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    '-์ง€๋งŒ', '-๋Š”๋ฐ', '-์œผ๋ฉด์„œ', '-๋”๋‹ˆ'๋ฅผ ์ค‘์‹ฌ์œผ๋กœ

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    ํ•™์œ„๋…ผ๋ฌธ (์„์‚ฌ) -- ์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› : ์‚ฌ๋ฒ”๋Œ€ํ•™ ๊ตญ์–ด๊ต์œก๊ณผ(ํ•œ๊ตญ์–ด๊ต์œก์ „๊ณต), 2021. 2. ๊ตฌ๋ณธ๊ด€.This study aims to suggest the effective teaching contents of similar grammar items focusing on Korean connective endings that express opposite relation, such as โ€“jiman, -neunde, -eumyeonseo, -deoni. For this purpose, distinctive features of these connective endings were analyzed and the difference of perception of these between Korean learners and native Korean speakers was examined. Korean connective endings are known that make Korean learners feel burdened because several items are included in one same semantic category, there are not only subtle differences in the implied meaning but also distinct constraints on the syntactic and pragmatic aspects. As a result, learners use only limited connective endings. However, since the goal of Korean grammar education is to make learners understand the subtle differences between grammar items and use them accurately and appropriately in communicative situations, it is essential to teach connective endings that could reveal the speakers intention. From this standpoint, a study on similar grammar items for Korean language education focusing on connective endings was conducted in this paper. Among the various semantic categories of connective endings, opposite relation was chosen for the following reasons: first, it was not discussed so far how these connective endings are differentiated even though there are some cases that cannot replace one another. In the second, although Korean learners learn a variety of connective endings that express opposite relation, they use only โ€“jiman or โ€“nuende. So that learners could develop their ability to use various connective endings, it should be provided explicit teaching contents based on the different points of these grammar items. Therefore this study was conducted to establish the foundation of teaching contents that deal with Korean similar connective endings that express opposite relations, examining the different points of connective endings by analyzing the native Korean speakers corpus and conducting an analysis on the perception of Korean learners and native Korean speakers through the investigation research. The second chapter focused on the theoretical concept of this paper based on prior studies of similar grammar items and the connective endings that express opposite relation. First of all, it was determined to the scope of the semantic similarity for the education of connective endings, which covers not only the basic meaning but also the extension meaning that includes the pragmatic homogeneity of connective endings. Next, the extended concept of the opposite relation was founded based on the semantic similarity of this study. Also reviewing the extended functions of the connective endings that express opposite relation, it was emphasized the necessity of analyzing them in the context of spoken dialogue. Based on these discussions โ€“jiman, -nuende, -eumyeonseo, -deoni were selected for the research and reviewing prior studies of the syntactic, semantic, and pragmatic characteristics of them some points that should be discussed more were commented on. In Chapter โ…ข, through analyzing the Korean native speakers colloquial dialogue corpus it was drawn to the points at which the connective endings that express opposite relation are differentiated. It was found that these connective endings used in spoken dialogue had various syntactic and semantic differences. And the uses of these are classified [formality], [argumentness], [politeness], and [impoliteness]. In Chapter IV, a survey was conducted on 92 intermediate and advanced Korean learners and 64 native Korean speakers. As a result, it came out that learners avoided certain connective endings due to the complexity of them and overapplied them by simplifying grammar rules. And it was also revealed that there was a lack of understanding of the different characteristics among these connective endings that express opposite relation. In Chapter โ…ค, the teaching contents were constructed based on the results of the spoken dialogue corpus analysis and the analysis of the Korean native speakers and Korean learners perceptions. It was proposed the explicit teaching contents that could distinguish them by linking syntactic and pragmatic features based on the semantic features. In the last chapter, a conclusion was drawn based on the result and the limitation of this study was stated.