144 research outputs found

    ๊ธฐ์—…์žฌ๋ฌด์— ๊ด€ํ•œ ์—ฐ๊ตฌ

    Get PDF
    ํ•™์œ„๋…ผ๋ฌธ(๋ฐ•์‚ฌ) -- ์„œ์šธ๋Œ€ํ•™๊ต๋Œ€ํ•™์› : ๊ฒฝ์˜๋Œ€ํ•™ ๊ฒฝ์˜ํ•™๊ณผ, 2021.8. ์ตœ์œค์˜.This thesis consists of two essays in corporate finance: distress resolution under concentrated equity ownership and the effect of the amount of analyst coverage on corporate innovation. The first essay examines the distress resolution mechanism under concentrated equity ownership and concentrated bank debt. Using a comprehensive sample of private and public Korean firms that entered distress between 2000 and 2019, we find that distress resolution is more likely when private placements of new equities are accompanied by a change in control. This effect is more pronounced when a large business-group-member firm is taken over. These findings suggest that equity capital injection and monitoring by the new controlling shareholder may be the key determinants of distress resolution under poor investor protection, which further explains why concentration of ownership may persist over time under such an environment. The second essay examines whether analyst coverage affects firm innovation in an economy characterized by family-controlled business groups. Using a sample of Korean publicly traded firms from 2010 to 2018, the second essay finds that an increase in analyst coverage leads covered firms to cut not only R&D, but also investments in corporate venture capital. The reduction in innovation efforts also occurs when analysts are from other brokerages affiliated with chaebols (family-controlled large business group). These findings suggest that, when the information environment is less transparent, unlike in the U.S., analyst coverage in Korea may function more as a pressure mechanism than an information mechanism.๋ณธ ์—ฐ๊ตฌ๋Š” ๊ธฐ์—…์˜ ์žฌ๋ฌด์  ๊ณค๊ฒฝ, ์• ๋„๋ฆฌ์ŠคํŠธ ์ปค๋ฒ„๋ฆฌ์ง€์™€ ๊ธฐ์—…ํ˜์‹  ๋“ฑ ๊ธฐ์—…์žฌ๋ฌด์— ๊ด€ํ•œ 2๊ฐœ์˜ ์†Œ๋…ผ๋ฌธ์œผ๋กœ ๊ตฌ์„ฑ๋˜์–ด ์žˆ๋‹ค. ์ฒซ ๋ฒˆ์งธ ๋…ผ๋ฌธ์—์„œ๋Š” ์ง‘์ค‘๋œ ์ฃผ์‹์˜ ์†Œ์œ ์™€ ์ง‘์ค‘๋œ ์€ํ–‰ ์ฑ„๋ฌด์— ๋Œ€ํ•œ ์˜์กด๋„๊ฐ€ ๋†’์€ ๊ตญ๋‚ด ํ™˜๊ฒฝ์—์„œ ๊ธฐ์—…์˜ ์„ฑ๊ณต์ ์ธ ๊ตฌ์กฐ์กฐ์ •์„ ์ด๋„๋Š” ์š”์ธ์„ ๋ถ„์„ํ•œ๋‹ค. ์žฌ๋ฌด์  ๊ณค๊ฒฝ์— ์ฒ˜ํ•œ ์™ธ๊ฐ ๊ธฐ์—…์„ ๋Œ€์ƒ์œผ๋กœ ๋ถ„์„ํ•œ ๊ฒฐ๊ณผ, ์„ฑ๊ณต์ ์ธ ๊ตฌ์กฐ ์กฐ์ •์„ ์œ„ํ•ด์„œ๋Š” ์ตœ๋Œ€ ์ฃผ์ฃผ ๋ณ€๊ฒฝ์„ ๋™๋ฐ˜ํ•œ ์ œ 3์ž ๋ฐฐ์ • ์‹ ์ฃผ ๋ฐœํ–‰์ด ์ˆ˜๋ฐ˜๋˜์–ด์•ผ ํ•˜๋Š” ๊ฒƒ์œผ๋กœ ๋‚˜ํƒ€๋‚ฌ๋‹ค. ์ด๋Ÿฌํ•œ ๊ฒฐ๊ณผ๋Š” ํ”ผ์ธ์ˆ˜๊ธฐ์—…์ด ๋Œ€๊ธฐ์—… ์ง‘๋‹จ์— ์†ํ•  ๋•Œ ๋” ๋šœ๋ ทํ•˜๊ฒŒ ๋‚˜ํƒ€๋‚จ์„ ํ™•์ธํ•˜์˜€๋‹ค. ๋˜ํ•œ ์ด๋Ÿฌํ•œ ํ˜„์ƒ์€ Cox ์œ„ํ—˜ ๋ชจํ˜• (Cox regression)๊ณผ ๋‚ด์ƒ์„ฑ์„ ๊ฐ์•ˆํ•œ ๋งค์นญ ๋ถ„์„ ๊ฒฐ๊ณผ๋ฅผ ํ†ตํ•ด์„œ๋„ ํ™•์ธ๋˜์—ˆ๋‹ค. ๋ณธ ์—ฐ๊ตฌ ๊ฒฐ๊ณผ๋Š” ํˆฌ์ž์ž ๋ณดํ˜ธ๊ฐ€ ์•ฝํ•˜๊ณ , ์€ํ–‰ ๋ถ€์ฑ„ ์˜์กด์œผ๋กœ ์ธํ•œ ์ ๊ทน์ ์ธ ์ฑ„๊ถŒ์ž์˜ ์—ญํ• ์ด ๋ถ€์žฌํ•œ ํ™˜๊ฒฝ์—์„œ๋Š” ์ž๊ธฐ ์ž๋ณธ ์œ ์ž…๊ณผ ์ตœ๋Œ€ ์ฃผ์ฃผ ๋ณ€๊ฒฝ์ด ๊ธฐ์—… ๊ตฌ์กฐ์กฐ์ •์— ์ค‘์š”ํ•œ ์š”์ธ์ž„์„ ์‹œ์‚ฌํ•œ๋‹ค. ๋˜ํ•œ ๋ณธ ์—ฐ๊ตฌ๋Š” ์†Œ์œ ์˜ ์ง‘์ค‘์ด ๊ตญ๋‚ด ์‹œ์žฅ๊ณผ ๊ฐ™์€ ํ™˜๊ฒฝ์—์„œ ์ง€์†๋  ์ˆ˜ ๋ฐ–์— ์—†๋Š” ์ด์œ ์— ๋Œ€ํ•˜์—ฌ ์„ค๋ช…์„ ์ œ๊ณตํ•œ๋‹ค๋Š” ์ ์—์„œ ์—ฐ๊ตฌ ์˜์˜๋ฅผ ์ง€๋‹Œ๋‹ค. ๋‘ ๋ฒˆ์งธ ๋…ผ๋ฌธ์—์„œ๋Š” ์• ๋„๋ฆฌ์ŠคํŠธ ์ปค๋ฒ„๋ฆฌ์ง€๊ฐ€ ๊ธฐ์—… ํ˜์‹ ์— ๋ฏธ์น˜๋Š” ์˜ํ–ฅ์„ ๋ถ„์„ํ•˜์˜€๋‹ค. ๊ตญ๋‚ด ์ƒ์žฅ ๊ธฐ์—…์„ ๋Œ€์ƒ์œผ๋กœ ๋ถ„์„ํ•œ ๊ฒฐ๊ณผ, ์• ๋„๋ฆฌ์ŠคํŠธ๊ฐ€ ๋งŽ์ด ์ปค๋ฒ„ํ•˜๋Š” ํšŒ์‚ฌ์ผ์ˆ˜๋ก ํ˜์‹  ๊ด€๋ จ ํˆฌ์ž์ธ R&D์™€ ๊ธฐ์—…๋ฒค์ฒ˜์บํ”ผํƒˆ ํˆฌ์ž๊ฐ€ ๊ฐ์†Œํ•˜๋Š” ๊ฒƒ์„ ํ™•์ธํ•˜์˜€๋‹ค. ์ด๋Ÿฌํ•œ ํ˜์‹  ๊ด€๋ จ ํˆฌ์ž์˜ ๊ฐ์†Œ๋Š” ๋Œ€๊ธฐ์—… ์ง‘๋‹จ์— ์†ํ•˜๋Š” ์ฆ๊ถŒ ํšŒ์‚ฌ ์†Œ์† ์• ๋„๋ฆฌ์ŠคํŠธ๋“ค์ด ๋Œ€๊ธฐ์—… ์ง‘๋‹จ์— ์†ํ•˜๋Š” ๊ธฐ์—…์„ ๋ถ„์„ํ•  ๋•Œ ๋”์šฑ ๋šœ๋ ทํ•œ ๋ชจ์Šต์„ ๋ณด์˜€๋‹ค. ํŠนํžˆ ์• ๋„๋ฆฌ์ŠคํŠธ์˜ ์˜ˆ์ธก์น˜๊ฐ€ ๊ฒฝ์˜์ง„์—๊ฒŒ ์˜ˆ์ธก์น˜ ๋‹ฌ์„ฑ์„ ์œ„ํ•œ ์••๋ ฅ์œผ๋กœ ์ž‘์šฉํ•ด ํ˜์‹ ์„ ์ €ํ•ดํ•  ์ˆ˜ ์žˆ๋‹ค๋Š” ์„ค๋ช…์€ (์••๋ ฅ ํšจ๊ณผ) ์• ๋„๋ฆฌ์ŠคํŠธ๋“ค์ด ๊ทธ๋“ค์ด ์†ํ•œ ๋Œ€๊ธฐ์—… ์ง‘๋‹จ๊ณผ ๋‹ค๋ฅธ ๋Œ€๊ธฐ์—… ์ง‘๋‹จ ์†Œ์†์ธ ๊ธฐ์—…์„ ๋ถ„์„ํ•  ๋•Œ์—๋„ ๋šœ๋ ทํ•˜๊ฒŒ ๋‚˜ํƒ€๋‚˜๋Š” ๊ฒƒ์„ ํ™•์ธํ•˜์˜€๋‹ค. ๋ณธ ์—ฐ๊ตฌ ๊ฒฐ๊ณผ๋Š” ์„ ์ง„ ๊ฒฝ์ œ๋ฅผ ๋Œ€์ƒ์œผ๋กœ ํ•œ ๊ธฐ์กด ์—ฐ๊ตฌ์—์„œ ์• ๋„๋ฆฌ์ŠคํŠธ ์ปค๋ฒ„๋ฆฌ์ง€๊ฐ€ ์ •๋ณด ๋น„๋Œ€์นญ์„ฑ์„ ๋‚ฎ์ถ”์–ด ๊ธฐ์—… ํ˜์‹ ์„ ์ฆ๊ฐ€์‹œํ‚จ๋‹ค๋Š” ์ •๋ณด ํšจ๊ณผ๋ฅผ ๋ณด์ธ ๊ฒƒ๊ณผ ์ƒ์ดํ•œ ๊ฒฐ๊ณผ๋ฅผ ๋ณด์ธ๋‹ค. ์˜คํžˆ๋ ค ํˆฌ์ž์ž ๋ณดํ˜ธ๊ฐ€ ์•ฝํ•œ ๊ฐœ๋ฐœ ๋„์ƒ๊ตญ์—์„œ๋Š” ์• ๋„๋ฆฌ์ŠคํŠธ๋“ค์ด ๋งŽ์ด ์ปค๋ฒ„ํ•˜๋Š” ํšŒ์‚ฌ์ผ์ˆ˜๋ก ๊ฒฝ์˜์ง„์— ๋Œ€ํ•œ ์˜ˆ์ธก์น˜ ๋‹ฌ์„ฑ ์••๋ ฅ์œผ๋กœ ์ž‘์šฉํ•˜์—ฌ ํ˜์‹  ๊ด€๋ จ ํˆฌ์ž๋ฅผ ๋‚ฎ์ถ˜๋‹ค๋Š” ๊ฒƒ์„ ๋ณด์˜€๋‹ค๋Š” ์ ์—์„œ ์—ฐ๊ตฌ ์˜์˜๋ฅผ ์ง€๋‹Œ๋‹ค.Chapter 1. Distress Resolution and Ownership Concentration: The Case of Bank-oriented Economies 1 1.1 Introduction 1 1.2 Institutional background 6 1.3 Literature review 7 1.4 Data and methodology 10 1.4.1 Sample construction 10 1.4.2 Methodology 16 1.5 Findings 21 1.5.1 Baseline results 21 1.5.2 Cross-sectional analysis 22 1.5.3 Robustness tests 23 1.6 Conclusion 28 References 30 Appendix 35 Chapter 2. Does Analyst Coverage Encourage Firm Innovation? Evidence From Korea 54 2.1 Introduction 54 2.2 Literature review 59 2.3 Data and methodology 61 2.3.1 Sample construction 61 2.3.2 Variables 63 2.3.3 Methodology 65 2.4 Findings 71 2.4.1 Baseline results 71 2.4.2 Cross-section variation and robustness tests 74 2.5 Conclusion 79 References 81 Appendix 86 ๊ตญ๋ฌธ์ดˆ๋ก 103๋ฐ•

