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An increasing number of investors have begun using financial data to develop optimal investment portfolios; therefore, the public financial data shared in the capital market plays a critical role in credit ratings. These data enable investors to understand the credit levels of debtors from a bank perspective; this facilitates predicting the debtor default rate to efficiently evaluate investment risks. The paper aims to discuss these issues.

A credit rating model can be developed to reduce the risk of adverse selection and moral hazard caused by information asymmetry in the loan market. In this study, a random forest (RF) was used to evaluate financial variables and construct credit rating prediction models. Data-mining techniques, including an RF, decision tree, neural networks, and support vector machine, were used to search for suitable credit rating forecasting methods. The distance to default from the KMV model was then incorporated into the credit rating model as a research variable to increase predictive power of various data-mining techniques. In addition, four-level and nine-level classification were set to investigate the accuracy rates of various models.

The experimental results indicated that applying the RF in the variable feature selection process and developing a forecasting model was the most effective method of predicting credit ratings; the four-level and nine-level feature-selection settings achieved 95.5 and 87.8 percent accuracy rates, respectively, indicating that RF demonstrated outstanding feature selection and forecasting capacity.

The experimental cases were based on financial data from public companies in North America.

Practical implication of this study indicates the most effective financial variables were dividends common/ordinary, cash dividends, volatility assumption, and risk-free rate assumption.

The RF model can be used to perform feature selection and efficiently filter numerous financial variables to obtain crediting rating information instantly.

Credit ratings have become one of the primary references for financial institutions to assess credit risk. Conventional credit rating approaches mainly concentrated on two-class classification (i.e. good or bad credit), which lacks adequate precision to perform credit risk evaluations in practice. In addition, most of previous researches directly focussed on employing various data mining techniques, but rare studies discussed the influence of data preprocessing before classifier construction. The paper aims to discuss these issues.

This study considers nine-class classification (i.e. nine credit risk level) to credit rating prediction. For the development of more accurate classifiers, the paper adopts two-stage analysis, which integrates multiple data preprocessing and supervised learning techniques. Specifically, the first stage applies feature selection, data clustering, and data resampling methods to preprocess the data, and then the second stage utilizes several classification techniques and classifier ensembles to construct prediction models.

The results show that Bagging-DT with data resampling method achieves excellent accuracy (82.96 percent), indicating that the proposed two-stage prediction model is better than conventional one-stage models.

Practical implication of this study can lower credit rating expenses and also allow corporations to gain credit rating information instantly.

The research framework of this study is based on tri-component attitude model (cognition-affect-conation) which explores consumers’ positive or negative emotions, as well as various types of thoughts and actions, triggered by their perceived justice in the context of service failure. This study aims to probe the possible mediating and moderating effects caused by the process where consumers form their thoughts and actions.

This study conducts a survey to consumers after restaurant dining. This study collects data from 262 respondents and analyzes the data with the structural equation modeling.

The results indicate that perceived justice has significant effect on empathy, anger, positive word-of-mouth, repurchase intention and revenge. Empathy has a significant and positive effect on positive word-of-mouth. Anger has significant and positive effects on revenge and avoidance. Empathy is a mediator between perceived justice and positive word-of-mouth. Blame attribution and service failure severity are the moderators in the relationship between perceived justice and empathy/anger.

Consumers might have experienced the scenarios described in the questionnaire and their responses might be based on recall of their previous dining experiences in other restaurants, thereby resulting in a time lapse problem and affecting the conclusions of this study.

It is not adequate to gain consumers’ choices just demonstrate favorable customer perceived justice and empathy in today’s industrial highly competitiveness because blame attribution and perception of service failure severity result in different positive and negative emotions and behavioral intentions. Therefore, food and beverage industry must have a various recovery approaches to recover service failure and create a more appealing relationship with consumers.

This study investigates the relationships among perceived justice, emotions and behavioral intentions which are seldom discussed in the past studies. In addition, this study investigates the mediating effect of empathy in the relationship between perceived justice and positive word-of-mouth. The results of this study indicate that blame attribution and service failure severity are the moderators between perceived justice and emotions (empathy/anger). The mediator of empathy and the moderators of blame attribution and service failure severity can enhance the research gap in the context of service recovery for the tri-component attitude model.