Chia-Huei Wu

Grey system theory and fuzzy time series forecasting for the growth of green electronic materials

The development of green materials is an important part of corporate social responsibility. Companies need to use resources legitimately and have environmental protection responsibility. Forecasting the growth trend of green copper clad laminate (CCL) material is crucial for manufacturers of printed circuit boards and green CCLs. Early and accurate understanding of such trends in this industry can lead to the early acquisition of opportunities for related markets and technological development. Because historical data samples associated with green CCL are small and typically lack a normal distribution, employing conventional regression analysis or time series models for forecasting is not suitable for testing-related presumptions. To address this issue, this study adopts grey system theory (GST) and a fuzzy time series (FTS) model as forecasting methods. The results show that effective forecasting can be achieved by applying either a GM (1,1) α model or heuristic FTS model to the data of a small and non-normally distributed sample.

Using Mahalanobis–Taguchi system, logistic regression, and neural network method to evaluate purchasing audit quality

The feature selection function in data mining facilitates the classification of vast data volumes and reduces attribute variables, enabling the construction of classification prediction models. For binary data, the Mahalanobis–Taguchi system, the logistic regression method, and the neural network method all feature high stability and accuracy. The Mahalanobis–Taguchi system differs from the other two methods in that models are developed through a measurement scale rather than from the learning of analytical data. We analyzed the audit quality of Taipei City Government public procurement from supply chains (hereafter abbreviated as public procurement) and applied the three feature selection methods to determine the items used in public procurement audit quality questionnaires. The results showed that the predictive powers of Mahalanobis–Taguchi system and logistic regression methods for the reduced question items were 93.8% and 92.5%, respectively. Furthermore, the prediction accuracy rate of the neural network method was 100%, showing that the prediction model constructed using the feature selections to reduce the number of attributes can effectively lower the number of questionnaire items while maintaining high prediction accuracy.

Evaluating the green corporate social responsibility of manufacturing corporations from a green industry law perspective

Corporate social responsibility (CSR) refers to corporations exhibiting ethical behaviours towards societal members. This responsibility should be demonstrated towards all stakeholders rather than only stockholders. Corporations should be responsible for the effects that their actions have on stakeholders by assuming a fair and responsible attitude and valuing sustainable development. Corporations that affect their stakeholders must assume their responsibilities by focusing on sustainable development and exhibiting a fair attitude; for example, providing staff members with a positive work environment, introducing green products, sustaining environmental quality, offering satisfactory after-sales services and maintaining positive interaction with the community. Relevant studies that have integrated green production (GP) with CSR evaluation were scarce in the past. Moreover, previous studies regarding standard criteria for CSR evaluation have involved a major shortcoming, in that a majority of these studies have assumed mutual independence and no influencing and causal relationships among CSR criteria. The present study focused on GP and employed a novel decision-making trial and evaluation laboratory method to evaluate and investigate green corporate social responsibility (GCSR) indicators. In addition, the degrees of influence and the causal relationships among GCSR indicators were explored.

A Duo-Theme DEMATEL approach for exploring the driving factors of online luxury goods sales: E-retailers’ perceptions

Abstract Factors affecting online luxury goods sales are complicated and intertwined. To stimulate online luxury goods sales, understanding of key influencing factors and causal relationships among the intertwined factors is of essential importance. To this end, this study first divides factors affecting online luxury goods sales into two distinct aspects: perceived benefits (PB) and perceived risks (PR), and then introduces a duo-theme Decision Making Trial and Evaluation Laboratory (DEMATEL) approach for the depiction of the driving factors that have the most effects on others in both aspects. An example of online luxury bags sales, from e-retailers’ perceptions, is presented. Results show that “wider selection” is the foremost driving factor, followed by “convenience,” “better price,” and “cost” in the PB aspect; whereas “trust and security” is the most influential driving factor, followed by “uncertainty” in the PR aspect. Accordingly, the luxury bags e-retailers are suggested to offer more information about the choices available, easier for ordering, and lower search and transaction costs through the Internet. In the meantime, they should also provide warranty of security for personal/financial information and product quality.

The use of importance-performance analysis (IPA) in evaluating bank services

The survey was based on SERVQUAL, a diagnostic tool developed in the 1980s, which measures service quality in terms of customer expectations and perceptions of service and integrates importance-performance analysis (IPA) which substitute for the original ones to produce a revised matrix. Beside that, we identifying critical service attributes to improve overall quality under resource-limiting conditions have become absolutely necessary. From the results of the study, managers can focus their efforts on providing quality service and facilities that customers perceive as being important in determining their view of the overall service quality of the bank service.

A Comparison Study on the Evaluation Criteria for Corporate Social Responsibility

All over the world the topic of corporate social responsibility (CSR) is becoming more and more important. Currently, there are nine different sets of CSR evaluation criteria being used worldwide. As each of the different evaluation methods vary from one another, it is difficult to compare the results from two different tests in order to evaluate the CSR scores of one company against another. This research selects four sets of CSR criteria, three which have international credibility, the SA8000, the GRI, and the ISO26000, as well as the Corporate Social Responsibility Best Practice Principles for TWSE/GTSM-Listed Companies (known as the TW Principles) released in 2010. In our research, we will review the current literature on the evaluation criteria to compare and contrast the different CSR evaluation criteria, present our results and make conclusions.

Measuring the library website service by integrating DEMATEL and DTPB model

The research used a library website as an individual case, and illustrated the benefits of the combination of DEMATEL and DTPB, so as to provide a reference for library administrators when promoting library website services. According to the analysis result of the combination of DEMATEL and DTPB, when DTPB variables were distributed within quadrants of high centrality and degree of causality, they became the key influential variables in planning library website services; when distributed within quadrants of low centrality and degree of causality, the variables needed urgent management. The newly added multinomial variable relationship of the re-established DTPB would depict factors that influence the intention of using the library website more clearly, when analyzing the use behavior of the university library website.