• Journal of Intelligence and Information Systems
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• v.19 no.2
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• pp.39-54
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• 2013

Recent explosive increase of electronic commerce provides many advantageous purchase opportunities to customers. In this situation, customers who do not have enough knowledge about their purchases, may accept product recommendations. Product recommender systems automatically reflect user's preference and provide recommendation list to the users. Thus, product recommender system in online shopping store has been known as one of the most popular tools for one-to-one marketing. However, recommender systems which do not properly reflect user's preference cause user's disappointment and waste of time. In this study, we propose a novel recommender system which uses data mining and multi-model ensemble techniques to enhance the recommendation performance through reflecting the precise user's preference. The research data is collected from the real-world online shopping store, which deals products from famous art galleries and museums in Korea. The data initially contain 5759 transaction data, but finally remain 3167 transaction data after deletion of null data. In this study, we transform the categorical variables into dummy variables and exclude outlier data. The proposed model consists of two steps. The first step predicts customers who have high likelihood to purchase products in the online shopping store. In this step, we first use logistic regression, decision trees, and artificial neural networks to predict customers who have high likelihood to purchase products in each product group. We perform above data mining techniques using SAS E-Miner software. In this study, we partition datasets into two sets as modeling and validation sets for the logistic regression and decision trees. We also partition datasets into three sets as training, test, and validation sets for the artificial neural network model. The validation dataset is equal for the all experiments. Then we composite the results of each predictor using the multi-model ensemble techniques such as bagging and bumping. Bagging is the abbreviation of "Bootstrap Aggregation" and it composite outputs from several machine learning techniques for raising the performance and stability of prediction or classification. This technique is special form of the averaging method. Bumping is the abbreviation of "Bootstrap Umbrella of Model Parameter," and it only considers the model which has the lowest error value. The results show that bumping outperforms bagging and the other predictors except for "Poster" product group. For the "Poster" product group, artificial neural network model performs better than the other models. In the second step, we use the market basket analysis to extract association rules for co-purchased products. We can extract thirty one association rules according to values of Lift, Support, and Confidence measure. We set the minimum transaction frequency to support associations as 5%, maximum number of items in an association as 4, and minimum confidence for rule generation as 10%. This study also excludes the extracted association rules below 1 of lift value. We finally get fifteen association rules by excluding duplicate rules. Among the fifteen association rules, eleven rules contain association between products in "Office Supplies" product group, one rules include the association between "Office Supplies" and "Fashion" product groups, and other three rules contain association between "Office Supplies" and "Home Decoration" product groups. Finally, the proposed product recommender systems provides list of recommendations to the proper customers. We test the usability of the proposed system by using prototype and real-world transaction and profile data. For this end, we construct the prototype system by using the ASP, Java Script and Microsoft Access. In addition, we survey about user satisfaction for the recommended product list from the proposed system and the randomly selected product lists. The participants for the survey are 173 persons who use MSN Messenger, Daum Caf$\acute{e}$, and P2P services. We evaluate the user satisfaction using five-scale Likert measure. This study also performs "Paired Sample T-test" for the results of the survey. The results show that the proposed model outperforms the random selection model with 1% statistical significance level. It means that the users satisfied the recommended product list significantly. The results also show that the proposed system may be useful in real-world online shopping store.

• Asia pacific journal of information systems
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• v.23 no.4
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• pp.1-28
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• 2013

