• Journal of vocational education research
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• v.36 no.1
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• pp.77-103
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• 2017

The purposes of this study are 1) to analyse the importance and performance of the success factors in steps of Formation process Of Cooperation NetWork(FoCNW) of educational institutions and businesses, and 2) to draw the success factors that need improvement. To achieve these purposes a survey research was conducted with a group of 1,098 (professors, teachers and workers in charge of cooperation at universities, technical colleges, vocational high schools and businesses which are equipped with a cooperation system), and 339 questionnaires were analyzed. The major results of the study are as follows. Firstly, while cooperation workers showed high recognition on the importance of success factors in steps of FoCNW, their performance of those factors was relatively low. Secondly, both the recognition of importance and performance of success factors in steps of FoCNW was higher in educational institutions than in businesses. Thirdly, the success factors in need of improvements from both educational institutions and businesses are 'collecting outer information and consulting' and 'publicizing cooperation competency', 'setting cooperative activities (programs) and contents of agreements', 'managing cooperation quality formally', 'drawing plans for sustainable cooperation improvement' and 'diffusing cooperation network'. The success factors in need of improvements from educational institutions are 'perceiving necessity of and forming consensus on cooperation' and 'deciding and supporting the worker in charge of cooperation', and 'participating and sharing of organization members'. The success factors in need of improvements from businesses are 'deciding goals and performance strategies', 'developing mutual understanding and checking possibility of cooperation with potential partners', and 'evaluating cooperation activities and their outcomes and providing feedback' and 'adjusting contents of agreements and deciding whether to maintain cooperation'. Therefore, efforts from each partner to improve those factors and support from related organizations are necessary.

• 한국노년학
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• v.37 no.2
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• pp.461-474
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• 2017

The purpose of this study was to develop the caregiver guideline for participation in activities daily living for the elderly with early dementia in home including easily adaptable and professional strategies as early intervetion. The process of this study was 3 stage. First stage, the development of preliminary caregiver guideline as the first stage of this study was to translate to Korean and reclassify the items about caregiver guideline for participation from reports of foreign association or government related dementia, and it consisted of 3 areas, 128 statements for questionnaire for Delphi investigation. Second stage, the guideline was to conduct the content validity, and delete, reclassify, integrate, revise inappropriate items through 2 rounds and 16 Delphi panels. Third stage, the establishment of the final version of caregiver guideline. It consisted of 8 areas: home modification and assistive device, home activities of daily living, health management, communication, psycho-emotional support, leisure activities, social participation, general strategies. All 68 items was arranged in important order. The content validity, stability, agreement index in this study were 0.81, 0.15, 0.79 respectively. When content validity, stability was above 0.49, below 0.5 in Delphi survey with 15 panels, it was not required additional survey. The result of this study meaned that it was not required to additional Delphi survey, and the result was stable and agreeable. This developed guideline was useful and practical to maintain the elderly's with dementia independent and healthy life as education materials for their caregivers, so it will expect to decrease caregiver's burden. Lastly, it stated limitation and suggestion for further study.

• Journal of vocational education research
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• v.37 no.3
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• pp.85-111
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• 2018

The purpose of this study is to derive the competence of nursing teacher in Specialized vocational high school. The knowledge, skills, and attitudes required for the nursing teachers were verified and the competency groups and sub-competencies were verified and priorities were suggested. To do this, 23 nursing teachers who were currently working in the Specialized vocational high school were selected as expert panels and the study was conducted using Delphi and Layered Analysis(AHP) technique. The results of this study were as follows. First, the competency group of the nursing teacher in Specialized vocational high school showed teaching and learning methods & techniques, student guidance, curriculum development and operation, school management support, industry-academy cooperation, self-development and professionalism improvement. The total number was 6. Second, the sub-competency is composed of knowledge and skills related to the curriculum, planning and preparation of instruction, instruction operation, guidance of experiment and practice, instruction evaluation, development and utilization of teaching and learning medium, instruction environment, guidance on student education activities outside the curriculum, career guidance, life guidance, class management, guidance of technology and qualification, development of school curriculum, organization and operation of school curriculum, planning and operation of field practice, work planning, school management support, teacher evaluation and personnel management support, leaning support(practice place and equipment), establishment and operation of industry-academia cooperation, strengthening community cooperation, public relations in the school(department), field research for improvement of educational activities, participation in conferences and training, exploration of new knowledge and technology, The total number was 26. The most important of the relative importance was the curriculum development and operation. The subordinate competencies that have a high priority in each competency group were guidance of experiment and practice, guidance of technology and qualification, planning and operation of field practice, leaning support(practice place and equipment), management and work planning, establishment and operation of industry-academia cooperation, exploration of new knowledge and technology. The competency system derived from this study will be applied to the training and evaluation of nursing teachers in the future and can be used as basic data for related research.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.15 no.6
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• pp.189-207
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• 2020

