• Journal of Korean Society of Industrial and Systems Engineering
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• v.42 no.3
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• pp.116-130
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• 2019

This study investigates passenger-authored online reviews of airline services using social network analysis to compare the differences in customer perceptions between full service carriers (FSCs) and low cost carriers (LCCs). While deriving words with high frequency and weight matrix based on the text analysis for FSCs and LCCs respectively, we analyze the semantic network (betweenness centrality, eigenvector centrality, degree centrality) to compare the degree of connection between words in online reviews of each airline types using the social network analysis. Then we compare the words with high frequency and the connection degree to gauge their influences in the network. Moreover, we group eight clusters for FSCs and LCCs using the convergence of iterated correlations (CONCOR) analysis. Using the resultant clusters, we match the clusters to dimensions of two types of service quality models ($Gr{\ddot{o}}nroos$, Brady & Cronin (B&C)) to compare the airline service quality and determine which model fits better. From the semantic network analysis, FSCs are mainly related to inflight service words and LCCs are primarily related to the ground service words. The CONCOR analysis reveals that FSCs are mainly related to the dimension of outcome quality in $Gr{\ddot{o}}nroos$ model, but evenly distributed to the dimensions in B&C model. On the other hand, LCCs are primarily related to the dimensions of process quality in both $Gr{\ddot{o}}nroos$ and B&C models. From the CONCOR analysis, we also observe that B&C model fits better than $Gr{\ddot{o}}nroos$ model for the airline service because the former model can capture passenger perceptions more specifically than the latter model can.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.19 no.12
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• pp.416-431
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• 2018

The purpose of this study was to analyze in-depth the nurses' experience of hospital work and the factors affecting career interruption of nurses. For this purpose, 20 inactive nurses aged 26-55 participated in interviews about their experience as nurses, characteristics of nursing work, and reasons for retirement, and applied the analysis method of Colaizzi among phenomenological research methods. The analysis resulted in 104 significant statements, 17 formulated meanings, 6 clusters of theme, and 2 categories. The two categories are nursing work characteristics and work environment characteristics. In the characteristics of nursing work, there are four clusters of theme of 'time pressure', 'lack of autonomy', 'physical and emotional labor', 'low wage increase', and in the characteristics of nursing work environment, there are two clusters of theme of 'physical environment' and 'human environment'. Therefore, in order to improve the nurse career interruption, efforts should be made to alleviate excessive work intensity, raise the appropriate level of pay and provide a safe working environment.

• Journal of Korean Society for Quality Management
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• v.39 no.1
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• pp.98-108
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• 2011

Clustering is an useful method to classify objects into subsets that have some meaning in the context of a particular problem and has been applied in variety of fields, customer relationship management, data mining, pattern recognition, and biotechnology etc. This paper addresses the unknown K clustering problems and presents a new approach based on a coevolutionary algorithm to solve it. Coevolutionary algorithms are known as very efficient tools to solve the integrated optimization problems with high degree of complexity compared to classical ones. The problem considered in this paper can be divided into two sub-problems; finding the number of clusters and classifying the data into these clusters. To apply to coevolutionary algorithm, the framework of algorithm and genetic elements suitable for the sub-problems are proposed. Also, a neighborhood-based evolutionary strategy is employed to maintain the population diversity. To analyze the proposed algorithm, the experiments are performed with various test-bed problems which are grouped into several classes. The experimental results confirm the effectiveness of the proposed algorithm.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.25 no.6
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• pp.1583-1593
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• 2015

This study applied the BSC performance consulting experience and diverse variable factors and whether there any differences in terms of satisfaction and the relationship between the support for the government consulting firm specializing in human resources by the resident of the innovation cluster area was investigated through empirical research. According to the study, a new high in recent local government support Consulting Consulting satisfaction by professionals residing in the area that applies to industrial clusters leads to practical consultancy, which was being evaluated as a significant boon to real performance of the company.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.13 no.10
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• pp.4440-4444
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• 2012

The purpose of the paper is to draw conclusions form the EU's cluster policy for Korean government policy. The paper consists of five chapters. Chapter two develops theoretical underpinnings of clusters. In chapter three are the research methods shown. Chapter four describes the EU policy for clusters. In chapter is drawn a conclusion and policy implications are discussed. Academic contribution of this study is academically analyzed the first time the research of EU Cluster and Policy of the EU Cluster is to enhance the innovative for SMEs technology.

