• Fashion & Textile Research Journal
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• v.12 no.1
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• pp.38-45
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• 2010

Cosmetics retailers would benefit from studies that examine which shopping-mall attributes can be manipulated to favorably affect consumer satisfaction and revisit intention at Internet. The purposes of this study were (1) to examine the dimensionality of shopping-mall attribute for cosmetics retailers, (2) to determine which dimensions of shopping-mall attribute were significant predictors of consumer satisfaction and revisit intention and (3) to find out the moderating effect of consumer satisfaction through shopping-mall attributes on revisit intention to buy cosmetics across the types of shopping-mall at Internet (i.e., total mall and specialty mall). Data were collected from 209 online cosmetic shoppers among high school girls. Factor analysis identified five dimensions of shopping-mall attributes at Internet, such as Convenience, Price, Loading speed, Sales promotion, and Service. Only two dimensions(i.e., convenience and service) were significant predictors of online shopper satisfaction in both total mall and specialty mall. The moderating effect of consumer satisfaction on revisit intention was significant in both two mall types at Internet. For total mall, price was a significant predictor through consumer satisfaction on revisit intention, while loading speed was a significant predictor directly on revisit intention for specialty mall. In light of the major findings, this study sets forth strategic implications for consumer satisfaction and revisit intention to buy cosmetics in the setting of electronic commerce.

• Preventive Nutrition and Food Science
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• v.7 no.1
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• pp.95-104
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• 2002

A survey on Korean families′purchase of fresh fruits and vegetables was conducted to increase understanding of families′food decision making. Two hundred ninety seven families with at least one elementary-school age child were selected from four elementary schools to complete a questionnaire during April, 2001 in Gyeongju, Korea. Descriptive statistics, Chi-square test, T-test, and ANOVA statistics were used to analyze the data. The major findings are as follows: Families bought fresh fruits and vegetables at the public markets or the farmer′s markets and a large supermarket most frequently in both summer and winter. Families grew produce by themselves and bought them from farmers directly least frequently in both summer and winter. Families whose housewives had less than middle school education brought fruits and vegetables from Agricultural Co-ops and grew thens by themselves more frequently compared to those who had higher education. On the other hand, families whose housewives had graduated from 4 year college bought fruits and vegetables from large supermarkets more frequently compared to those who had lesser education. "Quality"and "safety production"of fruits and vegetables and "clean environment of store"were the three most important factors when they decided the place to buy fruits and vegetables. "Being treated as a valuable customer" and "ease of finding things"were the two least important factors. Families whose housewives were in their thirties valued "cleanness of the store"and "being treated as a valuable customer"important factors when they decided the place. Families whose housewives had less than middle school education thought that price, availability of public transportation, and availability of locally grown food were the important factors for deciding the place compared to those who had higher education. The price was the factor which low-income families thought important for decision making on the place to buy fruits and vegetables.

• Journal of the Korea Convergence Society
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• v.12 no.4
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• pp.249-258
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• 2021

Bitcoin prices have been soaring recently as investors flock to cryptocurrency exchanges. The purpose of this study is to predict the Bitcoin price using a deep learning model and analyze whether Bitcoin is profitable through investment strategy. LSTM is utilized as Bitcoin prediction model with nonlinearity and long-term memory and the profitability of MA cross-over strategy with predicted prices as input variables is analyzed. Investment performance of Bitcoin strategy using LSTM forecast prices from 2013 to 2021 showed return improvement of 5.5% and 46% more than market price MA cross-over strategy and benchmark Buy & Hold strategy, respectively. The results of this study, which expanded to recent data, supported the inefficiency of the cryptocurrency market, as did previous studies, and showed the feasibility of using the deep learning model for Bitcoin investors. In future research, it is necessary to develop optimal prediction models and improve the profitability of Bitcoin investment strategies through performance comparison of various deep learning models.

• Journal of the Korean Society of Floral Art and Design
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• no.40
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• pp.125-136
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• 2019

This study aims to examine the important factors of flower shops' store attributes influencing the repurchase intention for people who buy flowers for themselves with a hedonic shopping motivation. This study was verified by empirical survey and the results are summarized as follow. First, it was found that price, salesperson's professionalism, store accessibility had a significant positive impact on the repurchasing intention of flowers. Second, it was found that price is the first important factor influencing the repurchasing intention of flowers, and next salesperson's professionalism is the second important one, and lastly store accessibility is the third important one. For implications of this study, this study investigated the cause-and-effect relationship between flower shops' store attributes and the repurchasing intention of flowers for people who buy flowers for themselves with a hedonic shopping motivation, so it provides a theoretical foundation for further studies.

• Korean Journal of Construction Engineering and Management
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• v.17 no.1
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• pp.76-82
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• 2016

Decision-making in construction projects often include options features. Such embedded options are difficult to value properly and many decision makers do not have experience in option analysis. The purpose of this paper is to demonstrate how real option analysis can be used to value capital expenditures on construction materials. We propose a real option framework to evaluate decision-making processes involving the purchase of construction materials. A case study was conducted by evaluating the purchase decision-making of solar cells, a good with high price volatility. Using real option analysis two strategies to improve the financial feasibility of installing a solar panel system were derived. The first strategy involves using a price cap that gives the project manager the right, but not obligation, to buy the modules for a predefined price during the next year. The second strategy is to defer the purchase of the solar cells until future price information becomes clearer. Both of the strategies in the case study were valued using the binominal model. This study will help to improve the financial feasibility of purchasing construction materials with high price volatility by including the value of managerial flexibility.

