• Journal of Intelligence and Information Systems
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• v.25 no.1
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• pp.21-41
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• 2019

Thanks to the rapid development of information technologies, the data available on Internet have grown rapidly. In this era of big data, many studies have attempted to offer insights and express the effects of data analysis. In the tourism and hospitality industry, many firms and studies in the era of big data have paid attention to online reviews on social media because of their large influence over customers. As tourism is an information-intensive industry, the effect of these information networks on social media platforms is more remarkable compared to any other types of media. However, there are some limitations to the improvements in service quality that can be made based on opinions on social media platforms. Users on social media platforms represent their opinions as text, images, and so on. Raw data sets from these reviews are unstructured. Moreover, these data sets are too big to extract new information and hidden knowledge by human competences. To use them for business intelligence and analytics applications, proper big data techniques like Natural Language Processing and data mining techniques are needed. This study suggests an analytical approach to directly yield insights from these reviews to improve the service quality of hotels. Our proposed approach consists of topic mining to extract topics contained in the reviews and the decision tree modeling to explain the relationship between topics and ratings. Topic mining refers to a method for finding a group of words from a collection of documents that represents a document. Among several topic mining methods, we adopted the Latent Dirichlet Allocation algorithm, which is considered as the most universal algorithm. However, LDA is not enough to find insights that can improve service quality because it cannot find the relationship between topics and ratings. To overcome this limitation, we also use the Classification and Regression Tree method, which is a kind of decision tree technique. Through the CART method, we can find what topics are related to positive or negative ratings of a hotel and visualize the results. Therefore, this study aims to investigate the representation of an analytical approach for the improvement of hotel service quality from unstructured review data sets. Through experiments for four hotels in Hong Kong, we can find the strengths and weaknesses of services for each hotel and suggest improvements to aid in customer satisfaction. Especially from positive reviews, we find what these hotels should maintain for service quality. For example, compared with the other hotels, a hotel has a good location and room condition which are extracted from positive reviews for it. In contrast, we also find what they should modify in their services from negative reviews. For example, a hotel should improve room condition related to soundproof. These results mean that our approach is useful in finding some insights for the service quality of hotels. That is, from the enormous size of review data, our approach can provide practical suggestions for hotel managers to improve their service quality. In the past, studies for improving service quality relied on surveys or interviews of customers. However, these methods are often costly and time consuming and the results may be biased by biased sampling or untrustworthy answers. The proposed approach directly obtains honest feedback from customers' online reviews and draws some insights through a type of big data analysis. So it will be a more useful tool to overcome the limitations of surveys or interviews. Moreover, our approach easily obtains the service quality information of other hotels or services in the tourism industry because it needs only open online reviews and ratings as input data. Furthermore, the performance of our approach will be better if other structured and unstructured data sources are added.

• The Korean Journal of Archival Studies
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• no.29
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• pp.175-223
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• 2011

