• Journal of Information Technology Applications and Management
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• v.21 no.3
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• pp.35-52
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• 2014

This study aims to examine how the relationship with an information source affects people to perceive credibility of online information. We developed a conceptual framework that explained how people perceived information credibility when they were familiar with the information source and/or when the information source seemed credible. We then compared the models in two different contexts, namely, online review and social media sites, to examine differences. We surveyed 136 online social media users with their online shopping experiences. Among our eight hypotheses, three (H6: the personality similarity between an information provider and an information seeker enhances the perceived credibility of the former; H7: the credibility of an information provider produces a much stronger mediating effect in review sites than in social media sites; H8: the familiarity of an information seeker with an information provider produces a stronger mediating effect in social media sites than in review sites) are fully supported and four (H1: the credibility of an information provider has a positive influence on the perceived credibility of the online information; H2: the familiarity of an information seeker with an information provider has a positive influence on the perceived credibility of the online information; H3: the goal similarity between an information provider and an information seeker enhances the perceived familiarity of the latter with the former; H5: the personality similarity between an information provider and an information seeker enhances the perceived familiarity of the latter with the former) are partially supported. The hypothesis of H4: the goal similarity between an information provider and an information seeker enhances the perceived credibility of the former is rejected. The result confirms that credibility of information is strongly mediated by credibility of information source than familiarity with information source in online review sites and vice versa in social media sites.

• Asia pacific journal of information systems
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• v.32 no.3
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• pp.514-536
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• 2022

Online reviews offer valuable information to businesses by reflecting consumer experiences about their products and services. Two crucial aspects of online reviews are the topics consumers choose to address, and the sentiments expressed in their reviews. Building upon previous literature that shows online reviews are context-dependent, we employ the Expectation-Confirmation Theory (ECT) to examine the impact of topic distribution on review sentiment in South Korea and the U.S. during pre- and post-pandemic periods. After applying a topic modeling to Airbnb app review data, we measure the contribution of each topic on review sentiment using SHAP values. Our results indicate variations in topic distribution trends between 2018 and 2021. In addition, the order and magnitude of topics' impact on review sentiment change between pre- and post-pandemic periods for both countries. This study can help businesses understand how topics and sentiments associated with their products and services changed after the pandemic and thus identify areas of improvement.

• 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.

• Korean Journal of Artificial Intelligence
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• v.12 no.2
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• pp.25-33
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• 2024

This study examines the relationship between customer satisfaction and management response in the hotel industry amid the COVID-19 pandemic. By applying regression analysis and topic modeling to consumer reviews on online platforms, we assess how consumer perceptions and management behaviors have shifted since the onset of the pandemic. The findings reveal a significant decline in customer satisfaction linked to COVID-19. Significantly, while the pandemic has reduced overall customer satisfaction levels, high response rates and high review-response content similarity mitigate the impact of the crises. These results highlight the critical need for hotel managers to continuously monitor online reviews and adapt their engagement strategies to maintain and enhance customer satisfaction during ongoing and future crises. This research not only corroborates existing theories on customer satisfaction but also exposes novel dynamics introduced by the pandemic, offering new insights for effective customer relationship management in turbulent times.

• Korea Trade Review
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• v.47 no.3
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• pp.93-110
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• 2022

This study aims to develop online convention service quality and examine the effect of online convention service quality on re-participation intention in the same convention and other types of online conventions. Based on an extensive literature review, the study chose five main factors of online convention service quality: human service, program service, platform service, platform aesthetics, and interaction. A total of 284 data were collected from online convention participants from July 26 to August 6, 2021. For the hypotheses test, multiple regression analysis was used. As a result, interaction and program service quality had positive effects on re-participation intention in the same convention, but except for platform aesthetic, all factors positively affected re-participation intention in other types of online conventions. This study also found that online service quality factors are more helpful in predicting the intention of re-participation in other types of online conventions rather than re-participation in the same convention. Based on the results, theoretical and practical implications were discussed

• International Journal of Internet, Broadcasting and Communication
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• v.16 no.4
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• pp.352-359
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• 2024

We analyze the digital marketing strategies of Rare Beauty, a cosmetic brand founded by Selena Gomez in 2020, focusing on inclusivity and mental health advocacy as core pillars of its brand mission. Through an in-depth review of the brand's website design, SEO performance, social media engagement, and online review management-the key elements of a firm's digital marketing activities-we reveal Rare Beauty's success in authentically connecting with diverse audiences and fostering brand loyalty. Our analysis uncovers noteworthy findings: while Rare Beauty excels in creating a mission-driven aesthetic across digital platforms, there are areas for improvement, particularly in enhancing user experience by improving website readability, refining the review filtering system, and expanding social media engagement. Optimizing technical SEO could further increase discoverability. We propose these recommendations to strengthen Rare Beauty's online presence and demonstrate how the brand's unique approach offers valuable insights for industry professionals aiming to integrate social values into digital marketing strategies.

• Asia pacific journal of information systems
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• v.26 no.3
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• pp.449-476
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• 2016

This study examines how an initially perceived product value affects consumer's purchase intention after reading online reviews with various tones. The study proposes that associations among initially perceived overall product value, degree of confirmation resulting from reading the reviews, and final purchase intention differ across review tones such that 1) when the tone is favorable, the effect of an initially perceived product value is stronger than when the tone is critical, and 2) when the tone is extreme, the effect of confirmation is stronger than when the tone is moderate. The survey was conducted with 276 online shopping mall users in Korea, and most of the hypotheses were supported. This study asserts that the effects of online reviews should be considered together with customer's level of expectation formed prior to reading online reviews, which resulted from extensive search and screening processes that the customer went through before reading online reviews.

• Knowledge Management Research
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• v.23 no.1
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• pp.187-201
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• 2022

Online reviews written by tourists provide important information for the management and operation of the tourism industry. The star rating of online reviews is a simple quantitative evaluation of a product or service, but it is difficult to reflect the sincere attitude of tourists. There is also an issue; the star rating and review content are not matched. In this study, a star rating prediction model based on online review content was proposed to solve the discrepancy problem. We compared the differences in star ratings and sentiment by continent through sentiment analysis on tourist attractions and hotels written by foreign tourists who visited Korea. Variables were selected through TF-IDF vectorization and sentiment analysis results. Logit, artificial neural network, and SVM(Support Vector Machine) were used for the classification model, and artificial neural network and SVR(Support Vector regression) were applied for the rating prediction model. The online review rating prediction model proposed in this study could solve inconsistency problems and also could be applied even if when there is no star rating.

• Journal of Digital Convergence
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• v.10 no.10
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• pp.55-60
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• 2012

In this paper, introduction and developing guide to the online submission and peer review system is studied. Such system must provides fare and fast review of drafted papers. In addition, the publication of accepted papers by reviewers is quick and easy. Today, Some societies use KISTI-ACOM which is supplied freely by KISTI(Korea Institute of Science and Technology Information) and some other societies develop and use their own system in Korea. It is sure that each system will be improved and developed further. Based on the consideration about a typical peer review and publication in online submission system, the guideline of online submission and peer review system is suggested in this paper.

• 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.