3 research outputs found

    The Case of Mongolia

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    ํ•™์œ„๋…ผ๋ฌธ(์„์‚ฌ) -- ์„œ์šธ๋Œ€ํ•™๊ต๋Œ€ํ•™์› : ๊ณต๊ณผ๋Œ€ํ•™ ํ˜‘๋™๊ณผ์ • ๊ธฐ์ˆ ๊ฒฝ์˜ยท๊ฒฝ์ œยท์ •์ฑ…์ „๊ณต, 2021.8. Jorn Altmann.Small and medium enterprises (SMEs) are considered key players in any country's social and economic development. Adopting innovative technologies such as Big Data Analytics (BDA) can bring better performance and competitive advantage for SMEs, which is important for a country's economic growth. This study aims to assess the main challenges and potentials of BDA adoptions in SMEs and examine the impacts of its adoption into business performance for SMEs in developing countries aspect. To achieve the study's goal, a systematic literature review (SLR) is conducted regarding the adoption of BDA in SMEs. The most common SLR method among the researchers in information system research, which was initiated by Kitchencham et al. (Kitchenham, Budgen, & Brereton, 2015) and Okoli et al.(Okoli & Schabram, 2010), is adapted in the study. In doing so, the SLR is focused on defining SMEs within various aspects and is directed to determine the most common influencing factors in BDA adoption in SMEs. In the result of the SLR, widely discussed 34 distinct influencing factors are identified in the adoption of BDA in SMEs from the previous literature. In addition, the hypotheses are developed based on the influencing factors, which show consensus among the researchers. After that, a conceptual framework is developed for developing the country aspect and control variables, and the moderating variablesโ€™ effect is also estimated. To evaluate hypotheses and the conceptual framework, an online questionnaire is conducted among Mongolia SMEs which run businesses in various industries. The online questionnaire is distributed to decision-makers and information technology specialists in the firm. In total, 170 respondents participated in the online survey. Based on the survey result, hypotheses are tested. As a consequence, the collected data and proposed framework are analyzed by using Partial Least Squares (PLS). This is a method of Structure Equation Modeling (SEM) that allows investigating the inter-relationship between the latent and observed variables. In terms of statistical software tools, Smart PLS v3.3.3 was employed, which is one of the useriv friendly tools for data analysis. Finally, policies and recommendations are deployed based on the findings.