• Asia Marketing Journal
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• v.15 no.1
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• pp.1-22
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• 2013

This case focuses on Shanghai Tang, the first truly Chinese luxury brand that appeals to both Westerners and, more recently, to Chinese consumers worldwide. A visionary and wealthy businessman Sir David Tang created this company from scratch in 1994 in Hong Kong. Its story, spanned over almost two decades, has been fascinating. It went from what best a Chinese brand could be in the eyes of Westerners who love the Chinese culture, to a nearly-bankrupted company in 1998, before being acquired by Richemont, the second largest luxury group in the world. Since then, its turnaround has been spectacular with a growing appeal among Chinese luxury consumers who represent the core segment of the luxury industry today. The main objective of this case study is to formally examine how Shanghai Tang overcame its downfall and re-emerged as one the very few well- known Chinese luxury brands. More specifically, this case highlights the ways with which Shanghai Tang made a transitional change from a brand for Westerners who love the Chinese culture, to a brand for both, Westerners who love the Chinese culture and Chinese who love luxury. A close examination reveals that Shanghai Tang has followed the brand identity concept that consists of two major components: functional and emotional. The functional component for developing a luxury brand concerns all product characteristics that will make a product 'luxurious' in the eyes of the consumer, such as premium quality of cachemire from Mongolia, Chinese silk, lacquer, finest leather, porcelain, and jade in the case of Shanghai Tang. The emotional component consists of non-functional symbolic meanings of a brand. The symbolic meaning marks the major difference between a premium and a luxury brand. In the case of Shanghai Tang, its symbolic meaning refers to the Chinese culture and the brand aims to represent the best of Chinese traditions and establish itself as "the ambassador of modern Chinese style". It touches the Chinese heritage and emotions. Shanghai Tang has reinvented the modern Chinese chic by drawing back to the stylish decadence of Shanghai in the 1930s, which was then called the "Paris of the East", and this is where the brand finds inspiration to create its own myth. Once the functional and emotional components assured, Shanghai Tang has gone through a four-stage development to become the first global Chinese luxury brand: introduction, deepening, expansion, and revitalization. Introduction: David Tang discovered a market gap and had a vision to launch the first Chinese luxury brand to the world. The key success drivers for the introduction and management of a Chinese luxury brand are a solid brand identity and, above all, a creative mind, an inspired person. This was David Tang then, and this is now Raphael Le Masne de Chermont, the current Executive Chairman. Shanghai Tang combines Chinese and Western elements, which it finds to be the most sustainable platform for drawing consumers. Deepening: A major objective of the next phase is to become recognized as a luxury brand and a fashion or design authority. For this purpose, Shanghai Tang has cooperated with other well-regarded luxury and lifestyle brands such as Puma and Swarovski. It also expanded its product lines from high-end custom-made garments to music CDs and restaurant. Expansion: After the opening of his first store in Hong Kong in 1994, David Tang went on to open his second store in New York City three years later. However this New York retail operation was a financial disaster. Barely nineteen months after the opening, the store was shut down and quietly relocated to a cheaper location of Madison Avenue. Despite this failure, Shanghai Tang products found numerous followers especially among Western tourists and became "souvenir-like" must-haves. However, despite its strong brand DNA, the brand did not generate enough repeated sales and over the years the company cumulated heavy debts and became unprofitable. Revitalizing: After its purchase by Richemont in 1998, Le Masne de Chermont was appointed to lead the company, reposition the brand and undertake some major strategic changes such as revising the "Shanghai Tang" designs to appeal not only to Westerners but also to Chinese consumers, and to open new stores around the world. Since then, Shanghai Tang has become synonymous to a modern Chinese luxury lifestyle brand.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.17 no.4
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• pp.205-218
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• 2022

