The Social Origins of Reputation: Barrier and Key to

Transcription

The Social Origins of Reputation:
Barrier and Key to Change
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 2015). 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 Governance,” for summary overview text).
Network Advantage
Trust & Reputation: Closure
Creates Bandwidth
Creates Echo
Key Points
Sociogram of the Org Chart for a
Large EU Healthcare Organization
CEO
C-Suite
Heir Apparent
Other, Respondent
Other, NonRespondent
Bill
Strategic Leadership
Network Brokerage (page 2)
Bob
Figure 1.1 in Burt (2015, Structural Holes in Virtual Worlds).
Sociogram of Senior Leadership in the
Healthcare Organization
Asia
US
Lines indicate frequent and
substantive work discussion;
heavy lines especially
close relationships.
Bill
EU and
Emerging
Markets
Bob
B
Front
Office
B
B
B
Network Brokerage (page 3)
B
B
CEO
C-Suite
Heir Apparent
Back
Office
B
R&D
Other Senior Person
B. Yielding Performance Scores
Higher than Peers
A. Brokers Are More Likely to
Detect & Articulate Good Ideas
(r = -.58, t = -6.78, n = 85)
(evaluation, compensation, promotion)
Z-Score Residual Performance
Average Z-Score Idea Value
(r = -.80, t = -9.67, n = 54)
Network Constraint
many ——— Structural Holes ——— few
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).
Trust and Reputation Are Critical:
To the extent that a broker is advocating 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.
B. Regardless
of a Banker’s
Status
Is Sufficent
Are you trusted by the people you arePositive
tryingReputation
to bridge?
to Get High Returns to Brokerage
Significant Contingencies (page 12)
Graph is from Figure 3.1 in Burt (2015,
Structural Holes in Virtual Worlds).
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)
r = -.86
Z-Score Compensation
(total annual)
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).
Banker
Reputations:
Top 50%
Bottom 50%
r = -.28
Network Constraint (C)
Z-Score Relative Compensation
Acquiring Management
r = -.40, t = -4.92, P < .001
Leader Development
Acquired Management
r = .11, t = 1.06, P = .29
All But One Division of Firm
r = -.36, t = -5.66, P < .001
Network Constraint
Network Constraint
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)
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.
M&A Integration
Advanced Topics in Management Networks
Managing Barriers to Coordination: Strategic Partners (page 9)
Illustrative Tests
for Pathology
The One Other Division
r = .09, t = 1.05, P = .30
see Appendix VI for network metrics identifying people treated as outsiders in an organization.
Illustrative Tests
for Pathology
(continued)
Senior Men
r = -.40
t = -5.56
P < .001
Network Constraint
"That's an excellent suggestion, Miss Triggs.
Perhaps one of the men here would like to
suggest it." (Punch, 8 January, 1988)
Early Promotion (in years)
Advanced Topics in Management Networks
Diversity
Early Promotion (in years)
C. Diversity
Women and Junior Men
r = .30
t = 3.38
P < .01
Network Advantage
Creates Bandwidth
Creates Echo
Key Points
Closure Creates a Reputation Cost for Misbehavior,
Which Facilitates Trust and Collaboration
Robert
Jessica
Situation A
Robert New Acquaintance
Delivering Value: Network Closure (page 9)
(no embedding)
Robert
Jessica
Robert
Jessica
Situation B
Robert Long-Time Colleague
Situation C
Robert Co-Member Group
("relational" embedding)
("structural" embedding)
More connections allow more rapid communication, so poor behavior can be more
readily detected and punished. Bureaucratic authority was the traditional engine for
coordination in organizations (budget, head count). The new engine is reputation (e.g., eBay).
In flattened-down organizations, leader roles are often ambiguous, so people need help knowing
who to trust, and the boss needs help supervising her direct reports. Multi-point evaluation
systems, often discussed as 360° evaluation systems, gather evaluative data from the people
who work with an employee. These are "reputational" systems in that evaluations are the
same data that define an employee's reputation in the company. In essence, reputation is the
governance mechanism in social networks.
