• Journal of Venture Innovation
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• v.6 no.1
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• pp.75-93
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• 2023

As non-face-to-face contact was activated due to the progress of the 4th Industrial Revolution and COVID-19, the government's civil service was also rapidly reflecting this phenomenon. However, there were cases in which it was quite burdensome and rather uncomfortable for the elderly who were relatively alienated from the digital culture. Therefore, in this study, we tried to empirically analyze the factors that affect the use of government 24(a government civil integration service) by the elderly. For this purpose, the characteristics of the elderly and the characteristics of services were paid attention. As the characteristic factors of the elderly, cognitive characteristics, psychological characteristics, and physical characteristics were derived, and as service characteristics, usefulness, ease of use, security, and government support were derived when using government 24. The effect of these factors on the intention to use Government 24 was empirically analyzed. For empirical analysis, a survey was conducted targeting the elderly in their 60s and older, and 250 valid sample were used for analysis. The analysis results were as follows. Among the characteristics of the elderly, cognitive characteristics, psychological characteristics, and physical characteristics were all found to had a significant negative (-) effect on the intention to use Government 24. On the other hand, usefulness, ease of use, and government support were found to had a significant positive (+) effect on the intention to use government 24. On the other hand, security was not tested for a significant relationship with government 24 use intention. Among the variables that have a significant impact, the psychological characteristics of the elderly had the greatest impact on the intention to use Government 24. Usefulness, cognitive characteristics, physical characteristics, ease of use, and government support were in order. The fact that this study conducted an empirical analysis by combining the characteristics of the elderly and the characteristics of services was meaningful at the academic level. In addition, considering that psychological characteristics appeared to be the most important factor, it seemed necessary to consider these points to promote the use of Government 24 by the elderly.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.18 no.3
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• pp.157-172
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• 2023

The recent Fourth Industrial Revolution is accelerating changes due to digital transformation. According to this trend, the existing start-up paradigm is changing, and new business models based on new technologies and creative ideas are emerging. In addition, the diversity of mentoring relationships and environments such as online mentoring, reverse mentoring, group mentoring, and multiple mentoring is also increasing. However, most mentors in their 50s and 60s, who are mainly active in the start-up field, have been able to help mentees a lot based on their own experience and expertise, but they are having difficulty responding to the changing environment due to a lack of understanding and experience of new technologies and environments. To cope with these changes well, mentors must constantly study, acquire and apply the latest technologies to improve their understanding of new technologies and the environment. In addition, it is necessary to have an understanding and respect for the diversity of mentoring relationships and environments, and to maximize the effectiveness of mentoring by actively utilizing them. Therefore, mentors should recognize that they directly affect the growth and development of mentees, constantly acquire new knowledge and skills to maintain and develop expertise, and actively deliver their knowledge and experiences to mentees. Therefore, in this study, was tried to empirically analyze the relationship between mentoring's influence on mentor's job satisfaction through mentor's personal learning and self-efficacy. The results of the empirical analysis were as follows. Among the functions of mentoring, career function and role modeling were found to have a positive effect on both personal learning and self-efficacy, which are parameters, and job satisfaction, which is a dependent variable. On the other hand, psychological and social functions have a positive effect on personal learning, but they do not have an effect on self-efficacy and job satisfaction. In addition, as a result of analyzing the mediating effect, all mediating effects were confirmed for career functions, and only the mediating effect of self-efficacy was confirmed for role modeling. Through this study, mentoring is an important factor in promoting job satisfaction, personal learning and self-efficacy, and this study can be said to be academically and practically meaningful in that it confirmed personal learning and self-efficacy as factors that increase mentor's job satisfaction, and the focus of mentoring research was shifted from mentee to mentor to study the impact of mentoring on mentors.

• Journal of Practical Agriculture & Fisheries Research
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• v.23 no.2
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• pp.71-86
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• 2021

