• 한국정보시스템학회지:정보시스템연구
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• 제31권2호
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• pp.159-177
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• 2022

Purpose Online reviews are critical for sales of online shopping platforms because they provide useful information to consumers. As the eCommerce market grows rapidly, the role of online reviews is becoming more important. The purpose of this study is to analyze how online reviews written by domestic consumers affect product sales by classifying the types of products. Design/methodology/approach This study analyzed how the effects of review characteristics(reviewer reputation, reviewer exposure, review length, time, rating, image, and emotional score) on the usefulness of online reviews differ depending on the product types. Subsequently, how the impact of review attributes (review usefulness, number of reviews, ratings, and emotional scores) on product sales differs according to each product type was compared. Based on the FCB Grid model, the product type was classified into high involvement-rational, high involvement-emotional, low involvement -rational, and low involvement-emotional product types. Findings According to the analysis result, the characteristics of reviews useful to consumers were different for each product type, and the review attributes affecting product sales were also different for each product type. This study confirmed that it revealed that product characteristics are major consideration in evaluating the review usefulness and the factors affecting product sales.

• 유통과학연구
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• 제22권3호
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• pp.113-124
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• 2024

Purpose: This study is a quantitative study and analyzes the effect of evaluating the extreme and usefulness of product reviews on sales performance by using text mining techniques based on product review big data. We investigate whether the perceived helpfulness of product reviews serves as a mediating factor in the impact of product review extremity on sales performance. Research design, data and methodology: The analysis emphasizes customer interaction factors associated with both product review helpfulness and sales performance. Out of the 8.26 million Amazon product reviews in the book category collected by He & McAuley (2016), text mining using natural language processing methodology was performed on 300,000 product reviews, and the hypothesis was verified through hierarchical regression analysis. Results: The extremity of product reviews exhibited a negative impact on the evaluation of helpfulness. And the helpfulness played a mediating role between the extremity of product reviews and sales performance. Conclusion: Increased inclusion of extreme content in the product review's text correlates with a diminished evaluation of helpfulness. The evaluation of helpfulness exerts a negative mediating effect on sales performance. This study offers empirical insights for digital market distributors and sellers, contributing to the research field related to product reviews based on review ratings.

• 유통과학연구
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• 제17권5호
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• pp.85-94
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• 2019

Purpose - The purpose of this study is to analyze the impact of customer's communication on sales performance in the online market. Research design, data, and methodology - This study uses linear regression analysis to examine the effects of product review characteristics which are the result of customer's communication, on sales performance by using product reviews of online marketplace Amazon. Result - The increase in the number of product reviews positively affected sales performance. An increase in extreme opinions in the product review has a positive effect on sales performance. The product review length has a negative effect on sales performance. Conclusions - This study has shown the online marketplace customers' communication can influence sales performance using product review big data. This study contributed to the theoretical completeness by analyzing all the products of the book category in Amazon online market. This research will complement the theories regard to the customer behavior affecting sales performance. We expect the empirical analysis result will provide empirical help to sellers, online marketplace operators, and customers. In particular, the number of letters in the product may negatively affect sales performance, so sellers need to consider this effect carefully when exposing product reviews.

• 한국정보시스템학회지:정보시스템연구
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• 제32권4호
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• pp.29-50
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• 2023

Purpose The impact of online reviews is widely acknowledged, with extensive research focused on text-based reviews. However, there's a lack of research regarding reviews in video format. To address this gap, this study aims to explore the connection between company-sponsored product review videos and the extent of directive speech within them. This article analyzed viewer sentiments expressed in video comments based on the level of directive speech used by the presenter. Design/methodology/approach This study involved analyzing speech acts in review videos based on sponsorship and examining consumer reactions through sentiment analysis of comments. We used Speech Act theory to perform the analysis. Findings YouTubers who receive company sponsorship for review videos tend to employ more directive speech. Furthermore, this increased use of directive speech is associated with a higher occurrence of negative consumer comments. This study's outcomes are valuable for the realm of user-generated content and natural language processing, offering practical insights for YouTube marketing strategies.

