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dc.contributor.advisorBall, Kirstie
dc.contributor.authorvon Laufenberg, Roger Ferdinand Francois
dc.coverage.spatialxiii, 229 p.en_US
dc.date.accessioned2021-04-08T12:06:55Z
dc.date.available2021-04-08T12:06:55Z
dc.date.issued2021-07-02
dc.identifier.urihttp://hdl.handle.net/10023/22987
dc.description.abstractThe proliferation of big data analytics in marketing appears to be having significant effects on the field, such as changing how marketers perceive their consumers and how they act on them. As I discuss in my research, marketers are not satisfied to work solely with approximate, imagined conceptualisations of consumers as a basis for advertisements and offers. Instead, they are looking for exact virtual data doubles of existing and potential consumers, which is something they hope to achieve through big data analytics. In my thesis, I explore the question of how and why marketers conceptualise consumers differently when using big data analytics compared with traditional market and consumer research methods. This is embedded in the theory of the co-production of knowledge and empirically relies on interviews with marketers and data analysts, case studies, and participant observations at industry conferences. In my research, I show to what extent the idea of the data double consumer conceptualisation is considered an ideal case for marketers, and that it is believed to be made possible through big data analytics, which is expected to create an exact knowledge about consumers. However, my findings show that in practice, big data analytics should be considered a sociotechnical assemblage that produces knowledge which contains inaccuracies, errors and uncertainties. Knowledge about consumers is not just discovered – neither through traditional market and consumer research methods nor through big data analytics. Instead, it is the outcome of a co-production that involves different steps, individuals, teams, normativities, and technologies. Hence, knowledge about consumers is never an exact representation of reality, irrespective of its methods of production. Consequently, consumer conceptualisations expected to be exact data doubles cannot be attained. Instead, postulations are established that are believed to be accurate, without having actual proof. Yet, my findings show that knowledge resulting from big data analytics has a higher credibility and epistemic authority amongst the participants, explaining the persistence of the data double consumer conceptualisation in digital marketing.en_US
dc.description.sponsorship"This work was supported by the Social Sciences and Humanities Research Council of Canada through the Big Data Surveillance Project and the University of St Andrews." -- Fundingen
dc.language.isoenen_US
dc.publisherUniversity of St Andrews
dc.relationPostulating consumers: how marketers conceptualise consumers in the era of big data analytics (thesis data) Von Laufenberg, R.F.F., University of St Andrews, 2021. DOI: https://doi.org/10.17630/af965a99-1c23-4a3d-a49d-f2b54529fccaen
dc.relation.urihttps://doi.org/10.17630/af965a99-1c23-4a3d-a49d-f2b54529fcca
dc.rightsCreative Commons Attribution-NonCommercial-NoDerivatives 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/*
dc.subjectOrganisational sociologyen_US
dc.subjectDigital marketingen_US
dc.subjectBig data analyticsen_US
dc.subjectEpistemic authorityen_US
dc.subjectCo-production of knowledgeen_US
dc.subjectCritical marketingen_US
dc.subjectActor-network theoryen_US
dc.subjectSurveillance studiesen_US
dc.subject.lccHF5415.L2
dc.subject.lcshMarketingen
dc.subject.lcshMarketing--Data processingen
dc.subject.lcshBig dataeng
dc.titlePostulating consumers : how marketers conceptualise consumers in the era of big data analyticsen_US
dc.typeThesisen_US
dc.contributor.sponsorSocial Sciences and Humanities Research Council of Canada (SSHRC)en_US
dc.type.qualificationlevelDoctoralen_US
dc.type.qualificationnamePhD Doctor of Philosophyen_US
dc.publisher.institutionThe University of St Andrewsen_US
dc.identifier.doihttps://doi.org/10.17630/sta/58
dc.identifier.grantnumber895-2015-1003en_US


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    Except where otherwise noted within the work, this item's license for re-use is described as Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International