• Proceedings of the Korean Society for Information Management Conference
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• 2014.08a
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• pp.73-78
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• 2014

피어리뷰(Peer Review)는 17세기 학술지가 만들어진 이래 오늘날까지 가장 널리 사용되는 논문의 질적인 수준과 학술지 게재 여부를 판단하는 전통적인 평가도구이다. 그러나 피어리뷰의 과정에서 발생되는 공정성 저해와 학술출판 분야에서 오픈 액세스 (OA, Open Access) 저널이 계속적으로 증가하는 디지털 미디어 시대에서 소수의 전문가가 검증하는 피어리뷰 시스템에 관한 불만들이 제기되면서 현 피어리뷰 시스템의 새로운 대안으로 오픈 피어리뷰(Open Peer Review)가 제시되기도 하였다. 이에 본 연구에서는 피어리뷰의 이론적 배경을 살펴보고 이를 토대로 새로운 대안으로 떠오르고 있는 오픈피어리뷰의 평가도구로써의 활용가능성에 대하여 살펴보고자 한다.

• Knowledge Management Research
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• v.24 no.4
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• pp.153-176
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• 2023

Generative AI, especially conversational AI like ChatGPT, has recently gained traction as a technological alternative for automating customer service. However, there is still a lack of research on whether current generative AI technologies can effectively replace traditional human managers in customer service automation, and whether they are advantageous in some situations and disadvantageous in others, depending on the conditions and environment. To answer the question, "Can generative AI replace human managers in customer service activities?", this study conducted experiments and surveys on customer online reviews of a food delivery platform. We applied the perspective of the elaboration likelihood model to generate hypotheses about whether there is a difference between positive and negative online reviews, and analyzed whether the hypotheses were supported. The analysis results indicate that for positive reviews, generative AI can effectively replace human managers. However, for negative reviews, complete replacement is challenging, and human managerial intervention is considered more desirable. The results of this study can provide valuable practical insights for organizations looking to automate customer service using generative AI.

• KDI북한경제리뷰
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• v.23 no.12
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• pp.69-72
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• 2021

• KDI북한경제리뷰
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• v.22 no.10
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• pp.47-69
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• 2020

• Journal of the Korea Academia-Industrial cooperation Society
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• v.9 no.5
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• pp.1234-1241
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• 2008

Cost effectiveness is greatly related with the degree of reducing the testing cost by the technical reviews. In this paper, we present a new metric My for evaluating the cost effectiveness of technical reviews during software development. First, we estimate and compare My with conventional measure using data collected during practical software development procedure, then we show the validity and usefulness of the proposed measure My. Also by formulating the relationship between the data collected during the reviews and the test, we present a method to estimate the value of the metric My using only the data collected during review phase.

• Information Systems Review
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• v.23 no.1
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• pp.45-68
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• 2021

As the influence of online reviews on consumer decision-making increases, concerns about review manipulation are also increasing. Fake reviews or review manipulations are emerging as an important problem by posting untrue reviews in order to increase sales volume, causing the consumer's reverse choice, and acting at a high cost to the society as a whole. Most of the related prior studies have focused on predicting review manipulation through data mining methods, and research from a consumer perspective is insufficient. However, since the possibility of manipulation of reviews perceived by consumers can affect the usefulness of reviews, it can provide important implications for online word-of-mouth management regardless of whether it is false or not. Therefore, in this study, we analyzed whether there is a difference between the review evaluated by the consumer as being manipulated and the general review, and verified whether the manipulated review negatively affects the review usefulness. For empirical analysis, 34,711 online book reviews on the LibraryThing website were analyzed using multilevel logistic regression analysis and Poisson regression analysis. As a result of the analysis, it was found that there were differences in product level, reviewer level, and review level factors between reviews that consumers perceived as being manipulated and reviews that were not. In addition, manipulated reviews have been shown to negatively affect review usefulness.

• The Journal of Society for e-Business Studies
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• v.27 no.2
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• pp.21-36
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• 2022

Recently, as COVID-19 has made the e-commerce market expand widely, customers who have different consumption patterns appear in the market. Because companies can obtain opinions and information of customers from reviews, they increasingly face the requirements of managing customer reviews on online platform. In this study, we analyze customers and carry out market segmentation for classifying and defining type of customers in e-commerce. Specifically, K-means clustering was conducted on customer review data collected from Wemakeprice online shopping platform, which leads to the result that six clusters were derived. Finally, we define the characteristics of each cluster and propose a customer management plan. This paper is possible to be used as materials which identify types of customers and it can reduce the cost of customer management and make a profit for online platforms.

• Knowledge Management Research
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• v.23 no.1
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• pp.187-201
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• 2022

Online reviews written by tourists provide important information for the management and operation of the tourism industry. The star rating of online reviews is a simple quantitative evaluation of a product or service, but it is difficult to reflect the sincere attitude of tourists. There is also an issue; the star rating and review content are not matched. In this study, a star rating prediction model based on online review content was proposed to solve the discrepancy problem. We compared the differences in star ratings and sentiment by continent through sentiment analysis on tourist attractions and hotels written by foreign tourists who visited Korea. Variables were selected through TF-IDF vectorization and sentiment analysis results. Logit, artificial neural network, and SVM(Support Vector Machine) were used for the classification model, and artificial neural network and SVR(Support Vector regression) were applied for the rating prediction model. The online review rating prediction model proposed in this study could solve inconsistency problems and also could be applied even if when there is no star rating.

• Proceedings of the Korean Society for Information Management Conference
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• 2015.08a
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• pp.109-114
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• 2015

인터넷 서점은 대형 프랜차이즈 서점만으로도 1백 60여 종에 이른다. 국내 서적 거래의 규모는 약 3조원인데, 이 중 인터넷 서점이 차지하는 규모는 1999년의 150억 이후 꾸준히 증가하여 2001년에는 1200억으로 전체 규모의 약 4%를 차지할 정도다. 출판시장이 감소되고 인터넷 서점의 시장점유율이 기하급수적으로 증가하고 있는 상황에서, 인터넷 서점의 도서 구매를 적극 활용하는 것은 출판시장을 되살리는 계기가 될 수 있다. 본 연구는 인터넷 서점의 활용 방안 중 이용자들에게 제공되는 인터넷 서점 내 리뷰가 이용자들의 구매 확정 의사에 영향을 미칠 것이라고 예상했다. 이에 따라 공공도서관 이용자 85명을 대상으로 설문조사를 실시하였고, 설문결과를 바탕으로 인터넷 서점 리뷰가 인터넷 서점 이용자의 구매 의사결정에 미치는 영향을 분석하였다. 인터넷 서점 리뷰 외에 구매자의 구매 의사 결정에 영향을 주는 다른 요인이 있는지 연구하였다.

• Journal of the Korea Society of Computer and Information
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• v.29 no.7
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• pp.139-148
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• 2024

In the post-pandemic era, the demand for online education platforms has surged, leading to increased consumer reliance on online reviews for decision-making. This study investigates the impact of Chinese online customer reviews on consumer purchase behavior in online education. By examining the role of trust, review sentiment, and the quantity and timeliness of reviews, the research aims to understand how these factors influence consumer decisions. By using regression model, findings reveal that negative reviews, timely feedback, and a higher volume of reviews positively affect consumer purchase decisions, while course pricing demonstrates an inverse relationship. Furthermore, cognitive and affective trust mediate the relationship between reviews and purchase behavior, highlighting a reverse U-shaped effect on consumer decision inclination. These insights provide valuable implications for online education providers, emphasizing the need to manage and leverage online reviews to foster consumer trust and improve sales performance.