Strategic group identification using evolutionary computation
| Autor: | S. L. Toral-Marín, María del Rocío Martínez-Torres |
|---|---|
| Přispěvatelé: | Universidad de Sevilla. Departamento de Administración de Empresas y Comercialización e Investigación de Mercados (Marketing), Universidad de Sevilla. Departamento de Ingeniería Electrónica |
| Jazyk: | angličtina |
| Rok vydání: | 2010 |
| Předmět: |
business.industry
Cultural algorithm Computer science Genetic Algorithms Performance General Engineering Strategic group Interactive evolutionary computation Evolutionary computation Machine learning computer.software_genre Computer Science Applications Java Evolutionary Computation Toolkit Franchising Human-based evolutionary computation Artificial Intelligence Genetic algorithm Artificial intelligence Genetic representation Strategic groups business computer Evolutionary programming |
| Zdroj: | idUS. Depósito de Investigación de la Universidad de Sevilla instname |
| Popis: | This paper proposes to identify strategic groups among franchisors from a big set of franchisor variables. Genetic evolutionary computation was used to reduce a set of variables efficiently, and factor analysis was used to make up the strategic groups. Franchise 500 was used as database. The results suggest both that the general map of franchisor has changed since Carney and Gedajlovic’s study, and that genetic evolutionary computation is a valid way to extract knowledge from a huge set of data. This paper proposes useful information for those retail firms considering internationalization via franchising. The originality of this paper is in the use of Genetic Algorithm to discriminate the final set of variables to be used for the identification of strategic groups instead of evaluating one by one the adequacy of each variable theoretically. The ability of evolutionary computation to create new knowledge is good to produce new insights into this topic |
| Databáze: | OpenAIRE |
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