Business, Sauder School of
Marketing, Division of
DSpace
UBCV
Strejcek, Brendan
Doctor of Philosophy - PhD
University of British Columbia
In this dissertation I explore the consequences of perceptions of victimhood in two substantive domains: intergroup conflict between disadvantaged groups and consumer hostility toward firms. In the intergroup conflict domain, I examine competitive victimhood, which is seeing oneâs own group as having suffered more than an outgroup (Young & Sullivan, 2016). In the domain of consumer/firm interactions, the service failure literature has considered consequences of service failures, such as consumer complaining behavior, and methods to recover from failures, but little work has considered the psychology underlying hostile reactions of consumers to firms following commercial interactions where service failure is absent. Hostile reactive attitudes prompted by victim framing provides a unifying framework with which I consider intergroup relations between members of disadvantaged groups and interactions between consumers and firms. Essay 1 explores the conditions under which disadvantaged groups engage in mutual derogation. Particular identities, often connected to perceptions of historical injustice or contemporary discrimination, suffuse social life, both in political discourse (Fukuyama, 2018; Schlesinger, 1998) and, increasingly, both the marketplace and the workplace. Historically, groups have mostly sought portrayal as powerful, as power signified virtue. However, in modern times, portrayal as a victim is one way to justify concern and extract resources, providing a benefit from competing for victimhood. I propose that competitive victimhood of this form emerges from a zero-sum perception that resources available are limited. Essay 2 identifies a practical consequence for firms and consumers of victimhood beliefs. Specifically, I investigate victim signaling behaviors (Ok et al., 2020), a stable individual difference. Across four studies, I demonstrate that victim signaling is positively associated with hostile reactive attitudes toward firms when service is neutral (compared to service failure). Essay 2 contributes to the existing literature on consumer reactions to service failure and individual differences in victim signaling behavior. Together, these two chapters show how victim framing influences intergroup relations and consumer to firm dynamics. I propose, and find evidence for, intrapsychic processes to explain the relationships between victim framing and competitive victimhood (zero-sum thinking) and between victim framing and hostile reactive attitudes (consumer perception of discrimination).
Two Essays on Perceptions ofVictimhoodbyBrendan StrejcekB.A., The University of Chicago, 2006M.Sc., Northwestern University, 2017A DISSERTATION SUBMITTED IN PARTIAL FULFILLMENT OFTHE REQUIREMENTS FOR THE DEGREE OFDOCTOR OF PHILOSOPHYinTHE FACULTY OF GRADUATE AND POSTDOCTORAL STUDIES(Business Administration)THE UNIVERSITY OF BRITISH COLUMBIA(Vancouver)March 2022© Brendan Strejcek 2022The following individuals certify that they have read, and recommend to the Faculty of Graduateand Postdoctoral Studies for acceptance, the dissertation entitled:Two Essays on Perceptions of Victimhoodsubmitted by Brendan Strejcek in partial fulfillment of the requirements forthe degree of Doctor of Philosophyin Business AdministrationExamining Committee:Karl Aquino, Professor, Marketing and Behavioural Science, UBCCo-supervisorKatherine White, Professor, Marketing and Behavioural Science, UBCCo-supervisorSteven Heine, Professor, Psychology, UBCSupervisory Committee MemberMark Schaller, Professor, Psychology, UBCUniversity ExaminerJohn Ries, Professor, Strategy and Business Economics, UBCUniversity ExamineriiAbstractIn this dissertation I explore the consequences of perceptions of victimhood in two substantivedomains: intergroup conflict between disadvantaged groups and consumer hostility toward firms.In the intergroup conflict domain, I examine competitive victimhood, which is seeing oneâs owngroup as having suffered more than an outgroup (Young & Sullivan, 2016). In the domain ofconsumer/firm interactions, the service failure literature has considered consequences of servicefailures, such as consumer complaining behavior, and methods to recover from failures, but littlework has considered the psychology underlying hostile reactions of consumers to firms followingcommercial interactions where service failure is absent. Hostile reactive attitudes prompted byvictim framing provides a unifying framework with which I consider intergroup relations betweenmembers of disadvantaged groups and interactions between consumers and firms.Essay 1 explores the conditions under which disadvantaged groups engage in mutual deroga-tion. Particular identities, often connected to perceptions of historical injustice or contemporarydiscrimination, suffuse social life, both in political discourse (Fukuyama, 2018; Schlesinger, 1998)and, increasingly, both the marketplace and the workplace. Historically, groups have mostly soughtportrayal as powerful, as power signified virtue. However, in modern times, portrayal as a victimis one way to justify concern and extract resources, providing a benefit from competing for victim-hood. I propose that competitive victimhood of this form emerges from a zero-sum perception thatresources available are limited.Essay 2 identifies a practical consequence for firms and consumers of victimhood beliefs. Specif-ically, I investigate victim signaling behaviors (Ok et al., 2020), a stable individual difference. Acrossfour studies, I demonstrate that victim signaling is positively associated with hostile reactive at-titudes toward firms when service is neutral (compared to service failure). Essay 2 contributes tothe existing literature on consumer reactions to service failure and individual differences in victimsignaling behavior.iiiAbstractTogether, these two chapters show how victim framing influences intergroup relations andconsumer to firm dynamics. I propose, and find evidence for, intrapsychic processes to explain therelationships between victim framing and competitive victimhood (zero-sum thinking) and betweenvictim framing and hostile reactive attitudes (consumer perception of discrimination).ivLay SummaryThis dissertation investigates two aspects of victimhood perception. In the first part, I examinecompetitive victimhood between two disadvantaged groups, where groups struggle over recognitionof suffering. I propose that zero-sum thinking, the belief that resources are fundamentally lim-ited, like a pie of fixed size, is one psychological explanation of competitive victimhood betweendisadvantaged groups. In the second part, I examine reactions of consumers to firms following com-mercial interactions, showing that consumer self-perceptions involving victimhood are associatedwith negative reactions. Based on these results, I discuss implications of victimhood perception ingeneral for social interactions involving members of identity-based groups.vPrefaceI am the primary author of the work presented in this Ph.D. dissertation. I performed the literaturereview, developed the hypotheses, designed the experiments, collected the data, analyzed the data,and prepared the manuscript. I discuss additional contributions below.For essay 1, Karl Aquino and Katherine White assisted in designing the experiments andprovided intellectual contributions.For essay 2, Karl Aquino and Katherine White assisted in designing the experiments andprovided intellectual contributions. Ekin Ok discussed the design of study 3 with me.The UBC Office of Research Services Behavioural Review Board (Human Ethics) providedethical approval for all experimental studies under the following certificates: Essay 1: H19-01656,Essay 2: H18-02925.viContentsAbstract . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . iiiLay Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . vPreface . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . viContents . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . viiList of Tables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xiList of Figures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xiiAcknowledgements . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xiii1 Competitive Victimhood Between Disadvantaged Groups . . . . . . . . . . . . . 11.1 Conceptual Development . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31.2 Competitive Victimhood Between Disadvantaged Groups . . . . . . . . . . . . . . . 41.3 Zero-Sum Thinking . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 61.4 Social Identity Theory . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 71.5 Hypotheses . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 101.6 Studies Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 131.7 Study 1A: Twitter #BiVisibilityDay . . . . . . . . . . . . . . . . . . . . . . . . . . 151.7.1 Data Description . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 161.7.2 Method . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 161.7.3 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 181.7.4 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 181.8 Study 1B: Reddit r/NonBinary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19viiContents1.8.1 Data Description . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 191.8.2 Method . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 201.8.3 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 201.8.4 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 211.9 Study 2: Lay Beliefs of Attitudes Between Disadvantaged Groups . . . . . . . . . . 221.9.1 Method . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 221.9.2 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 231.9.3 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 241.10 Study 3: Zero-Sum Thinking and Competitive Victimhood . . . . . . . . . . . . . . 241.10.1 Method . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 251.10.2 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 271.10.3 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 311.11 Study 4: Minimal Group Paradigm . . . . . . . . . . . . . . . . . . . . . . . . . . . 331.11.1 Method . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 341.11.2 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 371.11.3 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 411.12 General Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 431.12.1 Limitations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 451.12.2 Future Directions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 462 Victimhood Perception and Commercial Interactions . . . . . . . . . . . . . . . . 502.1 Hostile Reactive Attitudes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 522.2 Virtuous Victim Signaling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 532.3 Hypotheses . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 542.4 Study 1: Customer Reaction to Service Failure . . . . . . . . . . . . . . . . . . . . . 552.4.1 Method . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 552.4.2 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 572.4.3 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 592.5 Study 2: Customer Reaction to Service Failure (Conceptual Replication) . . . . . . 592.5.1 Method . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 59viiiContents2.5.2 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 612.5.3 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 632.6 Study 3: Customer Reaction to Product Failure . . . . . . . . . . . . . . . . . . . . 642.6.1 Method . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 642.6.2 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 652.6.3 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 672.7 Study 4: Consumer Perception of Discrimination . . . . . . . . . . . . . . . . . . . . 682.7.1 Method . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 682.7.2 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 692.7.3 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 732.8 General Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 733 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 76References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 83AppendicesA Common Measures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 101A.1 Belief in Zero-Sum Game . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 101A.2 Moral Identity Scale . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 101A.3 Victim Signaling Scale . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 102A.4 Demographic, Miscellaneous, and Concluding Questions . . . . . . . . . . . . . . . . 103A.4.1 Ethnicity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 103A.4.2 Socioeconomic Status . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 104A.5 Concluding Questions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 104A.6 Attention Check . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 104B Study Procedures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 105B.1 Chapter 1 Study 2: Lay Beliefs of Attitudes Between Disadvantaged Groups . . . . 105B.2 Chapter 1 Study 3: Zero-Sum Thinking and Competitive Victimhood . . . . . . . . 107ixContentsB.3 Chapter 1 Study 4: Minimal Group Paradigm . . . . . . . . . . . . . . . . . . . . . 111B.4 Chapter 2 Study 1: Customer Reaction to Service Failure . . . . . . . . . . . . . . . 113B.5 Chapter 2 Study 2: Customer Reaction to Service Failure (Conceptual Replication) 116B.6 Chapter 2 Study 3: Customer Reaction to Product Failure . . . . . . . . . . . . . . 119B.7 Chapter 2 Study 4: Consumer Perception of Discrimination . . . . . . . . . . . . . . 121C Supplementary Analyses . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 124C.1 Chapter 1 Study 2: Lay Beliefs of Attitudes Between Disadvantaged Groups . . . . 124C.1.1 Sample Demographics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 124C.1.2 Zero-Order Correlations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 125C.2 Chapter 1 Study 3: Zero-Sum Thinking and Competitive Victimhood . . . . . . . . 126C.2.1 Sample Demographics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 126C.2.2 Zero-Order Correlations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 127C.2.3 Moderation by Social Dominance Orientation . . . . . . . . . . . . . . . . . 128C.2.4 Group-Based Disadvantage Beliefs . . . . . . . . . . . . . . . . . . . . . . . . 128C.3 Chapter 1 Study 4: Minimal Group Paradigm . . . . . . . . . . . . . . . . . . . . . 129C.3.1 Sample Demographics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 129C.3.2 Zero-Order Correlations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 130C.4 Chapter 2 Study 1: Customer Reaction to Service Failure . . . . . . . . . . . . . . . 131C.4.1 Zero-Order Correlations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 131C.5 Chapter 2 Study 2: Customer Reaction to Service Failure (Conceptual Replication) 131C.5.1 Zero-Order Correlations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 132C.6 Chapter 2 Study 3: Customer Reaction to Product Failure . . . . . . . . . . . . . . 132C.6.1 Zero-Order Correlations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 133C.7 Chapter 2 Study 4: Consumer Perception of Discrimination . . . . . . . . . . . . . . 133C.7.1 Zero-Order Correlations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 134xList of Tables1.1 Chapter 1: Studies Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 141.2 Twitter: Regression Results (DV: Sentiment Composite) . . . . . . . . . . . . . . . 181.3 Reddit: Regression Results (DV: Sentiment Composite) . . . . . . . . . . . . . . . . 201.4 Reddit: Regression Results, 10+ Words (DV: Sentiment Composite) . . . . . . . . . 211.5 Summary of Scenarios and Expected Group Attitudes . . . . . . . . . . . . . . . . . 231.6 Minimal Group Paradigm: Regression Results (DV: Competitive Victimhood) . . . . 281.7 Study 3: Regression Results (DV: âDevaluesâ Measure) . . . . . . . . . . . . . . . . 301.8 Regression Results (DV: Attitude Measure) . . . . . . . . . . . . . . . . . . . . . . . 301.9 Ingroup Favoritism Choice Matrix . . . . . . . . . . . . . . . . . . . . . . . . . . . . 371.10 Minimal Group Paradigm: Regression Results (DV: Competitive Victimhood) . . . . 381.11 Minimal Group Paradigm: Regression Results (DV: Ingroup Favoritism) . . . . . . 402.1 Regression Results Virtuous Victimhood (DV: Hostile Reactive Attitudes) . . . . . . 572.2 Regression Results Victim Signaling (DV: Hostile Reactive Attitudes) . . . . . . . . 582.3 Regression Results Moral Identity Symbolization (DV: Hostile Reactive Attitudes) . 592.4 Regression Results Virtuous Victimhood (DV: Hostile Reactive Attitudes) . . . . . . 622.5 Regression Results Victim Signaling (DV: Hostile Reactive Attitudes) . . . . . . . . 632.6 Regression Results Moral Identity Symbolization (DV: Hostile Reactive Attitudes) . 632.7 Regression Results Virtuous Victimhood (DV: Hostile Reactive Attitudes) . . . . . . 672.8 Regression Results Victim Signaling (DV: Hostile Reactive Attitudes) . . . . . . . . 672.9 Regression Results Moral Identity Symbolization (DV: Hostile Reactive Attitudes) . 672.10 Regression Results Virtuous Victimhood (DV: Hostile Reactive Attitudes) . . . . . . 702.11 Regression Results Victim Signaling (DV: Hostile Reactive Attitudes) . . . . . . . . 712.12 Regression Results Moral Identity Symbolization (DV: Hostile Reactive Attitudes) . 71xiList of Figures1.1 Sample Tweets . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 171.2 Study 3: Competitive Victimhood (Civil Rights Measure) . . . . . . . . . . . . . . . 281.3 Study 3: Region of Significance (Civil Rights Measure) . . . . . . . . . . . . . . . . . 291.4 Minimal Group Paradigm: Competitive Victimhood . . . . . . . . . . . . . . . . . . 391.5 Minimal Group Paradigm: Ingroup Favoritism . . . . . . . . . . . . . . . . . . . . . 412.1 Study 1: Airline . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 582.2 Study 2: Airbnb . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 622.3 Study 3: Ikea . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 662.4 Study 4: Airbnb . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 702.5 Mechanism: Perceived Discrimination . . . . . . . . . . . . . . . . . . . . . . . . . . 72xiiAcknowledgementsThanks to everyone at UBC who believed in me. Special thanks to Karl, Kate, Steve, Darren, andJoey.Additionally, the encouragement of Ulf, Kent, Moran, Chelsea, Jessica, Chris (Ã2), Blake, andMegan was invaluable.Finally, thanks to Chenbo for everything.xiiiChapter 1Competitive Victimhood BetweenDisadvantaged Groups. . . at one in their faith in the morality of mutualpity, as if it were morality in itself and the pinnacle,the attained pinnacle of man, the sole hope of thefuture, the consolation of the present and the greatredemption from all the guilt of the past â at one,one and all, in their faith in the community as thesaviour . . .Beyond Good and EvilNietzsche (1886/2003, section 202)Despite often sharing interests in the struggle against oppression, intergroup relations betweendisadvantaged groups are regularly contentious. For example, racial minorities have found theslogan âgay rights are civil rightsâ to be offensive, devaluing the historical experience of racialstruggle (Victoria & Belcher, 2009, p. 4). The relations between other disadvantaged groups canbe similarly antagonistic. A notable book by a lesbian feminist scholar, The Transsexual Empire(Raymond, 1979), argued that trans women appropriate female bodies and by so doing participatein patriarchal misogyny. The scholar Carol Riddell wrote that the work of Raymond âdid not in-vent anti-transsexual prejudice, but it did more to justify and perpetuate it than perhaps any otherbook ever writtenâ (Riddell, 2006, p. 131). People concerned about economic inequality have high-lighted the negative consequences of gay gentrification on lower socioeconomic status communities(Knopp, 1997; Maxwell, 2019). The senator and presidential candidate Bernie Sanders has prior-itized economic disadvantage (Waldman, 2015), and he was criticized for speaking relatively lessabout racial issues (Clark, 2019; Zhou, 2019). The wide variety of disadvantaged groups struggling1Chapter 1. Competitive Victimhood Between Disadvantaged Groupsto highlight their own disadvantage poses the question of how to theoretically explain these similarbut diverse phenomena. I propose to investigate the psychological processes involved in internecinestruggle between disadvantaged groups, building on and extending the competitive victimhood andintergroup relations literatures. I will argue that zero-sum thinkingâthe belief that gains for onegroup necessarily come with losses for other groupsâleads people with disadvantaged identities tohighlight their own victimhood to recruit support from others. When people think there is only somuch to go around the implicit belief may be that one must prove oneâs own suffering to stand outfrom other potential causes.Competitive victimhood is defined as a response to claims about the suffering of an outgroupby belief or counterclaim that oneâs own group has suffered comparatively more (Young & Sullivan,2016). For example, to engage in competitive victimhood, a white person who identifies as gaymight, when confronted with claims about racism affecting an outgroup, focus on the sufferingand discrimination gay people face. Research has documented competitive victimhood responsesin struggles between adversarial groups, such as belligerents engaged in violent conflict or identitygroups that sometimes come into direct social conflict, such as men and women. For example,Catholics and Protestants in the geopolitical conflict over Northern Ireland highlight their groupâsown suffering when confronted with the suffering of an adversarial group (Noor, Brown, Gonzalez, etal., 2008). A series of experiments by Sullivan et al. (2012) found that when confronted with claimsof sexism, men will respond by increasing belief in male disadvantage. In such direct, adversarialconflicts, the mantle of wronged party can be a valuable resource, vindicating aggression, justifyingrecompense, and garnering the sympathy of third parties.The conflicts between disadvantaged groups described above, such as between self-identifiedfeminists and transgender women, demonstrate a competition for victimhood, but the conflicts areoften indirect, with groups lacking direct antagonism, unlike the direct antagonism studied by ear-lier research on competitive victimhood. Subsequent experimental work has examined support bymembers of disadvantaged groups for other disadvantaged groups when ingroup discrimination issalient (Craig et al., 2012; Craig & Richeson, 2014), but little research exists on competitive victim-hood responses when confronted with the plight of other disadvantaged groups. In this dissertation,I document phenomena of derogation and competitive victimhood between disadvantaged groups.Additionally, I investigate the role of a potential mechanism to explain such competitive victimhood21.1. Conceptual Developmentbetween groups lacking a direct, antagonistic relationship: belief in zero-sum outcomes.1.1 Conceptual DevelopmentHistorically, groups, such as tribes, polities, or family lineages, have mostly sought to be portrayedas powerful, as power signified virtue. However, in modern times, portrayal as a victim is one way torecruit support from others (Campbell & Manning, 2018), leading to what Nietzsche characterizedas a reversal of values (Nietzsche, 1887/1992). This perception of ingroup suffering expressed incompetitive victimhood can either be in the same domain as that perceived in the outgroup orin a different domain. For example, people can focus on personal hardships of a general naturealso suffered by an outgroup (Taylor Phillips & Lowery, 2015) or lack of recognition (compared toother groups) of specific past suffering (De Guissme´ & Licata, 2017). Conceptually, one could seedrawing attention to a previously overlooked aspect of suffering as being a viable way to recruitsupport, but most of the existing experimental work, such as that previously referenced by Youngand colleagues, focuses on perceptions in the same domain (partly because it is easier to compareresponses across groups to the same question).Research has found that adversarial groups exhibit competitive victimhood responses (Nooret al., 2012). This research has often focused on intractable, violent geopolitical conflicts suchas in the Balkans (Andrighetto et al., 2012), Chile (Noor, Brown, & Prentice, 2008), NorthernIreland (Ferguson et al., 2010; Noor, Brown, et al., 2008; Rosland, 2009) and Palestine (Maoz &McCauley, 2005). Researchers have examined the role of various beliefs in perpetuating such seem-ingly intractable conflicts (Bar-Tal, 1998, 2000), including beliefs about ingroup justness, societalsecurity, positive ingroup self-image, victimization, and delegitimation of opponents. Participantsin these struggles often perceive that gain for one side corresponds to loss for the antagonist (Nash,1951; Ro´zËycka-Tran et al., 2015), and group members often see such conflicts as having zero-sumoutcomes (Bar-Tal, 1998, p. 724). Additionally, research has found that zero-sum thinking is asignificant, though not the only, predictor of conflict between groups that engage in competitivevictimhood (Maoz & McCauley, 2009). Unlike other predictors, such as particular religious orideological beliefs, zero-sum thinking is a psychological process which may support a general ex-planation of competitive victimhood dynamics across various group identities or societal contexts31.2. Competitive Victimhood Between Disadvantaged Groupsand so provides a good candidate for a generalized account of competitive victimhood processes.The emergence of competitive victimhood responses has several consequences, including re-inforcing a sense of historical victimhood (Bar-Tal, 2000), creating a victim perspective (Bar-Tal,2007), increasing ingroup identification (Kelman, 2008), and decreasing the chances of reconcilia-tion (Noor, Brown, Gonzalez, et al., 2008). Direct antagonists in open conflict are not the onlygroups that compete for victimhood, however.1.2 Competitive Victimhood Between Disadvantaged GroupsOne reason for parties involved in direct conflict to compete for victimhood is to avoid being cast asthe aggressor in a conflict (Sullivan et al., 2012). However, there are other reasons that groups maycompete for victimhood. Two disadvantaged groups may compete for resources, such as publicityor donations, by claiming victimhood. A disadvantaged group is one that has been subjected toprejudice within society without regard to individual qualities (CFR, 2016). For the purposes of thisresearch, having a disadvantaged identity involves the personal endorsement of belonging to sucha group. As the subjective belief about disadvantage is the key factor relevant to the psychologicalprocesses I investigate, it is not necessary for a person to suffer objectively measurable group-basedprejudice or be a member of a minority in the numerical sense to count as having a disadvantagedidentity; the belief that the group-based disadvantage exists is sufficient, though on average thetwo criteria may overlap heavily. That is, objectively measurable group-based disadvantage, suchas apartheid-era ethnic restrictions in South Africa (Dubow, 2014), Jim Crow segregation laws inthe southern states of the USA prior to the major advances of the civil rights movement in NorthAmerica (Haws, 1978), or the lack of womenâs voting rights prior to the womenâs suffrage move-ment (Crawford, 2003), is responsible for the existence of many disadvantaged identities. However,historically advantaged groups may also sometimes see themselves as examples of identities thatsuffer group-based disadvantage. For example, some Christians have complained about increas-ing levels of societal anti-Christian prejudice (Yancey, 2018) and the contemporary menâs rightsmovement believe in the dominance of âfeminist ideologyâ or unfair treatment toward men when itcomes to legal disputes such as alimony (Rafail & Freitas, 2019). Whether or not these grievancesreflect objective facts, they still constitute subjective disadvantaged identities as studied in this41.2. Competitive Victimhood Between Disadvantaged Groupsdissertation.Unlike the struggles between direct antagonists, such as intractable geopolitical conflict, vari-ous disadvantaged groups often occupy a less adversarial position by default, and might even shareinterests in resisting some dominant, oppressive third party. In non-adversarial competitive vic-timhood, the contest is over which group suffered more, the cause of which can be specificâsuchas the experiences of different groups at the hands of the Nazis during World War Two (Gar-rard, 1995; Grau & Shoppmann, 2012; Kelso, 2013; Lukas, 1986; Rosenfeld, 1999; Stein, 