4

5

6

7CliqueC = 54.0(.80 density,

.00 hierarchy)

BowtieC = 46.3(.40 density,

.00 hierarchy)

PartnerC = 51.7(.40 density,

.21 hierarchy)

BrokerC = 23.6(.07 density,

.05 hierarchy)

You

2

45

6

7

You 2

4

56

7

You

2

4

56

7

3

3

3

3

Common Network FormsWhat Is the Active Ingredient

in Closure that is the Advantage for Outsiders?

from Burt, "Sometimes they don't want to hear it from a person like you," (2012, L'Impresa)

Stra

tegi

c Lea

ders

hip

Mana

ging

Bar

riers

to C

oord

inat

ion:

Stra

tegi

c Par

tner

s (pa

ge 12

)

0%

10%

20%

30%

40%

-2.0

-1.0

0.0

1.0

High-RankMen

Women andEntry-Rank Men

(71) (66)(N) (45) (46) (23)(33)

Mea

n E

arly

Pro

mot

ion

(in y

ears

)P

erce

nt M

anag

ers

42%

19%

39% 39%

20%

40%

1.4 years

-.3 years

-1.8 years

-.7 years

-1.8 years

0.9 years

Kinds of Networks AreSimilarly Likely across

Kinds of Managers (χ2 = 0.15, 2 d.f., P = .93)

Kinds of Networks HaveDifferent Consequencesfor Kinds of Managers

(F = 3.77, 5-278 d.f., P < .01)

Broker networkClique (closed dense network)Partner network (closed hierarchical)

Partnering Is the Active Ingredient that LinksNetwork Constraint with Success

for People Excludedfrom Brokerage

(In other words, pick a network for what it can do, not for the kind of people who picked it in the past.)

from Burt, "Gender of social capital" (1998, Rationality and Society) and Figure 7.4 in Neighbor Networks. See Appendix II on mapping individuals into the three network categories, Appendix VI on network diagnostics identifying outsiders.

Stra

tegi

c Lea

ders

hip

Mana

ging

Bar

riers

to C

oord

inat

ion:

Stra

tegi

c Par

tner

s (pa

ge 16

)

Building a

Partner Network

When a strategic partner

sponsors your access to structural holes,

it creates hierarchy in your network.

You Connect with a Strategic Partner

So You End Up with a Network that is Hierarchical in the sense that One Contact Poses More Constraint

than the Others. Your partner is the source of constraint,

you

you

Strategic Partner Introduces You toHis or Her Contacts, which Can Connect

You across Structural Holes

and the Resulting Hierarchical Structure around you Can Be Discussed as a “Partner” Network.

you

from Burt, "Gender of social capital" (1998, Rationality and Society) and Figure 7.5 in Neighbor Networks.