• Korean Journal of Human Ecology
• /
• v.22 no.2
• /
• pp.343-355
• /
• 2013

E-reviews, electronic reviews, are generally perceived as trustworthy and credible by the consumers, because it is based on the experiences of other consumers who are independent of the marketers. Therefore, consumers may rely more on the review information as an important cue than direct experience or advertising. This paper explored the structural equation model to investigate the relationships among search motives of e-reviews, attributes of e-review, trust, and purchase intention for cosmetics. A self-questionnaire was developed based on previous researches. Data were collected from 300 female university students experienced purchasing cosmetics at the Internet and were analyzed by AMOS 20.0. Results showed that e-review attributes consisted of three factors: expertise/visuality, quality/functionality and advertising/design. Utilitarian and hedonic search motives were significantly related to expertise/ visuality attributes of e-review and then influenced the purchase intention for cosmetics, mediated by the trust of e-review. However, quality/functionality attributes related by utilitarian motive did not have a significant effect to trust of e-review and purchase intention for cosmetics. Regardless of search motives and trust of e-review, advertising/design attributes of e-review directly related to purchase intention of cosmetics. As predicted, the trust of e-review was an important mediated variable to stimulate the purchase intention of cosmetics at internet. The implications of findings for research and practice are discussed.

• Korean Journal of Human Ecology
• /
• v.21 no.5
• /
• pp.1005-1016
• /
• 2012

Recently, as growing number of consumers publish product and service reviews on the Internet, e-review has received attention from retailers and researchers. E-review, a form of electronic word-of-mouth (eWOM) which is typically shared between strangers whose identity and credibility are unknown, has become an important product information source as social media has facilitated information exchanges between more consumers. The objective of this study was to investigate the effects of e-review attributes on purchase intention for fashion products, which is mediated by trust of e-review, as well as to explore the differences between consumer communities and cooperative communities. A questionnaire was developed based on previous researches. Data were gathered from adults living in Busan. The results were analyzed by factor analysis, t-test, and regression using SPSS 18.0. The results showed that consumers tended to recognize e-reviews from consumer communities as exaggerated information, while they considered reviews from cooperative communities as reliable information, which gave the latter higher purchase intention. There were significant differences in e-review attributes for fashion products (e.g., Exaggeration, Entertainment, Innocence, and Agreement), purchase intention between consumer communities (e.g: Blog, Internet cafe) and cooperative communities (e.g: general malls and specialty malls). For both communities, purchase intention of fashion products was influenced by its entertainment attributes and perceived trust of e-reviews. These results suggest that e-retailers need to focus on understanding the causes of purchase intention with e-reviews for fashion products. Specifically, e-retailers should recognize that e-reviews of fashion products were associated primarily with entertaining and with consumers' trust. Based on these findings, managerial implications are presented.

• Journal of Digital Convergence
• /
• v.19 no.7
• /
• pp.113-127
• /
• 2021

This study classifies review attributes into ratings, number of comments and image information in online shopping mall to verify their impact on brand attitude and purchase decision and e-WOM intention. Use SPSS 23.0 for frequency analysis, factor analysis and regression analysis. The results showed that review attributes have a positive effect on brand attitudes, purchase decision and e-WOM intention, but the number of comments has not affect on purchase decision. Brand attitude has a positive effect on purchase decision and e-WOM intention. Brand attitude has media effect in the relationship between ratings, image information and purchase decision, and in the relationship between review attributes and e-WOM intention. As these results, consumers don't always like to have a lot of comments. and should allow to focus on high ratings and photo reviews as much as possible when writing reviews.

• The Journal of Information Systems
• /
• v.31 no.2
• /
• pp.159-177
• /
• 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.

