• Asia pacific journal of information systems
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• 제25권4호
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• pp.763-783
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• 2015

Online customer reviews (i.e., electronic word-of-mouth) has gained considerable interest over the past years. However, a knowledge gap exists in explaining the mechanisms among the factors that determine the product sales in online retailing environment. To fill the gap, this study adopts a principal-agent perspective to investigate the effect of customer reviews and customer incentives on product sales in online retail stores. Two customer review factors (i.e., average review ratings and the number of reviews) and two customer incentive factors (i.e., price discounts and special shipping offers) are used to predict product sales in regression analysis. The sales ranking data collected from the video game titles at Amazon.com are used to analyze the direct effects of the four factors and the interaction effects between customer review and customer incentive factors to product sales. Result reveals that most relationships exist as hypothesized. The findings support both the direct and interaction effects of customer reviews and incentive factors on product sales. Based on the findings, discussions are provided with regard to the academic and practical contributions.

• 지능정보연구
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• 제17권3호
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• pp.115-130
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• 2011

온라인 스토어들은 다양한 방식으로 사용자들에게 신뢰감을 가져다 줄 수 있는 요인들을 제공하려고 한다. 대표적인 방식이 고객이 좋아할 만한 제품의 추천과 고객제품리뷰의 제공이다. 각각의 제공을 통해 신뢰의 선행요인이 되는 사회적 실재감을 향상시킬 수 있다는 연구들이 있어왔다. 따라서 본 연구에서는 추천 상황에 따른 사회적 실재감에 미치는 영향과 추천 상황과 제품군의 유형, 고객제품리뷰의 제공여부에 따라 사회적 실재감의 증가에 미치는 영향을 실험을 통해 분석하였다. 개인화 추천을 통해 사회적 실재감을 증대시킬 수 있었으며, 쾌락재에서는 고객제품리뷰의 제공을 통해 어떤 추천 상황에서든 사회적 실재감이 증대되나 유의한 차이를 보이지는 않았다.

• 한국IT서비스학회지
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• 제14권4호
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• pp.159-169
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• 2015

Customer reviews are one of the important sources for purchase decision makings in online stores. Online stores have tried to provide useful reviews in product pages to customers. To assess the usefulness of customer reviews before other users have voted enough on the reviews, diverse aspects of reviews were utilized in prevous studies. Style and semantic information were utilized in many studies. This study aims to test diverse alogrithms and datasets for identifying a proper classification method and threshold to classify useful reviews. In particular, most researches utilized ratio type helpfulness index as Amazon.com used. However, there is another type of usefulness index utilized in TripAdviser.com or Yelp.com, count type helpfulness index. There was no proper threshold to classify useful reviews yet for count type helpfulness index. This study used reivews and their usefulness votes on restaurnats from Yelp.com to devise diverse datasets and applied text mining approaches to classify useful reviews. Random Forest, SVM, and GLMNET showed the greater values of accuracy than other approaches.

• International Journal of Contents
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• 제14권2호
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• pp.16-23
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• 2018

Although there have been studies regarding the influence of customer reviews on consumer decision making at online shopping sites, research on factors affecting the perceived customer review quality for online shopping sites is limited. This study posits that sociability, which is one of the environmental factors of an online shopping site, can affect the quality of customer reviews. Sociability is a key factor in building a collaborative environment online, but studies have been limited to applying sociability to customer reviews that are the result of a collaborative environment. This study expects that sociability affects the performance of online shopping sites through the perceived information quality of customer reviews, and customers' efficacy. More specifically this study investigates the structural relationship between sociability, self-efficacy, collective efficacy, and the perceived information quality of the reviews in an online shopping context, regarding the patronage intention of customers. This study was conducted using a survey of 361 college students. The structural equation model results indicate that user perception of sociability increases self-efficacy and collective efficacy. The improved efficacy enhances the perceived information quality of reviews for online shopping sites, which increases patronage intention of customers. This study found that online shopping sites require a platform for customers to engage in social interaction to enhance their customers' loyalty and lifetime value.

• ETRI Journal
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• 제38권3호
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• pp.494-501
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• 2016

Sentiment analysis incorporates natural language processing and artificial intelligence and has evolved as an important research area. Sentiment analysis on product reviews has been used in widespread applications to improve customer retention and business processes. In this paper, we propose a method for performing an intensified sentiment analysis on customer product reviews. The method involves the extraction of two feature sets from each of the given customer product reviews, a set of acoustic features (representing emotions) and a set of lexical features (representing sentiments). These sets are then combined and used in a supervised classifier to predict the sentiments of customers. We use an audio speech dataset prepared from Amazon product reviews and downloaded from the YouTube portal for the purposes of our experimental evaluations.