์ค‘์†Œ๊ธฐ์—… (SME)์€ ๋ชจ๋“  ๊ตญ๊ฐ€์˜ ์‚ฌํšŒ ๋ฐ ๊ฒฝ์ œ ๊ฐœ๋ฐœ์—์„œ ํ•ต์‹ฌ์ ์ธ ์—ญํ• ์„ ํ•˜๊ณ  ์žˆ๋Š” ๊ฒƒ์œผ๋กœ ๊ฐ„์ฃผ๋œ๋‹ค. ๋น… ๋ฐ์ดํ„ฐ ๋ถ„์„ (BDA)๊ณผ ๊ฐ™์€ ํ˜์‹ ์ ์ธ ๊ธฐ์ˆ ์˜ ์ฑ„ํƒ์€ ๊ตญ๊ฐ€ ๊ฒฝ์ œ ์„ฑ์žฅ์— ์ค‘์š”ํ•œ ์—ญํ• ์„ ํ•˜๋Š” ์žˆ๋Š” ์ค‘์†Œ๊ธฐ์—…์— ๋” ๋‚˜์€ ๊ฒฝ์˜ ์„ฑ๊ณผ์™€ ๊ฒฝ์Ÿ๋ ฅ์„ ๊ฐ€์ ธ์˜ฌ ์ˆ˜ ์žˆ๋‹ค. ๋ณธ ์—ฐ๊ตฌ๋Š” ์ค‘์†Œ๊ธฐ์—…์—์„œ BDA ์ฑ„ํƒํ•˜๋Š” ๋ฐ์— ์žˆ๋Š” ์ฃผ์š” ๊ณผ์ œ์™€ ์ž ์žฌ๋ ฅ์„ ํ‰๊ฐ€ํ•˜๊ณ  ๊ฐœ๋ฐœ ๋„์ƒ๊ตญ ์ธก๋ฉด์—์„œ BDA ์ฑ„ํƒ์€ ์ค‘์†Œ๊ธฐ์—…์˜ ๊ฒฝ์˜ ์„ฑ๊ณผ์— ๋Œ€ํ•œ ์˜ํ–ฅ์„ ์กฐ์‚ฌํ•˜๋Š” ๊ฒƒ์„ ๋ชฉํ‘œ๋กœ ํ•œ๋‹ค. ๋ณธ ์—ฐ๊ตฌ์˜ ๋ชฉํ‘œ๋ฅผ ์ด๋ฃจ๊ธฐ ์œ„ํ•ด ์šฐ์„  SME์—์„œ BDA ์ฑ„ํƒ๊ณผ ๊ด€๋ จํ•œ ๋ฌธํ—Œ๊ฒ€ํ† (systematic literature review (SLR))๋ฅผ ํ•˜์˜€๋‹ค. ์ •๋ณด ์‹œ์Šคํ…œ ์—ฐ๊ตฌ์ž๋“ค ์ค‘์— Kitchencham et al [1]๊ณผ Okoli et al. [2]์— ์˜ํ•ด ์‹œ์ž‘๋œ ์ •๋ณด ์‹œ์Šคํ…œ ์—ฐ๊ตฌ๋Š” ๊ฐ€์žฅ ์ผ๋ฐ˜์ ์ธ SLR ๋ฐฉ๋ฒ•์ด๋ผ๊ณ  ํ•  ์ˆ˜ ์žˆ๋‹ค. ์ด ๋ฐฉ๋ฒ•์€ ๋ณธ ์—ฐ๊ตฌ์— ์ ์šฉ๋ฉ๋‹ˆ๋‹ค. ๋ณธ ์—ฐ๊ตฌ๋Š” ๋ฌธํ—Œ ๊ฒ€ํ† ๋ฅผ ํ†ตํ•ด์„œ ๋‹ค์–‘ํ•œ ์ธก๋ฉด์—์„œ SME๋ฅผ ์ •์˜ํ•˜๋Š” ๋ฐ ์ดˆ์ ์„ ๋งž์ถ”๊ณ  ์žˆ์œผ๋ฉฐ SME์—์„œ BDA ์ฑ„ํƒ์˜ ๊ฐ€์žฅ ์ผ๋ฐ˜์ ์ธ ์˜ํ–ฅ ์š”์ธ์„ ๋ฐํ˜”๋‹ค . ๋ฌธํ—Œ ๊ฒ€ํ† ํ•œ ๊ฒฐ๊ณผ๋ฅผ ๋ณด๋ฉด, ์„ ํ–‰ ์—ฐ๊ตฌ์—์„œ SME์˜ BDA ์ฑ„ํƒ์— ์žˆ์–ด์„œ 34 ๊ฐœ์˜ ๋šœ๋ ทํ•œ ์˜ํ–ฅ ์š”์ธ์„ ๋…ผ์˜ํ–ˆ๋‹ค๋Š” ๊ฒƒ์„ ํ™•์ธ๋˜์—ˆ๋‹ค. ๋ณธ ์—ฐ๊ตฌ์˜ ๊ฐ€์„ค์€ ์—ฐ๊ตฌ์ž๋“ค์˜ ์ผ์น˜ํ•œ ๊ด€์ ์„ ๋ณด์—ฌ์ฃผ๋Š” ์˜ํ–ฅ ์š”์ธ์„ ๊ธฐ๋ฐ˜์œผ๋กœ ์„ค์ •ํ•˜์—ˆ๋‹ค. ๊ทธ ๋‹ค์Œ์— ๊ฐœ๋ฐœ ๋„์ƒ๊ตญ์„ ์œ„ํ•œ ๊ฐœ๋…์˜ ์ฒด๊ณ„๋ฅผ ์„ธ์šฐ๊ณ  ํ†ต์ œ ๋ณ€์ธ๊ณผ ์กฐ์ ˆ ๋ณ€์ธ์˜ ์˜ํ–ฅ๋„ ์ถ”์ •ํ•˜์˜€๋‹ค. ๊ฐ€์„ค๊ณผ ๊ฐœ๋… ์ฒด๊ณ„๋ฅผ ํ‰๊ฐ€ํ•˜๊ธฐ ์œ„ํ•ด ๋ณธ ์—ฐ๊ตฌ๋Š” ๋ชฝ๊ณจ์˜ ๋‹ค์–‘ํ•œ ์‚ฌ์—…์„ ์šด์˜ํ•˜๊ณ  ์žˆ๋Š” ์ค‘์†Œ๊ธฐ์—…์„ ๋Œ€์ƒ์œผ๋กœ ์˜จ๋ผ์ธ ์„ค๋ฌธ์กฐ์‚ฌ๋ฅผ ์‹ค์‹œํ•˜์˜€๋‹ค. ์˜จ๋ผ์ธ 141 ์„ค๋ฌธ์กฐ์‚ฌ์˜ ์ฐธ์—ฌ์ž๋Š” ํšŒ์‚ฌ์˜ ์ฃผ์š” ์˜์‚ฌ ๊ฒฐ์ •์ž ๋ฐ ์ •๋ณด ๊ธฐ์ˆ  ์ „๋ฌธ๊ฐ€์˜€๋‹ค. ์ด๋ฅผ ํ†ตํ•ด ์ˆ˜์ง‘ ๋œ ๋ฐ์ดํ„ฐ์™€ ์ œ์•ˆ ๋œ ์ฒด๊ณ„๋ฅผ PLS (Partial Least Squire)๋ฅผ ์‚ฌ์šฉํ•˜์—ฌ ๋ถ„์„ํ•˜์˜€๋‹ค. ์ด ๋ฐฉ๋ฒ•์€ ์ž ์žฌ ๋ณ€์ˆ˜์™€ ๊ด€์ฐฐ ๋ณ€์ˆ˜ ๊ฐ„์˜ ์ƒํ˜ธ ๊ด€๊ณ„๋ฅผ ์กฐ์‚ฌ ํ•  ์ˆ˜์žˆ๋Š” ๊ตฌ์กฐ ๋ฐฉ์ •์‹ ๋ชจํ˜• (SEM) ๋ฐฉ๋ฒ•์ด๋‹ค. ํ†ต๊ณ„ ์†Œํ”„ํŠธ์›จ์–ด ๋„๊ตฌ ์ธก๋ฉด์—์„œ๋Š” ์ ‘ํ•˜๊ธฐ๊ฐ€ ์‰ฌ์šด ๋ฐ์ดํ„ฐ ๋ถ„์„ ๋„๊ตฌ ์ค‘ ํ•˜๋‚˜์ธ SmartPLS v3.3.3 ์„ ์ด์šฉํ•˜์˜€๋‹ค. ๋งˆ์ง€๋ง‰์œผ๋กœ, ๋ณธ ์—ฐ๊ตฌ๋Š” ๋ถ„์„ํ•œ ๊ฒฐ๊ณผ๋ฅผ ๊ธฐ๋ฐ˜ํ•˜์—ฌ ์ •์ฑ… ๋ฐ ์ œ์•ˆ์„ ์ œ์‹œํ•˜์˜€๋‹ค.Chapter 1. Introduction 1 Chapter 2. Background on Big Data Analytics Adoption 6 2.1 Defination of Big Data 6 2.2 Defination of Small and Medium enterprises 9 2.3 Role of Big Data 10 2.4 Charateristics of developing countries 11 Chapter 3. Methodology and Model Design 13 3.1 Methdogology fused for analyzing Big Data Analytics in Small and Medium Enterprises in Developing countries 13 3.2. Model