A social enterprise is an enterprise that pursues a social purpose with financial resources obtained through its own profit-making activities.. In a rapidly changing market environment, it is becoming increasingly difficult for social enterprises to maintain sustainability. Due to the poor and limited resources of social enterprises, the core competency of human resources is the most important point. In order for social enterprises to maintain their sustainability, it is very urgent to create conditions so that workers can stably commit themselves in their work. Therefore, this study tried to increase the sustainability of social enterprises by empirically analyzing the factors that affect job commitment among workers of social enterprises. As factors affecting job commitment, job fit and organizational justice factors were derived. In addition, the moderating effect of emotional leadership was also analyzed in the relationship between them. For this, a questionnaire survey was conducted on employees of social enterprises and 260 valid samples were used for the analysis. The empirical analysis results were as follows. Job knowledge, job skill, job experience, job desire as sub-factors of job fit, and distributive justice, procedural justice, which were sub-factors of organizational justice, had a significant positive (+) effect on job commitment. The impact of influence was in the order of procedural justice, job experience, job desire, distributive justice, job skill, and job knowledge. On the other hand, the significance of interactional justice was not tested. Emotional leadership was analyzed to play a moderating role between job knowledge, job skill, job desire, distributive justice, procedural justice and job commitment. However, moderating effect were not tested between job experience, interactional justice and job commitment. The results of this empirical study expanded the scope of existing job-related studies by empirically analyzing the factors affecting job commitment within the category of social enterprises at the academic level. At the psychological level, job experience was found to be one of the most important factors among various influencing factors. Significant implications necessary for future training for workers and implementation of various systems were presented.

• Journal of Intelligence and Information Systems
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• v.19 no.2
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• pp.101-123
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• 2013

Recently due to the advancements of science and information technology, the socio-economic business areas are changing from the industrial economy to a knowledge economy. Furthermore, companies need to do creation of new value through continuous innovation, development of core competencies and technologies, and technological convergence. Therefore, the identification of major trends in technology research and the interdisciplinary knowledge-based prediction of integrated technologies and promising techniques are required for firms to gain and sustain competitive advantage and future growth engines. The aim of this paper is to understand the recent research trend in management of technology (MOT) and to foresee promising technologies with deep knowledge for both technology and business. Furthermore, this study intends to give a clear way to find new technical value for constant innovation and to capture core technology and technology convergence. Bibliometrics is a metrical analysis to understand literature's characteristics. Traditional bibliometrics has its limitation not to understand relationship between trend in technology management and technology itself, since it focuses on quantitative indices such as quotation frequency. To overcome this issue, the network focused bibliometrics has been used instead of traditional one. The network focused bibliometrics mainly uses "Co-citation" and "Co-word" analysis. In this study, a keywords network analysis, one of social network analysis, is performed to analyze recent research trend in MOT. For the analysis, we collected keywords from research papers published in international journals related MOT between 2002 and 2011, constructed a keyword network, and then conducted the keywords network analysis. Over the past 40 years, the studies in social network have attempted to understand the social interactions through the network structure represented by connection patterns. In other words, social network analysis has been used to explain the structures and behaviors of various social formations such as teams, organizations, and industries. In general, the social network analysis uses data as a form of matrix. In our context, the matrix depicts the relations between rows as papers and columns as keywords, where the relations are represented as binary. Even though there are no direct relations between papers who have been published, the relations between papers can be derived artificially as in the paper-keyword matrix, in which each cell has 1 for including or 0 for not including. For example, a keywords network can be configured in a way to connect the papers which have included one or more same keywords. After constructing a keywords network, we analyzed frequency of keywords, structural characteristics of keywords network, preferential attachment and growth of new keywords, component, and centrality. The results of this study are as follows. First, a paper has 4.574 keywords on the average. 90% of keywords were used three or less times for past 10 years and about 75% of keywords appeared only one time. Second, the keyword network in MOT is a small world network and a scale free network in which a small number of keywords have a tendency to become a monopoly. Third, the gap between the rich (with more edges) and the poor (with fewer edges) in the network is getting bigger as time goes on. Fourth, most of newly entering keywords become poor nodes within about 2~3 years. Finally, keywords with high degree centrality, betweenness centrality, and closeness centrality are "Innovation," "R&D," "Patent," "Forecast," "Technology transfer," "Technology," and "SME". The results of analysis will help researchers identify major trends in MOT research and then seek a new research topic. We hope that the result of the analysis will help researchers of MOT identify major trends in technology research, and utilize as useful reference information when they seek consilience with other fields of study and select a new research topic.

