• Information Systems Review
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• v.25 no.2
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• pp.181-203
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• 2023

With the fierce competition of companies for the attraction of outstanding individuals, job satisfaction of employees has been of importance. In this circumstance, many companies try to invest in job satisfaction improvement by finding employees' everyday experiences and difficulties. However, due to a lack of understanding of the employee experience, their investments are not paying off. This study examined the relationship between employee experience and job satisfaction using employee reviews and company ratings from Glassdoor, one of the largest employee communities worldwide. We use text mining techniques such as K-means clustering and LDA topic-based sentiment analysis to extract key experience factors by job level, and DistilBERT sentiment analysis to measure the sentiment score of each employee experience factor. The drawn employee experience factors and each sentiment score were analyzed quantitatively, and thereby relations between each employee experience factor and job satisfaction were analyzed. As a result, this study found that there is a significant difference between the workplace experiences of managers and general employees. In addition, employee experiences that affect job satisfaction also differed between positions, such as customer relationship and autonomy, which did not affect the satisfaction of managers. This study used text mining and quantitative modeling method based on theory of work adjustment so as to find and verify main factors of employee experience, and thus expanded research literature. In addition, the results of this study are applicable to the personnel management strategy for improving employees' job satisfaction, and are expected to improve corporate productivity ultimately.

• Korean Management Science Review
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• v.16 no.1
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• pp.157-171
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• 1999

In a increasingly competitive marketplace, the manufacturing companies have no choice but looking for ways to improve productivity to sustain their competitiveness and survive in the industry. Recently cellular manufacturing has been under discussion as an option to be easily implemented without burdensome capital investment. The objective of cellular manufacturing is to realize many aspects of efficiencies associated with mass production in the less repetitive job-shop production systems. The very first step for cellular manufacturing is to group the sets of parts having similar processing requirements into part families, and the equipment needed to process a particular part family into machine cells. The underlying problem to determine the part and machine assignments to each manufacturing cell is called the cell formation. The purpose of this study is to develop a clustering algorithm based on the neural network approach which overcomes the drawbacks of ART1 algorithm for cell formation problems. In this paper, a generalized learning vector quantization(GLVQ) algorithm was devised in order to transform a 0/1 part-machine assignment matrix into the matrix with diagonal blocks in such a way to increase clustering performance. Furthermore, an assignment problem model and a rearrangement procedure has been embedded to increase efficiency. The performance of the proposed algorithm has been evaluated using data sets adopted by prior studies on cell formation. The proposed algorithm dominates almost all the cell formation reported so far, based on the grouping index($\alpha$ = 0.2). Among 27 cell formation problems investigated, the result by the proposed algorithm was superior in 11, equal 15, and inferior only in 1.

• The KIPS Transactions:PartD
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• v.12D no.5 s.101
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• pp.719-728
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• 2005

2-dimensional electrophoresis(2DE) is a separation technique to identify proteins contained in a sample. However, the image is very sensitive to its experimental conditions as well as the quality of scanning. In order to adjust the possible variation of spots in a particular image, a user should manually annotate landmark spots on each gel image to analyze the spots of different images together. However, this operation is an error-prone and tedious job. This thesis develops an automated method of extracting the landmark spots of an image based on landmark profile. The landmark profile is created by clustering the previously identified landmarks of sample images of the same type. The profile contains the various properties of clusters identified for each landmark. When the landmarks of a new image need to be fount all the candidate spots of each landmark are first identified by examining the properties of its clusters. Subsequently, all the landmark spots of the new image are collectively found by the well-known optimization algorithm $A^*$. The performance of this method is illustrated by various experiments on real 2DE images of mouse's brain-tissues.

• Journal of the military operations research society of Korea
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• v.33 no.2
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• pp.101-113
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• 2007

The period of military personnel service will be phased down by 2014 according to 'The law of National Defense Reformation' issued by the Ministry of National Defense. For this reason, the ROK army provides discrimination education to 'newly recruited privates' for more effective individual performance in the on-the-job training. For the training to be more effective, it would be essential to predict the degree of achievements by new privates in the training. Thus, we used data mining techniques to develop a classification model which classifies the new privates into one of two achievements groups, so that different skills of education are applied to each group. The target variable for this model is a binary variable, whose value can be either 'a group of general control' or 'a group of special control'. We developed four pure classification models using Neural Network, Decision Tree, Support Vector Machine and Naive Bayesian. We also built four hybrid models, each of which combines k-means clustering algorithm with one of these four mining technique. Experimental results demonstrated that the highest performance model was the hybrid model of k-means and Neural Network. We expect that various military education programs could be supported by these classification models for better educational performance.

• Journal of the Korea Computer Graphics Society
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• v.12 no.4
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• pp.23-32
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• 2006

In recent years, since the digital camera has been popularized, so users can easily collect hundreds of photos in a single usage. Thus the managing of hundreds of digital photos is not a simple job comparing to the keeping paper photos. We know that managing and classifying a number of digital photo files are burdensome and annoying sometimes. So people hope to use an automated system for managing digital photos especially for their own purposes. The previous studies, e.g. content-based image retrieval, were focused on the clustering of general images, which it is not to be applied on digital photo clustering and classification. Recently, some specialized clustering algorithms for images clustering digital camera images were proposed. These algorithms exploit mainly the statistics of time gap between sequent photos. Though they showed a quite good result in image clustering for digital cameras, still lots of improvements are remained and unsolved. For example the current tools ignore completely the image transformation with the different focal lengths. In this paper, we present a photo considering focal length information recorded in EXIF. We propose an algorithms based on MVA(Matching Vector Analysis) for classification of digital images taken in the every day activity. Our experiment shows that our algorithm gives more than 95% success rates, which is competitive among all available methods in terms of sensitivity, specificity and flexibility.

