Suchergebnisse

The case focuses on events surrounding Ernst & Young's tax shelter advice to senior executives of Sprint. The principal ethical lens used for analysis is Integrative Social Contracts Theory (ISCT). We believe that this is the first instance of ISCT applied specifically to a situation in accounting and to an ethical problem in a nonglobal context. Additionally, it is one of a small number of tax-oriented cases focusing on ethics. ISCT offers several benefits over the traditional models applied in ethical analysis (Mintz 1997). ISCT integrates implicit and explicit contracts among affected business communities that enable a detailed normative assessment of rich ethical problems in a realistic economic world. The use of ISCT in this particular case provides students, instructors, and practitioners with a model to aid in: (1) understanding the normative justification for business decisions, and (2) assessing the quality of ethical decision making in a complex business setting.

There is a lack of knowledge about which risk factors are more important in West Nile virus (WNV) transmission and risk magnitude. A better understanding of the risk factors is of great help in developing effective new technologies and appropriate prevention strategies for WNV infection. A contribution analysis of all risk factors in WNV infection would identify those major risk factors. Based on the identified major risk factors, measures to control WNV proliferation could be directed toward those significant risk factors, thus improving the effectiveness and efficiency in developing WNV control and prevention strategies. Neural networks have many generally accepted advantages over conventional analytical techniques, for instance, ability to automatically learn the relationship between the inputs and outputs from training data, powerful generalization ability, and capability of handling nonlinear interactions. In this article, a neural network model was developed for analysis of risk factors in WNV infection. To reveal the relative contribution of the input variables, the neural network was trained using an algorithm called structural learning with forgetting. During the learning, weak neural connections are forced to fade away while a skeletal network with strong connections emerges. The significant risk factors can be identified by analyzing this skeletal network. The proposed approach is tested with the dead bird surveillance data in Ontario, Canada. The results demonstrate the effectiveness of the proposed approach.