• 산업경영시스템학회지
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• 제45권4호
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• pp.1-7
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• 2022

Explainable AI (XAI) is an approach that leverages artificial intelligence to support human decision-making. Recently, governments of several countries including Korea are attempting objective evidence-based analyses of R&D investments with returns by analyzing quantitative data. Over the past decade, governments have invested in relevant researches, allowing government officials to gain insights to help them evaluate past performances and discuss future policy directions. Compared to the size that has not been used yet, the utilization of the text information (accumulated in national DBs) so far is low level. The current study utilizes a text mining strategy for monitoring innovations along with a case study of smart-farms in the Honam region.

• 한국문헌정보학회지
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• 제46권1호
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• pp.29-53
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• 2012

본 연구에서는 지난 20년간(1991년~2011년)의 한국 문헌정보학 교과과정을 분석함으로써 교과목의 변화를 분석하고자 하였다. 그 결과 첫째, 총 개설과목 수가 전체적으로 증가하여 왔고, 학부제로 전환하면서 학부기초과목 수는 증가하였고, 핵심과목 수는 감소하였으며, 다시 학과제로 전환하면서 학부기초과목 수는 감소하고, 핵심과목 수는 증가한 것을 알 수 있었다. 둘째, 전국 34개 4년제 문헌정보학과의 교과과정을 문헌정보학일반, 정보조직학, 정보조사제공학, 도서관 정보센터경영학, 정보학, 서지학, 기록관리학으로 구분하여 각 영역별 분포도를 조사하였는데, 지난 20년간 정보조사제공학과 정보학 분야를 제외한 대부분의 영역에서 그 교과목 수가 줄어드는 것으로 나타났다. 셋째, 본 연구를 통해서 사라진 과목, 과목 명칭이 바뀐 과목, 여러 과목이 통합된 과목, 하나의 과목이 여러 과목으로 세분화된 과목, 그리고 시대의 발전에 따라 새롭게 등장한 과목들이 밝혀졌다.

• 한국도서관정보학회지
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• 제44권1호
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• pp.143-175
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• 2013

본 연구에서는 지식정보사회의 능력있는 전문사서 양성을 위한 주요 전공과목의 교과내용을 개발, 제안하고자 하였다. 이를 위해 국내 39개 문헌정보학과에 개설된 핵심 전공과목의 강의계획서를 조사 분석하여 국내 문헌정보학 교육내용의 현황을 파악하였다. 주요 교과목의 교과내용인 강의계획서 개발대상과목은 2011년 선행연구에서 제안된 핵심 교과목으로, "문헌정보학개론", "정보조직론", "정보서비스론", "도서관경영론", "정보검색론", "도서관실습" 6개 과목이다. 강의계획서는 강의개요(목표 및 목적), 교수법, 평가방법, 주요 교재, 15주 또는 16주의 강의일정 및 내용(강의계획서)을 포함한다.

• 한국정보시스템학회지:정보시스템연구
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• 제30권3호
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• pp.201-216
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• 2021

Purpose The purpose of this study is to analyze the trend of patent technology in textile materials using text mining methodology based on Dynamic Embedded Topic Model and Structural Topic Model. It is expected that this study will have positive impact on revitalizing and developing textile materials industry as finding out technology trends. Design/methodology/approach The data used in this study is 866 domestic patent text data in textile material from 1974 to 2020. In order to analyze technology trends from various aspect, Dynamic Embedded Topic Model and Structural Topic Model mechanism were used. The word embedding technique used in DETM is the GloVe technique. For Stable learning of topic modeling, amortized variational inference was performed based on the Recurrent Neural Network. Findings As a result of this analysis, it was found that 'manufacture' topics had the largest share among the six topics. Keyword trend analysis found the fact that natural and nanotechnology have recently been attracting attention. The metadata analysis results showed that manufacture technologies could have a high probability of patent registration in entire time series, but the analysis results in recent years showed that the trend of elasticity and safety technology is increasing.

• 한국정보시스템학회지:정보시스템연구
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• 제30권2호
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• pp.1-27
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• 2021

Purpose Even if many startups firms have developed innovative items and a potential for success, they often have a limited financial resources, which makes them difficult to do business. To overcome this financial difficulty, startups have used one of fintech services, called crowdfunding that can be a good alternative to solving the difficulty of financing. The purpose of this study is to empirically validate the proposed research model that investigates the reasons of trusting crowdfunding platform, which positively leads to two outcomes - intention to participate and word-of-mouth for reward-based crowdfunding project. Design/methodology/approach We proposed several factors categorized as trust, information quality, and platform traits that have a positive impact on trust of crowdfunding platform, which positively leads to intention to participate and word-of-mouth of crowdfunding. The collected(n=285) from individuals who have participated in crowdfunding project was analyzed with SmartPLS 3.0 to test proposed hypotheses. Findings The results showed that all proposed variables (website reputation, crowdfunding familiarity, digital storytelling, information quality, and interaction) had a significant impact on crowfunding platform trust with exception of product differentiation. In addition, crowfunding platform trust was positively associated with participating intention and word-of-mouth. Based on findings, we discussed the research results and implication alone with a direction for future studies.

