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    Differences in Korean Youth Employees' Quality of Work and its Influencing Factors

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    ํ•™์œ„๋…ผ๋ฌธ(์„์‚ฌ) -- ์„œ์šธ๋Œ€ํ•™๊ต๋Œ€ํ•™์› : ๋†์—…์ƒ๋ช…๊ณผํ•™๋Œ€ํ•™ ๋†์‚ฐ์—…๊ต์œก๊ณผ, 2023. 2. ์ตœ์ˆ˜์ •.์ด ์—ฐ๊ตฌ์˜ ๋ชฉ์ ์€ ์ฒญ๋…„์ทจ์—…์ž์˜ ๊ณ ์šฉ์˜ ์งˆ์„ ์ถ”์ •ํ•˜๊ณ  ์ด๋“ค์˜ ๋ฐฐ๊ฒฝ์  ๋ณ€์ธ๊ณผ ๊ณ ์šฉ์˜ ์งˆ ์ง€์ˆ˜ ๋ฐ ํ•˜์œ„์š”์ธ๊ณผ์˜ ์˜ํ–ฅ ๊ด€๊ณ„๋ฅผ ๊ตฌ๋ช…ํ•˜๋Š” ๋ฐ ์žˆ์œผ๋ฉฐ, ์ดˆ๊ธฐ ์ผ์ž๋ฆฌ์—์„œ์˜ ๊ณ ์šฉ์˜ ์งˆ ์œ ํ˜•์˜ ์žฅ๊ธฐ์  ํšจ๊ณผ๋ฅผ ๊ตฌ๋ช…ํ•˜๋Š” ๋ฐ ์žˆ์—ˆ๋‹ค. ์„ธ๋ถ€์ ์ธ ์—ฐ๊ตฌ๋ชฉํ‘œ๋Š” ๋‹ค์Œ๊ณผ ๊ฐ™๋‹ค. ์ฒซ์งธ, ์ฒญ๋…„์ทจ์—…์ž์˜ ์ง๋ฌดํŠน์„ฑ, ๊ณ ์šฉ์•ˆ์ •์„ฑ, ์ˆ™๋ จํ–ฅ์ƒ ๋ฐ ๋ฐœ์ „๊ฐ€๋Šฅ์„ฑ, ๊ฒฝ์ œ์  ๋ณด์ƒ, ๊ทผ๋ฌด์กฐ๊ฑด ๋ฐ ์ž‘์—…ํ™˜๊ฒฝ, ์ฐธ์—ฌ ๋ฐ ๊ด€๊ณ„ ์ˆ˜์ค€์„ ํŒŒ์•…ํ•˜๊ณ , ์ด๋ฅผ ๋ฐ”ํƒ•์œผ๋กœ ์ฒซ ์ผ์ž๋ฆฌ ๊ณ ์šฉ์˜ ์งˆ ์ง€์ˆ˜๋ฅผ ์ถ”์ •ํ•œ๋‹ค. ๋‘˜์งธ, ์ฒญ๋…„์ทจ์—…์ž์˜ ๋ฐฐ๊ฒฝ์  ํŠน์„ฑ์— ๋”ฐ๋ฅธ ๊ณ ์šฉ์˜ ์งˆ ์ด์งˆ์„ฑ์„ ๊ฒ€์ฆํ•˜๊ณ  ์ผ๋ฐ˜์  ํŠน์„ฑ๊ณผ ํ•™์—… ํŠน์„ฑ, ๊ทธ๋ฆฌ๊ณ  ์ผ์ž๋ฆฌ ํŠน์„ฑ๊ณผ์˜ ์˜ํ–ฅ๊ด€๊ณ„๋ฅผ ๊ตฌ๋ช…ํ•œ๋‹ค. ์…‹์งธ, ์ฒญ๋…„์ทจ์—…์ž์˜ ๊ณ ์šฉ์˜ ์งˆ์— ๋”ฐ๋ฅธ ์ž ์žฌ ํ”„๋กœํŒŒ์ผ์„ ๋ถ„๋ฅ˜ํ•˜์—ฌ ๊ฐœ์ธ์˜ ๋ฐฐ๊ฒฝ์  ํŠน์„ฑ๊ณผ ๊ณ ์šฉ์˜ ์งˆ ์ž ์žฌ์ง‘๋‹จ ๊ฐ„์˜ ์˜ํ–ฅ๊ด€๊ณ„๋ฅผ ๊ตฌ๋ช…ํ•œ๋‹ค. ๋„ท์งธ, ์ฒญ๋…„์ทจ์—…์ž์˜ ์ดˆ๊ธฐ ์ผ์ž๋ฆฌ ๊ณ ์šฉ์˜ ์งˆ ์œ ํ˜• ๋ฐฐ์น˜์— ๋”ฐ๋ฅธ ์ดˆ๊ธฐ ๊ฒฝ๋ ฅ ์ดํ›„ ๊ณ ์šฉ์˜ ์งˆ ์ง€์ˆ˜์—์˜ ์žฅ๊ธฐ์  ์˜ํ–ฅ๊ด€๊ณ„๋ฅผ ๊ตฌ๋ช…ํ•œ๋‹ค. ์—ฐ๊ตฌ๋Œ€์ƒ์€ ํ•œ๊ตญ๊ณ ์šฉ์ •๋ณด์›์—์„œ ์‹ค์‹œํ•œ ์ฒญ๋…„ํŒจ๋„์กฐ์‚ฌ์— ์ฐธ์—ฌํ•œ ์‘๋‹ต์ž ์ค‘ ๊ณ ๋“ฑํ•™๊ต, ์ „๋ฌธ๋Œ€ํ•™, ๋˜๋Š” 4๋…„์ œ ๋Œ€ํ•™์œผ๋กœ ๋Œ€ํ‘œ๋˜๋Š” ์ •๊ทœ๊ต์œก๊ธฐ๊ด€์„ ์กธ์—…ํ•œ ๋’ค ๋…ธ๋™์‹œ์žฅ์— ์ง„์ž…ํ•˜์—ฌ ์ฒซ ์ผ์ž๋ฆฌ๋ฅผ ์ทจ๋“ํ•œ ์ฒญ๋…„์ทจ์—…์ž ์ด๋‹ค. ์ด๋“ค์˜ ์ง์—…๋ ฅ ์ž๋ฃŒ๋ฅผ ํ™œ์šฉํ•˜์—ฌ ์ทจ์—… ๊ฒฝํ—˜์ด ์žˆ๋Š” ์ž๋ฅผ ์ค‘์‹ฌ์œผ๋กœ ์ฒซ ์ผ์ž๋ฆฌ์˜ ๋ฐ์ดํ„ฐ๋ฅผ ์ถ”์ถœํ•˜์˜€์œผ๋ฉฐ, ํ•™๊ต์œ ํ˜•๊ณผ ์ž…์ง์—ฐ๋ น, ๊ทผ๋กœ์‹œ๊ฐ„ ๋“ฑ์„ ๊ณ ๋ คํ•˜์—ฌ ๋ถ„์„์„ ์œ„ํ•œ ํ‘œ๋ณธ์„ ๊ตฌ์ถ•ํ•˜์˜€๋‹ค. ์ž๋ฃŒ ๋ถ„์„์€ R 4.2.1 ํ”„๋กœ๊ทธ๋žจ์œผ๋กœ ์ฒญ๋…„์ทจ์—…์ž์˜ ๋ฐฐ๊ฒฝ์  ํŠน์„ฑ๊ณผ ๊ณ ์šฉ์˜ ์งˆ ๊ด€๋ จ ๋ณ€์ธ์— ๋Œ€ํ•œ ๊ธฐ์ˆ ํ†ต๊ณ„ ๋ฐ ์ƒ๊ด€ ๋ถ„์„, ๋‹ค์ค‘ํšŒ๊ท€๋ถ„์„, ์ž ์žฌ ํ”„๋กœํŒŒ์ผ ๋ถ„์„์„ ์‹ค์‹œํ•˜์˜€๋‹ค. ํ†ต๊ณ„์  ์œ ์˜์ˆ˜์ค€์€ 0.05์˜€์œผ๋ฉฐ, ์ž ์žฌ ํ”„๋กœํŒŒ์ผ ๋ชจํ˜•์€ ํ†ต๊ณ„์  ํŒ๋‹จ๊ธฐ์ค€๊ณผ ํ•ด์„์ƒ์˜ ๊ฐ„๋ช…์„ฑ์„ ๊ณ ๋ คํ•˜์—ฌ ๊ฐ€์žฅ ์ ํ•ฉํ•œ ๋ชจํ˜•์„ ์„ ํƒํ•˜์˜€๋‹ค. ์ฃผ์š” ์—ฐ๊ตฌ ๊ฒฐ๊ณผ๋Š” ๋‹ค์Œ๊ณผ ๊ฐ™๋‹ค. ์ฒซ์งธ, ์ฒญ๋…„์ทจ์—…์ž์˜ ๊ณ ์šฉ์˜ ์งˆ ์ง€์ˆ˜๋Š” ๋ฐฉํ•˜๋‚จ ์™ธ(2007)๊ฐ€ ์ œ์‹œํ•œ ๊ณ ์šฉ์˜ ์งˆ ์ง€์ˆ˜์˜ ํ•˜์œ„์š”์ธ์„ ๋ฐ”ํƒ•์œผ๋กœ ์ถ”์ •๋  ์ˆ˜ ์žˆ์œผ๋ฉฐ, ์ด๋Ÿฌํ•œ ๊ณ ์šฉ์˜ ์งˆ ์ง€์ˆ˜๋Š” ์ฒญ๋…„์ทจ์—…์ž์˜ ์„ฑ๋ณ„, ๊ต์œก์ˆ˜์ค€, ์ตœ์ข…ํ•™๊ต ์„ฑ์ , ์ง์ข…, ๊ธฐ์—…๊ทœ๋ชจ์— ๋”ฐ๋ผ ์œ ์˜ํ•œ ์ˆ˜์ค€์˜ ์ฐจ์ด๊ฐ€ ์žˆ๋Š” ๊ฒƒ์œผ๋กœ ํ™•์ธ๋˜์—ˆ๋‹ค. ๋‘˜์งธ, ๊ฐœ์ธ ์ˆ˜์ค€์˜ ํŠน์„ฑ ์ค‘ ์ฒญ๋…„์ทจ์—…์ž์˜ ๊ต์œก์ˆ˜์ค€๊ณผ ์„ฑ๋ณ„, ์•„๋ฒ„์ง€ ํ•™๋ ฅ ๋ฐ ๊ฐ€๊ตฌ์†Œ๋“์ˆ˜์ค€, ๊ทธ๋ฆฌ๊ณ  ์ตœ์ข…ํ•™๊ต ์„ฑ์  ๋ฐ ํ•™๊ต์ƒํ™œ ๋งŒ์กฑ๋„๋Š” ๊ณ ์šฉ์˜ ์งˆ ํ•˜์œ„์š”์ธ์ธ ์ง์—…์ž์œจ์„ฑ ๋ฐ ๊ถŒํ•œ, ์Šคํ‚ฌํ–ฅ์ƒ ๋ฐ ๋ฐœ์ „๊ฐ€๋Šฅ์„ฑ, ๋กœ๊ทธ ์›”ํ‰๊ท  ์ž„๊ธˆ์— ์ •์  ์˜ํ–ฅ์„ ๋ฏธ์น˜๋Š” ๊ฒƒ์œผ๋กœ ๋‚˜ํƒ€๋‚ฌ๋‹ค. ์ผ์ž๋ฆฌ ์ˆ˜์ค€์˜ ํŠน์„ฑ์˜ ๊ฒฝ์šฐ, ์—ฐ๊ตฌ์งยท๊ณตํ•™ ๊ธฐ์ˆ ์ง๊ณผ ๋ณด๊ฑดยท์˜๋ฃŒ์ง์€ ์ •์  ์˜ํ–ฅ์„ ๋‚˜ํƒ€๋‚ธ ๋ฐ˜๋ฉด, ๋ฏธ์šฉยท์—ฌํ–‰ยท์ˆ™๋ฐ•ยท์Œ์‹ยท๊ฒฝ๋น„ยท์ฒญ์†Œ์ง๊ณผ ์˜์—…ยทํŒ๋งคยท์šด์ „ยท์šด์†ก์ง, ์„ค์น˜ยท์ •๋น„ยท์ƒ์‚ฐ์ง์€ ๊ณ ์šฉ์˜ ์งˆ ์ง€์ˆ˜ ๋ฐ ํ•˜์œ„์š”์ธ์— ๋ถ€์  ์˜ํ–ฅ์„ ๋ฏธ์น˜๋Š” ๊ฒƒ์œผ๋กœ ๋‚˜ํƒ€๋‚ฌ๋‹ค. ๋˜ํ•œ ์ง์žฅ๊ทœ๋ชจ๊ฐ€ ํด์ˆ˜๋ก, ๊ณต๊ณต๋ถ€๋ฌธ์ผ์ˆ˜๋ก ๊ณ ์šฉ์˜ ์งˆ ์ง€์ˆ˜ ๋ฐ ํ•˜์œ„์š”์ธ์— ์ •์  ์˜ํ–ฅ์„ ๋ฏธ์น˜๋Š” ๊ฒƒ์œผ๋กœ ํ™•์ธ๋˜์—ˆ๋‹ค. ์…‹์งธ, ์ž ์žฌ ํ”„๋กœํŒŒ์ผ ๋ถ„์„์„ ํ†ตํ•ด 5๊ฐœ์˜ ํ”„๋กœํŒŒ์ผ์ด ์ฒญ๋…„์ทจ์—…์ž์˜ ๊ณ ์šฉ์˜ ์งˆ ์–‘์ƒ์„ ํฌ์ฐฉํ•˜๋Š”๋ฐ ๊ฐ€์žฅ ์ ํ•ฉํ•œ ๋ชจํ˜•์œผ๋กœ ํ™•์ธ๋˜์—ˆ๋‹ค. ์ž ์žฌ ํ”„๋กœํŒŒ์ผ๋ณ„ ์˜ํ–ฅ์š”์ธ์˜ ๊ฒฝ์šฐ ์„ฑ๋ณ„๊ณผ ์ตœ์ข…ํ•™๊ต ์„ฑ์ , ํ•™๊ต์ƒํ™œ ๋งŒ์กฑ๋„, ์ง์ข… ๋ฐ ๊ธฐ์—…๊ทœ๋ชจ๊ฐ€ ์œ ์˜ํ•œ ์˜ํ–ฅ์„ ๋ฏธ์น˜๋Š” ๊ฒƒ์œผ๋กœ ๋‚˜ํƒ€๋‚ฌ๋‹ค. ๋งˆ์ง€๋ง‰์œผ๋กœ ์ดˆ๊ธฐ ์ž…์ง ์‹œ์ ์˜ ๊ณ ์šฉ์˜ ์งˆ ์œ ํ˜•์€ 5๋…„ ํ›„ ๊ณ ์šฉ์˜ ์งˆ ์ง€์ˆ˜์— ์œ ์˜ํ•œ ์˜ํ–ฅ์„ ๋ฏธ์น˜๋Š” ๊ฒƒ์œผ๋กœ ๋‚˜ํƒ€๋‚ฌ๋‹ค. ๋‹ค๋ฅธ ์ง‘๋‹จ์— ๋น„ํ•ด ๋‚ฎ์€ ์ˆ˜์ค€์˜ ๊ณ ์šฉ์˜ ์งˆ์„ ๋‚˜ํƒ€๋‚ธ ์œ ํ˜• ์ง‘๋‹จ์€ ์žฅ๊ธฐ์ ์ธ ๋ถ€์  ์˜ํ–ฅ๊ด€๊ณ„๊ฐ€ ๋‚˜ํƒ€๋‚ฌ์œผ๋‚˜, ์ดˆ๊ธฐ ์ €์ˆ˜์ค€ ๊ณ ์šฉ์˜ ์งˆ ์ง‘๋‹จ์˜ ์žฅ๊ธฐ์  ๋ถ€์  ํšจ๊ณผ๋Š” ๋Œ€์กธ ์ด์ƒ์˜ ๊ต์œก์ˆ˜์ค€์— ์˜ํ•œ ์กฐ์ ˆํšจ๊ณผ์— ์˜ํ•ด ์ƒ๋‹น ๋ถ€๋ถ„ ์ƒ์‡„๋˜์—ˆ๋‹ค. ์ฃผ์š” ๊ฒฐ๋ก ์€ ๋‹ค์Œ๊ณผ ๊ฐ™๋‹ค. ์ฒซ์งธ, ๊ณ ์šฉ์˜ ์งˆ ํ•˜์œ„์š”์†Œ์ธ ์ง๋ฌด/์ง์—… ํŠน์„ฑ, ๊ณ ์šฉ์•ˆ์ •์„ฑ, ์ˆ™๋ จํ–ฅ์ƒ ๋ฐ ๋ฐœ์ „๊ฐ€๋Šฅ์„ฑ, ๊ฒฝ์ œ์  ๋ณด์ƒ, ๊ทผ๋ฌด์กฐ๊ฑด ๋ฐ ์ž‘์—…ํ™˜๊ฒฝ, ์ฐธ์—ฌ ๋ฐ ๊ด€๊ณ„ ์ˆ˜์ค€์„ ๋ฐ”ํƒ•์œผ๋กœ ์ฒญ๋…„์ทจ์—…์ž์˜ ์ดˆ๊ธฐ ์ผ์ž๋ฆฌ์—์„œ์˜ ๊ณ ์šฉ์˜ ์งˆ ์ง€์ˆ˜๋ฅผ ์ถ”์ •ํ•  ์ˆ˜ ์žˆ์œผ๋ฉฐ, ์ฒญ๋…„์ทจ์—…์ž์˜ ๊ณ ์šฉ์˜ ์งˆ์€ ์ผ๋ฐ˜์  ํŠน์„ฑ, ํ•™์—… ํŠน์„ฑ, ์ผ์ž๋ฆฌ ํŠน์„ฑ์— ๋”ฐ๋ผ ๊ทธ ์ˆ˜์ค€์˜ ์ฐจ์ด๊ฐ€ ์กด์žฌํ•˜๋Š” ๊ฒƒ์œผ๋กœ ๋‚˜ํƒ€๋‚œ๋‹ค. ์šฐ๋ฆฌ๋‚˜๋ผ์˜ ๊ณ ์šฉ ํ˜•ํƒœ์— ๋”ฐ๋ฅธ ๋…ธ๋™์‹œ์žฅ ๋ถ„์ ˆ๊ตฌ์กฐ๋ฅผ ๊ณ ๋ คํ•œ๋‹ค๋ฉด ๊ณ ์šฉ์˜ ์งˆ ํ•˜์œ„์š”์ธ์œผ๋กœ์จ ์ƒ์šฉ์ง ์—ฌ๋ถ€ ์ด์™ธ์—๋„ ๋น„์ •๊ทœ์ง ์—ฌ๋ถ€์˜ ํ™œ์šฉ์„ ์žฌ๊ณ ํ•ด๋ณผ ์ˆ˜ ์žˆ๋‹ค. ๋˜ํ•œ, ์ „๋ฐ˜์ ์ธ ๊ณ ์šฉ์˜ ์งˆ์€ ์ผ์ž๋ฆฌ ์ž์ฒด์˜ ํŠน์„ฑ๋ฟ๋งŒ ์•„๋‹ˆ๋ผ ์ผ์ž๋ฆฌ ์ง„์ž… ์‹œ์  ์ด์ „์— ํ˜•์„ฑ๋œ ๊ฐœ์ธ ์ˆ˜์ค€์˜ ๋ฐฐ๊ฒฝ์  ํŠน์„ฑ๊ณผ๋„ ์งยท๊ฐ„์ ‘์ ์ธ ๊ด€๊ณ„๊ฐ€ ์žˆ๋‹ค๊ณ  ๊ฒฐ๋ก  ๋‚ด๋ฆด ์ˆ˜ ์žˆ๋‹ค. ๋‘˜์งธ, ์ฒญ๋…„์ทจ์—…์ž์˜ ๊ต์œก์ˆ˜์ค€๊ณผ ์ผ๋ฐ˜์  ํŠน์„ฑ, ํ•™์—… ํŠน์„ฑ๊ณผ ์ผ์ž๋ฆฌ ํŠน์„ฑ์€ ๊ณ ์šฉ์˜ ์งˆ ์ง€์ˆ˜์™€ ์ฃผ์š” ํ•˜์œ„์š”์ธ์ธ ์ง์—…์ž์œจ์„ฑ ๋ฐ ๊ถŒํ•œ, ์Šคํ‚ฌํ–ฅ์ƒ ๋ฐ ๋ฐœ์ „๊ฐ€๋Šฅ์„ฑ, ์›”ํ‰๊ท  ์ž„๊ธˆ์ˆ˜์ค€์— ์œ ์˜ํ•œ ์ˆ˜์ค€์˜ ์˜ํ–ฅ์„ ๋ฏธ์นœ๋‹ค. ํŠนํžˆ ๊ทผ๋กœ์ž๊ฐ€ ์†ํ•ด์žˆ๋Š” ์—…์ข… ๋ฐ ์ง์ข… ํŠน์„ฑ์€ ์ž…์ง ์ดˆ๊ธฐ์˜ ๊ฐœ์ธ์ด ์ธ์‹ํ•˜๋Š” ์—…๋ฌด์ˆ˜ํ–‰์—์„œ์˜ ์ž์œจ์„ฑ์ด๋‚˜ ์Šคํ‚ฌํ–ฅ์ƒ ๊ฐ€๋Šฅ์„ฑ ์ˆ˜์ค€์— ์ผ์ • ์ˆ˜์ค€ ์˜ํ–ฅ์„ ๋ฏธ์นœ๋‹ค๋Š” ๊ฒƒ์„ ๊ณ ๋ คํ–ˆ์„ ๋•Œ, ์ €์ˆ™๋ จ ์ง์ข…์— ๋Œ€ํ•œ ์ง์—…๋Šฅ๋ ฅ๊ฐœ๋ฐœ ๊ธฐํšŒ๋ฅผ ํ™•๋Œ€ยท๋ณด์žฅํ•˜๋Š” ํ˜•ํƒœ๋กœ ์ดˆ๊ธฐ ๊ฒฝ๋ ฅ์ž๋“ค์˜ ์žฅ๊ธฐ์  ์ธก๋ฉด์˜ ๊ฒฝ๋ ฅ๊ฐœ๋ฐœ ๊ธฐ๋ฐ˜์„ ๋งˆ๋ จํ•  ํ•„์š”๊ฐ€ ์žˆ์„ ๊ฒƒ์ด๋‹ค. ์…‹์งธ, ์ฒญ๋…„์ทจ์—…์ž์˜ ์ดˆ๊ธฐ ์ผ์ž๋ฆฌ์—์„œ์˜ ๊ณ ์šฉ์˜ ์งˆ ์ˆ˜์ค€์— ๋Œ€ํ•œ ์ž ์žฌ์ง‘๋‹จ์„ ๋ถ„๋ฅ˜ํ•œ ๊ฒฐ๊ณผ, 5๊ฐœ์˜ ์ž ์žฌ ํ”„๋กœํŒŒ์ผ ๋ชจํ˜•์„ ๋ฐ”ํƒ•์œผ๋กœ ์ฒญ๋…„์ทจ์—…์ž์˜ ๊ณ ์šฉ์˜ ์งˆ์˜ ์œ ํ˜•ํ™”๊ฐ€ ๊ฐ€๋Šฅํ•˜๋ฉฐ, ๋ถ„๋ฅ˜๋œ ์ง‘๋‹จ์€ ์ง‘๋‹จ ์—ฌ์„ฏ ๊ฐœ์˜ ๊ณ ์šฉ์˜ ์งˆ ํ•˜์œ„์š”์ธ์— ๋”ฐ๋ผ ์„œ๋กœ ๊ตฌ๋ถ„๋˜๋Š” ์ด์งˆ์„ฑ์„ ๋‚˜ํƒ€๋‚ธ๋‹ค. ์ž ์žฌ ํ”„๋กœํŒŒ์ผ ๋ถ„์„์„ ํ†ตํ•œ ์ดˆ๊ธฐ ์ผ์ž๋ฆฌ์˜ ๊ณ ์šฉ์˜ ์งˆ ์œ ํ˜•ํ™” ๋ถ„์„์€ ์šฐ๋ฆฌ ์‚ฌํšŒ์— ์กด์žฌํ•˜๋Š” ์ค‘๊ฐ„ ์ˆ˜์ค€์˜ ์ผ์ž๋ฆฌ์— ๋Œ€ํ•œ ๋‹ค์–‘ํ•œ ํŠน์„ฑ์„ ๋ณด๋‹ค ์„ธ๋ถ€์ ์œผ๋กœ ํฌ์ฐฉํ•˜๋Š”๋ฐ ์œ ์šฉํ•œ ์ ‘๊ทผ๋ฒ•์ผ ์ˆ˜ ์žˆ์œผ๋ฉฐ, ์ค‘๊ฐ„ ์ˆ˜์ค€์˜ ์ผ์ž๋ฆฌ์˜ ๊ฐœ๋ณ„ ํ•˜์œ„์š”์ธ์— ๋”ฐ๋ผ ์–ด๋–ป๊ฒŒ ์„ธ๋ถ€์ ์ธ ๊ฐœ์ž…์ด ์ด๋ฃจ์–ด์ ธ์•ผ ํ•˜๋Š”์ง€์— ๋Œ€ํ•œ ๋…ผ์˜์˜ ๋’ท๋ฐ›์นจ์ด ๋  ์ˆ˜ ์žˆ์„ ๊ฒƒ์ด๋‹ค. ๋„ท์งธ, ์ฒญ๋…„์ทจ์—…์ž์˜ ์ž…์ง ์‹œ์ ์˜ ๊ณ ์šฉ์˜ ์งˆ ํ”„๋กœํŒŒ์ผ์€ 5๋…„ ํ›„ ๊ณ ์šฉ์˜ ์งˆ ์ง€์ˆ˜์— ์œ ์˜ํ•œ ์ˆ˜์ค€์˜ ์˜ํ–ฅ์„ ๋ฏธ์น˜๋ฉฐ, ๊ณ ์šฉ์˜ ์งˆ ์œ ํ˜•์— ๋”ฐ๋ฅธ ๋ถ€์  ํšจ๊ณผ๋Š” ๊ทผ๋กœ์ž์˜ ๋†’์€ ๊ต์œก ์ˆ˜์ค€์„ ์กฐ๊ฑด๋ถ€๋กœ ํ•˜์—ฌ ์ƒ๋‹น ๋ถ€๋ถ„ ์ƒ์‡„๋œ๋‹ค. ์ด๋Š” ๋Œ€์กธ ์ทจ์—…์ž์™€ ๊ทธ ์™ธ ์ทจ์—…์ž ๊ฐ„ ๊ณ ์šฉ์ง€ํ‘œ ์ƒ์˜ ๊ฒฉ์ฐจ๊ฐ€ ๊ฐ์†Œํ–ˆ์Œ์—๋„, ํ•™๋ ฅ์˜ ์ค‘์žฅ๊ธฐ์  ๊ณ ์šฉ์˜ ์งˆ ํ”„๋ฆฌ๋ฏธ์—„์€ ์ผ์ • ์ˆ˜์ค€ ์œ ์ง€๋˜๊ณ  ์žˆ์Œ์„ ์‹œ์‚ฌํ•œ๋‹ค. ์—ฐ๊ตฌ์˜ ๊ฒฐ๊ณผ ๋ฐ ๊ฒฐ๋ก ์„ ๋ฐ”ํƒ•์œผ๋กœ ์ฒญ๋…„์ทจ์—…์ž์˜ ๊ณ ์šฉ์˜ ์งˆ ์œ ํ˜•ํ™”์™€ ํ›„์†์—ฐ๊ตฌ๋ฅผ ์œ„ํ•œ ์ œ์–ธ์€ ๋‹ค์Œ๊ณผ ๊ฐ™๋‹ค. ์ฒซ์งธ, ๊ณ ์šฉ์˜ ์งˆ ์ง€์ˆ˜๋ฅผ ๋ฐ”ํƒ•์œผ๋กœ ์ฒญ๋…„์ทจ์—…์ž์˜ ์ดˆ๊ธฐ ์ผ์ž๋ฆฌ์˜ ๊ณ ์šฉ์˜ ์งˆ๊ณผ ๊ด€๋ จ๋œ ๋‹ค๊ฐ์ ์ธ ๋ถ„์„์€ ์ดˆ๊ธฐ ๋…ธ๋™์‹œ์žฅ ์ดํ–‰๊ณผ์ •์—์„œ ๋‹ค์–‘ํ•œ ์˜ํ–ฅ์š”์ธ์— ๋”ฐ๋ฅธ ์ผ์ž๋ฆฌ ๋ฐฐ์น˜ ์–‘์ƒ๊ณผ ์ดˆ๊ธฐ๊ฒฝ๋ ฅ์ž๋“ค์˜ ๊ฒฝ๋ ฅ๊ฐœ๋ฐœ์— ๋Œ€ํ•œ ์ธ์‹ ๋“ฑ์„ ์ดํ•ดํ•  ์ˆ˜ ์žˆ๋Š” ๊ธฐ์ดˆ์ ์ธ ๊ทผ๊ฑฐ๋กœ ํ™œ์šฉ๋  ์ˆ˜ ์žˆ์„ ๊ฒƒ์ด๋‹ค. ๋‘˜์งธ, ์ฒญ๋…„์ทจ์—…์ž์™€ ๊ฐ™์€ ์ดˆ๊ธฐ ๊ฒฝ๋ ฅ์ž์˜ ๊ฒฝ๋ ฅ๊ฐœ๋ฐœ์„ ์œ„ํ•ด ์ด๋“ค์˜ ์ง์ข…๊ณผ ๊ธฐ์—…๊ทœ๋ชจ, ์ง์žฅ์†Œ์žฌ์ง€ ๋“ฑ์„ ๊ณ ๋ คํ•œ ํฌ๊ด„์ ์ธ ๊ฒฝ๋ ฅ๊ฐœ๋ฐœ ๊ธฐํšŒ๋ฅผ ์ œ๋„์ ์œผ๋กœ ๋งˆ๋ จํ•  ํ•„์š”๊ฐ€ ์žˆ๊ณ , ํŠนํžˆ, ์ €์ˆ™๋ จ ์ง์ข…๊ณผ ์˜์„ธ๊ธฐ์—…, ๋น„์ˆ˜๋„๊ถŒ ์ผ์ž๋ฆฌ์—์„œ ๋‚˜ํƒ€๋‚˜๋Š” ํŠน์„ฑ์„ ๊ณ ๋ คํ•˜์—ฌ ํ•ด๋‹น ์ง์—… ๋‚ด ๊ทผ๋กœ์ž๋“ค์— ๋Œ€ํ•œ ํฌ๊ด„์ ์ธ ์—ญ๋Ÿ‰๊ฐœ๋ฐœ ๋ฐฉ์•ˆ์ด ์ œ์‹œ๋˜์–ด์•ผ ํ•  ๊ฒƒ์ด๋‹ค. ์…‹์งธ, ํ›„์†์—ฐ๊ตฌ์—์„œ๋Š” ๋‹ค์–‘ํ•œ ํŒจ๋„์ž๋ฃŒ๋ฅผ ๋ฐ”ํƒ•์œผ๋กœ ๊ฐœ๋ณ„ ๊ทผ๋กœ์ž๋“ค์˜ ๊ณ ์šฉ์˜ ์งˆ์˜ ๋ณ€ํ™”์–‘์ƒ์„ ์ถ”์ ํ•˜๋Š” ํ˜•ํƒœ์˜ ์‹œ๊ณ„์—ด ๋ถ„์„์ด ์ด๋ฃจ์–ด์งˆ ํ•„์š”๊ฐ€ ์žˆ๋‹ค. ํŠนํžˆ ์ดˆ๊ธฐ ๋…ธ๋™์‹œ์žฅ ์ดํ–‰ ์‹œ๊ธฐ ๋ฟ๋งŒ ์•„๋‹ˆ๋ผ ๊ทผ๋กœ์ž์˜ ๊ฒฝ๋ ฅ๋‹จ๊ณ„์— ๋”ฐ๋ฅธ ๊ณ ์šฉ์˜ ์งˆ ๋ณ€ํ™” ์–‘์ƒ์„ ์ฃผ๋ชฉํ•  ํ•„์š”๊ฐ€ ์žˆ์œผ๋ฉฐ, ๋…ธ๋™์‹œ์žฅ ์ดํƒˆ ๋ฐ ๊ฒฝ๋ ฅ๋‹จ์ ˆ ์ดํ›„ ์žฌ์ทจ์—…์ž์— ๋Œ€ํ•œ ๊ณ ์šฉ์˜ ์งˆ ๋ณ€ํ™” ์—ฌ๋ถ€ ๋˜ํ•œ ํ›„์† ์—ฐ๊ตฌ๋ฅผ ํ†ตํ•ด ๊ฒ€ํ† ๋  ์ˆ˜ ์žˆ์„ ๊ฒƒ์ด๋‹ค.This study aims to estimate the quality of work index (QWI) of youth employees, investigate the relationship between their background attributes and the quality of work index and its sub-factors, and investigate the long-term effects of their initial quality of work. The detailed research goals are as follows. The first goal is identifying sub-factors of the quality of work index, job characteristics, employment stability, skill improvement and developmental potential, economic compensation, working conditions and working environment, participation and relationship levels, and estimating the quality of work index in the first job of the