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    Never Let Me Go์— ๋‚˜ํƒ€๋‚œ ์‹  ์ž์œ ์ฃผ์˜ ๋งฅ๋ฝ ์† ๋ณต์ง€๊ตญ๊ฐ€์˜ ๋ชฐ๋ฝ

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    ํ•™์œ„๋…ผ๋ฌธ(์„์‚ฌ) -- ์„œ์šธ๋Œ€ํ•™๊ต๋Œ€ํ•™์› : ์‚ฌ๋ฒ”๋Œ€ํ•™ ์™ธ๊ตญ์–ด๊ต์œก๊ณผ(์˜์–ด์ „๊ณต), 2021.8. ๋‚˜์Šน์ค€.๊ฐ€์ฆˆ์˜ค ์ด์‹œ๊ตฌ๋กœ์˜ ใ€Ž๋‚˜๋ฅผ ๋ณด๋‚ด์ง€๋งˆ ใ€Never Let Me Go (2005)๋Š” ๊ทธ์˜ ์†Œ์„ค ์ค‘ ๊ฐ€์žฅ ํ˜„๋Œ€์ ์œผ๋กœ, ์กด์žฌํ–ˆ์„ ๋ฒ•ํ•œ 1990๋…„๋Œ€ ํ›„๋ฐ˜ ์˜๊ตญ์„ ๋ฐฐ๊ฒฝ์œผ๋กœ ํ•œ๋‹ค. ใ€Ž๋‚จ์•„์žˆ๋Š” ๋‚˜๋‚ ใ€ The Remains of the Day (1989)์— ๋งŒ์—ฐํ•œ ๋ฐ˜๋Œ€์ฒ˜๋ฆฌ์ฆ˜(Anti-Thatcherite) ์ •์„œ๋ฅผ ์ด์–ด๊ฐ€๋ฉด์„œ ์ด์‹œ๊ตฌ๋กœ๋Š” ์˜๊ตญ์ธ์˜ ์‚ถ์— ๊นŠ์ˆ™์ด ๋…น์•„ ๋“  ์‹ ์ž์œ ์ฃผ๋ฅผ ๋น„ํŒํ•˜๊ณ  ์žˆ๋‹ค. ์ด ์†Œ์„ค์€ ์ธ๊ฐ„์—๊ฒŒ ์žฅ๊ธฐ๊ธฐ์ฆ์„ ํ•˜๊ธฐ ์œ„ํ•ด ๋งŒ๋“ค์–ด์ง„ ๋ณต์ œ์ธ๊ฐ„์ธ ์ฃผ์ธ๊ณต๋“ค์ด ์‹ ์ž์œ ์ฃผ์˜์˜ ๋‹ด๋ก ์ธ ์ž์œ ์™€ ์ž์•„ ์‹คํ˜„์„ ์ซ“์•„๊ฐ€์ง€๋งŒ ๊ฒฐ๊ตญ ๊ทธ๋“ค์„ ๋ฉธ๋ง์œผ๋กœ ์ด๋„๋Š” ์‹œ์Šคํ…œ์˜ ์ž”์ธํ•จ์„ ๋ฌ˜์‚ฌํ•œ๋‹ค. ๋˜ ์‹ ์ž์œ ์ฃผ์˜์  ํ™˜์ƒ์ด ๋ฌด๋„ˆ์ง„ ํ›„ ํ™˜๋ฉธ์— ์ฐฌ ์ฃผ์ธ๊ณต๋“ค์ด ๋‚ด๋ฆฐ ๊ฒฐ์ •์„ ํ†ตํ•ด ํ—ค๊ฒŒ๋ชจ๋‹ˆ์˜ ๋Œ€์•ˆ์  ๋ฐ˜์‘์„ ์‹œ์‚ฌํ•œ๋‹ค. ์ตœ๊ทผ ๋ช‡๋ช‡ ๋น„ํ‰๊ฐ€๋“ค์€ ์ด์‹œ๊ตฌ๋กœ์˜ ใ€Ž๋‚˜๋ฅผ ๋ณด๋‚ด์ง€ ๋งˆใ€์—์„œ ๋‚˜ํƒ€๋‚˜๋Š” ๋Œ€์•ˆ ํ˜„์‹ค์˜ ์—ญ์‚ฌ์  ๋ฐฐ๊ฒฝ์„ ์ „ํ›„ ์˜๊ตญ์‚ฌ์— ๋“ฑ์žฅํ–ˆ๋˜ ๋ณต์ง€๊ตญ๊ฐ€๋กœ ๋ณด๋Š” ๊ฒฝํ–ฅ์ด ์žˆ๋Š”๋ฐ, ์ด ์ ์€ ์ด ์†Œ์„ค์—์„œ ๋ฌ˜์‚ฌ๋˜๋Š” ํ—ค์ผ์ƒด (Hailsham) ๊ธฐ์ˆ™ํ•™๊ต์˜ ์˜จํ™”ํ•œ ํ™˜๊ฒฝ๊ณผ ๊ทธ ์ดํ›„ ๊ธฐ์ฆ์ž์—๊ฒŒ ์ฃผ์–ด์ง€๋Š” ๊ฐ„๋ณ‘์ธ๋“ค์˜ ์—ญํ•  ๋“ฑ์—์„œ ์ž˜ ๋“œ๋Ÿฌ๋‚˜๊ณ  ์žˆ๋‹ค๊ณ  ๋ณธ๋‹ค. ๋‹ค์‹œ ๋งํ•ด, ์ด๋Ÿฌํ•œ ๋น„ํ‰๊ฐ€๋“ค์€ ์˜ค๋Š˜๋‚ ๊นŒ์ง€ ์ง€์†๋˜๊ณ  ์žˆ๋Š” ์˜๊ตญ ๋ณต์ง€ ์ฒด์ œ์˜ ๊ธฐ๋Šฅ๊ณผ ์–‘์ƒ์ด ์–ด๋–ป๊ฒŒ ์ด ์†Œ์„ค์— ๋ฐ˜์˜๋˜๋Š”์ง€์— ์ดˆ์ ์„ ๋งž์ถฐ ์ด ์†Œ์„ค์„ ์ฝ๊ณ  ์žˆ๋Š” ๊ฒƒ์ด๋‹ค. ๋ฐ˜๋ฉด์—, ๋‹ค๋ฅธ ๋น„ํ‰๊ฐ€๋“ค์€ ์ด ์†Œ์„ค์˜ ์—ญ์‚ฌ์  ๋ฐฐ๊ฒฝ์„ ๋Œ€์ฒ˜ ์ •๊ถŒ ์ดํ›„์— ๋“ฑ์žฅํ•œ ์‹ ์ž์œ ์ฃผ์˜์  ํ—ค๊ฒŒ๋ชจ๋‹ˆ๋กœ ๋ณด๋ฉด์„œ ์ž์•„ ์‹คํ˜„์ด๋‚˜ ๊ฐœ์ธ์ฃผ์˜ ๊ฐ™์€ ์‹ ์ž์œ ์ฃผ์˜์  ์ด์ƒ์ด ์–ด๋–ป๊ฒŒ ๊ฐœ์ง„๋˜๋Š”๊ฐ€์— ์ดˆ์ ์„ ๋‘๊ณ  ์†Œ์„ค์„ ์ฝ์œผ๋ ค ํ•œ๋‹ค. ๋ณธ ๋…ผ๋ฌธ์€ ์ด ๋‘ ์ฃผ์žฅ์ด ์„œ๋กœ ๋ชจ์ˆœ๋˜์ง€ ์•Š๋Š”๋‹ค๋Š” ์ ์„ ์ง€์ ํ•˜๋ฉด์„œ, ์†Œ์„ค์— ๋‹ด๊ธด ๋ณด๋‹ค ํฌ๊ด„์ ์ธ ์—ญ์‚ฌ์  ์ƒํ™ฉ์„ ํŒŒ์•…ํ•˜๊ธฐ ์œ„ํ•ด์„œ๋Š” ์ด ๋‘ ๊ด€์ ์„ ๋‹ค ๊ฐ™์ด ๊ณ ๋ คํ•ด์•ผ ํ•œ๋‹ค๊ณ  ์ฃผ์žฅํ•œ๋‹ค. ์ฆ‰, ๋ณธ ๋…ผ๋ฌธ์€ ๋ณต์ง€ ๊ตญ๊ฐ€์™€ ์‹ ์ž์œ ์ฃผ์˜๊ฐ€ ๊ณตํžˆ ์ด ์†Œ์„ค์˜ ์—ญ์‚ฌ์  ๋ฐฐ๊ฒฝ์œผ๋กœ ์ž‘์šฉํ•˜๊ณ  ์žˆ์Œ์„ ์ œ์‹œํ•˜๋ฉด์„œ ์ด์‹œ๊ตฌ๋กœ ๋ณธ์ธ์ด "๋ฏธ๊ตญ์˜ ๋‚™๊ด€์ฃผ์˜"๋ผ ์นญํ–ˆ๋˜ ํ˜„๋Œ€ ์„ธ๊ณ„๋ฅผ ์ง€๋ฐฐํ•˜๋Š” ํ—ค๊ฒŒ๋ชจ๋‹ˆ์— ๋Œ€ํ•ด ์ด ์†Œ์„ค์ด ์–ด๋–ป๊ฒŒ ๋น„ํŒ์  ์ž…์žฅ์„ ๊ฒฌ์ง€ํ•˜๋Š”์ง€๋ฅผ ์‚ดํ”ผ๋ ค๊ณ  ํ•œ๋‹ค. ์†Œ์„ค์— ๋“ฑ์žฅํ•˜๋Š” ์‹œ๊ธฐ๋Š” 1970๋…„๋Œ€๋ถ€ํ„ฐ 1990๋…„๋Œ€ ํ›„๋ฐ˜์œผ๋กœ ์ฃผ์ธ๊ณต์ด ํ—ค์ผ์ƒด์—์„œ ๋ณด๋‚ด๋Š” ์œ ๋…„์‹œ์ ˆ๋ถ€ํ„ฐ ๋ณธ๊ฒฉ์ ์œผ๋กœ ์ผ์„ ์‹œ์ž‘ํ•˜๊ธฐ ์ „ ์ฝ”ํ‹ฐ์ง€(cottage)๋ผ๋Š” ๊ณณ์—์„œ ๋ณด๋‚ด๋Š” ์ฒญ๋…„๊ธฐ, ๊ทธ๋ฆฌ๊ณ  ๊ฐ„๋ณ‘์ธ๊ณผ ๊ธฐ์ฆ์ž์˜ ์—ญํ• ์„ ์ˆ˜ํ–‰ํ•˜๋Š” ์„ฑ์ธ๊ธฐ๋กœ ๋‚˜๋‰œ๋‹ค. ์ด๋Š” ์—ญ์‚ฌ์ ์œผ๋กœ ๋ณผ ๋•Œ, ๋ณต์ง€๊ตญ๊ฐ€์˜ ์‡ ํ‡ด์—์„œ ๋Œ€์ฒ˜์™€ ๋ฉ”์ด์ € ์ •๋ถ€ ์•„๋ž˜ ์‹ ์ž์œ ์ฃผ์˜๋กœ์˜ ๊ธ‰๊ฒฉํ•œ ์ „ํ™”, ๊ทธ๋ฆฌ๊ณ  ์‹ ์ž์œ ์ฃผ์˜๊ฐ€ ์—ฌ์ „ํžˆ ๋ฒˆ์˜ํ•˜์˜€๋˜ ๋ธ”๋ ˆ์–ด ์ •๊ถŒ์œผ๋กœ์˜ ์‹œ๊ธฐ์™€ ๋งž๋ฌผ๋ฆฌ๊ธฐ์— ๋ณธ ๋…ผ๋ฌธ์€ ใ€Ž๋‚˜๋ฅผ ๋ณด๋‚ด์ง€๋งˆใ€๋ฅผ ํ—ค๊ฒŒ๋ชจ๋‹ˆ์˜ ๊ฐ•์••์œผ๋กœ ์ธํ•œ ๊ฐœ์ธ์˜ ๋ถ„๋ฆฌ์™€ ์ •์ฒด์„ฑ ์ƒ์‹ค์„ ๋น„ํŒํ•˜๋Š” ๋ฌธํ™”์  ํ…์ŠคํŠธ๋กœ ๋ถ„์„ํ•˜๋ ค ํ•œ๋‹ค. ๋”ฐ๋ผ์„œ ๋ณธ ๋…ผ๋ฌธ์€ ์‹œ๊ฐ„ ์ˆœ์„œ๋ฅผ ๋”ฐ๋ผ ์šฐ์„  ๋ณต์ง€๊ตฌ๊ฐ€์˜ ๊ฐ€์น˜์™€ ๊ธฐ๋Šฅ์„ ํ—ค์ผ์ƒด๊ณผ ๋น„๊ตํ•˜๊ณ  ํ—ค๊ฒŒ๋ชจ๋‹ˆ๋กœ ์ •๋ ฌ๋˜๋Š” ์ฃผ์ธ๊ณต๋“ค์˜ ํƒœ๋„์™€ ๊ฐ€์น˜๊ด€์„ ๋ถ„์„ํ•˜๋ฉฐ ๊ธฐ์ฆ์ž ํ˜น์€ ๊ฐ„๋ณ‘์ธ์ด ๋œ ์ฃผ์ธ๊ณต๋“ค์ด ๋ณด๋Š” ๊ธฐ๊ดดํ•œ ์ด๋ฏธ์ง€๋“ค๋กœ ๊นจ์–ด์ ธ๋ฒ„๋ฆฐ ์‹ ์ž์œ ์ฃผ์˜ ์ด์ƒ์„ ์„ค๋ช…ํ•˜๋ ค ํ•œ๋‹ค. ๋งˆ์ง€๋ง‰์œผ๋กœ๋Š” ๊ฐ์„ฑ๋œ ์ฃผ์ธ๊ณต๋“ค์˜ ํƒœ๋„๋ฅผ ํ†ตํ•ด ํ—ค๊ฒŒ๋ชจ๋‹ˆ์— ๋ฐ˜ํ•˜๋Š” ๋Œ€์•ˆ์  ํƒœ๋„๋ฅผ ์‚ดํŽด๋ณด๊ณ ์ž ํ•œ๋‹ค.Given a version of Britain that might have existed, Kazuo Ishiguroโ€™s Never Let Me Go (2005) takes place in the most contemporary period of all his novels: late-1990s Britain. Continuing the anti-Thatcherite discourse present in The Remains of the Day (1989), Ishiguro criticizes neoliberalism embedded deeply into the daily life of contemporary Britain. The thesis examines the characters (clones destined to donate organs for humans) that rehearse the neoliberal rhetoric of freedom and self-empowerment but end up facing the Systemโ€™s cruelty that eventually leads to their destruction (they die after giving their last donation). Decisions made by disillusioned characters after their neoliberal fantasies break apart serve as an alternative response to hegemony. Many critics point to the welfare state as the contemporary historical account that the alternative reality of the novel reflects in the current political climate. The benign environment of Hailsham (the boarding school for clone that the narrator spends her childhood in) and the role as a carer are considered to be part of the System that still continues today: Britainโ€™s welfare state. Such critics focus on how the function of the current welfare system is reflected in the novel. Others point to the neoliberal hegemony of the contemporary society as its historical backdrop. In that case, they focus on one aspect of neoliberal ideal that promote free market economy, such as self-aspiration or individualism. Since both claims are not contradicting one another, I claim that both perspectives need to be taken into account in order to give a more comprehensive picture of the history the novel reflects. Hence, my thesis focuses on both the welfare state and neoliberal ideals to reveal authorโ€™s critical attitude toward a contemporary hegemony which he calls in an interview โ€œAmerican optimism.โ€ Flashback in the novel covers a period starting from the late 1970s to late 1990s, corresponding to three different periods in the story and broadly overlapping with three historical years: the last years of the Welfare State, the years of drastic neoliberal turn under Thatcher and Major administrations, and the following years where neoliberal ideals still prospered under Blair administration. I map the three critical periods in the story to these historical moments to argue that Never Let Me Go is a cultural text that reveals individualโ€™s alienation and their loss of identity coerced by neoliberalism. My thesis unfolds as follows. Following the chronological order of the story, the first part compares the values and functions of Hailsham to those of the Welfare State before the neoliberal turn. The second part analyzes the changes in each of the main characters realigning themselves to the ideals promoted by the System. The third chapter depicts images of dismantled freedom and self-empowerment to reflect disillusioned state of each character from their neoliberal ideals. Finally, the last chapter analyzes the response of each disillusioned characters when they are fully aware of their political stance to suggest an alternative response to the hegemony.ABSTRACT 1 TABLE OF CONTENTS 3 CHAPTER I. Introduction 4 CHAPTER II. Hailsham as Emblem of Welfare State 16 CHAPTER III. Driving Through Realms of Neoliberalism 32 CHAPTER IV. At the Dead End of Neoliberal Myth 44 CHAPTER V. Conclusion 54 WORKS CITED 58 ABSTRACT IN KOREAN 61์„