A university is an organization charged with publicity and has accountability to the community for the operating process. Students account for a majority of members in a university. In universities, numerous creatures are pouring out every year and university students are major producers of these records. However, roles and functions of university students producing enormous amount of records as main agents of universities and focused concentration on produced records have not been made yet. It is reality that from the archival point of view, the importance of produced records of which main agents are university students has been relatively underestimated. In this background, this study attempted approach in archival point of view on records produced by university students, main agents. There are various types of records that university students produce such as records produced in the process of research and teaching as well as records produced in the process of various autonomy activities like clubs, students' associations. This study especially focused on university student autonomy activity process and placed emphasis on accountability securing measures on autonomy activity process of university students. To secure accountability of activities, records management should be based. Therefore, as a way to ensure accountability of unversity students autonomy activity, we tried to present records management systematization and records utilization measures. For this, a student body, a university student autonomy organization was analyzed and a student body of Myongji University Humanities Campus was selected as a specific target. First, to identify records management status, activities and organization and functions of the student body, we conducted an interview with the president of the student body. Through this, we analyzed the activities of the university student body and examined the necessity of accountability accordingly. Also, we derived the types and characteristics of records to be produced at each stage by analyzing the organization and functions of the student body of Myongji University. Like this, after deriving the types of production records according to the necessity, organization and functions of accountability and activities of the student body, we analyzed records management status of the present student body. First, to identify the general process status of activities of the student body, we analyzed activity process by stage of the student body of Myongji University. And we analyzed records management method of the student body and responsibility principal and conducted real condition analysis. Through this analysis, we presented the measures to ensure accountability of a university student body in three categories such as systematization of records management process, establishment of records management infrastructure, accountability guarantee measures. This study discussed accountability on society by analyzing activities and functions of a student body, targeting a student body, an autonomy organization of university students. And as a measure to secure accountability of a student body, we proposed a model for records management environment settlement. But in terms that a student body is an organization operated in one year basis, there is a limit that records management environment is hard to settle. This study pointed out this limit and was to provide clues when more active researches were carried out in the field of student records management in the future through presentation of student body records management model. Also, it is expected that the analysis results derived from this research will have significance in terms of school history arrangement and conservation.

• Journal of Intelligence and Information Systems
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• v.25 no.1
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• pp.163-177
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• 2019

As smartphones are getting widely used, human activity recognition (HAR) tasks for recognizing personal activities of smartphone users with multimodal data have been actively studied recently. The research area is expanding from the recognition of the simple body movement of an individual user to the recognition of low-level behavior and high-level behavior. However, HAR tasks for recognizing interaction behavior with other people, such as whether the user is accompanying or communicating with someone else, have gotten less attention so far. And previous research for recognizing interaction behavior has usually depended on audio, Bluetooth, and Wi-Fi sensors, which are vulnerable to privacy issues and require much time to collect enough data. Whereas physical sensors including accelerometer, magnetic field and gyroscope sensors are less vulnerable to privacy issues and can collect a large amount of data within a short time. In this paper, a method for detecting accompanying status based on deep learning model by only using multimodal physical sensor data, such as an accelerometer, magnetic field and gyroscope, was proposed. The accompanying status was defined as a redefinition of a part of the user interaction behavior, including whether the user is accompanying with an acquaintance at a close distance and the user is actively communicating with the acquaintance. A framework based on convolutional neural networks (CNN) and long short-term memory (LSTM) recurrent networks for classifying accompanying and conversation was proposed. First, a data preprocessing method which consists of time synchronization of multimodal data from different physical sensors, data normalization and sequence data generation was introduced. We applied the nearest interpolation to synchronize the time of collected data from different sensors. Normalization was performed for each x, y, z axis value of the sensor data, and the sequence data was generated according to the sliding window method. Then, the sequence data became the input for CNN, where feature maps representing local dependencies of the original sequence are extracted. The CNN consisted of 3 convolutional layers and did not have a pooling layer to maintain the temporal information of the sequence data. Next, LSTM recurrent networks received the feature maps, learned long-term dependencies from them and extracted features. The LSTM recurrent networks consisted of two layers, each with 128 cells. Finally, the extracted features were used for classification by softmax classifier. The loss function of the model was cross entropy function and the weights of the model were randomly initialized on a normal distribution with an average of 0 and a standard deviation of 0.1. The model was trained using adaptive moment estimation (ADAM) optimization algorithm and the mini batch size was set to 128. We applied dropout to input values of the LSTM recurrent networks to prevent overfitting. The initial learning rate was set to 0.001, and it decreased exponentially by 0.99 at the end of each epoch training. An Android smartphone application was developed and released to collect data. We collected smartphone data for a total of 18 subjects. Using the data, the model classified accompanying and conversation by 98.74% and 98.83% accuracy each. Both the F1 score and accuracy of the model were higher than the F1 score and accuracy of the majority vote classifier, support vector machine, and deep recurrent neural network. In the future research, we will focus on more rigorous multimodal sensor data synchronization methods that minimize the time stamp differences. In addition, we will further study transfer learning method that enables transfer of trained models tailored to the training data to the evaluation data that follows a different distribution. It is expected that a model capable of exhibiting robust recognition performance against changes in data that is not considered in the model learning stage will be obtained.

