综合参考书目

M. Mahmood
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引用次数: 19

摘要

在最近几起引人注目的事件的刺激下,对商业道德的担忧在过去十年有所增加。作为回应,研究的重点是发展理论和实证框架来理解道德决策。迄今为止,实证研究大多采用传统的定量工具,如回归分析或多元判别分析(MDA)。需要更先进的工具。在这一探索性研究中,提出了一种对伦理决策情境进行分类、分类和分析的新方法。人工神经网络、MDA和chance的性能对比分析表明,人工神经网络在训练和测试阶段的预测效果都更好。虽然这种方法存在一些局限性,但在商业道德领域,这种网络作为传统分析工具(如MDA)的替代方案是有希望的。
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Comprehensive Bibliography
Stimulated by recent high-profile incidents, concerns about business ethics have increased over the last decade. In response, research has focused on developing theoretical and empirical frameworks to understand ethical decision making. So far, empirical studies have used traditional quantitative tools, such as regression or multiple discriminant analysis (MDA), in ethics research. More advanced tools are needed. In this exploratory research, a new approach to classifying, categorizing and analyzing ethical decision situations is presented. A comparative performance analysis of artificial neural networks, MDA and chance showed that artificial neural networks predict better in both training and testing phases. While some limitations of this approach were noted, in the field of business ethics, such networks are promising as an alternative to traditional analytic tools like MDA.
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