Learning Links: Social Networks and Organizational Learning

Presentation to the PhD Tribunal

IESE/Universidad de Navarra

Jordi Comas, Candidate, 2007

Let’s Study Org Learning

• How do organizations “go from ideas to action, from the spark of the possible to the application of the practical?”

• Organizations are all about learning in response to the environment.– Learning includes processes in which the members of an

organization 1) acquire information about the organization and its environment 2) turn that information into various kinds of knowledge and 3) perform based on the knowledge at the individual and collective level (synthesized from Leavitt and March, Schulz, Argote)

• Organizations are made up of people and knowledge. • People and knowledge come from a social context=they

have a social component.

Networks…

• Networks are the concrete differences in social context. They embody the past and shape the future. They are the structures of information and influence which enable and constrain actors.– Hence, time is critical to unlocking enabling and

constraining.• Networks exist at many levels: dyad, group,

whole network (organization), and society.• Network research focuses on dynamics of

networks (endogenous effects) and dynamics on networks (exogenous effects).

Black Box 1- Org Learning

Adaptation (Routines, Artefacts, Strategy)

Stimulus (Dissatisfaction, Puzzle, Curiosity)

1) Org Learning

Learning

Black Box 2- Network EffectsNetworks of information and influence

Outcomes (Adaptation and Changed Networks)

2) Networks ↓

StructuredAction

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 ↓

Figure 1.2: Inside the Black Box

Organizational Learning is the creation, retention, and transfer of knowledge about the problems and solutions an organization faces.

Knowledge is multidimensional (tacit-explicit and group-individual).

Networks are the multilevel architecture (dyad-group-network) of flows of information and influence.

Networks

Knowledge Creation

Networks

Knowledge Retention

Networks

Knowledge Transfer

The Learning-Network Nexus (the inside of the two overlapping black boxes).

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).

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).

Research site

• 4 Companies of Management 101 [Mg 101] students in Fall 2003. – MG 101 companies start as undifferentiated groups assigned

according to individual schedule preferences (pseudo-experiment).

– Double-bottom line companies (financial and social performance).

– Intense experience for participants

• Overall, constrained environment • Overall, valuable for longitudinal data on networks and

learning• Applicability is more to the process than the type of

organization.

Methods Used

• Mixed methodology of quantitative (network surveys) and qualitative data (census, interviews, papers, archives, observations).

• Data collected and analyzed simultaneously.

Breaking Down the Learning-Network Nexus

• I looked at particular moments in the learning-network nexus.

• One moment is individuals and creating knowledge (Ch 2).

• A second is groups and retaining and transferring knowledge (Ch 3).

Overview of a Mg 101 Company

Brokerage and/or Closure

• How does the individual’s network position effect learning in terms of knowledge creation and retention?

• Knowledge creation and retention observed as ideas:– Idea formation– Idea quality (radical or incremental)– Idea adoption

• Network position=social capital.

Closure

•Ego is central and embedded

•Greater trust, control, cultural consistency

•Effective access to others’ knowledge

Brokerage

•Ego is central and his alters’ are not linked.

•Greater opportunity to leverage alters

•Greater access to diversity of ideas.

Summing up…

• Having ideas, in this limited case, has little to do with one’s social capital of advice-seeking relationships.

• Brokerage does matter for having radical ideas• There are two kinds of brokerage…

– Brokerage as flow matters for having ideas adopted even while radical ideas are less likely to be adopted.

• Idea involvement (agency) boosts closure for an actor over time.

• Such agency has little effect on brokerage as constraint. Brokerage, important for radical ideas and idea adoption, comes from endogenous network effects.– Initial closure, centrality and later popularity lead to later LOW

constraint.– Initial flow leads to later HIGH constraint.

Suggested research strands…apparent paradoxes

• The paradox of radical ideas:– The social capital that helps an actor have a radical idea is not the same

social capital that can helps get an idea adopted. What’s worse, radical ideas by themselves endure a liability of being radical. Organizations in need of more radical ideas to feed into strategy making, organizational learning, or innovation will want to unlock this paradox since the people best able to generate radical ideas may not have the social capital to overcome the inertia of incremental conservatism.

• The paradox of brokering: – Actors who fill structural holes, those who in this context were more

likely to generate radical ideas, may lose their advantageous position as they enact their brokerage opportunity. Ideas bring people together; as we saw here having ideas tended to add to one’s centrality. The impulse to connect one’s alters, to enact the latent value of structural holes may have the effect of winnowing future brokering opportunities.

Juggling Exploration/Exploitation

• Tackling other elements of learning-network nexus: knowledge retention and transfer at level of subgroup (clique) and whole network.

• This has been discussed as the trade off between exploration and exploitation.– “The essence of exploration is experimentation with

new alternatives. Its returns are uncertain, distant, and often negative.”

– The essence of exploitation is “the refinement and extension of existing competences, technologies, and paradigms. Its returns are positive, proximate, and predictable” (March 1991, 85).

The stakes to balancing (or juggling)

• Levinthal and March (1993) argued that “The basic problem confronting an organization is to engage in sufficient exploitation to ensure its current viability, and, at the same time, to devote enough energy to exploration to ensure future viability” (105).

