• Management & Information Systems Review
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• v.35 no.3
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• pp.115-130
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• 2016

This study aims to identify which social information have significant influence on the improvement of recommendation trust and how these effects can be different according to the product involvement level. Based on the relevant literature reviews, this study posits four characteristics of recommendation trust, which are closeness, similarity, sincerity, and reputation, and established a research model for the relationship between social information and recommendation trust. And we found a moderating effect of product involvement on the relationship between social information and recommendation trust. 205 trust relationships(links) from 55 respondents of Google Docs. survey data have been collected and tested using multiple regression and hierarchical regression analysis. The results of our hypotheses testing are summarized as follows. Firstly, four social information characteristics of closeness, similarity, sincerity, and reputation have a significantly positive effect on recommendation trust. Secondly, a moderating effect of product involvement between recommendation trust and antecedents (e.g., closeness and reputation) of social information is significant. From the results, we provide theoretical and managerial implications, and suggestions for further research.

• 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.

• Journal of the Korean Society of Clothing and Textiles
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• v.48 no.2
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• pp.350-366
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• 2024

This study aimed to clarify the continuance intention of users of size recommendation services. The expectation-confirmation model framed the analysis of the 180 data points collected. The analysis determined the mediating effects of perceived usefulness and satisfaction on the relationship between expectation-confirmation and continuance intention. The moderated mediation effect of familiarity was also analyzed, and a path analysis was conducted using PROCESS macro. Results showed that expectation-confirmation had a significant effect on perceived usefulness, satisfaction, and continuance intention. Findings indicated that perceived usefulness affected satisfaction and continuance intention and confirmed that satisfaction affected continuance intention. In the relationship between expectation-confirmation and continuance intention, mediation analysis verified the mediation and double mediation of perceived usefulness and satisfaction. In the group with an above-average familiarity value, moderation analysis confirmed a moderating effect between perceived usefulness and satisfaction. Above-average familiarity values also confirmed that the moderating effect on continuance intention was significant.

• Information Systems Review
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• v.9 no.2
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• pp.109-127
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• 2007

In a buyer seller transaction process, "value for money" is one of the most important criteria for a buyer's purchasing decision. The terms "value" and "money" represent a composite measure of what a buyer receives from goods and/or services and a measure of what he/she pays for them, respectively. The purpose of this paper is to help buyers select the best seller in terms of value for money. We suggest DEA models for buyer seller transactions and apply them to the case of an Internet comparison shopping site in Korea. We expect our DEA models to provide valuable information for rational buyers who want to pay the least price for high quality products/services. Moreover, we expect that our models can help sellers be more competitive by showing them how to attract buyers.

• Convergence Security Journal
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• v.8 no.4
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• pp.81-89
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• 2008

This paper introduces u-learning service model based on context awareness. Also, it concentrates on agent-based WPAN technology, OSGi based middleware design, and the application mechanism such as context manager/profile manager provided by agents/server. Especially, we'll introduce the meta structure and its management algorithm, which can be updated with learning experience dynamically. So, we can provide learner with personalized profile and dynamic context for seamless learning service. The OSGi middleware is applied to our meta structure as a conceptual infrastructure.

• Proceedings of the Korea Water Resources Association Conference
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• 2015.05a
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• pp.422-422
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• 2015

수문학적 모델링은 수자원계획에 있어 가장 핵심적인 도구 중에 하나이다. 강우-유출모형의 매개변수 추정시 장기간의 자료를 활용하는데 초점이 맞추어져 있으며, 일반적으로 5년 이상의 자료를 활용하여 매개변수를 추정하는 경년변동(inter-annual variability) 매개변수 추정 방법이 추천되고 있다. 수문학적 변동성 측면에서 볼 때 강우, 온도, 유역의 조건 등의 연내변동성(intra-annual variability)이 경년보다 크게 나타나고 있으나, 이러한 특성을 고려한 수문모형의 매개변수 추정은 이루어지고 있지 않다. 이러한 점에서 연내변동성으로 기인하는 비정상성을 고려한 매개변수 추정 방법의 도입이 필요할 것으로 판단되며, 본 연구에서는 계측유역을 대상으로 다양한 시간규모에서 매개변수 추정을 수행하고 최적의 시간규모를 도출하고자 한다. 이를 위해서 DDS(dynamically dimensioned search) 알고리즘을 도입하여 최적화를 수행하였으며, 다양한 시간 규모에서 모형의 적합특성을 평가하였다. 교차검증을 통하여 매개변수의 통계적 유의성을 확보하였으며, 전통적인 매개변수 추정 절차와 비교 검토를 수행하였다.

