• Review of Culture and Economy
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• v.21 no.2
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• pp.111-139
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• 2018

This study is about classical music lovers who write a lengthy concert review on instagram. The intention and objective of writing a review is discussed in addition to inter-communication between those reviewers. For the analysis, an interview with 8 reviewers are mainly analyzed with their reviews. As a result, it is found that some affordances of Instagram, easiness, randomness, and friendliness affects them to use Instagram more than other social media. Hence, since Instagram is image-based platform, it helps writers to keep their reviews from getting an attention by other users. Because of their sense of inferiority that they are lacking in classical music knowledge, continuous writing and reading of reviews help them accumulating some amount of cultural capital needed for understanding classical music in a proper way.

• Journal of Intelligence and Information Systems
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• v.28 no.2
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• pp.171-189
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• 2022

Online reviews play an essential role in the consumer's purchasing decision-making process, and thus, providing helpful and reliable reviews is essential to consumers. Previous online review helpfulness prediction studies mainly predicted review helpfulness based on the consistency of text and rating information of online reviews. However, there is a limitation in that representation capacity or review text and rating interaction. We propose a CNN-RHP model that effectively learns the interaction between review text and rating information to improve the limitations of previous studies. Multi-channel CNNs were applied to extract the semantic representation of the review text. We also converted rating into independent high-dimensional embedding vectors representing the same dimension as the text vector. The consistency between the review text and the rating information is learned based on element-wise operations between the review text and the star rating vector. To evaluate the performance of the proposed CNN-RHP model in this study, we used online reviews collected from Amazom.com. Experimental results show that the CNN-RHP model indicates excellent performance compared to several benchmark models. The results of this study can provide practical implications when providing services related to review helpfulness on online e-commerce platforms.

• 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.

• Journal of Intelligence and Information Systems
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• v.29 no.1
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• pp.41-63
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• 2023

As the online e-commerce market growing, the need for a recommender system that can provide suitable products or services to customer is emerging. Recently, many studies using the sentiment score of online review have been proposed to improve the limitations of study on recommender systems that utilize only quantitative information. However, this methodology has limitation in extracting specific preference information related to customer within online reviews, making it difficult to improve recommendation performance. To address the limitation of previous studies, this study proposes a novel recommendation methodology that applies deep learning technique and uses various linguistic factors within online reviews to elaborately learn customer preferences. First, the interaction was learned nonlinearly using deep learning technique for the purpose to extract complex interactions between customer and product. And to effectively utilize online review, cognitive contents, affective contents, and linguistic style matching that have an important influence on customer's purchasing decisions among linguistic factors were used. To verify the proposed methodology, an experiment was conducted using online review data in Amazon.com, and the experimental results confirmed the superiority of the proposed model. This study contributed to the theoretical and methodological aspects of recommender system study by proposing a methodology that effectively utilizes characteristics of customer's preferences in online reviews.

• Journal of Internet of Things and Convergence
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• v.8 no.5
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• pp.47-54
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• 2022

The development of the Internet of Things led to new services that did not exist before. This required a change to the existing network. This study aims to verify the service quality, satisfaction, repurchase intention relationship, and the moderating effect of online reviews of Chinese consumers using fashion shopping malls. The results of the study showed that from the perspective of consumers in their 20s and 30s in China, the type, reliability, convenience, and interaction of service quality had a positive effect on customer satisfaction and repurchase intention. In addition, negative reviews among online reviews had a great influence on repurchase intention. Based on the results of the study, it will help improve the effect on online product reviews and in-depth understanding of the acceptance of online product reviews for online fashion shopping malls, and establish strategies for fashion companies to effectively manage online product reviews information.

• The Journal of the Convergence on Culture Technology
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• v.9 no.1
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• pp.787-797
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• 2023

The use of service robots, particularly guide robots, is becoming increasingly prevalent in public institutions. However, there has been limited research into the interactions between users and guide robots. To explore the customer experience with the guidance robot, we selected 'QI', which has been meeting customers for the longest time, and collected all reviews since the service was launched in public institutions. By using text mining techniques, we identified the main keywords and user experience factors and examined factors that hinder user experience. As a result, the guide robot's functionality, appearance, interaction methods, and role as a cultural commentator and helper were key factors that influenced the user experience. After identifying hindrance factors, we suggested solutions such as improved interaction design, multimodal interface service design, and content development. This study contributes to the understanding of user experience with guide robots and provides practical suggestions for improvement.

