Organizational Learning:
New Directions
GOOD MORNING……
Learning is often depicted as a relatively
simple process of accumulation of experience
Actions
Outcome
Feedback
Details of the learning process and its
challenges
Actions
Outcome
Feedback
Paper
Challenges
Method
Alex and Pino
Performance feedback
needs to be interpreted
and constructed
Lab experiments
Erica, Carolyn, Linda and Feedback pertains to
Dennis
multiple products and
organizations experience
turnover
Empirical data analysis
Karen and Zur
The asymmetry in
anticipated
consequences
Case studies
Christina
Learning is complicated
by others in the network
Computational modeling
4
Organizational Learning in
Evolving Networks:
the Role of Alternative Growth Logics
Christina Fang
New York University
Interpersonal learning
 One key way that
individuals learn is
through sharing
ideas
With those connected to us…
7
Interpersonal Network Structure 
Interaction Pattern
Tradeoff between diffusion and
performance
 Individuals that rapidly identify and adopt higher
performing ideas from others learn efficiently
 However, too rapid diffusion of higher performing
beliefs through a population eliminates variety 
lower organizational performance in the long run
(March, 1991).
 Interpersonal structure influences the balance
between diffusion and performance (Fang, Lee &
Schilling, OS, forthcoming)
9
However
 Available evidence on the association between
structural configurations and organizational learning
is still limited
 Argote, McEvily and Reagans, 2003; Reagans and
McEvily, 2003;
 Are some networks more effective at creating and
retaining knowledge?
This work
 Extends prior works
 by
 Incorporating TWO notable empirical observations
1. Networks evolve
 Real-world interpersonal networks evolve over time
 Networks grow and constantly change through the
addition and/or removal of new members
 Network evolution follows distinctive growth logics

Preferential attachment (Barabasi and Albert, 1999)
 Existing number of links
 Distance
 Performance
12
2. Growth and learning happen
simultaneously
 As new members join the network, organizational
members need to learn and improve their performance
over time
 In reality, networks grow and learn at the same time
13
Key Question
 How do different growth logics impact
organizational learning and
performance?
14
A Formal Model
 Evolving interpersonal networks


Initial number of members
New members arrive in each period and preferentially
attach to the existing members
 Learning

Learning from superior performing individuals
(March, 1991) with whom one is connected
 Payoff

Varying degree of interdependency (Fang, Lee and
Schilling, 2009)
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How it works….
 Initial number of nodes
Network Growth Logics
 As a new node i arrives in a network, it
preferentially attaches to an existing node j
based on:
 Number of existing links of j – Link-based
 Distance between i and j- Distance-based
 Performance of j – Performance-based
Link-based logic
(init=50, time=50)
Link-based logic
(Init=50, period =150)
Link-based logic
(Init=50, period =250)
Distance-based logic
(Init=50, period =50)
Distance-based logic
(Init=50, period =150)
Distance-based logic
(Init= 50, period = 250)
Network structure matters
Degree Distribution
Number of Nodes (log)
1000
100
LinkNum
Distance
10
Performance
1
1
10
100
0.1
0.01
Number of Links (log)
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Performance over time differs
60
50
Performance
40
30
LinkNum
20
Distance
10
Performance
0
0
100
200
300
400
500
600
700
800
900
1000
Time
25
Dual rule of hubs
 Hubs facilitate diffusion of good ideas
 dramatically shorten the path length of a network
 But also speed up convergence and reduce
heterogeneity
26
Initial heterogeneity matters
80
LinkNum
70
Distance
Performance
60
Performance
50
40
30
20
10
0
Init=3
Init=10
Init=50
Init=100
Init=150
Inititial number of nodes
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Eventual heterogeneity less so
70
60
Total=300
Total=600
Final Performance
50
40
30
20
10
0
LinkNum
Distance
Performance
Total Nodes and Growth Logics
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Why such waste of new ideas?
80
70
60
Performance
50
40
Total=300
30
20
Total=600
10
0
0
200
400
600
800
Time
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Two possible explanations
 Rigidities of the learning mechanism
 Majority learning rule – superior performer may not be the
majority?
 New ideas are on average of extremely low quality
 Majority of newcomers perform around zero
30
If a high performer comes in…
Performance
Effect of High Performer Injection
(Injected Performance=90)
100
90
80
70
60
50
40
30
20
10
0
No Injection
With Injection
at t=150
LinkNum
Distance
Performance
Growing Mechanism & With/Without Injection
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Some preliminary results
 Distance based logic outperforms both link-based and
performance based logics
 Partly explained by network structure - the dual role of hubs
 Network growth introduces new ideas and new sources of
novelty
 Yet initial heterogeneity seems much more important in
determining the outcome of learning and performance
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