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

Enablers and Inhibitors of AI-Powered Voice Assistants: A Dual-Factor Approach by Integrating the Status Quo Bias and Technology Acceptance Model

This study investigates the factors that build resistance and attitude towards AI voice assistants (AIVA). A theoretical model is proposed using the dual-factor framework by integrating status quo bias factors (sunk cost, regret avoidance, inertia, perceived value, switching costs, and perceived threat) and Technology Acceptance Model (TAM; perceived ease of use and perceived usefulness) variables. The study model investigates the relationship between the status quo factors and resistance towards adoption of AIVA, and the relationship between TAM factors and attitudes towards AIVA. A sample of four hundred and twenty was analysed using structural equation modeling to investigate the proposed hypotheses. The results indicate an insignificant relationship between inertia and resistance to AIVA. Perceived value was found to have a negative but significant relationship with resistance to AIVA. Further, the study also found that inertia significantly differs across gender (male/female) and age groupings. The study's framework and results are posited as adding value to the extant literature and practice, directly related to status quo bias theory, dual-factor model and TAM

Antecedents of business intelligence system use

This thesis was submitted for the award of Doctor of Philosophy and was awarded by Brunel University London.Organisational reliance on information has become vital for organisational competitiveness. With increasing data volumes, Business Intelligence (BI) becomes a cornerstone of the decision-support system. However, employee resistance to use Business Intelligence Systems (BIS) is evident. This creates a problem to organisations in realising the benefits of BIS. It is thus important to study the enablers of sustained use of BIS amongst employees. This thesis identifies existing theories that can be used to study BI system use. It integrates and extends technology use theories through a framework focusing on Business Intelligence System Use (BISU). Empirical research is then conducted in Kuwait’s telecom and banking industries through a close-ended, self-administered questionnaire using a five-point Likert scale. Responses were received from 211 BI users. The data was analysed using SmartPLS to study the convergent and discriminant validity and reliability. Partial least squares structural equation modelling (PLS-SEM) was used to study the direct and indirect relationships between constructs and answer the hypotheses. In addition to SmartPLS, SPSS was used for descriptive analysis. The results indicated that UTAUT factors consisting of performance expectancy, effort expectancy and social influence positively impact BI system use. Voluntariness of use was found to positively moderate the relationship between social influence and BI system use. Furthermore, BI system quality positively impacts both performance expectancy and effort expectancy. The BI user’s self-efficacy also positively impacts effort expectancy. In addition, social influence was found to be positively influenced by organisational factors, namely top management support and information culture. The findings of this research contribute to literature by determining and quantifying the factors that influence BISU through the lens of employee perspectives. This thesis also explains how employees’ object-based beliefs about BI affect their behavioural beliefs, which in turn impact BISU. Limitations of this research include the omission of UTAUT’s facilitating conditions and the limited variance of respondent demographics

Big Data in operations and supply chain management: a systematic literature review and future research agenda

In the era of digitalisation, the role of Big Data is proliferating, receiving considerable attention in all sectors and domains. The domain of operations and supply chain management (OSCM) is no different since it offers multiple opportunities to generate a large magnitude of data in real-time. Such extensive opportunities for data generation have attracted academics and practitioners alike who are eager to tap different elements of Big Data application in OSCM. Despite the richness of prior studies, there is limited research that extensively reviews the extant findings to present an overview of the different facets of this area. The current study addresses this gap by conducting a systematic literature review (SLR) to uncover the existing research trends, distil key themes, and identify areas for future research. For this purpose, 116 studies were identified through a stringent search protocol and critically analysed. The key outcome of this SLR is the development of a conceptual framework titled the Dimensions-Avenues-Benefits (DAB) model for BDA adoption as well as potential research questions to support novel investigations in the area, offering actionable implications for managers working in different verticals and sectors.publishedVersio

Adopting DevOps practices: an enhanced unified theory of acceptance and use of technology framework

