When Are Apologies Effective?
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1 When Are Apologies Effective? An Investigation of the Components that Increase an Apology’s Efficacy A dissertation presented by Krista M. Hill To The Department of Psychology In partial fulfillment of the requirements for the degree of Doctor of Philosophy in the field of Psychology Northeastern University Boston, Massachusetts May 10, 2013 2 WHEN ARE APOLOGIES EFFECTIVE? AN INVESTIGATION OF THE COMPONENTS THAT INCREASE AN APOLOGY’S EFFICACY by Krista M. Hill ABSTRACT OF DISSERTATION Submitted in partial fulfillment of the requirements For the degree of Doctor of Philosophy in Psychology In the College of Science of Northeastern University May 10, 2013 3 ABSTRACT Although apologies are a staple of civil society, it is unclear whether they are effective and if effective, what components are involved in the perfect apology. The term “components” refers to general categories of actions (both verbal and nonverbal) that may be present in an apology. Two studies were conducted to examine (1) whether apologies are effective in eliciting positive outcomes for an apologizer and (2) potential apology components that may obtain positive outcomes for an apologizer. For Study 1, six meta-analyses of previously published studies, examined the relation between apologies and offended parties’ (1) forgiveness, (2) attributions of positive qualities to the apologizer, (3) positive emotions toward the apologizer, (4) positive legal outcomes for the apologizer, (5) intentions to purchase goods from the apologizer, and (6) overall positive reactions and behaviors toward the apologizer (i.e., combining across all outcomes). High- inference coding was used to determine which theory-driven components contribute most to the effectiveness of apologies. Analyses revealed a significant influence of apologizing on forgiveness (k = 79, r = .32, random effects Z = 8.16 p < .001), positive attributions of the apologizer (k = 60, r = .24, random effects Z = 6.69, p < .001), positive emotions toward the apologizer (k = 43, r = .33, random effects Z = 9.41, p < .001), legal sentencing (k = 11, r = .13, random effects Z = 3.49, p < .001), and purchase intentions (k = 10, r = .23, random effects Z = 2.85, p < .01). Combining across all outcomes apologizing was effective (k = 144, r = .27, random effects Z = 10.72, p < .001). All distributions of effect sizes were significantly heterogeneous. Significant moderators included the apology components of remorse, offers of compensation, and an acknowledgment of violated rules and norms. 4 The aim of Study 2 was to examine the relationship between apology components and judge-rated outcomes. Participants apologized for a transgression they committed on video. Trained coders then rated the apologies for apology components. Finally, videos were watched by new participants (i.e., judges) who rated the apologies on various outcomes. This was the first time this paradigm was used to study apologies. The apology components included both apologizer-rated emotions and coder-rated verbal (i.e., remorse, acknowledgment of violated rules and norms, and compensation) and expressive behavior (i.e., guilt and shame). The judge- rated outcomes included empathy, sympathy, dispositional attributions, forgiveness, trust, and sincerity. Analyses revealed that coder-rated remorse, guilt, and shame were significant predictors of judge-rated empathy, sympathy, forgiveness, trust, and sincerity. Similarly, apologizer-rated self-conscious emotions also predicted these outcomes. These relationships remained significant even when controlling for judge-rated severity of the transgression. 5 Acknowledgments First and foremost I would like to thank my family for their endless encouragement and support. Thank you for being my biggest fans throughout this process and throughout my life. Words cannot express how much your support and love have meant to me. From a very young age you instilled in me the importance of hard work, which made this degree possible. I would also like to extend my deepest thanks to the faculty that have helped me tremendously during this process. To my advisor, Randy Colvin, I can never thank you enough for taking a chance on me as an undergraduate so many years ago. Thank you for your guidance, support, and believing in my abilities. I hope we continue our friendship and collaborate for years to come. Thank you, Judith Hall and Nancy Kim, for your helpful comments on the many stages of my dissertation. To my research brother and sister, Sun Park and Stefanie Tignor, thank you for your professional advice, encouragement, and bringing joy to this experience. I loved working with the both of you and hope to continue to do so. Thank you Allison Seitchik and Sarah Gunnery. Not only I am leaving graduate school with a degree, but also with two best friends. I feel so fortunate to have gone through this process with such wonderful and intelligent women and I promise many “networking” meetings in the future. To Martha Caffrey, Amy DiBattista, Mollie Ruben, Jolie Baumann, Leah Dickens, Danielle Blanch Hartigan, and Susan Andrzejewski, you are the reasons why I enjoyed coming to the office everyday. Thank you for the coffee breaks and laughs. 