Figure 3.1 in Brokerage and Closure (for discussion, see pages 105-111).
See Appendix IX on network embedding in the theory of the firm.
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Network Advantage
Creates Bandwidth
Creates Echo
Key Points
III. Network
Closure as
the Source:
Echo Story
(vs good behavior
or closure bandwidth)
Third parties selectively
repeat information and
enforcement,
and so
amplify relations
to extremes of trust and
distrust.
See Section 4.1 in
Brokerage and Closure,
Appendix IV on
susceptibility to gossip,
Dunbar (1996) Grooming,
Gossip, and the Evolution of
Language.
"Echo" vs "Bandwidth"
versions of closure
argument: more channels
of communication create
more frequent selective
reinforcement.
Third parties do not enhance information and protection so much as they create an echo that makes people
feel more certain in their opinion of you.
Bias in selecting third parties (balance mechanism) — Faced with a decision about whether to trust you, the
other person (ego) turns to trusted contacts before less close contacts for information on you. Trusted contacts are likely
to have views similar to ego’s, so they are likely to report accounts of you consistent with ego’s own view. A preference for
trusted third parties means that ego draws a sample of information on you consistent with his or her predisposition toward you.
Bias in what third parties say (etiquette mechanism) — It is polite in conversation to go along with the flow of
sentiment being shared. We tend to share in conversations those of our facts consistent with the perceived predispositions
of the people with whom we speak, and facts shared with other people are facts more likely to be remembered. The biased
sample of facts shared in conversations becomes the population of information on, and so the reality of, the people discussed.
For example (Higgans, 1992), an undergraduate subject is given a written description of a hypothetical person
(Donald). The subject is asked to describe Donald to a second student who walks into the lab. The second person is a
Quidnunc (KWID-nunk, from
confederate who primes the conversation by leaking his predisposition toward Donald (“kinda likes” or “kinda dislikes” Donald).
Latin "what now", to English in Subjects distort their descriptions of Donald toward the expressed predisposition. Positive predisposition elicits positive words
1709) - a person who seeks
about Donald’s ambiguous characteristics and neglect of negative concrete characteristics. Negative predisposition elicits
to know all the latest news or
gossip. Example: I lowered my negative words about Donald’s ambiguous characteristics and neglect of positive concrete characteristics.
voice when I noticed that Nancy, the office quidnunc, was standing In sum, echo has the other person (ego) in vicarious play with you in gossip relayed by third parties. The sample of your
right next to my cubicle hoping to
behavior to which ego is exposed is biased by etiquette to be consistent with ego’s predisposition toward you. The result is
overhear what I was saying.
that ego becomes ignorantly certain about you, and so more likely to trust or distrust you (as opposed to remaining undecided
between the two extremes). Favorable opinion is amplified into trust. Doubt is amplified into distrust. The trust expected in
strong relations is more likely and intense in relations embedded in strong third-party ties. The distrust expected in weak and
negative relations is more likely and intense in relations embedded in strong third-party ties.
Lovegety
From Wikipedia, the free encyclopedia
Lovegety is a wireless-enabled,
spontaneous matchmaking service
that originated in Japan in 1998.
Mr.Takeya Takafuji and his friends
created Lovegety.
Users enter their profile of interests into the device and when the
device, with a limited wireless communications range, discovers a
user with a “matching” profile, LoveGety notifies the user that their
matched partner is nearby. Notification is done via device vibration.
LoveGety was the inspiration for countless bluetooth-enabled
matchmaking services for mobile phones, see Bluedating.
Detail on Gossip Creating Ignorant Certainty.
Expect extreme opinions
amplified by gossip in closed networks (regardless of the bandwidth focus on positive versus negative
indirect connections
through mutual contacts).