    ํŒŒ๋ผ๋ฏธํ„ฐ ํ•™์Šต ํ†ตํ•œ ๋ฐ์ดํ„ฐ ์žก์Œ ๋ฐ ๊ฐ„์„ญ๊ทน๋ณต ์—ฐ๊ตฌ

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    ํ•™์œ„๋…ผ๋ฌธ (๋ฐ•์‚ฌ) -- ์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› : ๊ณต๊ณผ๋Œ€ํ•™ ์ „๊ธฐยท์ปดํ“จํ„ฐ๊ณตํ•™๋ถ€, 2021. 2. ์ •๊ต๋ฏผ.์ธ๊ณต์‹ ๊ฒฝ๋ง ๋ชจ๋ธ์— ๋‹ค๋Ÿ‰์˜ ๋ฐ์ดํ„ฐ๋ฅผ ํ•™์Šต์‹œํ‚ค๋Š” ๋ฐฉ์‹์€ ์ปดํ“จํ„ฐ ๋น„์ „ ๋ฐ ์ž์—ฐ์–ด ์ฒ˜๋ฆฌ ๋ถ„์•ผ์˜ ๋ฌธ์ œ๋“ค์„ ํ•ด๊ฒฐํ•˜๋Š”๋ฐ ์ƒˆ๋กœ์šด ํŒจ๋Ÿฌ๋‹ค์ž„์œผ๋กœ ์ž๋ฆฌ๋งค๊น€ํ•˜์˜€๋‹ค. ๊ธฐ์กด ์‚ฌ๋žŒ์˜ ์ง๊ด€์œผ๋กœ ๋ชจ๋ธ์„ ์„ค์ •ํ•˜๋Š” ๋ฐฉ์‹๊ณผ ๋น„๊ตํ•˜์—ฌ ๋†’์€ ์„ฑ๋Šฅ์„ ๋‹ฌ์„ฑํ•  ์ˆ˜ ์žˆ์—ˆ์œผ๋‚˜, ํ•™์Šต๋ฐ์ดํ„ฐ์˜ ์–‘๊ณผ ํ’ˆ์งˆ์— ๋”ฐ๋ผ์„œ ๊ทธ ์„ฑ๋Šฅ์ด ํฌ๊ฒŒ ์ขŒ์šฐ๋œ๋‹ค. ์ด๋ ‡๊ฒŒ ์ธ๊ณต ์‹ ๊ฒฝ๋ง์„ ํšจ๊ณผ์ ์œผ๋กœ ํ›ˆ๋ จํ•˜๋ ค๋ฉด ๋งŽ์€ ์–‘์˜ ๋ฐ์ดํ„ฐ๋ฅผ ๋ชจ์œผ๋Š” ๊ฒƒ๊ณผ ๋ฐ์ดํ„ฐ์˜ ํ’ˆ์งˆ์„ ์ €ํ•˜์‹œํ‚ค๋Š” ์š”์ธ์„ ํŒŒ์•…ํ•˜๋Š” ๊ฒƒ์ด ์ค‘์š”ํ•˜๋‹ค. ๋ณธ ์—ฐ๊ตฌ์—์„œ๋Š” ๋ผ๋ฒจ๋ง๋œ ๋ฐ์ดํ„ฐ์˜ ํ’ˆ์งˆ์„ ๊ฒฐ์ •ํ•˜๋Š” ์ฃผ์š” ์š”์ธ์œผ๋กœ ์•Œ๋ ค์ ธ ์žˆ๋Š” ์žก์Œ(Noise)๊ณผ ๊ฐ„์„ญ(Interference)์„ ๊ทน๋ณตํ•  ์ˆ˜ ์žˆ๋Š” ๊ธฐ๋ฒ•์„ ์ œ์‹œํ•œ๋‹ค. ์—ฐ๊ตฌ์ž๋“ค์€ ์ผ๋ฐ˜์ ์œผ๋กœ ์›น๊ธฐ๋ฐ˜์˜ ํฌ๋ผ์šฐ๋“œ ์†Œ์‹ฑ์‹œ์Šคํ…œ์„ ์‚ฌ์šฉํ•˜์—ฌ ๋‹ค์–‘ํ•œ ์‚ฌ๋žŒ๋“ค๋กœ๋ถ€ํ„ฐ ๋‹ต๋ณ€์„ ์ˆ˜์ง‘ํ•˜์—ฌ ๋ฐ์ดํ„ฐ๊ทธ๋ฃน์„ ๊ตฌ์„ฑํ•œ๋‹ค\cite{simonyan2014very}. ๊ทธ๋Ÿฌ๋‚˜ ์‚ฌ๋žŒ๋“ค์˜ ๋‹ต๋ณ€์œผ๋กœ ์–ป๋Š” ๋ฐ์ดํ„ฐ๋Š” ์ž‘์—… ์ง€์นจ์— ๋Œ€ํ•œ ์˜คํ•ด, ์ฑ…์ž„ ๋ถ€์กฑ ๋ฐ ๊ณ ์œ ํ•œ ์˜ค๋ฅ˜๋กœ ์ธํ•ด์„œ ๋ฐ์ดํ„ฐ ์ž…๋ ฅ(Input)๊ณผ ์ถœ๋ ฅ(Target)์‚ฌ์ด์— ์žก์Œ์ด ํฌํ•จ๋œ๋‹ค. ๋ณธ ์—ฐ๊ตฌ์—์„œ๋Š” ์ด๋ ‡๊ฒŒ ํฌ๋ผ์šฐ๋“œ ์†Œ์‹ฑ์„ ํ†ตํ•ด ๋ผ๋ฒจ๋ง๋œ ๋ฐ์ดํ„ฐ์— ์กด์žฌํ•˜๋Š” ์žก์Œ์„ ๊ทน๋ณตํ•˜๊ธฐ ์œ„ํ•œ ์ถ”๋ก  ์•Œ๊ณ ๋ฆฌ์ฆ˜์„ ์ œ์•ˆํ•œ๋‹ค. ๋‘๋ฒˆ์งธ๋กœ, ๋ชจ๋ธ์˜ ํ•™์Šต์„ฑ๋Šฅ์„ ์ €ํ•˜์‹œํ‚ค๋Š” ์š”์ธ์ธ ๋ฐ์ดํ„ฐ๊ฐ„์˜ ๊ฐ„์„ญ์„ ๋‹ค๋ฃฌ๋‹ค. ์žก์Œ์ด ์ œ๊ฑฐ๋˜์–ด ์ •์ œ๋œ ์ž…๋ ฅ๊ณผ ์ถœ๋ ฅ์„ ๋ผ๋ฒจ๋ง๋œ ๋ฐ์ดํ„ฐ ์ƒ˜ํ”Œ์ด๋ผ๊ณ  ํ•˜๋ฉด, ํ•™์Šต์‹œ์— ์ƒ˜ํ”Œ๋“ค ์‚ฌ์ด์˜ ๊ด€๊ณ„๋ฅผ ์ƒ๊ฐํ•  ์ˆ˜ ์žˆ๋‹ค. ์‚ฌ๋žŒ ์ˆ˜์ค€์˜ ์ธ๊ณต์ง€๋Šฅ์— ๋„๋‹ฌํ•˜๊ธฐ ์œ„ํ•ด์„œ๋Š” ํ•˜๋‚˜์˜ ๋ชจ๋ธ์ด ํ•˜๋‚˜์˜ ๋ฌธ์ œ๋งŒ์„ ํ•ด๊ฒฐํ•˜๋Š” ๊ฒƒ์ด ์•„๋‹ˆ๋ผ ์‹œ๊ฐ„์ƒ ์ˆœ์ฐจ์ ์œผ๋กœ ์ง๋ฉดํ•˜๋Š” ์—ฌ๋Ÿฌ ๋ฌธ์ œ๋ฅผ ๋™์‹œ์— ํ•ด๊ฒฐํ•  ์ˆ˜ ์žˆ์–ด์•ผ ํ•œ๋‹ค. ์ด๋Ÿฌํ•œ ์ƒํ™ฉ์—์„œ, ์ƒ˜ํ”Œ๋“ค ์‚ฌ์ด์— ๊ฐ„์„ญ์ด ๋ฐœ์ƒํ•  ์ˆ˜ ์žˆ๊ณ , ํ•™๊ณ„์—์„œ๋Š” ์—ฐ์†ํ•™์Šต(Continual Learning)์—์„œ์˜ "Catastrophic Forgetting"๋˜๋Š” "Semantic Drift"์œผ๋กœ ์ •์˜ํ•˜๊ณ  ์žˆ๋‹ค. ๋ณธ ์—ฐ๊ตฌ์—์„œ๋Š” ์ด๋Ÿฌํ•œ ๊ฐ„์„ญ์„ ํšจ๊ณผ์ ์œผ๋กœ ๊ทน๋ณตํ•˜๊ธฐ ์œ„ํ•œ ๋ฐฉ๋ฒ•์— ๋Œ€ํ•œ ์—ฐ๊ตฌ๋ฅผ ๋‹ค๋ฃฌ๋‹ค. ์•ž์„œ ์–ธ๊ธ‰ํ•œ ๋ฐ์ดํ„ฐ ์žก์Œ์„ ๊ทน๋ณตํ•˜๊ธฐ ์œ„ํ•ด์„œ ์ฒซ ๋ฒˆ์งธ ์žฅ์—์„œ๋Š” ํฌ๋ผ์šฐ๋“œ ์†Œ์‹ฑ ์‹œ์Šคํ…œ์˜ ์ด์‚ฐ ๊ฐ๊ด€์‹ ๋ฐ ์‹ค์ˆ˜ ๋ฒกํ„ฐ ํšŒ๊ท€ ์ž‘์—…์— ๋Œ€ํ•œ ์ƒˆ๋กœ์šด ์ถ”๋ก  ์•Œ๊ณ ๋ฆฌ์ฆ˜์„ ๊ฐ๊ฐ ์ œ์•ˆํ•œ๋‹ค. ์ œ์•ˆ ๋œ ์•Œ๊ณ ๋ฆฌ์ฆ˜์€ ํฌ๋ผ์šฐ๋“œ ์†Œ์‹ฑ ๋ชจ๋ธ์„ ๊ทธ๋ž˜ํ”„ ๋ชจ๋ธ(Graphical Model)๋กœ์„œ ์ƒ์ •ํ•˜๊ณ , ํ…Œ์Šคํฌ์™€ ๋‹ต๋ณ€์„ ์ฃผ๋Š” ์‚ฌ๋žŒ๋“ค๊ฐ„์˜ ๋‘ ๊ฐ€์ง€ ์œ ํ˜•์˜ ๋ฉ”์‹œ์ง€๋ฅผ ๋ฐ˜๋ณต์ ์œผ๋กœ ์ฃผ๊ณ  ๋ฐ›์Œ์œผ๋กœ์จ ๊ฐ ์ž‘์—…์˜ ์ •๋‹ต๊ณผ ๊ฐ ์ž‘์—…์ž์˜ ์‹ ๋ขฐ์„ฑ์„ ์ถ”์ • ํ•  ์ˆ˜ ์žˆ๋‹ค. ๋˜ํ•œ ์ด๋“ค์˜ ํ‰๊ท  ์„ฑ๋Šฅ์€ ํ™•๋ฅ ์  ๊ตฐ์ค‘ ๋ชจ๋ธ์„ ์ด์šฉํ•˜์—ฌ ๋ถ„์„ํ•˜๊ณ  ์ž…์ฆํ•œ๋‹ค. ์ด๋Ÿฌํ•œ ์„ฑ๋Šฅ์—๋Ÿฌ ํ•œ๊ณ„๋Š” ์ž‘์—…๋‹น ํ• ๋‹น๋˜๋Š” ์‚ฌ๋žŒ๋“ค์˜ ์ˆ˜์™€ ์ž‘์—…์ž์˜ ํ‰๊ท  ์‹ ๋ขฐ์„ฑ์˜ํ•ด ๊ฒฐ์ •๋œ๋‹ค. ์‚ฌ๋žŒ๋“ค์˜ ํ‰๊ท  ์‹ ๋ขฐ๋„๊ฐ€ ์ผ์ • ์ˆ˜์ค€์„ ๋„˜์–ด์„œ๋ฉด, ์ œ์•ˆ๋œ ์•Œ๊ณ ๋ฆฌ์ฆ˜์˜ ํ‰๊ท  ์„ฑ๋Šฅ์€ ๋ชจ๋“  ์ž‘์—…์ž์˜ ์‹ ๋ขฐ์„ฑ์„ ์•Œ๊ณ ์žˆ๋Š” ์˜ค๋ผํด ์ถ”์ •๊ธฐ (์ด๋ก ์ ์ธ ํ•œ๊ณ„)์— ์ˆ˜๋ ดํ•œ๋‹ค. ์‹ค์ œ ๋ฐ์ดํ„ฐ ์„ธํŠธ์™€ ํ•ฉ์„ฑ ๋ฐ์ดํ„ฐ ์„ธํŠธ ๋ชจ๋‘์— ๋Œ€ํ•œ ๊ด‘๋ฒ”์œ„ํ•œ ์‹คํ—˜์„ ํ†ตํ•ด, ์ œ์•ˆ๋œ ์•Œ๊ณ ๋ฆฌ์ฆ˜์˜ ์‹ค์ œ ์„ฑ๋Šฅ์ด ์ด์ „์˜ state-of-the-art ์•Œ๊ณ ๋ฆฌ์ฆ˜๋“ค ๋ณด๋‹ค ์šฐ์ˆ˜ํ•˜๋‹ค๋Š” ๊ฒƒ์„ ์ž…์ฆํ•œ๋‹ค. ๋…ผ๋ฌธ์˜ ๋‘ ๋ฒˆ์งธ ์žฅ์—์„œ๋Š” ์—ฐ์†ํ•™์Šต์ƒํ™ฉ์—์„œ ๋ฐ์ดํ„ฐ์ƒ˜ํ”Œ์‚ฌ์ด์— ๋ฐœ์ƒํ•˜๋Š” ๊ฐ„์„ญ์„ ํ•ด๊ฒฐํ•˜๊ธฐ ์œ„ํ•ด, ํ•ญ์ƒ์„ฑ๊ธฐ๋ฐ˜์˜ ๋ฉ”ํƒ€ ํ•™์Šต ๊ตฌ์กฐ (Homeostatic Meta Model)๋ฅผ ์ œ์•ˆํ•œ๋‹ค. ๊ตฌ์ฒด์ ์œผ๋กœ, ์ด์ „ ํ…Œ์Šคํฌ ์ค‘์š”ํ•œ ํ•™์Šต ๋ณ€์ˆ˜๋ฅผ ์ฐพ๊ณ  ์ •๊ทœํ™”์— ์„ ๋ณ„์ ์œผ๋กœ ์ ์šฉํ•˜๋Š” ๋ฐฉ๋ฒ•์„ ์‚ฌ์šฉํ•˜๋Š”๋ฐ, ์ œ์•ˆ๋œ ๋ชจ๋ธ์€ ์ด๋Ÿฌํ•œ ์ •๊ทœํ™”์˜ ๊ฐ•๋„๋ฅผ ์ž๋™์œผ๋กœ ์ œ์–ดํ•œ๋‹ค. ์ด๋Ÿฌํ•œ ๊ธฐ๋ฒ•์€ ์ƒˆ๋กœ์šด ํ•™์Šต์„ ์ง„ํ–‰ํ•  ๋•Œ ์ด์ „์— ํš๋“ํ•œ ์ง€์‹์„ ์ตœ์†Œํ•œ์œผ๋กœ ์žƒ์–ด๋ฒ„๋ฆฌ๋„๋ก ์ธ๊ณต์‹ ๊ฒฝ๋ง์˜ ํ•™์Šต์„ ์œ ๋„ํ•œ๋‹ค. ๋‹ค์–‘ํ•œ ์œ ํ˜•์˜ ์—ฐ์† ํ•™์Šต ๊ณผ์ œ์—์„œ ์ œ์•ˆ๋œ ๋ฐฉ๋ฒ•์„ ๊ฒ€์ฆํ•˜๋Š”๋ฐ, ์‹คํ—˜์ ์œผ๋กœ ์ œ์•ˆ๋œ ๋ฐฉ๋ฒ•์ด ํ•™์Šต์˜ ๊ฐ„์„ญ์™„ํ™” ์ธก๋ฉด์—์„œ ๊ธฐ์กด ๋ฐฉ๋ฒ•๋ณด๋‹ค ์šฐ์ˆ˜ํ•˜๋‹ค๋Š” ์ ์„ ๋ณด์ธ๋‹ค.๋˜ํ•œ ๊ธฐ์กด ์‹œ๋ƒ…์Šค ๊ฐ€์†Œ์„ฑ ๊ธฐ๋ฐ˜ ๋ฐฉ๋ฒ•๋“ค์— ๋น„ํ•ด ์ƒ๋Œ€์ ์œผ๋กœ ๋ณ€ํ™”์— ๊ฐ•์ธํ•˜๋‹ค.์ œ์•ˆ๋œ ๋ชจ๋ธ์— ์˜ํ•ด ์ƒ์„ฑ๋œ ์ •๊ทœํ™”์˜ ๊ฐ•๋„ ๊ฐ’์€ ์‹œ๋ƒ…์Šค์—์„œ ํ•ญ์ƒ์„ฑ ์˜ ์Œ์˜ ํ”ผ๋“œ๋ฐฑ ๋ฉ”์ปค๋‹ˆ์ฆ˜๊ณผ ์œ ์‚ฌํ•˜๊ฒŒ, ํŠน์ • ๋ฒ”์œ„ ๋‚ด์—์„œ ๋Šฅ๋™์ ์œผ๋กœ ์ œ์–ด๋œ๋‹ค.Data-driven approaches based on neural networks have emerged as new paradigm to solve problems in computer vision and natural language processing fields. These approaches achieve better performance compared to existing human-design approaches (heuristic), however, these performance gains solely relies on a large amount of high quality labeled data. Accordingly, it is important to collect a large amount of data and improve the quality of data by analyzing degrading factors in order to well-train a model. In this dissertation, I propose iterative algorithms to relieve noise of labeled data in crowdsourcing system and meta architecture to alleviate interference among them in continual learning scenarios respectively. Researchers generally collect data using crowdsourcing system which utilizes human evaluations. However, human annotators' decisions may vary significantly due to misconceptions of task instructions, the lack of responsibility, and inherent noise. To relieve the noise in responses from crowd annotators, I propose novel inference algorithms for discrete multiple choice and real-valued vector regression tasks. Web-based crowdsourcing platforms are widely used for collecting large amount of labeled data. Due to low-paid workers and inherent noise, the quality of acquired data could be easily degraded. The proposed algorithms can overcome the noise by estimating the true answer of each task and a reliability of each worker updating two types of messages iteratively. For performance guarantee, the performances of the algorithms are theoretically proved under probabilistic crowd model. Interestingly, their performance bounds depend on the number of queries per task and the average quality of workers. Under a certain condition, each average performance becomes close to an oracle estimator which knows the reliability of every worker (theoretical upper bound). Through extensive experiments with both real-world and synthetic datasets, the practical performance of algorithms are verified. In fact, they are superior to other state-of-the-art algorithms. Second, when a model learns a sequence of tasks one by one (continual learning), previously learned knowledge may conflict with new knowledge. It is well-known phenomenon called "Catastrophic Forgetting" or "Semantic Drift". In this dissertation, we call the phenomena "Interference" since it occurs between two knowledge from labeled data separated in time. It is essential to control the amount of noise and interference for neural network to be well-trained. In the second part of dissertation, to solve the Interference among labeled data from consecutive tasks in continual learning scenario, a homeostasis-inspired meta learning architecture (HM) is proposed. The HM automatically controls the intensity of regularization (IoR) by capturing important parameters from the previous tasks and the current learning direction. By adjusting IoR, a learner can balance the amount of interference and degrees of freedom for its current learning. Experimental results are provided on various types of continual learning tasks. Those results show that the proposed method notably outperforms the conventional methods in terms of average accuracy and amount of the interference. In experiments, I verify that HM is relatively stable and robust compared to the existing Synaptic Plasticity based methods. Interestingly, the IoR generated by HM appears to be proactively controlled within a certain range, which resembles a negative feedback mechanism of homeostasis in synapses.Contents Abstract Contents List of Tables List of Figures 1 INTRODUCTION 1 2 Reliable multiple-choice iterative algorithm for crowdsourcing systems 6 2.1 Setup . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6 2.2 Algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7 2.2.1 Task Allocation . . . . . . . . . . . . . . . . . . . . . . . . . 7 2.2.2 Multiple Iterative Algorithm . . . . . . . . . . . . . . . . . . 8 2.2.3 Task Allocation for General Setting . . . . . . . . . . . . . . 10 2.3 Applications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12 2.4 Analysis of algorithms . . . . . . . . . . . . . . . . . . . . . . . . . 16 2.4.1 Quality of workers . . . . . . . . . . . . . . . . . . . . . . . 16 2.4.2 Bound on the Average Error Probability . . . . . . . . . . . . 18 2.4.3 Proof of the Theorem 1 . . . . . . . . . . . . . . . . . . . . . 20 2.4.4 Proof of Sub-Gaussianity . . . . . . . . . . . . . . . . . . . . 22 2.5 Experiments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27 iii2.6 Related Literature . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34 3 Reliable Aggregation Method for Vector Regression in Crowdsourcing 38 3.1 Preliminaries . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38 3.2 Inference Algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . 39 3.2.1 Task Message . . . . . . . . . . . . . . . . . . . . . . . . . . 40 3.2.2 Worker Message . . . . . . . . . . . . . . . . . . . . . . . . 40 3.3 Experimental Results . . . . . . . . . . . . . . . . . . . . . . . . . . 42 3.3.1 Real crowdsourcing data . . . . . . . . . . . . . . . . . . . . 43 3.4 Performance Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . 47 3.4.1 Dirichlet crowd model . . . . . . . . . . . . . . . . . . . . . 48 3.4.2 Error Bound . . . . . . . . . . . . . . . . . . . . . . . . . . 49 3.4.3 Optimality of Oracle Estimator . . . . . . . . . . . . . . . . . 51 3.4.4 Performance Proofs . . . . . . . . . . . . . . . . . . . . . . . 52 3.5 Related Literature . . . . . . . . . . . . . . . . . . . . . . . . . . . . 57 4 Homeostasis-Inspired Meta Continual Learning 60 4.1 Preliminaries . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 60 4.1.1 Continual Learning . . . . . . . . . . . . . . . . . . . . . . . 60 4.1.2 Meta Learning . . . . . . . . . . . . . . . . . . . . . . . . . 62 4.2 Homeostatic Meta-Model . . . . . . . . . . . . . . . . . . . . . . . . 63 4.3 Preliminary Experiments and Findings . . . . . . . . . . . . . . . . . 66 4.3.1 Block-wise Permutation . . . . . . . . . . . . . . . . . . . . 67 4.3.2 Performance Metrics . . . . . . . . . . . . . . . . . . . . . . 68 4.4 Experiment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 68 4.4.1 Datasets . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 69 4.4.2 Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . 69 4.4.3 Overall Performance . . . . . . . . . . . . . . . . . . . . . . 70 4.5 Related Literature . . . . . . . . . . . . . . . . . . . . . . . . . . . . 70 iv4.6 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 71 5 Conclusion 78 Abstract (In Korean) 89Docto

