• Title/Summary/Keyword: 개인적 신뢰

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The Study on The Cyber Communities of Migrant Workers in Korea (한국 이주 노동자의 '사이버 공동체'에 관한 연구)

  • Lee, Jeong Hyang;Kim, Yeong Kyeong
    • Journal of the Korean association of regional geographers
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    • v.19 no.2
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    • pp.324-339
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    • 2013
  • This study aims to investigate the characteristics of cyber communities composed of migrant workers from communities without propinquity in Korea. Its methods are both qualitative and quantitative. It further seeks to discover the relationship between the social capital formed and reproduced within these cyber communities and participants' cultural adaptation to Korean society. The study revealed that ethnic and non-ethnic communities differed in terms of strength of cohesion, space constraints, and links with the outside world. The former showed characteristics of a localized community type. The main motivations for migrant workers' participation in the ethnic cyber community were communication and friendship rather than cooperation and sharing among members. They usually used cyber communication media to communicate with one another. Conversely, the latter showed characteristics of an integrative type. Despite the difficulties in applying for membership and information provided in Korean, a high percentage of migrant workers participated in the community to obtain crucial information. The results did not show a significant correlation between social capital and migrant workers' traits within the cyber community, while a strong correlation emerged among four factors of social capital: faith, norms, networking, and political participation. The study showed that social capital in the cyber community was in direct proportion to an integrative type of cultural adaptation to Korean society. In particular, there was a strong connection between the cultural adaptation exhibited by members of the migrant subculture and their participation in discussions on political issues and human rights, with some migrants even functioning as agents of social change as participants in citizens' movements. The findings suggest that the cyber community facilitates the migrant subculture's communication with and integration into the indigenous Korean culture. Migrant workers' participation in the cyber community is therefore validated as an instrumental practice for members of this subculture to adapt to Korean society.

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A Study on the Implications of Korea Through the Policy Analysis of AI Start-up Companies in Major Countries (주요국 AI 창업기업 정책 분석을 통한 국내 시사점 연구)

  • Kim, Dong Jin;Lee, Seong Yeob
    • Asia-Pacific Journal of Business Venturing and Entrepreneurship
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    • v.19 no.2
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    • pp.215-235
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    • 2024
  • As artificial intelligence (AI) technology is recognized as a key technology that will determine future national competitiveness, competition for AI technology and industry promotion policies in major countries is intensifying. This study aims to present implications for domestic policy making by analyzing the policies of major countries on the start-up of AI companies, which are the basis of the AI industry ecosystem. The top four countries and the EU for the number of new investment attraction companies in the 2023 AI Index announced by the HAI Research Institute at Stanford University in the United States were selected, The United States enacted the National AI Initiative Act (NAIIA) in 2021. Through this law, The US Government is promoting continued leadership in the United States in AI R&D, developing reliable AI systems in the public and private sectors, building an AI system ecosystem across society, and strengthening DB management and access to AI policies conducted by all federal agencies. In the 14th Five-Year (2021-2025) Plan and 2035 Long-term Goals held in 2021, China has specified AI as the first of the seven strategic high-tech technologies, and is developing policies aimed at becoming the No. 1 AI global powerhouse by 2030. The UK is investing in innovative R&D companies through the 'Future Fund Breakthrough' in 2021, and is expanding related investments by preparing national strategies to leap forward as AI leaders, such as the implementation plan of the national AI strategy in 2022. Israel is supporting technology investment in start-up companies centered on the Innovation Agency, and the Innovation Agency is leading mid- to long-term investments of 2 to 15 years and regulatory reforms for new technologies. The EU is strengthening its digital innovation hub network and creating the InvestEU (European Strategic Investment Fund) and AI investment fund to support the use of AI by SMEs. This study aims to contribute to analyzing the policies of major foreign countries in making AI company start-up policies and providing a basis for Korea's strategy search. The limitations of the study are the limitations of the countries to be analyzed and the failure to attempt comparative analysis of the policy environments of the countries under the same conditions.