• 한국콘텐츠학회논문지
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• 제20권7호
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• pp.316-330
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• 2020

이 연구는 텍스트 마이닝을 사용하여 하드웨어 제품에 대한 리뷰, 서비스 상품에 대한 리뷰, ICT분야의 하드웨어와 클라우드 서비스가 융합된 형태의 상품을 대상으로 소비자 리뷰를 분석한다. 분석을 위해 각 리뷰의 키워드를 도출하고 토픽 도출에 사용된 단어의 차별성을 찾는다. 마지막으로 전체 리뷰를 대상으로 군집분석을 실시하고 각각의 상품군의 리뷰가 어떤 군집에 속하는지를 검토한다. 이 연구를 통해서 각 상품의 유형별로 특화되어 사용된 핵심어를 도출하였고, 토픽모델링을 사용하여 제품과 서비스의 특성을 표현하는 주제를 도출하였다. 서비스 상품 리뷰에서는 공급자의 우수성을 의미하는 professional, technician과 같은 핵심어를 도출하였고, 융합제품서비스상품으로서 아마존 에코 리뷰에서는 favorite, fine, fun, nice, smart, unlimited, useful 등의 긍정적 의미의 형용사를 도출하였다. 군집분석을 사용하여 전체 리뷰를 분석하였고, 3개의 상품 유형별 리뷰가 배타적으로 서로 다른 각각의 군집에 속하는 결과를 발견하였다. 이 연구는 소비자의 니즈(needs)를 상품의 유형별로 온라인 리뷰를 이용하여 차이점을 분석하였고 실무적으로 상품 유형에 기반한 상품기획과 마케팅 프로모션 차별화의 필요성을 제시하였다.

• 한국정보시스템학회지:정보시스템연구
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• 제30권4호
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• pp.1-20
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• 2021

Purpose Nowadays, Autonomous Sensory Meridian Response (ASMR) is rapidly growing in popularity and increasingly appearing in marketing. Not even in TV commercial advertisement, ASMR also fast growing in one-person media communication, many brands and social media influencers used ASMR for their marketing contents. The purpose of this study is to measure consumers' perceptions about the products in ASMR marketing content and compare the differences in communication effect of ASMR content creator between product review and how-to in the same Macro tier influencer - the YouTuber that has 10,000-100,000 subscribers. Design/methodology/approach The research methods selected ASMRtist that do product review content and how-to content, Text comments data was collected from 200 videos of tech-device review videos and beauty-fashion videos. A total of 52,833 text comments were analyzed by applying the LDA topic modeling algorithm and social network analysis. Findings Through the result, we can know that ASMR is good at taking attention of viewers with ASMR triggers. In the Tech device reviews field, ASMR viewers also focus on the product like product's performance and purchase. However, there are many topics related to reaction of ASMR sound, trigger, relaxation. In the Beauty-fashion field, viewers' topics mainly focus on the reaction of the ASMR trigger, response to ASMRtist and other topics are talking about makeup - fashion, product, purchase. From LDA result, many ASMR viewers comment that they feel more comfortable when watching the marketing content that uses ASMR. This result has shown that ASMR marketing contents have a good performance in terms of user watching experience, so applying ASMR can take more consumer intention. And the result of social network analysis showed that product review ASMRtist have a higher communication effectiveness than how-to ASMRtist in the same tier. As an influencer marketing strategy, this study provides information to establish an efficient advertising strategy by using influencers that create ASMR content.

