• The Journal of Bigdata
• /
• v.9 no.1
• /
• pp.169-186
• /
• 2024

This study aims to analyze the acceptance factors for expanding the adoption of AI by SMEs and draw practical and policy implications. To this, we conducted an empirical analysis of AI acceptance factors among 315 SMEs in various industries such as manufacturing, service, and information and communication sectors located in Korea. Based on the UTAUT, we examined the influence of decision-making reliability, perceived awareness, policy support, education and training, perceived cost, perceived risk, and system complexity, and found that decision-making reliability positively affects performance expectancy and social influence, perceived awareness positively affects performance expectancy and effort expectancy, policy support positively affects social influence and facilitating conditions, and education and training positively affects effort expectancy and facilitating conditions. Perceived cost had a negative effect on social influence and facilitating conditions, and perceived risk had a negative effect on performance expectancy and social influence. System complexity had a negative effect on effort expectancy but no effect on facilitating conditions. These results are expected to be widely utilized as basic research for the diffusion of AI in industry and provide practical and policy implications for promoting the adoption of AI in SMEs.

• Proceedings of the Korean Operations and Management Science Society Conference
• /
• 2005.05a
• /
• pp.664-668
• /
• 2005

ERP(Enterprise Resource Planning) 시스템은 급여, 회계, 인사, 생산, 판매, 물류 등을 포함한 핵심 비즈니스 프로세스를 담고 있는 소프트웨어이다. 인터넷의 등장으로 ERP 시스템이 WEB 기반으로 전환되었고 ERP의 영역도 기업내부에 국한된 것이 아니라 기업간의 거래를 모두 포함하기에 이르렀다. 그러나 ERP 시스템은 회사의 중요한 의사결정 데이터들과 한 Application 안에서 일어나는 Transaction 처리 등의 정보 보안에 대한 취약성을 가지고 있다. 이에 ERP 시스템에도 보안에 대한 필요성이 대두되었으나 ERP 업체들의 대다수는 아직도 구체적인 보안에 대한 방안을 제시하지 않고 있는 실정이다. 결국 비즈니스의 성공도 취약한 기업의 자원을 인증되지 않은 다른 사용자로부터 보호하는 능력에 달려 있으며, 이 연구의 목적은 ERP 시스템의 보안 이슈를 어떻게 다룰 것이며, ERP 보안 요구 사항을 어떻게 모델링 할 것인가에 대한 연구 이다. 현재 나와있는 SAP R/3와 EAGLE ERP의 제품 보안 모델을 비교함으로써 외산 제품과 국산 제품의 보안 요구 사항을 분석 하고, ERP시스템 보안에 대한 고려사항 및 접근 방법을 모델링 할 것이다. 본 연구에서는 UML을 이용하여 ERP 시스템 안에서 찾을 수 있는 모든 보안 사항을 점검 하고,UML의 물리적 요소와 논리적 요소를 이용함으로써 ERP 보안을 모델링 하고자 한다. 이 논문을 통하여 ERP 시스템의 각 분야의 담당자가 ERP 보안을 개념적으로 접근 할 수 있도록 할 것이다. 차후 연구 과제는 좀더 구체적인 모델링을 통한 ERP 보안 solution 개발이다.

• The Journal of Information Systems
• /
• v.29 no.3
• /
• pp.1-34
• /
• 2020

Purpose Due to the complexity of policy environment in modern society, it is accepted as common basics of policy design to mix up a variety of policy instruments aiming the multiple functions. However, under the current situation of written-down policy specification, not only the public officers but also the policy researchers cannot easily grasp such frameworks as policy portfolio. The purpose of this study is to develop "Policy Attributes Taxonomy" identifying and classifying the public programs to help making decisions for allocative efficiency with effectiveness-based information. Design/methodology/approach To figure out the main scheme and classification criteria of Policy Attributes Taxonomy which represents characteristics of public policies, previous theories and researches on policy components were explored. In addition, to test taxonomic feasibility of certain information system, a set of "Feasibility Standards" was drawn from "requirements for well-organized criteria" of eminent taxonomy literatures. Finally, current government classification system in the area of social service was tested to visualize the application of Taxonomy and Standards. Findings Program Taxonomy Schemes were set including "policy goals", "policy targets", "policy tools", "logical relation" and "delivery system". Each program and project could be condensed into these attributes, making their design more easily distinguishable. Policy portfolio could be readily made out by extracting certain characteristics according to this scheme. Moreover, this taxonomy could be used for rearrangement of present "Program Budget System" or estimation of "Basic Income".