This study was conducted to find information on the university's image from words related to 'Korea National College of Agriculture and Fisheries (KNCAF)' in Naver News. For this purpose, word frequency analysis, TF-IDF evaluation and semantic network analysis were performed using web crawling technology. In word frequency analysis, 'agriculture', 'education', 'support', 'farmer', 'youth', 'university', 'business', 'rural', 'CEO' were important words. In the TF-IDF evaluation, the key words were 'farmer', 'dron', 'agricultural and livestock food department', 'Jeonbuk', 'young farmer', 'agriculture', 'Chonju', 'university', 'device', 'spreading'. In the semantic network analysis, the Bigrams showed high correlations in the order of 'youth' - 'farmer', 'digital' - 'agriculture', 'farming' - 'settlement', 'agriculture' - 'rural', 'digital' - 'turnover'. As a result of evaluating the importance of keywords as five central index, 'agriculture' ranked first. And the keywords in the second place of the centrality index were 'farmers' (Cc, Cb), 'education' (Cd, Cp) and 'future' (Ce). The sperman's rank correlation coefficient by centrality index showed the most similar rank between Degree centrality and Pagerank centrality. The KNCAF articles of Naver News were used as important words such as 'agriculture', 'education', 'support', 'farmer', 'youth' in terms of word frequency. However, in the evaluation including document frequency, the words such as 'farmer', 'dron', 'Ministry of Agriculture, Food and Rural Affairs', 'Jeonbuk', and 'young farmers' were found to be key words. The centrality analysis considering the network connectivity between words was suitable for evaluation by Cd and Cp. And the words with strong centrality were 'agriculture', 'education', 'future', 'farmer', 'digital', 'support', 'utilization'.

• Journal of Intelligence and Information Systems
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• v.26 no.1
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• pp.97-117
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• 2020

Product evaluation criteria is an indicator describing attributes or values of products, which enable users or manufacturers measure and understand the products. When companies analyze their products or compare them with competitors, appropriate criteria must be selected for objective evaluation. The criteria should show the features of products that consumers considered when they purchased, used and evaluated the products. However, current evaluation criteria do not reflect different consumers' opinion from product to product. Previous studies tried to used online reviews from e-commerce sites that reflect consumer opinions to extract the features and topics of products and use them as evaluation criteria. However, there is still a limit that they produce irrelevant criteria to products due to extracted or improper words are not refined. To overcome this limitation, this research suggests LDA-k-NN model which extracts possible criteria words from online reviews by using LDA and refines them with k-nearest neighbor. Proposed approach starts with preparation phase, which is constructed with 6 steps. At first, it collects review data from e-commerce websites. Most e-commerce websites classify their selling items by high-level, middle-level, and low-level categories. Review data for preparation phase are gathered from each middle-level category and collapsed later, which is to present single high-level category. Next, nouns, adjectives, adverbs, and verbs are extracted from reviews by getting part of speech information using morpheme analysis module. After preprocessing, words per each topic from review are shown with LDA and only nouns in topic words are chosen as potential words for criteria. Then, words are tagged based on possibility of criteria for each middle-level category. Next, every tagged word is vectorized by pre-trained word embedding model. Finally, k-nearest neighbor case-based approach is used to classify each word with tags. After setting up preparation phase, criteria extraction phase is conducted with low-level categories. This phase starts with crawling reviews in the corresponding low-level category. Same preprocessing as preparation phase is conducted using morpheme analysis module and LDA. Possible criteria words are extracted by getting nouns from the data and vectorized by pre-trained word embedding model. Finally, evaluation criteria are extracted by refining possible criteria words using k-nearest neighbor approach and reference proportion of each word in the words set. To evaluate the performance of the proposed model, an experiment was conducted with review on '11st', one of the biggest e-commerce companies in Korea. Review data were from 'Electronics/Digital' section, one of high-level categories in 11st. For performance evaluation of suggested model, three other models were used for comparing with the suggested model; actual criteria of 11st, a model that extracts nouns by morpheme analysis module and refines them according to word frequency, and a model that extracts nouns from LDA topics and refines them by word frequency. The performance evaluation was set to predict evaluation criteria of 10 low-level categories with the suggested model and 3 models above. Criteria words extracted from each model were combined into a single words set and it was used for survey questionnaires. In the survey, respondents chose every item they consider as appropriate criteria for each category. Each model got its score when chosen words were extracted from that model. The suggested model had higher scores than other models in 8 out of 10 low-level categories. By conducting paired t-tests on scores of each model, we confirmed that the suggested model shows better performance in 26 tests out of 30. In addition, the suggested model was the best model in terms of accuracy. This research proposes evaluation criteria extracting method that combines topic extraction using LDA and refinement with k-nearest neighbor approach. This method overcomes the limits of previous dictionary-based models and frequency-based refinement models. This study can contribute to improve review analysis for deriving business insights in e-commerce market.