• Journal of Intelligence and Information Systems
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• v.23 no.4
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• pp.127-146
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• 2017

Services using artificial intelligence have begun to emerge in daily life. Artificial intelligence is applied to products in consumer electronics and communications such as artificial intelligence refrigerators and speakers. In the financial sector, using Kensho's artificial intelligence technology, the process of the stock trading system in Goldman Sachs was improved. For example, two stock traders could handle the work of 600 stock traders and the analytical work for 15 people for 4weeks could be processed in 5 minutes. Especially, big data analysis through machine learning among artificial intelligence fields is actively applied throughout the financial industry. The stock market analysis and investment modeling through machine learning theory are also actively studied. The limits of linearity problem existing in financial time series studies are overcome by using machine learning theory such as artificial intelligence prediction model. The study of quantitative financial data based on the past stock market-related numerical data is widely performed using artificial intelligence to forecast future movements of stock price or indices. Various other studies have been conducted to predict the future direction of the market or the stock price of companies by learning based on a large amount of text data such as various news and comments related to the stock market. Investing on commodity asset, one of alternative assets, is usually used for enhancing the stability and safety of traditional stock and bond asset portfolio. There are relatively few researches on the investment model about commodity asset than mainstream assets like equity and bond. Recently machine learning techniques are widely applied on financial world, especially on stock and bond investment model and it makes better trading model on this field and makes the change on the whole financial area. In this study we made investment model using Support Vector Machine among the machine learning models. There are some researches on commodity asset focusing on the price prediction of the specific commodity but it is hard to find the researches about investment model of commodity as asset allocation using machine learning model. We propose a method of forecasting four major commodity indices, portfolio made of commodity futures, and individual commodity futures, using SVM model. The four major commodity indices are Goldman Sachs Commodity Index(GSCI), Dow Jones UBS Commodity Index(DJUI), Thomson Reuters/Core Commodity CRB Index(TRCI), and Rogers International Commodity Index(RI). We selected each two individual futures among three sectors as energy, agriculture, and metals that are actively traded on CME market and have enough liquidity. They are Crude Oil, Natural Gas, Corn, Wheat, Gold and Silver Futures. We made the equally weighted portfolio with six commodity futures for comparing with other commodity indices. We set the 19 macroeconomic indicators including stock market indices, exports & imports trade data, labor market data, and composite leading indicators as the input data of the model because commodity asset is very closely related with the macroeconomic activities. They are 14 US economic indicators, two Chinese economic indicators and two Korean economic indicators. Data period is from January 1990 to May 2017. We set the former 195 monthly data as training data and the latter 125 monthly data as test data. In this study, we verified that the performance of the equally weighted commodity futures portfolio rebalanced by the SVM model is better than that of other commodity indices. The prediction accuracy of the model for the commodity indices does not exceed 50% regardless of the SVM kernel function. On the other hand, the prediction accuracy of equally weighted commodity futures portfolio is 53%. The prediction accuracy of the individual commodity futures model is better than that of commodity indices model especially in agriculture and metal sectors. The individual commodity futures portfolio excluding the energy sector has outperformed the three sectors covered by individual commodity futures portfolio. In order to verify the validity of the model, it is judged that the analysis results should be similar despite variations in data period. So we also examined the odd numbered year data as training data and the even numbered year data as test data and we confirmed that the analysis results are similar. As a result, when we allocate commodity assets to traditional portfolio composed of stock, bond, and cash, we can get more effective investment performance not by investing commodity indices but by investing commodity futures. Especially we can get better performance by rebalanced commodity futures portfolio designed by SVM model.