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Incorporating Social Relationship discovered from User's Behavior into Collaborative Filtering (사용자 행동 기반의 사회적 관계를 결합한 사용자 협업적 여과 방법)

  • Thay, Setha;Ha, Inay;Jo, Geun-Sik
    • Journal of Intelligence and Information Systems
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    • v.19 no.2
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    • pp.1-20
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    • 2013
  • Nowadays, social network is a huge communication platform for providing people to connect with one another and to bring users together to share common interests, experiences, and their daily activities. Users spend hours per day in maintaining personal information and interacting with other people via posting, commenting, messaging, games, social events, and applications. Due to the growth of user's distributed information in social network, there is a great potential to utilize the social data to enhance the quality of recommender system. There are some researches focusing on social network analysis that investigate how social network can be used in recommendation domain. Among these researches, we are interested in taking advantages of the interaction between a user and others in social network that can be determined and known as social relationship. Furthermore, mostly user's decisions before purchasing some products depend on suggestion of people who have either the same preferences or closer relationship. For this reason, we believe that user's relationship in social network can provide an effective way to increase the quality in prediction user's interests of recommender system. Therefore, social relationship between users encountered from social network is a common factor to improve the way of predicting user's preferences in the conventional approach. Recommender system is dramatically increasing in popularity and currently being used by many e-commerce sites such as Amazon.com, Last.fm, eBay.com, etc. Collaborative filtering (CF) method is one of the essential and powerful techniques in recommender system for suggesting the appropriate items to user by learning user's preferences. CF method focuses on user data and generates automatic prediction about user's interests by gathering information from users who share similar background and preferences. Specifically, the intension of CF method is to find users who have similar preferences and to suggest target user items that were mostly preferred by those nearest neighbor users. There are two basic units that need to be considered by CF method, the user and the item. Each user needs to provide his rating value on items i.e. movies, products, books, etc to indicate their interests on those items. In addition, CF uses the user-rating matrix to find a group of users who have similar rating with target user. Then, it predicts unknown rating value for items that target user has not rated. Currently, CF has been successfully implemented in both information filtering and e-commerce applications. However, it remains some important challenges such as cold start, data sparsity, and scalability reflected on quality and accuracy of prediction. In order to overcome these challenges, many researchers have proposed various kinds of CF method such as hybrid CF, trust-based CF, social network-based CF, etc. In the purpose of improving the recommendation performance and prediction accuracy of standard CF, in this paper we propose a method which integrates traditional CF technique with social relationship between users discovered from user's behavior in social network i.e. Facebook. We identify user's relationship from behavior of user such as posts and comments interacted with friends in Facebook. We believe that social relationship implicitly inferred from user's behavior can be likely applied to compensate the limitation of conventional approach. Therefore, we extract posts and comments of each user by using Facebook Graph API and calculate feature score among each term to obtain feature vector for computing similarity of user. Then, we combine the result with similarity value computed using traditional CF technique. Finally, our system provides a list of recommended items according to neighbor users who have the biggest total similarity value to the target user. In order to verify and evaluate our proposed method we have performed an experiment on data collected from our Movies Rating System. Prediction accuracy evaluation is conducted to demonstrate how much our algorithm gives the correctness of recommendation to user in terms of MAE. Then, the evaluation of performance is made to show the effectiveness of our method in terms of precision, recall, and F1-measure. Evaluation on coverage is also included in our experiment to see the ability of generating recommendation. The experimental results show that our proposed method outperform and more accurate in suggesting items to users with better performance. The effectiveness of user's behavior in social network particularly shows the significant improvement by up to 6% on recommendation accuracy. Moreover, experiment of recommendation performance shows that incorporating social relationship observed from user's behavior into CF is beneficial and useful to generate recommendation with 7% improvement of performance compared with benchmark methods. Finally, we confirm that interaction between users in social network is able to enhance the accuracy and give better recommendation in conventional approach.

A Study on the Market Structure Analysis for Durable Goods Using Consideration Set:An Exploratory Approach for Automotive Market (고려상표군을 이용한 내구재 시장구조 분석에 관한 연구: 자동차 시장에 대한 탐색적 분석방법)

