university of navarra complexity and strategy eiasm academic council prof. joan e. ricart iese...
TRANSCRIPT
University of Navarra
Complexity and Strategy
EIASM Academic Council
Prof. Joan E. RicartIESE Business School
October 11, 2006
University of Navarra
2
Prof. Joan Enric Ricart [email protected]
Agenda
1. Limits on classical organizational dynamics
2. Complex adaptive systems
3. Complexity in management literature
4. An example: Corporate level decision in turbulent environments.
University of Navarra
3
Prof. Joan Enric Ricart [email protected]
Analysis anddiagnostic
Of the current statusChoose corrective
action
Select and Realization of
the futureImplement corrective
action
ResultsForecast vs. realized
FeedbackDeviation fromplan
Classical Organizational Dynamics
The search for equilibrium
BASED ON: The search of a goal and the need to adapt to the environment: Strategic Planning and Control Systems
University of Navarra
4
Prof. Joan Enric Ricart [email protected]
1. Not possible to use scenarios for all possible events.
2. From the 70’s more difficult to forecast due to:Deregulation and privatizationGlobalizationTechnological development
3. Three key ignored factors:The existence of positive feedbacksAmbiguity and paradox are inherent to the firmThe social construction of reality
Limitations
Classical Organizational Dynamics
University of Navarra
5
Prof. Joan Enric Ricart [email protected]
Four Alternative Views
Process Engineering
SystemsDynamics
Mathematical Complexity
Social Complexity
Taylor, Demming, Hammer, Argyris, Senge, Checkland,
Langton, Kauffman, Wolfram Stacey, Cilliers, Juarerro
ObjectiveRule-based
Subjective Heuristics-based
Established OrderProspective Coherence
Emergent Order Retrospective
Coherence
Complexity sciences as an explanation of how novelty emerges
University of Navarra
6
Prof. Joan Enric Ricart [email protected]
Complex Adaptive Systems: Definition
• Nº of agents behaving according to their own principles of local interaction (“Microinteractions”)
Stable equilibrium Random chaos Edge of chaos
• Patterns of evolution emerge in the interaction between agents, neither by choice of “designer” nor by chance
No agent capable of determining patterns of whole system No agent is “designer” from outside the system CAS also display a broad category of dynamics
Eg: food distribution in a big city
University of Navarra
7
Prof. Joan Enric Ricart [email protected]
Complex Adaptive Systems: Modeling in biology
K=0“smooth landscape”
stable attractorsurvival strategy is easy to copyremove “competitive advantage”
K is high“rugged landscape”
high number of attractorsextreme: properties of mathematical chaos
as conflicting constraints multiply
Fitness reflected by height of positions in a “fitness landscape”
• Network evolve trying to survive increasing their fitness
Highest fitness at intermediate levels of K “edge of chaos”
N entities or agents form the network (gene)K= nº of connectionsThe different agents can take two values (0;1)Each value has an associated “fitness value
• Kauffman’s NK boolean networks (biology, genetics)
University of Navarra
8
Prof. Joan Enric Ricart [email protected]
Complexity in management literature
1. Complexity sciences used as source of loose metaphors
2. Complexity sciences as a framework about learning systems
• NK modeling
• Industry-level studies
• Self-organized interaction driven by simple rules “hidden order”
• Dynamics at “the edge of chaos”
• Fitness landscapes as set of possible structures to choose
University of Navarra
9
Prof. Joan Enric Ricart [email protected]
“Self organizing based in Simple rules”
“Designed emergence” (Pascale, 1999) They choose broadly what emerges
• Idea: Managers should manage the context and allow self- organizing to arise fruitfully (Morgan, 1997; Eisenhardt & Brown, 1998) Issue set of “simple rules” (Eisenhardt & Sull, 2001) Let the organization evolve freely within them
• Managers condition emergence
• Implications The message of complex sciences on how novelty emerges is lost Designer of “simple rules” outside the system No novelty, just unfolding of states within the simple rules Message already present in Systems Dynamics Emergence “allowed” only at superficial level Control is centralized in “designer", not property of micro-interactions
University of Navarra
10
Prof. Joan Enric Ricart [email protected]
NK networks in Social Science
• Problem: biology assumes total decomposability of the network
• Several papers use NK networks in social science
• Solution: works assuming near decomposability (Gavetti, 1999; Gavetti, Levinthal & Rivkin, 2003; Caldart & Ricart 2003, Siggelkow & Levinthal, 2003; Siggelkow and Rivkin, 2003)
Levinthal (1997), Mc Kelvey (1999)
High level decisions impose “majority rule” to low level decisions
High level decisions made on the basis of bounded knowledge of the network’s payout (fitness) structure
Decomposability solved by bringing back “the designer” to the picture
Firms are near decomposable systems (Simon, 1968) Interactions within units more intense than between units
University of Navarra
An example
”Corporate Level Decisions in Turbulent Environments:
A View from Complexity Theory”
Adrián Caldart & Joan Enric Ricart
University of Navarra
12
Prof. Joan Enric Ricart [email protected]
Long lasting (and open) debate on whether and how thecorporate level contributes to competitive advantage
CL contributes (Brush & Bromiley, 1997; Bowman & Helfat, 2001) CL doesn’t contribute. (Schmalensee, 1985; Rumelt, 1991; Mc Gahan & Porter 1997)
Mixed results suggest that new approaches would be welcomed
Recent literature focuses on design issues approached from the complexity paradigmCase studies of companies exposed to “turbulent environments”
Turbulent environments: high dynamism, complexity and uncertainty
(Galunic & Eisenhardt, 2001; Chakravarthy et al, 2001)Agent based simulations exploring how design issues affect firm’s evolution
(Levinthal, 1997; Mc Kelvey 1999; Gavetti and Levinthal, 2000)
Research Question:
How does the corporate level affect competitive advantage in turbulent environments?