• Journal of Global Scholars of Marketing Science
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• v.20 no.3
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• pp.262-268
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• 2010

Virtual communities (VCs) have developed rapidly, with more and more people participating in them to exchange information and opinions. A virtual community is a group of people who may or may not meet one another face to face, and who exchange words and ideas through the mediation of computer bulletin boards and networks. A business-to-consumer virtual community (B2CVC) is a commercial group that creates a trustworthy environment intended to motivate consumers to be more willing to buy from an online store. B2CVCs create a social atmosphere through information contribution such as recommendations, reviews, and ratings of buyers and sellers. Although the importance of B2CVCs has been recognized, few studies have been conducted to examine members' word-of-mouth behavior within these communities. This study proposes a model of involvement, statistics, trust, "stickiness," and word-of-mouth in a B2CVC and explores the relationships among these elements based on empirical data. The objectives are threefold: (i) to empirically test a B2CVC model that integrates measures of beliefs, attitudes, and behaviors; (ii) to better understand the nature of these relationships, specifically through word-of-mouth as a measure of revenue generation; and (iii) to better understand the role of stickiness of B2CVC in CRM marketing. The model incorporates three key elements concerning community members: (i) their beliefs, measured in terms of their involvement assessment; (ii) their attitudes, measured in terms of their satisfaction and trust; and, (iii) their behavior, measured in terms of site stickiness and their word-of-mouth. Involvement is considered the motivation for consumers to participate in a virtual community. For B2CVC members, information searching and posting have been proposed as the main purpose for their involvement. Satisfaction has been reviewed as an important indicator of a member's overall community evaluation, and conceptualized by different levels of member interactions with their VC. The formation and expansion of a VC depends on the willingness of members to share information and services. Researchers have found that trust is a core component facilitating the anonymous interaction in VCs and e-commerce, and therefore trust-building in VCs has been a common research topic. It is clear that the success of a B2CVC depends on the stickiness of its members to enhance purchasing potential. Opinions communicated and information exchanged between members may represent a type of written word-of-mouth. Therefore, word-of-mouth is one of the primary factors driving the diffusion of B2CVCs across the Internet. Figure 1 presents the research model and hypotheses. The model was tested through the implementation of an online survey of CTrip Travel VC members. A total of 243 collected questionnaires was reduced to 204 usable questionnaires through an empirical process of data cleaning. The study's hypotheses examined the extent to which involvement, satisfaction, and trust influence B2CVC stickiness and members' word-of-mouth. Structural Equation Modeling tested the hypotheses in the analysis, and the structural model fit indices were within accepted thresholds: ${\chi}^2^$/df was 2.76, NFI was .904, IFI was .931, CFI was .930, and RMSEA was .017. Results indicated that involvement has a significant influence on satisfaction (p<0.001, ${\beta}$=0.809). The proportion of variance in satisfaction explained by members' involvement was over half (adjusted $R^2$=0.654), reflecting a strong association. The effect of involvement on trust was also statistically significant (p<0.001, ${\beta}$=0.751), with 57 percent of the variance in trust explained by involvement (adjusted $R^2$=0.563). When the construct "stickiness" was treated as a dependent variable, the proportion of variance explained by the variables of trust and satisfaction was relatively low (adjusted $R^2$=0.331). Satisfaction did have a significant influence on stickiness, with ${\beta}$=0.514. However, unexpectedly, the influence of trust was not even significant (p=0.231, t=1.197), rejecting that proposed hypothesis. The importance of stickiness in the model was more significant because of its effect on e-WOM with ${\beta}$=0.920 (p<0.001). Here, the measures of Stickiness explain over eighty of the variance in e-WOM (Adjusted $R^2$=0.846). Overall, the results of the study supported the hypothesized relationships between members' involvement in a B2CVC and their satisfaction with and trust of it. However, trust, as a traditional measure in behavioral models, has no significant influence on stickiness in the B2CVC environment. This study contributes to the growing body of literature on B2CVCs, specifically addressing gaps in the academic research by integrating measures of beliefs, attitudes, and behaviors in one model. The results provide additional insights to behavioral factors in a B2CVC environment, helping to sort out relationships between traditional measures and relatively new measures. For practitioners, the identification of factors, such as member involvement, that strongly influence B2CVC member satisfaction can help focus technological resources in key areas. Global e-marketers can develop marketing strategies directly targeting B2CVC members. In the global tourism business, they can target Chinese members of a B2CVC by providing special discounts for active community members or developing early adopter programs to encourage stickiness in the community. Future studies are called for, and more sophisticated modeling, to expand the measurement of B2CVC member behavior and to conduct experiments across industries, communities, and cultures.