1998)âordiffuseâsuch as beliefs about structural inequality (Kahalon et al., 2019). There is also extensivedocumentation of non-adversarial competitive victimhood in treatments of specific historical orpolitical contexts, journalistic accounts, or advocacy (Chang, 1997; Williams, 2013).Research on intergroup relations between disadvantaged groups has found that salience of dis-crimination against oneâs own group can influence attitudes concerning and behaviors toward otherdisadvantaged outgroups. For example, among white women, reading about pervasive sexism inten-sifies negative attitudes about racial minorities (Craig et al., 2012). However, similarity betweendisadvantaged ingroup and outgroup, such as shared stereotypes, mitigates intergroup hostility(Craig & Richeson, 2014). When perceiving discrimination against an ingroup, people react morepositively toward other disadvantaged groups stigmatized along similar dimensions of identity andmore negatively to groups stigmatized along different dimensions (Craig & Richeson, 2016).Though the perceived interests of disadvantaged groups might align when considering sharedoppression, perceived interests might diverge when competing for resources perceived as scarce,such as attention or money. Previous research has described the zero-sum nature of direct conflictbetween two parties as an important precondition of competitive victimhood reactions (Sullivanet al., 2012, p. 783). When groups are directly in conflict, advancement of one group may come atthe expense of another, such as contested claims over physical territory. For intergroup relationsbetween disadvantaged groups, where the conflict between groups is indirect rather than directantagonism, group members might see attention that society bestows on one disadvantaged groupas unavailable to help other disadvantaged groups. This proposition is consistent with researchsuggesting that competitive victimhood claims strategically pursue the satisfaction of perceivedgroup needs (McNeill et al., 2017).51.3. Zero-Sum Thinking1.3 Zero-Sum ThinkingIn any particular intergroup relation, people can have various beliefs about how resources can bedistributed. In game theory, a zero sum game is one where the sum of all payments equals zero(Von Neumann & Morgenstern, 1953, section 4.2.3). That is, in a zero-sum game, gains for oneplayer come at the loss of another player. Outside of formal game theoretic specifications, peoplerarely possess full knowledge of potential outcomes, making potential resource distribution a matterof personal belief, which then guides related judgments and decisions. Researchers have studiedthe general belief that outcomes are governed by the principles of zero-sum games and validated ascale to measure such beliefs (Ro´zËycka-Tran et al., 2015), defining the construct as a belief that onepersonâs gain is possible only based on the loss of othersâthat is, social relations are inherentlyantagonistic. Researchers have also studied domain specific beliefs about zero-sum outcomes forcategories such as gender, race (Wilkins et al., 2015), and romantic relationships (Burleigh et al.,2017).Research suggests several reasons that zero-sum thinking might be implicated in the tendencyto engage in competitive victimhood. In particular, zero-sum thinking leads to decreased coopera-tion (Ro´zËycka-Tran et al., 2015), lower support for immigration (Esses et al., 2001), rationalizationfor discriminatory behavior (Burleigh, 2016), decreased workplace helping (Sirola & Pitesa, 2017),and prejudice against consensual non-monogamy in romantic relationships (Burleigh et al., 2017).Notably, many of these consequences involve intergroup conflict, competition, derogation or hostil-ity.Several studies have shown that zero-sum thinking can play a role in intergroup relations be-tween majority and disadvantaged groups. For example, whites in North Americaâa majoritygroupâperceive decreases in bias against blacksâan outgroupâas increases in bias against theracial ingroup (Norton & Sommers, 2011). One study presented data showing that men in hyper-competitive work cultures reacted to information about female status gains (women representedat higher levels) with increased gender bias, and this effect was mediated by changes in zero-sumthinking (Kuchynka et al., 2018). Together, this work suggests that zero-sum thinking may alsoplay a role when people perceive limited limited resources to be critical to advancing the causeof a disadvantaged group. This belief in zero-sum outcomes may cause people to present their61.4. Social Identity Theoryown identity group as relatively more victimized, and outgroup disadvantaged identity groups asrelatively less victimized. This can be a benefit to a group looking for assistance, as victimhoodis associated with deservingness (Smyth, 2001). Thus, claiming victimhood helps a group pursueexternal support but this status may be perceived as threatened by other groups making similarclaims (whether or not they are in direct conflict), resulting in competitive victimhood beliefs.Many instances of competitive victimhood documented in the literature depend on historicaldetails of particular group conflicts, such as beliefs about patriarchal oppression or geopoliticalconflicts. A claim about explaining intergroup hostility or competitive victimhood by means ofpsychological processes has the limitation that group-based details are generally present in ob-servational data or experimental stimuli. As such, historical particularity, even given conceptualreplication across multiple group comparisons, often remains a potential alternative explanation.Discovering similar dynamics in groups without connection to history, such as those induced in lab-oratory experiments, would provide some evidence against such an alternative theoretical account.Social identity theory provides a framework for testing identity processes with less content speci-ficity (that is, unconnected to the historical struggle of conflict between particular groups)âtheminimal group paradigm, which I use in study 4.1.4 Social Identity TheoryAccording to social identity theory, people naturally define themselves in terms of ingroup andoutgroup, with mere categorization being sufficient to induce group differentiation and promptdiscrimination (Brewer, 1979; Locksley et al., 1980; Tajfel et al., 1971). The theory holds thatsocial identities satisfy psychological needs such as seeing the self positively. Many different socialidentities can serve this psychological role, including national, ethnic, interest-based, and otheridentities. According to social identity theory, such identities provide ways to form a self-conceptbased on social categories. Tajfel and Turner (1979) argued that the underlying motivationalprinciple driving the formation of social identity is the need for positive self-concept, and evenobjectively low-status group memberships can provide ways to maintain positive self-concept basedon the social component of the self-concept. For example, a member of a disadvantaged group cangenerate positive self-concept from a social self-concept through downward positive comparisons71.4. Social Identity Theorywith another group, ignoring ingroup weaknesses, and focusing on ingroup strengths (Hornsey,2008).The starting point for social identity theory was investigating ingroup favoritism, such as theclassic Robbers Cave field experiment (Sherif, 1951; Sherif et al., 1961). In this field experiment(participants were 11 year old boys), researchers induced group identities that did not previouslyexist by presenting common goals (within groups) and then observed the intergroup conflict betweenthese groups based on competition due to incompatible goals (between groups). The researchersdemonstrated a subsequent reduction of intergroup conflict after introducing superordinate (shared)goals. This study, and others like it, contributed to Sherifâs broader realistic conflict theory, aprogram of research which examined the influence of resource competition on intergroup conflict.Later theories in the social identity theory tradition, such as optimal distinctiveness theory(Brewer, 1991), focused on the factors necessary for, or limiting of, the expression of social identitydynamics. In the case of optimal distinctiveness theory, which argued that people are motivatedto maintain positive self-concept both in terms of group affiliation and personal uniqueness, themotivations can sometimes be in tension, with threats to individual identity leading to decreasedfocus on the social self-concept and threats to collective identity leading to decreased focus on theindividual self-concept. Self-categorization theory (Turner et al., 1987; Turner et al., 1994) wasanother development in the social identity theory tradition which proposed factors underlying thesalience of social categorization, the kinds of social categories that developed, and consequencesof social category activation. In this way, social category theory speaks to the processes of groupformation and intergroup behavior.Based on several bodies of research, one would expect disadvantaged groups that have expe-rienced discrimination would support each other and cooperate. For example, research has foundthat shared experiences can make negative experiences less painful (Raghunathan & Corfman,2006). In the context of race, philosophers have argued that shared experience is one of the threebases of solidarity among people experiencing adversity (Blum, 2007). Such processes may con-tribute to traditional maxims such as âmisery loves companyâ and similar proverbs. The socialidentity approach, which explains behavior in terms of social affiliations and group labels, wouldalso predict several mechanisms by which members of disadvantaged groups might make commoncause in the face of discrimination. According to self-categorization theory, a development of so-81.4. Social Identity Theorycial identity theory originally developed to explain group identity formation, social identities arefluid and people have some degree of flexibility, depending on the situation and other social affor-dances, to categorize themselves (or others) as members of different groups. One such affordanceis a fault line, defined as a set of attributes that divide groups into subgroups (Lau & Murnighan,1998). Shared fault lines dividing members of disadvantaged groups from others could, accordingto self-categorization theory, provide the basis for superordinate identity formation, facilitatingcooperation and decreasing derogation when confronted with discrimination.Yet, research has documented intergroup conflict between disadvantaged groups, to the degreethat various common vernacular shorthand has emerged to characterize the predictable strife suchas âoppression olympicsâ (Cepeda, 2017; A.-M. Hancock, 2011). This may at first seem surprisingbased on the shared fault line attributes described above, but optimal distinctiveness theory, an-other development of social identity theory, provides a possible explanation. According to optimaldistinctiveness theory, people have needs beyond simple affiliative needs such as the need to belong.People also have a need for distinctiveness at the group identity level. While coalitions based onsuperordinate identity may benefit group members with regard to overcoming oppression, it may bemore difficult to establish favorable distinctiveness contrasted with a dominant perceived oppressorcompared to another disadvantaged group. In this case, a common superordinate identity sharedwith other disadvantaged groups would end up threatening the need for distinctiveness more thanit satisfied the need to belong, making some form of competition to establish distinctiveness moreprobable.The core of optimal distinctiveness theory is agnostic about the methods people can use torepair deficits in distinctiveness. However, the concerns salient to members of disadvantaged groupssuggest several possible strategies. One would involve a downward social comparison, as might bepredicted by the original formulation of social identity theory, to boost distinctiveness (and thus es-teem) of the collective self. This could take many forms, such as derogation of a non-disadvantagedoutgroup. Such a strategy may be difficult, almost by definition, on dimensions related to agenticvirtues such as influence, for a group experiencing disadvantage. Other methods to bolster dis-tinctiveness of the collective self are available, in the form of comparison with other disadvantagedgroups. Given the salience of mitigating disadvantage, a common concern among highly identifiedgroup members is to cultivate interest in the disadvantage suffered by their group (Westbrook,91.5. Hypotheses2020a). According to Westbrook (2020b, p. 207), victimhood is a âcompelling narrative withinthe social problems marketplace.â In this way, the practical imperative of attracting attention tothe disadvantaged ingroup cause and the psychological imperative of establishing distinctive socialidentity compared to other disadvantaged groups dovetail, suggesting that resource dependence(both in the indirect form of attention and the direct form of monetary resources) might contributeto a propensity to compete for victimhood status in part to cultivate optimal distinctiveness.In this dissertation, I draw primarily on two aspects of the social identity theory tradition, oneconceptual and one operational. Conceptually, the basic assumption of social identity theory thatcompetitive intergroup behavior can arise from social identity motivations (Hornsey, 2008, p. 207).Specifically, the need to see the ingroup as distinctive in a positive way, as suggested by optimaldistinctiveness theory, means that when people think in zero-sum terms, engaging in competitivevictimhood might be more likely. Operationally, the experimental procedures that studies in thesocial identity tradition use generally abstract the group processes from details of particular groupmemberships. Using an approach such as the minimal group paradigm, described in more detail forstudy 4 below, allows me to explore the relationship between membership in a disadvantaged groupand zero-sum thinking without using particular, historically-grounded groups. Using the minimalgroup paradigm also makes some alternative explanations of the competitive victimhood behaviorsI observe less plausible. For example, from self-categorization theory I could derive the predictionthat prototypic dissimilarity might explain competitive victimhood, with members of disadvantagedgroups likely to engage in less competitive victimhood with members of disadvantaged outgroupssharing prototypic characteristics. However, if the effect emerges in contexts lacking such details,I can have more confidence in zero-sum thinking as an explanatory factor.1.5 HypothesesAs noted above, much previous work on competitive victimhood explores phenomena involvinggroups that are in direct conflict (such as the geopolitical examples) or involves identities wheretension is often explicit, such as conflict between the genders (Sullivan et al., 2012). As such, theperspective of the group in conflict seems likely to match the perception of a general observer, whois a potential source of support, at least in terms of the general acknowledgement of grounds for101.5. Hypothesesconflict, even if attitudes may vary regarding the degree to which a conflict is truly intractable.However, the general perceptions matter practically to disadvantaged groups as sources of support,especially if the disadvantaged group perception of zero-sum outcomes diverges from the generalobserver perspective. My first hypothesis, that general observers are likely to predict that dis-advantaged groups will be more likely to cooperate rather that engage in derogation or hostilityarises from these observations. However, the behavior of members of disadvantaged groups maybe different than these expectations, though my perspective suggests that this should be true onlyif they engage in zero-sum thinking. My intent with hypothesis 1 is to document that my coreprediction is counter to conventional lay wisdom.If people with disadvantaged identities feel like support from third parties is limited to agreater degree, then they might be more likely to emphasize ingroup disadvantage, or downplaythe disadvantage of other possibly disadvantaged outgroups, compared to how people with non-disadvantaged identities might react to such disadvantaged outgroups. Further, I would expect thatthe disadvantage experienced by members of disadvantaged groups would make recruiting supporta greater immediate consideration for disadvantaged groups compared to non-disadvantaged groups(which may not feel a need to recruit support at all). This leads to my second hypothesis whichpredicts that the relationship between zero-sum thinking and competitive victimhood beliefs willbe stronger for people with disadvantaged identity, compared to people with non-disadvantagedidentity, when confronted with the plight of a disadvantaged outgroup.Hypothesis 2 concerns the comparison of reactions between members of disadvantaged and non-disadvantaged groups when presented with the disadvantage of a disadvantaged outgroup. If zero-sum thinking processes contribute to competitive victimhood between members of disadvantagedgroups, then framings of disadvantage which facilitated (or inhibited) consideration of relativedisadvantage in comparative terms would allow me to experimentally identify the effect, increasingthe internal validity of my potential findings, since I can induce different framings of disadvantageusing random assignment. This is the design that I use in the study that uses the minimal groupparadigm (study 4).Several framings of the disadvantage suffered by a disadvantaged outgroup are possible. Toillustrate these different framings, I will use a running example: imagine that the participant belongsto a racial minority that is disadvantaged. As part of this example, consider a disadvantaged111.5. Hypothesesoutgroup defined by sexual preference. First, the outgroup disadvantage could be presented, butwithout direct comparison. In the running example, this might occur when a member of the racialminority group is presented with information about the disadvantage suffered by members of thesexual preference minority, such as a charity donation appeal. Second, the ingroup disadvantagecould be presented alone, making ingroup concerns salient. In the running example, this mightoccur when a member of the racial minority group reads a news story about a racist incident thatan ingroup member experienced (with no mention explicitly made of any disadvantaged outgroup).Third, the outgroup disadvantage could be presented in explicitly comparative terms, making therelative suffering unambiguous. In the running example, this might occur when a member of theracial minority looks up statistics about incarceration and discovers that ingroup members tend tohave different criminal justice outcomes compared to members of the minority sexual preference.(This running example is only intended to summarize the structural setup of the disadvantageframings, rather than reflecting objective conditions faced by actual disadvantaged groups.)This does not exhaust all possible framings, but it allows me to test whether exposure toappeals regarding the disadvantage faced by a disadvantaged outgroup is sufficient to trigger per-ceptions of competitive victimhood, and if zero-sum thinking explains this difference in reactions.I predict that zero-sum thinking will be more-weakly associated with competitive victimhood re-sponses for the ingroup disadvantage and comparative disadvantage framings compared to thecommon disadvantage framing. Both of these framings provide less scope for influence of zero-sum thinking. In the ingroup disadvantage framing, participants only receive information aboutthe ingroup disadvantage. In the comparative framing, participants receive explicit informationthat ingroup disadvantage is greater than outgroup disadvantage. Though serves as a strong testof my framework because though the framing seems like it might facilitate conflict, the explicitinformation provides less scope for the role of zero-sum thinking in beliefs about intergroup re-lations. In contrast, when the disadvantage of an outgroup becomes salient, members of (other)disadvantaged groups adopt the mindset of competitive victimhood to the degree that they engagein zero-sum thinking. Based on this logic, I propose hypothesis 3: zero-sum thinking is associatedwith competitive victimhood responses to a greater degree when the disadvantage framing presentsthe disadvantage of both ingroup and outgroup but lacks direct comparative information aboutthe relative objective suffering of each group. In study 4, I represent this situation in the com-121.6. Studies Overviewmon framing condition, and compare responses in that condition to an ingroup framing condition(where no cues about the suffering of a disadvantaged outgroup are present) and a comparativeframing condition, which provides direct comparative information (in favor of the ingroup) aboutthe relative disadvantage suffered.In summary, my hypotheses for chapter 1 are:H1. Third-party observers of intergroup relations between disadvantaged groups expect suchgroups to cooperate rather than engage in derogation or hostility.H2. When the disadvantage of a outgroup is salient, zero-sum thinking is positively associatedwith competitive victimhood responses to a greater degree for people with disadvantagedidentities compared to people without disadvantaged identities.H3. Zero-sum thinking is associated with competitive victimhood responses when presented withthe disadvantage of an outgroup using common framing, compared to ingroup framing orcomparative framing.1.6 Studies OverviewI first present several preliminary studies to document the phenomenon of disadvantaged groupsengaging in mutual hostility and competitive victimhood. In these preliminary studies, I analyzesocial media content to document intergroup derogation and competitive victimhood responsesbetween multiple pairings of disadvantaged groups. Study 1A measures sentiment around the 2019#BiVisibilityWeek hashtag on Twitter, testing whether tweets containing this hashtag tend to bemore or less positive when mentioning words related to the broader LGBT community. I assume,based both on examining tweet contents and the broader discourse around this hashtag, that mostof the people using this hashtag have a bisexual identity, so I can measure intergroup relations andattitudes by examining how these tweets discuss other disadvantaged groups. In this study, I useLGBT identity as a disadvantaged outgroup 1. This study finds that such tweets tend to be morenegative when mentioning LGBT-related words, consistent with intergroup derogation. Study 1Breplicates this effect using a different social media platformâRedditâand an alternative pair of1Though âbisexualâ nominally is part of the LGBT initialism (as the B), the broader LGBT identity functions asa disadvantaged outgroup in this context (Marcus, 2018).131.6. Studies OverviewStudy Method Sample Description1a social media Twitter intergroup conflict; sentiment1b social media Reddit conceptual replication (diff. identities); sentiment2 survey Mturk lay judgments3 survey Mturk zero-sum thinking and competitive victimhood4 experiment Mturk zero-sum thinking, minimal group paradigmTable 1.1: Chapter 1: Studies Overviewcomparison groupsânonbinary people and transgender people. On Reddit, posts in r/NonBinarytend to be more negative when mentioning transgender-related words compared to mentions ofthe word straight. Study 2 examines the beliefs third parties have about intergroup attitudesbetween disadvantaged groups (and tests hypothesis 1). This study looks at whether lay beliefsare consistent with historical, journalistic, and anecdotal examples described above, finding thatrespondents believe that disadvantaged groups are likely to have attitudes that facilitate coalitionformation.In Study 3, I find that zero-sum thinking is positively associated with competitive victimhoodbeliefs for members of disadvantaged groups when confronted with the plight of disadvantagedoutgroups (preliminary correlational support for hypothesis 2). In addition to examining the roleof zero-sum thinking, the expression of competitive victimhood beliefs in study 3 is contrary to theattitudes expected by respondents in study 2, who report that they believe disadvantaged groupsshare interests and are likely to work together.The minimal group paradigm (Smith, 2010), a methodology developed my social identity theoryresearchers, provides a way to test whether competitive victimhood effects between disadvantagedgroups stem from particularities of historical identities or whether the response arises as part ofgroup membership as such, when combined with zero-sum thinking. I use this approach in study4. In this experiment, I find that zero-sum thinking is associated with competitive victimhoodresponses when confronted with the disadvantage of an outgroup that may also be trying to recruitthe same limited resources, providing experimental evidence for hypotheses 2 and 3.In summary, studies 1a, 1b, and 2 document the phenomenon of competitive victimhood be-tween disadvantaged groups and explore the assumptions of third parties about such intergroupinteractions, which are relevant to whether competitive victimhood behaviors are actually adaptive141.7. Study 1A: Twitter #BiVisibilityDaywhen recruiting support from third parties. These initial studies explore the tenor of intergrouprelations, and the attitudes of both general observers and observers belonging to a disadvantagedidentity, without examining specific scarce resources that might be contested or measuring zero-sum thinking. I designed studies 3 and 4 to investigate the processes underlying the phenomenaobserved in the earlier studies. These phenomena include hostile intergroup interactions, repre-sented by negative sentiment expressed in social media messages (Studies 1a and 1b) and generalassumptions of cooperation between disadvantaged groups (Study 2). Studies 3 and 4 investigatepsychological processes that may explain these observed phenomena. Study 3 examines whetherzero-sum thinking is associated with competitive victimhood beliefs using stimuli involving twohistorical groups, black people and gay people. Study 4 extends study 3 by using the minimalgroup paradigm, rather than actual identities with historically-grounded associations, to test myhypothesis that zero-sum thinking is involved in the psychological processes that result in com-petitive victimhood dynamics. Study 4 also measures both attitudinal and behavioral competitivevictimhood outcomes.1.7 Study 1A: Twitter #BiVisibilityDayTo investigate competitive victimhood between groups based on disadvantaged identity, I lookedat tweets around a recent âhashtag eventâ on Twitter, #BiVisibilityDay. In a hashtag event,people use a common hashtag to discuss an issue or happening (Blaszka et al., 2012). The pointof #BiVisibilityDay is to allow bisexual people an opportunity to display their identity. Thoughthe broader LGBT acronym stands for lesbian, gay, bisexual, and transgenderârepresenting sexualpreference and gender identification minoritiesâbisexual people often feel like they are not acceptedby either heterosexual or homosexual communities. If people with bisexual identities compete forvictimhood with other disadvantaged groups over perceived scarce resources, this phenomenonmay emerge in social media contexts, given publicity of the signal and the tendency of people tocluster in social networks around shared identities. This case is particularly notable, as peoplewith bisexual identities are potentially competing with and derogating a broader grouping whichnominally contains their own identity group. However, the broader group can still serve as anoutgroup from the bisexual perspective, as expressed by the notion of âbisexual erasureâ (Marcus,151.7. Study 1A: Twitter #BiVisibilityDay2018; Yoshino, 2000).In my analysis of this social media data, I examine sentiment scores as a proxy for intergroupattitudes related to competitive victimhood. Sentiment scores capture the postive and negativeattitudes toward topics in text and have been used as a proxy for interpersonal and intergroupattitudes. Intergroup hostility is correlated with competitive victimhood behavior more generally(Young & Sullivan, 2016), making sentiment scores a reasonable, if imperfect, first approximation ofpicking up on intergroup conflict in large bodies of textual data. The tool I used to assign sentimentscores, Vader (Hutto & Gilbert, 2014) has been used to explore conflict between communities online(Kumar et al., 2018). Additionally, Direct examination of many messages in this data suggest theexpressed hostility is often characteristic of competitive victimhood beliefs.Though I make no attempt to measure explicit competition for resources or zero-sum thinkingin the textual data, instead focusing on documenting derogation between disadvantaged groups,there is a clear resource that people compete for on social media: attention (Jung, 2021). Asresearchers studying nongovernmental organizations seeking publicity have written (Thrall et al.,2014, p. 138):As a result, the competition for attention is a zero-sum game. Any attention one actorgains is attention lost by another. On any given day, the volume of issues and voiceslooking for attention vastly exceeds the carrying capacity of any of these audiences.1.7.1 Data DescriptionUsing the Python module Tweepy I downloaded all the tweets containing the hashtag #BiVisibilityDayin September 2019, up through September 27th, 2019. This resulted in 215,845 tweets (Mchars =118.15, SDchars = 31.79). 