• Journal of Korean Neurosurgical Society
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• v.65 no.2
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• pp.315-324
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• 2022

Objective : The aim of this study was to verify the equivalence and effectiveness of the tablet-administered Korean Repeatable Battery for the Assessment of Neuropsychological Status (K-RBANS) for the prevention and early detection of dementia. Methods : Data from 88 psychiatry and neurology patient samples were examined to evaluate the equivalence between tablet and paper administrations of the K-RBANS using a non-randomly equivalent group design. We calculated the prediction scores of the tablet-administered K-RBANS based on demographics and covariate-test scores for focal tests using norm samples and tested format effects. In addition, we compared the receiver operating characteristic curves to confirm the effectiveness of the K-RBANS for preventing and detecting dementia. Results : In the analysis of raw scores, line orientation showed a significant difference (t=-2.94, p<0.001), and subtests showed small to large effect sizes (0.04-0.86) between paper- and tablet-administered K-RBANS. To investigate the format effect, we compared the predicted scaled scores of the tablet sample to the scaled scores of the norm sample. Consequently, a small effect size (d≤0.20) was observed in most of the subtests, except word list and story recall, which showed a medium effect size (d=0.21), while picture naming and subtests of delayed memory showed significant differences in the one-sample t-test. In addition, the area under the curve of the total scale index (TSI) (0.827; 95% confidence interval, 0.738-0.916) was higher than that of the five indices, ranging from 0.688 to 0.820. The sensitivity and specificity of TSI were 80% and 76%, respectively. Conclusion : The overall results of this study suggest that the tablet-administered K-RBANS showed significant equivalence to the norm sample, although some subtests showed format effects, and it may be used as a valid tool for the brief screening of patients with neuropsychological disorders in Korea.

• Journal of Venture Innovation
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• v.7 no.1
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• pp.167-182
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• 2024

The aim of this study is to empirically explore the operational spillover effect among companies within chaebol groups, prominent corporate conglomerates in South Korea. Chaebols are known for their horizontal and vertical integration, fostering close collaboration among their constituent companies from a supply chain standpoint. Existing literature highlights the sharing of tangible and intangible resources within chaebol structures, leading to increased efficiency by minimizing transaction costs through resource sharing. This research investigates whether operational management performance within chaebol structures can be transmitted through cooperative resource utilization. To achieve this objective, we categorize leading companies and affiliate companies within chaebols and examine whether the operational management performance of leading companies significantly influences that of affiliate companies. Data on conglomerates, as defined by the Korea Fair Trade Commission, were collected, along with information on companies within these groups. Subsequently, the company with the highest revenue within each group was identified as the leading company, while the remaining companies were designated as affiliate companies. Our analysis reveals a significant positive relationship between the performance of inventory and facility resource management of leading companies and that of affiliate companies. This study sheds light on the transfer of operational management performance within conglomerates from a managerial perspective, underscoring the importance of reinforcing cooperation systems within the chaebol group. Furthermore, this research contributes to the academic discourse by delineating conglomerates from an operational management perspective and empirically demonstrating the transfer effect of operational management performance.

• Journal of the Korean Society of Costume
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• v.38
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• pp.193-211
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• 1998

