© Hazy, Tivnan, & Schwandt Boundary Spanning in Organizational Learning: Preliminary computational explorations Jim Hazy, Brian Tivnan & David Schwandt The George Washington University Managing the Complex IV December 7-10, 2002

© Hazy, Tivnan, & Schwandt Overview Research Questions and Theoretical Basis The Value Chain Agent-based Model Hypotheses & Results Future Research Conclusions

© Hazy, Tivnan, & Schwandt Research Questions Are aspects of organizational learning emergent? –Do macro properties emerge from the stochastic, local interaction of individual agents socially and practically situated in a network? –Can computational empirical evidence be obtained to begin to answer the above? –Can this evidence be derived from a computational model built upon an axiomatic theoretical base consistent with complexity science research?

© Hazy, Tivnan, & Schwandt 20024

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6 Network Effects and Structuration Agent A 1. Resource transformation New Connection Task AResource A Resource B Transformation Knowledge A Transformation Random Connection R R

© Hazy, Tivnan, & Schwandt Network Effects and Structuration Agent B Knowledge B Agent A Task B Resource B 1. Resource transformation New Connection Task AResource A Resource B Transformation Knowledge A Transformation Random Connection R R

© Hazy, Tivnan, & Schwandt Network Effects and Structuration Agent B Knowledge B 2a. Knowledge Exchange and Learning Agent A Task B Resource B New Connection Task AResource BKnowledge A Transformation R Random Connection R

© Hazy, Tivnan, & Schwandt Network Effects and Structuration Agent B Knowledge B 2a. Knowledge Exchange and Learning Agent A Task B Resource B New Connection Task AResource BKnowledge A Transformation R Random Connection R

© Hazy, Tivnan, & Schwandt Network Effects and Structuration Agent B Knowledge B 2b. Task self-assignment based on learning (cross training assumption) Agent A Task B Resource B New Connection Task AResource BKnowledge A Transformation Random Connection R

© Hazy, Tivnan, & Schwandt Network Effects and Structuration Agent B Knowledge B Agent A Task B Resource B Resource C Transformation New Connection Task AResource BKnowledge A Transformation 3. Resource transformation Random Connection R R

© Hazy, Tivnan, & Schwandt A model of collective action: Task & reward interdependency and collective potency Collective action has been characterized as including three factors (Shea and Guzzo, 1985; Lestor, Meglino & Korsgaard, 2002) This model satisfies these factors. They are: –Task interdependency Tasks organized in precedence pattern. Success requires all tasks be executed. –Reward interdependency All tasks must be completed to have resources revitalized, I.e. for the individual agents to survive. No one agent can survive without other agents being successful –Potency Resources and knowledge are potentially available to lead to success Agent success dependent on collective success and upon available knowledge

© Hazy, Tivnan, & Schwandt The Organizational Learning Systems Model Area of Focus for this Study (Schwandt, 1997)

© Hazy, Tivnan, & Schwandt OLSM Variables Focus The Environmental Interface Sub-system outputs New Information –Measure amount of New Information (e.g., new generations of knowledge) crossing boundary under various boundary spanner conditions Dissemination and Diffusion Sub-system outputs Structuration –Measure changes to the organizational network and impact of changes on outcomes due to dissemination and diffusion of New Information (e.g., new generations of knowledge) among agents

© Hazy, Tivnan, & Schwandt Value Chain Agent-based Model The Value Chain Value Creation and Revitalization Change in the Environment Boundary Spanners Knowledge Diffusion

© Hazy, Tivnan, & Schwandt The Value Chain (Porter, 1980;1990): An organizationally realistic model

© Hazy, Tivnan, & Schwandt OR

© Hazy, Tivnan, & Schwandt Mt. Fuji Land

© Hazy, Tivnan, & Schwandt Value Creation and Revitalization But with environmental turbulence, knowledge changes through time

© Hazy, Tivnan, & Schwandt Change in the Environment The impact of frequency of change Change to the performance landscape itself through disruptive technologies or market changes is not discussed (Henderson & Clark, 1990; Christensen, 1997; Siggelkow, 2001)

© Hazy, Tivnan, & Schwandt Everyone carries knowledge but its usefulness decays

© Hazy, Tivnan, & Schwandt

© Hazy, Tivnan, & Schwandt

© Hazy, Tivnan, & Schwandt

© Hazy, Tivnan, & Schwandt Value of final product determined by the flow of new market information & the efficient diffusion of knowledge through the system

© Hazy, Tivnan, & Schwandt Model through time is stochastic: Agent’s move randomly on the grid In travels they encounter resources and other agents to interact with To avoid edges, ends of the grid are connected into a continuous torus

© Hazy, Tivnan, & Schwandt

© Hazy, Tivnan, & Schwandt

© Hazy, Tivnan, & Schwandt Hypotheses & Results Three hypotheses tested

© Hazy, Tivnan, & Schwandt Hypothesis 1: The relationship between # of boundary spanners and output is non-linear

© Hazy, Tivnan, & Schwandt Hypothesis 1: The relationship between # of boundary spanners and output is non-linear  Supported

© Hazy, Tivnan, & Schwandt Hypothesis 2: In turbulent environments, relatively more boundary spanners are associated with higher output

© Hazy, Tivnan, & Schwandt Hypothesis 2: In turbulent environments, relatively more boundary spanners are associated with higher output  Partially supported; holds for less than 50 (of 100) boundary spanners

© Hazy, Tivnan, & Schwandt Hypothesis 3: Cross-training results in increased organizational output

© Hazy, Tivnan, & Schwandt Hypothesis 3: Cross-training results in increased organizational output  Supported; plus exhibits shift to the left, i.e., fewer boundary spanners needed

© Hazy, Tivnan, & Schwandt Future Research Add to organizational realism of the model by increasing the intentionality of agents, adding agent-level replication, variation and selection and allowing new agents to be “hired” (Holland, 1975; 1995; 2001). More fluid & interconnected task & resource environment (Levinthal, 1997) –More rugged landscape and complex change scenarios, e.g., epistatic effects –Knowledge developed internally Incorporate explicit network effects (Barabasi, 2002) enabling and constraining agent action –Social networks as “small worlds” –Knowledge networks as “scale-free” Emergent persistent formal organization structures and roles (Carley, 1994) –E.g., Leadership Multiple organizations in competition –Alliances and Joint Venture –Technology and knowledge sharing scenarios Test ontological adequacy of the model (McKelvey 1999)

© Hazy, Tivnan, & Schwandt Conclusions Are aspects of organizational learning emergent? –Support for hypothesis 3 shows that macro properties can emerge from the local interaction of individual agents. –The model is derived from an axiomatic theoretical base consistent with complexity science research. –Results support experimental adequacy (McKelvey 1999) of model as representation of theory.