16,250 of these tweets contained any of the strings âLGBTâ, âlesbianâ,or âgayâ (case-insensitive). I used the presence of these substrings as a proxy for attitudes towardmembers of a disadvantaged outgroup.1.7.2 MethodI first coded tweets for whether they contained any of the strings âLGBTâ, âlesbianâ, or âgayâ (case-insensitive). There are many different potential sets of words that could be chosen to represent161.7. Study 1A: Twitter #BiVisibilityDayFigure 1.1: Sample Tweetsmention of LGBT-related concepts. Given the potential flexibility of choice, I chose the simplestpossible set of terms drawn from the initialism LGBT apart from the relevant ingroup (which isâbisexualâ for the purposes of this study). This led to the set of words listed above: the initialismitself (âLGBTâ) and the two other identity subgroups (âlesbianâ and âgayâ). Then, I used Vader(Hutto & Gilbert, 2014) to assign sentiment scores to each tweet. Vader is optimized for scoringdata on social media, handling content such as emojis, punctuation, and slang (Elbagir & Yang,2019). Vader has been used in hundreds of scholarly publications in domains ranging from tourism(Alaei et al., 2017) to consumer research (Humphreys & Wang, 2018). Vader assigns four scores toeach datum: positive, negative, neutral, and a normed composite score ranging from â1 (negativesentiment) to +1 (positive sentiment). For example, one tweet that received a score below â.75:QUEER ENOUGH TO DIE, BUT NOT GAY ENOUGH TO STAY STOP DEPORT-ING BI+ MIGRANTS! Weâre holding a vigil today at the Vauxhall Pleasure Gardensfor #BiVisibilityDay, to stand together against the harm, violence and abuse caused bybi erasure and the deportation of Bi+ people.One tweet that received a positive normed composite score above +.75:171.7. Study 1A: Twitter #BiVisibilityDayHonored to be part of this amazing community where all itâs about love BE PROUD OFWHO YOU ARE AND WHO YOU LOVE #BiVisibilityDay.1.7.3 ResultsTo test whether tweets containing words associated with LGBT identity were more emotionallynegative, I regressed the Vader composite sentiment score on dummy-coded variables for each term(F (1, 215842) = 626.63, p < .001). Across all comments, mentioning LGBT-related words wasassociated with more negative (less emotionally positive) sentiment (bLGBT = â.11, SE = .003,t = â33.97, p < .001, 95% CI [â.114,â.102]). Mentioning âstraightâ was associated with lessnegative (more emotionally positive) sentiment (bstraight = â.03, SE = .008, t = â3.98, p < .001,95% CI [â.048,â.016]). It is notable that the tweets containing LGBT-related terms involvedmore negative sentiment compared to tweets presumably related to a non-disadvantaged outgroupprovides. See table 1.2 for model estimates.Predictor b SE t pLGBT (indicator 0/1) â.11 .003 â33.97 < .001Straight (indicator 0/1) â.03 .008 â3.98 < .001Constant .40 < .001 468.93 < .001Table 1.2: Twitter: Regression Results (DV: Sentiment Composite)1.7.4 DiscussionThis pilot study provides an example of competitive victimhood between disadvantaged groups in alarge, recent dataset from a popular social media platform. Specifically, when people tweeting about#BiVisibilityDay expressed more negative sentiment when discussing topics that mentioned termsrelated to a disadvantaged outgroup (the broader âLGBTâ community). Mentioning âstraightââanon-disadvantaged outgroup, so presumably not in direct competition for supportâwas associatedwith less negative sentiment.This study, and the additional social media study that uses a comparison between differentidentity groups, have several limitations. They are constrained by measurement instruments avail-able, but speak to the tendency of disadvantaged groups to compare their ingroup with anotherdisadvantaged outgroup, which is a necessary component of expressing competitive victimhood181.8. Study 1B: Reddit r/NonBinary(and, as will be seen, is contrary to the general lay beliefs). Specifically, I do not measure zero-sumthinking or attempt to establish the presence of psychological causes in the behavior reflected bythis data. That said, the context is rife with dynamics involving competition for scarce resourcessuch as attention (as mentioned above).Though the effect of mentioning LGBT on tweet sentiment is substantial, even given the largenumber of observations, it is possible that the effect is driven by details specific to these twoparticular identity groups. However, in the next study, I replicate this effect using different identitygroups and a different social media platform: people with nonbinary identities on Reddit.1.8 Study 1B: Reddit r/NonBinaryI examined online discourse between members of a disadvantaged groupâpeople with nonbinarygender identityâto determine whether discussion included expressions of competitive victimhood.People with nonbinary gender identity see themselves as having a gender different than male orfemale (Richards et al., 2017). People with nonbinary gender identity report experiencing op-pression (A. Austin, 2016), feel misunderstood by healthcare providers (Lykens et al., 2018), andexperience negative workplace outcomes (Davidson, 2016). If people with disadvantaged identitiesbelieve that victimhood is a scarce resource over which various disadvantaged groups must struggle,then discussion focusing on groups with which they might be in competition should exhibit morederogation, hostility, or negativity compared to discussions about non-disadvantaged groups. Inthis study, people with transgender identity represent another disadvantaged group.1.8.1 Data DescriptionReddit is particularly well-suited to investigating phenomena related to disadvantaged identitiesdue to the topic-based discussion format. 11 percent of Americans use Reddit (Perrin & Anderson,2019) and Reddit is the sixth most popular website in the United States (Alexa, 2019). Reddit isorganized around topic-focused forums called subreddits.I downloaded all comments within the r/NonBinary subreddit from February 14, 2019 tothe September 24, 2019 (N = 71500). The r/NonBinary subreddit bills itself as: for peopleof every stripe who feel that they donât fit into a preference-binary or gender-binary culture and191.8. Study 1B: Reddit r/NonBinaryexamination suggests that most participants identify as nonbinary themselves. r/NonBinary has29.6k membersâpeople that can post on the subredditâwith several hundred active (âOnlineâ)at any given time. In the following analyses, I assume that a substantial majority of participantsidentify as nonbinary. Of these comments, 3402 included the text âtransâ and 510 included thetext âstraightâ (all classification is case insensitive).1.8.2 MethodAs with the Twitter study, I used Vader (Hutto & Gilbert, 2014) to assign sentiment scores toeach comment, resulting in four scores for each datum: positive, negative, neutral, and a normedcomposite score ranging from â1 (negative sentiment) to +1 (positive sentiment).1.8.3 ResultsTo test whether discussions including the text âtransâ were more negative compared to discussionsoverall (baseline) and discussions including the text âtrans,â I regressed the Vader composite sen-timent score on dummy-coded variables for each term (F (2, 71497) = 22.54, p < .001). Acrossall comments, mentioning âtransâ was associated with more negative sentiment (btrans = â.02,p = .022) while mentioning âstraightâ was associated with more positive sentiment (bstraight = .13,p < .001). See table 1.3 for model estimates.Predictor b SE t pTrans (indicator 0/1) â.02 .008 â2.30 < .022Straight (indicator 0/1) â.13 .020 6.45 < .001Constant .43 .002 249.15 < .001Table 1.3: Reddit: Regression Results (DV: Sentiment Composite)Given that Reddit comments have no character limit, the variance in message length is higherthan on Twitter. To check whether message length influenced results, I performed the same analysison comments restricted to the arbitrary threshold of 10 words or greater (nwords=10+ = 37927,ntrans = 3249, nstraight = 486, F (2, 37924) = 22.54, p < .001). Again, mentioning âtransâ wasassociated with more negative sentiment (btrans = â.06, t = â6.67, p < .001) while mentioningâstraightâ was associated with more positive sentiment (btrans = .09, t = 3.89, p < .001). See table201.8. Study 1B: Reddit r/NonBinary1.4 for model estimates.Predictor b SE t pTrans (indicator 0/1) â.06 .009 â6.67 < .001Straight (indicator 0/1) .09 .023 3.89 < .001Constant .49 .003 179.73 < .001Table 1.4: Reddit: Regression Results, 10+ Words (DV: Sentiment Composite)1.8.4 DiscussionIn this study, I showed, in a substantial real-world dataset, that people with nonbinary identityexpressed more negative sentiment when discussing topics that mentioned âtransââa disadvan-taged outgroup relative to the assumed commenters in the dataset. Mentioning âstraightââa non-disadvantaged outgroup, so presumably not in direct competition for resources such as donationsor publicityâwas associated with more positive sentiment.There are many different ways to analyze textual data such as this Reddit dataset, and onelimitation of these results is the inherent flexibility. I chose a simple, easy to understand operational-ization, and I reported all the coding schemes I tried to avoid capitalizing on chance associations.Given that I code messages by the presence of words, it is also possible this may introduce somemeasurement error due to missing details of context or other complexities of language such assarcasm.There are a number of other methodologies under active development that could be usedto investigate these dynamics in social media datasets potentially given proper validation studiesto ensure that the constructs measured map to concepts of interest (Conway & OâConnor, 2016;Markowetz et al., 2014). For example, machine learning techniques can be used to infer authorgender in social media text (Sboev et al., 2016) and political ideology (Ansari et al., 2020). However,challenges remain before such techniques are practical for behavioral research (Yan et al., 2017).Despite these limitations, these results conceptually replicate the results of the Twitter study,showing a similar effect using alternative identity groups and a different social media platform,increasing confidence in the existence of a more general phenomenon of competitive victimhoodbetween disadvantaged groups. In the next study, I examine general lay beliefs about predictedconflict between disadvantaged groups.211.9. Study 2: Lay Beliefs of Attitudes Between Disadvantaged Groups1.9 Study 2: Lay Beliefs of Attitudes Between DisadvantagedGroupsThis study examines the lay beliefs about whether members of disadvantaged groups are likely tohave positive or negative attitudes towards other disadvantaged groups. Specifically, I hypothesizethat general observers of intergroup relations between disadvantaged groups expect such groups tocooperate (H1).1.9.1 MethodParticipants and DesignI recruited 204 participants from Amazonâs Mechanical Turk. After excluding 12 responses forfailing an attention check, 192 responses remained for analysis (Mage = 36.41, SDage = 10.68).Gender breakdown was 81 Female, 111 Male, 0 Nonbinary. Ethnicity was 6 Asian, 38 Black orAfrican American, 4 Other, 5 Two or more races, and 139 White.ProcedureParticipants read four scenarios, presented in a random order. Each scenario presented a messageadvocating for the issues of a disadvantaged group. After each scenario, participants judged whethera majority or disadvantaged group would be more likely to have positive attitudes toward themessage, captured by three items.For example, one stimulus (noted as âACLU statementâ below; see table 1.5) was:The ACLU has the following statement about LGBT rights: The ACLU works toensure that lesbian, gay, bisexual, and transgender people can live openly without dis-crimination and enjoy equal rights, personal autonomy, and freedom of expression andassociation. Please consider how members of the following two groups would respondto this statement: Straight People, Bisexual People.The other stimuli had a similar structure, but using different contexts and groups. The Lesbianstrength march stimulus described a march celebrating all lesbians past and present from all walks221.9. Study 2: Lay Beliefs of Attitudes Between Disadvantaged Groupsof life. The CNN town hall stimulus described Bernie Sanders highlighting the plight of the pooracross all races. See section B.1 on page 105 for details of all stimuli.For the attitude measures after each scenario, the three items represented affective (which groupwould like this statement more? ), cognitive (which group would benefit more from this statement? ),and behavioural (which group would be more likely to support this statement? ) attitude componentswith the two groups as anchors. For example, in the âpovertyâ context listed in 1.5, the scale formatof these items was (1 = White People . . . 7 = Black People). I based the three components fromthe classic model of attitudes (Fazio & Petty, 2008), anchoring each side of the scale with relevantsocial groups. For example, in scenario B, participants read about a âLesbian Strength Marchâand judged whether non-transgender or transgender people would like the scenario described usingthree different attitude measures.Stimuli Scenario Context Privileged Group Disadvantaged GroupA LGBT ACLU statement Straight BisexualB Lesbian Strength March Non-Transgender TransgenderC Gay Workplace Policies White BlackD Poverty CNN Town Hall White BlackTable 1.5: Summary of Scenarios and Expected Group AttitudesParticipants also completed the zero-sum thinking scale (Ro´zËycka-Tran et al., 2015), includedfor exploratory purposes. Sample items include âSuccesses of some people are usually failures of oth-ersâ and âLife is so devised that when somebody gains, others have to loseâ (1 = Strongly Disagree. . . 7 = Strongly Agree). Finally, participants provided demographicsâage, gender, race/ethnicity,political ideology on two dimensions (social, economic), sexual orientation, transgender identity,and socioeconomic status. I embedded an attention check within the demographic measures.1.9.2 ResultsFor each scenario, I scored the attitude expectation judgments by averaging the indicators: LGBTrights (α = 0.88; 3 items), lesbian strength march (α = 0.90; 3 items), gay rights HR policy(α = 0.84; 3 items), and CNN town hall (α = 0.82; 3 items). The means for expected attitudeswere significantly above the midpoint of the scale for all four scenarios: LGBT rights (t(191) =21.47, p < 0.001, M = 5.94, H0 = 4, dCohen = 1.55; one-sample t-test) lesbian strength march231.10. Study 3: Zero-Sum Thinking and Competitive Victimhood(t(191) = 6.38, p < 0.001, M = 4.75, H0 = 4, dCohen = 0.46; one-sample t-test) gay rights HRpolicy (t(191) = 6.80, p < 0.001, M = 4.71, H0 = 4, dCohen = 0.49; one-sample t-test) and BernieSanders town hall (t(191) = 17.40, p < 0.001, M = 5.61, H0 = 4, dCohen = 1.26; one-sample t-test).In other words, on average, respondents believed that disadvantaged groups are likely to have apositive attitude toward the attempts of other disadvantaged groups to advocate for change.1.9.3 DiscussionThese results show that general observers tend to have the intuitive beliefs that the interests ofdisadvantaged groups align, based on scores for anticipated attitudes toward various messagesadvocating for particular group issues. In the preliminary studies using social media datasets, incontrast, I show that members of disadvantaged groups often engage in derogation and competitionfor victimhood status.This mismatch between lay beliefs and behavior of people with disadvantaged identity meansthat competitive victimhood behaviors may be less effective than other potential strategies forrecruiting support or improving societal conditions for disadvantaged groups, as competing claims ofvictimhood may increase observer uncertainty (Belavadi & Hogg, 2018). It also poses a question tothe researcher: how can we explain this phenomenon that is contrary to observerâs expectations? Instudy three, I provide initial evidence for why zero-sum thinking may provide such an explanation.While I examine some of the group pairings used in the stimuli for this study in other studies(such as bisexual/LGBT and black/gay), the other pairings show the wide variety of potentialintergroup conflict between members of disadvantaged groups. Future research can explore thegenerality of the competitive victimhood effect across all these, and other, group pairings.1.10 Study 3: Zero-Sum Thinking and Competitive VictimhoodThe purpose of this study is to examine whether zero-sum thinking is associated with competitivevictimhood beliefs in members of disadvantaged groups. Specifically, I look at the beliefs of membersof a disadvantaged group, compared to members of a non-disadvantaged group, when presentedwith an appeal to address the suffering of another disadvantaged outgroup. I operationalize thedisadvantaged ingroup by recruiting members of a historically disadvantaged group (black people).241.10. Study 3: Zero-Sum Thinking and Competitive VictimhoodThe non-disadvantaged comparison group is white people. The appeal to address the suffering ofanother disadvantaged outgroup is a scenario where a gay HR manager discusses gay rights.I measure competitive victimhood beliefs in two ways. First, I look at whether members of adisadvantaged group perceive the struggle of the ingroup as being devalued by an appeal to addressthe suffering of a disadvantaged outgroup. Second, I look at the attitude of disadvantaged ingroupmembers toward the suffering of the disadvantaged outgroup.Operationally, this study examines competitive victimhood expressed by black participants(compared to white participants) when confronting the suffering of gay people (which representsthe disadvantaged outgroup).1.10.1 MethodParticipants and DesignI recruited 222 participants from Amazonâs Mechanical Turk using TurkPrime to get an equalsample of heterosexual participants that identify as white and black. After excluding 19 participantsfor failing an attention check, 203 responses remained for analysis (Mage = 45.07, SDage = 15.46).Gender breakdown was 112 Female and 91 Male.ProcedureFollowing consent, participants read a scenario about workplace HR policies where a managercompares the progress gay rights has made unfavourably with the progress racial civil rights hasmade. All participants read the same scenario:Please imagine that you work for the HR department of a medium-sized, growing busi-ness. One manager, known to be out and gay, discussing the progress of gay rights, saidthe following at a recent meeting: âThe progress of gay rights still has a long way to go.Other groups, such as blacks, have made much more substantial progress. We need toprevent discrimination based on sexual orientation, as distinct from racial discrimina-tion. It is illegal in this country to fire a black person without cause, but in many placesitâs still legal to fire a gay person for being gay. The struggle for gay rights is a strugglefor civil rights.â The manager then proposed the following policies:251.10. Study 3: Zero-Sum Thinking and Competitive Victimhoodâ¢ Company civil rights policies should include protections based on sexual preference.â¢ Given two equally qualified candidates, gay or lesbian candidates should be pre-ferred.â¢ Company policy should prevent firing employees for being gay, just as with race.â¢ Develop and promote anti-harassment and anti-discrimination policies that addresshomophobia.â¢ Set up networking opportunities LGBT+ staff.Participants responded to several post-scenario questions, as well as reporting attitude towardthe policy using three semantic-differential items (Bad/Good, Unhelpful/Helpful, and Unfair/Fair,all scored from 1 to 7). Several of the belief questions concerned the hypothetical situation, suchas asking whether black employees would support the policies. Embedded within these items weretwo measures which capture competitive victimhood: The sentiment in the scenario devalues theracial struggle for civil rights and Gay rights are civil rights. These two measures constitute mytwo dependent variables and are both single-item measures. These measures capture competitivevictimhood by looking at whether participants believe that the scenario increases discriminationtoward the ingroup. Existing measures of competitive victimhood within the literature are oftensingle-item measures based on face-validity, such as the measure of competitive victimhood fromSullivan et al. (2012), which measures competitive victimhood by judging whether the ingroup(gender) experiences more or less discrimination overall. I developed these two items to assess theconstruct of competitive victimhood in the context of the scenario that participants read. I addressthis limitation in more detail in the discussion. I also included a scenario comprehension check,which asked participants to choose the topic of the scenario from several options. The belief itemsused seven-point scales (1 = Strongly Disagree . . . 7 = Strongly Agree).Next, participants answered questions about the degree to which various groups faced discrim-ination, a short scale measuring intolerance of uncertainty (for an unrelated project), the belief inzero-sum game scale (Ro´zËycka-Tran et al., 2015), and the social dominance orientation scale (Prattoet al., 1994), included for exploratory purposes (example item: Some groups of people are simplyinferior to other groups). Finally, participants made a hypothetical lottery decision (for an unre-lated project) and reported demographics, which included age, gender, ethnicity, political ideology261.10. Study 3: Zero-Sum Thinking and Competitive Victimhoodon an economic dimension, political ideology on a social dimension, sexual preference, transgenderidentity, socio-economic status on a ladder measure (Adler et al., 2000), attitude toward the survey,and were offered a changes to share thoughts with the researchers using a free-response text box.See section B.2 on page 107 for additional details about measures.1.10.2 ResultsI averaged scale item responses to create scores for zero-sum thinking (α = 0.82; 8 items2) andsocial-dominance orientation (α = .90). I also averaged the three attitude toward the policy items(α = .94).To assess whether participants believed they belonged to disadvantaged groups, I examinedbeliefs participants had about group disadvantage. In general, participants believed that Blackpeople faced more discrimination than White people (MBlack = 5.70, SEBlack = .10, MWhite =2.77, SEWhite = .13, n = 203). These results held when considering either Black participants(MBlack = 6.26, SEBlack = .12, MWhite = 2.44, SEWhite = .18, n = 96) or White participants alone(MBlack = 5.20, SEBlack = .15, MWhite = 3.07, SEBlack = .17, n = 107). This result providessome empirical support for the assumption that participant ethnicity is a reasonable proxy fordisadvantaged identity. See supplementary analyses for additional summary statistics (C.2.4 onpage 128). Participant attitudes toward the policy did not differ by ethnicity (t(200.881) = -1.22,MBlack = 5.01, MWhite = 4.72).To test my hypothesis that zero-sum thinking is positively associated with competitive victim-hood for participants belonging to a disadvantaged group, I performed analyses of covariance forthe two measures relevant to competitive victimhood beliefs, the civil rights measure and the de-values racial struggle measure. For both analyses, the measure relevant to competitive victimhoodbeliefs was the dependent variable. Independent variables were participant group (black = 1, white= 0), zero-sum thinking (z-scored), and the interaction term.For the âcivil rightsâ belief measure, there was a no main effect of participant group (p = .199)and no main effect of zero-sum thinking (p = .528). However, there was a significant interaction(b = â.57, SE = .25, t = â2.28, p = .024). Examining the slopes of the regression lines within2Participants responded to 12 items; four were from an older version of the scale included for psychometricinvestigation in a separate project. The eight indicators that contribute to the score here are those validated by thescale authors (Ro´zËycka-Tran et al., 2015, pp. 537-538) and used in my other studies.271.10. Study 3: Zero-Sum Thinking and Competitive Victimhoodparticipant groupsâthe simple main effectsâis insufficient to clarify this interaction, as one slopeis significant ( dydxwhite = .36, SE = .18, t = 2.00, p < .047) but the other slope fails to reachconventional significance levels ( dydxblack = â.21, SE = .17, t = â1.20, p = .230). See figure 1.2 fora graphical representation. See table 1.6 for model estimates.Predictor b SE t pBlack (indicator 0/1) â.32 .25 â1.29 .199Zero-sum thinking (continuous; z-scored) .36 .18 2.00 .047Black Ã Zero-sum thinking â.57 .25 â2.28 .024Constant 5.43 .17 31.70 < .001Table 1.6: Minimal Group Paradigm: Regression Results (DV: Competitive Victimhood)Figure 1.2: Study 3: Competitive Victimhood (Civil Rights Measure)To further probe the interaction, since simple main effects were insufficient, I used the Johnson-Neyman procedure (Spiller et al., 2013) to determine the region of significance for the relationship281.10. Study 3: Zero-Sum Thinking and Competitive Victimhoodbetween zero-sum thinking and competitive victimhood response. According to this procedure, theregion of significance begins slightly above four (the midpoint) of the zero-sum thinking scale. Seefigure 1.3 for a graphical representation of the region of significance (black participants).Figure 1.3: Study 3: Region of Significance (Civil Rights Measure)For the âdevalues racial struggleâ measure, there was a main effect of participant group(b = .68, SE = .23, t = 2.91, p = .004) and a main effect of zero-sum thinking (b = .42, SE = .17,t = 2.44, p < .016), but no significant interaction (p = .300). See table 1.7 for model estimates.That is, while participants from a disadvantaged group were more likely to see the appeal of adisadvantaged group as devaluing their own struggle, and zero-sum thinking was positively associ-ated with responses on this âdevaluingâ measure, in these results the relationship was not strongerfor disadvantaged than for non-disadvantaged participants. However, the lack of interaction isless relevant for this measure because the interpretation may differ between participant groups.Specifically, it may only be possible to interpret the belief as an expression of competitive victim-291.10. Study 3: Zero-Sum Thinking and Competitive Victimhoodhood among black participants given that the item devalues the racial struggle for civil rights onlyinvolves amplifying ingroup suffering for the black participants.Predictor b SE t pBlack (indicator 0/1) â.32 .25 â1.29 .199Zero-sum thinking (continuous; z-scored) .36 .18 2.00 .047Black Ã Zero-sum thinking â.57 .25 â2.28 .024Constant 5.43 .17 31.70 < .001Table 1.7: Study 3: Regression Results (DV: âDevaluesâ Measure)The two measures of competitive victimhood were negatively correlated (r(201) = -.16, p =.02), suggesting that they may not cleanly reflect a single, underlying latent competitive victimhoodbelief.It could be argued that the attitude toward the policy items also captures a competitivevictimhood belief similar to a belief statement that gay rights are civil rights. As such, I alsoperformed a similar analysis to that performed for the other competitive victimhood beliefs aboveusing the attitude measure as dependent variable. There were no effects other than a main effectof zero-sum thinking (b = .43, p = .017). See table 1.8 for model estimates.Predictor b SE t pBlack (indicator 0/1) .18 .24 .73 .468Zero-sum thinking (continuous; z-scored) .43 .18 2.41 .017Black Ã Zero-sum thinking â.08 .24 â.31 .755Constant 4.78 .17 28.65 < .001Table 1.8: Regression Results (DV: Attitude Measure)Exploration of Moderation by Social Dominance OrientationAs an exploratory analysis, I investigated whether social dominance orientation might moderatethe relationship between zero-sum thinking and competitive victimhood. Social dominance ori-entation is the âpreference for inequality among social groupsâ (Pratto et al., 1994, p. 741). Ispeculated that a high social dominance orientation could amplify the effect of zero-sum thinkingon competitive victimhood