• Science of Emotion and Sensibility
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• v.13 no.1
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• pp.1-10
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• 2010

This study is about what process and methodology could make UI designer be able to achieve the interface which considers user's mental model through implementing corporate line-up model, when people design an interface between product of multi-function printer and user. The most important concern of UI designers who are dealing with an interface between product and user is how they can make product system image match user's mental model so that users can utilize products without any confusion and discomfort. If concept model which designers bring up and mental model which users expect and recognize could be of one accord, then users can feel ease of use toward products. The understanding and observation for user behavior and use pattern is prerequisite to develop user-centered interface between product and user. However, UI designers do design interface from their own perspective and assumption in many business areas, and users do not react as designers assumed and intended in many cases or examples. This study is to find inappropriate system images against users' mental model on basic function of multi-function printer, and the relationship of system image and user's mental model is diagramed to build up a hypothesis. The hypothesis from this study is validated through evaluation of domestic and international users. In addition, two suggestions to improve usability problems revealed from user test are proposed. The optimal solution is designed based on the result of user evaluation and consideration of many user environments, and then it is implemented to line-up product. In conclusion, this study considers how UI designers can create system image which is close to user's mental model.

• Korean Journal of Remote Sensing
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• v.35 no.5_1
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• pp.665-679
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• 2019

Mesoscale soil moisture measurement from the promising Cosmic-Ray Neutron Probe (CRNP) is expected to bridge the gap between large scale microwave remote sensing and point-based in-situ soil moisture observations. Traditional calibration based on $N_0$ method is used to convert neutron intensity measured at the CRNP to field scale soil moisture. However, the static calibration parameter $N_0$ used in traditional technique is insufficient to quantify long term soil moisture variation and easily influenced by different time-variant factors, contributing to the high uncertainties in CRNP soil moisture product. Consequently, in this study, we proposed a modified traditional calibration method, so-called Dynamic-$N_0$ method, which take into account the temporal variation of $N_0$ to improve the CRNP based soil moisture estimation. In particular, a nonlinear regression method has been developed to directly estimate the time series of $N_0$ data from the corrected neutron intensity. The $N_0$ time series were then reapplied to generate the soil moisture. We evaluated the performance of Dynamic-$N_0$ method for soil moisture estimation compared with the traditional one by using a weighted in-situ soil moisture product. The results indicated that Dynamic-$N_0$ method outperformed the traditional calibration technique, where correlation coefficient increased from 0.70 to 0.72 and RMSE and bias reduced from 0.036 to 0.026 and -0.006 to $-0.001m^3m^{-3}$. Superior performance of the Dynamic-$N_0$ calibration method revealed that the temporal variability of $N_0$ was caused by hydrogen pools surrounding the CRNP. Although several uncertainty sources contributed to the variation of $N_0$ were not fully identified, this proposed calibration method gave a new insight to improve field scale soil moisture estimation from the CRNP.

• Journal of Intelligence and Information Systems
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• v.25 no.1
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• pp.219-239
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• 2019