    Application of Item Response Theory

    Get PDF
    ํ•™์œ„๋…ผ๋ฌธ(์„์‚ฌ) -- ์„œ์šธ๋Œ€ํ•™๊ต๋Œ€ํ•™์› : ๋ณด๊ฑด๋Œ€ํ•™์› ๋ณด๊ฑดํ•™๊ณผ, 2023. 2. ์กฐ์„ฑ์ผ.The purpose of this study was to look into the psychometric qualities of the Korean versions of the Freiburg Mindfulness Inventory (FMI) and Types of Positive Affect Scale (TPAS) in a cross-sectional design using item response theory and factor analysis. The scale validation involves 352 healthy Korean individuals. Although the FMI scale showed one-factoriality, a two-factor model fits better. A two-factorial approach without item 13 also fitted well. For the Rasch analysis of items of FMI, item 13 did not show adequate fitting (INFIT=1.42). TPAS is not represented by a single factorial model. The TPAS two-factor model fit the data adequately. Item 4 (INFIT=1.51) and 7 (INFIT=1.76) are not consistent with the Rasch model analysis. Except for FMI item 13, the scale appears to function equally well across a variety of subgroups such as sex and patient group. In conclusion, the study reveals that the FMI-13's two-factorial model provides a decent approximation to Rasch requirements, albeit further debate of how to interpret the results is required. The one-factorial TPAS solution did not fit well. With the exception of items 4 and 7, the two-factorial model meets the Rasch criterion. As a result, these two items should be removed for more validity.๋ณธ ์—ฐ๊ตฌ๋Š” ๋ฌธํ•ญ๋ฐ˜์‘์ด๋ก (IRT)๊ณผ ์š”์ธ๋ถ„์„์„ ์ด์šฉํ•˜์—ฌ ํ•œ๊ตญ์–ด ๋ฒˆ์—ญ๋ณธ Freiburg Mindfulness Inventory(FMI)์™€ Type of Positive Affect Scale(TPAS)์˜ ํƒ€๋‹น๋„๋ฅผ ๋ถ„์„ํ•˜์˜€๋‹ค. ๊ฑด๊ฐ•ํ•œ ํ•œ๊ตญ ์„ฑ์ธ 352๋ช…์ด ๋ถ„์„ ๋Œ€์ƒ์ด ์˜€๋‹ค. FMI ์ฒ™๋„๋Š” 2-์š”์ธ ๋ชจํ˜•์ด ๋” ์ ํ•ฉํ•˜์ง€๋งŒ 1-์š”์ธ ๋ชจํ˜• ๋˜ํ•œ ์ ํ•ฉํ•œ ์ˆ˜์ค€์œผ๋กœ ๋‚˜ํƒ€๋‚ฌ๋‹ค. ๋˜ํ•œ, ๋ฌธํ•ญ13์„ ์ œ์™ธํ•œ 2-์š”์ธ ๋ชจํ˜•์—์„œ ๊ฐ€์žฅ ๋†’์€ ์ ํ•ฉ๋„๊ฐ€ ๋ถ„์„๋˜์—ˆ๋‹ค. TPAS๋Š” ๋‹จ์ผ ์š”์ธ ๋ชจํ˜•์œผ๋กœ ์ ํ•ฉํ•˜์ง€ ์•Š์•˜๊ณ , 2-์š”์ธ ๋ชจํ˜•์—์„œ๋Š” ์ˆ˜์šฉ๊ฐ€๋Šฅํ•œ ์ ํ•ฉ๋„๊ฐ€ ๋‚˜ํƒ€๋‚ฌ๋‹ค. TPAS์˜ ๋ฌธํ•ญ 4๊ณผ ๋ฌธํ•ญ 7์€ ๋ผ์‰ฌ ๋ชจํ˜• ๋ถ„์„์œผ๋กœ ์ ํ•ฉํ•˜์ง€ ์•Š์€ ๊ฒƒ์œผ๋กœ ๋‚˜ํƒ€๋‚ฌ๋‹ค. ์ฐจ๋ณ„๋ฌธํ•ญ๊ธฐ๋Šฅ(DIF)๋ถ„์„์—์„œ FMI ๋ฌธํ•ญ 13์„ ์ œ์™ธํ•˜๊ณ  ์„ฑ๋ณ„, ํ™˜์ž ๊ทธ๋ฃน์˜ ํ•˜์œ„ ๊ทธ๋ฃน์—์„œ ๋‘ ์ฒ™๋„์˜ ์ฐจ์ด๊ฐ€ ์—†๋Š” ๊ฒƒ์œผ๋กœ ๋‚˜ํƒ€๋‚ฌ๋‹ค. ๋”ฐ๋ผ์„œ, ๋ผ์‰ฌ๋ถ„์„๊ณผ ์š”์ธ๋ถ„์„์„ ์ข…ํ•ฉํ•ด ๋ณด์•˜์„ ๋•Œ, FMI13์˜ 2-์š”์ธ ๋ชจ๋ธ์ด ๊ฐ€์žฅ ํ•ฉ๋ฆฌ์ ์ธ ๋ชจํ˜•์ด๋‹ค. ํ•˜์ง€๋งŒ, ๋ฌธํ•ญ์˜ ๋‚œ์ด๋„๋ฅผ ๊ณ ๋ คํ•ด ๊ฒฐ๊ณผ ํ•ด์„์— ๋Œ€ํ•œ ์ถ”๊ฐ€์ ์ธ ๋…ผ์˜๊ฐ€ ํ•„์š”ํ•˜๋‹ค. TPAS ๋Š” ๋‹จ์ผ ์š”์ธ ๋ชจ๋ธ์ด ์ ํ•ฉํ•˜์ง€ ์•Š์œผ๋ฉฐ, ๋ฌธํ•ญ7์„ ์ œ์™ธํ•œ 2-์š”์ธ ๋ชจ๋ธ์ด ๋ผ์‰ฌ๋ถ„์„๊ณผ ์š”์ธ๋ถ„์„์„ ์ข…ํ•ฉํ–ˆ์„ ๋•Œ ๊ฐ€์žฅ ์ ํ•ฉํ•œ ๊ฒƒ์œผ๋กœ ๋‚˜ํƒ€๋‚ฌ๋‹ค.Chapter 1. Introduction 1 1.1 Study Background 1 1.2 Mindfulness 3 1.2.1 Mindfulness 3 1.2.2 Measures 5 1.2.3 Freiburg Mindfulness Inventory (FMI) 6 1.3 Affect 7 1.3.1 Affect 7 1.3.2 Measures 8 1.3.3 Types of Positive Affect Scale (TPAS) 9 1.4 Objectives 9 Chapter 2. Methods 10 2.1 Translation 10 2.1.1. Translation Process of FMI 10 2.1.2. Translation Process of TPAS 11 2.2 Data Collection 11 2.2.1 Participants 11 2.2.2 Measures 12 2.2.3 Collection Procedure 15 2.3 Data Analysis 16 2.3.1 Principal Component Analysis (PCA) 17 2.3.2. Exploratory Factor Analysis (EFA) 17 2.3.3. Confirmatory Factor Analysis (CFA) 18 2.3.4. Item Response Theory 19 2.3.5. Correlation Analysis 21 Chapter 3. Result 22 3.1 Descriptive Analysis 22 3.1.1 Participants Characteristic 22 3.1.2 Descriptive analysis of FMI and TPAS 24 3.2 Factor Analysis 28 3.2.1 Parallel Analysis 28 3.2.2 Principal Component Analysis (PCA) 28 3.2.3 Exploratory Factor Analysis (EFA) 31 3.2.4 CFA 32 3.3 Item validation 36 3.3.1 Item Response Model 36 3.3.2. Differential Item Functioning (DIF) 48 3.3.3 Correlation Analysis 51 Chapter 4. Conclusion and discussion 54 4.1 Conclusion 54 4.1.1 FMI 54 4.1.2. TPAS 58 Acknowledgment 73 Appendix 74 Abstract in Korean 110์„