The government has achieved quantitative growth in university start-up education while supporting start-up education. However, it failed to systematize start-up education from an academic, policy, and practical perspective and to reveal the relationship between education and achievements in supporting start-ups. Therefore, there is a lack of interest and effort to promote effective education. In Europe, in-depth research has already been done over many years to establish an EntreComp system. Competences create values for others and attempt to apply them to education, viewing them as the people's lifelong competitiveness. On the other hand, it is urgent to improve the education system as domestic university start-up education is mainly focused on cultural level start-up skills and easy-to-access education from a business administration perspective. Based on this, the entrepreneurship competence-based start-up education system was designed. Next, eight EntreComp frames were drawn for university students through the Focus Group Interview (FGI) and Delphi survey methods, as well as domestic and international prior studies on EntreComp. In 2018, 919 start-up education programs of 42 start-up leading universities were conducted to derive the status of education by EntreComp. Prior studies of 25 entrepreneurship competences, including data from Bacigalupo et al.(2016), which studied EntreComp in the EU, were investigated and reflected the frequency of research and the importance of education and start-up perspectives. Based on the purpose of the university start-up education presented in this study, the entrepreneurship competence frame consisting of a total of eight, including spotting opportunities, value creation, self improvement, mobilising resources, technology application, strategic management, relationship, and learning through experience, was derived through expert verification. It also investigated the current status of education by competence, the degree of reflection of competence education, and the relationship with the results of support for start-ups that reflect the number of students enrolled in each university. Through this, it was suggested that future start-up education at universities could be improved from the EntreComp perspective. It has a differentiation in research in that it conducted a thorough survey using the data on start-up courses operated by leading startup universities for a certain period. However, it is difficult to generalize because the number of samples of leading startup universities is limited. Nevertheless, this study proposes the educational goal of advancing university start-up education from the perspective of entrepreneurial competence, cultivating future required competences, and fostering entrepreneurial talents that create value for others. In addition, it is meaningful in that it presents a clear direction for subsequent research by preparing a framework for research from a more essential perspective on the entrepreneurship competence frame.

• Journal of the Society of Disaster Information
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• v.18 no.1
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• pp.202-217
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• 2022

Purpose: The recent increased use of Personal Mobility (PM) has been accompanied by a rise in the annual number of accidents. Accordingly, the safety requirements for PM use are being strengthened, but the laws/systems, infrastructure, and management systems remain insufficient for fostering a safe environment. Therefore, this study comprehensively searches the main problems and improvement methods through a review of previous studies that are related to PM. Then the priorities according to the importance of the improvement methods are presented through the Delphi survey. Method: The research method is mainly composed of a literature study and an expert survey (Delphi survey). Prior research and improvement cases (local governments, government departments, companies, etc.) are reviewed to derive problems and improvements, and a problem/improvement classification table is created based on keywords. Based on the classification contents, an expert survey is conducted to derive a priority improvement plan. Result: The PM-related problems were in 'non-compliance with traffic laws, lack of knowledge, inexperienced operation, and lack of safety awareness' in relation to human factors, and 'device characteristics, road-drivable space, road facilities, parking facilities' in relation to physical factors. 'Management/supervision, product management, user management, education/training' as administrative factors and legal factors are divided into 'absence/sufficiency of law, confusion/duplication, reduced effectiveness'. Improvement tasks related to this include 'PM education/public relations, parking/return, road improvement, PM registration/management, insurance, safety standards, traffic standards, PM device safety, PM supplementary facilities, enforcement/management, dedicated organization, service providers, management system, and related laws/institutional improvement', and 42 detailed tasks are derived for these 14 core tasks. The results for the importance evaluation of detailed tasks show that the tasks with a high overall average for the evaluation items of cost, time, effect, urgency, and feasibility were 'strengthening crackdown/instruction activities, education publicity/campaign, truancy PM management, and clarification of traffic rules'. Conclusion: The PM market is experiencing gradual growth based on shared services and a safe environment for PM use must be ensured along with industrial revitalization. In this respect, this study seeks out the major problems and improvement plans related to PM from a comprehensive point of view and prioritizes the necessary improvement measures. Therefore, it can serve as a basis of data for future policy establishment. In the future, in-depth data supplementation will be required for each key improvement area for practical policy application.