• Journal of Information Technology Applications and Management
• /
• v.14 no.4
• /
• pp.97-120
• /
• 2007

Development of information technology and Internet brought big changes in information society. Quantity of information increased rapidly and various types of information were presented through diverse channels. This change brought an impact in electronic commerce environment. A large number of products are transacted in online market. And various search functions and product information are presented for supporting customer's decision making. This study examined the effect of external information on purchase decision-making in electronic commerce environment. An experiment was conducted to see the customer product review, unit sales, etc. on purchase decision-making process in online shopping mall based electronic commerce. As a result of study, external information referring to number of purchasing, positive product review, and reliability of information has a positive effect on purchase-decision. The significance of the study can be found in that it defined 1) external information has an effect on decision-making, 2) positiveness and reliability of product information showed that they have an influence on customer, and 3) when self opinion and other person's opinion are different, one is not satisfied with decision making process. The results of the study can be of practical use in the design and implementation of online shopping mall in electronic commerce.

• Proceedings of the Korea Information Processing Society Conference
• /
• 2001.10a
• /
• pp.61-64
• /
• 2001

최근 공간정보를 다루는 GIS, SIIS, GNSS, ITS 네 개의 시스템에 대한 통합연계 기술인 4S 기술이 각광받고 있다. 본 논문에서는 컴포넌트 기반의 4S 시스템을 소개하고 4S 기술을 화재, 홍수, 태풍 등의 재난재해 분야에 적용한 시스템을 4S 핵심 컴포넌트를 중심으로 설계하였다. 4S 기술을 이동 환경에서 적용하는 예는 매우 많으나 재난재해 업무에 적용될 경우 매우 큰 효과를 가져올 것이다. 공간데이터를 처리하는 핵심 기술과 재난재해 업무간 공통된 기능은 4S 핵심 컴포넌트로 구현되며, 업무별로 다른 기능은 별도의 업무 컴포넌트로 분리하여 분야별 응용시스템에 따라 사용하게 하였다. 특히 4S-Van 이라는 차량 및 이동단말기 등 현장과 중앙관제센터간에 실시간으로 위치정보와 영상정보 등을 전송하는 방법 및 전송된 정보를 중앙관제센터에서 공간 데이타와 연동.분석하는 방안을 제시하여 신속하고 정확한 상황파악, 의사결정, 지령관제 등을 가능하게 하고 재난재해 분야에 획기적인 업무효율 향상을 가져올 것으로 기대된다.

• KIPS Transactions on Computer and Communication Systems
• /
• v.9 no.10
• /
• pp.239-246
• /
• 2020

In the medical field where artificial intelligence technology is introduced, research related to clinical decision support system(CDSS) in relation to diagnosis and prediction is actively being conducted. In particular, medical imaging-based disease diagnosis area applied AI technologies at various products. However, medical imaging data consists of inconsistent data, and it is a reality that it takes considerable time to prepare and use it for research. This paper describes a one-stop AI learning platform for converting to medical image standard R_CDM(Radiology Common Data Model) and supporting AI algorithm development research based on the dataset. To this, the focus is on linking with the existing CDM(common data model) and model the system, including the schema of the medical imaging standard model and report information for multi-center research based on DICOM(Digital Imaging and Communications in Medicine) tag information. And also, we show the execution results based on generated datasets through the AI learning platform. As a proposed platform, it is expected to be used for various image-based artificial intelligence researches.

