Networks
15.301 Managerial Psychology
John S. Carroll
Congruence Model
(Nadler & Tushman)
Transformation Process
Environ.
Resources
History
Informal
Org’n
St
ra
te
gy
Inputs
Task
Formal
Org’n
Outputs
Org’n
Group
Individual
People
Feedback
Social Networks
• Two powerful social principles:
– Similarity: people who are similar tend to like
each other and spend more time together
– Propinquity: people who are in proximity
tend to spend more time together
• Your friends are most likely people who
are similar to you (on what dimensions?)
and who are nearby
• What about in the workplace?
Communication At Work (T. Allen)
0.35
0.30
Probability of communication
0.25
0.20
0.15
- Samples A and B combined
> 200’ apart,
people don’t talk
- Samples C
0.10
0.05
0
20
40
60
80
100 120 140 160 180 200 220 240
Distance (feet)
Probability of communication as a function of the
distance separating pairs of people
Figure by MIT OCW.
What Makes It A Network?
• Similarity and propinquity tend to be
transitive, i.e., A is similar to B, B is similar
to C, so A is probably similar to C
• Balance Theory – we like things to be
consistent
A
A
+
B
+
+
C
+
B
A
-
+
C
+
B
-
C
Sample Network
• Who are your 5 best
friends at MIT?
• What do you have in
common with each
(similarity) and how
near are they in
space (e.g., same
dorm room, same
floor,…)?
Name
1_____
2_____
3_____
4_____
5_____
Common Near
Are Your Friends Friends?
1____
2____
3____
4____
5____
1____
2____
3____
4____
5____
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
Network Diagram
ME
5____
1____
2____
JOHN
JAMAL
JILL
4____
3____
JOE
JANE
JUAN
Are You A “Connector”?
• “Connectors” (Malcolm Gladwell, The
Tipping Point) or “Stars” (Tom Allen) are
people who link to many other people
• Gladwell has a test (see attached) based
on random names from Manhattan phone
book
• Students average around 20, older people
around 40
• Connectors score around 100 or more
Score a point for anyone you know (friends and acquaintances) with
that last name. You may count multiple people for the same name.
Algazi, Alvarez, Alpern, Ametrano, Andrews, Aran, Arnstein, Ashford, Bailey, Ballout,
Bamberger, Baptista, Barr, Barrows, Baskerville, Bassiri, Bell, Bokgese, Brandao, Bravo,
Booke, Brightman, Billy, Blau, Bohen, Bohn, Borsuk, Brendle, Butler, Calle, Cantwell,
Carrell, Chinlund, Cirker, Cohen, Collas, Couch, Callegher, Calcaterra, Cook, Carey,
Cassell, Chen, Chung, Clarke, Cohn, Carton, Crowley, Curbelo, Dellamanna, Diaz, Dirar,
Duncan, Dagostino, Delakas, Dillon, Donaghey, Daly, Dawson, Edery, Ellis, Elliott,
Eastman, Easton, Famous, Fermin, Fialco, Finkelstein, Farber, Falkin, Feinman,
Friedman, Gardner, Gelpi, Glascock, Grandfield, Greenbaum, Greenwood, Gruber, Garil,
Goff, Gladwell, Greenup, Gannon, Ganshaw, Garcia, Gennis, Gerard, Gericke, Gilbert,
Glassman, Glazer, Gomendio, Gonzalez, Greenstein, Guglielmo, Gurman, Haberkorn,
Hoskins, Hussein, Hamm, Hardwick, Harrell, Hauptman, Hawkins, Henderson, Hayman,
Hibara, Hehmann, Herbst, Hedges, Hogan, Hoffman, Horowitz, Hsu, Huber, Ikiz, Jaroschy,
Johann, Jacobs, Jara, Johnson, Kassel, Keegan, Kuroda, Kavanau, Keller, Kevill, Kiew,
Kimbrough, Kline, Kossoff, Kotzitzky, Kahn, Kiesler, Korte, Liebowitz, Lin, Liu, Lowrance,
Lundh, Laux, Leifer, Leung, Levine, Leiw, Lockwood, Logrono, Lohnes, Lowet, Laber,
Leonardi, Marten, McLean, Michaels, Miranda, Moy, Marin, Muir, Murphy, Marodon,
Matos, Mendoza, Muraki, Neck, Needham, Noboa, Null, O’Flynn, O’Neill, Orlowski,
Perkins, Pieper, Pierre, Pons, Pruska, Paulino, Popper, Potter, Purpura, Palma, Perez,
Portocarrero, Punwasi, Rader, Rankin, Ray, Reyes, Richardson, Ritter, Roos, Rose,
Rosenfeld, Roth, Rutherford, Rustin, Ramos, Regan, Reisman, Renkert, Roberts, Rowan,
Rene, Rosario, Rothbart, Saperstein, Schoenbrod, Schwed, Sears, Statosky, Sutphen,
Sheehy, Silverton, Silverman, Silverstein, Sklar, Slotkin, Speros, Stollman, Sadowski,
Schles, Shapiro, Sigdel, Snow, Spencer, Steinkol, Stewart, Stires, Stopnik, Stonehill,
Tayss, Tilney, Temple, Torfield, Townsend, Trimpin, Turchin, Villa, Vasillov, Voda, Waring,
Weber, Weinstein, Wang, Wegimont, Weed, Weishaus
R&D Lab Network (T. Allen)
27
21
13
41
4
36
70
14
19
39
24
74
29
7
56
71
57
9
54
66
10
30
59
53
15
32
12
11
45
61
65
16
62
17
51
77
63
49
26
34
40
44
37
68
15
50
31
4
73
64
35
52
67
75
58
47
20
38
72
63
20
3
48
Typical communication network of a functional department in a large R&D laboratory
Figure by MIT OCW.
