Chapter 3: When size does not matter: compositional data analysis in marketing research
dc.contributor.author
dc.date.accessioned
2025-04-04T09:32:47Z
dc.date.available
2025-04-04T09:32:47Z
dc.date.issued
2021-10-22
dc.identifier.isbn
9781788976947
dc.identifier.uri
dc.description.abstract
Compositional Data analysis (CoDa) is the standard statistical methodology when data contain information about the relative importance of parts of a whole. Many research questions in marketing have to do with distribution of a whole (e.g., market share, product portfolio, spending distribution), or with relative importance (e.g., advertising content or style, preferred product attributes). CoDa solves the statistical problems that arise when treating compositional data with classical statistical methods and focuses on research questions about relative importance. In a costumer opinion platform the dominant types of reviews matter more than the number of reviews. We show how to apply the most common CoDa tools (visualization and linear models), by means of real data from an electronic word-of-mouth platform: are hotel characteristics affecting the share of valuation categories (e.g., from terrible to excellent reviews), or is it related to other compositions (e.g., by type of travelers)?
dc.format.mimetype
application/pdf
dc.language.iso
eng
dc.publisher
Edward Elgar Publishing
dc.relation.isformatof
Versió postprint del capítol de llibre publicat a: https://doi.org/10.4337/9781788976954.00009
dc.relation.ispartof
© Nunkoo, R., Teeroovengadum, V. & Ringle, Ch.M. (eds). 2021. Handbook of Research Methods for Marketing Management. Edward Elgar Publishing. https://doi.org/10.4337/9781788976954, p. 73-90
dc.relation.ispartofseries
Llibres / Capítols de LLibre (D-EC)
dc.rights
Tots els drets reservats
dc.title
Chapter 3: When size does not matter: compositional data analysis in marketing research
dc.type
info:eu-repo/semantics/bookPart
dc.rights.accessRights
info:eu-repo/semantics/openAccess
dc.type.version
info:eu-repo/semantics/acceptedVersion
dc.identifier.doi
dc.type.peerreviewed
peer-reviewed
dc.identifier.eisbn
9781788976954