  • Lee, Seokoo
    • Asia Marketing Journal
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    • v.14 no.2
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    • pp.157-176
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    • 2012
  • Brand switching data frequently used in market structure analysis is adequate to analyze non- durable goods, because it can capture competition between specific two brands. But brand switching data sometimes can not be used to analyze goods like automobiles having long term duration because one of main assumptions that consumer preference toward brand attributes is not changed against time can be violated. Therefore a new type of data which can precisely capture competition among durable goods is needed. Another problem of using brand switching data collected from actual purchase behavior is short of explanation why consumers consider different set of brands. Considering above problems, main purpose of this study is to analyze market structure for durable goods with consideration set. The author uses exploratory approach and latent class clustering to identify market structure based on heterogeneous consideration set among consumers. Then the relationship between some factors and consideration set formation is analyzed. Some benefits and two demographic variables - age and income - are selected as factors based on consumer behavior theory. The author analyzed USA automotive market with top 11 brands using exploratory approach and latent class clustering. 2,500 respondents are randomly selected from the total sample and used for analysis. Six models concerning market structure are established to test. Model 1 means non-structured market and model 6 means market structure composed of six sub-markets. It is exploratory approach because any hypothetical market structure is not defined. The result showed that model 1 is insufficient to fit data. It implies that USA automotive market is a structured market. Model 3 with three market structures is significant and identified as the optimal market structure in USA automotive market. Three sub markets are named as USA brands, Asian Brands, and European Brands. And it implies that country of origin effect may exist in USA automotive market. Comparison between modal classification by derived market structures and probabilistic classification by research model was conducted to test how model 3 can correctly classify respondents. The model classify 97% of respondents exactly. The result of this study is different from those of previous research. Previous research used confirmatory approach. Car type and price were chosen as criteria for market structuring and car type-price structure was revealed as the optimal structure for USA automotive market. But this research used exploratory approach without hypothetical market structures. It is not concluded yet which approach is superior. For confirmatory approach, hypothetical market structures should be established exhaustively, because the optimal market structure is selected among hypothetical structures. On the other hand, exploratory approach has a potential problem that validity for derived optimal market structure is somewhat difficult to verify. There also exist market boundary difference between this research and previous research. While previous research analyzed seven car brands, this research analyzed eleven car brands. Both researches seemed to represent entire car market, because cumulative market shares for analyzed brands exceeds 50%. But market boundary difference might affect the different results. Though both researches showed different results, it is obvious that country of origin effect among brands should be considered as important criteria to analyze USA automotive market structure. This research tried to explain heterogeneity of consideration sets among consumers using benefits and two demographic factors, sex and income. Benefit works as a key variable for consumer decision process, and also works as an important criterion in market segmentation. Three factors - trust/safety, image/fun to drive, and economy - are identified among nine benefit related measure. Then the relationship between market structures and independent variables is analyzed using multinomial regression. Independent variables are three benefit factors and two demographic factors. The result showed that all independent variables can be used to explain why there exist different market structures in USA automotive market. For example, a male consumer who perceives all benefits important and has lower income tends to consider domestic brands more than European brands. And the result also showed benefits, sex, and income have an effect to consideration set formation. Though it is generally perceived that a consumer who has higher income is likely to purchase a high priced car, it is notable that American consumers perceived benefits of domestic brands much positive regardless of income. Male consumers especially showed higher loyalty for domestic brands. Managerial implications of this research are as follow. Though implication may be confined to the USA automotive market, the effect of sex on automotive buying behavior should be analyzed. The automotive market is traditionally conceived as male consumers oriented market. But the proportion of female consumers has grown over the years in the automotive market. It is natural outcome that Volvo and Hyundai motors recently developed new cars which are targeted for women market. Secondly, the model used in this research can be applied easier than that of previous researches. Exploratory approach has many advantages except difficulty to apply for practice, because it tends to accompany with complicated model and to require various types of data. The data needed for the model in this research are a few items such as purchased brands, consideration set, some benefits, and some demographic factors and easy to collect from consumers.

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A study on the use of a Business Intelligence system : the role of explanations (비즈니스 인텔리전스 시스템의 활용 방안에 관한 연구: 설명 기능을 중심으로)

  • Kwon, YoungOk
    • Journal of Intelligence and Information Systems
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    • v.20 no.4
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    • pp.155-169
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    • 2014
  • With the rapid advances in technologies, organizations are more likely to depend on information systems in their decision-making processes. Business Intelligence (BI) systems, in particular, have become a mainstay in dealing with complex problems in an organization, partly because a variety of advanced computational methods from statistics, machine learning, and artificial intelligence can be applied to solve business problems such as demand forecasting. In addition to the ability to analyze past and present trends, these predictive analytics capabilities provide huge value to an organization's ability to respond to change in markets, business risks, and customer trends. While the performance effects of BI system use in organization settings have been studied, it has been little discussed on the use of predictive analytics technologies embedded in BI systems for forecasting tasks. Thus, this study aims to find important factors that can help to take advantage of the benefits of advanced technologies of a BI system. More generally, a BI system can be viewed as an advisor, defined as the one that formulates judgments or recommends alternatives and communicates these to the person in the role of the judge, and the information generated by the BI system as advice that a decision maker (judge) can follow. Thus, we refer to the findings from the advice-giving and advice-taking literature, focusing on the role of explanations of the system in users' advice taking. It has been shown that advice discounting could occur when an advisor's reasoning or evidence justifying the advisor's decision is not available. However, the majority of current BI systems merely provide a number, which may influence decision makers in accepting the advice and inferring the quality of advice. We in this study explore the following key factors that can influence users' advice taking within the setting of a BI system: explanations on how the box-office grosses are predicted, types of advisor, i.e., system (data mining technique) or human-based business advice mechanisms such as prediction markets (aggregated human advice) and human advisors (individual human expert advice), users' evaluations of the provided advice, and individual differences in decision-makers. Each subject performs the following four tasks, by going through a series of display screens on the computer. First, given the information of the given movie such as director and genre, the subjects are asked to predict the opening weekend box office of the movie. Second, in light of the information generated by an advisor, the subjects are asked to adjust their original predictions, if they desire to do so. Third, they are asked to evaluate the value of the given information (e.g., perceived usefulness, trust, satisfaction). Lastly, a short survey is conducted to identify individual differences that may affect advice-taking. The results from the experiment show that subjects are more likely to follow system-generated advice than human advice when the advice is provided with an explanation. When the subjects as system users think the information provided by the system is useful, they are also more likely to take the advice. In addition, individual differences affect advice-taking. The subjects with more expertise on advisors or that tend to agree with others adjust their predictions, following the advice. On the other hand, the subjects with more knowledge on movies are less affected by the advice and their final decisions are close to their original predictions. The advances in predictive analytics of a BI system demonstrate a great potential to support increasingly complex business decisions. This study shows how the designs of a BI system can play a role in influencing users' acceptance of the system-generated advice, and the findings provide valuable insights on how to leverage the advanced predictive analytics of the BI system in an organization's forecasting practices.