Without a solid marketing system in placed, the fashion industry cannot flourish on out-standing design or technology alone. Even though the significance of collecting and analyzing information, merchandising, and retail distribution is recognized, these functions are not firmly rooted or prevalent in our industry. In contrast, Italy which possesses similar demographic traits such as the lack of natural resources and other physical factors has succeed-ed in globalizing its fashion market by responding swiftly and exercising flexiblity to its constantly changing consumer demand. This in turn has earned Italy the competitive edge in the global fashion arena. Italy's unique management skills and operation know-how, along with successful market strategies come into play in bringing competitiveness to Italy's fashion market. Firstly, smaller companies with ability to adopt swiftly to the ever changing market. Secondly, fashion friendly social environment. Thirdly, niche marketing through highly specialized system and differentiation. Fourthly, timeless innovation through intense corporate competition. Lastly, establishment of foundations to support the industry through diverse networking. The alone building blocks have formed a basis for erecting an unparalleled market with a reputation for excellence in design and quality in the global fashion world. This study has examined how Italy's fashion industry has evolved from an underdeveloped textile business into a cutting edge fashion in-dustry. Italy's unique business processes and practices were studied to come up with a collection and merchandising ideas in a niche market. By selecting this venue we are able to continuously grow and develop in a market with diverse consumer needs. To analyze the Italian fashion market, data from 3 institutions were utilized, namely, CIT-ER which has provided consumer trends and sales analysis, SITA,a data service provided statistics from the textile and apparel businesses, and NBI has also furnished valuable data. Italian consumer preference, buying behavior, consumer profile, retail channels and other related data from the above institutions has formed a backbone for market segmentation and target markets, and as a result, we were able to zero in on the type of consumer, produce, pricing and retail channels for our womenswear. Going forward the direction is to elevate product image and pretige, and create syn-ergy between related industries, and at the same note, in order to develop internationally recognized brands such as Max Mara and Benetton. Certain elements such as the specialization of the fashion industry, alon-g with fashion-related data base and systems support, and most importantly experts with acute fashion sense and capacity to analyze pertinent data are in need. I firmly believe that we can achieve Italy's level in the fashion market with support from the government and unrelenting effort within the industry itself, and hope that this report can prove to be useful.

• Journal of Venture Innovation
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• v.4 no.1
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• pp.1-21
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• 2021

Recently, as the number of foreigners residing in Korea has increased, Korea is preparing to enter a multicultural country. This study was conducted to present a social integration model for the purpose of solving the social problem of social integration of multicultural families. The purpose of this study is as follows. First, this study examines the role of local social capital for social integration by improving the quality of life of multicultural families and increasing their intention to participate in society. Second, the purpose of this study is to examine the effects of multicultural family members on the formation of local social capital, subjective quality of life and social participation intention, focusing on the role of social enterprises. To achieve the purpose of this study, members of multicultural families living in Seoul and Gyeonggi Province were selected as samples, and responses to local social capital, subjective quality of life, social participation intention and social identity were collected through structured questionnaires. A total of 363 valid questionnaires were tested for the relationship between variables through the structural equation model. The analysis result of this study is that first, human social capital and corporate social capital of members of multicultural families have a significant positive effect on subjective quality of life. Second, it was found that the corporate social capital and community social capital of members of multicultural families had a significant positive effect on the intention to participate in society. Third, it was found that the subjective quality of life of members of multicultural families did not significantly affect their intention to participate in society. Finally, it was found that social identity plays a partly controlling role when community capital of multicultural family members affects their intention to participate in society. Through this analysis result, it is expected that it will play a meaningful role as basic data for policy proposals for social integration of multicultural families.

• Journal of Korean society of Dental Hygiene
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• v.11 no.2
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• pp.179-188
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• 2011

