• Journal of Distribution Science
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• v.15 no.10
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• pp.41-50
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• 2017

Purpose - Online product review has become a crucial part of the online retailer's market performance for a wide range of products. This research aims to investigate how an individual reviewer's review frequency and timing affect her/his average attitude toward products. Research design, data, and methodology - To conduct reviewer-level analysis, this study uses 42,172 posted online review messages generated by 6,941 identified reviewers for 59 movies released in the South Korea from July 2015 to December 2015. This study adopts Tobit model specification to take into account the censored nature and the selection bias arising from the nature of J-shaped distribution of movie rating. Results - Our estimation results support that the negative impact of review frequency and timing on valence. Furthermore, review timing has an inverted-U relationship with the user's average valence and enhance the negative effect of review frequency. Conclusions - This study contributes to the growing literature on the understanding how eWOM is generated at the individual consumer level. On the basis of the main empirical findings, this study provides insights into building a recommendation system in online retail store based on the consumer's review history data - frequency, timing, and valence.

• Knowledge Management Research
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• v.22 no.3
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• pp.273-293
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• 2021

Due to the COVID-19 pandemic, the size of the e-commerce has been increased rapidly. This pandemic, which made contact-less communication culture in everyday life made the e-commerce market to be opened even to the consumers who would hesitate to purchase and pay by electronic device without any personal contacts and seeing or touching the real products. Consumers who have experienced the easy access and convenience of the online purchase would continue to take those advantages even after the pandemic. During this time of transformation, however, the size of information source for the consumers has become even shrunk into a flat screen and limited to visual only. To provide differentiated and competitive information on products, companies are adopting AR/VR and steaming technologies but the reviews from the honest users need to be recognized as important in that it is regarded as strong as the well refined product information provided by marketing professionals of the company and companies may obtain useful insight for product development, marketing and sales strategies. Then from the consumer's point of view, if the ratings of reviews are widely diverged how consumers would process the review information before purchase? Are non-converged ratings always unreliable and worthless? In this study, we analyzed how consumer's regulatory focus moderate the attitude to process the diverged information. This experiment was designed as a 2x2 factorial study to see how the variance of product review ratings (high vs. low) for cosmetics affects product attitudes by the consumers' regulatory focus (prevention focus vs. improvement focus). As a result of the study, it was found that prevention-focused consumers showed high product attitude when the review variance was low, whereas promotion-focused consumers showed high product attitude when the review variance was high. With such a study, this thesis can explain that even if a product with exactly the same average rating, the converged or diverged review can be interpreted differently by customer's regulatory focus. This paper has a theoretical contribution to elucidate the mechanism of consumer's information process when the information is not converged. In practice, as reviews and sales records of each product are accumulated, as an one of applied knowledge management types with big data, companies may develop and provide even reinforced customer experience by providing personalized and optimized products and review information.

• Journal of Information Technology Applications and Management
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• v.30 no.2
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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.

• Advances in Traditional Medicine
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• v.7 no.1
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• pp.1-10
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• 2007

Mangifera indica Linn. (MI) (Family: Anacardiaceae) is commonly known worldwide as mango and ‘Aam’ in India. MI shares an important place in treatment of several diseases in various ancient system of medicine like Ayurveda and other Indian System of Medicine and some other Traditional Medicines world wide. Almost all the parts of MI have been used in oriental medicine, so in this review attempt has being made to review the history, traditional uses, phytoconstituents and therapeutic potentials of mango.

• Journal of Distribution Science
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• v.18 no.10
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• pp.65-75
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• 2020

Purpose: This study aims to examine how to review contents of experiential and utilitarian products (e.g., skincare products) and how to affect review helpfulness by applying natural language processing techniques. Research design, data, and methodology: This study uses 69,633 online reviews generated for the products registered at Amazon.com by 13 Korean cosmetic firms. The authors identify key topics that emerge about consumers' use of skincare products such as skin type and skin trouble, by applying bigram analysis. The review content variables are included in the review helpfulness model, including other important determinants. Results: The estimation results support the positive effect of review extremity and content on the helpfulness. In particular, the reviewer's skin type information was recognized as highly useful when presented together as a basis for high-rated reviews. Moreover, the content related to skin issues positively affects review helpfulness. Conclusions: The positive relationship between extreme reviews and helpfulness of reviews challenges the findings from prior literature. This result implies that an in-depth study of the effect of product types on review helpfulness is needed. Furthermore, a positive effect of review content on helpfulness suggests that applying big data analytics can provide meaningful customer insights in the online retail industry.

• KIPS Transactions on Software and Data Engineering
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• v.4 no.3
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• pp.121-128
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• 2015

A given product's online product reviews build up to form largely positive or negative reviews or mixed reviews that include both the positive and negative reviews. While the homogeneously positive or negative reviews help readers identify the generally praised or criticized product, the mixed reviews with minority opinions potentially contain valuable information about the product. We present a method of retrieving minority opinions from the online product reviews using the skewness of positive/negative reviews. The proposed method first classifies the positive/negative product reviews using a sentiment dictionary and then calculates the skewness of the classified results to identify minority reviews. Minority review retrieval experiments were conducted on smartphone and movie reviews, and the F1-measures were 24.6% (smartphone) and 15.9% (movie) and the accuracies were 56.8% and 46.8% when the individual reviews' sentiment classification accuracies were 85.3% and 78.8%. The theoretical performance of minority review retrieval is also discussed.

• The Journal of Society for e-Business Studies
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• v.16 no.3
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• pp.93-112
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• 2011

For the success of an online retail market, it is important to allow consumers to get more helpful reviews by figuring out the factors determining the helpfulness of online reviews. On the basis of elaboration likelihood model, this study analyzes which factors determine the helpfulness of reviews and how the factors affecting the helpfulness of an online consumer review differ for product price. For this study, 75,226 online consumer reviews were collected from Amazon.com. Furthermore, additional information on review messages was also gathered by carrying out a content analysis on the review messages. This study shows that both of peripheral cues such as review rating and reviewer's credibility and central cues such as word count of review message and the proportion of negative words influence the helpfulness of review. In addition, the result of this study reveals that each consumer focuses on different information sources of reviews depending on the product price.

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

• Journal of the Korea Institute of Military Science and Technology
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• v.10 no.4
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• pp.97-104
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• 2007

In this paper, we have proposed an integrated aircraft development environment which can support virtual design and development for Systems Engineering and IPPD(Integrated Product and Process Development). We have also proposed the ViMS(Virtual Mockup System) which can perform a system design review on an integrated aircraft development environment. The ViMS is an integrated virtual design support system with immersive design review functionality to make a virtual mockup instead of a physical mockup through the virtual reality technology. The functionality of the ViMS consists design data management, design technology, design verification, and design assessment. We have described the detailed development artifact, case studies and conclusions of using the ViMS functionality.

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