youth employees. The second goal is to investigate the relationship between the quality of work index and sub-factors according to the youth employees' general, educational, and occupational characteristics. The third goal is categorizing latent groups on their first job's quality of work sub-factors and investigating the relationship between latent group assignments and background attributes. The fourth goal is estimating the long-term impact on the quality of work index five years after entering the initial job according to the initial job quality group assignment. Among the respondents surveyed by the Youth Panel(YP), the study subjects were young workers who entered the labor market and obtained their first job after graduating from a regular educational institution represented by a high school, a junior college, or a four-year university. Based on occupational history data, data on the first job was extracted and centered on those with employment experience. A sample was constructed considering the school type, employed age, and working hours. For data analysis, the R 4.2.1 program was utilized for descriptive statistics and correlation analysis, multiple regression analysis, and latent profile analysis of the variables related to the background characteristics and quality of work index. The statistical significance level was 0.05, and for the latent profile model, the most suitable model was selected considering the statistical criteria and clarity of interpretation. The main research results are as follows. First, the quality of work index of the youth employees can be estimated based on the sub-factors of the quality of work index suggested by Bang et al. (2007). There was a significant difference in the level of quality of work according to gender, educational level, final school grades, occupation, and company size. Second, among the characteristics of the individual level, the educational level, and gender, the father's educational level and household income, and final school grades and satisfaction with school life of the youth employees significantly influence sub-factors of employment quality; professional autonomy and authority, skill improvement and development potential, and log monthly wage. Regarding occupational characteristics, researchยทengineeringยทtechnical jobs and healthยทmedical jobs showed a positive effect on the quality of work. In contrast, beautyยทtravelยทlodgingยทfoodยทsecurityยทcleaning jobs, and salesยทdrivingยทtransport jobs, and installationยทmaintenanceยทproduction jobs have a negative effect on the quality of work and its sub-factors. In addition, there were more positive effects on the work quality and sub-factors according to the company's attributes; larger size and public sector. Third, through latent profile analysis, five profiles were identified as the most suitable model to capture youth employees' quality of work. As for the influencing factors for each latent profile, gender, final school grades, school-life satisfaction, occupation, and company size had a significant effect on assignment of each latent profile. Finally, the latent profiles of quality of work at the time of initial employment significantly affected the quality of work index after five years of first employment. The long-term negative impact relationship was found in the specific type of latent groups, which showed a low quality of work compared to other groups. However, the long-term negative effect of the initial low-level quality group was substantially offset by the moderating effect of the educational level with a college degree or higher. The main conclusions are as follows. First, the quality of work index in the initial job of youth employees was successfully estimated based on job characteristics, job stability, skill improvement and developmental potential, economic compensation, working conditions and work environment, and participation and relationship level, which are sub-factors of quality of work index proposed by Bang et al. (2007). Youth employees' quality of work appears to have a significant difference in level depending on general, educational, and occupational characteristics. Considering the attributes of the dual labor market structure in Korea, it is possible to reconsider the use of non-regular workers in addition to regular workers as a sub-factor of employment