• Asia Marketing Journal
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• v.14 no.1
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• pp.1-24
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• 2012

Nowadays, customer satisfaction has been one of company's major objectives, and the index to measure and communicate customer satisfaction has been generally accepted among business practices. The major issues of CSI(customer satisfaction index) are three questions, as follows: (a)what level of customer satisfaction is tolerable, (b)whether customer satisfaction and company performance has positive causality, and (c)what to do to improve customer satisfaction. Among these, the second issue is recently attracting academic research in several perspectives. On this study, the second issue will be addressed. Many researchers including Anderson have regarded customer satisfaction as core competencies, such as brand equity, customer equity. They want to verify following causality "customer satisfaction → market performance(market share, sales growth rate) → financial performance(operating margin, profitability) → corporate value performance(stock price, credit ratings)" based on the process model of marketing performance. On the other hand, Insoo Jeon and Aeju Jeong(2009) verified sequential causality based on the process model by the domestic data. According to the rejection of several hypotheses, they suggested the balance model of marketing performance as an alternative. The objective of this study, based on the existing process model, is to examine the causal relationship between customer satisfaction and corporate value performance. Anderson and Mansi(2009) proved the relationship between ACSI(American Customer Satisfaction Index) and credit ratings using 2,574 samples from 1994 to 2004 on the assumption that credit rating could be an indicator of a corporate value performance. The similar study(Sangwoon Yoon, 2010) was processed in Korean data, but it didn't confirm the relationship between KCSI(Korean CSI) and credit ratings, unlike the results of Anderson and Mansi(2009). The summary of these studies is in the Table 1. Two studies analyzing the relationship between customer satisfaction and credit ratings weren't consistent results. So, in this study we are to test the conflicting results of the relationship between customer satisfaction and credit ratings based on the research model considering Korean credit ratings. To prove the hypothesis, we suggest the research model as follows. Two important features of this model are the inclusion of important variables in the existing Korean credit rating system and government support. To control their influences on credit ratings, we included three important variables of Korean credit rating system and government support, in case of financial institutions including banks. ROA, ER, TA, these three variables are chosen among various kinds of financial indicators since they are the most frequent variables in many previous studies. The results of the research model are relatively favorable : R², F-value and p-value is .631, 233.15 and .000 respectively. Thus, the explanatory power of the research model as a whole is good and the model is statistically significant. The research model has good explanatory power, the regression coefficients of the KCSI is .096 as positive(+) and t-value and p-value is 2.220 and .0135 respectively. As a results, we can say the hypothesis is supported. Meanwhile, all other explanatory variables including ROA, ER, log(TA), GS_DV are identified as significant and each variables has a positive(+) relationship with CRS. In particular, the t-value of log(TA) is 23.557 and log(TA) as an explanatory variables of the corporate credit ratings shows very high level of statistical significance. Considering interrelationship between financial indicators such as ROA, ER which include total asset in their formula, we can expect multicollinearity problem. But indicators like VIF and tolerance limits that shows whether multicollinearity exists or not, say that there is no statistically significant multicollinearity in all the explanatory variables. KCSI, the main subject of this study, is a statistically significant level even though the standardized regression coefficients and t-value of KCSI is .055 and 2.220 respectively and a relatively low level among explanatory variables. Considering that we chose other explanatory variables based on the level of explanatory power out of many indicators in the previous studies, KCSI is validated as one of the most significant explanatory variables for credit rating score. And this result can provide new insights on the determinants of credit ratings. However, KCSI has relatively lower impact than main financial indicators like log(TA), ER. Therefore, KCSI is one of the determinants of credit ratings, but don't have an exceedingly significant influence. In addition, this study found that customer satisfaction had more meaningful impact on corporations of small asset size than those of big asset size, and on service companies than manufacturers. The findings of this study is consistent with Anderson and Mansi(2009), but different from Sangwoon Yoon(2010). Although research model of this study is a bit different from Anderson and Mansi(2009), we can conclude that customer satisfaction has a significant influence on company's credit ratings either Korea or the United State. In addition, this paper found that customer satisfaction had more meaningful impact on corporations of small asset size than those of big asset size and on service companies than manufacturers. Until now there are a few of researches about the relationship between customer satisfaction and various business performance, some of which were supported, some weren't. The contribution of this study is that credit rating is applied as a corporate value performance in addition to stock price. It is somewhat important, because credit ratings determine the cost of debt. But so far it doesn't get attention of marketing researches. Based on this study, we can say that customer satisfaction is partially related to all indicators of corporate business performances. Practical meanings for customer satisfaction department are that it needs to actively invest in the customer satisfaction, because active investment also contributes to higher credit ratings and other business performances. A suggestion for credit evaluators is that they need to design new credit rating model which reflect qualitative customer satisfaction as well as existing variables like ROA, ER, TA.

