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Training, Warning, and Media Richness Effects on Computer-Mediated Deception and Its Detection Patricia Ann Tilley

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Florida State University Libraries Electronic Theses, Treatises and Dissertations The Graduate School 2005 Training, Warning, and Media Richness Effects on Computer-Mediated Deception and Its Detection Patricia Ann Tilley Follow this and additional works at the FSU Digital Library. For more information, please contact [email protected] THE FLORIDA STATE UNIVERSITY COLLEGE OF BUSINESS TRAINING, WARNING, AND MEDIA RICHNESS EFFECTS ON COMPUTER-MEDIATED DECEPTION AND ITS DETECTION By PATRICIA ANN TILLEY A Dissertation Submitted to the Department of Management Information Systems in partial fulfillment of the requirements for the degree of Doctor of Philosophy Degree Awarded: Summer Semester, 2005 The members of the Committee approve the dissertation of Patricia Ann Tilley defended on June 24, 2005. ____________________________________ Joey F. George Professor Directing Dissertation ____________________________________ Gerald R. Ferris Outside Committee Member ____________________________________ David B. Paradice Committee Member ____________________________________ Michael H. Dickey Committee Member ____________________________________ Pamela L. Perrewe Committee Member _________________________________________ E. Joe Nosari, Interim Dean, College of Business The Office of Graduate Studies has verified and approved the above named committee members. Dedicated to Rick for all his loving support. His help and understanding are gratefully appreciated. iii ACKNOWLEDGEMENTS There are several people I would like to thank for their time and assistance. First, I would like to thank all the members of my dissertation committee for their invaluable comments and guidance. I would especially like to thank my dissertation chair, Professor Joey George for his superb guidance and wisdom. I would also like to thank Gabe Giordano for his assistance in collecting data for my dissertation. He unselfishly contributed many hours to help with conducting the experiment at the same time that he was working on his own dissertation and teaching. His help was greatly appreciated. I would also like to thank Brian Keane for helping me with my data collection. Another person I would like to thank is Cate Serino for her support as a cohort in going through the doctoral program with me. I appreciate all the experiences and discussions we shared together. Finally, I would like to thank my son, Rick, who brightened my life and gave me encouragement. His love and friendship were instrumental in helping me through this process. I appreciate his contributions to making these years a rich experience. I would also like to thank my parents for their love and support, which helped me immensely in completing my research. iv TABLE OF CONTENTS List of Tables vii List of Figures vii Abstract ix 1. INTRODUCTION 1 2. LITERATURE REVIEW 5 Media Richness Theory 5 Social Presence Theory 8 Deception Literature and Theory 9 Interpersonal Deception Theory and Computer-Mediated Communication 18 Individual Differences in Social Skill and Political Skill 23 Media Richness Theory, Social Presence Theory, and Deception 25 Effects of Warning on Deception Detection 29 Effects of Training on Deception Detection 30 3. RESEARCH MODEL 34 Research Model of Training, Warning, and Media Richness on Deception Detection When Using Computer-Mediated Communication 34 Variable Descriptions, Hypothesis Development and Model Operationalizations 35 4. METHODOLOGY 40 Method Selection 40 Study Design 41 v The Matrix Design 43 5. RESULTS 53 Analysis of Control Variables 53 Session Duration, Lies, and Detection Accuracy 56 Training Manipulation Check 58 Tests of Hypotheses 59 6. DISCUSSION 62 Media Richness and Deception Detection Accuracy 63 Warning and Deception Detection Accuracy 64 Training and Deception Detection Accuracy 65 Interaction of Training with Warning and Deception Detection Accuracy 66 Summary 67 7. CONCLUSION 68 Summary of Findings 68 Strengths 71 Limitations 72 Implications for Future Academic Research 73 Management Implications 75 Summary 76 APPENDICES 78 REFERENCES 109 BIOGRAPHICAL SKETCH 119 vi LIST OF TABLES 2.1. Reliable deception cues from Zuckerman and Driver, 1985 13 2.2. Significant deception cues from DePaulo et al. (2003) 15 2.3. Reliable deceptive indicators across media from Rao & Lim (2000) 26 2.4. Significant deceptive indicators from DePaulo et al. (2003) across media 27 2.5. Training for deception cue recognition 32 3.1. Lean (e-mail) and rich (audio over Internet chat relay) media differences based on media richness theory 37 5.1. Descriptive Statistics for Control Variables 54 5.2. Reliability Statistics for Control Variables 54 5.3. Correlations 55 5.4. ANOVA for