• The Journal of Information Systems
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• v.31 no.2
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• pp.1-18
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• 2022

Purpose Customer review is a major factor in choosing certain restaurants. This study investigates the key factors affecting customer's evaluation about restaurants. With the recent intensification of competition among restaurants in the service industry, the analysis results are expected to provide in-depth insights for enhancing customer experiences. Design/methodology/approach We collected information and reviews provided at the restaurants in the Kakao Map platform. The information collected is based on the information of 3,785 restaurants in Daegu registered on Kakao Map. Based on the information collected, seven independent variables, including number of rating registered, number of reviews, presence or absence of safe restaurants, presence or absence of a posting about holding facilities, presence or absence of a posting about business hours, presence or absence of a posting about hashtags, and presence or absence of break times, were used. Dependent variable is restaurant rating. Multiple regression between independent variables and restaurant rating was carried out. Findings The results of the study confirmed that number of rating registered, presence or absence of a posting about business hours, and presence or absence of a posting about hash tags have an positive effects on the restaurant rating. The number of reviews had a negative effect on the restaurant rating. In addition, in order to confirm the role of customer's reviews, we carried out LDA topic modeling. We divided the topics into the positive review and the negative reviews.

• Journal of Intelligence and Information Systems
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• v.12 no.4
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• pp.109-126
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• 2006

An effective customer classification has been essential for the successful customer relationship management. The typical customer rating is carried out by the proportionally allocating the customers into classes in terms of their life time values. However, since this method does not accurately reflect the homogeneity within a class along with the heterogeneity between classes, there would be many problems incurred due to the misclassification. This paper suggests a new method of rating customer using Holistic profit technique, and validates the new method using the customer data provided by an insurance company. Holistic profit is one of the methods used for deciding the cutoff score in screening the loan application. By rating customers using the proposed techniques, insurance companies could effectively perform customer relationship management and diverse marketing activities.

• Asia pacific journal of information systems
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• v.20 no.2
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• pp.23-37
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• 2010

Conventional Support Vector Machines (SVMs) have been utilized as classifiers for binary classification problems. However, certain real world problems, including corporate bond rating, cannot be addressed by binary classifiers because these are multi-class problems. For this reason, numerous studies have attempted to transform the original SVM into a multiclass classifier. These studies, however, have only considered nominal classification problems. Thus, these approaches have been limited by the existence of multiclass classification problems where classes are not nominal but ordinal in real world, such as corporate bond rating and multiclass customer classification. In this study, we adopt a novel multiclass SVM which can address ordinal classification problems using ordinal pairwise partitioning (OPP). The proposed model in our study may use fewer classifiers, but it classifies more accurately because it considers the characteristics of the order of the classes. Although it can be applied to all kinds of ordinal multiclass classification problems, most prior studies have applied it to finance area like bond rating. Thus, this study applies it to a real world customer level classification case for implementing customer relationship management. The result shows that the ordinal multiclass SVM model may also be effective for customer level classification.

• Proceedings of the Korea Inteligent Information System Society Conference
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• 1999.10a
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• pp.347-354
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• 1999

The credit rating represents an assessment of the relative level of risk associated with the timely payments required by the debt obligation. In this paper, we present a new approach to credit rating of customers based on the rough set theory. The concept of a rough set appeared to be an effective tool for the analysis of customer information systems representing knowledge gained by experience. The customer information system describes a set of customers by a set of multi-valued attributes, called condition attributes. The customers are classified into groups of risk subject to an expert's opinion, called decision attribute. A natural problem of knowledge analysis consists then in discovering relationships, in terms of decision rules, between description of customers by condition attributes and particular decisions. The rough set approach enables one to discover minimal subsets of condition attributes ensuring an acceptable quality of classification of the customers analyzed and to derive decision rules from the customer information system which can be used to support decisions about rating new customers. Using the rough set approach one analyses only facts hidden in data, it does not need any additional information about data and does not correct inconsistencies manifested in data; instead, rules produced are categorized into certain and possible. A real problem of the evaluation of the evaluation of credit rating by a department store is studied using the rough set approach.

• 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.

• The Journal of the Korea Contents Association
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• v.13 no.7
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• pp.379-385
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• 2013

On the internet, the rating scores assigned by customers are considered as the preference information of themselves and thus, these can be used efficiently in the customer profile generation process of recommender system. However, since anyone is free to assign a score that has a biased rating, using this without any filtering can exhibit a reliability problem. In this study, we suggest the methodology that measures the reliability of rating scores and then applies them to the customer profile creation process. Unlikely to some related studies which measure the reliability on the user level, we measure the reliability on the individual rating score level. Experimental results show that prediction accuracy of recommender system can be enhanced when ratings with higher reliability are selectively used for the customer profile configuration.