quality. In addition, it can be concluded that the overall quality of work is directly or indirectly related not only to the characteristics of the occupation itself but also to the background characteristics of the individual level formed before the time of entry into the labor market. Second, youth employees' educational level and general characteristics, academic characteristics, and job characteristics significantly affect the quality of work index and its major sub-factors: occupational autonomy and authority, skill improvement and development potential, and monthly average wage. In particular, considering that the characteristics of the industry and occupation to which the worker belongs have a certain level of influence on the level of autonomy in job performance and the possibility of skill improvement perceived by individuals at the initial stage of employment, expand opportunities for vocational competency development for low-skilled occupations. It will be necessary to prepare a basis for long-term career development for those with early careers in the form of a guarantee. Third, as a result of classifying the latent profiles for the quality of work level in the initial job, it was possible to classify youth employees' quality of work based on the five latent profile models. It indicates heterogeneity that is differentiated according to the quality of work sub-factors. Analysis of the latent group assignment of quality of work through latent profile analysis can be a useful approach to capture more detailed characteristics of middle-level jobs in our society. It may support the discussion of detailed interventions for enhancing the overall job quality of early-career workers. Fourth, the quality of work profile when entering the job significantly affects the employment quality index after five years. The high level of education of the workers largely offsets the negative effect according to the type of quality of work. This suggests that the mid-to-long-term employment premium of educational attainment still exists at a certain level, even though the gap in employment indicators between university graduates and other educational-level employees has decreased. Based on the results and conclusions of the study, youth employees' types of quality of work and suggestions for follow-up research are as follows. First, based on the quality of work index, a multi-faceted analysis related to the youth employees' quality of work in their early days can be used as a fundamental basis for understanding the job placement patterns according to various influencing factors in the early labor market transition phase and the perceptions of career development of early-career workers. Second, it is necessary to systematically prepare comprehensive career development opportunities considering their occupation, company size, and location of work for the career development of early-career workers. Considering the characteristics of the job, a comprehensive competency development plan for the workers can be suggested. Third, in the follow-up study, it is necessary to conduct a time series analysis to track changes in individual workers' quality of work based on various datasets. In particular, it is necessary to pay attention to the changes in the quality of work according to the career stage of workers and the initial labor market transition phase. In addition, it is also necessary to review whether the quality of work for re-employed workers after leaving the labor market and career interruption is changed through follow-up studies.I. ์„œ๋ก  1 1. ์—ฐ๊ตฌ์˜ ํ•„์š”์„ฑ 1 2. ์—ฐ๊ตฌ ๋ชฉ์  4 3. ์—ฐ๊ตฌ ๋ฌธ์ œ 5 4. ์šฉ์–ด์˜ ์ •์˜ 6 II. ์ด๋ก ์  ๋ฐฐ๊ฒฝ 9 1. ์ฒญ๋…„์ทจ์—…์ž์˜ ๊ฐœ๋… ๋ฐ ํŠน์„ฑ 9 2. ๊ณ ์šฉ์˜ ์งˆ์˜ ๊ฐœ๋… ๋ฐ ์ธก์ • 14 3. ๊ณ ์šฉ์˜ ์งˆ ๊ด€๋ จ ์ด๋ก  21 4. ์ฒญ๋…„์ทจ์—…์ž์˜ ๊ณ ์šฉ์˜ ์งˆ ํ˜„ํ™ฉ ๋ฐ ์˜ํ–ฅ์š”์ธ 26 III. ์—ฐ๊ตฌ ๋ฐฉ๋ฒ• 32 1. ์—ฐ๊ตฌ์„ค๊ณ„ 32 2. ๋ถ„์„์ž๋ฃŒ ๋ฐ ๋Œ€์ƒ 33 3. ์ธก์ •๋ณ€์ธ 37 4. ์ž๋ฃŒ๋ถ„์„ ์ ˆ์ฐจ ๋ฐ ๋ฐฉ๋ฒ• 47 IV. ์—ฐ๊ตฌ ๊ฒฐ๊ณผ 54 1. ์ฒญ๋…„์ทจ์—…์ž์˜ ๊ณ ์šฉ์˜ ์งˆ ํŠน์„ฑ 54 2. ์ฒญ๋…„์ทจ์—…์ž์˜ ๋ฐฐ๊ฒฝ์  ํŠน์„ฑ๊ณผ ๊ณ ์šฉ์˜ ์งˆ ํ•˜์œ„์š”์ธ์˜ ๊ด€๊ณ„ 63 3. ์ฒญ๋…„์ทจ์—…์ž์˜ ๊ณ ์šฉ์˜ ์งˆ ์œ ํ˜•ํ™” ๋ฐ ์˜ํ–ฅ์š”์ธ ๋ถ„์„ 68 4. ์ฒญ๋…„์ทจ์—…์ž์˜ ์ฒซ ์ผ์ž๋ฆฌ ๊ณ ์šฉ์˜ ์งˆ ์œ ํ˜•๊ณผ 5๋…„ ํ›„ ์‹œ์ ์˜ ๊ณ ์šฉ์˜ ์งˆ ์ง€์ˆ˜์˜ ๊ด€๊ณ„ 79 V. ์š”์•ฝ, ๊ฒฐ๋ก  ๋ฐ ์ œ์–ธ 81 1. ์š”์•ฝ 81 2. ๊ฒฐ๋ก  82 3. ์ œ์–ธ 84 ์ฐธ๊ณ ๋ฌธํ—Œ 86์„

