• 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.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.19 no.5
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• pp.106-116
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• 2018

Recently, the role of the contact center for business-to-consumer (B2C) operations is becoming more and more important as the customer contact point. In particular, an Internet Protocol (IP)-based contact center system is made up of a complicated information system in order to accommodate various customer channels, in addition to the telephone, and to respond in real time. However, until now, evaluations of contact centers have focused on customer service-based research from inbound contact centers. We used the contact center as a measure of performance, focusing on indicators that have traditionally influenced customer satisfaction, such as response rates and service levels. There is insufficient research on the characteristics of the services that a contact center should have and on the evaluation models for information systems. The role of information systems is becoming important as the latest contact center, which has moved from the TDM-driven digital phone system center to the IP-based contact center, accommodates a variety of digital channels other than voice phones. In particular, as offline branches decrease due to the development of the Internet and mobile phones, non-facing responses to customers are important, so the contact center has influenced the enterprise. Therefore, we developed an evaluation model not only in terms of customer service, but also from information system and business aspects, using the AHP and verifying the evaluation model through empirical cases. In particular, content analysis was used to ensure objectivity of AHP evaluation items.

• Journal of Nutrition and Health
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• v.38 no.9
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• pp.765-776
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• 2005

The purposes of this study were to a) examine IC (intellectual capital) circumstance of CFMC (contract foodservice management company), b) identify the correlation between IC of CFMC, c) analyze the cause and effect of IC in the viewpoint of contractor, client, and customer. The questionnaires of IC measurement were handed out to 108 CfHCs, there composing of main office employees, foodservice managers, customers, and clients of 207 school, 38 hospital, and 86 business/industry foodservices. The statistical data analysis was completed using SPSS Win (ver 12.0) for descriptive analysis, correlation analysis, simple linear regression analysis. First, CFMCs had operational experience for an average of 8 years and 8 months, and served an average of 38,540 meals a day. Most of the respondent companies operated 'food supply/distribution($50\%$)', 'catering ($46.7\%$)', and restaurant business ($43.3\%$)' except for institutional foodservice and managed an average of 66 clients for the contract period of 2 years and 3 months. Second, there was positive correlation between $\ulcorner$sales of foodservice$\lrcorner$ and 'market ability', $\ulcorner$client satisfaction$\lrcorner$ and necessary intellectual capital for managing branch/chain foodservice office, and $\ulcorner$customer satisfaction$\lrcorner$ and $\ulcorner$renewal and development$\lrcorner$, 'market ability', 'infrastructure support for foodservice operation', 'employee satisfaction', respectively. Finally, the result of the cause and effect analysis on CFMCs, clients, and customers was positively influenced by 'client satisfaction' with 'customer satisfaction', 'infrastructure support for foodservice operation' and 'customer satisfaction' with 'employee satisfaction', and 'infrastructure support for foodservice operation'. In conclusion, if CFMCs were to perform a routine checkups by utilizing CFMC's IC measuring tool, improvements in CFMC operational capacities as well as foodservice quality can be noted. Additionally, CFMCS can satisfy their client-customer relationship by employing internal marketing thechniques for employee, a more efficient infrastructure support system, and construc tive infrastructure utilization. Therefore, CFMCs can show significant improvement in their sales and foodservice quali-ty though continuous maintenance of the client and customer satisfaction.

• The Journal of the Korea Contents Association
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• v.12 no.12
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• pp.372-384
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• 2012

CEO's business network is important to the establishment and growth of companies and it is recognized as an essential element in regional development, but the related research and studies including surveys on the characteristics and performance of the CEO's network and its activities are lacking. This study aimed at companies located in the northwestern part of the Chungnam to survey about CEO's network activities, and research about the characteristics and performance expectancy of the network was carried out. As research methods, we discussed the previous studies, designed and analyzed the research models empirically using the survey. Analysis based 3 stages approach showed that the performance expectancy on human resource such as recruit was not affected by any factors overall. CEO's satisfaction affect significantly to the both of performance expectations of finance and general management by types of the network and its differentiated program. Executive activities, # of joining network, and period of activity affected also conditionally. This study have contributions that enable businessmen can take advantage of strategic use on the region's business network activity.

• Information Systems Review
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• v.25 no.1
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• pp.233-255
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• 2023

The development of information technology(IT) has brought so-called "online experience" to satisfy our daily needs. The market for online experiences grew more during the COVID-19 pandemic. Therefore, this study attempted to analyze how the features of online experience services affect customer satisfaction by crawling structured and unstructured data from the online experience web site newly launched by Airbnb after COVID-19. As a result of the analysis, it was found that the structured data generated by service users on a C2C online sharing platform had a positive effect on the satisfaction of other users. In addition, unstructured text data such as experience introductions and host introductions generated by service providers turned out to have different subjectivity scores depending on the purpose of its text. It was confirmed that the subjective host introduction and the objective experience introduction affect customer satisfaction positively. The results of this study are to provide various implications to stakeholders of the online sharing economy platform and researchers interested in online experience knowledge management.