A Legal Study on the Certificate System for Light Sports Aircraft Repairman (경량항공기 정비사 자격증명제도에 관한 법적 고찰)

  • Kim, Woong-Yi;Shin, Dai-Won;Lee, Gi-Myung
    • The Korean Journal of Air & Space Law and Policy
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    • v.33 no.1
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    • pp.175-204
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    • 2018
  • Recently, the aviation leisure business has been legislated, and related industries have become active base with increasing the light sports aircraft within the legislation system. However, in the light sports aircraft safety problem, it is often mentioned that the flight is in violation of the regulations, the lack of safety consciousness of the operator and lack of ability, and the personal operators have a risk of accident of light aircraft such as insufficient safety management and poor maintenance. At present, the maintenance of light sports aircraft is carried out by the A & P mechanic in accordance with the relevant laws and regulations, but it is difficult to say that it is equipped with qualification and expertise. It is not a legal issue to undertake light sports aircraft maintenance work on the regulation system. However, the problem of reliability and appropriateness is constantly being raised because airplanes, light sports aircraft, and ultra-light vehicle are classified and serviced in a legal method. Although legal and institutional frameworks for light sports aircraft are separated, much of it is stipulated in the aviation law provisions. Light sports aircraft maintenance work also follows the current aircraft maintenance system. In the United States, Europe, and Australia where General Aviation developed, legal and institutional devices related to maintenance of light aircraft were introduced, and specialized maintenance tasks are covered in the light aircraft mechanics system. As a result of analysis of domestic and foreign laws and regulations, it is necessary to introduce the qualification system for maintenance of light aircraft. In advanced aviation countries such as the United States, Europe, and Australia, a light sports aircraft repairman system is installed to perform safety management. This is to cope with changes in the operating environment of the new light sports aircraft. This study does not suggest the need for a light aircraft repairman system. From the viewpoint of the legal system, the examination of the relevant laws and regulations revealed that the supplementary part of the system is necessary. It is also require that the necessity of introduction is raised in comparison with overseas cases. Based on these results, it is necessary to introduce the system into the light aircraft repairman system, and suggestions for how to improve it are suggested.

A Study on Improvement of Collaborative Filtering Based on Implicit User Feedback Using RFM Multidimensional Analysis (RFM 다차원 분석 기법을 활용한 암시적 사용자 피드백 기반 협업 필터링 개선 연구)

