• Information Systems Review
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• v.23 no.3
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• pp.127-158
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• 2021

Many online travel agencies (OTAs) provide average ratings and time-relevant information or the most recently posted reviews regarding hotels to satisfy customers. To identify these two factors' relative influence on behavioral decision-making processes, we conducted two studies: (1) an experimental research design to explore the relative influence of the two on online review consumption and (2) an empirical approach to examine their relative impact on online review generation. The results show that when review posters observe an inconsistency between average ratings and recent reviews, they tend to deviate from the recent reviews regardless of the overall direction (reactance behavior). Meanwhile, review consumers tend to conform to the opinions presented in recent reviews (herding behavior). Additionally, in both cases, the effects are amplified in case of a negative aberration. Based on the findings, this study provides theoretical and practical implications regarding the relative influences of average rating and recently posted reviews and their different impacts on online review consumption and generation.

• Knowledge Management Research
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• v.22 no.3
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• pp.273-293
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• 2021

Due to the COVID-19 pandemic, the size of the e-commerce has been increased rapidly. This pandemic, which made contact-less communication culture in everyday life made the e-commerce market to be opened even to the consumers who would hesitate to purchase and pay by electronic device without any personal contacts and seeing or touching the real products. Consumers who have experienced the easy access and convenience of the online purchase would continue to take those advantages even after the pandemic. During this time of transformation, however, the size of information source for the consumers has become even shrunk into a flat screen and limited to visual only. To provide differentiated and competitive information on products, companies are adopting AR/VR and steaming technologies but the reviews from the honest users need to be recognized as important in that it is regarded as strong as the well refined product information provided by marketing professionals of the company and companies may obtain useful insight for product development, marketing and sales strategies. Then from the consumer's point of view, if the ratings of reviews are widely diverged how consumers would process the review information before purchase? Are non-converged ratings always unreliable and worthless? In this study, we analyzed how consumer's regulatory focus moderate the attitude to process the diverged information. This experiment was designed as a 2x2 factorial study to see how the variance of product review ratings (high vs. low) for cosmetics affects product attitudes by the consumers' regulatory focus (prevention focus vs. improvement focus). As a result of the study, it was found that prevention-focused consumers showed high product attitude when the review variance was low, whereas promotion-focused consumers showed high product attitude when the review variance was high. With such a study, this thesis can explain that even if a product with exactly the same average rating, the converged or diverged review can be interpreted differently by customer's regulatory focus. This paper has a theoretical contribution to elucidate the mechanism of consumer's information process when the information is not converged. In practice, as reviews and sales records of each product are accumulated, as an one of applied knowledge management types with big data, companies may develop and provide even reinforced customer experience by providing personalized and optimized products and review information.

• Monthly Newsletter
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• no.80
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• pp.1-4
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• 1994

시설공사 수의계약사유평점제도 규제완화 건의 배경 - 예산회계법령 개정내용

• Proceedings of the Korea Database Society Conference
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• 1997.10a
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• pp.438-454
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• 1997

본 논문에서는 정보기술을 활용하고자 하는 정부 관리자들의 보다 전술적인 문제로써 정보 기술사업의 선정방안에 대해서 미국 뉴욕주의 사례를 중심으로 논하였다. 뉴욕주의 정보기술사업 선정에 대한 분석은 선정절차, 선정기준, 평점방법, 그리고 선정조직의 측면에서 이루어졌다. 뉴욕주 기술사업 선정은 평가절차가 정확성에 보다 치중하고 있으며, 평가기준도 제안조직의 능력보다는 제안된 사업의 개발로 인한 영향 평가에 초점을 두고 있다. 평점모형에서는 구체적인 가중치를 부여하여 계량화하기보다는 영향의 (+)와 (-)를 밝히고 있다. 평가조직은 내ㆍ외부 전문가를 충분히 혼합하여 활용하고 있다. 이러한 특징은 의사결정의 문화성, 합리성, 그리고 합법성의 측면에서 다시 논의되었다. 뉴욕주정부의 정보 기술 사업 선정과정에서의 특징이 정보정책에 갖는 함의 또한 논의되었다.

• KIISE Transactions on Computing Practices
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• v.21 no.2
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• pp.121-125
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• 2015

In recent years, research on collaborative filtering-based recommendation systems emphasized the accuracy of rating predictions, and this has led to an increase in computation time. As a result, such systems have divergeded from the original purpose of making quick recommendations. In this paper, we propose a recommendation algorithm that uses a Preference Difference Metric to reduce the computation time and to maintain adequate performance. The system recommends items according to their preference classification.

• Journal of the Korean Data and Information Science Society
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• v.25 no.6
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• pp.1475-1480
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• 2014

This article analyzes the academic achievement of students who entered A university from 2011 to 2012 using grade point average (GPA). The purpose of this analysis is to find the relationship between admission factors and academic achievement. Contrary to our expectation, GPA of student selected from KSAT is higher than that of selected from CSAT. So, designing and running university entrance type, it is necessary to consider admission factors deliberately.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2019.05a
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• pp.496-499
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• 2019

The study aims to collect detailed movie information from box office of the Korea Film Council and data on Naver's movie ratings to analyze important factors affecting the movie's popularity based on movie audiences and ratings.

• Proceedings of the Korea Information Processing Society Conference
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• 2023.05a
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• pp.459-460
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• 2023

기존의 추천시스템은 상품간 혹은 사용자 간의 유사도를 기반으로 작동한다. 하지만 이는 사용자가 유사한 상품 추천 속에 갇히게 되는 필터 버블의 문제와 추천시스템의 고질적인 문제인 데이터 희소성 문제를 피할 수 없게 된다. 따라서 본 연구에서는 사용자의 취향과 체형 정보를 반영하여 사용자의 평점을 예측하는 협업 필터링 기반 딥러닝 추천과 상품간 비유사성을 고려하여 사용자의 평점을 예측하는 내용 기반 추천을 혼합한 하이브리드 추천 모델을 구축하여 기존 추천시스템의 문제점을 해결하였다. 모델의 성능평가를 위해 인터넷 의류 쇼핑몰을 대상으로 유사한 이미지를 활용한 하이브리드 추천 모델과 NDCG 값을 비교하였고 유사도가 낮은 이미지를 활용한 모델이 더 우수한 성능을 보였다. 이는 다른 제품과는 달리 소비자가 의류를 구매할 경우 이미 구매한 상품과 유사한 상품보다는 유사하지 않은 상품을 구매할 가능성이 크다는 것을 보여준다.

• Proceedings of the Korean Society of Computer Information Conference
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• 2024.01a
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• pp.175-177
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• 2024

OTT(Over-The-Top) 플랫폼의 부상은 미디어 콘텐츠 소비 방식을 혁명적으로 변화시키고 있다. 본 논문은 Netflix, Amazon Prime Video, Disney+, Hulu 등 주요 OTT 플랫폼에 등록된 영화들을 IMDb 평점과 러닝타임, Rotten Tomatoes 지수를 중심으로 분석한다. 이를 통해 현재의 영화 시장 트렌드와 소비자 선택, 시장 전략에 중요한 정보를 제공하려 한다. 분석 결과, 플랫폼별로 제공하는 영화의 품질과 러닝타임이 다양하며, 소비자들이 선호하는 영화 테마를 시각적으로 파악할 수 있는 워드 클라우드를 포함한다. 이러한 결과는 OTT 플랫폼의 전략적 콘텐츠 제공과 소비자 행동 이해에 기여할 수 있는 중요한 통찰력을 제공한다.

• The Journal of the Korean dental association
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• v.35 no.1 s.332
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• pp.43-49
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• 1997