• International Journal of Contents
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• v.14 no.3
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• pp.46-52
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• 2018

The objective of this study was to determine the status of star chart game applications in the Google play store in Korea. The share of game genres in paid and free star charts of game applications was searched. Also, the average reviewer's rating, average number of reviews, and average age rating based on the genre of paid and free star charts of game applications, and the average price of paid applications based on genre were analyzed. Hypothesis tests for the differences in average reviewer's rating, average number of reviews, average age rating according to the genre of game applications were performed. Also, hypothesis tests for the differences in average reviewer's rating, average number of reviews, average age rating between the paid and free game applications along with the hypothesis test for the differences in price according to the genre of paid game applications were performed. Lastly, hypothesis tests for the correlation between the start chart ranking and number of reviews in association with the correlation between the start chart ranking and reviewer's rating were performed. Statistically significant differences in average reviewer's rating, average number of reviews, average age rating according to the genre of game applications, and between the paid and free game applications were verified. However, the correlation between the start chart ranking and number of reviews in association with the correlation between the start chart ranking and reviewer's rating were not statistically significant.

• 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.

• The Journal of Industrial Distribution & Business
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• v.14 no.1
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• pp.21-28
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• 2023

Purpose: This study aims to analysis the difference by Michelin rating in customer satisfaction of restaurant listed in the Korea Michelin Guide. There are opinions that the Michelin Guide's rating system and evaluation criteria are somewhat ambiguous. Research design, data, and methodology: This study collected 145 actual online reviews published on TripAdvisor to examine how the effect of the content attributes of reviews on consumer satisfaction varies according to the Michelin grade. Based on this, two studies were conducted. Study 1 examined the effect of strong and weak positive reviews on consumer satisfaction according to the rating. Study 2 examined the effect of image information on consumer satisfaction. Results: The results revealed that the lower the Michelin rating, the more positive review had a significant effect on consumer satisfaction. The higher the rating, the more image information had an effect on consumer satisfaction. Expectations for Michelin three-star restaurants are higher than those of two-star restaurants, so customers are more likely to be used negatively when writing reviews. Conclusions: Accurate information on Michelin selection criteria should be delivered so as not to form high expectations and not to disappoint. For consumers to be satisfied with the name Michelin, the standards should be stricter.

• Journal of Periodontal and Implant Science
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• v.42 no.1
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• pp.25-29
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• 2012

Purpose: There has been no attempt to establish an objective implant surgical evaluation protocol to assess residents' surgical competence and improve their surgical outcomes. The present study presents a newly developed assessment and rating system and simulation model that can assist the teaching staffs to evaluate the surgical events and surgical skills of residents objectively. Methods: Articles published in peer-reviewed English journals were selected using several scientific databases and subsequently reviewed regarding surgical competence and assessment tools. Particularly, medical journals reporting rating and evaluation protocols for various types of medical surgeries were thoroughly analyzed. Based on these studies, an implant surgical technique assessment and rating system (iSTAR) has been developed. Also, a specialized dental typodont was developed for the valid and reliable assessment of surgery. Results: The iSTAR consists of two parts including surgical information and task-specific checklists. Specialized simulation model was subsequently produced and can be used in combination with iSTAR. Conclusions: The assessment and rating system provided may serve as a reference guide for teaching staffs to evaluate the residents' implant surgical techniques.

• Asia Marketing Journal
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• v.21 no.1
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• pp.1-21
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• 2019

Online platforms often provide rating information to customers to relieve the uncertainty they encounter when purchasing experience goods. Prior research has focused mostly on the roles of rating volume and the valence of an average rating among the various possibilities. However, less frequently investigated is the effect of rating dispersion, which may be associated with uncertainty regarding how well a product fits a customer's personal preference, on new trials of experience goods. In this study, we examine the effect of rating dispersion on new trials of experience goods and identify the conditions which intensify or reduce the effect. Empirical analyses of movie box office sales data and online rating data reveal three interesting findings. First, movie sales decrease as movie ratings become increasingly dispersed. Second, the negative effect of rating dispersion on movie sales is more pronounced with more rating volume. Third, this negative effect weakens when additional information about a movie is available (i.e., higher average rating, greater star power, and time since its release). We discuss the academic and practical implications of our findings.

• The Journal of Information Systems
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• v.32 no.3
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• pp.133-150
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• 2023

Purpose This study aims to develop classification models using a decision tree algorithm to identify core keywords and rules influencing online consumer review evaluations for the robot vacuum cleaner on Amazon.com. The difference from previous studies is that we analyze core keywords that affect the evaluation results by dividing the subjects that evaluate online consumer reviews into self-evaluation (star ratings) and peer evaluation (helpfulness votes). We investigate whether the core keywords influencing star ratings and helpfulness votes vary across different products and whether there is a similarity in the core keywords related to star ratings or helpfulness votes across all products. Design/methodology/approach We used random under-sampling to balance the dataset. We progressively removed independent variables based on decreasing importance through backwards elimination to evaluate the classification model's performance. As a result, we identified classification models that best predict star ratings and helpfulness votes for each product's online consumer reviews. Findings We have identified that the core keywords influencing self-evaluation and peer evaluation vary across different products, and even for the same model or features, the core keywords are not consistent. Therefore, companies' producers and marketing managers need to analyze the core keywords of each product to highlight the advantages and prepare customized strategies that compensate for the shortcomings.

• Journal of Power Electronics
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• v.18 no.6
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• pp.1697-1707
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• 2018

In this paper, two large current rectifiers are proposed based on two wye-connected autotransformers. The requirements of the ideal large current rectifier are analyzed, and it is concluded that the large current rectifier has a higher power density and a higher energy conversion efficiency when it is made up of two three-phase half-wave rectifiers and a wye-connected autotransformer. According to theoretical analysis results, the two novel wye-connected autotransformers are designed to feed two three-phase half-wave rectifiers. The two autotransformers can generate two groups of three-phase voltages with a 60o phase shifting, and their kVA ratings account for 95% and 80% of the load power, respectively. These values are less than those of a double star rectifier at 30% and 46%. From the input mains and output side, the power quality of the proposed rectifiers is the same as that of the double star rectifier. Some experiments validate the correctness of the theoretical analysis.

• The Journal of the Korea Contents Association
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• v.18 no.5
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• pp.357-366
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• 2018

This study analyzed the factors affecting box office performance of 200 movies released at the Chinese theater in 2015. The results showed that main actor power, online rating, production power, and Chinese film were sighificant factors which influenced box office, while the distribution power, genre, IP utilization and integration of production and distribution were insignificant. These results mean that online marketing factors such as the popularity index of the main actors evaluated on the internet and the online rating are affecting box office performances in Chinese theaters.

• Journal of Korea Society of Digital Industry and Information Management
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• v.19 no.1
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• pp.91-107
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• 2023

With the recent expansion of the commerce ecosystem, a large number of user evaluations have been produced. Accordingly, attempts to create business insights using user evaluation data have been actively made. However, since user evaluation can change after the user experiences the product, it is difficult to say that the analysis based only on reviews immediately after purchase fully reflects the user's evaluation of the product. Moreover, studies conducted so far on user evaluation have overlooked the fact that the length of time a user has used a product can affect the user's product evaluation. Therefore, in this study, we build a model that predicts the direction of change in the user's rating after use from the user's rating and reviews immediately after purchase. In particular, the proposed model reflects the product's period of use in predicting the change direction of the star rating. However, since the posterior information on the duration of product use cannot be used as input in the inference process, we propose a structure that utilizes information about the product's period of use using an auxiliary classifier. As a result of an experiment using 599,889 user evaluation data collected from the shopping platform 'N' company, we confirmed that the proposed model performed better than the existing model in terms of accuracy.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2012.05a
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• pp.1105-1106
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• 2012