  • Lee, Jae-Seong;Kim, Jaeyoung;Kang, Byeongwook
    • Journal of Intelligence and Information Systems
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    • v.25 no.1
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    • pp.139-161
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    • 2019
  • The utilization of the e-commerce market has become a common life style in today. It has become important part to know where and how to make reasonable purchases of good quality products for customers. This change in purchase psychology tends to make it difficult for customers to make purchasing decisions in vast amounts of information. In this case, the recommendation system has the effect of reducing the cost of information retrieval and improving the satisfaction by analyzing the purchasing behavior of the customer. Amazon and Netflix are considered to be the well-known examples of sales marketing using the recommendation system. In the case of Amazon, 60% of the recommendation is made by purchasing goods, and 35% of the sales increase was achieved. Netflix, on the other hand, found that 75% of movie recommendations were made using services. This personalization technique is considered to be one of the key strategies for one-to-one marketing that can be useful in online markets where salespeople do not exist. Recommendation techniques that are mainly used in recommendation systems today include collaborative filtering and content-based filtering. Furthermore, hybrid techniques and association rules that use these techniques in combination are also being used in various fields. Of these, collaborative filtering recommendation techniques are the most popular today. Collaborative filtering is a method of recommending products preferred by neighbors who have similar preferences or purchasing behavior, based on the assumption that users who have exhibited similar tendencies in purchasing or evaluating products in the past will have a similar tendency to other products. However, most of the existed systems are recommended only within the same category of products such as books and movies. This is because the recommendation system estimates the purchase satisfaction about new item which have never been bought yet using customer's purchase rating points of a similar commodity based on the transaction data. In addition, there is a problem about the reliability of purchase ratings used in the recommendation system. Reliability of customer purchase ratings is causing serious problems. In particular, 'Compensatory Review' refers to the intentional manipulation of a customer purchase rating by a company intervention. In fact, Amazon has been hard-pressed for these "compassionate reviews" since 2016 and has worked hard to reduce false information and increase credibility. The survey showed that the average rating for products with 'Compensated Review' was higher than those without 'Compensation Review'. And it turns out that 'Compensatory Review' is about 12 times less likely to give the lowest rating, and about 4 times less likely to leave a critical opinion. As such, customer purchase ratings are full of various noises. This problem is directly related to the performance of recommendation systems aimed at maximizing profits by attracting highly satisfied customers in most e-commerce transactions. In this study, we propose the possibility of using new indicators that can objectively substitute existing customer 's purchase ratings by using RFM multi-dimensional analysis technique to solve a series of problems. RFM multi-dimensional analysis technique is the most widely used analytical method in customer relationship management marketing(CRM), and is a data analysis method for selecting customers who are likely to purchase goods. As a result of verifying the actual purchase history data using the relevant index, the accuracy was as high as about 55%. This is a result of recommending a total of 4,386 different types of products that have never been bought before, thus the verification result means relatively high accuracy and utilization value. And this study suggests the possibility of general recommendation system that can be applied to various offline product data. If additional data is acquired in the future, the accuracy of the proposed recommendation system can be improved.

Re-validation of the Revised Systems Thinking Measuring Instrument for Vietnamese High School Students and Comparison of Latent Means between Korean and Vietnamese High School Students (베트남 고등학생을 대상으로 한 개정 시스템 사고 검사 도구 재타당화 및 한국과 베트남 고등학생의 잠재 평균 비교)

  • Hyonyong Lee;Nguyen Thi Thuy;Byung-Yeol Park;Jaedon Jeon;Hyundong Lee
    • Journal of the Korean earth science society
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    • v.45 no.2
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    • pp.157-171
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    • 2024
  • The purposes of this study were: (1) to revalidate the revised Systems Thinking Measuring Instrument (Re_STMI) reported by Lee et al. (2024) among Vietnamese high school students and (2) to investigate the differences in systems thinking abilities between Korean and Vietnamese high school students. To achieve this, data from 234 Vietnamese high school students who responded to translated Re_STMI consisting of 20 items and an Scale consisting of 20 items were used. Validity analysis was conducted through item response analysis (Item Reliability, Item Map, Infit and Outfit MNSQ, DIF between male and female) and exploratory factor analysis (principal axis factor analysis using Promax). Furthermore, structural equation modeling was employed with data from 475 Korean high school students to verify the latent mean analysis. The results were as follows: First, in the item response analysis of the 20 translated Re_STMI items in Vietnamese, the Item Reliability was .97, and the Infit MNSQ ranged from .67 to 1.38. The results from the Item Map and DIF analysis align with previous findings. In the exploratory factor analysis, all items were loaded onto intended sub-factors, with sub-factor reliabilities ranging from .662 to .833 and total reliability at .876. Confirmatory factor analysis for latent mean analysis between Korean and Vietnamese students yielded acceptable model fit indices (χ2/df: 2.830, CFI: .931, TLI: .918, SRMR: .043, RMSEA: .051). Lastly, the latent mean analysis between Korean and Vietnamese students revealed a small effect size in systems analysis, mental models, team learning, and shared vision factors, whereas a medium effect size was observed in personal mastery factors, with Vietnamese high school students showing significantly higher results in systems thinking. This study confirmed the reliability and validity of the Re_STMI items. Furthermore, international comparative studies on systems thinking using Re_STMI translated into Vietnamese, English, and other languages are warranted in the context of students' systems thinking analysis.