    ๋šœ๋ ทํ•œ ํŠน์ง•์„ ์ง€๋‹ˆ๋Š” ๋ถ„์ž๋กœ ์„ค๊ณ„๋œ ๋ธ”๋ก ๊ณต์ค‘ํ•ฉ์ฒด์˜ ์šฉ์•ก์ƒ์—์„œ์˜ ์ž๊ธฐ์กฐ๋ฆฝ๊ฑฐ๋™๊ณผ ๊ทธ ์‘์šฉ

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    ํ•™์œ„๋…ผ๋ฌธ(๋ฐ•์‚ฌ) -- ์„œ์šธ๋Œ€ํ•™๊ต๋Œ€ํ•™์› : ์ž์—ฐ๊ณผํ•™๋Œ€ํ•™ ํ™”ํ•™๊ณผ, 2022. 8. ๊น€๊ฒฝํƒ.Constructing well-defined nanostructures with self-assembly becomes significant topic in modern research. Because sorts of morphologies made from self-assembly were so diverse, precise prediction of the nanostructure from molecular level was considered as important subject. Polymers with functional groups or distinct characteristics largely affects their self-assembly behavior in the solution. Their nanostructures can be vary according to the environment of the solution. This dissertation describes the various preparation methods for the well-defined self-assembly structure of the block copolymers in the solution. For the well-defined self-assembled nanostructurs, molecular engineering of the block copolymers with distinct characteristics were preceded. Various nanostructures were demonstrated using block copolymers composed of different building blocks. In Chapter 2, we prepared oxidation sensitive poly(ethylene glycol)-b-poly(acrylbenzylborate) (PEG-b-PABB). Binary mixture of PEG-b-PABB and PEG-b-PS made the oxidation-responsive polymersomes. The presence of H2O2 in the medium triggers the oxidation of benzyl borate pendants of PABB to form poly(acrylic acid) (PAA). This transformation results in the perforation of the compartmentalizing membrane of polymersomes by the dissolution of PEG-b-PAA domains embedded in the inert PEG-b-PS matrix. By controlling the composition of the stimuli-responsive block copolymer, the polymersomes of the binary blend exhibit size-selective permeability without losing the structural integrity. Chapter 3 demonstrated the preparation method for the poly(3-hexylthiophene) (P3HT) based nanofiber networks. Using pseudo-living nature of KCTP, ABA type P(3HT-b-3EHT-b-3HT) and P(3HT-b-3EHT) were synthesized with good manner. The block copolymer could be self-assembled to form 1D nanofibers and their lengths can be controlled in anisole with self-seeding method. ABA type triBCPs form linkage between nanofibers and generate networks. Furthermore, at higher concentration, nanonetwork beame organogel with increased viscosity. In TEM image, interwined network of the nanofibers were observed. After the freeze-drying the organic solvent, we demonstrated the fabrication of porous foam of nanofiber organogels. In Chapter 4, Discrete polymers with charged ionic blocks were synthesized and self-assembled in the solution. We used the convergent method to synthesize monodisperse and precisely defined block co-oligomers (BCOs) having pendant carboxylic acid or amine as a hydrophilic functional block and oligo(lactic acid) as a hydrophobic block. These oppositely charged BCOs, with an absolutely defined number of cations or anions were mixed for the co-assembly in water. Indeed, ionic interaction between assembled block co-oligomers opposes the membraneโ€™s natural curvature and leads to the faceting of the membranes.์ž๊ธฐ์กฐ๋ฆฝ์„ ์ด์šฉํ•œ ์ž˜ ์ •์˜๋œ ๋‚˜๋…ธ๊ตฌ์กฐ์˜ ํ•ฉ์„ฑ์€ ํ˜„๋Œ€ ์—ฐ๊ตฌ์—์„œ ๋งค์šฐ ์ค‘์š”ํ•œ ์—ฐ๊ตฌ ์ฃผ์ œ์ด๋‹ค. ์ž๊ธฐ์กฐ๋ฆฝ์œผ๋กœ ๋งŒ๋“ค์–ด์ง€๋Š” ํ˜•ํƒœ์˜ ์ข…๋ฅ˜๊ฐ€ ๋งค์šฐ ๋‹ค์–‘ํ•˜๊ธฐ ๋•Œ๋ฌธ์— ๋ถ„์ž ์ˆ˜์ค€์—์„œ ๋‚˜๋…ธ๊ตฌ์กฐ๋ฅผ ์ •ํ™•ํ•˜๊ฒŒ ์˜ˆ์ธกํ•˜๋Š” ๊ฒƒ ๋˜ํ•œ ์ค‘์š”ํ•œ ์ˆ™์ œ์ด๋‹ค. ์ž‘์šฉ๊ธฐ ๋˜๋Š” ๋šœ๋ ทํ•œ ํŠน์„ฑ์„ ๊ฐ€์ง„ ํด๋ฆฌ๋จธ๋Š” ์šฉ์•ก์— ์ฒจ๊ฐ€๋˜๋Š” ๋ฌผ์งˆ ๋˜๋Š” ๋‚ด๋ถ€ ๋ฐ ์™ธ๋ถ€์˜ ํ™˜๊ฒฝ์— ๋ฏผ๊ฐํ•˜๊ฒŒ ๋ฐ˜์‘ํ•˜์—ฌ ์šฉ์•ก์ƒ์—์„œ ์ž๊ธฐ ์กฐ๋ฆฝ ๊ฑฐ๋™์— ํฌ๊ฒŒ ์˜ํ–ฅ์„ ๋ฏธ์น˜๊ฒŒ ๋œ๋‹ค. ๋ณธ ์—ฐ๊ตฌ์—์„œ๋Š” ๋ธ”๋ก ๊ณต์ค‘ํ•ฉ์ฒด์˜ ์ž˜ ์ •์˜๋œ ์ž๊ธฐ ์กฐ๋ฆฝ ๊ตฌ์กฐ์— ๋Œ€ํ•œ ๋‹ค์–‘ํ•œ ์ œ์กฐ ๋ฐฉ๋ฒ•์„ ๋‹ค๋ฃฌ๋‹ค. ์ž˜ ์ •์˜๋œ ๋ณต์žกํ•œ ๊ตฌ์กฐ์˜ ๋‚˜๋…ธ ๊ตฌ์กฐ๋ฅผ ๊ด€์ฐฐํ•˜๊ธฐ ์œ„ํ•ด ๋…ํŠนํ•œ ์ž‘์šฉ๊ธฐ ๋˜๋Š” ํŠน์„ฑ์„ ์ง€๋‹Œ ๋‹จ๋Ÿ‰์ฒด๋ฅผ ์ด์šฉํ•ด ๋ธ”๋ก ๊ณต์ค‘ํ•ฉ์ฒด์˜ ํ•ฉ์„ฑ์ด ์„ ํ–‰๋˜์—ˆ๋‹ค. ์ด๋ฅผ ์ด์šฉํ•ด ๋‹ค์–‘ํ•œ ๋‚˜๋…ธ ๊ตฌ์กฐ์ฒด์˜ ํ•ฉ์„ฑ ์‚ฌ๋ก€๋ฅผ ๋ณด๊ณ ํ•œ๋‹ค. 2์žฅ์—์„œ๋Š” ํด๋ฆฌ(์—ํ‹ธ๋ Œ ๊ธ€๋ฆฌ์ฝœ) ํด๋ฆฌ(์•„ํฌ๋ฆด๋ฒค์งˆ๋ณด๋ ˆ์ดํŠธ) ๋ธ”๋ก ๊ณต์ค‘ํ•ฉ์ฒด (PEG-b-PABB)๋ฅผ ์ค€๋น„ํ•˜์—ฌ PEG-b-PS ์™€์˜ ํ˜ผํ•ฉ์„ ํ†ตํ•ด ์‚ฐํ™” ๋ฐ˜์‘์„ฑ ํด๋ฆฌ๋จธ์ข€์„ ํ•ฉ์„ฑํ•˜์˜€๋‹ค. ์‚ฐํ™”๋ฐ˜์‘์„ ํ†ตํ•ด PABB๋ฅผ ์นœ์ˆ˜์„ฑ PAA (ํด๋ฆฌ์•„ํฌ๋ฆด์‚ฐ)๋กœ ์ „ํ™˜ํ•˜์—ฌ ์„ ํƒ์  ์šฉํ•ด๋ฅผ ํ†ตํ•ด ํด๋ฆฌ๋จธ์ข€ ๋ง‰์— ํฌ์–ด๋ฅผ ์ƒ์„ฑํ•˜๊ฒŒ ๋œ๋‹ค. ์ž๊ทน ๋ฐ˜์‘์„ฑ ๋ธ”๋ก ๊ณต์ค‘ํ•ฉ์ฒด์˜ ์กฐ์„ฑ์„ ์ œ์–ดํ•จ์œผ๋กœ์จ, ํฌ๊ธฐ ์„ ํƒ์  ํˆฌ๊ณผ๋„๋ฅผ ์ง€๋‹ˆ๋Š” ํด๋ฆฌ๋จธ์ข€์„ ํ•ฉ์„ฑํ•  ์ˆ˜ ์žˆ์Œ์„ ๋ณด๊ณ ํ•œ๋‹ค. 3์žฅ์—์„œ๋Š” ๋ฌด์ •ํ˜•์˜ ํด๋ฆฌ์—ํ‹ธํ—ฅ์‹ค์‹ธ์ด์˜คํŽœ์„ KCTP๋ฅผ ํ†ตํ•ด ๊ฒฐ์ •ํ˜•์˜ ํด๋ฆฌํ—ฅ์‹ค์‹ธ์ด์˜คํŽœ์— ์—ฐ๊ฒฐํ•˜์—ฌ ์šฉํ•ด๋„ ๋ฐ ๊ฒฐ์ •์„ฑ์˜ ๋ณ€ํ™”๊ฐ€ ์ž๊ธฐ ์กฐ๋ฆฝ ๊ฑฐ๋™์— ๋ฏธ์น˜๋Š” ์˜ํ–ฅ์„ ์กฐ์‚ฌํ•˜์˜€๋‹ค. ์ด๋ฅผ ํ† ๋Œ€๋กœ ํด๋ฆฌ์‹ธ์ด์˜คํŽœ ๋ธ”๋ก๊ณต์ค‘ํ•ฉ์ฒด๋ฅผ ์ด์šฉํ•ด ๊ธธ์ด ์กฐ์ ˆ์ด ๊ฐ€๋Šฅํ•œ ๋‚˜๋…ธ์„ฌ์œ ์˜ ์ œ์กฐ๊ฐ€ ๊ฐ€๋Šฅํ•จ์„ ๋ณด์ด๋ฉฐ ๋˜ํ•œ, ํŠธ๋ฆฌ๋ธ”๋ก๊ณต์ค‘ํ•ฉ์ฒด๊ฐ€ ๋‚˜๋…ธ์„ฌ์œ ์˜ ์—ฐ๊ฒฐ์„ ๋„์™€ ๋‚˜๋…ธ๋„คํŠธ์›Œํฌ๋ฅผ ์ด๋ฃจ๋Š” ๊ฒƒ์„ ์ด์šฉํ•ด ๋‚˜๋…ธ์„ฌ์œ  ๊ธฐ๋ฐ˜์˜ ์œ ๊ธฐ์ ค์˜ ํ•ฉ์„ฑ์ด ๊ฐ€๋Šฅํ•จ์„ ๋ณด์ธ๋‹ค. 4์žฅ์—์„œ๋Š” ์ˆ˜๋ ด ๋ฐฉ๋ฒ•์„ ํ†ตํ•ด ํ•ฉ์„ฑ๋œ ์นด๋ฅด๋ณต์‹ค์‚ฐ ๋˜๋Š” ์•„๋ฏผ์„ ์นœ์ˆ˜์„ฑ ๊ธฐ๋Šฅ ๋ธ”๋ก์œผ๋กœ, ์˜ฌ๋ฆฌ๊ณ (๋ฝํŠธ์‚ฐ)๋ฅผ ์†Œ์ˆ˜์„ฑ ๋ธ”๋ก์œผ๋กœ ๊ฐ–๋Š” ์ •๋ฐ€ํ•˜๊ฒŒ ์ •์˜๋œ ์ด์˜จ์„ฑ ๋ธ”๋ก ๊ณต์˜ฌ๋ฆฌ๊ณ ๋จธ๋ฅผ ์ด์šฉํ•˜์—ฌ ์šฉ์•ก์ƒ์—์„œ์˜ ์ž๊ธฐ์กฐ๋ฆฝ ๊ฑฐ๋™์„ ์กฐ์‚ฌํ•˜์˜€๋‹ค. ์ด๋ฅผ ํ†ตํ•ด ๊ฒฐ์ •์„ฑ์˜ ๊ฐ์ง„ ๊ตฌ์กฐ๋ฅผ ์ง€๋‹ˆ๋Š” ๊ณ ๋ถ„์ž ์ฃผ๋จธ๋‹ˆ๋ฅผ ๊ด€์ฐฐํ•˜์˜€์Œ์„ ๋ณด๊ณ ํ•œ๋‹ค.Table of Contents Abstract i Table of Contents โ…ณ List of Schemes โ…ถ List of Tables โ…ท List of Figures โ…ธ Abbreviations โ…นโ…ท Chapter 1. Introduction 1.1 Self-assembly of polmyersomes as nanoreactors 22 1.2 Discrete polymer 34 1.3 Summary of thesis 45 1.4 References 47 Chapter 2. Polymersome-based modular nanoreactors with size-seletive transmembrane permeability 2.1 Abstract 52 2.2 Introduction 53 2.3 Experimental Section 56 2.4 Results and Discussion 60 2.5 Conclusion 78 2.6 References 79 Chapter 3. Nanofiber organogel of all-conjugated block copolymers 3.1 Abstract 89 3.2 Introduction 90 3.3 Experimental Section 82 3.4 Results and Discussion 97 3.5 Conclusion 108 3.6 References 109 Chapter 4. Self-assembly of Oppositely Charged Ionic Block Copolymer Complex with Discrete Molecular Weight 3.1 Abstract 113 3.2 Introduction 114 3.3 Experimental Section 116 3.4 Results and Discussion 130 4.5 Conclusion 142 4.6 References 143 Abstract (Korean) 147๋ฐ•

    ๋ฃจ์ด์Šค์˜ ์ƒ๋Œ€์—ญ ์ด๋ก (Counterpart Theory)๊ณผ ๋ณธ์งˆ์ฃผ์˜

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    ๋ฐ์ด๋น— ๋ฃจ์ด์Šค(David Lewis : 1941โˆผ2001)๊ฐ€ ์ œ์‹œํ•œ ์ƒ๋Œ€์—ญ ์ด๋ก (Counterpart Theory)์€ ์ž์—ฐ์–ธ์–ด์˜ ์–‘์ƒ๋ฌธ์žฅ์„ ์ผ์ฐจ์ˆ ์–ด๋…ผ๋ฆฌ๋กœ ๋ฒˆ์—ญํ•˜๋Š” ํ˜•์‹์ฒด๊ณ„์ด์ž, ๊ทธ๊ฒƒ์— ๋Œ€ํ•œ ์˜๋ฏธ๋ฅผ ์ •ํ•ด์ฃผ๋Š” ์˜๋ฏธ๋ก ์ด๋‹ค. ์˜ˆ๋ฅผ๋“ค๋ฉด, ํ—ค๊ฒ”์€ ํ•„์—ฐ์ ์œผ๋กœ ์กด์žฌํ•œ๋‹ค๋ผ๋Š” ๋ฌธ์žฅ์€ (x)(Wxโ†’(โˆƒ y)(Iyx&Cya))์™€ ๊ฐ™์ด ์ƒ๋Œ€์—ญ ์ด๋ก ์œผ๋กœ ๋ฒˆ์—ญ๋  ์ˆ˜ ์žˆ์œผ๋ฉฐ, ์ด๋Š” ํ—ค๊ฒ”์˜ ์ƒ๋Œ€์—ญ์ด ๋ชจ๋“  ๊ฐ€๋Šฅ ์„ธ๊ณ„์— ์กด์žฌํ•œ๋‹ค๋ผ๋Š” ์‹์œผ๋กœ ํ’€์ด๋  ์ˆ˜ ์žˆ๋‹ค. ๋ฃจ์ด์Šค๋Š” ๊ธฐ์กด์— ์กด์žฌํ•˜๋˜ ์–‘์ƒ๋ฌธ์žฅ์— ๋Œ€ํ•œ ํ˜•์‹์ฒด๊ณ„์™€ ์˜๋ฏธ๋ก ์ด ๊ฐ€์ง„ ๋ฌธ์ œ์ ๋“ค์„ ๊ทน๋ณตํ•˜๊ธฐ ์œ„ํ•ด ์ด๋Ÿฌํ•œ ์ด๋ก ์„ ๊ตฌ์ƒํ•˜๊ฒŒ ๋˜์—ˆ๋‹ค. ๋ณธ ๋…ผ๋ฌธ์€ ํฌ๊ฒŒ ๋‘ ๊ฐ€์ง€์˜ ๋ชฉ์ ์„ ๊ฐ€์ง€๊ณ  ์žˆ๋‹ค. ์ผ์ฐจ์ ์ธ ๋ชฉ์ ์€ ๋ฃจ์ด์Šค๊ฐ€ ์ฃผ์žฅํ•˜๋Š” ์ƒ๋Œ€์—ญ ์ด๋ก ์˜ ํ˜•์ด์ƒํ•™์  ์ฒด๊ณ„๋ฅผ ์‚ดํŽด๋ณด๊ณ , ์ƒ๋Œ€์—ญ ๊ด€๊ณ„์— ๋Œ€ํ•ด ๊ณ ์ฐฐํ•ด๋ณด๋Š” ๊ฒƒ์ด๋‹ค. ์ƒ๋Œ€์—ญ ์ด๋ก ์˜ ํ˜•์ด์ƒํ•™์  ์ฒด๊ณ„๋Š” ๊ทธ๊ฐ€ ์ œ์‹œํ•˜๋Š” 8๊ฐœ์˜ ๊ณต์ค€๋งŒ์œผ๋กœ ์ถฉ๋ถ„ํžˆ ์„ค๋ช…๋  ์ˆ˜ ์žˆ๋Š” ๋ช…๋ฃŒํ•œ ์ฒด๊ณ„์ด๋‹ค. ๊ทธ๋Ÿฌ๋‚˜ ์ƒ๋Œ€์—ญ์„ ๊ฒฐ์ •ํ•ด์ฃผ๋Š” ์ƒ๋Œ€์—ญ ๊ด€๊ณ„(counterpartrelation)๋Š” ๊ทธ๋ ‡๊ฒŒ ๋ช…๋ฃŒํ•˜์ง€๋งŒ์€ ์•Š์€๋ฐ, ์ด๋Ÿฌํ•œ ๋ฌธ์ œ๋Š” ํŽ ๋“œ๋จผ(Feldman)์— ์˜ํ•ด ์ œ๊ธฐ๋˜์—ˆ๋‹ค. ๋ฃจ์ด์Šค๋Š” ์ƒ๋Œ€์—ญ ๊ด€๊ณ„๋ฅผ ๋งฅ๋ฝ ์˜์กด์ ์ธ ๊ฐœ๋…์œผ๋กœ ์ˆ˜์ •ํ•จ์œผ๋กœ์จ ์ด๋Ÿฌํ•œ ๋ฌธ์ œ๋ฅผ ํ•ด๊ฒฐํ•  ์ˆ˜ ์žˆ์—ˆ์œผ๋‚˜, ๊ฒฐ๊ณผ ์ ์œผ๋กœ ์ƒ๋Œ€์—ญ ๊ฐœ๋…์ด ์ƒ๋‹นํžˆ ๋ชจํ˜ธํ•œ ๊ฐœ๋…์ด ๋˜์—ˆ๋‹ค. ํŠนํžˆ, ์ด๋Ÿฌํ•œ ์ˆ˜์ •๋œ ์ƒ๋Œ€์—ญ ์ด๋ก ์œผ๋กœ 'Counterpart Theory and Quantified ModalLogic'(1968)์—์„œ ๊ทธ๊ฐ€ ์ œ์‹œํ•œ ๋ณธ์งˆ์  ์†์„ฑ ์ •์˜์— ์•ฝ๊ฐ„์˜ ๋ฌธ์ œ๊ฐ€ ์ƒ ๊ธฐ๋Š” ๊ฒƒ ๊ฐ™๋‹ค. ์ด์— ๋ณธ ๋…ผ๋ฌธ์˜ ๋˜ ๋‹ค๋ฅธ ๋ชฉ์ ์€, ์ƒ๋Œ€์—ญ ์ด๋ก ์„ ๋ฐ”ํƒ•์œผ๋กœ ํ•œ ์ƒˆ๋กœ์šด ๋ณธ์งˆ์  ์†์„ฑ ์ •์˜๋ฅผ ์ œ์‹œํ•˜๊ณ ์ž ํ•˜๋Š” ๊ฒƒ์ด๋‹ค

