• Proceedings of the Korean Society of Computer Information Conference
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• 2022.07a
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• pp.19-22
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• 2022

In this work, we studied consumers' attractiveness/usefulness perceptions (CAUP) of online commerce product photos when exposed to alternative dark/light user interface (UI) modes. We analyzed time-series EEG data from 31 individuals and performed neuroscience mining (NSM) to ascertain (a) how the CAUP of products differs among UI modes; and (b) which deep learning model provides the most accurate assessment of such neuroscience mining (NSM) business difficulties. The dark UI style increased the CAUP of the products displayed and was predicted with the greatest accuracy using a unique EEG power spectra separated wave brainwave 2D-ConvLSTM model. Then, using relative importance analysis, we used this model to determine the most relevant power spectra. Our findings are considered to contribute to the discovery of objective truths about online customers' reactions to various user interface modes used by various online marketplaces that cannot be uncovered through more traditional research approaches like as surveys.

• Journal of Korea Society of Digital Industry and Information Management
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• v.15 no.1
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• pp.123-132
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• 2019

As the one-person households with economic power have increased, the consumption culture changed as well. The primary purpose of this study is to investigate the on-line shopping motivation of one-person households in terms of consumer value. Economic value, emotional value, convenience value, social value were identified as affecting factors of satisfaction and intention to re-use of on-line shopping purchasing based on prior studies of on-line shopping behavior. This study tested the hypothesized model targeting 244 one-person households who have purchased products in on-line shopping mall. According to the results of analysis by using R, economic value, emotional value are significantly related to the consumer satisfaction but convenience value, social value are not. Consumer satisfaction of online purchasing was also shown to be related to the intention to re-use. However no difference between men and female was shown in shopping motivations. The research result can provide useful guidelines and strategies for one-person households with online shopping malls.

• Proceedings of the Korean Society of Computer Information Conference
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• 2008.06a
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• pp.279-284
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• 2008

부가서비스는 공급자가 소비자에게 제공하는 본질적인 재화 또는 서비스에 추가하여 부수적으로 제공되는 서비스라는 사전적 의미를 갖고 있다. 최근에는 IT 기술이 발전함에 따라, 웹을 기반으로 한 다양한 부가서비스가 각 산업에서 등장하였으며, 때로는 기본적인 서비스처럼 여겨지기도 할 만큼 성장하고 있다. 전력산업에서도 이러한 시대적 요구와 경쟁력 강화를 위한 수단으로 부가서비스가 도입되고 있다. 초기에는 주로 이메일 청구서 형태의 단순한 서비스였으나, 현재는 다양한 전력 인프라와 데이터를 이용한 전력부가서비스가 시도되고 있다. 국내에도 전력부가서비스 개념이 도입되어, 초기에는 전력부가서비스의 필요성과 이에 대한 인식이 부족하여 제한적인 분야에서만 개발되어왔다. 그러나 최근들어 공급자는 고객을 위한 부가서비스의 중요성을 인식하게 되고, 수용가 또한 이에 대한 높은 관심을 보임에 따라, 점차 다양한 분야로 확대되는 경향을 보이고 있다. 본 논문에서는 이와 같은 시장의 변화를 고려하여, 국내의 전력산업 환경에 적합한 부가서비스 BM을 개발하되, 성공적인 전력부가서비스로 구축하기 위해 수용가의 선호도와 요구사항을 조사하고 이를 충분히 반영한 전력부가서비스 BM(Business Model) 개발 및 시스템 구축방안에 대하여 소개하고자 한다.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2005.05a
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• pp.798-805
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• 2005

Estimating the effects of price increase on a company's sales is important task faced by managers. If consumer has prior information on price increase or expect it, there would be stockpiling and subsequent drops in sales. In addition, consumer can suppress demand in the short run. Above factors make the sales dynamic and unstable. We develop a time series model to evaluate the sales patterns with stockpiling and short term suppression of demand and also propose a forecasting procedure. For estimation, we use panel data and extend the model to Bayesian hierarchical structure. By borrowing strength across cross-sectional units, this estimation scheme gives more robust and reasonable result than one from the individual estimation. Furthermore, the proposed scheme yields improved predictive power in the forecasting of hold-out sample periods.

• Journal of the Korean Operations Research and Management Science Society
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• v.31 no.2
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• pp.157-167
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• 2006

Estimating the effects of price increase on a company's sales is important task faced by managers. If consumer has prior information on price increase or expects it, there would be stockpiling and subsequent drops in sales. In addition, consumer can suppress demand in the short run. These factors make the sales dynamic and unstable. In this paper we develop a time series model to evaluate the sales patterns with stockpiling and short-term suppression of demand and also propose a forecasting procedure. For estimation, we use panel data and extend the model to Bayesian hierarchical structure. By borrowing strength across cross-sectional units, this estimation scheme gives more robust and reasonable result than one from the individual estimation. Furthermore, the proposed scheme yields improved predictive power in the forecasting of hold-out sample periods.

