• International Journal of Contents
• /
• v.14 no.2
• /
• pp.16-23
• /
• 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.

• Journal of Korean Society of Industrial and Systems Engineering
• /
• v.44 no.4
• /
• pp.85-97
• /
• 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.

• Journal of Intelligence and Information Systems
• /
• v.17 no.3
• /
• pp.115-130
• /
• 2011

Many online stores bring features that can build trust in their customers. More so, the number of products or content services on online stores has been increasing rapidly. Hence, personalization on online stores is considered to be an important technology to companies and customers. Recommender systems that provide favorable products and customer product reviews to users are the most commonly used features in this purpose. There are many studies to that investigated the relationship between social presence as an antecedent of trust and provision of recommender systems or customer product reviews. Many online stores have made efforts to increase perceived social presence of their customers through customer reviews, recommender systems, and analyzing associations among products. Primarily because social presence can increase customer trust or reuse intention for online stores. However, there were few studies that investigated the interactions between recommendation type, product type and provision of customer product reviews on social presence. Therefore, one of the purposes of this study is to identify the effects of personalized recommender systems and compare the role of customer reviews with product types. This study performed an experiment to see these interactions. Experimental web pages were developed with $2{\times}2$ factorial setting based on how to provide social presence to users with customer reviews and two product types such as hedonic and utilitarian. The hedonic type was a ringtone chosen from Nate.com while the utilitarian was a TOEIC study aid book selected from Yes24.com. To conduct the experiment, web based experiments were conducted for the participants who have been shopping on the online stores. Participants were a total of 240 and 30% of the participants had the chance of getting the presents. We found out that social presence increased for hedonic products when personalized recommendations were given compared to non.personalized recommendations. Although providing customer reviews for two product types did not significantly increase social presence, provision of customer product reviews for hedonic (ringtone) increased perceived social presence. Otherwise, provision of customer product reviews could not increase social presence when the systems recommend utilitarian products (TOEIC study.aid books). Therefore, it appears that the effects of increasing perceived social presence with customer reviews have a difference for product types. In short, the role of customer reviews could be different based on which product types were considered by customers when they are making a decision related to purchasing on the online stores. Additionally, there were no differences for increasing perceived social presence when providing customer reviews. Our participants might have focused on how recommendations had been provided and what products were recommended because our developed systems were providing recommendations after participants rating their preferences. Thus, the effects of customer reviews could appear more clearly if our participants had actual purchase opportunity for the recommendations. Personalized recommender systems can increase social presence of customers more than nonpersonalized recommender systems by using user preference. Online stores could find out how they can increase perceived social presence and satisfaction of their customers when customers want to find the proper products with recommender systems and customer reviews. In addition, the role of customer reviews of the personalized recommendations can be different based on types of the recommended products. Even if this study conducted two product types such as hedonic and utilitarian, the results revealed that customer reviews for hedonic increased social presence of customers more than customer reviews for utilitarian. Thus, online stores need to consider the role of providing customer reviews with highly personalized information based on their product types when they develop the personalized recommender systems.

• The Journal of the Korea Contents Association
• /
• v.9 no.8
• /
• pp.272-284
• /
• 2009

In this paper, we proposed the methodology for analyzing online customer reviews by using text mining technologies. We introduced marketing segmentation into the methodology because it would be efficient and effective to analyze the online customers by grouping them into similar online customers that might include similar opinions and experiences of the customers. That is, the methodology uses categorization and information extraction functions among text mining technologies, matched up with the concept of market segmentation. In particular, the methodology also uses cross-tabulations analysis function which is a kind of traditional statistics analysis functions to derive rigorous results of the analysis. In order to confirm the validity of the methodology, we actually analyzed online customer reviews related with tourism by using the methodology.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.9 no.5
• /
• pp.1702-1716
• /
• 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.

• Journal of Information Technology Services
• /
• v.16 no.3
• /
• pp.113-125
• /
• 2017

Customers have been affected by others' opinions when they make a purchase. Thanks to the development of technologies, people are sharing their experiences such as reviews or ratings through online or social network services, However, although ratings are intuitive information for others, many reviews include only texts without ratings. Also, because of huge amount of reviews, customers and companies can't read all of them so they are hard to evaluate to a product without ratings. Therefore, in this study, we propose a methodology to predict ratings based on reviews for a product. In a methodology, we first estimate the topic-review matrix using the Latent Dirichlet Allocation technic which is widely used in topic modeling. Next, we predict ratings based on the topic-review matrix using the artificial neural network model which is based on the backpropagation algorithm. Through experiments with actual reviews, we find that our methodology can predict ratings based on customers' reviews. And our methodology performs better with reviews which include certain opinions. As a result, our study can be used for customers and companies that want to know exactly a product with ratings. Moreover, we hope that our study leads to the implementation of future studies that combine machine learning and topic modeling.

• Journal of Distribution Science
• /
• v.17 no.5
• /
• pp.85-94
• /
• 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.

• The Journal of Information Systems
• /
• v.20 no.2
• /
• pp.21-36
• /
• 2011

The Web has become an excellent source for gathering consumer opinions. There are now numerous Web sites containing such opinions, e.g., customer reviews of products, forums, discussion groups, and blogs. This paper focuses on online customer reviews of products. It makes some contributions. Especially it proposes minimalism and chunking framework for analyzing and comparing consumer opinions of competing products. Users are able to clearly see the strengths and weaknesses of each product in the minds of consumers in terms of various product features. This comparison is useful to both potential customers and product manufacturers. For a product manufacturer, the comparison enables it to easily gather marketing intelligence and product benchmarking information. In this paper, we only focus on mining opinion/product features that the reviewers have commented on. Five types of online review presentations are presented to mine such features. Our experimental results show that these techniques are useful to identify customers' opinions and trends.

• Asia Marketing Journal
• /
• v.24 no.1
• /
• pp.13-28
• /
• 2022

Despite the benefits of online shopping, we easily observe consumer behaviour when making purchases through offline channels. Why do they choose to go offline by taking the effort to go there? As a factor influencing decision-making, this study assumes that distrust of online shopping increases webrooming intentions that online consumers move to offline channels. Consumers check online reviews as well as seller information to increase their purchasing confidence. There are few studies on the effect of negative online reviews on consumers' purchasing decisions. Contrary to the pessimistic results of previous studies, the results of this study explain the mechanism by which consumers who saw negative online reviews feel distrust of online shopping and go to offline stores. It provides implications for understanding the migration phenomenon of online shoppers to offline channels and what strategies should be prepared to retain and attract customers to each channel.

• Journal of Korea Society of Digital Industry and Information Management
• /
• v.8 no.1
• /
• pp.191-205
• /
• 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.