• Korean Journal of Remote Sensing
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• v.38 no.5_3
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• pp.939-951
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• 2022

The most important factor in designing autonomous driving systems is to recognize the exact location of the vehicle within the surrounding environment. To date, various sensors and navigation systems have been used for autonomous driving systems; however, all have limitations. Therefore, the need for high-definition (HD) maps that provide high-precision infrastructure information for safe and convenient autonomous driving is increasing. HD maps are drawn using three-dimensional point cloud data acquired through a mobile mapping system (MMS). However, this process requires manual work due to the large numbers of points and drawing layers, increasing the cost and effort associated with HD mapping. The objective of this study was to improve the efficiency of HD mapping by segmenting semantic information in an MMS point cloud into six classes: roads, curbs, sidewalks, medians, lanes, and other elements. Segmentation was performed using various machine learning techniques including random forest (RF), support vector machine (SVM), k-nearest neighbor (KNN), and gradient-boosting machine (GBM), and 11 variables including geometry, color, intensity, and other road design features. MMS point cloud data for a 130-m section of a five-lane road near Minam Station in Busan, were used to evaluate the segmentation models; the average F1 scores of the models were 95.43% for RF, 92.1% for SVM, 91.05% for GBM, and 82.63% for KNN. The RF model showed the best segmentation performance, with F1 scores of 99.3%, 95.5%, 94.5%, 93.5%, and 90.1% for roads, sidewalks, curbs, medians, and lanes, respectively. The variable importance results of the RF model showed high mean decrease accuracy and mean decrease gini for XY dist. and Z dist. variables related to road design, respectively. Thus, variables related to road design contributed significantly to the segmentation of semantic information. The results of this study demonstrate the applicability of segmentation of MMS point cloud data based on machine learning, and will help to reduce the cost and effort associated with HD mapping.

• 한국노년학
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• v.29 no.2
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• pp.761-779
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• 2009

The purpose of this study is to analyze the factors affecting the quality of life of elderly people from the ecological perspective. Specifically, this study attempts to compare the relative contributions of the variables from the individual system, microsystem, mesosystem, and macrosystem on the quality of life of the elderly people. The subjects for this study consisted of 443 elderly over 60 years old in Daegu city, Gyeongsang-do, and Chungchung-do areas. The data were collected randomly by interviews with a structured questionnaire, and analyzed by frequencies, means, ANOVA, and hierarchial regression method. The major-findings of this study were as follows: 1. The quality of life of the elderly consisted of four factors; physical, economic, psychological, and social factors. The highest satisfaction score for the quality of life was found in psychological factors, The global satisfaction score for the quality of life was found to be relatively high. 2. Out of the individual factors influencing the quality of life of the elderly, the economic status of the respondent was found to be the most important. Out of the microsystemic factors, the most important factors were found to be the intimacy with the spouse and with the children. Out of the mesosysystemic factors, the degree of the social participation of the elderly was found to be the most significant. However, none of the macrosystemic factors were found to be important in influencing the quality of life of the elderly people. 3. The relative significances of the variables contributing to the quality of life of the elderly were analyzed using a hierarchical regression technique. It was found that there was no significant factor in social background and mesosystemic variables. On the other hand, the degree of the self-efficacy and the level of the economic status from the individual factors, and the intimacy with the spouse and the children from microsystemic factors were found to be very significant in contributing the quality of life of the elderly.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.18 no.1
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• pp.159-172
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• 2023

