• Journal of Service Research and Studies
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• v.12 no.2
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• pp.80-93
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• 2022

This study is a study on a methodology that can extract various factors that affect purchase and use of products/services from the consumer's point of view through previous studies, and analyze the types and tendencies of consumers according to age and gender. To this end, we quantify factors in terms of general personal propensity, consumption influence, consumption decision, etc. to check the consistency of data, and based on these studies, we conduct research to suggest and prove data analysis methodologies of consumer types that are meaningful from the perspectives of startups and SMEs. did As a result, it was confirmed through cross-validation that there is a correlation between the three main factors assumed for data analysis from the consumer's point of view, the general tendency, the general consumption tendency, and the factors influencing the consumption decision. verified. This study presented a data analysis methodology and a framework for consumer data analysis from the consumer's point of view. In the current data analysis trend, where digital infrastructure develops exponentially and seeks ways to project individual preferences, this data analysis perspective can be a valid insight.

• Journal of Korean Home Economics Education Association
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• v.35 no.2
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• pp.73-89
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• 2023

This study aimed to analyze 'consumer life' units in middle school home economics textbooks in Korea and Japan based on the ESD concept (diversity, interaction, finiteness, fairness, cooperation, responsibility). The objective was to compare how the ESD concept was reflected in Korean and Japanese textbooks. The analysis focused on the units related to 'adolescent consumer life' in Korean textbooks and 'money management and purchase' as well as 'consumer rights and responsibilities' in Japanese textbooks. Results showed that in Korea, responsibility (23.36%) was most emphasized, followed by interaction (22.43%), cooperation (19.63%), fairness (18.69%), finiteness (10.28%), and diversity (5.61%). In Japan, cooperation (21.74%) and interaction (21.45%) received significant attention, followed by fairness (16.23%), responsibility (13.91%), finiteness (13.33%), and diversity (13.33%). Korean textbooks exhibited a wider range of ESD concept percentages compared to Japan. In the Korean textbooks, responsibility was emphasized for promoting rational and ethical consumption, while Japanese textbooks highlighted cooperation in resolving consumer issues and collaborating with local and international communities to address environmental concerns. Interaction was emphasized regarding the impact of individual and family consumption on society, economy, and the environment. Overall, both Korean and Japanese home economics textbooks reflected elements that foster sustainable consumer behaviors.

• Journal of Convergence for Information Technology
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• v.12 no.3
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• pp.222-235
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• 2022

The Government provides a financial assistance to stimulate firm R&D and innovation activities. Previous papers on the impact of public subsidies on firm R&D investments mainly had a focus on an individual policy tool regardless of potential impacts of other policy instruments. This study addresses this gap by examining the effects of policy mix regarding a subsidy and a tax credit. The empirical analyses from fixed effect model using Survey on Technology of SMEs 2015-2017 revealed valuable points. First, policy mix induces more R&D investment of SMEs, which in turn, shows a complementary relationship between two instruments. Second, even if impact of tax credit controlled, subsidy is positively associated with SMEs R&D investment. These findings justify policy mix interventions to promote SME R&D activity. Moreover, grants can be applied as a more useful policy tool for SMEs that are constrained by resources and capabilities.

• The Korean Journal of Franchise Management
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• v.2 no.1
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• pp.100-118
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• 2011

Since 1990, income has been grown rapidly in Korea. Thus, concerns of environmental pollution and health have been increased among Korea's consumers. As a result of this concern, demand for safe food and agricultural products has been growing in Korea. Recently, purchasing patterns of Korea's consumers have been changed as Korea's society has changed to an aging society, growth of unmarried person, and low birthrate. Korea's consumers prefer to buy only volume that they need. Thus, the volume of agricultural products that they purchase became small. Therefore, retailers should reflect such needs of consumers to their business. The purpose of this study is to build up new strategies in order to make a high profit through customer's satisfaction when selling agricultural products. Using literature review, this study has drawn results. The results of this study is that retailers should lay products with brand in their store and establish trust with customers in oder to make loyal customers. In addition, retailers should prepare individual package of agricultural products for sales of a small volume to keep pace with social changes.