๋ณธ ์—ฐ๊ตฌ๋Š” ๋ฌธ๋ฒ• ๊ต์œก์˜ ๊ด€์ ์—์„œ ํ•œ๊ตญ์–ด ๋Œ€๋ฆฝ ํ‘œํ˜„ ์œ ์‚ฌ ์—ฐ๊ฒฐ์–ด๋ฏธ -์ง€๋งŒ, -๋Š”๋ฐ, -์œผ๋ฉด์„œ, -๋”๋‹ˆ๋ฅผ ๋Œ€์ƒ์œผ๋กœ ๊ฐ ์–ด๋ฏธ์˜ ํŠน์„ฑ์„ ๋ณ€๋ณ„ํ•˜์—ฌ ์ฒด๊ณ„ํ™”ํ•˜๊ณ  ์ด์— ๋Œ€ํ•œ ํ•œ๊ตญ์–ด ํ•™์Šต์ž์™€ ๋ชจ์–ด ํ™”์ž์˜ ์ธ์‹ ์ฐจ์ด๋ฅผ ํ™•์ธํ•˜์—ฌ ํšจ๊ณผ์ ์ธ ๋Œ€๋ฆฝ ํ‘œํ˜„ ์œ ์‚ฌ ์—ฐ๊ฒฐ์–ด๋ฏธ์˜ ๊ต์œก ๋‚ด์šฉ์„ ์ œ์•ˆํ•˜๋Š” ๊ฒƒ์„ ๋ชฉํ‘œ๋กœ ํ•œ๋‹ค. ์—ฐ๊ฒฐ์–ด๋ฏธ๋Š” ํ•˜๋‚˜์˜ ์˜๋ฏธ ๋ฒ”์ฃผ์— ์—ฌ๋Ÿฌ ํ˜•ํƒœ๊ฐ€ ์†ํ•ด ์žˆ์„ ๋ฟ๋งŒ ์•„๋‹ˆ๋ผ ์–ด๋ฏธ๋งˆ๋‹ค ๋ฏธ๋ฌ˜ํ•œ ์˜๋ฏธ ์ฐจ์ด๋ฅผ ๋ณด์ด๋ฉฐ ํ†ต์‚ฌ, ํ™”์šฉ ์ธก๋ฉด์˜ ์ œ์•ฝ๋„ ๋‹ฌ๋ผ ํ•™์Šต์ž๋“ค์ด ํ•™์Šต์— ๋ถ€๋‹ด์„ ๋Š๋ผ๋Š” ๊ฒƒ์œผ๋กœ ์•Œ๋ ค์ ธ ์žˆ๋‹ค. ์ด์— ๋”ฐ๋ผ ํ•™์Šต์ž๋“ค์€ ์˜๋ฏธ ๋ฒ”์ฃผ๋ฅผ ๋Œ€ํ‘œํ•˜๋Š” ์—ฐ๊ฒฐ์–ด๋ฏธ๋งŒ์„ ํ•œ์ •์ ์œผ๋กœ ์‚ฌ์šฉํ•˜๊ฑฐ๋‚˜ ์—ฐ๊ฒฐ์–ด๋ฏธ๋ฅผ ์‚ฌ์šฉํ•˜์ง€ ์•Š๊ณ  ๋‹จ๋ฌธ์œผ๋กœ ๋ฐœํ™”ํ•˜๋Š” ๋ชจ์Šต์„ ๋ณด์ด๊ธฐ๋„ ํ•œ๋‹ค. ๊ทธ๋Ÿฌ๋‚˜ ํ•œ๊ตญ์–ด ๋ฌธ๋ฒ• ํ•™์Šต์˜ ๋ชฉํ‘œ๊ฐ€ ๋ฌธ๋ฒ• ํ•ญ๋ชฉ ์‚ฌ์ด์˜ ๋ฏธ๋ฌ˜ํ•œ ์ฐจ์ด๋ฅผ ํŒŒ์•…ํ•˜์—ฌ ์ด๋ฅผ ์ ํ™•ํ•˜๊ฒŒ ์‚ฌ์šฉํ•˜๋Š” ๋ฐ์— ์žˆ๋‹ค๋Š” ์ ์—์„œ ํ•™์Šต์ž์˜ ๋ฐœํ™” ์˜๋„์™€ ๋ฐ€์ ‘ํ•˜๊ฒŒ ๊ด€๋ จ๋˜๋Š” ์—ฐ๊ฒฐ์–ด๋ฏธ์— ๋Œ€ํ•œ ๊ต์œก์€ ๋ฐ˜๋“œ์‹œ ํ•„์š”ํ•˜๋‹ค. ์ด์— ๋”ฐ๋ผ ๋ณธ๊ณ ์—์„œ๋Š” ์—ฐ๊ฒฐ์–ด๋ฏธ๋ฅผ ๋Œ€์ƒ์œผ๋กœ ์œ ์‚ฌ ๋ฌธ๋ฒ• ํ•ญ๋ชฉ ๊ต์œก์— ๋Œ€ํ•œ ์—ฐ๊ตฌ๋ฅผ ์ง„ํ–‰ํ•˜์˜€๋‹ค. ๊ทธ๋ฆฌ๊ณ  ์—ฐ๊ฒฐ์–ด๋ฏธ์˜ ๋‹ค์–‘ํ•œ ์˜๋ฏธ ๋ฒ”์ฃผ ์ค‘ ๋Œ€๋ฆฝ ๋ฒ”์ฃผ๋ฅผ ๋Œ€์ƒ์œผ๋กœ ์‚ผ์•˜๋Š”๋ฐ ๊ทธ ์ด์œ ๋Š” ๋‹ค์Œ๊ณผ ๊ฐ™๋‹ค. ์ฒซ์งธ, ๋Œ€๋ฆฝ์˜ ๋งฅ๋ฝ์—์„œ ์–ด๋ฏธ๋ผ๋ฆฌ ์„œ๋กœ ๋Œ€์น˜๋˜์ง€ ์•Š๋Š” ๊ฒฝ์šฐ๊ฐ€ ์žˆ์Œ์—๋„ ๋ถˆ๊ตฌํ•˜๊ณ  ์ด๋“ค์˜ ์ฐจ๋ณ„์  ํŠน์„ฑ์— ๋Œ€ํ•œ ๋…ผ์˜๊ฐ€ ์ถฉ๋ถ„ํžˆ ์ด๋ฃจ์–ด์ง€์ง€ ์•Š์•˜๋‹ค. ๋‘˜์งธ, ๋‹ค์–‘ํ•œ ๋Œ€๋ฆฝ ํ‘œํ˜„ ์—ฐ๊ฒฐ์–ด๋ฏธ๋ฅผ ํ•™์Šตํ•จ์—๋„ ๋ถˆ๊ตฌํ•˜๊ณ  -์ง€๋งŒ๊ณผ -๋Š”๋ฐ์— ํ•™์Šต์ž๋“ค์˜ ์‚ฌ์šฉ์ด ํŽธ์ค‘๋˜์–ด ์žˆ๋‹ค. ํ•™์Šต์ž๋“ค์ด ๋งฅ๋ฝ๊ณผ ๋ฐœํ™” ์˜๋„์— ๋งž๋Š” ์—ฐ๊ฒฐ์–ด๋ฏธ๋ฅผ ๊ณจ๋ผ ์‚ฌ์šฉํ•  ์ˆ˜ ์žˆ๋„๋ก ๋•๊ธฐ ์œ„ํ•ด์„œ๋Š” ์–ด๋ฏธ๋“ค์˜ ๋ณ€๋ณ„์  ํŠน์„ฑ์ด ๋ช…์‹œ์ ์œผ๋กœ ์ œ์‹œ๋˜์–ด ์žˆ๋Š” ๊ต์œก ๋‚ด์šฉ์ด ์ œ๊ณต๋˜์–ด์•ผ ํ•  ๊ฒƒ์ด๋‹ค. ์ด์— ๋ณธ๊ณ ์—์„œ๋Š” ํ•œ๊ตญ์–ด ๋ชจ์–ด ํ™”์ž ๋ง๋ญ‰์น˜ ๋ถ„์„์„ ํ†ตํ•ด ๋Œ€๋ฆฝ ํ‘œํ˜„ ์œ ์‚ฌ ์—ฐ๊ฒฐ์–ด๋ฏธ์˜ ํ†ต์‚ฌ, ์˜๋ฏธ, ํ™”์šฉ ์ธต์œ„์˜ ๋ณ€๋ณ„์  ํŠน์„ฑ์„ ํ™•์ธํ•˜๊ณ  ์กฐ์‚ฌ ์—ฐ๊ตฌ๋ฅผ ํ†ตํ•ด ํ•™์Šต์ž ์ดํ•ด ์–‘์ƒ ๋ถ„์„์„ ์‹ค์‹œํ•˜์—ฌ ๋Œ€๋ฆฝ ํ‘œํ˜„ ์œ ์‚ฌ ์—ฐ๊ฒฐ์–ด๋ฏธ์˜ ๊ต์œก ๋‚ด์šฉ์„ ๊ตฌ์„ฑํ•˜๊ธฐ ์œ„ํ•œ ํ† ๋Œ€๋ฅผ ๋งˆ๋ จํ•˜๊ณ ์ž ํ•˜์˜€๋‹ค. โ…ก์žฅ์—์„œ๋Š” ๋Œ€๋ฆฝ ํ‘œํ˜„ ์œ ์‚ฌ ์—ฐ๊ฒฐ์–ด๋ฏธ์— ๊ด€ํ•œ ์ด๋ก ์ ์ธ ๋ฐฐ๊ฒฝ์„ ์‚ดํˆ๋‹ค. ์šฐ์„  ์œ ์‚ฌ ๋ฌธ๋ฒ• ํ•ญ๋ชฉ๊ณผ ๊ด€๋ จ๋œ ์„ ํ–‰ ๋…ผ์˜๋ฅผ ์‚ดํŽด ์œ ์‚ฌ์„ฑ๊ณผ ์œ ์‚ฌ ๋ฌธ๋ฒ• ํ•ญ๋ชฉ์˜ ๊ฐœ๋…์— ๋Œ€ํ•ด ์ •๋ฆฌํ•˜์˜€๋‹ค. ๊ทธ๋ฆฌ๊ณ  ์‹ค์ œ ๊ต์œก์—์„œ๋Š” ์—ฐ๊ฒฐ์–ด๋ฏธ์˜ ์‚ฌ์šฉ ์˜๋ฏธ ์—ญ์‹œ ๊ธฐ๋ณธ ์˜๋ฏธ์™€ ์ง€์œ„๊ฐ€ ๋™์ผํ•˜๋‹ค๋Š” ์ ์—์„œ ๋Œ€๋ฆฝ ํ‘œํ˜„ ์œ ์‚ฌ ์—ฐ๊ฒฐ์–ด๋ฏธ ๊ต์œก์„ ์œ„ํ•œ ์˜๋ฏธ์  ์œ ์‚ฌ์„ฑ์˜ ๋ฒ”์œ„์— ๊ธฐ๋ณธ ์˜๋ฏธ์˜ ๋™์งˆ์„ฑ๋ฟ๋งŒ ์•„๋‹ˆ๋ผ ๋งฅ๋ฝ์„ ํฌํ•จํ•˜๋Š” ํ™”์šฉ์  ๋™์งˆ์„ฑ์„ ํฌ๊ด„ํ•˜์—ฌ ๋‹ค๋ฃจ์—ˆ๋‹ค. ๋‹ค์Œ์œผ๋กœ๋Š” ํ™•์žฅ๋œ ์˜๋ฏธ์  ์œ ์‚ฌ์„ฑ์˜ ๋ฒ”์œ„๋ฅผ ๊ธฐ๋ฐ˜์œผ๋กœ ๋Œ€๋ฆฝ ํ‘œํ˜„์˜ ๊ฐœ๋…์„ ์ •๋ฆฝํ•˜๊ณ  ๊ทธ ์˜๋ฏธ์— ๋Œ€ํ•ด ์‚ดํˆ์œผ๋ฉฐ, ๋Œ€๋ฆฝ ํ‘œํ˜„์˜ ์˜๋ฏธ๊ฐ€ ๊ตฌ์–ด ์ƒํ™ฉ์—์„œ ํ™”โ‹…์ฒญ์ž์˜ ๊ด€๊ณ„ ์œ ์ง€๋ฅผ ์œ„ํ•œ ๊ธฐ๋Šฅ์œผ๋กœ ํ™•์žฅ๋˜๋Š” ์–‘์ƒ์— ๋Œ€ํ•ด ์•Œ์•„๋ณด์•˜๋‹ค. ์ด๋ฅผ ํ†ตํ•ด ๊ธฐ์กด์˜ ๋ฌธ์žฅ ์ค‘์‹ฌ์˜ ์—ฐ๊ตฌ์—์„œ ๋‚˜์•„๊ฐ€ ์ฒญ์ž๊ฐ€ ๊ฐ€์ •๋œ ๊ตฌ์–ด ๋Œ€ํ™” ์ƒํ™ฉ์—์„œ ๋Œ€๋ฆฝ ํ‘œํ˜„ ์—ฐ๊ฒฐ์–ด๋ฏธ๊ฐ€ ๋ณ€๋ณ„๋˜๋Š” ํŠน์„ฑ์„ ํ™•์ธํ•˜์—ฌ์•ผ ํ•จ์„ ์ฃผ์žฅํ•˜์˜€๋‹ค. ์ด์ƒ์˜ ๋…ผ์˜๋ฅผ ๋ฐ”ํƒ•์œผ๋กœ ๋ณธ๊ณ ์˜ ์—ฐ๊ตฌ ๋Œ€์ƒ์ด ๋˜๋Š” ๋Œ€๋ฆฝ ํ‘œํ˜„ ์œ ์‚ฌ ์—ฐ๊ฒฐ์–ด๋ฏธ๋กœ -์ง€๋งŒ, -๋Š”๋ฐ, -์œผ๋ฉด์„œ, -๋”๋‹ˆ๋ฅผ ์„ ์ •ํ•˜์˜€์œผ๋ฉฐ ๊ฐœ๋ณ„ ์—ฐ๊ฒฐ์–ด๋ฏธ์˜ ํ†ต์‚ฌ์ , ์˜๋ฏธ์ , ํ™”์šฉ์  ํŠน์„ฑ์— ๋Œ€ํ•ด ์‚ดํ”ผ๊ณ  ๊ธฐ์กด ์—ฐ๊ตฌ์˜ ํ•œ๊ณ„์ ์„ ํ™•์ธํ•˜์˜€๋‹ค. โ…ข์žฅ์—์„œ๋Š” ๋ชจ์–ด ํ™”์ž์˜ ๊ตฌ์–ด ๋Œ€ํ™” ๋ง๋ญ‰์น˜๋ฅผ ๋ถ„์„ํ•˜์—ฌ ์—ฐ๊ตฌ ๋Œ€์ƒ ๋Œ€๋ฆฝ ํ‘œํ˜„ ์œ ์‚ฌ ์—ฐ๊ฒฐ์–ด๋ฏธ๋“ค์ด ์˜๋ฏธ, ํ†ต์‚ฌ, ํ™”์šฉ ์ธต์œ„์—์„œ ๋ณ€๋ณ„๋˜๋Š” ์ง€์ ์„ ๋„์ถœํ•˜์˜€๋‹ค. ๊ฐœ๋ณ„ ๋Œ€๋ฆฝ ํ‘œํ˜„ ์—ฐ๊ฒฐ์–ด๋ฏธ๊ฐ€ ๊ตฌ์–ด ๋Œ€ํ™”์—์„œ ์‚ฌ์šฉ๋  ๋•Œ ๊ฐ–๋Š” ํ†ต์‚ฌ ์ œ์•ฝ๊ณผ ๊ณต๊ธฐ ํ‘œํ˜„ ์–‘์ƒ์„ ์‚ดํ”ผ๊ณ  ์‹œ๊ฐ„์„ฑ, ์˜์™ธ์„ฑ, ์ œํ•œ์„ฑ์˜ ์˜๋ฏธ๋ฅผ ํ™•์ธํ•˜์˜€๋‹ค. ๊ทธ๋ฆฌ๊ณ  ๊ตฌ์–ด ๋Œ€ํ™” ์ƒํ™ฉ์—์„œ ๋Œ€๋ฆฝ ํ‘œํ˜„ ์œ ์‚ฌ ์—ฐ๊ฒฐ์–ด๋ฏธ๋“ค์ด ๊ฐ–๋Š” ํ™”์šฉ์  ํšจ๊ณผ๋ฅผ ๊ฒฉ์‹์„ฑ, ๋…ผ์ฆ์„ฑ, ๊ณต์†์„ฑ, ๋ถˆ์†์„ฑ์œผ๋กœ ๋‚˜๋ˆ„์–ด ์‚ดํŽด๋ณด์•˜๋‹ค. โ…ฃ์žฅ์—์„œ๋Š” ๊ตฌ์–ด ๋Œ€ํ™” ๋ง๋ญ‰์น˜ ๋ถ„์„ ๊ฒฐ๊ณผ๋ฅผ ๋ฐ”ํƒ•์œผ๋กœ ํ•œ๊ตญ์–ด ํ•™์Šต์ž์˜ ๋Œ€๋ฆฝ ํ‘œํ˜„ ์œ ์‚ฌ ์—ฐ๊ฒฐ์–ด๋ฏธ์— ๋Œ€ํ•œ ์ดํ•ด ์–‘์ƒ์„ ํ™•์ธํ•˜๊ณ ์ž ํ•˜์˜€๋‹ค. ์ด๋ฅผ ์œ„ํ•ด ์ค‘โ‹…๊ณ ๊ธ‰ ํ•™์Šต์ž 