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http://dx.doi.org/10.5850/JKSCT.2022.46.5.868

Fashion Brand Sales Forecasting Analysis Using ARDL Time Series Model -Focusing on Brand and Advertising Endorser's Web Search Volume, Information Amount, and Brand Promotion-  

Seo, Jooyeon (Dept. of Fashion Industry, Ewha Womans University)
Kim, Hyojung (Dept. of Fashion Industry, Ewha Womans University)
Park, Minjung (Dept. of Fashion Industry, Ewha Womans University)
Publication Information
Journal of the Korean Society of Clothing and Textiles / v.46, no.5, 2022 , pp. 868-889 More about this Journal
Abstract
Fashion companies are using a big data approach as a key strategic analysis to predict and forecast sales. This study investigated the effectiveness of the past sales, web search volume, information amount, brand promotion, and the advertising endorser on the sales forecasting model. The study conducted the autoregressive distributed lag (ARDL) time series model using the internal and external social big data of a national fashion brand. Results indicated that the brand's past sales, search volume, promotion, and amount of advertising endorser information amount significantly affected the sales forecast, whereas the brand's advertising endorser search volume and information amount did not significantly influence the sales forecast. Moreover, the brand's promotion had the highest correlation with sales forecasting. This study adds to information-searching behavior theory by measuring consumers' brand involvement. Last, this study provides digital marketers with implications for developing profitable marketing strategies on the basis of consumers' interest in the brand and advertising endorser.
Keywords
Information-searching behavior; Web search volume; Information amount; Advertising endorser; ARDL time series analysis;
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