    ์„ฑ์ธ๋‹น๋‡จํ™˜์ž์˜ ๊ตฌ๊ฐ•๊ฑด๊ฐ•์ƒํƒœ์™€ ์‚ถ์˜ ์งˆ๊ณผ์˜ ๊ด€๋ จ์„ฑ

    Get PDF
    ํ•™์œ„๋…ผ๋ฌธ (๋ฐ•์‚ฌ)-- ์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› : ์น˜์˜ํ•™๋Œ€ํ•™์› ์น˜์˜ํ•™๊ณผ ์˜ˆ๋ฐฉ์น˜ํ•™์ „๊ณต, 2016. 2. ์ง„๋ณดํ˜•.๋‹น๋‡จ๋ณ‘์€ ๋Œ€ํ‘œ์ ์ธ ๋งŒ์„ฑ์งˆํ™˜์œผ๋กœ, ์ธ์Š๋ฆฐ์˜ ๋ถ„๋น„๋Ÿ‰์ด ๋ถ€์กฑํ•˜๊ฑฐ๋‚˜ ์ •์ƒ์ ์ธ ๊ธฐ๋Šฅ์ด ์ด๋ฃจ์–ด์ง€์ง€ ์•Š๋Š” ๋Œ€์‚ฌ์งˆํ™˜์˜ ์ผ์ข…์ด๋‹ค. ๋‹น๋‡จ๋ณ‘์˜ ํ•ฉ๋ณ‘์ฆ์€ ์ฒด๋‚ด ์—ฌ๋Ÿฌ ๋Œ€์‚ฌ์žฅ์• ๊ฐ€ ์œ ๋ฐœ๋œ ๊ฒฐ๊ณผ, ๋‹ค์–‘ํ•œ ํ˜•ํƒœ๋กœ ๋‚˜ํƒ€๋‚œ๋‹ค. ๊ทธ์ค‘์—์„œ๋„ ๋‹น๋‡จ๋ณ‘์˜ 6๋ฒˆ์งธ ์ฃผ์š” ํ•ฉ๋ณ‘์ฆ์ธ ์น˜์ฃผ์งˆํ™˜์€ ์น˜์ฃผ์กฐ์ง์˜ ๋งŒ์„ฑ์ ์ธ ๊ฐ์—ผ์œผ๋กœ, ์‹ฌ๊ฐํ•œ ๊ฒฝ์šฐ์—๋Š” ์น˜์•„์ƒ์‹ค์„ ์ดˆ๋ž˜ํ•˜๋ฉฐ, ์„ฑ์ธ์— ์žˆ์–ด์„œ๋Š” ์น˜์•„๋ฐœ๊ฑฐ์›์ธ์งˆํ™˜ ์ค‘ ๊ฐ€์žฅ ํฐ ๋น„์ค‘์„ ์ฐจ์ง€ํ•œ๋‹ค. ๋‹น๋‡จ๋ณ‘๊ณผ ์น˜์ฃผ์งˆํ™˜์€ ์ ๊ทน์ ์œผ๋กœ ๊ด€๋ฆฌํ•˜์ง€ ์•Š์œผ๋ฉด ์˜ค๋žœ ๊ธฐ๊ฐ„ ์‚ถ์˜ ์งˆ์„ ์•…ํ™”์‹œ์ผœ ์‹ฌ๊ฐํ•œ ์‚ฌํšŒ์  ๋ฌธ์ œ๋ฅผ ์•ผ๊ธฐํ•œ๋‹ค. ๋ณธ ์—ฐ๊ตฌ๋Š” ์„ฑ์ธ๋‹น๋‡จํ™˜์ž์˜ ๊ตฌ๊ฐ•๊ฑด๊ฐ•์ƒํƒœ์™€ ์‚ถ์˜ ์งˆ์„ ํ‰๊ฐ€ํ•˜๊ณ  ์ด๋“ค์˜ ์ƒํ˜ธ์—ฐ๊ด€์„ฑ์„ ๊ทœ๋ช…ํ•จ๊ณผ ๋™์‹œ์—, ๋น„์™ธ๊ณผ์  ์น˜์ฃผ์น˜๋ฃŒ์— ์˜ํ•œ ๊ตฌ๊ฐ•๊ฑด๊ฐ•์ƒํƒœ ๊ฐœ์„ ์ด ์‚ถ์˜ ์งˆ์— ๋ฏธ์น˜๋Š” ์˜ํ–ฅ์„ ํŒŒ์•…ํ•˜์—ฌ, ๋‹น๋‡จํ™˜์ž์˜ ๊ตฌ๊ฐ•๊ฑด๊ฐ•๊ณผ ์‚ถ์˜ ์งˆ ํ–ฅ์ƒ์— ๊ธฐ์—ฌํ•  ์ˆ˜ ์žˆ๋Š” ๋ฐฉ์•ˆ์„ ๋งˆ๋ จํ•˜๊ธฐ ์œ„ํ•œ ๋ชฉ์ ์œผ๋กœ, ์„œ์šธ์‹œ ๊ด‘์ง„๊ตฌ ๋ณด๊ฑด์†Œ์—์„œ ๋‹น๋‡จ ๊ด€๋ฆฌ๋ฅผ ๋ฐ›๊ณ  ์žˆ๋Š” 42-86์„ธ ์„ฑ์ธ๋‹น๋‡จํ™˜์ž ์ด194๋ช…์„ ๋Œ€์ƒ์œผ๋กœ ๊ตฌ๊ฐ•๊ฑด๊ฐ•๊ฒ€์‚ฌ ๋ฐ ์‚ถ์˜ ์งˆ์— ๊ด€ํ•œ SF-36 ์„ค๋ฌธ์กฐ์‚ฌ๋ฅผ ์‹œํ–‰ํ•œ ํ›„, ์—ฐ๋ น๋ณ„๋กœ 50๋Œ€ ์ดํ•˜, 60๋Œ€, 70๋Œ€, 80๋Œ€์˜ 4๊ฐœ ๊ตฐ์œผ๋กœ ๋‚˜๋ˆ„์–ด ์ž๋ฃŒ๋ฅผ ๋ถ„์„ํ•˜์˜€๋‹ค. ๋˜ํ•œ, ์—ฐ๊ตฌ ์„ ์ •๊ธฐ์ค€๊ณผ ์ œ์™ธ๊ธฐ์ค€์— ๋”ฐ๋ผ 194๋ช… ์ค‘ ์ด 55๋ช…์˜ 44-85์„ธ ํ™˜์ž๋ฅผ ๋Œ€์ƒ์œผ๋กœ ์Šค์ผ€์ผ๋ง ๋ฐ ์น˜๊ทผ๋ฉดํ™œํƒ์ˆ ๊ตฐ, ์ „๋ฌธ๊ฐ€๊ตฌ๊ฐ•๊ฑด๊ฐ•๊ด€๋ฆฌ๊ตฐ, ๋Œ€์กฐ๊ตฐ์˜ 3๊ฐœ ๊ตฐ์œผ๋กœ ๋‚˜๋ˆ„์–ด ๋น„์™ธ๊ณผ์  ์น˜์ฃผ์น˜๋ฃŒ ์ „ํ›„์˜ ๊ตฌ๊ฐ•๊ฑด๊ฐ•์ƒํƒœ ๋ณ€ํ™”์™€ ์‚ถ์˜ ์งˆ ์ง€์ˆ˜ ๋ณ€ํ™”๋ฅผ ๋ถ„์„ ๊ฒ€ํ† ํ•˜์—ฌ, ๋‹ค์Œ๊ณผ ๊ฐ™์€ ๊ฒฐ๋ก ์„ ์–ป์—ˆ๋‹ค. 1. ์‚ถ์˜ ์งˆ ๋งŒ์กฑ๋„๋Š” 100์  ๋งŒ์  ์ค‘ 51.40์ ์—์„œ 91.08์ ์ด์—ˆ๋‹ค. 2. ์‹ ์ฒด๊ธฐ๋Šฅ๊ณผ ๊ฑด๊ฐ•๋ณ€ํ™” ์ธก๋ฉด์˜ ์‚ถ์˜ ์งˆ์€ ์—ฐ๋ น ์ฆ๊ฐ€์— ๋”ฐ๋ผ ํ•˜๋ฝํ•˜์˜€๋‹ค (P<0.05). 3. ์ผ๋ฐ˜๊ฑด๊ฐ• ์ธก๋ฉด์˜ ์‚ถ์˜ ์งˆ์€ ์—ฐ๋ น ์ฆ๊ฐ€์— ๋”ฐ๋ผ ํ–ฅ์ƒ๋˜์—ˆ๋‹ค(P<0.05). 4. ์‹ ์ฒด๊ธฐ๋Šฅ ์ธก๋ฉด์˜ ์‚ถ์˜ ์งˆ์€ ์ž”์กด ์น˜์•„ ์ˆ˜, DMFT ์ง€์ˆ˜์™€ ์œ ์˜ํ•œ ์ƒ๊ด€์„ฑ์„ ๋ณด์˜€๋‹ค(P<0.05). 5. ์Šค์ผ€์ผ๋ง ๋ฐ ์น˜๊ทผ๋ฉดํ™œํƒ์ˆ ๊ตฐ์—์„œ ์น˜์ฃผ์น˜๋ฃŒ ํ›„ ์‹ ์ฒด๊ธฐ๋Šฅ๊ณผ ์ •์‹ ๊ฑด๊ฐ• ์ธก๋ฉด์˜ ์‚ถ์˜ ์งˆ์ด ํ–ฅ์ƒ๋˜์—ˆ๋‹ค(P<0.05). ์ด์ƒ์˜ ๊ฒฐ๊ณผ๋กœ๋ถ€ํ„ฐ, ์„ฑ์ธ๋‹น๋‡จํ™˜์ž์˜ ๊ตฌ๊ฐ•๊ฑด๊ฐ•์ƒํƒœ๊ฐ€ ๊ฑด๊ฐ•๊ด€๋ จ ์‚ถ์˜ ์งˆ๊ณผ ์—ฐ๊ด€์ด ์žˆ์œผ๋ฉฐ, ๋น„์™ธ๊ณผ์  ์น˜์ฃผ์น˜๋ฃŒ์— ์˜ํ•œ ๊ตฌ๊ฐ•๊ฑด๊ฐ•์ƒํƒœ ๊ฐœ์„ ์ด ๊ฑด๊ฐ•๊ด€๋ จ ์‚ถ์˜ ์งˆ์„ ํ–ฅ์ƒ์‹œํ‚ค๋Š” ๋ฐ์— ํšจ๊ณผ์ ์ด์—ˆ๋‹ค๊ณ  ํ•  ์ˆ˜ ์žˆ๋‹ค. ๋”ฐ๋ผ์„œ ๋‹น๋‡จํ™˜์ž์˜ ๊ตฌ๊ฐ•๊ฑด๊ฐ•์ƒํƒœ ๊ฐœ์„ ๊ณผ ๊ฑด๊ฐ•๊ด€๋ จ ์‚ถ์˜ ์งˆ ํ–ฅ์ƒ์„ ์œ„ํ•ด ๊ตฌ๊ฐ•๊ฑด๊ฐ•๊ด€๋ จ์ „๋ฌธ๊ฐ€๋Š” ๋‹น๋‡จํ™˜์ž์˜ ๊ตฌ๊ฐ•๊ด€๋ฆฌ์— ์žˆ์–ด์„œ ๋ณด๋‹ค ์ ๊ทน์ ์ธ ์ง€๋„์™€ ์น˜๋ฃŒ๋ฅผ ํ–‰ํ•˜๋Š” ์ž์„ธ๊ฐ€ ํ•„์š”ํ•  ๊ฒƒ์ด๋‹ค.โ…  ์„œ ๋ก  1 โ…ก ์—ฐ๊ตฌ๋ฐฐ๊ฒฝ 4 โ…ข ์—ฐ๊ตฌ๋Œ€์ƒ ๋ฐ ๋ฐฉ๋ฒ• 8 โ…ฃ ์—ฐ๊ตฌ๊ฒฐ๊ณผ 20 โ…ค ๊ณ  ์•ˆ 30 โ…ฅ ๊ฒฐ ๋ก  35 REFERENCES 36 ๋ถ€ ๋ก 44 Abstract 51Docto

