• Journal of Intelligence and Information Systems
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• v.16 no.3
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• pp.99-120
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• 2010

Due to the wide spread of customers' frequent access of non face-to-face services, there have been many attempts to improve customer satisfaction using huge amounts of data accumulated throughnon face-to-face channels. Usually, a call center is regarded to be one of the most representative non-faced channels. Therefore, it is important that a call center has enough agents to offer high level customer satisfaction. However, managing too many agents would increase the operational costs of a call center by increasing labor costs. Therefore, predicting and calculating the appropriate size of human resources of a call center is one of the most critical success factors of call center management. For this reason, most call centers are currently establishing a department of WFM(Work Force Management) to estimate the appropriate number of agents and to direct much effort to predict the volume of inbound calls. In real world applications, inbound call prediction is usually performed based on the intuition and experience of a domain expert. In other words, a domain expert usually predicts the volume of calls by calculating the average call of some periods and adjusting the average according tohis/her subjective estimation. However, this kind of approach has radical limitations in that the result of prediction might be strongly affected by the expert's personal experience and competence. It is often the case that a domain expert may predict inbound calls quite differently from anotherif the two experts have mutually different opinions on selecting influential variables and priorities among the variables. Moreover, it is almost impossible to logically clarify the process of expert's subjective prediction. Currently, to overcome the limitations of subjective call prediction, most call centers are adopting a WFMS(Workforce Management System) package in which expert's best practices are systemized. With WFMS, a user can predict the volume of calls by calculating the average call of each day of the week, excluding some eventful days. However, WFMS costs too much capital during the early stage of system establishment. Moreover, it is hard to reflect new information ontothe system when some factors affecting the amount of calls have been changed. In this paper, we attempt to devise a new model for predicting inbound calls that is not only based on theoretical background but also easily applicable to real world applications. Our model was mainly developed by the interactive decision tree technique, one of the most popular techniques in data mining. Therefore, we expect that our model can predict inbound calls automatically based on historical data, and it can utilize expert's domain knowledge during the process of tree construction. To analyze the accuracy of our model, we performed intensive experiments on a real case of one of the largest car insurance companies in Korea. In the case study, the prediction accuracy of the devised two models and traditional WFMS are analyzed with respect to the various error rates allowable. The experiments reveal that our data mining-based two models outperform WFMS in terms of predicting the amount of accident calls and fault calls in most experimental situations examined.

• 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.

• Journal of Korean Society of Forest Science
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• v.108 no.3
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• pp.429-439
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• 2019

The objectives of this study were to gauge the extent of the forestry business through establishing the definition of forestry industry from the perspective of economic convergence and to analyze key components that affect each growth phase of a forestry business entity by classifying them. A total of 1,397 "sixth-sector industry" management entities were certified by the Ministry of Agriculture, Food, and Rural Affairs in South Korea from 2012-2017. Of these, 259 (18.5%) were in the forestry sector. In this study, the 259 forestry management entities were further classified into three phases based on sales distribution: entrance, development, and maturity. The entrance phase (<100 million KRW), development phase (>100 million and <1 billion KRW), and maturity phase (>1 billion KRW) constituted 33.2%, 55.4%, and 12.4% of the total 259 entities, respectively. The results showed that most of the management entities were either in the entrance or development phases, and only a small portion was in the maturity phase. To identify the key variables that affect each of the phases, chi-square analysis was used. We designed the "sixth-sector industry" type as an independent variable, whereas selected region, business organization, manager age group, forest product, processing type, and service type were designated as dependent variables. The results of the analysis showed that the processing and service types influenced all three developmental phases. Moreover, as the phase advanced, processing type showed a higher proportion of health-functional ingredients, such as powder or extract from forest products, which enable to develop and produce a variety of products. Service type also changed from simple experience to integrated experience tourism and finally to tourism education. Distribution and sales channel also turned out to be a significant factor during the development phase. This study provides the basic information needed to guide government support in the implementation of a formal forestry business through convergence as well as to increase the efficiency of business management.

• The Korean Journal of Coaching Psychology
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• v.7 no.1
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• pp.59-89
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• 2023

In this study, change motivation for growth and development is defined as 'the power to set a specific action direction for change based on the perception of one's current behavior in order to achieve a goal that one considers important, and to be willing to act'. In addition, the purpose of this study was to develop and validate a scale to measure the motivation for change for growth and development of general adults. To develop preliminary questions, interviews were conducted with 7 coaching experts and 9 experienced coaches, and an open-ended questionnaire was conducted with 55 adults. Afterwards, 7 factors and 83 questions were selected through three rounds of item classification and content validity verification, and a preliminary survey was conducted targeting 321 general adults, and 42 items, 4 factors, were derived through exploratory factor analysis. did Finally, the main survey was conducted with 631 adults in order to verify the validity of the construct concept of the change motivation scale and the validity of the criterion. Divided into two groups, 315 people in group 1 conducted exploratory factor analysis and 316 people in group 2 conducted confirmatory factor analysis to verify the concept of change motivation scale. As a result of the factor analysis of Group 1, it was found that the 3 factor structure consisting of 31 items was appropriate, and as a result of the confirmatory factor analysis of Group 2, the goodness of fit of the modified model of the 3 factor structure was confirmed, which motivated change. The construct validity of the scale was demonstrated. As a result of analyzing the correlations with various variables for the analysis of convergent validity and criterion-related validity of the Motivation for Change scale, each of the three factors was found to be significantly related to most variables. Finally, the significance, implications and limitations of this study, and future research were discussed.

• Health Policy and Management
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• v.34 no.2
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• pp.178-184
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• 2024

Background: This study aimed to determine the effectiveness of drug utilization review (DUR) evaluation indicators on safe drug use by comparing the changes in DUR inspection rates and drug duplication prescription prevention rates between the pre- and post-implementation of the DUR evaluation indicators of the Healthcare Quality Evaluation Grant Initiative. Methods: This study used DUR data from the Health Insurance Review and Assessment Service in 2018 (pre-implementation) and the evaluation results of the Healthcare Quality Evaluation Grant Initiative in 2023 (post-implementation). The dependent variables were the DUR evaluation indicators, including DUR inspection rate and drug duplicate prescription prevention rate. The independent variable was the implementation of the DUR evaluation indicators, and the control variables included medical institution characteristics such as type, establishment classification, location, DUR billing software company, and number of beds. Results: The results of the analysis of the difference in the prevention rate of drug duplicate prescriptions between the pre- and post-implementation of the DUR evaluation indicators of the Healthcare Quality Evaluation Grant Initiative showed that the prevention rate of drug duplicate prescriptions increased statistically significantly after the implementation of the DUR evaluation indicators. Conclusion: The policy implications of this study are as follows: First, ongoing evaluation of DUR systems is needed. Second, it is necessary to establish a collaborative partnership between healthcare organizations that utilize DUR system information and the organizations that manage it.

• Journal of Intelligence and Information Systems
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• v.23 no.3
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• pp.95-118
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• 2017

