• 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.

• Journal of Fashion Business
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• v.21 no.1
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• pp.124-133
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• 2017

This study analyzed consumers' online fashion shopping site usage by site type and analyzed how product information search and product purchase differ. In addition, by comparing and analyzing preference and selection factors of PC and mobile shopping, we tried to provide basic data of effective mobile commerce operation strategy according to expansion of mobile market in fashion industry. We surveyed the use of 6 types of online sites (portal site, open market, general shopping mall, social commerce shopping mall, brand shopping mall and non-brand shopping mall) to understand the usage of mobile shopping site for consumers' fashion products. Consumer survey subjects were 373 college students in their early 20s who had the most mobile shopping experiences. The questionnaire consisted of online fashion site usage items, internet and mobile fashion shopping preference (6 items), internet and mobile shopping choice behavior (10 items), and purchase intention (3 items) .Data were analyzed using SPSS 14.0 program. The high preference of portal sites, open market, social commerce, and non-brand shopping malls in shopping online fashion products is a result of the perceptual changes of major distribution channels and the trends of consumers' value consumption tendency. Therefore, it is necessary for fashion companies to actively cope with fashion product distribution competition with huge shopping sites of current online market by well understanding consumers' preference trends and factors of online sites.

• Knowledge Management Research
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• v.15 no.3
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• pp.141-168
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• 2014

Purchasing through Internet shopping mall has more uncertainty compared with offline shopping mall. Previous studies have presented that trust plays a role of reducing uncertainty and increasing purchasing intention. In this study, we suggest that third-party assurance and word-of-mouth contribute to the formation of trust. In addition, we also propose that ease of product evaluation plays moderating roles in the relationships between third-party assurance, word-of-mouth and trust. For this study, we collected sample data from two groups consisting of online shoppers purchasing the search goods and experience goods categorized by type of ease of product evaluation. Empirical results show that word-of-mouth and third-party assurance have different effects on trust in two groups. The third-party assurance has a stronger impact on trust in online shopping group of the search goods than in the experience goods, while word-of-mouth in the online community has a stronger impact on trust in online shopping group of the experience goods than in the search goods. We expect that this result will provide researchers and managers who are interested in trust formation factors in online shopping mall with useful theoretical and practical implications.

• Soft Computing and Machine Intelligence
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• v.1 no.1
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• pp.31-35
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• 2021

In recent years, due to COVID-19, the process of shopping has become more restricted and difficult for customers. Based on this aspect, customers are more interested in online shopping to keep the Untact rules and stay safe, similarly ordering their product based on their need and interest with most straightforward and fastest ways. In this paper, the reinforcement learning technique is applied in the product recommendation system to improve the recommendation system quality for better and more related suggestions based on click patterns and users' profile information. The dataset used in this system was taken from an online shopping mall in Jeju island, South Korea. We have compared the proposed method with the recent state-of-the-art and research results, which show that reinforcement learning effectiveness is higher than other approaches.

• Journal of Intelligence and Information Systems
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• v.17 no.3
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• pp.115-130
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• 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
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• v.12 no.12
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• pp.733-741
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• 2012

In recent, many consumers visit the online shopping malls or price comparison sites to collect the information on the product category that they are interested in. However, the volumes of the data provided by such web sites are often too enormous, and significant number of consumers have trouble in making purchase decision based on the plethora of products and sellers. In this context, modern online shopping agents need to process the retrieved information in more intelligent way before providing them to the users. This paper proposes a novel approach for identifying the main price ranges hidden in a single product category. To this end, the price of an item in the category is represented as a row vector and k-means clustering analysis is applied to the price vectors to produce the clusters that consists of the product items with similar price vectors. Then, the main price ranges of the product category can be identified from the result of clustering analysis. In general, the price is one of the most important factors in the consumers' purchase decision, and the identified main price ranges will be helpful for the online shoppers to find appropriate items effectively.

• Fashion & Textile Research Journal
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• v.20 no.5
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• pp.556-568
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• 2018

In this study, we surveyed the current status and sizing system of the custom dress shirts sold through online shopping, compared with the sizing system of the ready-made dress shirts. We tried to collect the information needed to make the well fitted dress shirts for middle-aged men from this study. The 17 online custom dress shirt brands were selected and the sales type, sales price, design options and size options of each brand were analyzed. The sizing systems of online custom dress shirt brands were compared with the sizing system of the 10 ready made dress shirt brands. The result showed that online custom dress shirts brands offered a variety of design options and size options to meet the consumers' individuality, taste and demand for good fit. In the ready-made brands, all 10 brands were using the same size notation system. In the same size designation, the difference in product size among the ready-made brands showed a tendency to be smaller than the online custom brands. The online custom brands had the different size notation system among brands. The size notation, the number of size designation and the size interval were different for each brand. Also, in the online custom brands, the product size among brands differed from each other in the same size designation. Therefore, the standardized size information and sizing system for middle-aged men that could be used as criteria when making the product size and pattern design in online custom brands were needed.

• KIPS Transactions on Software and Data Engineering
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• v.9 no.9
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• pp.259-266
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• 2020

In this paper, we propose a novel scheme for product effect analysis, termed PEM, to find out the effectiveness of products used for improving the current condition, such as health supplements and cosmetics, by utilizing online customer reviews. The proposed technique preprocesses online customer reviews to remove advertisements automatically, constructs the word dictionary composed of symptoms, effects, increases, and decreases, and measures products' effects from online customer reviews. Using Naver Shopping Review datasets collected through crawling, we evaluated the performance of PEM compared to those of two methods using traditional sentiment dictionary and an RNN model, respectively. Our experimental results shows that the proposed technique outperforms the other two methods. In addition, by applying the proposed technique to the online customer reviews of atopic dermatitis and acne, effective treatments for them were found appeared on online social media. The proposed product effect analysis technique presented in this paper can be applied to various products and social media because it can score the effect of products from reviews of various media including blogs.

• Information Systems Review
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• v.21 no.1
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• pp.61-90
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• 2019

From the digital perspective, customer journey and customer experience management are emerging as important issues for companies. While digital customer experience has become more important due to the recent surge in online sales of the home appliance products, customers' experience in online is not differentiated as offline-focused traditional methods are maintained. This study aims to analyze the characteristics and mutual influences of customer experiences at each stage of online purchase journey, and to explore the effects on the product repurchase intention, focusing on online purchasers of home appliance which are high-involvement products. As a result, both cognitive and affective experiences of the research phase directly affect satisfaction, whereas affective experience at the purchasing stage indicated indirect effects through cognitive experience. The experience of the research phase positively affects the next phase, the purchasing experience, and the experience of the purchasing phase leads to the intention to repurchase the product. However, it is also found that, depending on the choice of online channels, the experience of research phase may affect the product repurchase intention than the purchase experience.

• 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.