• Journal of Korea Technology Innovation Society
• /
• v.20 no.4
• /
• pp.1262-1287
• /
• 2017

Recently, forecasting for next-generation technologies have influenced the competitiveness of companies. However, in previous studies, only extract factors influencing the adoption of technology have been investigated. Also, there are few researches on the importance of each decision factors or the competition between technologies. In this research, Lotka-Volterra model is used to confirm the technological competition in the new technology choice timing when the competition is intensified due to the emergence of new technologies. For purpose of this study, estimate the LVC model based on the data of the past competition and then derived the factors affecting the technology of competition and substitution from the literature survey. After that, we confirmed the factor value between the past and the present technology competition. The difference between the factor values derived from the previous step is used to revise the model estimated from the past data base. At this stage, regression analysis is used to derive the importance of each factor and use it as the weight. Through the correction model, the competitiveness is identified through 1:1 comparison with competition candidate technology and existing dominant design technology. In this research, we quantitatively propose the possibility that a specific technology can become a dominant design in the next generation, based on the difference in factor values and importance. This results will help the company's R&D strategy and decision making.

• Journal of the Korean Society of Marine Environment & Safety
• /
• v.26 no.7
• /
• pp.798-809
• /
• 2020

Evidence-based policymaking and assessments for scientific administration have increased the importance of statistics (data) utilization. Statistics can explain specific phenomena by providing numerical values and are a public resource for national decision making. Due to these inherent attributes, statistics are utilized as baseline and base data for government policy determinations and the analysis of various phenomena. However, compared to the importance, the role of statistics is limited, and statistics are often used as simple abstracts, produced mainly for suppliers, not for consumers' perspectives to create value. This study explores the statistical data and other attributes that can be utilized for policies or research to address the problems mentioned above. The baseline statistical data used in this study is from the Maritime Distress Accident Statistical Yearbook published by the South Korean Coast Guard, and other additional attributes are from text analyses of vessel casualty situation reports from the South Korean Maritime Police. Collecting 56 attributes drawn from the text analysis and executing an EDA resulted in 88 attribute unions: 18 attribute unions had a satisfactory significance probability (p-value < .05) and a strong correlation coefficient above 0.7, and 70 attribute unions had a middle correlation. (over 0.4 and under 0.7). Additionally, to utilize the extra attributes discovered from the EDA politically, a keyword analysis for each detailed strategy of the disaster Preparation basic plan was executed, the utilization availability of the attributes was obtained using a matching process of keywords, and the EDA deducted attributes were examined.

• KSCE Journal of Civil and Environmental Engineering Research
• /
• v.29 no.3D
• /
• pp.373-379
• /
• 2009

Cost estimation in the early phase enables government to plan public budgeting more efficiently by providing information about construction cost. However, cost estimation in the early phase is difficult to predict because only a little information can be utilized. The cost estimation method now being used by the government is calculated by length of the road multiplied by unit cost per length and shows high error rate because it cannot reflect the unique characteristics of each project. As the project is being proceeded, level of available information also changed. So, reflecting available information of a project is important. This paper divided early phase into two parts : planning phase and early design phase, and developed cost estimation model considering level of available information of each phase. Total 143 cases are utilized to find influencing variables and develop cost estimation model and model validation is done by adopting required accuracy level. This cost estimation model reflecting level of available information can be applied to public budgeting, feasibility test, and comparison between routes.

• Journal of the Korea Convergence Society
• /
• v.12 no.1
• /
• pp.99-104
• /
• 2021

In this paper, we propose health risk management using feature extraction and cluster analysis considering time flow. The proposed method proceeds in three steps. The first is the pre-processing and feature extraction step. It collects user's lifelog using a wearable device, removes incomplete data, errors, noise, and contradictory data, and processes missing values. Then, for feature extraction, important variables are selected through principal component analysis, and data similar to the relationship between the data are classified through correlation coefficient and covariance. In order to analyze the features extracted from the lifelog, dynamic clustering is performed through the K-means algorithm in consideration of the passage of time. The new data is clustered through the similarity distance measurement method based on the increment of the sum of squared errors. Next is to extract information about the cluster by considering the passage of time. Therefore, using the health decision-making system through feature clusters, risks able to managed through factors such as physical characteristics, lifestyle habits, disease status, health care event occurrence risk, and predictability. The performance evaluation compares the proposed method using Precision, Recall, and F-measure with the fuzzy and kernel-based clustering. As a result of the evaluation, the proposed method is excellently evaluated. Therefore, through the proposed method, it is possible to accurately predict and appropriately manage the user's potential health risk by using the similarity with the patient.