    ๊ธฐ์—…๊ทœ๋ชจ๋ณ„ ์ž„๊ธˆ๊ฒฉ์ฐจ ์ถ•์†Œ๋ฐฉ์•ˆ: ์„ฑ๊ณผ๊ณต์œ ์ œ์˜ ๋„์ž…์„ ์ค‘์‹ฌ์œผ๋กœ

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    ํ•™์œ„๋…ผ๋ฌธ (์„์‚ฌ)-- ์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› : ํ–‰์ •๋Œ€ํ•™์› ํ–‰์ •ํ•™๊ณผ(ํ–‰์ •ํ•™์ „๊ณต), 2018. 8. ์ตœ์ข…์›.๋ณธ ์—ฐ๊ตฌ์—์„œ๋Š” ์šฐ๋ฆฌ๋‚˜๋ผ์˜ ์ž„๊ธˆ๊ฒฉ์ฐจ๋Š” ์ฃผ๋กœ ๊ธฐ์—…๊ทœ๋ชจ๋กœ ๊ตฌ๋ถ„ํ•œ ๋Œ€๊ธฐ์—…๊ณผ ์ค‘์†Œ๊ธฐ์—…์—์„œ ์ง€์†์ ์œผ๋กœ ๋ฐœ์ƒํ•˜๊ณ  ๊ทธ ๊ทœ๋ชจ๋„ ํ™•๋Œ€๋˜๊ณ  ์žˆ๋‹ค๋Š” ์‚ฌ์‹ค๊ณผ, ์ž„๊ธˆ ์ˆ˜์ค€์˜ ๊ฒฐ์ •์š”์ธ ์ค‘ ์ค‘์š”ํ•œ ์‚ฌํ•ญ์€ ์ƒ์‚ฐ์„ฑ์ด๊ณ  ๋”๊ตฌ๋‚˜ ์žฅ๊ธฐ์ ์œผ๋กœ๋Š” ์ƒ์‚ฐ์„ฑ์— ๋”ฐ๋ผ ์ž„๊ธˆ์ˆ˜์ค€์ด ๊ฒฐ์ •๋˜๋ฏ€๋กœ ์ด ๊ฐ™์€ ๊ฒฉ์ฐจ๊ฐ€ ๋ฐœ์ƒํ•œ ์ฃผ์š” ์›์ธ์ด ์ƒ์‚ฐ์„ฑ์— ์ฐจ์ด๊ฐ€ ์žˆ๊ธฐ ๋•Œ๋ฌธ์ด๋ผ๋Š” ๊ฐ€์ • ํ•˜์— ๋Œ€๊ธฐ์—…๊ณผ ์ค‘์†Œ๊ธฐ์—… ๊ฐ๊ฐ์˜ ์ž„๊ธˆ๊ณผ ์ƒ์‚ฐ์„ฑ๊ณผ์˜ ๊ด€๊ณ„๋ฅผ ์‹ค์ฆ์—ฐ๊ตฌ์™€ ๊ธฐ์กด ์—ฐ๊ตฌ์˜ ๋ถ„์„์„ ํ†ตํ•ด ์‚ดํŽด๋ณด์•˜๋‹ค. ๋˜ํ•œ ์ƒ์‚ฐ์„ฑ ์ฆ๊ฐ€์— ๋”ฐ๋ผ ํ–ฅ์ƒ๋œ ๊ธฐ์—…์˜ ์„ฑ๊ณผ๋ฅผ ๊ณต์œ ํ•˜๋Š” ์„ฑ๊ณผ๊ณต์œ ์ œ์˜ ๋„์ž…๊ฐ•ํ™”๋ฅผ ํ†ตํ•ด ์ƒ์‚ฐ์„ฑ์˜ ํ–ฅ์ƒ์„ ๋„๋ชจํ•  ์ˆ˜ ์žˆ๋‹ค๋Š” ๊ฒƒ์„ ์ด๋ก ์  ๊ฒ€ํ† , ๊ธฐ์กด ์—ฐ๊ตฌ์˜ ๋ถ„์„ ๋ฐ ํ†ต๊ณ„์ž๋ฃŒ์˜ ๋ถ„์„์„ ํ†ตํ•ด ์•Œ์•„๋ณด์•˜๋‹ค. ๋˜ํ•œ ํ˜„ํ–‰ ์ค‘์†Œ๊ธฐ์—…๋“ค์€ ์„ฑ๊ณผ๊ณต์œ ์ œ์˜ ๋„์ž…๊ณผ ํ™œ์šฉ์—์„œ ๋งค์šฐ ๋‚ฎ์€ ์ˆ˜์ค€์— ๋จธ๋ฌด๋ฅด๊ณ  ์žˆ์œผ๋ฉฐ ์ค‘์†Œ๊ธฐ์—…์˜ ์„ฑ๊ณผ๊ณต์œ ์ œ ๋„์ž…์„ ํ™œ์„ฑํ™” ์‹œํ‚ฌ ์ˆ˜ ์žˆ๋Š” ์ •์ฑ…๋ฐฉ์•ˆ์„ ๊ฒ€ํ† ํ•ด ๋ณด์•˜๋‹ค.๋ชฉ ์ฐจ ์ œ 1 ์žฅ ์„œ ๋ก  1 ์ œ 1 ์ ˆ ์—ฐ๊ตฌ์˜ ๋ชฉ์  1 ์ œ 2 ์ ˆ ์—ฐ๊ตฌ์˜ ๋ฒ”์œ„ ๋ฐ ๋ฐฉ๋ฒ• 5 1. ์—ฐ๊ตฌ์˜ ๋ฒ”์œ„: ๊ธฐ์—…๊ทœ๋ชจ๋ณ„ ์ž„๊ธˆ๊ฒฉ์ฐจ 5 2. ์—ฐ๊ตฌ์˜ ๋ฐฉ๋ฒ• 8 ์ œ 2 ์žฅ ์ด๋ก ์  ๋ฐฐ๊ฒฝ ๋ฐ ๋ถ„์„ํ‹€ 10 1. ์ž„๊ธˆ๊ฒฉ์ฐจ ์ „๋ฐ˜์— ๊ด€ํ•œ ์—ฐ๊ตฌ 10 2. ์ž„๊ธˆ๊ณผ ์ƒ์‚ฐ์„ฑ ์ „๋ฐ˜์— ๊ด€ํ•œ ์—ฐ๊ตฌ 12 3. ์†Œ๋“๋ถˆํ‰๋“ฑ ์ „๋ฐ˜์— ๊ด€ํ•œ ์—ฐ๊ตฌ 19 4. ์†Œ๋“๊ฒฉ์ฐจ๋ฅผ ์ค„์ด๊ธฐ ์œ„ํ•œ ์ •์ฑ…๋ฐฉ์•ˆ์— ๊ด€ํ•œ ์—ฐ๊ตฌ 22 5. ๋ณธ ์—ฐ๊ตฌ์˜ ๋ถ„์„ํ‹€ 27 ์ œ 3 ์žฅ ๊ธฐ์—…๊ทœ๋ชจ๋ณ„ ์ž„๊ธˆ๊ฒฉ์ฐจ ์›์ธ๊ณผ ๊ฒฉ์ฐจ ์™„ํ™” ์ „๋žต 28 ์ œ 1 ์ ˆ ๊ธฐ์—…๊ทœ๋ชจ๋ณ„ ์ž„๊ธˆ๊ฒฉ์ฐจ ๋ฐœ์ƒ์›์ธ 28 1. ์ž„๊ธˆ๊ฒฉ์ฐจ ๋ฐœ์ƒ์›์ธ ๊ฒ€ํ†  28 2. ์šฐ๋ฆฌ๋‚˜๋ผ ๊ธฐ์—…๊ทœ๋ชจ๋ณ„ ์ž„๊ธˆ๊ฒฉ์ฐจ์˜ ํŠน์ง• 30 ์ œ 2 ์ ˆ ์ค‘์†Œ๊ธฐ์—…์˜ ์ƒ์‚ฐ์„ฑ ์ œ๊ณ ์™€ ์ž„๊ธˆ๊ฒฉ์ฐจ ์ถ•์†Œ 33 1. R&D ํˆฌ์ž ํ™œ์„ฑํ™” ๋“ฑ์„ ํ†ตํ•œ ์ƒ์‚ฐ์„ฑ ์ œ๊ณ ๋ฐฉ์•ˆ 33 (1) R&D ํˆฌ์ž ํ™œ์„ฑํ™” 33 (2) ์ง์—…ํ›ˆ๋ จ ์ด‰์ง„โ€ค๊ฐ•ํ™” 36 (3) ๊ธฐํƒ€ ์ƒ์‚ฐ์„ฑ ๊ฐ•ํ™” ๋ฐฉ์•ˆ 38 2. ์„ฑ๊ณผ๊ณต์œ ์ œ ๋„์ž… ๊ฐ•ํ™”๋ฅผ ํ†ตํ•œ ์ƒ์‚ฐ์„ฑ ์ œ๊ณ ๋ฐฉ์•ˆ 39 (1) ์ด๋ก ์  ๊ณ ์ฐฐ: ์ •๋ณด๋น„๋Œ€์นญ ์ƒํ™ฉ์—์„œ ๋ณธ์ธ-๋Œ€๋ฆฌ์ธ ๋ฌธ์ œ 39 (2) ์„ฑ๊ณผ๊ณต์œ ์ œ์™€ R&Dํˆฌ์ž์™€์˜ ๊ด€๊ณ„ 41 ์ œ 4 ์žฅ ์„ฑ๊ณผ๊ณต์œ ์ œ์˜ ํ˜„ํ™ฉ๊ณผ ์ž„๊ธˆ๊ฒฉ์ฐจ ์™„ํ™” 42 ์ œ 1 ์ ˆ ์„ฑ๊ณผ๊ณต์œ ์ œ ๋„์ž… ํ˜„ํ™ฉ 42 1. ์ค‘์†Œ๊ธฐ์—…์˜ ์„ฑ๊ณผ๊ณต์œ ์ œ ๋„์ž… ํ˜„ํ™ฉ 42 2. ํ•ด์™ธ ์ฃผ์š”๊ตญ์˜ ์„ฑ๊ณผ๊ณต์œ ์ œ ์ •์ฑ… ๋™ํ–ฅ 45 (1) ๋ฏธ ๊ตญ 45 (2) ์ผ ๋ณธ 46 (3) ์˜ ๊ตญ 49 (4) ๋… ์ผ 52 ์ œ 2 ์ ˆ ์„ฑ๊ณผ๊ณต์œ ์ œ์˜ ์˜ํ–ฅ๋ถ„์„ 57 1. ๊ธฐ์กด ์—ฐ๊ตฌ ๋ถ„์„ 57 2. ์ƒ์‚ฐ์„ฑ๊ณผ ์„ฑ๊ณผ๊ณต์œ ์ œ์™€์˜ ๊ด€๊ณ„ ๊ฒ€ํ†  59 (1) ์ฃผ์š”์Ÿ์  59 โ–ช ์ž„๊ธˆ์ฆ๊ฐ€์œจ๊ณผ 1์ธ๋‹น ๋ถ€๊ฐ€๊ฐ€์น˜์ฆ๊ฐ€์œจ์˜ ์—ฐ๋„๋ณ„ ์ถ”์ด 59 โ–ช ์ž„๊ธˆ์„ ์„ฑ๊ณผ๊ธ‰๊ณผ ๊ณ ์ •๊ธ‰์œผ๋กœ ๊ตฌ๋ถ„(๋Œ€๊ธฐ์—…) 61 โ–ช ์ž„๊ธˆ์„ ์„ฑ๊ณผ๊ธ‰๊ณผ ๊ณ ์ •๊ธ‰์œผ๋กœ ๊ตฌ๋ถ„(์ค‘์†Œ๊ธฐ์—…) 62 (2) ์„ฑ๊ณผ๊ธ‰ ๋ฐ ๊ณ ์ •๊ธ‰์˜ ์ƒ์‚ฐ์„ฑ๊ณผ์˜ ๊ด€๊ณ„ 63 3. ์ƒ์‚ฐ์„ฑ ์ฆ๊ฐ€์™€ ์„ฑ๊ณผ๊ณต์œ ์ œ 64 ์ œ 5 ์žฅ ๊ฒฐ๋ก  ๋ฐ ์ •์ฑ…์‹œ์‚ฌ์  66 ์ œ 1 ์ ˆ ์ค‘์†Œ๊ธฐ์—…์˜ ์„ฑ๊ณผ๊ณต์œ ์ œ ํ™•์‚ฐ์„ ์œ„ํ•œ ์ •์ฑ…๋ฐฉ์•ˆ 66 1. ์œ ์ธ์ •์ฑ…๊ณผ ๊ฐ•์ œ์ •์ฑ… 66 (1) ์ค‘์†Œ๊ธฐ์—… ์„ฑ๊ณผ๊ณต์œ ์ œ ํ™•์‚ฐ์„ ์œ„ํ•œ ์ •์ฑ…๋ฐฉํ–ฅ 66 (2) ์„ฑ๊ณผ๊ณต์œ ์ œ ์„ค๊ณ„ ์‹œ ๊ณ ๋ ค์‚ฌํ•ญ 68 2. ์ถ”์ง„์ „๋žต๊ณผ ๊ณ ์ •๊ธ‰ ์Ÿ์  71 (1) ์ ์ฆ์ฃผ์˜์  ์ ‘๊ทผ 71 (2) ๊ณ ์ •๊ธ‰์—ฌ์™€์˜ ๊ด€๊ณ„ 73 ์ œ 2 ์ ˆ ์„ฑ๊ณผ๊ณต์œ ์ œ ํ™•์‚ฐ ๊ด€๋ จ ์ •์ฑ…์‹œ์‚ฌ์  74Maste

