Optimal structure, market dynamism, and the strategy of simple rules

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Citation: Jason P. Davis, Kathleen M. Eisenhardt, Christopher B. Bingham (2009) Optimal structure, market dynamism, and the strategy of simple rules. Administrative Science Quarterly (RSS)
Internet Archive Scholar (fulltext): Optimal structure, market dynamism, and the strategy of simple rules
Tagged: Business (RSS) organization theory (RSS), learning (RSS), routines (RSS), structure (RSS)

Summary (Abstract)

The basic literature on organizations, strategy, networks, and complexity have suggested that in the context of organizations, there is a balance between too much structure and too little structure and that either extreme leads to a unoptimal situation. This question is particularly interesting to the authors in the context of dynamic high velocity environments.

Using theory, the authors suggest the following propositions (quoted verbatim):

  • Proposition (P1): Performance has an inverted-U shaped relationship with the amount of structure.
  • Proposition 2 (P2): As environmental dynamism increases, the optimal amount of structure decreases.

Davis et al. use a stochastic computational simulation to measure the effect of too much and too little structure on organizations. The authors modeled organization structure as rules. They modeled environments as a set of heterogeneous opportunities. They modeled each opportunities has having a set of binary features. Organizations have "rules" which are similar vectors and when enough of these rules match the opportunities, they are considered to captures these opportunities. The actions could either be based on rules or improvised. Not all spaces in a rule vector was filled and the "blanks" in the rules would be improvised and the number of blanks corresponded to the inverse of the amount of structure.

The authors also modeled four dimensions of organization dynamism which included the rate at which new opportunities emerge (velocity), the number of features of an opportunity that must be correct executed to match (complexity), the proportion fo perceived factors that different from reality (ambiguity) and the amount of disorder or turbulence in the flow of opportunities (unpredictability).

The predicted U-shaped relationship was clearly evident in the results of the simulations. That said, it clearly favored too much structure leading to a new proposition:

  • Proposition 1a (P1a): Performance has a unimodal, asymmetric right relationship with the amount of structure.

P2 was not supported by the model. The amount of structure predicted was very similar in both the high and low velocity environments. The results in P1a are surprisingly robust against different types of environments. There is still a middle-ground, but it seems safer to err toward too much structure.

Their results led them to more modified version which moved away from dynamism to a statement about predictability:

  • Proposition 2a (P2a): As environmental unpredictability increases, the optimal amount of structure decreases.