    Short-Term Outcomes of Intracorporeal Delta-Shaped Gastroduodenostomy Versus Extracorporeal Gastroduodenostomy after Laparoscopic Distal Gastrectomy for Gastric Cancer

    Get PDF
    BACKGROUND: Billroth I anastomosis is one of the most common reconstruction methods after distal gastrectomy for gastric cancer. Intracorporeal Billroth I (ICBI) anastomosis and extracorporeal Billroth I (ECBI) anastomosis are widely used in laparoscopic surgery. Here we compared ICBI and ECBI outcomes at a major gastric cancer center. METHODS: We retrospectively analyzed data from 2,284 gastric cancer patients who underwent laparoscopic distal gastrectomy between 2009 and 2017. We divided the subjects into ECBI (n=1,681) and ICBI (n=603) groups, compared the patients' clinical characteristics and surgical and short-term outcomes, and performed risk factor analyses of postoperative complication development. RESULTS: The ICBI group experienced shorter operation times, less blood loss, and shorter hospital stays than the ECBI group. There were no clinically significant intergroup differences in diet initiation. Changes in white blood cell counts and C-reactive protein levels were similar between groups. Grade II-IV surgical complication rates were 2.7% and 4.0% in the ECBI and ICBI groups, respectively, with no significant intergroup differences. Male sex and a body mass index (BMI) โ‰ฅ30 were independent risk factors for surgical complication development. In the ECBI group, patients with a BMI โ‰ฅ30 experienced a significantly higher surgical complication rate than those with a lower BMI, while no such difference was observed in the ICBI group. CONCLUSION: The surgical safety of ICBI was similar to that of ECBI. Although the chosen anastomotic technique was not a risk factor for surgical complications, ECBI was more vulnerable to surgical complications than ICBI in patients with a high BMI (โ‰ฅ30).ope

    Behavioral motivation for target selection in acquisition- A linkage between aspiration and environment -

    Get PDF
    ํ•™์œ„๋…ผ๋ฌธ (์„์‚ฌ)-- ์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› : ๊ฒฝ์˜ํ•™๊ณผ, 2016. 8. ๋ฐ•์ฒ ์ˆœ.This paper explores how acquirers prior performance influences their selection of target firms across different industries contingent upon environmental conditions. Although extensive stream of research has advanced our understandings on what motivates a particular firm to pursue the acquisitions, a critical question still remains on what leads firms to select targets in different industries once they have decided to acquire. Using the sample of U.S. manufacturing firms, the paper argue that motivation to employ specific acquisition strategy (i.e., related or unrelated acquisition) is simultaneously affected by an individual performance feedback condition and environmental characteristics that acquiring firm compete in. Through incorporating behavioral perspectives with task environment dimensions (i.e., dynamism), the study examines the contingency effects of environmental condition upon firms acquisition strategies. Therefore, the study contributes to both streams of performance feedback theory and acquisition literature by identifying more integrative approach of antecedents that explains differences in acquisition behaviors.1. Introduction 1 2. Theory & Literature Review 4 2.1 The Behavioral Theory of the Firm 4 2.2 Organizational Task Environments 5 2.3 Related acquisition vs. Unrelated acquisition. 7 3. Hypotheses 8 3.1 Problemistic Search 8 3.2 Slack Search 10 3.3 Moderating effect of environment 12 3.3.1 Environmental Dynamism 12 4. Data & Method 16 4.1 Dependent Variable 17 4.2 Independent Variable 17 4.3 Control Variable 18 5. Result 20 6. Conclusion 31 7. Limitation and Future Research 33 REFERENCE 35 ABSTRACT IN KOREAN 45Maste

    ๊ณ ์†๋„๋กœ ์†๋„์˜ˆ์ธก์„ ์œ„ํ•œ ๊ธฐ๊ณ„ํ•™์Šต ์ ‘๊ทผ๋ฒ•

    Get PDF
    ํ•™์œ„๋…ผ๋ฌธ (๋ฐ•์‚ฌ)-- ์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› : ๊ณต๊ณผ๋Œ€ํ•™ ๊ฑด์„คํ™˜๊ฒฝ๊ณตํ•™๋ถ€, 2018. 2. ๊ณ ์Šน์˜.Prediction of freeway traffic speed can be used for predictive traffic management to improve the quality of the intelligent transportation system. The data-driven prediction is widely used due to its predictive capability. Recently, the non - parametric method using machine learning shows excellent predictive capability. In these methods, the feature extraction or selection is used to mitigate the overfitting and reflect the congestion mechanism. Although this nonparametric approach can be used as advanced traveler information system due to its excellent capability, it cannot provide any information on the congestion mechanism. Lack of information makes it difficult to establish a strategy for use in operational management. This study proposes a highway speed prediction model based on machine learning approach with a feature selection that provides both high predictive performance and interpretation of traffic flow characteristics. To do this, a supervised feature selection is applied using principal component analysis (PCA) based variable grouping and ordering and support vector machine (SVM) based variable selection. Varimax rotation is also applied to obtain the simple structure. In the variable ranking, the variables in the PC are ranked by using the nonlinear correlation coefficient which implies the predictive capability in the machine learning model. The cross-correlation coefficients were used in this study. With this grouped and ranked variables, the variables are selected by the forward selection method. The machine learning regression model in this study is SVM regression which has excellent generalization performance and low computational cost. Empirical data evaluation was implemented based on the several month's data of Kyungbu freeway in Korea and the interstate (I-880) freeway in the United States. Comparing other approaches, the proposed feature selection approach well captured the characteristics of traffic flow among spatiotemporal variables. In particular, the feature selection performance is somewhat better than that of the artificial neural network feature extraction model, stacked auto-encoder, and the ensemble learning model, random forest. The vector space of the PCA is transformed into the traffic phase diagram between two spatiotemporal variables to obtain the implication of proposed approach in traffic engineering area. Based on the traffic phase interpretation, principal components with some loading of dependent variable can explain the propagation of traffic state. The proposed approach captures the propagation of traffic state well according to prediction step. The proposed approach would be used to establish strategies for avoiding congestion or preventing rear-end accidents because it has advantages in the multi-step prediction on congested areas and in identifying the congestion mechanism.Chapter 1. Introduction 1 1.1 Background 1 1.2 Objective and Scope 4 Chapter 2. Literature Review 8 2.1 Traffic Flow Based Model 8 2.2 Data Based Model 11 2.3 Review Result and Study Direction 18 Chapter 3. Method 20 3.1 Principal Component Analysis (PCA) 20 3.2 PCA Based Supervised Feature Selection 29 3.3 Comparison Models 40 Chapter 4. Empirical Data Evaluation 45 4.1 Evaluation Strategy 45 4.2 Case 1: Korean Freeway 47 4.3 Case 2: Interstate Freeway 59 4.4 Comparison of Predictive Capability 68 Chapter 5. Implication for Traffic Analysis 74 5.1 Traffic Phase of Principal Components 74 5.2 Comparison of Selected Variables 94 Chapter 6. Conclusions 103 Reference 106Docto

