• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.19 no.3
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• pp.9-15
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• 2019

Large global online shopping malls, such as Amazon, offer services in English or in the language of a country when their products are sold. Since many customers purchase products based on the product reviews, the shopping malls actively utilize the sentimental analysis technique in judging preference of each product using the large amount of review data that the customer has written. And the result of such analysis can be used for the marketing to look the potential shoppers. However, it is difficult to apply this English-based semantic analysis system to different languages used around the world. In this study, more than 500,000 data from Amazon fine food reviews was used for training a deep learning based system. First, sentiment analysis evaluation experiments were carried out with three models of English test data. Secondly, the same data was translated into seven languages (Korean, Japanese, Chinese, Vietnamese, French, German and English) and then the similar experiments were done. The result suggests that although the accuracy of the sentimental analysis was 2.77% lower than the average of the seven countries (91.59%) compared to the English (94.35%), it is believed that the results of the experiment can be used for practical applications.

• Information Systems Review
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• v.21 no.2
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• pp.29-50
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• 2019

Social media is divided into Owned Media, operated by companies according to information sources, and Earned Media, which third parties produce contents. Social media research developing the logic that brand-related content in social media increases awareness of potential customers and positively changes brand attitudes, resulting in increased sales and business performance. However, there are limitations in previous researches that can not fully explain the difference of media synergy effect according to the information source of social media. it is very important for the consumer to integrate media management because consumers are more likely to choose appropriate media information for the information needed at each decision making stage. The purpose of this study is to analyze the effect of eWOM of review site and social media (owned media and earned media) on movie sales. To do this, we collected 3,589 review data from films released in 2017. The results of the study showed that eWOM of review site, social media (owned media and earned media) had a positive effect on movie sales. However, it was found that the effect of moderating eWOM of review site was different between the owned media and the earend media.

• 한국벤처창업학회:학술대회논문집
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• 2021.11a
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• pp.167-172
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• 2021

구매 의사 결정 단계에서 이미지나 동영상의 영향력이 지속해서 증가하고 있음에도 불구하고 시각적 정보가 소비자의 구매 의사 결정 과정에 영향을 미치는 메커니즘과 이에 따른 기업, 특히, 오프라인 기업의 성과에 대해서는 아직 알려진 바가 많지 않다. 이에 본 연구는 O2O 플랫폼에 입점한 10만 개 식당의 실제 예약 매출 데이터를 분석하여 이용자와 사업자가 O2O 플랫폼에 업로드한 사진 등의 시각적 정보가 기업의 성과에 미치는 영향과 메커니즘을 이중 처리 이론 기반으로 파악하고자 한다. 또한 저장하기, 공유, 리뷰 등 고객 참여와 프랜차이즈 가맹의 시각적 정보와 성과 간 관계에 대한 조절된 매개 효과를 분석한다. 본 연구는 이중 처리 이론 기반으로 시각적 정보가 구매 의사 결정 과정에서 작동하는 메커니즘을 설명하는데 기여할 것으로 기대한다. 그뿐만 아니라 실제 예약 매출 데이터를 활용하여 분석함으로써 직접적이고, 수익적인 측면의 기업 성과를 측정할 수 있을 것이다. 또한 오프라인 소매점에서 활용할 수 있는 온라인 채널 전략에 대한 실무적인 시사점을 제공할 것으로 기대한다.

• 한국벤처창업학회:학술대회논문집
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• 2017.04a
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• pp.52-52
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• 2017

글로벌 금융위기 이후 다양한 형태로 등장한 금융상품과 ICT의 결합은 그 동안 생각하지 못한 방식으로 전 세계에 다양한 수요를 충족시키면서 폭발적으로 성장했다. 하지만 IT강국이라고 자부하는 대한민국은 다양한 규제와 시스템의 복잡성 때문에 은행상품이 온라인에서 거래되는 것은 아직까지 익숙하지 않다. 다행히 이러한 규제가 조금씩 완화되어 가면서 2016년은 모바일 송금, 금융상품 추천 플랫폼 등 비 금융업체 주도의 금융시장 온라인화가 소극적으로 이루어지는 과도기로 볼 수 있다. 이러한 시점에서 기존 오프라인 채널이 아닌 온라인 채널을 통해 금융상품을 구매하거나 가입하는 고객의 만족요인에 대해 연구하는 것은 향후 폭발적으로 증가할 수요에 앞서 연구하고, 현상을 주도할 기업에서도 소비자의 만족요인을 미리 파악한다는 점에서 시기적으로 적절하다. 해당 연구는 신용대출, 정기예금, 전세대출, 주택담보대출, 정기적금, 그리고 P2P투자 상품 별 만족도에 영향을 미치는 요인과 영향력을 SERVPERF 모델을 이용하여 분석한 뒤, 회귀분석과 텍스트간의 공동 출현단어에 대해 파이선을 통해 메트릭스를 형성하고, 사회연결망 분석으로 네트워크 중심성을 분석하여 단어간의 관계를 살펴보았다. 해당 연구는 국내 최초 온라인 금융상품 비교 추천 플랫폼인 "Finda"의 리뷰/평점데이터를 이용하였다.