• Journal of Nutrition and Health
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• v.43 no.2
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• pp.171-180
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• 2010

This study aims at investigating the health and nutritional status of the adults according to frequency of eating out. One day food intake data were collected by 24 hr recall dietary survey and body size, blood pressure and some blood lipids and other constituents were measured on 137 Daejeon metropolitan citizens 20 yrs old and above who visited Chungnam National University Hospital for physical examination during the period of December 6, to December 15, 2008. The frequency of eating out were categorized into four levels: less than once a week, once a week, 2-3 times a week, 4 times a week and above. Body mass Index, waist circumference, blood pressure, blood lipid, blood glucose, GPT and GOT did not showed any significant difference according to the frequency of eating out of the subjects. Though, systolic blood pressures and serum levels of total cholesterol and LDL-cholesterol showed a little tendency to be high in the subjects eating out 2-3 times a week. In the contrary, serum triglyceride level tended to be low in the same group. The subjects eating out 4 times a week and over took more total protein, animal protein, animal fat, phosphorus and vitamin $B_2$ than any other group. Also protein energy ratio was the highest in the group eating out 4 times a week and above and they took more animal food group, other food group, beverages teas and alcohols than other groups eating out. These results showed that higher frequency of eating out leads to higher intake of protein, fat, phosphorus, animal food groups and other food groups (oils, beverages, seasonings) and to lower intake of vitamin C and plant food groups. It, thus, suggested that the strategy for restaurant business is required to provide the menu substituted animal food by more plant food.

• Journal of Intelligence and Information Systems
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• v.16 no.4
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• pp.67-84
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• 2010

In terms of business, forecasting is a work of what is expected to happen in the future to make managerial decisions and plans. Therefore, the accurate forecasting is very important for major managerial decision making and is the basis for making various strategies of business. But it is very difficult to make an unbiased and consistent estimate because of uncertainty and complexity in the future business environment. That is why we should use scientific forecasting model to support business decision making, and make an effort to minimize the model's forecasting error which is difference between observation and estimator. Nevertheless, minimizing the error is not an easy task. Case-based reasoning is a problem solving method that utilizes the past similar case to solve the current problem. To build the successful case-based reasoning models, retrieving the case not only the most similar case but also the most relevant case is very important. To retrieve the similar and relevant case from past cases, the measurement of similarities between cases is an important key factor. Especially, if the cases contain symbolic data, it is more difficult to measure the distances. The purpose of this study is to improve the forecasting accuracy of case-based reasoning approach using fuzzy relation and composition. Especially, two methods are adopted to measure the similarity between cases containing symbolic data. One is to deduct the similarity matrix following binary logic(the judgment of sameness between two symbolic data), the other is to deduct the similarity matrix following fuzzy relation and composition. This study is conducted in the following order; data gathering and preprocessing, model building and analysis, validation analysis, conclusion. First, in the progress of data gathering and preprocessing we collect data set including categorical dependent variables. Also, the data set gathered is cross-section data and independent variables of the data set include several qualitative variables expressed symbolic data. The research data consists of many financial ratios and the corresponding bond ratings of Korean companies. The ratings we employ in this study cover all bonds rated by one of the bond rating agencies in Korea. Our total sample includes 1,816 companies whose commercial papers have been rated in the period 1997~2000. Credit grades are defined as outputs and classified into 5 rating categories(A1, A2, A3, B, C) according to credit levels. Second, in the progress of model building and analysis we deduct the similarity matrix following binary logic and fuzzy composition to measure the similarity between cases containing symbolic data. In this process, the used types of fuzzy composition are max-min, max-product, max-average. And then, the analysis is carried out by case-based reasoning approach with the deducted similarity matrix. Third, in the progress of validation analysis we verify the validation of model through McNemar test based on hit ratio. Finally, we draw a conclusion from the study. As a result, the similarity measuring method using fuzzy relation and composition shows good forecasting performance compared to the similarity measuring method using binary logic for similarity measurement between two symbolic data. But the results of the analysis are not statistically significant in forecasting performance among the types of fuzzy composition. The contributions of this study are as follows. We propose another methodology that fuzzy relation and fuzzy composition could be applied for the similarity measurement between two symbolic data. That is the most important factor to build case-based reasoning model.

