• Science of Emotion and Sensibility
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• v.24 no.4
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• pp.79-90
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• 2021

Consumers have an increasingly active role in the marketing cycle, using social media channels to create, distribute, and consume digital content. In this context, this paper investigates the impact of user- and marketer-generated content on consumer purchase intentions and the approach to designing an effective social media marketing platform. Referencing a literature review of social media marketing and consumer purchase intentions, a case study of the social media-marketing platform, 0.8L, was undertaken using both qualitative and quantitative results through content analysis and a participatory survey. First, about 450 consumer reviews for ten sunscreen products posted on the 0.8L platform were compared with products' marketer-generated content. Next, 55 subjects participated in a survey regarding purchase intentions toward moisturizing creams on the 0.8L platform. The results indicated that user-generated content (i.e., texts and photos) provided more personal experiences of the product usage process, whereas marketers focused on distinctive product photos and features. Moreover, customer reviews (particularly high volume and narrative format) had more impact on purchase decisions than marketer information in the online cosmetics market. Real users' honest reviews (both positive and negative) were found to aid companies' prompt and straightforward assessment of newly released products. In addition to the importance of customer-driven marketing practices, distinctive user experience design features of a competitive social media-marketing platform are identified to facilitate the creation and sharing of sincere customer reviews that resonate with potential buyers.

• Journal of Information Technology Services
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• v.15 no.2
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• pp.169-184
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• 2016

Smartphone use is rapidly spreading due to the advantage of being able to connect to the Internet anytime, anywhere--and mobile app development is developing accordingly. The characteristic of the mobile app market is the ability to launch one's app into foreign markets with ease as long as the platform is the same. However, a large amount of prior research asserts that consumers behave differently depending on their culture and, from this perspective, various studies comparing the differences between consumer behaviors in different countries exist. Accordingly, this research, which uses online product reviews (OPRs) in order to analyze the cultural differences in consumer behavior comparatively by nationality, proposes to compare the U.S. and South Korea by selecting ten apps which were released in both countries in order to perform a sentimental analysis on the basis of star ratings and, based on those ratings, to interpret the sentiments in reviews. This research was carried out to determine whether, on the basis of ratings analysis, analysis of review contents for sentiment differences, analysis of LDA topic modeling, and co-occurrence analysis, actual differences in online reviews in South Korea and the U.S. exist due to cultural differences. The results confirm that the sentiments of reviews for both countries appear to be more negative than those of star ratings. Furthermore, while no great differences in high-raking review topics between the U.S. and South Korea were revealed through topic modeling and co-occurrence analyses, numerous differences in sentiment appeared-confirming that Koreans evaluated the mobile apps' specialized functions, while Americans evaluated the mobile apps in their entirety. This research reveals that differences in sentiments regarding mobile app reviews due to cultural differences between Koreans and Americans can be seen through sentiment analysis and topic modeling, and, through co-occurrence analysis, that they were able to examine trends in review-writing for each country.

• Journal of Korea Society of Digital Industry and Information Management
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• v.8 no.1
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• pp.191-205
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• 2012

A book review is one of the most important sources of information which provide the descriptive and evaluative contents about books. Reviews have great influence on consumer behavior because they are believed to be more reliable than information provided by sellers. Readers who read a book review includes information about book decide whether they will buy or not. This study examines customer attitude change by book reviews with regarding to different type of information sources(experts and prior customers) and different directions of messages. We address the following research questions: (1) Can positive book reviews with credibility have a positive impact on acceptance of books? (2) Can negative book reviews with credibility have a negative impact on acceptance of books? The results shows that a credibility is an essential factor for affecting customers' mind. When positive book reviews were written, both expert and customer opinions have a positive impact on acceptance of customers. Given negative book reviews of experts, trustworthiness is more important than expertise. However, a objectivity of customer's reviews is more important.

• Journal of Information Technology Services
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• v.22 no.3
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• pp.141-165
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• 2023

With the advent of the fourth industrial revolution, technologies such as the Internet of Things, artificial intelligence and cloud computing are developing rapidly, and smart homes enabled by these technologies are rapidly gaining popularity. To gain a competitive advantage in the global market, companies must understand the differences in consumer needs in different countries and cultures and develop corresponding business strategies. Therefore, this study conducts a comparative analysis of consumer reviews of smart homes in South Korea and China. This study collected online reviews of SmartThings, ThinQ, Msmarthom, and MiHome, the four most commonly used smart home apps in Korea and China. The collected review data is divided into satisfied reviews and dissatisfied reviews according to the ratings, and topics are extracted for each review dataset using LDA topic modeling. Next, the extracted topics are classified according to five evaluation factors of Perceived Usefulness, Reachability, Interoperability,Trustness, and Product Brand proposed by previous studies. Then, by comparing the importance of each evaluation factor in the two datasets of satisfaction and dissatisfaction, we find out the factors that affect consumer satisfaction and dissatisfaction, and compare the differences between users in Korea and China. We found Trustness and Reachability are very important factors. Finally, through language network analysis, the relationship between dissatisfied factors is analyzed from a more microscopic level, and improvement plans are proposed to the companies according to the analysis results.

