A Descriptive Study on the Purchase Timing Effect in Athletic Shoes -Focused on Day-of-the-week Effect and Intra-month Effect-

Abstract

The influence of a marketing mix on the consumer purchasing process is well documented in academic fields; however, studies of when consumers purchase fashion brands or products in terms of the day-ofthe-week effect and 10-days-of-the-month effect on various channels to a given brand in practice are limited in the fashion industry. This study empirically describes the purchase timing behavior for athletic shoes using daily sales data from a domestic brand in Korea from January in 2006 to December in 2010. The results show that Saturday and Sunday have significantly higher sales than weekdays. In addition, the first and third 10-days-of-the-month yield a higher sales volume than the second 10-days-of-the-month. The department store's sales volume (compared with discount and franchised stores) was higher in the first 10-days-of-the-month; however, the discount and franchised stores have a higher sales volume in the second 10-days-of-the-month. Three store types have no significant differences in sales volume for the third 10-days-of-the-month. Based on the results, in practice, marketers for a specific domestic brand can develop a new marketing expenditure plan, store supply plan, and cash-in and cash-out plan to maximize profits. This research can introduce constructs such as purchase timing distribution, the day-of-the-week effect, and the ten-days-of-the-month effect for the fashion industry.

An Empirical Study on Asymmetric and Non-Proportional Price Response Function in the Shipment Timing Decision-Making Support System for Agricultural Products

“…BIC refers to a difference in maximum likelihood values between the observation and the measurement model, thus a smaller value is regarded to have higher explanation power. In other words, two equations with the same form as equation (7), hiding in this observation, are shown as one equation in the whole market. In analyzing the sample with two segmented markets, the explanation power of observation increased to 0.679.…”

“…Kwak (2015) reported that the delivery amounts before those holidays was greater than during ordinary days, and decreased after those holidays. Additionally, Lee, et al, (2012) reported that there is a day of the week effect on agricultural products and prices. Therefore, this study developed a model with the holiday effect and the day of the week effect added to the basic model of equation ( 6).…”

“…The daily apple wholesale transaction volume of Garak Agro-Fishery Market is used as the dependent variable in equation (7). This is because the market does not only announce the transaction volume of Fuji apples but price, an independent variable in equation ( 7), is measured by changes in the daily wholesale prices of 15kg of Fuji apples.…”

“…This is because the market does not only announce the transaction volume of Fuji apples but price, an independent variable in equation ( 7), is measured by changes in the daily wholesale prices of 15kg of Fuji apples. In other words, equation (7) calculates the difference between the average price for the previous day and today of the wholesale transaction volume for 15kg of fine grade Fuji apples. The average price is used because same grade apples are sold at different prices on the same day.…”

“…The purpose of this study is to present and to analyze empirically a dynamic model that traces non-proportional price responses with the directionality of the price increase or decrease based on price changes and simultaneously measure non-proportional changes in demand. This study substituted apple wholesale price and delivery volume data in equation (7). Table 1 shows the result of linear regression analysis using LatendGold software program.…”