GOSSIP
(data filtered by etiquette)
CREATES
IGNORANT CERTAINTY
Distribution of the
stories known
Opinion of Business Leader
E Stories they know
Extreme
Positive
Ego’s
Initial
Extreme
Negative
J Stories they share
E
E
E
E
E
E
J
E
E
E
E
E
E
E
E
E
E
E
E
E
1
E
J
E
E
E
E
E
E
E
E
2
E
E
E
J
E
E
E
E
E
3
Distribution of the
stories ego hears
E
J
E
E
E
E
E
E
E
4
5
...
Ego’s sequence of conversations
in which business leader is discussed
For discussion, read the footnotes on pages 98-99 and 106 of Brokerage
and Closure. For selected illustration from a team of employees driven into
ignorant certainty, see Levy, "The Nut Island Effect" (2001, HBR). Several
examples are briefly described in Chapter 4 of Brokerage and Closure.
Confidence interval
around ego’s opinion
is the average datum,
plus and minus the
standard error, which is S .
√N
2
Variance S is severely underestimated
by the stories shared with ego.
The number of observations N is
increasing as ego hears more stories.
So the confidence interval around ego’s opinion
becomes tight, making ego feel certain,
but only because etiquette has filtered out
data inconsistent with ego’s opinion.
Despite a High
Average Rate of
Network Decay
4.0
(which implies volatile
reputations because so much
of evaluation variance is in
the pair of people connected
rather than either individual,
see Appendix VI),
(average evaluation by colleagues)
Reputation next Year
3.5
3.0
Reputations
Persist from
One Year
to the Next
2.5
2.0
Bankers (r = .61, t = 13.16)
Analysts (r = .55, t = 9.78)
1.5
1.5
2.0
2.5
3.0
3.5
4.0
Reputation this Year
(average evaluation by colleagues)
Figure 6.3 in Neighbor Networks.
R(t+1)
Solid regression line and white dots
describe stability in positive reputations
(8.1 routine t-test)
4
0.8
3
2
1
1
2
Rt
3
4
0.7
(13-person subsample)
Correlation Between
Banker’s Reputation
This Year and Next
0.6
4
0.4
0.3
0.1
3
0.5
0.2
R(t+1)
2
1
Dashed regression line and black dots
describe stability in negative reputations
(6.9 routine t-test)
2
Rt
3
4
Positive and
Negative
Reputations
Quickly
Stabilize.
e.g, sociogram bottom-right
0.0
1
Closure Is
Essential to
Reputation
Stability.
0
banker
2
4
6
8
10 or
more
Average Number of Mutual Contacts banker
Linking Banker this Year with Colleagues
From Figure 4.6 in Brokerage and Closure, Figure 6.4 in Neighbor Networks.
What
Implications
for Building
Reputation?
See Appendix V on third-party ties as a network-closure metric. See Appendix VII for detail separating
positive versus negative embedding, and analysts versus bankers. See Appendix VIII on groupthink and unlearning.
Implications for Managing Reputation
Questions:
When Closure
When Closure
Creates Bandwidth Creates Echo (most
(e.g., Amazon, eBay) social networks)
1. What makes your
reputation persist?
Your consistent behavior, on which
others are informed. The
bandwidth provided by a closed
network enhances information
distribution and consistency.
2. Therefore, who owns
your reputation?
You do. It is defined directly and
indirectly by your behavior.
3. So, what are the
implications for effectively
building reputation?
Behave well and get the word out.
4. How many reputations
do you have? (Does the
relevant network distribute
or filter information?)
One reputation, defined by your
behavior. Variation can exist from
imperfect information distribution
or conflicting interests, but
variation is resolved by finding the
true, authentic you.
Table 2.4 in Burt, "Network Structure of Advantage" (2013 manuscript)
Questions:
When Closure
When Closure
1. What makes your
reputation persist?
your reputation?
