1. Social Origins
of Your Reputation
the social psychology
of competitive
advantage
Related teaching and research materials can be downloaded from my website (esp.
course handouts “12brokerage, ”34closure,” and a book chapter “Network Structure
of Advantage”): http://www.chicagobooth.edu/faculty/directory/b/ronald-s-burt.

2. Trust and Reputation Can Be 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.
A. High Status is a Good Signal
B. Regardless of a Banker’s Status
Are you trusted by the people youPositive Reputation Is Sufficent
are trying to bridge?
of Positive Reputation, but Low
These are data averaged across a few hundred
investment bankers in the mid-1990s sorted by
reputation into those with positive (solid dots),
average (grey dots), or poor (hollow dots are
bankers in the bottom third of peer evaluations).
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 (free Moelis case at
www.sbs.oxford.edu/reputation/cases).
Network Status
(eigenvector score / mean score)
to Get High Returns to Brokerage
Z-Score Compensation
(total annual)
Strategic Leadership
Creating Value, Contingencies: The Social Capital of Brokerage (page 12)
Status Is an Ambiguous Signal
Banker
Reputations:
Top third
Middle third
Bottom third
Network Constraint (C)
many ——— Structural Holes ——— few

3. Closure Creates a Reputation Cost for Misbehavior,
Which Facilitates Trust and Collaboration
Robert
Jessica
Strategic Leadership
Delivering Value: The Social Capital of Closure (page 4)
Situation A
Robert New Acquaintance
(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.

4. Search
Check out LIVE auctions on eBay.
tips
Search titles and descriptions
Overall profile makeup
ID card
222 positives. 203 are from
unique users and count toward
the final rating.
dan (200)
Member since Thursday, May 07,
1998
Summary of Most Recent Comments
3 neutrals. 3 are from users no
longer registered.
Past 7 days
Past month
Past 6 mo.
Positive
5
84
Neutral
0
0
0
Negative
Total
0
0
0
5
1
85
Bid Retractions
3 negatives. 3 are from unique
users and count toward the final
rating.
0
0
0
0
Auctions by dan
Note: There are 3 comments that were converted to neutral because the commenting users are no longer
registered.
You can leave feedback for this user. Visit the Feedback Forum for more info on feedback profiles.
If you are dan (200)
, you can respond to comments in this Feedback Profile.
Items 1-25 of 228 total
= 1 = [2] [3] [4] [5] [6] [7] [8] [9] [10] (next page)
User: chache@fyi.net (4) Date: Jan-04-01 09:36:46 PST
Item: 508790896
Praise: probably the best seller on ebay. goes all out to make sure the buyer is happy!
User: marywdwrth@aol.com (58)
Date: Dec-31-00 16:02:19 PST
Item: 512484383
Praise: item in perfect condition, great to deal with, quick service
User: sararah1 (121)
Date: Dec-30-00 13:20:51 PST
Item: 512488662

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6. III. Network
Closure as
the Source:
Echo Story
(vs good behavior
or closure bandwidth)
Strategic Leadership
Delivering Value: The Social Capital of Closure (page 13)
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.

7. 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.

8. 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
Strategic Leadership
Delivering Value: The Social Capital of Closure (page 14)
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 is severely underestimated
Variance S
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.

9. A. Stability from Year to Year
B. Disappears Without Closure
4
3
2
2
3
4
1
Mean Correlation for
Banker’s Reputation
from this Year to Next
(13-person subsample)
Reputation Next Year
(average evaluation by colleagues)
1
Bold line through white dots describes above
average reputations (8.1 routine t-test). Dashed
line through black dots describes reputations
average and below (6.1 routine t-test).
Analysts
Bankers
Reputation This Year
(average evaluation by colleagues)
banker
Mean Number of Third Parties
Connecting Banker with
Colleagues This Year
10 or
more
4
3
2
1
2
banker
Figure 2.11
Closure Essential to Reputation
Graph A plots analyst and banker reputations this year versus next. Squares are analysts (r = .55, t = 9.78), and circles are
bankers (r = .61, t = 13.16). Graph B describes for the bankers subsample correlations between positive (above average)
and negative (below average) reputations this year and next year. Adapted from Burt (2010:162, 166).
From R. S. Burt, "The Network Structure of Advantage" (available at http://faculty.chicagobooth.edu/ronald.burt/research)
3
4
1