• Journal of the Korean Academic Society of Industrial Cluster
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• v.3 no.1
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• pp.26-38
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• 2009

Many economic development practitioners view cluster theory and analysis as constituting a general approach to strategy making in economic development, which may lead them to prioritize policy and planning interventions that cannot address the actual development challenges in their cities and regions. This paper discusses the distinction between strategy formation and strategic planning, where the latter is the programming of development strategies that are identified through a blend of experience, intuition, and analysis. Cluster theories and analytical tools can provide useful informational inputs into a strategy making effort and they can also be helpful for programming specific interventions (i.e., strategic planning). However, they should not be used as the exclusive or even predominant framework for filtering information about the competitive advantages of a region or for formulating strategy. To do so forces strategy making into a conceptual box defined by only one highly stylized theory of regional growth and development.

• Journal of Technology Innovation
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• v.13 no.2
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• pp.207-222
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• 2005

A prerequisite for making R&D more productive is to able to measure its productivity. Most of the previous studies on this topic have attempted to measure R&D productivity at the firm or industry levels. In this study, however, R&D productivity is measured at the national level to provide R&D policy implications, particularly for Asian countries. Contrary to the previous studies where total factor productivity was adopted, this study employs the data envelopment analysis (DEA) approach to measure R&D productivity. DEA is a multi-factor productivity analysis model for measuring the relative efficiency of each Decision Making Unit (DMU). In addition to the basic DEA model that includes all inputs and outputs, five additional models are constructed by combining single input with all outputs and single output with all inputs in order to measure specialized R&D efficiency. In this study, the twenty-seven countries are classified into four clusters based on the output-specialized R&D efficiency: inventors, merchandisers, academicians, and duds. Then, the characteristics of the Asian countries with respect to R&D efficiency are identified. It is found that Singapore ranks high in total efficiency, and Japan in patent-oriented efficiency. Meanwhile, China, Korea, and Taiwan are found to be relatively inefficient in R&D. We expect that the findings from this study will be able to provide directions for R&D policy-making of the Asian countries.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.8 no.3
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• pp.569-575
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• 2007

The main issue of this study is to find ways to lengthen the lifetime of network mainly by reducing energy consumption. This paper proposes how to reduce the amount of data transmitted in each sensor and cluster head in order to lengthen the lifetime of sensor network. The most important factor of reducing the sensor's energy dissipation is to reduce the amount of messages transmitted. This study proposes cluster cohesion for the purposes. The method is to use the cluster cohesion and manage the number of clusters adaptively and reduce the amount of message transmitted in network topology. This method should be much more efficient and effective as it reduces the network traffic significantly and increases the network's lifetime.

• Journal of Korean Institute of Industrial Engineers
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• v.37 no.3
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• pp.180-190
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• 2011

Efficient energy consumption is a critical factor for deployment and operation of wireless sensor networks (WSNs). To achieve energy efficiency there have been several hierarchical routing protocols that organize sensors into clusters where one sensor is a cluster-head to forward messages received from its cluster-member sensors to the base station of the WSN. In this paper, we propose a self-organized clustering method for cluster-head selection and cluster based routing for a WSN. To select cluster-heads and organize clustermembers for each cluster, every sensor uses only local information and simple decision mechanisms which are aimed at configuring a self-organized system. By these self-organized interactions among sensors and selforganized selection of cluster-heads, the suggested method can form clusters for a WSN and decide routing paths energy efficiently. We compare our clustering method with a clustering method that is a well known routing protocol for the WSNs. In our computational experiments, we show that the energy consumptions and the lifetimes of our method are better than those of the compared method. The experiments also shows that the suggested method demonstrate properly some self-organized properties such as robustness and adaptability against uncertainty for WSN's.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.19 no.39
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• pp.47-61
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• 1996

We propose a parallel neural network model in which patterns are clustered and patterns in a cluster are studied in a parallel neural network. The learning algorithm used in this paper is based on LVQ algorithm of Kohonen(1990) for clustering and ADALINE(Adaptive Linear Neuron) network of Widrow and Hoff(1990) for parallel learning. The proposed algorithm consists of two parts. First, N patterns to be learned are categorized into C clusters by LVQ clustering algorithm. Second, C patterns that was selected from each cluster of C are learned as input pattern of ADALINE(Adaptive Linear Neuron). Data used in this paper consists of 250 patterns of ASCII characters normalized into $8\times16$ and 1124. The proposed algorithm consists of two parts. First, N patterns to be learned are categorized into C clusters by LVQ clustering algorithm. Second, C patterns that was selected from each cluster of C are learned as input pattern of ADALINE(Adaptive Linear Neuron). Data used in this paper consists 250 patterns of ASCII characters normalized into $8\times16$ and 1124 samples acquired from signals generated from 9 car models that passed Inductive Loop Detector(ILD) at 10 points. In ASCII character experiment, 191(179) out of 250 patterns are recognized with 3%(5%) noise and with 1124 car model data. 807 car models were recognized showing 71.8% recognition ratio. This result is 10.2% improvement over backpropagation algorithm.