• Proceedings of the Korea Society for Industrial Systems Conference
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• 2008.10b
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• pp.545-545
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• 2008

• Knowledge Management Research
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• v.22 no.1
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• pp.269-286
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• 2021

As the un-tact and on-tact consumption culture has proliferated due to the impact of COVID-19, 'live commerce', a form of shopping while communicating with customers through real-time streaming broadcasting, is emerging in the commerce and distribution industry. Live commerce provides an environment where customers can get the convenience of online shopping and enjoy un-tact shopping more realistically while communicating with the broadcaster in real time, as if purchasing directly from an offline store. Therefore, purchases using live commerce are expected to increase further. In this study, based on the characteristics of live commerce, the main factors influencing the increase in purchase intention through live commerce were derived and their influences were verified. In particular, this study examined these factors in multiple dimensions with focusing on strong interactivity, realistic presence, and providing detailed reviews with high credibility for products as the features of live commerce. This research collected sample data from actual users of live commerce and empirically analyzed the significance of the factors influencing the purchase increase of live commerce, thereby providing implications for knowledge management in a newly changed commerce environment in the un-tact era.

• 한국HCI학회:학술대회논문집
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• 2006.02b
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• pp.721-726
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• 2006

인터넷 게시판을 통한 커뮤니케이션에서는 상호작용의 결과물이 지속적으로 남아 있어, 상호작용의 당사자뿐 아니라 나중에 참여하는 다른 이용자들의 게시판에 대한 지각에도 영향을 미치게 된다. 따라서 인터넷 게시판에서의 이용자들 간의 상호작용은 게시판의 기술적 형식이나 주제뿐 아니라 게시판에 있는 기존 이용자들의 메시지를 읽고 받은 인상에 따라서도 달라질 수 있다. 본 연구에서는 게시판의 익명성과 메시지의 전문성을 독립변인으로 한 $2{\times}2$ 실험설계에 따라 가상적 CMC 상황에서 피험자들이 영화 리뷰 게시판의 글을 읽도록 하였다. 게시판 기존 이용자 집단에 대해 받은 인상의 긍정성, 모호성, 전문성, 피험자 자신과의 지각된 유사성 및 해당 게시판에 대한 참여의사를 측정하였다. 결과, 전문성이 높은 메시지를 읽은 피험자들은 게시판의 기존 이용자들이 보다 전문적이라는 인상을 받았으며, 해당 게시판에 참여하고자 하는 의사를 더 많이 표시했다. 또한 기존 이용자들에 대한 인상의 긍정성과 유사성, 참여의사 사이에는 상관관계가 있었다. 이 결과는 인터넷 게시판 이용이 기술적 요인뿐만 아니라 게시판의 기존 이용자 집단에 대한 인상에 따라서도 달라질 수 있음을 시사한다.

• The Journal of the Korea Contents Association
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• v.21 no.11
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• pp.1-10
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• 2021

This study analyzes the use and meaning of consumer personality implication words in the review text of the Used Transaction Application . From of May 2021, the data were collected for the past six months by our Web crawler in Seoul and Gyeonggi Province, and a total of 1368 cases were collected first by random sampling, and finally 570 cases were preprocessed. The results are as follows. First, 48.2% of review texts were related to the personality of consumers even though it was a commercial platform of products. Second, the review text is mainly positive, which formed a text network structure based on the keyword 'gratitude'. Third, the review text, which implies consumer character, was divided into two groups: 'extrovert personality' and 'introvert personality' of consumers. And the individuality of the two groups worked together on the platform. In conclusion, we would like to suggest that consumer personality plays an important role in the platform transaction process, that consumer personality will play a role in the services of the platform in the future, and that consumer personality should be studied from various perspectives.