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This paper investigates consumer's attitudes towards fashion product assortment in UK mid-market department stores. It aims to determine whether changes to assortment will increase purchase intention and help regain competitive advantage through aligning customer perceptions of product quality and fit with brand image. Our findings challenge the traditional role of the department store in curating fashion assortment. We find that increases in perceived quality, perceptions of brand portfolio and brand fit will increase the purchase intention of UK mid-market department store consumers, whilst reduced assortment sizes would lead to a decrease in purchase intent.
Shannon Donnelly; Liz Gee; Emmanuel Sirimal Silva. UK mid-market department stores: Is fashion product assortment one key to regaining competitive advantage? Journal of Retailing and Consumer Services 2020, 54, 102043 .
AMA StyleShannon Donnelly, Liz Gee, Emmanuel Sirimal Silva. UK mid-market department stores: Is fashion product assortment one key to regaining competitive advantage? Journal of Retailing and Consumer Services. 2020; 54 ():102043.
Chicago/Turabian StyleShannon Donnelly; Liz Gee; Emmanuel Sirimal Silva. 2020. "UK mid-market department stores: Is fashion product assortment one key to regaining competitive advantage?" Journal of Retailing and Consumer Services 54, no. : 102043.
This paper aims to discuss the current state of Google Trends as a useful tool for fashion consumer analytics, show the importance of being able to forecast fashion consumer trends and then presents a univariate forecast evaluation of fashion consumer Google Trends to motivate more academic research in this subject area. Using Burberry—a British luxury fashion house—as an example, we compare several parametric and nonparametric forecasting techniques to determine the best univariate forecasting model for “Burberry” Google Trends. In addition, we also introduce singular spectrum analysis as a useful tool for denoising fashion consumer Google Trends and apply a recently developed hybrid neural network model to generate forecasts. Our initial results indicate that there is no single univariate model (out of ARIMA, exponential smoothing, TBATS, and neural network autoregression) that can provide the best forecast of fashion consumer Google Trends for Burberry across all horizons. In fact, we find neural network autoregression (NNAR) to be the worst contender. We then seek to improve the accuracy of NNAR forecasts for fashion consumer Google Trends via the introduction of singular spectrum analysis for noise reduction in fashion data. The hybrid neural network model (Denoised NNAR) succeeds in outperforming all competing models across all horizons, with a majority of statistically significant outcomes at providing the best forecast for Burberry’s highly seasonal fashion consumer Google Trends. In an era of big data, we show the usefulness of Google Trends, denoising and forecasting consumer behaviour for the fashion industry.
Emmanuel Sirimal Silva; Hossein Hassani; Dag Øivind Madsen; Liz Gee. Googling Fashion: Forecasting Fashion Consumer Behaviour Using Google Trends. Social Sciences 2019, 8, 111 .
AMA StyleEmmanuel Sirimal Silva, Hossein Hassani, Dag Øivind Madsen, Liz Gee. Googling Fashion: Forecasting Fashion Consumer Behaviour Using Google Trends. Social Sciences. 2019; 8 (4):111.
Chicago/Turabian StyleEmmanuel Sirimal Silva; Hossein Hassani; Dag Øivind Madsen; Liz Gee. 2019. "Googling Fashion: Forecasting Fashion Consumer Behaviour Using Google Trends." Social Sciences 8, no. 4: 111.