Exploration/exploitation

• Due to limited resources, exploration/exploitation are orthogonally related at any one moment in time.– Resources include:

• Capital• Attention• Cognition

– I argue that network structure is a fourth constraint leading to the trade-off between exploration and exploitation.

Figure 4.1 Exploration-Exploitation Balance as Network Problem

Exploration and Exploitation

Exploration Network Flows Exploitation Network Flows

Lower Density Higher Density

Less Cohesive More Cohesive

High Clustering Low clustering

Not centralized (Dispersed) Centralized

Balanced Unbalanced

Unembedded

Embedded

Each property is a continuum: learning

tradeoffs are associated with network tradeoffs

Dynamic View of Exploration/Exploitation

My approach

• Look for a relationship between exploration/exploitation and network structure.

• Examine how changes in exploration/exploitation and networks evolve over time.

• A holistic approach to observing exploration/exploitation

• Quantitative, descriptive, approach to networks

“Connected Clustering”

• To ascertain clustering (cohesion):– Number of components– Number of overlapping cliques

• To ascertain connection– Advice/communication embedding– Strong advice/weak communication

embedding– Overall cohesion (strength of weakest path)

Ascertaining exploration/exploitation II

OnTrack: Early Exploration

Data Display 3.1 Number of Ideas in Play 10 Number Unique to the Company 5 Number of Unique Ideas with Longevity

4 (80%)

Business Project: “Tooters” T-shirts Exploratory Service Project: Building library and renovating SIMON House

Exploratory

Backtracker: Exploitative Learning

Data Display 3.3 Number of Ideas in Play 14 Number Unique to the Company 5 Number of Unique Ideas with Longevity

2 (40%)

Final Business Project: Pint Glasses Exploitative Final Service Project: Habitat for Humanity [HfH]

Exploitative

On Track Explorer Back-tracker

Inertia

Exploratory Mild Explorator

Exploitative Mild Exploitative

Basics Advice Density (Standard Deviation)

0.03 (0.18)0.041

(0.198)0.062 (0.243) 0.10 (0.30)

Advice Betweenness, Mean and (Standard Deviation)

0.25 (0.76) 1.1 (2.5)  1.5 (2.5) 5.4 (8.6)

Advice Sub Groups

0.00% 10.70% 37.50% 23.20%

7 3 3 1

0 1 6 12

Communication Connection 0.31*** 0.49*** 0.56*** 0.468***

0.06 0.124 -0.015*** 0.21***

46.30% 56.00% 42.50% 43.00%

Network Property

Advice Clustering

Table 3.3- Comparative Network PropertiesCompany

Asymmetric Embedding: Strong Advice and Weak CommunuictionCommunication Cohesion  (for all strengths)

For QAP Correlations: *** p<.001, All calculations made using Borgatti, Everett, and Freeman 2002.

Number of ComponentsNumber of Strong Cliques (of at least 3)

Overall Embedding: Communication and Advice

Research Hypothesis

☼ Hypothesis One: Dynamic networks and shifts in exploration-exploitation (ambidexterity) are associated with each other

☼ Hypothesis Two: The learning-network association leads to better adaptation.

Table 4.4- Summary of Learning Trajectory, Adaptation, and Performance.

A: On Track B: Inertia C: Backtracker D: Explorer Network Learning Trajectory (time1 to time2) Exploration to

Exploitation Exploitation to

Exploitation Mild Exploitation

to Mild Exploration

Exploration to Exploration

Adaptation by Planning (100%=Perfect Prediction) 88% 66% 77% 83%

Effectiveness Rank 1 2 3 4 Efficiency Rank 1 2 4 3

Changes in Key Network Dynamics

Density R T TT T R TR

Clustering R T R T RR TT

Centralization R T TT TR RR

Balance R T TT TT RR

Embedding in Friendship R T TT RT TR

Embedding in Advice TT TT TT TR

Conclusions• OnTrack benefited the most, with the highest evaluations of their

performance and the highest adaptation score. • Inertia was persistently exploitative. It suffered less for going against

the grain from exploration to exploitation, than Backtracker or Explorer did (they ended in more exploratory learning and networks.)

• Suggestively reinforces echoes March’s (1991) finding that short-term pressures to be myopic towards favoring (seeing) exploitation opportunities perpetuate exploitative learning.

• Inertia did well in this setting, but if it needed to exist for longer or adapt to more dynamic circumstances, its lock in to exploitative learning may have left it disadvantaged, relative to a company like Backtracker that demonstrated more dynamic network formations.

Overall Conclusions

• Why “Learning Links”? The two fields need each other:– The actors who do the learning in organizations are

always embedded in networks, “on-going systems of concrete relations.”

– Knowledge, the material of learning processes, is socially mediated.

– Actors, in the course of learning, create and recreate their networks. Often in ways that are surprising or counter-intuitive.

From here?

1. Examine the apparent paradox of brokering with more rigorous research design. Examine individuals in multiple networks and track network and action over longer period of time.

2. Develop more precise theory of how knowledge types are effected by network ties.

3. Examine networks in new, synthetic worlds.4. Explore network forms of organization as a

development of the Information Age (Castells). One salient example is Al-Qaeda from the mid 1990s-2001 and 2001-2007.