• Journal of Intelligence and Information Systems
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• v.25 no.1
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• pp.109-125
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• 2019

As interest on intelligent search engines increases, various studies have been conducted to extract and utilize the features related to products intelligencely. In particular, when users search for goods in e-commerce search engines, the 'color' of a product is an important feature that describes the product. Therefore, it is necessary to deal with the synonyms of color terms in order to produce accurate results to user's color-related queries. Previous studies have suggested dictionary-based approach to process synonyms for color features. However, the dictionary-based approach has a limitation that it cannot handle unregistered color-related terms in user queries. In order to overcome the limitation of the conventional methods, this research proposes a model which extracts RGB values from an internet search engine in real time, and outputs similar color names based on designated color information. At first, a color term dictionary was constructed which includes color names and R, G, B values of each color from Korean color standard digital palette program and the Wikipedia color list for the basic color search. The dictionary has been made more robust by adding 138 color names converted from English color names to foreign words in Korean, and with corresponding RGB values. Therefore, the fininal color dictionary includes a total of 671 color names and corresponding RGB values. The method proposed in this research starts by searching for a specific color which a user searched for. Then, the presence of the searched color in the built-in color dictionary is checked. If there exists the color in the dictionary, the RGB values of the color in the dictioanry are used as reference values of the retrieved color. If the searched color does not exist in the dictionary, the top-5 Google image search results of the searched color are crawled and average RGB values are extracted in certain middle area of each image. To extract the RGB values in images, a variety of different ways was attempted since there are limits to simply obtain the average of the RGB values of the center area of images. As a result, clustering RGB values in image's certain area and making average value of the cluster with the highest density as the reference values showed the best performance. Based on the reference RGB values of the searched color, the RGB values of all the colors in the color dictionary constructed aforetime are compared. Then a color list is created with colors within the range of ${\pm}50$ for each R value, G value, and B value. Finally, using the Euclidean distance between the above results and the reference RGB values of the searched color, the color with the highest similarity from up to five colors becomes the final outcome. In order to evaluate the usefulness of the proposed method, we performed an experiment. In the experiment, 300 color names and corresponding color RGB values by the questionnaires were obtained. They are used to compare the RGB values obtained from four different methods including the proposed method. The average euclidean distance of CIE-Lab using our method was about 13.85, which showed a relatively low distance compared to 3088 for the case using synonym dictionary only and 30.38 for the case using the dictionary with Korean synonym website WordNet. The case which didn't use clustering method of the proposed method showed 13.88 of average euclidean distance, which implies the DBSCAN clustering of the proposed method can reduce the Euclidean distance. This research suggests a new color synonym processing method based on RGB values that combines the dictionary method with the real time synonym processing method for new color names. This method enables to get rid of the limit of the dictionary-based approach which is a conventional synonym processing method. This research can contribute to improve the intelligence of e-commerce search systems especially on the color searching feature.

• Journal of Intelligence and Information Systems
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• v.27 no.3
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• pp.231-252
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• 2021

Artificial intelligence (AI) is a key technology that will change the future the most. It affects the industry as a whole and daily life in various ways. As data availability increases, artificial intelligence finds an optimal solution and infers/predicts through self-learning. Research and investment related to automation that discovers and solves problems on its own are ongoing continuously. Automation of artificial intelligence has benefits such as cost reduction, minimization of human intervention and the difference of human capability. However, there are side effects, such as limiting the artificial intelligence's autonomy and erroneous results due to algorithmic bias. In the labor market, it raises the fear of job replacement. Prior studies on the utilization of artificial intelligence have shown that individuals do not necessarily use the information (or advice) it provides. Algorithm error is more sensitive than human error; so, people avoid algorithms after seeing errors, which is called "algorithm aversion." Recently, artificial intelligence has begun to be understood from the perspective of the augmentation of human intelligence. We have started to be interested in Human-AI collaboration rather than AI alone without human. A study of 1500 companies in various industries found that human-AI collaboration outperformed AI alone. In the medicine area, pathologist-deep learning collaboration dropped the pathologist cancer diagnosis error rate by 85%. Leading AI companies, such as IBM and Microsoft, are starting to adopt the direction of AI as augmented intelligence. Human-AI collaboration is emphasized in the decision-making process, because artificial intelligence is superior in analysis ability based on information. Intuition is a unique human capability so that human-AI collaboration can make optimal decisions. In an environment where change is getting faster and uncertainty increases, the need for artificial intelligence in decision-making will increase. In addition, active discussions are expected on approaches that utilize artificial intelligence for rational decision-making. This study investigates the impact of artificial intelligence on decision-making focuses on human-AI collaboration and the interaction between the decision maker personal traits and advisor type. The advisors were classified into three types: human, artificial intelligence, and human-AI collaboration. We investigated perceived usefulness of advice and the utilization of advice in decision making and whether the decision-maker's personal traits are influencing factors. Three hundred and eleven adult male and female experimenters conducted a task that predicts the age of faces in photos and the results showed that the advisor type does not directly affect the utilization of advice. The decision-maker utilizes it only when they believed advice can improve prediction performance. In the case of human-AI collaboration, decision-makers higher evaluated the perceived usefulness of advice, regardless of the decision maker's personal traits and the advice was more actively utilized. If the type of advisor was artificial intelligence alone, decision-makers who scored high in conscientiousness, high in extroversion, or low in neuroticism, high evaluated the perceived usefulness of the advice so they utilized advice actively. This study has academic significance in that it focuses on human-AI collaboration that the recent growing interest in artificial intelligence roles. It has expanded the relevant research area by considering the role of artificial intelligence as an advisor of decision-making and judgment research, and in aspects of practical significance, suggested views that companies should consider in order to enhance AI capability. To improve the effectiveness of AI-based systems, companies not only must introduce high-performance systems, but also need employees who properly understand digital information presented by AI, and can add non-digital information to make decisions. Moreover, to increase utilization in AI-based systems, task-oriented competencies, such as analytical skills and information technology capabilities, are important. in addition, it is expected that greater performance will be achieved if employee's personal traits are considered.