• Journal of Digital Convergence
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• v.12 no.11
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• pp.49-55
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• 2014

Many changes in growth in the lifestyle of consumers for online shopping mall has surfaced. consumers will appear brand product will tend to buy products at more affordable prices and a department store's brand product sale increase in online shopping mall. This study verified the effects of Utilitarian shopping value and Hedonic shopping value, the shopping value, on the shopping-mall satisfactions, and the moderating effects of Price sensitivity. The result states that the shopping value affect the shopping-mall satisfaction, In terms of Price sensitivity, the group with higher Price sensitivity of utilization showed more significant result on the Utilitarian shopping value affects on the shopping-mall satisfaction, the group with higher Price sensitivity of utilization showed more significant result on the Utilitarian shopping value and Hedonic shopping value.

• Journal of the Korea Society of Computer and Information
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• v.28 no.8
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• pp.67-75
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• 2023

Stock price prediction is an important topic extensively discussed in the financial market, but it is considered a challenging subject due to numerous factors that can influence it. In this research, performance was compared and analyzed by applying time series prediction models (LSTM, GRU) and non-time series prediction models (RF, SVR, KNN, LGBM) that do not take into account the temporal dependence of data into stock price prediction. In addition, various data such as stock price data, technical indicators, financial statements indicators, buy sell indicators, short selling, and foreign indicators were combined to find optimal predictors and analyze major factors affecting stock price prediction by industry. Through the hyperparameter optimization process, the process of improving the prediction performance for each algorithm was also conducted to analyze the factors affecting the performance. As a result of feature selection and hyperparameter optimization, it was found that the forecast accuracy of the time series prediction algorithm GRU and LSTM+GRU was the highest.

• Journal of Distribution Science
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• v.16 no.3
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• pp.13-21
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• 2018

Purpose - This study examined the effect of the importance of selective attribute of HMR(Home Meal Replacement) on customers' satisfaction and repurchase intention which is rapidly increasing with the changes of demographic, social and cultural trends as well as the influence of on and offline shopping channel moderating role. Research design, data, and methodology - Based on the research of previous studies, it assumed the selection attributes of HMR products were price, convenience, menu, freshness. With 231 surveyed questionnaires, this study was conducted by AMOS 21.0 and the SEM(structural equation model) was used as statistical method for examining the hypotheses in this study. Results - The analysis showed that price, convenience, and freshness had a significant effect on satisfaction, whereas menu did not affect satisfaction and the effect of satisfaction on repurchase intention was statistically significant. However, the results were different depending on the on and offline shopping channel for customers to buy HMR products. Price, menu and freshness are affected by online shopping, meanwhile convenience is more influenced by offline. Conclusions - This study analyzed the effect of selection attribute of HMR products on the satisfaction, repurchase intention, and the influence of each shopping channel, and provided practical implications.

• International Journal of Human Ecology
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• v.4 no.1
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• pp.45-54
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• 2003

The purpose of this study was primarily to examine various variables influencing consumer purchasing behavior on perceived product quality, value toward product including brand loyalty, price, consumer's willingness to pay for the product, and their expenditure patterns in Korean apparel market. Factor analysis was used to evaluate the credibility of dependent variables, and T-test was used to compare the effect of brand label, country of origin, brand effect between Korean and U.S, and jacket price and quality on consumer characteristics. Discriminated analysis was used to find the effective variables influencing the two reference group differences when they evaluated Korean and U.S. labeled and non-labeled apparel products. Multiple Regression analysis was used to examine the effects of consumer characteristics on perceived quality, perceived value, perceived price, and their willingness to buy. The results of this study also provides useful information of consumer purchasing behavior on U.S. branded apparel which may or already launched the Korean fashion merchandizing market.

• Journal of Environmental Science International
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• v.27 no.12
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• pp.1195-1203
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• 2018

Many consumers intend to make environmentally purchase. However, this is not always what occurs. A gap exists between consumer intentions to purchase environmentally friendly products and their actual purchase behavior. In this study, we examined the effect of situational factors such as price sensitivity, product quality perception, and label trust on the relationship between purchase intention and purchase behavior for environmentally friendly products. First, we conducted a theoretical consideration through a review of literature on price sensitivity, product quality perception, label trust, purchase intention and purchase behavior. Based on the literature review, we designed a structural model and developed the hypotheses. Next, we collected data using a survey and processed them statistically in order to verify the hypotheses. A total of 213 samples were collected and the data were analyzed using a structural equation model (LISREL 8.70). The results suggest that the situational factor of label trust has a moderating effect on the relationship between purchase intention and purchase behavior for environmentally friendly products. However, price sensitivity and product quality perception have no moderating effect. This means that trust in labels is important when consumers want to buy environmentally friendly products. Although this study has some limitations, it is expected that it will positively trigger follow-up research.