10. Implications for Managing Reputation
Questions:
When Closure
When Closure
Creates Bandwidth Creates Echo (most
(e.g., Amazon, eBay) social networks)
Your consistent behavior, on which
others are informed. The
bandwidth provided by a closed
network enhances information
distribution and consistency.
Consistent stories circulating among
them about your behavior. The echo
produced by etiquette enhances
story distribution and consistency in
a closed network.
2. Therefore, who owns
your reputation?
Strategic Leadership
Delivering Value: The Social Capital of Closure (page 24)
1. What makes your
reputation persist?
You do. It is defined directly and
indirectly by your behavior.
They do. It is defined by people
gossiping about you. Reputation
quickly decays in open networks.
3. So, what are the
implications for effectively
building reputation?
Behave well and get the word out.
Put a premium on projects, products,
and services likely to be talked
about.
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.
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.
Table 2.4 in Burt, "Network Structure of Advantage" (2013 manuscript)

11. Appendix Materials

12. 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).

13. Figure 2.4
Network Brokers Tend To Be Recognized Leaders
Constraint and status are computed from work discussion networks around twelve hundred managers in four organizations.
A. In the formal
organization
B. And in the informal
organization
Most Senior Job Ranks
(29.5 mean network constraint)
1%
Next-Lower,
Senior Ranks
(41.9 mean constraint)
r2 = .61
Next-Lower,
Middle Ranks
(56.4 mean
constraint)
Network Constraint
many ——— Structural Holes ——— few
Network Status (S)
(Si = Σj zji Sj, divided by mean so average is 1.0)
Percent of People within Each Level of Job Ranks
18%
Network Constraint
many ——— Structural Holes ——— few
From R. S. Burt, "The Network Structure of Advantage" (available at http://faculty.chicagobooth.edu/ronald.burt/research)

14. Figure 2.9
Diagnostic Contingency in Three Organizations
Z-Score Relative Compensation
Network Constraint
The One Other Division
r = .09, t = 1.05, P = .30
Z-Score Relative Compensation
Z-Score Relative Compensation
Network Constraint
Acquiring Management
r = -.40, t = -4.92, P < .001
Acquired Management
r = .11, t = 1.06, P = .29
Senior Men
r = -.40
t = -5.56
P < .001
Network Constraint
Early Promotion (in years)
Z-Score Relative Compensation
All But One Division of Firm
r = -.36, t = -5.66, P < .001
C. Diversity
Early Promotion (in years)
B. Merger & Acquisition
A. Leader Development
Women and Junior Men
r = .30
t = 3.38
P < .01
From R. S. Burt, "The Network Structure of Advantage" (available at http://faculty.chicagobooth.edu/ronald.burt/research)

15. 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
Figure 2.13
Essential Closure Is Around Contacts, Maintaining the
Reputations of Brokers and People in Closed Networks
Vertical axis is same as in Figure 2.11B. 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.
From R. S. Burt, "The Network Structure of Advantage" (available at http://faculty.chicagobooth.edu/ronald.burt/research)

16. 409
Who Talks
To Whom
472
255
169
219
182
629
936
948
337
355
932
682
427 690
933
484
587
947
480
440
554
252
292
441
732
59
930
949
328
394
859
935
929
924
695
899
920
855
898
895
901
894
893
892
406
557
888
563
213
628
572
139
421 36
565
503
487
907
673
769
919
297
314
144
425
692
376
356
152
747
67
937
461
131
455
78
385
883
648
613
771
538
109
685
636
887
908
900
107
889
121
999
209
11
890
848
917
821
237
120
624
20
871
194
766
164
915
74
232
761
192
760
73
137
788
926
39
884
891
33
299
804
488
818
352
311
934
927
616
312
669
506
244
801
564
428
842
882
75
556
50
48
99
797
662
931
353
689
315
380
851
540
370
95
632
190
781
885
443
126
709
879
174
896
535
384
390
110
44
61
483
656
108
303
340
91
868
270
532
909
92
254
84
34
897
770
294
677
672
905
510
782
914
886
903
922
128
47
90
155
902
923
726
220
96
802
287
561
928
145
787
132
277
910
437
306
524
258
916
Nonrespondent
904
739
301
584
234
490
832
Respondent (reports to participant)
906
318
485
911
559
Senior Executive Team (not invited to survey)
921
743
381
913
Program Participant
912
188
426
Lines indicate frequent and substantive
work contact (.15 ≤ connect ≤ 1.00)
143
321
469
233
925
172
68
387
175
105
641
69
330
432
372
324
594
434 23
918
684
98
803
223
72
55