    Improving Subgraph Isomorphism with Pruning by Maximum Bipartite Matching

    Get PDF
    ํ•™์œ„๋…ผ๋ฌธ (์„์‚ฌ) -- ์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› : ๊ณต๊ณผ๋Œ€ํ•™ ์ปดํ“จํ„ฐ๊ณตํ•™๋ถ€, 2021. 2. ๋ฐ•๊ทผ์ˆ˜.๋Œ€ํ˜• ๊ทธ๋ž˜ํ”„์— ๋Œ€ํ•œ ๋ถ„์„์€ ์ตœ๊ทผ ์†Œ์…œ ๋„คํŠธ์›Œํฌ, ์ƒ๋ฌผ ์ •๋ณดํ•™, ํ™”ํ•™ ๋“ฑ ๋‹ค์–‘ํ•œ ๋ถ„์•ผ์—์„œ ์ ์ฐจ ์ค‘์š”ํ•ด์ง€๊ณ  ์žˆ๋‹ค. ๊ทธ๋ž˜ํ”„ ๋ถ„์„์—์„œ ๊ฐ€์žฅ ํ•ต์‹ฌ์ ์ธ ๋ฌธ์ œ ์ค‘ ํ•˜๋‚˜๋กœ ๋ถ€๋ถ„ ๊ทธ๋ž˜ํ”„ ๋™ํ˜• ๋ฌธ์ œ (subgraph isomorphism) ๊ฐ€ ์žˆ๋‹ค. ๋ถ€๋ถ„ ๋™ํ˜• ๊ทธ๋ž˜ํ”„ ๋ฌธ์ œ๋ฅผ backtracking ํ™œ์šฉํ•˜์—ฌ ํ•ด๊ฒฐํ•˜๋Š” ๋งŽ์€ ์•Œ๊ณ ๋ฆฌ์ฆ˜์ด ์ œ์‹œ๋˜์–ด์™”๋‹ค. ํ•˜์ง€๋งŒ ์ตœ์•…์˜ ๊ฒฝ์šฐ ์ง€์ˆ˜์ ์ธ ์‹œ๊ฐ„ ๋ณต์žก๋„๋ฅผ ๊ฐ€์ง€๋Š” backtracking ํŠน์„ฑ์ƒ ์—ฌ์ „ํžˆ ํŠน์ • ์ž…๋ ฅ์—์„œ๋Š” ๋‹ต์„ ์ฐพ๊ธฐ ์œ„ํ•ด์„œ ์ง€๋‚˜์น˜๊ฒŒ ๊ธด ์‹œ๊ฐ„์„ ์†Œ๋ชจํ•˜๊ธฐ ๋•Œ๋ฌธ์— ์—ฌ์ „ํžˆ ์‹ค์ œ ๋ฌธ์ œ์— ์ ์šฉ์ด ์–ด๋ ค์šด ๊ฒฝ์šฐ๊ฐ€ ์กด์žฌํ•œ๋‹ค. ๋ณธ ๋…ผ๋ฌธ์—์„œ๋Š” backtracking ๊ณผ์ •์—์„œ ์•Œ๊ณ ๋ฆฌ์ฆ˜ ๋™์ž‘ ์‹œ๊ฐ„์„ ๊ธธ๊ฒŒ ๋งŒ๋“ค ์ˆ˜ ์žˆ ๋Š” ์š”์ธ์„ ํƒ๊ตฌํ•˜๊ณ , ์ด๋ฅผ ํ•ด๊ฒฐํ•จ์œผ๋กœ์จ ์‹คํ–‰ ์‹œ๊ฐ„์„ ํš๊ธฐ์ ์œผ๋กœ ์ค„์ผ ์ˆ˜ ์žˆ๋Š” ์ƒˆ๋กœ์šด ๊ธฐ๋ฒ•์„ ์†Œ๊ฐœํ•œ๋‹ค. ๋˜ํ•œ, ๊ธฐ์กด ์•Œ๊ณ ๋ฆฌ์ฆ˜ ์ค‘ state-of-the-art ์ธ DAF[4] ์— ๊ธฐ๋ฒ•์„ ์ ์šฉํ•œ ๊ฒฝ์šฐ์™€ ๊ทธ๋ ‡์ง€ ์•Š์€ ๊ฒฝ์šฐ๋ฅผ ๊ตฌํ˜„ํ•˜๊ณ  ์‹ค์ œ ๊ทธ๋ž˜ํ”„ ๋ฐ์ดํ„ฐ ์ƒ์—์„œ ์‹คํ—˜์„ ์ง„ํ–‰ํ•˜์—ฌ ์ œ์‹œํ•œ ๊ธฐ๋ฒ•์ด ๋ฌธ์ œ๋ฅผ ํšจ๊ณผ์ ์œผ๋กœ ํ•ด๊ฒฐํ•˜๋Š”๋ฐ ๋„์›€์ด ๋  ์ˆ˜ ์žˆ๋Š” ๋ฐฉ๋ฒ•์ž„์„ ์ž…์ฆํ–ˆ๋‹ค.In recent years, graphs have been playing an increasingly important role in various domains, e.g., social networks [3], bioinformatics [8], chemistry [10] etc. One of the most fundamental problems in graph analysis is subgraph isomor- phism. Many practical solutions have been suggested for subgraph isomorphism. However, those algorithms show limited response time and scalability in han- dling real-world applications because, by the nature of backtracking, there could be many redundant computations. In this paper, we develop a new technique to prune out some parts of the search space. Furthermore, we incorporate our method to one of them and show the efficacy by conducting experiments on several real-world datasets.Chapter 1 ์„œ๋ก  1 Chapter 2 ๋ฐฐ๊ฒฝ์ง€์‹ 3 Chapter 3 DAF ์•Œ๊ณ ๋ฆฌ์ฆ˜ 8 Chapter 4 ์ตœ๋Œ€ ์ด๋ถ„ ๋งค์นญ์— ํ™œ์šฉํ•œ ๊ฐ€์ง€์น˜๊ธฐ 16 Chapter 5 ์„ฑ๋Šฅ ํ‰๊ฐ€ 26Maste