    Treatment of medication-related osteonecrosis of the jaw around the dental implant in a patient with multiple myeloma: a case report

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    The management of medication-related osteonecrosis of the jaw (MRONJ) around a dental implant is difficult. Conservative treatment is recommended, but patients do not exhibit improvement. Thus, several adjunctive therapies have been introduced. This paper describes a case of MRONJ around a dental implant in a 68-year-old man. Treatment with teriparatide was contraindicated because of the patientโ€™s history of multiple myeloma. Successful results with hyperbaric oxygen therapy and surgical intervention were achieved. Clinicians treating patients with MRONJ should conduct a thorough examination before selecting the modalities of adjuvant therapy, thereby establishing a solid treatment strategy.ope

    ํฌ๋ฆฝํ‚ค์˜ ํšŒ์˜์  ์—ญ์„ค๊ณผ ์–ธ์–ด๊ณต๋™์ฒด์˜ ๋ฌธ์ œ

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    ํฌ๋ฆฝํ‚ค๋Š” ๋น„ํŠธ๊ฒ์Šˆํƒ€์ธ์˜ ํ›„๊ธฐ ์ €์„œใ€Ž์ฒ ํ•™์  ํƒ๊ตฌใ€๋ฅผ ์ฝ๊ณ  ๊ทธ์— ๋Œ€ํ•œ ์ž์‹ ๋งŒ์˜ ๋…ํŠนํ•œ ํ•ด์„์„ ์ž์‹ ์˜ ์ €์„œ ใ€Ž๋น„ํŠธ๊ฒ์Šˆํƒ€์ธ ๊ทœ์น™๊ณผ ์‚ฌ์  ์–ธ์–ดใ€๋ฅผ ํ†ตํ•ด ๋ฐœํ‘œํ–ˆ๋‹ค. ์ด ์ฑ…์€ ๋งŽ์€ ๋ฐ˜ํ–ฅ์„ ์ผ์œผ์ผฐ์ง€๋งŒ, ์ด๊ฒƒ์ด ๋น„ํŠธ๊ฒ์Šˆํƒ€์ธ์˜ ํƒ๊ตฌ์— ๋Œ€ํ•œ ์ •์„์ ์ธ ํ•ด์„์€ ์•„๋‹ˆ๋ผ๋Š” ๊ฒƒ์œผ๋กœ ํ•™์ž๋“ค์˜ ์˜๊ฒฌ์ด ๋ชจ์•„์ง€๋Š” ๋“ฏํ•˜๋‹ค. ๊ทธ๋Ÿผ์—๋„ ๋ถˆ๊ตฌํ•˜๊ณ  ๊ทธ๊ฐ€ ์ด ์ฑ…์—์„œ ์ „๊ฐœํ•˜๋Š” ํšŒ์˜์  ์—ญ์„ค์€ ๊ทธ ์ž์ฒด๋กœ ๋งค์šฐ ํฅ๋ฏธ๋กญ๊ณ  ๋งค๋ ฅ์ ์ด๊ธฐ ๋•Œ๋ฌธ์— ๊ทธ ํ›„ ์ˆ˜๋งŽ์€ ์ฒ ํ•™์ž๋“ค์—๊ฒŒ ๋…ผ์Ÿ๊ฑฐ๋ฆฌ๊ฐ€ ๋˜์—ˆ๋‹ค. ํฌ๋ฆฝํ‚ค๋Š” ์ด ์ฑ…์—์„œ ์ „๊ฐœํ•˜๋Š” ๋…ผ์ฆ์ด ์˜จ์ „ํžˆ ์ž์‹ ์˜ ๊ฒƒ์ด ์•„๋‹ˆ๋ผ ๋น„ํŠธ๊ฒ์Šˆํƒ€์ธ์— ๋Œ€ํ•œ ์ž์‹ ์˜ ํ•ด์„์ด๋ผ๋Š” ์ ์„ ๊ฑฐ๋“ญ ๊ฐ•์กฐํ•˜๋Š”๋ฐ, ์•ž์—์„œ ์–ธ๊ธ‰ํ•œ ๋Œ€๋กœ ํฌ ๋ฆฝํ‚ค์˜ ํ•ด์„์€ ๋น„ํŠธ๊ฒ์Šˆํƒ€์ธ์— ๋Œ€ํ•œ ์ •์„์ ์ธ ํ•ด์„์ด๋ผ๊ณ  ๋ณผ ์ˆ˜๋Š” ์—†๊ธฐ ๋•Œ๋ฌธ์— ์ด ์ฑ…์˜ ํ™”์ž๋ฅผ ํฌ๋ฆฝ์ผ„์Šˆํƒ€์ธ์ด๋ผ๊ณ  ๋ถ€๋ฅด๊ธฐ๋„ ํ•œ๋‹ค. ์ด ๋…ผ๋ฌธ์—์„œ๋Š” ํŠน๋ณ„ํžˆ ํฌ๋ฆฝ์ผ„์Šˆํƒ€์ธ, ํ˜น์€ ํฌ๋ฆฝํ‚ค์˜ ๋น„ํŠธ๊ฒ์Šˆํƒ€์ธ๊ณผ ๊ฐ™์€ ์šฉ์–ด๋Š” ์“ฐ์ง€ ์•Š๊ณ  ๊ทธ๋ƒฅ ํฌ๋ฆฝํ‚ค๋ผ๊ณ  ํ†ต์นญํ•  ๊ฒƒ์ด๋‹ค. ํ•„์ž๋Š” ์ผ๋‹จ ํฌ๋ฆฝํ‚ค์˜ ํšŒ์˜์  ์—ญ์„ค๊ณผ ํšŒ์˜์  ํ•ด๊ฒฐ์ฑ…์— ๋Œ€ํ•œ ๋‚ด์šฉ์„ ์‚ดํŽด๋ณธ ํ›„, ๊ทธ์— ๋Œ€ํ•œ ํ‰๊ฐ€์™€ ๋…ผ์˜๋ฅผ ์ง„ํ–‰ํ•  ๊ฒƒ์ด๋‹ค. ํšŒ์˜์  ํ•ด๊ฒฐ์ฑ…์— ๋Œ€๋น„๋˜๋Š” ์ง์ ‘์  ํ•ด๊ฒฐ์ฑ…์— ๊ด€ํ•œ ๋‚ด์šฉ์€ ๊ฑฐ์˜ ์–ธ๊ธ‰ํ•˜์ง€ ์•Š์•˜๋Š”๋ฐ, ๋…์ž์ ์œผ๋กœ ์ง์ ‘์  ํ•ด๊ฒฐ์ฑ…์„ ์ „๊ฐœํ•˜๋Š” ๊ฒƒ์€ ํ˜„์žฌ ๋ณธ์ธ์˜ ๋Šฅ๋ ฅ์„ ๋ฒ—์–ด๋‚˜๋Š”๋ฐ๋‹ค ์ด ๊ธ€์˜ ์ „์ฒด์ ์ธ ๊ตฌ์„ฑ๊ณผ๋„ ๋งž์ง€ ์•Š๊ธฐ ๋•Œ๋ฌธ์ด๋‹ค. ํšŒ์˜์  ํ•ด๊ฒฐ์ฑ…์— ๋Œ€ํ•œ ํ•„์ž์˜ ๋…ผ์˜๋Š” ์ฃผ๋กœ ์–ธ์–ด ๊ณต๋™์ฒด์™€ ๊ด€๋ จํ•˜์—ฌ, ๊ทœ์น™ ์ค€์ˆ˜๋ฅผ ์œ„ํ•ด ์–ธ์–ด์  ๊ณต๋™์ฒด๊ฐ€ ๊ผญ ํ•„์š”ํ•œ์ง€์— ๋Œ€ํ•œ ๋ฌธ์ œ์— ์ง‘์ค‘ํ•  ๊ฒƒ์ด๋‹ค. ํ•„์ž๋Š” ํšŒ์˜์  ํ•ด๊ฒฐ์ฑ…์˜ ์‚ถ์˜ ํ˜•์‹๋“ค์—์˜ ์ผ์น˜๋ผ๋Š” ์ธก๋ฉด์—์„œ, ์–ธ์–ด ๊ณต๋™์ฒด๊ฐ€ ์—†์ด๋„ ๊ทœ์น™ ์ค€์ˆ˜๋Š” ์ผ์–ด๋‚œ๋‹ค๋Š” ์ ์„ ์—ญ์„คํ•  ๊ฒƒ์ด๋‹ค

    Allogeneic Dentin Graft: A Review on Its Osteoinductivity and Antigenicity

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    Studies on allogeneic demineralized dentin matrix (Allo-DDM) implantation in the 1960s and 1970s provided the most reliable preclinical evidence of bone formation and antigenicity in an extraosseous site. Recently, applications of Allo-DDM at skeletal sites were studied, and have provided reliable evidence of bone-forming capacity and negligible antigenicity. However, the osteoinductivity and antigenicity properties of Allo-DDM in extraskeletal sites have not yet been investigated due to the lack of follow-up studies after the initial research. The clinical applications of autogenous DDM (Auto-DDM) have been standardized in some countries. Long-term clinical studies have reported the development of several shapes of Auto-DDM, such as powders, blocks, moldable forms, and composites, with recombinant human bone morphogenetic protein-2. For the development of Allo-DDM as a reliable bone graft substitute next to Auto-DDM, we reviewed preclinical studies on the bone induction capacity of allogeneic dentin at extraskeletal as well as skeletal sites. Electronic databases were screened for this review in January 2020 and searched from 1960 to 2019. This review aims to provide a foundation on the preclinical studies of Allo-DDM, which could enable future researches on its osteogenic capability and antigenicity. In conclusion, Allo-DDM showed great potential for osteoinductivity in extraskeletal sites with low antigenicity, which neither adversely affected osteogenic capability nor provoked immunologic reactions. However, the risk of viral disease transmission should be researched before the clinical application of Allo-DDM.ope

    ๋„์‹œ์ •๋น„์‚ฌ์—… ์˜ˆ์ •์ง€์˜ ๋ฐฉ๊ธฐ๋ถ€๋™์‚ฐ์—์„œ ๋‚˜ํƒ€๋‚˜๋Š” ๋ฌธํ™”์˜ˆ์ˆ ์ธ์˜ ์ผ์‹œ์  ๊ณต๊ฐ„ ํ™œ์šฉ์ด ์ง€์—ญ์— ๋ฏธ์น˜๋Š” ์˜ํ–ฅ