Objectives : The study set out to analyze correlations between emotional labor and job stress among dental hygienist, who have direct and lasting relationships with patients in dental clinic, and provide basic data to resolve stressors and manage stress. Methods : A survey was taken among dental hygienists working at dental clinics, and 200 questionnaires were used in analysis. The gathered data were put to Cronbach's alpha with the SPSS WIN12.0 program to test the reliability of the inventories to measure their emotional labor and job stress. In addition, correlation analysis was conducted to examine relations between the items of emotional labor and those of job stress along with regression analysis to examine relations between emotional labor and job stress. Finally, t-test and One-way ANOVA were conducted to test mean differences in the job stress items according to the degrees of emotional labor with the statistical significance level set at 0.05. Results : 1. The measurement tool used in the study recorded 0.7 for all the areas of Cronbach's alpha for internal reliability and thus achieved high reliability. 2. The overall mean of emotional labor was 2.74, which indicates that the dental hygienists had 'average' or higher stress for emotional labor. 3. Emotional labor had statistically significant relations with educational background, place of work, motivation to choose to be a dental hygienist, and religion of their general characteristics. 4. There was statistical significance in relations between general characteristics and job stress according to educational background, position at work, and experience with change of occupation. 5. The correlations between emotional labor and the stress areas were analyzed. As a result, emotional labor was in positive(+) correlations with job demand, lack of job autonomy, relational conflict, job instability, organizational system, and corporate culture. In addition, regression analysis was conducted to test causal relations between emotional labor and job stress. The results indicate that there were positive(+) influences between emotional labor and job stress. Conclusions : The results show that emotional labor can serve as a mediating variable for job stress in dental clinic. Thus both dental clinics and dental hygienists need to have ways to deal with job stress derived from emotional labor in which they are forced to process their emotions according to the dental clinics' demands, properly. The study will hopefully trigger ongoing follow-up researches on the deployment of dental hygienists according to their job characteristics and the situational variables to alleviate the negative results of emotional labor.

• The Journal of the Convergence on Culture Technology
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• v.9 no.2
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• pp.119-126
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• 2023

Due to the COVID-19 phenomenon, more than one-third of SMEs in Korea have been working from home. Therefore, we tried to find out the management status of SMEs and find policy support. The survey data was based on the Bank of Korea's corporate management analysis 2021 data. As a result of the study, the debt of SMEs increased from 362 trillion won(2019) to 409 trillion won(2022), while their capital decreased from 489 trillion won(2019) to 336 trillion won(2022). Net profit and loss increased to 14.9 trillion won(2019) and 23.3 trillion won(2021). As a result of the company's financial soundness analysis, First, for stability, the current ratio was high compared to the total industry and the dependence on borrowings was high. Second, profitability improved from 3.20%(2019) to 4.28%(2021), but it was lower than 5.01%(2021) for all industries. Third, the growth rate showed an increase of 12.43%, which is 1.57 times faster than the total asset growth rate of 7.94%(2021) for all industries. As for the growth rate of sales, all industries(2021) showed (-)growth, while SMEs among manufacturing industries showed a growth rate of 14.78%. Fourth, as for activity, the total asset turnover ratio was higher at 0.96% compared to 0.73 for all industries. In conclusion, stability and profitability were low and growth potential was high compared to all industries. In the future, policies that focus on industries with high growth potential are needed.

• Journal of Intelligence and Information Systems
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• v.25 no.2
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• pp.99-122
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• 2019