and that a low social dominance orientation could suppress the effectof zero-sum thinking on competitive victimhood. To test this possibility, I performed analyses ofcovariance as before, using in turn the civil rights measure and the devalues racial struggle measure301.10. Study 3: Zero-Sum Thinking and Competitive Victimhoodas dependent variables. The independent variables were also the same, apart from the addition ofsocial dominance orientation (z-scored), and all possible factorial interaction terms. For the civilrights measure dependent variable, the ethnicity Ã zero-sum thinking interaction was significant(p = .01), the ethnicity Ã social dominance orientation interaction was significant (p = .01), andthe zero-sum thinking Ã social dominance orientation interaction was significant (p = .004). Tounderstand the meaning of these interactions, I repeated the analysis within each participant groupseparately. The interaction terms were not significant within Black participants, though they weresignificant within White participants. Given that my research question involves competitive vic-timhood between members of disadvantaged groups, I do not discuss these analyses further. Forthe devalues racial struggle dependent variable, none of the interaction terms were significant. Asa cautionary note about interpretation, it would only be possible to detect large two- or three-wayinteraction effects with this sample size. An analysis of variance with three predictors and factorialdesign (2Ã 2Ã 2 = 8; 203/8 = 25.38 participants per cell) has relatively low statistical power. Seesupplementary analyses in section C.2.3 on page 128 for additional details.1.10.3 DiscussionThis study provides preliminary evidence that zero-sum thinking is positively associated with com-petitive victimhood beliefs for members of a disadvantaged group, compared to members of anon-disadvantaged group, when confronted with appeals to address the suffering of a disadvan-taged outgroup using one of the two competitive victimhood measures. The measures used tocapture competitive were single measures tied to the hypothetical scenario I used. I based thesemeasures on the existing literature on competitive victimhood, which generally uses belief measureswithout formal psychometric construct validation, such as the measure from Sullivan et al. (2012),which assesses competitive victimhood by asking simply whether the ingroup experiences more orless discrimination than the outgroup. These results are complemented by additional measures instudy 4 which provide some degree of convergent validity, though still rely on face validity. The dif-ferent results observed for the two measures suggest that measurement of competitive victimhoodoutcomes would benefit from additional scale validation (or other research geared toward psycho-metric precision) studies. In particular, it would be helpful to create guidelines by which one couldevaluate beliefs about group victimhood for several identity groups relevant to intergroup conflict311.10. Study 3: Zero-Sum Thinking and Competitive Victimhoodbetween disadvantaged groups. In terms of choosing between the two measures in this study, fewhard conclusions can be drawn given that there are only two items, apart from arguing for theadvantage of using multiple indicators when developing a measurement instrument.Interpreting both results raises several questions. My identification strategy of using non-disadvantaged participants as a comparison group allowed me to distinguish a clear interaction forone of my measuresâthe gay rights are civil rights belief. Though the interaction was clear, thesimple main effect among black participants failed to reach conventional significance levels. Thesimple main effect among white participants was significant. This could be due to some specificgroup-specific attitudes. Results were equivocal regarding another measureâthe devalues strugglebelief. In the second measure, zero-sum thinking predicted the competitive victimhood beliefs forboth groups of participants, though there was a main effect of race such that black participantsscored higher on this measure than white participants. However, it is worth noting that devalues theracial struggle for civil rights only directly references ingroup disadvantage for one of the participantgroupsâthe black participants. As such, interpreting the association between zero-sum thinkingand the devalues struggle belief may only be meaningful within the black participant group. Sincethe effect was only significant within white participants, the results are ambiguous for testing myhypothesis with this dependent variable (though this would not be true if there had been a simplemain effect of within the group of black participants). From this result, we can only identify a maineffect of zero-sum thinking on the devalues struggle measure. Additionally, there was no effect onthe attitude measure for another potential measure of competitive victimhood.This study has several limitations regarding specific identities. While recruiting members ofspecific disadvantaged groups and using stimuli related to real identities increases the externalvalidity of findings, it also increases the chances that any effects may be driven by specific detailsof the actual groups or aspects of the particular stimuli. The study design introduces anotherlimitation. To identify effects related to the beliefs of people belonging to disadvantaged groups,I used non-disadvantaged participants as a comparison. In doing so, I assume that people self-reporting identification with a disadvantaged (or non-disadvantaged) group perceive themselves asmore (or less) disadvantaged in line with historical group stereotypes. I provide evidence withoutrelying on identities with historical associations in the study using the minimal group paradigm.321.11. Study 4: Minimal Group Paradigm1.11 Study 4: Minimal Group ParadigmI designed this study to investigate the relationship between zero-sum thinking and competitivevictimhood while addressing some of the limitations of previous studies, such as the dependence ongroup identities with historical characteristics, the limitations of comparison between disadvantagedand non-disadvantaged participants with regard to identifying competitive victimhood beliefs, andthe inherently correlational nature of comparisons between participants recruited from specificgroups.This study used a minimal group paradigm (Diehl, 1990; Tajfel, 1970; Tajfel & Turner, 1979)to experimentally control for details particular to group identities with rich historical meaning, alimitation of several earlier studies, and in so doing increases the internal validity of any findings.As discussed in the conceptual development above, the minimal group paradigm creates groupidentities using âmereâ categorization, which means categorization based on seemingly arbitrarycharacteristics such as randomly assigned shapes (for example, assigning some people the triangleidentity and some people the circle identity) or individual idiosyncrasies with little substantivecontent, such as telling participants (using deception) that they belong to an âover-estimatorâor âunder-estimatorâ group, when in reality the labels are purely based on random assignment.Though a more immersive methodology might better capture an ecologically valid situation or ahistorically weighty identity group, the purpose of this study is to prioritize the internal validity ofrelationships between constructs, which random assignment supports.To operationalize group-based disadvantage, I presented participants with information aboutchallenges faced by (minimal) ingroup and outgroup, providing some groups with information aboutingroup disadvantage. Random assignment determined the framing of relative disadvantage. Thatis, rather than compare the reactions of participants from disadvantaged and non-disadvantagedgroups, my identification strategy for this study was to induce disadvantaged minimal group iden-tity and then compare the responses of participants to different framings of outgroup disadvantage:ingroup disadvantage, comparative disadvantage, and common disadvantage. I use these framingsto investigate whether zero-sum thinking is responsible for competitive victimhood beliefs betweendisadvantaged groups. The common disadvantage condition represents the beliefs members of onedisadvantaged group form about other disadvantaged groups, as information is provided about331.11. Study 4: Minimal Group Paradigmdisadvantage faced by the ingroup and disadvantage faced by an outgroup. I provide no explicitcomparative information about the relative disadvantage faced by the two groups in the commondisadvantage condition, which reflects the way people generally confront information about out-groups beyond the lab. The other two conditions provide comparisons. The ingroup disadvantagecondition represents beliefs a member of a disadvantaged group might hold regarding a member ofan unspecified group. In the comparative disadvantage condition, the stimuli provides informationabout which group suffers greater disadvantage, giving less scope for inferences driven by zero-sumthinking, but potentially still allowing other forms of ingroup-favoring or outgroup-disfavoring bi-ases to emerge if processes other than zero-sum thinking explain competitive victimhood betweendisadvantaged groups. This framing treatment is not designed to manipulate zero-sum thinking(which is measured as an individual difference), but to create the most experimentally clean, un-confounded example of group identity possible in the context of study constraints.For consistency with previous studies and past work on competitive victimhood, competitivevictim belief was the primary dependent variable. Additionally, the minimal group paradigmtypically assesses intergroup discrimination by comparing reward allocation between ingroup andoutgroup members (Billig & Tajfel, 1973) yielding a measure of ingroup favoritism. I used a versionof this ingroup favoritism measure where participants divided a small bonus sum between (minimal)ingroup and outgroup members. In summary, this study builds on previous results by increasinginternal validity (through experimental design) and by measuring a real economic behavior (bonusallocation) in addition to measuring competitive victimhood beliefs.1.11.1 MethodParticipants and DesignI recruited 400 participants using Amazonâs Mechanical Turk. 379 participants passed a bot check(see below for details). I excluded an additional 30 responses for failing an attention check, leaving349 responses for analysis (Mage = 37.45, SDage = 11.49, 134 female, 214 male, 1 nonbinary). Sur-vey software assigned participants randomly to one of three experimental conditions (disadvantageframing: common, ingroup, and comparative).341.11. Study 4: Minimal Group ParadigmProcedureAs a cover story for the minimal group paradigm, participants first completed a Big 5 personalityassessment (BFI2-S; 30 items) prior to minimal group categorization (Soto & John, 2017). Thisassessment included items such as I am someone who keeps things neat and tidy and I am someonewho has few artistic interests, general personality questions across many different aspects of per-sonality (1 = Strongly Agree . . . 7 = Strongly Agree). Categorization was random and did notdepend upon Big 5 scores. After completing the personality assessment, all participants read:Thank you for completing the personality questionnaire. Research has found that thisquestionnaire is predictive of outcomes in the workplace and other domains of life, in-cluding how others treat you. People can be categorized into different groups based theirresults. Based on your responses, you belong to INGROUP. Members of INGROUPtend to have doubts about whether they have made the right decision or done the rightthing, have some aspirations that are pretty unrealistic, possess a great deal of unusedpotential which has yet to be explored, and have a tendency to be self-critical.Research has found that on average people tend to rate this general description as accurate(Forer, 1949). Survey software randomly counterbalanced whether the ingroup label was Group Bor Group C ; software used the other label for the outgroup later during the scenario task. On thefollowing screen, participants completed a comprehension check, choosing assigned ingroup labelfrom several options (Group AâGroup D).Next, participants each read one scenario corresponding to randomly assigned experimentalcondition and then completed three post-scenario questions. In all conditions, participants readabout disadvantages that members of the ingroup faced (for example, greater likelihood of beingcriticized):Please imagine that research is published showing that, compared to the average worker,members of INGROUP earn lower wages, are less likely to be hired, are less likely tobe promoted, and are more likely to be criticized in front of colleagues. However,independent examination of work quality reveals no difference between members ofINGROUP and the average worker.351.11. Study 4: Minimal Group ParadigmThis consisted of the full text for participants in the ingroup disadvantage condition. In thecommon disadvantage condition, participants also read about how members of an outgroup werebringing attention to their own disadvantages:Additionally, the research found that members of OUTGROUP, another group, facedvarious disadvantages in the workplace. Members of OUTGROUP have begun to bringattention to the disadvantages faced by members of their group and advocate for newworkplace policies.The text in the common disadvantage lacked any direct comparison between disadvantagedgroups, but based on my theorization, I would expect people high in zero-sum thinking to reactwith competitive victimhood to defend access to potentially scarce resources such as support fromthird-parties. In contrast, in the comparative disadvantage condition participants read about howthe disadvantage faced by ingroup members was greater than the disadvantage faced by membersof the outgroup:Additionally, the research found that members of INGROUP face significantly greaterdisadvantage compared to OUTGROUP, another group. However, members of OUT-GROUP have begun to advocate for new workplace policies to benefit their group,claiming that disadvantages faced by OUTGROUP deserve greater recognition.For example, if the ingroup was Group B and the outgroup was Group C, then in the com-parative disadvantage condition a participant would have read about the disadvantages faced byGroup B, the disadvantages faced by group C, and about how Group B faced greater disadvantagethan Group C.After reading the scenario, participants responded to post-scenario questions about discrimi-nation beliefs (Members of INGROUP are discriminated against ; 1 = Strongly Disagree . . . 7 =Strongly Agree), competitive victimhood (Compared with OUTGROUP, members of INGROUPexperience discrimination; 1 = Less Overall, 4 = As Much, 7 = More Overall), and socialmedia sharing behavior (How likely would you be to share a news article on social media explainingthe treatment of INGROUP? ; 1 = Extremely Unlikely . . . 7 = Extremely Likely). I derived the361.11. Study 4: Minimal Group Paradigmfirst two items from previous work on competitive victimhood (Sullivan et al., 2012). The thirdwas an exploratory measure.Ingroup Favoritism (Allocation Task) Next, participants engaged in a bonus allocation taskto measure ingroup favoritism (Allen & Wilder, 1975). In the bonus allocation task, participantsdistributed a small monetary rewardâ75 cents US, in 5-cent stepsâbetween non-self ingroup andoutgroup members. In the allocation, the participant chose one column from a choice matrixindicating a bonus split between a non-self ingroup and outgroup member. The top row alwayscorresponded to the ingroup and the bottom row always corresponded to the outgroup, with groupname labels in a leftmost column. For example, choosing the second column from the choicematrix would award a 10 cent bonus to a non-self ingroup member and award a 65 cent bonus toan outgroup member. Such a decision would represent relatively weak ingroup favoritism.INGROUP .05 .10 .15 .20 .25 .30 .35 .40 .45 .50 .55 .60 .65 .70OUTGROUP .70 .65 .60 .55 .50 .45 .40 .35 .30 .25 .20 .15 .10 .05Table 1.9: Ingroup Favoritism Choice MatrixAfter the scenario and post-scenario questions, participants completed a measure of zero-sumthinking (Ro´zËycka-Tran et al., 2015) and demographic questions. Embedded near the end of thedemographic questions were an attention check and bot check. The attention check prompt wasI often visit the moon. This item is just to see if you are paying attention. Please select stronglydisagree. (1 = Strongly Disagree . . . 7 = Strongly Agree). The bot check was a free-responsetext item (prompt: What did you eat for your most recent meal or snack? ). I coded responses asbot or human based on whether the text response made sense with respect to the prompt. Withinquestion blocks, survey software randomized presentation of post-scenario questions and zero-sumthinking items.1.11.2 ResultsI scored zero-sum thinking by averaging the scale items (α = .93, 8 items). Since I measuredzero-sum thinking near the end of the survey, to ensure that experimental condition did not affectthis score I performed an analysis of variance with zero-sum thinking score as dependent variable371.11. Study 4: Minimal Group Paradigmand experimental condition as independent variable. The analysis of variance revealed no effect oftreatment on zero-sum thinking (F (2, 346) = 0.02, p = .980), suggesting that the score is acting asan individual difference measure.To test my hypothesis that competitive victimhood arises from zero-sum thinking when con-fronted with the disadvantage of an outgroup, I performed an analysis of covariance with competi-tive victimhood beliefs as dependent variable. Independent variables were experimental condition,zero-sum thinking (z-scored), and the interaction term (adjusted R2 = .15). There were signifi-cant effects associated with all predictors (p < .001). To examine estimates of the effects, I ran aregression with competitive victimhood beliefs as the dependent variable and the same indepen-dent variables, using common disadvantage framing condition as baseline. See table 1.10 for modelestimates. Interpreting these estimates, participants in the common disadvantage exhibit lowercompetitive victimhood beliefs compared to either of the other two conditions (the main effect ofexperimental condition). Additionally, zero-sum thinking is positively associated with competitivevictimhood beliefs (the main effect of zero-sum thinking, a continuous variable). The negative(and significant) coefficient of the comparative disadvantage Ã zero-sum thinking term indicatesthat zero-sum thinking shapes competitive victimhood beliefs for members of disadvantaged groupswhen presented with the suffering of a disadvantaged outgroup, consistent with my hypothesis. Thefact that the ingroup disadvantage Ã zero-sum thinking term is not significant indicates that theinfluence of zero-sum thinking on competitive victimhood beliefs functions similarly in these twoconditions. See figure 1.4 for a visual presentation and below for a statistical analysis of the slopesbetween conditions.Predictor b SE t pIngroup disadvantage (indicator 0/1) .83 .19 4.41 < .001Comparative disadvantage (indicator 0/1) .82 .18 4.60 < .001Zero-sum thinking (continuous; z-scored) .67 .13 5.30 < .001Ingroup disadvantage Ã Zero-sum thinking â.22 .19 â1.16 .247Comparative disadvantage Ã Zero-sum thinking â.75 .19 â4.14 < .001Constant 4.49 .12 36.06 < .001Table 1.10: Minimal Group Paradigm: Regression Results (DV: Competitive Victimhood)I compared the effect of zero-sum thinking on competitive victimhood across conditions, fo-cusing on the comparisons between common disadvantage and the other two conditions. The rela-381.11. Study 4: Minimal Group Paradigmtionship between zero-sum thinking and competitive victimhood beliefs is captured by the slope ofthe linear prediction by condition: common disadvantage framing ( dydx = 0.67, SE = .13, p < .001),ingroup disadvantage framing ( dydx = 0.45, SE = .14, p = .003), and comparative disadvantageframing ( dydx = â0.08, SE = .13, p = 1.000). The comparison between the common disadvantagecondition and the other two conditions together was significant ( dydx = .48, SE = .16, p = .002).Figure 1.4: Minimal Group Paradigm: Competitive VictimhoodTo test whether ingroup favoritism behavior, a common operationalization of discriminationunder in a minimal group paradigm, is similarly a function of zero-sum thinking in the commondisadvantage context, I performed an analysis of covariance with ingroup favoritism (bonus awardallocation) as dependent variable. Independent variables were experimental condition, zero-sumthinking (z-scored), and the interaction term (adjusted R2 = .07). There was a significant maineffect of zero-sum thinking (p < .001) and a significant interaction (p = .033). To examine estimatesof the effects, I ran a regression with ingroup favoritism as the dependent variable and the same391.11. Study 4: Minimal Group Paradigmindependent variables. See table 1.11 for model estimates.Predictor b SE t pIngroup disadvantage (indicator 0/1) 2.97 1.59 1.87 .062Comparative disadvantage (indicator 0/1) 2.73 1.52 1.80 .073Zero-sum thinking (continuous; z-scored) 4.88 1.07 4.57 < .001Ingroup disadvantage Ã Zero-sum thinking â4.07 1.58 â2.58 .010Comparative disadvantage Ã Zero-sum thinking â2.50 1.54 â1.63 .104Constant 47.22 1.06 44.71 < .001Table 1.11: Minimal Group Paradigm: Regression Results (DV: Ingroup Favoritism)I compared the effect of zero-sum thinking across conditions, focusing on the comparisonsbetween common disadvantage and the other two conditions. The relationship between zero-sumthinking and ingroup favoritism is captured by the slope of the linear prediction by condition:common ( dydx = 4.88, SE = 1.07, p < .001), ingroupdydx = 0.80, SE = 1.16, p = .491), andcomparative dydx = 2.37, SE = 1.10, p = .032). The comparison between the common disadvantagecondition and the other two conditions together was significant (p = .014).401.11. Study 4: Minimal Group ParadigmFigure 1.5: Minimal Group Paradigm: Ingroup FavoritismWhen we emphasize comparative disadvantage, people will adopt the mindset of competitivevictimhood, based on prioritizing ingroup favoritism. I had no theoretical predictions for differencebetween comparative and ingroup disadvantage framings in competitive victimhood responses asmy hypothesis primarily concerned the comparison between common disadvantage and the otherconditions. Based on these results, however, my post hoc interpretation is that comparative disad-vantage does not completely negate the effect of zero-sum beliefs but weakens the effect of rejectingthese beliefs to behave with greater impartiality and less ingroup favoritism.1.11.3 DiscussionThe framing of disadvantage changed how zero-sum thinking influenced competitive victimhood.Specifically, in the comparative condition, zero-sum thinking was not a factor in competitive vic-timhood responses. However, in the common condition, which represents when a member of a411.11. Study 4: Minimal Group Paradigmdisadvantaged group is exposed to the appeals of a disadvantaged outgroup, zero-sum thinkingshaped responses, consistent with hypothesis 3. Though the influence of zero-sum thinking oncompetitive victimhood was highest in the common disadvantage condition, one result that is con-sistent with previous research is worth highlighting: the common condition was also the conditionwith lowest overall competitive victimhood scores. One way of interpreting this result is thatmembers of disadvantaged groups tend to have more positive attitudes toward other disadvantagedgroups that are stigmatized along similar dimensions, as documented in the work of Craig andRicheson (2016), unless zero-sum thinking is high. From another perspective, making only ingroupdisadvantage salient, or explicitly providing comparative framing in favor of the ingroup seems toneutralize the effect of zero-sum thinking. This study also contributes to the literature on compet-itive victimhood by demonstrating the role of zero-sum thinking in both competitive victimhoodbeliefs and ingroup favoritism behaviors. The pattern of results was slightly different for thesetwo measures, though in both measures the influence of zero-sum thinking was strongest in thecommon condition. In particular, the slope of effect when in the ingroup condition was steeper forthe competitive victimhood item than for the favoritism measure. I had no prediction about thiscomparison, but speculatively ingroup favoritism and competitive victimhood beliefs, while related,may have different antecedents.One limitation of this study is that several attitude or belief outcome measures display mod-erate ceiling effects based on examination of scatter plots, with some degree of clustering towardone end of the scale. Ceiling effects can sometimes inflate the incidence of type 1 errors, but thisdanger is most pronounced for measures with objective characteristics such as income and alcoholconsumption, rather than the subjective Likert-type scales I employ in this research (P. C. Austin& Brunner, 2003).These results suggest a future direction for research involving the beliefs contrary to zero-sumthinking. Whether such beliefs involve merely the absence of beliefs about the inherent conflictover resources or an explicit constellation of beliefs related to the idea that the success of otherscan benefit the self or ingroup as well requires further theoretical clarification. Early versions of thezero-sum thinking scale (Ro´zËycka-Tran et al., 2015) included four additional reverse-coded itemswhich could be read as characteristic of such shared-interest beliefs. These four items were:421.12. General Discussionâ¢ When a person does much for the good of others, he or she profits as well.â¢ Those who give much to others receive much from them.â¢ People who do much for their own good frequently benefit others as well.