• Asia Marketing Journal
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• v.15 no.4
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• pp.51-60
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• 2014

Previous research on loyalty program has verified the factors that influence redemption behavior and the understanding of the mechanism of redemption behavior with academic and practical implications. However, these research has not proven boundary conditions in which the phenomena can be strengthened or weakened- that is, the moderating effect remains unclear. The inclusion of moderating variables can provide a more extensive understanding of the mechanism of this behavior from academic and managerial perspectives alike. Therefore, this current research proposes regulatory focus as a moderating variable, which has received scarce attention in the study of loyalty program behavior, especially individual characteristic variables that, in turn, affect the consumers' purchasing behavior in various ways. Previous research on consumer decision making investigates the differential role of regulatory focus as a series of stages. Regulatory focus theory posits that people depend on the two types of regulatory focus when pursuing goals: promotion focus vs. prevention focus. The former induces tendencies to recognize a goal as a hope and ideal, as something that satisfies the need for accomplishment, and to be sensitive to the presence of a positive outcome of the match and to match the pursuit of goals. On the other hand, the latter tends to regard a goal as the responsibility or obligation to achieve the goal, has a tendency to avoid failure to meet a target, and is sensitive to the presence of the negative consequences that do not reach the target. The following propositions are suggested: 1) The effect of higher accumulation effort level on delaying point redemption speed will be relatively more pronounced for customers with prevention focus. 2) The effect of higher accumulation effort level on large redemption unit size will be relatively more pronounced for customers with prevention focus. 3) The effect of higher accumulation effort level on hedonic redemption ratio will be relatively more pronounced for customers with promotion focus. Therefore, this research provides a moderating variable that has the potential to be used as a reference for market segmentation and affects the relationship between point accumulation effort and three sides of point redemption behavior. On this basis, the direction for the future research on this issue is recommended. Future research could verify these propositions conducting a survey of customers' propensity of regulatory focus in conjunction with the history of the loyalty program of data. This would provide a more realistic effect on the usage behavior of loyalty program consumers by providing useful implications for both marketing practitioners and researchers.

• Information Systems Review
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• v.25 no.1
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• pp.111-128
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• 2023

With the development of artificial intelligence technology, interest in data-based product preference estimation and personalized recommender systems is increasing. However, if the recommendation is not suitable, there is a risk that it may reduce the purchase intention of the customer and even extend to a huge financial loss due to the characteristics of the financial product. Therefore, developing a recommender system that comprehensively reflects customer characteristics and product preferences is very important for business performance creation and response to compliance issues. In the case of financial products, product preference is clearly divided according to individual investment propensity and risk aversion, so it is necessary to provide customized recommendation service by utilizing accumulated customer data. In addition to using these customer behavioral characteristics and transaction history data, we intend to solve the cold-start problem of the recommender system, including customer demographic information, asset information, and stock holding information. Therefore, this study found that the model proposed deep learning-based collaborative filtering by deriving customer latent preferences through characteristic information such as customer investment propensity, transaction history, and financial product information based on customer transaction log records was the best. Based on the customer's financial investment mechanism, this study is meaningful in developing a service that recommends a high-priority group by establishing a recommendation model that derives expected preferences for untraded financial products through financial product transaction data.

• The Journal of the Convergence on Culture Technology
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• v.10 no.5
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• pp.163-171
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• 2024

This study applies neural network analysis techniques to examine the factors influencing the purchasing decisions of fashion eyewear among women in their 30s and 40s, comparing these findings with traditional parametric analysis methods. In the fashion area, machine learning techniques are utilized for personalized fashion recommendation systems. However, research on such applications in Korea remains insufficient. By reanalyzing a study conducted in 2017 using traditional quantitative methods with these new techniques, this study aims to confirm the utility of neural network methods. Notably, the study finds that the classification accuracy of preferred sunglasses design is highest, at 86.2%, when the L-BFGS-B neural network is activated using the hyperbolic tangent function. The most critical factors influencing purchasing decisions were consumers' occupations and their pursuit of new styles. It is interpreted that Korean sunglasses consumers prefer "safe changes." These findings are consistent for selecting both the frames and lenses of sunglasses. Traditional quantitative analysis suggests that the type of sunglasses preferred varies according to the group to which a consumer belongs. In contrast, neural network analysis predicts the preferred sunglasses for each individual, thereby facilitating the development of personalized sunglasses recommendation systems.