• Journal of Information Science Theory and Practice
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• v.10 no.2
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• pp.45-73
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• 2022

The research in the literature review on Internet of Things (IoT) adoption from an individual consumer viewpoint is minimal and has not yet been fully investigated. Therefore, the objectives of this study are to analyze the growth of IoT in recent years and to conduct a weight analysis of the factors that affect acceptance intentions and real usage of IoT-enabled services. For the review, we analyzed 87 publications from 13 conferences and 54 journals published during the period 2014-2020 about consumer adoption of IoT. Following the study, we discovered an unprecedented increase in the number of articles published in the last seven years, which points to an emerging area with an enormous prospect. Furthermore, the weight analysis outcome was associated with the diagrammatic representation in this study. After that, this research developed a generalized consumer IoT adoption model based on the 12 best predictors derived from frequency count and weight analysis, which had the highest predictive power for calculating IoT adoption. This paper further acknowledges the study's theoretical and practical contributions, as well as its shortcomings, and proposes further research directions for future researchers.

• Journal of Communications and Networks
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• v.15 no.3
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• pp.292-300
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• 2013

Nowadays, energy saving has become a hot topic and information and communication technology has become a major power consumer. In long term evolution advanced (LTE-A) networks, heterogeneous deployments of low-power nodes and conventional macrocells provide some new features, such as coverage extension, throughput enhancement, and load balancing. However, a large-scale deployment of low-power nodes brings substantial energy consumption and interference problems. In this paper, we propose a novel switching strategy (NS), which adaptively switches on or off some low-power nodes based on the instantaneous load of the system. It is compatible with the microcells' load balancing feature and can be easily implemented on the basis of existing LTE-A specifications. Moreover, we develop an analytical model for analyzing the performance of system energy consumption, block rate, throughput, and energy efficiency. The performance of NS is evaluated by comparison with existing strategies. Theoretical analysis and simulation results show that NS not only has a low block rate, but also achieves a high energy efficiency.

• Journal of Cadastre & Land InformatiX
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• v.52 no.2
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• pp.211-231
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• 2022

This study attempted to identify the effects of macroeconomic variables such as the All Industry Production Index, Consumer Price Index, CD Interest Rate, and KOSPI on apartment lease prices divided into nationwide, Seoul, metropolitan, and region, and to present a methodological prediction model of apartment lease prices by region using Long Short Term Memory (LSTM). According to VAR analysis results, the nationwide apartment lease price index and consumer price index in Lag1 and 2 had a significant effect on the nationwide apartment lease price, and likewise, the Seoul apartment lease price index, the consumer price index, and the CD interest rate in Lag1 and 2 affect the apartment lease price in Seoul. In addition, it was confirmed that the wide-area apartment jeonse price index and the consumer price index had a significant effect on Lag1, and the local apartment jeonse price index and the consumer price index had a significant effect on Lag1. As a result of the establishment of the LSTM prediction model, the predictive power was the highest with RMSE 0.008, MAE 0.006, and R-Suared values of 0.999 for the local apartment lease price prediction model. In the future, it is expected that more meaningful results can be obtained by applying an advanced model based on deep learning, including major policy variables

• The Transactions of The Korean Institute of Electrical Engineers
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• v.65 no.1
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• pp.16-22
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• 2016

This paper addresses the load usage scheduling in the HEMS for residential power consumers. The HEMS would lead the residential users to change their power usage, so as to minimize the cost in response to external information such as a time-varying electricity price, the outside temperature. However, there may be a consumer's inconvenience in the change of the power usage. In order to improve this, it is required to understand the pattern of load usage according to the external information. Therefore, this paper suggests a methodology to model the load usage pattern, which classifies home appliances according to external information affecting the load usage and models the usage pattern for each appliance based on a copula function representing the correlation between variables. The modeled pattern would be reflected as a constraint condition for an optimal load usage scheduling problem in HEMS. To explain an application of the methodology, a case study is performed on an electrical water heater (EWH) and an optimal load usage scheduling for EHW is performed based on the branch-and-bound method. From the case study, it is shown that the load usage pattern can contribute to an efficient power consumption.

• Korean Journal of Agricultural Science
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• v.39 no.1
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• pp.133-142
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• 2012

In this paper, animal welfare egg was focused in Korea. This study was built to establish marketing strategies for the animal welfare egg. Data were collected by consumer survey on November 4th, 2011 at Chuncheon Hanaromart in Chuncheon. 355 questionnaires were distributed and collected. SAS 9.1 and Excel 2007 were used as statistical packages and ranked logit model was used to analyze. From the results of the study the following improving plans were suggested for the consideration factors affecting to purchase for animal welfare egg. First, strategies using public relations are needed only for package design. The package design will be able to complements gaps for low advertisement. Moreover, when consumer will be provided information on packaging for consumers, the consumer will be able to reduce anxiety. Second, package design of identity strategies are needed. There are so many package designs in market. The package design of identity may give competition power to the animal welfare egg.