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    ํ•™์œ„๋…ผ๋ฌธ (์„์‚ฌ)-- ์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› : ์ง€๋ฆฌํ•™๊ณผ, 2015. 8. ๊น€์šฉ์ฐฝ.๋„์‹œ ๋‚ด ํŠน์ • ์‚ฐ์—…์ง€๊ตฌ์˜ ์‡ ํ‡ด, ๊ตฌ๋„์‹ฌ ๊ณต๋™ํ™”์™€ ๊ฐ™์€ ํ˜„์ƒ์€ ๋„์‹œ ๋‚ด์— ๋ฐฉ๊ธฐ๋œ ๊ณต๊ฐ„์„ ๋งŒ๋“ค์–ด๋‚ด๊ณ , ์ด๋ ‡๊ฒŒ ๋งŒ๋“ค์–ด์ง„ ๊ณต๊ฐ„์„ ์ง€์—ญ ์žฌ์ƒ์ด๋‚˜ ์ง€์—ญ ๊ฐœ๋ฐœ์„ ์œ„ํ•œ ์ž์‚ฐ์œผ๋กœ ํ™œ์šฉํ•˜๊ณ ์ž ํ•˜๋Š” ์‹œ๋„๋“ค์ด ์ด๋ฃจ์–ด์ง€๊ณ  ์žˆ๋‹ค. ๊ทธ๋Ÿฌ๋‚˜ ์ด์— ๋Œ€ํ•œ ๊ธฐ์กด์˜ ๋…ผ์˜๋“ค์€ ๋ฐฉ๊ธฐ๋œ ๊ณต๊ฐ„ ํ™œ์šฉ์— ๋Œ€ํ•œ ์ •์ฑ…์  ๊ตฌ์ƒ์—๋งŒ ์ดˆ์ ์„ ๋‘๊ณ  ์žˆ๊ณ , ์‹ค์ œ ๋ฐฉ๊ธฐ๋œ ๊ณต๊ฐ„์„ ํ™œ์šฉํ•˜๋Š” ๊ฐœ์ธ๋“ค๊ณผ ์ด๋Ÿฌํ•œ ๊ฐœ์ธ๋“ค์˜ ํ™œ๋™์— ๋”ฐ๋ผ ๋‚˜ํƒ€๋‚  ์ˆ˜ ์žˆ๋Š” ๊ณต๊ฐ„์˜ ๋ณ€ํ™”์— ๋Œ€ํ•ด์„œ๋Š” ๊ด€์‹ฌ์ด ๋ถ€์กฑํ•˜์˜€๋‹ค. ์ด์— ๋ณธ ์—ฐ๊ตฌ์—์„œ๋Š” ์ •๋ฆ‰๊ณจ๊ณผ ๊ทธ๊ณณ์— ์ด์ฃผํ•œ ์˜ˆ์ˆ ๊ฐ€๋“ค์˜ ์‚ฌ๋ก€๋ฅผ ํ†ตํ•ด ๋ฐฉ๊ธฐ๋œ ๊ณต๊ฐ„์„ ์ ์œ ํ•˜๊ณ  ์‚ฌ์šฉํ•˜๋Š” ์ผ์‹œ์  ๊ณต๊ฐ„ ํ™œ์šฉ์˜ ๋ฐฉ์‹์ด ๊ณต๊ฐ„์ , ์‹œ๊ฐ„์ ์œผ๋กœ ํ™•์žฅ๋˜์–ด ๋‚˜๊ฐ€๋Š” ๊ณผ์ •์— ์ฃผ๋ชฉํ•˜์˜€๋‹ค. ๋‚˜์•„๊ฐ€์„œ ์ด๋Ÿฌํ•œ ์‹œ๋„๋“ค์ด ์ง€์—ญ ์žฌ์ƒ ๋‹ด๋ก ์—๊นŒ์ง€ ์˜ํ–ฅ์„ ๋ฏธ์น  ์ˆ˜ ์žˆ๋Š”์ง€๋ฅผ ์‚ดํŽด๋ณด๊ณ ์ž ํ•˜์˜€๋‹ค. ๋ณธ ์—ฐ๊ตฌ์˜ ๊ฒฐ๊ณผ๋Š” ๋‹ค์Œ๊ณผ ๊ฐ™๋‹ค. ์ฒซ์งธ, ์˜ˆ์ˆ ๊ฐ€๋“ค์˜ ์ด์ฃผ ์ดˆ๊ธฐ ์ผ์‹œ์  ๊ณต๊ฐ„ ํ™œ์šฉ์€ ์žฌ๊ฐœ๋ฐœ ์˜ˆ์ •์ง€์ธ ์ •๋ฆ‰๊ณจ์˜ ๋นˆ์ง‘์„ ์˜ˆ์ˆ ๊ฐ€๋“ค์˜ ์ฃผ๊ฑฐ์ง€์ด์ž ์ž‘์—…์žฅ์œผ๋กœ ๋งŒ๋“œ๋Š” ๊ฒƒ์—์„œ ์‹œ์ž‘ํ•˜์˜€๋‹ค. ํ™ฉํํ•ด์ง„ ๊ณต๊ฐ„์„ ์˜ˆ์ˆ ๊ฐ€๋“ค์ด ์ž‘์—…์žฅ์œผ๋กœ ๋งŒ๋“ค๋ฉด์„œ ์ •๋ฆ‰๊ณจ์˜ ๋นˆ์ง‘๋“ค์€ ์ƒˆ๋กญ๊ฒŒ ํ™œ๋ ฅ์„ ์ฐพ๊ฒŒ ๋˜์—ˆ๋‹ค. ๋‘˜์งธ, ์˜ˆ์ˆ ๊ฐ€๋“ค์˜ ํ™œ๋™์€ ์ ์ฐจ ํ™•์žฅ๋˜์–ด ์˜ˆ์ˆ ๊ฐ€์™€ ์ฃผ๋ฏผ, ๊ทธ๋ฆฌ๊ณ  ์ง€์—ญ ์™ธ๋ถ€์˜ ํ–‰์œ„์ฃผ์ฒด๋“ค์ด ํ•จ๊ป˜ ์—ฐ๋Œ€ํ•˜๊ณ  ์ฐธ์—ฌํ•˜๋Š” ํ™œ๋™์œผ๋กœ ๋ฐœ์ „ํ•œ๋‹ค. ๋นˆ์ง‘์„ ์ž„๋Œ€ํ•˜์—ฌ ๊ณต๋™ ์ž‘์—…์žฅ์„ ๊พธ๋ฏธ๊ณ , ์ฃผ๋ฏผ ์ฐธ์—ฌํ•  ์ˆ˜ ์žˆ๋Š” ๋งˆ์„ ํ™˜๊ฒฝ ์ •๋น„์‚ฌ์—…๊ณผ ๋งˆ์„ ์ž”์น˜๋ฅผ ๊ธฐํšํ•˜์—ฌ ํ™œ๋™์˜ ๋ฒ”์œ„๋ฅผ ์˜ˆ์ˆ ๊ฐ€ ๋‚ด๋ถ€์—์„œ ์ง€์—ญ์‚ฌํšŒ ์ „์ฒด๋กœ ํ™•์žฅ์‹œ์ผฐ๋‹ค. ๋‚˜์•„๊ฐ€ ์ง€์—ญ ์ •๋ถ€์˜ ์žฌ์ •์  ์ง€์› ๋ฐ ์™ธ๋ถ€ ํ™œ๋™๊ฐ€๋“ค๊ณผ์˜ ์—ฐ๋Œ€๋ฅผ ํ†ตํ•ด ํ™œ๋™์ด ์ง€์†์ ์œผ๋กœ ์ด๋ฃจ์–ด์งˆ ์ˆ˜ ์žˆ๋Š” ๊ธฐ๋ฐ˜์„ ๋งˆ๋ จํ•˜์˜€๋‹ค. ์…‹์งธ, ์žฌ๊ฐœ๋ฐœ ์‚ฌ์—…์˜ ์‹œํ–‰์ด ์ง€์—ฐ๋˜๋Š” ์ƒํ™ฉ ์†์—์„œ ์ •๋ฆ‰๊ณจ ์˜ˆ์ˆ ๊ฐ€๋“ค์˜ ํ™œ๋™์€ ์ง€์†๋  ์ˆ˜ ์žˆ์—ˆ๋‹ค. ์—ฌ๊ธฐ์— ๋”ํ•˜์—ฌ ์ฃผ๋ฏผ๋“ค์˜ ์ฐธ์—ฌ, ์ง€์—ญ ์™ธ๋ถ€ ํ™œ๋™๊ฐ€์™€์˜ ์—ฐ๋Œ€, ์ง€์—ญ ์ •๋ถ€์˜ ์ง€์› ๋“ฑ์ด ๊ณ„์† ์ด์–ด์ง€๋ฉด์„œ ์ •๋ฆ‰๊ณจ ์ง€์—ญ ์‚ฌํšŒ๋‚ด๋ถ€์—์„œ๋Š”, ๊ธฐ์กด์˜ ์ „๋ฉด ์žฌ๊ฐœ๋ฐœ ๋ฐฉ์‹์„ ์ถ”์ง„ํ•ด ๋‚˜๊ฐ€๋˜ ์ฃผ๋ฏผํ˜‘์˜์ฒด์— ๋ฐ˜๋Œ€ํ•˜๋Š” ์˜๊ฒฌ์„ ๊ฐ€์ง„ ์ฃผ๋ฏผ๋“ค์ด ๋ชฉ์†Œ๋ฆฌ๋ฅผ ๋†’์ด๊ธฐ ์‹œ์ž‘ํ•˜์˜€๋‹ค. ์ด์— ๋”ฐ๋ผ ๊ธฐ์กด์˜ ์žฌ๊ฐœ๋ฐœ ๋ฐฉ์‹์„ ์ฃผ์žฅํ•˜๋Š” ์ฃผ๋ฏผ์˜๊ฒฌ๊ณผ ๋Œ€์•ˆ์  ์ง€์—ญ ์žฌ์ƒ ์ „๋žต์„ ์ถ”์ง„ํ•˜๊ณ ์ž ํ•˜๋Š” ์ฃผ๋ฏผ์˜ ์˜๊ฒฌ์ด ๊ณต์กดํ•˜๊ณ  ์žˆ๋Š” ์ƒํ™ฉ์ด๋‹ค. ๋ณธ ์—ฐ๊ตฌ๊ฒฐ๊ณผ๋Š” ๋ฐฉ๊ธฐ๋œ ๊ณต๊ฐ„์„ ์‚ฌ์šฉํ•˜๋Š” ๊ฐœ์ธ์˜ ์ผ์‹œ์  ๊ณต๊ฐ„ ํ™œ์šฉ ํ–‰์œ„๊ฐ€ ํ™•์žฅ๋˜์–ด ์ง€์—ญ ๊ฐœ๋ฐœ ๋ฐ ์ง€์—ญ ์žฌ์ƒ ๋…ผ์˜์— ๋Œ€ํ•ด ์ƒˆ๋กœ์šด ๋‹ด๋ก ์„ ์ œ์‹œํ•  ๊ฐ€๋Šฅ์„ฑ์ด ์žˆ์Œ์„ ์‹œ์‚ฌํ•œ๋‹ค. ์ผ์‹œ์  ๊ณต๊ฐ„ ์‚ฌ์šฉ์€ ๋‹จ์ˆœํžˆ ๋ฐฉ๊ธฐ๋œ ๊ณต๊ฐ„์„ ์ž„์‹œ๋กœ ๋ฐ”๊พธ๋Š” ๊ฒƒ๋ฟ๋งŒ ์•„๋‹ˆ๋ผ ์ง€์†์ ์œผ๋กœ ์˜ํ–ฅ์„ ๋ฏธ์น  ์ˆ˜ ์žˆ๋Š” ๋Œ€์•ˆ์  ๊ณต๊ฐ„ ์‚ฌ์šฉ์ด๋ผ๊ณ  ํ•  ์ˆ˜ ์žˆ๋‹ค.์ œ 1์žฅ ์„œ ๋ก  1 ์ œ 1์ ˆ ์—ฐ๊ตฌ์˜ ๋ฐฐ๊ฒฝ๊ณผ ๋ชฉ์  1 ์ œ 2์ ˆ ์—ฐ๊ตฌ๋Œ€์ƒ๊ณผ ์—ฐ๊ตฌ๋ฐฉ๋ฒ• 7 1. ์—ฐ๊ตฌ๋Œ€์ƒ : ์„œ์šธ์‹œ ์„ฑ๋ถ๊ตฌ ์ •๋ฆ‰3๋™ ์ •๋ฆ‰๊ณจ(์ดํ•˜ ์ •๋ฆ‰๊ณจ) ์ง€์—ญ 7 2. ์—ฐ๊ตฌ๋ฐฉ๋ฒ• 10 ์ œ 2 ์žฅ ๋ฌธํ—Œ์—ฐ๊ตฌ 13 ์ œ 1์ ˆ ์ฃผํƒ์˜ ๋ฐฉ๊ธฐ(abandonment)์— ๋Œ€ํ•œ ๋…ผ์˜ 14 1. ์ผ์‹œ์  ๊ณต๊ฐ„์œผ๋กœ์„œ ์ฃผํƒ ๋ฐฉ๊ธฐ ์ง€์—ญ 14 2. ์ฃผํƒ ๋ฐฉ๊ธฐ์— ๋Œ€ํ•œ ๊ธฐ์กด ๋…ผ์˜์˜ ํ•œ๊ณ„ 17 ์ œ 2์ ˆ ์ผ์‹œ์  ํ™œ์šฉ์„ ํ†ตํ•œ ๋Œ€์•ˆ์  ๊ณต๊ฐ„ ํ˜•์„ฑ 18 1. ์ผ์‹œ์  ํ™œ์šฉ์—์„œ ์ผ์‹œ์„ฑ์ด ๊ฐ€์ง€๋Š” ์˜๋ฏธ 19 2. ์ผ์‹œ์  ํ™œ์šฉ์˜ ์‚ฌ๋ก€๋กœ์„œ ๋นˆ์ง‘ ํ™œ์šฉ๊ณผ ๋Œ€์•ˆ์  ๊ณต๊ฐ„ ํ˜•์„ฑ 21 ์ œ 3์ ˆ ์ž๋ฐœ์  ํ–‰์œ„์ž๋กœ์„œ ์˜ˆ์ˆ ๊ฐ€์˜ ๋นˆ์ง‘ ์ ์œ ํ–‰์œ„๊ฐ€ ์ง€์—ญ์— ๋ฏธ์น˜๋Š” ํšจ๊ณผ 25 1. ์ž๋ฐœ์  ์ฃผ์ฒด์— ์˜ํ•œ ์ผ์‹œ์  ํ™œ์šฉ๊ณผ ์ง€์—ญ ์˜์ œ(agenda) ์„ค์ • 26 2. ์ผ์‹œ์  ํ™œ์šฉ๊ณผ ๋„์‹œ ์žฌ์ƒ ์ „๋žต 27 ์ œ 4์ ˆ ๋…ผ์˜์˜ ์ข…ํ•ฉ 31 ์ œ 3 ์žฅ ์—ฐ๊ตฌ์ง€์—ญ ํŠน์„ฑ 32 ์ œ 1์ ˆ ์ •๋ฆ‰๊ณจ์˜ ์ž์—ฐํ™˜๊ฒฝ๊ณผ ์ธ๋ฌธํ™˜๊ฒฝ ํŠน์„ฑ 33 ์ œ 2์ ˆ ์ •๋ฆ‰๊ณจ ํ˜•์„ฑ๊ณผ์ • 38 ์ œ 3์ ˆ ์žฌ๊ฐœ๋ฐœ ์ง€๊ตฌ ์ง€์ • ์ดํ›„ ์ง€์—ญ ๋ณ€ํ™” 42 1. ์›์ฃผ๋ฏผ ๊ฑฐ์ฃผ์ธ๊ตฌ ๊ฐ์†Œ์— ๋”ฐ๋ฅธ ๋นˆ์ง‘์˜ ์ฆ๊ฐ€ 42 2. ์ Š์€ ๊ณ„์ธต ์™ธ๋ถ€์ธ๊ตฌ์˜ ์œ ์ž… 49 ์ œ 4์žฅ ์˜ˆ์ˆ ๊ฐ€๋“ค์˜ ์ด์ฃผ์™€ ์ •์ฐฉ ๊ณผ์ • 51 ์ œ 1์ ˆ ์ •๋ฆ‰๊ณจ์— ์˜ˆ์ˆ ๊ฐ€๋“ค์ด ์ด์ฃผํ•˜๊ฒŒ ๋œ ๋ฐฐ๊ฒฝ 52 1. ์˜ˆ์ˆ ๊ฐ€ ๋„คํŠธ์›Œํฌ๋ฅผ ํ†ตํ•œ ์—ฐ์‡„ ์ด์ฃผ 52 2. ๊ฒฝ์ œ์  ์ด์œ ์™€ ์ž์—ฐํ™˜๊ฒฝ 55 ์ œ 2์ ˆ ์˜ˆ์ˆ ๊ฐ€์™€ ์ง€์—ญ ์ฃผ๋ฏผ์˜ ๊ด€๊ณ„ 58 1. ์ด์ฃผ์ดˆ๊ธฐ์˜ ๊ฐˆ๋“ฑ ์š”์ธ 58 2. ์˜ˆ์ˆ ๊ฐ€์— ๋Œ€ํ•œ ์ฃผ๋ฏผ ์ธ์‹์˜ ๋ณ€ํ™” 61 ์ œ 3์ ˆ ์˜ˆ์ˆ ๊ฐ€ ์ค‘์‹ฌ์˜ ๋Œ€์•ˆ์  ์ง€์—ญ ๊ฐœ๋ฐœ ๋…ผ์˜์˜ ์ „๊ฐœ 66 1. ๋นˆ์ง‘์„ ์ด์šฉํ•œ ๊ฒŒ์ŠคํŠธ ํ•˜์šฐ์Šค ์„ค์น˜ 67 2, ์ •๋ฆ‰์ƒ๋ช…ํ‰ํ™”๋งˆ์„ํ”„๋กœ์ ํŠธ๋ฅผ ํ†ตํ•œ ๋Œ€์•ˆ์  ์ง€์—ญ ๋ฐœ์ „ ์ „๋žต 69 ์ œ 5์žฅ ์ผ์‹œ์  ๊ณต๊ฐ„ ํ™œ์šฉ์˜ ์ง€์†์„ฑ์ด ์ง€์—ญ๊ฐœ๋ฐœ์ •์ฑ…์— ๋ฏธ์น˜๋Š” ์˜ํ–ฅ 72 ์ œ1์ ˆ ์˜ˆ์ˆ ๊ฐ€ ํ™œ๋™์— ๋Œ€ํ•œ ์žฌ๊ฐœ๋ฐœ ์ถ”์ง„ ์ฃผ๋ฏผ ํ˜‘์˜์ฒด์˜ ๋Œ€์‘ 73 ์ œ 2์ ˆ ๋ฏผ๊ด€ ์—ฐ๊ณ„๋ฅผ ํ†ตํ•œ ์ง€์›๊ณผ ์ผ์‹œ์  ๊ณต๊ฐ„ ์‚ฌ์šฉ์˜ ์ง€์†์„ฑ ํ™•๋ณด 78 1. ์„œ์šธ์‹œ์™€ ์„ฑ๋ถ๊ตฌ์˜ ์ •์ฑ…์  ์ž…์žฅ ๋ณ€ํ™”์™€์— ๋”ฐ๋ฅธ ์ •๋ฆ‰๊ณจ ์žฌ๊ฐœ๋ฐœ ๊ณ„ํš์˜ ์ง€์—ฐ 78 2. ์ผ์‹œ์  ๊ณต๊ฐ„ ํ™œ์šฉ์— ๋Œ€ํ•œ ์ง€์—ญ์ •๋ถ€์˜ ์ง€์› 82 ์ œ 3์ ˆ ์ž์œจ์  ์ฃผ์ฒด๋“ค์˜ ์ฐธ์—ฌ๋ฅผ ํ†ตํ•œ ์ผ์‹œ์  ๊ณต๊ฐ„ ํ™œ์šฉ์˜ ์ง€์†์„ฑ ํ™•๋ณด 83 ์ œ 6์žฅ ๊ฒฐ ๋ก  : ์—ฐ๊ตฌ์˜ ๊ฒฐ๊ณผ 86 ์ฐธ๊ณ ๋ฌธํ—Œ 88 Abstract 93Maste