• Journal of Information Technology Applications and Management
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• 제30권2호
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• pp.1-18
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• 2023

Recently, as technology development has accelerated and product life cycles have been shortened, it is necessary to derive key product features from customers in the R&D planning and evaluation stage. More companies want differentiated competitiveness by providing consumer-tailored products based on big data and artificial intelligence technology. To achieve this, the need to correctly grasp the required quality, which is a requirement of consumers, is increasing. However, the existing methods are centered on suppliers or domain experts, so there is a gap from the actual perspective of consumers. In other words, product attributes were defined by suppliers or field experts, but this may not consider consumers' actual perspective. Accordingly, the demand for deriving the product's main attributes through reviews containing consumers' perspectives has recently increased. Therefore, we propose a review data analysis-based required quality methodology containing customer requirements. Specifically, a pre-training language model with a good understanding of Korean reviews was established, consumer intent was correctly identified, and key contents were extracted from the review through a combination of KeyBERT and topic modeling to derive the required quality for each product. RevBERT, a Korean review domain-specific pre-training language model, was established through further pre-training. By comparing the existing pre-training language model KcBERT, we confirmed that RevBERT had a deeper understanding of customer reviews. In addition, all processes other than that of selecting the required quality were linked to the automation process, resulting in the automation of deriving the required quality based on data.

• 경영정보학연구
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• 제3권2호
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• pp.337-347
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• 2001

• 한국전자거래학회지
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• 제22권1호
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• pp.65-87
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• 2017

온라인 쇼핑몰에서 상품에 대한 사용자 리뷰와 평점을 분석하여 상품의 특징을 자동으로 추출하고 평점이 어떤 특징에 의해 부여된 것인지 판단하여 각 특징에 분배하여 점수화함으로써 상품의 특징을 파악할 수 있는 방법을 제안한다. 기존 방식은 상품 구매 여부를 결정하기 위해서 많은 리뷰와 평점을 읽는데 시간을 허비하거나, 상품의 장단점을 파악하기 어려울 뿐더러 상품에 부여된 평점이 어떠한 특징에 의해서 부여되었는지 알 수 없는 구조로 되어있다. 따라서 본 논문에서는 이러한 문제를 해소하기 위하여 사용자 리뷰에서 상품의 특징을 자동으로 추출하고 각 특징별 평점을 전체 평점에서 자동으로 분배 계산하여 보여주는 방법을 제안한다. 제안하는 방법은 상품별 리뷰와 평점을 수집하여 형태소 분석을 수행하고 이를 통해 상품의 특징과 이에 대한 감성어를 추출한다. 또한, 상품의 특징을 파악할 수 있도록 각 특징에 대한 가중치를 특징이 출현한 문장의 극성을 판단하여 부여하는 방법을 기술한다. 실험을 통하여 얻은 결과와 기존 방법을 비교하는 설문조사를 통하여 제안하는 방법의 유용성을 입증하였고, 상품 리뷰 전문가의 분석과 실험의 결과를 비교함으로써 타당성을 입증하였다.

• 한국융합학회논문지
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• 제8권4호
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• pp.1-8
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• 2017

현대 사회에서 패션 시장의 규모는 해외와 국내 모두 지속적으로 증가하고 있다. 전자상거래를 통해 상품을 구입하는 경우 다른 소비자들이 작성한 상품에 대한 평가 데이터는 소비자가 상품의 구입 여부를 결정하는데에 영향을 미친다. 기업의 입장에서도 상품에 대한 소비자의 평가 데이터를 분석하여 소비자의 피드백을 반영한다면 기업의 성과에 긍정적인 영향을 미칠 수 있다. 이에 본 논문에서는 아마존 패션 상품의 리뷰 데이터를 학습하여 형성된 워드임베딩 공간을 이용하여 사용자의 감성을 분석하는 모델을 구축하는 방법을 제안한다. 실험은 아마존 리뷰 데이터 570만건을 학습하여 형성된 워드임베딩 공간을 이용하여 긍정, 부정 리뷰 데이터의 개수에 따라 총 3개의 SVM 분류기 모델을 학습하는 방식으로 진행하였다. 실험 결과 긍정 리뷰 데이터 5만건, 부정 리뷰데이터 5만건을 이용하여 SVM 분류기를 학습하였을 때 88.0%로 가장 높은 정확도(accuracy)를 나타냈다.