As the importance of providing customized services to individuals becomes important, researches on personalized recommendation systems are constantly being carried out. Collaborative filtering is one of the most popular systems in academia and industry. However, there exists limitation in a sense that recommendations were mostly based on quantitative information such as users' ratings, which made the accuracy be lowered. To solve these problems, many studies have been actively attempted to improve the performance of the recommendation system by using other information besides the quantitative information. Good examples are the usages of the sentiment analysis on customer review text data. Nevertheless, the existing research has not directly combined the results of the sentiment analysis and quantitative rating scores in the recommendation system. Therefore, this study aims to reflect the sentiments shown in the reviews into the rating scores. In other words, we propose a new algorithm that can directly convert the user 's own review into the empirically quantitative information and reflect it directly to the recommendation system. To do this, we needed to quantify users' reviews, which were originally qualitative information. In this study, sentiment score was calculated through sentiment analysis technique of text mining. The data was targeted for movie review. Based on the data, a domain specific sentiment dictionary is constructed for the movie reviews. Regression analysis was used as a method to construct sentiment dictionary. Each positive / negative dictionary was constructed using Lasso regression, Ridge regression, and ElasticNet methods. Based on this constructed sentiment dictionary, the accuracy was verified through confusion matrix. The accuracy of the Lasso based dictionary was 70%, the accuracy of the Ridge based dictionary was 79%, and that of the ElasticNet (${\alpha}=0.3$) was 83%. Therefore, in this study, the sentiment score of the review is calculated based on the dictionary of the ElasticNet method. It was combined with a rating to create a new rating. In this paper, we show that the collaborative filtering that reflects sentiment scores of user review is superior to the traditional method that only considers the existing rating. In order to show that the proposed algorithm is based on memory-based user collaboration filtering, item-based collaborative filtering and model based matrix factorization SVD, and SVD ++. Based on the above algorithm, the mean absolute error (MAE) and the root mean square error (RMSE) are calculated to evaluate the recommendation system with a score that combines sentiment scores with a system that only considers scores. When the evaluation index was MAE, it was improved by 0.059 for UBCF, 0.0862 for IBCF, 0.1012 for SVD and 0.188 for SVD ++. When the evaluation index is RMSE, UBCF is 0.0431, IBCF is 0.0882, SVD is 0.1103, and SVD ++ is 0.1756. As a result, it can be seen that the prediction performance of the evaluation point reflecting the sentiment score proposed in this paper is superior to that of the conventional evaluation method. In other words, in this paper, it is confirmed that the collaborative filtering that reflects the sentiment score of the user review shows superior accuracy as compared with the conventional type of collaborative filtering that only considers the quantitative score. We then attempted paired t-test validation to ensure that the proposed model was a better approach and concluded that the proposed model is better. In this study, to overcome limitations of previous researches that judge user's sentiment only by quantitative rating score, the review was numerically calculated and a user's opinion was more refined and considered into the recommendation system to improve the accuracy. The findings of this study have managerial implications to recommendation system developers who need to consider both quantitative information and qualitative information it is expect. The way of constructing the combined system in this paper might be directly used by the developers.

• Journal of Intelligence and Information Systems
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• v.26 no.4
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• pp.173-198
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• 2020

For a long time, many studies have been conducted on predicting the success of campaigns for customers in academia, and prediction models applying various techniques are still being studied. Recently, as campaign channels have been expanded in various ways due to the rapid revitalization of online, various types of campaigns are being carried out by companies at a level that cannot be compared to the past. However, customers tend to perceive it as spam as the fatigue of campaigns due to duplicate exposure increases. Also, from a corporate standpoint, there is a problem that the effectiveness of the campaign itself is decreasing, such as increasing the cost of investing in the campaign, which leads to the low actual campaign success rate. Accordingly, various studies are ongoing to improve the effectiveness of the campaign in practice. This campaign system has the ultimate purpose to increase the success rate of various campaigns by collecting and analyzing various data related to customers and using them for campaigns. In particular, recent attempts to make various predictions related to the response of campaigns using machine learning have been made. It is very important to select appropriate features due to the various features of campaign data. If all of the input data are used in the process of classifying a large amount of data, it takes a lot of learning time as the classification class expands, so the minimum input data set must be extracted and used from the entire data. In addition, when a trained model is generated by using too many features, prediction accuracy may be degraded due to overfitting or correlation between features. Therefore, in order to improve accuracy, a feature selection technique that removes features close to noise should be applied, and feature selection is a necessary process in order to analyze a high-dimensional data set. Among the greedy algorithms, SFS (Sequential Forward Selection), SBS (Sequential Backward Selection), SFFS (Sequential Floating Forward Selection), etc. are widely used as traditional feature selection techniques. It is also true that if there are many risks and many features, there is a limitation in that the performance for classification prediction is poor and it takes a lot of learning time. Therefore, in this study, we propose an improved feature selection algorithm to enhance the effectiveness of the existing campaign. The purpose of this study is to improve the existing SFFS sequential method in the process of searching for feature subsets that are the basis for improving machine learning model performance using statistical characteristics of the data to be processed in the campaign system. Through this, features that have a lot of influence on performance are first derived, features that have a negative effect are removed, and then the sequential method is applied to increase the efficiency for search performance and to apply an improved algorithm to enable generalized prediction. Through this, it was confirmed that the proposed model showed better search and prediction performance than the traditional greed algorithm. Compared with the original data set, greed algorithm, genetic algorithm (GA), and recursive feature elimination (RFE), the campaign success prediction was higher. In addition, when performing campaign success prediction, the improved feature selection algorithm was found to be helpful in analyzing and interpreting the prediction results by providing the importance of the derived features. This is important features such as age, customer rating, and sales, which were previously known statistically. Unlike the previous campaign planners, features such as the combined product name, average 3-month data consumption rate, and the last 3-month wireless data usage were unexpectedly selected as important features for the campaign response, which they rarely used to select campaign targets. It was confirmed that base attributes can also be very important features depending on the type of campaign. Through this, it is possible to analyze and understand the important characteristics of each campaign type.