    d-๋ฐ˜์ˆœ์„œ์˜ ๊ฒฝ์Ÿ๊ทธ๋ž˜ํ”„์˜ ์—ฐ๊ตฌ

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    ํ•™์œ„๋…ผ๋ฌธ (๋ฐ•์‚ฌ)-- ์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› : ์‚ฌ๋ฒ”๋Œ€ํ•™ ์ˆ˜ํ•™๊ต์œก๊ณผ, 2018. 2. ๊น€์„œ๋ น.The \emph{competition graph} C(D)C(D) of a digraph DD is defined to be a graph whose vertex set is the same as DD and which has an edge joining two distinct vertices xx and yy if and only if there are arcs (x,z)(x,z) and (y,z)(y,z) for some vertex zz in DD. Competition graphs have been extensively studied for more than four decades. Cohen~\cite{cohen1968interval, cohen1977food, cohen1978food} empirically observed that most competition graphs of acyclic digraphs representing food webs are interval graphs. Roberts~\cite{roberts1978food} asked whether or not Cohen's observation was just an artifact of the construction, and then concluded that it was not by showing that if GG is an arbitrary graph, then GG together with additional isolated vertices as many as the number of edges of GG is the competition graph of some acyclic digraph. Then he asked for a characterization of acyclic digraphs whose competition graphs are interval graphs. Since then, the problem has remained elusive and it has been one of the basic open problems in the study of competition graphs. There have been a lot of efforts to settle the problem and some progress has been made. While Cho and Kim~\cite{cho2005class} tried to answer his question, they could show that the competition graphs of doubly partial orders are interval graphs. They also showed that an interval graph together with sufficiently many isolated vertices is the competition graph of a doubly partial order. In this thesis, we study the competition graphs of dd-partial orders some of which generalize the results on the competition graphs of doubly partial orders. For a positive integer dd, a digraph DD is called a \emph{dd-partial order} if V(D) \subset \RR^d and there is an arc from a vertex x\mathbf{x} to a vertex y\mathbf{y} if and only if x\mathbf{x} is componentwise greater than y\mathbf{y}. A doubly partial order is a 22-partial order. We show that every graph GG is the competition graph of a dd-partial order for some nonnegative integer dd, call the smallest such dd the \emph{partial order competition dimension} of GG, and denote it by dimโกpoc(G)\dim_\text{poc}(G). This notion extends the statement that the competition graph of a doubly partial order is interval and the statement that any interval graph can be the competition graph of a doubly partial order as long as sufficiently many isolated vertices are added, which were proven by Cho and Kim~\cite{cho2005class}. Then we study the partial order competition dimensions of some interesting families of graphs. We also study the mm-step competition graphs and the competition hypergraph of dd-partial orders.1 Introduction 1 1.1 Basic notions in graph theory 1 1.2 Competition graphs 6 1.2.1 A brief history of competition graphs 6 1.2.2 Competition numbers 7 1.2.3 Interval competition graphs 10 1.3 Variants of competition graphs 14 1.3.1 m-step competition graphs 15 1.3.2 Competition hypergraphs 16 1.4 A preview of the thesis 18 2 On the competition graphs of d-partial orders 1 20 2.1 The notion of d-partial order 20 2.2 The competition graphs of d-partial orders 21 2.2.1 The regular (d โˆ’ 1)-dimensional simplex โ–ณ dโˆ’1 (p) 22 2.2.2 A bijection from H d + to a set of regular (d โˆ’ 1)-simplices 23 2.2.3 A characterization of the competition graphs of d-partial orders 25 2.2.4 Intersection graphs and competition graphs of d-partial orders 27 2.3 The partial order competition dimension of a graph 29 3 On the partial order competition dimensions of chordal graphs 2 38 3.1 Basic properties on the competition graphs of 3-partial orders 39 3.2 The partial order competition dimensions of diamond-free chordal graphs 42 3.3 Chordal graphs having partial order competition dimension greater than three 46 4 The partial order competition dimensions of bipartite graphs 3 53 4.1 Order types of two points in R 3 53 4.2 An upper bound for the the partial order competition dimension of a graph 57 4.3 Partial order competition dimensions of bipartite graphs 64 5 On the m-step competition graphs of d-partial orders 4 69 5.1 A characterization of the m-step competition graphs of dpartial orders 69 5.2 Partial order m-step competition dimensions of graphs 71 5.3 dim poc (Gm) in the aspect of dim poc (G) 76 5.4 Partial order competition exponents of graphs 79 6 On the competition hypergraphs of d-partial orders 5 81 6.1 A characterization of the competition hypergraphs of d-partial orders 81 6.2 The partial order competition hyper-dimension of a hypergraph 82 6.3 Interval competition hypergraphs 88 Abstract (in Korean) 99Docto