92๋ช…๊ณผ ๋ชจ์–ด ํ™”์ž 64๋ช…์„ ๋Œ€์ƒ์œผ๋กœ ์„ค๋ฌธ ์กฐ์‚ฌ ๋ฐ ์‚ฌํ›„ ๋ฉด๋‹ด ์กฐ์‚ฌ๋ฅผ ์‹ค์‹œํ•˜์˜€๋‹ค. ๊ทธ ๊ฒฐ๊ณผ ํ•™์Šต์ž๋“ค์€ ๋Œ€๋ฆฝ ํ‘œํ˜„ ์œ ์‚ฌ ์—ฐ๊ฒฐ์–ด๋ฏธ์˜ ํ†ต์‚ฌ์ , ์˜๋ฏธ์ , ํ™”์šฉ์  ๋ณต์žก์„ฑ์œผ๋กœ ์ธํ•ด ํŠน์ • ์–ด๋ฏธ์˜ ์‚ฌ์šฉ์„ ํšŒํ”ผํ•˜๋ฉฐ ์˜๋ฏธ ๊ทœ์น™์„ ๋‹จ์ˆœํ™”ํ•˜์—ฌ ๊ณผ์ž‰ ์ ์šฉํ•˜๋Š” ์–‘์ƒ์„ ๋ณด์ž„์„ ์•Œ ์ˆ˜ ์žˆ์—ˆ๋‹ค. ๊ทธ๋ฆฌ๊ณ  ๋Œ€๋ฆฝ ํ‘œํ˜„ ์œ ์‚ฌ ์—ฐ๊ฒฐ์–ด๋ฏธ ๊ต์œก์˜ ๋ถ€์žฌ๋กœ ์ธํ•ด ์—ฐ๊ฒฐ์–ด๋ฏธ ๊ฐ„์˜ ๋ณ€๋ณ„์  ํŠน์„ฑ์— ๋Œ€ํ•œ ํ•™์Šต์ž๋“ค์˜ ์ดํ•ด๊ฐ€ ๋ถ€์กฑํ•จ์„ ํ™•์ธํ•˜์˜€๋‹ค. โ…ค์žฅ์—์„œ๋Š” โ…ข์žฅ์˜ ๊ตฌ์–ด ๋Œ€ํ™” ๋ง๋ญ‰์น˜ ๋ถ„์„ ๊ฒฐ๊ณผ์™€ โ…ฃ์žฅ์˜ ํ•™์Šต์ž ๋ฐ ๋ชจ์–ด ํ™”์ž์˜ ์ดํ•ด ์–‘์ƒ ๋ถ„์„ ๊ฒฐ๊ณผ๋ฅผ ๋ฐ”ํƒ•์œผ๋กœ ํšจ๊ณผ์ ์ธ ๋Œ€๋ฆฝ ํ‘œํ˜„ ์œ ์‚ฌ ์—ฐ๊ฒฐ์–ด๋ฏธ์˜ ๊ต์œก ๋‚ด์šฉ์„ ๊ตฌ์•ˆํ•˜์˜€๋‹ค. ๋Œ€๋ฆฝ ํ‘œํ˜„ ์œ ์‚ฌ ์—ฐ๊ฒฐ์–ด๋ฏธ์˜ ๊ต์œก ๋‚ด์šฉ ๊ตฌ์„ฑ ์›๋ฆฌ๋ฅผ ๋ฐํžˆ๊ณ , ๊ต์œก์  ํšจ๊ณผ๋ฅผ ๊ณ ๋ คํ•˜์—ฌ ์–ด๋ฏธ๋ฅผ ๋ฐฐ์—ดํ•˜์˜€๋‹ค. ๊ทธ๋ฆฌ๊ณ  ๋ฐฐ์—ด ์ˆœ์„œ์— ๋”ฐ๋ผ ๊ฐ๊ฐ์˜ ๋Œ€๋ฆฝ ํ‘œํ˜„ ์œ ์‚ฌ ์—ฐ๊ฒฐ์–ด๋ฏธ๋ฅผ ๊ต์œกํ•  ๋•Œ ์ œ๊ณต๋˜์–ด์•ผ ํ•˜๋Š” ์ •๋ณด๋ฅผ ์ •๋ฆฌํ•˜์—ฌ ๊ธฐ์ˆ ํ•˜์˜€๋‹ค. โ…ฅ์žฅ์—์„œ๋Š” ์ด์ƒ์˜ ๋…ผ์˜๋ฅผ ์ •๋ฆฌํ•˜๊ณ  ์—ฐ๊ตฌ์˜ ์˜์˜ ๋ฐ ํ•œ๊ณ„์ ์„ ๊ธฐ์ˆ ํ•˜์˜€๋‹ค.I. ์„œ๋ก  1 1. ์—ฐ๊ตฌ์˜ ํ•„์š”์„ฑ ๋ฐ ๋ชฉ์  1 2. ์„ ํ–‰ ์—ฐ๊ตฌ 5 2.1. ์œ ์‚ฌ ๋ฌธ๋ฒ• ํ•ญ๋ชฉ ๊ต์œก ๊ด€๋ จ ๋…ผ์˜ 5 2.2. ๋Œ€๋ฆฝ ํ‘œํ˜„ ์—ฐ๊ฒฐ์–ด๋ฏธ ๊ต์œก ๊ด€๋ จ ๋…ผ์˜ 7 3. ์—ฐ๊ตฌ ๋ฐฉ๋ฒ• ๋ฐ ์ ˆ์ฐจ 10 II. ๋Œ€๋ฆฝ ํ‘œํ˜„ ์œ ์‚ฌ ์—ฐ๊ฒฐ์–ด๋ฏธ ๊ต์œก์„ ์œ„ํ•œ ์ด๋ก ์  ๋ฐฐ๊ฒฝ 13 1. ์œ ์‚ฌ์„ฑ๊ณผ ์œ ์‚ฌ ๋ฌธ๋ฒ• ํ•ญ๋ชฉ 13 1.1. ์œ ์‚ฌ ๋ฌธ๋ฒ• ํ•ญ๋ชฉ์˜ ์ •์˜ 13 1.2. ์—ฐ๊ฒฐ์–ด๋ฏธ ๊ต์œก์„ ์œ„ํ•œ ์˜๋ฏธ์  ์œ ์‚ฌ์„ฑ์˜ ๋ฒ”์œ„ 18 2. ๋Œ€๋ฆฝ ํ‘œํ˜„์˜ ๊ฐœ๋… ๋ฐ ์˜๋ฏธ 21 2.1. ๋Œ€๋ฆฝ ํ‘œํ˜„์˜ ๊ฐœ๋… 21 2.1.1. ๋Œ€๋ฆฝ๊ณผ ์ธ์ ‘ ์˜๋ฏธ ๋ฒ”์ฃผ์™€์˜ ๊ตฌ๋ถ„ 21 2.1.2. ๋Œ€๋ฆฝ ํ‘œํ˜„ ๊ฐœ๋…์˜ ์ •๋ฆฝ 26 2.2. ๋Œ€๋ฆฝ ํ‘œํ˜„์˜ ์˜๋ฏธ 27 2.2.1. ๋Œ€๋ฆฝ ํ‘œํ˜„์˜ ๋ณธ์งˆ์  ์˜๋ฏธ 27 2.2.2. ๊ตฌ์–ด์—์„œ ๋Œ€๋ฆฝ ํ‘œํ˜„ ์˜๋ฏธ์˜ ํ™•์žฅ 32 3. ๋Œ€๋ฆฝ ํ‘œํ˜„ ์œ ์‚ฌ ์—ฐ๊ฒฐ์–ด๋ฏธ์˜ ํŠน์„ฑ 36 3.1. ๋Œ€๋ฆฝ ํ‘œํ˜„ ์œ ์‚ฌ ์—ฐ๊ฒฐ์–ด๋ฏธ์˜ ์„ ์ • 36 3.2. ๊ฐœ๋ณ„ ์—ฐ๊ฒฐ์–ด๋ฏธ์˜ ํŠน์„ฑ 45 3.2.1. ํ†ต์‚ฌ์  ํŠน์ง• 46 3.2.2. ์˜๋ฏธ์  ํŠน์ง• 50 3.2.3. ํ™”์šฉ์  ํŠน์ง• 56 3.2.4. ์†Œ๊ฒฐ๋ก  59 III. ๊ตฌ์–ด ๋Œ€ํ™” ๋ง๋ญ‰์น˜ ๊ธฐ๋ฐ˜ ๋Œ€๋ฆฝ ํ‘œํ˜„ ์œ ์‚ฌ ์—ฐ๊ฒฐ์–ด๋ฏธ์˜ ๋ณ€๋ณ„์  ํŠน์„ฑ ๋ถ„์„ 61 1. ๋ถ„์„ ๋ฐฉ๋ฒ• 61 1.1. ๋ถ„์„ ๋Œ€์ƒ 61 1.2. ๋ถ„์„ ๊ธฐ์ค€ 62 1.3. ๋ถ„์„ ์ ˆ์ฐจ 68 2. ๋ถ„์„ ๊ฒฐ๊ณผ 69 2.1. ํ†ต์‚ฌ ์ธต์œ„์˜ ๋ณ€๋ณ„์  ํŠน์„ฑ 69 2.1.1. ์ฃผ์–ด ์ธ์นญ ์ œ์•ฝ ์–‘์ƒ 69 2.1.2. ์„ ํ–‰ ์„œ์ˆ ์–ด ์ œ์•ฝ ์–‘์ƒ 70 2.1.3. ์‹œ์ œ ์„ ์–ด๋ง์–ด๋ฏธ ์ œ์•ฝ ์–‘์ƒ 72 2.1.4. ํ›„ํ–‰์ ˆ ์„œ๋ฒ• ์ œ์•ฝ ์–‘์ƒ 73 2.1.5. ๊ณต๊ธฐ ํ‘œํ˜„ ์–‘์ƒ 75 2.1.6. ์‹คํ˜„ ์œ„์น˜ ๋ณ€ํ™” ์–‘์ƒ 79 2.2. ์˜๋ฏธ ์ธต์œ„์˜ ๋ณ€๋ณ„์  ํŠน์„ฑ 82 2.2.1. ์‹œ๊ฐ„์„ฑ 82 2.2.2. ์˜์™ธ์„ฑ 85 2.2.3. ์ œํ•œ์„ฑ 89 2.3. ํ™”์šฉ ์ธต์œ„์˜ ๋ณ€๋ณ„์  ํŠน์„ฑ 92 2.3.1. ๊ฒฉ์‹์„ฑ 92 2.3.2. ๋…ผ์ฆ์„ฑ 93 2.3.3. ๊ณต์†์„ฑ 99 2.3.4. ๋ถˆ์†์„ฑ 109 2.3.5. ์†Œ๊ฒฐ๋ก  115 IV. ํ•œ๊ตญ์–ด ํ•™์Šต์ž์˜ ๋Œ€๋ฆฝ ํ‘œํ˜„ ์œ ์‚ฌ ์—ฐ๊ฒฐ์–ด๋ฏธ ์ดํ•ด ์–‘์ƒ 117 1. ์กฐ์‚ฌ ๋ฐฉ๋ฒ• 117 1.1. ์กฐ์‚ฌ ๋Œ€์ƒ 117 1.2. ์กฐ์‚ฌ ๋„๊ตฌ 118 1.3. ์กฐ์‚ฌ ๊ฒฐ๊ณผ ๋ถ„์„ ์ ˆ์ฐจ 125 2. ์กฐ์‚ฌ ๊ฒฐ๊ณผ 126 2.1. ํ†ต์‚ฌ ์ธต์œ„์˜ ๋ณ€๋ณ„์  ํŠน์„ฑ์— ๋”ฐ๋ฅธ ์ดํ•ด ์–‘์ƒ 126 2.1.1. ์ „์ฒด์  ์ดํ•ด ์–‘์ƒ 126 2.1.2. ๋ณ€๋ณ„์  ํŠน์„ฑ๋ณ„ ์ดํ•ด ์–‘์ƒ 127 2.2. ์˜๋ฏธ ์ธต์œ„์˜ ๋ณ€๋ณ„์  ํŠน์„ฑ์— ๋”ฐ๋ฅธ ์ดํ•ด ์–‘์ƒ 136 2.2.1. ์‹œ๊ฐ„์„ฑ 136 2.2.2. ์˜์™ธ์„ฑ 142 2.2.3. ์ œํ•œ์„ฑ 149 2.3. ํ™”์šฉ ์ธต์œ„์˜ ๋ณ€๋ณ„์  ํŠน์„ฑ์— ๋”ฐ๋ฅธ ์ดํ•ด ์–‘์ƒ 154 2.3.1. ๊ฒฉ์‹์„ฑ 154 2.3.2. ๋…ผ์ฆ์„ฑ 157 2.3.3. ๊ณต์†์„ฑ 163 2.3.4. ๋ถˆ์†์„ฑ 173 2.4. ํ•™์Šต ์ •๋ณด์— ๋”ฐ๋ฅธ ์ดํ•ด ์–‘์ƒ 177 2.4.1. ๋Œ€๋ฆฝ ํ‘œํ˜„ ์œ ์‚ฌ ์—ฐ๊ฒฐ์–ด๋ฏธ์˜ ์ฐจ์ด์  ํ•™์Šต ์—ฌ๋ถ€ 178 2.4.2. ๋Œ€๋ฆฝ ํ‘œํ˜„ ์œ ์‚ฌ ์—ฐ๊ฒฐ์–ด๋ฏธ์˜ ์‚ฌ์šฉ ๋นˆ๋„ 179 2.4.3. ๋Œ€๋ฆฝ ํ‘œํ˜„ ์œ ์‚ฌ ์—ฐ๊ฒฐ์–ด๋ฏธ์˜ ์ˆ™๋‹ฌ๋„ 181 2.4.4. ๋Œ€๋ฆฝ ํ‘œํ˜„ ์œ ์‚ฌ ์—ฐ๊ฒฐ์–ด๋ฏธ์— ๋Œ€ํ•œ ๊ต์žฌ ๋ฐ ๊ต์‚ฌ ์„ค๋ช…์—์˜ ๋งŒ์กฑ๋„ 182 3. ์˜ค๋ฅ˜ ์›์ธ์˜ ๋ถ„์„ 184 3.1. ๋ชฉํ‘œ์–ด์˜ ์˜ํ–ฅ์— ์˜ํ•œ ์˜ค๋ฅ˜ 184 3.1.1. ์—ฐ๊ฒฐ์–ด๋ฏธ์˜ ๋ณต์žก์„ฑ์œผ๋กœ ์ธํ•œ ์˜ค๋ฅ˜ 185 3.1.2. ์˜๋ฏธ ๊ทœ์น™์˜ ๋‹จ์ˆœํ™”์™€ ๊ณผ์ž‰ ์ ์šฉ์œผ๋กœ ์ธํ•œ ์˜ค๋ฅ˜ 186 3.2. ๊ต์œก ๊ณผ์ •์— ์˜ํ•œ ์˜ค๋ฅ˜ 188 V. ๋Œ€๋ฆฝ ํ‘œํ˜„ ์œ ์‚ฌ ์—ฐ๊ฒฐ์–ด๋ฏธ ๊ต์œก ๋‚ด์šฉ์˜ ์„ค๊ณ„ 191 1. ๋Œ€๋ฆฝ ํ‘œํ˜„ ์œ ์‚ฌ ์—ฐ๊ฒฐ์–ด๋ฏธ ๊ต์œก์˜ ๋ชฉํ‘œ 191 2. ๋Œ€๋ฆฝ ํ‘œํ˜„ ์œ ์‚ฌ ์—ฐ๊ฒฐ์–ด๋ฏธ ๊ต์œก์˜ ๋‚ด์šฉ 192 2.1. ๊ต์œก ๋‚ด์šฉ์˜ ์ฒด๊ณ„ํ™” 192 2.1.1. ๊ต์œก ๋‚ด์šฉ์˜ ๊ตฌ์„ฑ ์›๋ฆฌ 192 2.1.2. ์ œ์‹œ ์ˆœ์„œ์˜ ์œ„๊ณ„ํ™” 195 2.2. ๊ต์œก ๋‚ด์šฉ์˜ ์‹ค์ œ 198 2.2.1. -์ง€๋งŒ์˜ ๊ต์œก ๋‚ด์šฉ 198 2.2.2. -๋Š”๋ฐ์˜ ๊ต์œก ๋‚ด์šฉ 202 2.2.3. -์œผ๋ฉด์„œ์˜ ๊ต์œก ๋‚ด์šฉ 207 2.2.4. -๋”๋‹ˆ์˜ ๊ต์œก ๋‚ด์šฉ 210 VI. ๊ฒฐ๋ก  214 ์ฐธ๊ณ ๋ฌธํ—Œ 216 ๋ถ€ ๋ก 232 Abstract 242Maste

    Producing and isolating monoclonal antibodies to neuronal stem cells.