• 지식경영연구
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• 제20권2호
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• pp.25-42
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• 2019

AI speakers which are wireless speakers with smart features have released from many manufacturers and adopted by many customers. Though smart features including voice recognition, controlling connected devices and providing information are embedded in many mobile phones, AI speakers are sitting in home and has a role of the central en-tertainment and information provider. Many surveys have investigated the important factors to adopt AI speakers and influ-encing factors on satisfaction. Though most surveys on AI speakers are cross sectional, we can track customer attitude toward AI speakers longitudinally by analyzing customer reviews on AI speakers. However, there is not much research on the change of customer attitude toward AI speaker. Therefore, in this study, we try to grasp how the attitude of AI speaker changes with time by applying text mining-based analysis. We collected the customer reviews on Amazon Echo which has the highest share of AI speakers in the global market from Amazon.com. Since Amazon Echo already have two generations, we can analyze the characteristics of reviews and compare the attitude ac-cording to the adoption time. We identified all sub topics of customer reviews and specified the topics for smart features. And we analyzed how the share of topics varied with time and analyzed diverse meta data for comparisons. The proportions of the topics for general satisfaction and satisfaction on music were increasing while the proportions of the topics for music quality, speakers and wireless speakers were decreasing over time. Though the proportions of topics for smart fea-tures were similar according to time, the share of the topics in positive reviews and importance metrics were reduced in the 2nd generation of Amazon Echo. Even though smart features were mentioned similarly in the reviews, the influential effect on satisfac-tion were reduced over time and especially in the 2nd generation of Amazon Echo.

• 산업경영시스템학회지
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• 제44권4호
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• pp.85-97
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• 2021

Online consumer activities have increased considerably since the COVID-19 outbreak. For the products and services which have an impact on everyday life, online reviews and recommendations can play a significant role in consumer decision-making processes. Thus, to better serve their customers, online firms are required to build online-centric marketing strategies. Especially, it is essential to define core value of customers based on the online customer reviews and to propose these values to their customers. This study discovers specific perceived values of customers in regard to a certain product and service, using online customer reviews and proposes a customer value proposition methodology which enables online firms to develop more effective marketing strategies. In order to discover customers value, the methodology employs a text-mining technology, which combines a sentiment analysis and topic modeling. By the methodology, customer emotions and value factors can be more clearly defined. It is expected that online firms can better identify value elements of their respective customers, provide appropriate value propositions, and thus gain sustainable competitive advantage.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제9권5호
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• pp.1702-1716
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• 2015

Customer product reviews have become great influencers of purchase decision making. To assist potential customers, online stores provide various ways to sort customer reviews. Different methods have been developed to identify and recommend useful reviews to customers, primarily using feedback provided by customers about the helpfulness of reviews. Most of the methods consider the preferences of all users to determine whether reviews are helpful, and all users receive the same recommendations.

• 디지털산업정보학회논문지
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• 제8권1호
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• pp.191-205
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• 2012

A book review is one of the most important sources of information which provide the descriptive and evaluative contents about books. Reviews have great influence on consumer behavior because they are believed to be more reliable than information provided by sellers. Readers who read a book review includes information about book decide whether they will buy or not. This study examines customer attitude change by book reviews with regarding to different type of information sources(experts and prior customers) and different directions of messages. We address the following research questions: (1) Can positive book reviews with credibility have a positive impact on acceptance of books? (2) Can negative book reviews with credibility have a negative impact on acceptance of books? The results shows that a credibility is an essential factor for affecting customers' mind. When positive book reviews were written, both expert and customer opinions have a positive impact on acceptance of customers. Given negative book reviews of experts, trustworthiness is more important than expertise. However, a objectivity of customer's reviews is more important.

• 품질경영학회지
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• 제51권4호
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• pp.701-713
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• 2023

Purpose: The primary purpose of this study is to integrate text mining and Quality Function Deployment (QFD) to automatically extract valuable information from customer reviews, thereby establishing a QFD frame- work to confirm genuine customer needs for New Product Development (NPD). Methods: Our approach combines text mining and QFD through topic modeling and sentiment analysis on a large data set of 56,873 customer reviews from Zappos.com, spanning five running shoe brands. This process objectively identifies customer requirements, establishes priorities, and assesses competitive strengths. Results: Through the analysis of customer reviews, the study successfully extracts customer requirements and translates customer experience insights and emotions into quantifiable indicators of competitiveness. Conclusion: The findings obtained from this research offer essential design guidance for new product develop- ment endeavors. Importantly, the significance of these results extends beyond the running shoe industry, presenting broad and promising applications across diverse sectors.