    ์ด์ „๊ฐ€๊ฒฉ์„ธ์ œ ์ค‘ BEPS ํ”„๋กœ์ ํŠธ์™€ ๊ด€๋ จ๋œ ์ฃผ์š” ์Ÿ์ ์— ๊ด€ํ•œ ์—ฐ๊ตฌ

    Get PDF
    ํ•™์œ„๋…ผ๋ฌธ (์„์‚ฌ)-- ์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› ๋ฒ•๊ณผ๋Œ€ํ•™ ๋ฒ•ํ•™๊ณผ, 2017. 8. ์ด์ฐฝํฌ.๋ณธ ๋…ผ๋ฌธ์—์„œ ๋‹ค๋ฃจ๊ณ ์ž ํ•˜๋Š” ๋‚ด์šฉ์€ OECD/G20์ด 2015๋…„ ๊ธฐ์กด์˜ ๊ตญ์ œ์กฐ์„ธ์ฒด์ œ๋งŒ์œผ๋กœ๋Š” ๋Œ€์‘ํ•˜๊ธฐ ์–ด๋ ค์šด ์„ธ์›์ž ์‹ ๋ฐ ์†Œ๋“์ด์ „์˜ ๋ฌธ์ œ(Base Erosion and Profit Shifting)์— ๋Œ€์‘ํ•˜๊ธฐ ์œ„ํ•˜์—ฌ ์ถœ๋ฒ”ํ•œ BEPS ํ”„๋กœ์ ํŠธ ์ค‘ ์ด์ „๊ฐ€๊ฒฉ์„ธ์ œ์— ๊ด€ํ•œ ๋ถ€๋ถ„(Action 8-10, 13)์ด ๊ธฐ์กด์˜ OECD ์ด์ „๊ฐ€๊ฒฉ์ง€์นจ, ๊ตญ์ œ์กฐ์„ธ์กฐ์ • ๋“ฑ์— ๊ด€ํ•œ ๋ฒ•๋ฅ (์ดํ•˜ ๊ตญ์กฐ๋ฒ•์ด๋ผ ํ•œ๋‹ค)๊ณผ ๋น„๊ตํ•˜์—ฌ ์–ด๋– ํ•œ ๋ฐœ์ „๋œ ๋‚ด์šฉ์„ ๋‹ด๊ณ  ์žˆ๋Š”์ง€, ๊ตญ์กฐ๋ฒ• ๊ฐœ์ •์˜ ํ•„์š”์„ฑ์ด ์žˆ๋Š”์ง€, ๊ทธ๋Ÿฌํ•˜๋‹ค๋ฉด ๊ทธ ๋ฐฉํ–ฅ์ด ์–ด๋–ป๊ฒŒ ๋˜์–ด์•ผ ํ•˜๋Š”์ง€๋ฅผ ์‚ดํ”ผ๊ณ , BEPS ํ”„๋กœ์ ํŠธ์˜ ๊ฐ ์ด์Šˆ(๋ฌดํ˜•์ž์‚ฐ, ์ €๋ถ€๊ฐ€๊ฐ€์น˜ ๊ทธ๋ฃน๋‚ด๋ถ€์šฉ์—ญ, ์›๊ฐ€๋ถ„๋‹ด์•ฝ์ •)๋ณ„๋กœ ์ด์— ํ•ด๋‹นํ•˜๋Š” ๋ฒ•์›์˜ ํŒ๋ก€ ๋ฐ ์กฐ์„ธ์‹ฌํŒ์›์˜ ๊ฒฐ์ •๋ก€์˜ ๊ตฌ์ฒด์  ์‚ฌ์•ˆ๊ณผ ๊ทธ ํŒ์‹œ์‚ฌํ•ญ์— BEPS ํ”„๋กœ์ ํŠธ์˜ ๋‚ด์šฉ์„ ๋Œ€์ž…ํ•˜์—ฌ ๋ด„์œผ๋กœ์จ BEPS ํ”„๋กœ์ ํŠธ์˜ ์‹ค์ฒœ์  ์˜๋ฏธ๋ฅผ ์ฐพ๊ณ , BEPS ํ”„๋กœ์ ํŠธ์˜ ์ถœ๋ฒ”์—๋„ ๋ถˆ๊ตฌํ•˜๊ณ  ์ด์ „๊ฐ€๊ฒฉ์„ธ์ œ๊ฐ€ ์—ฌ์ „ํžˆ ๊ฐ€์ง€๋Š” ํ•œ๊ณ„์— ๊ด€ํ•˜์—ฌ ๋…ผ์˜ํ•˜๋Š” ๊ฒƒ์ด๋‹ค. BEPS ํ”„๋กœ์ ํŠธ Action 8-10 ์ค‘ ๋ฌดํ˜•์ž์‚ฐ์— ๊ด€ํ•œ ๋ถ€๋ถ„์€ ๋ฌดํ˜•์ž์‚ฐ์˜ ์ •์˜๋ฅผ ๋ถ„๋ช…ํžˆ ํ•˜๊ณ , ๋น„๊ต๊ฐ€๋Šฅ์„ฑ ๋ถ„์„์˜ ํ‹€๊ณผ ์ฃผ์š” ๋น„๊ต๊ฐ€๋Šฅ์„ฑ ๋ถ„์„ ์š”์†Œ๋ฅผ ์ƒ์„ธํžˆ ์ œ์‹œํ•˜๋ฉฐ, ๋ฌดํ˜•์ž์‚ฐ์˜ ์ •์ƒ๊ฐ€๊ฒฉ ์‚ฐ์ถœ๋ฐฉ๋ฒ•์œผ๋กœ ํ˜„๊ธˆํ๋ฆ„ํ• ์ธ๋ฒ• ๋“ฑ ๊ฐ€์น˜ํ‰๊ฐ€๋ฐฉ๋ฒ•์„ ๋„์ž…ํ•˜๊ณ , ๊ฐ€์น˜ํ‰๊ฐ€๊ฐ€ ์–ด๋ ค์šด ๋ฌดํ˜•์ž์‚ฐ์— ๊ด€ํ•˜์—ฌ ์ผ์ •ํ•œ ์š”๊ฑด ํ•˜์—์„œ ์‚ฌํ›„์  ๊ฒฐ๊ณผ์— ๊ธฐ์ดˆํ•œ ์กฐ์ •์ด ๊ฐ€๋Šฅํ•˜๋‹ค๋Š” ์ ์„ ๋ช…ํ™•ํžˆ ํ•˜๋Š” ๋ฐฉํ–ฅ์œผ๋กœ ๊ฐœ์ •๋˜์—ˆ๋‹ค. ๊ตญ์กฐ๋ฒ•์˜ ๊ฐœ์ •๋ฐฉํ–ฅ์— ๊ด€ํ•˜์—ฌ ๋Œ€์ฒด์ ์œผ๋กœ ์ด์— ๋ฐœ๋งž์ถ˜ ๊ฐœ์ •์˜ ํ•„์š”์„ฑ์ด ์žˆ๋‹ค๊ณ  ์ƒ๊ฐํ•œ๋‹ค. ๋‹ค๋งŒ BEPS ํ”„๋กœ์ ํŠธ๋Š” ๋น„๊ต๋Œ€์ƒ๊ฑฐ๋ž˜๋ฅผ ์ฐพ๊ธฐ ์–ด๋ ค์šด ๋ฌดํ˜•์ž์‚ฐ๊ณผ ๊ด€๋ จ๋œ ๊ฑฐ๋ž˜์— ์žˆ์–ด์„œ๋Š” ์ผ๋ฐ˜์ ์œผ๋กœ ์ด์ต๋ถ„ํ• ๋ฐฉ๋ฒ•์„ ์‚ฌ์šฉํ•˜๋Š” ๊ฒƒ์ด ์ ์ ˆํ•˜๋‹ค๊ณ  ๋ณด๋ฉด์„œ๋„ ํ•œํŽธ์œผ๋กœ๋Š” ๋…๋ฆฝ๊ธฐ์—…์˜ ์›์น™์„ ๊ณ ์ˆ˜ํ•˜๊ณ  ์žˆ๋Š”๋ฐ, ์ด์ต๋ถ„ํ• ๋ฐฉ๋ฒ•์˜ ๋„์ž… ์—ฐํ˜๊ณผ ๊ทธ ํŠน์„ฑ ๋“ฑ์— ๋น„์ถ”์–ด ๋ณผ ๋•Œ ์ด์ต๋ถ„ํ• ๋ฐฉ๋ฒ•์€ ๊ธฐ์กด ์ด์ „๊ฐ€๊ฒฉ์„ธ์ œ์˜ ์ฃผ์š” ์›์น™์ธ ๋…๋ฆฝ๊ธฐ์—…์˜ ์›์น™๊ณผ ๊ฐ€์žฅ ๋™๋–จ์–ด์ง„ ์ •์ƒ๊ฐ€๊ฒฉ ์‚ฐ์ถœ๋ฐฉ๋ฒ•์— ํ•ด๋‹นํ•˜๋ฏ€๋กœ, ์ด์ต๋ถ„ํ• ๋ฐฉ๋ฒ• ์ž์ฒด์˜ ํŠน์ˆ˜์„ฑ์„ ์ธ์ •ํ•˜๊ณ , ๋…์ž์ ์ธ ๋ฐœ์ „๋ฐฉํ–ฅ์„ ๋ชจ์ƒ‰ํ•˜๋Š” ๊ฒƒ์ด ํ•„์š”ํ•˜๋‹ค. BEPS ํ”„๋กœ์ ํŠธ Action 8-10 ์ค‘ ๊ทธ๋ฃน๋‚ด๋ถ€์šฉ์—ญ์— ๊ด€ํ•œ ๋ถ€๋ถ„์€ ์ €๋ถ€๊ฐ€๊ฐ€์น˜ ๊ทธ๋ฃน๋‚ด๋ถ€์šฉ์—ญ์— ๊ด€ํ•œ ๋‚ด์šฉ์„ ์ถ”๊ฐ€ํ•˜์—ฌ ์ €๋ถ€๊ฐ€๊ฐ€์น˜ ๊ทธ๋ฃน๋‚ด๋ถ€์šฉ์—ญ์— ํ•ด๋‹นํ•  ๊ฒฝ์šฐ ์šฉ์—ญ ์›๊ฐ€์— 5%์˜ ์ด์œค์„ ๊ฐ€์‚ฐํ•˜๊ณ , ๊ฐ ๊ตฌ์„ฑ์›๋“ค์˜ ์›๊ฐ€ ๋น„์œจ์— ๋”ฐ๋ผ ์ด๋ฅผ ๋ฐฐ๋ถ„ํ•˜๋Š” ๋ฐฉ์‹์˜ ๊ฐ„์†Œํ™”๋œ ์ •์ƒ๋Œ€๊ฐ€ ์‚ฐ์ถœ์ด ๊ฐ€๋Šฅํ•˜๋‹ค๋Š” ๋‚ด์šฉ์„ ๋„์ž…ํ•˜์˜€๋‹ค. ์ด๋Ÿฌํ•œ ๊ฐ„์†Œํ™”๋œ ์ ‘๊ทผ๋ฐฉ์‹์„ ์‹ค๋ฌด์ ์œผ๋กœ ๋‚ฉ์„ธ์ž ๋ฐ ๊ณผ์„ธ๊ด€์ฒญ์—๊ฒŒ ๋‚ฉ์„ธ๋น„์šฉ ๋“ฑ์˜ ์ ˆ๊ฐํšจ๊ณผ๋ฅผ ๊ฐ€์ ธ์˜ฌ ๊ฒƒ์œผ๋กœ ๋ณด์ด๊ณ , ์ •์ƒ์ด์œค ๋น„์œจ์˜ ๋ฒ”์œ„ ๋“ฑ์— ๊ด€ํ•œ ์ถ”๊ฐ€์ ์ธ ๊ฒ€ํ† ๋ฅผ ๊ฑฐ์ณ ๊ตญ์กฐ๋ฒ•์— ๋ฐ˜์˜ํ•˜๋Š” ๊ฒƒ์ด ํƒ€๋‹นํ•˜๋‹ค๊ณ  ์ƒ๊ฐํ•œ๋‹ค. ํ•œํŽธ ์ด์™€ ๊ฐ™์€ ๊ฐ„์†Œํ™”๋œ ์ ‘๊ทผ๋ฐฉ์‹์€ ์ •์ƒ์ด์œค ๋น„์œจ์„ ์‚ฌ์ „์— ์ •ํ•˜๊ณ  ๊ทธ์— ๋”ฐ๋ผ ์ด์ „๊ฐ€๊ฒฉ์ด ์ •์ƒ๊ฐ€๊ฒฉ์— ํ•ด๋‹นํ•˜๋Š”์ง€ ์—ฌ๋ถ€๋ฅผ ํŒ๋‹จํ•˜๋Š” ๊ฒƒ์ด๋ฏ€๋กœ, ์ „ํ†ต์ ์ธ ๋…๋ฆฝ๊ธฐ์—…์˜ ์›์น™๊ณผ๋Š” ๊ฑฐ๋ฆฌ๊ฐ€ ์žˆ๋‹ค. BEPS ํ”„๋กœ์ ํŠธ Action 8-10 ์ค‘ ์›๊ฐ€๋ถ„๋‹ด์•ฝ์ •์— ๊ด€ํ•œ ๋ถ€๋ถ„์€ ๊ธฐ์กด OECD ์ด์ „๊ฐ€๊ฒฉ์ง€์นจ ์ค‘ ์ด์— ๊ด€ํ•œ ๋ถ€๋ถ„์ด ์ฐธ์—ฌ์ž์˜ ๊ณตํ—Œ๊ฐ€์น˜๋ฅผ ์–ด๋–ป๊ฒŒ ์ธก์ •ํ•˜์—ฌ์•ผ ํ•˜๋Š” ๊ฒƒ์ธ์ง€ ์•Œ๊ธฐ ์–ด๋ ค์›Œ ์‹ค์ฒœ์ ์ธ ์˜๋ฏธ๊ฐ€ ์—†๋‹ค๋Š” ๋น„ํŒ์„ ๊ทน๋ณตํ•˜๊ธฐ ์œ„ํ•˜์—ฌ ์›๊ฐ€๋ถ„๋‹ด์•ฝ์ •์˜ ์œ ํ˜•์„ ๋‚˜๋ˆ„๊ณ , ์ •์ƒ๊ฐ€๊ฒฉ ๋ถ„์„์˜ ์ƒ์„ธํ•œ ํ‹€์„ ์ œ์‹œํ•˜๊ณ ์ž ํ•œ๋‹ค. ํ˜„ํ–‰ ๊ตญ์กฐ๋ฒ•์€ ๋ฌดํ˜•์ž์‚ฐ์˜ ๊ฐœ๋ฐœ๊ณผ ๊ด€๋ จํ•œ ์›๊ฐ€๋ถ„๋‹ด์•ฝ์ •์— ๊ด€ํ•˜์—ฌ๋งŒ ๊ทœ์ •ํ•˜๊ณ  ์žˆ๋Š”๋ฐ, ๊ทธ ์ ์šฉ๋ฒ”์œ„๋ฅผ ๋ฌดํ˜•์ž์‚ฐ์— ํ•œ์ •ํ•  ํ•„์š”๋Š” ์—†๋‹ค๊ณ  ์ƒ๊ฐํ•œ๋‹ค. ๋˜ํ•œ ๊ตญ์กฐ๋ฒ•์—์„œ ์˜ˆ์ƒํŽธ์ต์˜ ์ธก์ •ํ•˜๋Š” ์—ฌ๋Ÿฌ ๊ฐ€์ง€ ์ง€ํ‘œ๋ฅผ ๋ณ‘๋ ฌ์ ์œผ๋กœ ๋‚˜์—ดํ•˜๊ณ  ์žˆ๋Š”๋ฐ, ๊ฐ ์‚ฌ์•ˆ์˜ ํŠน์ˆ˜์„ฑ์— ๊ธฐ์ดˆํ•˜์—ฌ ๊ฐ€์žฅ ์ ์ ˆํ•œ ์ง€ํ‘œ๋ฅผ ์„ ํƒํ•˜๊ธฐ ์œ„ํ•œ ๊ธฐ์ค€์„ ์ œ์‹œํ•  ํ•„์š”๊ฐ€ ์žˆ๋‹ค. ํ•œํŽธ ์›๊ฐ€๋ถ„๋‹ด์•ฝ์ •์— ๊ด€ํ•ด BEPS ํ”„๋กœ์ ํŠธ๊ฐ€ ์ œ์‹œํ•˜๋Š” ๋ถ„์„์˜ ํ‹€์ด ์ œ๋Œ€๋กœ ์ž‘๋™ํ•˜์—ฌ ๊ฐ ์ฐธ์—ฌ์ž๋“ค ์‚ฌ์ด์— ์ƒ๋Œ€์  ๊ณตํ—Œ๋„์˜ ์‚ฐ์ •์ด ๊ฐ€๋Šฅํ•˜๋‹ค๋ฉด, ์ด์ต๋ถ„ํ• ๋ฐฉ๋ฒ•์—์„œ์˜ ๋…ผ์˜์™€ ๊ฐ™์ด ์ „ํ†ต์ ์ธ ์˜๋ฏธ์˜ ๋…๋ฆฝ๊ธฐ์—…์˜ ์›์น™์ด ์‚ฌ์‹ค์ƒ ์ž‘์šฉํ•˜์ง€ ์•Š๋Š” ๊ฒฐ๋ก ์— ์ด๋ฅผ ์ˆ˜๋„ ์žˆ๋‹ค. BEPS ํ”„๋กœ์ ํŠธ๋ฅผ ๋Œ€๋ฒ•์›์˜ ํŒ๋ก€๋‚˜ ์กฐ์„ธ์‹ฌํŒ์›์˜ ๊ฒฐ์ •๋ก€์— ๋‚˜ํƒ€๋‚œ ์‚ฌ์•ˆ์— ๋Œ€์ž…ํ•˜์—ฌ ๋ณด๋ฉด, ์‚ฌ์•ˆํ•ด๊ฒฐ์— ๋Œ€ํ•ด ํ•ฉ๋ฆฌ์ ์ธ ๋ถ„์„์˜ ํ‹€์„ ์ œ๊ณตํ•˜๊ณ , ๊ณ ๋ ค์‚ฌํ•ญ์„ ๋‹ค์–‘ํ•˜๊ฒŒ ์ œ์‹œํ•จ์œผ๋กœ์จ ๊ณผ์„ธ๋‹น๊ตญ์˜ ์ž์˜์ ์ธ ์ •์ƒ๊ฐ€๊ฒฉ ์‚ฐ์ถœ์„ ๋ฐฉ์ง€ํ•˜๊ณ , ๊ทธ์— ๊ด€ํ•œ ํŒ๋‹จ์ž๋ฃŒ๋ฅผ ์ œ๊ณตํ•˜์—ฌ ์ค„ ๊ฒƒ์œผ๋กœ ๋ณด์ธ๋‹ค. ๊ทธ๋Ÿฌ๋‚˜ ํ•œํŽธ์œผ๋กœ๋Š” ์ด์ „๊ฐ€๊ฒฉ์„ธ์ œ๊ฐ€ ๊ฐ€์ง„ ์ œ๋„ ์ž์ฒด์˜ ํ•œ๊ณ„๋กœ ์ธํ•ด ์—ฌ์ „ํžˆ ๋ช…ํ™•ํ•œ ํ•˜๋‚˜์˜ ๋‹ต์„ ์ œ์‹œํ•˜๊ณ  ์žˆ์ง€ ๋ชปํ•˜๊ณ , ๊ทธ ๋ถˆ๋ณต๊ณผ์ •์—์„œ ์„ธ๋ฌด๋‹น๊ตญ ๋ฐ ๋‚ฉ์„ธ์ž ๋ชจ๋‘์—๊ฒŒ ๊ณผ๋„ํ•œ ๋น„์šฉ์ด ์†Œ์š”๋  ์ˆ˜ ์žˆ๋‹ค๋Š” ๋‹จ์ ๋„ ๊ฐ€์ง€๊ณ  ์žˆ๋‹ค. ๋…๋ฆฝ๊ธฐ์—…์˜ ์›์น™์— ๊ธฐ์ดˆํ•œ ์ด์ „๊ฐ€๊ฒฉ์„ธ์ œ๊ฐ€ ์—ฌ์ „ํžˆ ์ „ ์„ธ๊ณ„์ ์œผ๋กœ ํ†ต์šฉ๋˜๊ณ  ์žˆ์ง€๋งŒ, BEPS ํ”„๋กœ์ ํŠธ๋Š” ๊ทธ ๋‚ด์šฉ์— ๋น„์ถ”์–ด ๋ณผ ๋•Œ ์ „ํ†ต์ ์ธ ๋…๋ฆฝ๊ธฐ์—…์˜ ์›์น™์„ ๊ณ ์ˆ˜ํ•˜๊ธฐ๋ณด๋‹ค๋Š” ์ด์ต๋ถ„ํ• ๋ฐฉ๋ฒ•์˜ ํ™œ์šฉ์ด๋‚˜ ์›๊ฐ€๋ถ„๋‹ด์•ฝ์ • ์ž์ฒด์˜ ๊ณ ์œ ์„ฑ ๋“ฑ์„ ๊ฐ•์กฐํ•จ์œผ๋กœ์จ ๋””์ง€ํ„ธ ๊ฒฝ์ œํ™” ์‹œ๋Œ€์— ์ฃผ๋กœ ๋ฌธ์ œ๊ฐ€ ๋˜๋Š” ์ด์Šˆ์— ๋Œ€ํ•ด ๋ณด๋‹ค ํ•ฉ๋ฆฌ์ ์ด๊ณ  ์ฒด๊ณ„์ ์ธ ์ ‘๊ทผ๋ฐฉ์‹์„ ์ œ์‹œํ•˜๊ณ  ์žˆ์œผ๋ฉฐ, ๊ตฌ์ฒด์ ์ธ ๋ถ„์„์š”์†Œ๋ฅผ ๊ณต๊ฐœํ•จ์œผ๋กœ์จ ํ–ฅํ›„ ์ด์ „๊ฐ€๊ฒฉ์„ธ์ œ์˜ ๋ฐœ์ „์— ์žˆ์–ด ํฐ ์—ญํ• ์„ ํ•˜๋ฆฌ๋ผ๊ณ  ๋ณธ๋‹ค.์ œ1์žฅ ์„œ๋ก  1 ์ œ1์ ˆ ์—ฐ๊ตฌ์˜ ๋ชฉ์  1 ์ œ2์ ˆ ์—ฐ๊ตฌ์˜ ๋ฒ”์œ„ ๋ฐ ๋…ผ์˜์˜ ์ˆœ์„œ 4 ์ œ2์žฅ BEPS ํ˜„์ƒ๊ณผ BEPS ํ”„๋กœ์ ํŠธ 5 ์ œ1์ ˆ ๋‹ค๊ตญ์ ๊ธฐ์—…์˜ ๋“ฑ์žฅ๊ณผ ์กฐ์„ธ์ „๋žต 5 1. ๊ฑฐ์ฃผ์ง€์ฃผ์˜์™€ ์›์ฒœ์ง€์ฃผ์˜ 5 ๊ฐ€. ์˜์˜ 5 ๋‚˜. ๊ฑฐ์ฃผ์ง€์ฃผ์˜์™€ ์›์ฒœ์ง€์ฃผ์˜์˜ ํ˜ผ์žฌ 7 2. ๋‹ค๊ตญ์ ๊ธฐ์—…๊ณผ ์ด์ „๊ฐ€๊ฒฉ 8 3. BEPS ํ˜„์ƒ ๋ฐ ๊ทธ ๊ตฌ์กฐ์  ์š”์ธ 9 ๊ฐ€. BEPS ํ˜„์ƒ 9 ๋‚˜. BEPS ํ˜„์ƒ์˜ ๊ตฌ์กฐ์  ์š”์ธ 9 ์ œ2์ ˆ BEPS ํ”„๋กœ์ ํŠธ 11 1. ๋ฐฐ๊ฒฝ 11 2. ๋‚ด์šฉ 12 ๊ฐ€. ๊ฐœ๊ด€ 13 ๋‚˜. BEPS ํ”„๋กœ์ ํŠธ์˜ ์˜ํ–ฅ 15 3. BEPS ํ”„๋กœ์ ํŠธ ์ค‘ ์ด์ „๊ฐ€๊ฒฉ์„ธ์ œ(Action 8 ๋‚ด์ง€ 10, 13)์˜ ์ฃผ์š” ๋‚ด์šฉ 18 ๊ฐ€. ๋…๋ฆฝ๊ธฐ์—…์›์น™์˜ ์ ์šฉ์ง€์นจ 18 ๋‚˜. ๋ฌดํ˜•์ž์‚ฐ 21 ๋‹ค. ์ €๋ถ€๊ฐ€๊ฐ€์น˜ ๊ทธ๋ฃน๋‚ด๋ถ€์šฉ์—ญ 27 ๋ผ. ์›๊ฐ€๋ถ„๋‹ด์•ฝ์ • 31 ๋งˆ. ์ด์ „๊ฐ€๊ฒฉ์˜ ๋ฌธ์„œํ™” ๋ฐ ๊ตญ๊ฐ€๋ณ„ ๋ณด๊ณ ์„œ์— ๋Œ€ํ•œ ์ง€์นจ 33 ์ œ3์žฅ BEPS ํ”„๋กœ์ ํŠธ์˜ ์‹ค์ฒœ์  ์˜๋ฏธ 35 ์ œ1์ ˆ ๋…ผ์˜์˜ ์˜์˜ 35 ์ œ2์ ˆ BEPS ๋ณด๊ณ ์„œ์™€ OECD ์ด์ „๊ฐ€๊ฒฉ์ง€์นจ ๋ฐ ๊ตญ์กฐ๋ฒ•๊ณผ์˜ ๋น„๊ต 38 1. ๋ฌดํ˜•์ž์‚ฐ 38 ๊ฐ€. OECD ์ด์ „๊ฐ€๊ฒฉ์ง€์นจ ์ œ6์žฅ์˜ ์ฃผ์š” ๋‚ด์šฉ 38 ๋‚˜. ๊ตญ์กฐ๋ฒ•์˜ ๊ด€๋ จ ๊ทœ์ • 40 ๋‹ค. BEPS ํ”„๋กœ์ ํŠธ์˜ ์˜์˜ 41 ๋ผ. ๊ตญ์กฐ๋ฒ• ๊ฐœ์ •์˜ ํ•„์š”์„ฑ ๋ฐ ๊ทธ ๋ฐฉํ–ฅ 45 2. ์ €๋ถ€๊ฐ€๊ฐ€์น˜ ๊ทธ๋ฃน๋‚ด๋ถ€์šฉ์—ญ 54 ๊ฐ€. OECD ์ด์ „๊ฐ€๊ฒฉ์ง€์นจ ์ œ7์žฅ์˜ ์ฃผ์š” ๋‚ด์šฉ 54 ๋‚˜. ๊ตญ์กฐ๋ฒ•์˜ ๊ด€๋ จ ๊ทœ์ • 55 ๋‹ค. BEPS ํ”„๋กœ์ ํŠธ์˜ ์˜์˜ 55 ๋ผ. ๊ตญ์กฐ๋ฒ• ๊ฐœ์ •์˜ ํ•„์š”์„ฑ ๋ฐ ๊ทธ ๋ฐฉํ–ฅ 59 3. ์›๊ฐ€๋ถ„๋‹ด์•ฝ์ • 62 ๊ฐ€. OECD ์ด์ „๊ฐ€๊ฒฉ์ง€์นจ ์ œ8์žฅ์˜ ์ฃผ์š” ๋‚ด์šฉ 62 ๋‚˜. ๊ตญ์กฐ๋ฒ•์˜ ๊ด€๋ จ ๊ทœ์ • 63 ๋‹ค. BEPS ํ”„๋กœ์ ํŠธ์˜ ์˜์˜ 65 ๋ผ. ๊ตญ์กฐ๋ฒ• ๊ฐœ์ •์˜ ํ•„์š”์„ฑ ๋ฐ ๊ทธ ๋ฐฉํ–ฅ 67 ์ œ3์ ˆ ํŒ๋ก€์‚ฌ์•ˆ์— ๋Œ€ํ•œ BEPS ํ”„๋กœ์ ํŠธ์˜ ๋„์ž… 71 1. ๋„์ž… 71 ๊ฐ€. BEPS ํ”„๋กœ์ ํŠธ์˜ ํŠน์ง• ์š”์•ฝ 71 ๋‚˜. ํŒ๋ก€์‚ฌ์•ˆ์— ๋Œ€์ž…์˜ ์˜์˜ 72 2. ๋ฌดํ˜•์ž์‚ฐ 72 ๊ฐ€. ๋Œ€๋ฒ•์› 2001. 10. 23. ์„ ๊ณ  99๋‘3423 ํŒ๊ฒฐ 72 ๋‚˜. ์กฐ์„ธ์‹ฌํŒ์› 2011. 12. 30.์ž 2009์„œ2136 ๊ฒฐ์ • ๋ฐ ์กฐ์„ธ์‹ฌํŒ์› 2010. 11. 23.์ž 2008์„œ2529 ๊ฒฐ์ • 83 3. ์ €๋ถ€๊ฐ€๊ฐ€์น˜ ๊ทธ๋ฃน๋‚ด๋ถ€์šฉ์—ญ 90 ๊ฐ€. ๊ตญ์„ธ์‹ฌํŒ์› 2004. 5. 4.์ž 2003์ค‘3619 ๊ฒฐ์ • 90 ๋‚˜. ์กฐ์„ธ์‹ฌํŒ์› 2014. 1. 22.์ž 2011์„œ2372 ๊ฒฐ์ • 95 ๋‹ค. ๋Œ€๋ฒ•์› 2007. 8. 24. ์„ ๊ณ  2007๋‘11429 ํŒ๊ฒฐ 101 4. ์›๊ฐ€๋ถ„๋‹ด์•ฝ์ • 107 ๊ฐ€. ๋ฒ•์›์˜ ํŒ๊ฒฐ 107 ๋‚˜. ๊ตญ์„ธ์‹ฌํŒ์› 2007. 3. 26.์ž 2006์„œ2503 ๊ฒฐ์ • 107 5. ์†Œ๊ฒฐ๋ก  111 ๊ฐ€. ๋ฌดํ˜•์ž์‚ฐ์— ๊ด€ํ•˜์—ฌ 111 ๋‚˜. ์ €๋ถ€๊ฐ€๊ฐ€์น˜ ๊ทธ๋ฃน๋‚ด๋ถ€์šฉ์—ญ์— ๊ด€ํ•˜์—ฌ 114 ๋‹ค. ์›๊ฐ€๋ถ„๋‹ด์•ฝ์ •์— ๊ด€ํ•˜์—ฌ 116 ์ œ4์žฅ ๊ฒฐ๋ก  118 ์ฐธ๊ณ ๋ฌธํ—Œ 121 Abstract 124Maste