3. So, what are the
Consistent stories circulating among
them about your behavior. The echo
produced by etiquette enhances
story distribution and consistency in
a closed network.
Questions:
When Closure
When Closure
1. What makes your
reputation persist?
a closed network.
your reputation?
They do. It is defined by people
gossiping about you. Reputation
quickly decays in open networks.
3. So, what are the
Questions:
When Closure
When Closure
1. What makes your
reputation persist?
a closed network.
your reputation?
3. So, what are the
Put a premium on projects, products,
and services likely to be talked
about.
Questions:
When Closure
When Closure
1. What makes your
reputation persist?
a closed network.
your reputation?
3. So, what are the
Put a premium on projects, products,
and services likely to be talked
about.
Multiple, depending on gossip. You
have as many reputations as there
are groups in which you are
discussed. The reputations can be
similar, but they are generated and
maintained separately.
Network Advantage
Creates Bandwidth
Creates Echo
Key Points
Key Points
Competitive Advantage in Networks is created by the information breadth, timing, and
arbitrage advantages of bridging structural holes. Benefiting from the advantage depends
on having sufficient Social Standing to be accepted as a network broker (formal
authority, informal authority, and especially reputation which most allows new talent to
rise up).
In other words, understanding the Origins of Reputation is critical to understanding
competitive advantage. In some networks, reputation emerges within closed networks
distributing trusted information from repeated observation (eBay, Amazon, etc.;
Bandwidth Effect). In social networks, however, closed networks play a more active role
in defining reputations. Closed networks operate as echo chambers in which people
share selected stories in casual gossip intended to strengthen connections with one
another (Echo Effect). Sharing the same opinion again and again makes people feel
connected but they become ignorantly certain in their opinions of others, and reputations
are amplified to persistent positive and negative extremes (key graph).
The difference between closed-network bandwidth versus echo effects on reputation has
Significant Implications for Managing Reputation (key table & network diagnostics).
Appendix Slides
Common Network Forms
5
What Is the Active Ingredient
in Closure that is the
Advantage for Outsiders?
6
4
2
3
7
5
Broker
You
C = 23.6
2
3
(.07 density,
.05 hierarchy)
4
You
Bowtie
C = 46.3
6
(.40 density,
.00 hierarchy)
7
6
5
5
7
4
Partner
C = 51.7
(.40 density,
.21 hierarchy)
You
4
6
2
3
from Burt, "Sometimes they don't want to hear it from
a person like you," (2012, L'Impresa)
You
Clique
C = 54.0 7
(.80 density,
.00 hierarchy)
3
2
(N)
Percent Managers
(71)
42%
(66)
(33)
39%
(45)
(46)
39%
40%
(23)
30%
19%
20%
20%
Kinds of Networks Are
Similarly Likely across
Kinds of Managers
(χ2 = 0.15, 2 d.f., P = .93)
10%
0%
1.0
(in years)
Mean Early Promotion
40%
Partnering Is the Active Ingredient that Links
Network Constraint with Success
for People Excluded
Women and
High-Rank
Entry-Rank Men
Men
from Brokerage
1.4 years
Broker network
0.9 years
Clique (closed dense network)
(In other words, pick a
network for what it can do,
not for the kind of people
who picked it in the past.)
Partner network (closed hierarchical)
0.0
-.3 years
-.7 years
-1.0
-2.0
-1.8 years
-1.8 years
Kinds of Networks Have
Different Consequences
for Kinds of Managers
(F = 3.77, 5-278 d.f., P < .01)
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.
But although many people have sufficient reputation
without having sufficient status, most people have too little of
either, so knowing how to build reputation is critical knowledge.
Horizontal axis ranks banker observations from highest status (hollow dots) or most-positive reputation (solid dots) to the opposite
extreme. Vertical axis is the correlation between compensation and log network constraint for a sample of observations adjacent to each
banker (24 of higher social standing plus 24 of lower). Displayed data are smoothed by averaging across 24 adjacent observations.