• Journal of Global Scholars of Marketing Science
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• v.20 no.2
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• pp.135-148
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• 2010

Arab boycotts of Danish products, Australian boycotts of French products and Chinese consumer aversion toward Japanese products are all examples of how adverse actions at the country level might impact consumers' behavior. The animosity literature has examined how consumers react to the adverse actions of other countries, and how such animosity impacts consumers' attitudes and preferences for products from the transgressing country. For example, Chinese consumers are less likely to buy Japanese products because of Japanese atrocities during World War II and the unjust economic dealings of the Japanese (Klein, Ettenson and Morris 1998). The marketing literature, however, has not examined how consumers react to adverse actions committed by their own country against other countries, and whether such actions affect their attitudes towards purchasing products that originated from the adversely affected country. The social psychology literature argues that consumers will experience a feeling called collective guilt, in response to such adverse actions. Collective guilt stems from the distress experienced by group members when they accept that their group is responsible for actions that have harmed another group (Branscombe, Slugoski, and Kappenn 2004). Examples include Americans feeling guilty about the atrocities committed by the U.S. military at Abu Ghraib prison (Iyer, Schamder and Lickel 2007), and the Dutch about their occupation of Indonesia in the past (Doosje et al. 1998). The primary aim of this study is to examine consumers' perceptions of adverse actions by members of one's own country against another country and whether such perceptions affected their attitudes towards products originating from the country transgressed against. More specifically, one objective of this study is to examine the perceptual antecedents of collective guilt, an emotional reaction to adverse actions performed by members of one's country against another country. Another objective is to examine the impact of collective guilt on consumers' perceptions of, and preference for, products originating from the country transgressed against by the consumers' own country. If collective guilt emerges as a significant predictor, companies originating from countries that have been transgressed against might be able to capitalize on such unfortunate events. This research utilizes the animosity model introduced by Klein, Ettenson and Morris (1998) and later expanded on by Klein (2002). Klein finds that U.S. consumers harbor animosity toward the Japanese. This animosity is experienced in response to events that occurred during World War II (i.e., the bombing of Pearl Harbor) and more recently the perceived economic threat from Japan. Thus this study argues that the events of Word War II (i.e., bombing of Hiroshima and Nagasaki) might lead U.S. consumers to experience collective guilt. A series of three hypotheses were introduced. The first hypothesis deals with the antecedents of collective guilt. Previous research argues that collective guilt is experienced when consumers perceive that the harm following a transgression is illegitimate and that the country from which the transgressors originate should be responsible for the adverse actions. (Wohl, Branscombe, and Klar 2006). Therefore the following hypothesis was offered: H1a. Higher levels of perceived illegitimacy for the harm committed will result in higher levels of collective guilt. H1b. Higher levels of responsibility will be positively associated with higher levels of collective guilt. The second and third hypotheses deal with the impact of collective guilt on the preferences for Japanese products. Klein (2002) found that higher levels of animosity toward Japan resulted in a lower preference for a Japanese product relative to a South Korean product but not a lower preference for a Japanese product relative to a U.S. product. These results therefore indicate that the experience of collective guilt will lead to a higher preference for a Japanese product if consumers are contemplating a choice that inv olves a decision to buy Japanese versus South Korean product but not if the choice involves a decision to buy a Japanese versus a U.S. product. H2. Collective guilt will be positively related to the preference for a Japanese product over a South Korean product, but will not be related to the preference for a Japanese product over a U.S. product. H3. Collective guilt will be positively related to the preference for a Japanese product over a South Korean product, holding constant product judgments and animosity. An experiment was conducted to test the hypotheses. The illegitimacy of the harm and responsibility were manipulated by exposing respondents to a description of adverse events occurring during World War II. Data were collected using an online consumer panel in the United States. Subjects were randomly assigned to either the low levels of responsibility and illegitimacy condition (n=259) or the high levels of responsibility and illigitemacy (n=268) condition. Latent Variable Structural Equation Modeling (LVSEM) was used to test the hypothesized relationships. The first hypothesis is supported as both the illegitimacy of the harm and responsibility assigned to the Americans for the harm committed against the Japanese during WWII have a positive impact on collective guilt. The second hypothesis is also supported as collective guilt is positively related to preference for a Japanese product over a South Korean product but is not related to preference for a Japanese product over a U.S. product. Finally there is support for the third hypothesis, since collective guilt is positively related to the preference for a Japanese product over a South Korean product while controlling for the effect of product judgments about Japanese products and animosity. The results of these studies lead to several conclusions. First, the illegitimacy of harm and responsibility can be manipulated and that they are antecedents of collective guilt. Second, collective guilt has an impact on a consumers' decision when they face a choice set that includes a product from the country that was the target of the adverse action and a product from another foreign country. This impact however disappears from a consumers' decision when they face a choice set that includes a product from the country that was the target of the adverse action and a domestic product. This result suggests that collective guilt might be a viable factor for company originating from the country transgressed against if its competitors are foreign but not if they are local.

