• Journal of information and communication convergence engineering
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• v.18 no.1
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• pp.55-60
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• 2020

As personalized customer services create a society that emphasizes the personality of an individual, the number of product reviews and quantity of user data generated by users on the internet in mobile shopping apps and sites are increasing. Such product review data are classified as unstructured data. Unstructured data have the potential to be transformed into information that companies and users can employ, using appropriate processing and analyses. However, existing systems do not reflect the detailed information they collect, such as user characteristics, purchase preference, or purchase priority while analyzing review data. Thus, it is challenging to provide customized recommendations for various users. Therefore, in this study, we have developed a product recommendation system that takes into account the user's priority, which they select, when searching for and purchasing a product. The recommendation system then displays the results to the user by processing and analyzing their preferences. Since the user's preference is considered, the user can obtain results that are more relevant.

• Journal of Information Technology Applications and Management
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• v.25 no.2
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• pp.53-72
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• 2018

This empirical study explored characteristics that affect the usefulness of online reviews, in the China e-commerce platform, and implemented multiple regressions to find factors that significantly influence on product sales, ultimately. Till now, prior studies have continuously revealed what factor affects usefulness of online review or product sales, only in respective terms. The point of our study is that we built two-level regression models, thereby being able to comprehensively analyze these two different targets. Before plunging into running regressions, we carefully collected 192,764 online review data for 200 products extracted from the Jingdong, the second biggest e-commerce platform in China. Also, we gathered "review sentimental scores" variable from each review and used that one as a core variable in our regression model, thus we were able to implement both quantitative and qualitative research. The evidences from the two-level regression models showed that the extent to which a product is experience good positively affects both usefulness of a review and product sales, again the usefulness of a review contributes to product sales in sequence. Also, the property of experience good has interaction effect on both for two-level regression models. Our main findings highlight the importance of role of online review to business performance of e-commerce firms.

• Fashion & Textile Research Journal
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• v.19 no.2
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• pp.180-193
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• 2017

The increase use of smartphones has paved the way for quick dissemination of online information. This has a huge influence on consumers' purchase decision making and the formation of a company's image. As such, this study focuses on product review from mobile beauty applications(apps); in particular, the perceived information authenticity. The purpose is as follows. First, to examine if there is any difference in perceived information authenticity based on the types of product review. Second, to analyze how perceived information authenticity influences brand attitude, purchase intention, and electronic word of mouth(e-WOM) intention. The study targets consumers in their 20s and 30s who have experience buying a product via a mobile beauty app. Three hundred responses are analyzed using SPSS 21.0 and AMOS 18.0. The results reveal that, first of all, consumers derive higher perceived information authenticity from a multi-facet review rather than a double-facet review. Second, among the traits of perceived information authenticity, only a brand's perceived reliability has a significant influence on brand attitude. Third, this brand attitude has a positive influence on purchase intention and e-WOM intention. In conclusion, these findings can serve as an important discussion point for companies developing a mobile beauty app, drawing attention to perceived information authenticity, based on the types of product review.

• 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 Information Systems
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• v.30 no.4
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• pp.1-20
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• 2021

Purpose Nowadays, Autonomous Sensory Meridian Response (ASMR) is rapidly growing in popularity and increasingly appearing in marketing. Not even in TV commercial advertisement, ASMR also fast growing in one-person media communication, many brands and social media influencers used ASMR for their marketing contents. The purpose of this study is to measure consumers' perceptions about the products in ASMR marketing content and compare the differences in communication effect of ASMR content creator between product review and how-to in the same Macro tier influencer - the YouTuber that has 10,000-100,000 subscribers. Design/methodology/approach The research methods selected ASMRtist that do product review content and how-to content, Text comments data was collected from 200 videos of tech-device review videos and beauty-fashion videos. A total of 52,833 text comments were analyzed by applying the LDA topic modeling algorithm and social network analysis. Findings Through the result, we can know that ASMR is good at taking attention of viewers with ASMR triggers. In the Tech device reviews field, ASMR viewers also focus on the product like product's performance and purchase. However, there are many topics related to reaction of ASMR sound, trigger, relaxation. In the Beauty-fashion field, viewers' topics mainly focus on the reaction of the ASMR trigger, response to ASMRtist and other topics are talking about makeup - fashion, product, purchase. From LDA result, many ASMR viewers comment that they feel more comfortable when watching the marketing content that uses ASMR. This result has shown that ASMR marketing contents have a good performance in terms of user watching experience, so applying ASMR can take more consumer intention. And the result of social network analysis showed that product review ASMRtist have a higher communication effectiveness than how-to ASMRtist in the same tier. As an influencer marketing strategy, this study provides information to establish an efficient advertising strategy by using influencers that create ASMR content.