• Journal of Korean Society of Archives and Records Management
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• v.1 no.1
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• pp.69-94
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• 2001

The purpose of this paper is to provide an overview of the current status of the records management science education in Japan, and to examine the implications of the rapid growth of this filed while noting some of its significant issues and problems. The goal of records management science education is to improve the quality of information services and to assure an adequate supply of information professionals. Because records management science programs prepare students for a professional career, their curricula must encompass elements of both education and practical training. This is often expressed as a contrast between theory and practice. The confluence of the social, economic and technological realities of the environment where the learning takes place affects both. This paper reviews the historical background and current trends of records management science education in Japan. It also analyzes the various types of curriculum and the teaching staff of these institutions, with focus on the status of the undergraduate program at Surugadai University, the first comprehensive, university level program in Japan. The Faculty of Cultural Information Resources, Surugadai University, a new school toward an integrated information disciplines, was opened in 1994, to explore the theory and practice of the management diverse cultural information resources. Its purpose was to stimulate and promote research in additional fields of information science by offering professional training in archival science, records management, and museum curatorship, as well as librarianship. In 1999, the school introduced a master program, the first in Japan. The Faculty has two departments and each of them has two courses; Department of Sensory Information Resources Management; -Sound and Audiovisual Information Management, -Landscape and Tourism Information Management, Department of Knowledge Information Resources Management; -Library and Information Management, -Records and Archives Management The structure of the entire curriculum is also organized in stages from the time of entrance through basic instruction and onwards. Orientation subjects which a student takes immediately upon entering university is an introduction to specialized education, in which he learns the basic methods of university education and study, During his first and second years, he arranges Basic and Core courses as essential steps towards specialization at university. For this purpose, the courses offer a wide variety of study topics. The number of courses offered, including these, amounts to approximately 150. While from his third year onwards, he begins specific courses that apply to his major field, and in a gradual accumulation of seminar classes and practical training, puts his knowledge grained to practical use. Courses pertaining to these departments are offered to students beginning their second year. However, there is no impenetrable wall between the two departments, and there are only minor differences with regard requirements for graduation. Students may select third or fourth year seminars regardless of the department to which they belong. To be awarded a B.A. in Cultural Information Resources, the student is required to earn 34 credits in Basic Courses(such as, Social History of Cultural Information, Cultural Anthropology, History of Science, Behavioral Sciences, Communication, etc.), 16 credits in Foreign Languages(including 10 in English), 14 credits on Information Processing(including both theory and practice), and 60 credits in the courses for his or her major. Finally, several of the issues and problems currently facing records management science education in Japan are briefly summarized below; -Integration and Incorporation of related areas and similar programs, -Curriculum Improvement, -Insufficient of Textbooks, -Lack of qualified Teachers, -Problems of the employment of Graduates. As we moved toward more sophisticated, integrated, multimedia information services, information professionals will need to work more closely with colleagues in other specialties. It will become essential to the survival of the information professions for librarians to work with archivists, record managers and museum curators. Managing the changes in our increasingly information-intensive society demands strong coalitions among everyone in cultural Institutions. To provide our future colleagues with these competencies will require building and strengthening partnerships within and across the information professions and across national borders.