DevOps software development approach is widely used in the software engineering discipline. DevOps eliminates the development and operations department barriers. The paper aims to develop a conceptual model for adopting DevOps practices in software development organizations by extending the unified theory of acceptance and use of technology (UTAUT). The research also aims to determine the influencing factors of DevOps practices’ acceptance and adoption in software organizations, determine gaps in the software development literature, and introduce a clear picture of current technology acceptance and adoption research in the software industry. A comprehensive literature review clarifies how users accept and adopt new technologies and what leads to adopting DevOps practices in the software industry as the starting point for developing a conceptual framework for adopting DevOps in software organizations. The literature results have formulated the conceptual framework for adopting DevOps practices. The resulting model is expected to improve understanding of software organizations’ acceptance and adoption of DevOps practices. The research hypotheses must be tested to validate the model. Future work will include surveys and expert interviews for model enhancement and validation. This research fulfills the necessity to study how software organizations accept and adopt DevOps practices by enhancing UTAUT

Unveiling AI Aversion: Understanding Antecedents and Task Complexity Effects

Artificial Intelligence (AI) has generated significant interest due to its potential to augment human intelligence. However, user attitudes towards AI are diverse, with some individuals embracing it enthusiastically while others harbor concerns and actively avoid its use. This two essays\u27 dissertation explores the reasons behind user aversion to AI. In the first essay, I develop a concise research model to explain users\u27 AI aversion based on the theory of effective use and the adaptive structuration theory. I then employ an online experiment to test my hypotheses empirically. The multigroup analysis by Structural Equation Modeling shows that users\u27 perceptions of human dissimilarity, AI bias, and social influence strongly drive AI aversion. Moreover, I find a significant difference between the simple and the complex task groups. This study reveals why users avert using AI by systematically examining the factors related to technology, user, task, and environment, thus making a significant contribution to the emerging field of AI aversion research. Next, while trust and distrust have been recognized as influential factors shaping users\u27 attitudes towards IT artifacts, their intricate relationship with task characteristics and their impact on AI aversion remains largely unexplored. In my second essay, I conduct an online randomized controlled experiment on Amazon Mechanical Turk to bridge this critical research gap. My comprehensive analytic approach, including structural equation modeling (SEM), ANOVA, and PROCESS conditional analysis, allowed me to shed light on the intricate web of factors influencing users\u27 AI aversion. I discovered that distrust and trust mediate between task complexity and AI aversion. Moreover, this study unveiled intriguing differences in these mediated relationships between subjective and objective task groups. Specifically, my findings demonstrate that, for objective tasks, task complexity can significantly increase aversion by reducing trust and significantly decrease aversion by reducing distrust. In contrast, for subjective tasks, task complexity only significantly increases aversion by enhancing distrust. By considering various task characteristics and recognizing trust and distrust as vital mediators, my research not only pushes the boundaries of the human-AI literature but also significantly contributes to the field of AI aversion

How nurses perceive the impact of health IT applications on their performance and satisfaction: Examining the organizational, social, and personal factors

Applying information technology tools in the healthcare industry is an appropriate solution to integrate and record medical data and provide complete access of patients’ information. However, the effectiveness of these technologies depends on their successful implementation and adaptation. This study addresses the impact of result observability, autonomy, perceived barriers, task structure, privacy and security anxiety, and communication (social) patterns on the perception of the performance and satisfaction of nurses using IT applications in healthcare. Furthermore, the effects of nurses’ years of experience, age, different hospitals, different electronic medical records (EMR) applications, and personality factors are examined as a moderator factors on the relationships between the organizational and social factors, and nurses’ performance and satisfaction. This study proposes a model of the relationship of organizational and social variables as predictor factors on the perception of performance and satisfaction with EMR among nurses. Multivariate linear regression was used to build models for the perception of performance and the perception of EMR satisfaction. Professional autonomy, communication patterns, privacy and security anxiety, and result observability are the most important predictors for the nurses’ perception of performance relationship. Personality factors do not have a direct relationship with the perception of performance and satisfaction; however, they have moderator effects on the relationship of the independent and dependent variables. Based on the result, financial incentives and sufficient training could influence the nurses’ perception of EMR effectiveness. Based on the findings of this study, the healthcare administrators could focus on increasing employee awareness about the results and tangible benefits of EMR applications and their effects on their performance and satisfaction. EMR development companies in collaboration with healthcare administrators could design the EMR applications more flexible in terms of professional autonomy and give the healthcare staff more freedom to make decisions and deliver care to patients. Moreover, EMR companies may need to reconsider the communication patterns among healthcare staff and patients