Recent centrally the downtown area, the transaction between the row housing and multiplex housing is activated and platform services such as Zigbang and Dabang are growing. The row housing and multiplex housing is a blind spot for real estate information. Because there is a social problem, due to the change in market size and information asymmetry due to changes in demand. Also, the 5 or 25 districts used by the Seoul Metropolitan Government or the Korean Appraisal Board(hereafter, KAB) were established within the administrative boundaries and used in existing real estate studies. This is not a district classification for real estate researches because it is zoned urban planning. Based on the existing study, this study found that the city needs to reset the Seoul Metropolitan Government's spatial structure in estimating future housing prices. So, This study attempted to classify the area without spatial heterogeneity by the reflected the property price characteristics of row housing and Multiplex housing. In other words, There has been a problem that an inefficient side has arisen due to the simple division by the existing administrative district. Therefore, this study aims to cluster Seoul as a new area for more efficient real estate analysis. This study was applied to the hedonic model based on the real transactions price data of row housing and multiplex housing. And the K-Means Clustering algorithm was used to cluster the spatial structure of Seoul. In this study, data onto real transactions price of the Seoul Row housing and Multiplex Housing from January 2014 to December 2016, and the official land value of 2016 was used and it provided by Ministry of Land, Infrastructure and Transport(hereafter, MOLIT). Data preprocessing was followed by the following processing procedures: Removal of underground transaction, Price standardization per area, Removal of Real transaction case(above 5 and below -5). In this study, we analyzed data from 132,707 cases to 126,759 data through data preprocessing. The data analysis tool used the R program. After data preprocessing, data model was constructed. Priority, the K-means Clustering was performed. In addition, a regression analysis was conducted using Hedonic model and it was conducted a cosine similarity analysis. Based on the constructed data model, we clustered on the basis of the longitude and latitude of Seoul and conducted comparative analysis of existing area. The results of this study indicated that the goodness of fit of the model was above 75 % and the variables used for the Hedonic model were significant. In other words, 5 or 25 districts that is the area of the existing administrative area are divided into 16 districts. So, this study derived a clustering method of row housing and multiplex housing in Seoul using K-Means Clustering algorithm and hedonic model by the reflected the property price characteristics. Moreover, they presented academic and practical implications and presented the limitations of this study and the direction of future research. Academic implication has clustered by reflecting the property price characteristics in order to improve the problems of the areas used in the Seoul Metropolitan Government, KAB, and Existing Real Estate Research. Another academic implications are that apartments were the main study of existing real estate research, and has proposed a method of classifying area in Seoul using public information(i.e., real-data of MOLIT) of government 3.0. Practical implication is that it can be used as a basic data for real estate related research on row housing and multiplex housing. Another practical implications are that is expected the activation of row housing and multiplex housing research and, that is expected to increase the accuracy of the model of the actual transaction. The future research direction of this study involves conducting various analyses to overcome the limitations of the threshold and indicates the need for deeper research.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.18 no.3
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• pp.211-226
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• 2023

As we transition into the post-COVID era, economic activities that were stagnant are regaining momentum. In particular, there is a growing trend of technology entrepreneurship driven by the opportunities of digital transformation in the Fourth Industrial Revolution. However, entrepreneurship education content is struggling to keep up with the rapid pace of technological change. This study aims to emphasize the importance of entrepreneurship mentoring as a crucial component of entrepreneurship education content that requires adaptation and advancement due to the increasing demand for technology entrepreneurship. This study redefines startup mentoring, which is differentiated from general mentoring, at the present time when the demand for startups, which increases with the declining employment rate, increases, and the development of quality startup education contents and securing professional startup mentors are required. According to the start-up stage, it is divided into preliminary entrepreneurs and early entrepreneurs, and the effect of entrepreneurship knowledge and self-efficacy among start-up mentoring functions on entrepreneurial will and mentoring satisfaction is improved by empirically researching the effects of start-up mentoring functions in the case of initial entrepreneurs as a moderating effect. To confirm the importance of entrepreneurship mentoring effect for. To this end, among the mentoring functions, entrepreneurship knowledge and self-efficacy were set as independent variables, and entrepreneurial will and mentoring satisfaction were set as dependent variables. The research model was designed and hypotheses were established. In addition, empirical analysis was conducted by conducting a questionnaire survey on trainees who received entrepreneurship mentoring education at ICCE Startup School and Opus Startup School. To summarize the results of the empirical analysis, first, among the entrepreneurship mentoring functions, entrepreneurship knowledge and self-efficacy were analyzed to have a significant positive (+) effect on entrepreneurial will. Second, among the entrepreneurship mentoring functions, entrepreneurship knowledge and self-efficacy were analyzed to have a significant positive (+) effect on mentoring satisfaction. Third, it was analyzed that entrepreneurship had no significant moderating effect on entrepreneurial knowledge and entrepreneurial will. Fourth, it was analyzed that entrepreneurship had no significant moderating effect on mentoring satisfaction. Fifth, it was found that entrepreneurship had a significant moderating effect between self-efficacy and will to start a business. As a result of the research analysis, the first implication is that the mentoring function in start-up education is analyzed to produce meaningful results for both the initial entrepreneurs and the prospective entrepreneurs in the will to start a business and satisfaction. . Second, it was analyzed that there was no significant relationship between whether a business was started and the mentoring function and effect. However, it was analyzed that the will to start a business through improvement of self-efficacy through mentoring was significantly related to whether or not to start a business. turned out to be helpful. Many start-up education programs currently conducted in Korea educate both early-stage entrepreneurs and prospective entrepreneurs at the same time for reasons such as convenience. However, through the results of this study, even in small-scale entrepreneurship mentoring, it is suggested that customized mentoring through detailed classification such as whether the mentee has started a business can be a method for successful entrepreneurship and high satisfaction of the mentee.