• Fashion & Textile Research Journal
• /
• v.18 no.1
• /
• pp.15-26
• /
• 2016

Many brands are attempting to use consumers as a part of their marketing strategies, due to the fashion industry's sensitive response to consumers' reaction. In addition, due to the popularity of e-WOM(electronic Word-Of-Mouth), fashion brands are highly sensitive to their supporters' online reviews. Amid this background, the main objectives of this study are as follows: 1) to analyze the effect of online reviews' attributes and valences on forming an impression about a fashion brand; 2) to examine the online re-WOM(word-of-mouth) effect of online reviews by fashion brand supporters on brand attitude; and 3) to measure the moderating effect of fashion involvement in online re-WOM intention. In order to verify the research model and to test the proposed hypotheses, a 2 (utilitarian vs. hedonic review attributes) by 2 (positive vs. negative review valences) model is constructed and gathers 215 respondents. The results demonstrate that consumers form the highest reliable impression based on utilitarian and negative online reviews. However, there is no relationship between the types of online reviews and the formation of a favorable impression. Findings also reveal that the impression formed by online reviews has a positive effect on re-WOM intention, contributing to brand attitude. In addition, the hypothesis about the moderating effect produced by fashion involvement on re-WOM is supported. In conclusion, these results suggest that online reviews by fashion brand supporters have a powerful effect on forming a consumer's impression towards a fashion brand, affecting re-WOM intention and brand attitude.

• Journal of Fashion Business
• /
• v.27 no.3
• /
• pp.50-62
• /
• 2023

The e-commerce fashion market has experienced a remarkable growth, leading to an overwhelming availability of shared information and numerous choices for users. In light of this, chatbots have emerged as a promising technological solution to enhance personalized services in this context. This study aimed to develop user-product attributes for a chatbot-based personalized fashion recommendation service using big data text mining techniques. To accomplish this, over one million consumer reviews from Coupang, an e-commerce platform, were collected and analyzed using frequency analyses to identify the upper-level attributes of users and products. Attribute terms were then assigned to each user-product attribute, including user body shape (body proportion, BMI), user needs (functional, expressive, aesthetic), user TPO (time, place, occasion), product design elements (fit, color, material, detail), product size (label, measurement), and product care (laundry, maintenance). The classification of user-product attributes was found to be applicable to the knowledge graph of the Conversational Path Reasoning model. A testing environment was established to evaluate the usefulness of attributes based on real e-commerce users and purchased product information. This study is significant in proposing a new research methodology in the field of Fashion Informatics for constructing the knowledge base of a chatbot based on text mining analysis. The proposed research methodology is expected to enhance fashion technology and improve personalized fashion recommendation service and user experience with a chatbot in the e-commerce market.

• Asia pacific journal of information systems
• /
• v.25 no.4
• /
• pp.784-804
• /
• 2015

This study investigates the effects of a two-sided message on product attitude and purchase intention by using a message structure variable, such as attribute importance in the context of online consumer reviews (OCRs). Study 1 explains the previous inconsistent results of a two-side message by comparing a one-side message and a two-side message by using the attribute importance in negative reviews. Study 2 determines the reasons for the inconsistent results of a refutational two-sided message research by using the attribute importance in negative reviews and website trust. Two experiments are designed to test our hypotheses. The first experiment is a $2{\times}2$ factorial design with 84 participants. The second experiment uses a $2{\times}2{\times}2$ factorial design with 196 participants. In study 1, two-sided OCRs are more credible than one-sided OCRs, and two-sided OCRs that use low important attributes are more effective in making favorable product attitude/purchase intention. In study 2, refutational two-sided OCRs that use high attribute importance render positive effects on product attitudes in trustworthy websites. However, the refutation could negatively affect product attitude/purchase intention in low trustworthy websites.

• Journal of the Korean Society of Clothing and Textiles
• /
• v.35 no.9
• /
• pp.1099-1111
• /
• 2011

This study identified the types of consumer review information about apparel products based on consumer satisfaction/dissatisfaction with the availability/non-availability of consumer review information for online stores. Data were collected from 318 females aged 20s' to 30s', who had significant experience in reading consumer reviews posted on online stores. Consumer satisfaction/dissatisfaction with availability or non-availability of review information on online stores is different for information in regards to apparel product attributes, product benefits, and store attributes. According to the concept of quality elements suggested by the Kano model, two types of consumer review information were determined: Must-have information (product attribute information about size, fabric, color and design of the apparel product; benefit information about washing & care and comport of the apparel product; store attribute information about responsiveness, disclosure, delivery and after service of the store) and attracting information (attribute information about price comparison; benefit information about coordination with other items, fashionability, price discounts, value for price, reaction from others, emotion experienced during transaction, symbolic features for status, health functionality, and eco-friendly feature; store attribute information about return/refund, damage compensation and reputation/credibility of online store and interactive and dynamic nature of reviews among customers). There were significant differences between the high and low involvement groups in their perceptions of consumer review information.

