1. Learning Links: Social Networks and Organizational Learning Presentation to the PhD Tribunal IESE/Universidad de Navarra Jordi Comas, Candidate, 2007

5. Black Box 1- Org Learning Adaptation (Routines, Artefacts, Strategy) Stimulus (Dissatisfaction, Puzzle, Curiosity) 1) Org Learning  Learning

6. Black Box 2- Network Effects 2) Networks ↓ Networks of information and influence Outcomes (Adaptation and Changed Networks) StructuredAction

7. Inside the two 2 Black Boxes 1) “Learning” 2) “Action” Stimulus (Dissatisfaction, Puzzle, Curiosity) Adaptation (Routines, Artefacts, Strategy) Networks of information and influence Outcomes (Adaptation and Changed Networks) 1) Org Learning  2) Structure ↓

9. The Learning-Network Nexus (the inside of the two overlapping black boxes). Networks Knowledge Creation Networks Knowledge Retention Networks Knowledge Transfer

10. Network-Theoretical Perspectives A fruitful analysis of any human action-- including economic action, my subject here—requires us to avoid the atomization implicit in the theoretical extremes of under- and over-socialized views. Actors do not behave or decide as atoms outside a social context, nor do they adhere slavishly to a script written for them by the particular intersection of socio-cultural categories they happen to occupy. Their attempts at purposive action are instead embedded in concrete, ongoing systems of social relations (Granovetter, 1992, 32). People do learn, but a person and her knowledge are not bounded by the skin and skull; both are dispersed across network ties. The actor is a nexus of relationships, and knowledge is stored and used through activating ties. This perspective is a relational perspective, and it is an approach that threads the needle between over and under-socialized views of people and actions (Comas 2007). The shift is away from mechanistic, steady-state concepts of organizations and towards concepts that incorporate change, flux, and real time distributed action and decision-making. … An action perspective grasps organizations as complex systems where many different things are always happening at once, where the global behavior of the organization as a whole is grasped as ‘emergent’ out of local and individual action rather than from any top-down plan or design (Nohria and Berkley, 1994, 73).

11. Convergence with Recent Trends in Organizational Learning Social relationships matter for knowledge creation, retention, and transfer. When properties of units, properties of relationships and properties of knowledge fit or are congruent with each other, knowledge retention, and transfer increase. Knowledge creation, by contrast, may be stimulated by a lack of congruence or parts that do not fit together. Experience can be structured to promote learning outcomes in firms. Where boundaries are drawn matters for knowledge creation, retention, and transfer…And embedding knowledge in transactive memory systems, short-hand languages, routines, technologies, and other knowledge repositories can promote knowledge retention and transfer in firms (Argote, McEvily, & Reagans, 2003a). “… sophisticated forms of intelligence emerge from the interactions among loosely linked organizational components …This also implies that the most critical aspect of knowledge management is not the management of knowledge content per se . Rather, it has to do with creating an environment rich with knowledge cues and managing the social processes by which organizational units interact ” (Fiol 2002, 120).

21. Knowledge Census

23. Overview of a Mg 101 Company

30. Research Questions Brokerage Closure Idea Generation ? ? Research Question 1: Which Form of Social Capital Will Make an Actor More Likely to Have an Idea?

31. Research Question 2: Does Brokerage or Closure Affect Radical Ideas? Research Question 3: Does Brokerage or Closure Affect Adoption? Can Either Overcome the Liability of Radical Ideas? Brokerage Closure Radical Ideas Adopted ▬ ? ? ? ?

33. Descriptive Statistics

34. Descriptive Statistics

36. Results (using binary logistic regression) Idea Generation RQ1: What effects idea generation? Results : The model explains almost no variance. Individual action matters more than social capital for generating ideas Agency, Creativity

37. Results (using binary logistic regression) RQ 2: What effects idea quality? Results: Brokerage as constraint does. A more likely effect is 1.5 times (SD of constraint x coefficient). RQ 3: What effects idea adoption? Results: Closure has a slight negative effect when it is simply more connections (In Degree). Brokerage as flow has a strong effect and is greater than the liability of radical ideas. Brokerage as Constraint Closure as InDegree Radical Ideas Adopted 1/3 4 - 5% 2 Brokerage as Flow

