Dynamics of organizational growth in the international automobile industry.
KeywordsAutomobile industry and trade -- Management -- Mathematical models.
Organizational sociology -- Mathematical models.
Committee ChairHamblin, Robert L.
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PublisherThe University of Arizona.
RightsCopyright © is held by the author. Digital access to this material is made possible by the University Libraries, University of Arizona. Further transmission, reproduction or presentation (such as public display or performance) of protected items is prohibited except with permission of the author.
AbstractThe phenomenon of organizational growth has traditionally been assumed to be indeterminate largely due to chance or accidents found in organizational worlds. This research takes up the causal processes underlying the growth (and decline) of virtually all world-class manufacturers in the international automobile industry from 1946 to 1989. Two models are developed as alternative explanations for the long-term trends observed in growth rates and their differences across firms. The models are estimated with a nonlinear method and tested through various empirical implications. The model that seems most consistent with the data shows unambiguously that they were not generated by a random or chance process but by underlying processes of collective learning, innovation, and outnovation in technologies and organizational routines. Firms that had generated different rates in these processes differed as hypothesized in their long-term growth performance. The dynamics of collective learning processes, as measured by the parameters of the model, largely explain the dynamics of organizational growth in the world automobile industry, hence, the dynamics of interorganizational competition. The results from tests of ecological hypotheses suggest that organizational ecology might benefit from the application of matrices of collective learning rates generated from interorganizational learning curves, particularly where ecology seeks to explain patterns of competition by organizational size. As shown, this research strategy is general and gauges directly interactions among organizations over long periods. It is also flexible in dealing with various levels of analysis in longitudinal and cross-sectional dimensions. As also shown, the collective learning theory, its model, and the ecology of interorganizational learning curves derived from them can help in evaluating empirically the competitive potential of firms by indicators of innovation and outnovation relative to other firms, patterns of competition (gauged by relative learning rates) among firms, and any changes of those patterns over time. Thus, the research strategy used here provides potentially useful causal analyses as well as meaningful measures on which different organizations can be compared, with each other and with themselves. These measures may also provide important benchmarks and diagnostics for strategic management.