Special Issue "Fashion Merchandising and Consumer Behavior"

A special issue of Social Sciences (ISSN 2076-0760). This special issue belongs to the section "Social Economics".

Deadline for manuscript submissions: 15 June 2019

Special Issue Editor

Guest Editor
Dr. Sanjukta Pookulangara

College of Merchandising, Hospitality & Tourism, University of North Texas, Denton, TX 76203-5017, USA
Website | E-Mail
Interests: retail; consumer behavior; digital

Special Issue Information

Dear Colleagues,

The retail industry is constantly re-inventing itself. The industry has always been at the forefront of innovation, from the invention of the sewing machine to the rise of e-commerce—always forward-looking and cyclical (The Future Of Fashion, 2018). According to Stone & Farnan (2018), “‘Fashion merchandising’ is defined as the buying and selling of goods for the purpose of making a profit.” Consumer behavior impacts fashion merchandising decisions, such as product (e.g., slow fashion vs. fast fashion), channel strategy (e.g., omni-channel vs. single channel), promotional strategy (e.g., digital platforms or traditional media), and pricing strategy (e.g., price transparency vs. dynamic pricing). Consumers today are in charge of their experience in a retail format, and this is especially true for the Millennials and Generation Z. Thus, this Special Issue will focus on how the area of fashion merchandising has evolved with changing consumer behavior. Manuscripts are invited on topics that include, but are not limited to:

  1. The impact of technology
  2. New retail formats and consumer behavior
  3. Merchandising and channel strategy (e.g., channel convergence)
  4. Sustainability
  5. The emergence of social savvy retailers and brands
  6. Crowdsourcing, shortening of the production cycle, and supply chain

(Stone & Farnan 2018) Stone, E., and Farnan, S. A. 2018. The dynamics of fashion. Bloomsbury Publishing USA.

(The Future Of Fashion 2018) The Future Of Fashion: From Design To Merchandising, How Tech Is Reshaping The Industry. 2018. CBInsights. Retrieved from: https://www.cbinsights.com/research/fashion-tech-future-trends/

Dr. Sanjukta Pookulangara
Guest Editor

Manuscript Submission Information

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Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a double-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Social Sciences is an international peer-reviewed open access monthly journal published by MDPI.

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Keywords

  • fashion merchandising
  • retailing
  • consumer behavior
  • technology
  • innovation

Published Papers (2 papers)

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Research

Open AccessArticle Googling Fashion: Forecasting Fashion Consumer Behaviour Using Google Trends
Soc. Sci. 2019, 8(4), 111; https://doi.org/10.3390/socsci8040111
Received: 22 February 2019 / Revised: 25 March 2019 / Accepted: 29 March 2019 / Published: 4 April 2019
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Abstract
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 [...] Read more.
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. Full article
(This article belongs to the Special Issue Fashion Merchandising and Consumer Behavior)
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Open AccessArticle Online Store Locator: An Essential Resource for Retailers in the 21st Century
Soc. Sci. 2019, 8(2), 53; https://doi.org/10.3390/socsci8020053
Received: 4 January 2019 / Revised: 8 February 2019 / Accepted: 12 February 2019 / Published: 14 February 2019
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Abstract
Most retailers use their websites and social media to increase their visibility, while potential customers get information about these retailers using the Internet on electronic devices. Many papers have previously studied online marketing strategies used by retailers, but little attention has been paid [...] Read more.
Most retailers use their websites and social media to increase their visibility, while potential customers get information about these retailers using the Internet on electronic devices. Many papers have previously studied online marketing strategies used by retailers, but little attention has been paid to determine how these companies provide information through the Internet about the location and characteristics of their stores. This paper aims to obtain evidence about the inclusion of interactive web maps on retailers’ websites to provide information about the location of their stores. With this purpose, the store locator interactive tools of specialty retailers’ websites included in the report “Global Powers of Retailing 2015” are studied in detail using different procedures, such as frequency analysis and word clouds. From the results obtained, it was concluded that most of these firms use interactive maps to provide information about their offline stores, but today some of them still use non-interactive (static) maps or text format to present this information. Moreover, some differences were observed among the search filters used in the store locator services, according to the retailer’s specialty. These results provided insight into the important role of online store locator tools on retailers’ websites. Full article
(This article belongs to the Special Issue Fashion Merchandising and Consumer Behavior)
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