45. Figure 4.1 Exploration-Exploitation Balance as Network Problem Dynamic View of Exploration/Exploitation

49. Ascertaining exploration/exploitation

50. Ascertaining exploration/exploitation II

58. Response Rates to Network Surveys

59. Regression Models from Chapter 2

60. Table 4.3- Overview of Network Measures Valued for both Embedding Advice and Friendship Valued for both Embedding Advice and Communication Binary (Strong Advice) x i ↔x j =(1,0) Weighted Clustering Coefficient Valued, Digraph (Advice) x i  x j =(1-5), x j  x i =(1-5) Balance - Weak Transitivity Valued, Symmetrical (Advice) x i ↔x j =(1-5) Centralization to most central (eigenvector) Binary (Strong Advice) x i ↔x j =(1,0) Centralization- Valued Digraph (Advice) x i  x j =(1-5), x j  x i =(1-5) Cohesion II- Valued Digraph (Advice) x i  x j =(1-5), x j  x i =(1-5) Cohesion I- Binary (Strong Advice) x i ↔x j =(1,0) Density- Relationship Network Property

62. R  T Even more dramatic example of above. 0.68 0.233 Embedding Advice and Friendship T  T More embedding will help norm reinforcement. 0.619 0.46 Embedding Advice and Communication R  T Same as above. 28.9 0 Weighted Clustering Coefficient R  T Much more closure. 73.8 38.2 Balance Transitivity- R  T This is highest centralization to central actors. 16.05% 13.07% Centralization to most central (eigenvector) R  T. Central actors, in this case the officers, have many more links to them. 49.90% 10.57% Centralization- R  T Getting less cohesive while more dense suggest centralization. 0.5 0.563 Cohesion II- R  R Least cohesive of four, and increasing. 2.3 2.08 Cohesion I- R  T. Most change of the four companies and becomes densest company. Biggest SD of density which is consistent with more centralization. .13 (.39) .03 (.18) Density (Standard Deviation)- Trajectory R=Exploratory; T=Exploitative Time 2 Time 1 Category Company A- OnTrack

63. Company B: Inertia: Inertial Density Leads to Exploitation T  T Communication and friendship are embedded with advice, a trend that simply continues. 0.59 0.47 Embedding Advice and Friendship T  T Communication and advice are embedded with advice, a trend that simply continues. 0.62 0.5 Embedding Advice and Communication R  T They ended with the lowest clustering after staring with the second highest. 23.5 23.3 Weighted Clustering Coefficient T  T Triples that exist close off. 66.5 48.1 Balance Transitivity- T  T Each actor closer to central. Most centralized of the four. 16.96% 11.81% Centralization to most central (eigenvector) T  T Closer to star. 45.87% 19.89% Centralization- T  T Closer to each being one degree. 0.52 0.43 Cohesion II- T  T Getting more cohesive. Shorter paths among members. Only company to move in this direction. 2.33 2.84 Cohesion I- T  T Starts as the most dense, ends as second most. .10(.30) .081 (.273) Density- Trajectory R=Exploratory; T=Exploitative Time 2 Time 1 Category Company B- Inertia

64. R  T Towards embedding and exploitation. 0.41 0.19 Embedding Advice and Friendship T  T Flat and towards exploitation. 0.62 0.56 Embedding Advice and Communication R  R Clustering not increasing, and is fairly high in beginning and end. 39.3 37.5 Weighted Clustering Coefficient T  T More closure of triples. Reflects move to exploitation. 76.8 50.8 Balance Transitivity- T  R Moving to less centralized in terms of connection to central actors on average. Means that slight increases in density and balance (below) are not contributing to centralization. 9.67% 13.13% Centralization to most central (eigenvector) T  R More centralization, but by end much less than A and B. 23.65% 11.82% Centralization- T  R Longer path length, less cohesive. 0.43 0.48 Cohesion II- T  R Less cohesive, increasing possibility for exploration. 3.0 2.5 Cohesion I- T  R Flat-- Still low density and with substantial standard density. Not very dense at the end in line with more exploration. .07 (.26) .06 (.24) Density- Trajectory R=Exploratory; T=Exploitative Time 2 Time 1 Category Company C- Backtracker

65. Company D: Explorer: Flattening Network Leads to Perpetual Exploring T  R Starts high, but little increase in embedding. 0.59 0.49 Embedding Advice and Friendship T  R Starts high, but little increase in embedding. 0.46 0.48 Embedding Advice and Communication T  T Low clustering early and late. 24.2 10.7 Weighted Clustering Coefficient R  R Given the increase in density but lack of increase in centralization, the extra connections have to go somewhere. In clustering and transitivity, D is increasing. 74.6 56.7 Balance Transitivity- R  R Also flat. 13% 13% Centralization to most central (eigenvector) R  R Very uncentralized. No star actor. 18.85% 13.45% Centralization- R  R Cohesiveness declining slightly from initially moderate level 0.48 0.5 Cohesion II- R  R Cohesion does not budge. Longer paths between actors means more social distance. 2.74 2.75 Cohesion I- T  R Density stays low. .09(.286) .041 (0.198) Density- Trajectory Time 2 Time 1 Category Company D