Organizational Data Mining

Successfully competing in the new global economy requires immediate decision capability. This immediate decision capability requires quick analysis of both timely and relevant data. To support this analysis, organizations are piling up mountains of business data in their databases every day. Terabyte-sized (1,000 megabytes) databases are commonplace in organizations today, and this enormous growth will make petabyte-sized databases (1,000 terabytes) a reality within the next few years (Whiting, 2002). Those organizations making swift, fact-based decisions by optimally leveraging their data resources will outperform those organizations that do not. A technology that facilitates this process of optimal decision-making is known as Organizational Data Mining (ODM). Organizational Data Mining: Leveraging Enterprise Data Resources for Optimal Performance demonstrates how organizations can leverage ODM for enhanced competitiveness and optimal performance.

Preface

viii

Hamid R. Nemati, University of North Carolina at Greensboro, USA

Christopher D. Barko, University of North Carolina at Greensboro, USA

SECTION I: STRATEGIC IMPLICATIONS OF ODM

Chapter I. Organizational Data Mining (ODM): An Introduction

1

(8)

Hamid R. Nemati, University of North Carolina at Greensboro, USA

Christopher D. Barko, University of North Carolina at Greensboro, USA

Chapter II. Multinational Corporate Sustainability: A Content Analysis Approach

9

(16)

Riad A. Ajami, University of North Carolina at Greensboro, USA

Marca Marie Bear, University of Tampa, USA

Hanne Norreklit, Aarhus School of Business, Denmark

Chapter III. A Porter Framework for Understanding the Strategic Potential of Data Mining for the Australian Banking Industry

25

(21)

Kate A. Smith, Monash University, Australia

Mark S. Dale, Monash University, Australia

Chapter IV. The Role of Data Mining in Organizational Cognition

46

(15)

Chandra S. Amaravadi, Western Illinois University, USA

Farhad Daneshgar, University of New South Wales, Australia

Chapter V. Privacy Implications of Organizational Data Mining

61

(18)

Hamid R. Nemati, University of North Carolina at Greensboro, USA

Charmion Brathwaite, University of North Carolina at Greensboro, USA

Kara Harrington, University of North Carolina at Greensboro, USA

SECTION II: BUSINESS PROCESS INNOVATIONS THROUGH ODM

Chapter VI. Knowledge Exchange in Organizations is a Potential, Not a Given: Methodologies for Assessment and Management of a Knowledge-Sharing Culture

79

(13)

Richard E. Potter, University of Illinois at Chicago, USA

Pierre A. Balthazard, Arizona State University West, USA

Chapter VII. Organic Knowledge Management for Web-Based Customer Service

92

(17)

Stephen D. Durbin, RightNow Technologies, USA

Doug Warner, RightNow Technologies, USA

J. Neal Richter, RightNow Technologies, USA

Zuzana Gedeon, RightNow Technologies, USA

Chapter VIII. A Data Mining Approach to Formulating a Successful Purchasing Negotiation Strategy

109

(16)

Hokey Min, University of Louisville-Shelby, USA

Ahmed Emam, Western Kentucky University, USA

Chapter IX. Mining Meaning: Extracting Value from Virtual Discussions

125

(16)

William L. Tullar, University of North Carolina at Greensboro, USA

SECTION III: ODM ANALYTICS AND ALGORITHMS

Chapter X. An Intelligent Support System Integrating Data Mining and Online Analytical Processing

141

(10)

Rahul Singh, University of North Carolina at Greensboro, USA

Richard T. Redmond, Virginia Commonwealth University, USA

Victoria Yoon, University of Maryland Baltimore County, USA

Chapter XI. Knowledge Mining in DSS Model Analysis

151

David M. Steiger, University of Maine, USA

Natalie M. Steiger, University of Maine, USA

Chapter XII. Empowering Modern Managers: Towards an Agent-Based Decision Support System

110

(78)

Rustam Vahidov, Concordia University, Canada

Chapter XIII. Mining Message Board Content on the World Wide Web for Organizational Information

188

(14)

Cheryl Aasheim, Georgia Southern University, USA

Gary J. Koehler, University of Florida, USA

SECTION IV: INDUSTRIAL ODM APPLICATIONS

Chapter XIV. Data Warehousing: The 3M Experience

202

(15)

Hugh J. Watson, University of Georgia, USA

Barbara H. Wixom, University of Virginia, USA

Dale L. Goodhue, University of Georgia, USA

Chapter XV. Data Mining in Franchise Organizations

217

(13)

Ye-Sho Chen, Louisiana State University, USA

Robert Justis, Louisiana State University, USA

P. Pete Chong, University of Houston-Downtown, USA

Chapter XVI. The Use of Fuzzy Logic and Expert Reasoning for Knowledge Management and Discovery of Financial Reporting Fraud

230

(11)

Mary Jane Lenard, University of North Carolina at Greensboro, USA

Pervaiz Alam, Kent State University, USA

Chapter XVII. Gaining Strategic Advantage Through Bibliomining: Data Mining for Management Decisions in Corporate, Special, Digital, and Traditional Libraries

241

(22)

Scott Nicholson, Syracuse University, USA

Jeffrey Stanton, Syracuse University, USA

Chapter XVIII. Translating Advances in Data Mining to Business Operations: The Art of Data Mining in Retailing

263

(17)

Henry Dillon, Independent Consultant, UK

Beverley Hope, Victoria University of Wellington, New Zealand

SECTION V: ODM CHALLENGES AND OPPORTUNITIES

Chapter XIX. Impediments to Exploratory Data Mining Success

280

(20)

Jeff Zeanah, Z Solutions, Inc., USA

Chapter XX. Towards Constructionist Organizational Data Mining (ODM): Changing the Focus from Technology to Social Construction of Knowledge

300

(21)

Isabel Ramos, Universidade do Minho, Portugal

João Alvaro Carvalho, Universidade do Minho, Portugal

Chapter XXI. E-Commerce and Data Mining: Integration Issues and Challenges

321

(13)

Parviz Partow-Navid, California State University, Los Angeles, USA

Ludwig Slusky, California State University, Los Angeles, USA

Chapter XXII. A Framework for Organizational Data Analysis and Organizational Data Mining

334

(17)

Bernd Knobloch, University of Bamberg, Germany

About the Authors

351

(10)

Index

361

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Organizational Data Mining

Summary

Table of Contents

Supplemental Materials