• Journal of Intelligence and Information Systems
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• v.23 no.3
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• pp.155-175
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• 2017

As social data become into the spotlight, mainstream web search engines provide data indicate how many people searched specific keyword: Web Search Traffic data. Web search traffic information is collection of each crowd that search for specific keyword. In a various area, web search traffic can be used as one of useful variables that represent the attention of common users on specific interests. A lot of studies uses web search traffic data to nowcast or forecast social phenomenon such as epidemic prediction, consumer pattern analysis, product life cycle, financial invest modeling and so on. Also web search traffic data have begun to be applied to predict tourist inbound. Proper demand prediction is needed because tourism is high value-added industry as increasing employment and foreign exchange. Among those tourists, especially Chinese tourists: Youke is continuously growing nowadays, Youke has been largest tourist inbound of Korea tourism for many years and tourism profits per one Youke as well. It is important that research into proper demand prediction approaches of Youke in both public and private sector. Accurate tourism demands prediction is important to efficient decision making in a limited resource. This study suggests improved model that reflects latest issue of society by presented the attention from group of individual. Trip abroad is generally high-involvement activity so that potential tourists likely deep into searching for information about their own trip. Web search traffic data presents tourists' attention in the process of preparation their journey instantaneous and dynamic way. So that this study attempted select key words that potential Chinese tourists likely searched out internet. Baidu-Chinese biggest web search engine that share over 80%- provides users with accessing to web search traffic data. Qualitative interview with potential tourists helps us to understand the information search behavior before a trip and identify the keywords for this study. Selected key words of web search traffic are categorized by how much directly related to "Korean Tourism" in a three levels. Classifying categories helps to find out which keyword can explain Youke inbound demands from close one to far one as distance of category. Web search traffic data of each key words gathered by web crawler developed to crawling web search data onto Baidu Index. Using automatically gathered variable data, linear model is designed by multiple regression analysis for suitable for operational application of decision and policy making because of easiness to explanation about variables' effective relationship. After regression linear models have composed, comparing with model composed traditional variables and model additional input web search traffic data variables to traditional model has conducted by significance and R squared. after comparing performance of models, final model is composed. Final regression model has improved explanation and advantage of real-time immediacy and convenience than traditional model. Furthermore, this study demonstrates system intuitively visualized to general use -Youke Mining solution has several functions of tourist decision making including embed final regression model. Youke Mining solution has algorithm based on data science and well-designed simple interface. In the end this research suggests three significant meanings on theoretical, practical and political aspects. Theoretically, Youke Mining system and the model in this research are the first step on the Youke inbound prediction using interactive and instant variable: web search traffic information represents tourists' attention while prepare their trip. Baidu web search traffic data has more than 80% of web search engine market. Practically, Baidu data could represent attention of the potential tourists who prepare their own tour as real-time. Finally, in political way, designed Chinese tourist demands prediction model based on web search traffic can be used to tourism decision making for efficient managing of resource and optimizing opportunity for successful policy.

