• Korean Management Science Review
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• v.26 no.2
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• pp.1-18
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• 2009

Recent market environment shows a new relationship between customer and company. You can easily observe customers take initiatives to get involved in the marketing activities such as product development, pricing and distribution etc. Despite the fact there are a lot of marketing activities initiated by customers in the online travel market, customer participation has been given little attention in the academic literature. Our research is grounded in the well-known commitment-trust theory of relationship marketing, originally proposed by Morgan and Hunt(1994). According to the theory, trust is central to successful relationship marketing. This study explores the effect of trust on customer participation in the online travel market. Consequently, this study suggests some managerial implications for marketing strategies in response to customer initiative In the marketing activities on the online travel market.

• 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.

• 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.

• Proceedings of the Korean Society for Emotion and Sensibility Conference
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• 2011.05a
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• pp.49-50
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• 2011

• Korean Management Science Review
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• v.23 no.2
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• pp.41-57
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• 2006

The purposes of this research are to identify causalities among flow and customer loyalty In Chinese online games, and to identify the factors by which flow are influenced. This research tests the model with Chinese on-line game users and compare this result with Korea and Japanese results which were conducted by Lee's research. These implications are thought to be helpful for Korean online game companies to understand the Chinese online game user and to develop the penetration strategies. The results indicated that significant Path coefficients to flow were the convenience of operator, the provision of information, the reality of design. The results indicated that significant path coefficients to customer loyalty were the involvement of virtual community and flow. The involvement of virtual community to flow was not significant but to customer loyalty was significant. The provision of information was negatively influenced on flow. The result of comparison indicated that the path coefficients were different among nations. Korea online game companies need to develop the indigenized online game and to Provide the information to their Chinese partner correctly and quickly.

• The Journal of Information Systems
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• v.26 no.4
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• pp.327-353
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• 2017

Purpose Social media such as social network services, online forums, and customer reviews have produced a plethora amount of information online. Yet, the information deluge has created both opportunities and challenges at the same time. This research particularly focuses on the challenges in order to discover and track the service defects over time derived by mining publicly available online customer reviews. Design/methodology/approach Synthesizing the streams of research from text analytics, we apply two stages of methods of sentiment analysis and structural topic model incorporating meta-information buried in review texts into the topics. Findings As a result, our study reveals that the research framework effectively leverages textual information to detect, prioritize, and categorize service defects by considering the moving trend over time. Our approach also highlights several implications theoretically and practically of how methods in computational linguistics can offer enriched insights by leveraging the online medium.

• Journal of Korean Society for Quality Management
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• v.49 no.4
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• pp.595-607
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• 2021

Purpose: This study introduces a systematic framework to evaluate service satisfaction of cosmetic brands through online review analysis utilizing Text-Mining technique. Methods: The framework assumes that the service satisfaction is evaluated by positive comments from online reviews. That is, the service satisfaction of a cosmetic brand is evaluated higher as more positive opinions are commented in the online reviews. This study focuses on two approaches. First, it collects online review comments from the top 50 global cosmetic brands and evaluates customer service satisfaction for each cosmetic brands by applying Sentimental Analysis and Latent Dirichlet Allocation. Second, it analyzes the determinants that induce or influence service satisfaction and suggests the guidelines for cosmetic brands with low satisfaction to improve their service satisfaction. Results: For the satisfaction evaluation, online review data were extracted from the top 50 global cosmetic brands in the world based on 2018 sales announced by Brand Finance in the UK. As a result of the satisfaction analysis, it was found that overall there were more positive opinions than negative opinions and the averages for polarity, subjectivity, positive ratio, and negative ratio were calculated as 0.50, 0.76, 0.57, and 0.19, respectively. Polarity, subjectivity and positive ratio showed the opposite pattern to negative ratio, and although there was a slight difference in fluctuation range and ranking between them, the patterns are almost same. Conclusion: The usefulness of the proposed framework was verified through case study. Although some studies have suggested a method to analyze online reviews, they didn't deal with the satisfaction evaluation among competitors and cause analysis. This study is different from previous studies in that it evaluates service satisfaction from a relative point of view among cosmetic brands and analyze determinants.

• 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.

• 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.18 no.10
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• pp.65-75
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• 2020

Purpose: This study aims to examine how to review contents of experiential and utilitarian products (e.g., skincare products) and how to affect review helpfulness by applying natural language processing techniques. Research design, data, and methodology: This study uses 69,633 online reviews generated for the products registered at Amazon.com by 13 Korean cosmetic firms. The authors identify key topics that emerge about consumers' use of skincare products such as skin type and skin trouble, by applying bigram analysis. The review content variables are included in the review helpfulness model, including other important determinants. Results: The estimation results support the positive effect of review extremity and content on the helpfulness. In particular, the reviewer's skin type information was recognized as highly useful when presented together as a basis for high-rated reviews. Moreover, the content related to skin issues positively affects review helpfulness. Conclusions: The positive relationship between extreme reviews and helpfulness of reviews challenges the findings from prior literature. This result implies that an in-depth study of the effect of product types on review helpfulness is needed. Furthermore, a positive effect of review content on helpfulness suggests that applying big data analytics can provide meaningful customer insights in the online retail industry.