Niche width and the dynamics of organizational populations
Citation: John Freeman, Michael T. Hannan (1983) Niche width and the dynamics of organizational populations. The American Journal of Sociology (RSS)
Internet Archive Scholar (fulltext): Niche width and the dynamics of organizational populations
Tagged: Sociology (RSS) population ecology (RSS), organization theory (RSS)
In their influential article essentially beginning the field of population ecology, Hannan and Freeman (1977) discuss the concept of niche-width in depth and suggest the idea that specialists organizations will fair better in stable environments and generalists will do better in unstable environments. Building on that work, this paper explores the effect of both environmental variability and grain (or the pattern of variability) on niche width of organizational populations.
The authors explain that in biological ecology, authors pursue the concept of equilibrium theory. The authors suggest that organizational environments are too dynamic to make equilibrium theory appropriate to organizational ecology. They aim to answer three questions:
- Does population ecology explain, at least approximately, the processes governing organizational niche width?
- Do organizational niche-width processes operate at least partly as a force of mortality?
- Are our procedures for inferring differences in specialism/generalism of organizations from strategy and structure sound?
Borrowing from niche-width theory in biology, the authors suggest that level of variability (which is reasonably straightforward) and grain (which is the the degree of mixing of different types over spacial and temporal dimensions) are two key variables.
The key hypotheses are that:
- In fine-grained environments death rates of generalists will exceed those of generalists will be higher those of specialists at all levels of variability.
- In course grain environments, generalists will have lower death rates when environmental variation is large.
The authors offer a test of their theory using an population of restaurants in 18 Californian cities chosen to maximize both variation and grain.
The authors then code each restaurants menus along a set of different qualities (i.e., seasonality, menu length, variability, etc). How much variation there is in monthly demand and whether there are spikes (i.e., grain). Specialism and generalism is measured by saying that a specialist has a smaller menu that focuses on a single time of food. Their argument is specialists do better when change is on frequent and the variability in demand is not very much. The generalists do better when there are course grain and fast changes
For the case of fine-grained environments, the results are weakly supportive. For the case of coarse-grained environments, there is strong support to the model.