• Journal of Intelligence and Information Systems
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• v.25 no.1
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• pp.85-107
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• 2019

Online consumers browse products belonging to a particular product line or brand for purchase, or simply leave a wide range of navigation without making purchase. The research on the behavior and purchase of online consumers has been steadily progressed, and related services and applications based on behavior data of consumers have been developed in practice. In recent years, customization strategies and recommendation systems of consumers have been utilized due to the development of big data technology, and attempts are being made to optimize users' shopping experience. However, even in such an attempt, it is very unlikely that online consumers will actually be able to visit the website and switch to the purchase stage. This is because online consumers do not just visit the website to purchase products but use and browse the websites differently according to their shopping motives and purposes. Therefore, it is important to analyze various types of visits as well as visits to purchase, which is important for understanding the behaviors of online consumers. In this study, we explored the clustering analysis of session based on click stream data of e-commerce company in order to explain diversity and complexity of search behavior of online consumers and typified search behavior. For the analysis, we converted data points of more than 8 million pages units into visit units' sessions, resulting in a total of over 500,000 website visit sessions. For each visit session, 12 characteristics such as page view, duration, search diversity, and page type concentration were extracted for clustering analysis. Considering the size of the data set, we performed the analysis using the Mini-Batch K-means algorithm, which has advantages in terms of learning speed and efficiency while maintaining the clustering performance similar to that of the clustering algorithm K-means. The most optimized number of clusters was derived from four, and the differences in session unit characteristics and purchasing rates were identified for each cluster. The online consumer visits the website several times and learns about the product and decides the purchase. In order to analyze the purchasing process over several visits of the online consumer, we constructed the visiting sequence data of the consumer based on the navigation patterns in the web site derived clustering analysis. The visit sequence data includes a series of visiting sequences until one purchase is made, and the items constituting one sequence become cluster labels derived from the foregoing. We have separately established a sequence data for consumers who have made purchases and data on visits for consumers who have only explored products without making purchases during the same period of time. And then sequential pattern mining was applied to extract frequent patterns from each sequence data. The minimum support is set to 10%, and frequent patterns consist of a sequence of cluster labels. While there are common derived patterns in both sequence data, there are also frequent patterns derived only from one side of sequence data. We found that the consumers who made purchases through the comparative analysis of the extracted frequent patterns showed the visiting pattern to decide to purchase the product repeatedly while searching for the specific product. The implication of this study is that we analyze the search type of online consumers by using large - scale click stream data and analyze the patterns of them to explain the behavior of purchasing process with data-driven point. Most studies that typology of online consumers have focused on the characteristics of the type and what factors are key in distinguishing that type. In this study, we carried out an analysis to type the behavior of online consumers, and further analyzed what order the types could be organized into one another and become a series of search patterns. In addition, online retailers will be able to try to improve their purchasing conversion through marketing strategies and recommendations for various types of visit and will be able to evaluate the effect of the strategy through changes in consumers' visit patterns.

• Journal of Intelligence and Information Systems
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• v.25 no.1
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• pp.179-196
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• 2019