Motivation
University of Navarra
13
Prof. Joan Enric Ricart [email protected]
Framework :Corporate Strategy Triangle
Purpose: to provide lenses to approach the field study
Cognition Representing the fitness landscapeImperfect due to bounded rationality
Corporate search strategyLocal searchOn line long jumps (commitment)Off line long jumps (real options,
alliances)Recombination
Architectural design Management of interdependenciesCenter-unit / Unit-Unit. Self-organizationAction-payoff relationshipsBalance. Prevent “error” or “complexity” catastrophes
CorporateStrategy
University of Navarra
14
Prof. Joan Enric Ricart [email protected]
Simulation experiments: Purpose • To explore the relationship between the three building
blocks of the CST in a formal and general way
• To observe the behavior and the relative performance of varied configurations of the CST under different environmental settings
• Findings in a previous fieldwork (Caldart & Ricart; 2003) led us to explore a particular concern:
Environmental turbulence requires to increase internal
complexity (Ashby’s law). Then,
Should a change in internal complexity affect qualitatively
corporate strategy making?
University of Navarra
15
Prof. Joan Enric Ricart [email protected]
Simulation experiments: Model Adaptation of Kauffman’s NK model
Simulated firms have P=3 divisions with D=3 functionalpolicies each (N=9). Hierarchy of choices.
Parameter K is divided in two: KW (intra-divisional links) and KB (interdivisional links)
Divisional strategy limited by majority ruleEight possible corporate strategies (23)Each CS has 64 possible configurations (43)
Firms are assumed to match environmental variety throughtheir architectural design (Ashby’s law)
Higher KW and KB imply an attempt to match internally a higherdegree of external turbulence
Software: Java-based ad-hoc program
University of Navarra
16
Prof. Joan Enric Ricart [email protected]
Simulation experiments: Model Seven Evolution Patterns (combinations of cognition and search strategy)
are released on each kind of landscape:
Corporate Strategy Cognition1 Intelligent Disciplined (ID) Smart Local search led by cognition
2 Intelligent Moderately Flexible (IMF) Smart Local search led by cognition if successful in long term
Otherwise, purely experiential search
3 Intelligent Highly Flexible (IHF) Smart Local search led by cognition if successful in short-term
Otherwise, purely experiential search
4 Mediocre Disciplined (MD) Poor Local search led by cognition
5 Mediocre Moderately Flexible (MMF) Poor Local search led by cognition if successful in long term
Otherwise, purely experiential search
6 Mediocre Highly Flexible (MHF) Poor Local search led by cognition if successful in short-term
Otherwise, purely experiential search
7 Totally Emergent and Flexible (EF) None Purely experiential search
Search Strategy
University of Navarra
17
Prof. Joan Enric Ricart [email protected]
Simulation experiments: Model
Each evolution pattern is released on eight kinds of fitness landscapes, each of them reflecting different structural designs
A Kb=0 implies an M-form design As Kb increases, we have increasingly complex CM-form designs 7 different patterns of evolution under eight different architectural
designs conform 56 configurations of the CST
Architectural designKb: interdivisional interdependencies
Kw: intradivisional interdependencies
KB
0 0,25 0,5 0,750
KW 1 Stable Relat. Stable Relat. Turb. Turbulent2 Stable Relat. Stable Relat. Turb. Turbulent
Cases matching Simon's near decomposability assumption
University of Navarra
18
Prof. Joan Enric Ricart [email protected]
Simulation experiments: Simulation run II Relatively Turbulent Environment
KW=1 KB=0.5
0,45
0,5
0,55
0,6
0,65
0,7
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30
Emergent Flexible Intelligent Disciplined Intelligent Highly Flexible Intelligent Moderately Flexible
Mediocre Disciplined Mediocre Highly Flexible Mediocre Moderately Flexible
University of Navarra
19
Prof. Joan Enric Ricart [email protected]
Simulation experiments: Findings
KW=1; KB=0 KW=2; KB=0 KW=1; KB=0,25 KW=2; KB=0,25 KW=1; KB=0,5 KW=2; KB=0,5 KW=1; KB=0,75 KW=2; KB=0,75
ID ID ID ID ID ID ID IDIMF IMF IMF IMF IMF IMF IMF IMFMD MD IHF IHF IHF IHF IHF IHFIHF IHF MD MD MHF MHF MHF MHF
MMF MMF MMF MMF MMF MMF EF EFMHF MHF MHF MHF EF EF MMF MMFEF EF EF EF MD MD MD MD
Intelligent cognitionMediocre cognitionTrial and error
Stable Environment
Relatively StableEnvironment
Relatively Turbulent Environment
TurbulentEnvironment
University of Navarra
20
Prof. Joan Enric Ricart [email protected]
Simulation experiments: Findings The importance of cognition is contingent to the degree of
environmental turbulence
• Stable environments: discipline ALWAYS pays • Turbulent environments: discipline only advisable if cognition is
“intelligent”.
In turbulent environments, if the initial cognition is mediocre, results favor strategies based on its opportunistic application
• Realized strategy as a mix of intended and emergent features Purely opportunistic strategy always underperforms
University of Navarra
21
Prof. Joan Enric Ricart [email protected]
General discussion and conclusions
Corporate Strategy
Decision level that drives, paces and frames corporate wide evolution through the choice, at the corporate level of the firm, of a particular equilibrium configuration of the CST.
Evolution is driven by the cognitive representation
Corporate decisions pace evolution shifting between initiatives that involve local search/long jumps/recombinations
The corporate level develops broad organizational arrangements that frame the emergence of self-organized processes as sources of corporate advantage