Strategic group identification using evolutionary computation

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...

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Detalles Bibliográficos
Autores: Martínez Torres, María del Rocío, Toral, S. L.
Tipo de recurso: artículo
Estado:Versión enviada para evaluación y publicación
Fecha de publicación:2010
País:España
Institución:Universidad de Sevilla (US)
Repositorio:idUS. Depósito de Investigación de la Universidad de Sevilla
OAI Identifier:oai:idus.us.es:11441/39475
Acceso en línea:http://hdl.handle.net/11441/39475
https://doi.org/10.1016/j.eswa.2009.12.019
Access Level:acceso abierto
Palabra clave:Franchising
Strategic groups
Genetic Algorithms
Evolutionary computation
Performance
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spelling Strategic group identification using evolutionary computationMartínez Torres, María del RocíoToral, S. L.FranchisingStrategic groupsGenetic AlgorithmsEvolutionary computationPerformanceThis 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 topicAdministración de Empresas y MarketingIngeniería Electrónica2010info:eu-repo/semantics/articleinfo:eu-repo/semantics/submittedVersionapplication/pdfapplication/pdfhttp://hdl.handle.net/11441/39475https://doi.org/10.1016/j.eswa.2009.12.019reponame:idUS. Depósito de Investigación de la Universidad de Sevillainstname:Universidad de Sevilla (US)InglésExpert Systems with Applications, 37(7), 4948-4954info:eu-repo/semantics/openAccessoai:idus.us.es:11441/394752026-06-17T12:51:07Z
dc.title.none.fl_str_mv Strategic group identification using evolutionary computation
title Strategic group identification using evolutionary computation
spellingShingle Strategic group identification using evolutionary computation
Martínez Torres, María del Rocío
Franchising
Strategic groups
Genetic Algorithms
Evolutionary computation
Performance
title_short Strategic group identification using evolutionary computation
title_full Strategic group identification using evolutionary computation
title_fullStr Strategic group identification using evolutionary computation
title_full_unstemmed Strategic group identification using evolutionary computation
title_sort Strategic group identification using evolutionary computation
dc.creator.none.fl_str_mv Martínez Torres, María del Rocío
Toral, S. L.
author Martínez Torres, María del Rocío
author_facet Martínez Torres, María del Rocío
Toral, S. L.
author_role author
author2 Toral, S. L.
author2_role author
dc.contributor.none.fl_str_mv Administración de Empresas y Marketing
Ingeniería Electrónica
dc.subject.none.fl_str_mv Franchising
Strategic groups
Genetic Algorithms
Evolutionary computation
Performance
topic Franchising
Strategic groups
Genetic Algorithms
Evolutionary computation
Performance
description 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
publishDate 2010
dc.date.none.fl_str_mv 2010
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/submittedVersion
format article
status_str submittedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/11441/39475
https://doi.org/10.1016/j.eswa.2009.12.019
url http://hdl.handle.net/11441/39475
https://doi.org/10.1016/j.eswa.2009.12.019
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv Expert Systems with Applications, 37(7), 4948-4954
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
application/pdf
dc.source.none.fl_str_mv reponame:idUS. Depósito de Investigación de la Universidad de Sevilla
instname:Universidad de Sevilla (US)
instname_str Universidad de Sevilla (US)
reponame_str idUS. Depósito de Investigación de la Universidad de Sevilla
collection idUS. Depósito de Investigación de la Universidad de Sevilla
repository.name.fl_str_mv
repository.mail.fl_str_mv
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