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The Case of Mongolia
ํ์๋
ผ๋ฌธ(์์ฌ) -- ์์ธ๋ํ๊ต๋ํ์ : ๊ณต๊ณผ๋ํ ํ๋๊ณผ์ ๊ธฐ์ ๊ฒฝ์ยท๊ฒฝ์ ยท์ ์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.์ค์๊ธฐ์
(SME)์ ๋ชจ๋ ๊ตญ๊ฐ์ ์ฌํ ๋ฐ ๊ฒฝ์ ๊ฐ๋ฐ์์
ํต์ฌ์ ์ธ ์ญํ ์ ํ๊ณ ์๋ ๊ฒ์ผ๋ก ๊ฐ์ฃผ๋๋ค. ๋น
๋ฐ์ดํฐ ๋ถ์ (BDA)๊ณผ
๊ฐ์ ํ์ ์ ์ธ ๊ธฐ์ ์ ์ฑํ์ ๊ตญ๊ฐ ๊ฒฝ์ ์ฑ์ฅ์ ์ค์ํ ์ญํ ์ ํ๋
์๋ ์ค์๊ธฐ์
์ ๋ ๋์ ๊ฒฝ์ ์ฑ๊ณผ์ ๊ฒฝ์๋ ฅ์ ๊ฐ์ ธ์ฌ ์ ์๋ค. ๋ณธ
์ฐ๊ตฌ๋ ์ค์๊ธฐ์
์์ BDA ์ฑํํ๋ ๋ฐ์ ์๋ ์ฃผ์ ๊ณผ์ ์ ์ ์ฌ๋ ฅ์
ํ๊ฐํ๊ณ ๊ฐ๋ฐ ๋์๊ตญ ์ธก๋ฉด์์ BDA ์ฑํ์ ์ค์๊ธฐ์
์ ๊ฒฝ์ ์ฑ๊ณผ์
๋ํ ์ํฅ์ ์กฐ์ฌํ๋ ๊ฒ์ ๋ชฉํ๋ก ํ๋ค. ๋ณธ ์ฐ๊ตฌ์ ๋ชฉํ๋ฅผ ์ด๋ฃจ๊ธฐ ์ํด
์ฐ์ SME์์ BDA ์ฑํ๊ณผ ๊ด๋ จํ ๋ฌธํ๊ฒํ (systematic literature
review (SLR))๋ฅผ ํ์๋ค.
์ ๋ณด ์์คํ
์ฐ๊ตฌ์๋ค ์ค์ Kitchencham et al [1]๊ณผ Okoli et
al. [2]์ ์ํด ์์๋ ์ ๋ณด ์์คํ
์ฐ๊ตฌ๋ ๊ฐ์ฅ ์ผ๋ฐ์ ์ธ SLR
๋ฐฉ๋ฒ์ด๋ผ๊ณ ํ ์ ์๋ค. ์ด ๋ฐฉ๋ฒ์ ๋ณธ ์ฐ๊ตฌ์ ์ ์ฉ๋ฉ๋๋ค. ๋ณธ ์ฐ๊ตฌ๋ ๋ฌธํ
๊ฒํ ๋ฅผ ํตํด์ ๋ค์ํ ์ธก๋ฉด์์ SME๋ฅผ ์ ์ํ๋ ๋ฐ ์ด์ ์ ๋ง์ถ๊ณ
์์ผ๋ฉฐ SME์์ BDA ์ฑํ์ ๊ฐ์ฅ ์ผ๋ฐ์ ์ธ ์ํฅ ์์ธ์ ๋ฐํ๋ค .
๋ฌธํ ๊ฒํ ํ ๊ฒฐ๊ณผ๋ฅผ ๋ณด๋ฉด, ์ ํ ์ฐ๊ตฌ์์ SME์ BDA ์ฑํ์ ์์ด์
34 ๊ฐ์ ๋๋ ทํ ์ํฅ ์์ธ์ ๋
ผ์ํ๋ค๋ ๊ฒ์ ํ์ธ๋์๋ค.
๋ณธ ์ฐ๊ตฌ์ ๊ฐ์ค์ ์ฐ๊ตฌ์๋ค์ ์ผ์นํ ๊ด์ ์ ๋ณด์ฌ์ฃผ๋ ์ํฅ
์์ธ์ ๊ธฐ๋ฐ์ผ๋ก ์ค์ ํ์๋ค. ๊ทธ ๋ค์์ ๊ฐ๋ฐ ๋์๊ตญ์ ์ํ ๊ฐ๋
์
์ฒด๊ณ๋ฅผ ์ธ์ฐ๊ณ ํต์ ๋ณ์ธ๊ณผ ์กฐ์ ๋ณ์ธ์ ์ํฅ๋ ์ถ์ ํ์๋ค. ๊ฐ์ค๊ณผ
๊ฐ๋
์ฒด๊ณ๋ฅผ ํ๊ฐํ๊ธฐ ์ํด ๋ณธ ์ฐ๊ตฌ๋ ๋ชฝ๊ณจ์ ๋ค์ํ ์ฌ์
์ ์ด์ํ๊ณ
์๋ ์ค์๊ธฐ์
์ ๋์์ผ๋ก ์จ๋ผ์ธ ์ค๋ฌธ์กฐ์ฌ๋ฅผ ์ค์ํ์๋ค. ์จ๋ผ์ธ
141
์ค๋ฌธ์กฐ์ฌ์ ์ฐธ์ฌ์๋ ํ์ฌ์ ์ฃผ์ ์์ฌ ๊ฒฐ์ ์ ๋ฐ ์ ๋ณด ๊ธฐ์ ์ ๋ฌธ๊ฐ์๋ค.
์ด๋ฅผ ํตํด ์์ง ๋ ๋ฐ์ดํฐ์ ์ ์ ๋ ์ฒด๊ณ๋ฅผ PLS (Partial Least
Squire)๋ฅผ ์ฌ์ฉํ์ฌ ๋ถ์ํ์๋ค. ์ด ๋ฐฉ๋ฒ์ ์ ์ฌ ๋ณ์์ ๊ด์ฐฐ ๋ณ์ ๊ฐ์
์ํธ ๊ด๊ณ๋ฅผ ์กฐ์ฌ ํ ์์๋ ๊ตฌ์กฐ ๋ฐฉ์ ์ ๋ชจํ (SEM) ๋ฐฉ๋ฒ์ด๋ค. ํต๊ณ
์ํํธ์จ์ด ๋๊ตฌ ์ธก๋ฉด์์๋ ์ ํ๊ธฐ๊ฐ ์ฌ์ด ๋ฐ์ดํฐ ๋ถ์ ๋๊ตฌ ์ค ํ๋์ธ
SmartPLS v3.3.3 ์ ์ด์ฉํ์๋ค. ๋ง์ง๋ง์ผ๋ก, ๋ณธ ์ฐ๊ตฌ๋ ๋ถ์ํ
๊ฒฐ๊ณผ๋ฅผ ๊ธฐ๋ฐํ์ฌ ์ ์ฑ
๋ฐ ์ ์์ ์ ์ํ์๋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