• Journal of the Korean Academic Society of Industrial Cluster
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• v.1 no.1
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• pp.29-46
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• 2007

The purpose of this paper is to analyses current status and issues of cluster formation and extract policy implications. To this end, the questionnaire which surveyed the level of cluster formation were executed targeting the actors of regional innovation projects(RIPs). The results show that the situations and development stage of the cluster formation between capital region and non-capital region, large cities and small and medium sized cites are different. The level of clustering is also satisfactory, which is a requirement for cluster formation at its early stage. However, the capacity for phase II of cluster growth is not sufficient yet in terns of relationships between ventures and large corporations, institutions supporting management, finance and marketing, researchers from each individual sector of strategic industries and spin-off fines. Therefore, RIPs should be promoted with different policy tools for various regions that are devised according to the varying development stage of each region. The location of RIPs should be determined considering efficiency rather than equity, clustering rather than decentralization, and specialization rather than multiple development. In the long term, developed regions should pursue balanced regional development, with underdeveloped regions targeting specialization.

• Survey Research
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• v.7 no.1
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• pp.55-84
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• 2006

This study has classified development stages (Embryonic-Growth-Maturity) of mobile telecommunication industry based on Industry Life Cycle theory. There are two steps to be analyzed in this study, In the first step, cluster was investigated through cluster analysis using mobile density to categorize development stages of mobile telecommunication industry. In the second step, we compared on indexes of market structure, market efficiency and market performance to find out characteristics of each stage of development. The results are as follows. First, HHI is higher at embryonic stage than at growth and maturity stages, Second, ARPU(Average Revenue Per User) and RPM(Revenue Per Minute) are getting higher as the stages move on. Third, EBITDA margins, an index of market performance, is decreasing along the three stages. Finally, this study presents a clue to define the stage of development of mobile telecommunication industry and build a proper strategy for the market change.

• Korean Journal of Microbiology
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• v.49 no.1
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• pp.8-16
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• 2013

A GAL4 type transcription factor gene (formally annotated as AN3912) locating downstream of sndA (AN3911) was characterized. The putative transcription factor carries both Zn(II)2Cys6 binuclear cluster DNA-binding domain and transcription activator domain. The gene named gtfA (gal4 type transcription factor) had an open reading frame which consisted of 762 amino acids and was disrupted by three introns. The deletion mutant produced reduced amount of conidia but increased amount of fruiting bodies, suggesting that the GtfA make function in decision of asexual preferential to sexual development. The forced over expression of gtfA caused the retardation of fruiting body formation on high glucose concentration. The transcript level of gtfA was kept constant through the life cycle except late vegetative stage and early sexual development stage during which slight increase was found. The expression of gtfA was not significantly affected by sexual or asexual development regulators, such as VeA, NsdD or FluG, FadA, and SfaD. The GtfA repressed the nsdC transcription, which suggested that GftA control sexual development negatively via negative regulation of nsdC expression.

• Journal of Global Scholars of Marketing Science
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• v.17 no.3
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• pp.1-19
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• 2007

1. Purpose Rural tourism is made by individuals with different characteristics, needs and wants. It is important to have information on the characteristics and preferences of the consumers of the different types of existing rural accommodation. The stud aims to identify the determinants of consumer preference by type of accommodations. 2. Methodology 2.1 Sample Data were collected from 1000 people by telephone survey with three-stage stratified random sampling in seven metropolitan areas in Korea. Respondents were chosen by sampling internal on telephone book published in 2006. We surveyed from four to ten-thirty 0'clock afternoon so as to systematic sampling considering respondents' life cycle. 2.2 Two-step cluster Analysis Our study is accomplished through the use of a two-step cluster method to classify the accommodation in a reduced number of groups, so that each group constitutes a type. This method had been suggested as appropriate in clustering large data sets with mixed attributes. The method is based on a distance measure that enables data with both continuous and categorical attributes to be clustered. This is derived from a probabilistic model in which the distance between two clusters in equivalent to the decrease in log-likelihood function as a result of merging. 2.3 Multinomial Logit Analysis The estimation of a Multionmial Logit model determines the characteristics of tourist who is most likely to opt for each type of accommodation. The Multinomial Logit model constitutes an appropriate framework to explore and explain choice process where the choice set consists of more than two alternatives. Due to its ease and quick estimation of parameters, the Multinomial Logit model has been used for many empirical studies of choice in tourism. 3. Findings The auto-clustering algorithm indicated that a five-cluster solution was the best model, because it minimized the BIC value and the change in them between adjacent numbers of clusters. The accommodation establishments can be classified into five types: Traditional House, Typical Farmhouse, Farmstay house for group Tour, Log Cabin for Family, and Log Cabin for Individuals. Group 1 (Traditional House) includes mainly the large accommodation establishments, i.e. those with ondoll style room providing meals and one shower room on family tourist, of original construction style house. Group 2 (Typical Farmhouse) encompasses accommodation establishments of Ondoll rooms and each bathroom providing meals. It includes, in other words, the tourist accommodations Known as "rural houses." Group 3 (Farmstay House for Group) has accommodation establishments of Ondoll rooms not providing meals and self cooking facilities, large room size over five persons. Group 4 (Log Cabin for Family) includes mainly the popular accommodation establishments, i.e. those with Ondoll style room with on shower room on family tourist, of western styled log house. While the accommodations in this group are not defined as regards type of construction, the group does include all the original Korean style construction, Finally, group 5 (Log Cabin for Individuals)includes those accommodations that are bedroom western styled wooden house with each bathroom. First Multinomial Logit model is estimated including all the explicative variables considered and taking accommodation group 2 as base alternative. The results show that the variables and the estimated values of the parameters for the model giving the probability of each of the five different types of accommodation available in rural tourism village in Korea, according to the socio-economic and trip related characteristics of the individuals. An initial observation of the analysis reveals that none of variables income, the number of journey, distance, and residential style of house is explicative in the choice of rural accommodation. The age and accompany variables are significant for accommodation establishment of group 1. The education and rural residential experience variables are significant for accommodation establishment of groups 4 and 5. The expenditure and marital status variables are significant for accommodation establishment of group 4. The gender and occupation variable are significant for accommodation establishment of group 3. The loyalty variable is significant for accommodation establishment of groups 3 and 4. The study indicates that significant differences exist among the individuals who choose each type of accommodation at a destination. From this investigation is evident that several profiles of tourists can be attracted by a rural destination according to the types of existing accommodations at this destination. Besides, the tourist profiles may be used as the basis for investment policy and promotion for each type of accommodation, making use in each case of the variables that indicate a greater likelihood of influencing the tourist choice of accommodation.