• 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.

• Proceedings of the CALSEC Conference
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• 1998.10b
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• pp.585-594
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• 1998

이미 전자상거래(EC)가 시간적ㆍ공간적 제약을 극복하고 국경을 초월한 새로운 교역시장 (Cyber Market)으로 등장하고 있으며 세계 자동차 산업은 표준부품의 공동개발 및 조달을 통해 중복투자 방지, 신차개발기간 단축 등 전략적 제휴를 통한 공조ㆍ공생체계 구축을 경쟁적으로 추진하고 있으나 국내 자동차업계는 제품개발, 부품조달, 판매 및 A/S 등 모든 부문을 독자적으로 해결함으로써 경쟁력 제고에 역행하는 경향이 있다. 또한 자동차 선진국과는 달리 국제 경쟁력 강화를 위한 CALS/EC 정보 기반 기술의 실질적인 활용이 미흡한 실정이다. 이러한 현실을 개선하기 위해 최근에 자동차공업협회(KAMA)와 현대, 대우, 기아 자동차 3사는 자동차 산업 CALS 추진 모델(Autopia)의 구축을 추진하고 있다. 추진 내용은 자동차 산업의 전체 Life-Cycle인 제품기획 단계부터 설계, 생산, 구매/조달, 고객지원 단계등 전 분야를 3개 부문(신차개발 프로세스, 구매조달 프로세스, 고객지원 서비스)으로 구분되어 있다. 신차개발 프로세스 부문은 차세대 PDM을 통하여 제품개발 사이클 단축을 추구하며 STEP을 통한 범용적 설계정보 교환 체계 구현이 기반이 된다. 또한 업무 흐름의 불투명성으로 인한 업무의 불균형 현상 타파와 설계 변경의 효율적 대응을 위하여 Workflow Management가 동시공학에 바탕을 두고 도입 적용되어야 한다. CAD 데이터를 비롯한 방대한 데이터의 효율적 관리를 위해서는 각 프로세스별로 독립된 정보를 체계적으로 관리할 수 있는 통합 환경(Integrated Data Environment)을 구성하여 각 프로세스에 걸쳐 데이터의 처리효율을 증대하여야 한다. 신차개발 부문의 핵심 기술이면서도 현업 적용이 초기 단계인 Digital Mockup과 Virtual Reality의 적용을 위해서는 3D 모델링이 기본 설계 방법으로 적용되어야 하며 이를 통한 어셈블리 및 부품구조의 관리가 이루어져야 한다. 구매조달 프로세스 부문은 자동차 업계의 공통 EDI/EC 네트워크 구축을 통한 경제적인 인프라 구조와 함께 부품 조달 체계의 간소화를 추구함으로써 자동차 산업의 대외 경쟁력 강화가 이루어 질 수 있다. 공개구매 시스템의 구축을 통하여 완성차별로 전속 계열화된 수직적인 부품조달 체계와 업체간 정보공유의 폐쇄성을 제거할 수 있고 완전 경쟁에 의한 우량 협력업체 발굴 기회의 확대가 용이하다. 이를 통하여 궁극적으로는 Global Vendor망의 구축이 실현될 것이다. 종합물류 시스템이 구현되면 판매는 경쟁체제, 물류는 공동화가 됨으로써 국가적으로 물류 비용의 절감이 엄청날 것으로 예상된다. 전국에 산재되어 있는 1,000여개의 대리점과 7,000여개의 정비업소를 대상으로 한 정비부품 EDI/EC 시스템이 구축되면 고객 서비스의 효율 향상과 함께 정비업소의 물류 및 재고 비용의 감소, 조달 속도의 향상, 조달 업무의 간소화 등의 효과를 보게 될 것이다. 고객지원 서비스는 정비정보 시스템, 산업정보 시스템, 쇼핑몰 시스템, 등록대행 시스템등을 통하여 일반 국민들이 피부로 느낄 수 있는 시스템으로 구축 되어야 할 것이다.