Medium, Warning, Training, and Lies 57 5.5. Descriptive Statistics of Lies for Medium, Warning, and Training 57 5.6. Detection Accuracy Rates 58 5.7. Descriptive Statistics of the Training Pre-test and Post-test 59 5.8. ANCOVA results for Medium, Warning, Training, and Receiver Motivation on Deception Detection Accuracy 60 5.9. Descriptive Statistics for Medium, Warning, and Training on Deception Detection Accuracy 61 6.1. Summary of Findings 63 vii LIST OF FIGURES 2.1. Interpersonal Deception Theory 17 2.2. A Model of Deceptive Communication and Its Detection from Carlson, George, Burgoon, Adkins & White (2004) 19 2.3. The Deceptive Communication Event from Carlson, George, Burgoon, Adkins & White (2004) 22 3.1. Training, Warning, and Media Richness on Deception Detection Accuracy 35 4.1. Laboratory Experiment Model 44 viii ABSTRACT Although deception research in the communication field has a long history, it is a relatively new topic of research in management information systems. Deception detection research has expanded to include lies transmitted via computer-mediated communication. Recent studies have only begun to look at the influence of media richness, training, and warning on deception detection accuracy. Studies on the effect of training on deception cue recognition with cross-media comparison are scarce. In addition, few studies have been conducted on the effects of training with warning on deception detection. This study examines the effects of media richness, training, warning, and the combination of training and warning on deception detection accuracy. To test the hypotheses, a laboratory experiment, in which deceivers were interviewed based upon deceptive information in their enhanced resumes, was conducted. Results of the study indicate that training in deceptive cue recognition improves deception detection success. ix CHAPTER 1 INTRODUCTION Deception has a long history in the human experience, from the earliest mythical religious stories of the fallen Archangel lying to Adam and Eve about what would happen to them if they took a bite out of an apple to today’s deceptions surrounding terrorism and the buildup of weapons of mass destruction. Various communication mass media transmit stories of corruption and lies in business and everyday life. It is hard to view television or read a newspaper without learning of someone who created harm through distorting the truth. People have been interested in learning about deception and its detection for many years (Trovillo 1939). Various topics related to deception have been studied, including deception in business practices. Employees’ personal beliefs and attitudes toward lying and the organizational ethical climate are some factors that can influence the prevalence of lies in business (Leonard et al. 2001). Also, people tell more lies when they want to appear likeable or competent, both important aspects for success in business (Feldman et al. 2002). Since deception in business tends to hurt productivity and profitability (Prater et al. 2002), any insights that researchers obtain from the study of deception may have beneficial consequences. For example, auditors can develop a set of heuristics to help them detect financial fraud (Johnson et al. 2001). One area essential to business success is the ability of businesses to hire the most qualified applicants. But research has shown that from 25% to 67% of applicants lie on their resumes and defend these lies in job interviews (Prater et al. 2002). This can be an enormous problem when firms are looking to hire the most qualified applicants (Snell et al. 1999). Detection of deceptive information on resumes is important to improve the interviewing and hiring processes. False information on resumes tends to pertain to the most job relevant items (Becker et al. 1992). For this to be true, applicants must be aware that many companies do not conduct extensive background checks. It is often common knowledge that to compete for jobs, applicants need to present themselves in a positive 1 light. Often, as seen from the above statistics about lies on resumes, applicants cross the line between presenting a positive image and presenting a deceptive image. One example of the perils of real life resume deception is that of Notre Dame football coach George O’Leary who fabricated information on his resume. He said that he had been a letterman and had a football career at the University of New Hampshire, and held a masters’ degree from New York University. After Notre Dame found out about these fabrications, he resigned after only five days on the job (Hughes et al. 2003). Although the above example was a high-profile case, other cases of deception occur that can affect many people and organizations. Hi-technology applicants have more resume fraud than applicants in other industries (Prater et al. 2002). Since technology
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