Given that smartphone-based social network services (SNS), such as KakaoStory is now being widely used as a way for people to connect and communicate with each other, this study examines key factors leading to the continued use of SNS. People have been using PC-based SNS, such as Cyworld, for years are now using smartphone-based SNS, such as KakaoStory. KakaoStory developed by KakaoTalk has rapidly grown up as the largest smartphone-based SNS in Korea as smartphone penetration increases. It is more difficult for firms to maintain their current users over time in that alternative SNSs satisfying people's new needs are constantly emerging and evolving. In this sense, one of the most challenging issues for SNS firms is how to retain their current users in the long run. However, there are few empirical studies on this issue. Applying the IS continuance model proposed by Bhattacherjee [2001], this study explores key determinants of users' smartphone-based SNS continuance intention. The model suggests that perceived usefulness and user satisfaction are the key determinants of IS continuance intention. However, the model includes only the utilitarian value that users can obtain through the use of smartphone-based SNS, by considering perceived usefulness. Therefore, the study attempts to extend the IS continuance model by considering hedonic and social values simultaneously. More specifically, we consider subjective norms as social value that are proposed by the theory of reasoned action and the theory of planned behavior. We also consider perceived enjoyment as hedonic value that is emphasized as a key factor influencing users' behavior intention and actual behavior, particularly in the context of hedonic IS use. By considering the three values in our model simultaneously, we could offer a deeper understanding of smartphone-based SNS continuance. That is, this study could offer an explanation of how each value is associated with user satisfaction and SNS continuance intention. The customer value-satisfaction-loyalty model can strengthen the assertion that smartphone-based SNS continuance intention is determined by various different types of customer values, such as utilitarian, hedonic, and social ones. Moreover, the model provides a theoretical basis for the assertion that customer values lead to increased loyalty via customer satisfaction. In this regard, we theorize that SNS continuance intention is influenced by users' various values, namely perceived usefulness, perceived enjoyment, and subjective norms, via user satisfaction. To test the proposed research model and hypotheses, we conducted a partial least squares analysis using a total of 253 data collected on the users of smartphone-based SNS (i.e., KakaoStory). The key findings are as follows: First, it has been found that SNS continuance intention considerably depends on user satisfaction. Second, user satisfaction is determined by confirmation, perceived usefulness, and perceived enjoyment. Third, concerning the effects of the three values on SNS continuance intention, only perceived enjoyment regarded as hedonic value was statistically significant. That is, perceived usefulness considered as utilitarian value and subjective norms considered as social value had no effect on SNS continuance intention. Finally, our results indicate that confirmation increases perceived usefulness, perceived enjoyment, and user satisfaction. The results reconfirm the effectiveness of IS continuance model in predicting smartphone-based SNS continuance intention. Moreover, the results demonstrate that perceived enjoyment reflecting hedonic value is the most important predictor of SNS continuance intention. Therefore, it is imperative for firms to meet SNS users' hedonic value to retain them in the long run. Meanwhile, we could not find any empirical evidence to support the assertion that subjective norms are associated with user satisfaction and SNS continuance intention. The results lead us to conclude that when users have enough direct experience in SNS use, subjective norms have no effect on SNS continuance intention. Discussions and implications of the results are provided.

• KOREAN JOURNAL OF CROP SCIENCE
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• v.55 no.4
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• pp.312-318
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• 2010

This study was carried out to develop and test a prototype program that recommends the nitrogen topdressing rate using the color digital camera image taken from rice field at panicle initiation stage (PIS). This program comprises four models to estimate shoot N content (PNup) by color digital image analysis, shoot N accumulation from PIS to maturity (PHNup), yield, and protein content of rice. The models were formulated using data set from N rate experiments in 2008. PNup was found to be estimated by non-linear regression model using canopy cover and normalized green values calculated from color digital image analysis as predictor variables. PHNup could be predicted by quadratic regression model from PNup and N fertilization rate at panicle initiation stage with $R^2$ of 0.923. Yield and protein content of rice could also be predicted by quadratic regression models using PNup and PHNup as predictor variables with $R^2$ of 0.859 and 0.804, respectively. The performance of the program integrating the above models to recommend N topdressing rate at PIS was field-tested in 2009. N topdressing rate prescribed for the target protein content of 6.0% by the program were lower by about 30% compared to the fixed rate of 30% that is recommended conventionally as the split application rate of N fertilizer at PIS, while rice yield in the plots top-dressed with the prescribed N rate were not different from those of the plots top-dressed with the fixed N rates of 30% and showed a little lower or similar protein content of rice as well. And coefficients of variation in rice yield and quality parameters were reduced substantially by the prescribed N topdressing. These results indicate that the N rate recommendation using the analysis of color digital camera image is promising to be applied for precise management of N fertilization. However, for the universal and practical application the component models of the program are needed to be improved so as to be applicable to the diverse edaphic and climatic condition.