• Distribution Business Review
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• no.2
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• pp.65-78
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• 2002

1980년대 말 화장품 유통경로는 커다란 환경변화를 맞게 되었다. '86년 아시안 게임 개최와' 88년 올림픽 개최로 우리나라 경제는 드디어 혹자시대를 맞게 되었다. 이러한 경제의 비약적인 발전은 화장품의 고객인 여성의 경제력을 향상시키게 되었다. 뿐만 아니라 이 시기 여성의 사회진출도 급속히 증가하게 되었다. 이러한 경제적, 사회문화적 환경의 변화는 화장품 유통경로의 변화로 이어져 소위 말하는 "화장품 전문점"을 탄생시키게 되었다.(중략)게 되었다.(중략)

• Journal of the Korea Society of Computer and Information
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• v.28 no.12
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• pp.259-266
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• 2023

In this paper, we propose an algorithm that can improve the accuracy performance of collaborative filtering using attribute-based opinion mining (ABOM). For the experiment, a total of 1,227 online consumer review data about smartphone apps from domestic smartphone users were used for analysis. After morpheme analysis using the KKMA (Kkokkoma) analyzer and emotional word analysis using KOSAC, attribute extraction is performed using LDA topic modeling, and the topic modeling results for each weighted review are used to add up the ratings of collaborative filtering and the sentiment score. MAE, MAPE, and RMSE, which are statistical model performance evaluations that calculate the average accuracy error, were used. Through experiments, we predicted the accuracy of online customers' app ratings (APP_Score) by combining traditional collaborative filtering among the recommendation algorithms and the attribute-based opinion mining (ABOM) technique, which combines LDA attribute extraction and sentiment analysis. As a result of the analysis, it was found that the prediction accuracy of ratings using attribute-based opinion mining CF was better than that of ratings implementing traditional collaborative filtering.

• Information Systems Review
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• v.20 no.4
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• pp.59-78
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• 2018

With the advent of the various device types and marketing communication, customer's search and purchase behavior have become more complex and segmented. However, extant research on multichannel marketing effects of the purchase funnel has not reflected the specific features of device User Interface (UI) and User Experience (UX). In this study, we analyzed the marketing channel effects of multi-device shoppers using a unique click stream dataset from global online retailers. We examined device types that activate online shopping and compared the differences between marketing channels that promote visits. In addition, we estimated the direct and indirect effects on visits and purchase revenue through customer's accumulated experience and channel conversions. The findings indicate that the same customer selects a different marketing channel according to the device selection. These results can help retailers gain a better understanding of customers' decision-making process in multi-marketing channel environment and devise the optimal strategy taking into account various device types. Our empirical analyses yield business implications based on the significant results from global big data analytics and contribute academically meaningful theoretical framework using an economic model. We also provide strategic insights attributed to the practical value of an online marketing manager.

• Information Systems Review
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• v.23 no.1
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• pp.155-172
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• 2021

With the recent increase in online product purchases, a recommender system that recommends products considering users' preferences has still been studied. The recommender system provides personalized product recommendation services to users. Collaborative Filtering (CF) using user ratings on products is one of the most widely used recommendation algorithms. During CF, the item-based method identifies the user's product by using ratings left on the product purchased by the user and obtains the similarity between the purchased product and the unpurchased product. CF takes a lot of time to calculate the similarity between products. In particular, it takes more time when using text-based big data such as review data of Amazon store. This paper suggests a hybrid recommendation system using a 2-phase methodology and text data mining to calculate the similarity between products easily and quickly. To this end, we collected about 980,000 online consumer ratings and review data from the online commerce store, Amazon Kinder Store. As a result of several experiments, it was confirmed that the suggested hybrid recommendation system reflecting the user's rating and review data has resulted in similar recommendation time, but higher accuracy compared to the CF-based benchmark recommender systems. Therefore, the suggested system is expected to increase the user's satisfaction and increase its sales.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.22 no.9
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• pp.1153-1158
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• 2018

Online customer evaluations and social media information among a variety of information sources are critical for businesses as it influences the customer's decision making. There are limitations on the time and money that the survey will ask to identify a variety of customers' needs and complaints. The customer review data at online shopping malls provide the ideal data sources for analyzing customer sentiment about their products. In this study, we collected product reviews data on the smartphone of Samsung and Apple from Amazon. We applied five classification algorithms which are used as representative sentiment analysis techniques in previous studies. The five algorithms are based on support vector machines, bagging, random forest, classification or regression tree and maximum entropy. In this study, we proposed entropy score which can comprehensively evaluate the performance of classification algorithm. As a result of evaluating five algorithms using an entropy score, the SVMs algorithm's entropy score was ranked highest.

• Knowledge Management Research
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• v.24 no.3
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• pp.95-125
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• 2023

As the aviation industry has rapidly been grown, there are more factors for customers to consider when choosing an airline. In response, airlines are trying to increase customer value by providing high-quality services and differentiated experiential value. While early customer value research centered on utilitarian value, which is the trade-off between cost and benefit in terms of utility for products and services, the importance of experiential value has recently been emphasized. However, experiential value needs to be studied in a specific context that fully represents customer preferences because what constitutes customer value changes depending on the product or service context. In addition, customer value has an important influence on customers' decision-making, so it is necessary for airlines to accurately understand what constitutes customer value. In this study, we collected customer reviews and ratings from Skytrax, a website specializing in airlines, and utilized the BERTopic technique to derive factors of customer value. The results revealed nine factors that constitute customer value in airlines, and six of them are related to customer satisfaction. This study proposes a new methodology that enables a granular understanding of customer value and provides airlines with specific directions for improving service quality.