    ๊ฐœ์ธ-์กฐ์ง ์ ํ•ฉ์„ฑ๊ณผ ๊ฐœ์ธ-์ง๋ฌด ์ ํ•ฉ์„ฑ์˜ ์กฐ์ ˆํšจ๊ณผ๋ฅผ ์ค‘์‹ฌ์œผ๋กœ

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    ํ•™์œ„๋…ผ๋ฌธ(์„์‚ฌ) -- ์„œ์šธ๋Œ€ํ•™๊ต๋Œ€ํ•™์› : ํ–‰์ •๋Œ€ํ•™์› ํ–‰์ •ํ•™๊ณผ(ํ–‰์ •ํ•™์ „๊ณต), 2022. 8. ์ด์ˆ˜์˜.๋ณธ ์—ฐ๊ตฌ๋Š” ์ง๋ฌด์ž์œจ์„ฑ์ด ๊ณต๋ฌด์›์˜ ํ˜์‹ ํ–‰๋™์— ๋ฏธ์น˜๋Š” ์˜ํ–ฅ๊ณผ ํ•จ๊ป˜ ๊ฐœ์ธ-์กฐ์ง ์ ํ•ฉ์„ฑ๊ณผ ๊ฐœ์ธ-์ง๋ฌด ์ ํ•ฉ์„ฑ์˜ ์กฐ์ ˆํšจ๊ณผ๋ฅผ ๋ถ„์„ํ•˜์˜€๋‹ค. ํŠนํžˆ ์ง๋ฌด์ž์œจ์„ฑ์„ ๋ฐฉ๋ฒ•, ์ผ์ •, ๊ธฐ์ค€์ด๋ผ๋Š” ์„ธ ๊ฐ€์ง€ ์ธก๋ฉด์œผ๋กœ ๊ตฌ๋ถ„ํ•˜์—ฌ ํ˜์‹ ํ–‰๋™์— ๋Œ€ํ•œ ๊ฐ๊ฐ์˜ ์˜ํ–ฅ์— ์ฐจ์ด๊ฐ€ ์žˆ๋Š”์ง€๋ฅผ ํ™•์ธํ•˜์˜€๋‹ค. ์ง๋ฌด์ž์œจ์„ฑ์— ๋Œ€ํ•œ ๋‹ค๊ฐ์  ๋ถ„์„๊ณผ ํ•จ๊ป˜ ๊ฐœ์ธ, ์กฐ์ง, ์ง๋ฌด ๊ฐ„ ์ƒํ˜ธ์ž‘์šฉ์„ ๊ณ ๋ คํ•จ์œผ๋กœ์จ ๊ณต๋ฌด์›์˜ ํ˜์‹ ํ–‰๋™์„ ์ด๋Œ์–ด๋‚ด๊ธฐ ์œ„ํ•œ ์‹ค์ฒœ์  ์‹œ์‚ฌ์ ์„ ์ œ๊ณตํ•˜๊ณ ์ž ํ•˜์˜€๋‹ค. ๋ณธ ์—ฐ๊ตฌ๋Š” ๋ฐฉ๋ฒ•์ž์œจ์„ฑ, ์ผ์ •์ž์œจ์„ฑ, ๊ธฐ์ค€์ž์œจ์„ฑ์„ ๋…๋ฆฝ๋ณ€์ˆ˜๋กœ, ๊ณต๋ฌด์›์˜ ํ˜์‹ ํ–‰๋™์„ ์ข…์†๋ณ€์ˆ˜๋กœ, ๊ฐœ์ธ-์กฐ์ง ์ ํ•ฉ์„ฑ๊ณผ ๊ฐœ์ธ-์ง๋ฌด ์ ํ•ฉ์„ฑ์„ ์กฐ์ ˆ๋ณ€์ˆ˜๋กœ ์„ค์ •ํ•˜์—ฌ ์œ„๊ณ„์  ํšŒ๊ท€๋ถ„์„์„ ์‹ค์‹œํ•˜์˜€๋‹ค. ๋ถ„์„์„ ์œ„ํ•œ ์—ฐ๊ตฌ์ž๋ฃŒ๋กœ์„œ ํ•œ๊ตญํ–‰์ •์—ฐ๊ตฌ์›์˜ โ€˜2020๋…„ ๊ณต์ง์ƒํ™œ์‹คํƒœ์กฐ์‚ฌโ€™๋ฅผ ํ™œ์šฉํ•˜์˜€๊ณ , ์ค‘์•™์ •๋ถ€ ๋ฐ ๊ด‘์—ญ์ž์น˜๋‹จ์ฒด ์†Œ์† ์ผ๋ฐ˜์ง ๊ณต๋ฌด์› 4,339๋ช…์„ ํ‘œ๋ณธ์œผ๋กœ ํ•˜์˜€๋‹ค. ๋ถ„์„ ๊ฒฐ๊ณผ๋ฅผ ์š”์•ฝํ•˜๋ฉด ๋‹ค์Œ๊ณผ ๊ฐ™๋‹ค. ์ฒซ์งธ, ๋ฐฉ๋ฒ•์ž์œจ์„ฑ๊ณผ ๊ธฐ์ค€์ž์œจ์„ฑ์€ ํ˜์‹ ํ–‰๋™์— ์œ ์˜๋ฏธํ•œ ์ •(+)์˜ ์˜ํ–ฅ์„ ๋ฏธ์น˜๋Š” ๊ฒƒ์œผ๋กœ ๋‚˜ํƒ€๋‚œ ๋ฐ˜๋ฉด ์ผ์ •์ž์œจ์„ฑ์€ ํ˜์‹ ํ–‰๋™์— ์œ ์˜๋ฏธํ•œ ์˜ํ–ฅ์„ ๋ฏธ์น˜์ง€ ์•Š๋Š” ๊ฒƒ์œผ๋กœ ๋‚˜ํƒ€๋‚ฌ๋‹ค. ์ง๋ฌด์˜ ๋ฐฉ๋ฒ•๊ณผ ํ‰๊ฐ€ ๊ธฐ์ค€์— ๋Œ€ํ•ด ์ž์œจ์„ฑ์„ ๋ถ€์—ฌํ• ์ˆ˜๋ก ๊ตฌ์„ฑ์›์˜ ํ˜์‹ ํ–‰๋™์€ ์ฆ๊ฐ€ํ•˜์ง€๋งŒ, ์ผ์ •์— ๋Œ€ํ•ด ์ž์œจ์„ฑ์„ ๋ถ€์—ฌํ•˜๋Š” ๊ฒƒ๋งŒ์œผ๋กœ๋Š” ํ˜์‹ ํ–‰๋™์ด ์ฆ๊ฐ€ํ•˜์ง€ ์•Š์„ ์ˆ˜ ์žˆ๋‹ค. ์ด๋Ÿฌํ•œ ๊ฒฐ๊ณผ๋Š” ํ˜์‹ ํ–‰๋™์„ ์œ„ํ•ด ์ง๋ฌด์ž์œจ์„ฑ์„ ํ™•๋Œ€ํ•  ๋•Œ ์ง๋ฌด์˜ ์ˆ˜ํ–‰๋ฐฉ๋ฒ•๊ณผ ํ‰๊ฐ€๊ธฐ์ค€์— ์ค‘์ ์„ ๋‘˜ ํ•„์š”๊ฐ€ ์žˆ์Œ์„ ์‹œ์‚ฌํ•œ๋‹ค. ๋‘˜์งธ, ๊ฐœ์ธ-์ง๋ฌด ์ ํ•ฉ์„ฑ์ด ๊ธฐ์ค€์ž์œจ์„ฑ๊ณผ ํ˜์‹ ํ–‰๋™ ๊ฐ„์— ์•ฝํ•œ ์ •(+)์˜ ์กฐ์ ˆํšจ๊ณผ๋ฅผ ๊ฐ–๋Š” ๊ฒƒ์œผ๋กœ ๋‚˜ํƒ€๋‚ฌ๋‹ค. ๊ฐœ์ธ-์ง๋ฌด ์ ํ•ฉ์„ฑ์ด ๋†’์€ ๊ตฌ์„ฑ์›์€ ๊ฐœ์ธ-์ง๋ฌด ์ ํ•ฉ์„ฑ์ด ๋‚ฎ์€ ๊ตฌ์„ฑ์›์— ๋น„ํ•ด ๊ธฐ์ค€์ž์œจ์„ฑ์ด ํ˜์‹ ํ–‰๋™์— ๋ฏธ์น˜๋Š” ๊ธ์ •์ ์ธ ์˜ํ–ฅ์ด ๋” ๊ฐ•ํ•˜๊ฒŒ ๋‚˜ํƒ€๋‚ฌ๋‹ค. ๋ฐ˜๋ฉด ๊ฐœ์ธ-์กฐ์ง ์ ํ•ฉ์„ฑ์€ ๊ฐ ์ž์œจ์„ฑ๊ณผ ํ˜์‹ ํ–‰๋™ ๊ฐ„์— ์กฐ์ ˆํšจ๊ณผ๊ฐ€ ์—†์—ˆ์œผ๋ฉฐ, ๊ฐœ์ธ-์ง๋ฌด ์ ํ•ฉ์„ฑ์€ ๋ฐฉ๋ฒ•์ž์œจ์„ฑ๊ณผ ์ผ์ •์ž์œจ์„ฑ์— ๋Œ€ํ•ด์„œ๋„ ์กฐ์ ˆํšจ๊ณผ๊ฐ€ ์—†์—ˆ๋‹ค. ์ด๋Ÿฌํ•œ ๊ฒฐ๊ณผ๊ฐ€ ์‹œ์‚ฌํ•˜๋Š” ๋ฐ”๋Š”, ์ง๋ฌด์˜ ํ‰๊ฐ€ ๊ธฐ์ค€์— ๋Œ€ํ•ด ์ž์œจ์„ฑ์„ ๋ถ€์—ฌํ•  ๋•Œ ๊ตฌ์„ฑ์›์ด ๊ฐ€์ง„ ์—ญ๋Ÿ‰ ์ˆ˜์ค€๊ณผ ์ ์„ฑ์ด ์ง๋ฌด์— ์ ํ•ฉํ•œ์ง€๋ฅผ ํ•จ๊ป˜ ๊ณ ๋ คํ•œ๋‹ค๋ฉด ํ˜์‹ ํ–‰๋™์„ ๋ณด๋‹ค ํšจ๊ณผ์ ์œผ๋กœ ์ด๋Œ์–ด๋‚ผ ์ˆ˜ ์žˆ๋‹ค๋Š” ๊ฒƒ์ด๋‹ค. ๋”ฐ๋ผ์„œ ๊ตฌ์„ฑ์›์˜ ์ง๋ฌด ์—ญ๋Ÿ‰๊ณผ ์ ์„ฑ์— ๋Œ€ํ•ด ์กฐ์ง์  ์ฐจ์›์—์„œ ๊ด€์‹ฌ์„ ๊ธฐ์šธ์—ฌ ์ง๋ฌด์— ์ ํ•ฉํ•œ ์ธ๋ ฅ์„ ๋ฐฐ์น˜ํ•  ํ•„์š”๊ฐ€ ์žˆ๋‹ค. ๋ณธ ์—ฐ๊ตฌ๊ฐ€ ๊ฐ–๋Š” ์ด๋ก ์ , ์‹ค์ฒœ์  ์ธก๋ฉด์˜ ์˜์˜๋Š” ๋‹ค์Œ๊ณผ ๊ฐ™๋‹ค. ์ด๋ก ์  ์ธก๋ฉด์—์„œ, ๋ณธ ์—ฐ๊ตฌ๋Š” ์ง๋ฌด์ž์œจ์„ฑ์„ ๋‹จ์ผ๋ณ€์ˆ˜๋กœ ์„ค์ •ํ–ˆ๋˜ ๊ธฐ์กด ์—ฐ๊ตฌ๋“ค๊ณผ ๋‹ฌ๋ฆฌ ์ง๋ฌด์ž์œจ์„ฑ์„ ๋ฐฉ๋ฒ•, ์ผ์ •, ๊ธฐ์ค€์œผ๋กœ ์„ธ๋ถ„ํ™”ํ•˜์—ฌ ๊ฐ ์ž์œจ์„ฑ์ด ํ˜์‹ ํ–‰๋™์— ๋ฏธ์น˜๋Š” ์˜ํ–ฅ์ด ์„œ๋กœ ์ƒ์ดํ•  ์ˆ˜ ์žˆ์Œ์„ ํ™•์ธํ•˜์˜€๋‹ค. ํ•œํŽธ ๊ฐœ์ธ-์ง๋ฌด ์ ํ•ฉ์„ฑ์ด ๊ธฐ์ค€์ž์œจ์„ฑ๊ณผ ํ˜์‹ ํ–‰๋™ ๊ฐ„์˜ ๊ด€๊ณ„๋ฅผ ์กฐ์ ˆํ•˜๋Š” ํšจ๊ณผ๊ฐ€ ์žˆ์Œ์„ ํ™•์ธํ•˜์˜€๋‹ค๋Š” ์ ์—์„œ๋„ ๋ณธ ์—ฐ๊ตฌ์˜ ์ด๋ก ์  ์˜์˜๊ฐ€ ์žˆ๋‹ค. ์‹ค์ฒœ์  ์ธก๋ฉด์—์„œ, ๋ณธ ์—ฐ๊ตฌ๋Š” ๊ตฌ์„ฑ์›์—๊ฒŒ ์ง๋ฌด์ž์œจ์„ฑ์„ ๋ถ€์—ฌํ•  ๋•Œ ๊ตฌ์ฒด์ ์ธ ๋ฐฉํ–ฅ์„ฑ์„ ์ œ์‹œํ•ด์ค„ ์ˆ˜ ์žˆ๋‹ค๋Š” ์ ์—์„œ ์˜์˜๊ฐ€ ์žˆ๋‹ค. ์ฆ‰ ๋ณธ ์—ฐ๊ตฌ๋Š” ๊ณต๋ฌด์›์˜ ํ˜์‹ ํ–‰๋™์„ ํ™œ์„ฑํ™”ํ•˜๊ธฐ ์œ„ํ•ด ์ง๋ฌด์˜ ์ˆ˜ํ–‰ ๋ฐฉ๋ฒ•๊ณผ ํ‰๊ฐ€ ๊ธฐ์ค€์— ์ค‘์ ์„ ๋‘๊ณ  ์ง๋ฌด์ž์œจ์„ฑ์„ ๊ฐ•ํ™”ํ•  ํ•„์š”๊ฐ€ ์žˆ๋‹ค๋Š” ์ ์„ ์ œ์‹œํ•˜์˜€๋‹ค. ๋˜ํ•œ ๊ณต๋ฌด์› ๊ฐœ์ธ์˜ ์—ญ๋Ÿ‰๊ณผ ์š•๊ตฌ๋ฅผ ์ถฉ๋ถ„ํžˆ ๊ณ ๋ คํ•˜์—ฌ ์ ํ•ฉํ•œ ์ง๋ฌด๋ฅผ ๋ฐฐ์น˜ํ•  ๋•Œ ์ง๋ฌด์ž์œจ์„ฑ์ด ํ˜์‹ ํ–‰๋™์„ ๋” ํšจ๊ณผ์ ์œผ๋กœ ์ด๋Œ์–ด๋‚ผ ์ˆ˜ ์žˆ์Œ์„ ๋ณธ ์—ฐ๊ตฌ๋ฅผ ํ†ตํ•ด ์•Œ ์ˆ˜ ์žˆ๋‹ค. ๋‹ค๋งŒ ๋ณธ ์—ฐ๊ตฌ์—๋Š” ๋‹ค์Œ๊ณผ ๊ฐ™์€ ํ•œ๊ณ„๊ฐ€ ์กด์žฌํ•œ๋‹ค. ์ฒซ์งธ, ๋…๋ฆฝ๋ณ€์ˆ˜์ธ ๋ฐฉ๋ฒ•์ž์œจ์„ฑ, ์ผ์ •์ž์œจ์„ฑ, ๊ธฐ์ค€์ž์œจ์„ฑ์„ ๋‹จ์ผํ•ญ๋ชฉ์œผ๋กœ ์ธก์ •ํ•˜์˜€์œผ๋ฏ€๋กœ ์ธก์ •์˜ ํƒ€๋‹น์„ฑ์— ํ•œ๊ณ„๊ฐ€ ์žˆ๋‹ค. ๋‘˜์งธ, ๋ณธ ์—ฐ๊ตฌ๋Š” 2์ฐจ ์ž๋ฃŒ๋ฅผ ํ™œ์šฉํ•˜์˜€์œผ๋ฏ€๋กœ ๋ณ€์ˆ˜์˜ ๊ฐœ๋…์  ๊ตฌ์„ฑ์ด ์ •ํ™•ํ•˜์ง€ ์•Š์„ ์ˆ˜ ์žˆ๋‹ค๋Š” ํ•œ๊ณ„๊ฐ€ ์žˆ๋‹ค. ๋ณ€์ˆ˜๋ฅผ ์ธก์ •ํ•˜๊ธฐ ์œ„ํ•œ ์งˆ๋ฌธ์„ ์ง์ ‘ ์„ค์ •ํ•˜๋Š” ๋Œ€์‹  2์ฐจ ์ž๋ฃŒ์˜ ์งˆ๋ฌธ์„ ๋ณ€์ˆ˜์˜ ๊ฐœ๋…์ •์˜์— ์œ ์‚ฌํ•˜๊ฒŒ ๊ตฌ์„ฑํ•˜์˜€์œผ๋ฏ€๋กœ ์ธก์ •์ƒ ํ•œ๊ณ„๊ฐ€ ์กด์žฌํ•  ์ˆ˜ ์žˆ๋‹ค. ์…‹์งธ, ๋ณธ ์—ฐ๊ตฌ์˜ ๋ณ€์ˆ˜๋“ค์€ ๋™์ผ ์‹œ์ ์— ๋™์ผํ•œ ์„ค๋ฌธ์กฐ์‚ฌ๋กœ ์ธก์ •๋˜์—ˆ์œผ๋ฏ€๋กœ ๋™์ผ๋ฐฉ๋ฒ•ํŽธ์˜๊ฐ€ ์กด์žฌํ•˜์—ฌ ๋ณ€์ˆ˜ ๊ฐ„ ๊ด€๊ณ„๋ฅผ ์™œ๊ณกํ•  ์šฐ๋ ค๊ฐ€ ์žˆ๋‹ค. ๋”ฐ๋ผ์„œ ํ–ฅํ›„ ์—ฐ๊ตฌ์—์„œ๋Š” ์„ค๋ฌธ์กฐ์‚ฌ ์‹œ ์ต๋ช…์„ฑ์„ ์ฒ ์ €ํžˆ ๋ณด์žฅํ•˜๊ณ  ์„ค๋ฌธ ์‹œ์ ๊ณผ ์งˆ๋ฌธ ๊ฐ„ ์ˆœ์„œ๋ฅผ ๋‹ค๋ฅด๊ฒŒ ํ•˜๋Š” ๋“ฑ์˜ ์กฐ์น˜๋ฅผ ์ทจํ•  ํ•„์š”๊ฐ€ ์žˆ๊ณ , ๊ฐ๊ด€์ ์œผ๋กœ ๋ณ€์ˆ˜๋ฅผ ์ธก์ •ํ•  ์ˆ˜ ์žˆ๋Š” ์ง€ํ‘œ๋ฅผ ๋งˆ๋ จํ•ด์•ผ ํ•  ๊ฒƒ์ด๋‹ค.This study examined the effect of job autonomy on the public servantโ€™s innovative behavior, as well as moderating effect of person-organization fit(P-O fit) and person-job fit(P-J fit). Particularly, this study differentiated job autonomy into three facets, i.e., method, scheduling, and criteria, to investigate whether each aspect of job autonomy has a different effect on innovative behavior. With the multifaceted analysis of job autonomy, this study took into consideration the interaction of individuals, organizations, and jobs to have practical implications for increasing public servantsโ€™ innovative behavior. For the analysis, hierarchical regression was conducted with method autonomy, scheduling autonomy, and criteria autonomy as independent variables, public servantโ€™s innovative behavior as a dependent variable, P-O fit and P-J fit as moderating variables. โ€˜2020 Public Employee Perception Surveyโ€™ of the Korea Institute of Public Administration was utilized for analysis. The subject of this study was 4,339 public servants in central government and metropolitan government. The result of the analysis is as follows: first, method autonomy and criteria autonomy had a significant positive effect on innovative behavior. On the contrary, scheduling autonomy did not have a significant effect. Encouraging job autonomy on method and criteria increases innovative behavior, while scheduling autonomy itself is not likely to increase innovative behavior. This result implies that it is important to focus on job methods and criteria when promoting job autonomy for innovative behavior. Second, P-J fit had a weak moderating effect on the relationship between criteria autonomy and innovative behavior. The positive effect of criteria autonomy on innovative behavior was stronger for an individual with a high level of P-J fit than for an individual with a low level of P-J fit. In contrast, P-O fit did not have a moderating effect on the relationship between each autonomy and innovative behavior, nor did P-J fit on method autonomy and scheduling autonomy. This result implies that innovative behavior can be facilitated more effectively if an organization takes an individualโ€™s abilities and aptitude into account when encouraging autonomy of job criteria. Thus, an organization has to assign the right job to the right person with consideration of a personโ€™s abilities and aptitude. The theoretical and practical significance of this study is as follows: it is theoretically notable that this study, unlike precedent studies that evaluated job autonomy as a global construct, differentiated job autonomy into method, scheduling, and criteria, to prove each autonomy could have a different effect on innovative behavior. This study has also theoretical significance in that the moderating effect of P-J fit was confirmed between criteria autonomy and innovative behavior. In a practical sense, this study can suggest a detailed direction for how to give job autonomy. That is, this study shows the importance of emphasizing method and criteria autonomy when job autonomy is encouraged to increase innovative behavior. Furthermore, this study suggests that public servantsโ€™ innovative behavior can be increased by job autonomy more effectively when the right job is assigned to the right person with consideration of oneโ€™s abilities and need. Still, this study has limitations as follows: first, each independent variable was estimated with a single item, which is likely to compromise the validity of measurement. Second, the variables might not be exactly constructed due to the use of secondary data. This study did not measure variables directly but utilized questions from secondary data for measurement. Therefore, limitations of measurement might exist in this study. Third, because variables in this study were measured with the same questionnaire at the same time, the relationships among variables might be distorted by common method bias. Thus, future studies should implement measures to avoid common method bias. It is necessary to guarantee complete anonymity and conduct a survey at different time points or with different question sequences. Additionally, developing objective indicators is required in future studies.์ œ 1 ์žฅ ์„œ๋ก  1 ์ œ 1 ์ ˆ ์—ฐ๊ตฌ์˜ ๋ชฉ์  ๋ฐ ํ•„์š”์„ฑ 1 ์ œ 2 ์ ˆ ์—ฐ๊ตฌ์˜ ๋ฒ”์œ„ ๋ฐ ๋ฐฉ๋ฒ• 4 ์ œ 2 ์žฅ ์ด๋ก ์  ๊ฒ€ํ†  ๋ฐ ๊ฐ€์„ค 5 ์ œ 1 ์ ˆ ํ˜์‹ ํ–‰๋™ 5 1. ํ˜์‹ ํ–‰๋™์˜ ์ •์˜ 5 2. ํ˜์‹ ํ–‰๋™์— ๊ด€ํ•œ ์„ ํ–‰์—ฐ๊ตฌ 6 1) ๊ฐœ์ธ์  ์š”์ธ 7 2) ์กฐ์ง์  ์š”์ธ 10 3) ์ง๋ฌด์  ์š”์ธ 13 ์ œ 2 ์ ˆ ์ง๋ฌด์ž์œจ์„ฑ 21 1. ์ง๋ฌด์ž์œจ์„ฑ์˜ ๊ฐœ๋… 21 2. ์ง๋ฌด์ž์œจ์„ฑ์— ๊ด€ํ•œ ์„ ํ–‰์—ฐ๊ตฌ 22 ์ œ 3 ์ ˆ ๊ฐœ์ธ-์กฐ์ง ์ ํ•ฉ์„ฑ ๋ฐ ๊ฐœ์ธ-์ง๋ฌด ์ ํ•ฉ์„ฑ 28 1. ๊ฐœ์ธ-ํ™˜๊ฒฝ ์ ํ•ฉ์„ฑ์˜ ๊ฐœ๋… 28 2. ๊ฐœ์ธ-ํ™˜๊ฒฝ ์ ํ•ฉ์„ฑ์— ๊ด€ํ•œ ์„ ํ–‰์—ฐ๊ตฌ 29 ์ œ 4 ์ ˆ ๊ฐ€์„ค ์„ค์ • 36 1. ์„ ํ–‰์—ฐ๊ตฌ ๊ฒ€ํ† ์˜ ์‹œ์‚ฌ์  36 2. ์ง๋ฌด์ž์œจ์„ฑ๊ณผ ํ˜์‹ ํ–‰๋™ 37 3. ๊ฐœ์ธ-์กฐ์ง ์ ํ•ฉ์„ฑ ๋ฐ ๊ฐœ์ธ-์ง๋ฌด ์ ํ•ฉ์„ฑ์˜ ์กฐ์ ˆํšจ๊ณผ 39 1) ๊ฐœ์ธ-์กฐ์ง ์ ํ•ฉ์„ฑ์˜ ์กฐ์ ˆํšจ๊ณผ 40 2) ๊ฐœ์ธ-์ง๋ฌด ์ ํ•ฉ์„ฑ์˜ ์กฐ์ ˆํšจ๊ณผ 42 ์ œ 3 ์žฅ ์—ฐ๊ตฌ๋ฐฉ๋ฒ• 45 ์ œ 1 ์ ˆ ์—ฐ๊ตฌ๋Œ€์ƒ ๋ฐ ์ž๋ฃŒ 45 ์ œ 2 ์ ˆ ๋ณ€์ˆ˜ ์ธก์ • 48 1. ์ข…์†๋ณ€์ˆ˜: ํ˜์‹ ํ–‰๋™ 48 2. ๋…๋ฆฝ๋ณ€์ˆ˜: ๋ฐฉ๋ฒ•์ž์œจ์„ฑ, ์ผ์ •์ž์œจ์„ฑ, ๊ธฐ์ค€์ž์œจ์„ฑ 48 3. ์กฐ์ ˆ๋ณ€์ˆ˜: ๊ฐœ์ธ-์กฐ์ง ์ ํ•ฉ์„ฑ, ๊ฐœ์ธ-์ง๋ฌด ์ ํ•ฉ์„ฑ 50 4. ํ†ต์ œ๋ณ€์ˆ˜: ๊ฐœ์ธ์  ์š”์ธ, ์กฐ์ง์  ์š”์ธ 51 ์ œ 3 ์ ˆ ๋ถ„์„๋ฐฉ๋ฒ• 54 ์ œ 4 ์žฅ ์—ฐ๊ตฌ๊ฒฐ๊ณผ 56 ์ œ 1 ์ ˆ ๊ธฐ์ˆ ํ†ต๊ณ„ 56 1. ๋ณ€์ˆ˜์˜ ํ‰๊ท  ๋ฐ ํ‘œ์ค€ํŽธ์ฐจ 56 2. ๋ณ€์ˆ˜ ๊ฐ„ ์ƒ๊ด€๊ด€๊ณ„ 57 ์ œ 2 ์ ˆ ์ธก์ • ๋ชจํ˜•์˜ ๋ถ„์„ 59 1. ์ธก์ • ๋ชจํ˜•์˜ ์ ํ•ฉ๋„ 59 2. ์ธก์ • ๋ชจํ˜•์˜ ํƒ€๋‹น๋„ ๋ฐ ์‹ ๋ขฐ๋„ 60 1) ํƒ€๋‹น๋„ 60 2) ์‹ ๋ขฐ๋„ 62 ์ œ 3 ์ ˆ ๊ฐ€์„ค ๊ฒ€์ • 66 1. ์ง์ ‘ํšจ๊ณผ ๊ฒ€์ฆ 73 2. ์กฐ์ ˆํšจ๊ณผ ๊ฒ€์ฆ 74 1) ๊ฐœ์ธ-์กฐ์ง ์ ํ•ฉ์„ฑ์˜ ์กฐ์ ˆํšจ๊ณผ ๊ฒ€์ฆ 74 2) ๊ฐœ์ธ-์ง๋ฌด ์ ํ•ฉ์„ฑ์˜ ์กฐ์ ˆํšจ๊ณผ ๊ฒ€์ฆ 75 ์ œ 5 ์žฅ ๊ฒฐ๋ก  79 ์ œ 1 ์ ˆ ์—ฐ๊ตฌ๊ฒฐ๊ณผ์˜ ์š”์•ฝ 79 ์ œ 2 ์ ˆ ์—ฐ๊ตฌ์˜ ์˜์˜ ๋ฐ ํ•œ๊ณ„ 80 1. ์—ฐ๊ตฌ์˜ ์˜์˜ ๋ฐ ์‹œ์‚ฌ์  80 2. ์—ฐ๊ตฌ์˜ ํ•œ๊ณ„ 82 ์ฐธ๊ณ ๋ฌธํ—Œ 84 Abstract 96์„