• Journal of Internet of Things and Convergence
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• v.7 no.4
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• pp.59-68
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• 2021

The purpose of this study is to develop a personality education tea culture therapy program to effectively improve juvenile delinquency prevention and social adaptation. In order to verify the effectiveness of the tea culture therapy program through social intervention, we analyzed whether there were significant differences in the scores of social characteristics such as self-efficacy, self-control, and interpersonal relationships of juvenile detention students before and after participating in the program. Ten juvenile detention students between the ages of 14 and 17 who were accommodated to a juvenile detention center in B city were selected as the experimental group for the study. The tea culture therapy program was conducted 10 times as a social intervention personality education for juvenile delinquents, and as a result, self-efficacy was found to have a pre-mean of 2.37 (SD 0.33) and a post-mean of 2.49 (SD 0.31), showing a significant difference (Z=-5.874. P=.000), self-control showed a significant difference with the pre-mean 2.06 (SD 0.20) and the post-mean of 2.16 (SD 0.19) (Z=-4.743, P=.001). The interpersonal relationship was found to have a significant difference, with a pre-mean of 1.90 (SD 0.32) and a post-mean of 2.15 (SD 0.21) (Z=-5.892, P=.000). The above results show that this program has a significant effect on improving social characteristics such as self-efficacy, self-control, and interpersonal relationships among juvenile detention students. Therefore, the tea culture therapy program developed in this study for personality education for juveniles can be used as a personality education program for various types of adolescents as well as in the field of correctional welfare in the future.

• Journal of Intelligence and Information Systems
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• v.24 no.1
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• pp.167-181
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• 2018

Over the past decade, deep learning has been in spotlight among various machine learning algorithms. In particular, CNN(Convolutional Neural Network), which is known as the effective solution for recognizing and classifying images or voices, has been popularly applied to classification and prediction problems. In this study, we investigate the way to apply CNN in business problem solving. Specifically, this study propose to apply CNN to stock market prediction, one of the most challenging tasks in the machine learning research. As mentioned, CNN has strength in interpreting images. Thus, the model proposed in this study adopts CNN as the binary classifier that predicts stock market direction (upward or downward) by using time series graphs as its inputs. That is, our proposal is to build a machine learning algorithm that mimics an experts called 'technical analysts' who examine the graph of past price movement, and predict future financial price movements. Our proposed model named 'CNN-FG(Convolutional Neural Network using Fluctuation Graph)' consists of five steps. In the first step, it divides the dataset into the intervals of 5 days. And then, it creates time series graphs for the divided dataset in step 2. The size of the image in which the graph is drawn is $40(pixels){\times}40(pixels)$, and the graph of each independent variable was drawn using different colors. In step 3, the model converts the images into the matrices. Each image is converted into the combination of three matrices in order to express the value of the color using R(red), G(green), and B(blue) scale. In the next step, it splits the dataset of the graph images into training and validation datasets. We used 80% of the total dataset as the training dataset, and the remaining 20% as the validation dataset. And then, CNN classifiers are trained using the images of training dataset in the final step. Regarding the parameters of CNN-FG, we adopted two convolution filters ($5{\times}5{\times}6$ and $5{\times}5{\times}9$) in the convolution layer. In the pooling layer, $2{\times}2$ max pooling filter was used. The numbers of the nodes in two hidden layers were set to, respectively, 900 and 32, and the number of the nodes in the output layer was set to 2(one is for the prediction of upward trend, and the other one is for downward trend). Activation functions for the convolution layer and the hidden layer were set to ReLU(Rectified Linear Unit), and one for the output layer set to Softmax function. To validate our model - CNN-FG, we applied it to the prediction of KOSPI200 for 2,026 days in eight years (from 2009 to 2016). To match the proportions of the two groups in the independent variable (i.e. tomorrow's stock market movement), we selected 1,950 samples by applying random sampling. Finally, we built the training dataset using 80% of the total dataset (1,560 samples), and the validation dataset using 20% (390 samples). The dependent variables of the experimental dataset included twelve technical indicators popularly been used in the previous studies. They include Stochastic %K, Stochastic %D, Momentum, ROC(rate of change), LW %R(Larry William's %R), A/D oscillator(accumulation/distribution oscillator), OSCP(price oscillator), CCI(commodity channel index), and so on. To confirm the superiority of CNN-FG, we compared its prediction accuracy with the ones of other classification models. Experimental results showed that CNN-FG outperforms LOGIT(logistic regression), ANN(artificial neural network), and SVM(support vector machine) with the statistical significance. These empirical results imply that converting time series business data into graphs and building CNN-based classification models using these graphs can be effective from the perspective of prediction accuracy. Thus, this paper sheds a light on how to apply deep learning techniques to the domain of business problem solving.