Search results

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.

Research into the use/adoption of online news services (ONSs) is still in its infancy, Scholars have indicated that there is no comprehensive theoretical framework for understanding or predicting consumers' online adoption behavior. The purpose of the paper is to propose a theoretical framework as a foundation for better understanding and further analyzing the adoption of ONSs.

A literature review was conducted together with a series of in‐depth interviews with selected key industry experts. Three paradigms (i.e. the Diffusion of Innovation Theory, the Technology Acceptance Model, the Uses and Gratifications Theory) were examined and included findings from research into some aspects of online behaviour and these are discussed in relation to the objectives of this paper.

Six factors are identified as potential key drivers in the adoption of ONSs These are based on the findings from the literature review and the in‐depth interviews with the industry experts. The six factors are: Perceived Usefulness (PU), Perceived Core Service Quality (PCSQ), Perceived Supplementary Service Quality (PSSQ), Trust, Networking, Interface and Subjective Norm. A theoretical framework for better understanding and analyzing the adoption of ONSs is built that shows the relationship among these factors and adoption of ONSs.

For researchers, this paper provides a framework to identify and understand the way the potential key factors contribute to the adoption of online news services. For practitioners, this framework lists the features that specifically attract online news users. Understanding users' preferences is of major importance in e‐businesses for making strategic decisions to increase user satisfaction, as well as improving the performance of the business.