• The Journal of Society for e-Business Studies
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• v.16 no.3
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• pp.23-45
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• 2011

People could possess different self-identity under virtual world from physical world because of anonymity of the virtual world and this difference could influence their behavior in the virtual world. Based on self-discrepancy theory, this research proposes that continuous use model in self-expression goal. We defined the difference bet ween actual self~identity and ideal self~identity in the virtual world as "self-discrepancy in virtual world", and the difference between actual self-identity in the physical world and actual self-identity in the virtual world as "cross-world self-discrepancy". Before testing hypothesis, we compare the actual self-identity in the online community with the actual self-identity in the physical world, and with ideal self-identity in the virtual world. We derived an index for two different types of self-identity in terms of Personal Self-identity and Social Self-identity through factor analysis. Our results show that online community members have a higher level of ideal self-identity than actual self-identity in online community, and they have better personal self-identity in online community than physical world while a lower level of social self-identity in online community than physical world. The results of the hypothesis testing analysis based on 300 respondents showed that "self-discrepancy in virtual world" negatively influenced perceived usefulness for self-expression while "cross-world self-discrepancy" positively influenced perceived usefulness for self-expression. The perceived usefulness for self-expression and ease of use positively influence both continuous use and knowledge contribution. Specially, the effect of perceived usefulness for self-expression on knowledge contribution is much bigger than the effect of ease of use. This study extends self-discrepancy theory to virtual worlds by suggesting various types of self-discrepancy and by applying the effect of self-discrepancies in online community. Also, this study extends technology acceptance model in the personal goal in terms of self-expression. This study hopes to offer practical insights by suggesting positive effect of self-discrepancy on behavior in the online community.

• Korean Journal of Remote Sensing
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• v.30 no.1
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• pp.61-73
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• 2014

This study reviewed an application of water quality estimation using an Airborne Hyperspectral Imagery (A-HSI) and tested a part of Han River water quality (especially suspended solid) estimation with available in-situ data. The estimation of water quality was processed two methods. One is using observation data as downwelling radiance to water surface and as scattering and reflectance into water body. Other is linear regression analysis with water quality in-situ measurement and upwelling data as at-sensor radiance (or reflectance). Both methods drive meaningful results of RS estimation. However it has more effects on the auxiliary dataset as water quality in-situ measurement and water body scattering measurement. The test processed a part of Han River located Paldang-dam downstream. We applied linear regression analysis with AISA eagle hyperspectral sensor data and water quality measurement in-situ data. The result of linear regression for a meaningful band combination shows $-24.847+0.013L_{560}$ as 560 nm in radiance (L) with 0.985 R-square. To comparison with Multispectral Imagery (MSI) case, we make simulated Landsat TM by spectral resampling. The regression using MSI shows -55.932 + 33.881 (TM1/TM3) as radiance with 0.968 R-square. Suspended Solid (SS) concentration was about 3.75 mg/l at in-situ data and estimated SS concentration by A-HIS was about 3.65 mg/l, and about 5.85mg/l with MSI with same location. It shows overestimation trends case of estimating using MSI. In order to upgrade value for practical use and to estimate more precisely, it needs that minimizing sun glint effect into whole image, constructing elaborate flight plan considering solar altitude angle, and making good pre-processing and calibration system. We found some limitations and restrictions such as precise atmospheric correction, sample count of water quality measurement, retrieve spectral bands into A-HSI, adequate linear regression model selection, and quantitative calibration/validation method through the literature review and test adopted general methods.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.12 no.5
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• pp.127-139
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• 2017

In recent 10 years the attention to social entrepreneurship has raised increasing among scholars, public sector, and community development. However less research has been conducted on how social entrepreneurship intention create a social enterprise and what factors can be affected to the social entrepreneurial intentions. This paper aims at contributing to identify the antecedents of entrepreneurial behavior and intentions. Especially, we have had a strong interests in compassion factors which haven't been used as important variables to encourage for people to do social entrepreneurial activities. Also, we try to find the moral obligation and perceived social support as antecedents of social entrepreneurial intentions. Finding show that compassion and moral obligation affect to the social entrepreneurial intention. Especially this study identify the external factor of society with the variable, perceived social support. Once individuals recognize that the infrastructure and societal positive mood on social entrepreneurship is friendly to social entrepreneurship, people have a tendency to try to do some social entrepreneurial activities. Only few empirical studies exist in this research domain. A study of more than 271 Korean college students has studied which personal traits predict certain characteristics of social entrepreneurs (such as having social vision or looking for social innovational opportunities). In addition to those antecedents, students experience is the critical factor that enabled continued expansion of the social entrepreneurial activities. The results of this research show how we can nurture social entrepreneurs and how we can develop the social environment to promote social entrepreneurship.