â¢ When the number of rich people increases in the country, the poorer people benefit as well.Psychometric analysis led the scaleâs original authors to recommend dropping those items andonly using the other eight items where high scores represented agreement with beliefs characteristicof zero-sum thinking; I followed this recommendation in my studies, based on my initial primaryinterest in zero-sum thinking as explanation. However, based on my findings, it might yield divi-dends to explore concepts related to those discarded items, treating them as a separate but relatedconstruct that could be a potential predictor of decreased intergroup derogation and decreasedcompetitive victimhood when both ingroup and outgroup disadvantages are salient to members ofdisadvantaged groups.1.12 General DiscussionIn chapter 1 of my dissertation, I have shown that, contrary to general expectations (study 2),members of disadvantaged groups sometimes engage in intergroup conflict, including responding toclaims of disadvantaged outgroup victimhood by competing for victim status (studies 3â4). Thisadds to the existing literature in two ways. First, I provide empirical evidence consistent withhistorical and journalistic accounts of disadvantaged groups competing for victim status. Second, Idemonstrate the role of a psychological process, zero-sum thinking, in explaining this phenomenon.I demonstrate this effect using a multi-method approach, with content analysis of social mediatextual data demonstrating intergroup derogation and survey-based experiments demonstratingcompetitive victimhood between disadvantaged groups.To explore potential psychological mechanisms, I hypothesized that zero-sum thinking mightexplain when members of disadvantaged groups engage in competitive victimhood. Zero-sum think-ing is difficult to operationalize in textual data, so I performed two survey-based experiments thatused a validated self-report measure of zero-sum thinking.Additionally, to explore whether this effect is bound strongly to particular historical identities,I used the minimal group paradigm (Smith, 2010) to investigate whether people given transient431.12. General Discussionidentities without historical weight might react similarly and whether this effect would also be asso-ciated with zero-sum thinking. Results of an experiment supported the role of zero-sum thinking incompetitive victimhood between disadvantaged groups in the minimal group paradigm. Along withproviding evidence that competitive victimhood seems to occur as a result of general social identityprocesses, rather than being entirely expressions of particular historical intergroup comparisons,study 5 also demonstrated a behavioral outcome involving allocation of bonuses between ingroupand (disadvantaged) outgroup members.Though I have primarily investigated one mechanism (zero-sum thinking) to explain compet-itive victimhood behavior, there are many other potential alternative explanations. For example,earlier when explaining the predictions of self-categorization theory, I mentioned the categorizationcriteria based on differences or similarities, specifically the role that superordinate identities couldplay in decreasing competitive victimhood behavior. In the case of complex identity-related phe-nomena such as competitive victimhood, it seems most plausible that many mechanisms work inparallel to influence beliefs and behaviors. However, some alternative mechanisms identified by pre-vious research may be less likely to be a factor in competitive victimhood as operationalized in thesestudies. For example, White and colleagues have investigated a phenomenon they term âhorizontalhostilityâ (White et al., 2006). In this research, they argue that more distinctive minority groupswill engage in prejudice against less distinctive minority groups. Or, put another way, minoritieswill be prejudiced against other minorities that are more mainstream. According to this concep-tual formulation, âHorizontal hostility . . . is unidirectional: The targets of horizontal hostility arealways members of a similar minority group that is more mainstream than the minority ingroupâ(White & Langer, 1999, p. 555). In many of the conflicts identified in journalistic and historicalaccounts, one could indeed categorize the groups into more mainstream and less mainstream: incompetitive victimhood between some feminists and transgender women, feminists would probablybe seen as more mainstream. Similarly, in competitive victimhood between racial minorities andminorities defined by sexuality, the racial minority identity might often be seen as more mainstream(by virtue of the civil rights movement history). Despite this seeming applicability, asymmetrichostility does not seem to characterize competitive victimhood, either conceptually or as I haveobserved empirically in my data. That is, the âmore mainstreamâ disadvantaged identity groupoften seems to engage in hostility toward the âless mainstreamâ disadvantaged identity, as well441.12. General Discussionas vice versa. As such, though I have every reason to believe that horizontal hostility dynamicsmight emerge as well when degree of group distinctiveness is at play, the two explanations seemorthogonal rather than competing alternative explanations. Future research could perhaps exploreadditional linkages between these mechanisms.1.12.1 LimitationsDespite detecting this effect on several different social media platforms, using multiple disadvan-taged identities as stimuli, and in online samples with wide demographic coverage, these studieshave several limitations. Though the Amazon Mturk participants are more representative of generalNorth American population than many other common convenience samples (such as undergrad-uate students, which at the very least have a much-constrained range of age and life experiencescompared to online panel participants), nonetheless Mturk fails to be a truly representative sampleof the North American population (Stritch et al., 2017). As such, I am cautious about generalizingthese results, even though the dynamic seems to emerge using the minimal group paradigm. Thesample being mostly Western is an even larger limitation, from the perspective of generalization,as results may be peculiar to social dynamics in Western, educated, industrialized nations (Henrichet al., 2010). Additionally, I operationalize competitive victimhood in several different ways, andmeasure related, but not identical, constructs including intergroup derogation, ingroup favoritism,and general perceptions of whether disadvantaged groups will support appeals to help other disad-vantaged groups. This wide variety of beliefs, behaviors, and attitudes represents ways that thesedynamics often occur in the field, increasing the external validity (for some outcomes and groups)but presenting challenges to internal validity regarding the application of the zero-sum thinkingframework I propose. Finally, many of my studies rely on imagining hypothetical scenarios andself-reports of anticipated behaviors. It is possible that participants might fail to accurately reportor predict future behavior, due to issues such as affective forecasting deficits (Wilson & Gilbert,2005) or lack of salience with regard to personal priorities.Studies 1A and 1B, my studies that use social media datasets, suffer from limitations presentin analyses that use pre-existing objective classifications as independent variables, rather thanmanipulating constructs of interest directly (such as through experimental random assignment).As such, though my independent variable classifications were based on an examination of common451.12. General Discussionterms used on these platforms, I could have chosen a different set of terms, or included additionalwords. I performed no unreported analyses using alternative operationalizations of the independentvariables in the social media studies.Several dependent measures indicate some degree of ceiling effect, displaying decreased sensi-tivity to detect variation at extreme ends of the construct. For example, the belief that gay rightsare civil rights seems to display such a pattern upon examination of the scatter plot.1.12.2 Future DirectionsMany avenues exist for development of future research from these findings. First, my studies focusprimarily on antecedents of competitive victimhood between disadvantaged groups. Additionalstudies could explore potential psychological consequences for people who engage in competitivevictimhood. Even if competitive victimhood is sometimes adaptive in bolstering ingroup solidarityor recruiting support for an ingroupâs cause, the act of continually seeing oneâs self in the victimrole might have various deleterious consequences. There is a notable gap in the existing literatureregarding psychological consequences, as most research seems to focus on understanding causes andinterventions from an intergroup conflict perspective (for a representative example, see the commoningroup identity model: Gaertner et al., 1993). For example, it is possible that engaging incompetitive victimhood over time with regularity may have downstream consequences on outcomessuch as wellbeing or reputation.My research so far has focused on disadvantaged groups engaged in competitive victimhoodwhen confronted with the suffering of other disadvantaged groups. However, an individual persongenerally has many identities (Deaux, 1996), some of which could involve disadvantage while othersmight be situationally neutral or even provide advantages. As such, it is only generally within thecontext of a specific situation that a personâs constellation of identities settles into specific identitysalience, either by intrapsychic processes of attention (thinking about oneâs self as a national ofa particular country, for example) or external stimuli (such as someone shouting a racial slur).Given that I have found that zero-sum thinking plays a role in competitive victimhood behavior, itis possible that some identities might entail a greater likelihood of engaging in zero-sum thinkingcompared to others, which would have implications both for moderation by identity type (or perhapsidentity class) as well as potential interventions (such as activating identities that are, from a461.12. General Discussionpersonâs subjective view, less disadvantaged).Future research could explore whether the historical recency of an identity is more or lessassociated with the propensity to engage in competitive victimhood. It is possible that the modernbusiness cycle might be too short to enable the examination of new identities, though social mediaplatforms seem to have accelerated the formation of new identities (Gu¨ndu¨z, 2017) or amplifythe relevance of existing identities (Ray et al., 2017). And the changes in resources available todifferent groups as fortunes ebb and flow might also have consequences for competitive victimhoodbased on my findings. During periods of economic growth, zero-sum thinking might decrease,while the reverse might happen during economic contractions, leading to corresponding decreasesand increases in competitive victimhood behavior, assuming appropriate proxies can be found inarchival data.The complexity of identity also may help explain why members of groups that have manyobjective advantages might choose to engage in competitive victimhood driven by zero-sum thinkingas well. For example, a wealthy professional who is also a member of a disadvantaged ethnic grouphas several identities that could become active, as does a poor person of the dominant ethnicgroup. In fact, it could be that thinking in zero-sum terms makes it more likely for people toactivate identities involving disadvantage.Another avenue to investigate in future research, as suggested by my preliminary studies,is social media, which is a rich source of data regarding intergroup relations between disadvan-taged groups specifically, and identity-based groups more generally, but this very richness is itselfa challenge to operationalizing psychological concepts, as many existing validated measures requireinstruments such as self-report questionnaires. However, automated content analysis, using adap-tive techniques such machine learning, can support analysis of large data sets. In this dissertation, Iuse software to analyze sentiment in text, but this software is unable to measure more sophisticatedpsychological concepts such as zero-sum thinking and personality traits. However, researchers arebeginning to apply these techniques to measurement of user personality traits by analyzing textualcontent traces (Bleidorn & Hopwood, 2019; Stachl et al., 2020; Tay et al., 2020). For example,there is work on using deep neural network techniques to predict dark triad personality traits ofuses on social media platforms (Ahmad et al., 2020). I hope to use this sort of approach to explorethese ideas using social media datasets in the future beyond documenting phenomena.471.12. General DiscussionOne interpretation consistent with these results is that the salience of a disadvantaged out-groupâs disadvantage functions as a social identity threat because it indicates that other groupsmight be more deserving of help or recompense than the ingroup. By engaging in competitivevictimhood, participants could be bolstering their social identity by virtue of being worse off thanothers. This account thus identifies both a novel form of social identity threatâan outgroup beingmore deservingâand a novel form of coping with the social identity threatâreinforcing the sense ofingroup victimhood. The social identity threat is novel with respect to existing literature becausemost social identity threats fit the model of devaluation based on social identity, such as lack ofperceived positive qualities due to social stereotype (Steele et al., 2002). Future research could po-tentially investigate whether other ways to cope with social identity threat might substitute for thecompetitive victimhood response. For example, might some other opportunity for self-affirmationdecrease competitive victimhood beliefs in members of disadvantaged groups when confronted withthe plight of disadvantaged outgroups?Along with this methodological avenue, these results suggest that understanding the causesof zero-sum thinking, and related constructs, might help paint a fuller picture of the dynamicsof competitive victimhood, as identifying such causes would allow identification of when, or bywhom, competitive victimhood is likely to occur. For example, the situational sense of scarcity (orplenty) might amplify (or dampen) the effect of zero-sum thinking. In my studies, I either measureproxies for existing identity (such as race) or induce identity using the minimal group paradigm. Itseems plausible that some degree of identity salience might be necessary for competitive victimhoodresponses to occur. be particularly relevant in commercial contexts, where marketers sometimesuse scarcity framing to increase desirability. Such work would also add to the broader literature onzero-sum thinking.Additionally, such understanding would be useful for those who wish to develop interventionsto curtail competitive victimhood dynamics, as, according to journalistic and historical accounts,competitive victimhood is associated with intergroup hostility and other negative outcomes, suchas intractable conflicts. In the discussion of study 4, I outline one approach which seems to havepromise for understanding other constructs that might explain decreased competitive victimhoodresponses that flows directly from the zero-sum thinking literature: looking at whether there is ashared outcome alternative to zero-sum thinking. It is also possible that other personality or situ-481.12. General Discussionational traits may influence when disadvantaged groups in particular engage in zero-sum thinking,with consequences for competitive victimhood beliefs and behavior. For example, high neuroti-cism or emotional reactivity might amplify the social identity threat experienced and lead to evengreater competitive victimhood reaction (or the reverse effect for low neuroticism. In chapter 2 ofthis dissertation, I explore the effect of an individual difference concerning signaling victimhood;it is plausible that such stable individual differences in victim signaling might also moderate thecompetitive victimhood outcomes I observe in study 1. Finally, though several of my preliminarystudies use extensive real-world data sets, these studies have demonstrated phenomena rather thanisolating psychological mechanisms. As such, it would be useful for future research to tackle theexternal validity of my explanatory framework. This could involve field studies or, as discussedabove, other approaches to analyzing data from social media platforms.Chapter 1 examined perceptions of victimhood as an outcome with implications for intergrouprelations, including derogation, ingroup favoritism, and the likelihood of cooperation. In the nextessay, I examine a signaling behavior related to the perception of victimhood as a predictor variablerather than as an outcome. I look at the way that differences in virtuous victim signalingâanindividual difference that captures whether people signal to others that they are both virtuous buthave sufferedâpredict different attitudes and behaviors for consumers in commercial interactions.49Chapter 2Victimhood Perception andCommercial InteractionsConsumers can form negative attitudes toward firms based on many factors related to attributesof a specific firm, or its behaviors, including product failure (Day & Ash, 1979), firm moral vio-lations or unethical behaviors (Ferrell et al., 2019; Folkes & Kamins, 1999; Romani et al., 2015),positions on social issues (Sen & Morwitz, 1996), political beliefs (Hollenbeck & Zinkhan, 2006),and corporate scandals (Dawar & Pillutla, 2000; Fong & Wyer, 2012), among other causes. How-ever, consumers also sometimes react negatively toward firms based on factors unrelated to firmattributes or behaviors, such as consumer personality traits. For example, the dark triad person-ality traits are associated with malicious consumer behavior directed at harming firms or brands(T. Hancock, 2020). Additionally, the online phenomena of âtrollingââa deliberate attempt toprovoke reactions from other users (Golf-Papez & Veer, 2017)âshows that some consumers seemto be motivated to attack other people and firms because they find it pleasurable or are attention-seeking by disposition (Lopes & Yu, 2017). The wide variety of firm- and brand-related stimulionline provides ready grist for the trollâs mill (Pehlivan et al., 2011). These examples show thatconsumer negativity toward firms can emerge from effects grounded in the person of the consumer,rather than entirely as reasoned or emotional consequences of firm attributes or behaviors. Inthis research, I add to the literature on consumer negativity toward firms grounded in consumerindividual differences, examining the role of consumer victimhood perceptions in service experiencecontexts where personal harm or insult is lacking.Previous research on consumer hostility in retail contexts has primarily focused on interactionsbetween consumers and sales representatives (Richins, 1983a; Rose & Neidermeyer, 1999). Thisresearch often focuses on consumer misbehavior and disruption, often as contingent upon particular50Chapter 2. Victimhood Perception and Commercial Interactionsconsumer experiences such as service failure (Gelbrich, 2010; Strizhakova et al., 2012). Otherforms of consumer negativity that has been studied primarily within the context of causes suchas preexisting brand attitudes or service failure include negative word-of-mouth (Laczniak et al.,2001; Richins, 1983b), and consumer complaining (Gilly & Gelb, 1982; Maxham III & Netemeyer,2002).Also within the broad umbrella of causes related to preexisting brand attitudes, some consumerhostility is explained by firm- or brand-based hate (Fetscherin, 2019; Kucuk, 2019a). Research hasfound that poor performance or unethical reputation are antecedents of brand hate (Kucuk, 2019b).Along with such factors, which firms have some degree of direct control over, I suggest that someconsumers may, by disposition, be more likely to interpret ambiguous or neutral service experiencesnegatively.I investigate this phenomenon using the construct of virtuous victim signaling (Ok et al., 2020),a set of behaviors signaling both virtue and victimization that people use to recruit support fromothers as signaling victimization indicates harm suffered and signaling virtue deflects attributionfrom the signaler. For example, behaviors characteristic of this constellation of signals might includepublicizing donations to a worthy charity (a virtue signal) and complaining about an ingroupidentity is underrepresented in the media (a victim signal). By signaling virtuous victimhood,consumers can make a case for their deservingness based on experiencing unfair oppression ordisadvantage. Though virtuous victimhood is a multi-dimensional construct, it can be measuredusing a validated self-report scale and is an individual difference that is stable over time. As a setof behavioral tendencies, virtuous victim signaling is likely the expression of underlying personalitytraits. For example, Ok et al. (2020) explored virtuous victim signaling as a strategy positivelyassociated with dark triad traits. I propose that consumers high in virtuous victim signalingwill be ready to interpret all commercial interactions through a more critical, grievance-relatedlens, even when the interaction itself is innocuous, because such consumers have a particular self-view, that they are the kind of person likely to be treated unfairly or discriminated against incommercial interactions. Previous research has show that the effect of virtuous victim signaling onoutcomes such as unethical behavior occurs when controlling for identities historically associatedwith disadvantage (Ok et al., 2020), suggesting that, at least in some cases, virtuous victim signalingneed not be tied to specific visible identity categories. In summary, I suggest that consumers high in512.1. Hostile Reactive Attitudesvirtuous victimhood will be more likely to react with hostility toward firms when service infractionor product failure is ambiguous or absent, unlike consumers low in virtuous victimhood, who resortto dramatic reparative actions only when confronted with clear service failure.2.1 Hostile Reactive AttitudesFollowing many commercial interactions with firms, consumers must decide how to react, suchas when evaluating service quality, responding to a salesperson, or deciding whether to leave areview. In particular, evaluative reactions to such interactions, such as judging quality or demandingrestitution, involve interpreting insult or affront and deciding upon appropriate reactions, whichcould include ignoring slights, demanding restitution, or even responding with violent aggression(Cohen et al., 1996). Previous research has found that the interpretation of ambiguous situationsby consumers is shaped by consumer dispositions. For example, research on the personality ofaggression has found that propensity to interpret ambiguous behavior as hostile are more likely toengage in aggression (Dill et al., 1997). Dodge and Coie (1987), in studies among male children,further distinguished between two types of aggressive personality: proactive and reactive, findingthat reactive-aggressive types exhibited a bias leading to likelihood to interpret an ambiguoussituation as provocation and a consequent propensity to display hostile reactive attitudes andengage in aggressive behavior. These findings have a number of limitations when applied to thegeneral consumers due to the population and study context, but suggest hostile reactive attitudesin ambiguous or neutral cases may be shaped by individual differences in sensitivity to provocation.The literature documents instances where consumers react negatively to companies even whenthe consumer has not, themselves, had a negative experience. In one series of studies, researchersshowed that schadenfreude associated with product failure experienced by other consumers whenusing a status brand was associated with negative attitudes toward the brand and led to spread-ing negative word-of-mouth about the brand (Sundie et al., 2009). Consumers also may react tothreatening information about the ingroup by derogating a product associated with an outgroup(Chae et al., 2017), another example of consumer behavior related to negative attitudes withoutexperience product or service failure personally. Johnson et al. (2011) found that consumers hav-ing strong relationships with a brand, following brand transgression, were more likely to engage522.2. Virtuous Victim Signalingin anti-brand activities, even in the absence of a subsequent commercial experience. In contrastto these findings, which often focus on situational or brand-specific causes, many consumer hostilereactive attitudes toward firms are contingent upon behaviors or attributes of firms. For example,a consumer may react with anger or disappointment upon receiving the wrong item in an order.For an example of negative response based on firm trait, consider the reaction of a consumer whovalues avoiding animal cruelty when that consumer learns that a firm is engaged in testing productson animals. Even lacking particular commercial failure, such values may prompt hostile reactiveattitudes in this consumer toward the firm. However, when a commercial interaction is ambigu-ous, it may be more fruitful to consider individual differences between consumers when explainingdifferences in consumer responses, such as personality traits. Research has identified several per-sonality traits associated with hostile reactions. For example, Daunt and Harris (2011) proposedthat consumer alienation, Machiavellianism, sensation seeking, aggressiveness, and self-esteem areall associated with consumer misbehavior. Several of these traits are consistent with beliefs andpersonality elements underlying virtuous victim signaling, such as alienation and elements of thedark triad.2.2 Virtuous Victim SignalingVirtuous victim signaling is a behavioral tendency to highlight personal disadvantage combinedwith personal virtue (Ok et al., 2020). Signal contents have two underlying dimensions, with oneset of signals involving victimhood, such as accusation and unfairness, and the other set of signalsinvolving personal virtue, measured as moral identity symbolization (Aquino & Reed II, 2002).The perceived victimhood dimension includes beliefs about systemic underrepresentation, lack ofpolitical voice, and anxiety about broad identity-based rejection. The virtue dimension includesbehaviors such as engaging in hobbies, belonging to organization, or buying products that identifyone as being virtuous. One function of such signaling is to recruit support from sympathetic thirdparties, and, as such, virtuous victim signaling behaviors are likely most adaptive in in culturesthat prioritize moral status of victims (Campbell & Manning, 2018).Though claims of victimization and virtue can reflect actual disadvantage, suffering and mis-treatment, previous research has found that virtuous victim signaling, after controlling for various532.3. Hypothesesdemographic factors associated with systemic disadvantage, is associated with dark triad personal-ity traits. Additionally, the association between virtuous victimhood and several classes of unethicalbehavior, such as lying and exaggeration to benefit the self, seems to be an expression of dark triadpersonality traits (Ok et al., 2020). These findings also suggest, though do not test directly, thatpeople high in virtuous victim signaling would find evidence of mistreatment useful for signalingpurposes and so would remain vigilant for potential affronts or examples of being treated unfairly,leading to a greater likelihood of detecting affront and then expressing hostile reactive attitudes,such as posting negative reviews or complaining to friends about the experience.2.3 HypothesesI take an individual difference perspective, examining how aspects of consumer traits systematicallyshape hostile reactive attitudes of consumers toward firms, such as complaints, negative word ofmouth, and negative reviews. My first hypothesis flows from the tendency of consumers high invirtuous victim signaling to tell others how they are treated unfairly. I expect that such consumerswould be vigilant in any commercial interaction for signs of being treated badly and so would bemore likely to have negative reactions in all commercial interactions, rather than only in contextswhere service failures (or similar events) have occurred. Further, given the self- and identity-focus ofthe beliefs associated with virtuous victim signaling, I propose a second hypothesis, that consumershigh in victim signaling are more likely to see themselves as the kind of person that experiencesdiscrimination in commercial contexts. Thus:H1. Virtuous victim signaling is more positively associated with hostile reactive attitudes whencommercial experiences are neutral, compared to when commercial experiences are negative.H2. People who engage in more virtuous victim signaling behaviors are more likely to see them-selves as the kind of person who faces discrimination in commercial experiences and thisself-perception explains the relationship between virtuous victim signaling and hostile reac-tive attitudes toward firms.542.4. Study 1: Customer Reaction to Service Failure2.4 Study 1: Customer Reaction to Service FailureStudy 1 tests my first hypothesis, that virtuous victim signaling is positively associated with hostilereactive attitudes when commercial experiences are neutral (as opposed to negative). This studyuses an airline service scenario where service quality is either unambiguously negativeâbeing askedto change seats without any justification based on fairnessâor neutral.2.4.1 MethodParticipants and DesignI recruited 343 participants using Amazonâs Mechanical Turk in exchange for small monetary com-pensation. I excluded 27 responses for failing either of two attention checks, leaving 326 responsesfor analysis (Mage = 38.50, SDage = 11.86, 182 female, 141 male, 3 nonbinary). Survey softwareassigned participants randomly to one of two experimental conditions (service experience: neutral,failure). The design was mixed: 2 (experimental condition) Ã continuous (virtuous victim signal-ing individual difference). I expect service failure will lead to hostile reactive attitudes generally,but high virtuous victim signalers will also exhibit hostile reactive attitudes in the neutral servicecondition.ProcedureParticipants each read one scenario corresponding to randomly assigned experimental condition andthen completed several post-scenario questions. Both scenarios began with the same introductoryparagraph:You board a small plane with a friend in the early hours of the morning. As there arefew passengers, the flight attendant tells you to sit anywhere, so you choose seats nearthe front of the plane and across the aisle from one another.In the neutral condition, participants imagined an airline commercial experience where a flightattendant asks several passengers to switch seats based on boarding precedence, in an effort tobetter balance distribution of weight on the plane.552.4. Study 1: Customer Reaction to Service FailureAt the last minute, three passengers enter the plane and take seats in front of you. Justbefore takeoff, the flight attendant tells those three people to move to the back of theaircraft to better balance the planeâs weight. They move to the back.One of the three other passengers asks the flight attendant why they need to moverather than some other passengers. The flight attendant tells them they need to movebecause they boarded last. Those passengers take their bags and move to the back ofthe plane.In the service failure condition, participants imagined an airline commercial experience withsimilar events, but the situation involves ambiguous fairness regarding passenger selection.At the last minute, three passengers enter the plane and take seats in front of you. Justbefore takeoff, the flight attendant tells you to move to the back of the aircraft to betterbalance the planeâs weight.Since you had entered the plane first, you ask why the flight attendant did not requestthe people in front to move instead of you. The attendant becomes indignant, statingthat you had misunderstood intentions, and refuses to talk about the matter any further.You move to the back of the plane.Following the scenario task, participants responded to several questions about the scenario(fairness, service quality) as manipulation checks and reported hostile reactive attitudes. Examplesof hostile reactive attitudes included Post a negative review about the airline online after the flightand Complain to friends about the experience (1 = V ery Inappropriate . . . 