According to the recent IDC (International Data Corporation) report, as from 2025, the total volume of data is estimated to reach ten times higher than that of 2016, corresponding to 163 zettabytes. then the main body of generating information is moving more toward corporations than consumers. So-called "the wave of Big-data" is arriving, and the following aftermath affects entire industries and firms, respectively and collectively. Therefore, effective management of vast amounts of data is more important than ever in terms of the firm. However, there have been no previous studies that measure the effects of big data investment, even though there are number of previous studies that quantitatively the effects of IT investment. Therefore, we quantitatively analyze the Big-data investment effects, which assists firm's investment decision making. This study applied the Event Study Methodology, which is based on the efficient market hypothesis as the theoretical basis, to measure the effect of the big data investment of firms on the response of market investors. In addition, five sub-variables were set to analyze this effect in more depth: the contents are firm size classification, industry classification (finance and ICT), investment completion classification, and vendor existence classification. To measure the impact of Big data investment announcements, Data from 91 announcements from 2010 to 2017 were used as data, and the effect of investment was more empirically observed by observing changes in corporate value immediately after the disclosure. This study collected data on Big Data Investment related to Naver 's' News' category, the largest portal site in Korea. In addition, when selecting the target companies, we extracted the disclosures of listed companies in the KOSPI and KOSDAQ market. During the collection process, the search keywords were searched through the keywords 'Big data construction', 'Big data introduction', 'Big data investment', 'Big data order', and 'Big data development'. The results of the empirically proved analysis are as follows. First, we found that the market value of 91 publicly listed firms, who announced Big-data investment, increased by 0.92%. In particular, we can see that the market value of finance firms, non-ICT firms, small-cap firms are significantly increased. This result can be interpreted as the market investors perceive positively the big data investment of the enterprise, allowing market investors to better understand the company's big data investment. Second, statistical demonstration that the market value of financial firms and non - ICT firms increases after Big data investment announcement is proved statistically. Third, this study measured the effect of big data investment by dividing by company size and classified it into the top 30% and the bottom 30% of company size standard (market capitalization) without measuring the median value. To maximize the difference. The analysis showed that the investment effect of small sample companies was greater, and the difference between the two groups was also clear. Fourth, one of the most significant features of this study is that the Big Data Investment announcements are classified and structured according to vendor status. We have shown that the investment effect of a group with vendor involvement (with or without a vendor) is very large, indicating that market investors are very positive about the involvement of big data specialist vendors. Lastly but not least, it is also interesting that market investors are evaluating investment more positively at the time of the Big data Investment announcement, which is scheduled to be built rather than completed. Applying this to the industry, it would be effective for a company to make a disclosure when it decided to invest in big data in terms of increasing the market value. Our study has an academic implication, as prior research looked for the impact of Big-data investment has been nonexistent. This study also has a practical implication in that it can be a practical reference material for business decision makers considering big data investment.

• Journal of Intelligence and Information Systems
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• v.17 no.1
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• pp.153-169
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• 2011

Today using Internet environment is considered absolutely essential for establishing corporate marketing strategy. Companies have promoted their products and services through various ways of on-line marketing activities such as providing gifts and points to customers in exchange for participating in events, which is based on customers' membership data. Since companies can use these membership data to enhance their marketing efforts through various data analysis, appropriate website membership management may play an important role in increasing the effectiveness of on-line marketing campaign. Despite the growing interests in proper membership management, however, there have been difficulties in identifying inappropriate members who can weaken on-line marketing effectiveness. In on-line environment, customers tend to not reveal themselves clearly compared to off-line market. Customers who have malicious intent are able to create duplicate IDs by using others' names illegally or faking login information during joining membership. Since the duplicate members are likely to intercept gifts and points that should be sent to appropriate customers who deserve them, this can result in ineffective marketing efforts. Considering that the number of website members and its related marketing costs are significantly increasing, it is necessary for companies to find efficient ways to screen and exclude unfavorable troublemakers who are duplicate members. With this motivation, this study proposes an approach for managing duplicate membership based on the social network analysis and verifies its effectiveness using membership data gathered from real websites. A social network is a social structure made up of actors called nodes, which are tied by one or more specific types of interdependency. Social networks represent the relationship between the nodes and show the direction and strength of the relationship. Various analytical techniques have been proposed based on the social relationships, such as centrality analysis, structural holes analysis, structural equivalents analysis, and so on. Component analysis, one of the social network analysis techniques, deals with the sub-networks that form meaningful information in the group connection. We propose a method for managing duplicate memberships using component analysis. The procedure is as follows. First step is to identify membership attributes that will be used for analyzing relationship patterns among memberships. Membership attributes include ID, telephone number, address, posting time, IP address, and so on. Second step is to compose social matrices based on the identified membership attributes and aggregate the values of each social matrix into a combined social matrix. The combined social matrix represents how strong pairs of nodes are connected together. When a pair of nodes is strongly connected, we expect that those nodes are likely to be duplicate memberships. The combined social matrix is transformed into a binary matrix with '0' or '1' of cell values using a relationship criterion that determines whether the membership is duplicate or not. Third step is to conduct a component analysis for the combined social matrix in order to identify component nodes and isolated nodes. Fourth, identify the number of real memberships and calculate the reliability of website membership based on the component analysis results. The proposed procedure was applied to three real websites operated by a pharmaceutical company. The empirical results showed that the proposed method was superior to the traditional database approach using simple address comparison. In conclusion, this study is expected to shed some light on how social network analysis can enhance a reliable on-line marketing performance by efficiently and effectively identifying duplicate memberships of websites.

