• Journal of the Korean Operations Research and Management Science Society
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• v.32 no.2
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• pp.53-69
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• 2007

Internet shopping websites have offered comfort to consumers in shopping and built trust relationships with them by providing electronic agents for recommendation, escrow services, and customer centers etc. But as there is little big difference among the shopping websites in terms of technical competence, website design, operational policy, they recognize online feedback (reviews or recommendation of consumers or experts) and online feedback mechanism as important marketing tools. Based on online feedback related studies, this study explores antecedents (consensus, vividness of reviews, interactions in review boards) of the perceived effectiveness of review boards which are text-based feedback mechanisms and its consequences such as trust, satisfaction, and intention to use. The results show that the perceived effectiveness of review boards is significantly affected by vividness of reviews and interactions in review boards, and the impact of interaction in review boards on the perceived effectiveness of review boards is stronger than that of vividness of reviews. The results also show that the perceived effectiveness of review boards has a significant influence on trust and satisfaction with the shopping websites, and intention to use is influenced by both trust and satisfaction.

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

• Journal of Distribution Science
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• v.11 no.4
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• pp.13-21
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• 2013

Purpose - The purpose of this research is to find out the relationship between cue utilization and perceived website quality and purchase intention for an online store. To achieve this, we suggest a conceptual model that examines the relationship among product introductions, online communications, online reviews, perceived quality, and online purchase intention. Research design, data, and methodology - This research utilizes SPSS 19.0 and AMOS17.0 to analyze the data. We used factor analysis to shape the structure of the original data and saved the information with multiple dimensions. We then deployed the AMOS software to analyze the model. We performed both factor analysis and structural equation analysis. Results - The findings of this study show that graphic and word descriptions, online chatting, and online reviews have a positive influence on perceived quality. Furthermore, perceived quality has a positive influence on purchase intention. Conclusions - First, detailed product information should be added to influence quality perception. Second, consumers expect a certain level of service while shopping. Simultaneously, online products reviews from consumers deserve attention as they can impact consumer purchase intention.

• Asia pacific journal of information systems
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• v.29 no.4
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• pp.838-855
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• 2019

Online product reviews are a vital source for companies in that they contain consumers' opinions of products. The earlier methods of opinion mining, which involve drawing semantic information from text, have been mostly applied in one dimension. This is not sufficient in itself to elicit reviewers' comprehensive views on products. In this paper, we propose a novel approach in opinion mining by projecting online consumers' reviews in a multidimensional framework to improve review interpretation of products. First of all, we set up a new framework consisting of six dimensions based on a marketing management theory. To calculate the distances of review sentences and each dimension, we embed words in reviews utilizing Google's pre-trained word2vector model. We classified each sentence of the reviews into the respective dimensions of our new framework. After the classification, we measured the sentiment degrees for each sentence. The results were plotted using a radar graph in which the axes are the dimensions of the framework. We tested the strategy on Amazon product reviews of the iPhone and Galaxy smartphone series with a total of around 21,000 sentences. The results showed that the radar graphs visually reflected several issues associated with the products. The proposed method is not for specific product categories. It can be generally applied for opinion mining on reviews of any product category.

• Journal of Information Technology Services
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• v.13 no.1
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• pp.43-55
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• 2014

More and more people are gravitating to reading products reviews prior to making purchasing decisions. As a number of reviews that vary in usefulness are posted every day, much attention is being paid to measuring their helpfulness. The goal of this paper is to investigate firstly various determinants of the helpfulness of reviews, and intends to examine the moderating effect of product type, i.e., search or experience goods on the product sales, helpfulness and helpfulness votes of online reviews. The determinants include product data, review characteristics, and textual characteristics of reviews. The results indicate that the direct effect exists for the determinants of product sales, helpfulness, and helpfulness votes. Further, the moderating effects of product type exist for these determinants on three dependent variables. The results of study will identify helpful online review and design review sites effectively.

• 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 Information Technology Services
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• v.14 no.4
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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.

