The DOI matches its Crossref record; 11 of the 11 references it cites were found in a registry or an index.
Exploring how social media platforms influence fashion consumer decisions in the UK retail sector
Open accessAbstract
Purpose: The purpose of this paper is to explore the levels social media (SM) platforms are influencing consumer decision-making process for Generation X and Y consumers in the retail fashion environment. Design/methodology/approach: This study adopts an interpretive, exploratory approach, applying a qualitative design. The research involved eight in-depth interviews and two focus groups in order to gain in-depth insights of two generational cohorts opinions, arguments, motivations and ideas. Findings: The findings revealed that consumers use a variety of internal and external motivations that influence their behaviours and perceptions of high-street fashion retailers, and these factors are aided and facilitated by the use of SM. However, the research also revealed that SM is not the only source that motivates their consumer decision-making process, and with the proliferation of active users on SM, these platforms are (and will continue to have) an ever more increasing impact on consumer decision-makings. Participants were found to actively use SM to gain inspiration and information regarding high-street fashion retailers, however their final intentions to purchase were not as highly influenced by the content produced as previously expected. Research limitations/implications: Prior to consumers’ making any decisions they are impacted by various stimuli, however, with the proliferation of active users on SM, the influence these platforms unconsciously have on consumer behaviour is vast. Therefore, suggesting that consumers are not as predictable as traditional consumer decision-making process (CDMP) theory postulates. Although thought processes do appear to exist, consumers now demonstrate a much more complex process, which create multiple layers of motivations where internal and external factors overlap. Practical implications: The findings of this study present valuable implications for high-street fashion marketers and are anticipated to enhance and deepen fashion marketers understanding of CDMP when using SM. Thus, they must strive to understand and provide relevant content to consumers’, not only for Generation Y but also Generation X, responding to quickly changing consumer demands and expectations with both of these cohorts. Overall, these findings propose that high-street fashion retailers on SM must understand the complexity of internal and external factors motivating consumers to interact with retailers online and off, and therefore, generating added value for their consumers. This is useful within marketing practises. Social implications: This paper gives some clarity and insight into the motivations of consumers in the fashion retail environment, from a digital influential perspective. Originality/value: The findings concluded that SM has created a consumer that can be impacted at any stage of the decision process and has added to the research regarding unplanned consumer behaviour. Participants behaviours were stimuli-driven and user-controlled. Therefore, this had a larger impact on inspiration as opposed to modifying their behaviours to align exactly with trends or products.
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Cited by
‘Instagram made Me buy it’: Generation Z impulse purchases in fashion industry
Djafarova, E., & Bowes, T. (2021) · Journal of Retailing and Consumer Services · doi:10.1016/j.jretconser.2020.102345 · Cited by 650
Hewei, T. (2022) · PLOS ONE · doi:10.1371/journal.pone.0273968 · Cited by 39
Metadata
- Authors
- Nash, J.
- Venue
- Journal of Fashion Marketing and Management: An International Journal
- Publisher
- Emerald
- Published
- 2019-03-11
- Volume
- 23
- Issue
- 1
- Pages
- 82-103
- ISSN
- 1361-2026 (ISSN-L)
- OpenAlex
- W2900019070
- Cited by
- 171
- References
- 11
- FWCI
- 35.31
- Type
- journal article
- Language
- en
- Open access
- green
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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