• Horticultural Science & Technology
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• v.29 no.6
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• pp.633-640
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• 2011

Brassica rapa is an A genome model species for Brassica crop genetics, genomics, and breeding. With the completion of sequencing the B. rapa genome, functional analysis of the genome is forthcoming issue. The expressed sequence tags are fundamental resources supporting annotation and functional analysis of the genome including identification of tissue-specific genes and promoters. As of July 2011, 147,217 ESTs from 39 cDNA libraries of B. rapa are reported in the public database. However, little information can be retrieved from the sequences due to lack of organized databases. To leverage the sequence information and to maximize the use of publicly-available EST collections, the Brassica rapa tissue-specific EST database (BrTED) is developed. BrTED includes sequence information of 23,962 unigenes assembled by StackPack program. The unigene set is used as a query unit for various analyses such as BLAST against TAIR gene model, functional annotation using MIPS and UniProt, gene ontology analysis, and prediction of tissue-specific unigene sets based on statistics test. The database is composed of two main units, EST sequence processing and information retrieving unit and tissue-specific expression profile analysis unit. Information and data in both units are tightly inter-connected to each other using a web based browsing system. RT-PCR evaluation of 29 selected unigene sets successfully amplified amplicons from the target tissues of B. rapa. BrTED provided here allows the user to identify and analyze the expression of genes of interest and aid efforts to interpret the B. rapa genome through functional genomics. In addition, it can be used as a public resource in providing reference information to study the genus Brassica and other closely related crop crucifer plants.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.15 no.2
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• pp.221-234
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• 2020

The role of entrepreneurship has become important for the continuous growth (scale-up) of companies in a rapidly changing economic environment. However, research has focused mainly on business-oriented growth that emphasizes efficiency only. However, in the era of the fourth industrial revolution, people have become more important than corporate efficiency. In particular, there are few studies on SMEs. The purpose of this study is to investigate how the characteristics of human - centered organizational culture affects the business ecosystem and firm excellence of SMEs, and how business ecosystem and firm excellence affect the performance of corporations. Therefore, this study focuses on the empathy, enablement, empowerment, and engagement of the corporate culture characteristics of the enterprise and the effects of the independent variables on the business ecosystem and the flower excellence as the parameters and the business ecosystem and company excellence. The main research subjects are the causality analysis that examines the effects on the management performance. The results of the study are as follows. Empathy, enablement, empowerment, engagement have a significant impact on the business ecosystem. In addition, the engagement have a positive effect on firm excellence. The results of empirical studies on the causal relationship between business ecosystem, corporate competence, and company excellence and business performance show that business ecosystem has no statistically significant effect on business performance. Corporate excellence has a significant effect on business performance Respectively. In addition to the above findings, this study can suggest the following implications. First, it is an empirical study of small and medium-sized enterprises (SMEs) by utilizing people-oriented entrepreneurship in addition to existing research. Small and medium-sized enterprises (SMEs) are expected to have lower perceptions of people-oriented management than large corporations or public institutions, but the analysis shows that people-centered entrepreneurship has a significant impact on the business ecosystem. Second, the research results of the serial multiple mediating effect analysis show that the higher the atmosphere of entrepreneurship in a company, the higher the atmosphere of entrepreneurship, and the parameters of business ecosystem and company excellence in the effect of people-oriented entrepreneurship on management performance. It is that the path through which people-oriented entrepreneurship influences management performance was verified by verifying the mediating effect by inputting. Lastly, it is hoped that research on people-oriented entrepreneurship in Korea's SMEs will be activated, providing a theoretical basis for transforming SMEs' business models into innovative types.