Use of Technology and Big Data in E-Health Services

[Abstract]: The objective of this work has been to analyze the factors that determine the acceptance and use of technology (TAM) in the field of health services as well as the design of an app focused on the prevention of cardiovascular diseases. The factors that determine the use of electronic devices in the health field are the utility or perceived value, the ease of use (simple and attractive interface), the interactivity of the user with the device, the attitude towards technology and the reduction of the perceived risk (protection of privacy and health risk). From these determining factors, an app named Heart Focus App has been developed. This app would also make it possible to collect massive data from users or from databases from different official sources (FAO, INE, Ministry of Health) with the aim of predicting risk factors and providing information on healthier lifestyle habits. A data analysis based on statistical analysis techniques such as correlation analysis has identified a strong association between the elderly population and deaths from cardiovascular disease. Therefore, in view of the inevitable aging of the population, the development and use of electronic devices or apps with simple and easy-to-use interfaces, and the exploitation of big data derived from these can allow not only to improve the quality of life of patients, but also to reduce health costs and improve the quality of online and offline service.[Resumen]: El objetivo de este trabajo ha sido analizar los factores que determinan la aceptación y uso de la tecnología (TAM) en el ámbito de los servicios sanitarios así como el diseño de una app focalizada en la prevención de enfermedades cardiovasculares. Los factores que determinan el uso de dispositivos electrónicos en el ámbito sanitario son la utilidad o valor percibido, la facilidad de uso (interfaz sencilla y atractiva), la interactividad del usuario con el dispositivo, la actitud hacia la tecnología y la reducción del riesgo percibido (protección de la privacidad y los riesgos para la salud). A partir de estos factores determinantes, se ha desarrollado una app denominada Heart Focus App. Esta app también permitiría recoger datos masivos procedentes de los usuarios o de bancos de datos de diferentes fuentes oficiales (FAO, INE, Ministerio de Sanidad) con el objetivo de predecir factores de riesgo y proporcionar información sobre hábitos de vida más saludables. Un análisis de datos basado en técnicas de análisis estadístico como el análisis de correlación ha identificado una fuerte asociación entre la población de edad avanzada y las muertes por enfermedades cardiovasculares. Por ello, ante el inevitable envejecimiento de la población, el desarrollo y uso de dispositivos electrónicos o apps con interfaces sencillas y fáciles de usar, y la explotación de datos masivos derivados de estas puede permitir no solo mejorar la calidad de vida de los pacientes, sino también disminuir los costes sanitarios y mejorar la calidad del servicio online y offline.Traballo fin de grao (UDC.ECO). ADE. Curso 2019/202

Extended unified theory of acceptance and use of technology: the influence of behavioral intention on big data management adoption by Malaysian public research universities

The higher education environment has been experiencing a record of challenges such as declines in students’ enrolment, retention rates, and graduation rates. Combined with cuts in institutional funding, executive leaders in higher education institutions (HEIs) in Malaysia and internationally have found it challenging to address these challenges effectively due to the dynamic and highly competitive education landscape. However, HEIs, like many organizations, are rapidly changing because of advancements in technology. The application of big data management (BDM) has been acknowledged as a potential solution to difficulties experienced in HEIs. So far, its adoption is relatively new, as there are currently numerous unknowns regarding its use. The focus of this study is to provide both institutional executive decision-makers and strategic managers with insight into factors related to the behavioral intention for the adoption of BDM in Malaysian public research universities. The contribution of this study is to bridge the gap in existing research on the behavioral intention for the adoption of BDM. Venkatesh's Unified Theory of Acceptance and Use of Technology model was used to determine if the independent variables: performance expectancy, effort expectancy, social influence and facilitating conditions are predictors of the dependent variable; the behavioral intention with moderating variable of transformational leadership (TL) for adoption of BDM by both institutional executive decision-makers and strategic managers whose universities are considering using BDM in their operations. The findings of the study based on a total of 171 valid survey collected showed that social influence and facilitating conditions have significant effects on behavioral intention for BDM adoption. Worth noting is the fact that TL this study’s results found no impact on the relationship between performance expectancy, effort expectancy, social influence variables and the behavioral intention. The sector is at the initial phase of adopting BDM technology, and the main challenge of adopting BDM use is transforming the processes, culture, and people in the institutions. The need for further research into the behavioral intention factors and leadership may in turn universities in being better prepared for the implementation and the costs of the technology