p.s. look for “stars” and “isolates”
Network Structure
• Density is measured as the number of ties out
of the number possible (is more always better?)
• Symmetry is whether ties are reciprocated –
liking often is, but respect?
• Structural holes are places where ties are
missing, and therefore opportunities for brokers
to gain power or social capital by becoming key
links, at the extreme becoming “bowties” where
many people are dependent on a single link
• Networks also connect externally, e.g.,
gatekeepers are conduits for external info (in
R&D labs, they go to conferences, read journals)
Corporate Architecture
• If communication patterns follow distance
between people, then how is that distance
determined?
• For face-to-face communication, it’s the
architecture of buildings that matters
• What kinds of designs inhibit or facilitate
communication? E.g., is engineering “inside the
fence”?
• We are just beginning to understand what new
technologies (email, IM, etc.) do to networks.
What would you predict?
Strength of Ties
• Strong ties are like “cohesiveness” in that they
create closed systems: “us” and “them”
• Granovetter’s famous paper “The Strength of
Weak Ties” found that people get jobs through
acquaintances, i.e., weak ties, rather than close
friends (strong ties)
• Hansen found that different kinds of information
travels by strong vs. weak ties: you need weak
ties to search broadly for quick and simple stuff,
but transmission of tacit or complex information
requires strong ties
Kinds of Networks
• We drew a friendship or liking network, but
networks can be drawn on other bases:
– Who do you talk to every day?
– Who do you go to for help or advice?
– Who do you respect?
– Who do you trust?
• These networks are separate facets, not
necessarily a single network
Network Analysis
• The informal network may explain why things happen in
organizations, good and bad
• Krackhardt & Hanson report studying a bank with 80%
turnover rate among tellers. It turned out that when key
players who were trusted by tellers left, the others in
their trust network left in droves
• One bank fired someone who appeared to be an
“isolate” in their internal network, but neglected to
consider that the person had very strong ties with the
bank’s largest customer!
• Following a transfer between divisions of a large
chemical firm, the transferred persons provided an
increasingly effective communication link for 1½ years,
then decreased so after 5 years, isolation and poor perf.
Computer Company Case
• Customized office information systems
• Field design group designs and installs,
generates most revenue, led by tech superstar
• CEO tried to address missed opportunities from
not using other divisions more; conflict and
jealousy among divisions
• CEO formed a strategic task force, put a
credible, proven performer in charge, but the
task force deadlocked in conflict. Why? What
would you do as CEO to fix this?
Org Chart Shows Who’s On Top
Leers (CEO)
Software
Applications
Field
Design
Integrated
Communications
Technologies
Data
Control
Systems
O'Hara (SVP)
Calder (SVP)
Lang (SVP)
Stern (SVP)
Blair
Harris
Muller
Huttle
Stewart
Ruiz
Benson
Jules
Atkins
Fleming
Baker
Kibler
Church
Daven
Martin
Thomas
Zanado
Lee
Wilson
Swinney
Carlson
Hoberman
Fiola
Figure by MIT OCW.
Advice Network Reveals Experts
Blair
Church
Jules
Baker
Thomas
Swinney
Harris
Lee
Muller
Daven
Lang (SVP)
O'Hara (SVP)
Martin
Calder (SVP)
Fiola
Wilson
Zanado
Leers (CEO)
Ruiz
Carlson
Fleming
Hoberman
Benson
Stern (SVP)
Huttle
Atkins
Kibler
Figure by MIT OCW.
But When It Comes To Trust…
Church
Lee
O'Hara (SVP)
Huttle
Lang (SVP)
Stewart
Ruiz
Calder (SVP)
Benson
Leers (CEO)
Hoberman
Daven
Carlson
Baker
Blair
Swinney
Fleming
Fiola
Harris
Thomas
Martin
Kibler
Atkins
Muller
Figure by MIT OCW.
Another Trust Problem
• Look back at the charts to find another problem
with a key manager who had also been
promoted because he was the best producer
• Unfortunately, this person regularly told people
they were stupid and paid little attention to their
professional concerns. The CEO had assumed
that this leader had good relationships with this
team (see next chart on CEO’s perception of the
trust network).
• What to do?
How the CEO Saw Trust
Martin
Harris
Church
Calder (SVP)
Wilson
Lee
Hoberman
Fiola
Benson
Fleming
Swinney
Carlson
Figure by MIT OCW.