    Numerical analysis on underwater propeller noise with hull-appendage effect

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    ํ•™์œ„๋…ผ๋ฌธ (์„์‚ฌ)-- ์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› : ๊ณต๊ณผ๋Œ€ํ•™ ๊ธฐ๊ณ„ํ•ญ๊ณต๊ณตํ•™๋ถ€, 2019. 2. ์ด์ˆ˜๊ฐ‘.์ฃผ๋กœ ๊ตฐ์‚ฌ์ ์ธ ๋ชฉ์ ์—์„œ ํ•จ์ •์˜ ์ƒ์กด์„ฑ ๋ฐ ์ž‘์ „์ˆ˜ํ–‰ ๋Šฅ๋ ฅ๊ณผ ์ง๊ฒฐ๋˜๋Š” ๋ฌธ์ œ๋กœ ์ค‘์š”ํ•˜๊ฒŒ ์—ฌ๊ฒจ์ง€๋Š” ์ˆ˜์ค‘ ์ถ”์ง„๊ธฐ ์†Œ์Œ์€ ์ตœ๊ทผ ํ•ด์–‘ ์˜ค์—ผ ๋ฌธ์ œ์˜ ์ธก๋ฉด์—์„œ ๋ฏผ๊ฐ„ ์‚ฐ์—…๊ณ„์—์„œ๋„ ์ฃผ๋ชฉ์„ ๋ฐ›๊ณ  ์žˆ๋‹ค. ์ด์— ๋”ฐ๋ผ ์ถ”์ง„๊ธฐ ์†Œ์Œ์— ๋Œ€ํ•œ ๋งŽ์€ ์—ฐ๊ตฌ๊ฐ€ ์ง„ํ–‰๋˜๊ณ  ์žˆ์ง€๋งŒ ์„ ์ฒด์˜ ๋‚œ๋ฅ˜ ๊ฒฝ๊ณ„์ธต๊ณผ ๋ถ€๊ฐ€๋ฌผ ํ›„๋ฅ˜์— ์˜ํ•œ ๋งค์šฐ ๋ณต์žกํ•œ ๋น„์ •์ƒ ๋น„๊ท ์ผ ์œ ์ž…๋ฅ˜(unsteady non-uniform inflow)๋ฅผ ์†Œ์Œํ•ด์„์— ๊ณ ๋ คํ•œ ์—ฐ๊ตฌ๋Š” ๊ฑฐ์˜ ์—†๋Š” ์‹ค์ •์ด๋‹ค. ๋ณธ ๋…ผ๋ฌธ์—์„œ๋Š” ์„ ์ฒด-๋ถ€๊ฐ€๋ฌผ-์ถ”์ง„๊ธฐ์˜ ์œ ๋™ ์ƒํ˜ธ์ž‘์šฉ์„ ์œ ๋™ ํ•ด์„ ๋‹จ๊ณ„์—์„œ ๊ณ ๋ คํ•˜์—ฌ ์„ ์ฒด์™€ ์ถ”์ง„๊ธฐ ๋ชจ๋‘๋ฅผ ํฌํ•จํ•˜๋Š” ์„ ๋ฐ• ์ „์ฒด ์˜์—ญ์— ๋Œ€ํ•œ ๋น„๊ณต๋™/๊ณต๋™ ์ƒํƒœ์˜ CFD ํ•ด์„์„ ์ˆ˜ํ–‰ํ•˜๊ณ , ์ด๋ฅผ ํ† ๋Œ€๋กœ ๋ถˆ์—ฐ์† ์ฃผํŒŒ์ˆ˜ ์†Œ์Œ์„ ํ•ด์„ํ•˜์˜€๋‹ค. ์œ ๋™ ํ•ด์„์€ ์ƒ์šฉ ์œ ๋™ ํ•ด์„ ํ”„๋กœ๊ทธ๋žจ์ธ STAR-CCM+๋ฅผ ์ด์šฉํ•˜์—ฌ ๋น„๊ณต๋™ ์œ ๋™์˜ ๊ฒฝ์šฐ ๋น„์ •์‚ฐ RANS(unsteady Reynolds Averaged Navier-Stokes) ํ•ด์„์„ ์ˆ˜ํ–‰ํ•˜์˜€๊ณ , ๊ณต๋™์œ ๋™์˜ ๊ฒฝ์šฐ VOF(Volume of Fraction) ๋ฐฉ๋ฒ•์— Schnerr-Sauer ์บ๋น„ํ…Œ์ด์…˜ ๋ชจ๋ธ์„ ์ ์šฉํ•˜์—ฌ ํ•ด์„ํ•˜์˜€๋‹ค. ์†Œ์Œ ํ•ด์„์€ Ffowcs Williams-Hawkings ๋ฐฉ์ •์‹ ๊ธฐ๋ฐ˜์˜ ์‹œ๊ฐ„ ์˜์—ญ ์Œํ–ฅ์ƒ์‚ฌ๋ฒ•์„ ์ด์šฉํ•˜์—ฌ ๋น„๊ณต๋™/๊ณต๋™ ์ถ”์ง„๊ธฐ์— ๋Œ€ํ•˜์—ฌ ์ด๋ฃจ์–ด์กŒ๋‹ค. ์œ ๋™ ํ•ด์„๊ณผ ์†Œ์Œ ํ•ด์„ ๊ฒฐ๊ณผ๋Š” ์„ ๋ฐ•ํ•ด์–‘ํ”Œ๋žœํŠธ์—ฐ๊ตฌ์†Œ์˜ ๋Œ€ํ˜• ์บ๋น„ํ…Œ์ด์…˜ ํ„ฐ๋„(Large Cavitation Tunnel, LCT)์—์„œ ์‹ค์‹œ๋œ ๋ชจํ˜• ์‹œํ—˜ ๊ฒฐ๊ณผ์™€ ๋น„๊ต๋˜์—ˆ์œผ๋ฉฐ, ์†Œ์Œ ํ•ด์„ ๊ฒฐ๊ณผ๋Š” ์ €์ฃผํŒŒ ๋Œ€์—ญ์—์„œ ๋†’์€ ์ •ํ™•๋„๋ฅผ ๋ณด์˜€๋‹ค. ๋ณธ ์—ฐ๊ตฌ์˜ ๊ฒฐ๊ณผ๋Š” ์ˆ˜์ค‘ ์ถ”์ง„๊ธฐ์˜ ์ „์ฒด ์†Œ์Œ ์ˆ˜์ค€๊ณผ ์ €์ฃผํŒŒ ๋Œ€์—ญ์˜ ์†Œ์Œ ํŠน์„ฑ์„ ๋†’์€ ์ •ํ™•๋„๋กœ ์˜ˆ์ธกํ•  ์ˆ˜ ์žˆ๋Š” ๊ธฐ๋ฒ•์„ ์ •๋ฆฝํ•จ์œผ๋กœ์จ, ์ด๋ฅผ ํ™œ์šฉํ•˜๋ฉด ์ถ”์ง„๊ธฐ ์„ค๊ณ„ ๋‹จ๊ณ„์—์„œ ์†Œ์Œ์„ ๊ณ ๋ คํ•˜์—ฌ ์ €์†Œ์Œ ์ถ”์ง„๊ธฐ์˜ ๊ฐœ๋ฐœ์— ๋„์›€์ด ๋  ์ˆ˜ ์žˆ๋‹ค.The underwater propeller noise, which is considered to be important for the survival and operational performance of the warship mainly for military purposes, has recently been attracting attention from the industry in terms of marine pollution problem. As a result, many researches have been conducted on the noise of the propeller. In this paper, the CFD analysis of the non-cavitating/cavitating condition for the entire vessel domain is performed with considering the flow interaction of the hull-appendages-propeller in the flow analysis stage. And the discrete frequency tonal noise is analyzed based on the CFD analysis. The flow analysis was performed using STAR-CCM +, a commercial flow analysis program. And noise analysis was performed on the non-cavitating/cavitating propeller using Ffowcs Williams-Hawkings equation based time-domain acoustic analogy. The results of flow analysis and noise analysis were compared with the model test results in the large cavitation tunnels of the KRISO. Noise analysis results showed high accuracy in the low frequency band. Through this, it is possible to predict the total noise level of the underwater propeller and the noise characteristics of the low frequency band with high accuracy, and it can help to the low noise propeller design.1. ์„œ๋ก  1 1.1 ์—ฐ๊ตฌ๋ฐฐ๊ฒฝ 1 1.2 ์—ฐ๊ตฌ ๋ชฉ์  ๋ฐ ๋‚ด์šฉ 5 2. ๋ชจํ˜• ์‹œํ—˜ 6 3. ์œ ๋™ ์ˆ˜์น˜ ํ•ด์„ 8 3.1 ํ•ด์„ ๋Œ€์ƒ ๋ชจ๋ธ 8 3.2 ์œ ๋™ ์ˆ˜์น˜ ํ•ด์„ ๊ธฐ๋ฒ• 9 3.2.1 ์ง€๋ฐฐ ๋ฐฉ์ •์‹ 9 3.2.2 ๋‚œ๋ฅ˜ ๋ชจ๋ธ๋ง 11 3.2.3 ์บ๋น„ํ…Œ์ด์…˜ ๋ชจ๋ธ๋ง 13 3.3 ๊ฒฉ์ž๊ณ„ ๋ฐ ๊ฒฝ๊ณ„์กฐ๊ฑด 14 3.4 ์œ ๋™ ํ•ด์„ ์กฐ๊ฑด 16 3.5 ์œ ๋™ ํ•ด์„ ๊ฒฐ๊ณผ ๋ฐ ๊ฒ€์ฆ 17 4. ์ถ”์ง„๊ธฐ ์†Œ์Œ ์ˆ˜์น˜ ํ•ด์„ 22 4.1 ์†Œ์Œ ์ˆ˜์น˜ ํ•ด์„ ๊ธฐ๋ฒ• 22 4.1.1 ์ถ”์ง„๊ธฐ ๋น„๊ณต๋™ ์†Œ์Œ 22 4.1.2 ์ถ”์ง„๊ธฐ ๊ณต๋™ ์†Œ์Œ 27 4.1.3 ์ง€์—ฐ์‹œ๊ฐ„์˜ ๊ณ„์‚ฐ 32 4.2 ์ถ”์ง„๊ธฐ ์†Œ์Œ์› ์ถ”์ถœ 32 4.3 ์ถ”์ง„๊ธฐ ์†Œ์Œ ์ˆ˜์น˜ ํ•ด์„ ๊ฒฐ๊ณผ ๋ฐ ๊ฒ€์ฆ 37 4.3.1 ์ถ”์ง„๊ธฐ ๋น„๊ณต๋™ ์†Œ์Œ ์ˆ˜์น˜ํ•ด์„ ๊ฒฐ๊ณผ ๋ฐ ๊ฒ€์ฆ 37 4.3.2 ์ถ”์ง„๊ธฐ ๊ณต๋™ ์†Œ์Œ ์ˆ˜์น˜ํ•ด์„ ๊ฒฐ๊ณผ ๋ฐ ๊ฒ€์ฆ 40 5. ๊ฒฐ ๋ก  44Maste

    On the matrix sequence {Gamma(A^m)}_{m=1}^infinity for a Boolean matrix A whose digraph is linearly connected

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    ํ•™์œ„๋…ผ๋ฌธ (์„์‚ฌ)-- ์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› : ์ˆ˜ํ•™๊ต์œก๊ณผ, 2014. 8. ๊น€์„œ๋ น.In this thesis, we extend the results given by Park et al. [12] by studying the convergence of the matrix sequence {Gamma(A^m)}_{m=1}^infinity for a matrix A in {B}_n the digraph of which is linearly connected with an arbitrary number of strong components. In the process for generalization, we concretize ideas behind their arguments. We completely characterize A for which {Gamma(A^m)}_{m=1}^infinity converges. Then we find its limit when all of the irreducible diagonal blocks are of order at least two. We go further to characterize A for which the limit of {Gamma(A^m)}_{m=1}^infinity is a J block diagonal matrix. All of these results are derived by studying the m-step competition graph of the digraph of A.Abstract 1 Introduction 1.1 Preliminaries 1.2 A preview of thesis 2 Convergence of {Gamma(A^m)}_{m=1}^infinity 3 The limit of {Gamma(A^m)}_{m=1}^infinity 3.1 The limit of {Gamma(A^m)}_{m=1}^infinity 3.2 Limit of a particular form: the disjoint union of complete subgraphs 4 Conclusions and closing remarks Abstract (in Korean)Maste

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    ํ•™์œ„๋…ผ๋ฌธ(์„์‚ฌ)--์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› :์žฌ๋ฃŒ๊ณตํ•™๋ถ€,2004.Maste

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    ํ•™์œ„๋…ผ๋ฌธ(์„์‚ฌ)--์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› :์ง€๋ฆฌํ•™๊ณผ,1999.Maste

    Cytotoxic Constituents of Rhus trichocarpa Roots on Human Gastric Adenocarcinoma AGS Cells