• Culinary science and hospitality research
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• v.22 no.3
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• pp.183-197
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• 2016

This study was conducted to investigate the relationships among service quality, perceived benefit, perceived value and behavioral intention as perceived by franchise snack bars customers. The service quality of franchise snack bars' was tested in three sub-dimensions: environmental quality interaction quality, and outcome quality, which are based on Brady & Cronin's third-dimensional model. A total of 450 survey questionaires were distributed from March 9th to November 12th in 2015, of whi 411 questionnaires were deemed suitable for statistical analysis. SPSS 20.0 program was employed to conduct frequency analysis and reliability analysis, while AMOS 20.0 program was used to test the hypotheses. The results revealed that all three elements of service quality have a positive impact on perceived benefit. In particular, the outcome quality element had the greatest influence on perceived benefit. In sum, customers of a franchise snack bar considered outcome variables such as food taste, reasonable amount, and general quality of food as the most important factors to fulfill the benefit. This results suggest that Korean snack bar franchise companies need to consider improvements to outcome quality features, such as food quality. In addition, perceived benefit was a critical antecedent of perceived value, which was itself a significant predictor of behavioral intention. In conclusion, this study applied the means-end chain theory on franchise sank bar segmentation, as well as three dimension service quality model as developed by Brady and Cronin, and found results that will enable meaningful strategics for snack bar foodservice segmentation in pursuit of the development of efficient business plans, and that can be utilized as a theoretical data for future studies.

• Korean journal of applied entomology
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• v.55 no.2
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• pp.139-148
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• 2016

A seasonal outlook for crop insect pests is most valuable when it provides accurate information for timely management decisions. In this study, we investigated probable tele-connections between climatic phenomena and pest infestations in Korea using a statistical method. A rice insect pest, brown planthopper (BPH), was selected because of its migration characteristics, which fits well with the concept of our statistical modelling - utilizing a long-term, multi-regional influence of selected climatic phenomena to predict a dominant biological event at certain time and place. Variables of the seasonal climate forecast from 10 climate models were used as a predictor, and annual infestation area for BPH as a predictand in the statistical analyses. The Moving Window Regression model showed high correlation between the national infestation trends of BPH in South Korea and selected tempo-spatial climatic variables along with its sequential migration path. Overall, the statistical models developed in this study showed a promising predictability for BPH infestation in Korea, although the dynamical relationships between the infestation and selected climatic phenomena need to be further elucidated.

• Journal of Korean Academy of Nursing
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• v.29 no.5
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• pp.1087-1102
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• 1999

Diabetes mellitus, a universal and prevalent chronic disease, is projected to be one of the most formidable worldwide health problems in the 21st century. For those living with diabetes, there is a need for self-care skills to manage a complex medical regimen. Self-efficacy which refers to one's belief in his/her capability to monitor and perform the daily activities required to manage diabetes has be found to be related to self-care. The concept of self-efficacy comes from social cognitive theory which maintains that cognitive mechanism mediate the performance of behavior. The literature cites several research studies which show a strong relationship between self-efficacy and self-care behavior. Meta-analysis is a technique that enables systematic review and quantitative integration of the results from multiple primary studies that are relevant to a particular research question. Therefore, this study was done using meta-analysis to quantitatively integrate the results of independent research studies to obtain numerical estimates of the overall effect of a self-efficacy with diabetic patient on self-care behaviors. The research proceeded in three stages : 1) literature search and retrieval of studies in which self-efficacy was related to self-care, 2) coding, and 3) calculation of mean effect size and data analysis. Seventeen studies which met the research criteria included study population of adults with diabetes, measures of self-care and measures of self-efficacy as a predictive variable. Computation of effect size was done on DSTAT which is a statistical computer program specifically designed for meta-analysis. To determine the effect of self-efficacy on self-care practice homogeneity tests were conducted. Pooled effect size estimates, to determine the best subvariable for composite variables, metabolic control variables and component of self-efficacy and self-care, indicated that the effect of self-efficacy composite on self-care composite was moderate to large. The weighted mean effect size of self-efficacy composite and self-care composite were ＋.76 and the confidence interval was from ＋.66 to ＋.86 with the number of subjects being 1,545. The total for this meta-analysis result showed that the weighted mean effect sizes ranged from ＋.70 to ＋1.81 which indicates a large effect. But since reliabilities of the instruments in the primary studies were low or not stated, caution must be applied in unconditionally accepting the results from these effect sizes. Meta-analysis is a useful took for clarifying the status of knowledge development and guiding decision making about future research and this study confirmed that there is a relationship between self-efficacy and self-care in patients with diabetes. It, thus, provides support for nurses to promote self-efficacy in their patients. While most of the studies included in this meta-analysis used social cognitive theory as a framework for the study, some studies use Fishbein & Ajzen's attitude model as a model for active self-care. Future research is needed to more fully define the concept of self-care and to determine what it is that makes patients feel competent in their self-care activities. The results of this study showed that self-efficacy can promote self-care. Future research is needed with experimental design to determine nursing interventions that will increase self-efficacy.

