Neural Networks in Organizational Research: Applying Pattern Recognition to the Analysis of Organizational Behavior

Pages: 187
Item #: 4316077
ISBN: 978-1-59147-415-9
List Price: $19.95
Member/Affiliate Price: $19.95
Copyright: 2006
Format: Hardcover
Availability: In Stock
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Overview

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While the term neural networks may be unfamiliar to many organizational psychologists, exciting new applications of artificial intelligence are attracting notice among organizational behavior researchers. In Neural Networks in Organizational Research: Applying Pattern Recognition to the Analysis of Organizational Behavior, authors David Scarborough and Mark Somers bring researchers, academics, and practitioners up to speed on this emerging field, in which powerful computing capabilities offer new insights into longstanding, complex I/O questions such as employee selection and behavioral prediction.

Neural networks mimic the way the human brain works, using interconnected nodes and feedback loops to “learn” to recognize even subtle patterns in vast amounts of data. They can process data far more quickly and efficiently than conventional techniques can, and produce better empirical results. They are especially useful for modeling nonlinear processes. The book traces the development of this methodology and demonstrates how it opens up new ways of thinking about traditional problems. Academic researchers will gain a design template for studying both the linear and non-linear elements of a given problem, and thus enhance their own research.

Table of Contents

Foreword
G. David Garson

Acknowledgments

Introduction

I. Orientation

  1. Neural Networks in Organizational Research
  2. Science, Organizational Research, and Neural Networks
  3. Neural Network Theory, History, and Concepts
  4. Neural Networks as a Theory Development Tool
  5. Using Neural Networks in Organizational Research

II. Applications

  1. Statistics, Neural Networks, and Behavioral Research
  2. Using Neural Networks in Employee Selection
  3. Using Self-Organizing Maps to Study Organizational Commitment

III. Implications

  1. Limitations and Myths
  2. Trends and Future Directions

Appendix: Backpropagation Algorithm

Glossary

References

Index

About the Authors

Author Bios

David Scarborough holds an MBA and a PhD in human resources from the University of North Texas in Denton. Currently, he is chief scientist at Unicru, Inc., a provider of talent management solutions based in Beaverton, Oregon. Dr. Scarborough and his team wrote Unicru’s patents and prepared the patent applications for the first commercial use of neural network predictive modeling for employee selection decision support. Prior to joining Unicru, Dr. Scarborough held consulting and research positions with SHL USA, Batrus Hollweg Ph.D.s, Inc., and American Airlines. He is a member of the American Psychological Association, the Academy of Management, the International Neural Networks Society, the Society for Industrial and Organizational Psychology, and the Society for Human Resource Management.

Mark John Somers, PhD, MBA, is a professor of management at New Jersey Institute of Technology (NJIT) and a member of the doctoral faculty in management at Rutgers University. Dr. Somers holds a BS from Tulane University in New Orleans, Louisiana; an MBA from Baruch College in New York, New York; and a PhD in business with a specialization in organizational behavior from the City University of New York, New York. He joined academia from research groups at IBM, DDB Advertising, and Citibank and served as the dean of the NJIT School of Management from 2000 to 2005. Dr. Somers’s research interests are in the micro aspects of organizational behavior, including work attitudes, organizational commitment, job performance, employee turnover, and employee socialization. His interest in neural networks stems from the desire to look at old problems in new ways, with an emphasis on nonlinear thinking and methods. Dr. Somers is currently interested in the application of complexity theory to human behavior in organizations.

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