The Market Segmentation of Coffee Shops and the Difference Analysis of Consumer Behavior: A Case based on Caffe Bene (커피전문점의 시장세분화와 소비자행동 차이 분석 : 카페베네 사례를 중심으로)

  • Yu, Jong-Pil;Yoon, Nam-Soo
    • Journal of Distribution Science
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    • v.9 no.4
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    • pp.5-13
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    • 2011
  • This study provides analysis of the effectiveness of domestic marketing strategies of the Korean coffee shop "Caffe Bene". It bases its evaluation on statistical outputs of 'choice attributes,' "market segmentation," demographic characteristics," and "satisfaction differences." The results are summarized in four points. First, five choice attributes were extracted from factor analysis: price, atmosphere, comfort, taste, and location; these are related to coffee shop selection behavior. Based on these five factors, cluster analysis was conducted, with statistical results classifying customers into three major groups: atmosphere oriented; comfort oriented; and taste oriented. Second, discriminant analysis tested cluster analysis and showed two discriminant functions: location and atmosphere. Third, cross-tabulation analysis based on demographic characteristics showed distinctive demographic characteristics within the three groups. Atmosphere oriented group, early-20s, as women of all ages was found to be 'walking down the street 'and 'through acquaintances' in many cases, as the cognitive path, and mostly found the store through 'outdoor advertising', and 'introduction'. Comfort oriented group was mainly women who are students in their early twenties or professionals, and appeared as a group to be very loyal because of high recommendation to other customers compared to other groups. Taste oriented group, unlike the other group, was mainly late-20s' college graduates, and was confirmed, as low loyalty, with lower recommendation activity. Fourth, to analyze satisfaction differences, one-way ANOVA was conducted. It shows that groups which show high satisfaction in the five main factors also show high menu satisfaction and high overall satisfaction. This results show that segmented marketing strategies are necessary because customers are considering price, atmosphere, comfort, taste, location when they choose coffee shop and demographics show different attributes based on segmented groups. For example, atmosphere oriented group is satisfied with shop interior and comfort while dissatisfied with price because most of the customers in this group are early 20s and do not have great financial capability. Thus, price discounting marketing strategies based on individual situations through CRM system is critical. Comfort oriented group shows high satisfaction level about location and shop comfort. Also, in this group, there are many early 20s female customers, students, and self-employed people. This group customers show high word of mouth tendency, hence providing positive brand image to the customers would be important. In case of taste oriented group, while the scores of taste and location are high, word of mouth score is low. This group is mainly composed of educated and professional many late 20s customers, therefore, menu differentiation, increasing quality of coffee taste and price discrimination is critical to increase customers' satisfaction. However, it is hard to generalize the results of study to other coffee shop brand, because this study have researched only one domestic coffee shop, Caffe Bene. Thus if future study expand the scope of locations, brands, and occupations, the results of the study would provide more generalizable results. Finally, research of customer satisfactions of menu, trust, loyalty, and switching cost would be critical in the future study.

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Preparation of Students for Future Challenge (미래의 요구에 부응하는 미래를 위한 간호교육)