• The Journal of the Korea Contents Association
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• v.20 no.3
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• pp.542-559
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• 2020

This study was conducted to derive theoretical and practical implications in situations where innovation of the business is desperate in the face of the emergence of agile organizations and digital transformation. To do so, we tried to verify the correlation between negative feedback-seeking behavior and innovative behavior and whether the learning goal orientation of these two variables has a moderated mediating effect by coaching leadership. It analyzed the collected questionnaire from 381 members working in domestic companies; SPSS 25.0, AMOS 25.0, and Process Macro 3.0 were used. The analysis result showed that the negative feedback seeking behavior had a positive effect on the learning goal orientation, and the leader's coaching leadership found to have a moderating effect between the negative feedback seeking behavior and the learning goal orientation. Learning goal orientation has been found to have a moderated mediating effect between negative feedback seeking behavior and innovative behavior. This study is significant in the sense that it reveals the process of how members seeking negative feedback in the organization could be led to innovative behavior and shows the necessity of organizational support for coaching leadership for the vitalization of innovative behavior.

• The Journal of Korean Academy of Prosthodontics
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• v.41 no.4
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• pp.503-518
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• 2003

Statement of problem : There have been previous studies about considerable variations in machining accuracy and consistency in the implant-abutment-screw interfaces. Purpose : The purpose of this study was to evaluate the machining accuracy and consistency of implant/abutment/screw combinations on two randomly selected implants from each of four manufactures. Material and methods : In this study, screws were respectively used to secure a cemented abutment, to a hexlock implant fixture ; teflon coated titanium alloy screw(Torq-Tite) and titanium alloy screw in Steri--Oss system, gold-plated gold-palladium alloy screw(Gold-Tite) and titanium alloy screw in 3i system gild screw ana titanium screw in AVANA Dental Implant system, and titanium screws in Paragon System. The implants were perpendicularly mounted in polymethyl methacrylate autopolymerizing acrylic resin block(Orthodontic resin, Densply International Inc. USA) by use of dental surveyer. Each abutment screw was secured to the implant with recommended torque value using a digital torque controller. Each screw was again tightened after 10 minutes. All samples were cross sectioned with grinder-polisher unit(Omnilap 2000 SBT Inc) after embeded in liquid unsaturated polyester (Epovia, Cray Valley Inc) Results : There were the largest gaps in the neck areas of screws in hexagonal extension implants which were examined in this study. The leading edge of the abutment screw thread (superior surface) was in contact with the implant body thread, and the majority of the contacting surfaces were localized to the middle portion of the mating threads. Considerable variation in the contacting surfaces was noted in the samples evaluated. Amounts of contact in the abutment screw thread were larger for assemblies with Gold-Tite screw, gold alloy screw. Torq-Tite screw than those with titanium screws. The findings of intimate contact between the screw and screw seat were seen in all samples, regardless of manufacturers. However, microgap between the head and lateral neck surface of the screw and the abutment could be dectected in all samples. The findings of intimate contact between the platform of the implant and the bottom of the abutment were consistent in all samples, regardless of manufacturers. However, microgaps between the lateral surface of external hex of the fixture and the abutment could be dectected in all samples. Conclusion : Considerable variations in machining accuracy and consistency were noted in the samples and the implant-abutment-screw interfaces were incomplete. From the results of this study, further development of the system will be required, including improvements in pattern design.