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The evaluative conditioning of well-established liked, disliked, and neutral brands. A new, multidimensional approach to understanding consumer behaviour

thesis
posted on 2025-05-09, 13:00 authored by Shannon Stefan Bosshard
Traditional marketing research, up until recently, has seen an over reliance on surveys and questionnaires to gain an insight into consumer preferences. Although these explicit methods were once thought to provide a comprehensive insight into the true attitudes of the consumer, recent literature suggests otherwise. The assertion that explicit measures are polluted by cognitive processes is becoming more common. As a result, the future of marketing is one that sees marketers and advertisers assess consumer attitudes via the use of tools which are unaffected by cognitions and thus, do not require a verbal or cognitive response. To test the notion that implicit measures are a more reliable and a more sensitive means to assessing attitudes and attitude changes than traditional explicit measures, the current project involved the simultaneous collection of electroencephalography (EEG) data and self-report data whilst participants were presented with familiar liked, disliked, and neutral brands. In addition, further implicit insight was provided via the implementation of the implicit association test (IAT). The collective results of the present research indicate that whilst self-report and the IAT are sensitive to baseline like and dislike, conditioning resulted in no changes in attitudes. In contrast, EEG appeared to not only be capable of distinguishing between liked, disliked, and neutral brands at baseline, but also after subsequent conditioning. Taken together, the findings of the current thesis not only suggest that neural activity may provide an insight into the affective nature of brand attitudes, but also that an implicit approach may allow for an accurate prediction of future purchase behaviour.

History

Year awarded

2017.0

Thesis category

  • Doctoral Degree

Degree

Doctor of Philosophy (PhD)

Supervisors

Walla, Peter (University of Newcastle); Dennis, Simon (University of Newcastle)

Language

  • en, English

College/Research Centre

Faculty of Science and Information Technology

School

School of Psychology

Rights statement

Copyright 2017 Shannon Stefan Bosshard

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