7 = V ery Appropriate).Participants completed the victim signaling and moral identity scales. As exploratory mea-sures, participants answered questions about personal values drawn from the European Social Sur-vey (ESS, 2016), a four-item measure of beliefs about personal dignity (Ijzerman & Cohen, 2011).Finally, participants completed demographic questions (age, gender, ethnicity, political orientationon the social dimension, and political orientation on the economic dimension). Embedded nearthe end of the demographic questions was an attention check. The attention check prompt was Ioften visit the moon. This item is just to see if you are paying attention. Please select strongly dis-agree. (1 = Strongly Disagree, . . . 7 = Strongly Agree). Within question blocks, survey software562.4. Study 1: Customer Reaction to Service Failurerandomized presentation of scale items.2.4.2 ResultsTo create aggregate scores for scales, I averaged the items for hostile reactive attitudes (α = .78),victim signaling (α = .89), and moral identity symbolization (α = .91). The moral identitysymbolization score and the victim signaling score are correlated (r(324) = .11, p = .046). Followingthe procedure validated in Ok et al. (2020), I then standardized the victim signaling and moralidentity symbolization items and averaged the two scores to compute a virtuous victim signalingscore. Along with reporting results using this aggregate virtuous victim signaling score for ease ofcomparison with other published findings, I also report analyses using each score (moral identitysymbolization and victim signaling) separately to examine whether either of the components areindependently more influential.To test my hypothesis that victim signaling is positively associated with hostile reactive atti-tudes when service is neutral, I performed an analysis of covariance with hostile reactive attitudes asdependent variable. Independent variables were experimental condition, virtuous victim signaling(z-scored), and the interaction term (adjusted R2 = .53). There was a main effect of experimentalcondition (b = 1.65, SE = .13, t = 12.41, p < .001), a main effect of victim signaling (b = .53,SE = .11, t = 4.79, p < .001), and a significant interaction (b = â.40, SE = .14, t = â2.88,p = .004). See table 2.1 for model estimates.Predictor b SE t pCommercial Failure (indicator 0/1) 1.65 .13 12.41 < .001Virtuous Victimhood (continuous; z-scored) .53 .11 4.79 < .001Commercial Failure Ã Virtuous Victimhood â.40 .14 â2.88 .004Constant 2.83 .10 28.65 < .001Table 2.1: Regression Results Virtuous Victimhood (DV: Hostile Reactive Attitudes)To further explore the interaction, I computed the marginal effect of virtuous victimhood onhostile reactive attitudes for each experimental condition: neutral ( dydx = .53, SE = .11, t = 4.79,p < .001) and failure ( dydx = 0.13, SE = .08, t = 1.59, p = .114). The significant slope for theneutral condition means that virtuous victimhood is a good predictor of hostile reactive attitudesonly in the experimental condition without commercial failure. See also figure 2.1 for a graphical572.4. Study 1: Customer Reaction to Service Failurerepresentation.Figure 2.1: Study 1: AirlineAs noted above, I also performed regressions using each of the components of virtuous victim-hood individually. See tables 2.2 and 2.3 for model estimates. As you can see, in this study thevictim signaling component seems to be primarily responsible for the effect observed.Predictor b SE t pCommercial Failure (indicator 0/1) 1.62 .13 12.46 < .001Victim Signaling (continuous; z-scored) .63 .10 6.02 < .001Commercial Failure Ã Victim Signaling â.41 .13 â3.05 .002Constant 2.85 .10 29.57 < .001Table 2.2: Regression Results Victim Signaling (DV: Hostile Reactive Attitudes)582.5. Study 2: Customer Reaction to Service Failure (Conceptual Replication)Predictor b SE t pCommercial Failure (indicator 0/1) 1.69 .14 12.33 < .001Moral Identity Symbolization (continuous; z-scored) .13 .12 1.12 < .263Commercial Failure Ã Moral Identity Symbolization â.15 .14 â1.07 .287Constant 2.79 .10 27.37 < .001Table 2.3: Regression Results Moral Identity Symbolization (DV: Hostile Reactive Attitudes)2.4.3 DiscussionStudy 1 provides preliminary evidence for the relationship between virtuous victim signaling andhostile reactive attitudes, where consumers high in virtuous victimhood are more likely to reactwith aggressive behaviors in contexts of neutral service compared to service failure than consumerslow in virtuous victimhood.Based on my initial plan of analysis, I analyzed my data using the aggregate virtuous victim-hood score. Reanalysis revealed that the victim signaling component was the aspect responsiblefor the effect observed.2.5 Study 2: Customer Reaction to Service Failure (ConceptualReplication)To explore the generality of the effect I observed in study 1, I designed study 2 using an alternativeservice context, Airbnb. This service is a hospitality platform that competes with hotels, allowingpeople to rent out their own homes for short stays.2.5.1 MethodParticipants and DesignI recruited 352 participants using Amazonâs Mechanical Turk in exchange for small monetary com-pensation. I excluded 30 responses for failing either of two attention checks, leaving 322 responsesfor analysis (Mage = 41.43, SDage = 13.72, 190 female, 130 male, 2 nonbinary). Survey softwareassigned participants randomly to one of two experimental conditions (service experience: neutral,failure). The design was mixed: 2 (experimental condition) Ã continuous (virtuous victim signaling592.5. Study 2: Customer Reaction to Service Failure (Conceptual Replication)individual difference).ProcedureParticipants each read one scenario corresponding to randomly assigned experimental conditionand then completed several post-scenario questions. The design and method were similar to study1, though I manipulated service failure in a different consumer context to test the generality ofthe effect across commercial domains. Rather than imagining potential affront in an airline serviceexperience, in the service failure condition we asked participants to imagine booking an Airbnband then having their booking canceled after updating their profile, providing info about interestsand uploading a photo. In the neutral condition, participants imagined booking an Airbnb withoutincident. In both conditions, the scenario began:You have been planning a vacation. You decide to book an Airbnb rather than stay in ahotel as the amenities look good, there is a wide selection, and the cost is substantiallycheaper than comparable hotels. It is sometimes hard to find good options in thecity you have chosen to visit, but you started looking early and were able to find agreat location. You make the reservation and the host confirms the reservation shortlythereafter. You buy tickets to some local attractions. All your preparation is completeand you assume everything is in order. A month passes.About a week before your trip, you login to Airbnb to check your reservation andeverything looks good. While checking your reservation, you notice that your profile isincomplete. You go ahead and complete your profile, adding information about yourinterests and uploading a photo.In the neutral service condition, the scenario continues:The next day you receive a message from the host, wishing you an enjoyable visit. Yourtrip is less than a week away! You notice no other Airbnb options are available at suchshort notice and even hotel rooms look scarce. It was definitely a good choice to bookearly.In the service failure condition, the scenario continues:602.5. Study 2: Customer Reaction to Service Failure (Conceptual Replication)The next day you receive an email saying that the host has cancelled your booking.Your trip is less than a week away! You notice no other Airbnb options are available atsuch short notice and even hotel rooms look scarce. The host gives no reason, writingonly that the apartment is no longer available on those dates.Following the scenario task, participants reported hostile reactive attitudes and responded toseveral other questions about the scenario. Next, participants completed a battery of measures,including victim signaling, moral identity, and universal values.Finally, participants completed demographic questions. Embedded near the end of the de-mographic questions was an attention check. The attention check prompt was I often visit themoon. This item is just to see if you are paying attention. Please select strongly disagree. (1 =Strongly Disagree, . . . 7 = Strongly Agree). Within question blocks, survey software randomizedpresentation of scale items.2.5.2 ResultsTo create aggregate scores for scales, I averaged the items for hostile reactive attitudes (α = .83),victim signaling (α = .86), and moral identity symbolization (α = .85). The moral identitysymbolization score and the victim signaling score are correlated (r(320) = .76, p < .001). Istandardized the victim signaling and moral identity symbolization items and averaged the twoscores to compute a virtuous victim signaling score.To test my hypothesis that victim signaling is positively associated with hostile reactive atti-tudes when service is neutral, I performed an analysis of covariance with hostile reactive attitudesas dependent variable. As with study 1, I also report analyses using each score (moral identitysymbolization and victim signaling) separately to examine whether either of the components areindependently more influential. Independent variables were experimental condition, virtuous vic-tim signaling (z-scored), and the interaction term (adjusted R2 = .53). There was a main effect ofexperimental condition (b = 2.54, SE = .14, t = 18.26, p < .001), a main effect of victim signaling(b = .58, SE = .10, t = 5.58, p < .001), and a significant interaction (b = â.44, SE = .14,t = â3.17, p = .002). To further explore the interaction, I computed the marginal effect of vir-tuous victimhood on hostile reactive attitudes for each experimental condition: neutral ( dydx = .58,612.5. Study 2: Customer Reaction to Service Failure (Conceptual Replication)SE = .10, t = 5.58, p < .001) and failure ( dydx = 0.14, SE = .09, t = 1.45, p = .148). Thesignificant slope for the neutral condition means that virtuous victimhood is a good predictor ofhostile reactive attitudes only in the experimental condition without commercial failure. See alsofigure 2.2 for a graphical representation. See table 2.4 for model estimates.Figure 2.2: Study 2: AirbnbPredictor b SE t pCommercial Failure (indicator 0/1) 2.54 .14 18.26 < .001Victim Signaling (continuous; z-scored) .58 .10 5.58 < .001Commercial Failure Ã Victim Signaling â.44 .14 â3.17 .002Constant 2.44 .10 23.41 < .001Table 2.4: Regression Results Virtuous Victimhood (DV: Hostile Reactive Attitudes)Unlike study 1, in study 2 both components of virtuous victimhood were important independentpredictors of hostile reactive attitudes. See tables 2.5 and 2.6 for model estimates. Additionally,622.5. Study 2: Customer Reaction to Service Failure (Conceptual Replication)though my sample size is too small to be adequately powered to interpret a three-way interactiongiven the effect size I observed, as an exploratory analysis I fit a model using all three predictorsusing a factorial design (all possible interactions). In this model, the interaction between victimsignaling and moral identity symbolization was significant (b = .33, p = .001) which is consistentwith both components of virtuous victimhood working together to facilitate hostility toward firms.Predictor b SE t pCommercial Failure (indicator 0/1) 2.52 .14 18.20 < .001Virtuous Victimhood (continuous; z-scored) .60 .11 5.71 < .001Commercial Failure Ã Virtuous Victimhood â.44 .14 â3.14 .002Constant 2.46 .10 23.67 < .001Table 2.5: Regression Results Victim Signaling (DV: Hostile Reactive Attitudes)Predictor b SE t pCommercial Failure (indicator 0/1) 2.54 .14 17.54 < .001Moral Identity Symbolization (continuous; z-scored) .31 .11 2.86 .005Commercial Failure Ã Moral Identity Symbolization â.28 .15 â1.95 .052Constant 2.45 .11 22.60 < .001Table 2.6: Regression Results Moral Identity Symbolization (DV: Hostile Reactive Attitudes)2.5.3 DiscussionStudy 2 replicates and generalizes the effect discovered in study 1, that consumers high in virtuousvictimhood will be more likely to react with hostility toward a firm even in a neutral servicecontext, using a different commercial context. I observed a similar effect for Airbnb as I did forairline service. This conceptual replication provides preliminary evidence that the effect is due tomore than some particularity of the airline scenario.As with study 1, I also reanalyzed my data to examine whether the effect was driven pri-marily by one or the other components of virtuous victimhood. Unlike study 1, both componentscontributed to the effect observed.632.6. Study 3: Customer Reaction to Product Failure2.6 Study 3: Customer Reaction to Product FailureThe purpose of study 3 was to conceptually replicate the relationship between virtuous victimhoodand hostile reactive attitudes in the product domain. Specifically, the context involves purchasingfurniture and the resulting performance of the product. This study helps ensure that there is notsomething specifically about services, such as interpersonal interaction, that might be an importantprecondition for the effect to emerge.2.6.1 MethodParticipants and DesignI recruited 403 participants from Amazonâs Mechanical Turk who completed a survey in exchangefor small monetary compensation. Due to increased concerns about data quality in online samplesduring data collection (Matherly, 2019), the survey included an initial attention screen, whichdirected participants to ignore the text of a prompt (How often do you watch horror movies? )and select both Often and Never from several options. I excluded 100 responses that answeredless than 5% of survey questions 3. The initial attention screen resulted in no exclusionsâthoughnote the heavy attrition due to responses which provided almost no data, but finished the survey.I further excluded 16 responses for failing either of two attention checks, leaving 287 responsesfor analysis (Mage = 38.32, SDage = 12.77, 168 female, 118 male, 1 nonbinary). Survey softwareassigned participants randomly to one of two experimental conditions (service experience: neutral,failure). The design was mixed: 2 (experimental condition) Ã continuous (virtuous victim signalingindividual difference).ProcedureIn study 3, participants completed the measure of victim signaling and moral identity, using thesame items as studies 1 and 2. Then, participants were asked to read and imagine a scenarioinvolving a bookcase from Ikea. In all conditions, the scenario began:3I noticed a potential issue when the initial attention screen seemed to be excluding around a quarter of responses.Upon examination, the initially excluded participants belonged entirely to a group that clicked through the surveyand provided answers for less than 5% of items requesting responses. I believe it is clearer to exclude these non-participants directly rather than report the attrition based on the initial attention screen, despite being a post-hocdecision. The substantive results remain the same either way.642.6. Study 3: Customer Reaction to Product FailurePlease imagine that you bought a bookcase from Ikea that required home assembly.The bookcase arrived. When you went to assemble it, you found the instructions to becomplex. After spending some time, however, you were able to assemble the bookcase.You put some books and other things on the bookcase.In the neutral condition, the scenario continued:The next day, after you returned home, you noticed the bookcase as you came in, yourpossessions on the shelf, and it seemed to be just what you needed for the space.In the failure condition, the scenario continued:The next day, after you returned home, you noticed the bookcase had collapsed, spillingyour possessions on the floor, causing damage to some of them.After the scenario, participants reported hostile reactive attitudes and answered several ques-tions about the scenario. The prompts measuring hostile reactive attitudes were similar to studies1 and 2, but adjusted to match the scenario stimulusâPost a negative review about the bookcase,Organize a boycott of Ikea, Complain to friends about the bookcaseâ(1 = V ery Inappropriate. . . 7 = V ery Appropriate). Participants also completed an exploratory three-item measure ofblame (example item: An incident with this bookcase is the fault of Ikea). Finally, participantsprovided demographics (age, gender, ethnicity, education, sexual orientation, social political orien-tation, and economic political orientation).2.6.2 ResultsTo create aggregate scores for scales, I averaged the items for hostile reactive attitudes (α = .76),victim signaling (α = .83), moral identity symbolization (α = .84), and blame (α = .83). I thenstandardized the victim signaling and moral identity symbolization items and averaged the twoscores to compute a virtuous victim signaling score. The moral identity symbolization score andthe victim signaling score are correlated (r(285) = .73, p < .001). There were no relationshipsbetween any of the primary independent variables and the exploratory measure of blame so I donot discuss it further.652.6. Study 3: Customer Reaction to Product FailureTo test my hypothesis that victim signaling is positively associated with hostile reactive atti-tudes when service is neutral, I performed an analysis of covariance with hostile reactive attitudes asdependent variable. Independent variables were experimental condition, virtuous victim signaling(z-scored), and the interaction term (adjusted R2 = .53). There was a main effect of experimentalcondition (b = 2.04, SE = .12, t = 17.24, p < .001), a main effect of victim signaling (b = .36,SE = .08, t = 4.48, p < .001), and a significant interaction (b = â.23, SE = .12, t = â1.98,p = .049). To further explore the interaction, I computed the marginal effect of virtuous victim-hood on hostile reactive attitudes for each experimental condition: neutral ( dydx = .36, SE = .08,t = 4.48, p < .001) and failure ( dydx = 0.13, SE = .09, t = 1.46, p = .144). The significant slopefor the neutral condition means that virtuous victimhood is a good predictor of hostile reactiveattitudes only in the experimental condition without commercial failure. See also figure 2.3 for agraphical representation. See table 2.7 for model estimates.Figure 2.3: Study 3: Ikea662.6. Study 3: Customer Reaction to Product FailurePredictor b SE t pCommercial Failure (indicator 0/1) 2.01 .12 17.24 < .001Virtuous Victimhood (continuous; z-scored) .36 .08 4.48 < .001Commercial Failure Ã Virtuous Victimhood â.23 .12 â1.98 .049Constant 2.05 .08 24.19 < .001Table 2.7: Regression Results Virtuous Victimhood (DV: Hostile Reactive Attitudes)In this study, in neither analysis looking at separate components was the interaction significantby conventional thresholds. See tables 2.8 and 2.9 for model estimates. As an exploratory analysisI fit a model using all three predictors using a factorial design (all possible interactions). In thismodel, the interaction between victim signaling and moral identity symbolization was significant(b = .23, p = .003). As with the previous study, I would be cautious about interpreting this resultdue to the sample size, but it does indicate some possibility of the two subcomponents workingtogether.Predictor b SE t pCommercial Failure (indicator 0/1) 2.00 .12 17.00 < .001Victim Signaling (continuous; z-scored) .36 .08 4.49 < .001Commercial Failure Ã Victim Signaling â.16 .12 â1.38 .169Constant 2.07 .08 24.59 < .001Table 2.8: Regression Results Victim Signaling (DV: Hostile Reactive Attitudes)Predictor b SE t pCommercial Failure (indicator 0/1) 2.04 .12 16.70 < .001Moral Identity Symbolization (continuous; z-scored) .18 .09 2.09 < .038Commercial Failure Ã Moral Identity Symbolization â.19 .12 â1.51 .131Constant 2.04 .09 23.34 < .001Table 2.9: Regression Results Moral Identity Symbolization (DV: Hostile Reactive Attitudes)2.6.3 DiscussionStudy 3 conceptually replicates the effect I observed in studies 1 and 2 using product, rather thanservice, stimuli. The effect was slightly weaker in study 3 compared to the other studies reportedin this chapter. One difference between this study and others in chapter 2 is that participantscompleted the items related to virtuous victim signaling before, rather than after, the scenario672.7. Study 4: Consumer Perception of Discriminationtask. In the other studies from this chapter, virtuous victim signaling scores were unaffected byexperimental condition, but it is possible that exposure to the items related to virtuous victimsignaling influences responses to the scenario in this study, which could potentially explain thedampened effect I report. This could also be due to the particular stimulus, or could indicate thatthe effect is stronger for service interactions in general. This distinction remains a potential avenuefor future research. Despite the slightly weaker effect, the pattern remains consistent across all fourstudies.As noted above, a clear interaction did not emerge when reanalyzing the data using sepa-rate components (victim signaling and moral identity symbolization) as predictors. It is difficultto interpret this result given that the analysis was post-hoc and the aggregate measure as origi-nally planned did lead to a significant interaction. One interpretation of this result is that bothcomponents of virtuous victimhood are necessary to consistently observe the effect of increased hos-tility. That would, additionally, be consistent with the broader conceptual framing of the virtuousvictimhood construct and the empirical results considered across all four studies in chapter 2.2.7 Study 4: Consumer Perception of DiscriminationIn study 4, I explore one potential mechanism underlying the relationship between virtuous victimsignaling and hostile reactive attitudes: the perception of discrimination in a commercial inter-action. Study 4 uses the same service context as study 2: booking an Airbnb for a vacation.Additionally, this study serves to directly replicate the main finding of studies 1 and 2.2.7.1 MethodParticipants and DesignI recruited 351 participants using Amazonâs Mechanical Turk in exchange for small monetarycompensation. I excluded 14 responses for failing an attention check, leaving 337 responses foranalysis (Mage = 38.36, SDage = 12.24, 164 female, 172 male, 1 nonbinary). Survey softwareassigned participants randomly to one of two experimental conditions (service experience: neutral,failure). The design was mixed: 2 (experimental condition) Ã continuous (virtuous victim signalingindividual difference).682.7. Study 4: Consumer Perception of DiscriminationProcedureThe scenario details, including random assignment and post-scenario questions, were identical tostudy 2. After completing the scenario-related tasks, participants completed the victim signalingmeasure and the moral identity scale. Additionally, in study 4 participants completed a 15 itemmeasure of consumer perception of discrimination (Klinner & Walsh, 2013). Several example itemsinclude: Compared to other customers, employees are often offensive toward me, Compared toother customers, it sometimes happens that employees insult me, and Compared to other customers,employees often do not heed my needs or problems (1 = Strongly Disagree, 7 = Strongly Agree).Finally, participants completed the same collection of demographics.2.7.2 ResultsTo create aggregate scores for scales, I averaged the items for hostile reactive attitudes (α = .86),victim signaling (α = .89), moral identity symbolization (α = .88), and consumer perception ofdiscrimination (α = .98). I then standardized the victim signaling and moral identity symbolizationitems and averaged the two scores to compute a virtuous victim signaling score. The moral identitysymbolization score and the victim signaling score are correlated (r(335) = .75, p < .001).To test my hypothesis that virtuous victim signaling is positively associated with hostile reac-tive attitudes when service is neutral, I performed an analysis of covariance with hostile reactiveattitudes as dependent variable. Independent variables were experimental condition, virtuous vic-tim signaling (z-scored), and the interaction term (adjusted R2 = .65). There was a main effectof experimental condition (b = 2.96, SE = .12, t = 23.84, p < .001), a main effect of virtuousvictim signaling (b = .62, SE = .09, t = 6.99, p < .001), and a significant interaction (b = â.63,SE = .12, t = â5.09, p < .001). To further explore the interaction, I computed the marginaleffect of virtuous victimhood on hostile reactive attitudes for each experimental condition: neutral( dydx = .63, SE = .09, t = 6.99, p < .001) and failure (dydx = â.00, SE = .09, t = â.03, p = .976).The significant slope for the neutral condition means that virtuous victimhood is a good predictorof hostile reactive attitudes only in the experimental condition without commercial failure. See alsofigure 2.4 for a graphical representation. See table 2.10 for model estimates.692.7. Study 4: Consumer Perception of DiscriminationFigure 2.4: Study 4: AirbnbPredictor b SE t pCommercial Failure (indicator 0/1) 2.96 .12 23.84 < .001Virtuous Victimhood (continuous; z-scored) .63 .09 6.99 < .001Commercial Failure Ã Virtuous Victimhood â.63 .12 â5.09 ¡ .001Constant 2.06 .09 22.70 < .001Table 2.10: Regression Results Virtuous Victimhood (DV: Hostile Reactive Attitudes)Like study 1, victim signaling seemed to be a more reliable predictor of hostile reactive atti-tudes, as the model which only included moral identity symbolization did not result in a significantinteraction. See tables 2.11 and 2.12 for model estimates. As an exploratory analysis I fit a modelusing all three predictors using a factorial design (all possible interactions). In this model, theinteraction between victim signaling and moral identity symbolization was significant (b = .40, p= .001).702.7. Study 4: Consumer Perception of DiscriminationPredictor b SE t pCommercial Failure (indicator 0/1) 2.93 .12 23.67 < .001Victim Signaling (continuous; z-scored) .62 .08 7.03 < .001Commercial Failure Ã Victim Signaling â.69 .12 â5.58 < .001Constant 2.09 .09 23.03 < .001Table 2.11: Regression Results Victim Signaling (DV: Hostile Reactive Attitudes)Predictor b SE t pCommercial Failure (indicator 0/1) 2.96 .13 22.60 < .001Moral Identity Symbolization (continuous; z-scored) .29 .09 3.11 < .002Commercial Failure Ã Moral Identity Symbolization â.22 .13 â1.70 .091Constant 2.06 .10 21.50 < .001Table 2.12: Regression Results Moral Identity Symbolization (DV: Hostile Reactive Attitudes)To test whether perception of discrimination explained part of the relationship between virtu-ous victim signaling and hostile reactive attitudes, I fit a moderated mediation model (Hayes, 2017,model 14). The independent variable (X) was virtuous victim signaling score. The dependent vari-able (Y) was hostile reactive attitudes score. The mediator variable (M) was consumer perceptionof discrimination score (standardized). The moderator variable (V) was experimental condition. Inmodel 14, the moderator affects the path from the mediator to the dependent variable. I ran thismodel using a Python implementation of the PROCESS macros (Andre´, 2019) with 5000 bootstrapsamples to compute bias-corrected confidence intervals. There was a significant index of moderatedmediation (index = â0.26, SE = 0.04, 95% CI [-0.353, -0.180]). For ease of visualization, I alsoperformed separate mediation analyses splitting the sample by experimental condition (see figure2.5).712.7. Study 4: Consumer Perception of DiscriminationFigure 2.5: Mechanism: Perceived DiscriminationIf experimental condition (service failure) moderates the relationship between the mediator(consumer perception of discrimination) and the dependent variable (hostile reactive attitudes),one would expect the three variable