Recently, the "Smart Consumer" has been emerging. He or she is increasingly inclined to search for and purchase products by taking into account personal judgment or expert reviews rather than by relying on information delivered through manufacturers' advertising. This is especially true when purchasing cosmetics. Because cosmetics act directly on the skin, consumers respond seriously to dangerous chemical elements they contain or to skin problems they may cause. Above all, cosmetics should fit well with the purchaser's skin type. In addition, changes in global cosmetics consumer trends make it necessary to study this field. The desire to find one's own individualized cosmetics is being revealed to consumers around the world and is known as "Finding the Holy Grail." Many consumers show a deep interest in customized cosmetics with the cultural boom known as "K-Beauty" (an aspect of "Han-Ryu"), the growth of personal grooming, and the emergence of "self-culture" that includes "self-beauty" and "self-interior." These trends have led to the explosive popularity of cosmetics made in Korea in the Chinese and Southeast Asian markets. In order to meet the customized cosmetics needs of consumers, cosmetics manufacturers and related companies are responding by concentrating on delivering premium services through the convergence of ICT(Information, Communication and Technology). Despite the evolution of companies' responses regarding market trends toward customized cosmetics, there is no "Intelligent Data Platform" that deals holistically with consumers' skin condition experience and thus attaches emotions to products and services. To find the Holy Grail of customized cosmetics, it is important to acquire and analyze consumer data on what they want in order to address their experiences and emotions. The emotions consumers are addressing when purchasing cosmetics varies by their age, sex, skin type, and specific skin issues and influences what price is considered reasonable. Therefore, it is necessary to classify emotions regarding cosmetics by individual consumer. Because of its importance, consumer emotion analysis has been used for both services and products. Given the trends identified above, we judge that consumer emotion analysis can be used in our study. Therefore, we collected and indexed data on consumers' emotions regarding their cosmetics experiences focusing on consumers' language. We crawled the cosmetics emotion data from SNS (blog and Twitter) according to sales ranking ($1^{st}$ to $99^{th}$), focusing on the ample/serum category. A total of 357 emotional adjectives were collected, and we combined and abstracted similar or duplicate emotional adjectives. We conducted a "Consumer Sentiment Journey" workshop to build a "Consumer Sentiment Dictionary," and this resulted in a total of 76 emotional adjectives regarding cosmetics consumer experience. Using these 76 emotional adjectives, we performed clustering with the Self-Organizing Map (SOM) method. As a result of the analysis, we derived eight final clusters of cosmetics consumer sentiments. Using the vector values of each node for each cluster, the characteristics of each cluster were derived based on the top ten most frequently appearing consumer sentiments. Different characteristics were found in consumer sentiments in each cluster. We also developed a cosmetics experience pattern map. The study results confirmed that recommendation and classification systems that consider consumer emotions and sentiments are needed because each consumer differs in what he or she pursues and prefers. Furthermore, this study reaffirms that the application of emotion and sentiment analysis can be extended to various fields other than cosmetics, and it implies that consumer insights can be derived using these methods. They can be used not only to build a specialized sentiment dictionary using scientific processes and "Design Thinking Methodology," but we also expect that these methods can help us to understand consumers' psychological reactions and cognitive behaviors. If this study is further developed, we believe that it will be able to provide solutions based on consumer experience, and therefore that it can be developed as an aspect of marketing intelligence.

• Journal of the Korean Institute of Traditional Landscape Architecture
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• v.37 no.1
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• pp.56-67
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• 2019