• The Journal of Information Systems
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• v.32 no.4
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• pp.29-50
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• 2023

Purpose The impact of online reviews is widely acknowledged, with extensive research focused on text-based reviews. However, there's a lack of research regarding reviews in video format. To address this gap, this study aims to explore the connection between company-sponsored product review videos and the extent of directive speech within them. This article analyzed viewer sentiments expressed in video comments based on the level of directive speech used by the presenter. Design/methodology/approach This study involved analyzing speech acts in review videos based on sponsorship and examining consumer reactions through sentiment analysis of comments. We used Speech Act theory to perform the analysis. Findings YouTubers who receive company sponsorship for review videos tend to employ more directive speech. Furthermore, this increased use of directive speech is associated with a higher occurrence of negative consumer comments. This study's outcomes are valuable for the realm of user-generated content and natural language processing, offering practical insights for YouTube marketing strategies.

• IE interfaces
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• v.17 no.1
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• pp.56-70
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• 2004

Expanding product variety and high customer service provision place an enormous burden on demand forecasting and the matching of supply with demand in a supply chain. Postponement of product differentiation has been found to be powerful means to improve supply chain performance in the presence of increasing product variety. Delaying the point of product differentiation implies that the process would not commit the work-in-process into a particular finished product until a later point. This paper reviews the recent analytical models that quantify the value of delayed product differentiation. We conclude the literature review by summarizing and synthesizing the economic evaluation of the postponement and outline directions for future research.

• Journal of KIISE:Databases
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• v.36 no.4
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• pp.254-262
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• 2009

As the trend of e-business activities develop, customers come into contact with products through on-line shopping sites and lots of customers refer product reviews before the purchasing on-line. However, as the volume of product reviews grow, it takes a great deal of time and effort for customers to read and evaluate voluminous product reviews. Lately, attention is being paid to Opinion Mining(OM) as one of the effective solutions to this problem. In this paper, we propose an efficient method for opinion sentiment classification of product reviews using product specific context information of words occurred in the reviews. We define the context information of words and propose the application of context for sentiment classification and we show the performance of our method through the experiments. Additionally, in case of word corpus construction, we propose the method to construct word corpus automatically using the review texts and review scores in order to prevent traditional manual process. In consequence, we can easily get exact sentiment polarities of opinion words in product reviews.

• Journal of KIISE
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• v.44 no.4
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• pp.339-343
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• 2017

As the number of customers who write fake reviews is increasing, online shopping sites have difficulty in providing reliable reviews. Fake reviews are called review spam, and they are written to promote or defame the product. They directly affect sales volume of the product; therefore, it is important to detect review spam. Review spam detection methods suggested in prior researches were only based on an international site even though review spam is a widespread problem in domestic shopping sites. In this paper, we have presented new review features of the domestic shopping site NAVER, and we have applied the formerly introduced method to this site for performing an evaluation.

• Journal of the Korea Convergence Society
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• v.8 no.4
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• pp.1-8
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• 2017

In the modern society, the size of the fashion market is continuously increasing both overseas and domestic. When purchasing a product through e-commerce, the evaluation data for the product created by other consumers has an effect on the consumer's decision to purchase the product. By analysing the consumer's evaluation data on the product the company can reflect consumer's opinion which can leads to positive affect of performance to company. In this paper, we propose a method to construct a model to analyze user's sentiment using word embedding space formed by learning review data of amazon fashion products. Experiments were conducted by learning three SVM classifiers according to the number of positive and negative review data using the formed word embedding space which is formed by learning 5.7 million Amazon review data.. Experimental results showed the highest accuracy of 88.0% when learning SVM classifier using 50,000 positive review data and 50,000 negative review data.