model to show a significant mediation in one condition butnot the other. This is indeed what I observe in figure 2.5, where the mediation is significant in theneutral condition but not significant in the failure condition. That is, participants high in virtuousvictim signaling seem to consider themselves the kind of person likely to be discriminated againstin a commercial interaction generally, but this self-perception is only related to hostile reactiveattitudes in the neutral service condition.722.8. General Discussion2.7.3 DiscussionThe results of study 4 directly replicated the effect I observed in study 2, that hostile reactiveattitudes are associated with virtuous victimhood only in the neutral condition. Additionally, study3 provided evidence for a psychological process explaining this effect, with consumer perception ofdiscrimination mediating the relationship.Consumer perception of discrimination is consistently associated with virtuous victim signaling,but only associated with hostile reactive attitudes in the neutral service condition, suggesting thatthe people reacting with hostility to companies might be bringing a sense of persistent grievance tobear on an unrelated situation. It seems plausible to assume that such respondents might assumethey are being treated unfairly in other contexts as well.In the reanalysis examining the subcomponents of virtuous victimhood independently, whilethe aggregate score resulted in a significant interaction, only the victim signaling score showed thesame result. This could be an indicator that both components are important to the construct ofvirtuous victimhood. In any case, it does seem like victim signaling is more consistently related tothe outcome of hostile reactive attitudes when examined alone, as this was true of three out of fourstudies that I ran for chapter 2.2.8 General DiscussionConsumers increasingly prioritize identity concerns when making purchase decisions and whendiscussing companies or brands. Firms have responded to this preoccupation using many elementsof the marketing mix, from products aimed at particular disadvantaged groups to promotions andadvertising highlighting the plight of victims (Chaney et al., 2019). In this research, I identify akind of consumer which tends to interpret commercial interactions through the lens of virtuousvictimhood, and a mechanismâincreased perception of discriminationâthat explains why theseconsumers may react with hostility toward firms even when no direct commercial failure occurs.Based on these findings, marketing managers may wish to think twice before basing promo-tions heavily on disadvantage narratives, as such appeals may draw the attention of customersparticularly sensitive to slight and prone to interpret commercial service as discrimination.This research contributes to literatures on moral cultures (Cohen & Nisbett, 1994; L. Leung,732.8. General Discussion2009), service failure, and perception of victimhood. The responses I detect in these studies may becharacteristic of the developing victimhood culture which involves recruiting support from othersand greater sensitivity to moral insult (Campbell & Manning, 2014, 2018). Much of the work todate on victimhood culture involves sociological theory. The current research contributes to thisliterature by presenting experimental findings that align with the predictions made by the theoryof victimhood culture (Campbell & Manning, 2018). Additionally, I add to a growing body ofwork on the way in which victimhood can function as a positional good (Hirsch, 2005) in moderncommercial society, with people signaling virtuous victimhood as a way to recruit support fromothers that may otherwise be disinclined to provide assistance.Future directions for research include more fully mapping out the ways that virtuous victimsignaling may shape the emerging dynamics of victimhood culture. It is possible that virtuous vic-tim signaling may be associated with other victimhood culture behaviors, such as gossip, protest,and various kinds of formalized complaints (Campbell & Manning, 2014). In addition to consumerbehavior in the marketplace, such phenomena may influence the behavior in organizational con-texts as well. Based on the likelihood of consumers high in virtuous victim signaling to interpretambiguous stimuli as discriminatory, it is also possible that such consumers might have broadernotions of what constitutes harms, consistent with what Haslam (2016) has discussed as conceptcreep. Further, existing research has found evidence for several antecedents of virtuous victimhood,such as dark triad personality traits, specifically the âamoral manipulationâ dimension of Machi-avellianism and communal narcission, a self-perceptions of superior prosociality (Ok et al., 2020,study 5). In addition to looking at causes of virtuous victimhood, there is also space to elaborateon the consequences side of the nomological net. Existing consequences include lying to earn abonus, indication of willingness to purchase counterfeit products, and exaggerating claims of harmin a workplace context for personal benefit. However, the research to date does not examine indetail how the virtuous victim signal is perceived by others. Whether consciously or not, presum-ably consumers engaging in virtuous victim signaling assume that it serves broader goals such asdrawing sympathy or recruiting support. However, it remains an open question how third-partyperceivers interpret virtuous victim signaling behaviors.The performative aspect of social media interactions (Choi & Bazarova, 2015), where peoplechange their behavior or expression due to expectation of wider audience, make such data sets742.8. General Discussionparticularly well suited to measuring signaling behavior, and it may be possible to measure manyof the dynamics I document in the chapter 2 experiments in the (social media) field, expanding thepractical relevance of this conceptual relationship.75Chapter 3ConclusionThis dissertation demonstrates the role that perceptions of victimhood can play in the judgmentsand decisions of consumers. Using multiple methods, I show mechanisms through which perceivingvictimhood can influence intergroup attitudes, with implications for both cooperative behaviorsand hostility. Specifically, chapter 1 shows how zero-sum thinking contributes to competitive vic-timhood between disadvantaged groups. Chapter 2 shows how self-perceptions of victimhood shapeconsumer attitudes toward firms. Together, these essays reveal both antecedents and consequencesof victimhood perceptions.This research contributes to the broader field of intergroup relations by drawing attention togroup comparison processes in the interactions of disadvantaged groups. This applies to interac-tions both with other disadvantaged groups and with other social actors, such as firms. Previousperspectives, such as the common ingroup identity model (Gaertner et al., 1993; Shnabel et al.,2013), have focused more on social categorization processes, emerging as they did from the socialidentity approach to studying intergroup interaction. As Gaertner et al. (1993, p. 2) note, thisleads to prioritizing transformations of cognitive representation as a path to shaping or decreasingintergroup bias and conflict. While these findings and conclusions are useful and illuminating, inpractice group boundaries often resist redefinition, limiting the scope or effectiveness of strategiesinvolving recategorization. These findings add to the literature by suggesting some reasons whymembers of disadvantaged groups particularly might resist such identity redefinition. Specifically,members of disadvantaged groups might be motivated to maintain currently conceived identityschemas due to perceived pressures in the situation, such as scarcity or competition with othergroups. As such, even the mention of the plights other groups face might make zero-sum con-strual more likely as a defensive reaction, leading to a downward spiral (from the perspective offorming a common ingroup with members of other disadvantaged groups). This suggests a com-76Chapter 3. Conclusionplex interplay between motivated processes (to protect the ingroup and defend limited resources)and cognitive processes (the process of drawing group boundaries and self-categorizing) that isparticularly fraught for members of disadvantaged groups.If anything, group boundaries, particularly those defined by disadvantaged identity, have be-come more entrenched (Fukuyama, 2018), and previously secondary forms of division, such asideological or political partisanship, take more prominent positions on the stage of ingroup con-flict (West & Iyengar, 2020). Intersectional categorization increases the complexity of the identitylandscape (Petsko & Bodenhausen, 2020). While this trend may have benefits for acknowledgingthe diversity of lived experience, it challenges common ingroup approaches to intergroup bias re-duction. As such, group comparison approaches, such as the role of victimhood perception, mayhave an increasing role to play in understanding and navigating group identity dynamics. Alongthese lines, there are some reasons to suspect that various historical dynamics have been, andmight continue to, increase the prevalence of several key variables studies in this research, includ-ing independent variables, dependent variables, and psychological mechanisms. That is, thoughcompetitive victimhood is not a new phenomenon historically speaking, it does seem to be on therise. For example, as discussed in the conceptual development of chapter 1, culture seems to havechanged such that people are more likely to publicize their victimhood with a corresponding de-crease in victim blaming. For just a few examples of this shift, consider the increasing literature onrape culture (Phillips, 2016) and moral condemnation on social media platforms (Hillstrom, 2018;Rentschler, 2015). Additionally, some research has shown that perceptions of competitive climatehave risen over time, a reasonable proxy for zero-sum thinking (Sa´nchez-Rodr´ıguez et al., 2019). Ifthis holds more generally, my research would predict an increase over time of competitive victim-hood, due to increased zero-sum thinking, and an increase over time of hostile reactive attitudes,due to increased willingness to signal victimhood, all else equal.Across both essays, this research has explored different ways that people experience themselvesas victims. In chapter 1, people seem to increase their beliefs in victimhood when in a comparativeintergroup context. In chapter 2, I explore how propensity to signal victimhood is related to othermarketplace behaviors. Neither of these studies looks directly at the welfare of people experiencingthemselves in this way, though the broader conceptual framing offers the possibility that peoplemay be attempting to recruit support from others for their ingroup, a potentially altruistic motive77Chapter 3. Conclusion(though also limited in terms of moral horizon). However, the means of achieving this potentiallyaltruistic motive involves externalizing personal outcomes as the very nature of victimhood isto accept the narrative of others (the victimizing agent) determining outcomes. Much researchhas found negative consequences for well-being in such externalization, and research in clinicalpsychology on exposure therapy would suggest that accepting (or even embracing) victimhood mightcompromise the ability of people to deal with setbacks and adversities. As such, the consequencesof living life thinking one is a victim (whether or not this accurately reflects specific personal eventsor societal opportunities) may be at best ambivalent. Over time, perhaps such identity based ongrievance could be part of the means to overturn unjust relations, but based on historical examplesoutlined in chapter 1, it also seems likely to entrench conflict.My research also connects two mostly unrelated bodies of research concerning intergroup be-havior by considering a trait-level explanation of conflict-related (competitive victimhood) behavior.Previous explanations have focused mostly on social-cognitive factors such as identity strength andcommon ingroup. For reviews, see Noor et al. (2012), Young and Sullivan (2016), and Noor et al.(2017). When considering specific identity conflicts, particularly intractable geopolitical or ethnicconflict, explanations often incorporate situational factors or beliefs relevant to the particular con-flict in question. For example, forgiveness and beliefs about past violence are key to the modeladvanced by Noor, Brown, et al. (2008). But what about when competitive victimhood emergesbetween groups that do not have a clear history of direct violence or the possibility of forgiveness,such as competitive victimhood between Black people and gay people (Davis, 2021), between somefeminists and transgender people (Jeffreys, 2014), or between Asian people and Black people (Mor-ris, 2021)? The frameworks used to understand competitive victimhood between majority/minoritygroups, such as racism or sexism in the North American context, or between adversarial groups,such as the geopolitical conflicts in Northern Ireland or Palestine, may be less well equipped tooffer guidance regarding intergroup relations between disadvantaged groups.My research considers a dispositional explanation, as zero-sum thinking appears to be a stabletrait (Ro´zËycka-Tran et al., 2015) that affects a wide variety of social behavior. Social psychologicalexplanations sometimes consider zero-sum framing of intergroup relations as a factor relevant toconflict and competitive victimhood at the situation level, but have yet to investigate psychologicalprocesses directly involved with zero-sum thinking. Though zero-sum thinking behaves like a78Chapter 3. Conclusiontrait in terms of stability over time, as a âsocial axiomâ it has important differences from classicpersonality psychology traits such as agreeableness or extraversion. A social axiom is defined asa generalized expectancy at a level of abstraction âlikely to relate to social behaviors across avariety of contexts, actors, targets, and time periodsâ (K. Leung et al., 2002). This perspective hasadopted trait constructs such as locus of control (Rotter, 1966) as examples of social axioms. Onecan think of social axioms as existing at a higher, less fundamental level than the core substrate ofpersonality, but a lower, more fundamental level than situational cues, such as framing effects, ortransient internal psychological states, such as anxiety. As such, social axiom beliefs probably existat an intermediate position on the state-trait continuum (Kenny & Zautra, 2001), Research is stillneeded to fully articulate the degree to which these traits overlap or represent emergent dimensionsat different levels of analysis. However, in the context of competitive victimhood behavior, theintermediate abstraction of social axioms, such as zero-sum thinking, offers a useful perspective,given the intransigence of competitive victimhood in the face of situational interventions and thefact that more fundamental personality theories may be too general to apply easily in contextsconcerning particular identity conflicts.Though there is a developing body of research on zero-sum thinking in intergroup relations,most research that uses zero-sum thinking as an explanatory factor involves topics such as politicalideology, group membership, and attitudes toward refugees (Davidai & Ongis, 2019; Piotrowskiet al., 2019; Smithson et al., 2015). My research contributes by showing a direct way in whichzero-sum thinking can influence intergroup conflict.Though I have studied the two constructs related to perception of victimhood (competitivevictimhood and victim signaling) separately, there are conceptual reasons to suspect that zero-sumthinking may also be an antecedent of virtuous victim signaling, and indeed could be a manifestationof competitive victimhood beliefs or behaviors in certain contexts. While I do not at present havedata that speaks to this question, connecting these two streams of research at the operational level isanother opportunity for future directions. Given that competitive victimhood involves seeing oneâsown group as suffering more than relevant outgroups, and victim signaling involves communicatingto others about how one has been treated unfairly, there seems to be enough conceptual overlapthat I might predict the frequency of virtuous victim signaling to increase when zero-sum thinkingis high. Zero-sum thinking could in this case either be measured as an individual difference, as I do79Chapter 3. Conclusionin chapter 1, or perhaps investigated using variations in the situation, such as the sense of societalscarcity. Additionally, a virtuous victim signaler might be more likely to engage in competitivevictimhood as a way to more effectively signal victimhood to others, especially if the competitivevictimhood could be expressed in a context observed by others.Though I focus primarily on theoretical relationships, my findings have several practical impli-cations which are relevant for cultures, such as the contemporary West, where social and politicalpolarization have been rising (Rapp, 2016; Yarchi et al., 2020). Researchers of consumer behaviorare just beginning to investigate these phenomenaâfor example, in the literature on stigmatized-identity cues (Wooten & Rank-Christman, 2019). My research contributes to this growing bodyof knowledge, identifying processes relevant to intergroup conflict with special focus on conflict be-tween disadvantaged groups. In particular, zero-sum thinking, and related conceptual assumptionssuch as fixed pie bias (Bazerman & Neale, 1983; Thompson & Hastie, 1990) are likely to increasecompetitive victimhood beliefs and behaviors. This literature may provide additional methods todecrease fixed pie assumptions based on systemic changes, such as using accountability to inducegroup members to focus on accurate information (such as shared interests) rather than bolsteringesteem through competing for a blameless victim status at the expense of other disadvantagedgroups (de Dreu et al., 2000). By implication, societal or contextual factors which emphasize zero-sum thinking, such as scarcity, are also likely to amplify these dynamics. In terms of marketplacebehavior, I show how perceptions of victimhood are relevant to marketers and firms in the hos-tile reaction that consumers high in virtuous victim signaling exhibit toward firms in commercialinteractions.As discussed previously, consumer identity can be a powerful force shaping behavior, and thisincludes behavior in the marketplace and in organizations. It may be tempting for decision makersin organizations to appeal to identity for many reasons. For example, appealing to consumeridentity can be a coherent product or service marketing strategy, given that brands can be usedby consumers to buttress or construct identity (Aaker, 1997; Escalas & Bettman, 2005). In an erawhen competition has made many products comparable on basic dimensions of utility, appealingto consumer identity can create added value or even a durable competitive advantage if a brand iswell-positioned to satisfy particular identity needs. For example, multinational corporations haveappealed to disadvantaged identity in major marketing campaigns (Draper & Belson, 2018; Kates,80Chapter 3. Conclusion2002, 2004). Such campaigns come with several immediate benefits and risks. Some research hasfound limits to the ability of brands to satisfy consumer needs for identity (Chernev et al., 2011).There may be benefits for society in representation for disadvantaged groups. However, based on thefindings in chapter 1, such marketing campaigns may also increase competitive victimhood beliefsamong some members of disadvantaged groups, particularly those high in zero-sum thinking. I haveapproached this particular factor from an individual differences perspective in this dissertation, butit is likely that a commercial context exacerbates this dynamic due to the inherent nature of scarcityin marketing many products and services. I leave it to future research to investigate the details ofhow this psychological process may manifest in the marketplace.Chapter 2 offers some guidance specific to the commercial context apart from general societalconcerns potentially of interest to managers and marketers. Specifically, that consumers who tendto signal victimhood may be more likely to react with hostility toward firms even when nothing goeswrong with a product or service. Future research will need to be done to determine what kindsof marketing attracts such consumers high in such signaling behavior. Combined, the findingsfrom both chapters suggest that it may be wise for companies to tread carefully around marketingstrategies that rely on invoking consumer identity.However, I want to emphasize that this research was designed to test, in the formulation ofCalder et al. (1981), the application of general scientific theory rather than the application of spe-cific effects in particular contexts. This concern is also framed sometimes in terms of externalvalidity, which is âwhether or not an observed causal relationship should be generalized to andacross different measures, persons, settings, and timesâ (Calder et al., 1982, p. 240). Trouble withthe external validity of laboratory studies has long plagued behavioral science research (Mitchell,2012), particularly for findings involving small and medium effect sizes. Additionally, one of thelessons of the replication crisis (Shrout & Rodgers, 2018; Simmons et al., 2011), along with othermethodological critiques and advances (Friese & Frankenbach, 2020; Schimmack, 2020), is to ap-proach scientific findings involving the complexity of social and identity dynamics with a greaterdegree of epistemological humbleness.Taken together, this research has several limitations. Though I draw from multiple populationsby using both online data sources and textual content analyses, most or all data comes from NorthAmerica. As such, I would take care in generalizing these findings beyond this cultural context.81Chapter 3. ConclusionThough cross-cultural research suggests that zero-sum thinking may be a common psychologicaldynamic in many cultures (Ro´zËycka-Tran et al., 2015; Ro´zËycka-Tran et al., 2019), the stimuli I useto operationalize my investigations, whether identity groups or firms, remain bound historicallyand culturally to the populations I sampled. Additionally, though several studies in both essaysinvolve conceptual replications of various propositions, my studies still involve a limited collectionof stimuli drawn from a much larger potential set. There may remain undetected commonalitiesbetween stimuli which represent undiscovered boundary conditions or explanatory factors.Both major findings in this dissertation revolve around the interest people have in extractingresources from the environment. In chapter 1, members of different disadvantaged groups seemto compete for access to victimhood status, a potential resource in the struggle for attention andrecognition, necessary for garnering support. In chapter 2, people high in virtuous victim signaling,an individual difference associated with the dark triad of personality traits, were more likely toperceive affront and respond negatively toward firms even in neutral service contexts. While thesebehaviors may sometimes result in adaptive outcomes for the self or ingroup in the short term, suchas attracting preferential sympathy to the ingroup or gaining benefits in a commercial interaction,the long-term interests, both for the ingroup and society at large, may have downsides or be lesseffective than members of disadvantaged groups expect. For example, recent research has shownthat disruptive protest actions may decrease support for particular causes (Feinberg et al., 2020).Though appeals perceived as competitive victimhood by third parties may not always rise to thelevel of disruptive protest actions, there may be more effective ways to improve the well-being ofpeople across society.Broadly considered, the studies I ran for chapter 1 add to the literature by demonstratingthe role of zero-sum thinking in competitive victimhood, as well as demonstrating the relation-ship within existing experimental paradigms of intergroup interactions, such as the minimal groupparadigm. Additionally, though more work should be done to fully operationalize some of theseconstructs in field settings, the social media analyses increase confidence that these relationshipscould emerge in societally important contexts. 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It would make me feel good to be a person who has these characteristics.101A.3. Victim Signaling Scale2. Being someone who has these characteristics is an important part of who I am.3. I would be ashamed to be a person who has these characteristics.4. Having these characteristics is not really important to me.5. I strongly desire to have these characteristics.6. I often wear clothes that identify me as having these characteristics.7. The types of things I do in my spare time (e.g., hobbies) clearly identify me as having thesecharacteristics.8. The kinds of books and magazines that I read identify me as having these characteristics.9. The fact that I have these characteristics is communicated to others by my membership incertain organizations.10. I am actively involved in activities that communicate to others that I have these characteristics.Response options: 1 = Strongly Disagree . . . 7 = Strongly Agree.The first five items constitute the internalization dimension of moral identity. The last fiveitems constitute the symbolization dimension of moral identity. In these studies, I present the itemsin a random order.Aquino and Reed II (2002)A.3 Victim Signaling Scale1. Discussed how my concerns and needs are not being heard by political leaders.2. Discussed how I donât feel financially secure.3. Shared how I donât feel comfortable with my body.4. Pointed out how I am not able to pursue my goals and dreams because of external factors.5. Disclosed that I donât feel like I am in control of my future.6. Disclosed that I struggle with mental health issues.7. Explained how I donât feel accepted in the society because of my identity.8. Spoke about how people who share my identity are criminalized by society.9. Expressed how people like me are underrepresented in the media and leadership.*10. Made it known that I canât move freely within or outside of my country.102A.4. Demographic, Miscellaneous, and Concluding QuestionsResponse options: 1 = Never, 2 = Rarely, 3 = Sometimes, 4 = Often, and 5 = Always.Ok et al. (2020).A.4 Demographic, Miscellaneous, and Concluding QuestionsUnless otherwise specified, I included demographic measures to describe the characteristics of sam-ples and guide inferences about generalization.â¢ Age. What is your age? (free-response number)â¢ Gender. What is your gender? (Female, Male, Nonbinary)â¢ Sexual Preference. What is your sexual orientation? (Heterosexual, Homosexual, Bisexual,Other free-response text)â¢ Transgender. Are you transgender? (Yes, No, Prefer not to say)â¢ Political Orientation (Social). How would you describe your political outlook with regardto social issues? (1 = Very Left Wing . . . 7 = Very Right Wing)â¢ Political Orientation (Economic). How would you describe your political outlook withregard to economic issues? (1 = Very Left Wing . . . 7 = Very Right Wing)A.4.1 EthnicityFor measuring ethnicity, surveys included the following items all on one page and in this order.Race, Ethnicity, and OriginWhat is your race or ethnicity? (Asian, Black or African American, Native Americans andAlaska Natives, White Two or more races (specify below), Other (specify below))Are you of Hispanic or Latino origin? (Yes, No)Optional: if you selected âTwo or more racesâ or âOtherâ above, provide additional details inthe box below. You may also provide further specification here for any selected category (such asChinese, Dominican, German, Nigerian, and so forth). (free-response text)103A.5. Concluding QuestionsA.4.2 Socioeconomic StatusThink of this ladder as representing where peo-ple stand in our society. At the top of the ladderare the people who are the best off, those whohave the most money, most education, and bestjobs. At the bottom are the people who are theworst off, those who have me least money, leasteducation, and worst jobs or no job.Please select the number that best repre-sents the rung where you stand on the ladder.Responses were reverse coded so that the high numbers indicated higher status in therecorded data.A.5 Concluding Questionsâ¢ Attitude Toward Survey. Relative to other research surveys you have taken, how muchdid you like this survey? This question will help us design better surveys in the future. (1 =Strongly Dislike . . . 7 = Strongly Like)â¢ Pay Fairness. Relative to other research surveys you have taken, how fair was the compen-sation? (1 = Very Unfair . . . 