• Journal of Intelligence and Information Systems
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• v.28 no.4
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• pp.287-308
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• 2022

The explosive growth of cryptocurrency, led by Bitcoin has emerged as a major issue in the financial market recently. As a result, interest in cryptocurrency investment is increasing, but the market opens 24 hours and 365 days a year, price volatility, and exponentially increasing number of cryptocurrencies are provided as risks to cryptocurrency investors. For that reasons, It is raising the need for research to reduct investors' risks by dividing cryptocurrency which is not suitable for recommendation. Unlike the previous studies of maximizing returns by simply predicting the future of cryptocurrency prices or constructing cryptocurrency portfolios by focusing on returns, this paper reflects the tendencies of investors and presents an appropriate recommendation method with interpretation that can reduct investors' risks by selecting suitable Altcoins which are recommended using Apriori algorithm, one of the machine learning techniques, but based on the similarity and association rules of Bitocoin.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.19 no.2
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• pp.173-184
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• 2018

This study was conducted to construct and test structural equation modeling of the causal relationship of job satisfaction of nursing staff with satisfaction, revisit intention and recommendation of the hospital by patients. The data utilized in this study were the second data acquired from the 2012 Public Hospital Evaluation Programme. The subjects of this study were 2,375 nursing staff and 3,433 patients at 39 district public hospitals. The instrument of job satisfaction of nursing staff consisted of five factors and 13 items. The instruments of satisfaction, revisit intention, and recommendation to others of patients consisted of one question on an 11 point scale (0: very negative, 10: very positive). The data were analyzed using SPSS version 20.0 and AMOS version 20.0. Model fit indices for the hypothetical model were suitable for the recommended level: model of in-patient ${\chi}^2$ 904.598 (df=81, p<0.001), GFI 0.938, AGFI 0.900, RMSR 0.076, mode of out-patient ${\chi}^2$ 869.021(df=81, p<0.001), GFI 0.940, AGFI 0.900, RMSR 0.074. In conclusion, nursing staff are the largest group in public hospitals, and they provide direct care to patients. Therefore, job satisfaction of nursing staff should be enhanced to improve satisfaction of patients because their attitude significantly influences patient satisfaction.

• Journal of Intelligence and Information Systems
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• v.26 no.2
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• pp.27-42
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• 2020

Traditional companies with offline stores were unable to secure large display space due to the problems of cost. This limitation inevitably allowed limited kinds of products to be displayed on the shelves, which resulted in consumers being deprived of the opportunity to experience various items. Taking advantage of the virtual space called the Internet, online shopping goes beyond the limits of limitations in physical space of offline shopping and is now able to display numerous products on web pages that can satisfy consumers with a variety of needs. Paradoxically, however, this can also cause consumers to experience the difficulty of comparing and evaluating too many alternatives in their purchase decision-making process. As an effort to address this side effect, various kinds of consumer's purchase decision support systems have been studied, such as keyword-based item search service and recommender systems. These systems can reduce search time for items, prevent consumer from leaving while browsing, and contribute to the seller's increased sales. Among those systems, recommender systems based on association rule mining techniques can effectively detect interrelated products from transaction data such as orders. The association between products obtained by statistical analysis provides clues to predicting how interested consumers will be in another product. However, since its algorithm is based on the number of transactions, products not sold enough so far in the early days of launch may not be included in the list of recommendations even though they are highly likely to be sold. Such missing items may not have sufficient opportunities to be exposed to consumers to record sufficient sales, and then fall into a vicious cycle of a vicious cycle of declining sales and omission in the recommendation list. This situation is an inevitable outcome in situations in which recommendations are made based on past transaction histories, rather than on determining potential future sales possibilities. This study started with the idea that reflecting the means by which this potential possibility can be identified indirectly would help to select highly recommended products. In the light of the fact that the attributes of a product affect the consumer's purchasing decisions, this study was conducted to reflect them in the recommender systems. In other words, consumers who visit a product page have shown interest in the attributes of the product and would be also interested in other products with the same attributes. On such assumption, based on these attributes, the recommender system can select recommended products that can show a higher acceptance rate. Given that a category is one of the main attributes of a product, it can be a good indicator of not only direct associations between two items but also potential associations that have yet to be revealed. Based on this idea, the study devised a recommender system that reflects not only associations between products but also categories. Through regression analysis, two kinds of associations were combined to form a model that could predict the hit rate of recommendation. To evaluate the performance of the proposed model, another regression model was also developed based only on associations between products. Comparative experiments were designed to be similar to the environment in which products are actually recommended in online shopping malls. First, the association rules for all possible combinations of antecedent and consequent items were generated from the order data. Then, hit rates for each of the associated rules were predicted from the support and confidence that are calculated by each of the models. The comparative experiments using order data collected from an online shopping mall show that the recommendation accuracy can be improved by further reflecting not only the association between products but also categories in the recommendation of related products. The proposed model showed a 2 to 3 percent improvement in hit rates compared to the existing model. From a practical point of view, it is expected to have a positive effect on improving consumers' purchasing satisfaction and increasing sellers' sales.

• Journal of Korean Library and Information Science Society
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• v.35 no.4
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• pp.251-269
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• 2004

This paper recommends the use of information processing models for student's information literacy education. The author mantions that the proper use of an information processing model help enhance students' self-study, creativity, and academic attainment as psychological tools. An information processing model helps also students reach the Information literacy standards set by AASL and AECT. The author attempts to show the important linkage between the practical application of information technologies during the use of an information processing model and the attainment of the Information literacy standards.