• The Korean Journal of Financial Studies
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• v.3 no.1
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• pp.233-265
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• 1996

We examine the impact of public and private information on price movements using the thirty DJIA stocks and twenty-one NASDAQ stocks. We find that the standard deviation of daily returns on information days (dividend announcement, earnings announcement, insider purchase, or insider sale) is much higher than on no-information days. Both public information matters at the NYSE, probably due to masked identification of insiders. Earnings announcement has the greatest impact for both DJIA and NASDAQ stocks, and there is some evidence of positive impact of insider asle on return volatility of NASDAQ stocks. There has been considerable debate, e.g., French and Roll (1986), over whether market volatility is due to public information or private information-the latter gathered through costly search and only revealed through trading. Public information is composed of (1) marketwide public information such as regularly scheduled federal economic announcements (e.g., employment, GNP, leading indicators) and (2) company-specific public information such as dividend and earnings announcements. Policy makers and corporate insiders have a better access to marketwide private information (e.g., a new monetary policy decision made in the Federal Reserve Board meeting) and company-specific private information, respectively, compated to the general public. Ederington and Lee (1993) show that marketwide public information accounts for most of the observed volatility patterns in interest rate and foreign exchange futures markets. Company-specific public information is explored by Patell and Wolfson (1984) and Jennings and Starks (1985). They show that dividend and earnings announcements induce higher than normal volatility in equity prices. Kyle (1985), Admati and Pfleiderer (1988), Barclay, Litzenberger and Warner (1990), Foster and Viswanathan (1990), Back (1992), and Barclay and Warner (1993) show that the private information help by informed traders and revealed through trading influences market volatility. Cornell and Sirri (1992)' and Meulbroek (1992) investigate the actual insider trading activities in a tender offer case and the prosecuted illegal trading cased, respectively. This paper examines the aggregate and individual impact of marketwide information, company-specific public information, and company-specific private information on equity prices. Specifically, we use the thirty common stocks in the Dow Jones Industrial Average (DJIA) and twenty one National Association of Securities Dealers Automated Quotations (NASDAQ) common stocks to examine how their prices react to information. Marketwide information (public and private) is estimated by the movement in the Standard and Poors (S & P) 500 Index price for the DJIA stocks and the movement in the NASDAQ Composite Index price for the NASDAQ stocks. Divedend and earnings announcements are used as a subset of company-specific public information. The trading activity of corporate insiders (major corporate officers, members of the board of directors, and owners of at least 10 percent of any equity class) with an access to private information can be cannot legally trade on private information. Therefore, most insider transactions are not necessarily based on private information. Nevertheless, we hypothesize that market participants observe how insiders trade in order to infer any information that they cannot possess because insiders tend to buy (sell) when they have good (bad) information about their company. For example, Damodaran and Liu (1993) show that insiders of real estate investment trusts buy (sell) after they receive favorable (unfavorable) appraisal news before the information in these appraisals is released to the public. Price discovery in a competitive multiple-dealership market (NASDAQ) would be different from that in a monopolistic specialist system (NYSE). Consequently, we hypothesize that NASDAQ stocks are affected more by private information (or more precisely, insider trading) than the DJIA stocks. In the next section, we describe our choices of the fifty-one stocks and the public and private information set. We also discuss institutional differences between the NYSE and the NASDAQ market. In Section II, we examine the implications of public and private information for the volatility of daily returns of each stock. In Section III, we turn to the question of the relative importance of individual elements of our information set. Further analysis of the five DJIA stocks and the four NASDAQ stocks that are most sensitive to earnings announcements is given in Section IV, and our results are summarized in Section V.

• Korean Journal of Organic Agriculture
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• v.19 no.3
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• pp.357-372
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• 2011

