• Journal of the Korean Society of Clothing and Textiles
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• v.29 no.9_10 s.146
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• pp.1349-1358
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• 2005

Generally apparel products were recognized as an incongruent product type which direct search was needed for. But nowadays apparel products were ranked as a second product type next to electronics in sale amount of internet shopping mall. Therefore more detailed study on which attributes of fashion products are more successful and suitable in internet shopping mall is necessary. This study was executed to analyze common attributes of successful apparel in internet shopping mall on the basis of real sale data, and then to help internet apparel marketer plan marketing strategies more efficiently. Common attributes of successful apparel products in internet shopping-mall in terms of product, price and promotion attributes were identified. 240 products which were loaded in bestseller section of internet shopping-mall for 8 weeks from November to December, 2004 were analyzed. The results are as follows. First, products ratio for women ($76.7\%$) were higher than for men($18.3\%$). And the amount of each apparel product types purchased by consumers were ranked in the order of leather/fur coat, woven coat, inner wear, jacket, T-shirts, pants, training wear, suit, knit wear etc. and basic style dominated in $74.2\%$. General brands ($69.2\%$) that had price competitiveness were preferred to famous designer brand, national brand and PB brand. Preferred level of price was ten thousand to fifty thousand won. For promotion skill, in order of just one product, product adding promotion product and package set were preferred. Regular price, less than $50\%$ off and more than $50\%$ off price showed no difference in preference. Product planning strategy for internet apparel marketer was followed on the basis of these results.

• Journal of the Korean Society of Clothing and Textiles
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• v.43 no.1
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• pp.154-166
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• 2019

Consumers believe it is more profitable to buy products at a discount price than at a fixed price. However, consumers who miss an opportunity for a discount would experience regret; consequently, they may also forgo a purchase despite experiencing regret if a subsequent opportunity was no more attractive than the initial one. This phenomenon has been conceptualized as inaction inertia. In this study we investigate if consumers showed behavior of inaction inertia under the situation of fashion products purchase as well as if price and involvement had a moderating effect on product purchase. The following are the results of the research based on an empirical investigation. First, according to the results of influence of inaction inertia on purchase intention, respondents exposed to scenario of experiencing similar price discount in the past, both under high-price situations and low-price situations, made statistically fewer responses than those exposed to situations without such experience; therefore, effect of inaction inertia could be confirmed. Second, the moderating effect of product price on relationships between inaction inertia and purchase intention was not statistically significant.

• Journal of Korea Society of Digital Industry and Information Management
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• v.15 no.4
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• pp.173-187
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• 2019

Based on the theory of planning behavior, the purpose of this study was to validate the path to consumers organic bakery product purchasing process, and moderating effect of price sensitivity. The improvement of national income and changes in diet due to economic growth began to increase consumption of organic and eco-friendly farm products, and thus products using organic materials began to be released in bakeries. Thus, it was necessary to understand consumer awareness and purchasing processes for organic bakery products. Specifically, the study examined the structural relations among purchase reasons, purchase motives, and purchase behavioral intention of organic bakery products. A survey was conducted in May 2019 from adults who had experience purchasing organic bakery products in Seoul metropolitan area, and 327 responses were used for data analysis. The findings of the study showed that consumers' interests in health, food safety, and environment had positive effects on their attitudes, subjective norm, and perceived behavioral control toward organic bakery products, while their interests in organic products did not have an effect on perceived behavioral control. There were significant differences in the purchasing process of organic bakery products according to their price sensitivity. The study suggests that the correct delivery of information on organic ingredients and the price policies available to many consumers will contribute to the revitalization of organic bakery products.

• The Journal of Fisheries Business Administration
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• v.41 no.3
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• pp.59-78
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• 2010

This paper tries to apply the asymmetrical price transmission(APT) behavior observed in the agricultural industry to supply chains of the domestic fishery industry by a statistical manner. The fore mentioned asymmetrical price transmission refers to when price movements in the later stage of the supply chain do not move in a normal or symmetrical manner corresponding to price movements in the earlier stage of the supply chain. Therefore, when the earlier stage price increase and the later stage price increases to a larger degree, it is called positive(+) asymmetry and the opposite behavior is called negative(-) asymmetry. The study examines the data from domestic producers of three fresh fish types, hairtail, mackerel, and cuttlefish, and tries to examine the price asymmetry between the producer or farm, wholesaler, and retail prices via an APT test utilizing unit root, cointegration, and error correction model. The study found, hairtail wholesale and retail pricing bas a negative asymmetric relationship while mackerel has a negative asymmetric pricing relationship at the producer and retail levels of the supply chain. In the case of cuttlefish, all levels of the supply chain showed negative asymmetrical behavior in the supply chain price transmission, meaning the earlier stage price changes are more rapidly and greatly inputted in the later stage of the supply chain pricing. We believe that the reason why the analysis results show negative price asymmetry is due to the uniqueness of fishery products having an important variable such as freshness. If price increases are greater and quicker than price decreases, then consumer demand, which is sensitive to price increases will decrease and subsequently result in the increase of inventory levels, reducing profits for retailers. Also, frozen hairtail, mackerel, and cuttlefish will act as substitute goods to fresh fishery products. Therefore, fresh fishery products have a high demand of price elasticity. When prices increase, demand quickly decreases. Therefore the profit of wholesalers and retailers to decrease, I think this is the main reason of APT in the supply chain of Korea' s fisheries industry.