design 26 3.2.1 Factors 26 3.2.2. Theories 28 3.2.3. Classification of factors into categories 36 3.2.4. Impact on developing country 46 3.2.5. Impact on different industries 50 3.2.6. Theoritical background and hypothesis development 51 3.2.7. Technological context 54 3.2.8. Organizational context 58 3.2.9. Environmental context 61 3.2.10. Moderating variables 63 3.2.11. Control variables 65 Chapter 4. Framework for Mongolian case 67 4.1. Mongolia 67 4.2. Data collection 68 4.3. Basic understanding on moderating effect 70 4.4. Data analysis 71 4.5. Results 74 4.5.1. Reliability and validity 74 4.5.2. Structual model analysis 78 4.5.3. Moderating variables 82 Chapter 5. Conclusion 85 5.1. Discussion 85 5.1.1. Technological context 85 5.1.2. Organizational context 88 5.1.3. Environmental context 88 5.2. Contrubitions 89 5.3. Policy implication 90 5.4. Limitation and outlok 91 Appendix.1 93 Appendix.2 110 Bibliography 115 Abstract in Korean 140์„

    How Could Venture Capitalists Improve Their Performance at Fund Management Level via Better Allocating Their Financial and Human Capitals - A Cross-Fund Approach

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    Venture capitalistsโ€™ performance has been studied at the levels of portfolio company and fund. But no similar study at the fund management entity level has been documented. This study fills in the gap to examine the drivers of the venture capitalistsโ€™ performance at the fund management entity level. This study uses the capital allocation theory to develop five hypotheses on its performance. Each of them embodies an aspect of the feature of capital allocation in the venture capital investment process. With an expanded concept of capital for capital allocation, this study examines the allocation of both the financial capital and the human capital of venture capitalists. The capital allocation in venture capital investment is a process of capital leveraging and channeling by venture capitalists at the fund management level to the portfolio companies. Our results show that the amount of leveraging financial capital channeled to the individual portfolio company on average is negatively associated with venture capitalistsโ€™ performance at the fund management level, while the reserve ratio of leveraging the financial capital, the degree of human capital clustering, and the quality of human capital are positively related to venture capitalistsโ€™ performance at the fund management level
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