• The Journal of the Korea Contents Association
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• v.6 no.11
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• pp.25-37
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• 2006

The purpose of this study examines the interrelationships among antecedents and consequences of privacy concern on the online-shopping mall. Based on relevant literature review, a customer's attitude toward direct marketing, a customer's desire to information control, and a customer's prediction of negative effect as antecedents that affect the privacy concern. Also, consequences are a firm's reputation and a customer's purchase experience. Then related hypotheses were tested using data from 165 online shopping mall customer. The results for empirical analysis are as follows; 1) a customer's attitude toward direct marketing affected negatively the privacy concern, 2) a customer's desire to information control and a customer's prediction of negative effect affected positively the privacy concern, 3) a firm's reputation negatively related to the privacy concern, 4) a customer's purchase experience positively related to a firm's reputation.

• Information Systems Review
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• v.8 no.3
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• pp.125-134
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• 2006

This article adopts a decision tree algorithm(C5.0) to predict customer churning in online auto insurance environment. Using a sample of on-line auto insurance customers contracts sold between 2003 and 2004, we test how decision tree-based model(C5.0) works on the prediction of customer churning. We compare the result of C5.0 with those of logistic regression model(LRM), multivariate discriminant analysis(MDA) model. The result shows C5.0 outperforms other models in the predictability. Based on the result, this study suggests a way of setting marketing strategy and of developing online auto insurance business.

• The Journal of Asian Finance, Economics and Business
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• v.8 no.8
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• pp.443-451
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• 2021

The classification standards for hotels in Vietnam are different from many other hotel classification standards in the world. This study aims to analyze customer reviews on the TripAdvisor website to develop a new algorithm for hotel rating that is independent of Vietnam's hotel classification standards. This method can be applied to individual hotels, or hotels of a region or the whole country, while online booking sites only rate individual hotels. Data was crawled from TripAdvisor with 22,287 reviews of 5 cities in Vietnam. This study used a statistical model to analyze the review dataset and build an algorithm to rate hotels according to aspects or hotel overall. The results have less rating deviation when compared to the TripAdvisor system. This study also supports hotel managers to regularly update the status of their hotels using data from customer reviews, from which, managers can strategize long-term solutions to improve the quality of the hotel in all aspects and attract more travelers to Vietnam. Moreover, this method can be developed into an automatic system to rate hotels and update the status of service quality more quickly, thus, saving time and costs.

• Journal of Information Technology Services
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• v.14 no.4
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• pp.159-169
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• 2015

Customer reviews are one of the important sources for purchase decision makings in online stores. Online stores have tried to provide useful reviews in product pages to customers. To assess the usefulness of customer reviews before other users have voted enough on the reviews, diverse aspects of reviews were utilized in prevous studies. Style and semantic information were utilized in many studies. This study aims to test diverse alogrithms and datasets for identifying a proper classification method and threshold to classify useful reviews. In particular, most researches utilized ratio type helpfulness index as Amazon.com used. However, there is another type of usefulness index utilized in TripAdviser.com or Yelp.com, count type helpfulness index. There was no proper threshold to classify useful reviews yet for count type helpfulness index. This study used reivews and their usefulness votes on restaurnats from Yelp.com to devise diverse datasets and applied text mining approaches to classify useful reviews. Random Forest, SVM, and GLMNET showed the greater values of accuracy than other approaches.

• Journal of the Korea Society of Computer and Information
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• v.29 no.7
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• pp.139-148
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• 2024

In the post-pandemic era, the demand for online education platforms has surged, leading to increased consumer reliance on online reviews for decision-making. This study investigates the impact of Chinese online customer reviews on consumer purchase behavior in online education. By examining the role of trust, review sentiment, and the quantity and timeliness of reviews, the research aims to understand how these factors influence consumer decisions. By using regression model, findings reveal that negative reviews, timely feedback, and a higher volume of reviews positively affect consumer purchase decisions, while course pricing demonstrates an inverse relationship. Furthermore, cognitive and affective trust mediate the relationship between reviews and purchase behavior, highlighting a reverse U-shaped effect on consumer decision inclination. These insights provide valuable implications for online education providers, emphasizing the need to manage and leverage online reviews to foster consumer trust and improve sales performance.

• ETRI Journal
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• v.33 no.5
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• pp.720-730
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• 2011

Opinion mining involves the analysis of customer opinions using product reviews and provides meaningful information including the polarity of the opinions. In opinion mining, feature extraction is important since the customers do not normally express their product opinions holistically but separately according to its individual features. However, previous research on feature-based opinion mining has not had good results due to drawbacks, such as selecting a feature considering only syntactical grammar information or treating features with similar meanings as different. To solve these problems, this paper proposes an enhanced feature extraction and refinement method called FEROM that effectively extracts correct features from review data by exploiting both grammatical properties and semantic characteristics of feature words and refines the features by recognizing and merging similar ones. A series of experiments performed on actual online review data demonstrated that FEROM is highly effective at extracting and refining features for analyzing customer review data and eventually contributes to accurate and functional opinion mining.