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    ํ•™์œ„๋…ผ๋ฌธ (์„์‚ฌ)-- ์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› : ์•ฝํ•™๊ณผ, 2012. 8. ์„ฑ์ƒํ˜„.์•”์€ ์„ธํฌ ์ž์ฒด์˜ ์กฐ์ ˆ ๊ธฐ๋Šฅ์— ๋ฌธ์ œ๊ฐ€ ์ƒ๊ธฐ๋ฉด์„œ ์ •์ƒ์ ์œผ๋กœ๋Š” ์‚ฌ๋ฉธ๋˜์–ด์•ผ ํ•  ๋น„์ •์ƒ ์„ธํฌ๋“ค์ด ๊ณผ๋‹ค ์ฆ์‹ํ•˜๊ฒŒ ๋˜๋ฉฐ, ์กฐ์ง ๋ฐ ์žฅ๊ธฐ์— ์นจ์ž…ํ•˜์—ฌ ์ข…๊ดด๋ฅผ ํ˜•์„ฑํ•˜๊ณ  ๊ธฐ์กด์˜ ๊ตฌ์กฐ๋ฅผ ํŒŒ๊ดดํ•˜๊ฑฐ๋‚˜ ๋ณ€ํ˜•์‹œํ‚ค๋Š” ์งˆ๋ณ‘์ด๋‹ค. ํŠนํžˆ ์œ„์•”์€ ์•” ๊ด€๋ จ ์‚ฌ๋ง ์ค‘ ๋‘ ๋ฒˆ์งธ๋กœ ๋†’์€ ๋น„์œจ์„ ์ฐจ์ง€ํ•˜๊ณ  ์žˆ์œผ๋ฉฐ, ๊ทธ ์›์ธ์€ Helicobacter pylori ๊ฐ์—ผ์ด๋‚˜ ์‹์Šต๊ด€, ํ™˜๊ฒฝ์ โ€ข์œ ์ „์ ์ธ ์š”์ธ์ด๋ผ๊ณ  ๋ณด๊ณ  ๋˜๊ณ  ์žˆ๋‹ค. ์˜ป๋‚˜๋ฌด๊ณผ (Anacardiaceae)์— ์†ํ•˜๋Š” ๊ฐœ์˜ป๋‚˜๋ฌด (Rhus trichocarpa)๋Š” urushiols, flavonoid, polyphenolics ๊ณ„์—ด์„ ์ฃผ์„ฑ๋ถ„์œผ๋กœ ํ•˜๋ฉฐ, ์ „ํ†ต์ ์œผ๋กœ ๋ฏผ๊ฐ„์—์„œ ์œ„์žฅ ์งˆํ™˜, ๋™๋งฅ๊ฒฝํ™”๋“ฑ์˜ ์น˜๋ฃŒ๋ฅผ ์œ„ํ•ด ์‚ฌ์šฉ ๋˜์–ด ์™”์œผ๋ฉฐ, anti-apoptotic, anti-rhematoid, anti-mutagenic ๋“ฑ์˜ ํšจ๊ณผ๊ฐ€ ์žˆ๋‹ค๊ณ  ๋ณด๊ณ  ๋˜๊ณ  ์žˆ๋‹ค. ์ด์— ๋ณธ ์—ฐ๊ตฌ์—์„œ๋Š” ์œ„์•” ์„ธํฌ์ธ AGS cell์„ ์ด์šฉํ•˜์—ฌ ์„ธํฌ๋…์„ฑ์„ ๊ฐ–๋Š” ์„ฑ๋ถ„์„ ๊ฐœ์˜ป๋‚˜๋ฌด์˜ ๋ฟŒ๋ฆฌ๋กœ๋ถ€ํ„ฐ ํ™œ์„ฑ ์ง€ํ–ฅ์  ๋ถ„๋ฆฌ ๊ธฐ๋ฒ•์„ ์ด์šฉํ•˜์—ฌ ๋ถ„๋ฆฌํ•˜๊ณ  ๊ทธ ๊ตฌ์กฐ๋ฅผ ๊ทœ๋ช…ํ•˜๊ณ ์ž ํ•˜์˜€๋‹ค. ๊ฐœ์˜ป๋‚˜๋ฌด ๋ฟŒ๋ฆฌ๋ฅผ 80% ๋ฉ”ํƒ„์˜ฌ๋กœ ์ถ”์ถœํ•œ ๋‹ค์Œ, ์ถ”์ถœ๋ฌผ์„ ๋‹ค์‹œ n-hexane, EtOAc ๋ฐ n-BuOH ๋ถ„ํš์œผ๋กœ ๋‚˜๋ˆ„์—ˆ๊ณ , ์ด์ค‘ EtOAc ๋ถ„ํš์ด ์œ ์˜์„ฑ ์žˆ๋Š” ์„ธํฌ๋…์„ฑ์„ ํ™•์ธ ํ•˜์˜€๋‹ค. ๊ฐ์ข… column chromatography ๋ฐ RP-HPLC๋ฅผ ์ด์šฉํ•˜์—ฌ EtOAc ๋ถ„ํš์œผ๋กœ๋ถ€ํ„ฐ ์ด 13์ข…์˜ ํ™”ํ•ฉ๋ฌผ์„ ๋ถ„๋ฆฌํ•˜์˜€๋‹ค. ๋ถ„๋ฆฌํ•œ ํ™”ํ•ฉ๋ฌผ๋“ค์€ ๊ฐ์ข… ์ดํ™”ํ•™์  ์„ฑ์ƒ ๋ฐ ๋ถ„๊ด‘ํ•™์  ๋ฐ์ดํ„ฐ๋ฅผ ์ข…ํ•ฉํ•˜์—ฌ ๊ทธ ๊ตฌ์กฐ๋ฅผ ๊ฐ๊ฐ 3-methoxy semialactone (1), 4-(2,6-dihydroxy-4-methoxyphenyl)-4-oxobutanoic acid (2), gallic acid (3), 4-O-methylgallic acid (4), pentagalloyl glucose (5), (-)-fustin (6), (+)-taxifolin (7), 3,3,5,5,7-pentahydroxyflavanonol (8), fisetin (9), 3-methoxy-7,3,4-trihydroxyflavone (10), sulfuretin (11), 5,7-dihydroxy-4H-chromen-4-one (12) and Isolariciresinol (13) ์œผ๋กœ ๋™์ •ํ•˜์˜€์œผ๋ฉฐ, ์ด๋“ค ํ™”ํ•ฉ๋ฌผ ์ค‘ 1 ๊ณผ 2 ์€ ์ฒœ์—ฐ์—์„œ ์ฒ˜์Œ์œผ๋กœ ๋ถ„๋ฆฌ, ๋ณด๊ณ  ๋˜๋Š” ๋ฌผ์งˆ์ด๋‹ค. ๋ถ„๋ฆฌํ•œ ์ด 13 ์ข…์˜ ํ™”ํ•ฉ๋ฌผ์— ๋Œ€ํ•˜์—ฌ AGS cell์—์„œ ์„ธํฌ๋…์„ฑ์„ ํ™•์ธํ•œ ๊ฒฐ๊ณผ, ํ™”ํ•ฉ๋ฌผ 1-12 ๊ฐ€ ์œ ์˜์„ฑ ์žˆ๋Š” ์„ธํฌ๋…์„ฑ์„ ๋‚˜ํƒ€๋‚ด์—ˆ๋‹ค.Gastric cancer is a major health issue and the second cause of cancer-related deaths worldwide. For its therapy, Rhus trichocarpa (Anacardiaceae) has been used in traditional East Asia medicine, although the mechanism for the biological activity remains to be clarified. In Korean folk medicine, it has been used for the treatment of anti-inflammatory and anti-cancer, and urushiols, flavonoids and polyphenolics have been reported from Rhus species. This study aims to investigate the cytotoxic constituents of Rhus trichocarpa roots on AGS human gastric cells. It was found that an 80% MeOH extract of roots of Rhus trichocarpa showed cytotoxic effects on AGS cells proliferation. In the present study, its bioassay-guided fractionation resulted in the isolation of thirteen compounds. The isolated compounds were identified as 3-methoxy semialactone (1), 4-(2,6-dihydroxy-4-methoxyphenyl)-4-oxobutanoic acid (2), gallic acid (3), 4-O-methylgallic acid (4), pentagalloyl glucose (5), (-)-fustin (6), (+)-taxifolin (7), 3,3,5,5,7-pentahydroxyflavanonol (8), fisetin (9), 3-methoxy-7,3,4-trihydroxyflavone (10), sulfuretin (11), 5,7-dihydroxy-4H-chromen-4-one (12) and Isolariciresinol (13) respectively. Compounds 3 and 11 were newly reported in nature. Among the isolated compounds, compounds 4, 5 and 9 significantly showed the cytotoxic effect in on Human Gastric Adenocarcinoma AGS Cells.I. Introduction 1 II. Materials and Methods 4 1. Materials 4 1.1. Plant material 4 1.2. Reagents 4 1.3. Equipments 5 2. Methods 7 2.1. Extraction and fractionation of R. trichocarpa 7 2.2. Isolation of the compounds from the EtOAc fraction of R. trichocarpa root 8 2.2.1. Isolation of compound 1 10 2.2.2. Isolation of compound 2 11 2.2.3. Isolation of compound 3 12 2.2.4. Isolation of compound 4 12 2.2.5. Isolation of compound 5 13 2.2.6. Isolation of compound 6 14 2.2.7. Isolation of compound 7 14 2.2.8. Isolation of compound 8 15 2.2.9. Isolation of compound 9 16 2.2.10. Isolation of compound 10 16 2.2.11. Isolation of compound 11 17 2.2.12. Isolation of compound 12 18 2.2.13. Isolation of compound 13 19 2.3. Evalustion of the cytotoxic effect in vitro model 27 2.3.1. Cell line culture 27 2.3.2. Cell viability assay 27 2.3.3. Statistical analysis 28 III. Results and Discussion 29 1. Elucidation of chemical structure of compounds from R. trichocarpa roots 29 1.1. Compound 1 29 1.2. Compound 2 34 1.3. Compound 3 and 4 37 1.4. Compound 5 38 1.5. Compound 6 and 7 41 1.6. Compound 8 42 1.7. Compound 9 and 10 43 1.8. Compound 11 44 1.9. Compound 12 46 1.10. Compound 13 48 2. Cytotoxic effect of total extract, fractions and the compounds from R. trichocarpa roots 51 2.1. Cytotoxic effect of total extract and fractions on AGS cells 51 2.2. Cytotoxic effect of compound 1-13 53 2.3. Cytotoxic effect of compound 3,4 and 5 on AGS cells for 24 and 48 hr. 55 IV. Conclusion 57 V. References 58Maste

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    ํ•™์œ„๋…ผ๋ฌธ(์„์‚ฌ)--์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› :๋””์ž์ธํ•™๋ถ€ ๊ธˆ์†๊ณต์˜ˆ ์ „๊ณต,2006.Maste

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