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A strategic typology for UK small and medium sized enterprises. An investigation of influential factors and the development of a predictive typology

Kendrick, Sean
Publication Date
2012
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Creative Commons License
The University of Bradford theses are licenced under a Creative Commons Licence.
Peer-Reviewed
Open Access status
Accepted for publication
Institution
University of Bradford
Department
School of Management
Awarded
2012
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Abstract
The success of small and medium enterprises (SMEs) is critical to Europe’s economic health, however, our understanding of SME strategic behaviour is predominantly based on large enterprise theory. This study uses the Miles and Snow (1978) typology to examine the strategic behaviour of 150 UK SMEs. It also investigates whether strategy type, environment adaptation and organisational performance can be predicted by several contingency factors: organisation size, age, industry type, and management style. The findings confirm that the typology is not well suited for categorising SMEs; organisations that rarely develop through all three domains of the adaptive cycle to be sufficiently eligible for categorisation by one of the four pure archetypes. However, similar patterns of strategic behaviour were observed for certain dimensions, largely independent of the industry type or size of the SME, suggesting that an optimal configuration of mixed strategies may exist. Furthermore, Reactors, or those with mixed strategies, were found to perform similarly as Analysers and better than Defenders. The study also found that by fitting nominal logistic regression models to organisation age and size data, it was possible to predict strategic behaviour and environment adaptation, and to a lesser degree, financial performance. Surprisingly, the industry type and management style data were observed to exert minimal influence on the outcome variables. Finally, this research provides important insight relating to the validity concerns of the Miles and Snow typology and categorisation method employed, and demonstrates how these can be avoided.
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Type
Thesis
Qualification name
DBA
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