• 한국정보시스템학회지:정보시스템연구
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• 제31권2호
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• pp.289-320
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• 2022

Purpose The purpose of this study is to empirically investigate impacts of e-Commerce traits on intention to use platform extended services based on Value-Attitude-Behavior(VAB) framework as well as the moderating effect of network impact as the current e-Commerce platforms provide more extended services to their customers. Design/methodology/approach The research model was developed based on the literature review and VAB framework in order to empirically investigate impacts of e-Commerce platform characteristics. The survey was conducted in order to test proposed hypotheses. 298 collected responses were analyzed for the structural equational modeling(SEM) with SmartPLS 3.0. Findings Findings show that all proposed hypotheses were supported with exception of responsiveness. Among five constructs represented as e-Commerce platform characteristics, product variety had a highest impact on platform value. In addition, this study confirmed that the variable - network effect - strength the relationship between platform attitude and intention to use extended service.

• 한국정보시스템학회지:정보시스템연구
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• 제32권2호
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• pp.1-24
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• 2023

Purpose The aim of this study is to investigate how system quality, privacy concerns, and usefulness impact user satisfaction and word-of-mouth intention towards smart order services among Chinese consumers. The study also seeks to provide both theoretical and practical implications based on the findings. Design/methodology/approach This study conducted an empirical study on Chinese consumers through an online survey on 274 users of smart order service in China. To analyze the data, frequency analysis, reliability analysis, and confirmatory factor analysis were performed using SPSS 26.0 and AMOS 26.0 statistical programs, and structural equation model was used for hypothesis testing. Findings The study results are as follows. First, system quality was found to have a positive effect on usefulness and user satisfaction. Second, privacy concerns were found to have a positive effect on user satisfaction, but not on usefulness. Third, user satisfaction was found to have a positive effect on consumers' word-of-mouth intention of the smart order service. Finally, mediating effects were found between system quality and user satisfaction through usefulness, as well as between system quality, perceived privacy concern, and usefulness through user satisfaction.

• 한국정보시스템학회지:정보시스템연구
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• 제32권2호
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• pp.137-152
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• 2023

Purpose The purpose of this study is to analyze big data related to DURIBAL from the operation of taxis reserved for the disabled to identify the issues and suggest solutions. ESG management should be translated into "environmental factors, social responsibilities, and transparent management." Therefore, the current study used Big Data analysis to analyze the factors affecting the standby of taxis reserved for the disabled and relevant problems for implications on convenience of social weak. Design/methodology/approach The analysis method used R, Excel, Power BI, QGIS, and SPSS. We proposed several suggestions included problems with managing cancellation data, minimization of dark data, needs to develop an integrated database for scattered data, and system upgrades for additional analysis. Findings The results showed that the total duration of standby was 34 minutes 29 seconds. The reasons for cancellation data were mostly use of other modes of transportation or delayed arrival. The study suggests development of an integrated database for scattered data. Finally, follow-up studies may discuss government-initiated big data analysis to comparatively analyze the use of taxis reserved for the disabled nationwide for new social value.

• 한국정보시스템학회지:정보시스템연구
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• 제32권4호
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• pp.149-162
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• 2023

Purpose In the textile industry, fabric defects significantly impact product quality and consumer satisfaction. This research seeks to enhance defect detection by developing a transformer-based deep learning image segmentation model for learning high-dimensional image features, overcoming the limitations of traditional image classification methods. Design/methodology/approach This study utilizes the ZJU-Leaper dataset to develop a model for detecting defects in fabrics. The ZJU-Leaper dataset includes defects such as presses, stains, warps, and scratches across various fabric patterns. The dataset was built using the defect labeling and image files from ZJU-Leaper, and experiments were conducted with deep learning image segmentation models including Deeplabv3, SegformerB0, SegformerB1, and Dinov2. Findings The experimental results of this study indicate that the SegformerB1 model achieved the highest performance with an mIOU of 83.61% and a Pixel F1 Score of 81.84%. The SegformerB1 model excelled in sensitivity for detecting fabric defect areas compared to other models. Detailed analysis of its inferences showed accurate predictions of diverse defects, such as stains and fine scratches, within intricated fabric designs.

• 한국정보시스템학회지:정보시스템연구
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• 제32권3호
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• pp.225-251
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• 2023

Purpose The purpose of this study is to examine the future direction of in-vehicle app development and service, the relationship between potential customers' intention to use and the factors that affect it was explored. It was also checked whether the two types of app development platform and the experience of the existing smartphone app platform had a moderating effect on these relationships. Design/methodology/approach Data was gathered through surveys, collecting responses from 904 potential consumers of vehicle app services in Korea. Structural equation modeling was utilized to analyze the data. Findings According to the empirical analysis result, it was found that potential customers considered enjoyment as the most important benefit factor in in-vehicle app service, and the most important external factor affecting enjoyment was functional compatibility with smartphone. The type of vehicle app development platform did not have a meaningful moderating effect on the factor relationship, whereas the smartphone app platform experience showed a meaningful moderating effect on the relationship between factors. It was analyzed that the risk of app performance, personal information privacy, and driving safety data did not have a negative effect on the intention to use the vehicle app service.