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    ์˜ํ•™๊ณผ/์„์‚ฌ[ํ•œ๊ธ€] ์ฒ™์ถ”๋™๋ฌผ์˜ ์ค‘์ถ”์‹ ๊ฒฝ๊ณ„๋Š” ์‹ ๊ฒฝ์„ธํฌ, ๋ณ„์•„๊ต์„ธํฌ, ํฌ์†Œ๋Œ๊ธฐ์•„๊ต์„ธํฌ์™€ ๋ฏธ์„ธ์•„๊ต์„ธํฌ๋กœ ๊ตฌ์„ฑ๋˜์–ด ์žˆ๋Š”๋ฐ ์ด ์„ธํฌ๋“ค์€ ์‹ ๊ฒฝ๊ฐ„์„ธํฌ๋กœ๋ถ€ํ„ฐ ์ฆ์‹ ๋ฐ ๋ถ„ํ™”๋ฅผ ํ†ตํ•ด ์ด๋ฃจ์–ด์ง„๋‹ค. ์‹ ๊ฒฝ๊ฐ„์„ธํฌ๋กœ๋ถ€ํ„ฐ ์‹ ๊ฒฝ์„ธํฌ, ์•„๊ต์„ธํฌ, ํฌ์†Œ๋Œ๊ธฐ์•„๊ต์„ธํฌ๋กœ ์–ด๋Š ์‹œ๊ธฐ์— ์–ด๋–ค ๊ธฐ์ „์œผ๋กœ ๋ถ„ํ™”๋˜๋Š”์ง€๋Š” ์•„์ง ์•Œ๋ ค์ ธ ์žˆ์ง€ ์•Š๋‹ค. ๋ณธ ์‹คํ—˜์—์„œ๋Š” ์‹ ๊ฒฝ๊ฐ„์„ธํฌ์— ๋Œ€ํ•œ ๋‹จํด๋ก ํ•ญ์ฒด๋ฅผ ์ƒ์„ฑํ•˜์—ฌ ์‹ ๊ฒฝ๊ฐ„์„ธํฌ์˜ ๋ถ„ํ™”๊ณผ์ •์„ ์—ฐ๊ตฌํ•˜๋Š” ๊ธฐ๋ณธ์ ์ธ ์žฌ๋ฃŒ๋กœ ํ™œ์šฉํ•˜๊ณ ์ž ํ•˜์˜€๋‹ค. ์ƒํ›„ 1-2์ผ๋œ ํฐ ์ฅ์˜ ๋Œ€๋‡Œ์—์„œ ์‹ ๊ฒฝ๊ฐ„์„ธํฌ๋ฅผ ๋ถ„๋ฆฌํ•˜์—ฌ EGF๊ฐ€ ๋“ค์–ด๊ฐ„ ๋ฌดํ˜ˆ์ฒญ ๋ฐฐ์ง€์—์„œ ํ‚ค์šด ๊ฒฐ๊ณผ ์‹ ๊ฒฝ๊ฐ„์„ธํฌ๋ฅผ ์ œ์™ธํ•œ ๋Œ€๋ถ€๋ถ„์˜ ์„ธํฌ๋Š” ์ฃฝ๊ณ  ์‹ ๊ฒฝ๊ฐ„์„ธํฌ๋งŒ ์‚ด์•„์„œ ๋‘ฅ๊ธ€๊ณ  ๋น›๋‚˜๋Š” ์„ธํฌ๊ตฐ์„ ํ˜•์„ฑํ•˜์˜€๋‹ค. ๊ทธ ์„ธํฌ๊ตฐ์€ ๋ชจ๋‘ ์‹ ๊ฒฝ๊ฐ„์„ธํฌ์˜ ์„ธํฌ๊ณจ๊ฒฉ๋‹จ๋ฐฑ์งˆ์ธ nestin์„ ๋ฐœํ˜„ํ•˜์˜€๋‹ค. ๋ฐฐ์–‘ 7์ผ์งธ์— ์ด์ฐจ๋ฐฐ์–‘ ๊ฒฐ๊ณผ ์„ธํฌ๋“ค์ด ๋ถ„ํ™”ํ•˜์—ฌ ์‹ ๊ฒฝ์„ธํฌ, ๋ณ„์•„๊ต์„ธํฌ, ํฌ์†Œ๋Œ๊ธฐ์•„๊ต์„ธํฌ๊ฐ€ ๋˜์—ˆ๋‹ค. ๋ฐฐ์–‘ 7์ผ์งธ๋˜๋Š” ์‹ ๊ฒฝ๊ฐ„์„ธํฌ๋ฅผ ๋ถ„๋ฆฌํ•˜์—ฌ BALB/c์— ๋ฉด์—ญ๋ฐ˜์‘์‹œํ‚จ ํ›„ ๋น„์žฅ์„ธํฌ๋ฅผ ์–ป์–ด ๊ณจ์ˆ˜์ข…์„ธํฌ์™€ ์„ธํฌ์œตํ•ฉ์„ ์‹œ์ผฐ๋‹ค. ์„ธํฌ์œตํ•ฉ ํ›„ ํ•˜์ด๋ธŒ๋ฆฌ๋„๋งˆ ์ƒ์ธต์•ก์„ ์ด์šฉํ•˜์—ฌ ๋ฉด์—ญํ™”ํ•™์—ผ์ƒ‰์„ ์‹œํ–‰ํ•œ ๊ฒฐ๊ณผ ์–‘์„ฑ๋ฐ˜์‘์„ ๋‚˜ํƒ€๋ƒˆ๋‹ค. ๋‹จํด๋ก ํ•ญ์ฒด์˜ ์•„ํ˜•์„ ํ™•์ธํ•œ ๊ฒฐ๊ณผ 5๊ฐœ์˜ ๋‹จํด๋ก ํ•ญ์ฒด๊ฐ€ ๋ชจ๋‘ IgMฮบ type์ž„์„ ์•Œ ์ˆ˜ ์žˆ์—ˆ๋‹ค. -------------------- ํ•ต์‹ฌ๋˜๋Š” ๋ง: ์‹ ๊ฒฝ๊ฐ„์„ธํฌ, ๋‹จํด๋ก ํ•ญ์ฒด, ์„ธํฌ์œตํ•ฉ, epidermal growth factor [์˜๋ฌธ] Mammalian central nervous system consists of neurons, astrocytes, oligodendrocytes and microglia. These cells are originated from neuroepithelial cells. Neuroepithelial cells proliferate and differentiate to become mature neural cells. It has not been proven how neuroepithelial cells differentiate into glial precursor cells and neuronal precursor cells. In addition to that, it is to be studied what kind of stimuli lead glial precursor cells to glial cells. Several kinds of monoclonal antibodies to specific markers of differentiating cells have been isolated to detect how glial precursor cells differentiate into macroglial cells. However, it is unknown how neuronal precursor cells differentiate. Progenitor cells can be isolated by culturing in epidermal growth factor(EGF) containing serum-free medium. After isolating progenitor cells, cells were immunized to BALB/c mice. Spleen cells from immunized mice were fused with myeloma cells and fused cells were selected by HAT medium. After limited dilution, immunocytochemistry was performed. Some hybridoma supernatant were positive but some were negative. Testing with positive supernatant resulted that all positive supernatant were IgM type and Igฮบ-specific. These monoclonal antibodies to neuronal precursor cells can be used further study on differentiation of neuronal cells in central nervous system.prohibitio

    Comparative Advantage of Transit Modes by Travel Distance in the Seoul Metropolitan Area

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    ํ•™์œ„๋…ผ๋ฌธ (์„์‚ฌ)-- ์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› : ํ™˜๊ฒฝ๋Œ€ํ•™์› ํ™˜๊ฒฝ๊ณ„ํšํ•™๊ณผ, 2019. 2. ๊น€์„ฑ์ˆ˜.์„œ์šธ์„ ๋น„๋กฏํ•œ ์ˆ˜๋„๊ถŒ์˜ ํ™•์žฅ์— ๋”ฐ๋ผ ๋Œ€์ค‘๊ตํ†ต ์„œ๋น„์Šค์— ๋Œ€ํ•œ ์ˆ˜์š”๊ฐ€ ์ง€์†์ ์œผ๋กœ ์ฆ๊ฐ€ํ•˜๊ณ  ์žˆ๋‹ค. ์ด์— ๋Œ€์‘ํ•˜์—ฌ ์ˆ˜๋„๊ถŒ์—๋Š” ๋‹ค์–‘ํ•œ ๋Œ€์ค‘๊ตํ†ต ์„œ๋น„์Šค๊ฐ€ ๊ณต๊ธ‰๋˜๊ณ  ์žˆ๋‹ค. ๊ทธ๋Ÿฌ๋‚˜ ๋Œ€์ค‘๊ตํ†ต์˜ ๊ณต๊ธ‰์ด ํŠน์ • ๊ตํ†ต์ถ•์ด๋‚˜ ํŠน์ • ์ง€์—ญ์— ํŽธ์ค‘๋˜๊ฑฐ๋‚˜ ๊ณผ์ž‰๊ณต๊ธ‰๋˜๋Š” ํ˜„์ƒ์ด ๋‚˜ํƒ€๋‚˜๊ธฐ๋„ ํ•œ๋‹ค. ๋ฐ˜๋ฉด, ์ด๋Ÿฌํ•œ ํ˜„์ƒ์œผ๋กœ ์ธํ•˜์—ฌ ๋Œ€์ค‘๊ตํ†ต ๊ณต๊ธ‰์ด ๋ถ€์กฑํ•˜๊ฑฐ๋‚˜ ์ ์ ˆํ•˜์ง€ ์•Š์€ ๋Œ€์ค‘๊ตํ†ต ์„œ๋น„์Šค๊ฐ€ ๊ณต๊ธ‰๋˜๋Š” ์ง€์—ญ์ด ์กด์žฌํ•˜๊ฒŒ ๋œ๋‹ค. ๋”ฐ๋ผ์„œ ๋ณธ ์—ฐ๊ตฌ์—์„œ๋Š” ๋Œ€์ค‘๊ตํ†ต ์„œ๋น„์Šค์˜ ํšจ์œจ์ ์ธ ๊ณต๊ธ‰์„ ์œ„ํ•œ ๋Œ€์ค‘๊ตํ†ต ์ˆ˜๋‹จ์˜ ํ†ตํ–‰๊ฑฐ๋ฆฌ๋ณ„ ๋น„๊ต์šฐ์œ„ ๋ถ„์„๊ณผ ํ˜„์žฌ ๋Œ€์ค‘๊ตํ†ต ์„œ๋น„์Šค์ˆ˜์ค€์ด ๋‚ฎ์€ ์ง€์—ญ์„ ํŒŒ์•…ํ•  ์ˆ˜ ์žˆ๋Š” ์ด์ƒ์  ๋ถ„์„ ํ‹€์„ ์ œ์‹œํ•˜์˜€๋‹ค. ๋ณธ ์—ฐ๊ตฌ์—์„œ๋Š” ๊ฐœ๋ณ„์ด์šฉ์ž์˜ ๋Œ€์ค‘๊ตํ†ต ์ด์šฉ์‹ค์  ์ž๋ฃŒ์ธ ๊ตํ†ต์นด๋“œ์ž๋ฃŒ๋ฅผ ์ด์šฉํ•˜์—ฌ ํ†ตํ–‰์š”๊ธˆ๊ณผ ํ†ตํ–‰์‹œ๊ฐ„์„ ๋™์‹œ์— ๊ณ ๋ คํ•œ ์ผ๋ฐ˜ํ™” ๋น„์šฉ์„ ์‚ฐ์ •ํ•˜๊ณ  ๋Œ€์ค‘๊ตํ†ต ์ˆ˜๋‹จ์˜ ํ†ตํ–‰๊ฑฐ๋ฆฌ์™€ ์ผ๋ฐ˜ํ™”๋น„์šฉ์˜ ๊ด€๊ณ„์‹์„ ๋„์ถœํ•˜์˜€๋‹ค. ์ด ๊ด€๊ณ„์‹์„ ๋ฐ”ํƒ•์œผ๋กœ ๋Œ€์ค‘๊ตํ†ต์ˆ˜๋‹จ์˜ ํ†ตํ–‰๊ฑฐ๋ฆฌ๋ณ„ ๋น„๊ต์šฐ์œ„๋ฅผ ๋ถ„์„ํ•˜์˜€๋‹ค. ๋Œ€์ค‘๊ตํ†ต ์ˆ˜๋‹จ์€ ๋งˆ์„๋ฒ„์Šค, ์ผ๋ฐ˜๋ฒ„์Šค, ๊ด‘์—ญ๋ฒ„์Šค, ์ง€ํ•˜์ฒ ๋กœ ๊ตฌ๋ถ„ํ•˜์˜€์œผ๋ฉฐ ํ†ตํ–‰๊ฑฐ๋ฆฌ๊ฐ€ ๊ฐ€์žฅ ๊ธด ์ˆ˜๋‹จ์„ ์ฃผ์ˆ˜๋‹จ์œผ๋กœ ๊ฐ„์ฃผํ•˜์˜€๋‹ค. ์„œ์šธ์‹œ์—์„œ ๊ด€ํ• ํ•˜๋Š” ๋ฒ„์Šค๋…ธ์„ ๊ณผ ์ง€ํ•˜์ฒ  ๋…ธ์„ ์ด ์šดํ–‰๋˜๋Š” ์ง€์—ญ์—์„œ ์ˆ˜์ง‘๋œ ์ž๋ฃŒ ์ค‘ ์„œ์šธ ๋„์‹ฌ์ธ ์‚ฌ๋Œ€๋ฌธ ์•ˆ์„ ๋„์ฐฉ์ง€๋กœ ํ•˜๋Š” ํ‘œ๋ณธ์„ ์ถ”์ถœํ•˜์˜€๋‹ค. ๋˜ํ•œ ํ†ตํ–‰์‹œ๊ฐ„๋Œ€๋Š” ์˜ค์ „์ฒจ๋‘์‹œ๊ฐ„๋Œ€(์˜ค์ „ 07์‹œโˆผ์˜ค์ „09์‹œ). ์˜คํ›„์ฒจ๋‘์‹œ๊ฐ„๋Œ€(17์‹œโˆผ20์‹œ)๋ฅผ ์ œ์™ธํ•œ ๋น„์ฒจ๋‘ ์‹œ๊ฐ„๋Œ€๋กœ ๊ตฌ๋ถ„ํ•˜์˜€๋‹ค. ํ†ตํ–‰๊ฑฐ๋ฆฌ์™€ ์ผ๋ฐ˜ํ™”๋น„์šฉ์˜ ๊ด€๊ณ„์‹ ์ถ”์ • ์‹œ ๋งˆ์„๋ฒ„์Šค์™€ ๊ด‘์—ญ๋ฒ„์Šค๋Š” ๋…๋ฆฝ๋ณ€์ˆ˜์™€ ์ข…์†๋ณ€์ˆ˜ ๋ชจ๋‘ ์ž์—ฐ๋กœ๊ทธ๋ฅผ ์ทจํ•˜์—ฌ ๋ณ€ํ™˜ํ•œ ๋กœ๊ทธ-์„ ํ˜•๋ชจํ˜•์„ ์ ์šฉํ•˜์˜€๊ณ , ์ง€ํ•˜์ฒ ๊ณผ ์ผ๋ฐ˜๋ฒ„์Šค๋Š” ์„ ํ˜•๋ชจํ˜•์„ ์ ์šฉํ•˜์˜€๋‹ค. ๋Œ€์ค‘๊ตํ†ต ์ˆ˜๋‹จ์˜ ํ†ตํ–‰๊ฑฐ๋ฆฌ๋ณ„ ๋น„๊ต์šฐ์œ„ ๋ถ„์„๊ฒฐ๊ณผ ๋งˆ์„๋ฒ„์Šค์˜ ๊ฒฝ์šฐ ์•ฝ 3km ์ด๋‚ด์—์„œ ๋น„๊ต์šฐ์œ„๊ฐ€ ๋‚˜ํƒ€๋‚ฌ์œผ๋ฉฐ, ์ผ๋ฐ˜๋ฒ„์Šค์˜ ๊ฒฝ์šฐ ์‹œ๊ฐ„๋Œ€๋ณ„๋กœ ์ฐจ์ด๊ฐ€ ์žˆ์—ˆ๋Š”๋ฐ ์˜ค์ „์ฒจ๋‘์‹œ๊ฐ„๋Œ€์˜ ๊ฒฝ์šฐ ์•ฝ 3โˆผ4km์—์„œ ๋น„๊ต์šฐ์œ„๊ฐ€ ๋‚˜ํƒ€๋‚ฌ์œผ๋ฉฐ, ๋น„์ฒจ๋‘์‹œ๊ฐ„๋Œ€์˜ ๊ฒฝ์šฐ ์•ฝ 13km๊นŒ์ง€ ๋น„๊ต์šฐ์œ„๊ฐ€ ๋‚˜ํƒ€๋‚ฌ๋‹ค. ์ด๋Š” ๊ตํ†ตํ˜ผ์žก์˜ ์˜ํ–ฅ์œผ๋กœ ์ธํ•˜์—ฌ ์˜ค์ „์ฒจ๋‘์‹œ๊ฐ„๋Œ€์— ๋„๋กœ์˜ ํ†ตํ–‰์†๋„๊ฐ€ ๋‚ฎ๊ธฐ ๋–„๋ฌธ์ด๋‹ค. ์ง€ํ•˜์ฒ ์˜ ๊ฒฝ์šฐ ์˜ค์ „์ฒจ๋‘์‹œ๊ฐ„๋Œ€์— 4โˆผ23km๊นŒ์ง€ ๋น„๊ต์šฐ์œ„๊ฐ€ ๋‚˜ํƒ€๋‚ฌ๊ณ , ๋น„์ฒจ๋‘์‹œ๊ฐ„๋Œ€์˜ ๊ฒฝ์šฐ 13โˆผ15km๊นŒ์ง€ ๋น„๊ต์šฐ์œ„๊ฐ€ ๋‚˜ํƒ€๋‚ฌ๋‹ค. ๊ด‘์—ญ๋ฒ„์Šค๋Š” ์˜ค์ „์ฒจ๋‘์‹œ๊ฐ„๋Œ€๋Š” ์•ฝ 23km ์ด์ƒ, ๋น„์ฒจ๋‘์‹œ๊ฐ„๋Œ€๋Š” 15km ์ด์ƒ์˜ ํ†ตํ–‰๊ฑฐ๋ฆฌ์—์„œ ๋น„๊ต์šฐ์œ„๊ฐ€ ๋‚˜ํƒ€๋‚ฌ๋‹ค. ์˜ค์ „์ฒจ๋‘์‹œ๊ฐ„๋Œ€์˜ ๊ฒฝ์šฐ ๊ตํ†ตํ˜ผ์žก์˜ ์˜ํ–ฅ์œผ๋กœ ์ง€ํ•˜์ฒ ์˜ ๋น„๊ต์šฐ์œ„๊ฐ€ ๋‚˜ํƒ€๋‚˜๋Š” ํ†ตํ–‰๊ฑฐ๋ฆฌ ๊ตฌ๊ฐ„์ด ๋งค์šฐ ๋„“๊ฒŒ ๋‚˜ํƒ€๋‚ฌ๋‹ค. ๊ฐ ์ˆ˜๋‹จ ๋ณ„ ํ†ตํ–‰๊ฑฐ๋ฆฌ์™€ ์ผ๋ฐ˜ํ™”๋น„์šฉ์˜ ๊ด€๊ณ„์‹์œผ๋กœ๋ถ€ํ„ฐ ์ด๊ฒฉ๋œ ํ‘œ๋ณธ์„ ์ด์ƒ์  ํ‘œ๋ณธ์œผ๋กœ ๋ถ„๋ฅ˜ํ•˜์˜€์œผ๋ฉฐ, ์ด์ƒ์  ํ‘œ๋ณธ์˜ ๊ณต๊ฐ„์  ๋ถ„ํฌ๋ฅผ ๋ถ„์„ํ•˜์˜€๋‹ค. ๋ฒ„์Šค์ „์šฉ์ฐจ๋กœ, ๊ธ‰ํ–‰๋…ธ์„  ๋“ฑ์˜ ํ†ตํ–‰์†๋„๋ฅผ ๊ฐœ์„ ํ•˜๋Š” ์„œ๋น„์Šค๋‚˜ ์‹œ์„ค์ด ๊ณต๊ธ‰๋˜๋Š” ๊ตํ†ต์ถ•์—์„œ ์–‘(+)์˜ ํšจ๊ณผ๊ฐ€ ๋‚˜ํƒ€๋‚˜๋Š” ์ด์ƒ์ ์ด ๋‚˜ํƒ€๋‚ฌ๋‹ค. ๋ฐ˜๋ฉด, ์Œ(-)์˜ ํšจ๊ณผ๊ฐ€ ๋‚˜ํƒ€๋‚˜๋Š” ์ด์ƒ์ ์€ ์ „์ฒด์ ์œผ๋กœ ๋„“์€ ์ง€์—ญ์—์„œ ๋‚˜ํƒ€๋‚ฌ๋‹ค. ๋ณธ ์—ฐ๊ตฌ์—์„œ ์‚ฌ์šฉํ•œ ์ž๋ฃŒ์˜ ํ•œ๊ณ„๋กœ ์ธํ•˜์—ฌ ์Œ(-)์˜ ํšจ๊ณผ๋ฅผ ์ผ์œผํ‚ค๋Š” ์š”์ธ์„ ๋ถ„์„ํ•˜์ง€ ๋ชปํ•˜์˜€๋‹ค. ๋ณธ ์—ฐ๊ตฌ๋Š” ๋น„๋ก ์ ‘๊ทผํ†ตํ–‰์— ๊ด€ํ•œ ์ž๋ฃŒ๋ฅผ ๊ณ ๋ คํ•˜์ง€ ๋ชปํ•˜์˜€๋‹ค๋Š” ํ•œ๊ณ„์ ์ด ์กด์žฌํ•˜์ง€๋งŒ ์ฃผ๋กœ ์ง€์—ญ ๊ฐ„ ํ†ตํ–‰์„ ๋Œ€์ƒ์œผ๋กœ ์ˆ˜ํ–‰๋˜์—ˆ๋˜ ์ˆ˜๋‹จ ๊ฐ„ ํ†ตํ–‰๊ฑฐ๋ฆฌ๋ณ„ ๋น„๊ต์šฐ์œ„๋ถ„์„์„ ๋Œ€์ค‘๊ตํ†ต์ˆ˜๋‹จ์— ์ ์šฉํ•˜์˜€๊ณ , ์ด์ƒ์  ๋ถ„์„์„ ์ˆ˜ํ–‰ํ•˜์—ฌ ๋Œ€์ค‘๊ตํ†ต์˜ ํšจ์œจ์ ์ธ ๊ณต๊ธ‰๊ณผ ์ทจ์•ฝ์ง€์—ญ ํŒŒ์•…์„ ์œ„ํ•œ ๋ถ„์„ํ‹€์„ ์ œ์‹œํ•˜์˜€๋‹ค๋Š” ์ ์—์„œ ์˜์˜๊ฐ€ ์žˆ๋‹ค.Demand for public transport services continues to increase with the expansion of the Seoul metropolitan area. In response, various public transportation services are being expanded in the metropolitan area. However, these expanded services are concentrated in certain traffic corridor specific areas. Due to this phenomenon, there are regions where the public transportation provided is insufficient or inappropriate. This study presents a comparative advantage analysis for the effective supply of public transportation services, as well as an outlier analysis framework for identifying the areas with low public transportation service levels. In this study, we calculated the generalization cost by considering the traffic fare and the travel time cost by using traffic card data, which provides the trip history data of individual users, Using travel distances and the generalization cost of public transportation, we analyzed the comparative advantage of public transportation by trip distance. Public transport was classified into community buses, city buses, express buses and subways. The main mode of transport is that which has the longest trip distance of the total trip distance. A sample of Seoul CBD area as a destination was extracted from the data collected in the Seoul metropolitan area. Also, the peak hours are from 07:00 am to 09:00 am. Off-peak hours are all times except for peak hours and afternoon peak time(17:00 ~ 20:00). For the estimation of the relationship between the distance and the generalization cost, the log-linear model was applied to the community bus and the express bus. The logarithmic model was applied to both the independent variable and the dependent variable and the linear model was applied to the subway and city bus. As a result of the comparative advantage analysis by the distance of public transportation, the comparative advantage was found within 3km for the community bus, and the difference was found in the time of the city bus. In the peak time of the morning, comparative advantage was shown at about 3-4km, and the comparative advantage was shown up to about 13km in the peak time period. This is caused by low traffic speed due to traffic congestion. In the case of subway, comparative advantage was shown up to 4-23km in peak hours of the morning, and comparative advantage was shown at 13-15km in off-peak hours. The express buses showed a comparative advantage at a distance of more than 23km in the peak time period of the morning, and a distance of more than 15km in the off-peak hours. In the case of the morning peak hours, the distance range in which the subway has a comparative advantage due to traffic congestion is very wide. A sample separated from the relation between the travel distance and the generalization cost for each mode of transportation, was classified as an outlier sample and the spatial distribution of the outliers was analyzed. A(+) effect was shown in the traffic axis where services and facilities to improve the speed of transit modes(i.e. bus exclusive lanes and express routes) were supplied. On the other hand, the outliers in which negative (-) effects appear are broad in the overall regions. Because of the limitation of the data used in this study, the factors causing the negative effects were not analyzed. In this study, although there is a limitation that it does not consider the data on auxiliary trips, we applied the comparative advantage analysis by distance between transit modes, conducted for inter-provincial traffic. This study provides an analysis for the efficient supply of public transport and identification of the most underserved areas.