  • Kim Euisook
    • The Korean Nurse
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    • v.20 no.4 s.112
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    • pp.50-59
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    • 1981
  • 간호학생들이 당연하고 있는 문제점 미래의 간호학생들이 교육문제를 논하기 위하여는 간호학생들이 가지고 있는 문제점을 파악하고 또 이해하는 것이 우선순위가 될 것이다. 간호학생들이 문제점에 대한 연구는 한국에서 뿐아니라 미국에서도 꽤 많이 시행되어져 왔으며 특히 간호학과정에서 중간 탈락되는 중퇴자들에 대한 연구들 중에 이러한 문제점에 대해서 언급한 것이 많다. 고등학교를 졸업하고 곧 대학과정에 진학한 학생들을 대상으로 조사 보고될 Munro의 자료에 의하면 전문대학과정에서 27%, 대학과정에서는 41%의 간호학생들이 간호학과정에서 중간 탈락하고 있음이 보고되고 있다. 이들이 중간탈락하는 데에는 여러 가지 이유가 있으나 그 중 ''간호학에 흥미를 잃어서''가 가장 큰 이유로 보고되고 있다. 이곳 한국사회에서도 역시 비슷한 현상을 보이고 있다. 그러나 대학입시경쟁과 대학내에서의 전과가 거의 허용되지 않는 특수여건이기 때문에 학교를 중간 탈락하는 율은 미국이 보고만큼 높지는 않으나 역시 ''간호학에 흥미를 잃는다''는 것이 간호학생들의 가장 큰 문제점으로 대두되고 있다. 최근 한국에서 시행된 간호학생들에 관한 연구(표 1 참조)에 의하면 간호학생들의 학문에 대한 만족도는 조사자의 35\~50%정도에 불과하였고 더우기 이 비율은 고학년에 올라갈수록 더욱 감소되고 있는 경향을 보이고 있다. 한국에서 시행된 어느 연구보고에 의하면 간호학에 실망했다고 생각하는 학생이 전체의 67%였으며, 다른 학교로 전과를 희망한 경험이 있다는 학생이 71%나 되는 것으로 보고되고 있다. 그러나 왜 흥미를 잃게 되는지 그 이유에 대하여 설명해 주는 연구는 많지 않았다. 미국의 한 저자는 간호학생들이 간호학에 흥미를 잃게 되는 원인을 간호원의 역할에 대한 이해가 정확하지 못한 것과 졸업 후 진로기회에 대한 인식부족 때문이라고 추측하고 있다. 간호학에 흥미를 잃게 되는 이유는 크게 다음의 세 가지로 분류 요약될 수 있다. 첫째, 간호학을 전공으로 택한 동기이다. 간호학의 특수성으로 인하여 학생들이 간호학을 전공으로 택한 동기도 다른 전공분야보다는 훨씬 다른 여러 종류를 보이고 있다. 즉, 종교적 이유, 다른 사람들에게 봉사할 수 있는 직업이기 때문에, 쉽게 취업을 할 수 있어서, 결혼 후에도 직업을 가질 수 있기 때문에, 외국으로 쉽게 취업할 수 있어서 등이 간호학을 선택한 이유로 보고되고 있다. 흥미나 적성에 맞다고 생각하기 때문에 간호학을 택한 학생의 수는 다른 과에 비하여 훨씬 적다. 이러한 흥미나 적성 때문이 아닌 여러 가지 다른 이유들로 인하여 간호학을 택한 경우에 특히 간호학에 쉽게 흥미를 잃어버리는 것을 볼 수 있다. 간호학에 현실적인 개념을 가지고 있는 학생들일수록 추상적이고 현실적인 개념을 가지고 있는 학생들보다 더 간호학에 지속적인 흥미를 가지며 중간에 탈락하는 율이 훨씬 적다는 것이 많은 연구에서 보고되었다. 또한 흥미나 적성 때문에 간호학을 택하였다는 학생들이 다른 과로 전과를 희망하는 율이 낮다는 것도 보고되었다. 둘째, 교과내용자체나 실습에 대한 불만족이다. 간호학에 대한 체계적인 교과내용의 결여, 과중한 과제물, 임상실습에서의 욕구불만, 실습으로 인한 부담, 지식과 실습의 차이점에 대한 갈등 등이 주요 이유로 보고되고 있다. 대부분의 연구들이 이 교과목이나 실습에 대한 불만족, 특히 실습경험에서의 갈등을 학생들이 흥미를 잃는 가장 중요한 요인이 되는 것으로 보고하고 있다. 어느 한 연구에서는 응답자의 90%가 임상실습에 만족하지 못한다고 응답하였으며 그들 중의 88%가 실습감독에 문제가 있다고 생각한다고 보고하였다. 셋째, 교수들에 대한 불만족이다. 대부분의 연구들이 학년이 올라가면 갈수록 교수에 대한 신뢰도가 낮아지며 또한 그에 비례하여 간호학에 대한 만족도가 낮아진다고 보고하고 있다. 교육내용에 대한 전문지식의 결여, 학생들과의 인간적인 관계의 결여, 교수법에 대한 불만족 등이 교수에 대한 불만의 주요내용으로 보고되었다. 미래의 간호에 부응할 학생교육 계속적인 사회적 변동과 더불어 급격하게 변화하고 있는 일반인들의 건강에 대한 요구도와 앞에서 기술한 문제점 등을 감안할 때 학생들에게 동기를 부여하고 간호학에 확신감을 가질 수 있도록 준비시키므로써 간호환경에서 실망하기보다는 오히려 그것을 받아들여 변화하는 사회요구에 책임감을 느낄 수 있도록 교육시키는 것이 미래의 간호학생을 준비시키는데 가장 중요한 요인이라고 할 수 있겠다. 