• Culinary science and hospitality research
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• v.22 no.8
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• pp.109-121
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• 2016

This study analyzed the effect of five sub-variables' attribute of HMR: features of information, diversity, promptness, price and convenience, on the consumer purchasing intention. In addition, the regulation effect of positive reviews and negative reviews of consumers' online reviews between HMR selection attribute and purchasing intention was also tested. Results are following. First, convenience feature (B=.577, p<.001) and diversity feature (B=.093, p<.01) among the effect of HMR selection attribute had a positive (+) effect on purchasing intention. On the other hand, promptness feature (B=.235, p<.001) and price feature (B=.161, p<.001), and information feature (B=.288, p<.001) were not significant effect on purchasing intention. Second, result of regulation effect of the positive reviews of consumer's online review between the selection attribute of the HMR product and consumers' purchasing intention, in the first-stage model in which the selection attribute of the HMR product is input as an independent variable, there was a significant positive (+) effect on all the features of convenience, diversity, promptness, price, and information. In addition, there was significant positive (+) main effect (B=.472, p<.001) in the second step model in which the consumers' positive reviews, that is a regulation variable. Furthermore, the feature of price (B=.068, p<.05) had a significant positive (+) effect in the third stage in which the selection attribute of the HMR product that is an independent variable and the interaction of the positive review. However, the feature of information (B=-.063, p<.05) showed negative (-) effect, and there was no effect on the features of convenience, diversity, and promptness. Third, as a result of testing the regulation effect of the negative reviews of consumers' online reviews between HMR product selection attribute and consumers' purchasing intention, in the first-stage model in which the selection attribute of the HMR product was a positive (+) effect on all the features of convenience, diversity, promptness, price, and information. In the second-stage model in which consumers' negative reviews (B=-.113, p<.001) had negative (-) effect. In the third-stage in which the selection attribute of the HMR product and the interactions of the negative reviews was a positive (+) effect with the feature of price (B=.113, p<.01). Last, there was no effect at all on the features of convenience, promptness, and information.

• The Journal of Information Systems
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• v.32 no.3
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• pp.133-150
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• 2023

Purpose This study aims to develop classification models using a decision tree algorithm to identify core keywords and rules influencing online consumer review evaluations for the robot vacuum cleaner on Amazon.com. The difference from previous studies is that we analyze core keywords that affect the evaluation results by dividing the subjects that evaluate online consumer reviews into self-evaluation (star ratings) and peer evaluation (helpfulness votes). We investigate whether the core keywords influencing star ratings and helpfulness votes vary across different products and whether there is a similarity in the core keywords related to star ratings or helpfulness votes across all products. Design/methodology/approach We used random under-sampling to balance the dataset. We progressively removed independent variables based on decreasing importance through backwards elimination to evaluate the classification model's performance. As a result, we identified classification models that best predict star ratings and helpfulness votes for each product's online consumer reviews. Findings We have identified that the core keywords influencing self-evaluation and peer evaluation vary across different products, and even for the same model or features, the core keywords are not consistent. Therefore, companies' producers and marketing managers need to analyze the core keywords of each product to highlight the advantages and prepare customized strategies that compensate for the shortcomings.

• Information Systems Review
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• v.26 no.2
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• pp.23-43
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• 2024

Nowadays, online reviews have become a common word of mouth that many users produce and consume. Posting online reviews is a kind of job that consumers do themselves. Since posting online reviews is not mandatory, it entirely relies on the consumer's voluntary willingness. In this respect, this study aims to describe the motivation for posting online reviews and their behavior processes, such as why online reviewers generate reviews and what types of reviews they create. In this study, we have conducted an in-depth study with 18 participants who have experience in posting reviews. By analyzing interview manuscripts from the grounded theory method approach, we have ultimately presented motivating factors for review posting (mutual reciprocity, material rewards), determinants of review browsing (trust toward review contents, preference for review format), and shadow work (a job that must be done, voluntary data production, consumer's share). We have also proposed the dynamics between core dimensions for theorizing a cycle process of review production and consumption. Our findings could bridge the gap in the existing online review research and offer practical implications for platform companies that need review management.