    The Efficacy of Hypnotherapy in the Treatment of Irritable Bowel Syndrome: A Systematic Review and Meta-analysis

    Get PDF
    BACKGROUND/AIMS: Hypnotherapy is considered as a promising intervention for irritable bowel syndrome (IBS), but the evidence is still limited. The aims of this study were to conduct a systematic review and meta-analysis to estimate the efficacy of hypnotherapy for the treatment of IBS. METHODS: A literature search was performed using MEDLINE (PubMed), Embase, PsycINFO and the Cochrane Central Register of Controlled Trials (CENTRAL database). Only randomized controlled trials that compared hypnotherapy with any other conven-tional treatment or no treatment in patients with IBS were included. Studies had to report outcomes as IBS symptom score or quality of life. The mean change in outcome score was used to pool these outcomes for the meta-analysis. Data were syn-thesized using the standardized mean difference for continuous data. RESULTS: Seven randomized controlled trials (6 papers) involving 374 patients with IBS were identified. Performance bias was high in all trials because it was impossible to blind participants and therapists in this type of intervention. The outcomes in this meta-anal-ysis were evaluated at 3 months for short-term effects and at 1 year for long-term effects. The change in abdominal pain score at 3 months was significant in the hypnotherapy group (standardized mean difference, -0.83; 95% CI, -1.65 to -0.01). Three of the 4 trials showed greater improvement in overall gastrointestinal symptoms in the hypnotherapy group. CONCLUSIONS: This study provides clearer evidence that hypnotherapy has beneficial short-term effects in improving gastrointestinal symptoms of patients with IBS.ope

    Comparison of surgical outcomes between integrated robotic and conventional laparoscopic surgery for distal gastrectomy: a propensity score matching analysis

    Get PDF
    This study was aimed to compare the surgical outcomes between conventional laparoscopic distal gastrectomy (CLDG) and integrated robotic distal gastrectomy (IRDG) which used both Single-Site platform and fluorescence image-guided surgery technique simultaneously. Retrospective data of 56 patients who underwent IRDG and 152 patients who underwent CLDG were analyzed. Propensity score matching analysis was performed to control selection bias using age, sex, American Society of Anesthesiologists score, and body mass index. Fifty-one patients were selected for each group. Surgical success was defined as the absence of open conversion, readmission, major complications, positive resection margin, and inadequate lymph node retrieval (<16). Patients characteristics and surgical outcomes of IRDG group were comparable to those of CLDG group, except longer operation time (159.5 vs. 131.7โ€‰min; Pโ€‰<โ€‰0.001), less blood loss (30.7 vs. 73.3โ€‰mL; Pโ€‰=โ€‰0.004), higher number of retrieved lymph nodes (LNs) (50.4 vs. 41.9 LNs; Pโ€‰=โ€‰0.025), and lower readmission rate (2.0 vs. 15.7%; Pโ€‰=โ€‰0.031). Surgical success rate was higher in IRDG group compared to CLDG group (98.0 vs. 82.4%; Pโ€‰=โ€‰0.008). In conclusion, this study found that IRDG provides the benefits of higher number of retrieved LNs, less blood loss, and lower readmission rate compared with CLDG in patients with early gastric cancer.ope
    • โ€ฆ
    corecore