• Journal of dental hygiene science
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• v.17 no.6
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• pp.523-532
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• 2017

The purpose of this study was to provide basic data to standardize the clinical dental hygiene curriculum, based on analysis of current clinical dental hygiene curricula in Korea. We emailed questionnaires to 12 schools to investigate clinical dental hygiene curricula, from February to March, 2017. We analyzed the clinical dental hygiene curricula in 5 schools with a 3-year program and in 7 schools with a 4-year program. The questionnaire comprised nine items on topics relating to clinical dental hygiene, and four items relating to the dental hygiene process and oral prophylaxis. The questionnaire included details regarding the subject name, the grade/semester/credit system, course content and class hours, the number of senior professors, and the number of patients available for dental hygiene clinical training purposes. In total, there were 96 topics listed in the curricula relating to clinical dental hygiene training, and topics varied between the schools. There was an average of 20.4 topic credits, and more credits and hours were allocated to the 4-year program than to the 3-year program. On average, the ratio of students to professors was 21.4:1. Course content included infection control, concepts for dental hygiene processes, dental hygiene assessment, intervention and evaluation, case studies, and periodontal instrumentation. An average of 2 hours per patient was spent on dental hygiene practice, with an average of 1.9 visits. On average, student clinical training involved 19 patients and 26.6 patients in the 3-year and 4-year programs, respectively. The average participation time per student per topic was 38.0 hours and 53.1 hours, in the 3-year and 4-year programs, respectively. Standardizing the clinical dental hygiene curricula in Korea will require consensus guidelines on topics, the number of classes required to achieve core competencies as a dental hygienist, and theory and practice time.

• Journal of Intelligence and Information Systems
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• v.19 no.3
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• pp.113-125
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• 2013

Every company wants to know customer's requirement and makes an effort to meet them. Cause that, communication between customer and company became core competition of business and that important is increasing continuously. There are several strategies to find customer's needs, but VOC (Voice of customer) is one of most powerful communication tools and VOC gathering by several channels as telephone, post, e-mail, website and so on is so meaningful. So, almost company is gathering VOC and operating VOC system. VOC is important not only to business organization but also public organization such as government, education institute, and medical center that should drive up public service quality and customer satisfaction. Accordingly, they make a VOC gathering and analyzing System and then use for making a new product and service, and upgrade. In recent years, innovations in internet and ICT have made diverse channels such as SNS, mobile, website and call-center to collect VOC data. Although a lot of VOC data is collected through diverse channel, the proper utilization is still difficult. It is because the VOC data is made of very emotional contents by voice or text of informal style and the volume of the VOC data are so big. These unstructured big data make a difficult to store and analyze for use by human. So that, the organization need to automatic collecting, storing, classifying and analyzing system for unstructured big VOC data. This study propose an intelligent VOC analyzing system based on opinion mining to classify the unstructured VOC data automatically and determine the polarity as well as the type of VOC. And then, the basis of the VOC opinion analyzing system, called domain-oriented sentiment dictionary is created and corresponding stages are presented in detail. The experiment is conducted with 4,300 VOC data collected from a medical website to measure the effectiveness of the proposed system and utilized them to develop the sensitive data dictionary by determining the special sentiment vocabulary and their polarity value in a medical domain. Through the experiment, it comes out that positive terms such as "칭찬, 친절함, 감사, 무사히, 잘해, 감동, 미소" have high positive opinion value, and negative terms such as "퉁명, 뭡니까, 말하더군요, 무시하는" have strong negative opinion. These terms are in general use and the experiment result seems to be a high probability of opinion polarity. Furthermore, the accuracy of proposed VOC classification model has been compared and the highest classification accuracy of 77.8% is conformed at threshold with -0.50 of opinion classification of VOC. Through the proposed intelligent VOC analyzing system, the real time opinion classification and response priority of VOC can be predicted. Ultimately the positive effectiveness is expected to catch the customer complains at early stage and deal with it quickly with the lower number of staff to operate the VOC system. It can be made available human resource and time of customer service part. Above all, this study is new try to automatic analyzing the unstructured VOC data using opinion mining, and shows that the system could be used as variable to classify the positive or negative polarity of VOC opinion. It is expected to suggest practical framework of the VOC analysis to diverse use and the model can be used as real VOC analyzing system if it is implemented as system. Despite experiment results and expectation, this study has several limits. First of all, the sample data is only collected from a hospital web-site. It means that the sentimental dictionary made by sample data can be lean too much towards on that hospital and web-site. Therefore, next research has to take several channels such as call-center and SNS, and other domain like government, financial company, and education institute.