• Environmental Mutagens and Carcinogens
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• v.22 no.4
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• pp.303-311
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• 2002

This study evaluated the concentrations of urinary metabolites of polycyclic aromatic hydrocarbons (PAHs) in industrial waste incineration workers. The effect of genetic polymorphisms of xenobiotic metabolism enzymes on urinary concentration of PAH metabolites was assessed. And, aromatic DNA adduct levels were also determined in total white blood cells. Fifty employees were recruited from a company handling industrial wastes located in Ansan, Korea: non-exposed group (n=21), exposed group (n=29). Sixteen ambient PAHs were determined by GC/MSD (NIOSH method) from personal breathing zone samples of nine subjects near incinerators. Urinary 1-hydroxypyrene glucuronide (1-OHPG), a major pyrene metabolite, was assayed by synchronous fluorescence spectroscopy after immunoaffinity purification using monoclonal antibody 8E11 (SFS/IAC). Multiplex PCR was used for genotyping for GSTMI/TI and PCR-RFLP for genotyping of CYP1A1 (MspI and Ile/Val). PAH-DNA adducts in peripheral blood WBC were measured by the nuclease P1-enhanced postlabeling assay. Smoking habit, demographic and occupational information were collected by self-administered questionnaire. The range of total ambient PAH levels were 0.00-7.00 mg/㎥ (mean 3.31). Urinary 1-OHPG levels were significantly higher in workers handling industrial wastes than in those with presumed lower exposure to PAHs (p=0.006, by Kruskal-Wallis test). There was a statistically significant dose-response increase in 1-OHPG levels with the number of cigarettes consumed per day (Pearson correlation coefficient=0.686, p<0.001). Urinary 1-OHPG levels in occupationally exposed smoking workers were highest compared with non-occupationally exposed smokers (p=0.053, by Kruskal-Wallis test). Smoking and GSTMI genotype were significant predictors for log-transformed 1-OHPG by multiple regression analysis (overall model R²=0.565, p<0.001), whereas smoking was the only significant predictor for log-transformed aromatic DNA adducts (overall model R²=0.249, p=0.201). Aromatic DNA adducts was also a significantly correlation between log transferred urinary 1-OHPG levels (pearson's correlation coefficient=0.307, p=0.04). However, the partial correlation coefficient adjusting for Age, Sex, and cigarette consumption was not significant (r=0.154, p=0.169). The significant association exists only in individuals with the GSTMI null genotype (pearsons correlation coefficient=0.516, p=0.010; partial correlation coefficient adjusting for age, sex, and cigarette consumption, r=0.363, p=0.038). Our results suggest that the significant increase in urinary 1-OHPG in the exposed workers is due to higher prevalence of smokers among them, and that the association between urinary PAH metabolites and aromatic DNA adducts in workers of industrial waste handling may be modulated by GSTMI genotype. There results remain to be confirmed in future larger studies.