• Asia Marketing Journal
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• v.14 no.1
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• pp.1-24
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• 2012

Nowadays, customer satisfaction has been one of company's major objectives, and the index to measure and communicate customer satisfaction has been generally accepted among business practices. The major issues of CSI(customer satisfaction index) are three questions, as follows: (a)what level of customer satisfaction is tolerable, (b)whether customer satisfaction and company performance has positive causality, and (c)what to do to improve customer satisfaction. Among these, the second issue is recently attracting academic research in several perspectives. On this study, the second issue will be addressed. Many researchers including Anderson have regarded customer satisfaction as core competencies, such as brand equity, customer equity. They want to verify following causality "customer satisfaction → market performance(market share, sales growth rate) → financial performance(operating margin, profitability) → corporate value performance(stock price, credit ratings)" based on the process model of marketing performance. On the other hand, Insoo Jeon and Aeju Jeong(2009) verified sequential causality based on the process model by the domestic data. According to the rejection of several hypotheses, they suggested the balance model of marketing performance as an alternative. The objective of this study, based on the existing process model, is to examine the causal relationship between customer satisfaction and corporate value performance. Anderson and Mansi(2009) proved the relationship between ACSI(American Customer Satisfaction Index) and credit ratings using 2,574 samples from 1994 to 2004 on the assumption that credit rating could be an indicator of a corporate value performance. The similar study(Sangwoon Yoon, 2010) was processed in Korean data, but it didn't confirm the relationship between KCSI(Korean CSI) and credit ratings, unlike the results of Anderson and Mansi(2009). The summary of these studies is in the Table 1. Two studies analyzing the relationship between customer satisfaction and credit ratings weren't consistent results. So, in this study we are to test the conflicting results of the relationship between customer satisfaction and credit ratings based on the research model considering Korean credit ratings. To prove the hypothesis, we suggest the research model as follows. Two important features of this model are the inclusion of important variables in the existing Korean credit rating system and government support. To control their influences on credit ratings, we included three important variables of Korean credit rating system and government support, in case of financial institutions including banks. ROA, ER, TA, these three variables are chosen among various kinds of financial indicators since they are the most frequent variables in many previous studies. The results of the research model are relatively favorable : R², F-value and p-value is .631, 233.15 and .000 respectively. Thus, the explanatory power of the research model as a whole is good and the model is statistically significant. The research model has good explanatory power, the regression coefficients of the KCSI is .096 as positive(+) and t-value and p-value is 2.220 and .0135 respectively. As a results, we can say the hypothesis is supported. Meanwhile, all other explanatory variables including ROA, ER, log(TA), GS_DV are identified as significant and each variables has a positive(+) relationship with CRS. In particular, the t-value of log(TA) is 23.557 and log(TA) as an explanatory variables of the corporate credit ratings shows very high level of statistical significance. Considering interrelationship between financial indicators such as ROA, ER which include total asset in their formula, we can expect multicollinearity problem. But indicators like VIF and tolerance limits that shows whether multicollinearity exists or not, say that there is no statistically significant multicollinearity in all the explanatory variables. KCSI, the main subject of this study, is a statistically significant level even though the standardized regression coefficients and t-value of KCSI is .055 and 2.220 respectively and a relatively low level among explanatory variables. Considering that we chose other explanatory variables based on the level of explanatory power out of many indicators in the previous studies, KCSI is validated as one of the most significant explanatory variables for credit rating score. And this result can provide new insights on the determinants of credit ratings. However, KCSI has relatively lower impact than main financial indicators like log(TA), ER. Therefore, KCSI is one of the determinants of credit ratings, but don't have an exceedingly significant influence. In addition, this study found that customer satisfaction had more meaningful impact on corporations of small asset size than those of big asset size, and on service companies than manufacturers. The findings of this study is consistent with Anderson and Mansi(2009), but different from Sangwoon Yoon(2010). Although research model of this study is a bit different from Anderson and Mansi(2009), we can conclude that customer satisfaction has a significant influence on company's credit ratings either Korea or the United State. In addition, this paper found that customer satisfaction had more meaningful impact on corporations of small asset size than those of big asset size and on service companies than manufacturers. Until now there are a few of researches about the relationship between customer satisfaction and various business performance, some of which were supported, some weren't. The contribution of this study is that credit rating is applied as a corporate value performance in addition to stock price. It is somewhat important, because credit ratings determine the cost of debt. But so far it doesn't get attention of marketing researches. Based on this study, we can say that customer satisfaction is partially related to all indicators of corporate business performances. Practical meanings for customer satisfaction department are that it needs to actively invest in the customer satisfaction, because active investment also contributes to higher credit ratings and other business performances. A suggestion for credit evaluators is that they need to design new credit rating model which reflect qualitative customer satisfaction as well as existing variables like ROA, ER, TA.

• 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 the Ergonomics Society of Korea
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• v.17 no.3
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• pp.71-80
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• 1998

This study proposed a method to apply certain fuzzy-related Quality control concepts to design customer-oriented products considering user requirements and information starting with the product development stage. This approach showed how to define the importance level of design elements and how to Quantify complex subjective perception of products using the fuzzy linguistic rating method and quality function deployment concepts. Using this approach, various customer requirements could be interpreted and reflected on the early design phase of a new product. To validate the proposed method, an experiment was conducted for designing the shape of the beeper using 14 subjects and 10 commercial beeper products. Front area, width/length ratio, thickness, curve variance, weight, and display area were selected as design elements of the beeper. The results showed that among design elements, front area and weight are significantly related with the subjective perception of the products. Consequently, this study indicates that customer decision on product selection could be made by quantification of user perception for beeper products.

• 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.