In order to investigate the issue with the proper name of eshel(Tamarix aphylla) mentioned in the Bible, analysis of morphological taxonomy features of plants, studies on the symbolism of the Tamarix genus, analysis of examples in Korean classics and Chinese classics, and studies on the problems found in translations of Korean, Chinese and Japanese Bibles. The results are as follows. According to plant taxonomy, similar species of the Tamarix genus are differentiated by the leaf and flower, and because the size is very small about 2-4mm, it is difficult to differentiate by the naked eye. However, T. aphylla found in the plains of Israel and T. chinensis of China and Korea have distinctive differences in terms of the shape of the branch that droops and its blooming period. The Tamarix genus is a very precious tree that was planted in royal courtyards of ancient Mesopotamia and the Han(漢) Dynasty of China, and in ancient Egypt, it was said to be a tree that gave life to the dead. In the Bible, it was used as a sign of the covenant that God was with Abraham, and it also symbolized the prophet Samuel and the court of Samuel. When examining the example in Korean classics, the Tamarix genus was used as a common term in the Joseon Dynasty and it was often used as the medical term '$Ch{\bar{e}}ngli{\check{u}}$(檉柳)'. Meanwhile, the term 'wiseonglyu(渭城柳)' was used as a literary term. Upon researching the period and name of literature related to $Ch{\bar{e}}ngli{\check{u}}$(檉柳) among Chinese medicinal herb books, a total of 16 terms were used and among these terms, the term Chuísīliǔ(垂絲柳) used in the Chinese Bible cannot be found. There was no word called 'wiseonglyu(渭城柳)' that originated from the poem by Wang Wei(699-759) of Tang(唐) Dynasty and in fact, the word 'halyu(河柳)' that was related to Zhou(周) China. But when investigating the academic terms of China currently used, the words Chuísīliǔ(垂絲柳) and $Ch{\bar{e}}ngli{\check{u}}$(檉柳) are used equally, and therefore, it appears that the translation of eshel in the Chinese Bible as either Chuísīliǔ (垂絲柳) or $Ch{\bar{e}}ngli{\check{u}}$(檉柳) both appear to be of no issue. There were errors translating tamarix into 'やなぎ(willow)' in the Meiji Testaments(舊新約全書 1887), and translated correctly 'ぎょりゅう(檉柳)' since the Colloquial Japanese Bible(口語譯 聖書 1955). However, there are claims that 'gyoryu(ぎょりゅう 檉柳)' is not an indigenous species but an exotics species in the Edo Period, so it is necessary to reconsider the terminology. As apparent in the Korean classics examples analysis, there is high possibility that Korea's T. chinensis were grown in the Korean Peninsula for medicinal and gardening purposes. Therefore, the use of the medicinal term $Ch{\bar{e}}ngli{\check{u}}$(檉柳) or literary term 'wiseonglyu' in the Korean Bible may not be a big issue. However, the term 'wiseonglyu' is used very rarely even in China and as this may be connected to the admiration of China and Chinese things by literary persons of the Joseon Dynasty, so the use of this term should be reviewed carefully. Therefore, rather than using terms that may be of issue in the Bible, it is more feasible to transliterate the Hebrew word and call it eshel.

• The Korean Journal of Nuclear Medicine Technology
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• v.22 no.2
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• pp.67-73
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• 2018

$[^{11}C]PIB$ synthesis has been performed by a loop-methylation and HPLC purification in our lab. However, this method is time-consuming and requires complicated systems. Thus, we developed an on-cartridge method which simplified the synthetic procedure and reduced time greatly by removing HPLC purification step. We compared 6 different cartridges and evaluated the $[^{11}C]PIB$ production yields and specific activities. $[^{11}C]MeOTf$ was synthesized by using TRACERlab FXC Pro and was transferred into the cartridge by blowing with helium gas for 3 min. To remove byproducts and impurities, cartridges were washed out by 20 mL of 30% EtOH in 0.5 M $NaH_2PO_4$ solution (pH 5.1) and 10 mL of distilled water. And then, $[^{11}C]PIB$ was eluted by 5 mL of 30% EtOH in 0.5 M $NaH_2PO_4$ into the collecting vial containing 10 mL saline. Among the 6 cartridges, only tC18 environmental cartridge could remove impurities and byproducts from $[^{11}C]PIB$ completely and showed higher specific activity than traditional HPLC purification method. This method took only 8 ~ 9 min from methylation to formulation. For the tC18 environmental cartridge and conventional HPLC loop methods, the radiochemical yields were $12.3{\pm}2.2%$ and $13.9{\pm}4.4%$, respectively, and the molar activities were $420.6{\pm}20.4GBq/{\mu}mol$ (n=3) and $78.7{\pm}39.7GBq/{\mu}mol$ (n=41), respectively. We successfully developed a facile on-cartridge methylation method for $[^{11}C]PIB$ synthesis which enabled the procedure more simple and rapid, and showed higher molar radio-activity than HPLC purification method.