• Journal of the Korea Society for Simulation
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• v.23 no.1
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• pp.21-31
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• 2014

In this article, data mining simulation is applied to find a proper approach and results of analysis for study of variables related to organization. Also, turnover intention and organizational commitment are used as target (dependent) variables in this simulation. Classification and regression tree (CART) with ensemble methods are used in this study for simulation. Human capital corporate panel data of Korea Research Institute for Vocation Education & Training (KRIVET) is used. The panel data is collected in 2005, 2007, and 2009. Organizational commitment variables are analyzed with combined measure variables which are created after investigation of reliability and single dimensionality for multiple-item measurement details. The results of this study are as follows. First, major determinants of turnover intention are trust, communication, and talent management-oriented trend. Second, the main determining factors for organizational commitment are trust, the number of years worked, innovation, communication. CART with ensemble methods has two ensemble CART methods which are CART with Bagging and CART with Arcing. Comparing two methods, CART with Arcing (Arc-x4) extracted scenarios with very high coefficients of determination. In this study, a scenario with maximum coefficient of determinant and minimum error is obtained and practical implications are presented. Using one of data mining methods, CART with ensemble method. Also, the limitation and future research are discussed.

• International conference on construction engineering and project management
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• 2011.02a
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• pp.432-438
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• 2011

The purpose of the present thesis is to develop bankruptcy prediction models capable of being applied to the Korean construction industry and to deduce an optimal model through comparative evaluation of final developed models. A study population was selected as general contractors in the Korean construction industry. In order to ease the sample securing and reliability of data, it was limited to general contractors receiving external audit from the government. The study samples are divided into a bankrupt company group and a non-bankrupt company group. The bankruptcy, insolvency, declaration of insolvency, workout and corporate reorganization were used as selection criteria of a bankrupt company. A company that is not included in the selection criteria of the bankrupt company group was selected as a non-bankrupt company. Accordingly, the study sample is composed of a total of 112 samples and is composed of 48 bankrupt companies and 64 non-bankrupt companies. A financial ratio was used as early predictors for development of an estimation model. A total of 90 financial ratios were used and were divided into growth, profitability, productivity and added value. The MDA (Multivariate Discriminant Analysis) model and BLRA (Binary Logistic Regression Analysis) model were used for development of bankruptcy prediction models. The MDA model is an analysis method often used in the past bankruptcy prediction literature, and the BLRA is an analysis method capable of avoiding equal variance assumption. The stepwise (MDA) and forward stepwise method (BLRA) were used for selection of predictor variables in case of model construction. Twenty two variables were finally used in MDA and BLRA models according to timing of bankruptcy. The ROC-Curve Analysis and Classification Analysis were used for analysis of prediction performance of estimation models. The correct classification rate of an individual bankruptcy prediction model is as follows: 1) one year ago before the event of bankruptcy (MDA: 83.04%, BLRA: 93.75%); 2) two years ago before the event of bankruptcy (MDA: 77.68%, BLRA: 78.57%); 3) 3 years ago before the event of bankruptcy (MDA: 84.82%, BLRA: 91.96%). The AUC (Area Under Curve) of an individual bankruptcy prediction model is as follows. : 1) one year ago before the event of bankruptcy (MDA: 0.933, BLRA: 0.978); 2) two years ago before the event of bankruptcy (MDA: 0.852, BLRA: 0.875); 3) 3 years ago before the event of bankruptcy (MDA: 0.938, BLRA: 0.975). As a result of the present research, accuracy of the BLRA model is higher than the MDA model and its prediction performance is improved.