이러한 교육을 위하여 다음의 두가지 안을 제시한다. 1. 교수와 학생간의 관계-서로의 좋은 동반자 : 교수들이 학생에게 미치는 영향, 특히 학생들의 성취도에 대한 영향에 대하여는 이미 많은 연구가 시행되었다. Tetreault(1976)가 간호학생들의 전문의식에 영향을 미치는 요인에 대하여 연구한 바에 의하면 다른 어느 것보다도 교수의 전문의식여부가 학생들의 전문의식 조성에 가장 큰 영향을 미친다고 하였다. 또한 학생들이 교수에게 신뢰감을 가지고 있을때, 교수들이 전문가로서의 행동을 하는 것을 보았을때 비로서 배움이 증가된다고 하였다. Banduras는 엄격하고 무서운 교수보다는 따뜻하고 인간적인 교수에게 학생들이 더 Role Model로서 모방하려는 경향을 나타낸다고 보고 하였다. 그러면 어떻게 학생에게 신뢰받는 교수가 될 수 있겠는가? apos;학생들의 요구에 부응할 때apos;라고 한마디로 표현할 수 있을 것이다. Lussier(1972)가 언급한 것처럼 학생들의 요구에 부응하지 못하는 교육은 Piaget이 언급한 교육의 기본 목표, 즉 개인에게 선배들이 한 것을 그대로 반복하여 시행하도록 하는 것이 아니라 새로운 것을 시도할 수 있는 능력을 가지게 하는 목표에는 도달할 수 없으며 이러한 목표는 간호학에도 가장 기본이 되어야 할 기본목표이기 때문이다. 학생들이 현재 어떤 요구를 가지고 있으며 또 어떤 생각을 하고 있는지 계속 파악하고 있는 것이 학생요구에 부응하는 교육을 할 수 있는 기본조건이 될 것이다. 의외로 많은 교수들이 학생들을 이해하고 있다고 생각하고 있으나 잘못 이해하고 있는 경우가 많다. 표 2는 현 간호학생들이 생각하고 있는 가치관과 문제점을 파악하고 또 교수가 그 가치관과 문제점을 어느 정도 파악하고 있는지 알아보기 위하여 일개 4년제 대학 200여명의 학생과 그 대학에 근무하는 18명의 교수진을 대상으로 질문한 결과를 간략하게 보고한 것이다. 또한 여기에서 학생이 보고하는 가치관, 문제점, 교수에게 바라는 점이 교수가 이해하고 있는 것과 차이가 있다는 것도 보여주고 있다. 우리가 학생들의 요구를 파악할 수 있도록 귀를 기울이고 이해하며, 그 요구에 부응하려고 노력할때 진정한 교수와 학생간의 관계가 이루어질 수 있을 것이며 이때 비로서 우리는 apos;partnershipapos;을 이룰 수 있을 것이다. 이때 간호학에 대한 실망은 줄어들 수 있을 것이며 우리도 학생들에게 전문가적인 태도를 함양시켜줄 수 있는 기회를 부여할 수 있을 것이다. 이렇게 될때 앞으로 기다리고 있는 미지의 의무에 효과적으로 또 적극적으로 대처할 수 있는 자질을 형성한 학생들을 준비해 낼 수 있을 것이다. 2. 간호모델에 의한 교과과정의 확립과 임상실습에의 적용 : 교과과정이 학생들의 모양을 만들어주는 하나의 기본틀이라고 말할 수 있다면 미래의 요구에 부응하는 학생들을 준비시키기 위하여 지금까지와는 다른 새로운 방향의 교과과정이 필요하다는 것은 재론할 필요가 없을 것이다. 이미 진취적인 간호대학에서는 guided design systems approach 또는 integrated curriculum 등의 새로운 교과과정을 시도하고 있음은 알려진 사실이다. 물론 간호모델에 준한 교과과정을 발전시키는데 대한 장점과 이에 수반되는 여러가지 새로운 문제점에 대하여 많은 논란이 있으나 모든 교과과정이 처음 시도될 때부터 완전한 것이 있을 수 없으며 시간이 지남에 따라 성숙되는 것임을 감안해 볼 때 이러한 새로운 교과과정에의 시도는 미래의 새로운 간호방향에 필수적인 사업이라고 하겠다. 이러한 교과과정을 개발하는데 몇가지 게안점을 첨부하려 한다. (1) 새로운 교과과정의 개발은 처음부터 끝까지 모든 교수진의 협력과 참여로 이루어져야 한다. (2) 비록 처음에는 어렵고 혼란이 있더라도 교과과정은 의학모델이 아닌 간호모델을 중심으로 이루어져야 한다. (3) 간호모델에서 다루어지는 개념들은 모두 직접 간호업무에 적용될 수 있는 것으로 선택되어야 한다. (4) 교과과정의 결과로 배출되는 학생들의 준비정도는 그 지역사회에 적합하여야 한다. (5) 그 지역사회의 고유한 문화적 요소가 포함되어야 한다. 아직 우리는 간호분야 내부의 갈등을 해결하지 못하고 있는 시기에 있다. 우리 내부의 문제점을 잘 해결할 수 있을때 외부와의 갈등에 잘 대처할 수 있을 것이다. 내부의 갈등을 잘 해결하기 위한 힘을 모으기 위하여는 동반자, 즉 교수와 학생, 간호교육자와 임상간호원 등이 서로 진정한 의미의 동반자 될때 가장 중요한 해결의 실마리가 될 것이다.

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