e-space
Manchester Metropolitan University's Research Repository

    Fashion “see-now-buy-now”: implications and process adaptations

    Boardman, R, Haschka, Y, Chrimes, C ORCID logoORCID: https://orcid.org/0000-0002-4710-9885 and Alexander, B (2020) Fashion “see-now-buy-now”: implications and process adaptations. Journal of Fashion Marketing and Management, 24 (3). pp. 495-515. ISSN 1361-2026

    [img]
    Preview
    Accepted Version
    Available under License In Copyright.

    Download (1MB) | Preview

    Abstract

    Purpose: The purpose of this paper is to identify if and how the see-now-buy-now model impacts the traditional buying, merchandising and supply chain processes (BMSCP) of multi-brand fashion retailers (MBFR) and whether they need to be adapted in order to facilitate this development. Design/methodology/approach: This exploratory study includes three industry case studies, triangulated with external observers. A total of 11 semi-structured interviews were conducted within Germany and the UK. Findings: Findings demonstrate that in order to adopt the see-now-buy-now model there is a need for process-shortening, as well as better process and network alignment between MBFR and brands through agility, supplier–relationship management and vertical integration in order to stay competitive against time-based competition. Whilst most steps of the traditional BMSCP are still applicable under the see-now-buy-now model, they must be re-engineered and shortened, with the steps being rolling rather than linear, with buyers and merchandisers operating in a more hybrid role. Originality/value: This paper addresses the lack of research on the see-now-buy-now model as well as on the BMSCP of MBFR and the implications that see-now-buy-now could have on those processes. A modified buying, merchandising and supply chain framework adapted to incorporate see-now-buy-now is created which will be useful for academics and practitioners.

    Impact and Reach

    Statistics

    Activity Overview
    6 month trend
    797Downloads
    6 month trend
    165Hits

    Additional statistics for this dataset are available via IRStats2.

    Altmetric

    Repository staff only

    Edit record Edit record