โ… . ์„œ๋ก  1 1. ์—ฐ๊ตฌ์˜ ๋ชฉ์  ๋ฐ ํ•„์š”์„ฑ 1 2. ์—ฐ๊ตฌ์˜ ๋ฒ”์œ„ ๋ฐ ๋‚ด์šฉ 2 1) ์—ฐ๊ตฌ์˜ ๋ฒ”์œ„ 2 2) ์—ฐ๊ตฌ์˜ ๋‚ด์šฉ 2 โ…ก. ์„ ํ–‰์—ฐ๊ตฌ์˜ ๊ณ ์ฐฐ 4 1. ์„ ํ–‰์—ฐ๊ตฌ์˜ ๊ณ ์ฐฐ ๋ฐ ์‹œ์‚ฌ์  4 2. ๋ณธ ์—ฐ๊ตฌ์˜ ์ฐจ๋ณ„์„ฑ 7 โ…ข. ์—ฐ๊ตฌ๋ฐฉ๋ฒ•๋ก  ์ •๋ฆฝ 8 1. ๊ฐœ์š” 8 2. ๊ธฐ๋ณธ๊ฐ€์ • 8 1) ์ด์šฉ์ž์˜ ์ฃผ์ˆ˜๋‹จ์— ๋Œ€ํ•œ ๊ฐ€์ • 8 2) ๋Œ€์ค‘๊ตํ†ต ์ด์šฉ์ž์˜ ๋„๋ณด ํ†ตํ–‰ 9 3) ํ†ตํ–‰์‹œ๊ฐ„๋Œ€ ๊ตฌ๋ถ„ 9 3. ์—ฐ๊ตฌ๋ฐฉ๋ฒ•๋ก  10 1) ๋Œ€์ค‘๊ตํ†ต์ˆ˜๋‹จ์˜ ํ†ตํ–‰๊ฑฐ๋ฆฌ๋ณ„ ๋น„๊ต์šฐ์œ„ ๋ถ„์„ 10 2) ์ด์ƒ์  ํ‘œ๋ณธ ๋ถ„์„ 12 โ…ฃ. ์ž๋ฃŒ 16 1. ๊ตํ†ต์นด๋“œ์ž๋ฃŒ์˜ ํŠน์„ฑ 16 1) ์ž๋ฃŒ์˜ ์ˆ˜์ง‘ ๋ฒ”์œ„ 16 2) ์ž๋ฃŒ์˜ ๊ตฌ์„ฑ 16 2. ๋ถ„์„์ž๋ฃŒ ๊ตฌ์ถ• 17 1) ๋ชฉ์ ์ด์šฉ์ž๋ฃŒ ๊ตฌ์ถ• 17 2) ํ‘œ๋ณธ์ถ”์ถœ 18 3) ์ผ๋ฐ˜ํ™”๋น„์šฉ ์‚ฐ์ • 24 โ…ค. ๋ถ„์„๊ฒฐ๊ณผ 25 1. ๋Œ€์ค‘๊ตํ†ต์ˆ˜๋‹จ์˜ ํ†ตํ–‰๊ฑฐ๋ฆฌ๋ณ„ ์ผ๋ฐ˜ํ™”๋น„์šฉ 25 2. ๋Œ€์ค‘๊ตํ†ต์ˆ˜๋‹จ์˜ ํ†ตํ–‰๊ฑฐ๋ฆฌ๋ณ„ ๋น„๊ต์šฐ์œ„ 33 3. ์ด์ƒ์  ํ‘œ๋ณธ ๋ถ„์„ 42 1) ์ˆ˜๋‹จ๋ณ„ ์ž”์ฐจ ๋ถ„์„ 42 2) ์ด์ƒ์  ํ‘œ๋ณธ ์ถ”์ถœ๊ฒฐ๊ณผ 45 3) ์ด์ƒ์  ํ‘œ๋ณธ์˜ ์ถœ๋ฐœ์ง€ ๋ถ„์„ 59 โ…ฅ. ๊ฒฐ๋ก  ๋ฐ ์ œ์–ธ 67 1. ๊ฒฐ๋ก  67 2. ์—ฐ๊ตฌ์˜ ์˜์˜ ๋ฐ ํ•œ๊ณ„์  69 โ–  ์ฐธ๊ณ ๋ฌธํ—Œ 70Maste

    ๋™๋‚จ์•„์‹œ์•„์—์„œ ์ธ์ฒด๊ฐ์—ผ์„ ์ผ์œผํ‚ค๋Š” ์ดํ˜•ํก์ถฉ๋ฅ˜๊ณผ ๋‚œํ™ฉ์ง€ํก์ถฉ๋ฅ˜ ์ถฉ๋ž€์˜ ํ‘œํ”ผ๋ฏธ์„ธ๊ตฌ์กฐ ๊ด€์ฐฐ

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    Thesis(masters) --์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› :์˜ํ•™๊ณผ (๊ธฐ์ƒ์ถฉํ•™์ „๊ณต),2009.8.Maste

    Alliance Portfolio Complexity, Market Entry Timing, and Alliance Formation in the Global Airline Industry

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    ํ•™์œ„๋…ผ๋ฌธ (๋ฐ•์‚ฌ)-- ์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› : ๊ฒฝ์˜๋Œ€ํ•™ ๊ฒฝ์˜ํ•™๊ณผ ๊ฒฝ์˜ํ•™ ์ „๊ณต, 2016. 2. ๋ฐ•๋‚จ๊ทœ.Recent studies suggest that the average number of alliances per firm has increased over the years, and their scope extends to various stages of the value chain. As firms face greater challenges coordinating multiple simultaneous alliances across various value chains and functions, this new phenomenon has encouraged scholars and practitioners to shift their attention to understanding the strategic complexity and the impact of alliance portfolios. Possessing an alliance portfolio is an inevitable consequence for all firms because it naturally occurs when managing a set of multiple alliances. Investigating the continuous evolution of an alliance portfolio is very important to better understand alliance dynamics because it heavily influences firms value creation activities through its functional focus, the depth of collaboration, the mode of governance, partner selection, and so on. These influences of an alliance portfolio may critically affect future decisions regarding whether a specific firm should add an additional alliance or terminate an existing alliance. This approach calls for attention from scholars to further examine existing alliances as a structural portfolio of interlinkages rather than simply counting the total number of alliances. Nevertheless, most of the prior research on alliances has not paid sufficient attention to the fact that firms in fact evaluate the value of their new alliances based on the continuously evolving context of their alliance portfolios. Contrary to the traditional alliance research, the alliance portfolio-based view considers multiple existing alliances not as a simple set of aggregated individual alliances and but as an evolving portfolio of inter-related relationships. Therefore, alliance portfolio approach goes beyond the single-alliance evaluation approach and can explain why firms decide to form less efficient alliances or give up certain beneficial alliances to maximize the overall gains from their existing alliance portfolios. Particularly when adding volume and diversity to their existing alliance portfolio, firms are challenged to address the unintentional consequences of a state of increased complexity associated with alliance portfolio management. It is important for firms to understand the level of complexity at which firms can maximize the overall gains of their alliance portfolios. The main objective of this study is to advance the alliance literature by developing a new theoretical concept as well as a construct measurement for alliance portfolio complexity. The first study investigates the various theoretical drivers of alliance formation to extend the existing literature on alliance formation, with special attention paid to the moderating effect of alliance portfolio complexity and alliance termination. I show how the bounded pattern of alliance formation can be stronger when the focal firms alliance portfolio complexity is low or its alliance termination experience is high. The second study advances international alliance formation and alliance portfolio research by highlighting the role of alliance portfolio complexity and order-of-entry learning effects in international alliance