Average Subsample Correlation between
Compensation and Log Network Constraint
About 80 cases
have status high
enough to benefit
from brokerage
About 200 cases have reputation positive
enough to benefit from brokerage
Rank Order of Bankers from First to Last in Social Standing
(hollow dots for network status, solid dots for reputation)
Echo Amplifies
Opinions to
Extremes in
Closed
Networks:
Character
Assassination
These are explanations
from managers in electronic
equipment and financial
services; from Table 1 in Burt
“Entrepreneurs, Distrust, and
Third Parties” (1999, Shared
Cognition in Organizations).
Numbers in parentheses to the
left are the hostility scores on
next page.
(
(
(
(
(
(
(
(
(
(
Some Managers Blame the Situation (n = 88)
0) conflict of goals; what was good for him was bad for my group
25) different management style and motivation
0) I do not know; most likely a misunderstanding of my work rather than him personally
25) influential; has different view of importance and implementation of my current function
0) language barrier was very difficult
38) little or no interest in my functional area yet is my boss’ boss
0) managed a parallel sales organization with a different philosophy
13) personally we got along wonderfully, but people in her organization have a difficult style
0) representative of an organization that has goals and objectives in opposition to to mine
0) she is under a lot of pressure and it spills over to the people around her
Some Managers Blame the Other Person’s Competence (n = 200)
( 63) almost always makes unreasonable schedule and cost demands
( 13) does not understand his functional area
( 25) her planning requests do not take into account time difference between NY and Europe
(100) incompetent; can not make a decision and stick with it
( 75) inexperienced; too emotional and immature to manage his organization
( 50) micromanagement; poor understanding of our client group's needs
( 25) mixed messages; no road map of clear direction
( 0) not able to effectively affect change in organizational direction
( 88) promoted too high, too fast; beyond her level of experience
( 75) wastes people's time requiring work be done over 20-30 times, eventually doing it herself
Some Managers Blame the Other Person’s Character (n = 228)
(100) dishonest; self-serving; no integrity
(100) divide and conquer person; takes credit for my work; disempowers
(100) egotistical; self-oriented; liar; worst manager I have ever met
(100) jerk; power-hungry; political; etc....
(100) lone ranger type; my way is the only way
( 88) loses her temper and has a very unprofessional attitude with myself and external clients
(100) manipulative - insensitive to people - dishonest
(100) most territorial, uncooperative person I know
(100) my boss and a charlatan
(100) nasty, ill-tempered bitch
(100) not trustworthy; a back-stabber
( 88) person can not accept females
(100) secretive; insecure
( 88) shared private information with manager & peers
(100) unethical; uncooperative; unpleasant
Anger and Character Assassination in Closed Networks
Anger in the Explanation
Third-Party Ties
Surrounding Explained Relationship
(box shows 25%, mean, 75%; 11.56 t-test for
association with strong third-party ties, P < .001)
(93.33 chi-square, 2 d.f., P < .001)
0%
20%
40%
60%
Blame the Situation
0
20
40
60
80
100
21%
Blame Colleague’s
Competence
53%
(e.g., “promoted too high,
too fast;” n = 103)
100%
79%
(e.g., “language barrier,”
“parallel organization,”
conflict of goals;” n = 63)
Blame Colleague’s
Character (e.g., “unethical
80%
47%
4%
charlatan,” “back-stabber,”
“nasty, ill-tempered;’ n = 90)
96%
Weak third-party ties
Strong third-party ties
from Figure 4.4 in Brokerage and Closure
Reputation Stability Predicted
by Positive Closure versus Negative Closure
A. Positive Indirect Connections
-
Mutual
Contact
Emile
Colleague
-
-
Employee
+
+
Mutual
Contact
Philippe
Colleague
+
Employee
-
+
Mutual
Contact
Marc
B. Negative Indirect Connections
Mutual
Contact
Catherine
If bandwidth story true, then:
Stability of positive reputation increases with positive indirect, decreases with negative indirect (relations as info pipes)
Stability of negative reputation increases with negative indirect, decreaess with positive indirect (relations as info pipes)
If echo story true, then
Stability of positive reputation increases with positive or negative indirect (etiquette filter on info transmitted)
Stability of negative reputation increases with positive or negative indirect (etiquette filter on info transmitted)
Figure 3 in Burt, "Gossip and reputation" in Management et Réseaux Sociaux, edited by edited by Marc
Lecoutre and Pascal Lievre (2008 Hermes-Lavoisier, English language version on my website).
Stability of Positive and Negative Reputations
Increase with Either Positive or Negative Closure.
Relations Are Balanced in Amplitude, not Direction;
Reputations Are Defined by Network Echo, not Bandwidth.
Predict
Positive Reputations
(N = 899)
Predict
Negative Reputations
(N = 797)
1
2
3
4
5
6
.59
.50
.59
.45
.50
.51
Number of Positive
.77**
(28.1)
—
.66**
(11.7)
.67**
(21.2)
—
.21**
(3.6)
Number of Negative
—
.71**
(23.7)
.12*
(2.2)
—
.70**
(23.3)
.52**
(8.7)
R2
Average Number of Mutual
Contacts Linking Employee
this Year with Colleagues
NOTE — These are regression models predicting reputation stability from this year to next using network variables measured this year. Stability
is measured for an employee by the sub-correlation between reputation in adjacent years (vertical axis on pages 23 and 25 of this handout).
Average number of mutual contacts (horizontal axis on pages 23 and 25) are here log scores to capture the nonlinear association. T-tests in
parentheses are adjusted for autocorrelation between repeated observations (using "cluster" option in STATA), but they are only a heuristic
since routine statistical inference is not applicable for sub-sample correlations as a criterion variable (footnote 1 in the source paper cited
below). * P < .05 ** P < .001
Table 1 in Burt, "Gossip and reputation" in Management et Réseaux Sociaux, edited by edited by Marc
Lecoutre and Pascal Lievre (2008 Hermes-Lavoisier, English language version on my website).
4
3
2
2
3
4
1
Mean Correlation for
Banker’s Reputation
from this Year to Next
(13-person subsample)
1
Brokers (8): Y = .248 + .202 log(X), n = 894, t = 13.0
4
Other (J): Y = -.047 + .274 log(X), n = 897, t = 15.1
3
2
1
2
3
4
1
banker
Mean Number of Third Parties
Connecting People in the Networks
around Banker’s Contacts this Year
10 or
more
banker
Essential Closure Is Around Contacts, Maintaining the
Reputations of Brokers and People in Closed Networks
Vertical axis is same as on page 23. Horizontal axis is average number of third party connections in the networks around banker's
contacts (rounded to nearest whole number). Brokers are bankers with below-median network constraint this year. Regression
lines in graph go through averages. Regression equations estimated from 894 year-to-year banker transitions. Test statistics
are adjusted down for correlation between repeated observations of the same bankers using the "cluster" option in Stata.
Building Your Network: A Broker Network Can Result
from Always Being a Broker or from Network Oscillation
April
February
Coordinating across the Enterprise: Finding the Balance between Brokerage and Closure (page 12)
11
12
1
11
2
10
3
12
4
11
6
12
1
12
11
2
6
7
12
4
6
5
1
2
4
11
4
Figure 4 in Burt & Merluzzi
(2015, "Network Oscillation")
6
7
12
6
5
11
3
12
1
11
2
6
7
12
3
4
1
8
2
11
3
6
4
7
Bob Is
Always a
Broker
NonRedundant
Contacts
(thin
solid
line)
Cat
Alternates
between
Brokerage
& Closure
(Metrics
oscillate
through
reversals)
Bob
6
7
12
1
5
8
6
5
1
11
2
10
5
3
3
Cat
Cat
9
8
3
4
9
5
10
5
1
11
2
10
Cat
9
1
Bob
4
8
3
7
2
9
5
10
8
1
10
Cat
9
7
12
December
Network Survey
October
Bob
9
8
3
8
11
2
3
Cat
9
1
10
5
10
Cat
August
Bob
4
8
3
7
11
3
9
5
10
8
2
Bob
9
7
1
10
Bob
8
June
4
9
8
7
6
5
8
5
Network Density
& Constraint
(bold line
is constraint,
dashed line
is density)
(evaluation, compensation, promotion)
Z-Score Residual Achievement
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.