    ๋ฐœ๋‹ฌ์žฅ์• ์ธ์˜ ์‚ฌํšŒ๋ณต์ง€์„œ๋น„์Šค ์ด์šฉ ์˜ˆ์ธก์š”์ธ

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    ํ•™์œ„๋…ผ๋ฌธ (์„์‚ฌ)-- ์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› : ์‚ฌํšŒ๋ณต์ง€ํ•™๊ณผ, 2012. 8. ๊ฐ•์ƒ๊ฒฝ.์žฅ์• ์ธ์˜ ์ž๋ฆฝ์ƒํ™œ ๋ฐ ์ฐจ๋ณ„๊ธˆ์ง€์™€ ๊ด€๋ จ๋œ ๋‚ด์šฉ์ด ๋ณด๋‹ค ํ™•๋Œ€๋˜๋Š” ๋“ฑ ์žฅ์• ์ธ์˜ ์„ ํƒ๊ณผ ์ž๊ธฐ๊ฒฐ์ •์„ ๊ฐ•์กฐํ•œ ํŒจ๋Ÿฌ๋‹ค์ž„์˜ ๋ณ€ํ™”๊ฐ€ ๋ฒ•์ œ๋„๋ฅผ ํ†ตํ•ด ๊ตฌ์ฒดํ™”๋˜๋ฉด์„œ, ๊ตญ๋‚ด ์žฅ์• ์ธ ๋ณต์ง€์„œ๋น„์Šค๋Š” ์ผ๋ฐฉ์ ์œผ๋กœ ์ฃผ์–ด์ง€๋Š” ๊ณต๊ธ‰์ž ์œ„์ฃผ์˜ ์ฒด์ œ์—์„œ ๋‹น์‚ฌ์ž๊ฐ€ ์„ ํƒํ•˜๊ณ  ๊ฒฐ์ •ํ•˜๋Š” ์ด์šฉ์ž ์ค‘์‹ฌ์˜ ์ฒด์ œ๋กœ ๋ณ€ํ™”ํ•ด์™”๋‹ค. ํ•˜์ง€๋งŒ ์ด๋Ÿฌํ•œ ์žฅ์• ์ธ ๋ณต์ง€ํŒจ๋Ÿฌ๋‹ค์ž„์˜ ๋ณ€ํ™”์™€ ์ œ๋„์ ์ธ ๋ฐœ์ „์ด ๋ฐœ๋‹ฌ์žฅ์• ์ธ์—๊ฒŒ๊นŒ์ง€ ์˜ํ–ฅ๋ ฅ์„ ๋ฏธ์นœ๋‹ค๊ณ  ๋ณด๊ธฐ๋Š” ์–ด๋ ต๋‹ค. ์ด๋Š” ๋ฐœ๋‹ฌ์žฅ์• ์ธ๋“ค์ด ๊ฐ–๋Š” ๊ธฐ๋ณธ์ ์ธ ์š•๊ตฌ๊ฐ€ ํŠน์ˆ˜ํ•จ์—๋„ ๋ถˆ๊ตฌํ•˜๊ณ , ๋‹ค์ˆ˜์˜ ๋ชฉ์†Œ๋ฆฌ๋ฅผ ๋‚ด์–ด์˜จ ์‹ ์ฒด ์žฅ์• ์ธ์— ๊ฐ€๋ ค์ ธ์˜จ ๊ฒฐ๊ณผ๋กœ ๋ณผ ์ˆ˜ ์žˆ๋‹ค. ์ „ ์ƒ์• ๊ธฐ๊ฐ„ ๋™์•ˆ ํƒ€์ธ์˜ ๋Œ๋ด„์„ ํ•„์š”๋กœ ํ•˜๋Š” ๋ฐœ๋‹ฌ์žฅ์• ์ธ์˜ ์š•๊ตฌ์— ๋ถ€์‘ํ•˜๋Š”๋ฐ ํ•„์ˆ˜์ ์ธ ์‚ฌํšŒ๋ณต์ง€์„œ๋น„์Šค ์ด์šฉ์— ๊ด€ํ•œ ํ•™์ˆ ์  ๊ด€์‹ฌ์ด ๋ถ€์กฑํ•œ ์‹ค์ •์— ๊ทธ ์‹ฌ๊ฐ์„ฑ์ด ๋”ํ•œ๋‹ค. ๋ณธ ์—ฐ๊ตฌ๋Š” ์•ค๋”์Šจ์˜ ์„œ๋น„์Šค์ด์šฉ ํ–‰๋™๋ชจํ˜•(Andersen, 1995Andersen & Newman, 1973)์„ ์ทจ์•ฝํ•œ ์ง‘๋‹จ์˜ ์„œ๋น„์Šค ์ด์šฉ์„ ์ดํ•ดํ•˜๊ณ  ๊ทธ ์˜ˆ์ธก์š”์ธ์„ ๊ฒ€์ฆํ•˜๋Š”๋ฐ ์ ์šฉ๋˜์–ด์˜จ ์ทจ์•ฝ๊ณ„์ธต์„ ์œ„ํ•œ ์„œ๋น„์Šค์ด์šฉ ํ–‰๋™๋ชจํ˜•(Gelberg, Andersen & Leake, 2000)์„ ๋ฐ”ํƒ•์œผ๋กœ, ๊ตญ๋‚ด ๋ฐœ๋‹ฌ์žฅ์• ์ธ์˜ ์‚ฌํšŒ๋ณต์ง€์„œ๋น„์Šค ์ด์šฉ ๋ฐ ์„œ๋น„์Šค ์ด์šฉ์— ์˜ํ–ฅ์„ ์ฃผ๋Š” ์š”์ธ์„ ํ†ตํ•ฉ์ ์ด๊ณ  ์ฒด๊ณ„์ ์œผ๋กœ ๊ฒ€์ฆํ•˜๋Š”๋ฐ ๋ชฉ์ ์ด ์žˆ๋‹ค. ๋ฐœ๋‹ฌ์žฅ์• ์ธ ๋‚ด์—์„œ๋„ ๋‹ค์–‘ํ•˜๊ฒŒ ์กด์žฌํ•˜๋Š” ๋ณต์ง€ ์š•๊ตฌ๋Š” ์„œ๋น„์Šค ์ด์šฉ ํ–‰๋™์„ ์‹คํ˜„ํ™”ํ•˜๋Š”๋ฐ ์žˆ์–ด์„œ ๋‹ค๋ฅธ ํ˜•์‹์œผ๋กœ ์˜ํ–ฅ์„ ๋ฏธ์น  ์ˆ˜ ์žˆ๋‹ค. ์ด์—, ๋ณธ ์—ฐ๊ตฌ์—์„œ๋Š” ํ•œ๋ฐœ ๋” ๋‚˜์•„๊ฐ€ ๋ฐœ๋‹ฌ์žฅ์• ์˜ ํ•˜์œ„ ์žฅ์• ์œ ํ˜•์— ์†ํ•˜๋Š” ์žํ์„ฑ์žฅ์• ์ธ๊ณผ ์ง€์ ์žฅ์• ์ธ์˜ ์‚ฌํšŒ๋ณต์ง€์„œ๋น„์Šค ์ด์šฉ์˜ ์ฐจ์ด, ๊ทธ๋ฆฌ๊ณ  ์ƒ์• ์ฃผ๊ธฐ์— ์žˆ์–ด์„œ ์„ฑ์ธ๊ธฐ ์ด์ „๊ณผ ์„ฑ์ธ๊ธฐ์— ์žˆ๋Š” ๋ฐœ๋‹ฌ์žฅ์• ์ธ์˜ ์‚ฌํšŒ๋ณต์ง€์„œ๋น„์Šค ์ด์šฉ์˜ ์ฐจ์ด์— ๋Œ€ํ•ด์„œ ํƒ์ƒ‰ํ•˜์˜€๋‹ค. ๋ณธ ์—ฐ๊ตฌ์˜ ๋ชฉ์ ์„ ๋‹ฌ์„ฑํ•˜๊ธฐ ์œ„ํ•ด ์„ค์ •ํ•œ ์—ฐ๊ตฌ๋ฌธ์ œ๋Š” ๋‹ค์Œ๊ณผ ๊ฐ™๋‹ค. ์ฒซ์งธ, ๋ฐœ๋‹ฌ์žฅ์• ์ธ์˜ ์‚ฌํšŒ๋ณต์ง€์„œ๋น„์Šค ์ด์šฉ์˜ ์˜ˆ์ธก์š”์ธ(์„ ํ–‰์š”์ธ, ์ž์›์š”์ธ, ์š•๊ตฌ์š”์ธ)์€ ๋ฌด์—‡์ธ๊ฐ€? ๋‘˜์งธ, ์‚ฌํšŒ๋ณต์ง€์„œ๋น„์Šค ์ด์šฉ์˜ ์˜ˆ์ธก์š”์ธ์€ ๋ฐœ๋‹ฌ์žฅ์•  ํ•˜์œ„์œ ํ˜•(์ง€์ ์žฅ์•  ๋ฐ ์žํ์„ฑ์žฅ์• ) ๊ฐ„์— ์ฐจ์ด๊ฐ€ ์žˆ๋Š”๊ฐ€? ์…‹์งธ, ์‚ฌํšŒ๋ณต์ง€์„œ๋น„์Šค ์ด์šฉ์˜ ์˜ˆ์ธก์š”์ธ์€ ์„ฑ์ธ๊ธฐ ์ด์ „์˜ ๋ฐœ๋‹ฌ์žฅ์• ์ธ๊ณผ ์„ฑ์ธ๊ธฐ์˜ ๋ฐœ๋‹ฌ์žฅ์• ์ธ ๊ฐ„์— ์ฐจ์ด๊ฐ€ ์žˆ๋Š”๊ฐ€? ๋ณธ ์—ฐ๊ตฌ์—์„œ๋Š” ์—ฐ๊ตฌ๋ฌธ์ œ ๋ฐ ๊ฐ€์„ค์˜ ๊ฒ€์ •์„ ์œ„ํ•˜์—ฌ ๋ณด๊ฑด๋ณต์ง€๋ถ€์—์„œ ์‹œํ–‰ํ•œ 2011๋…„ ๋ฐœ๋‹ฌ์žฅ์• ์ธ ํ™œ๋™์ง€์› ๋“ฑ์„ ์œ„ํ•œ ์š•๊ตฌ์กฐ์‚ฌ ๋ฐ ์ •์ฑ…๊ณผ์ œ ์ˆ˜๋ฆฝ์—ฐ๊ตฌ๋ฅผ ํ†ตํ•ด ๋ฐœ๋‹ฌ์žฅ์• ์ธ์˜ ๋ณดํ˜ธ์ž 1,500๋ช…์„ ๋Œ€์ƒ์œผ๋กœ ์ˆ˜์ง‘๋œ ๋ฐœ๋‹ฌ์žฅ์• ์ธ ์‹คํƒœ์กฐ์‚ฌ์˜ ์ด์ฐจ ์ž๋ฃŒ๋ฅผ ํ™œ์šฉํ•˜์˜€๋‹ค. ์—ฐ๊ตฌ์ž๋ฃŒ์˜ ๋ฐœ๋‹ฌ์žฅ์•  ํ•˜์œ„์žฅ์•  ์œ ํ˜•๋ณ„ ๊ตฌ์„ฑ์€ ์ง€์ ์žฅ์• ๊ฐ€ 80.4%(1206๋ช…), ์žํ์„ฑ์žฅ์• ๊ฐ€ 19.6%(294๋ช…)์ด๋‹ค. 3-77์„ธ์— ๊ฑธ์นœ ๋ฐœ๋‹ฌ์žฅ์• ์ธ ํ‘œ๋ณธ์˜ 33%(495๋ช…)๊ฐ€ ์„ฑ์ธ๊ธฐ ์ด์ „์˜ ์ƒ์• ์ฃผ๊ธฐ์— ์žˆ์œผ๋ฉฐ 77%(1005๋ช…)๊ฐ€ ์„ฑ์ธ๊ธฐ์— ์žˆ์—ˆ๋‹ค. ๊ธฐ์ดˆ ๊ธฐ์ˆ ํ†ต๊ณ„ ๋ถ„์„์„ ์œ„ํ•ด SPSS 19.0 ํ†ต๊ณ„ํŒจํ‚ค์ง€๋ฅผ ์‚ฌ์šฉํ•˜์˜€๊ณ , AMOS 18.0์„ ํ™œ์šฉํ•˜์—ฌ ๊ตฌ์กฐ๋ฐฉ์ •์‹ ๋ชจํ˜•์„ ํ†ตํ•ด์„œ ๋ฐœ๋‹ฌ์žฅ์• ์ธ์˜ ์‚ฌํšŒ๋ณต์ง€์„œ๋น„์Šค ์ด์šฉ์˜ ์˜ˆ์ธก์š”์ธ์„ ๊ฒ€์ฆํ•˜์˜€๊ณ , ๋‹ค์ค‘์ง‘๋‹จ๋ถ„์„์„ ํ†ตํ•ด ์„œ๋น„์Šค ์ด์šฉ ์˜ˆ์ธก๊ฒฝ๋กœ์—์„œ ๋ฐœ๋‹ฌ์žฅ์•  ํ•˜์œ„์œ ํ˜• ๊ฐ„, ์—ฐ๋ น ์ง‘๋‹จ ๊ฐ„์— ์–ด๋– ํ•œ ์ฐจ์ด๋ฅผ ๋ณด์ด๋Š”์ง€๋ฅผ ํƒ์ƒ‰ํ•˜์˜€๋‹ค. ์ฃผ์š”ํ•œ ์—ฐ๊ตฌ๊ฒฐ๊ณผ๋ฅผ ์‚ดํŽด๋ณด๋ฉด ๋‹ค์Œ๊ณผ ๊ฐ™๋‹ค. ์ฒซ์งธ, ์„ ํ–‰์š”์ธ ์ค‘์—์„œ๋Š” ์—ฐ๋ น์ด, ์ž์›์š”์ธ ๊ฐ€์šด๋ฐ๋Š” ๊ฐ€์กฑ๊ณผ์˜ ๋™๊ฑฐ ์—ฌ๋ถ€์™€ ์žฅ์• ์ธ ์นœ๊ตฌ๋กœ๋ถ€ํ„ฐ์˜ ์‚ฌํšŒ์  ์ง€์ง€๊ฐ€, ์š•๊ตฌ์š”์ธ์—์„œ๋Š” ์„œ๋น„์Šค์— ๋Œ€ํ•œ ์ธ์ง€๋œ ์š•๊ตฌ์™€ ๋ฐœ๋‹ฌ์žฅ์•  ํ•˜์œ„์œ ํ˜•, ๊ทธ๋ฆฌ๊ณ  ์žฅ์• ๋“ฑ๊ธ‰์ด ๋ฐœ๋‹ฌ์žฅ์• ์ธ์˜ ์‚ฌํšŒ๋ณต์ง€์„œ๋น„์Šค ์ด์šฉ์„ ์œ ์˜ํ•˜๊ฒŒ ์˜ˆ์ธกํ•˜๋Š” ์š”์ธ์œผ๋กœ ๋‚˜ํƒ€๋‚ฌ๋‹ค. ์ฆ‰, ๋ฐœ๋‹ฌ์žฅ์• ์ธ์˜ ์—ฐ๋ น์ด ์–ด๋ฆฌ๊ณ , ๊ฐ€์กฑ๊ณผ ๋™๊ฑฐํ•˜๊ณ  ์žˆ์œผ๋ฉฐ, ์žฅ์• ์ธ ์นœ๊ตฌ์™€ ์‚ฌํšŒ์  ๊ด€๊ณ„๋ฅผ ํ˜•์„ฑํ•˜๊ณ  ์žˆ์œผ๋ฉฐ, ์„œ๋น„์Šค์— ๋Œ€ํ•œ ์ธ์ง€๋œ ์š•๊ตฌ๊ฐ€ ๋†’๊ณ , ์ง€์ ์žฅ์• ๋ณด๋‹ค ์žํ์„ฑ์žฅ์• ๋ฅผ ๊ฐ€์ง€๊ณ  ์žˆ๋Š” ๊ฒฝ์šฐ์—, ๊ทธ๋ฆฌ๊ณ  ์žฅ์•  ๋“ฑ๊ธ‰์ด ๋†’์„์ˆ˜๋ก (์žฅ์• ์ •๋„๊ฐ€ ์‹ฌํ• ์ˆ˜๋ก) ์‚ฌํšŒ๋ณต์ง€์„œ๋น„์Šค๋ฅผ ๋ณด๋‹ค ๋” ๋งŽ์ด ์ด์šฉํ•˜๋Š” ๊ฒƒ์œผ๋กœ ํ™•์ธ๋˜์—ˆ๋‹ค. ๋‘˜์งธ, ๋ฐœ๋‹ฌ์žฅ์•  ํ•˜์œ„์œ ํ˜• ๊ฐ„ ์‚ฌํšŒ๋ณต์ง€์„œ๋น„์Šค ์ด์šฉ์˜ ์˜ˆ์ธก์š”์ธ์€ ์žํ์„ฑ์žฅ์• ์ธ๊ณผ ์ง€์ ์žฅ์• ์ธ ๊ฐ„์— ๊ตญ๋ฏผ๊ธฐ์ดˆ์ƒํ™œ๋ณด์žฅ ์ˆ˜๊ธ‰ ์—ฌ๋ถ€, ์„œ๋น„์Šค์— ๋Œ€ํ•œ ์ธ์ง€๋œ ์š•๊ตฌ, ์žฅ์• ์œ ํ˜•์˜ ๊ฒฝ๋กœ์—์„œ ์œ ์˜ํ•˜๊ฒŒ ์ฐจ์ด๊ฐ€ ์žˆ๋Š” ๊ฒƒ์œผ๋กœ ๋‚˜ํƒ€๋‚ฌ๋‹ค. ์…‹์งธ, ์—ฐ๋ น ์ง‘๋‹จ ๊ฐ„ ์‚ฌํšŒ๋ณต์ง€์„œ๋น„์Šค ์ด์šฉ์˜ ์˜ˆ์ธก์š”์ธ์€ ์„ฑ์ธ๊ธฐ ์ด์ „์˜ ๋ฐœ๋‹ฌ์žฅ์• ์ธ๊ณผ ์„ฑ์ธ๊ธฐ์˜ ๋ฐœ๋‹ฌ์žฅ์• ์ธ ๊ฐ„์— ์—ฐ๋ น, ๊ต์œก, ์ด์ค‘์ง„๋‹จ์˜ ๊ฒฝ๋กœ์—์„œ ์œ ์˜ํ•˜๊ฒŒ ์ฐจ์ด๊ฐ€ ์žˆ๋Š” ๊ฒƒ์œผ๋กœ ๋‚˜ํƒ€๋‚ฌ๋‹ค. ์ด์ƒ์˜ ์—ฐ๊ตฌ๊ฒฐ๊ณผ๋ฅผ ํ†ตํ•ด ๋ณธ ์—ฐ๊ตฌ๋Š” ๊ตญ๋‚ด์—์„œ ์ตœ์ดˆ๋กœ ๋ฐœ๋‹ฌ์žฅ์• ์ธ์„ ๋Œ€์ƒ์œผ๋กœ ์ทจ์•ฝ๊ณ„์ธต์„ ์œ„ํ•œ ์„œ๋น„์Šค์ด์šฉ ํ–‰๋™๋ชจํ˜•์„ ์ ์šฉํ•˜์—ฌ ๊ทธ๋“ค์˜ ์‚ฌํšŒ๋ณต์ง€์„œ๋น„์Šค ์ด์šฉ์˜ ์˜ˆ์ธก์š”์ธ์„ ํ†ตํ•ฉ์ ์ด๊ณ  ์ฒด๊ณ„์ ์œผ๋กœ ๊ฒ€์ฆ ๊ฐ€๋Šฅํ•˜๋‹ค๋Š” ๊ฒƒ์„ ๋ณด์—ฌ์ฃผ์—ˆ๋‹ค. ์ด๋Š” ์„œ๋น„์Šค์ด์šฉ ํ–‰๋™๋ชจํ˜•์˜ ์ ์šฉ ๋Œ€์ƒ์˜ ํ™•๋Œ€๋ฅผ ์‹œ๋„ํ•˜์˜€๋‹ค๋Š” ์ ์—์„œ ์˜์˜๋ฅผ ๊ฐ€์ง„๋‹ค. ๊ทธ๋ฆฌ๊ณ  ๋ฐœ๋‹ฌ์žฅ์• ์ธ์˜ ํ•˜์œ„ ๋ฒ”์ฃผ์— ์†ํ•˜๋Š” ์ง€์ ์žฅ์• ์ธ๊ณผ ์žํ์„ฑ์žฅ์• ์ธ, ๊ทธ๋ฆฌ๊ณ  ์„ฑ์ธ๊ธฐ ์ด์ „๊ณผ ์„ฑ์ธ๊ธฐ ๋ฐœ๋‹ฌ์žฅ์• ์ธ์˜ ์‚ฌํšŒ๋ณต์ง€์„œ๋น„์Šค ์ด์šฉ ๋ฐ ์˜ˆ์ธก ์š”์ธ์˜ ์ฐจ์ด๋ฅผ ์‚ดํŽด๋ด„์œผ๋กœ์จ ๋‹ค์ฑ„๋กœ์šด ์š•๊ตฌ์— ์œ ์—ฐํ•˜๊ฒŒ ๋ถ€ํ•ฉํ•˜๋Š” ์‚ฌํšŒ๋ณต์ง€์„œ๋น„์Šค ๊ฐœ๋ฐœ ๋ฐ ์ „๋‹ฌ์— ์‹ค์ฆ์ ์ธ ๊ทผ๊ฑฐ๋ฅผ ์ œ๊ณตํ•˜์˜€๋‹ค. ๋ณธ ์—ฐ๊ตฌ์—์„œ๋Š” ์ „๊ตญ๋Œ€ํ‘œ ๋ฐœ๋‹ฌ์žฅ์• ์ธํ‘œ๋ณธ์„ ํ™œ์šฉํ•˜์—ฌ ์—ฐ๊ตฌ์˜ ๊ฒฐ๊ณผ๋ฅผ ์ผ๋ฐ˜ํ™”ํ•  ์ˆ˜ ์žˆ์–ด, ํ–ฅํ›„ ๋ฐœ๋‹ฌ์žฅ์• ์ธ ์‚ฌํšŒ๋ณต์ง€์„œ๋น„์Šค ๊ด€๋ จ ์—ฐ๊ตฌ ๋ฐ ์ •์ฑ… ์ˆ˜๋ฆฝ์˜ ๊ทผ๊ฐ„์„ ์ œ๊ณตํ•˜์˜€๋‹ค๋Š” ์ ์—์„œ ์˜์˜๊ฐ€ ํฌ๋‹ค. ๋ณธ ์—ฐ๊ตฌ๋Š” ์ „๊ตญ ๋“ฑ๋ก์žฅ์• ์ธ ํ˜„ํ™ฉ ๋ช…๋ถ€๋ฅผ ๋ชจ์ง‘๋‹จ์œผ๋กœ ์ทจํ•œ ๋ฐœ๋‹ฌ์žฅ์• ์ธ ์‹คํƒœ์กฐ์‚ฌ์˜ ์ž๋ฃŒ๋ฅผ ํ™œ์šฉํ•˜์—ฌ, ๋น„๋“ฑ๋ก ์žฅ์• ์ธ์„ ๊ณ ๋ คํ•˜์ง€ ๋ชปํ•˜๊ณ  ์žˆ๋‹ค. ์—ฌ๋Ÿฌ ์žฅ์• ์œ ํ˜• ์ค‘์—์„œ๋„ ํŠนํžˆ ์ž๊ธฐํ‘œํ˜„ ๋ฐ ์ž๊ธฐ๊ฒฐ์ • ๋Šฅ๋ ฅ์— ์ œํ•œ์„ ๋ณด์ด๋Š” ๋ฐœ๋‹ฌ์žฅ์• ์ธ์˜ ์‚ฌํšŒ๋ณต์ง€์„œ๋น„์Šค ์ด์šฉ์€ ๋ณดํ˜ธ์ž ์š”์ธ์„ ํ†ตํ•ด ์œ ์˜ํ•˜๊ฒŒ ์„ค๋ช…๋˜๋Š” ๋ถ€๋ถ„์ด ์žˆ๋‹ค. ๋˜ํ•œ ์ง€์—ญ์‚ฌํšŒ ์ž์›๋ณ€์ˆ˜ ๋“ฑ์˜ ๊ณต๊ธ‰์ž ์š”์ธ ๋˜ํ•œ ์ด์šฉ์ž ์š”์ธ๊ณผ ๋”๋ถˆ์–ด ์ทจ์•ฝ๊ณ„์ธต์ด ๊ณต์ ์ธ ๋Œ๋ด„ ์„œ๋น„์Šค๋ฅผ ์ด์šฉํ•˜๋Š”๋ฐ ์˜ํ–ฅ์„ ๋ฏธ์น  ๊ฒƒ์ด๋‹ค. ๊ทธ๋Ÿฌ๋‚˜ ์ด ์—ญ์‹œ ์ด์ฐจ์ž๋ฃŒ์˜ ํ•œ๊ณ„๋กœ ๋ณธ ์—ฐ๊ตฌ๋ชจํ˜•์— ํฌํ•จํ•˜์ง€ ๋ชปํ•˜์˜€๋‹ค. ์–‘์  ๋ฐ ์งˆ์  ํ˜•ํ‰์„ฑ์„ ๊ธฐ๋ฐ˜ํ•œ ์‚ฌํšŒ๋ณต์ง€์„œ๋น„์Šค ์ œ๊ณต๋ฐฉ์•ˆ์„ ๋„์ถœํ•˜๋Š” ๊ทผ๊ฑฐ๋ฅผ ๋งˆ๋ จํ•  ์ˆ˜ ์žˆ๋„๋ก ํ›„์†์—ฐ๊ตฌ์—์„œ๋Š” ์„œ๋น„์Šค ์ด์šฉํ–‰๋™์˜ ์งˆ์ ์ธ ์ฐจ์›์„ ์ธก์ •ํ•˜๋Š” ๋“ฑ ๋ณด๋‹ค ๊ตฌ์กฐํ™”๋œ ์„ค๋ฌธ์ง€๋ฅผ ๊ตฌ์„ฑํ•˜์—ฌ ๋ฐœ๋‹ฌ์žฅ์• ์ธ์˜ ์‚ฌํšŒ๋ณต์ง€์„œ๋น„์Šค ์ด์šฉ์„ ์ดํ•ดํ•ด์•ผ ํ•  ๊ฒƒ์ด๋‹ค. ๋˜ํ•œ, ์ข…๋‹จ์  ์ ‘๊ทผ์„ ํ†ตํ•ด ์‚ฌํšŒ์  ์ง€์ง€ ๋ฐ ์ธ์ง€๋œ ์š•๊ตฌ ๋“ฑ์˜ ์‹œ๋ณ€๋ณ€์ˆ˜๋“ค์˜ ํŠน์„ฑ์„ ๊ฐ์•ˆํ•œ ๋ณด๋‹ค ํƒ€๋‹นํ•œ ๊ฒฐ๊ณผ๋ฅผ ์ด๋Œ์–ด ๋‚ผ ์ˆ˜ ์žˆ๊ธฐ๋ฅผ ๊ธฐ๋Œ€ํ•œ๋‹ค. ์ฃผ์š”์–ด: ๋ฐœ๋‹ฌ์žฅ์• ์ธ, ์‚ฌํšŒ๋ณต์ง€์„œ๋น„์Šค ์ด์šฉ, ์ทจ์•ฝ๊ณ„์ธต์„ ์œ„ํ•œ ์„œ๋น„์Šค์ด์šฉ ํ–‰๋™๋ชจํ˜•, ๋ฐœ๋‹ฌ์žฅ์•  ํ•˜์œ„์œ ํ˜• ๋ฐ ์—ฐ๋ น ์ง‘๋‹จ ๊ฐ„ ์ฐจ์ด, ๊ตฌ์กฐ๋ฐฉ์ •์‹, ๋‹ค์ค‘์ง‘๋‹จ๋ถ„์„ ํ•™ ๋ฒˆ: 2010-20129The paradigm of self-determination has influenced the institutionalized system of support for individuals with disabilities, reflecting the voice of the target population in the design and direction of policies and prohibiting disability-based discrimination in all areas of society. Under this new paradigm, individuals with disabilities take more responsibility for adjusting the necessary services to their needs. The new arrangement, however, is unlikely to provide the same amount of benefits for individuals with developmental disabilities. This might be the result of the fact that the arrangement was primarily focused on individuals with physical disabilities, thereby alienating individuals with developmental disabilities with other particular needs. Individuals with developmental disabilities have a continuous need for social welfare services and support as they are limited in their capacity for self-care. However, little attention has been paid to this vulnerable population both in practice and policy. Much less research has examined the utilization of social welfare services that address the need for support among individuals with developmental disabilities throughout their lives. The purpose of the present study was to investigate the relationship between the social welfare service use and the predisposing, enabling, and need factors among individuals with developmental disabilities in Korea. This study adopted the Gelberg-Andersen Behavioral Model for Vulnerable Populations (Gelberg, Andersen & Leake, 2000) as a theoretical framework to guarantee a comprehensive, systematic approach in identifying contributing factors associated with their service use. A population referred to as individuals with developmental disabilities does not necessarily share common properties, which might affect them differently in actualizing the service use. Accordingly, this study further explored variations in the relationships among contributing factors and the social welfare service use by subordinate type of developmental disabilities and age group among individuals with developmental disabilities. Based on the objectives of the research, the current study aimed to answer the following questions: 1. What are the predisposing, enabling, and need factors predicting the social welfare service utilization among individuals with developmental disabilities? 