formation. This study sheds light on the various theoretical drivers of alliance formation to extend the existing literature on alliance formation, with special attention paid to order-of-entry learning effects in increasing the benefits and mitigating the costs of alliance portfolio complexity. The results show that early entry order learning exists for direct international alliance experience in both local and global markets. However, indirect entry order learning effects of international operations are limited to only the local market. The implications of this research are substantive with regard to predicting what alliances firms will form and what benefits and costs their cooperative strategies entail.CHAPTER โ… . INTRODUCTION 1 1. RESEARCH OBJECTIVE 3 2. EMPIRICAL SETTING 6 3. OVERVIEW OF CONTENT 7 CHAPTER โ…ก. REVISITING ALLIANCE FORMATION: THE MODERATING EFFECT OF ALLIANCE PORTFOLIO COMPLEIXTY AND ALLIANCE TERMINATION 9 1. INTRODUCTION 11 2. THEORY AND HYPOTHESES 15 2.1. Alliance Portfolios and Alliance Formation 15 2.2. Impact of Alliance Portfolio Size 19 2.3. Moderating Role of Alliance Portfolio Complexity 22 2.4. Moderating Role of Alliance Termination 25 3. METHOD 26 3.1. Research Setting 26 3.2. Data 27 3.3. Measures 29 3.4. Analysis 33 4. RESULTS 35 4.1. Descriptive Statistics 35 4.2. Regression Analysis 36 4.3. Robustness Check 39 5. DISCUSSION & CONCLUSION 39 5.1. Contributions 39 5.2. Limitations and Future Research 42 5.3. Conclusion 45 CHAPTER โ…ข. ALLIANCE PORTFOLIO COMPLEXITY AND ORDER-OF-ENTRY LEARNING EFFECTS IN INTERNATIONAL ALLIANCE FORMATION 51 1. INTRODUCTION 53 2. THEORY AND HYPOTHESES 56 2.1. Benefits of Building a Complex Alliance Portfolio 57 2.2. Costs of Building a Complex Alliance Portfolio 58 2.3. Moderate Role of Order-of-Entry Learning Effects 60 3. METHOD 63 3.1. Research Setting 63 3.2. Data 65 3.3. Measures 66 3.4. Analysis 71 4. RESULTS 73 4.1. Descriptive Statistics 73 4.2. Regression Analysis 73 4.3. Robustness Check 77 5. DISCUSSION & CONCLUSION 78 5.1. Contributions 78 5.2. Limitations and Future Research 79 5.3. Conclusion 81 CHAPTER โ…ฃ. OVERALL CONCLUSION 89 REFERENCES 95 APPENDIX 109 APPENDIX A 109 APPENDIX B 110 APPENDIX C 111 ๊ตญ๋ฌธ์ดˆ๋ก 113Docto

    ํƒˆ์ถค์Œ์•…์˜ ๊ทน์  ํ˜•์ƒํ™” ๋ฐฉ๋ฒ•๊ณผ ์†Œํ†ต์  ๊ธฐ๋Šฅ ์—ฐ๊ตฌ

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    ํ•™์œ„๋…ผ๋ฌธ(์„์‚ฌ) --์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› :ํ˜‘๋™๊ณผ์ •(๊ณต์—ฐ์˜ˆ์ˆ ํ•™์ „๊ณต),2008. 8.Maste

    ํ˜„๋Œ€ ์ฐฝ๊ทน์˜ ๊ณต์—ฐ ๊ธฐ๋ฒ•๊ณผ ์–‘์‹์  ํŠน์„ฑ ์—ฐ๊ตฌ

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    ํ•™์œ„๋…ผ๋ฌธ (๋ฐ•์‚ฌ)-- ์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› : ํ˜‘๋™๊ณผ์ • ๊ณต์—ฐ์˜ˆ์ˆ ํ•™์ „๊ณต, 2015. 2. ์–‘์Šน๊ตญ.์ฐฝ๊ทน์€ ์ „ํ†ต๊ณผ ํ˜„๋Œ€๊ฐ€ ๊ณต์กดํ•˜๋Š” ๋…ํŠนํ•œ ํ•œ๊ตญ ์Œ์•…๊ทน์œผ๋กœ์„œ ์ค‘์š”์„ฑ์„ ๊ฐ€์ง€๊ณ  ์žˆ๋‹ค. ๋ณธ ๋…ผ๋ฌธ์€ 2000๋…„๋Œ€ ๊ตญ๋ฆฝ์ฐฝ๊ทน๋‹จ์˜ ๊ณต์—ฐ์ž‘ํ’ˆ์„ ๋Œ€์ƒ์œผ๋กœ ํ•˜์—ฌ, ํ˜„๋Œ€ ์ฐฝ ๊ทน์˜ ๊ณต์—ฐ ๊ธฐ๋ฒ•์„ ๋ถ„์„ํ•˜๊ณ  ์–‘์‹์  ํŠน์„ฑ์„ ๋ฐํžˆ๋Š” ๊ฒƒ์„ ๋ชฉํ‘œ๋กœ ํ•œ๋‹ค. ํ˜„๋Œ€ ์ฐฝ ๊ทน์€ ์†Œ์žฌ๋ฅผ ํ™•์žฅํ•˜์˜€๊ณ , ์ž‘ํ’ˆ์˜ ๋‚ด์šฉ๊ณผ ํ˜•์‹, ๊ทธ๋ฆฌ๊ณ  ์Œ์•…์— ์ด๋ฅด๊ธฐ๊นŒ์ง€ ํ˜„๋Œ€ ์ ์ด๊ณ  ์‹คํ—˜์ ์ธ ์‹œ๋„๋ฅผ ๊ฑฐ๋“ญํ•˜์˜€๋‹ค. ์ฐฝ๊ทน๊ณผ ํŒ์†Œ๋ฆฌ์˜ ๊ตฌ๋ถ„์ด ํฌ๋ฏธํ–ˆ๋˜ ๊ณผ๊ฑฐ ์—, ์ฐฝ๊ทน์€ ๋ณต์›ํ•˜๊ณ  ๋ณด์กดํ•ด์•ผ ํ•  ์ „ํ†ต์œผ๋กœ ์ทจ๊ธ‰๋˜์—ˆ๋‹ค. ํ•˜์ง€๋งŒ ์ฐฝ์ž‘์ž๋“ค์€ ์  ์ฐจ ํŒ์†Œ๋ฆฌ์™€ ๋‹ฌ๋ฆฌ ์ฐฝ๊ทน์ด ๊ทผ๋Œ€์— ๋“ฑ์žฅํ•œ ์ƒˆ๋กœ์šด ์žฅ๋ฅด์ž„์„ ์ธ์‹ํ•˜๊ฒŒ ๋˜์—ˆ๊ณ , ๊ทธ์‚ฌ์ด ์ „ํ†ต์— ๋Œ€ํ•œ ๋‹ด๋ก ์€ ์ „ํ†ต์˜ ๋ณด์กด์—์„œ ์žฌ์ฐฝ์กฐ๋กœ ์ด๋™ํ•˜์˜€๋‹ค. ์ฐฝ๊ทน์€ ์„œ์‚ฌ์žฅ๋ฅด์ธ ํŒ์†Œ๋ฆฌ์™€ ๋น„๊ตํ–ˆ์„ ๋•Œ, ๊ทน์žฅ๋ฅด์˜ ํŠน์„ฑ์„ ๊ฐ€์ง€๊ณ  ์žˆ๋‹ค. ์ฐฝ ๊ทน์€ ํŒ์†Œ๋ฆฌ์™€ ๋‹ฌ๋ฆฌ, ์—ฌ๋Ÿฌ ๋“ฑ์žฅ์ธ๋ฌผ๋“ค์ด ๋ฌด๋Œ€ ์œ„์— ๊พธ๋ฉฐ์ง„ ์‹œ๊ฐ„๊ณผ ๊ณต๊ฐ„ ์•ˆ์— ์„œ ํ–‰๋™์œผ๋กœ ์‚ฌ๊ฑด์„ ์žฌํ˜„ํ•œ๋‹ค. ์ด์— ๋”ฐ๋ผ ์ฐฝ๊ทน์€ ์žฅ์†Œ๋ฅผ ์ œํ•œํ•˜๊ณ , ์—ํ”ผ์†Œ๋“œ๋ฅผ ์ค„์—ฌ์„œ ํ”Œ๋กฏ์— ํ†ต์ผ์„ฑ์„ ๋ถ€์—ฌํ•˜๋Š” ๋ฐฉํ–ฅ์œผ๋กœ ๋งŒ๋“ค์–ด์ง„๋‹ค. ๋˜ํ•œ ๋ฌด์—‡๋ณด๋‹ค ๋ฌด๋Œ€ ์ƒ์—์„œ ํŒ์†Œ๋ฆฌ์™€ ์ฐฝ๊ทน์˜ ๊ฐ€์žฅ ์ค‘์š”ํ•˜๊ณ  ๊ทผ๋ณธ์ ์ธ ์ฐจ์ด๋Š”, ์ฐฝ๊ทน์€ ๋…์—ฐ์ธ ํŒ์†Œ ๋ฆฌ์™€ ๋‹ฌ๋ฆฌ ๋“ฑ์žฅ์ธ๋ฌผ๋กœ์„œ ์‹ค์ œ์˜ ๋Œ€ํ™” ์ƒ๋Œ€๊ฐ€ ํ˜„์ „ํ•œ๋‹ค๋Š” ๊ฒƒ์ด๋‹ค. ํŒ์†Œ๋ฆฌ์™€ ๋‹ค ๋ฅธ, ์ฐฝ๊ทน์˜ ํŠน์žฅ์€ ๋‘ ์ธ๋ฌผ ์‚ฌ์ด์— ํ๋ฅด๋Š” ๊ธฐ๋ฅ˜๋ฅผ ๊ด€๊ฐ์ด ์ง์ ‘์ ์œผ๋กœ ํฌ์ฐฉํ•  ์ˆ˜ ์žˆ๋‹ค๋Š” ๊ฒƒ์ด๋‹ค. ๋งˆ์ง€๋ง‰์œผ๋กœ, ํŒ์†Œ๋ฆฌ๋Š” ์Œ์•…์˜ ์ ˆ์ •๊ณผ ๊ทน์˜ ์ ˆ์ •์ด ์ผ์น˜ํ•˜์ง€ ์•Š๋Š” ๊ฒฝ์šฐ๊ฐ€ ๋งŽ์€๋ฐ, ์ฐฝ๊ทน์€ ์ง€๋‚˜์น˜๊ฒŒ ๋Š˜์–ด์ง€๋Š” ๋ˆˆ๋Œ€๋ชฉ๊ณผ ์ ˆ์ •์—์„œ ๊ธด์žฅ๊ฐ์„ ๋–จ์–ด๋œจ๋ฆฌ๋Š” ๋Œ€๋ชฉ์„ ์ถ•์†Œ ํ˜น์€ ์‚ญ์ œํ•˜๊ณ , ์ด๋ฅผ ๋ฌด๋Œ€ํ™”ํ•จ์œผ๋กœ์จ ๊ทน์˜ ๊ท ํ˜•์„ ๋„ ๋ชจํ•œ๋‹ค. ๊ทธ๋Ÿฌ๋‚˜ ์ฐฝ๊ทน์˜ ์—ฐ๊ทน์  ํŠน์„ฑ์„ ์•„๋ฌด๋ฆฌ ๊ฐ•์กฐํ•œ๋‹ค๊ณ  ํ•ด๋„, ์ฐฝ๊ทน์— ๋‚ด์žฌ๋œ ํŒ์†Œ ๋ฆฌ์˜ ๋…ํŠนํ•œ ์Œ์•…๊ณผ ์„œ์‚ฌ์  ํŠน์„ฑ์€ ํ˜„๋Œ€์˜ ๋‹ค๋ฅธ ์Œ์•…๊ทน๊ณผ ๋น„๊ตํ–ˆ์„ ๋•Œ ์ฐฝ๊ทน์˜ ๋…์ž์„ฑ์„ ๊ฐ€์žฅ ์ž˜ ๋‚˜ํƒ€๋‚ด์ฃผ๋Š” ์š”์†Œ์ด๋‹ค. ์–ด๋ ค์šด ๊ณ ์–ด์ฒด๋กœ ๋œ ํŒ์†Œ๋ฆฌ ์‚ฌ์„ค์€ ๊ด€๊ฐ์˜ ์ฃผ์˜๋ฅผ ์•ฝํ™”์‹œํ‚ค๊ธฐ๋„ ํ•˜์ง€๋งŒ, ์–ด๋ ค์šด ์‚ฌ์„ค์— ํŒ์†Œ๋ฆฌ ์Œ์•…์˜ ๊ทน์  ํŠน์„ฑ ๊ณผ ์ฐฝ๊ทน์˜ ์‹œ๊ฐ์  ์žฌํ˜„์ด ๋”ํ•ด์ง€๋ฉด, ํŒ์†Œ๋ฆฌ์˜ ๋ฌธํ•™์„ฑ์„ ํ•ด์น˜์ง€ ์•Š์œผ๋ฉด์„œ๋„ ์ƒ ํ™ฉ์„ ๋ช…ํ™•ํ•˜๊ฒŒ ํ‘œํ˜„ํ•  ์ˆ˜ ์žˆ๋‹ค. ์ฐฝ๊ทน์—์„œ ํŒ์†Œ๋ฆฌ์˜ ์„œ์‚ฌ์  ํŠน์„ฑ์€ ๋„์ฐฝ์ด๋ผ ๋Š” ๋…ํŠนํ•œ ์žฅ์น˜๋กœ ๋‚จ์•„ ์žˆ๋‹ค. ๊ณผ๊ฑฐ ์ฐฝ๊ทน์˜ ๋„์ฐฝ์€ ํŒ์†Œ๋ฆฌ ์Œ์•…์„ ํ›ผ์†ํ•˜์ง€ ์•Š ๊ธฐ ์œ„ํ•ด ๋“ฑ์žฅ์ธ๋ฌผ์ด ํ–‰๋™์œผ๋กœ ๋ณด์—ฌ์ฃผ๋Š” ๋ถˆํ•„์š”ํ•œ ๊ฒƒ๊นŒ์ง€ ๋ชจ๋‘ ์„ค๋ช…ํ•˜์˜€์ง€๋งŒ, ํŒ์†Œ๋ฆฌ์™€ ์ฐฝ๊ทน์„ ๋ถ„๋ฆฌํ•œ ํ˜„๋Œ€ ์ฐฝ๊ทน์˜ ๋„์ฐฝ์€ ๊ทน์  ์ „๊ฐœ๋ฅผ ์œ„ํ•ด ์กด์žฌํ•œ๋‹ค. ํ•œ ํŽธ ์ฐฝ๊ทน์€ ์Œ์•…๊ทน์ž„์—๋„ ๋ถˆ๊ตฌํ•˜๊ณ  ๋“ค์„๋งŒํ•œ ์†Œ๋ฆฌ๊ฐ€ ์—†๋‹ค๋Š” ์ ์ด ์ž์ฃผ ์ง€์ ๋˜๋Š”๋ฐ, ํ˜„๋Œ€ ์ฐฝ๊ทน์€ ๊ธฐ์กด์˜ ์œ ๋ช…ํ•œ ํŒ์†Œ๋ฆฌ์˜ ์ฐฝ๋“ค์„ ์ƒํ˜ธํ…์ŠคํŠธ์ ์œผ๋กœ ํ™œ์šฉํ•จ ์œผ๋กœ์จ, ์ต์ˆ™ํ•œ ์Œ์•…์— ์ƒˆ๋กœ์šด ์ด์•ผ๊ธฐ๋ฅผ ๋”ํ•ด์„œ ์‹ ์„ ํ•œ ๊ฒฝํ—˜์„ ์ œ๊ณตํ•œ๋‹ค. ์Œ์•…๊ทน์œผ๋กœ์„œ ์ฐฝ๊ทน์€ ๋ฌด๋Œ€ ์žฅ๋ฉด๊ณผ ํŒ์†Œ๋ฆฌ ์ฐฝ, ๊ทธ๋ฆฌ๊ณ  ๋ฐ˜์ฃผ์Œ์•…์ด ๋‹ค์„ฑ์ ์œผ ๋กœ ์กฐํ™”์‹œ์ผœ์„œ ์„œ์‚ฌ๋ฅผ ๋”์šฑ ํ’๋ถ€ํ•˜๊ฒŒ ๋งŒ๋“ ๋‹ค. ํ˜„๋Œ€ ์ฐฝ๊ทน์—์„œ ์ ๊ทน์ ์œผ๋กœ ํ™œ์šฉ ํ•˜๋Š” ํ™”์„ฑ๋ฒ• ๋ฐ ๊ด€ํ˜„์•…, ๊ทธ๋ฆฌ๊ณ  ๋‹ค์–‘ํ•œ ํ˜„๋Œ€ ์•…๊ธฐ๋“ค์€ ๋ฐ˜์ฃผ์Œ์•…์„ ๋”์šฑ ๊ทน์  ์œผ๋กœ ๋งŒ๋“ค์–ด์ค€๋‹ค. ์ „ํ†ต ์„ ์œจ์˜ ์ฐฝ๊ณผ ์–‘์•…์— ๊ธฐ๋Œ„ ๋ฐ˜์ฃผ์Œ์•…์€ ๋Œ€์œ„์  ์•™์ƒ๋ธ”์„ ๋ณด์—ฌ์ฃผ๋ฉฐ, ์–‘์ž์˜ ์ด์งˆ์ ์ธ ๋Œ€๋น„๋Š” ํ‘œ๋ฉด์  ์ด์•ผ๊ธฐ์˜ ์ •ํ™•ํ•œ ์ „๋‹ฌ๋ณด๋‹ค๋Š” ์ด๋ฉด ์— ์ˆจ์€ ์˜๋ฏธ๋ฅผ ๋‹ค์ธต์ ์œผ๋กœ ์ œ์‹œํ•˜๋ฉด์„œ ํ’๋ถ€ํ•œ ํ•ด์„์˜ ์—ฌ์ง€๋ฅผ ์ œ๊ณตํ•œ๋‹ค. ์‚ฌ๊ฑด ์˜ ๊ฒ‰์œผ๋กœ ๋“œ๋Ÿฌ๋‚˜๋Š” ๋ฉด๊ณผ ๋‚ดํฌ๋œ ์˜๋ฏธ๊ฐ€ ๋‹ค๋ฅด๊ฒŒ ํ˜๋Ÿฌ๊ฐ€๋Š” ์•„์ด๋Ÿฌ๋‹ˆ๋ฅผ ํ‘œํ˜„ํ•˜ ๋Š” ์žฅ์น˜ ์ค‘์—์„œ ํŒ์†Œ๋ฆฌ์—์„œ ์˜จ ๊ฒƒ๋„ ์žˆ๋‹ค. ์Œ์•…๊ณผ ์‚ฌ์„ค์„ ๊ฒฐํ•ฉํ•˜๋Š” ํŒ์†Œ๋ฆฌ์˜ ๋‹ค์–‘ํ•œ ๋ถ™์ž„์ƒˆ ์ค‘์—์„œ ๋„์„ญ์€ ์žฅ๋‹จ์˜ ๋ฆฌ๋“ฌ๊ณผ ์„ ์œจ์˜ ๋ฆฌ๋“ฌ์„ ๋‹ค๋ฅด๊ฒŒ ํ‘œํ˜„ํ•จ์œผ ๋กœ์จ, ์„ ์œจ์— ๋ถ™์€ ๋ง์˜ ๋‚ด์šฉ๊ณผ ๋ฐ˜์ฃผ๋กœ ๋Œ€๋ณ€๋˜๋Š” ์ƒํ™ฉ์ด ๋‹ค๋ฅด๊ฒŒ ํ˜๋Ÿฌ๊ฐ€๋Š” ์•„ ์ด๋Ÿฌ๋‹ˆ๋ฅผ ํ‘œํ˜„ํ•œ๋‹ค. ์„œ๋กœ ๋‹ค๋ฅธ ์ƒ‰๊น”์„ ๊ฐ€์ง„ ์Œ์•…๊ณผ ์Œ์•… ์‚ฌ์ด์˜ ๋Œ€ํ™”๋Š” ๊ฐ€์‚ฌ์˜ ๋‚ด์šฉ๊ณผ ์ƒ๊ด€ ์—† ์ด, ์†Œ๋ฆฌ์˜ ์ข…๋ฅ˜์™€ ํ๋ฆ„๋งŒ์œผ๋กœ ์–‘์ž์˜ ๋Œ€๊ฒฐ์„ ์ฒญ๊ฐ์ ์œผ๋กœ ๋“œ๋Ÿฌ๋‚ด์คŒ์œผ๋กœ์จ, ์Œ ์•…๊ทน์—์„œ ์Œ์•…์ด ๊ทนํ–‰๋™์˜ ์ง์ ‘์  ๋™๋ ฅ์ด ๋  ์ˆ˜ ์žˆ์Œ์„ ๋ณด์—ฌ์ค€๋‹ค. ๋˜ํ•œ ๊ทน ์ค‘ ์—์„œ ๋‘ ์‚ฌ๋žŒ์ด ํ•˜๋‚˜์˜ ๋…ธ๋ž˜๋ฅผ ๋น„์Šทํ•œ ๋ณผ๋ฅจ๊ณผ ๊ธธ์ด๋กœ ๋‚˜๋ˆ„์–ด ๋ถ€๋ฅด๋Š” ๊ฒฝ์šฐ์—๋Š”, ๋‘ ์‚ฌ๋žŒ์˜ ํž˜์ด ๋น„๋“ฑํ•˜๋‹ค๋Š” ๊ฒƒ์„ ์•”์‹œํ•œ๋‹ค. ๋‘ ์ธ๋ฌผ์˜ ์ด์ค‘์ฐฝ์€ ๊ฐˆ๋“ฑ์˜ ์ ˆ์ • ์„ ํ˜•์ƒํ™”ํ•˜๋ฉฐ, ๊ทน์  ๊ฐˆ๋“ฑ์ด ์ตœ๊ณ ์กฐ์— ์ด๋ฅด๋ €์„ ๋•Œ ์ž์ฃผ ์‚ฌ์šฉ๋œ๋‹ค. ํ•œํŽธ ๋‹ค์ˆ˜ ์˜ ์ธ๋ฌผ์ด ๋“ฑ์žฅํ•˜๋Š” ๊ตฐ์ค‘์žฅ๋ฉด์€ ๋†€์ด์žฅ๋ฉด์œผ๋กœ ํ™•๋Œ€๋˜๋ฉด์„œ ๋– ๋“ค์ฉํ•œ ๋†€์ดํŒ ํ˜น ์€ ์ž”์น˜ํŒ์˜ ๋ถ„์œ„๊ธฐ๋ฅผ ๋งŒ๋“ค์–ด์ค€๋‹ค. ์ด๋Ÿฐ ๊ตฐ์ค‘์žฅ๋ฉด์€ ํ•ฉ์ฐฝ์œผ๋กœ ๋‚˜ํƒ€๋‚˜๊ธฐ๋„ ํ•˜ ๋Š”๋ฐ, ํ•ฉ์ฐฝ์€ ์—ฌ๋Ÿฌ ์‚ฌ๋žŒ์ด ๋ถ€๋ฅด๊ธฐ ๋•Œ๋ฌธ์— ์Œ๋Ÿ‰์ด ํฌ๊ณ  ๊ฐ€์‚ฌ์™€ ์„ ์œจ์ด ๋‹จ์ˆœํ•˜ ๊ฒŒ ๋ฐ˜๋ณต๋˜๊ณ , ์ด๊ฒƒ์ด ํŠน์ • ๋ฉ”์‹œ์ง€๋ฅผ ๊ฐ•ํ™”ํ•˜๋ฉฐ ๊ทน์ ์ธ ๋Š๋‚Œ์„ ์ค€๋‹ค. ๊ฒฐ๊ณผ์ ์œผ๋กœ, ์ฐฝ๊ทน์€ ํŒ์†Œ๋ฆฌ๊ฐ€ ๊ฐ€์ง„ ์ „ํ†ต ์š”์†Œ๋“ค๊ณผ ๋‹ค๋ฅธ ๊ณต์—ฐ์˜ˆ์ˆ ์—์„œ ์ˆ˜์šฉ ํ•œ ํ˜„๋Œ€์ ์ธ ์š”์†Œ๋“ค์„ ์กฐํ™”์‹œ์ผœ์„œ, ํ˜„๋Œ€์˜ ๋ฌธํ™”์  ์š”๊ตฌ ๋ฐ ๊ฐ๊ฐ๊ณผ ์—ฐ๋Œ€ํ•  ์ˆ˜ ์žˆ๋Š” ๋ฐฉ์‹์„ ์ฐพ๊ธฐ ์œ„ํ•ด ๋…ธ๋ ฅํ•˜๊ณ  ์žˆ๋‹ค.1. ์„œ ๋ก  .................................................................................... 1 1.1. ๋ฌธ์ œ์ œ๊ธฐ ๋ฐ ์—ฐ๊ตฌ์‚ฌ ๊ฒ€ํ†  ................................................... 1 1.2. ์—ฐ๊ตฌ๋Œ€์ƒ ๋ฐ ์—ฐ๊ตฌ๋ฐฉ๋ฒ• ..................................................... 14 2. ๊ตญ๋ฆฝ์ฐฝ๊ทน๋‹จ์˜ ๊ณต์—ฐ ๋ณ€๋ชจ ์–‘์ƒ๊ณผ 2000๋…„๋Œ€ ์ž‘ํ’ˆ๋“ค ........................ 22 2.1. ๊ตญ๋ฆฝ์ฐฝ๊ทน๋‹จ์˜ ๊ณต์—ฐ ๋ณ€๋ชจ ์–‘์ƒ ........................................... 22 2.2. ๊ตญ๋ฆฝ์ฐฝ๊ทน๋‹จ 2000๋…„๋Œ€ ์ž‘ํ’ˆ๋“ค์˜ ์žฅ๋ฉด ๋ฐ ์Œ์•… ๊ตฌ์„ฑ ............. 27 3. ๊ทน์žฅ๋ฅด๋กœ์„œ์˜ ์ฐฝ๊ทน ์ธ์‹๊ณผ ํŒ์†Œ๋ฆฌ ์–ธ์–ด์™€์˜ ๊ณต์กด ๋ชจ์ƒ‰ ................. 67 3.1. ์ŠคํŽ™ํ„ฐํด์— ์˜ํ•œ ๊ทน์ž‘์ˆ ์˜ ๋ณ€ํ™” ........................................ 67 3.1.1. ์„œ์‚ฌ์žฅ๋ฅด์—์„œ ๊ทน์žฅ๋ฅด๋กœ ์ „ํ™˜ .................................... 67 3.1.2. ๋Œ€ํ™” ์ƒ๋Œ€์˜ ๋“ฑ์žฅ: ๋“ฑ์žฅ์ธ๋ฌผ๋“ค ............................... 78 3.1.3. ํŒ์†Œ๋ฆฌ ๋ˆˆ๋Œ€๋ชฉ๊ณผ ๊ทน์˜ ์ ˆ์ • ์‚ฌ์ด์˜ ๊ดด๋ฆฌ์— ๋Œ€ํ•œ ์‹œ๊ฐ์  ํ•ด๊ฒฐ ..................................................................... 87 3.2. ์ŠคํŽ™ํ„ฐํด๊ณผ ํŒ์†Œ๋ฆฌ ์–ธ์–ด์˜ ์ƒํ˜ธ ๊ด€๊ณ„ ๊ตฌ์ถ• ......................... 98 3.2.1. ์‹œ๊ฐ์ ยท์–ธ์–ด์  ์žฌํ˜„์— ๋Œ€ํ•œ ๊ฐ๊ฐ์˜ ๋ถ„์‚ฐ๊ณผ ๋‹จ์ผํ™” ...... 98 3.2.2. ์„œ์ˆ ์ž์˜ ์ž”์กด๊ณผ ๋„์ฐฝ์—ญ(ๅฝน) ................................. 107 3.2.3. ๋“ค์„๋งŒํ•œ ์†Œ๋ฆฌ ๋ถ€์žฌ์™€ ํŒ์†Œ๋ฆฌ ์ฐฝ์˜ ์ƒํ˜ธํ…์ŠคํŠธ์  ํ™œ์šฉ ..................................................................................... 118 4. ์Œ์•…ยท์–ธ์–ดยท์žฅ๋ฉด์˜ ์‚ผ์ค‘ํŠธ๋ž™์„ ํ†ตํ•œ ๋‹ค์„ฑ์  ์Œ์•…๊ทน ์ฐฝ์ถœ ............... 126 4.1. ์ฐฝ๊ณผ ๋ฐ˜์ฃผ์Œ์•…์˜ ๋ถ„ํ™”๋ฅผ ํ†ตํ•œ ๋‹ค์ธต์  ์žฅ๋ฉด ๊ตฌํ˜„ ............... 126 4.1.1. ๋ฐ˜์ฃผ์Œ์•…์˜ ๋ชฉ์†Œ๋ฆฌ ํ™•์žฅ๊ณผ ์Œ์•…๋“ค ๊ฐ„ ์กฐํ™”์˜ ํ•œ๊ณ„ ... 126 4.1.2. ์ด์งˆ์  ์ฒญ๊ฐ์š”์†Œ๋“ค์˜ ํ˜ผ์„ฑ์  ์ƒํ˜ธ ์กฐ๋ช… ................... 132 4.1.3. ๋„์„ญ์˜ ์„œ์ˆ ํ–‰์œ„์— ์˜ํ•œ ๊ทน์  ํ˜•์ƒํ™” ...................... 143 4.2. ์Œ์•…์„ ํ†ตํ•œ ๋‹ค์ž๊ฐ„ ๋Œ€ํ™”: ์•™์ƒ๋ธ” ................................... 152 4.2.1. ์Œ์•… ๋Œ€ ์Œ์•…์˜ ๋Œ€๊ฒฐ๋กœ ๋งŒ๋“ค์–ด์ง„ ์žฅ๋ฉด: ํ–‰๋™์œผ๋กœ์„œ ์Œ์•… ..................................................................................... 152 4.2.2. ์ด์ค‘์ฐฝ์„ ํ†ตํ•œ ๊ฐˆ๋“ฑ ์žฅ๋ฉด ํ‹€ ์ง“๊ธฐ .......................... 158 4.2.3. ๊ตฐ์ค‘์žฅ๋ฉด์— ์˜ํ•œ ๋†€์ดํŒ์˜ ๋ณต์›๊ณผ ๋Œ€๋‹จ์› ํ•ฉ์ฐฝ์˜ ๋„์‹์„ฑ ..................................................................................... 166 5. ๊ฒฐ ๋ก  .............................................................................. 177Docto

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