The Case of Mongolia

학위논문(석사) -- 서울대학교대학원 : 공과대학 협동과정 기술경영·경제·정책전공, 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 variables’ 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.중소기업 (SME)은 모든 국가의 사회 및 경제 개발에서 핵심적인 역할을 하고 있는 것으로 간주된다. 빅 데이터 분석 (BDA)과 같은 혁신적인 기술의 채택은 국가 경제 성장에 중요한 역할을 하는 있는 중소기업에 더 나은 경영 성과와 경쟁력을 가져올 수 있다. 본 연구는 중소기업에서 BDA 채택하는 데에 있는 주요 과제와 잠재력을 평가하고 개발 도상국 측면에서 BDA 채택은 중소기업의 경영 성과에 대한 영향을 조사하는 것을 목표로 한다. 본 연구의 목표를 이루기 위해 우선 SME에서 BDA 채택과 관련한 문헌검토(systematic literature review (SLR))를 하였다. 정보 시스템 연구자들 중에 Kitchencham et al [1]과 Okoli et al. [2]에 의해 시작된 정보 시스템 연구는 가장 일반적인 SLR 방법이라고 할 수 있다. 이 방법은 본 연구에 적용됩니다. 본 연구는 문헌 검토를 통해서 다양한 측면에서 SME를 정의하는 데 초점을 맞추고 있으며 SME에서 BDA 채택의 가장 일반적인 영향 요인을 밝혔다 . 문헌 검토한 결과를 보면, 선행 연구에서 SME의 BDA 채택에 있어서 34 개의 뚜렷한 영향 요인을 논의했다는 것을 확인되었다. 본 연구의 가설은 연구자들의 일치한 관점을 보여주는 영향 요인을 기반으로 설정하었다. 그 다음에 개발 도상국을 위한 개념의 체계를 세우고 통제 변인과 조절 변인의 영향도 추정하였다. 가설과 개념 체계를 평가하기 위해 본 연구는 몽골의 다양한 사업을 운영하고 있는 중소기업을 대상으로 온라인 설문조사를 실시하였다. 온라인 141 설문조사의 참여자는 회사의 주요 의사 결정자 및 정보 기술 전문가였다. 이를 통해 수집 된 데이터와 제안 된 체계를 PLS (Partial Least Squire)를 사용하여 분석하였다. 이 방법은 잠재 변수와 관찰 변수 간의 상호 관계를 조사 할 수있는 구조 방정식 모형 (SEM) 방법이다. 통계 소프트웨어 도구 측면에서는 접하기가 쉬운 데이터 분석 도구 중 하나인 SmartPLS v3.3.3 을 이용하였다. 마지막으로, 본 연구는 분석한 결과를 기반하여 정책 및 제안을 제시하였다.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 140석

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