We carried out the experiment to select the suitable sprout soybean varieties for environment-friendly cultivation in paddy field of southern part area, compares of excess moisture injury degree and yield ability among 29 sprout soybean varieties. Plant growth of sprout soybean was generally low in beginning and recovered after flowering due to rainfall. In paddy field cultivation, number of pod per individual and number of seed per individual were less in difference than upland cultivation, and maturing date was delayed 5-14 days than upland cultivation in most species. When environment-friendly cultivation, pest injury was not caused major problem for the growth during the vegetative period of soybean due to ground spider as natural enemy to insect pest. However, damage of stink bugs showed severe during grain filling period, and Dawonkong, Anpyeongkong, Dachaekong and Wonhwangkong showed susceptible to sting bug. SMV infection was weak and showed some necrosis symptoms in Sokangkong, but black root rot was not infected at all. Bacterial pustule began to be infected slowly from pod enlargement stage in most species, displayed severe symptoms in Dawonkong, Pungsannamulkong, Seonamkong and Sobaeknamulkong. The symptoms of pod anthracnose, pod blight and purple spot were greatly appeared after flowering. Disease resistance varieties was Paldokong, Kwangankong, Doremikong, Somyeongkong, Pungsannamulkong, Iksa-namulkong, Seonamkong, Sojinkong, Pureunkong, Bosugkong, Namhaekong and Sorokkong. Lodging index showed 3 in Saebyeolkong, and other species displayed slight lodging in 0-3 degree. 100-seed weight is 9.8-17.2g extent and increased 0.1-3.7g than upland cultivation in most species, but decreased in some species. Government purchase standard, species correspond to small-seed-size namulkong (Sizing screen diameter 4.0-5.6 mm) was Dawonkong, Dachaekong, Bosugkong, Seonamkong, Sokangkong, Hannamkong, Somyeongkong and Wonhwangkong. Species which seed yield was higher than Pungsannamulkong (266kg/10a) were Sorokkong, Hannamkong, Bosugkong and Sowonkong. Considering sprout soybean species, disease endurance, insect resistance, lodging resistance, 100-seed weight, yield ability and excess moisture tolerances synthetically, Seonamkong, Hannamkong, Doremikong, Bosugkong, Pungwonkong, Kwangankong, Sowonkong, Dagikong, Paldokong, Eunhakong and Pungsannamulkong were promising for environment-friendly cultivation in paddy field.

• Journal of Intelligence and Information Systems
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• v.23 no.4
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• pp.77-110
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• 2017

Recently, the diversification and individualization of consumption patterns through the web and mobile devices based on the Internet have been rapid. As this happens, the efficient operation of the offline store, which is a traditional distribution channel, has become more important. In order to raise both the sales and profits of stores, stores need to supply and sell the most attractive products to consumers in a timely manner. However, there is a lack of research on which SKUs, out of many products, can increase sales probability and reduce inventory costs. In particular, if a company sells products through multiple in-store stores across multiple locations, it would be helpful to increase sales and profitability of stores if SKUs appealing to customers are recommended. In this study, the recommender system (recommender system such as collaborative filtering and hybrid filtering), which has been used for personalization recommendation, is suggested by SKU recommendation method of a store unit of a distribution company that handles a homogeneous brand through a plurality of sales stores by country and region. We calculated the similarity of each store by using the purchase data of each store's handling items, filtering the collaboration according to the sales history of each store by each SKU, and finally recommending the individual SKU to the store. In addition, the store is classified into four clusters through PCA (Principal Component Analysis) and cluster analysis (Clustering) using the store profile data. The recommendation system is implemented by the hybrid filtering method that applies the collaborative filtering in each cluster and measured the performance of both methods based on actual sales data. Most of the existing recommendation systems have been studied by recommending items such as movies and music to the users. In practice, industrial applications have also become popular. In the meantime, there has been little research on recommending SKUs for each store by applying these recommendation systems, which have been mainly dealt with in the field of personalization services, to the store units of distributors handling similar brands. If the recommendation method of the existing recommendation methodology was 'the individual field', this study expanded the scope of the store beyond the individual domain through a plurality of sales stores by country and region and dealt with the store unit of the distribution company handling the same brand SKU while suggesting a recommendation method. In addition, if the existing recommendation system is limited to online, it is recommended to apply the data mining technique to develop an algorithm suitable for expanding to the store area rather than expanding the utilization range offline and analyzing based on the existing individual. The significance of the results of this study is that the personalization recommendation algorithm is applied to a plurality of sales outlets handling the same brand. A meaningful result is derived and a concrete methodology that can be constructed and used as a system for actual companies is proposed. It is also meaningful that this is the first attempt to expand the research area of the academic field related to the existing recommendation system, which was focused on the personalization domain, to a sales store of a company handling the same brand. From 05 to 03 in 2014, the number of stores' sales volume of the top 100 SKUs are limited to 52 SKUs by collaborative filtering and the hybrid filtering method SKU recommended. We compared the performance of the two recommendation methods by totaling the sales results. The reason for comparing the two recommendation methods is that the recommendation method of this study is defined as the reference model in which offline collaborative filtering is applied to demonstrate higher performance than the existing recommendation method. The results of this model are compared with the Hybrid filtering method, which is a model that reflects the characteristics of the offline store view. The proposed method showed a higher performance than the existing recommendation method. The proposed method was proved by using actual sales data of large Korean apparel companies. In this study, we propose a method to extend the recommendation system of the individual level to the group level and to efficiently approach it. In addition to the theoretical framework, which is of great value.