• The Journal of Korean Society for Radiation Therapy
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• v.30 no.1_2
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• pp.9-16
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• 2018

Purpose : Brain Stereotactic Radiosurgery can treat non-invasive diseases with high rates of complications due to surgical operations. However, brain stereotactic radiosurgery may be accompanied by radiation induced side effects such as fractionation radiation therapy because it uses radiation. The effects of Coplanar Volumetric Modulated Arc Therapy(C-VMAT) and Non-Coplanar Volumetric Modulated Arc Therapy(NC-VMAT) on surrounding normal tissues were analyzed in order to reduce the side effects caused fractionation radiation therapy such as head and neck. But, brain stereotactic radiosurgery these contents were not analyzed. In this study, we evaluated the usefulness of NC-VMAT by comparing and analyzing C-VMAT and NC-VMAT in patients who underwent brain stereotactic radiosurgery. Methods and materials : With C-VMAT and NC-VMAT, 13 treatment plans for brain stereotactic radiosurgery were established. The Planning Target Volume ranged from a minimum of 0.78 cc to a maximum of 12.26 cc, Prescription doses were prescribed between 15 and 24 Gy. Treatment machine was TrueBeam STx (Varian Medical Systems, USA). The energy used in the treatment plan was 6 MV Flattening Filter Free (6FFF) X-ray. The C-VMAT treatment plan used a half 2 arc or full 2 arc treatment plan, and the NC-VMAT treatment plan used 3 to 7 Arc 40 to 190 degrees. The angle of the couch was planned to be 3-7 angles. Results : The mean value of the maximum dose was $105.1{\pm}1.37%$ in C-VMAT and $105.8{\pm}1.71%$ in NC-VMAT. Conformity index of C-VMAT was $1.08{\pm}0.08$ and homogeneity index was $1.03{\pm}0.01$. Conformity index of NC-VMAT was $1.17{\pm}0.1$ and homogeneity index was $1.04{\pm}0.01$. $V_2$, $V_8$, $V_{12}$, $V_{18}$, $V_{24}$ of the brain were $176{\pm}149.36cc$, $31.50{\pm}25.03cc$, $16.53{\pm}12.63cc$, $8.60{\pm}6.87cc$ and $4.03{\pm}3.43cc$ in the C-VMAT and $135.55{\pm}115.93cc$, $24.34{\pm}17.68cc$, $14.74{\pm}10.97cc$, $8.55{\pm}6.79cc$, $4.23{\pm}3.48cc$. Conclusions : The maximum dose, conformity index, and homogeneity index showed no significant difference between C-VMAT and NC-VMAT. $V_2$ to $V_{18}$ of the brain showed a difference of at least 0.5 % to 48 %. $V_{19}$ to $V_{24}$ of the brain showed a difference of at least 0.4 % to 4.8 %. When we compare the mean value of $V_{12}$ that Radione-crosis begins to generate, NC-VMAT has about 12.2 % less amount than C-VMAT. These results suggest that if NC-VMAT is used, the volume of $V_2$ to $V_{18}$ can be reduced, which can reduce Radionecrosis.

• The Journal of Korean Society for Radiation Therapy
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• v.30 no.1_2
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• pp.27-34
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• 2018

