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.
|Translated title of the contribution
|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-
|Number of pages
|Journal of the Korean Society of Clothing and Textiles
|Published - 2022
Bibliographical notePublisher Copyright:
© 2022, The Korean Society of Clothing and Textiles. All rights reserved.
- Advertising endorser
- Ardl time series analysis
- Information amount
- Information-searching behavior
- Web search volume