The purpose of this study is to explore a collaborative start-up space(Co-Space) configuration plan in terms of university-regional linkage through demand analysis on the composition of university-regional linkage startup space. To this end, a survey was conducted for request analysis, and the collected data were analyzed through the t-test, The Lotus for Focus model. In addition, FGI was implemented for entrepreneurs, and the direction of the composition of the university-region Co-Space was derived from various aspects. The results of this study are as follows. First, as a result of the analysis of the necessity of university-community Co-Space, the necessity of opening up the start-up space recognized by local residents and the necessity of building the start-up space in the region were high. In addition, men recognized the need to build a space for start-ups in the community more highly than women did women. Second, as a result of analysis of demands for university-regional Co-Space, the difference between current importance and future necessity of university-regional Co-Space was statistically significant. Third, as a result of analysis on the composition of the startup space by cooperation between universities and regions, different demands were made for composition of the startup space considering openness and closeness, and for composition of the startup space size. The implications of the study are as follows. First, Co-Spaces need to be constructed in conjunction with universities in accordance with the demands of start-up companies in the region by stage of development. Second, it is necessary to organize a customized Co-Space that takes into account the size and operation of the start-up space. Third, it is necessary to establish an experience-based open space for local residents in the remaining space of the university. Fourth, it is necessary to establish a Co-Space that enables an organic network between local communities, start-up investment companies, start-up support institutions, and start-up companies. This study is significant in that it proposed the regional startup ecosystem and the cooperative start-up space structure for strengthening start-up sustainability through cooperation between universities and local communities. The results of this study are expected to be used as useful basic data for Co-Space construction to build a regional start-up ecosystem in a trend emphasizing the importance of start-up space, which is a major factor affecting start-up companies.

• Journal of Intelligence and Information Systems
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• v.26 no.4
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• pp.111-126
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• 2020

Most industries have recently become aware of the importance of customer lifetime value as they are exposed to a competitive environment. As a result, preventing customers from churn is becoming a more important business issue than securing new customers. This is because maintaining churn customers is far more economical than securing new customers, and in fact, the acquisition cost of new customers is known to be five to six times higher than the maintenance cost of churn customers. Also, Companies that effectively prevent customer churn and improve customer retention rates are known to have a positive effect on not only increasing the company's profitability but also improving its brand image by improving customer satisfaction. Predicting customer churn, which had been conducted as a sub-research area for CRM, has recently become more important as a big data-based performance marketing theme due to the development of business machine learning technology. Until now, research on customer churn prediction has been carried out actively in such sectors as the mobile telecommunication industry, the financial industry, the distribution industry, and the game industry, which are highly competitive and urgent to manage churn. In addition, These churn prediction studies were focused on improving the performance of the churn prediction model itself, such as simply comparing the performance of various models, exploring features that are effective in forecasting departures, or developing new ensemble techniques, and were limited in terms of practical utilization because most studies considered the entire customer group as a group and developed a predictive model. As such, the main purpose of the existing related research was to improve the performance of the predictive model itself, and there was a relatively lack of research to improve the overall customer churn prediction process. In fact, customers in the business have different behavior characteristics due to heterogeneous transaction patterns, and the resulting churn rate is different, so it is unreasonable to assume the entire customer as a single customer group. Therefore, it is desirable to segment customers according to customer classification criteria, such as loyalty, and to operate an appropriate churn prediction model individually, in order to carry out effective customer churn predictions in heterogeneous industries. Of course, in some studies, there are studies in which customers are subdivided using clustering techniques and applied a churn prediction model for individual customer groups. Although this process of predicting churn can produce better predictions than a single predict model for the entire customer population, there is still room for improvement in that clustering is a mechanical, exploratory grouping technique that calculates distances based on inputs and does not reflect the strategic intent of an entity such as loyalties. This study proposes a segment-based customer departure prediction process (CCP/2DL: Customer Churn Prediction based on Two-Dimensional Loyalty segmentation) based on two-dimensional customer loyalty, assuming that successful customer churn management can be better done through improvements in the overall process than through the performance of the model itself. CCP/2DL is a series of churn prediction processes that segment two-way, quantitative and qualitative loyalty-based customer, conduct secondary grouping of customer segments according to churn patterns, and then independently apply heterogeneous churn prediction models for each churn pattern group. Performance comparisons were performed with the most commonly applied the General churn prediction process and the Clustering-based churn prediction process to assess the relative excellence of the proposed churn prediction process. The General churn prediction process used in this study refers to the process of predicting a single group of customers simply intended to be predicted as a machine learning model, using the most commonly used churn predicting method. And the Clustering-based churn prediction process is a method of first using clustering techniques to segment customers and implement a churn prediction model for each individual group. In cooperation with a global NGO, the proposed CCP/2DL performance showed better performance than other methodologies for predicting churn. This churn prediction process is not only effective in predicting churn, but can also be a strategic basis for obtaining a variety of customer observations and carrying out other related performance marketing activities.

