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dc.contributor.authorWeijters, Bert
dc.contributor.authorGeuens, Maggie
dc.contributor.authorBaumgartner, Hans
dc.date.accessioned2017-12-02T14:52:33Z
dc.date.available2017-12-02T14:52:33Z
dc.date.issued2013
dc.identifier.doi10.1086/670394
dc.identifier.urihttp://hdl.handle.net/20.500.12127/4852
dc.description.abstractNowadays, an increasing number of information technology tools are implemented in order to support decision making about marketing strategies and improve customer relationship management (CRM). Consequently, an improvement in CRM can be obtained by enhancing the databases on which these information technology tools are based. This study shows that data augmentation with situational variables of the purchase occasion can significantly improve purchasing behavior predictions for a home vending company. Three dimensions of situational variables are examined: physical surroundings, temporal perspective and social surroundings respectively represented by weather, time, and salesperson variables. The smallest, but still significant, increase in predictive performance was measured by enhancing the model with time variables. Besides the moment of the day, this study shows that the incorporation of weather variables, and more specifically sunshine, can also improve the accuracy of a CRM model. Finally, the best improvement in purchasing behavior predictions was obtained by taking the salesperson effect into account using a multilevel model.
dc.language.isoen
dc.subjectStyles
dc.subjectLanguage
dc.subjectAlternatives
dc.subjectMarketing-Research
dc.subjectRating-Scales
dc.subjectWords
dc.titleThe effect of familiarity with the response category labels on item response to likert scales
dc.identifier.journalJournal of Consumer Research
dc.source.volume40
dc.source.issue2
dc.source.beginpage368
dc.source.endpage381
vlerick.knowledgedomainMarketing & Sales
vlerick.typearticleFT ranked journal article  
vlerick.vlerickdepartmentMKT
dc.identifier.vperid140358
dc.identifier.vperid51213
dc.identifier.vperid43906
dc.identifier.vpubid5784


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