Creating Value, Contingencies: The Social Capital of Brokerage (page 11)
In Sum: Individuals Receive
Different Returns to Brokerage
Cat
B. But Vary Widely between
the Advantaged Individuals
(overall r = -.24,
t = -9.98, n = 1,989)
A. Achievement Scores for
People in Open Networks Are
Higher than Peers on Average
(r = -.58, t = -6.78, n = 85)
Network Constraint
Bob
Personality is not the slope adjustment here, see Appendix I, from Burt,
"Network-relevant personality and the agency question" (2012 AJS)
from the second handout, "Creating Value, Contingencies"
Broker Bankers
Clique Bankers
Z-Score Residual Compensation
(33% most open networks)
(33% most closed networks)
How Much Does
Oscillation Matter for
Each Category of
Bankers?
N Definite Oscillation
(r = -.49, t= -4.90, n = 76)
Probable Oscillation
(r = -.21, t= -2.51, n = 143)
No Oscillation
(r = .10, t= 1.13, n = 127)
Network Constraint
t-‐test P Broker Bankers 111 4.38 <.001 Middling Bankers 116 0.08 .94 Clique Bankers 119 -‐0.84 .84 NOTE -‐ Test for oscilla3on associa3on with rela3ve compensa3on for each row of bankers using a contrast of 1 for deﬁnite oscilla3on, 0 for probable oscilla3on, and -‐1 for no oscilla3on. Average z-‐score compensa3on across four years is predicted from average network constraint, holding constant job rank, seniority, peer evalua3ons, gender, race, and geography (Model IV, Table 1, Burt and Merluzzi, 2015). Returns to Brokerage Are Contingent on Oscillation
Vertical axis is a banker’s z-score annual compensation — adjusted for the banker’s job rank, evaluation by colleagues, years with the bank, gender, race, and
geographic location — averaged across the four-year observation period. Horizontal axis is annual network constraint averaged across the four years. Symbols
indicate averages of individual scores on the horizontal and vertical axes, within five-point intervals of network constraint. The three lines distinguish bankers by
the extent to which oscillation across the four years is visible in their annual networks: Definite oscillation refers to bankers who experienced reversals in network
status and constraint. Probable oscillation refers to bankers who experienced a reversal in status or constraint, but not both. No oscillation refers to bankers who
experienced no reversals. As a summary test for oscillation, compensation was regressed for all 346 bankers across the control variables plus a dummy variable
for probable oscillation and a dummy variable for definite oscillation, plus two interactions between the oscillation dummies and log network constraint. Negligible
association between constraint and compensation for “no oscillation” bankers (-.86 t-test, P ~ .39), increases to significantly higher associations for probable and
definite oscillation (15.28 F2,333, P < .001), yielding significantly higher levels of compensation for broker bankers (17.20 F2,333, P < .001).
Figure X. Returns to Brokerage Con3ngent on Oscilla3on Figure 6 in Burt and Merluzzi (2015, "Network Oscillation")
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).
Executive Function
Corporate Operations
Insurance
Other Divisions
Retail
Wholesale
What are the implications of such a dense network for bank
operations? Customer service? Employee engagement?
Bank adaptation to the changing business environment?

The Social Origins of Reputation: Barrier and Key to

Transcription

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