A New Approach to Evaluating Business Ethics: An Artificial Neural Networks Application

A New Approach to Evaluating Business Ethics: An Artificial Neural Networks Application

Mo Adam Mahmood, Gary L. Sullivan, Ray-Lin Tung
Copyright: © 1999 |Pages: 9
DOI: 10.4018/joeuc.1999070102
OnDemand:
(Individual Articles)
Available
$37.50
Current Special Offers
No Current Special Offers
TOTAL SAVINGS: $37.50

Abstract

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.

Complete Article List

Search this Journal:
Reset
Volume 36: 1 Issue (2024)
Volume 35: 3 Issues (2023)
Volume 34: 10 Issues (2022)
Volume 33: 6 Issues (2021)
Volume 32: 4 Issues (2020)
Volume 31: 4 Issues (2019)
Volume 30: 4 Issues (2018)
Volume 29: 4 Issues (2017)
Volume 28: 4 Issues (2016)
Volume 27: 4 Issues (2015)
Volume 26: 4 Issues (2014)
Volume 25: 4 Issues (2013)
Volume 24: 4 Issues (2012)
Volume 23: 4 Issues (2011)
Volume 22: 4 Issues (2010)
Volume 21: 4 Issues (2009)
Volume 20: 4 Issues (2008)
Volume 19: 4 Issues (2007)
Volume 18: 4 Issues (2006)
Volume 17: 4 Issues (2005)
Volume 16: 4 Issues (2004)
Volume 15: 4 Issues (2003)
Volume 14: 4 Issues (2002)
Volume 13: 4 Issues (2001)
Volume 12: 4 Issues (2000)
Volume 11: 4 Issues (1999)
Volume 10: 4 Issues (1998)
Volume 9: 4 Issues (1997)
Volume 8: 4 Issues (1996)
Volume 7: 4 Issues (1995)
Volume 6: 4 Issues (1994)
Volume 5: 4 Issues (1993)
Volume 4: 4 Issues (1992)
Volume 3: 4 Issues (1991)
Volume 2: 4 Issues (1990)
Volume 1: 3 Issues (1989)
View Complete Journal Contents Listing