• The Journal of Society for e-Business Studies
• /
• v.15 no.1
• /
• pp.17-37
• /
• 2010

Currently, online searching sites offer a variety of services such as just sorting by price, manufacturer, sorting by release date for sale as well as the product reviews to help and enjoy online shopping provided consumers with more shopping information. The purpose of this study is to examine the effects of online product reviews attributes (informativeness and usefulness) and familiarity on consumers' loyalty in online product searching site via trust and satisfaction. To identify these relationships, the secondary data or past studies were collected and theoretically arranged. I made the theoretical proposed model to explain the relationships between the constructs, identify the operational definitions and 8 Hypotheses were established, there was executed the survey of 175 customers. As the result of test that make the relations of used variables clear, i can get the conclusion; site familiarity and informativeness, Usefulness of online reviews have the positive effect empirically on trust building and loyalty. From the empirical test, i suggest the strategic advices in online product searching site. To increase the consumers' loyalty, it would be developed that a variety of methods and ways to raise the site familiarity and informativeness, usefulness in online product reviews. It is necessary for sticking the consumers to raise the positive trust building and satisfaction. The results of this study would help companies operating the online product searching site.

• Journal of Intelligence and Information Systems
• /
• v.22 no.3
• /
• pp.129-142
• /
• 2016

Customer product reviews have become one of the important factors for purchase decision makings. Customers believe that reviews written by others who have already had an experience with the product offer more reliable information than that provided by sellers. However, there are too many products and reviews, the advantage of e-commerce can be overwhelmed by increasing search costs. Reading all of the reviews to find out the pros and cons of a certain product can be exhausting. To help users find the most useful information about products without much difficulty, e-commerce companies try to provide various ways for customers to write and rate product reviews. To assist potential customers, online stores have devised various ways to provide useful customer reviews. Different methods have been developed to classify and recommend useful reviews to customers, primarily using feedback provided by customers about the helpfulness of reviews. Most shopping websites provide customer reviews and offer the following information: the average preference of a product, the number of customers who have participated in preference voting, and preference distribution. Most information on the helpfulness of product reviews is collected through a voting system. Amazon.com asks customers whether a review on a certain product is helpful, and it places the most helpful favorable and the most helpful critical review at the top of the list of product reviews. Some companies also predict the usefulness of a review based on certain attributes including length, author(s), and the words used, publishing only reviews that are likely to be useful. Text mining approaches have been used for classifying useful reviews in advance. To apply a text mining approach based on all reviews for a product, we need to build a term-document matrix. We have to extract all words from reviews and build a matrix with the number of occurrences of a term in a review. Since there are many reviews, the size of term-document matrix is so large. It caused difficulties to apply text mining algorithms with the large term-document matrix. Thus, researchers need to delete some terms in terms of sparsity since sparse words have little effects on classifications or predictions. The purpose of this study is to suggest a better way of building term-document matrix by deleting useless terms for review classification. In this study, we propose neutrality index to select words to be deleted. Many words still appear in both classifications - useful and not useful - and these words have little or negative effects on classification performances. Thus, we defined these words as neutral terms and deleted neutral terms which are appeared in both classifications similarly. After deleting sparse words, we selected words to be deleted in terms of neutrality. We tested our approach with Amazon.com's review data from five different product categories: Cellphones & Accessories, Movies & TV program, Automotive, CDs & Vinyl, Clothing, Shoes & Jewelry. We used reviews which got greater than four votes by users and 60% of the ratio of useful votes among total votes is the threshold to classify useful and not-useful reviews. We randomly selected 1,500 useful reviews and 1,500 not-useful reviews for each product category. And then we applied Information Gain and Support Vector Machine algorithms to classify the reviews and compared the classification performances in terms of precision, recall, and F-measure. Though the performances vary according to product categories and data sets, deleting terms with sparsity and neutrality showed the best performances in terms of F-measure for the two classification algorithms. However, deleting terms with sparsity only showed the best performances in terms of Recall for Information Gain and using all terms showed the best performances in terms of precision for SVM. Thus, it needs to be careful for selecting term deleting methods and classification algorithms based on data sets.