6 Thank you to my dear friends, specifically Jessie Canor and Corinne McHugh, for always being there to listen, especially during these last few years. You never stopped encouraging me to work hard and always brought a smile to my face with your daily emails. I would also like to thank the following undergraduate assistants who contributed to the completion of this project: Katie McEnaney, Lisa Bartucca, Gina Strumolo, Nicole Colley, Colleen Trinh, and Kelly Kolkmeyer. And finally I dedicate this dissertation to my wonderful husband-to-be, Matt. I can never thank you enough for your love and encouragement. Thank you for listening when I needed someone to listen, for opening your arms when I needed support, and for making me laugh when I needed to smile. These past five years have brought so many wonderful things for us and I look forward to our post-PhD life together. 7 TABLE OF CONTENTS Abstract 2 Acknowledgments 5 Table of Contents 7 List of Tables 8 List of Figures 9 Chapter 1: Introduction 11 I. What Is An Apology and Why Do People Apologize? 13 II. Are Apologies Effective in Producing Positive Outcomes? 15 III. Which Components Make Apologies More Effective? 17 IV. Why Do Apologies and The Components Work? 20 Chapter 2: Study 1- Meta-Analysis 23 I. Methods 24 II. Results 28 III. Discussion 35 Chapter 3: Study 2 40 I. Methods 40 II. Results 46 III. Discussion 54 Chapter 4: General Discussion 58 I. Limitations and Future directions 60 II. Conclusion 62 References 63 Footnotes 81 Tables 82 Figures 107 Appendix A 109 Appendix B 111 8 List of Tables Table 1: Characteristics of Studies Included in the Meta-Analysis 82 Table 2: Results for Meta-Analyses Examining Efficacy of Apologies 92 Table 3: Correlations Among Apology Components and Effect Size 93 Table 4: Interaction Analysis for Remorse, Acknowledgment of Violated Rules 94 and Norms, and Compensation for High Social Distance Relationships Table 5: Inter-Rater Reliability Indices and Inter-Correlations For Apology 95 Components Table 6: Phase 1 Apologizer Self-Rated Emotion Descriptive Statistics and 96 Factor Loadings Table 7: Apologizer Self-Rated Emotion Factor Descriptive Statistics and 98 Inter-correlations Table 8: Apologizer Self Rated Emotion Factors Correlated with Apology and 99 Transgression Specific Questions Table 9: Correlations Among Judge-Rated Items 100 Table 10: Coder-Rated Items Correlated with Apologizer and Judge-rated Items 101 Table 11: Apologizer-rated Items Correlated with Judge-rated Items 103 Table 12: Apologizer and Coder-Rated Components Correlated with 104 Judge-Rated Outcomes Controlling for Judge-Rated Severity Table 13: Apologizer-rated Emotions Predicting Judge Reactions 105 Table 14: Acknowledgment, Remorse, and Compensation Predicting Judge 106 Reactions 9 Table 15: Interaction Analysis for Coder-Rated Remorse, Coder-Rated 107 Guilt-Shame Aggregate, and Apologizer-Rated Self-conscious emotions Table 16: Coder Rated Remorse, Coder Rated Guilt-Shame Aggregate, and 108 Apologizer Self Conscious Emotions Predicting Judge Reactions 10 List of Figures Figure 1: Apology-Outcome Process Model 109 Figure 2: Mediation Analysis Diagram 110 11 Chapter 1: Introduction Apologies are a core feature of human relationships. Politicians, CEOs, public figures, criminals, romantic partners, and friends all use apologies to repair relationships and protect their reputations (Lazare, 2005). Although apologies are a staple of civil society, it is unclear when they are effective and which components are involved in the perfect apology. Some studies suggest that apologies are beneficial (e.g., Darby & Schlenker, 1982; Eaton, Struthers, & Santelli, 2006; Exline & Baumeister, 2000) and lead to positive outcomes such as forgiveness (e.g., Fehr, Gelfand, & Nag, 2010), positive feelings toward the transgressor (e.g., DeCremer, van Dijk, & Pillutla, 2010), and reduced sentencing in legal cases (e.g., Robbennolt, 2003). Other studies suggest that apologies can have detrimental effects, especially if not done well (e.g., DeCremer et al., 2010; Eaton, Struthers, Shomrony, & Santelli, 2007; Zechmeister, Garcia, Romero, Vas, 2004). Researchers who find the latter suggest that an apology’s effectiveness is highly dependent on whether certain components of the apology are present or absent. The term “components” refers to three general categories of actions (both verbal and nonverbal) that may be present in an apology. These three categories are (1) an expression of empathy for the offended party/an expression of remorse, (2) an acknowledgment of violated rules and norms, and (3) an offer of compensation. Researchers have found that when one or more of these components is (are) manipulated, an apology’s effectiveness is likely to change (e.g., Scher & Darley, 1997). This begs the question: which components produce the most positive outcomes?1 To date, there is no agreed upon empirical answer. For my dissertation two studies were conducted to answer this question.