• Journal of Intelligence and Information Systems
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• v.26 no.3
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• pp.71-90
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• 2020

Recently, the global insurance industry is rapidly developing digital transformation through the use of artificial intelligence technologies such as machine learning, natural language processing, and deep learning. As a result, more and more foreign insurers have achieved the success of artificial intelligence technology-based InsurTech and platform business, and Ping An Insurance Group Ltd., China's largest private company, is leading China's global fourth industrial revolution with remarkable achievements in InsurTech and Digital Platform as a result of its constant innovation, using 'finance and technology' and 'finance and ecosystem' as keywords for companies. In response, this study analyzed the InsurTech and platform business activities of Ping An Insurance Group Ltd. through the ser-M analysis model to provide strategic implications for revitalizing AI technology-based businesses of domestic insurers. The ser-M analysis model has been studied so that the vision and leadership of the CEO, the historical environment of the enterprise, the utilization of various resources, and the unique mechanism relationships can be interpreted in an integrated manner as a frame that can be interpreted in terms of the subject, environment, resource and mechanism. As a result of the case analysis, Ping An Insurance Group Ltd. has achieved cost reduction and customer service development by digitally innovating its entire business area such as sales, underwriting, claims, and loan service by utilizing core artificial intelligence technologies such as facial, voice, and facial expression recognition. In addition, "online data in China" and "the vast offline data and insights accumulated by the company" were combined with new technologies such as artificial intelligence and big data analysis to build a digital platform that integrates financial services and digital service businesses. Ping An Insurance Group Ltd. challenged constant innovation, and as of 2019, sales reached $155 billion, ranking seventh among all companies in the Global 2000 rankings selected by Forbes Magazine. Analyzing the background of the success of Ping An Insurance Group Ltd. from the perspective of ser-M, founder Mammingz quickly captured the development of digital technology, market competition and changes in population structure in the era of the fourth industrial revolution, and established a new vision and displayed an agile leadership of digital technology-focused. Based on the strong leadership led by the founder in response to environmental changes, the company has successfully led InsurTech and Platform Business through innovation of internal resources such as investment in artificial intelligence technology, securing excellent professionals, and strengthening big data capabilities, combining external absorption capabilities, and strategic alliances among various industries. Through this success story analysis of Ping An Insurance Group Ltd., the following implications can be given to domestic insurance companies that are preparing for digital transformation. First, CEOs of domestic companies also need to recognize the paradigm shift in industry due to the change in digital technology and quickly arm themselves with digital technology-oriented leadership to spearhead the digital transformation of enterprises. Second, the Korean government should urgently overhaul related laws and systems to further promote the use of data between different industries and provide drastic support such as deregulation, tax benefits and platform provision to help the domestic insurance industry secure global competitiveness. Third, Korean companies also need to make bolder investments in the development of artificial intelligence technology so that systematic securing of internal and external data, training of technical personnel, and patent applications can be expanded, and digital platforms should be quickly established so that diverse customer experiences can be integrated through learned artificial intelligence technology. Finally, since there may be limitations to generalization through a single case of an overseas insurance company, I hope that in the future, more extensive research will be conducted on various management strategies related to artificial intelligence technology by analyzing cases of multiple industries or multiple companies or conducting empirical research.