PLS path modeling in marketing and genetic algorithm segmentation

Ringle, C; Schlittgen, R; Sarstedt, M

PLS path modeling in marketing and genetic algorithm segmentation

Ringle, C Schlittgen, R Sarstedt, M

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Publisher:: ANZMAC
Publication Type:: Conference Proceeding
Citation:: Proceedings of the Australian and New Zealand Marketing Academy Conference, 2009, pp. 1 - 8
Issue Date:: 2009-01

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Field	Value	Language
dc.contributor.author	Ringle, C	en_US
dc.contributor.author	Schlittgen, R	en_US
dc.contributor.author	Sarstedt, M	en_US
dc.contributor.editor	Tojib, D	en_US
dc.date	2009-11-30	en_US
dc.date.issued	2009-01	en_US
dc.identifier.citation	Proceedings of the Australian and New Zealand Marketing Academy Conference, 2009, pp. 1 - 8	en_US
dc.identifier.isbn	1-86308-158-5	en_US
dc.identifier.uri	http://hdl.handle.net/10453/11403
dc.description.abstract	This paper presents the PLS genetic algorithm segmentation methodology which uses directed random searches to detect an optimum solution in the complex search space that underlies data partitioning tasks in PLS path modeling. The results of a simulation study allow a primary assessment of this novel approach and reveal its capabilities and effectiveness. Furthermore, applying the approach to the American Customer Satisfaction Index model allows unobserved heterogeneity and different consumer segments to be uncovered.	en_US
dc.publisher	ANZMAC	en_US
dc.relation.ispartof	Proceedings of the Australian and New Zealand Marketing Academy Conference	en_US
dc.relation.ispartof	Australian and New Zealand Marketing Academy Conference	en_US
dc.title	PLS path modeling in marketing and genetic algorithm segmentation	en_US
dc.type	Conference Proceeding
utslib.location	Melbourne, Australia	en_US
utslib.location.activity	Melbourne, Australia	en_US
utslib.for	150504 Marketing Measurement	en_US
dc.location.activity	Melbourne, Australia	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Business
pubs.organisational-group	/University of Technology Sydney/Faculty of Business/School of Marketing
utslib.copyright.status	open_access
pubs.consider-herdc	true	en_US
pubs.place-of-publication	Melbourne, Australia	en_US
pubs.start-date	2009-11-30	en_US

Abstract:

This paper presents the PLS genetic algorithm segmentation methodology which uses directed random searches to detect an optimum solution in the complex search space that underlies data partitioning tasks in PLS path modeling. The results of a simulation study allow a primary assessment of this novel approach and reveal its capabilities and effectiveness. Furthermore, applying the approach to the American Customer Satisfaction Index model allows unobserved heterogeneity and different consumer segments to be uncovered.

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/11403