• The Journal of Information Systems
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• v.20 no.2
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• pp.21-36
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• 2011

The Web has become an excellent source for gathering consumer opinions. There are now numerous Web sites containing such opinions, e.g., customer reviews of products, forums, discussion groups, and blogs. This paper focuses on online customer reviews of products. It makes some contributions. Especially it proposes minimalism and chunking framework for analyzing and comparing consumer opinions of competing products. Users are able to clearly see the strengths and weaknesses of each product in the minds of consumers in terms of various product features. This comparison is useful to both potential customers and product manufacturers. For a product manufacturer, the comparison enables it to easily gather marketing intelligence and product benchmarking information. In this paper, we only focus on mining opinion/product features that the reviewers have commented on. Five types of online review presentations are presented to mine such features. Our experimental results show that these techniques are useful to identify customers' opinions and trends.

• Journal of Information Technology Services
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• v.17 no.3
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• pp.1-16
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• 2018

There exist a number of studies pertaining to the determinants of customer satisfaction between low-cost and full-service carriers in the airline industry. Most studies measured service quality using SERVQUAL based on a survey method. This study offers a new perspective by employing a big data analytic approach using SNS data, which reflects the immediate response of customers as well as trends in real time. This study chose eight factors from TripAdvisor's customer review site as determinants of customer satisfaction and compared the differences between low-cost and full-service airlines. The factors analyzed were seat comfort, customer service, cleanliness, food and beverage, legroom, entertainment, value for money, and check-in and boarding. Additionally, ratings from domestic and foreign customers were compared. The findings show that customer service and value for money are significant factors in satisfaction with low-cost airlines while all variables except legroom and entertainment are significant for full-service airlines. The results show that SNS-based data and analysis of big data are important for improving decision-making effectiveness and increasing customer satisfaction in the airline industry.

• Journal of Korean Home Economics Education Association
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• v.22 no.3
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• pp.129-145
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• 2010

In this study, when buying fashion products through internet shopping malls, it is researched about the buying behavior, the awareness of customer review, the use and posting of customer review and the accompanying awareness. The difference of awareness on the customer review according to their involvement of clothes, are examined from high school students in Seoul. And it is examined if they experienced any dissatisfaction after their purchase and what their behavior were. The questionnaire survey was taken by 508 students from 6 high schools in Seoul. The average, the standard deviation, the frequency, the t-test, the One way ANOVA and Duncan's Multiple Test were conducted for data analysis using SPSS 17.0. In the fashion products purchase behavior of the students, The reasons of buying were mainly because of the diversity and the convenience. Some students don't shop online because screen product and actual product are not the same. The awareness of the customer review represented high in the reliability and usefulness. The awareness on the influence of the customer review represented high in the contents direction and the numbers of the customer reviews but represented low in the timeliness. As to the awareness of the customer review, the student using it represented higher in all elements such as the usefulness, the reliability, and the influence than students who not use customer review. The students posting customer review recognized higher on the usefulness and the reliability of the customer review than those who did not post it, and were highly influenced by the numbers of customer reviews. The awareness of the customer review according to the involvement of clothes was the difference only in the usefulness. As to coping actions of students experiencing dissatisfaction, the proportion of the students coping with the public action and those who do not perform any action represented high.

• Information Systems Review
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• v.17 no.3
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• pp.95-111
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• 2015

The recommendation system automatically provides the predicted items which are expected to be purchased by analyzing the previous customer behaviors. This recommendation system has been applied to many e-commerce businesses, and it is generating positive effects on user convenience as well as the company's revenue. However, there are several limitations of the existing recommendation systems. They do not reflect specific criteria for evaluating products or the factors that affect customer buying decisions. Thus, our research proposes a collaborative recommendation model algorithm that utilizes each customer's online product reviews. This study deploys topic modeling method for customer opinion mining. Also, it adopts a kernel-based machine learning concept by selecting kernels explaining individual similarities in accordance with customers' purchase history and online reviews. Our study further applies a multiple kernel learning algorithm to integrate the kernelsinto a combined model for predicting the product ratings, and it verifies its validity with a data set (including purchased item, product rating, and online review) of BestBuy, an online consumer electronics store. This study theoretically implicates by suggesting a new method for the online recommendation system, i.e., a collaborative recommendation method using topic modeling and kernel-based learning.