• Korean Management Science Review
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• v.13 no.3
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• pp.187-198
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• 1996

The traveling salesman problem has been studied for many years since the model can be used for various applications such as vehicle routing, job sequencing, clustering a data array, and so on. In this paper one of the typical exact algorithms for TSP, Minty's, will be modified to improve the performance of the algorithm on the applications without losing simplicity. The Little's algorithm gives good results, however, the simple and plain Minty's algorithm for solving shortest-route problems has the most intuitive appeal. The suggested Minty's modification is based on the creation of penalty-values on the matrix of a TSP. Computer experiments are made to verify the effectiveness of the modification.

• Journal of the Korean Society for Library and Information Science
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• v.50 no.3
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• pp.113-132
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• 2016

Positioning technique which has been widely used for making marketing strategy by analyzing customer's image also has been used for public and test-taker's image analysis about public facilities, entrepreneurs, universities. This study analyze image of library and Information science students who trying to find a job in library fields about diverse types of library and information centers by Positioning technique. As a result of Similarity cognition analysis by multidimensional Scaling and K-means clustering, it was found that students recognize that public, national, university, school library are similar, on the other hand, portal company and special library are different from those types. In the jobs, user service jobs and technical service jobs are recognized as separated clusters, and cultural program job is also recognized dissimilarly from those clusters. By the way, images about work satisfaction and stability of employment shows high in national library; high wage shows high in portal company; employee's growth potential shows high in special library; job importance shows high in reference service jobs; difficulty shows high in content's job. Anyway, in the workplace selection, almost students regard stability of employment as top priorities, accordingly they prefers public library at most. Such a preference concentration tendency is strongly appeared in local university students than in metropolitan area students as a result of Pearson's chi-square test.

• Korean Journal of Occupational Health Nursing
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• v.14 no.2
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• pp.138-152
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• 2005

Purposes: The purposes of this study are to investigate the definition, components, prevalence, and associated factors of metabolic syndrome and suggest the management strategies for workers. Method: This study was conducted by literature review. Results: Metabolic syndrome by the NCEP-ATP III is the clustering of three or more of five conditions: abdominal obesity, high triglycerides, low levels of HDL cholesterol, high blood pressure, and high glucose(blood sugar). The prevalence of the metabolic syndrome by modified NCEP-ATP III in South Korean workers was about 20 to 25%. Metabolic syndrome is caused by many associated factors, namely, age, family history, socioeconomic status, job strain, shift work, psychosocial distress, bad health behaviprs and so on. Conclusions: To prevent metabolic syndrome at worksites, multifactorial risk factor assessments and preventive approaches are required. Socioeconomic factors such as education, working status should be nationally importantly considered for the health inequality of workers. Occupational health nurse, at first, can start weight control, smoking cessation program. stress management, the improvement of work environment. Next stage, early diagnosis and treatment for metabolic risk group can be performed.

• Journal of Korean Academy of Nursing Administration
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• v.10 no.2
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• pp.255-264
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• 2004

Purpose: The Purpose of this study was to identify the relationship between 7S factors of the nursing organizational culture and organizational effectiveness. Method: The data were gathered from the self-reported questionnaires of 717 nurses who work for eight different general hospitals located around Seoul and Kyounggi province. The period of data collection was from November 12 to December 7, 2002. For data analysis, descriptive statistics, clustering analysis, and t-test with SPSS Program were used. Result: The nurses who highly perceived 7S factors of nursing organizational culture showed higher job satisfaction and organizational commitment in comparison with the nurses who lowly perceived 7S factors of nursing organizational culture. And the nurses who highly perceived 7S factors of nursing organizational culture showed higher organizational citizenship behavior in comparison with the nurses who lowly perceived 7S factors of nursing organizational culture. Among subdimension of organizational citizenship behavior, altruism and civic virtue were significant. Conclusion: From the above results, the high group with 7S factors of nursing organizational culture has strong culture, therefore nursing organization with strong culture is very implicative to enhance the organizational effectiveness.

• Quality Improvement in Health Care
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• v.5 no.2
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• pp.296-310
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• 1998

Background : Performance evaluation of medical care providers has become more important than before in Korea. Especially in university hospitals, job contents of medical staffs are so complicated that evaluation is not easily performed. In addition, in order that the feedback of evaluation be successful, acceptance of staffs to be evaluated is essential. This study is aimed at the development of items for evaluation and weighting of each item in one university hospital, and clustering departments by different weight given by medical staffs. Methods : Through resource group meeting. performance items were listed up by categories of education, research, medical services, and other activities in and out of the hospital. For each item, all the medical staffs were asked how important they thought, compared with publishing one original article. By factor analysis, the items in each category were grouped into a few subgroups. In turn, cluster analysis was done for the purpose of grouping departments by priority the medical staffs gave. Results and Conclusion : Among five major categories, medical staffs regard education, research, and medical services more important than other activities in and out of the hospital. Five categories consisted of two or three components. Departments in hospital were grouped into three. However, characteristics of each group was not clearly delineated. This result suggests that more comprehensive tool should be developed and applied in the process of performance evaluation in university hospitals.