• Journal of the Korea Society of Computer and Information
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• v.28 no.10
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• pp.229-239
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• 2023

This study aims to explore the impact of algorithmic price discrimination on negative word-of-mouth (NWOM) through the lens of attribution theory. It also examines the mediating roles of intentional attributions and negative emotions, as well as the moderating effect of price sensitivity. For this study, 772 consumers who had purchased flight tickets completed a questionnaire survey, and the collected data were analyzed and tested using SPSS 27.0 and AMOS 24.0 software. The research findings reveal that algorithmic price discrimination has a significant positive impact on intentional attributions, negative emotions, and NWOM. Specifically, deliberate attributions and negative emotions mediate the relationship between algorithmic price discrimination and NWOM, while price sensitivity positively moderates the relationship between negative emotions and NWOM. Therefore, companies should consider disclosing algorithm details transparently in their marketing strategies to mitigate consumers' negative emotions and implement targeted strategies for consumers with different levels of price sensitivity to enhance positive word-of-mouth.

• Journal of Industrial Convergence
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• v.21 no.12
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• pp.37-43
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• 2023

In today's business landscape, collaboration and interoperability are crucial for organizational success and profitability. However, integrating operations across multiple organizations is challenging due to differing roles and policies in Identity and Access Management (IAM). User-centric identity (UCI) adopts a personalized approach to digital identity management, centering on the end-user for authentication and access control. It provides a decentralized system that ensures secure and customized access for each user. UCI aims to address complex security challenges by aligning access privileges with individual user requirements. This research delves into UCI's ability to streamline resource access amidst conflicting IAM roles and protocols across various organizations. The study presents a UCI-based multi-domain access control (MDAC) framework, which encompasses an ontology, a unified method for articulating access roles and policies across domains, and software services melding with UCI infrastructure. The goal is to enhance organizational resource management and decision-making by offering clear guidelines on access roles and policy management across diverse domains, ultimately boosting companies' return on investment.

• Smart Media Journal
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• v.13 no.6
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• pp.44-53
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• 2024

Companies in modern society are increasingly recognizing sentiment classification as a crucial task, emphasizing the importance of accurately understanding consumer opinions opinions across various platforms such as social media, product reviews, and customer feedback for competitive success. Extensive research is being conducted on sentiment classification as it helps improve products or services by identifying the diverse opinions and emotions of consumers. In sentiment classification, fine-tuning with large-scale datasets and pre-trained language models is essential for enhancing performance. Recent advancements in artificial intelligence have led to high-performing sentiment classification models, with the ELECTRA model standing out due to its efficient learning methods and minimal computing resource requirements. Therefore, this paper proposes a method to enhance sentiment classification performance through efficient fine-tuning of various datasets using the KoELECTRA model, specifically trained for Korean.

• Proceedings of the Korea Information Processing Society Conference
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• 2007.11a
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• pp.991-994
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• 2007

최근 컴퓨터 기술의 발전과 네트워크의 개방화 그리고 무선 모바일 통신 기술의 비약적인 보급으로 인하여 컴퓨팅 환경을 이루고 있는 각종 장치(PC, 모바일 단말, 저장장치, 네트워크 기기 등)가 다양한 형태의 보안 위협에 노출되어 데이터의 유실, 조작, 유출되어 금전적인 피해를 입거나 프라이버시 침해를 받고 있다. 이러한 문제를 근본적으로 해소하기 위하여 설립된 TCG(Trusted Computing Group)는 세계적인 IT 핵심기업들을 중심으로 구성된 비영리 단체로서 PC 혹은 모바일 기기 등의 단말과 서버 장비 그리고 저장 장치 및 네트워크로 구성된 컴퓨팅 환경에서 보안성 향상 및 데이터의 신뢰성을 제고하기 위하여 TPM(Trusted Platform Module)이라는 반도체 칩을 신뢰의 기반(root of trust)으로 한 신뢰 플랫폼을 제안하고 있다. 한편 SaaS(Software as a Service)는 패키지 형태의 소프트웨어를 네트워크 서비스 형태로 바꾸어 사용량에 비례한 요금제로 과금하는 방식을 채택하고 사용자가 온디맨드로 요청한 서비스를 적시에 제공하는 기술로 최근 전세계적으로 각광을 받고 있다. 이때 다양한 컴퓨팅 환경 안의 사용자에게 높은 신뢰성과 보안성 그리고 연속성을 갖는 SaaS 서비스를 제공하고 데이터의 무결성 및 비밀유지와 정확한 서비스 사용시간을 기록하고 업로드하는 기능들을 제공하는 SaaS 플랫폼은 TPM기반의 신뢰컴퓨팅 기술을 통하여 쉽게 구현될 수 있다. 본 논문에서는 일시적으로 네트워크와 차단된 상태의 PC 혹은 모바일 단말에서도 위의 조건들을 만족하는 SaaS 서비스를 지원하는 신뢰 플랫폼이 가져야 할 기능들에 대하여 분석-도출한 후 그러한 기능들을 제공하는 컴포넌트로 구성된 신뢰형 SaaS 사용자 플랫폼을 설계하였다.