• Fashion & Textile Research Journal
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• v.26 no.1
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• pp.25-34
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• 2024

This study investigates consumer awareness and concerns regarding apparel sizing in the realm of online shopping. A survey was conducted with 450 women aged 18-59 who had engaged in online clothing purchases within the past year. It was observed that consumers shop for clothes online an average of 1.6 times per month, with those under 50 shopping more frequently. The importance of size is higher when buying pants than jackets, especially in online shopping compared to offline purchases. Key references guiding online shopping decisions encompassed product sizing codes, customer reviews, and garment dimensions, which were notably favored by consumers with significant concerns. Respondents opted for Korean-style sizing codes for jackets but chose inch-sizing codes for pants. While awareness of height and weight remains high, knowledge of specific body measurements crucial for clothing size design is lacking, suggesting inadequate communication of size information. Respondents prioritized specific areas for jacket and pants fit, yet the lack of comprehensive self-measurements beyond height and weight might present challenges in determining fit based solely on product dimensions. To address this issue, online retailers should display essential garment dimensions and visually suggest clothing sizes according to various body types. These findings provide valuable insights for online retailers to effectively present size information and lay a foundational framework for consumer size education.

• Knowledge Management Research
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• v.19 no.4
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• pp.1-17
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• 2018

Hotel consumers tend to rely on online reviews to reduce the risk to hotel products when they book hotel rooms because hotel products are high-risk products due to their intangibility. However, the development of ICT has caused information load, and it is an important issue to be perceived as useful information to consumer because a large amount of information complicates the decision making process of consumers. Drawn from Heuristic-Systematic Model(HSM), the present study explored the role of heuristic and systematic cues composing an online review influencing consumers' perception of hotel online reviews. More specifically, this study identified reviewers' identity, level of the reviewer, review star ratings, and attached hotel photo as heuristic cue, while review length, cognitive level of review and negativity in review as systematic cues. The binary logistic regression was adopted for analysis. This study found that only systematic cues of online review were found to affect the usefulness of it. Moreover, we preceded further study examining the moderating effect of seasonality in the relationships between systematic cues and usefulness.

• The Journal of the Korea Contents Association
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• v.20 no.7
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• pp.316-330
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• 2020

The study analyzes reviews of hardware products, customer service products, and products that take the form of a convergence of hardware and cloud services in ICT using text mining. We derive keywords of each review and find the differentiation of words that are used to derive topics. A cluster analysis is performed to categorize reviews into their respective clusters. Through this study, we observed which keywords are most often used for each product type and found topics that express the characteristics of products and services using topic modeling. We derived keywords such as "professional" and "technician" which are topics that suggest the excellence of the service provider in the review of service products. Further, we identified adjectives with positive connotations such as "favorite", "fine", "fun", "nice", "smart", "unlimited", and "useful" from Amazon Eco review, an integrated product and service. Using the cluster analysis, the entire review was clustered into three groups, and three product type reviews exclusively resulted in belonging to each different cluster. The study analyzed the differences whereby consumer needs are expressed differently in reviews depending on the type of product and suggested that it is necessary to differentiate product planning and marketing promotion according to the product type in practice.

• Information Systems Review
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• v.25 no.1
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• pp.27-46
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• 2023

Research on restaurant recommender systems has been proposed due to the development of the food service industry and the increasing demand for restaurants. Existing restaurant recommendation studies extracted consumer preference information through quantitative information or online review sensitivity analysis, but there is a limitation that it cannot reflect consumer semantic preference information. In addition, there is a lack of recommendation research that reflects the detailed attributes of restaurants. To solve this problem, this study proposed a model that can learn the interaction between consumer preferences and restaurant attributes by applying deep learning techniques. First, the convolutional neural network was applied to online reviews to extract semantic preference information from consumers, and embedded techniques were applied to restaurant information to extract detailed attributes of restaurants. Finally, the interaction between consumer preference and restaurant attributes was learned through the element-wise products to predict the consumer preference rating. Experiments using an online review of Yelp.com to evaluate the performance of the proposed model in this study confirmed that the proposed model in this study showed excellent recommendation performance. By proposing a customized restaurant recommendation system using big data from the restaurant industry, this study expects to provide various academic and practical implications.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2014.05a
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• pp.89-93
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• 2014

Reviews are very important for potential consumer' making choices. They are also used by manufacturers to find problems of their products and to collect competitors' business information. But someone write fake reviews to mislead readers to make wrong choices. Therefore detecting fake reviews is an important problem for the E-commerce sites. Support Vector Machines (SVMs) are very important text classification algorithms with excellent performance. In this paper, we propose a new incremental algorithm based on weight and the extension of Karush-Kuhn-Tucker(KKT) conditions and Convex Hull for online Review Spam Detection. Finally, we analyze its performance in theory.

• Journal of Information Technology Services
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• v.10 no.1
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• pp.1-20
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• 2011

The market structure and consumer characteristics are changing dynamically according to internet shopping industry developing based on Web 2.0. But, there is absent typical online service after 'Cyworld.' The social shopping sites based on social networking reflect to present phenomenon that collective intellect, information sharing, participate in making information. The social shopping sites are not limited in particular shopping sites but include all of sites in online. So, consumers can copy various products and display on their own blog provided from social shopping sites and make some purchase reviews and any comments about products can lead transactions among social shopping sites. So, it might be a one of meta-shopping mall like 'Naver.' As the social shopping sites are new form, we just applied to TAM theory to figure out acceptance factors using SEM. The perceived enjoyment affect to usefulness, ease of use and using intension. The perceived ease of use also affect to usefulness and the usefulness affect to using intension positively. But the perceived ease of use was for nothing in using intension. Finally, we provided managerial implications to activate domestic online shopping industry and theoritical meaning using extended TAM.