2. Do the factors that predict the social welfare service utilization of individuals with developmental disabilities vary by subordinate type of developmental disabilities, i.e., autistic disorders and intellectual disabilities? 3. Do the factors that predict the social welfare service utilization of individuals with developmental disabilities vary by age group, i.e., minors below the age of 19 and adults 19 and over? In order to empirically examine the research hypotheses, the present study employed secondary data from the 2011 Policy Design on Supporting People with Developmental Disabilities and Their Families through Need Assessment and Field Research, Koreas first nationally representative survey on individuals with developmental disabilities. Under the supervision of Ministry of Health and Welfare, the data were collected by one-on-one, face-to-face interviews with caregivers of individuals with developmental disabilities. The sample comprised 1,500 individuals with developmental disabilities in all ages ranging from 3 to 77those below the age of 19 accounted for 33% (495 cases) and those 19 and older for 67% (1,005 cases). As for subordinate types of individuals with developmental disabilities, 80.4% had intellectual disabilities (1,206 cases) and 19.6% had autistic disorders (294 cases). Descriptive analysis was conducted using the SPSS 19.0 statistical package. Structural equation modeling was performed to examine contributing factors that affect the social welfare service use among individuals with developmental disabilities, and multi-group analysis was conducted to explore within-group variations in predictors of the use of those services, using AMOS 18.0 software. The major findings of the current research are as follows. First, age was identified as a predisposing factor significantly associated with the social welfare service utilization among individuals with developmental disabilities. Living condition and informal social support from individuals with disabilities were significant enabling factors predicting the service use. Perceived need for care, subordinate types of developmental disabilities, and disability grade were important need factors in their access to the community-based care system. This indicates that younger age, living with family, having social relationships with friends with disabilities, more needs for formal support, autistic disorders, and more severe levels of disabilities were associated with the use of more care services among individuals with developmental disabilities in Korea. Second, the findings of the data analysis using multi-group structural equation modeling indicated significant variations in the paths that determine the social welfare service utilization across subordinate types of developmental disabilities. Particular paths, including national basic livelihood security recipient status, perceived need for services and disability grade, were found to be responsible for group variations. Third, significant variations in the paths determining the use of formal care services across age groups were identified. Specifically, there were age group differences in size or direction of influence of age, education, and dual diagnosis on the service use by individuals with developmental disabilities. The present study was the first attempt to adopt the Gelberg-Andersen Behavioral Model for Vulnerable Populations (Gelberg et al., 2000) as a theoretical framework to guarantee a holistic and systematic approach in identifying contributing factors predicting the social welfare service utilization among individuals with developmental disabilities in Korea. As demonstrated in the research findings, the present study validated the applicability of the behavioral model of service utilization to this vulnerable population. In addition, this study provided empirical evidence for the development and provision of social welfare services tailored to address the varied, particular needs of the target population by exploring variations in the factors that affect the service utilization across individuals with autistic disorders and individuals with intellectual disabilities, as well as minors and adults with developmental disabilities. The findings of the current research carry significant implications for building an empirical foundation for future research and related policy designs for social welfare services for individuals with developmental disabilities in Korea, as the study utilized a nationally representative sample of the vulnerable population and thus achieved high generalizablity. The current research used secondary data from the Survey on Individuals with Developmental Disabilities, which was based on registered individuals with developmental disabilities in the National Disability Registration Database, so that unregistered individuals were not taken into consideration. The use of social welfare services among individuals with developmental disabilities is likely determined by caregivers in terms of the nature of disabilities, such as a lack of self-determination skills in the process of managing independent living. Also, the service provision factor in the structural domain is expected to significantly predict their access to formal care services. Due to the limitation of employing secondary data, these factors could not be included in the research model. Future research might provide more validated findings by employing more structured, specified survey items. Through a longitudinal approach, future research would be able to estimate time-varying effects of contributing factors that predict the use of social welfare services. Keywords: Individuals with Developmental Disabilities, Social Welfare Service Utilization, the Gelberg-Andersen Behavioral Model for Vulnerable Populations, Variations by Subordinate Type of Developmental Disabilities and Age Group, Structural Equation Modeling, Multi-Group Analysis Student Number: 2010-20129CHAPTER 1. INTRODUCTION 1 1.1 Problem Statement 1 1.2 Research Questions 4 CHAPTER 2. INDIVIDUALS WITH DEVELOPMENTAL DISABILITIES AND RELATED SOCIAL WELFARE SERVICES 6 2.1. Individuals with Developmental Disabilities 6 2.2. Social Welfare Services for Individuals with Developmental Disabilities 11 CHATPER 3. THEORETICAL BACKGROUND 15 3.1. Theoretical Framework 15 3.2. The Andersen Behavioral Model in Social Welfare Service Research 20 CHATPER 4. LITERATURE REVIEW 24 4.1. Literature on Social Welfare Service Utilization of Individuals with Developmental Disabilities 24 4.2. Factors Associated with Social Welfare Service Utilization among Individuals with Developmental Disabilities 28 4.2.1. Predisposing Factors 28 4.2.2. Enabling Factors 30 4.2.3. Need Factors 32 CHATPER 5. RESEARCH MODEL AND HYPOTHESES 35 5.1. Research Model 35 5.2. Research Hypotheses 36 CHATPER 6. RESEARCH METHOD 39 6.1. Research Procedure and Sampling 39 6.2. Measurement of the Variables 41 6.2.1. Endogenous Variable: Social Welfare Service Utilization Behavior 41 6.2.2. Exogenous Variables 41 6.3. Data Analysis Procedures 47 CHATPER 7. RESEARCH FINDINGS 51 7.1. Descriptive Statistics of the Study Variables 51 7.2. Data Examination 57 7.3. Structural Equation Modeling for Social Welfare Service Utilization 60 7.4. Multi-group Structural Equation Modeling 65 7.4.1. Examining Variations by Subordinate Type of Developmental Disabilities in Social Welfare Service Utilization 65 7.4.2. Examining Variations by Age Group in Social Welfare Service Utilization 69 CHATPER 8. CONCLUSION 74 8.1. Summary of Findings 74 8.1.1. The Relationships among Predisposing, Enabling, Need Factors and Service Utilization Behavior 76 8.1.2. Variations by Subordinate Type of Developmental Disabilities in Social Welfare Service Utilization 78 8.1.3. Variations by Age Group in Social Welfare Service Utilization 79 8.2. Discussions 80 8.3. Research Implications 84 8.3.1. Theoretical Implications 84 8.3.2. Practice and Policy Implications 85 8.4. Limitation of the Study and Directions for Future Study 89Maste
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