• Analyses & Alternatives
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• v.8 no.1
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• pp.59-85
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• 2024

This study examines the impact of the development of the artificial intelligence (AI) industry on the economic growth of South Korea. The study uses variables such as the revenue and patent applications of AI-related companies, as well as industry-specific total factor productivity and GDP, to estimate the effects. The results suggest that the growth of the AI industry has a positive effect on the economic growth with a lag of about one year. Specifically, the effect of government AI revenue on GDP growth appears to be greater than that of private companies or consumer-focused AI revenue. This indicates that government policies aimed at promoting the diffusion of the AI industry have had significant effects. The study notes that the period covered by the AI industry survey data is relatively short, and there is a lack of detailed data for the manufacturing sector. I suggest that further improvements and accumulation of data could lead to more robust results.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.24 no.3
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• pp.171-176
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• 2024

IT-related fields are very diverse. As of 2018, the IT revolution from the Fourth Industrial Revolution not only brought out the new fields that were different from the previous ones, but it also made a reexamination of various fields that had already been an issue in the past. Companies and public institutions have a great interest in understanding IT trends in this situation. Therefore, in this paper, IT trends are identified through the analyzation of keywords provided by domestic papers. Moreover, unlike previous industry trend analysis or economic analysis, this paper focuses on analyzing the keyword provided by the doctoral thesis or master's thesis about direct IT-related research, and grasps the more basic and direct IT trend. This analysis predicts and presents the vision based on the data of the analysis from the academic papers that researched in IT technology for IT related students or IT related educators.

• Advanced Industrial SCIence
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• v.3 no.1
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• pp.30-36
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• 2024

Recently, the Internet of Things (IoT), which has been applied to various industrial fields, is constantly evolving in the process of automation and digitization. However, in the network where IoT devices are built, research on IoT critical information-related data sharing, personal information protection, and data integrity among intermediate nodes is still being actively studied. In this study, we propose a blockchain-based IoT critical information management technique that is easy to implement without burdening the intermediate node in the network environment where IoT is built. The proposed technique allocates a random value of a random size to the IoT critical information arriving at the intermediate node and manages it to become a decentralized P2P blockchain. In addition, the proposed technique makes it easier to manage IoT critical data by creating licenses such as time limit and device limitation according to the weight condition of IoT critical information. Performance evaluation and proposed techniques have improved delay time and processing time by 7.6% and 10.1% on average compared to existing techniques.

• Advanced Industrial SCIence
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• v.3 no.2
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• pp.17-22
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• 2024

Recently, as the ICT field has been used in various environments, it has become possible to analyze pests by crops, use robots when harvesting crops, and predict by big data by utilizing ICT technologies in a sustainable agricultural environment. However, in a sustainable agricultural environment, efforts to solve resource depletion, agricultural population decline, poverty increase, and environmental destruction are constantly being demanded. This paper proposes an artificial intelligence-based big data processing analysis method to reduce the production cost and increase the efficiency of crops based on a sustainable agricultural environment. The proposed technique strengthens the security and reliability of data by processing big data of crops combined with AI, and enables better decision-making and business value extraction. It can lead to innovative changes in various industries and fields and promote the development of data-oriented business models. During the experiment, the proposed technique gave an accurate answer to only a small amount of data, and at a farm site where it is difficult to tag the correct answer one by one, the performance similar to that of learning with a large amount of correct answer data (with an error rate within 0.05) was found.

• Journal of the Korea Society of Computer and Information
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• v.29 no.6
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• pp.211-221
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• 2024

Live streaming commerce has emerged as an innovative e-commerce model. This study, based on the Elaboration Likelihood Model (ELM), aims to explore the impact of Key Opinion Consumers' (KOCs) attributes in live streaming commerce on purchase intentions on short video platforms. A survey was conducted with 411 consumers, and data analysis and hypothesis testing were performed using SPSS 24.0 and AMOS 23.0 software. Research has found that differences in consumers' information processing abilities lead to different pathway selections. Central route factors such as recommendation consistency, product involvement, and professionalism, as well as peripheral route factors such as recommendation timeliness, all have significant positive effects on consumers' purchase intention. However, visual cues in the peripheral route do not have a significant impact. This study aims to provide theoretical support and practical guidance for the development of the live streaming commerce industry, and to help companies adjust their promotion strategies based on differences in consumer information processing, thereby improving purchase conversion rates.

• Management & Information Systems Review
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• v.36 no.3
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• pp.131-150
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• 2017

The purpose of this study is to observe the spread pattern of open source software (OSS) while establishing relations with surrounding actors during its operation period. In order to investigate the change pattern of participants in the OSS, we use a netnography on the basis of online data, which can trace the change patterns of the OSS depending on the passage of time. For this, the cases of three OSSs (e.g. jQuery, MooTools, and YUI), which are JavaScript frameworks, were compared, and the corresponding data were collected from the open application programming interface (API) of GitHub as well as blog and web searches. This research utilizes the translation process of the actor-network theory to categorize the stages of the change patterns on the OSS translation process. In the project commencement stage, we identified the type of three different OSS-related actors and defined associated relationships among them. The period, when a master commences a project at first, is refined through the course for the maintenance of source codes with persons concerned (i.e. project growth stage). Thereafter, the period when the users have gone through the observation and learning period by being exposed to promotion activities and codes usage respectively, and becoming to active participants, is regarded as the 'leap of participants' stage. Our results emphasize the importance of promotion processes in participants' selection of the OSS for participation and confirm the crowding-out effect that the rapid speed of OSS development retarded the emergence of participants.