• Asian Pacific Journal of Cancer Prevention
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• v.16 no.6
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• pp.2527-2530
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• 2015

Background: The cancer of the prostate risk assessment (CAPRA) score has been defined to predict prostate cancer recurrence based on the pre-clinical data, then pathological data have also been incorporated. Thus, CAPRA post-surgical (CAPRA-S) score has been developed based on six criteria (prostate specific antigen (PSA) at diagnosis, pathological Gleason score, and information on surgical margin, seminal vesicle invasion, extracapsular extension and lymph node involvement) for the prediction of post-surgical recurrences. In the present study, biochemical recurrence (BCR)-free probabilities after open retropubic radical prostatectomy (RP) were evaluated by the CAPRA-S scoring system and its three-risk level model. Materials and Methods: CAPRA-S scores (0-12) of our 240 radical prostatectomies performed between January 2000-May 2011 were calculated. Patients were distributed into CAPRA-S score groups and also into three-risk groups as low, intermediate and high. BCR-free probabilities were assessed and compared using Kaplan-Meier analysis and Cox proportional hazards regression. Ability of CAPRA-S in BCR detection was evaluated by concordance index (c-index). Results: BCR was present in 41 of total 240 patients (17.1%) and the mean follow-up time was $51.7{\pm}33.0$ months. Mean BCR-free survival time was 98.3 months (95% CI: 92.3-104.2). Of the patients in low, intermediate and high risk groups, 5.4%, 22.0% and 58.8% had BCR, respectively and the difference among the three groups was significant (P = 0.0001). C-indices of CAPRA-S score and three-risk groups for detecting BCR-free probabilities in 5-yr were 0.87 and 0.81, respectively. Conclusions: Both CAPRA-S score and its three-risk level model well predicted BCR after RP with high c-index levels in our center. Therefore, it is a clinically reliable post-operative risk stratifier and disease recurrence predictor for prostate cancer.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.17 no.7
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• pp.134-143
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• 2016

This study examined the effects of perfectionism and ego-resiliency on the anxiety in two groups of nursing students: leisure activity, and no leisure activity group. A total of 134 undergraduate students were recruited at two universities in A and B cities, Korea using the convenient sampling method. They were asked to complete a self-reported questionnaire for 11 days in October, 2015. The results showed that there were significant differences in the ego-resiliency (t=-3.80, p=.001) and anxiety (t=3.71, p<.001) according to the leisure activity. In the subjects who did not have leisure activities, perfectionism (${\beta}=.71$, p<.001) and ego-resiliency (${\beta}=-.29$, p=.003) were identified as significant predictors of anxiety and this model explained 58.0% of the variance in anxiety (F=34.50, p<.001). In the analysis according to the classification of current leisure activity, Perfectionism (${\beta}=.54$, p=.003) was identified as a significant predictor of anxiety in subjects doing travel watch and pastime amusement, and this model explained 26.0% of the variance in anxiety in nursing students (F=5.66, p=.009). Therefore, providing strategies to control anxiety can not only improve ego-resiliency, but also reduce perfectionism among nursing students. In addition, it is necessary to resolve the disturbance factors of the leisure activity and create an environment that promotes leisure activities in universities.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.44 no.1
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• pp.33-39
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• 2007

LonWorks over IP (LonWorks/IP) virtual device network (VDN) is an integrated form of LonWorks device network and IP data network. LonWorks/IP VDN can offer ubiquitous access to the information on the factory floor and make it possible for the predictive and preventive maintenance on the factory floor. Timely response is inevitable for predictive and preventive maintenance on the factory floor under the real-time distributed control. The network induced uncertain time delay deteriorates the performance and stability of the real-time distributed control system on LonWorks/IP virtual device network. Therefore, in order to guarantee the stability and to improve the performance of the networked distributed control system the time-varying uncertain time delay needs to be compensated for. In this paper, under the real-time distributed control on LonWorks/IP VDN with uncertain time delay, a control scheme based on disturbance observer and ZPETC(Zero Phase Error Tracking Controller) phase lag compensator is proposed and tested through computer simulation. The result of the proposed control is compared with that of internal model controller (IMC) based on Smith predictor and disturbance observer. It is shown that the proposed control scheme is disturbance and noise tolerant and can significantly improve the stability and the tracking performance of the periodic reference. Therefore, the proposed control scheme is well suited for the distributed servo control for predictive maintenance on LonWorks/IP-based virtual device network with time-varying delay.