Purpose : To evaluate the effect of carbon couch side rail and vacuum immobilization device in case of lung RPO irradiation. Materials and Methods : The 10, 20, 30 mm thickness of vac-lok's right side were obtained. To measure of doses, glass dosimeters were used and measured reference point is left lung center at the phantom. A, B, C, and D points are left, right, down, and up directions based on the center point. In the state of Side-Rail-Out, place the without vac-lok, with the thickness of 10, 20, and 30 mm vac-lok. After the glass dosimeters was inserted in center, A, B, C, and D points, 100 MU of 6 MV X-ray were irradiated to the referenced center point in the condition of $10{\times}10cm^2$ field size, SAD 100 cm, gantry angle 225, 300 MU/min dose rate. Five measurements were made for each point. In the state of Side-Rail-In, five measurement were made for each point under the same conditions. The average is measured on each of the five Side-Rail-Out and Side-Rail-In measurements. Results : In the presence of side rail, the dose reduction ratio was -11.8 %, -12.3 %, -4.1 %, -12.3 %, -7.3 % for each A, B, C, and D points. In the state of Side-Rail-Out, the dose reduction ratio for the using 10 mm thickness of vac-lok was -0.9 % than without vac-lok. The dose reduction ratio for the using 20 mm thickness of vac-lok was -2.0 %, for the using 30 mm thickness of the vac-lok was -3.0 % than without vac-lok. In the state of Side-Rail-In, the dose reduction ratio for the using 10 mm thickness of vac-lok was -1.0 % than without vac-lok. The dose reduction ratio for the using 20 mm vac-lok was -2.1 %, for the using 30 mm vac-lok was -3.0 % than without vac-lok. Based on the value of no vac-lok dose in the Side-Rail-In state, The dose reduction ratios for the using 10 mm, 20 mm and 30 mm thickness of vac-loks In the Side-Rail-Out that the center point were -12.7 %, -13.7 %, -14.2 % and -12.8 %, -13.8 %, -14.5 % respectively at point A. The dose reduction ratios for the same conditions to the B point were -4.9 %, -6.1 %, -7.1 % and -13.4 %, -14.4 %, -15.5 % respectively at point C. The dose reduction ratios for the same conditions to the D point were -8.4 %, -9.0 %, -10.4 % respectively. Conclusion : The attenuation was caused by presence of side rails and thickness of vac-lok. Pay attention to these attenuation factors, making it a more effective radiation therapy.

• Journal of Intelligence and Information Systems
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• v.26 no.4
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• pp.149-171
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• 2020

The period at university is to make decision about getting an actual job. As our society develops rapidly and highly, jobs are diversified, subdivided, and specialized, and students' job preparation period is also getting longer and longer. This study analyzed the log data of college students to see how the various activities that college students experience inside and outside of school might have influences on employment. For this experiment, students' various activities were systematically classified, recorded as an activity data and were divided into six core competencies (Job reinforcement competency, Leadership & teamwork competency, Globalization competency, Organizational commitment competency, Job exploration competency, and Autonomous implementation competency). The effect of the six competency levels on the employment status (employed group, unemployed group) was analyzed. As a result of the analysis, it was confirmed that the difference in level between the employed group and the unemployed group was significant for all of the six competencies, so it was possible to infer that the activities at the school are significant for employment. Next, in order to analyze the impact of the six competencies on the qualitative performance of employment, we had ANOVA analysis after dividing the each competency level into 2 groups (low and high group), and creating 6 groups by the range of first annual salary. Students with high levels of globalization capability, job search capability, and autonomous implementation capability were also found to belong to a higher annual salary group. The theoretical contributions of this study are as follows. First, it connects the competencies that can be extracted from the school experience with the competencies in the Human Resource Management field and adds job search competencies and autonomous implementation competencies which are required for university students to have their own successful career & life. Second, we have conducted this analysis with the competency data measured form actual activity and result data collected from the interview and research. Third, it analyzed not only quantitative performance (employment rate) but also qualitative performance (annual salary level). The practical use of this study is as follows. First, it can be a guide when establishing career development plans for college students. It is necessary to prepare for a job that can express one's strengths based on an analysis of the world of work and job, rather than having a no-strategy, unbalanced, or accumulating excessive specifications competition. Second, the person in charge of experience design for college students, at an organizations such as schools, businesses, local governments, and governments, can refer to the six competencies suggested in this study to for the user-useful experiences design that may motivate more participation. By doing so, one event may bring mutual benefits for both event designers and students. Third, in the era of digital transformation, the government's policy manager who envisions the balanced development of the country can make a policy in the direction of achieving the curiosity and energy of college students together with the balanced development of the country. A lot of manpower is required to start up novel platform services that have not existed before or to digitize existing analog products, services and corporate culture. The activities of current digital-generation-college-students are not only catalysts in all industries, but also for very benefit and necessary for college students by themselves for their own successful career development.