• The korean journal of orthodontics
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• v.34 no.3 s.104
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• pp.241-251
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• 2004

Previous studies have focused on the causes of root resorption after orthodontic treatment and treatment methods to reduce this phenomenon, and have been mainly associated with developed, mature roots. As parents become increasingly interested in their children's' dentition, orthodontists are performing fixed orthodontic treatment on patients of less than 10 years and before the completion of the immature root. Thus, the author evaluated the changes of root length and root form of maxillary immature incisors after orthodontic treatment, compared with those of mature teeth, and investigated the correlation according to gender, treatment duration, and displacement of incisors. The sample consisted of an immature root group of twenty-eight persons (between 8 and 10 years old) and a mature root group of thirty-one persons (between 11 and 15 years old). The crown and root length of the maxillary four incisors were measured with a periapical radiograph, changes in root length and crown-root ratio were calculated, and root form was classified according to a scoring system. The results were as follows. 1. The development of immature roots was not affected by orthodontic treatment and mostly showed normal root length and apical form. 2. Root length of immature teeth was sustained or became shorter, partially in long treatment duration or with open bite patients. Even though the teeth reached their normal root length, they demonstrated a blunt form. 3. Most of the mature roots showed mild resorption, and the form of mature roots was more blunt than the developed form of the immature roots (p<0.05). 4. The developed form of the immature roots was statistically related to treatment duration, while the form of the mature roots was significantly related to the displacement of incisors (p<0.05). 5. In contrast, other variables such as gender, classification of malocclusion, changes in overbite, and changes of U1 to SN showed no correlation with the root resorption of both groups.

• The Journal of Korean Academy of Prosthodontics
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• v.50 no.1
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• pp.61-66
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• 2012

Purpose: This study was to compare the cumulative survival rate (CSR) of Br${\aa}$nemark machined surface implants and TiUnite$^{TM}$ imlants and to analyze association between risk factors and the CSR of the implants. Materials and methods: A retrospective study design was used to collect long-term follow-up clinical data from dental records of 156 patients treated with 541 Br${\aa}$nemark machined and TiUnite$^{TM}$ implants at Korea University Guro hospital in South Korea from 1993 through 2008. Machined implant and TiUnite$^{TM}$ implant were compared by CSR. Exposure variables such as gender, systemic disease, location, implant length, diameter, prosthesis type, opposing occlusion type, date of implant placement, type of edentulous space, abutment type, existence of splinting with natural teeth, and existence of cantilever were collected. Life table analysis was undertaken to examine the CSR. Cox regression method was conducted to assess the association between potential risk factors and overall CSR (${\alpha}$=.05). Results: Patient ages ranged from 16 to 75 years old (mean age, 51 years old). Implants were more frequently placed in men than women (94 men versus 63 women). Since 1993, 264 Br${\aa}$nemark machined implants were inserted in 79 patients and since 2001, 277 TiUnite$^{TM}$ implants were inserted in 77 patients. A total survival rate of 86.07% was observed in Br${\aa}$nemark and Nobel Biocare TiUnite$^{TM}$ during 15 years. A survival rate of machined implant during 15 years was 82.89% and that of TiUnite$^{TM}$ implant during 5 years was 98.74%. The implant CSR revealed lower rates association with several risk factors such as, systemic disease, other accompanied surgery, implant location, and Kennedy classification. Conclusion: Clinical performance of Br${\aa}$nemark machined and TiUnite$^{TM}$ implant demonstrated a high level of predictability. In this study, TiUnite$^{TM}$ implant was more successful than machined implant. The implant CSR was associated with several risk factors.