7 = Very Fair)â¢ Share. Optional: Is there anything else you would like to share with us? (free-response text)A.6 Attention Checkâ¢ I often visit the moon without a spacesuit. This item is just to see if you are paying attention.Please select strongly disagree. (1 = Strongly Disagree . . . 7 = Strongly Agree)104Appendix BStudy ProceduresThis appendix contains a description of study measures. For common measures used in multiplestudies, such as the Belief in Zero-Sum Game scale and various demographic questions, I providereferences to the appropriate entry in Appendix A (which begins on page 101). For survey elementsunique to specific studies, I include the full text that I presented to participants, as well as detailsabout randomization. Unless otherwise stated, the order of item presentation is as specified below.Bold generally indicates a content summary from the perspective of the researcher and was notvisible to participants.B.1 Chapter 1 Study 2: Lay Beliefs of Attitudes BetweenDisadvantaged Groups1. Scenarios. All four presented in a random order.â¢ Scenario Aâ¢ Scenario B105B.1. Chapter 1 Study 2: Lay Beliefs of Attitudes Between Disadvantaged Groupsâ¢ Scenario Câ¢ Scenario D106B.2. Chapter 1 Study 3: Zero-Sum Thinking and Competitive Victimhood1. Belief in Zero-Sum Game. See section A.1 on page 1012. Demographics and Concluding Questions. Age, gender, ethnicity, political ideology(social), political ideology (economic), attention check, sexual preference, transgender status,socioeconomic status (ladder measure), attitude toward survey, pay fairnessB.2 Chapter 1 Study 3: Zero-Sum Thinking and CompetitiveVictimhood1. ScenarioWORKPLACE POLICIESPlease imagine that you work for the HR department of a medium-sized, growing business.One manager, known to be out and gay, discussing the progress of gay rights, said the followingat a recent meeting:The progress of gay rights still has a long way to go. Other groups, such as blacks, havemade much more substantial progress. We need to prevent discrimination based on sexualorientation, as distinct from racial discrimination.107B.2. Chapter 1 Study 3: Zero-Sum Thinking and Competitive VictimhoodIt is illegal in this country to fire a black person without cause, but in many places itâs stilllegal to fire a gay person for being gay.The struggle for gay rights is a struggle for civil rights.The manager then proposed the following policies:â¢ Company civil rights policies should include protections based on sexual preference.â¢ Given two equally qualified candidates, gay or lesbian candidates should be preferred.â¢ Company policy should prevent firing employees for being gay, just as with race.â¢ Develop and promote anti-harassment and anti-discrimination policies that address ho-mophobia.â¢ Set up networking opportunities LGBT+ staff.Next, we will ask you several questions about the passage you read.When you are ready to proceed, please click the button below.2. Post-scenario questions (presented one per page)â¢ Devalues. The sentiment in the scenario devalues the racial struggle for civil rights.(1 = Strongly Disagree . . . 7 = Strongly Agree)â¢ Deserves. The historical and ongoing discrimination gay people face in society deservesgreater recognition.(1 = Strongly Disagree . . . 7 = Strongly Agree)â¢ Decrease. The proposed policies would decrease efforts to address racial inequality.(1 = Strongly Disagree . . . 7 = Strongly Agree)â¢ Civil Rights. Gay rights are civil rights.(1 = Strongly Disagree . . . 7 = Strongly Agree)â¢ Support. Black employees would support the proposed policies.(1 = Strongly Disagree . . . 7 = Strongly Agree)â¢ Advocate. How likely do you think black people would be to advocate for these policies?(1 = Very Unlikely . . . 7 = Very Likely)108B.2. Chapter 1 Study 3: Zero-Sum Thinking and Competitive Victimhoodâ¢ Comprehension Check. The topic of the scenario I read was:(Gay rights, Washing machines, Feminism, Talent shows)3. Attitude toward the policy. The policy in the scenario is. . .â¢ 1 = Bad . . . 7 = Goodâ¢ 1 = Unhelpful . . . 7 = Helpfulâ¢ 1 = Unfair . . . 7 = Fair4. Transition to belief measuresNext, we will ask you several questions about your beliefs.Please click the button below when you are ready to proceed.5. Group Disadvantage (exploratory; group order randomized)6. Belief in Zero-Sum Game. See section A.1 on page 101.This survey included a 12-item version of this scale. Ro´zËycka-Tran et al. (2015) validate (andrecommend using) the eight-item version of the scale. I included the full preliminary 12-item109B.2. Chapter 1 Study 3: Zero-Sum Thinking and Competitive Victimhoodversion to explore the psychometric properties of the scale. The additional four items are: 9.When a person does much for the good of others, he or she profits as well. 10. Those whogive much to others receive much from them. 11. People who do much for their own goodfrequently benefit others as well. 12. When the number of rich people increases in the country,the poorer people benefit as well.7. Transition to social dominance orientation scaleOver the next few pages, we will show you several statements or objects. Please indicatewhether you have a positive or negative feeling toward each of these statements or objects.There are no right or wrong answers, so please be as honest as you can. Please click thebutton below when you are ready to proceed.8. Social dominance orientation scale. Presented in a random order (Pratto et al., 1994).(1 = Very Negative . . . 7 = Very Positive)â¢ Some groups of people are simply inferior to other groups.â¢ In getting what you want, it is sometimes necessary to use force against other groups.â¢ Itâs OK if some groups have more of a chance in life than others.â¢ To get ahead in life, it is sometimes necessary to step on other groups.â¢ If certain groups stayed in their place, we would have fewer problems.â¢ Itâs probably a good thing that certain groups are at the top and other groups are at thebottom.â¢ Inferior groups should stay in their place.â¢ Sometimes other groups must be kept in their place.â¢ It would be good if groups could be equal.â¢ Group equality should be our ideal.â¢ All groups should be given an equal chance in life.â¢ We should do what we can to equalize conditions for different groups.â¢ Increased social equality.â¢ We would have fewer problems if we treated people more equally.â¢ We should strive to make incomes as equal as possible.110B.3. Chapter 1 Study 4: Minimal Group Paradigmâ¢ No one group should dominate in society.9. Demographics and Concluding Questions. Age, gender, ethnicity, political ideology(economic), political ideology (social), attention check, sexual orientation, transgender, so-cioeconomic status, attitude toward survey, anything else to share?B.3 Chapter 1 Study 4: Minimal Group ParadigmParticipants were randomly assigned to one of three conditions: Common Disadvantage, IngroupDisadvantage, or Comparative Disadvantage. Participants were also randomly assigned an ingroupname (âGroup Bâ or âGroup Câ), referred to as INGROUP below. The name used for theoutgroup was the one not chosen as ingroup from that pair, referred to as OUTGROUP below.1. InstructionsThanks for your interest in our survey! First, we will ask to you complete a brief personalityquestionnaire. After the personality questionnaire, we will ask you to read a short passageand then answer several questions about it. Next, we will ask you about some of your beliefsand opinions. Finally, we will ask you to tell us a few things about yourself. Please click thebutton below when you are ready to proceed.2. BFI2S. 30 items (Soto & John, 2017). Completing this scale was only part of the merecategorization procedure for the minimal group paradigm.3. Categorization.Thank you for completing the personality questionnaire. Research has found that this ques-tionnaire is predictive of outcomes in the workplace and other domains of life, including howothers treat you. People can be categorized into different groups based their results.Based on your responses, you belong to INGROUP.Members of INGROUP tend to have doubts about whether they have made the right decisionor done the right thing, have some aspirations that are pretty unrealistic, possess a great dealof unused potential which has yet to be explored, and have a tendency to be self-critical.111B.3. Chapter 1 Study 4: Minimal Group Paradigm4. Comprehension. Based on the personality questionnaire at the beginning of this survey, towhich group do you belong?5. Experimental Conditions. Participants saw one of the following three:â¢ Common DisadvantagePlease imagine that research is published showing that, compared to the average worker,members of INGROUP earn lower wages, are less likely to be hired, are less likely to bepromoted, and are more likely to be criticized in front of colleagues. However, indepen-dent examination of work quality reveals no difference between members of INGROUPand the average worker.Additionally, the research found that members of OUTGROUP, another group, facedvarious disadvantages in the workplace. Members of OUTGROUP have begun to bringattention to the disadvantages faced by members of their group and advocate for newworkplace policies.â¢ Ingroup DisadvantagePlease imagine that research is published showing that, compared to the average worker,members of INGROUP earn lower wages, are less likely to be hired, are less likely to bepromoted, and are more likely to be criticized in front of colleagues. However, indepen-dent examination of work quality reveals no difference between members of INGROUPand the average worker.â¢ Comparative DisadvantagePlease imagine that research is published showing that, compared to the average worker,members of INGROUP earn lower wages, are less likely to be hired, are less likely to bepromoted, and are more likely to be criticized in front of colleagues. However, indepen-dent examination of work quality reveals no difference between members of INGROUPand the average worker.Additionally, the research found that members of INGROUP face significantly greaterdisadvantage compared to OUTGROUP, another group. However, members of OUT-GROUP have begun to advocate for new workplace policies to benefit their group, claim-112B.4. Chapter 2 Study 1: Customer Reaction to Service Failureing that disadvantages faced by OUTGROUP deserve greater recognition.6. Post-Scenario Questions1. Discrimination. Members of INGROUP are discriminated against.(1 = Strongly Disagree . . . 7 = Strongly Agree)2. Competitive Victimhood. Compared with OUTGROUP, members of INGROUPexperience discrimination.(1 = Less Overall . . . 4 = As Much . . . 7 = More Overall)3. Social Media Share. How likely would you be to share a news article on social mediaexplaining the treatment of INGROUP?(1 = Extremely Unlikely . . . 7 = Extremely Likely)7. Ingroup Favoritism. Bonus allocation between non-self INGROUP and OUTGROUP mem-bers. See section 1.11.1 on page 37 for explanation.8. Belief in Zero-Sum Game. See section A.1 on page 101.9. Demographics and Concluding Questions. Age, gender, transgender status, sexualpreference, ethnicity, socioeconomic status (ladder measure), attitude toward survey, anythingelse to share? (free-response text).B.4 Chapter 2 Study 1: Customer Reaction to Service Failure1. Random Assignment: Service Failure, Neutral Service2. InstructionsIn the following screen, we will ask you to read a short passage and imagine yourself in thesituation.We will ask for your thoughts about the passage afterwards, so please read it carefully.Please click the button below when you are ready to proceed.3. Experimental Condition113B.4. Chapter 2 Study 1: Customer Reaction to Service Failureâ¢ Service Failureâ¢ Neutral Service4. Post-Scenario QuestionsIn the next few screens, we will ask you to consider some actions in response to being askedto move seats.For each action, we would like to know how appropriate it would be in the situation you readabout.Please click the button below when you are ready to proceed.114B.4. Chapter 2 Study 1: Customer Reaction to Service Failure1. Organize a boycott of the airline.(1 = Very Inappropriate . . . 7 = Very Appropriate)2. Post a negative review about the airline online after the flight.(1 = Very Inappropriate . . . 7 = Very Appropriate)3. Demand that the flight attendant apologize for treating you unfairly.(1 = Very Inappropriate . . . 7 = Very Appropriate)4. Refuse to move and resist physically if necessary.(1 = Very Inappropriate . . . 7 = Very Appropriate)5. Complain to friends about the experience.(1 = Very Inappropriate . . . 7 = Very Appropriate)6. Raise voice toward the flight attendant.(1 = Very Inappropriate . . . 7 = Very Appropriate)7. In the scenario I read, the three other passengers were of what gender? (Mixed, bothmale & female, Male, Female)8. After the flight, you realize that the airline undercharged you by almost 50%. You aresure it was a mistake. How likely are you to bring this mistake to the airlineâs attention?(1 = Very Unlikely . . . 7 = Very Likely)9. How do you think the flight attendant decided which passengers should move? (free-response text)10. The outcome in the scenario I read was fair.(1 = Strongly Disagree . . . 7 = Strongly Agree)11. Imagining yourself the in the scenario you read, how would you rate the airlineâs qualityof service?(1 = Very Bad . . . 7 = Very Good)12. Comprehension Check. In the scenario I read, the flight attendant. . .â¢ Asked some passengers to be quietâ¢ Asked some passengers to move to the back of the plane115B.5. Chapter 2 Study 2: Customer Reaction to Service Failure (Conceptual Replication)â¢ Asked whether passengers would like to purchase snacks5. Victim Signaling. Randomized. See appendix A.3 on page 102.6. Moral Identity Scale. See appendix A.2 on page 101.7. Demographics and Concluding Questions. Age, gender, ethnicity (free-response text),political ideology (economic), political ideology (social), attention check, attitude towardsurvey, anything else to share? (free-response text)B.5 Chapter 2 Study 2: Customer Reaction to Service Failure(Conceptual Replication)1. InstructionsIn the following screen, we will ask you to read a short passage and imagine yourself in thesituation.We will ask for your thoughts about the passage afterwards, so please read it carefully.Please click the button below when you are ready to proceed.116B.5. Chapter 2 Study 2: Customer Reaction to Service Failure (Conceptual Replication)2. Experimental Conditionâ¢ Service Failureâ¢ Neutral Service117B.5. Chapter 2 Study 2: Customer Reaction to Service Failure (Conceptual Replication)3. Post-Scenario Questionsâ¢ Post a negative review about the experience.(1 = Very Inappropriate . . . 7 = Very Appropriate)â¢ Organize a boycott of Airbnb.(1 = Very Inappropriate . . . 7 = Very Appropriate)â¢ Complain to friends about the experience.(1 = Very Inappropriate . . . 7 = Very Appropriate)â¢ Alert Airbnb that you experienced discrimination or prejudice.(1 = Very Inappropriate . . . 7 = Very Appropriate)â¢ Post the hostâs name, address, and phone number on a forum for Airbnb users so otherguests know about the host.(1 = Very Inappropriate . . . 7 = Very Appropriate)â¢ Send a direct message to the host demanding compensation for the inconvenience.(1 = Very Inappropriate . . . 7 = Very Appropriate)â¢ Send a direct message to the host making clear the potential negative consequences forhow they treated you.(1 = Very Inappropriate . . . 7 = Very Appropriate)â¢ Demand that the host apologize for treating you unfairly.(1 = Very Inappropriate . . . 7 = Very Appropriate)â¢ The outcome in the scenario I read was fair.(1 = Strongly Disagree . . . 7 = Strongly Agree)â¢ Imagining yourself the in the scenario you read, how would you rate the Airbnb qualityof service?(1 = Very Bad . . . 7 = Very Good)â¢ Comprehension. In the scenario I read, I imagined myself. . .â Asking some people to be quietâ Booking accommodations for a trip118B.6. Chapter 2 Study 3: Customer Reaction to Product Failureâ Searching the web to find a good movie to watch4. Victim Signaling. Randomized. See appendix A.3 on page 102.1. Moral Identity Scale. Randomized. See appendix A.2 on page 101.2. Demographics and Concluding Questions. Age, gender, ethnicity (free-response text),political ideology (economic), political ideology (social), attention check, attitude towardsurvey, anything else to share? (free-response text)B.6 Chapter 2 Study 3: Customer Reaction to Product Failure1. Victim Signaling. Randomized. See appendix A.3 on page 1022. Moral Identity Scale. Randomized. See appendix A.2 on page 101119B.6. Chapter 2 Study 3: Customer Reaction to Product Failure3. Experimental Condition. One of the following two scenarios were randomly presented:â¢ Product Scenario: Failure.â¢ Product Scenario: Neutral.4. Transition to Post-Scenario Questions.In the next few screens, we will ask you to consider some actions you might take in responseto the scenario you imagined.For each action, we would like to know how appropriate it would be in the situation you readabout.Please click the button below when you are ready to proceed.5. Post-Scenario Questions120B.7. Chapter 2 Study 4: Consumer Perception of Discriminationâ¢ Post a negative review about the bookcase.(1 = Very Inappropriate . . . 7 = Very Appropriate)â¢ Organize a boycott of Ikea.(1 = Very Inappropriate . . . 7 = Very Appropriate)â¢ Complain to friends about the bookcase.(1 = Very Inappropriate . . . 7 = Very Appropriate)â¢ Send a message to Ikea demanding compensation.(1 = Very Inappropriate . . . 7 = Very Appropriate)â¢ Send a message to Ikea making clear the potential negative consequences for how theytreated you.(1 = Very Inappropriate . . . 7 = Very Appropriate)â¢ Demand that Ikea apologize for the treatment you received.(1 = Very Inappropriate . . . 7 = Very Appropriate)6. Blame. Exploratory (Klein & Dawar, 2004, p. 209).â¢ Ikea is responsible for the performance of the bookcase.â¢ Ikea should be held accountable if there were some problem with the bookcase.â¢ An incident with this bookcase is the fault of Ikea.7. Demographics and Concluding Questions. Gender, age, ethnicity, education, sexualpreference, political ideology (economic), political ideology (social), religiosity, English flu-ency, anything else to share? (free-response text)B.7 Chapter 2 Study 4: Consumer Perception of Discrimination1. Scenarios. See appendix B.5 on page 116; I used the same stimuli.2. Post-Scenario Questionsâ¢ Post a negative review about the experience.(1 = Very Inappropriate . . . 7 = Very Appropriate)121B.7. Chapter 2 Study 4: Consumer Perception of Discriminationâ¢ Organize a boycott of Airbnb.(1 = Very Inappropriate . . . 7 = Very Appropriate)â¢ Complain to friends about the experience.(1 = Very Inappropriate . . . 7 = Very Appropriate)â¢ Send a direct message to the host demanding compensation for the inconvenience.(1 = Very Inappropriate . . . 7 = Very Appropriate)â¢ Send a direct message to the host making clear the potential negative consequences forhow they treated you.(1 = Very Inappropriate . . . 7 = Very Appropriate)â¢ Demand that the host apologize for treating you unfairly.(1 = Very Inappropriate . . . 7 = Very Appropriate)3. Next, we are going to ask you about your experience in the scenario. Please click the buttonbelow when you are ready to proceed.â¢ In the scenario, I experienced inferior service.(1 = Strongly Disagree . . . 7 = Strongly Agree)4. Customer Perception of Discrimination. (Klinner & Walsh, 2013)â¢ Compared to other customers, I am often verbally abused by service employees.â¢ Compared to other customers, it sometimes happens that employees insult me.â¢ Compared to other customers, it often happens that employees make remarks that hu-miliate me.â¢ Compared to other customers, it often happens that the behavior of the employees humil-iates me.â¢ Compared to other customers, employees are often offensive toward me.â¢ Compared to other customers, employees often do not heed my needs or problems.â¢ Compared to other customers, employees are often patronizing toward me.â¢ Compared to other customers, employees often take little time to advise me and quicklygo to the next customer.122B.7. Chapter 2 Study 4: Consumer Perception of Discriminationâ¢ Compared to other customers, employees are often very distant to me.â¢ Compared to other customers, I am frequently being critically observed by employees.â¢ Compared to other customers, employees often make me wait longer.â¢ Compared to other customers, the tone of the employees is often condescending towardme.â¢ Compared to other customers, employees frequently give me derogatory looks.â¢ Compared to other customers, employees often give me condescending looks.â¢ Compared to other customers, the tone of employees is often patronizing toward me.5. Negative Emotions. Exploratory.â¢ Imagining myself in the scenario I read, I feel uneasy.â¢ Imagining myself in the scenario I read, I feel frustrated.â¢ Imagining myself in the scenario I read, I feel angry.â¢ Imagining myself in the scenario I read, I feel uncertain.6. Scenario Questions.â¢ The outcome in the scenario I read was fair.(1 = Strongly Disagree . . . 7 = Strongly Agree)â¢ In the scenario you read, how would you rate the Airbnb quality of service?(1 = Very Bad . . . 7 = Very Good)â¢ Comprehension. In the scenario I read, I imagined myself. . .â Asking some people to be quietâ Booking accommodations for a tripâ Searching the web to find a good movie to watch7. Moral Identity Scale. Randomized. See appendix A.2 on page 101.8. Demographics and Concluding Questions. Age, gender, ethnicity (free-response text),political ideology (economic), political ideology (social), attention check, attitude towardsurvey, pay fairness, anything else to share? (free-response text).123Appendix CSupplementary AnalysesC.1 Chapter 1 Study 2: Lay Beliefs of Attitudes BetweenDisadvantaged GroupsC.1.1 Sample DemographicsDemographicEthnicity 107 White 96 BlackHispanic 154 No 38 YesSexual Preference 154 Heterosexual 28 Bisexual 10 HomosexualTransgender 174 No 15 Yes 2 Prefer not to sayRace, Ethnicity, and Origin:â¢ 139 Whiteâ¢ 38 Black or African Americanâ¢ 6 Asianâ¢ 5 Two or more racesâ¢ 4 Other124C.1. Chapter 1 Study 2: Lay Beliefs of Attitudes Between Disadvantaged GroupsC.1.2 Zero-Order Correlations* means p < .05, ** means p < .01, *** means p < .001. Values in square brackets indicatethe 95% confidence interval for each correlation.â¢ Age is participant ageâ¢ Polit. Soc. is participant political ideology regarding social issuesâ¢ Polit. Econ. is participant political ideology regarding economic issuesâ¢ SES is participant subjective socioeconomic status (ladder measure)â¢ BZSG is score on the belief in zero-sum game scaleâ¢ A. Straight/Bi is the expected attitude of straight and bisexual people toward a scenario anACLU statment on LGBT rights (score is the average of 3 items)â¢ B. Cis./Trans. is the expected attitude of non-transgender and transgender people toward ascenario involving a Lesbian march (score is the average of 3 items)â¢ C. Wht./Black is the expected attitude of White and Black people toward a scenario involvinggay rights in the workplace (score is the average of 3 items)â¢ D. Wht./Black is the expected attitude of White and Black people toward a scenario involvingthe struggles of people with little wealth (score is the average of 3 items)In all cases for the scenarios, lower scores represent belief that groups facing less disadvantage125C.2. Chapter 1 Study 3: Zero-Sum Thinking and Competitive Victimhoodwould like the content presented in the scenario more while higher scores represent participantbelief that groups facing more disadvantage would like the content presented in the scenario more.See section B.1 on page 105 for item prompt wording.C.2 Chapter 1 Study 3: Zero-Sum Thinking and CompetitiveVictimhoodC.2.1 Sample DemographicsDemographicGender 112 Female 91 MaleSexual Preference 203 HeterosexualTransgender 202 No 1 YesEthnicity 107 White 96 BlackHispanic 194 No 5 Yes 4 missing values(Missing values indicate that the participant skipped the question.)Demographic Mean SD Min MaxAge 45.07 15.46 19 90SES 6.05 2.10 1 10Political Ideology (Social) 3.84 1.63 1 7Political Ideology (Economic) 3.87 1.61 1 7126C.2. Chapter 1 Study 3: Zero-Sum Thinking and Competitive VictimhoodC.2.2 Zero-Order Correlations* means p < .05, ** means p < .01, *** means p < .001. Values in square brackets indicatethe 95% confidence interval for each correlation. Ethnicity is coded as 1 = Black, 0 = White. Seesection B.2 on page 107 for item prompt wording.127C.2. Chapter 1 Study 3: Zero-Sum Thinking and Competitive VictimhoodC.2.3 Moderation by Social Dominance OrientationFollowing are the full results of analysis of covariance using the civil rights measure as dependentvariable and factorial design for independent variables to investigate potential moderation by socialdominance orientation. All continuous predictors (BZSG and SDO) are z-scored. Ethnicity iscoded 1 = Black, 0 = White. As described in Chapter 1 Study 3 (section 1.10.2 on page 30),despite the presence of significant interactions in the three-way interaction model, social dominanceorientation did not moderation the influence of zero-sum thinking on competitive victimhood forblack participants.N obs = 203Root MSE = 1.60948R-squared = 0.1984Adj R-squared = 0.1696Source Partial SS df MS F Prob>FModel 124.98583 7 17.855119 6.89 0.0000Ethnicity 6.2826642 1 6.2826642 2.43 0.1210BZSG 5.3520718 1 5.3520718 2.07 0.1522Ethnicity Ã BZSG 17.336495 1 17.336495 6.69 0.0104SDO 61.915043 1 61.915043 23.90 0.0000Ethnicity Ã SDO 16.645242 1 16.645242 6.43 0.0120BZSG Ã SDO 21.624420 1 21.062442 8.35 0.0043Ethnicity Ã BZSG Ã SDO 7.3212714 1 7.3212714 2.83 0.0943Residual 505.1324 195 2.5904225Total 630.11823 202 3.1193972C.2.4 Group-Based Disadvantage BeliefsTo explore beliefs about group-based disadvantage, participants reported the degree to which groupsexperienced discrimination (1 = Very little . . . 7 = Very much). The groups selected were basedon Jonathan Haidtâs discussion of victim groups (Leo, 2016). The measure presented groups in a128C.3. Chapter 1 Study 4: Minimal Group Paradigmrandom order. Below, I report summary statistics sorted by discrimination score.Group n Mean SD Min MaxTransgender People 203 5.76 1.42 1 7African Americans 203 5.70 1.47 2 7Muslims 203 5.70 1.40 1 7Lesbian, Gay, Bisexual 203 5.55 1.43 1 7Native Americans 203 5.19 1.63 1 7Latinos 203 5.18 1.55 1 7Disabled People 203 5.05 1.61 1 7Women 203 4.99 1.60 1 7White People 203 2.77 1.84 1 7See section B.2 on page 107 for measure details.C.3 Chapter 1 Study 4: Minimal Group ParadigmC.3.1 Sample DemographicsDemographicGender 214 Male 134 Female 3 NonbinarySexual Preference 235 Straight 98 Bisexual 12 HomosexualTransgender 328 No 17 YesHispanic 253 No 96 YesDemographic n Mean SD Min MaxAge 348 37.45 11.49 20 78SES 349 5.20 2.40 1 10Political Ideology (Social) 347 4.59 1.87 1 7129C.3. Chapter 1 Study 4: Minimal Group ParadigmDemographic n Mean SD Min MaxPolitical Ideology (Economic) 348 4.74 1.73 1 7Race, Ethnicity, and Origin:â¢ 221 Whiteâ¢ 92 Black or African Americanâ¢ 25 Asianâ¢ 4 Native Americans and Alaska Nativesâ¢ 3 Two or more racesâ¢ 1 OtherC.3.2 Zero-Order Correlations130C.4. Chapter 2 Study 1: Customer Reaction to Service FailureC.4 Chapter 2 Study 1: Customer Reaction to Service FailureC.4.1 Zero-Order Correlations* means p < .05, ** means p < .01, *** means p < .001. Values in square brackets indicatethe 95% confidence interval for each correlation. Condition is coded as 1 = Service Failure, 0 =Neutral Service. See section B.4 on page 113 for item prompt wording.C.5 Chapter 2 Study 2: Customer Reaction to Service Failure(Conceptual Replication)131C.6. Chapter 2 Study 3: Customer Reaction to Product FailureC.5.1 Zero-Order Correlations* means p < .05, ** means p < .01, *** means p < .001. Values in square brackets indicatethe 95% confidence interval for each correlation. Condition is coded as 1 = Service Failure, 0 =Neutral Service. See section B.5 on page 116 for item prompt wording.C.6 Chapter 2 Study 3: Customer Reaction to Product Failure132C.7. Chapter 2 Study 4: Consumer Perception of DiscriminationC.6.1 Zero-Order Correlations* means p < .05, ** means p < .01, *** means p < .001. Values in square brackets indicatethe 95% confidence interval for each correlation. Condition is coded as 1 = Service Failure, 0 =Neutral Service. See section B.6 on page 119 for item prompt wording.C.7 Chapter 2 Study 4: Consumer Perception of Discrimination133C.7. Chapter 2 Study 4: Consumer Perception of DiscriminationC.7.1 Zero-Order Correlations* means p < .05, ** means p < .01, *** means p < .001. Values in square brackets indicatethe 95% confidence interval for each correlation. Condition is coded as 1 = Service Failure, 0 =Neutral Service. See section B.7 on page 121 for item prompt wording.134
Thesis/Dissertation
eng
Business Administration - Marketing
Vancouver : University of British Columbia Library
Attribution-NonCommercial-NoDerivatives 4.0 International
Graduate
Aquino, Karl
White, Katherine, 1974-
Two essays on perceptions of victimhood
Text