• Journal of Intelligence and Information Systems
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• v.26 no.4
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• pp.173-198
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• 2020

For a long time, many studies have been conducted on predicting the success of campaigns for customers in academia, and prediction models applying various techniques are still being studied. Recently, as campaign channels have been expanded in various ways due to the rapid revitalization of online, various types of campaigns are being carried out by companies at a level that cannot be compared to the past. However, customers tend to perceive it as spam as the fatigue of campaigns due to duplicate exposure increases. Also, from a corporate standpoint, there is a problem that the effectiveness of the campaign itself is decreasing, such as increasing the cost of investing in the campaign, which leads to the low actual campaign success rate. Accordingly, various studies are ongoing to improve the effectiveness of the campaign in practice. This campaign system has the ultimate purpose to increase the success rate of various campaigns by collecting and analyzing various data related to customers and using them for campaigns. In particular, recent attempts to make various predictions related to the response of campaigns using machine learning have been made. It is very important to select appropriate features due to the various features of campaign data. If all of the input data are used in the process of classifying a large amount of data, it takes a lot of learning time as the classification class expands, so the minimum input data set must be extracted and used from the entire data. In addition, when a trained model is generated by using too many features, prediction accuracy may be degraded due to overfitting or correlation between features. Therefore, in order to improve accuracy, a feature selection technique that removes features close to noise should be applied, and feature selection is a necessary process in order to analyze a high-dimensional data set. Among the greedy algorithms, SFS (Sequential Forward Selection), SBS (Sequential Backward Selection), SFFS (Sequential Floating Forward Selection), etc. are widely used as traditional feature selection techniques. It is also true that if there are many risks and many features, there is a limitation in that the performance for classification prediction is poor and it takes a lot of learning time. Therefore, in this study, we propose an improved feature selection algorithm to enhance the effectiveness of the existing campaign. The purpose of this study is to improve the existing SFFS sequential method in the process of searching for feature subsets that are the basis for improving machine learning model performance using statistical characteristics of the data to be processed in the campaign system. Through this, features that have a lot of influence on performance are first derived, features that have a negative effect are removed, and then the sequential method is applied to increase the efficiency for search performance and to apply an improved algorithm to enable generalized prediction. Through this, it was confirmed that the proposed model showed better search and prediction performance than the traditional greed algorithm. Compared with the original data set, greed algorithm, genetic algorithm (GA), and recursive feature elimination (RFE), the campaign success prediction was higher. In addition, when performing campaign success prediction, the improved feature selection algorithm was found to be helpful in analyzing and interpreting the prediction results by providing the importance of the derived features. This is important features such as age, customer rating, and sales, which were previously known statistically. Unlike the previous campaign planners, features such as the combined product name, average 3-month data consumption rate, and the last 3-month wireless data usage were unexpectedly selected as important features for the campaign response, which they rarely used to select campaign targets. It was confirmed that base attributes can also be very important features depending on the type of campaign. Through this, it is possible to analyze and understand the important characteristics of each campaign type.