• Korean Journal of Organic Agriculture
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• v.17 no.3
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• pp.327-345
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• 2009

Environment-friendly agricultural products market has been experiencing various change through the quantitative growth in Korea. The food safety is considered seriously and the interest of the consumer is increasing about environment-friendly agricultural products and organic agricultural products. Environment-friendly agricultural products is encouraged in the link of the counter-measure which follows in the market opening. This research aims at making shorter the distance between the productive person and the consumer and to improve the income for organic farm and the price competitiveness for Environment-friendly agricultural products through the present condition analysis for organic produce's production, distribution and consumption market. To solve the problems above, the development of strategies for the establishment of distribution system for the organic produce's stable supply and the improvement of price competitiveness, the establishment of the cooperating system on the produce's demand and supply, the improvement of consumer's reliability by reinforcement with connectivity and transparency of the process, and the vitalization of regional economy and the exchange of rural and city area are needed.

• Journal of Family Resource Management and Policy Review
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• v.9 no.2
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• pp.77-92
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• 2005

This study examined the differences on the amount of purchase of and willingness-to-pay more for organic products by socio-economic variables, knowledge and reliability of organic products, and concern for food. In addition the effects of the reliability and the price of organic products on the amount of purchase and willingness-to-pay more for organic products in the Path analysis. The results were below. First, consumers who had high education, were the age of 40's, high income, high level of knowledge and reliability for organic products were more likely to purchase organic products. Second, the level of willingness-to-pay more for organic Products were high when consumers with more than three children, not lowest level of health, and higher reliability. Third, the reliability of organic products impact the amount of purchase of organic products, but not the perception of organic products' price. Forth, both the reliability and the perception of those price impact the level of willingness-to-pay more for organic products.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.12
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• pp.3923-3942
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• 2022

Environment, price, regulation, and other factors influence the price of agricultural products, which is a social signal of product supply and demand. The price of many agricultural products fluctuates greatly due to the asymmetry between production and marketing details. Horticultural goods are particularly price sensitive because they cannot be stored for long periods of time. It is very important and helpful to forecast the price of horticultural products which is crucial in designing a cropping plan. The proposed method guides the farmers in agricultural product production and harvesting plans. Farmers can benefit from long-term forecasting since it helps them plan their planting and harvesting schedules. Customers can also profit from daily average price estimates for the short term. This paper study the time series models such as ARIMA, SARIMA, and neural network models such as BPN, LSTM and are used for wheat cost prediction in India. A large scale available data set is collected and tested. The results shows that since ARIMA and SARIMA models are well suited for small-scale, continuous, and periodic data, the BPN and LSTM provide more accurate and faster results for predicting well weekly and monthly trends of price fluctuation.

• Korean Journal of Organic Agriculture
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• v.29 no.2
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• pp.159-171
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• 2021

The purpose of this paper is to analyze the relationship between the level of satisfaction of domestic Environment-friendly agricultural products and purchase intent of imported organic products. To accomplish the objective of the study a consumer survey was administered for quantitative analysis regarding consumption patterns. The bivariate probit with sample selection model was employed for empirical analysis on the relationship. The estimation results showed that to increase continuously the consumption, it is necessary to improve the quality satisfaction compared to the price, and that it is also necessary to increase the reliability of the certification system and the awareness that the consumption is helpful for health promotion to increase the quality satisfaction compared to price. In addition, it was concluded that in order to induce the purchase of domestic organic products rather than imported organic products, efforts to improve the safety of domestic products, remove the risk of residual pesticides, and increase the reliability of domestic products compared to imported products are needed. Therefore, to reduce the proportion of purchases of imported organic products and increase the consumption of domestic products, raising awareness that the consumption is conducive to health promotion, enhancing the safety of domestic products, and providing accurate information on the safety of imported products are required.

• The Journal of the Korea Contents Association
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• v.18 no.11
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• pp.416-429
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• 2018

Typhoons and floods are natural disasters that occur frequently, and the damage resulting from these disasters must be in advance predicted to establish appropriate responses. Direct damages such as building collapse, human casualties, and loss of farms and fields have more attention from people than indirect damages such as increase of consumer prices. But indirect damages also need to be considered for living. The agricultural products are typical consumer items affected by typhoons and floods. Sudden, powerful typhoons are mostly accompanied by heavy rains and damage agricultural products; this increases the retail price of such products. This study analyzes the influence of natural disasters on the price of agricultural products by using a deep learning algorithm. We decided rice, onion, green onion, spinach, and zucchini as target agricultural products, and used data on variables that influence the price of agricultural products to create a model that predicts the price of agricultural products. The result shows that the model's accuracy was about 0.069 measured by RMSE, which means that it could explain the changes in agricultural product prices. The accurate prediction on the price of agricultural products can be utilized by the government to respond natural disasters by controling amount of supplying agricultural products.

• Korean Journal of Fisheries and Aquatic Sciences
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• v.56 no.2
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• pp.243-252
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• 2023

Fluctuations in the price of aquaculture products have recently intensified. In particular, wholesale price fluctuations are adversely affecting consumers. Therefore, there is an emerging need for a study on forecasting the wholesale price of aquaculture products. The present study forecasted the wholesale price of olive flounder Paralichthys olivaceus, a representative farmed fish species in Korea, by constructing multivariate long-short term memory (LSTM) and gated recurrent unit (GRU) models. These deep learning models have recently been proven to be effective for forecasting in various fields. A total of 191 monthly data obtained for 17 variables were used to train and test the models. The results showed that the mean average percent error of LSTM and GRU models were 2.19% and 2.68%, respectively.